Latest News | Antal Dániel honlapja

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

Cooperation with the Slovak Ministry of Culture and Other Slovak Partners

Mon, 06 Mar 2023 12:00:00 +0100

Reprex signed a Memorandum of Understanding with the Ministry of Culture of the Slovak Republic, the Economic University of Bratislava, SOZA, and Sinus (on behalf of the Open Music Europe consortium) on utilizing the Open Policy Analysis results.

From left to right: Ľubomír Burgr, Chariman of the Board (SOZA); Dr James Edwards Open Music Europe program director; Ferdinand Daňo, rector of EUBA; Rado Kutaš, state secretary; Tomaš Mikš, SOZA; Daniel Antal, co-founder of Reprex. Photo: Dominika Semaňáková.

This cooperation is a very important milestone for our company: our reproducible research products will be used in an official national policy context in an EU member state. Our Slovak Music Industry Report has set a best practice within the Slovak Republic.

Our Smart Policy Documents will be used to create the Live Policy Document on Music Economy, on Diversity and Circulation,on Music and Society, and on Music Innovation in a national policy context. We will contribute with automatically refreshed web resources and high-quality indicators about cultural and creative industries, particularly music. Our work will be used to monitor the implementation of the Cultural and Creative Industries Strategy of the Slovak Republic 2030 Stratégia kultúry a kreatívneho priemyslu Slovenskej republiky 2030 national policy.

Reprex’s Smart Policy Documents technology will be used to monitor the national cultural and creative industry policies of the Slovak Republic. See Central Register of Contracts (in Slovak).

This will be the first high-profile national policy use of our Digital Music Observatory. Over the course of three years, in cooperation with our Slovak partners, we would like to persuade more and more national policymakers and also regional actors (like the city of Trenčín, the cultural capital of Europe in 2026) to use this collaborative open knowledge platform as a prototype of a European Music Observatory.

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.

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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:)

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!

How We Add Value to Public Data With Better Curation And Documentation?

Mon, 08 Nov 2021 09:00:00 +0000

In this example, we show a simple indicator: the Turnover in Radio Broadcasting Enterprises in many European countries. This is an important demand driver in the Music economy pillar of our Digital Music Observatory, and important indicator in our more general Cultural & Creative Sectors and Industries Observatory. Of course, if you work with competition policy or antitrust, than any industry may be interesting to you–but not all of them are well-serverd with data.

This dataset comes from a public datasource, the data warehouse of the European statistical agency, Eurostat. Yet it is not trivial to use: unless you are familiar with national accounts, you will not find this dataset on the Eurostat website.

The data can be retrieved from the Annual detailed enterprise statistics for services NACE Rev.2 H-N and S95 Eurostat folder.

Our version of this statistical indicator is documented following the FAIR principles: our data assets are findable, accessible, interoperable, and reusable. While the Eurostat data warehouse partly fulfills these important data quality expectations, we can improve them significantly. And we can also improve the dataset, too, as we will show in the next blogpost.

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Findable Data

Our data observatories add value by curating the data–we bring this indicator to light with a more descriptive name, and we place it in a domain-specific context with our Digital Music Observatory and Cultural & Creative Sectors and Industries Observatory and a policy-specific context with our Competition Data Observatory and Green Deal Data Observatory. While many people may need this dataset in the creative sectors, or among cultural policy designers, most of them have no training in working with national accounts, which imply decyphering national account data codes in records that measure economic activity at a national level. Our curated data observatories bring together many available data around important domains. Our Digital Music Observatory, for example, aims to form an ecosystem of music data users and producers.

We added descriptive metadata that help you find our data and match it with other relevant data sources.

We added descriptive metadata that help you find our data and match it with other relevant data sources. For example, we add keywords and standardized metadata identifiers from the Library of Congress Linked Data Services, probably the world’s largest standardized knowledge library description. This ensures that you can find relevant data around the same key term ("Radio broadcasting") in addition to our turnover data. This allows connecting our dataset unambiguously with other information sources that use the same concept, but may be listed under different keywords, such as Radio–Broadcasting, or Radio industry and trade, or maybe Hörfunkveranstalter in German, or Emitiranje radijskog programa in Croatian or Actividades de radiodifusão in Portugese.

Accessible Data

Our data is accessible in two forms: in csv tabular format (which can be read with Excel, OpenOffice, Numbers, SPSS and many similar spreadsheet or statistical applications) and in JSON for automated importing into your databases. We can also provide our users with SQLite databases, which are fully functional, single user relational databases.

Tidy datasets are easy to manipulate, model and visualize, and have a specific structure: each variable is a column, each observation is a row, and each type of observational unit is a table. This makes the data easier to clean, and far more easier to use in a much wider range of applications than the original data we used. In theory, this is a simple objective, yet we find that even governmental statistical agencies–and even scientific publications–often publish untidy data. This poses a significant problem that implies productivity loses: tidying data will require long hours of investment, and if a reproducible workflow is not used, data integrity can also be compromised: chances are that the process of tidying will overwrite, delete, or omit a data or a label.

Tidy datasets are easy to manipulate, model and visualize, and have a specific structure: each variable is a column, each observation is a row, and each type of observational unit is a table.

While the original data source, the Eurostat data warehouse is accessible, too, we added value with bringing the data into a tidy format. Tidy data can immediately be imported into a statistical application like SPSS or STATA, or into your own database. It is immediately available for plotting in Excel, OpenOffice or Numbers.

Interoperability

Our data can be easily imported with, or joined with data from other internal or external sources.

All our indicators come with standardized descriptive metadata, and statistical (processing) metadata. See our API

All our indicators come with standardized descriptive metadata, following two important standards, the Dublin Core and DataCite–implementing not only the mandatory, but the recommended descriptions, too. This will make it far easier to connect the data with other data sources, e.g. turnover with the number of radio broadcasting enterprises or radio stations within specific territories.

Our passion for documentation standards and best practices goes much further: our data uses Statistical Data and Metadata eXchange standardized codebooks, unit descriptions and other statistical and administrative metadata.

Reuse

All our datasets come with standardized information about reusabililty. We add citation, attribution data, and licensing terms. Most of our datasets can be used without commercial restriction after acknowledging the source, but we sometimes work with less permissible data licenses.

In the case presented here, we added further value to encourage re-use. In addition to tidying, we significantly increased the usability of public data by handling missing cases. This is the subject of our next blogpost.

Are you a data user? How could we serve you better?

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 get in touch with us!

How We Add Value to Public Data With Imputation and Forecasting

Mon, 08 Nov 2021 10:00:00 +0100

Public data sources are often plagued by missng values. Naively you may think that you can ignore them, but think twice: in most cases, missing data in a table is not missing information, but rather malformatted information. This approach of ignoring or dropping missing values will not be feasible or robust when you want to make a beautiful visualization, or use data in a business forecasting model, a machine learning (AI) applicaton, or a more complex scientific model. All of the above require complete datasets, and naively discarding missing data points amounts to an excessive waste of information. In this example we are continuing the example a not-so-easy to find public dataset.

In the previous blogpost we explained how we added value by documenting data following the FAIR principle and with the professional curatorial work of placing the data in context, and linking it to other information sources, such as other datasets, books, and publications, regardless of their natural language (i.e., whether these sources are described in English, German, Portugese or Croatian). Photo: Jack Sloop.

Completing missing datapoints requires statistical production information (why might the data be missing?) and data science knowhow (how to impute the missing value.) If you do not have a good statistician or data scientist in your team, you will need high-quality, complete datasets. This is what our automated data observatories provide.

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Why is data missing?

International organizations offer many statistical products, but usually they are on an ‘as-is’ basis. For example, Eurostat is the world’s premiere statistical agency, but it has no right to overrule whatever data the member states of the European Union, and some other cooperating European countries give to them. And they cannot force these countries to hand over data if they fail to do so. As a result, there will be many data points that are missing, and often data points that have wrong (obsolete) descriptions or geographical dimensions. We will show the geographical aspect of the problem in a separate blogpost; for now, we only focus on missing data.

Some countries have only recently started providing data to the Eurostat umbrella organization, and it is likely that you will find few datapoints for North Macedonia or Bosnia-Herzegovina. Other countries provide data with some delay, and the last one or two years are missing. And there are gaps in some countries’ data, too.

See the authoritative copy of the dataset.

This is a headache if you want to use the data in some machine learning application or in a multiple or panel regression model. You can, of course, discard countries or years where you do not have full data coverage, but this approach usually wastes too much information–if you work with 12 years, and only one data point is available, you would be discarding an entire country’s 11-years’ worth of data. Another option is to estimate the values, or otherwise impute the missing data, when this is possible with reasonable precision. This is where things get tricky, and you will likely need a statistician or a data scientist onboard.

What can we improve?

Consider that the data is only missing from one year for a particular country, 2015. The naive solution would be to omit 2015 or the country at hand from the dataset. This is pretty destructive, because we know a lot about the radio market turnover in this country and in this year! But leaving 2015 blank will not look good on a chart, and will make your machine learning application or your regression model stop.

A statistician or a radio market expert will tell you that you know more-or-less the missing information: the total turnover was certainly not zero in that year. With some statistical or radio domain-specific knowledge you will use the 2014, or 2016 value, or a combination of the two and keep the country and year in the dataset.

Our improved dataset added backcasted (using the best time series model fitting the country’s actually present data), forecasted (again, using the best time series model), and approximated data (using linear approximation.) In a few cases, we add the last or next known value. To give a few quantiative indicators about our work:

Increased number of observations: 65%
Reduced missing values: -48.1%
Increased non-missing subset for regression or AI: +66.67%

If your organization is working with panel (longitudional multiple) regressions or various machine learning applications, then your team knows that not havint the +66.67% gain would be a deal-breaker in the choice of models and punctuality of estimates or KPIs or other quantiative products. And that they would spent about 90% of their data resources on achieving this +66.67% gain in usability.

If you happen to work in an NGO, a business unit or a research institute that does not employ data scientists, then it is likely that you can never achieve this improvement, and you have to give up on a number of quantitative tools or visualizations. If you have a data scientist onboard, that professional can use our work as a starting point.

Can you trust our data?

We believe that you can trust our data better than the original public source. We use statistical expertise to find out why data may be missing. Often, it is present in a wrong location (for example, the name of a region changed.)

If you are reluctant to use estimates, think about discarding known actual data from your forecast or visualization, because one data point is missing. How do you provide more accurate information? By hiding known actual data, because one point is missing, or by using all known data and an estimate?

Our codebooks and our API uses the Statistical Data and Metadata eXchange documentation standards to clearly indicate which data is observed, which is missing, which is estimated, and of course, also how it is estimated. This example highlights another important aspect of data trustworthiness. If you have a better idea, you can replace them with a better estimate.

Our indicators come with standardized codebooks that do not only contain the descriptive metadata, but administrative metadata about the history of the indicator values. You will find very important information about the statistical method we used the fill in the data gaps, and even link the reliable, the peer-reviewed scientific, statistical software that made the calculations. For data scientists, we record the plenty of information about the computing environment, too-–this can come handy if your estimates need external authentication, or you suspect a bug.

Avoid the data Sisyphus

If you work in an academic institution, in an NGO or a consultancy, you can never be sure who downloaded the Annual detailed enterprise statistics for services (NACE Rev. 2 H-N and S95) Eurostat folder from Eurostat. Did they modify the dataset? Did they already make corrections with the missing data? What method did they use? To prevent many potential problems, you will likely download it again, and again, and again…

See our The Data Sisyphus blogpost.

We have a better solution. You can always rely on our API to import directly the latest, best data, but if you want to be sure, you can use our regular backups on Zenodo. Zenodo is an open science repository managed by CERN and supported by the European Union. On Zenodo, you can find an authoritative copy of our indicator (and its previous versions) with a digital object identifier, in this case, 10.5281/zenodo.5652118. These datasets will be preserved for decades, and nobody can manipulate them. You cannot accidentally overwrite them, and we have no backdoor access to modify them.

Get the data

How can we do better?

Are you a data user?

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 get in touch with us!

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.

Economic and Environment Impact Analysis, Automated for Data-as-Service

Thu, 03 Jun 2021 16:00:00 +0000

We have released a new version of iotables as part of the rOpenGov project. The package, as the name suggests, works with European symmetric input-output tables (SIOTs). SIOTs are among the most complex governmental statistical products. They show how each country’s 64 agricultural, industrial, service, and sometimes household sectors relate to each other. They are estimated from various components of the GDP, tax collection, at least every five years.

SIOTs offer great value to policy-makers and analysts to make more than educated guesses on how a million euros, pounds or Czech korunas spent on a certain sector will impact other sectors of the economy, employment or GDP. What happens when a bank starts to give new loans and advertise them? How is an increase in economic activity going to affect the amount of wages paid and and where will consumers most likely spend their wages? As the national economies begin to reopen after COVID-19 pandemic lockdowns, is to utilize SIOTs to calculate direct and indirect employment effects or value added effects of government grant programs to sectors such as cultural and creative industries or actors such as venues for performing arts, movie theaters, bars and restaurants.

Making such calculations requires a bit of matrix algebra, and understanding of input-output economics, direct, indirect effects, and multipliers. Economists, grant designers, policy makers have those skills, but until now, such calculations were either made in cumbersome Excel sheets, or proprietary software, as the key to these calculations is to keep vectors and matrices, which have at least one dimension of 64, perfectly aligned. We made this process reproducible with iotables and eurostat on rOpenGov

Our iotables package creates direct, indirect effects and multipliers programatically. Our observatory will make those indicators available for all European countries.

Accessing and tidying the data programmatically

The iotables package is in a way an extension to the eurostat R package, which provides a programmatic access to the Eurostat data warehouse. The reason for releasing a new package is that working with SIOTs requires plenty of meticulous data wrangling based on various metadata sources, apart from actually accessing the data itself. When working with matrix equations, the bar is higher than with tidy data. Not only your rows and columns must match, but their ordering must strictly conform the quadrants of the a matrix system, including the connecting trade or tax matrices.

When you download a country’s SIOT table, you receive a long form data frame, a very-very long one, which contains the matrix values and their labels like this:

## Table naio_10_cp1700 cached at C:\Users\...\Temp\RtmpGQF4gr/eurostat/naio_10_cp1700_date_code_FF.rds
# we save it for further reference here
saveRDS(naio_10_cp1700, "not_included/naio_10_cp1700_date_code_FF.rds")
# should you need to retrieve the large tempfiles, they are in
dir (file.path(tempdir(), "eurostat"))
dplyr::slice_head(naio_10_cp1700, n: 5)
## # A tibble: 5 x 7
## unit stk_flow induse prod_na geo time values
## <chr> <chr> <chr> <chr> <chr> <date> <dbl>
## 1 MIO_EUR DOM CPA_A01 B1G EA19 2019-01-01 141873.
## 2 MIO_EUR DOM CPA_A01 B1G EU27_2020 2019-01-01 174976.
## 3 MIO_EUR DOM CPA_A01 B1G EU28 2019-01-01 187814.
## 4 MIO_EUR DOM CPA_A01 B2A3G EA19 2019-01-01 0
## 5 MIO_EUR DOM CPA_A01 B2A3G EU27_2020 2019-01-01 0

The metadata reads like this: the units are in millions of euros, we are analyzing domestic flows, and the national account items B1-B2 for the industry A01. The information of a 64x64 matrix (the SIOT) and its connecting matrices, such as taxes, or employment, or C**O₂ emissions, must be placed exactly in one correct ordering of columns and rows. Every single data wrangling error will usually lead in an error (the matrix equation has no solution), or, what is worse, in a very difficult to trace algebraic error. Our package not only labels this data meaningfully, but creates very tidy data frames that contain each necessary matrix of vector with a key column.

iotables package contains the vocabularies (abbreviations and human readable labels) of three statistical vocabularies: the so called COICOP product codes, the NACE industry codes, and the vocabulary of the ESA2010 definition of national accounts (which is the government equivalent of corporate accounting).

Our package currently solves all equations for direct, indirect effects, multipliers and inter-industry linkages. Backward linkages show what happens with the suppliers of an industry, such as catering or advertising in the case of music festivals, if the festivals reopen. The forward linkages show how much extra demand this creates for connecting services that treat festivals as a ‘supplier’, such as cultural tourism.

Let’s seen an example

## Downloading employment data from the Eurostat database.
## Table lfsq_egan22d cached at C:\Users\...\Temp\RtmpGQF4gr/eurostat/lfsq_egan22d_date_code_FF.rds

and match it with the latest structural information on from the Symmetric input-output table at basic prices (product by product) Eurostat product. A quick look at the Eurostat website already shows that there is a lot of work ahead to make the data look like an actual Symmetric input-output table. Download it with iotable_get() which does basic labelling and preprocessing on the raw Eurostat files. Because of the size of the unfiltered dataset on Eurostat, the following code may take several minutes to run.

sk_io <- iotable_get ( labelled_io_data: NULL,
source: "naio_10_cp1700", geo: "SK",
year: 2015, unit: "MIO_EUR",
stk_flow: "TOTAL",
labelling: "iotables" )
## Reading cache file C:\Users\..\Temp\RtmpGQF4gr/eurostat/naio_10_cp1700_date_code_FF.rds
## Table naio_10_cp1700 read from cache file: C:\Users\..\Temp\RtmpGQF4gr/eurostat/naio_10_cp1700_date_code_FF.rds
## Saving 808 input-output tables into the temporary directory
## C:\Users\...\Temp\RtmpGQF4gr
## Saved the raw data of this table type in temporary directory C:\Users\...\Temp\RtmpGQF4gr/naio_10_cp1700.rds.

The input_coefficient_matrix_create() creates the input coefficient matrix, which is used for most of the analytical functions.

a_i**j: X_i**j / x_j

It checks the correct ordering of columns, and furthermore it fills up 0 values with 0.000001 to avoid division with zero.

input_coeff_matrix_sk <- input_coefficient_matrix_create(
data_table: sk_io
)
## Columns and rows of real_estate_imputed_a, extraterriorial_organizations are all zeros and will be removed.

Then you can create the Leontieff-inverse, which contains all the structural information about the relationships of 64x64 sectors of the chosen country, in this case, Slovakia, ready for the main equations of input-output economics.

I_sk <- leontieff_inverse_create(input_coeff_matrix_sk)

And take out the primary inputs:

primary_inputs_sk <- coefficient_matrix_create(
data_table: sk_io,
total: 'output',
return: 'primary_inputs')
## Columns and rows of real_estate_imputed_a, extraterriorial_organizations are all zeros and will be removed.

Now let’s see if there the government tries to stimulate the economy in three sectors, agricultulre, car manufacturing, and R&D with a billion euros. Direct effects measure the initial, direct impact of the change in demand and supply for a product. When production goes up, it will create demand in all supply industries (backward linkages) and create opportunities in the industries that use the product themselves (forward linkages.)

direct_effects_create( primary_inputs_sk, I_sk ) %>%
select ( all_of(c("iotables_row", "agriculture",
"motor_vechicles", "research_development"))) %>%
filter (.data$iotables_row %in% c("gva_effect", "wages_salaries_effect",
"imports_effect", "output_effect"))
## iotables_row agriculture motor_vechicles research_development
## 1 imports_effect 1.3684350 2.3028203 0.9764921
## 2 wages_salaries_effect 0.2713804 0.3183523 0.3828014
## 3 gva_effect 0.9669621 0.9790771 0.9669467
## 4 output_effect 2.2876287 3.9840251 2.2579634

Car manufacturing requires much imported components, so each extra demand will create a large importing activity. The R&D will create a the most local wages (and supports most jobs) because research is job-intensive. As we can see, the effect on imports, wages, gross value added (which will end up in the GDP) and output changes are very different in these three sectors.

This is not the total effect, because some of the increased production will translate into income, which in turn will be used to create further demand in all parts of the domestic economy. The total effect is characterized by multipliers.

Then solve for the multipliers:

multipliers_sk <- input_multipliers_create(
primary_inputs_sk %>%
filter (.data$iotables_row == "gva"), I_sk )

And select a few industries:

set.seed(12)
multipliers_sk %>%
tidyr::pivot_longer ( -all_of("iotables_row"),
names_to: "industry",
values_to: "GVA_multiplier") %>%
select (-all_of("iotables_row")) %>%
arrange( -.data$GVA_multiplier) %>%
dplyr::sample_n(8)
## # A tibble: 8 x 2
## industry GVA_multiplier
## <chr> <dbl>
## 1 motor_vechicles 7.81
## 2 wood_products 2.27
## 3 mineral_products 2.83
## 4 human_health 1.53
## 5 post_courier 2.23
## 6 sewage 1.82
## 7 basic_metals 4.16
## 8 real_estate_services_b 1.48

Vignettes

The Germany 1990 provides an introduction of input-output economics and re-creates the examples of the Eurostat Manual of Supply, Use and Input-Output Tables, by Jörg Beutel (Eurostat Manual).

The United Kingdom Input-Output Analytical Tables Daniel Antal, based on the work edited by Richard Wild is a use case on how to correctly import data from outside Eurostat (i.e. not with eurostat::get_eurostat()) and join it properly to a SIOT. We also used this example to create unit tests of our functions from a published, official government statistical release.

Finally, Working With Eurostat Data is a detailed use case of working with all the current functionalities of the package by comparing two economies, Czechia and Slovakia and guides you through a lot more examples than this short blogpost.

Our package was originally developed to calculate GVA and employment effects for the Slovak music industry (see our Slovak Music Industry Report), and similar calculations for the Hungarian film tax shelter. We can now programatically create reproducible multipliers for all European economies in the Digital Music Observatory, and create further indicators for economic policy making in the Economy Data Observatory.

Environmental Impact Analysis

Our package allows the calculation of various economic policy scenarios, such as changing the VAT on meat or effects of re-opening music festivals on aggregate demand, GDP, tax revenues, or employment. But what about the C**O₂, methane and other greenhouse gas effects of the reopening festivals, or the increasing meat prices?

Technically our package can already calculate such effects, but to do so, you have to carefully match further statistical vocabulary items used by the European Environmental Agency about air pollutants and greenhouse gases.

The last released version of iotables is Importing and Manipulating Symmetric Input-Output Tables (Version 0.4.4). Zenodo. https://doi.org/10.5281/zenodo.4897472, but we are alread working on a new major release. In that release, we are planning to build in the necessary vocabulary into the metadata functions to increase the functionality of the package, and create new indicators for our Green Deal Data Observatory. This experimental data observatory is creating new, high quality statistical indicators from open governmental and open science data sources that has not seen the daylight yet.

rOpenGov and the EU Datathon Challenges

rOpenGov, Reprex, and other open collaboration partners teamed up to build on our expertise of open source statistical software development further: we want to create a technologically and financially feasible data-as-service to put our reproducible research products into wider user for the business analyst, scientific researcher and evidence-based policy design communities.

rOpenGov is a community of open governmental data and statistics developers with many packages that make programmatic access and work with open data possible in the R language. Reprex is a Dutch-startup that teamed up with rOpenGov and other open collaboration partners to create a technologically and financially feasible service to exploit reproducible research products for the wider business, scientific and evidence-based policy design community. Open data is a legal concept - it means that you have the rigth to reuse the data, but often the reuse requires significant programming and statistical know-how. We entered into the annual EU Datathon competition in all three challenges with our applications to not only provide open-source software, but daily updated, validated, documented, high-quality statistical indicators as open data in an open database. Our iotables package is one of our many open-source building blocks to make open data more accessible to all.

Join our open collaboration Digital Music Observatory team as a data curator, developer or business developer. More interested in environmental impact analysis? Try our Green Deal Data Observatory team! Or economic policies, particularly computation antitrust, innovation and small enterprises? Check out our Economy Music Observatory team!

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

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.)

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!

Reproducible Survey Harmonization: retroharmonize Is Released

Mon, 21 Sep 2020 11:31:39 +0000

Our original intention was to make surveying more accessible for music and creative industry partners, by relying more on already existing survey data, and better designing complementary, smaller surveys, becasue surveying, opinion polling is becoming increasingly expensive in the develop world. People are less and less likely to sit down for an interview in their houses. We have tried to harmonize our custom surveys, particuarly with Kantar in Hungary and Focus in Slovakia with exisiting EU projects. But we ended up making a part of international survey harmonization across countries and throughout years easier to automate.

Surveys are like sensors for natural sciences and industrial production. They are essential for almost any social and economic statistical indicator, for calculating the inflation, parts of the GDP, participation in education programs. Making surveys easier to harmonize and exploit more already existing survey data can bring down research cost, and can increase research value at the same time. (See our earlier blog post Increase The Value Of Market Research With Open Data And Survey Harmonization.)

So, if you are an R user, you can use install.packages(“retroharmonize”) to get the released 0.1.13 version and make tutorials with real Eurobarometer or Afrobarometer microdata. With devtools::install_github("antaldaniel/retroharmonize") you can already install the current development version 0.1.14, which handles perl-like regex, which will be necessary for our next tutorial in the making for Arab Barometer.

Related:

Launching Our Demo Music Observatory

Tue, 15 Sep 2020 08:00:39 +0000

Today, on 15 September 2020, we officially launched our minimal viable product as we promised to partners back in February. This was a particularly difficult period for everybody. We aspired to deliver by September in a very different environment, our hopes for commissioned work went up in flames with the pandemic, and our targeted users, musicians and music entrepreneurs, talent managers, music venues lost most of their income. The organizations helping them, granting authorities, export offices and collective management societies are overwhelmed with the problem. During these troublesome times, our team expanded, attracted great new talent, and kept working.

Our first product is the Demo Music Observatory, a collaborative, automated research-based observatory for the music industry, one that is particularly hard hit by the COVID19 crisis. Not only great artists, composers, technicians, managers fell victim to the virus, but musicians lost about 50–90% of their income from live music. This translates to a 100% loss for the live music technicians and managers.

See our earlier blogpost on what you see on the video.

The music industry was never a place for great job security. For putting up a show, you usually need a network of 10–200 artists, technicians and managers to work together as freelancers without all those social benefits that many people enjoy in other walks of life. We have been trying to figure out how to help this microenterprise and freelancer-network based industry with research for five years. Our aim is to make them competitive when they are talking with their buyers: Google, Apple, Spotify, who are really heavy-weight data and AI pros. Our better plan their tours, when they will be back on the road, to understand what sort of audiences and purchasing power waits for them in different European cities.

We are launching at a time when the music industry is crying for help.Therefore, we have decided to make our demo observatory open and unfinished. Over the last 7 years, we have built up about 2000 music and creative sector indicators to be used for business KPIs, forecasting targets, grant evaluations, royalty valuations, concert demography target group analysis and other professional uses. We would like to open up, based on your needs, about 50 well-designed indicators, and pledge to keep it daily refreshed, corrected, documented, citaable, downloadable. Also, feel free to use our most valuable source code—use it for your own purposes, even modify it, as long as you keep it open.

For our smaller partners, we follow what musicians do these days on Bandcamp: name your price. We make a pledge to our small partners: if you need reliable data to plan your next grant calls, calculate royalties, compensations, predict hit candidates, give us the job—and name your price. Post-corona, you can take for a dollar the best music from Bandcamp. You can take our research products, for a limited period, for any amount you name, as long as it is for a good cause and serves the industry, musicians, technicians or managers. In return, we ask for your feedback. Help us validate whether we are on the right track, tell us how we can cooperate after the pandemic, in better times.

Our larger and better funded partners? We ask you to pay the price we name, because we believe that it is a well-justified, fair and competitive price, set by pricing experts.

We appreciate it if you take a look at our offering, or if you pass this blogpost on to your colleagues in the industry. Our main target audience initially are music professional in broader Europe, but we are planning to cover all major global markets very soon, too. Feedback from the U.S., Australia, Canada, Colombia, Brazil & Argentina is particularly welcome as we have great plans over there!

Who we are?

We started our operations on 1 September 2020 on the basis of CEEMID, a pan-European data observatory that created about 2000 music and creative industry indicators for its users. In the coming days, we are gradually opening up about 50 music industry and 50 broader creative industry indicators in a fully reproducible workflow, with daily re-freshed, re-processed, well-formatted and documented indicators for business and policy decisions.

We would like to validate this approach in one of the world’s most prestigious university-backed incubator programs, in the Yes!Delft AI/Blockchain Validation Lab. We’re finalist on their selection, and all help before 23 September from our friends in the music industry is more than appreciated. If we get there, we can rely on probably the best pros in Europe to make our offering better tailored and financially sustainable.

Get in touch!

We use the very simple and extremely secure keybase.io, a kind of mix of Whatsapp, Skype, Google Drive, One Drive and zoom. You can get in touch on that platform with us in anytime here.

You can easily contact on LinkedIn Daniel or Kátya and of course, we have a usually working email contact form, too. Our email is name.surname at our main domain.

Video credits

Data acquisition and processing: Daniel Antal, CFA and Marta Kołczyńska, PhD (survey data).
Documentation automation: Sandor Budai
Video art: Line Matson
Music: Moon Moon Moon.

Creating An Automated Data Observatory

Fri, 11 Sep 2020 16:00:39 +0000

We are building data ecosystems, so called observatories, where scientific, business, policy and civic users can find factual information, data, evidence for their domain. Our open source, open data, open collaboration approach allows to connect various open and proprietary data sources, and our reproducible research workflows allow us to automate data collection, processing, publication, documentation and presentation.

Our scripts are checking data sources, such as Eurostat’s Eurobase, Spotify’s API and other music industry sources every day for new information, and process any data corrections or new disclosure, interpolate, backcast or forecast missing values, make currency translations and unit conversions. This is shown illustrated with an earlier post.

For direct access to the file visit this link.

In the video we show automated the creation of an observatory website with well-formatted, statistical data dissemination, a technical document in PDF and an ebook can be automated. In our view, our technology is particularly useful technology in business and scientific researech projects, where it is important that always the most timely and correct data is being analyzed, and remains automatically documented and cited. We are ready deploy public, collaborative, or private data observatories in short time.

Data processing costs can be as high as 80% for any in-house AI deployment project. We work mainly with organization that do not have in house data science team, and acquire their data anyway from outside the organization. In their case, this rate can be as high as 95%, meaning that getting and processing the data for deploying AI can be 20x more expensive than the AI solution itself.

AI solutions require a large amount of standardized, well processed data to learn from. We want to radically decrease the cost of data acquisition and processing for our users so that exploiting AI becomes in their reach. This is particularly important in one of our target industries, the music industries, where most of the global sales is algorithmic and AI-driven. Artists, bands, small labels, publishers, even small country national associations cannot remain competitive if they cannot participate in this technological revolution.

We would like to validate this approach in one of the world’s most prestigious university-backed incubator programs, in the Yes!Delft AI/Blockchain Validation Lab.

Video credits

Data acquisition and processing: Daniel Antal, CFA and Marta Kołczyńska, PhD (survey data).
Documentation automation: Sandor Budai
Video art: Line Matson
Music: Moon Moon Moon.

Starting-up

Mon, 24 Aug 2020 10:15:00 +0000

The big day has come: the co-founders singed off the documents at the public notary and started the registration of a reproducible research start-up in Leiden. We got a lot of support from our friends! Your encouragement gives us a lot of energy to accomplish our first milestones, and to get Reprex B.V. going!

Reprex means ‘reproducible example’ in data science. When you are stuck with a problem, creating a reproducible example allows other computer scientists, statisticians, programmers or data users to solve it. In 80% of the cases, you usually find the solution while creating a generalized example. In the 20% other cases, you can reach out for help easily.

In the coming days, we are launching demo versions of our headline products, data observatories. music.dataobservatory.eu will be a fully automated online service that every day collects, processes, cleans, and publishes scientifically valid data about European music. Very soon after we will launch two other observatories.

The creative and cultural sector, NGOs, most research institutions, data journalism teams are usually very small, and they do not have internal IT or data science capacities. We would like to provide them a transparent, high quality, and fully open source solution to acquire data, process it without errors, document it and make sense of it. We would like to embrace the idea of open collaboration among creative enterprises, scientific researchers, NGOs, data journalists and policymakers with our work.

Our work will comply with the Open Policy Analysis standards developed by the Berkeley Initiative for Transparency in the Social Sciences & Center for Effective Global Action and the four principles of reproducible research: reviewability, replicability, confirmability and auditability. We believe that these standards apply in reproducible finance, empirical evidence presentation in courts, or advocating sound policies and producing high-quality journalism.

Do you want to help our start?

We would like to enter into the Validation Lab of one of the best artificial intelligence incubators in early September. Talented team members, letters of intents and assignments from organizations will give a lot of credibility to our start Meet our team ».

Put as in contact with people who love to write code in R and interested in automating business and social science research and primary data collection such as surveying. Check out what sort of code we create »
Introduce us to people who need data and information to make better informed decision and analysis in music, film, book publishing, photography services or socially responsible finance.
Share contacts of data journalists who would like to develop stories from big survey programs like Eurobarometer, Afrobarometer and Lationbarometro, or base their storytelling on data and its visualizations. See our survey harmonization examples »

Do you know such people? Send over this post or connect us in an email or social media message!

Thanks again for your good wishes and encouragements, and hope to hear from you soon!