Digital Agenda Scoreboard: A Statistical Anatomy of Europe's way into the Information Age

  • screenshot

Evidence-based policy is policy informed by rigorously established objective evidence. An important aspect of evidence-based policy is the use of scientifically rigorous studies to identify programs and practices capable of improving policy relevant outcomes. Statistics represent a crucial means to determine whether progress is made towards policy targets. In May 2010, the European Commission adopted the Digital Agenda for Europe (DAE), a strategy to take advantage of the potential offered by the rapid progress of digital technologies. The DAE is part of the overall Europe2020 strategy for smart, sustainable and inclusive growth. In order to chart the progress of both the announced policy actions and the key performance targets a scoreboard is published, thus allowing the monitoring and benchmarking of the main developments of information society in European countries. 

Homepage Homepage @ LOD2

The Digital Agenda contains commitments to undertake 101 specific policy actions (78 actions to be taken by the Commission, including 31 legal proposals, and 23 actions proposed to the Member States) intended to stimulate a virtuous circle of investment in and usage of digital technologies. It identifies 13 key performance targets to show whether Europe is making progress in this area. In order to chart the progress of both the announced policy actions and the key performance targets, the DAE calls for the publication of an annual scoreboard, supported by a large set of statistical indicators allowing monitoring and benchmarking of the main developments of information society in European countries. As an outcome, the visualization tool of the Digital Agenda Scoreboard (DAS) was published in June 2011. This application was developed for interested citizens and professionals (e.g. journalists) providing them with the possibility to browse statistical data with suitable visualization and interaction features. In addition to these human-readable access methods, machine-readable access facilitating re-usage and interlinkability of the underlying data in a dereferencable way is provided by means of RDF and Linked Open Data.

Project Team

Former Members

Partner

Publications

by (Editors: ) [BibTex of ]

News

SANSA 0.2 (Semantic Analytics Stack) Released ( 2017-06-13T18:18:28+02:00 by Prof. Dr. Jens Lehmann)

2017-06-13T18:18:28+02:00 by Prof. Dr. Jens Lehmann

The AKSW and Smart Data Analytics groups are happy to announce SANSA 0.2 – the second release of the Scalable Semantic Analytics Stack. Read more about "SANSA 0.2 (Semantic Analytics Stack) Released"

AKSW at ESWC 2017 ( 2017-06-12T10:53:35+02:00 Christopher Schulz)

2017-06-12T10:53:35+02:00 Christopher Schulz

Hello Community! The ESWC 2017 just ended and we give a short report of the course at the conference, especially regarding the AKSW-Group. Our members Dr. Muhammad Saleem, Dr. Mohamed Ahmed Sherif, Claus Stadler, Michael Röder, Prof. Dr. Read more about "AKSW at ESWC 2017"

Four papers accepted at WI 2017 ( 2017-06-10T15:01:31+02:00 Christopher Schulz)

2017-06-10T15:01:31+02:00 Christopher Schulz

Hello Community! We proudly announce that The International Conference on Web Intelligence (WI) accepted four papers by our group. The WI takes place in Leipzig between the 23th – 26th of August. Read more about "Four papers accepted at WI 2017"

AKSW Colloquium, 29.05.2017, Addressing open Machine Translation problems with Linked Data. ( 2017-05-26T13:51:11+02:00 by Diego Moussallem)

2017-05-26T13:51:11+02:00 by Diego Moussallem

At the AKSW Colloquium, on Monday 29th of May 2017, 3 PM, Diego Moussallem will present two papers related to his topic. First paper titled “Using BabelNet to Improve OOV Coverage in SMT” of Du et al. Read more about "AKSW Colloquium, 29.05.2017, Addressing open Machine Translation problems with Linked Data."

SML-Bench 0.2 Released ( 2017-05-11T13:01:45+02:00 by Patrick Westphal)

2017-05-11T13:01:45+02:00 by Patrick Westphal

Dear all, we are happy to announce the 0.2 release of SML-Bench, our Structured Machine Learning benchmark framework. SML-Bench provides full benchmarking scenarios for inductive supervised machine learning covering different knowledge representation languages like OWL and Prolog. Read more about "SML-Bench 0.2 Released"