open-science | Automated Data Observatories

Including Indicators from Arab Barometer in Our Observatory

Mon, 28 Jun 2021 09:00:00 +0200

A new version of the retroharmonize R package – which is working with retrospective, ex post harmonization of survey data – was released yesterday after peer-review on CRAN. It allows us to compare opinion polling data from the Arab Barometer with the Eurobarometer and Afrorbarometer. This is the first version that is released in the rOpenGov community, a community of R package developers on open government data analytics and related topics.

Surveys are the most important data sources in social and economic statistics – they ask people about their lives, their attitudes and self-reported actions, or record data from companies and NGOs. Survey harmonization makes survey data comparable across time and countries. It is very important, because often we do not know without comparison if an indicator value is low or high. If 40% of the people think that climate change is a very serious problem, it does not really tell us much without knowing what percentage of the people answered this question similarly a year ago, or in other parts of the world.

With the help of Ahmed Shabani and Yousef Ibrahim, we created a third case study after the Eurobarometer, and Afrobarometer, about working with the Arab Barometer harmonized survey data files.

Ex ante survey harmonization means that researchers design questionnaires that are asking the same questions with the same survey methodology in repeated, distinct times (waves), or across different countries with carefully harmonized question translations. Ex post harmonizations means that the resulting data has the same variable names, same variable coding, and can be joined into a tidy data frame for joint statistical analysis. While seemingly a simple task, it involves plenty of metadata adjustments, because established survey programs like Eurobarometer, Afrobarometer or Arab Barometer have several decades of history, and several decades of coding practices and file formatting legacy.

Variable harmonization means that if the same question is called in one microdata source Q108 and the other eval-parl-elections then we make sure that they get a harmonize and machine readable name without spaces and special characters.
Variable label harmonization means that the same questionnaire items get the same numeric coding and same categorical labels.
Missing case harmonization means that various forms of missingness are treated the same way.

For the climate awareness dataset get the country averages and aggregates from Zenodo, and the plot in jpg or png from figshare.

In our new Arab Barometer case study, the evaulation of parliamentary elections has the following labels. We code them consistently 1 = free_and_fair, 2 = some_minor_problems, 3 = some_major_problems and 4 = not_free.

“0. missing”	“1. they were completely free and fair”
“2. they were free and fair, with some minor problems”	“3. they were free and fair, with some major problems”
“4. they were not free and fair”	“8. i don’t know”
“9. declined to answer”	“Missing”
“They were completely free and fair”	“They were free and fair, with some minor breaches”
“They were free and fair, with some major breaches”	“They were not free and fair”
“Don’t know”	“Refuse”
“Completely free and fair”	“Free and fair, but with minor problems”
“Free and fair, with major problems”	“Not free or fair”
“Don’t know (Do not read)”	“Decline to answer (Do not read)”

Of course, this harmonization is essential to get clean results like this:

For evaluation or reuse of parliamentary elections dataset get the replication data and the code from the Zenodo open repository.

In our case study, we had three forms of missingness: the respondent did not know the answer, the respondent did not want to answer, and at last, in some cases the respondent was not asked, because the country held no parliamentary elections. While in numerical processing, all these answers must be left out from calculating averages, for example, in a more detailed, categorical analysis they represent very different cases. A high level of refusal to answer may be an indicator of surpressing democratic opinion forming in itself.

Survey harmonization with many countries entails tens of thousands of small data management task, which, unless automatically documented, logged, and created with a reproducible code, is a helplessly error-prone process. We believe that our open-source software will bring many new statistical information to the light, which, while legally open, was never processed due to the large investment needed.

We also started building experimental APIs data is running retroharmonize regularly. We will place cultural access and participation data in the Digital Music Observatory, climate awareness, policy support and self-reported mitigation strategies into the Green Deal Data Observatory, and economy and well-being data into our Economy Data Observatory.

Further plans

Retrospective survey harmonization is a far more complex task than this blogpost suggest. Retrospective survey harmonization is a far more complex task than this blogpost suggest, because established survey programs have gathered decades of legacy data in legacy coding schemes and legacy file formats. Putting the data right, and especially putting the invaluable descriptive and administrative (processing) metadata right is a huge undertaking. We are releasing example codes, datasets and charts for researchers to comapre our harmonized results with theirs, and improve our software. We are releasing example codes, datasets and charts for researchers to comapre our harmonized results with theirs, and improve our software.

Use our software

The retroharmonize R package can be freely used, modified and distributed under the GPL-3 license. For the main developer and contributors, see the package homepage. If you use it for your work, please kindly cite it as:

Daniel Antal (2021). retroharmonize: Ex Post Survey Data Harmonization. R package version 0.1.17. https://doi.org/10.5281/zenodo.5034752

Download the BibLaTeX entry.

Tutorial to work with the Arab Barometer survey data

Daniel Antal, & Ahmed Shaibani. (2021, June 26). Case Study: Working With Arab Barometer Surveys for the retroharmonize R package (Version 0.1.6). Zenodo. https://doi.org/10.5281/zenodo.5034759

For the replication data to report potential issues and improvement suggestions with the code:

Daniel Antal, & Ahmed Shaibani. (2021). Replication Data for the retroharmonize R Package Case Study: Working With Arab Barometer Surveys (Version 0.1.6) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5034741

Experimental API

We are also experimenting with the automated placement of authoritative and citeable figures and datasets in open repositories. For the climate awareness dataset get the country averages and aggregates from Zenodo, and the plot in jpg or png from figshare. Our plan is to release open data in a modern API with rich descriptive metadata meeting the Dublin Core and DataCite standards, and further administrative metadata for correct coding, joining and further manipulating or data, or for easy import into your database.

Join our open source effort

Want to help us improve our open data service? Include Lationbarómetro and the Caucasus Barometer in our offering? Join the rOpenGov community of R package developers, an our open collaboration to create the automated data observatories. We are not only looking for developers, but data curators and service design associates, too.

Open Data - The New Gold Without the Rush

Fri, 18 Jun 2021 17:00:00 +0200

If open data is the new gold, why even those who release fail to reuse it? We created an open collaboration of data curators and open-source developers to dig into novel open data sources and/or increase the usability of existing ones. We transform reproducible research software into research- as-service.

Every year, the EU announces that billions and billions of data are now “open” again, but this is not gold. At least not in the form of nicely minted gold coins, but in gold dust and nuggets found in the muddy banks of chilly rivers. There is no rush for it, because panning out its value requires a lot of hours of hard work. Our goal is to automate this work to make open data usable at scale, even in trustworthy AI solutions.

There is no rush for it, because panning out its value requires a lot of hours of hard work. Our goal is to automate this work to make open data usable at scale, even in trustworthy AI solutions.

Most open data is not public, it is not downloadable from the Internet – in the EU parlance, “open” only means a legal entitlement to get access to it. And even in the rare cases when data is open and public, often it is mired by data quality issues. We are working on the prototypes of a data-as-service and research-as-service built with open-source statistical software that taps into various and often neglected open data sources.

We are in the prototype phase in June and our intentions are to have a well-functioning service by the time of the conference, because we are working only with open-source software elements; our technological readiness level is already very high. The novelty of our process is that we are trying to further develop and integrate a few open-source technology items into technologically and financially sustainable data-as-service and even research-as-service solutions.

Our review of about 80 EU, UN and OECD data observatories reveals that most of them do not use these organizations’s open data - instead they use various, and often not well processed proprietary sources.

We are taking a new and modern approach to the data observatory concept, and modernizing it with the application of 21st century data and metadata standards, the new results of reproducible research and data science. Various UN and OECD bodies, and particularly the European Union support or maintain more than 60 data observatories, or permanent data collection and dissemination points, but even these do not use these organizations and their members open data. We are building open-source data observatories, which run open-source statistical software that automatically processes and documents reusable public sector data (from public transport, meteorology, tax offices, taxpayer funded satellite systems, etc.) and reusable scientific data (from EU taxpayer funded research) into new, high quality statistical indicators.

We are taking a new and modern approach to the ‘data observatory’ concept, and modernizing it with the application of 21st century data and metadata standards, the new results of reproducible research and data science

We are building various open-source data collection tools in R and Python to bring up data from big data APIs and legally open, but not public, and not well served data sources. For example, we are working on capturing representative data from the Spotify API or creating harmonized datasets from the Eurobarometer and Afrobarometer survey programs.
Open data is usually not public; whatever is legally accessible is usually not ready to use for commercial or scientific purposes. In Europe, almost all taxpayer funded data is legally open for reuse, but it is usually stored in heterogeneous formats, processed into an original government or scientific need, and with various and low documentation standards. Our expert data curators are looking for new data sources that should be (re-) processed and re-documented to be usable for a wider community. We would like to introduce our service flow, which touches upon many important aspects of data scientist, data engineer and data curatorial work.
We believe that even such generally trusted data sources as Eurostat often need to be reprocessed, because various legal and political constraints do not allow the common European statistical services to provide optimal quality data – for example, on the regional and city levels.
With rOpenGov and other partners, we are creating open-source statistical software in R to re-process these heterogenous and low-quality data into tidy statistical indicators to automatically validate and document it.
We are carefully documenting and releasing administrative, processing, and descriptive metadata, following international metadata standards, to make our data easy to find and easy to use for data analysts.
We are automatically creating depositions and authoritative copies marked with an individual digital object identifier (DOI) to maintain data integrity.
We are building simple databases and supporting APIs that release the data without restrictions, in a tidy format that is easy to join with other data, or easy to join into databases, together with standardized metadata.
We maintain observatory websites (see: Digital Music Observatory, Green Deal Data Observatory, Economy Data Observatory) where not only the data is available, but we provide tutorials and use cases to make it easier to use them. Our mission is to show a modern, 21st century reimagination of the data observatory concept developed and supported by the UN, EU and OECD, and we want to show that modern reproducible research and open data could make the existing 60 data observatories and the planned new ones grow faster into data ecosystems.

We are working around the open collaboration concept, which is well-known in open source software development and reproducible science, but we try to make this agile project management methodology more inclusive, and include data curators, and various institutional partners into this approach. Based around our early-stage startup, Reprex, and the open-source developer community rOpenGov, we are working together with other developers, data scientists, and domain specific data experts in climate change and mitigation, antitrust and innovation policies, and various aspects of the music and film industry.

Our open collaboration is truly open: new data curators,developers and service designers, even volunteers and citizen scientists are welcome to join.

Our open collaboration is truly open: new data curators, data scientists and data engineers are welcome to join. We develop open-source software in an agile way, so you can join in with an intermediate programming skill to build unit tests or add new functionality, and if you are a beginner, you can start with documentation and testing our tutorials. For business, policy, and scientific data analysts, we provide unexploited, exciting new datasets. Advanced developers can join our development team: the statistical data creation is mainly made in the R language, and the service infrastructure in Python and Go components.

Music Creators’ Earnings in the Streaming Era

Fri, 18 Jun 2021 08:00:00 +0200

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

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

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

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

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

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

Justified and Unjustified Differences in Earnings

Stating that the greatest difference among rightsholders’ earnings is related to the popularity of their works and recorded fixations can appear banal and trivial. Yet, because many payout problems appear in the hard-to-describe long tail, understanding the justified differences of rightsholder earnings is an important step towards identifying the unjustified differences. It would be a breach of copyright law if less popular, or never played artists, would receive significantly more payment at the expense of popular artists from streaming providers. The earnings must reflect the difference in use and the economic value in use among rightsholders.

In our analysis we quantify differences using the actual data of the CEEMID-CI Streaming Indexes, created from hundreds of millions of data points, and computer simulations under realistic scenarios.

Justified Difference & Changes Over Time

Among the justified differences we quantify four objective justifications:

The variability of the domestic price of a stream over time shows a diminishing, but variable value of streams. Depending on the release date of a recording, and how quickly it builds up or loses the interest of the audience, the same number of streams can result in about 28% different earnings. In the period 2015-2019, later releases were facing diminishing revenues on streaming platforms.
The variability of international market share and international streaming prices in International Competitiveness. Compared to UK streaming prices, most international markets, particularly emerging markets, have a much greater variability of streaming prices. The variability of prices in advanced foreign markets such as Germany was similar to that of the British market, but in emerging markets and smaller advanced markets–such as the Netherlands–we measured a variability of around 50-80%. Artists who have a significant foreign presence, depending on their foreign market share, can experience 2-3 times greater differences in earnings than artists whose audience is predominantly British.

In our simulated results, the depreciation of the GBP shielded the internationally competitive rightsholders from a significant part of the otherwise negative price change in streaming markets.

The variability of the exchange rate that is applied when translating foreign currency revenues to the British pound in Exchange Rate Effects. Our CEEMID-CI Streaming Indexes index covers the post-Brexit referendum period, when the British pound was generally depreciating against most currencies. This resulted in a GBP-denominated translation gain for artists with a foreign presence. We show that the variability of the GBP exchange rates can add bigger justified differences among rightsholders’ earnings than the entire British price variation. The exchange rate movements are typically in the range of 30%, or at the level of the British domestic price variations in streaming prices. In our simulated results, this effect shielded the internationally competitive rightsholders from a significant part of the otherwise negative price change in foreign markets.
We were also investigating the choice of distribution model. , i.e., Both models, the currently used pro-rata model and the user-centric distribution, which has many proponents (and was introduced by SoundCloud in 2021), changes the earnings of artists. We think that both models represent a bad compromise, but they are legal, and a change of zero-sum distribution change could potentially increase the income of less popular and older artists at the expense of very popular and younger artists. More about this in our forthcoming report!

In our understanding, there are some known and some hypothetical causes of unjustified earning differences.

We did not have systemic data on the uncollected revenues–these are earnings that are legally made, but due to documentation, matching, processing, accounting, or other problems, the earnings are not paid. We could not even attempt to estimate this problem in the absence of relevant British empirical data. The problem is likely to be greater in the case of composers than in producer and performer revenues. In ideal cases, of course, the unclaimed royalty is near 0% of the earnings; it seems that on advanced markets the magnitude of this problem is in the single digits, and in emerging markets much greater, sometimes up to 50%. Royalty distribution is a costly business, and the smaller the revenue, the smaller the cost base to manage billions of transactions and related micropayments. We are working with a large group of eminent copyright researchers to understand this program better and provide regulatory solutions. See Recommendation Systems: What can Go Wrong with the Algorithm? - Effects on equitable remuneration, fair value, cultural and media policy goals
More analysis is required to understand how the algorithmic, highly autonomous recommendation systems of digital platforms such as Spotify, YouTube, Apple, and Deezer, among others, impact music rightsholders’ earnings. There are some empirical findings that suggest that such biases are present in various platforms, but due to the high complexity of recommendation systems, it is impossible to intuitively assign blame to pre-existing user biases, wrong training datasets, improper algorithm design, and other factors. We are working with our data curators, competition economist Dr Peter Ormosi, antropologist and data scientist Dr Botond Vitos and musicologist Dominika Semaňáková - We Want Machine Learning Algorithms to Learn More About Slovak Music to understand what can go wrong here (see Trustworthy AI: Check Where the Machine Learning Algorithm is Learning From).
There is always a hypothetical possibility that organizations with monopolistic power try to corner the market or make the playing field uneven. The music industry requires a permanent market monitoring facility to win fights in competition tribunals, because it is increasingly disputing revenues with the world’s biggest data owners. We are working with our data curators, competition economist Dr Peter Ormosi and copyright lawyer dr Eszter Kabai - New Indicators for Royalty Pricing and Music Antitrust to find potential traces of an uneven playing field (see: Music Streaming: Is It a Level Playing Field?.)

Solidarity & Equitable Remuneration

Equitable remuneration is a legal concept which has an economic aspect. In international law, it simply means that men and women should receive equal pay for equal work. Within the context of international copyright law, it was introduced by the Rome Convention, and it means that equitable remuneration means the same payment for the same use, regardless of genre, gender or other unrelated characteristics of the rightsholder.

While the word equitable in everyday usage often implies some level of equality and solidarity, in the context of royalty payments, these terms should not be mixed.

Digital Music Observatory uses harmonized, anonimous surveys conducted among musicians to find out about their living conditions compared to their peers in other countries, and people in other professions.

Solidarity is present in many royalty payout schemes, but it is unrelated to the legal concept of equitable remuneration. Music earnings are very heavily skewed towards a small number of very successful composers, performers, and producers. The music streaming licensing model has little elements of solidarity, unlike some of the licensing models that it is replacing–particularly public performance licensing. However, in those cases, the solidarity element is decided by the rightsholders themselves, and not by external parties like radio broadcasters or streaming service providers.

The so-called socio-cultural funds that provide assistance for artists in financial need must be managed by rightsholders, not others, and the current streaming model makes the organizations of such solidaristic action particularly difficult. Our data curator, Katie Long is working on us to find metrics and measurement possibilities on solidarity among rightsolders.

The Size of the Pie and the Distribution of the Pie

The current debate in the United Kingdom is often organized around the submission of the #BrokenRecord campaign to the DCMS committee, which calls for a legal re-definition of equitable remuneration rights⁴. This idea is not unique to the United Kingdom, in various European jurisdictions, performers fought similar campaigns for legislation or went to court, sometimes successfully, for example, in Hungary.

Another hot redistribution topic is the choice between the so-called pro-rata versus user-centric distribution of streaming royalties. We think that both models represent a bad compromise, but they are legal, and a zero-sum distribution change could potentially increase the income of less popular and older artists at the expense of very popular and younger artists. We instead propose an alternative approach of artist-centric distribution that could be potentially a win-win for all rightsholder groups; further elaboration of the concept lies outside the scope of this report.

Digital Music Observatory uses monitors volumes, prices and revenues on the total market, and compares them to calculated fair values. By understand the entire music economy, we can highlight when music is devaluing in various uses or countries.

Our mission with the Digital Music Observatory is to help focus the policy debate with facts around the economics of music streaming. The legal concept equitable remuneration is inseparable from the economic concept of fair valuation, and music streaming earnings cannot be subject to a valid economic analysis without analyzing the economics of music. Streaming services are competing with digital downloads, physical sales, and radio broadcasting; and the media streaming of YouTube and similar services is competing with music streaming, radio, and television broadcasting as well as retransmissions. It is a critically important to determine if in replacing earlier services and sales channels, the new streaming licensing model (a mix of the mechanical and public performance licensing) is also capable of replacing the revenues for all rightsholders.

The current streaming licensing model in Europe is a mix of mechanical and public performance rights. Therefore, when we are talking about music streaming, we must compare the streaming sub-market with the digital downloads, physical sales and private copying markets (mechanical licensing), and with the radio, television, cable and satellite retransmission markets (public performance licensing.) Our experience outside the UK suggests that these replacement values are very low.

Join us

Join our open collaboration Music Data Observatory team as a data curator, developer or business developer. More interested in antitrust, innovation policy or economic impact analysis? Try our Economy Data Observatory team! Or your interest lies more in climate change, mitigation or climate action? Check out our Green Deal Data Observatory team!

Footnote References

state51 Music Group. 2020. “Written Evidence Submitted by The state51 Music Group. Economics of Music Streaming Review. Response to Call for Evidence.” UK Parliament website. https://committees.parliament.uk/writtenevidence/15422/html/. ↩︎
Artisjus, HDS, SOZA, and Candole Partners. 2014. “Measuring and Reporting Regional Economic Value Added, National Income and Employment by the Music Industry in a Creative Industries Perspective. Memorandum of Understanding to Create a Regional Music Database to Support Professional National Reporting, Economic Valuation and a Regional Music Study.” ↩︎
Antal, Daniel. 2021. “Launching Our Demo Music Observatory.” Data & Lyrics. Reprex. https://dataandlyrics.com/post/2020-09-15-music-observatory-launch/. ↩︎
Gray, Tom. 2020. “#BrokenRecord Campaign Submission (Supplementary to Oral Evidence).” UK Parliament website. https://committees.parliament.uk/writtenevidence/15512/html/. ↩︎

Analyze Locally, Act Globally: New regions R Package Release

Wed, 16 Jun 2021 12:00:00 +0200

The new version of our rOpenGov R package regions was released today on CRAN. This package is one of the engines of our experimental open data-as-service Green Deal Data Observatory, Economy Data Observatory, Digital Music Observatory prototypes, which aim to place open data packages into open-source applications.

In international comparison the use of nationally aggregated indicators often have many disadvantages: they inhibit very different levels of homogeneity, and data is often very limited in number of observations for a cross-sectional analysis. When comparing European countries, a few missing cases can limit the cross-section of countries to around 20 cases which disallows the use of many analytical methods. Working with sub-national statistics has many advantages: the similarity of the aggregation level and high number of observations can allow more precise control of model parameters and errors, and the number of observations grows from 20 to 200-300.

The change from national to sub-national level comes with a huge data processing price: internal administrative boundaries, their names, codes codes change very frequently.

Yet the change from national to sub-national level comes with a huge data processing price. While national boundaries are relatively stable, with only a handful of changes in each recent decade. The change of national boundaries requires a more-or-less global consensus. But states are free to change their internal administrative boundaries, and they do it with large frequency. This means that the names, identification codes and boundary definitions of sub-national regions change very frequently. Joining data from different sources and different years can be very difficult.

Our regions R package helps the data processing, validation and imputation of sub-national, regional datasets and their coding.

There are numerous advantages of switching from a national level of the analysis to a sub-national level comes with a huge price in data processing, validation and imputation, and the regions package aims to help this process.

You can review the problem, and the code that created the two map comparisons, in the Maping Regional Data, Maping Metadata Problems vignette article of the package. A more detailed problem description can be found in Working With Regional, Sub-National Statistical Products.

This package is an offspring of the eurostat package on rOpenGov. It started as a tool to validate and re-code regional Eurostat statistics, but it aims to be a general solution for all sub-national statistics. It will be developed parallel with other rOpenGov packages.

Get the Package

You can install the development version from GitHub with:

devtools::install_github("rOpenGov/regions")

or the released version from CRAN:

install.packages("regions")

You can review the complete package documentation on regions.dataobservaotry.eu. If you find any problems with the code, please raise an issue on Github. Pull requests are welcome if you agree with the Contributor Code of Conduct

If you use regions in your work, please cite the package as: Daniel Antal. (2021, June 16). regions (Version 0.1.7). CRAN. http://doi.org/10.5281/zenodo.4965909

Download the BibLaTeX entry.

Join us

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

Open Data is Like Gold in the Mud Below the Chilly Waves of Mountain Rivers

Thu, 10 Jun 2021 07:00:00 +0200

Open data is like gold in the mud below the chilly waves of mountain rivers. Panning it out requires a lot of patience, or a good machine.

As the founder of the automated data observatories that are part of Reprex’s core activities, what type of data do you usually use in your day-to-day work?

The automated data observatories are results of syndicated research, data pooling, and other creative solutions to the problem of missing or hard-to-find data. The music industry is a very fragmented industry, where market research budgets and data are scattered in tens of thousands of small organizations in Europe. Working for the music and film industry as a data analyst and economist was always a pain because most of the efforts went into trying to find any data that can be analyzed. I spent most of the last 7-8 years trying to find any sort of information—from satellites to government archives—that could be formed into actionable data. I see three big sources of information: textual,numeric, and continuous recordings for on-site, offsite, and satellite sensors. I am much better with numbers than with natural language processing, and I am improving with sensory sources. But technically, I can mint any systematic information—the text of an old book, a satellite image, or an opinion poll—into datasets.

For you, what would be the ultimate dataset, or datasets that you would like to see in the Green Deal Data Observatory?

Our retroharmonize and regions packages can create regional statistics from Eurobarometer and Afrobarometer surveys on how people think locally about climate change. I would like to combine this with local information on observable climate change, such as drought, urban heat, and extreme weather conditions. Do people have to feel the pain of climate change to believe in the phenomenon? How do self-reported mitigation steps correlate with what people already feel in their local environment? Suzan is talking about measuring mitigation and damage control, because she’s aware of the already present health risks in overheating urban environments. I am more interested in what people think.

See our case study on connecting local tax revenues, climate awareness poll data and drought data in Belgium - we want to extend this to Europe and then to Africa. We also published the code how to do it with tutorials 1, 2 for our International Open Data Day 2021 Event.

Is there a number or piece of information that recently surprised you? If so, what was it?

There were a few numbers that surprised me, and some of them were brought up by our observatory teams. Karel is talking about the fact that not all green energy is green at all: many hydropower stations contribute to the greenhouse effect and not reduce it. Annette brought up the growing interest in the Dalmatian breed after the Disney 101 Dalmatians movies, and it reminded me of the astonishing growth in interest for chess sets, chess tutorials, and platform subscriptions after the success of Netflix’s The Queen’s Gambit.

The Queen’s Gambit’ Chess Boom Moves Online By Rachael Dottle on bloomberg.com

Annette is talking about the importance of cultural influencers, and on that theme, what could be more exciting that Netflix’s biggest success so far is not a detective series or a soap opera but a coming-of-age story of a female chess prodigy. Intelligence is sexy, and we are in the intelligence business.

But to tell a more serious and more sobering number, I recently read with surprise that there are more people smoking cigarettes on Earth in 2021 than in 1990. Population growth in developing countries replaced the shrinking number of developed country smokers. While I live in Europe, where smoking is strongly declining, it reminds me that Europe’s population is a small part of the world. We cannot take for granted that our home-grown experiences about the world are globally valid.

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

FiveThirtyEight.com had a wonderful podcast series, produced by Jody Avirgan, called What’s the Point. It is exactly about good and bad uses of data, and each episode is super interesting. Maybe the most memorable is Why the Bronx Really Burned. New York City tried to measure fire response times, identify redundancies in service, and close or re-allocate fire stations accordingly. What resulted, though, was a perfect storm of bad data: The methodology was flawed, the analysis was rife with biases, and the results were interpreted in a way that stacked the deck against poorer neighborhoods. It is similar to many stories told in a very compelling argument by Catherine D’Ignazio and Lauren F. Klein in their much celebrated book, Data Feminism. Usually, the bad use of data starts with a bad data collection practice. Data analysts in corporations, NGOs, public policy organizations and even in science usually analyze the data that is available.

You can find these examples, together with many more that our contributors recommend, in the motivating examples of Create New Datasets and the Remain Critical parts of our onboarding material. We hope that more and more professionals and citizen scientist will help us to create high-quality and open data.

The real power lies in designing a data collection program. A consistent data collection program usually requires an investment that only powerful organizations, such as government agencies, very large corporations, or the richest universities can afford. You cannot really analyze the data that is not collected and recorded; and usually what is not recorded is more interesting than what is. Our observatories want to democratize the data collection process and make it more available, more shared with research automation and pooling.

You cannot really analyze the data that is not collected and recorded; and usually what is not recorded is more interesting than what is. Our observatories want to democratize the data collection process and make it more available, more shared with research automation and pooling.

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

I have been involved with open data policies since 2004. The problem has not changed much: more and more data are available from governmental and scientific sources, but in a form that makes them useless. Data without clear description and clear processing information is useless for analytical purposes: it cannot be integrated with other data, and it cannot be trusted and verified. If researchers or government entities that fall under the Open Data Directive release data for reuse in a way that does not have descriptive or processing metadata, it is almost as if they did not release anything. You need this additional information to make valid analyses of the data, and to reverse-engineer them may cost more than to recollect the data in a properly documented process. Our developers, particularly Leo and Pyry are talking eloquently about why you have to be careful even with governmental statistical products, and constantly be on the watch out for data quality.

Our API is not only publishing descriptive and processing metadata alongside with our data, but we also make all critical elements of our processing code available for peer-review on rOpenGov

What do you think the Green Deal Data Observatory, and our other automated observatories do, to make open data more credible in the European economic policy community and be accepted as verified information?

Most of our work is in research automation, and a very large part of our efforts are aiming to reverse engineer missing descriptive and processing metadata. In a way, I like to compare ourselves to the working method of the open-source intelligence platform Bellingcat. They were able to use publicly available, scattered information from satellites and social media to identify each member of the Russian military company that illegally entered the territory of Ukraine and shot down the Malaysian Airways MH17 with 297, mainly Dutch, civilians on board.

How we create value for research-oriented consultancies, public policy institutes, university research teams, journalists or NGOs.

We do not do such investigations but work very similarly to them in how we are filtering through many data sources and attempting to verify them when their descriptions and processing history is unknown. In the last years, we were able to estore the metadata of many European and African open data surveys, economic impact, and environmental impact data, or many other open data that was lying around for many years without users.

Open data is like gold in the mud below the chilly waves of mountain rivers. Panning it out requires a lot of patience, or a good machine. I think we will come to as surprising and strong findings as Bellingcat, but we are not focusing on individual events and stories, but on social and environmental processes and changes.

Join us

Join Copernicus Climate Data Store Data with Socio-Economic and Opinion Poll Data

Sun, 06 Jun 2021 10:00:00 +0200

In this series of blogposts we will show how to collect environmental data from the EU’s Copernicus Climate Data Store, and bring it to a data format that you can join with Eurostat’s socio-economic and environmental data. We have shown in a previous blogpost how to connect this to survey (opinion poll) and tax data, and a real policy problem in Belgium. We will create now subsequent tutorials to do more!

But first, why are we doing this? The European Union and its members states are releasing every year more and more data for open re-use since 2003, yet these are often not used in the EU’s data dissemination projects (the observatories) or in EU-funded research. We believe that there are many reasons behind this. Whilst more and more people can conduct business, scientific or policy analysis programmatically or with statistical software, knowledge how to systematically collect the data from the exponentially growing availability is not everybody’s specialty. And the lack of documentation, and high re-processing and validation need for open data is another drawback.

rOpenGov has long been producing high-quality, peer-reviewed R packages to work with open data, but their use is not for all. In an open collaboration, where you can join, too, rOpenGov teamed up with open source developers, knowledgeable data curators, and a service developer team lead by the Dutch reproducible research start-up Reprex to create a sustainable infrastructure that is permanently collecting, processing, documenting and visualizing open data. What we do is that we access open data (that is not always available for direct download) and re-process it to usable data that is tidy to be integrated with your existing data or databases. We are competing for the EU Datathon Challenge 1: supporting a European Green Deal agenda with open data as a service, and research as a servcie, and you are more than welcome to join our effort as a developer, a data curator, or as an occasional contributor to open government packages.

Register to the Copernicus Climate Data Store

Koen Hufkens, Reto Stauffer and Elio Campitelli created the ecmwfr R package for programmatically accessing the Copernicus Data Store service. Follow the CDS Functionality vignette to get started.

You will need to create a Register yourself for CDS services after accepting the Terms and conditions.

wf_set_key(user = "12345", 
           key = "00000000-aaaa-b1b1-0000-a1a1a1a1a1a1", 
           service = "cds")

You can check if you were successful with:

ecmwfr::wf_get_key(user = "12345", service = "cds")

Get the Data

Let us formulate our first request:

request_lai_hv_2019_06 <- list(
  "dataset_short_name" = "reanalysis-era5-land-monthly-means",
  "product_type"   = "monthly_averaged_reanalysis",
  "variable"       = "leaf_area_index_high_vegetation",
  "year"           = "2019",
  "month"          =  "06",
  "time"           = "00:00",
  "area"           = "70/-20/30/60",
  "format"         = "netcdf",
  "target"         = "demo_file.nc")

lai_hv_2019_06.nc  <- wf_request(user = "<your_ID>",
                     request = request_lai_hv_2019_06 ,
                     transfer = TRUE,
                     path = "data-raw",
                     verbose = FALSE)

Effective Leaf Area Index

You can find this data either in global computer raster images, or in re-processed monthly averages. Working with the raw data is not very practical – in case of cloudy weather you have missing data, and the files are extremely huge for a personal computer. For the purposes of our Green Deal Data Observatory the monthly average values are far more practical, which are called monthly_averaged_reanalysis product types.

For compatibility with other R packages, convert the data with the from raster package from rSpatial.org.

lai_file <- here::here( "data-raw", "demo_file.nc")
lai_raster <- raster::raster(lai_file)

## Loading required namespace: ncdf4

Let us convert this to a SpatialDataPointsDataFrame class, which is an augmented data frame class with coordinates.

LAI_df <- raster::rasterToPoints(lai_raster, fun=NULL, spatial=TRUE)

Get The Map

With the help fo rOpenGov, we are creating various R packages to programmatically access open data and put them into the right format. The popular eurostat package is not only useful to download data from Eurostat, but also to map it.

In this case, we want to create regional maps. Europe has five levels of geographical regions: NUTS0 for countries, NUTS1 for larger areas like states, provinces; NUTS2 for smaller areas like countries, NUTS3 for even smaller areas. The LAU level contains settlemens and their surrounding areas.

Country borders change sometimes (think about the unification of Germany, or the breakup of Czechoslovakia and Yugoslavia), but they are relatively stable entities. Sub-national regional border change very-very frequently – since 2000 there were many thousand changes in Europe. This means that you must choose one regional boundary definition. The latest edition is NUTS2021 but most of the data available is still in the NUTS2016 format, and often you will find NUTS2013 or even NUTS2010 data around. Our Green Deal Data Observatory uses the NUTS2016 definition, because it is far the most used in 2021. An offspring of the eurostat package, regions helps you take care of NUTS changes when you work, and can convert your data to NUTS2021 if you later need it.

## sf at resolution 1:60 read from local file

## Warning in eurostat::get_eurostat_geospatial(resolution = "60", nuts_level =
## "2", : Default of 'make_valid' for 'output_class="sf"' will be changed in the
## future (see function details).

plot(map_nuts_2)

Our measurement of the average Effective Leaf Area Index is a raster data, it is given for many points of Europe’s map. What we need to do is to overlay this raster information of the statistical map of Europe. We use the excellent sp: R Classes and Methods for Spatial Data package for this purpose. The sp::over() function decides if a point of Leaf Area Index measurement falls into the polygon (shape) of a particular NUTS2 regions, for example, Zuid-Holland or South Holland in the Netherlands, or Saarland in Germany, or not. Then it averages with the mean() function those measurements falling in the area.

LAI_nuts_2 = sp::over(sp::geometry(
  as(map_nuts_2, 'Spatial')), 
  LAI_df,
  fn=mean)

Let’s call the average LAI index lai, and bind it to the Eurostat map:

names(LAI_nuts_2)[1] <- "lai"
LAI_sfdf <- bind_cols ( map_nuts_2, LAI_nuts_2 )

If you want to work with the data in a numeric context, you do not need the geographical information, and you can “downgrade” the SpatialDataPointsDataFrame to a simple data frame.

set.seed(2019) #to always see the same sample
LAI_sfdf %>%
  as.data.frame() %>%
  select ( all_of(c("NUTS_NAME", "NUTS_ID", "lai")) ) %>%
  sample_n(10)

##                      NUTS_NAME NUTS_ID lai
## 281                       Vest    RO42  NA
## 125                     Kassel    DE73  NA
## 69              Friesland (NL)    NL12  NA
## 237 Agri, Kars, Igdir, Ardahan    TRA2  NA
## 273                East Anglia    UKH1  NA
## 119                Prov. Liège    BE33  NA
## 61                   Bourgogne    FRC1  NA
## 275                      Essex    UKH3  NA
## 282                   Istanbul    TR10  NA
## 174                    Leipzig    DED5  NA

We’ll plot the map with ggplot2.

library(ggplot2)
library(sf)
ggplot(data=LAI_sfdf) + 
  geom_sf(aes(fill=lai),
          color="dim grey", size=.1) + 
  scale_fill_gradient( low ="#FAE000", high = "#00843A") +
  guides(fill = guide_legend(reverse=T, title = "LAI")) +
  labs(title="Leaf Area Index",
       subtitle = "High vegetation half, NUTS2 regional avareage values",
       caption="\ua9 EuroGeographics for the administrative boundaries 
                \ua9 Copernicus Data Service, June 2019 average values
                Tutorial and ready-to-use data on greendeal.dataobservatory.eu") +
  theme_light() + theme(legend.position=c(.88,.78)) +
  coord_sf(xlim=c(-22,48), ylim=c(34,70))

Data Integrity

Our Green Deal Data Observatory has a data API where we place the new data with metadata for programmatic download in CSV, JSON or even with SQL queries. For data integrity purposes, we are placing an authoritative copy on Zenodo (Green Deal Data Observatory Community). You can use this for scientific citations. We are also happy if you place your own climate policy related research data here, so that we can include it in our observatory. In our subsequent tutorials, we will show how to do this programmatically in R. This particular dataset (not only with the month June, which we selected to streamline the tutorial) is available here with the digital object identifier doi.org/10.5281/zenodo.4903940.

Join us

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

Thu, 03 Jun 2021 16:00:00 +0200

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, 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 already working on a new major release. (Download the BibLaTeX entry.) 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 Economy Data Observatory team as a data curator, developer or business developer. More interested in economic policies, particularly computation antitrust, innovation and small enterprises? Check out our Economy Music Observatory team! Or your interest lies more in data governance, trustworthy AI and other digital market problems? Check out our Digital Music Observatory team!