Our journalism data lab
The SMC Lab is our data lab. This is where SMC develops software and services for its own editorial office and for the journalism community.
In the SMC Lab we develop smart software in the form of scanning, scouting and reporting tools that are designed to facilitate the everyday work of SMC’s editors. Take the job of identifying experts or latching onto completely new topics and trends: algorithms taken from artificial intelligence are used, especially machine learning techniques and statistical methods for natural language processing and text mining.
In the SMC Lab, moreover, services for the wider data journalism community evolve. By publishing filtered data sets and aggregated databases for general use as well as by providing web-based tools, the SMC Lab is set to become an important go-to point for evidence-based data journalism in Germany.
We also plan to actively engage with scientists, learned societies and institutuions that will be our "in-house methodologists", advising SMC on methods, statistics and ancillary sciences.
We value data: “Potential Analysis of Big Data Mining for Government Foresight and Science Communication”
- Funded by the Federal MInistry of Education and Research (Funding number 0150853)
- Duration: 01.08.2016 to 31.07.2017
- In cooperation with the Institut für Technologie und Arbeit e. V. (ITA) and the German Research Center for Artificial Intelligence (DFKI)
The study focuses on how IT-supported assistance systems for Big Data mining need to be designed so that they can best help the user to find and evaluate topics of relevance to the future in large volumes of data. For this purpose, usage scenarios for Big Data technologies in foresight and science communication are being developed, from which procedural, economic and technical requirements and potential can be derived. Existing assistance systems are being analysed and possible synergies between assistance systems for both purposes identified. The needs of research and development with regard to assistance systems for Big Data mining will complete the scope of the study on potential assessment and form the basis for tailored implementation projects.