Latest News | Daniel Antal

One Song, Many Free Uses

Thu, 19 Mar 2026 08:45:00 +0100

Read our Substack blogpost

Livonian is a critically endangered European language related to Estonian, Finnish, and Hungarian. A small community of Livonians are re-learning their ancestral language, and singing together is one of the most effective ways to practice.
For the first time, we made a recording of a Livonian dance song accessible on Spotify, Apple Music, and Amazon. These platforms make the song available in almost every country, and some—like Spotify—offer it for free if you are willing to listen to advertisements. Continue reading on Substack…

When the Heir to the Throne Met the Udmurts

Sun, 01 Mar 2026 08:45:00 +0100

Read our Substack blogpost

One of these images — the photograph of the Votyaks (Вотяки: Russian exonym for Udmurts) — stands today as a foundational anchor of Udmurt photography. Like the companion images of Hill and Meadow Mari groups, it presents a small group posed as representative “types” of their people. The three female and two male figures stand in a group, dressed in carefully arranged garments that emphasise ethnic distinctiveness. The image was not intended as an intimate portrait, but as documentation: a visual taxonomy of empire. Continue reading on Substack…

Mari Clothing Heritage Across Collections

Thu, 22 Jan 2026 08:45:00 +0100

Read our Substack blogpost

Rather than building a new platform or launching a large institutional programme, we focused on a carefully designed, hands-on intervention. The goal was to create a concrete, working example that could be expanded, criticised, reused, and—crucially—taken up by others. Continue reading on Substack…

Federating Music Library Data in Hungary – A Call to Action

Mon, 21 Jul 2025 08:45:00 +0200

How can music library services be modernized to compete with platforms like Spotify, YouTube, or Apple Music Classical? How can we make it easy for music students, educators, amateurs, or professional musicians to find the sheet music of a piece that interests them? And how can schoolchildren explore the music of their town or region—with the help of local musicians, teachers, or librarians—given the limited financial resources of university and public libraries worldwide?

Our tools are open-source and free to test, and we are happy to support those interested in exploring them. We are also planning a meetup in Budapest at the end of August or beginning of September to discuss these ideas further with IAML Hungary members.

🇭🇺 Olvasd magyarul el ezt a bejegyzést - Hungarian version of this post.

Daniel Antal, co-founder of Reprex, originally presented at the IAML 2025 Congress alongside librarian Anna Mester and Anna Žilková, head of IAML Slovakia. Their presentation and poster explored the legal, organizational, and information science aspects of the data-sharing infrastructure behind the Slovak Comprehensive Music Database (SKCMDb). A year earlier, the Hungarian professional community encountered this work at the Networkshop 2024: Digital Transformation of Education, Research, and Public Collections conference, in the talk and paper titled A szlovák adatkicserélési tér magyarországi föderációjának lehetőségei [Opportunities for Federating the Slovak Data Exchange Space in Hungary].

At the IAML Congress, we invited international partners to test, critique, and help develop a live demo service. Thanks to Salzburg’s geographical and cultural proximity, we were joined by many Hungarian colleagues.

The Reprex team is explaining our collaborative work to music metadata experts in Salzburg. Photo: Anna Žilková.

Our project’s ambitious goal is to make all music created within the territory of present-day Slovakia accessible through a semantic database. This includes a user-friendly graphical interface for individuals and API access for libraries and other institutions. The database connects known works and their variants, manuscript and published scores, and demo, archival, and commercial recordings. It also links composers and performers to secondary sources in libraries and archives that provide musicological context.

In Slovakia, we are integrating materials from the Music Fund, the Slovak Music Centre, and the SOZA database (sister organization to Artisjus). This enables us to substantially improve the data quality of public music library catalogues, which often lack detailed, work-level descriptions of scores. Metadata is frequently incomplete, outdated, or even incorrect. For example, a catalogue may refer to a composer’s “collected works” or “selected sonatas,” but rarely indicate which edition contains a particular sonata or movement a listener may have just encountered on a streaming playlist.

Our poster presented by Daniel Antal, Anna Márta Mester (librarian-data steward) and Anna Žilková (chairperson of IAML Slovakia) on 8 July 2025 on IAML 2025. You can download our poster in PDF here.

Our Salzburg presentation explored key questions:

How does data governance work between private and public organizations—such as between a rights management society or streaming platform and a university or public library?
How can we reduce redundant workflows through data exchange?
How can deeper, semantically enriched search improve music library services?
What transferable insights from information and library science can help libraries, archives, museums, and private actors mutually improve their data?
The development of this system is supported by the European Union’s Horizon Europe programme, through the Open Music Europe project. Our aim is to offer a federated, decentralized alternative to the centralized but never built European Music Observatory model—one that builds on existing national, regional, and pan-European data systems in a networked way and leaves data ownership and control in place following the subsidiarity principle.

Sneak peak: http://135.181.91.51:3007/en/

If you care about interoperability, cultural equity, and the future of library relevance in the streaming era—this is your moment to get involved.

Missed IAML2025?
👉 Presentation
👉 Poster
👉 Please contact us directly to try out our system.
👉 Blogpost about the wider context of our work, relevant for IAML, not only national chapters and invidivual members.

Help Us Build a Truly Inclusive European Music Observatory

Thu, 19 Jun 2025 18:45:00 +0200

Across Europe, music libraries are under pressure: greater expectations for digital services, growing metadata burdens, and increasingly fragmented infrastructure. At the same time, vital parts of our musical heritage—especially regional or minority repertoires—remain hidden from search, discovery, and policy.

Please meet us at 👉 IAML2025 in Salzburg on 7 or 8th July. Our presentation takes place in the session of Music Libraries of Tomorrow: Reaching out to Wider Audiences at the Mozarteum University E.001 HS Thomas Bernhard room on 7 July 2025, 16:00–17:30. The day after you can meet us in the Gallery for the poster session.

We initiated the Open Music Europe project, because we believe that in the music ecosystem, data centralisation always fails, and a new kind of cooperation is needed—one that respects local control while enabling international reuse.

Our Slovak pilot, the SKCMDb, connects libraries, music centres, rights organisations, and platforms through a shared metadata backbone based on open ontologies. Built as a national data sharing space, it enables coordinated cataloguing and discovery across public and private systems—from streaming services and printed scores to CD loans and digital archives.

Please visit our poster and talk with our team members, Daniel Antal, Anna Márta Mester (librarian-data steward) and Anna Zilkova (chairperson of IAML Slovakia) on 8 July 2025 10:30–11:00 in the Gallery. You can download our poster in PDF here.

But we also know that cultural and music policy is not only national. It is often regional, local, or community-based. That’s why we follow the principle of subsidiarity: letting decisions and innovation happen at the lowest competent level, close to the collections and communities themselves.

Our Finno-Ugric Data Sharing Space, including the LīvMDb (Livonian Music Database), shows how even the smallest communities—without formal cultural infrastructure—can take part in high-quality metadata production and digital discovery. We provide the tools and models to empower local custodians, in their language, on their terms, and without the need for large institutional support.

Please check out the demo version of the Finno-Ugric Dataspace or read the long-form project description.

Now we invite IAML members—national libraries, regional centres, municipal collections, and independent music librarians—to join us in building a federated, decentralised European Music Observatory. One that reflects Europe’s diversity. One that reduces data curation costs and improves visibility. One that connects music libraries with the open data and open science infrastructures already transforming other sectors.

Our platform is open-source, built on FAIR principles and the European Interoperability Framework. We use tools like Wikibase, Blazegraph, Sampo-UI, and R—packaged to work for libraries with limited technical capacity.

Sneak peak: http://135.181.91.51:3007/en/

If you care about interoperability, cultural equity, and the future of library relevance in the streaming era—this is your moment to get involved.

Not present at IAML2025?
👉 Presentation
👉 Poster
👉 Please contact us directly.

Let’s ensure music libraries remain vital entry points to Europe’s rich and evolving cultural soundscape.

Metadata Groundhog Day: What a Moribound Language Can Teach Spotify and Shopify

Thu, 19 Jun 2025 18:45:00 +0200

And if you want to fix these errors, you may find that you are back to the Data Sisyphus.

When you build systems in the cloud, or in your local architecture, at one point you will realise that naming things — places, people, products — or updating their whereabouts is probably the most time-consuming, most expensive, and most error-prone workflow.

In this blogpost, we want to talk about what seems like the easiest part of a location: the name of the city, town, or village.

Mazirbe Is Missing Again

We recently built a multilingual gazetteer — essentially a reconciled database of place names — for a tiny stretch of the Livonian coast in Latvia. At first glance, this might seem like a project rooted deeply in the digital humanities.

But here’s the twist: the very same problems we tackled here are the ones plaguing the music industry, global e-commerce platforms, and enterprise software stacks.

This is Gross-Irben 👉 Lielirbe / Īra / Irben / Suur-Irben 👉 Familiar with RDF: see in TTL; Klein-Irben will be near Gross-Irben, and Irē is almost Īra!

Mazirbe is a small, big place. It definitely exists, and it is the cultural center of a small nation: the Livonians. Yet, when you are looking for clothing, music, or photographs that should come from Mazirbe in a relevant database, you often find nothing. Not even the place.

But Mazirbe exists!

Depending on the record, it might appear as:

Mazirbe (Latvian) • Irē (Livonian) • Мазирбе (Russian) • Klein-Irben (German) • Suur-Irben (Finnish-German hybrid) • Мазирбе (Russian) • Mazirbė (Lithuanian)

Meyer‘s Zeitungsatlas 050 – Russland- Gouvernement Sankt Petersburg, Esthland, Liefland, Kurland

This kind of variation isn’t just a cultural footnote — it breaks databases, mismatches search results, and silently corrupts analytics.

If you’re in music metadata, this is your “JAY Z” vs. “Jay-Z” vs. “Shawn Carter” problem.

If you’re in e-commerce, it’s “Red Crewneck XXL” vs. “Crewneck, crimson, 2XL”.

Same data structure. Same unresolved chaos.

A Gazetteer That Works Like Real Life

We created a semantic, multilingual, multiscript gazetteer for the Livonian coast. Each place entry includes:

All known name variants across time, languages, and scripts
Structured links to global authority services (Wikidata, VIAF, GeoNames)
Canonical IDs, multilingual labels, and machine-readable formats (RDF, TTL, etc.)
Context about administrative boundaries, historical changes, and source provenance

Try us:

👉 Mazirbe / Irē / Klein-Irben / Мазирбе / Mazirbė 👉 Familiar with RDF: see in TTL

We published it using Wikibase — the same technology that powers Wikidata. It’s not just a spreadsheet; it’s a small, dynamic knowledge graph.

And we also put it into BlazeGraph, so you can find all these villages — and also the music, the clothing, or photographs that come from them.

So What?

Here’s why this matters outside the northern shores of Kurzeme, or beyond the borders of Latvia:

In global supply chains, location names and vendor names drift constantly. While country boundaries are relatively stable, subnational boundary changes — counties, parishes, provinces, municipal borders — happen thousands of times per year, even within Europe.
In streaming metadata, artists get duplicated, misspelled, or transliterated inconsistently. It’s not unusual to find dozens of same-named artists in a distributor’s or rights manager’s roster.
In CRM systems, customers have multiple entries because of one diacritic. Irē becomes Ire if the user didn’t have ē installed.
In museum heritage databases and webshops, items disappear because their place of origin changed names three times since the accession record was created.

Our little example was created to accompany a digital humanities publication, but it’s not just a “humanities” problem. It’s a cross-sector, multilingual, historical, bureaucratic, data problem.

And we’re all living in it.

Lessons We Took Away

Don’t fight ambiguity. Model it.
Linked data models (RDF, Wikibase) handle aliases and variants with elegance.
Small, local, curated vocabularies can scale conceptually to global systems.
Top-down standardization fails in diverse data ecosystems — context wins.

See It / Fork It / Repurpose It

You can explore the full Livonian Gazetteer here:

Web UI: https://reprexbase.eu/fu/Main_Page
RDF example: https://reprexbase.eu/fu/Special:EntityData/Q4429.ttl
Also check out our TextileBase project — same model, but for 19th-century Latvian shirts and skirts

If your stack includes messy location names, user-generated labels, non-English content, or legacy records — maybe this can help.

And if you feel like you’ve seen this movie before… you have.

It’s Data Sisyphus all over again.

👉 https://reprex.nl/post/2021-07-08-data-sisyphus/

Linked Open Datasets on Garments from the Latgale Region

Mon, 07 Apr 2025 17:00:00 +0100

Entry examples from Linked Open Datasets on Garments from the Latgale Region, from right to left: Q142, Q180, Q179, Q181.

The first published dataset, Linked Open Datasets on Garments from the Latgale Region contains data on Latvian traditional shirts and skirts from the Latgale region in Eastern Latvia. The The female and male shirts, and the skirts in the dataset are handmade and were worn in the 19th century. They represent both festive and daily wear of the local female and male peasants. The shirts are stored at the National History Museum of Latvia and the Ethnographic Open-Air Museum of Latvia. The data contain information on the locality of their origin, their approximate date of creation with various precisions, the materials they are made of, and the way of their fabrication, as well as their purpose of wearing (festive or daily wear) and wearer’s ethnicity and gender. They also include the name of the museum each shirt is stored at, supplemented with its unique inventory number. Data on some sample shirts also include a photo of the shirt.

Check out the properties (relations) in the data model: ListProperties
See every entry in the database: All items

Dataweek²⁴: Data-driven Compliance with the Corporate Social Responsibility Directive

Thu, 06 Jun 2024 16:22:00 +0100

All slides

This is a lightly edited version of the presentation at the Data-driven and Automated Compliance section of Dataweek²⁴ Event page.

It has often been said that ESG reporting is a data problem. If you want to fulfil the new requirements set by the Corporate Social Responsibility Directive, which is a legal act that changes European laws on financial accounting and its audit or assurance, you will encounter a very serious data linking and integration problem. If you have such sustainability bookkeeping, you must be able to factually link your financial accounts to your environmental and social accounts. In simple terms, if you expense the cost of 1 MW of electricity, then you cannot calculate the footprint of 0.98 MW in your sustainability report.

All slides

ESG introduces two related challenges, which are best addressed by explicit knowledge bases, enterprise graphs connected to open knowledge graphs, and data sharing spaces.

The company must be able to collect data and report on other companies and things that happen outside of the boundaries of the company or company group. Such a practice had been in place in some heavily regulated industries; for example, the food industry had to bear a qualified responsibility over the entire food supply chain or the nuclear industry for the lifecycle of the fossile fuels.
The company must also be able to curate trustworthy data about entirely new domains: data about the natural or ecological environment, such as data on biodiversity, recycling, water; and data about the social environment, for example, indicators about affected communities, workers in the supply chain or end users.

Joining a data sharing space is a good solution because the new data requirements are not one-time data upgrades but require a permanent data connection with the ecological and social environment. A company and its ERP system or its key performance management users cannot import new data, such as metadata of the EU Taxonomy regulation, and call it a day. Data curation must be an ongoing activity.

All slides

Most companies import relatively little data regularly, and therefore, they have little experience in data curation, i.e., the art of organising, annotating, and integrating data collected from various sources in a way that is presentable in the form of indicators or can be reused later. Perhaps their bookkeeping needs to import foreign exchange rates regularly if they export or import in their activities. Data curation will become an ongoing activity if they start to monitor and measure the environmental and social environment impacts of their actions.

So, if we agree that joining a data sharing space, i.e., an organisation that has pre-agreed terms and conditions on sharing and exchanging data, with pre-agreed terminology or vocabulary (“semantics”) and technology, the question is, what kind of data sharing organisation is the most appropriate?

The CSRD Directive has industry-agnostic elements, which must be fulfilled in every company, and industry-specific elements, which are being developed as we speak. For example, we work mainly with music and film production, both of which belong to the Media and Entertainment Group, which must apply the same industry-specific variations of the European Sustainability Development Standards. We think that the best is to create industry-specific data sharing spaces, such as the famous Data 4.0 for manufacturing, but allow them to be federated and to exploit further synergies: there are plenty of financial, economic, social or environmental data that are used by other existing data sharing spaces and it is not necessary to curate and produce them in, for example, a music or film-production oriented data sharing space.

Are for-profit and social enterprises technically ready to join data-sharing spaces? Not without help. While some large corporations have explicit knowledge bases and enterprise graphs, most smaller European enterprises do not necessarily have a distinct IT function. It is a simple relational database with a fixed schema if they manage databases. Bringing them on board requires simple systems and assistance.

All slides

Reprex is building a data sharing system, Reprexbase, which is built around Wikibase as a knowledge broker system. Wikibase is the open-source software that hosts Wikidata, the largest open knowledge graph in the world. We extend it with various ETL modules and an ecosystem of peer-reviewed statistical libraries to create scientifically correct impact indicators and benchmarks. These extensions are necessary not only because most enterprises are not familiar with working with linked data or graphs but also because Companies are usually familiar with creating financial indicators (for SMEs, simple accounting indicators, larger enterprises with a controlling function, and more complex indicators) but not with the creation of non-financial statistical indicators. The CSRD directive calls for reporting more than 200 indicators over five environmental and four social matter groups, which is more than a company would have on a balanced scorecard. Reliably producing so many indicators is no small feat.

Large and public companies are directly responsible for CSRD reporting, i.e., integrating financial, environmental and social accounting. They need to improve their bookkeeping or ERP systems because often they do not even organise into a database much of the contents of the current invoices (physical quantities, units), which would allow them to create the factual basis between energy cost, energy use and energy footprint, for example. However, they must ask their suppliers for further environmental and social data. Without readily applicable Digital Product Passports, this is a considerable cost, estimated at around 1500 euros per supplier. If you are a festival organiser or a film producer with hundreds of suppliers, this will quickly pay for a simple data sharing space; if you do it with a cluster in your industry, the costs can be significantly reduced.

Open Music Observatory: Towards a European Music Dataspace

Sun, 02 Jun 2024 17:00:00 +0100

Check out the full presentation here, or the forming observatory on music.dataobservatory.eu.

Daniel Antal will introduce part 2 of Data Week 2024 and LAILEC, the Open Music Observatory, the subjective take of the Open Music Europe consortium on creating a European data observatory with the help of a modern, federated, decentralised data-sharing place that is interoperable with various EU digital services. The short presentation will be in the plenary session (16.00-18.00.)

At 14.00, in the Data-driven automated compliance session (Room Erasmus 3), Daniel will speak about the Sustainability and Innovation pillars of the music observatory in light of the complex compliance issues raised by the CSRD Directive and the difficult-to-access but favourable green financing and insurance opportunities open for the music, film, and another creative sector.

If you are participating in Data Week Part 2 or LAILAC, please join the presentation or have an informal chat at the social dinner. Thanks to KU-Leuven and the Big Data Value Association for the organisation and invitation.

Building Public-Private Data Partnerships

Tue, 20 Feb 2024 15:48:00 +0000

While open data infrastructures are growing fast, we must bare in mind that private infrastructure is growing at an even faster scale. Last weekend, Europeana had 12,416 sound recordings that you could use without restrictions and altogether, 206,280 music sound recordings were made available over ten years. The same amount is made available with excellent intelligent recommenders and free listening options on Spotify in every 2 days. If we can find ways to connect private and public data infrastructures, the benefits are enormous for all cycles of data curation: in some cases, we can save much cost, and in other cases we can enrich the data and build fantastic new public applications, like we plan to in music libraries.

To put the scale of PPP advantages in a different scope, if we placed the music we handle in Slovakia alone on Europeana in our project, we would double Europeana’s music collection, even though the Slovak Republic accounts for only 1% of the European Union’s population and music creation.

Categorizing Cultural & Creative Sectors: An Impossible Task?

Sun, 12 Nov 2023 17:48:00 +0100

I am honoured to be invited to the final event of the wonderful Creative FLIP program. Our Digital Music Observatory in many ways gained momentum from the CCS Ecosystems: Evidence-Driven CCI Policy & The Central & Eastern European Music Industry Report discussion in 2020, just before the terrible pandemic hit us all. Are you in Brussels on 15-16 November? Interested? Get in touch and let us meet at the event.

It is one of our most fundamental cognitive abilities to identify shared features or similarities of objects, events, or ideas and then to group (“categorise”) these to make sense of the world. Without categorisation, learning, language, memory, and decision making (to name a few only) would be impossible. The production of cultural statistics also depends on categorisation, distinguishing what is to be measured and what is not.

Session: Categorizing CCS - an impossible task?

In this panel on the FLIP FORWARD Final Conference organised together with the CHARTER project (See: Factsheets: Families of competences pdf), we want to discuss the benefits but also the dangers of categorisation related to the self-image, the measurement, but also the visibility of the CCSI: categorisation enables a highlighting of similarities, but it also has the potential to exclude the unforeseen, the evolving, the fringes.

Our discussion will focus on cultural-creative occupations and be organised around the following questions:

To what extent do the categoriser’s viewpoint and goals shape the categories?
What are the limitations and dangers of categorising the occupational scope of the CCS?
Is it possible to develop an occupational categorisation of the CCS that serves all purposes? If not, how do you deal with this dilemma?

Are you in Brussels on 15-16 November? Interested? Get in touch.

One of the most fundamental problems of developing creative businesses in Europe and developing public policies to support them is that we have far less business and policy planning information available then any business in fruit farming, manufacturing of cars or providing banking services. Cultural statistics have a very low coverage in music or film. Indicators that an apple producer, a car component manufacturer or a bank supplier can take for granted, such as total or average gross value added in a country, employment, or wage statistics, do not exist.

The lack of statistical coverage makes it particularly difficult for such creative businesses and their industry organisations to comply with the expectations of the Corporate Social Responsibility Directive or participate in the advantages of green financing and the European Green Deal.

The production of statistics for the music or the film industry, like any other industry, relies on three fundamental categorisations:

the categorisations of economic activities that creative businesses and self-employed people do;
the categorisations of their products and services (such as films or sound recordings);
the categorisation of occupations and jobs available or filled with such occupations.

These categorisation do not work well in the cultural and creative sectors, and perhaps in some other services sector either.

To help small enterprises in music or film, we need different data collection and statistical or indicator production procedures and more flexible categorisation processes to support their business development on enterprise or a regional/national level. Photo: Jan Demiralp on Unsplash.

Why normal business and occupation categories fail to work in the creative sectors?

Unfortunately, these categorisations cannot be successfully applied in the music or the film industry for many reasons:

The entities are too small.
They have mixed activities.
They mainly employ people atypically without even checking their formal qualifications.

A small recording studio that works for films or the music industry, or a small company that offers stage or in-studio or on-set lightning typically employs people who learned their skills on the job; they often do various related activities, so their NACE code is not very characteristic. Also, because of their size, they hardly ever participate in governmental statistical surveys.

On a more fundamental level, in the creative industries, the dominant firm size is the microenterprise, where the management functions are usually not specialised and often not filled by people with business administration degrees: they do not have an HR department or even an HR function that would categorise their jobs; they do not have a management controlling system that would rely on a sophisticated bookkeeping or a well-categorised inventory of sales. This is what the International Labour Organization, or employment policymakers, describe as “informal enterprises”: they do not have formal internal structures, processes, and often even contracts that in larger enterprises enable a professional management of product, career, finances, or sustainability.

In Open Music Europe, building on a decade of experience at the Digital Music Observatory, we are using statistical processes and data innovation to serve the needs of the music industry by providing data on royalty collection, marketing, average artist remuneration, or gross value added. Our approach is based on innovative statistical procedures and recent regulatory changes that allow national statistical authorities to embrace them.

We do not have average musician remuneration statistics or gross value-added figures for music labels because even though musicians and record labels participate in the national statistical system, they are invisible as they do not have a clear category. The NACE categorisation system does not contain the activities of the “music industry”, only categories like “audiovisual” or “art performances” categories that mix up the numbers of the music industry with television, video production, or theatres. The same can be said of most occupations where music businesses employ workers or self-employed professionals. Even though they have a chance to participate in statistical surveys, which call upon a randomly selected list of small enterprises (in the case of business surveys) or people (in the case of the labour force surveys), they do not get an appropriate “musician” or “music business” label.

The categorisation or labelling of “performing arts” or “audiovisual” is unsuitable for a musician or filmmaker. If “audiovisual” average wages rise in a region, who are they supposed to know if this is due to outsourcing some film post/production to the region or an uptake in music recording activities? After all, film sound studios and music recording studios work for different markets and employ different people.

On the other hand, many creative industries know who belongs to their industry. Architects usually must fulfil severe qualification requirements and register into a local chamber of architects. Musicians must register with author’s and performer’s collective management societies if they want to receive copyright or neighbouring right royalties. If we can align such representative creative organisations with the statistical processes, we will get indicators that follow relevant categorisations for musicians, record labels or architects.

Several statistical and data innovations can make such an alignment or data coordination possible.

See: United Nations Guidelines on Statistical Business Registers pdf. European Business Statistics Methodological Manual for Statistical Business Registers. 2021 Edition.

Sample surveying, when musicians (as members of the working force) or their enterprises (as small businesses) are selected by a lottery to fill out a questionnaire, had been the most important source of official statistics between 1970 and 2010, but they are less and less used. Before the 1970s, we only had fewer statistics on full census-like questionnaires with no random sampling. In the last decade, we have seen more and more often that statistical offices directly connect to various “registers” or databases initially created for business or tax administration purposes to retrieve the data. Why ask a musician about her royalties if we can access her royalty account directly?

Why ask a musician about her royalties if we can access her royalty account directly? Accessing data that is available in a well-managed administrative source, such as a national royalty accounting system is cheaper, faster and more accurate.

From a royalty account, we can access more precise and timely information than from the memory of an artist.

In the last decade, UNECE and Eurostat have been the forerunners with several European national statistical offices in finding new ways to tap into so-called “privately held” registers, for example, the royalty accounts of copyright management societies, who have a full view of musicians in the country and precise and relevant information about their earnings, and indirectly about their employment or gross value added.

Economy of music in Europe: Novel data collection methods and indicators

In Slovakia, we are working out a process where the register and framework of the official data collection could be aligned with a private register set up by the music industry’s nationally representative stakeholders. Such a scheme would have many advantages both for the private and the public parties:

The collection of the statistical data would be faster, cheaper and more precise.
The data could be securely mapped to and from the NACE/ISCO categorisation used by the government and the relevant categorisations used by the music industry.
Whenever the music sector – a relatively minor part of the services business sector – would be under-sampled in a statistical survey methodology to produce reliable, music sector-specific data, additional, harmonised surveys or administrative data could ensure that minimum amount of music enterprise and musician data is present for quality-controlled statistical indicators.
The music businesses could professionalise their company planning HR processes, and, critically, have relevant benchmarks for environmental and social sustainability management; they could participate in the European Green Deal.

Are you in Brussels on 15-16 November? Interested? Get in touch!

Cover photo by Clem Onojeghuo on Unsplash.

ImpactFest Roundabout: ESRS Reporting Standards

Tue, 07 Nov 2023 19:48:00 +0100

Daniel Antal, the co-founder of Reprex, moderated a roundabout with experts on the 8th ImpactFest about the problems around the introduction of the European Sustainability Reporting Standards (ESRS).

What is ImpactFest?

ImpactFest is the leading impact event. The eighth edition of ImpactFest took place in the Fokker Terminal in The Hague.

ESRS

The European Union’s revolutionary new integrated financial and sustainability reporting system will be introduced for about 4100 companies in the Netherlands and about 50,000 companies in the entire EU in 2024/25. Companies will have to connect the environmental and social impact measures of their economic activities with their financial accounting system and with the data system of their suppliers and buyers. In effect, they must be able to report on the whole lifecycle of a product or service.

Daniel Antal started with their own subjective mindmap of the introduction of ESRS, aligning some positive thoughts on the left-hand side and sceptical ones on the right-hand side.

Our Roundabout

Not revealing this mindmap above, the expert participants in the Roundabout engaged in a structured conversation, bringing questions and thoughts from their practice in sustainability consulting (with a social sustainability focus), university research (with a carbon focus) and application development for reporting.

Mindmapping our understanding of the opportunities and problems with the introduction of ESRS from next year.

The shared view on the introduction of ESRS and, generally, the Corporate Sustainability Reporting Directive (CSRD) was that even though just in the Netherlands more than 4000 companies are expected to apply these standards directly from the 2024 business year, and tens of thousands of SMEs in the supply chain will need to supply structured data, there is a very low level of understanding on the requirements. Companies hope that their accountant, auditor, or sustainability consultant will develop a magic tool that will be installed as a plug-in to their ERP system, and it will solve the problem.

Such a magic bullet that will hit all requirements with one shot, of course, does not exist. The aim of any ESG reporting is not to produce a report but to focus the management’s attention on reducing harmful impacts and increasing beneficial impacts on their stakeholders, users, and the broader environment they work in.

At the end of the conversation, we were making educated guesses about how various corporate leaders, including CFOs and heads of HR, will be involved in the process.

Reprex is developing an intelligent, semantic model to connect data in the financial bookkeeping of companies with reliable scientific benchmarks and data sources for benchmarking and target setting and with harmonised survey data for the supply chain data. Our OpenProduction tool prepares such data integration in film and music production. It utilises various OSSH (open source software and hardware) solutions to provide necessary upgrades for ERP systems or to support suppliers to produce information for their corporate buyers, donors for banks and insurance companies in a trustworthy and cost-effective way.

Based on the success of this Roundabout, we will organise short meetups where experts and practitioners can exchange professional views in an informal setting. We’ll set up such meetings in The Hague, Amsterdam and Brussels in the near future. Interested? Get in touch and we will send you a Doodle about possible dates.

Connecting Collection and Repertoire Management to Wikidata

Fri, 24 Mar 2023 10:48:00 +0100

Tools to connect photographs about Béla Bartók collecting songs to the songs he collected and their modern interpretations. As sound recordings, journal articles and books at the same place. Regardless in which country and what type of institution it is catalogued and made available.

We love Wikidata, because we believe in open knowledge, and we believe in linking open data, and Wikidata is the easiest, most open, most accessible way to place information on linked open knowledge graphs.

Linking open data can make songs, films, books, photographs easier to find, to reuse. To connect photographs about Béla Bartók collecting songs to the songs he collected and their modern interpretations. The book of War and Peace with the locations in Leo Tolstoy’s monumental work. And then with other locations that were dubbing in the recent BBC adoption of the book. And connect them with the earlier one, filmed in the former Yugoslavia.

Reprex is seeking partners to connect archive and collection inventories automatically, actively rights-managed music, film, and photography catalogues to link public archive, rights management metadata via Wikidata into a self-correcting metadata system, improving and ensuring that machines find the correct information about European or global cultural content. We want to build an open-source tool to make this process automatic and painless.

The BBC’s adaptation of Leo Tolstoy’s novel, much of which was filmed in Lithuania, sparked sparked huge interest in the country abroad. The series featured Vilnius’ Old Town and major sights in Vilnius including Gediminas Castle, Vilnius University and other areas like Trakų Vokė. More than six million British viewers tuned in for its first episode in January.

Our next product is the development of an open source tool (and an app) that brings your reliable catalogue or collection information to the format of Wikidata. It will show what is already known about your collection or catalogue items “on the internet” and selects information that can be filled or improved, and provides a connection to the mass uploader.

A video tutorial on how to use QuickStatements to bulk upload data onto Wikidata by Dr Sara Thomas, Scotland Programme Coordinator, Wikimedia UK.

Providing a proper data representation to historically underrepresented groups, like women in the example of bulk updating monument data about monumental women by Dr Sara Thomas, or films from small countries, literature from small languages, or niche genre music is essential in the era of AI. Current global platforms for music, film, books, use machine learning that are trained on large open datasets like Wikidata and connecting sources. These automated systems, like automatic recommendations, will not recommend to suitable audiences this diverse content that lower quality or lacking machine-readable metadata representation.

Links

University of Edinburgh Media Hopper Create: How to use QuickStatements - a tool to bulk upload data onto Wikidata by Hannah Rothmann, September 7th, 2020.
Lithuania Tribune: War and Peace tourist trail launches in Vilnius, March 31, 2016.
Wikidata: Linked open data workflow

Create Datasets that are Easy to Combine and Reuse

Tue, 22 Nov 2022 09:09:00 +0100

The latest Reprex R package, dataset was released today on the Comprehensive R Archive Network. It is a very early, conceptual package that will help make scientific achievements more open, governmental data easier to find, and store information that can be better combined.

Data interoperability is almost a buzzword, yet we see very few comprehensive, good solutions to apply it. Try to find information on open government portals or on big open science repositories—apart from a few good examples, most datasets are as disorganized as any PC’s hard disk that is collecting dust in a shed.

The dataset package aims to bring together the best practices of data semantics, data organization, and the use of standard metadata to make sure that whatever you store in a data table, it will be immediately available for data analysis, activation, or combination in any new database.

Ambitious? It is, and dataset 0.1.9 is a very experimental product. While our other packages are aimed at intermediate users with a clear use case in mind, dataset at this point is aimed at package developers. Casual or even heavy R users are unlikely to download it as a standalone product. Instead, dataset aims to be a stable developer basis for our existing products, rOpenGov packages, and many new uses.

Download dataset

The metadata aim of dataset it to add standardized metadata to r data.frames, tibbles, data.tables and other similar structured, tabular objects. The organization and semantic objectives are to bring the tidy data concept closer to the datacube model, which is the basis of all statistical data exchanges, and W3C standards, which foster machine-to-machine data communications on the traditional web APIs and the semantic web.

Makes data importing easier and less error-prone;
Leaves plenty of room for documentation automation, resulting in far better reusability and reproducibility;
The publication of results from R following the FAIR principles is far easier, making the work of the R user more findable, more accessible, more interoperable and more reusable by other users;
Makes the placement into relational databases, semantic web applications, archives, repositories possible without time-consuming and costly data wrangling (See From dataset To RDF).

The first official release offers little immediate benefits. However, if you are an R package developer, we can bring you a few steps nearer to releasing your data products in a way that conforms the FAIR metadata principles. We can make a few steps to streamline your data wrangling. Make integration with relational databases easier. To make a step towards the semantic web.

Surveying Better Gender Diversity Within the Music Industry and Audiences

Tue, 22 Nov 2022 09:09:00 +0100

In this case study, we will collect responses in English and Italian among music professionals and fans on LineCheck, and compare their answers with 10,000s of answers from their countries, from other occupational groups, and other countries.

How to fill out this survey?

EN on Google Forms (will be our free tool)
IT su Google Forms (sarà il nostro strumento gratuito)
SurveyMonkey can be more suitable for more languages.

Table of Contents

What are the benefits of recycling and harmonizing surveys?

Surveyharmonies allows you to make more, bolder, and better-supported statements for your argument. Our technology vastly improves the quality and the inferential capacity of your surveys.

Retrospective survey harmonization means that your answers (we surveyed about 1500 music professionals in 2019) can be compared with other respondants (for example, the same questions asked in Eurobarometer from more than 30,000 people.)

Who we are asking?

We are asking music professionals in almost 100 artistic, technical or managerial roles. The way we form the questions make your answers comparable to the worlds largest database of musicians’ (anonymous) income and working condition database, and many non-music specific survey programs, such as Eurobarometer or the European Values Survey.

What are we asking?

We are asking questions that have been asked before. We created a small demonstration survey to show how we can better understand gender diversity in the music sector. We will anonymously record your answers, and connect them to the answers of more than 28,000 people who answered these questions earlier.

How to fill out this survey?

EN on Google Forms (will be our free tool)
IT su Google Forms (sarà il nostro strumento gratuito)
Both lanugages on SurveyMonkey (free for now)

In your opinion, are inequalities between women and men nowadays very widespread, fairly widespread, fairly rare or very rare in the country where you live?

This question was asked in the

Eurobarometer 82.4 survey

Eurobarometer 82.4: The European Parliament, Autonomous Systems, Gender Equality, and Smoking Habits, November-December 2014.

This means that we can compare what different occupational groups, men and women, different age groups answered to this question.

And within the music business or music non-profit sector of your country?

We are asking And within the music business or music non-profit sector of your country, are inequalities between women and men nowadays very widespread, fairly widespread, fairly rare, or very rare in the country that you chose (where you live and work?) to gain more insight about the potential differences within the music sector and the country where the music professional lives.

Works best if you use this from music professionals and non-music professionals in the same country.

In your opinion, in your country, in the year around 2030, do you think that …?

The image of women will be less stereotyped in the media (compare)
Men and women will earn the same pay (compare)
Men will take care of more household and family tasks (compare)
Women will be more represented among Heads of State and government (compare)
Women will be more present in traditionally masculine professions (compare
Acts of violence against women will have decreased (compare)
Women’s rights in developing countries will be better recognised (compare)

This question was asked in the

Eurobarometer 72.2 survey in 2009.

Eurobarometer 72.2: Nuclear Energy, Corruption, Gender Equality, Healthcare, and Civil Protection, September-October 2009.

This means that we can compare what different occupational groups, men and women, different age groups answered to this question. In 2009, 2030 was a long way many years ahead, but not any more. We could ask this question in two different ways again. We could ask what people think about 2040 to have a similar perspective, or re-ask how they think about 2030 which is only 8 years away by now.

See our earlier blogposts:

Big Data for All: Building Collaborative Data Observatories

Thu, 03 Nov 2022 17:30:00 +0000

Reprex’s co-founder, Daniel Antal talked in the Eindhoven Innovation Café about these issues. You can watch the recorded version of the the livestream that starts at 5 minutes and 22 seconds:

This is a past event. Check out our forthcoming events or write to Daniel Antal or to antaldaniel. Or send an email.

The event invitation text and links

Big data and AI creates inequalities. It puts historically marginalized people, like ethnic minorities, and womxn, at a disadvantage. Because AI and checking on AI require plenty of data, usually only giant corporations, the wealthiest governments, and university entities can make it work for them. Reprex is a Hague-based, international startup that wants to impact various sustainable development goals by enabling smaller organizations to join their smaller datasets, use open data, create linked available data, and collaboratively make a change.

Reprex is a finalist for the Hague Innovation Award for impact startup (please 🙏, vote for us!). Daniel Antal, one of the co-founders, will talk about their approach to building an international coalition of music organizations to pool data and challenge data monopolies using organizational techniques, a collaboration ethos, and data from the open-source developer world.

Using the example of independent music creators, who often find themselves in a position where it is more expensive to claim their money from global platforms, he will talk about how to reduce inequalities in the world of big data and AI with collaboration on web 3.0. In the Q&A he will take questions on how to apply their know-how, and generally linked open data to other art+tech or creative segments or problems for which everybody is too small, like meeting the Paris Accord greenhouse gas targets bit by bit, small company by small company.

In the Q&A, we can discuss many things

How can Reprex help an individual creator in music, or in fashion and design, or any other area?
What sort of help it can give to researchers, research institutes, specialist consultancies, law firms, and other knowledge-based actors?

What sort of partners is Reprex looking for in Eindhoven?

Check out our projects

Digital Music Observatory and Listen Local
Cultural & Creative Sectors and Industries Observatory and short call for potential partners.
Green Deal Data Observatory and simple, connected, financial and sustainability reporting for creative enterprises and others

Reprex: the impact startup

Check out our accomplishments since the foundation in 2020

Listen Local Nederland Meetup in Utrecht

Wed, 26 Oct 2022 16:17:00 +0200

We will carry out the research outlined in our feasibility study within the [OpenMuse] project from 1 January 2023 in an open collaboration with cultural policymakers, the local music ecosystem, and open source developers. Our partner, MXF tries to put the findings into actionable data services in Lithuania and Ukraine with the help of MusicAire. We develop open tools that can be applied in Utrecht, the Hague, Budapest, Tallinn, Vilnius, Bratislava, or anywhere.

Our ongoing project since 2014 is Listen Local. We would like to find out how can a local music ecosystem, or a small scene avoid being colonized by global players on streaming platforms, radio, or in-store music. How can we make sure that music recommendations connect bands in Utrecht with fans in Utrecht? If a Polish band is visiting Utrecht, music lovers will find their show?

We are arriving at opening to Klein Berlijn at 17.00 where you can sit down with a coffee or a beer to chat. No reservation needed. At 19.30 we are getting on our OV fiets and cycle over to the Vechtclub where we will meet people of the local indie scene, Tiny Rooms, and see two independent bands on stage.

As the sales and promotion of recorded music get fully automated, and even the curation of music for live events gets more and more influenced by machine learning outcomes or social media metrics, how can a DIY label remain relevant? How you can run a small club or a label without having to invest in a multi-million euro data engineering team? Make sure that the algorithm will learn successfully your music offering.

‘I feel at home where people care’ Read Gabija’s interview and check out the band with us.

Reprex’s motto is big data for all. We want to fight data inequalities, data monopolies, and make big data and AI work for self-released artists or small labels, too.

Gabija and Daniel are visiting Utrecht to meet DIY musicians, labels, researchers, fans and friends to find new partners for our Listen Local projects. On an intellectual level, we are interested in trustworthy AI, data feminism, and providing a proper digital representation to music and live performances for all. On a more emotional level, we want to meet musicians and music lovers from the indie scene. (Check out our event page.)

We want to help independent artists to find their next audience, and fans to find their next favorite record or a truly fulfilling live music experience. Gabija had a conversation with The Kurws in our Listen Local Interviews series. She asked the band about where they are coming from, where they want to go? Where they are local? And what they have to offer to the people who will join us on 31 October 2022 in Utrecht?

Check out our projects

Digital Music Observatory and Listen Local
Cultural & Creative Sectors and Industries Observatory and short call for potential partners.
Make interviews, get interviewed, write blog posts, or syndicate our content to and from Data & Lyrics.
Don’t forget to vote for Reprex in the Hague Innovators Award competition 2022. The audience voting starts on 1 November 2022.
Download our submission for the competition.
Get in touch!

Dutch AI Coalition Working Group Culture and Media

Thu, 22 Sep 2022 19:30:00 +0200

Reprex presented its Digital Music Observatory and Cultural Creative Sectors Industries Data Observatory as platforms for developing and evaluating trustworthy AI in the cultural domains. We hope to find new partners within the NLAIC community to join our open, collaborative projects.

We have reviewed more than 80 data observatories in the world, and we are building five modern ones.

It was particularly important for us to get away from the Hague, and meet organizations like DEN and the KB to find out how our ambitious plans could connect to their excellent work. Reprex is a finalist in the Hague Innovators Challenge 2022, and we would like to bring at least one global observatory, the planned European Music Observatory, into our beautiful and smart city. While knowledge graphs are virtual and live in the web 3.0, the Dutch AI Coalition and the country’s future competitiveness need to ensure that essential knowledge graphs will be managed by the ecosystem of Netherlands-based researchers, institutions, and startups. The ethical consciousness shown by the members of our Culture AI Lab shows that it is probably the best for future human generations globally, too.

The SABIO is one of the most interesting in the world and couuld be connected easily with our Cultural Creative Sectors Industries Data Observatory prototype.

The Culture AI Lab presented a handful of very interesting, ethical and interesting projects. Pressing Matter responds to growing concerns in the Netherlands and Europe about how to deal with the legacies of colonialism in museums and builds innovative tools for museums (and broader society) to address the question of ownership of objects collected in the colonial period. Dr Emily Hansell Clark, former editor of our Data&Lyrics blog, presented the Polyvocal Interpretation of Contested Colonial Heritage project.

Both projects are conceptually and technologically relevant to our Listen Local project. Our project aims to prevent the colonization or start the de-colonization of the local music ecosystem and make local artists of Utrecht, Vilnius, or Sarajevo visible and audible in their own cities’ public spaces or on the smartphones of their town.

The most compelling use case of Listen Local project is finding out why music recommender systems do not recommend some music at all. Or why is it so hard to connect Utrecht-based artists with fans living or visiting Utrecht on the Spotify or YouTube platform? The Responsible Recommenders in the Public Library Sector is looking for similar answers for librarians to avoid all recommendations to visitors pointing to U.S. authors and publishers.

Savvina Daniil’s excellent presentation raised very similar questions to our Feasibility Study On Promoting Slovak Music In Slovakia & Abroad.

Our deep dive into legislative and regulatory issues of AI highlighted that the past decade unleashed global web-based tools that have the potential to undermine our democratic and cultural cohesion. Reassuringly, we have seen that our thinking about the dangers of AI on European culture and the technological solutions to combat them are very widely shared by NLAIC Culture and Media members. We hope that our partners’ policy work in the Digital Music Observatory and our forming new observatories will support policy design and decision-making that will protect the Netherlands and the EU from some of these threats.

Learn more about the Dutch AI Coalition’s Cultur and Media Working Group (in Dutch:)

Trustworthy Autonomous Recommender Systems on Music Streaming Platforms

Mon, 28 Feb 2022 19:00:00 +0100

Currently almost 60% of the global recording industry sales are made via streaming platforms. Given the enormity of choice on these platforms, and that music listening is a low-key, routine consumption choice, consumers are more and more relying on the recommendations of autonomous recommendation systems. Streaming platforms are two-sided markets, where recommendations are deployed to enhance the user experience on the consumer side, but they also decide the fate of the investments that composers, lyricists, producers, and performers made into the music. We are going to contribute to a research on how such systems may lead to potentially tilted competition field between the content providers, and more specifically, between major labels and independents.

Reprex maintains the Digital Music Observatory and the Listen Local system for granular microdata about music use in small territories (i.e., on small country or sub-national level.) We will provide data/expertise in music streaming and recommendation systems and links to many relevant stakeholders with our considerable experience running experiments on music platforms.

A research team of the University of East Anglia (UEA) the University of Liverpool (UoL), The University of London (City), and King’s College (KCL), supported by the Competition Market Authority of the United Kingdom and Reprex won a prestigious research grant to understand how recommender systems on music streaming platforms can employ trustworthy AI.

The researchers will explore the relationship between the autonomous recommendation systems and entry barriers via simulation. Working closely with Reprex, they will simulate sets of users, and iteratively generate recommendation lists, which the simulated users will react to by deciding how long to engage for and which recommendations to listen to. Through their engagement their user profiles will be updated based on what they listen to which will feed into future recommendations.

See our Feasibility Study for Listen Local.

The empirical experiments of the project want to explore how autonomous recommendation systems are driving consumer choice in a real-life setting, and to establish causality between the recommendation systems and the barrier to entry. As part of the second work package, the researchers will conduct randomised trials by inviting participants to stream music through our own user interface. Reprex has extensive experience conducting similar experiments in the music domain (for various online, field experiments, and high-quality surveys.)

Link: Eight new TAS research projects announced

Reproducible Economic Impact Assessment

Sun, 19 Dec 2021 13:00:00 +0100

Get started with iotables.

We made an important, peer-reviewed release of iotables in the last week as a preparation to increase the functionality of our open-source software. The official release of the iotables R package currently works with economic impact assessments, and can evaluate the likely employment, tax, wage, or gross value added direct, indirect and multiplied impacts of various policy changes in about 30 countries.

Originally the package was developed to calculate the economic impact of the Hungarian film tax shelter and the impact of the music sector on the Slovak economy. (See Slovak Music Industry Report).

The new CRAN release improved the documentation of the function and removed most outdated dependencies. The new, development version (which did not go through peer-review yet) is adding new functionality for environmental impact analysis with the following pollutants: Carbon dioxide without emissions from biomass (CO2), Carbon dioxide from biomass (Biomass CO2), Nitroux oxide (N2O), Methane (CH4), Perfluorocarbons (PFCs), Hydrofluorocarbons (HFCs), Sulphur hexafluoride (SF6) including nitrogen trifluoride (NF3), Nitrogen oxides (NOx), Non-methane volatile organic compounds, (NMVOC), Carbon monoxide (CO), Particulate matter < 10μm (PM10), Particulate matter < 2,5μm (PM2,5), Sulphur dioxide (SO2), Ammonia (NH3) and their combinations (see Reference Metadata in Euro SDMX Metadata Structure (ESMS)).

Our aim is to develop new sustainable finance applications, and understand the sustainability impacts of bank’s lending activities and insurer’s underwriting activities on climate change mitigation and adoption, biodiversity, preservation of water reservers, preventing pollution, and promoting the circular economy.

EU Taxonomy on Sustainable Activities

The European Commission created an created an EU Taxonomy Compass, which provides a visual representation of the contents of the EU Taxonomy, starting with the Delegated Act on the climate objectives, as adopted on 4 June 2021. Whilst you can download the EU Taxonomy in xlsx or json format, they are not tidy datasets, and they are not particularly well-suited for calculations, filtering, or inclusion in applications.

Reprex created a tidy version of the EU Taxonomy for developing better sustainability indicators into the Green Deal Data Observatory.

Open Data

EU Taxonomy on Sustainable Activities (Tidy) download.

Using our iotables is not for the faint heart. It is a scientific software, and it requires a good command of national accounts, input-output economics and sustainability to work with. Our Green Deal Data Observaotry is designed to be an API of scientific software, and produce clean, ready to use data for researchers, policy-makers and business planners who do not have the skills to work with scientific software. We are planning to release well-designed datasets that go through dozens of checks to make sure they have the best data quality.

Do you want to develop input-output models for any European country to measure the direct and indirect green house gas impacts of policy actions? Do you need well-formatted data on interindustry linkages or other relevant topics for sustainable economy or susitainable finance research? Get in touch with us – we are happy to help and test our new software tool with data you need, and create high-quality, open datasets that are ready to use.

Jumping Ahead With the Digital Music Observatory

Thu, 02 Dec 2021 13:00:00 +0100

Our Digital Music Observatory project spent a year in the JUMP Music Market Accelerator’s program. Over the course of 9 months, co-founder Daniel Antal could meet many stakeholders from almost all European countries, meet other new music technology startups and projects, and got mentoring and other professional help to further develop the project.

The Digital Music Observatory is one of the several initiatives to fill the data gaps of the fragmented European music ecosystems. While most of Europe’s music is available and promoted on data-heavy, AI-driven autonomous platforms like TikTok, Spotify, YouTube, Deezer, music labels, publishers, national export offices are lacking the necessary data solutions to remain competitive.

Daniel is pitching for partnership with Music Tech Europe on Linechech and finding a music city that wants to be the seat of the future European Music Observatory. Photo: Wen Liu.

One of the recurring themes of 2021 was the notion that the music streaming economy is broken. Several JUMP fellows are working on various projects that aim to fix this, and our Digital Music Observatory has both the data and track record to provide evidence and test ideas about possible solutions – change in pricing, better targeting in export and domestic markets, and checking for algorithmic biases. See what we have done in the field this year in the UK IPO-initiated Music Creators’ Earning project; understanding algorithmic recommendation problems with the support of the Slovak Arts Council, and making recommendations about better music metadata and copyright regulation with our research consortium.

The other very interesting theme of the year was the emergence of new, immersive music tech companies. We hope that our Digital Music Observatory can grow into a hub for their data needs, too. How is the world of 2.7 billion gamer and music lovers is forming a new market for Ristband? We would also like to curate data about the healing effects of sound, and work in the future with immersive, functional music providers like Flower of Sound who place music and sound design into a less stressful, more healthy acoustic environment.

We were often criticized for placing too little emphasis on data visualization. Our next priority is to provide clear, beautiful infographs and charts to all of our datasets.

There were many professionals who helped us in the JUMP program. We are particularly thankful for Alessanra di Caro (partnership building), Elodie Crouzet (program coordination), Steve Farris (mentoring), Veronique Friedrich (team building), Thierry Giesler (improving our pitch) and Anna Zò (Music Tech Europe).

Are you a data user? Give us some feedback! Shall we do some further automatic data enhancements with our datasets? Document with different metadata? Link more information for business, policy, or academic use? Please ive us any feedback!

Reprex on MaMA

Fri, 15 Oct 2021 19:00:00 +0000

Reprex’s co-founder, the main developer of the Digital Music Observatory, Daniel Antal and Digital Music Observatory curator, Marie Zhorová participated in the MaMA Festival & Convention in Paris on 13-15 October within the JUMP Music Market Accelerator Program Program. We introduced our Digital Music Observatory to national music organizations and encouraged them to try out a cooperation with us. (See Use Cases below)

Our main aim was to find new users to our Digital Music Observatory, and to find partners for a future Horizon Europe R&D project to develop the scientific pillars of the Observatory in a manner that meets practical industry needs and the feature requirements laid out in hte Feasiblity Study for a Euroepan Music Observatory.

Our concept was introduced in Le Trianon to a wider audience during the JUMP Music Market Accelerator Pitch Session and in one-to-one meetings to representatives of French national organizations. We have also started to investigate the possibility to cooperate with two startups to bring our data services closer to artists, labels, and publishers.

Use Cases

Fair Streaming

Daniel introduced our work made for the UK IPO’s Music Creators’ Earnings in the Digital Era Project about the justified and not-justified differences among music rightsholders earnings and the diminishing market value of streams. We believe that our UK approach is a particularly interesting addition to join with the distribution analysis performed by the Centre Nationale de la Musique and Deloitte in France.

Fair Value

Daniel introduced to collective management professioanls our innovative approach for private copying valuation, royalty price setting, estimating the values of value transfer to media platforms, and other topics of interests for collective management and rights management organizations. Our approach has a proven track record to increase revenues for creators.

Open Music Observatory

We introduced our approach to building the European Music Observatory in a decentralized way, relying not only on the resources of Creative Europe but also on Open Science, Horizon Europe, bringing the music industry, music research in universities and cultural policy under one open collaboration. Because France is building its own music observatory of a kind, the decentralized approach could particularly benefit French stakeholders.

Listen Local

Why Data Observatory?

Our use cases highlight the value of having a wide range of data available for the industry players, researchers and policy-makers. In the era of big data, and when open data is becoming legally more and more available, it is important to have one place with a single data collection method. Copernicus built a permanent observatory for the ongoing observation of celestial bodies. We built an automated data observatory to permanently collect data about music.

Research & Analysis: Music Creators’ Earnings in the Digital Era

Thu, 23 Sep 2021 08:00:00 +0000

Reprex with its Digital Music Observatory team was commissioned to prepare an analysis on the justified and not justified differences in music creators’ earnings. We have posted our most important findings in an earlier blogpost (Music Creators’ Earnings in the Streaming Era. United Kingdom Research Cooperation With the Digital Music Observatory.

The UK Intellectual Property Office has published the entire report on the music creators’ earnings, and we have made our detailed analysis available in a side-publication. Reprex also signed an agreement with the researchers of the Music Creators’ Earnings project to deposit all data published in the report in the Digital Music Observatory, and to promote the building of the observatory further.

The research questions asked in this report are related to the Music Creator Earnings’ Project (MCE), exploring issues concerning equitable remuneration and earnings distributions. We were tasked with providing a longitudinal analysis of earnings development and relating our findings to equitable remuneration. The starting point of our work was centred around a very broadly defined problem: how much money music creators (rightsholders) earn from streaming, how these earnings are distributed, and how the earnings and their distribution have developed during the last decade.

The highly globalized music industry generates two important international reports, as well as several national reports, but these are not suitable for the analysis of the typical or average rightsholder, nor for small labels and publishers who do not represent a large and internationally diversified portfolio of music works or recordings. Copyright and neighboring right revenues are collected in national jurisdictions. Because British artists are almost never constrained by their use of language, and the UK Music Industry is highly competitive in the global music markets, even relatively less known rightsholders earn revenues from dozens of national markets. The lack of market information on music sales volumes, prices for each jurisdiction, and the unaccounted for national, domestic, and foreign revenues makes the analysis of the rightholder’s earnings, or the economics of a certain distribution channel like music streaming or media platforms, impossible.

The Effect of International Diversification on Revenues - a combination of international price differences and exchange rate fluctuations.

While total earnings are reported by international and national organizations, they hide five important economic variables: changes in sales volumes, changes in prices, market share on various national jurisdictions (which have their own volume and price movements), the exchange rates applied, and the share of the repertoire exploited. Even worse, the global music industry has no comprehensive database of rightsholders, music works, and recordings – this is the data gap that we would like fill with the Digital Music Observatory.

Our report highlights some important lessons. First, we show that in the era of global music sales platforms it is impossible to understand the economics of music streaming without international data harmonization and advanced surveying and sampling. Paradoxically, without careful adjustments for accruals, market shares in jurisdictions, and disaggregation of price and volume changes, the British industry cannot analyze its own economics because of its high level of integration to the global music economy. Furthermore, the replacement of former public performances, mechanical licensing, and private copying remunerations (which has been available for British rightsholders in their European markets for decades) with less valuable streaming licenses has left many rightsholders poorer. Making adjustments on the distribution system without modifying the definition of equitable remuneration rights or the pro-rata distribution scheme of streaming platforms opens up many conflicts while solving not enough fundamental problems. Therefore, we suggest participation in international data harmonization and policy coordination to help regain the historical value of music.

Context

The idea of our Digital Music Observatory was brought to the UK policy debate on music streaming by the Written evidence submitted by The state51 Music Group to the Economics of music streaming review of the UK Parliaments’ DCMS Committee¹.

The music industry requires a permanent market monitoring facility to win fights in competition tribunals, because it is increasingly disputing revenues with the world’s biggest data owners. This was precisely the role of the former CEEMID² program, which was initiated by a group of collective management societies. Starting with three relatively data-poor countries, where data pooling allowed rightsholders to increase revenues, the CEEMID data collection program was extended in 2019 to 12 countries.The final regional report, after the release of the detailed Hungarian, Slovak and Croatian reports of CEEMID was sponsored by Consolidated Independent (of the state51 music group.)

CEEMID was eventually to formed into the Demo Music Observatory in 2020³, following the planned structure of the European Music Observatory, and validated in the world’s 2nd ranked university-backed incubator, the Yes!Delft AI+Blockchain Validation Lab. In 2021, under the final name Digital Music Observatory, it became open for any rightsholder or stakeholder organization or music research institute, and it is being launched with the help of the JUMP European Music Market Accelerator Programme which is co-funded by the Creative Europe Programme of the European Union.

In December 2020, we started investigating how the music observatory concept could be introduced in the UK, and how our data and analytical skills could be used in the Music Creators’ Earnings in the Streaming Era (in short: MCE) project, which is taking place paralell to the heated political debates around the DCMS inquiry. After the state51 music group gave permission for the UK Intellectual Property Office to reuse the data that was originally published as the experimental CEEMID-CI Streaming Volume and Revenue Indexes, we came to a cooperation agreement between the MCE Project and the Digital Music Observatory. We provided a detailed historical analysis and computer simulation for the MCE Project, and we will host all the data of the Music Creators’ Earnings Report in our observatory, hopefully no later than early July 2021.

The Digital Music Observatory contributes to the Music Creators’ Earnings in the Streaming Era project with understanding the level of justified and unjustified differences in rightsholder earnings, and putting them into a broader music economy context.

We started our cooperation with the two principal investigators of the project, Prof David Hesmondhalgh and Dr Hyojugn Sun back in April and will start releasing the findings and the data in July 2021.

Join us

Do you need high-quality data for your music business or institution? Are you a music researcher? Join our open collaboration Digital Music Observatory team as a data curator, developer or business developer.

Footnote References

state51 Music Group. 2020. “Written Evidence Submitted by The state51 Music Group. Economics of Music Streaming Review. Response to Call for Evidence.” UK Parliament website. https://committees.parliament.uk/writtenevidence/15422/html/. ↩︎
Artisjus, HDS, SOZA, and Candole Partners. 2014. “Measuring and Reporting Regional Economic Value Added, National Income and Employment by the Music Industry in a Creative Industries Perspective. Memorandum of Understanding to Create a Regional Music Database to Support Professional National Reporting, Economic Valuation and a Regional Music Study.” ↩︎
Antal, Daniel. 2021. “Launching Our Demo Music Observatory.” Data & Lyrics. Reprex. https://dataandlyrics.com/post/2020-09-15-music-observatory-launch/. ↩︎

Comparing Data to Oil is a Cliché: Crude Oil Has to Go Through a Number of Steps and Pipes Before it Becomes Useful

Mon, 07 Jun 2021 10:00:00 +0000

As a developer at rOpenGov, and as an economic sociologist, what type of data do you usually use in your work?

Generally speaking, people’s access to (or inequalities in accessing) different types of resources and their ability in transforming these resources to other types of resources is what interests me. The data I usually work with is the kind of data that is actually nicely covered by existing rOpenGov tools: data about population demographics and administrative units from Statistics Finland, statistical information on welfare and health from Sotkanet and also data from Eurostat. Aside from these a lot of information is of course data from surveys and texts scraped from the internet.

We are placing the growing number of rOpenGov tools in a modern application with a user-friendly service and a modern data API.

In your ideal data world, what would be the ultimate dataset, or datasets that you would like to see in the Music Data Observatory?

Late spring and early summer time is, at least for me, defined by the Eurovision Song Contest. Every year watching the contest makes me ponder the state of the music industry in my home country Finland as well as in Europe. Was the song produced by homegrown talent or was it imported? Was it better received by the professional jury or the public? How well does the domestic appeal of an artist translate to the international stage? Many interesting phenomena are difficult to quantify in a meaningful way and writing a catchy song with international appeal is probably more an art than a science. Nevertheless that should not deter us from trying as music, too, is bound by certain rules and regularities that can be researched.

Music, too, is bound by certain rules and regularities that can be researched. Our Digital Music Observatory and its Listen Local experimental App does this exactly, and we would love to create Eurovision musicology datasets. Photo: Eurovision Song Contest 2021 press photo by Jordy Brada

Why did you decide to join the EU Datathon challenge team and why do you think that this would be a game changer for researchers and policymakers?

The challenge has, in my opinion, great potential in leading by example when it comes to open data access and reproducible research. Comparing data to oil is a common phrase but fitting in the sense that crude oil has to go through a number of steps and pipes before it becomes useful. Most users and especially policymakers appreciate ease-of-use of the finished product, but the quality of the product and the process must also be guaranteed somehow. Openness and peer-review practices are the best guarantors in the field of data, just as industrial standards and regulations are in the oil industry.

We provide many layers of fully transparent quality control about the data we are placing in our data APIs and provide for our end-users.

Join us

Join our open collaboration Economy Data Observatory team as a data curator, developer or business developer. More interested in environmental impact analysis? Try our Green Deal Data Observatory team! Or your interest lies more in data governance, trustworthy AI and other digital market problems? Check out our Digital Music Observatory team!

Creating Algorithmic Tools to Interpret and Communicate Open Data Efficiently

Fri, 04 Jun 2021 10:00:00 +0000

As a developer at rOpenGov, what type of data do you usually use in your work?

As an academic data scientist whose research focuses on the development of general-purpose algorithmic methods, I work with a range of applications from life sciences to humanities. Population studies play a big role in our research, and often the information that we can draw from public sources - geospatial, demographic, environmental - provides invaluable support. We typically use open data in combination with sensitive research data but some of the research questions can be readily addressed based on open data from statistical authorities such as Statistics Finland or Eurostat.

In your ideal data world, what would be the ultimate dataset, or datasets that you would like to see in the Music Data Observatory?

One line of our research analyses the historical trends and spread of knowledge production, in particular book printing based on large-scale metadata collections. It would be interesting to extend this research to music, to understand the contemporary trends as well as the broader historical developments. Gaining access to a large systematic collection of music and composition data from different countries across long periods of time would make this possible.

Why did you decide to join the challenge and why do you think that this would be a game changer for researchers and policymakers?

Joining the challenge was a natural development based on our overall activities in this area; the rOpenGov project has been around for a decade now, since the early days of the broader open data movement. This has also created an active international developer network and we felt well equipped for picking up the challenge. The game changer for researchers is that the project highlights the importance of data quality, even when dealing with official statistics, and provides new methods to solve these issues efficiently through the open collaboration model. For policymakers, this provides access to new high-quality curated data and case studies that can support evidence-based decision-making.

Do you have a favorite, or most used open governmental or open science data source? What do you think about it? Could it be improved?

Regarding open government data, one of my favorites is not a single data source but a data representation standard. The px format is widely used by statistical authorities in various countries, and this has allowed us to create R tools that allow the retrieval and analysis of official statistics from many countries across Europe, spanning dozens of statistical institutions. Standardization of open data formats allows us to build robust algorithmic tools for downstream data analysis and visualization. Open government data is still too often shared in obscure, non-standard or closed-source file formats and this is creating significant bottlenecks for the development of scalable and interoperable AI and machine learning methods that can harness the full potential of open data.

Regarding open government data, one of my favorites is not a single data source but a data representation standard, the Px format.

From your perspective, what do you see being the greatest problem with open data in 2021?

Although there are a variety of open data sources available (and the numbers continue to increase), the availability of open algorithmic tools to interpret and communicate open data efficiently is lagging behind. One of the greatest challenges for open data in 2021 is to demonstrate how we can maximize the potential of open data by designing smart tools for open data analytics.

What can our automated data observatories do to make open data more credible in the European economic policy community and be accepted as verified information?

The role of the professional network backing up the project, and the possibility of getting critical feedback and later adoption by the academic communities will support the efforts. Transparency of the data harmonization operations is the key to credibility, and will be further supported by concrete benchmarks that highlight the critical differences in drawing conclusions based on original sources versus the harmonized high-quality data sets.

We need to get critical feedback and later adoption by the academic communities.

How we can ensure the long-term sustainability of the efforts?

The extent of open data space is such that no single individual or institution can address all the emerging needs in this area. The open developer networks play a huge role in the development of algorithmic methods, and strong communities have developed around specific open data analytical environments such as R, Python, and Julia. These communities support networked collaboration and provide services such as software peer review. The long-term sustainability will depend on the support that such developer communities can receive, both from individual contributors as well as from institutions and governments.

Join us

New Indicators for Computational Antitrust

Wed, 02 Jun 2021 17:00:00 +0000

As someone who’s worked in data for almost 20 years, what type of data do you usually use in your research?

In my field (industrial organisation, competition policy), company level financial data, and product price and sales data have been the conventional building blocks of research papers. Ideally this has been the sort of data that I would seek out for my work. Of course as academic researchers we often get knocked back by the reality of data access and availability. I would think that industrial organisation is one of those fields where researchers have to be quite innovative in terms of answering interesting and relevant policy questions, whilst having to operate in an environment where most relevant data is proprietary and very expensive. Against this backdrop, I have worked with neatly organised proprietary datasets, self-assembled data collections, and also textual data.

From your experience working with various data sets, models, and frameworks, what would be the ultimate dataset, or datasets that you would like to see from the Economy Data Observatory?

There seems to be an emerging consensus that market concentration and markups have been continuously increasing across the economy. But most of these works use industry classification to define markets. One of the things I’d really like to see coming out of the Economy Data Observatory is a mapping of what we call antitrust markets.

Mapping NACE to Antitrust Markets.

Available datasets use standard industry classification (such as NACE in the EU), which is often very different from what we call a product market in microeconomics. Product markets are defined by demand, and supply-side substitutability, which is a dynamically evolving feature and difficult to capture systematically on a wider scale. But with the recent proliferation of data and the growth (and fall in price) of computing power, I am positive that we could attempt to map out the European economy along these product market boundaries. Of course this is not without any challenge. For example in digital markets, traditional ways to define markets have caused serious challenges to competition authorities around the world.

I believe that there is an immensely rich, and largely unexplored source of information in unstructured textual data that would be hugely useful for applied microeconomic works, including my own area of IO and competition policy. This includes a large corpus of administrative and court decisions that relate to businesses, such as merger control decisions of the European Commission. To give two examples from my experience, we’ve used a large corpus of news reports related to various firms to gauge the reputational impact of European Commission cartel investigations, or we’ve trained an algorithm to be able to classify US legislative bills and predict whether they have been lobbied or not. Finding a way to collect and convert this unstructured data into a format that is relevant and useful for users is not a trivial challenge, but is one of the most exciting parts of our Economy Data Observatory plans (see related project plan).

Finding a way to collect and convert this unstructured data into a format that is relevant and useful for users is not a trivial challenge, but is one of the most exciting parts of our Economy Data Observatory plans.

What is an idea that you consider will be a game changer for researchers and/or policymakers?

Partly talking in the past tense, the use of data driven approaches, automation in research, and machine learning have been increasingly influential and I think this trend will continue to all areas of social science. 10 years ago, to do machine learning, you had to build your models from scratch, typically requiring a solid understanding of programming and linear algebra. Today, there are readily available deep learning frameworks like TensorFlow, Keras, PyTorch, to design a neural network for your own application. 10 years ago, natural language processing would have only been relevant for a small group of computational linguists. Today we have massive word embedding models trained on an enormous corpus of texts, at the fingertip of any researcher. 10 years ago, the cost of computing power would have made it prohibitive for most researchers to run even relatively shallow neural networks. Today, I can run complex deep learning models on my laptop using cloud computing servers. As a result of these developments, whereas 10 years ago one would have needed a small (or large) research team to explore certain research questions, much of this can now be automated and be done by a single researcher. For researchers without access to large research grants and without the ability to hire a research team, this has truly been an amazing victory for the democratisation of research.

You can already try out our API.

Do you have a favorite, or most used open governmental or open science data source? What do you think about it? Could it be improved?

As a competition economist, I tend to need very specific data for each research question I’m working on, which has to be collected from scratch. On the other hand, most works do require us to use data that has already been collected and made available. For example, access to census data has been immensely useful in ensuring that we can control for local demographic features, in papers where local competition plays a role. Census data is made readily available by most governments, but I particularly liked the Australian data, partly because they run a census every 5 years, but also because they have made the data available through a great table making tool.

Is there a number that recently surprised you? What was it?

I have these moments of surprise fairly frequently. To give one example from something I’m currently working on, looking at the distributional impact of increasing market concentration, we’ve found that low income households experience a larger increase in the petrol retail margin when market concentration increases than high income households. This fits nicely with theoretical works on search in homogeneous costs, i.e. low income households are less good at engaging with the market, and, as a result, if suppliers can price discriminate, they will charge a higher margin to these households.

The figure below shows our raw data (18 years of petrol station level daily price data from Western Australia) for low and high income areas, and the increase in the margin following an increase in market concentration (vertical dotted line). The left hand side, low income areas, displays a large increase in the margin (when compared to a control group), whereas the right hand side (high income households) experience no change. In our paper of course we build a fairly data intensive quasi experiment for identification of the treatment effect of changing market concentration on the price margin applied to various demographic groups.

Surprising findings: market concentration and margin changes for petrol stations.

Do you have a good example of really good, or really bad use of data science /data curation?

Out of professional courtesy I really wouldn’t like to mention names from academic research as examples of bad use of data. But there are ample examples from newspaper coverage of data related work, or simply the misuse of data by newspapers. This may be intentional but is often a result of journalists not having the necessary training in using and analysing data.

When the press finds a piece of academic research interesting, often bad things come out of it. This is often because not all journalists are well equipped to interpret scientific findings. As a result, sometimes conclusions are drawn as a result of a misinterpretation of good data analysis. Correlation interpreted as causation is a frequently recurring example. Equally bad is press coverage changes the incentive system of producing good research, when scientists work too hard for their work to be noticed by the press, and sacrifice scientific rigour in data analysis for the sake of media attention. There can also be less discernible but equally damaging errors.

In some cases requiring to pre disclose the tests the research is going to run on data helps maintain credibility in many instances. Moreover, I am always a bit suspicious if the authors do not give access to their data for reproduction.

Our Economy Data Observatory places all new indicators on Zenodo with a DOI, and asks future individual contributors their data for replication there.

What do you see as the greatest challenge with open data in 2021?

The things I mentioned above about the democratisation of research driven by automation and access to big data does raise serious challenges as well. The obvious one is to do with the fact that there are enormous economies of scale in the use of data. As such, larger players will always be better positioned to outdo their smaller competitors, simply as a result of their superior data and infrastructure (for example having more granular consumer data allowing them to offer better designed customised experience for the consumer). Like many others, I see this as the biggest challenge for open data - to level the playing field for smaller players. This is not a trivial task at all; and even if, miraculously, small businesses could access the same data as the biggest players, they still would not have the capacity or the ability to use this data. So allowing access to data alone is unlikely to solve any of these problems. I would say that fostering engagement with open data is probably as big a challenge as creating the open data in the first place.

How do you envision the Economy Data Observatory making open data more credible in the European economic policy community and accepted as verified information?

I think starting with a focused agenda is a good idea. For example, linking up with the Centre for Competition Policy means that we have an initial focus of competition policy relevant economic data. This is still a large domain, but it is one where we have ample expertise. Starting with specific research questions such as linking competition enforcement and merger decisions to related information on innovation and ownership data puts the Economy Data Observatory at the heart of some of the most topical policy questions, such as the role of killer acquisitions (acquisitions with the intent to kill of sources of rival innovation), or common ownership, both of which are increasingly discussed in policy and practitioner circles. Once we established ourselves as a credible source of data in the competition policy community, we can look into joining this up with other policy areas, and also with our other Data Observatories (Music and Green Deal).

Join us

Reprex is Contesting all Three Challenges of the EU Datathon 2021 Prize

Fri, 21 May 2021 20:00:00 +0000

Reprex, a Dutch start-up enterprise formed to utilize open source software and open data, is looking for partners in an agile, open collaboration to win at least one of the three EU Datathon Prizes. We are looking for policy partners, academic partners and a consultancy partner. Our project is based on agile, open collaboration with three types of contributors.

With our competing prototypes we want to show that we have a research automation technology that can find open data, process it and validate it into high-quality business, policy or scientific indicators, and release it with daily refreshments in a modern API.

We are looking for institutions to challenge us with their data problems, and sponsors to increase our capacity. Over then next 5 months, we need to find a sustainable business model for a high-quality and open alternative to other public data programs.

The EU Datathon 2021 Challenge

To take part, you should propose the development of an application that links and uses open datasets. - our data curator team
Your application … is also expected to find suitable new approaches and solutions to help Europe achieve important goals set by the European Commission through the use of open data.” - this application is developed by our technology contributors
Your application should showcase opportunities for concrete business models or social enterprises. - our service development team is working to make this happen!
We use open source software and open data. The applications are hosted on the cloud resources of Reprex, an early-stage technology startup currently building a viable, open-source, open-data business model to create reproducible research products.
We are working together with experts in the domain as curators (check out our guidelines if you want to join: Data Curators: Get Inspired!).
Our development team works on an open collaboration basis. Our indicator R packages, and our services are developed together with rOpenGov.

Mission statement

We want to win an EU Datathon prize by processing the vast, already-available governmental and scientific open data made usable for policy-makers, scientific researchers, and business researcher end-users.

“To take part, you should propose the development of an application that links and uses open datasets. Your application should showcase opportunities for concrete business models or social enterprises. It is also expected to find suitable new approaches and solutions to help Europe achieve important goals set by the European Commission through the use of open data.”

We aim to win at least one first prize in the EU Datathon 2021. We are contesting all three challenges, which are related to the EU’s official strategic policies for the coming decade.

Challenge 1: A European Grean Deel

Our Green Deal Data Observatory connects socio-economic and environmental data to help understanding and combating climate change.

Challenge 1: A European Green Deal, with a particular focus on the The European Climate Pact, the Organic Action Plan, and the New European Bauhaus, i.e., mitigation strategies.

Climate change and environmental degradation are an existential threat to Europe and the world. To overcome these challenges, the European Union created the European Green Deal strategic plan, which aims to make the EU’s economy sustainable by turning climate and environmental challenges into opportunities and making the transition just and inclusive for all.

Our Green Deal Data Observatory is a modern reimagination of existing ‘data observatories’; currently, there are over 70 permanent international data collection and dissemination points. One of our objectives is to understand why the dozens of the EU’s observatories do not use open data and reproducible research. We want to show that open governmental data, open science, and reproducible research can lead to a higher quality and faster data ecosystem that fosters growth for policy, business, and academic data users.

We provide high quality, tidy data through a modern API which enables data flows between public and proprietary databases. We believe that introducing Open Policy Analysis standards with open data, open-source software, and research automation, can help the Green Deal policymaking process. Our collaboration is open for individuals, citizens scientists, research institutes, NGOS, and companies.

Challenge 2: A Europe fit for the digital age

Our Economy Data Observatory will focus on competition, small and medium sized enterprizes and robotization.

Challenge 2: An economy that works for people, with a particular focus on the Single market strategy, and particular attention to the strategy’s goals of 1. Modernising our standards system, 2. Consolidating Europe’s intellectual property framework, and 3. Enabling the balanced development of the collaborative economy strategic goals.

Big data and automation create new inequalities and injustices and have the potential to create a jobless growth economy. Our Economy Data Observatory is a fully automated, open source, open data observatory that produces new indicators from open data sources and experimental big data sources, with authoritative copies and a modern API.

Our observatory monitors the European economy to protect consumers and small companies from unfair competition, both from data and knowledge monopolization and robotization. We take a critical Small and Medium-Sized Enterprises (SME)-, intellectual property, and competition policy point of view of automation, robotization, and the AI revolution on the service-oriented European social market economy.

We would like to create early-warning, risk, economic effect, and impact indicators that can be used in scientific, business, and policy contexts for professionals who are working on re-setting the European economy after a devastating pandemic in the age of AI. We are particularly interested in designing indicators that can be early warnings for killer acquisitions, algorithmic and offline discrimination against consumers based on nationality or place of residence, and signs of undermining key economic and competition policy goals. Our goal is to help small and medium-sized enterprises and start-ups to grow, and to furnish data that encourages the financial sector to provide loans and equity funds for their growth.

Challenge 3: A Europe fit for the digital age

Our Digital Music Observatory is not only a demo of the European Music Observatory, but a testing ground for data governance, Digital Servcies Act, and trustworthy AI problems.

Challenge 3: A Europe fit for the digital age, with a particular focus Artificial Intelligence, the European Data Strategy, the Digital Services Act, Digital Skills and Connectivity.

The Digital Music Observatory (DMO) is a fully automated, open source, open data observatory that creates public datasets to provide a comprehensive view of the European music industry. It provides high-quality and timely indicators in all four pillars of the planned official European Music Observatory as a modern, open source and largely open data-based, automated, API-supported alternative solution for this planned observatory. The insight and methodologies we are refining in the DMO are applicable and transferable to about 60 other data observatories funded by the EU which do not currently employ governmental or scientific open data.

Music is one of the most data-driven service industries where most sales are currently executed by AI-driven autonomous systems that influence market shares and intellectual property remuneration. We provide a template that enables making these AI-driven systems accountable and trustworthy, with the goal of re-balancing the legitimate interests of creators, distributors, and consumers. Within Europe, this new balance will be an important use case of the European Data Strategy and the Digital Services Act.

The DMO is a fully functional service that can serve as a testing ground of the European Data Strategy. It can showcase the ways in which the music industry is affected by the problems that the Digital Services Act and European Trustworthy AI initiatives attempt to regulate. It is being built in open collaboration with national music stakeholders, NGOs, academic institutions, and industry groups.

Our Product/Market Fit was validated in the world’s 2nd ranked university-backed incubator program, the Yes!Delft AI Validation Lab. We are currently developing this project with the help of the JUMP European Music Market Accelerator program.

Problem Statement

The EU has an 18-year-old open data regime and it makes public taxpayer-funded data in the values of tens of billions of euros per year; the Eurostat program alone handles 20,000 international data products, including at least 5,000 pan-European environmental indicators.

As open science principles gain increased acceptance, scientific researchers are making hundreds of thousands of valuable datasets public and available for replication every year.

The EU, the OECD, and UN institutions run around 100 data collection programs, so-called ‘data observatories’ that more or less avoid touching this data, and buy proprietary data instead. Annually, each observatory spends between 50 thousand and 3 million EUR on collecting untidy and proprietary data of inconsistent quality, while never even considering open data.

Our automated data observatories are modern reimaginations of the existing observatories that do not use open data and research automation.

The problem with the current EU data strategy is that while it produces enormous quantities of valuable open data, in the absence of common basic data science and documentation principles, it seems often cheaper to create new data than to put the existing open data into shape.

This is an absolute waste of resources and efforts. With a few R packages and our deep understanding of advanced data science techniques, we can create valuable datasets from unprocessed open data. In most domains, we are able to repurpose data originally created for other purposes at a historical cost of several billions of euros, converting these unused data assets into valuable datasets that can replace tens of millions’ worth of proprietary data.

What we want to achieve with this project – and we believe such an accomplishment would merit one of the first prizes - is to add value to a significant portion of pre-existing EU open data (for example, available on data.europa.eu/data) by re-processing and integrating them into a modern, tidy database with an API access, and to find a business model that emphasises a triangular use of data in 1. business, 2. science and 3. policy-making. Our mission is to modernize the concept of data observatories.

Recommendation Systems: What can Go Wrong with the Algorithm?

Thu, 06 May 2021 07:10:00 +0000

Traitors in a war used to be executed by firing squad, and it was a psychologically burdensome task for soldiers to have to shoot former comrades. When a 10-marksman squad fired 8 blank and 2 live ammunition, the traitor would be 100% dead, and the soldiers firing would walk away with a semblance of consolation in the fact they had an 80% chance of not having been the one that killed a former comrade. This is a textbook example of assigning responsibility and blame in systems. AI-driven systems such as the YouTube or Spotify recommendation systems, the shelf organization of Amazon books, or the workings of a stock photo agency come together through complex processes, and when they produce undesirable results, or, on the contrary, they improve life, it is difficult to assign blame or credit.

This is the edited text of my presentation on Copyright Data Improvement in the EU – Towards Better Visibility of European Content and Broader Licensing Opportunities in the Light of New Technologies - download the entire webinar’s agenda.

Assigning and avoding blame.

If you do not see enough women on streaming charts, or if you think that the percentage of European films on your favorite streaming provider—or Slovak music on your music streaming service—is too low, you have to be able to distribute the blame in more precise terms than just saying “it’s the system” that is stacked up against women, small countries, or other groups. We need to be able to point the blame more precisely in order to effect change through economic incentives or legal constraints.

This is precisely the type of work we are doing with the continued support of the Slovak national rightsholder organizations, as well as in our research in the United Kingdom. We try to understand why classical musicians are paid less, or why 15% of Slovak, Estonian, Dutch, and Hungarian artists never appear on anybody’s personalized recommendations. We need to understand how various AI-driven systems operate, and one approach would at the very least model and assign blame for undesirable outcomes in probabilistic terms. The problem is usually not that an algorithm is nasty and malicious; Algorithms are often trained through “machine learning” techniques, and often, machines “learn” from biased, faulty, or low-quality information.

Outcomes: What Can Go Wrong With a Recommendation System?

In complex systems there are hardly ever singular causes that explain undesired outcomes; in the case of algorithmic bias in music streaming, there is no single bullet that eliminates women from charts or makes Slovak or Estonian language content less valuable than that in English. Some apparent causes may in fact be “blank cartridges,” and the real fire might come from unexpected directions. Systematic, robust approaches are needed in order to understand what it is that may be working against female or non-cisgender artists, long-tail works, or small-country repertoires.

Some examples of “undesirable outcomes” in recommendation engines might include:

Recommending too small a proportion of female or small country artists; or recommending artists that promote hate and violence.
Placing Slovak books on lower shelves.
Making the works of major labels easier to find than those of independent labels.
Placing a lower number of European works on your favorite video or music streaming platform’s start window than local television or radio regulations would require.
Filling up your social media newsfeed with fake news about covid-19 spread by some malevolent agents.

These undesirable outcomes are sometimes illegal as they may go against non-discrimination or competition law. (See our ideas on what can go wrong – Music Streaming: Is It a Level Playing Field?) They may undermine national or EU-level cultural policy goals, media regulation, child protection rules, and fundamental rights protection against discrimination without basis. They may make Slovak artists earn significantly less than American artists.

Metadata problems: no single bullet theory

In our work in Slovakia, we reverse engineered some of these undesirable outcomes. Popular video and music streaming recommendation systems have at least three major components based on machine learning:

The users’ history – Is it that users’ history is sexist, or perhaps the training metadata database is skewed against women?
The works’ characteristics – are Dvorak’s works as well documented for the algorithm as Taylor Swift’s or Drake’s?
Independent information from the internet – Does the internet write less about women artists?

In the making of a recommendation or an autonomous playlist, these sources of information can be seen as “metadata” concerning a copyright-protected work (as well as its right-protected recorded fixation.) More often than not, we are not facing a malicious algorithm when we see undesirable system outcomes. The usual problem is that the algorithm is learning from data that is historically biased against women or biased for British and American artists, or that it is only able to find data in English language film and music reviews. Metadata plays an incredibly important role in supporting or undermining general music education, media policy, copyright policy, or competition rules. If a video or music steaming platform’s algorithm is unaware of the music that music educators find suitable for Slovak or Estonian teenagers, then it will not recommend that music to your child.

Furthermore, metadata is very costly. In the case of cultural heritage, European states and the EU itself have been traditionally investing in metadata with each technological innovation. For Dvorak’s or Beethoven’s works, various library descriptions were made in the analogue world, then work and recording identifiers were assigned to CDs and mp3s, and eventually we must describe them again in a way intelligible for contemporary autonomous systems. In the case of classical music and literature, early cinema, or reproductions of artworks, we have public funding schemes for this work. But this seems not to be enough. In the current economy of streaming, the increasingly low income generated by most European works is insufficient to even cover the cost of proper documentation, which then sends that part of the European repertoire into a self-fulfilling oblivion: the algorithm cannot “learn” its properties and it never shows these works to users and audiences.

Until now, in most cases, it was assumed that it is the artists or their representative’s duty to provide high quality metadata, but in the analogue era, or in the era of individual digital copies, we did not anticipate that the sales value will not even cover the documentation cost. We must find technical solutions with interoperability and new economic incentives to create proper metadata for Europe’s cultural products. With that, we can cover one area out of the three possible problem terrains.

But this is not enough. We need to address the question of how new, better Algorithms can learn from user history and avoid amplifying pre-existing bias against women or hateful speech. We need to make sure that when Algorithms are “scraping” the internet, they do so in an accountable way that does not make small language repertoires vulnerable.

Incentives and investments into metadata

In our paper we argue for new regulatory considerations to create a better, and more accountable playing field for deploying Algorithms in a quasi-autonomous system, and we suggest further research to align economic incentives with the creation of higher quality and less biased metadata. The need for further research on how these large systems affect various fundamental rights, consumer or competition rights, or cultural and media policy goals cannot be overstated. The first step is to open and understand these autonomous systems. It is not enough to say that the firing squads of Big Tech are shooting women out from charts, ethnic minority artists from screens, and small language authors from the virtual bookshelves. We must put a lot more effort on researching the sources of the problems that make machine learning Algorithms behave in a way that is not compatible with our European values or regulations.

This blogpost was first published on our general interest blog Data & Lyrics

Feasibility Study On Promoting Slovak Music In Slovakia & Abroad

Thu, 25 Mar 2021 11:00:00 +0000

How to help promote local music?

The new study opens the question of the local music promotion within the digital environment. The Slovak Performing and Mechanical Rights Society (SOZA), the State51 music group in the United Kingdom, and the Slovak Arts Council commissioned Reprex to created a feasibility study which provides recommendations for better use of quotas for Slovak radio stations and which also maps the share and promotion of Slovak music within large streaming and media platforms such as Spotify.

What should a good local content policy (radio quota, recommendation system, streaming quota) achieve?

The study proposes best practices for the introduction of mandatory quotas for Slovak radio stations and points out how current recommendation systems used by large platforms such as Spotify, YouTube, or Apple hardly consider local music from smaller countries. Local music stands against competition consisting of million songs from the whole world, and for ordinary Slovak musicians, whose music doesn’t belong to the global hits playlists, it is almost impossible to get recommended by the recommendation systems of large platforms.

Listen Local App for discovering new music

We aimed to create a demo version of a utility-based, transparent, accountable recommendation system.

The solution to this problem could be the Listen Local App, built on a comprehensive reference database of local music, which we created as a demo version within the study. The app aims to help listeners discover more local music; the app also presents new and alternative ways for large digital platforms to recommend local artists. Through Listen Local, listeners search for artists and bands based on their taste and the city they are situated in. In this way, listeners can easily search for music by artists from particular cities or from the town they are about to visit. We are releasing today the feasibility study in English and Slovak. We call for an open consultation to evaluate the results of this work and continue developing the Slovak Music Database, the Listen Local recommendation, and the AI validation system.

Check out the Demo Listen Local App. We explain here why.

Screenshot of the first verison of the demo app.

Database

The Slovak Music Database is connected to Reprex’s flagship project, the Demo Music Observatory, an open collaboration-based demo version of the planned European Music Observatory, currently being further developed in the JUMP Music Market Accelerator Programme supported by Music Moves Europe.

The project website contains the demo version of the Slovak Music Database.

Download the Study

You can download the study herein Slovak or in English.

Next steps

In the next phase of the work, we add further data to our Slovak Demo Music Database and carry out more and more experiments and educational activities to understand how Slovak music can become more visible and targeted. We are also bringing this project into an international collaboration for better utilization of R&D efforts and experiences throughout Europe. This agile project method originated in reproducible scientific practice and open-source software development and allows participation in large projects on any scale: from individual musicians and educators to large research universities and music distributors. Anyone can join in on the effort.

Reprex is looking for further international partners; Reprex is currently part of the Dutch AI Coalition and the European AI Alliance project. SOZA and Reprex are committed to opening this project for international collaboration while ensuring that a significant part of the R&D activities remains in the Slovak Republic.

We are preparing informal, online information sessions for artists, promoters, researchers, and developers to join our project.

Contributors

The Reprex team who contributed to the English version:

Budai, Sándor, programming and deployment
Dr. Emily H. Clarke, musicologist
Stef Koenis, musicologist, musician
Dr. Andrés Garcia Molina, data scientist, musicologist, editor
Kátya Nagy, music journalist, research assistant;

and the Slovak version:

Dáša Bulíková, musician, translator
Dominika Semaňáková, musicologist, editor, layout.

Special thanks to Tammy Nižňanska & the Youniverse for the case study.

Our Music Observatory in the Jump European Music Market Accelerator: Meet the 2021 Fellows and their Tutors

Thu, 04 Mar 2021 15:00:00 +0200

According to the announcement of JUMP, the European Music Market Accelerator, after a careful screening of all applications received, the selection committee composed of all JUMP board members has selected the most promising ideas and projects to be developed together with renowned tutors for this 2021 fellowship.

For nine months, the 20 fellows living in many European countries will develop their innovative projects, while receiving a comprehensive 360° training. In addition to specialised workshops by highly qualified experts, each fellow will receive one-on-one tutoring sessions from the most renowned music professionals coming from all over Europe.

The 20 selected projects cover a great variety of urgent needs faced within the music sector. They will:

help fostering social change with projects focusing on diversity in the industry, more fairness and transparency as well as raising awareness on timely issues.
enhance technological development with projects using blockchain, immersive sound and VR and AR.
build bridges between different key actors of the ecosystem.

Download the entire JUMP press release.

Reprex’s project, the automated Demo Music Observatory will be represented by Daniel Antal, co-founder of Reprex among other building bridges projects. This project offers a different approach to the planned European Music Observatory based on the principles of open collaboration, which allows contributions from small organizations and even individuals, and which provides higher levels of quality in terms of auditability, timeliness, transparency and general ease of use. Our open collaboration approach allows to power trustworthy, ethical AI systems like our Listen Local that we started out from Slovakia with the support of the Slovak Arts Council.

JUMP fellows building bridges between different key actors of the ecosystem.

Apart from our Demo Music Observatory the build bridges section Groovly with Martin Zenzerovich, From Play To Rec by Jeremy Dunne, Hajde Radio by Thibaut Boudaud, LowDee by Alex Davidson and ONO-HU! by Gina Akers.

Meet all the JUMP 2021 Fellows, including the technology and social change professionals!

Reprex is a start-up company based in the Netherlands and the United States that validated its early products in the Yes!Delft AI+Blockchain Lab in the Hague. In 2021 we joined the Dutch AI Coalition – NL AIC and requested membership in the European AI Alliance. Reprex is committed to applying reproducible in an open collaboration with our business, scientific, policy and civil society partners, and facilitate the use of open data and open-source software. Many fellows in the program are connected to other regions, like North America and Australia – because music is one of the most globalized industries and forms of art in the world! Reprex is a startup based in the Netherlands and the United States, and we are very excited to collaborate with our peers in new European territories, and in Canada and Australia.

Hope to meet you in these great events - maybe not only online!

Further links:

From Play to Rec on Facebook
HAJDE FR/EN

Follow up:

Jumping Ahead With the Digital Music Observatory (2021.11.13.)

Open Data Day Interview: Mapping Data with Milos Popovic

Wed, 03 Mar 2021 22:23:00 +0200

Milos Popovic is a researcher, a data scientist, Marie Curie postdoc & Top 10 dataviz & R contributor on Twitter according to NodeXL. He took part in policy debates about terrorism and military intervention and appeared on a number of TV channels including N1 (the CNN affiliate in the Western Balkans), Serbian National Television and Al-Jazeera Balkans. My research interests are at the intersection of civil war dynamics and postwar politics in the Balkans. He is going to join the Data & Lyrics team on International Open Data Day to help us put harmonized environmental degradation perception and environmental sensory data on maps. We asked him four questions about his passion, mapping data. Please join us 6 March 2021 9.30 EST / 15.30 CET for an informal digital coffee.

As a researcher, why are you so much drawn into maps? Is this connected to your interest in territorial conflicts, or you have some other inspiration?

That’s a great question that really makes me pause and look back at the past 5 years. My mapping story started out of curiosity: I found interesting data on the post-WWII violence in Serbia and thought how cool it would be to make a map in R. I quickly made an unimpressive choropleth map and noticed some unexpected patterns. Then I realized just how much unused violence and census data sits out there while we have no clue about geographic patterns. So, it began. I started off with map-making but my curiosity took me to the world of georeferencing and geospatial analysis. In the process, I created over 300 maps hosted on my website as well as dozens of shapefiles from the scratch.

I used to think that my interest is linked to growing up in a war-torn country. But, as my map-making evolved, I discovered that my passion is to use maps as a way to democratize the data: to take the scores of unused, and often buried datasets, place them on the map and share the dataviz with people.

Can you show us an example of the best use of mapped data, and the best map that you have personally created? What is their distinctive value?

I’m immensely proud of my work that required making the shapefiles from the scratch. For instance, my shapefile of over 1500 Kosovo cadastral settlements came into being after I turned dozens of high-resolution raster files into a shapefile fully compatible with Open Street Maps. After months of hard work, I managed to merge the shapefile with the 2011 Kosovo census and present several laser-focused demographic maps to my audience. Same goes for the settlement shapefile of Republika Srpska [the Serb-speaking entity of Bosnia-Herzegovina — the editor], which I made out of a pdf file and merged with the 2013 census data. Whereas most existing maps take a bird’s eye view, my work offers a more fine-grained view of the local dynamics to stakeholders.

Another similar undertaking was my transformation of the pre-WWII German military map of Yugoslavia into a unique shapefile of a few hundred Yugoslav municipalities. I combined this shapefile with the 1931 census data, 80 years after it was first published (better late than never!). It took me almost a year to complete this tremendous project but I enjoyed every bit of it. I have teamed up with my brother who is a web developer and we even made an interactive map of Yugoslavia based on the 1931 census.[The screenshot of this interactive map is the top image in the post – the editor] We hope this project would serve not only scholars but also history enthusiasts to better understand a history of the country that is no more.

Check out Milos’s beautiful static and interactive maps on https://milosp.info/

What do you think about collaboration based on open data and open-source software that processes such data?

It’s a fantastic opportunity for small teams to bypass traditional gatekeepers such as state institutions or big companies and use open source apps for the benefit of their local communities. For example, the access to Open Street Map allows small teams to map pressing communal issues as crime, deceases, or environmental degradation and come up with innovative solutions. In my work, too, I used OSM has helped me create several fine-grained maps that shed more light on local problems in Serbia such as pollution, car accidents or violence.

We are hoping to bring together environmental, sensory data and public attitude data on environmental issues? How can mapping help? What do you expect from this project?

More than ever, we are compelled to figure out how maladies spreads locally. Without mapping the hotspots, our understanding of the consequences of, for example, viral transmission or pollution is shrouded with a lot of uncertainty. We might have no clue how environmental issues shape public attitudes in localities until we use the mapping to turn on the light. Mapping would help this project pin down geographic clusters that require immediate attention from the private and public stakeholders.

Please join us for a digital coffee, tea or beer on International Open Data Day - we will put never seen data on maps, and discuss how to build successful open collaborations, with little, independent contributions to build large data observatories. Make sure you check out Milos’ amazing website, too!

This blogpost was originally posted on our Data & Lyrics blog and its mutation on Medium.

Eurobarometer Surveys Used In Our Project

Wed, 03 Mar 2021 00:00:00 +0000

In our tutorial series, we are going to harmonize the following questionnaire items from five Eurobarometer harmonized survey files. The Eurobarometer survey files are harmonized across countries, but they are only partially harmonized in time.

All data must be downloaded from the GESIS Data Archive in Cologne. We are not affiliated with GESIS and you must read and accept their terms to use the data.

Eurobarometer 80.2 (2013)

GESIS Data Archive, Cologne. ZA5877 Data file Version 2.0.0, https://doi.org/10.4232/1.12792

Data file: ZA6595 data file (European Commission 2017).
Questionnaire: Eurobarometer 83.4 Basic Bilingual Questionnaire
Citation: ZA6595 Bibtex

QA1a Which of the following do you consider to be the single most serious problem facing the world as a whole? (single choice)

QA1b Which others do you consider to be serious problems? (multiple choice)

QA2 And how serious a problem do you think climate change is at this moment? Please use a scale from 1 to 10, with '1' meaning it is "not at all a serious problem (scale 1-10)

QA4 To what extent do you agree or disagree with each of the following statements? - Fighting climate change and using energy more efficiently can boost the economy and jobs in the EU (agreement-disagreement 4-scale)

QA4 To what extent do you agree or disagree with each of the following statements? - Reducing fossil fuel imports from outside the EU could benefit the EU economically (agreement-disagreement 4-scale)

QA5 Have you personally taken any action to fight climate change over the past six months? (binary)

Eurobarometer 83.4 (2015)

European Commission, Brussels; Directorate General Communication COMM.A.1 ´Strategy, Corporate Communication Actions and Eurobarometer´GESIS Data Archive, Cologne. ZA6595 Data file Version 3.0.0, https://doi.org/10.4232/1.13146

Data file: ZA6595 data file (European Commission 2018).
Questionnaire: Eurobarometer 83.4 Basic Bilingual Questionnaire
Citation: ZA6595 Bibtex

Eurobarometer 87.1 (2017)

European Commission, Brussels; Directorate General Communication, COMM.A.1 ‘Strategic Communication’; European Parliament, Directorate-General for Communication, Public Opinion Monitoring Unit GESIS Data Archive, Cologne. ZA6861 Data file Version 1.2.0, https://doi.org/10.4232/1.12922

Data file: ZA6861 data file.
Questionnaire: Eurobarometer 90.2 Basic Bilingual Questionnaire
Citation: ZA6861 Bibtex

QC1a Which of the following do you consider to be the single most serious problem facing the world as a whole? (single choice)

QC1b Which others do you consider to be serious problems? (multiple choice)

QC2 And how serious a problem do you think climate change is at this moment? Please use a scale from 1 to 10, with '1' meaning it is "not at all a serious problem (scale 1-10)

Qc4 To what extent do you agree or disagree with each of the following statements? - Fighting climate change and using energy more efficiently can boost the economy and jobs in the EU (agreement-disagreement 4-scale)

Qc4 To what extent do you agree or disagree with each of the following statements? - Promoting EU expertise in new clean technologies to countries outside the EU can benefit the EU economically (agreement-disagreement 4-scale)

Qc4 To what extent do you agree or disagree with each of the following statements? - Reducing fossil fuel imports from outside the EU can benefit the EU economically (agreement-disagreement 4-scale)

Qc4 To what extent do you agree or disagree with each of the following statements? - Reducing fossil fuel imports from outside the EU can increase the security of EU energy supplies (agreement-disagreement 4-scale)

Qc4 To what extent do you agree or disagree with each of the following statements? - More public financial support should be given to the transition to clean energies even if it means subsidies to fossil fuels should be reduced. (agreement-disagreement 4-scale)

Qc5 Have you personally taken any action to fight climate change over the past six months? (binary)

Eurobarometer 90.2 (2018)

European Commission, Brussels; Directorate General Communication, COMM.A.3 ‘Media Monitoring and Eurobarometer’ GESIS Data Archive, Cologne. ZA7488 Data file Version 1.0.0, https://doi.org/10.4232/1.13289

Data file: ZA7488 data file (European Commission 2019a)
Questionnaire: Eurobarometer 90.2 Basic Bilingual Questionnaire
Citation: ZA7488 Bibtex

QB5 To what extent do you agree or disagree with each of the following statements? - Fighting climate change and using energy more efficiently can boost the economy and jobs in the EU (agreement-disagreement 4-scale)

QB5 To what extent do you agree or disagree with each of the following statements? - Promoting EU expertise in new clean technologies to countries outside the EU can benefit the EU economically (agreement-disagreement 4-scale)

QB5 To what extent do you agree or disagree with each of the following statements? - Reducing fossil fuel imports from outside the EU can benefit the EU economically (agreement-disagreement 4-scale)

QB5 To what extent do you agree or disagree with each of the following statements? - Reducing fossil fuel imports from outside the EU can increase the security of EU energy supplies (agreement-disagreement 4-scale)

QB5 To what extent do you agree or disagree with each of the following statements? - More public financial support should be given to the transition to clean energies even if it means subsidies to fossil fuels should be reduced. (agreement-disagreement 4-scale)

Eurobarometer 91.3 (2019)

European Commission, Brussels; Directorate General Communication, COMM.A.3 ‘Media Monitoring and Eurobarometer’ GESIS Data Archive, Cologne. ZA7572 Data file Version 1.0.0, https://doi.org/10.4232/1.13372

Data file: ZA7572 data file (European Commission 2019b).
Questionnaire: Eurobarometer 91.3 Basic Bilingual Questionnaire
Citation: ZA7572 Bibtex

QB4 To what extent do you agree or disagree with each of the following statements? - Taking action on climate change will lead to innovation that will make EU companies more competitive (N) (agreement-disagreement 4-scale)

QB4 To what extent do you agree or disagree with each of the following statements? - Promoting EU expertise in new clean technologies to countries outside the EU can benefit the EU economically (agreement-disagreement 4-scale)

QB4 To what extent do you agree or disagree with each of the following statements? - Reducing fossil fuel imports from outside the EU can benefit the EU economically (agreement-disagreement 4-scale)

QB4 To what extent do you agree or disagree with each of the following statements? - Adapting to the adverse impacts of climate change can have positive outcomes for citizens in the EU (agreement-disagreement 4-scale)

QB5 Have you personally taken any action to fight climate change over the past six months? (binary)

References

European Commission, Brussels. 2017. “Eurobarometer 80.2 (2013).” GESIS Data Archive, Cologne. ZA5877 Data file Version 2.0.0, https://doi.org/10.4232/1.12792. https://doi.org/10.4232/1.12792.

———. 2018. “Eurobarometer 83.4 (2015).” GESIS Data Archive, Cologne. ZA6595 Data file Version 3.0.0, https://doi.org/10.4232/1.13146. https://doi.org/10.4232/1.13146.

———. 2019a. “Eurobarometer 90.2 (2018).” GESIS Data Archive, Cologne. ZA7488 Data file Version 1.0.0, https://doi.org/10.4232/1.13289. https://doi.org/10.4232/1.13289.

———. 2019b. “Eurobarometer 91.3 (2019).” GESIS Data Archive, Cologne. ZA7572 Data file Version 1.0.0, https://doi.org/10.4232/1.13372. https://doi.org/10.4232/1.13372.

Music Streaming: Is It a Level Playing Field?

Tue, 23 Feb 2021 21:23:00 +0200

Our article, Music Streaming: Is It a Level Playing Field? is published in the February 2021 issue of CPI Antitrust Chronicle, which is fully devoted to competition policy issues in the music industry.

The dramatic growth of music streaming over recent years is potentially very positive. Streaming provides consumers with low cost, easy access to a wide range of music, while it provides music creators with low cost, easy access to a potentially wide audience. But many creators are unhappy about the major streaming platforms. They consider that they act in an unfair way, create an unlevel playing field and threaten long-term creativity in the music industry.

Our paper describes and assesses the basis for one element of these concerns, competition between recordings on streaming platforms. We argue that fair competition is restricted by the nature of the remuneration arrangements between creators and the streaming platforms, the role of playlists, and the strong negotiating power of the major labels. It concludes that urgent consideration should be given to a user-centric payment system, as well as greater transparency of the factors underpinning playlist creation and of negotiated agreements.

You can read the entire issue and the full text of our article on Competition Policy International in pdf.

Daniel Antal, co-founder of Reprex Was Selected into the 2021 Fellowship Program of the European Music Market Accelerator

Mon, 22 Feb 2021 21:23:00 +0200

Daniel Antal, co-founder of Reprex, was selected into 2021 Fellowship program of JUMP, the European Music Market Accelerator. Jump provides a framework for music professionals to develop innovative business models, encouraging the music sector to work on a transnational level. The European Music Market Accelerator composed of MaMA Festival and Convention, UnConvention, MIL, Athens Music Week, Nouvelle Prague and Linecheck support him in the development of our two, interrelated projects over the next nine months.

Our Demo Music Observatory is a demo version of the European Music Observatory based on open data, open source, automated research in open collaboration with music stakeholders. We hope that we can further develop our business model and find new users, and help the recovery of the festival and live music segment.
Listen Local is our AI system that validated third party music AI, such as Spotify’s or YouTube’s recommendation systems, and provides trustworthy, accountable, transparent alternatives for the European music industry. We hope to expand our pilot project from Slovakia to several European countries in 2021.

Reprex is committed to applying reproducible in an open collaboration with our business, scientific, policy and civil society partners, and facilitate the use of open data and open-source software.

Reprex Joins The Dutch AI Coalition

Tue, 16 Feb 2021 17:10:00 +0200

Reprex, our start-up, is based in the Netherlands and the United States that validated its early products in the Yes!Delft AI+Blockchain Lab in the Hague. In 2021, we decided to join the Dutch AI Coalition – NL AIC.

The NL AIC is a public-private partnership in which the government, the business sector, educational and research institutions, as well as civil society organisations collaborate to accelerate and connect AI developments and initiatives. The ambition is to position the Netherlands at the forefront of knowledge and application of AI for prosperity and well-being. We are continually doing so with due observance of both the Dutch and European standards and values. The NL AIC functions as the catalyst for AI applications in our country.

We are particularly looking forward to participating in the Culture working group of NLAIC, but we will also take a look at the Security, Peace and Justice and the Energy and Sustainability working groups. Reprex is committed to use and further develop AI solutions that fulfil the requirements of trustworthy AI, a human-centric, ethical, and accountable use of artificial intelligence. We are committed to develop our data platforms, or automated data observatories, and our Listen Local system in this manner. Furthermore, we are involved in various scientific collaborations that are researching ideas on future regulation of copyright and fair competition with respect to AI algorithms.

We are committed to applying reproducible in an open collaboration with our business, scientific, policy and civil society partners, and facilitate the use of open data and open-source software.

Ensuring the Visibility and Accessibility of European Creative Content on the World Market: The Need for Copyright Data Improvement in the Light of New Technologies

Sat, 13 Feb 2021 18:10:00 +0200

The majority of music sales in the world is driven by AI-algorithm powered robots that create personalized playlists, recommendations and help programming radio music streams or festival lineups. It is critically important that an artist’s work is documented, described in a way that the algorithm can work with it.

In our research paper – soon to be published – made for the Listen Local Initiative we found that 15% of Dutch, Estonian, Hungarian, or Slovak artists had no chance to be recommended, and they usually end up on Forgetify, an app that lists never-played songs of Spotify. In another project with rights management organizations, we found that about half of the rightsholders are at risk of not getting all their royalties from the platforms because of poor documentation.

But how come that distributors give streaming platforms songs that are not properly documented? What sort of information is missing for the European repertoire’s visibility? Reprex is exploring this problem in a practical cooperation with SOZA, the Slovak Performing and Mechanical Rights Society, and in an academic cooperation that involves leading researchers in the field. A manuscript co-authored Martin Senftleben, director of the Institute for Information Law in Amsterdam, and eminent researchers in copyright law and music economics, Reprex’s co-founder makes the case that Europe must invest public money to resolve this problem, because in the current scenario, the documentation costs of a song exceed the expected income from streaming platforms.

In the European Strategy for Data, the European Commission highlighted the EU’s ambition to acquire a leading role in the data economy. At the same time, the Commission conceded that the EU would have to increase its pools of quality data available for use and re-use. In the creative industries, this need for enhanced data quality and interoperability is particularly strong. Without data improvement, unprecedented opportunities for monetising the wide variety of EU creative and making this content available for new technologies, such as artificial intelligence training systems, will most probably be lost. The problem has a worldwide dimension. While the US have already taken steps to provide an integrated data space for music as of 1 January 2021, the EU is facing major obstacles not only in the field of music but also in other creative industry sectors. Weighing costs and benefits, there can be little doubt that new data improvement initiatives and sufficient investment in a better copyright data infrastructure should play a central role in EU copyright policy. A trade-off between data harmonisation and interoperability on the one hand, and transparency and accountability of content recommender systems on the other, could pave the way for successful new initiatives. Download the manuscript from SSRN

Our Slovak Demo Music Database project is a best example for this. We started systematically collect publicly available information from Slovak artists (in our write-in process) and ask them to give GDPR-protected further data (in our opt-in process) to create a comprehensive database that can help recommendation engines as well as market-targeting or educational AI apps.

We believe that one of the problems of current AI algorithms that they solely or almost only work with English language documentation, putting other, particularly small language repertoires at risk of being buried below well-documented music mainly arriving from the United States.

We are looking for rightsholders and their organizations, artists, researchers to work with us to find out how we can increase the visibility of European music.

Demo Slovak Music Database

Thu, 17 Dec 2020 17:10:00 +0200

We are finalizing our first local recommendation system, Listen Local Slovakia, and the accompanying Demo Slovak Music Database. Our aim is

Show how the Slovak repertoire is seen by media and streaming platforms
What are the possibilities to give greater visibility to the Slovak repertoire in radio and streaming platforms
What are the specific problems why certain artists and music is almost invisible.

In the next year, we would like to create a modern, comprehensive national music database that serves music promotion in radio, streaming, live music within Slovakia and abroad.

To train our locally relevant, alternative recommendation system, we filled the Demo Slovak Music Database from two sources. In the opt-in process we asked artists to participate in Listen Local, and we selected those artists who opted in from Slovakia, or whose language is Slovak. In the write-in process we collected publicly available data from other artists that our musicology team considered to be Slovak, mainly on the basis of their language use, residence, and other public biographical information. The following artists form the basis of our experiment. (If you want to be excluded from the write-in list, write to us, or you want to be included, please, fill out this form.)

Click here to view the table on a separate page

Modern recommendation systems usually rely on data provided by artists or their representatives, data on who and how is listening to their music, and what music is listened to by the audience of the artists, and certain musicological features of the music. Usually they collect data from various data sources, but these data sources are mainly English language sources.

The problem with these recommendation systems is that they do not help music discovery, and make starting new acts very difficult. Recommendation systems tend to help already established artists, and artists whose work is well described in the English language.

Our alternative recommendation system is a utility-based system that gives a user-defined priority to artists released in Slovakia, or artists identified as Slovak, or both. The system can be extended for lyrics language priorities, too. Currently, our app is demonstration to provide a more comprehensive database-driven tool that can support various music discovery, recommendation or music export tools. Our Feasibility Study to build such tools and our Demo App is currently under consultation with Slovak stakeholders.

Listen Local is developing transparent algorithms and open source solutions to find new audiences for independent music. We want to correct the injustice and inherent bias of market leading big data algorithms. If you want your music and audience to be analysed in Listen Local, fill this form in. We will include you in our demo application for local music recommendations and our analysis to be revealed in December.

Listen Local: Why We Need Alternative Recommendation Systems

Mon, 14 Dec 2020 17:10:00 +0200

The first version of our demo application

Recommendation systems utilize knowledge about music content and their audiences while also pursuing the objectives or needs of recommenders.

The simplest recommendation systems just follow the charts: for example, they select from well-known current or perennial greatest hits. Such a system may work well for an amateur DJ in a home party or a small local radio that just wants to make sure that the music in its programme will be liked by many people. They reinforce existing trends and make already popular songs and their creators even more popular.

If the recommendation engine is supported by big data and a machine learning system – or increasingly, a combination of several machine learning algorithms – the general modus operandi is to exploit information about both content and users in order to achieve certain goals.

How algorithmic recommendation systems work?

How recommendation systems work?

Spotify’s recommendation system is a mix of content- and collaborative filtering that exploits information about users’ past behaviour (e.g. liked, skipped, and re-listened songs), the behaviour of similar users, as well as data collected from the users’ social media and other online activities, or from blogs. Deezer uses a similar system that is boosted by the acquisition of Last.fm – big data created from user comments are used to understand the mood of the songs, for example.

Spotify makes 16 billion music recommendations each month in 2020.

YouTube, which plays an even larger role in music discovery, uses a system comprised of two neural networks: one for candidate generation and one for ranking. The candidate generation deep neural network provides works on the basis of collaborative filtering, while the ranking system is based on content-based filtering and a form of utility ranking that takes into consideration the user’s languages, for example.

What makes these systems common is that they maximize the algorithm creators’ corporate key performance indicators. Spotify wants to be ‘your playlist to life’ and increase the amount of music played during work or sports in the background, during travelling, or active music listening –- i.e. maximizing the number of hours spent using it, and do not let empty timeslots for other music providers, such as radio stations. YouTube and Netflix have similar targets. They are in many ways like commercial radio targets, which want to maximize the time spent listening to the broadcast stream. Radios and YouTube, in particular, have similar goals because they are mainly financed through advertising. For Spotify or Netflix, their key financial motivation is to avoid users’ cancelling their subscriptions or changing it to different providers, such as Amzon, Apple or Deezer.

What is the problem with black box recommendation systems?

What they also have in common is that they do not aim to give a fair chance to each uploaded song, serve equally every artist, or provide whatever equality of chances for English, Slovak or Farsi language content.

They tend to reinforce trends similarly to music charts, but with far bigger efficiency. As the Dutch comedian, author and journalist Arjen Lubach explains the YouTube algorithm, to keep their personal recommendations engaging all the day and all of the night, they create a comfortable universe for the user allows little distraction in. If the user wants to listen to global hit music, or stoner rock, it will never be distracted with anything else.

Zondag met Lubach on Dutch public broadcaster VPRO. Click settings sign to change the language of the captions.

The problem with such hyper-personalized media is that they leave no room for public activities. Public broadcasters, which had a monopoly to television broadcasting in most European countries until the early 1990s, for example, were aiming to air a diversity of news, knowledge and access to local culture. Many countries on all continents have maintained local content guidelines for broadcasting on public, commercial and community television and radio channels, for example, local music and films, and reliable news as a public service. Personalized media-, social media- and streaming platforms do not have such obligations.

Black box recommendation systems usually maximize a corporate key performance indicator, and they are not subject to usual public new service or local content regulations that traditional broadcast media is.
The goal and the steps that the algorithm is pursuing is not know to content creators, and they do not know when will the algorithm work for their benefit or against them.

Transparent and regulated AI

In our view, utility-based recommendation system can provide a bridge between current, corporate-owned systems that maximize a media or streaming platforms’ business indicators.

Public new service requirements or local content requirements (“national quotas”) set for commercial broadcasting are similar to utility or knowledge-based recommendation systems. A utility-based recommendation system, for example, would prefer from two candidates for a playlist the one that has a Slovak composer, or a performer from Wales, or which has Farsi lyrics.

Our Demo App creates recommendations on the basis of a pre-existing radio or personal streaming playlist that, by choice, contains a pre-defined ratio of music produced in Slovakia, or performed by Slovak artists. We will soon add Dutch and Hungarian choices to this demo, but naturally, we could add any city’s, regions’, province’s our countries preferences into the app.

Our Demo App and accompanying Feasibility Study in Slovakia shows how can a regulator create better broadcasting regulations, learning from the experience with AI-driven streaming platforms, and how can it apply the goals of local content requirements (such as a certain visibility for Slovak or a city-based music) and public service requirements (for example, spreading reliable information or stopping hateful music.)

Access for all

We do not believe that the current heated discussion on the re-regulation of AI and music streaming will solve all the problems of independent artists, bands from ethnic or racial minorities, or otherwise vulnerable producers. New regulation can limit the unintended collateral damage of big data algorithms deployed by big corporations, but they will not bring down the benefits of AI to these creators.

Take the example of the controversial new initiative that let’s artists, labels and publishers to promote their music in exchange for a cut in royalty rates. While felt by many artists injust and even corrupt, it is an answer to the growing need to influence how the recommendation algorithms are promoting certain music at the expense of tens of millions of sound recordings that are not recommended.

We believe that algorithms create value for the users, and if artists are not happy to pay for corporations to influence black box algoritms, thatn they must collaborate and share data, and build large enough data pools so that they can deploy white, transparent algorithms that work for them.

Our Feasibility Study shows why it is important that creators have a control over what data describes their music, their biographies and other information online, because corporate streaming platforms use this information for their algorithms.
We should that with relatively little effort creators can pool enough information to create alternative recommendation systems that follow a more agreeable goal, that is sensitive to local content requirements and more access to new artists, women or black performers, or which suppress hateful lyrics.
The benefit of an open algorithm and pooled data is that artists can actively look for audiences in various age groups or in cities that are accessible for them on a performing tour after the pandemic.

Overall, we want to show that regulating black box, private algorithms and data monopolies is only a first step to damage control. Deploying white, transparent algorithms and building collaborative or open data pools can only guarantee fairness in the digital platforms, in recommendations, and generally in the use of AI.

Reproducible research in practice: empirical study on the structural conditions of book piracy in global and European academia

Sat, 05 Dec 2020 08:10:00 +0200

PLOS One is the fourth most influential multidisciplinary journal after Nature, and Science, and Proceedings of the National Academy of Sciences of the United States of America (based on H index.) On December 3, 2020 it published a paper co-authored by Dr. Balazs Bodo, associate professor at the Institute for Information Law (IViR), Daniel Antal (Reprex, Demo Music Observatory), a data scientist interested in reproducible research, as an independent researcher, and Zoltan Puha, a Data Science PhD at Tilburg University, JADS. PLOS (Public Library of Science) is a nonprofit Open Access publisher, empowering researchers to accelerate progress in science and medicine by leading a transformation in research communication.

The article utilizes the our reproducible datasets created with our regions package, and builds on many years of expertise in empirical research on the field of music and audiovisual piracy, home copying and private copying compensation (see for example Private Copying in Croatia.) Our aim is to provide reliable, high quality indicators for the creative industries not only on national, but provincial, state, regional and metropolitan area level, too, because these levels are often more relevant for creators, performers and policy-makers.

The topic of the paper is Library Genesis (LG), the biggest piratical scholarly library on the internet, which provides copyright infringing access to more than 2.5 million scientific monographs, edited volumes, and textbooks. The paper uses advanced statistical methods to explain why researchers around the globe use copyright infringing knowledge resources. The analysis is based on a huge usage dataset from LG, as well as data from the World Bank, Eurostat, and Eurobarometer, to identify the role of macroeconomic factors, such as R&D and higher education spending, GDP, researcher density in scholarly copyright infringing activities.

We created a global and a far more detailed European model for pirate book downloads.

The main finding of the paper is that open access, even if it is radical, is not a panacea. The hypothesis of the research was that researchers in low-income regions use piratical open knowledge resources relatively more to compensate for the limitations of their legal access infrastructures. The authors found evidence to the contrary. Researchers in high income countries and European regions with access to high quality knowledge infrastructures, and high levels of funding use radical open access resources more intensively than researchers in lower income countries and regions, with less resourceful libraries. This means that while open knowledge is an important resource to close the knowledge gap between centrum and periphery, equality in access does not translate into equality in use. Structural knowledge inequalities are both present and are being reproduced in the context of open access resources.

The paper is unique not just because of the data it is based on. It also sets new standards in interdisciplinary legal research by publishing the paper, the data and the software code in the same time in open access repositories, following reproducible research best practices — the practices that we want to promote in our Demo Music Observatory (later renamed: Digital Music Observatory) and further data observatories to serve business, evidence-based policy and scientific research.

Feasibility Study For The Establishment Of A European Music Observatory & The Demo Observatory

Mon, 16 Nov 2020 07:03:00 +0200

The Feasibility study for the establishment of a European Music Observatory was published on 13 November. Our private observatory, CEEMID was consulted in the creation of the Feasibility Study, and some of our recommendations found way into the consultant’s document. We created a Demo Music Observatory to provide a practical guidance on the decisions facing the European stakeholders, and to answer the questions that were left open in the Feasibility Study — particularly on data integration and the institutional model, where a wrong choice can lead to very long delivery time, quality control and budgeting.

We have been developing our Demo Music Observatory in the world’s 2nd ranked university-backed incubator program, the Yes!Delft AI Validation Lab since 15 September 2020. Our aim is to show a better organizational model, examples of research automation and other data integration innovation that can reduce the budgetary needs of the European Music Observatory by 80-90% and provide far more timely, accurate, and relevant service than most data observatories in Europe.

CEEMID has been creating a similar data observatory to the foreseen European Data Observatory, solely based on the contribution of about 60 European stakeholders. As the Feasibility Study suggests, we would be happy to transfer much of CEEMID’s content to the European Data Observatory, which could potentially fill up about 50-70% of the envisioned observatory. We are building our Demo Music Observatory based on the 2000 pan-European indicators collected by CEEMID since 2014.

Challenge Our Demo Observatory: Check out the Music Diversity & Circulation Pillar of our Demo Music Observatory. If you do not find what you are looking for, contact us — we will try to put the data there from our repositories.

Illusory data gap: active and music participation is available on EU level both for gender groups or four ethnic minorities – this is regularly featured in various European CAP surveys and in our national CAP surveys, too.

The Feasibility Study is based on perceived data gaps between data needs of the European stakeholders and data availability. We have shown earlier this year to the European stakeholders that much of these data gaps are illusory. We would like to give about 50 indicators with full documentation, automated, weekly, monthly, quarterly, or annual refreshment for free for all music industry users. We would like to challenge the stakeholders to formulate data requests to us and think together on the ways how could the European music industry build a better observatory faster and with less cost.

Challenge Our Demo Observatory: Check out the Music Economy Pillar of our Demo Music Observatory. If you do not find what you are looking for, contact us — we will try to put the data there from our repositories.

The Feasibility Study concludes that a “European Music Observatory would require a very significant allocation of funds, beyond what could be currently expected from the possible budget of the future Creative Europe programme”. While the Feasibility Study provide cost options, or any cost-benefit analysis, we are certain that this is an exaggeration. Most European data observatories operate with an annual 20,000-200,000-euro subsidy. We want to show with our Demo Music Observatory what can be achieved with an annual budget of 20,000 euros, 50,000 euros, 100,000 euros or 200,000 euros.

Challenge Our Demo Observatory: Check out the Music, Society and Citizenship Pillar of our Demo Music Observatory. If you do not find what you are looking for, contact us — we will try to put the data there from our repositories.

Product/Market Fit Validation in Yes!Delft

Fri, 25 Sep 2020 15:31:39 +0000

We would like to validate our product market/fit in two segments, business/policy research and scientific research, with a supporting role given to data journalism. Because we want to follow a bootstrapping strategy, we must focus on those clients where we find the highest value proposition, which is of course easier said than done. We see much interest in our offering from other continents, therefore we truly welcome the opportunity that we can do this on a truly global business canvas in one of the worlds’ top five incubators, the number 2 university-backed incubator in the world, second to none in Europe, in the Yes!Delft AI+Blockchain Validation Lab.

In Europe hundreds of thousands of microenterprises, such as record labels, video producers or book publishers are facing data and AI giants like Google’s YouTube, Apple Music, Spotify, Netflix or Amazon. If the recommendation engines of these giants do not recommend their songs, films or books, then their investments are doomed to fail, because about half of the global sales are driven by AI algorithms. When they make a claim for the missing money, they will immediately find themselves in a dispute with gigabytes of data that they can only handle with a data scientist, even though they do not even have an IT professional or an HR professional to make the hire.

An awful lot of money, creativity and real values are at stake, and we want to be on the creator’s side, their technician’s side, their manager’s side when they want to get a fair share from the pie and they want to help these industry leader to make the pie grow.

The UNESCO and the EU have been promoting as an organizational solution the fragmentation problem with the so-called data observatories that are pooling the business, policy, and scientific research needs of various domains, like music. This is an idea that we really like, and we believe that our research automation solutions can help these observatories to grow faster as ecosystems, create better quality and more timely data and research products and a far lower cost.

We define ourselves as a reproducible research company inspired by the philosophy of open collaboration, based on open-source software and open data. We want to explore various revenue models around these ideas.

We are not committed to open source licensing if more permissive licensing policies provide us with better opportunities.
We would like to explore various data-as-service models, because we do not want to be locked into the position of cheap open data vendors.
We want to deploy AI applications that really help earning money in these sectors with playlisting, recommendation engines, forecasting applications, or royalty valuations, because our open collaboration approach brings up enough data sooner to than its alternatives, because it manages inherent conflicts of interests, fragmentation, and decentralization better than hierarchical solutions.

Timeline

In January CEEMID reached its peak: we introduced a 12-country reproducible research project made with only freelancers in Brussels, presented as best use case of evidence-based policy design.
In February Daniel visited the Yes!Delft Co-Lab to find out who would be the best co-founder to re-launch CEEMID as an enterprise.
In April we started to release our data as open data for validation.
One month ago we started-up.
Then we launched the music.dataobservatory.eu project.
A few other data observatories.

Bonus:

Palato in the Hague, where we took our selfie and had an absolutely amazing dinner after the pitch. Check them out!