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

Tue, 01 Mar 2022 00:00:00 +0000

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.

The published article: https://www.jipitec.eu/issues/jipitec-13-1-2022/5515

Preprint version

The earlier preprint version on SSRN our for direct download here on Data & Lyrics. Senftleben, Martin and Margoni, Thomas and Antal, Daniel and Bodó, Balázs and Gompel, Stef van and Handke, Christian and Kretschmer, Martin and Poort, Joost and Quintais, João and Schwemer, Sebastian Felix, 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 and the Opportunity Arising from Article 17 of the CDSM Directive (February 12, 2021). Available at SSRN: https://ssrn.com/abstract=3785272 or http://dx.doi.org/10.2139/ssrn.3785272

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. We show a very similar example in our Green Deal Data Observatory with environmental R&D public spending in Europe.

Engage with us on LinkedIn @CultDataObs or check out our open data and open repositories, code, tutorials

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.

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 context with our Digital Music Observatory and Cultural & Creative Sectors and Industries 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 unambiguosly 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? 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 give us any feedback!