EU data cloud
In the LATC project we publish data sources of Institutions and Bodies of the European Union as Linked Open Data to seed the EU data cloud:
The following datasets have been published in LATC (note that we also provide a description of the datasets in VoID format):
- Business Datasets
- EU Commission Financial Transparency System via http://fintrans.publicdata.eu
- Community Research and Development Information Service (CORDIS) via http://www4.wiwiss.fu-berlin.de/cordis/
- European Employment Services (EURES) via http://www4.wiwiss.fu-berlin.de/eures/
- EURAXESS - Researchers in Motion via http://www4.wiwiss.fu-berlin.de/euraxess/
- EC Competition via http://www4.wiwiss.fu-berlin.de/eccompetition/
- Legal Datasets
- EUR-Lex Access to European Union law via http://eur-lex.publicdata.eu/
- N-Lex via http://n-lex.publicdata.eu
- European Patent Office via http://epo.publicdata.eu
- EU Agencies via http://agencies.publicdata.eu
- Prelex via http://prelex.publicdata.eu
- UNODC via http://unodc.publicdata.eu
- Institution Datasets
- Eurostat via http://eurostat.linked-statistics.org/
- EU Whoiswho, the official directory of the European Union via http://whoiswho.publicdata.eu
- Data about European Higher Education Institutions via http://eumida.publicdata.eu
- Data about multi-media content published by the European Parliament via http://eupmedia.publicdata.eu
- Eurobarometer via http://eurobarometer.publicdata.eu
- European Election Results via http://elections.publicdata.eu
- European Central Bank (ECB) via http://ecb.publicdata.eu
- EU Institutions via http://institutions.publicdata.eu
Related datasets that have been published by LATC:
- Eventseer via http://linkeddata.few.vu.nl/eventseer
- Scottish Neighbourhood Statistics via http://sns.linkedscotland.org/
- Sciencewise via http://data.sciencewise.info
Additional Pointers
- dataset checklist (used for quality assurance and peer review of the above datasets)
- sample SPARQL queries (to emphasize added value of the datasets)
- triplification experiences (lessons learned from converting 20+ datasets)