Competition | Reprex

Competition Data Observatory

Wed, 23 Jun 2021 00:00:00 +0000

Our observatory is monitoring the certain segments of the European economy, and develops tools for computational antitrust in Europe. We take a critical SME-, intellectual property policy and competition policy point of view automation, robotization, and the AI revolution on the service-oriented European social market economy.

We aim to create early-warning, risk, economic effect, and impact indicators that can be used in scientific, business and policy contexts for professionals who are working on re-setting the European economy after a devastating pandemic and in the age of AI. We would like to map data between economic activities (NACE), antitrust markets, and sub-national, regional, metropolitian area data.

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Finding reliable historic and new data that can fuel large market monitoring schemes or computational antitrust models is surprisingly hard. And it is even more hopeless if you work as a (data) journalist, a policy researcher in an NGO, or in a competition law practice that does not provide you with an army of (geo)statisticians, data engineers, and data scientists who can render various data into usable format, i.e. something that you can trust, quote, visualize, import, or copy & paste.

Better, Bigger, Faster, More

Get more information from public datasets, or data you had paid for. Find more data in higher quality that is available sooner than in other sources.

Novel data products	Official statistics at the national and European levels follow legal regulations, and in the EU, compromises between member states. New policy indicators often appear 5-10 years after demand appears. We employ the same methodology, software, and often even the same data that Eurostat might use to develop policy indicators, but we do not have to wait for a political and legal consensus to create new datasets. See our 100,000 Opinions on the Most Pressing Global Problem blogpost.
Better data	Statistical agencies, old fashioned observatories, and data providers often do not have the mandate, know-how or resources to improve data quality. Using peer-reviewed statistical software and hundreds of computational tests, we are able to correct mistakes, impute missing data, generate forecasts, and increase the information content of public data by 20-200% percent. This makes the data usable for NGOs, journalists, and visual artists—among other potential users—who do not have this statistical know-how to make incomplete, mislabelled or low quality data usable for their needs and applications. See our example with the Turnover of the Radio Broadcasting Industry in Europe indicator.
Never seen data	The 2019/1024 directive on open data and the re-use of public sector information of the European Union (which is an extension and modernization of the earlier directives on re-use of public sector information since 2003) makes data gathered in EU institutions, national institutions, and municipalities, as well as state-owned companies legally available. According to the European Data Portal the estimated historical cost of the data released annually is in the billions of euros. But if this data is a gold mine, its full potential can only be unlocked by an experienced data mining partner like Reprex. Here is why: data is not readily downloadable; it sits in various obsolete file formats in disorganized databases; it is documented in various languages, or not documented at all; it is plagued with various processing errors. We make the powerful promise Government Budget Allocations for R&D in Environment of the EU legislation a reality in the field of the Green Deal policy context.

Increase Your Impact, Avoid Old Mistakes

Reprex helps its policy, business, and scientific partners by providing efficient solutions for necessary data engineering, data processing and statistical tasks that are as complex as they are tedious to perform. We deploy validated, open-source, peer-reviewed scientific software to create up-to-date, reliable, high-quality, and immediately usable data and visualizations. Our partners can leave the burden of this task, share the cost of data processing, and concentrate on what they do best: disseminating and advocating, researching, or setting sustainable business or underwriting indicators and creating early warning systems.

Impact

We publish the data in a way that it is easy to find—as a separate data publication with a DOI, full library metadata, and place it in open science repositories. Our data is more findable than 99% of the open science data, and therefore makes far bigger impact. See our data on the European open science repository Zenodo managed by CERN (the European Organization for Nuclear Research).

Easy-to-use data

Our data follows the tidy data principle and comes with all the recommended Dublin Core and DataCite metadata. This increases our data compatibility, allowing users to open it in any spreadsheet application or import into their databases. We publish the data in tabular form, and in JSON form through our API enabling automatic retrieval for heavy users, especially if they plan to automatically use our data in daily or weekly updates. Using the best practice of data formatting and documentation with metadata ensures reproducibility and data integrity, rather than repeating data processing and preparation steps (e.g. changing data formats, removing unwanted characters, creating documentation, and other data processing steps that take up thousands of working hours. See our blogpost on the data Sisyphus.

Ethical Big Data for All

Big data creates inequalities, because only the largest corporations, government bureaucracies and best endowed universities can afford large data collection programs, the use of satellites, and the employment of many data scientists. Our open collaboration method of data pooling and cost sharing makes big data available for all.

Big picture

Integrating and joining data is hard—it requires engineering, mathematical, and geo-statistical know-how that a large amount of environmental users and stakeholders do not possess. Some examples of the challenges implicit in making data usable include addressing the changing boundaries of French departments (and European administrative-geographic borders, in general), various projections of coordinates on satellite images of land cover, different measurement areas for public opinion and hydrological data, public finance expressed in different orders (e.g. millions versus thousands of euros). We create data that is easy to combine, map, and visualize for end users. See our case study on the severity and awareness of flood risk in Belgium, as well as the financial capacity to manage it.

Better data

AI in 2021 increases data inequalities because large government and corporate entities with an army of data engineers can create proprietary, black box business algorithms that fundamentally alter our lives. We are involved in the R&D and advocacy of the EU’s trustworthy AI agenda which aims at similar protections like GDPR in privacy. We want to demystify AI by making it available for organizations who cannot finance a data engineering team, because 95% of a successful AI is cheap, complete, reliable data tested for negative outcomes – precisely what dwe offer to our users.

Open Collaboration

Reprex grew out of an international data cooperation and works in the open-source world. We use the agile open collaboration method that allows us to work with large corporations, NGOs, developers, university researcher institutes and individuals on an equal footing.

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

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

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

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

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

The EU Datathon 2021 Challenge

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

Mission statement

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

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

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

Challenge 1: A European Grean Deel

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

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

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

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

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

Challenge 2: A Europe fit for the digital age

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

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

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

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

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

Challenge 3: A Europe fit for the digital age

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

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

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

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

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

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

Problem Statement

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

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

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

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

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

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

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

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.

Music Creators' Earning Project

Tue, 29 Sep 2020 10:00:00 +0000

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

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

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

We have started to retrospectively harmonize the Music Creators Earnigs survey for the the Digital Music Observatory. The survey’s raw data is accessible on the website of the UKIPO here. Because of the bias of the survey, we did not include statistical indicators of the survey yet in our observatory, but we made the processed data available on our open repository space on Zenodo. Nevertheless, because of the relatively large sample size (n=708) we believe that important comparisons can be made with our CEEMID surveys, and we can shed some light on the earnings distribution of UK artists, and the way they distribute and finance their recordings.

see our Report
See our main findings in a blogpost
See the first version of the processed, machine readable, partially cleaned datafile of the Music Creators Earnings survey 2020.