FAIR-IMPACT identifies practices, policies, tools and technical specifications to guide researchers, repository managers, research performing organisations, policy makers and citizen scientists towards a FAIR data management cycle. The focus is on persistent identifiers (PIDs), metadata, ontologies, metrics, certification and interoperability, starting with real-life use cases on social sciences and humanities, the photon and neutron sciences, life sciences and agri-food and environmental sciences.
Omeka is a free, flexible, and open source web-publishing platform for the display of library, museum, archives, and scholarly collections and exhibitions. Its five-minute setup makes launching an online archive or exhibition as easy as launching a blog.
scholaraly infrastructure to provide access to global scholarly bibliographic and citation data with full provenance (where, wen, who of the source data + change tracking)
Data provided under CC0
Hackathons providing sandbox environments for practicing reproducible research. Use the Hub to organise events, submit papers for reproduction, record and feed back reviews
Decentralized identifiers (DIDs) are a new type of identifier that enables verifiable, decentralized digital identity. A DID refers to any subject (e.g., a person, organization, thing, data model, abstract entity, etc.) as determined by the controller of the DID. In contrast to typical, federated identifiers, DIDs have been designed so that they may be decoupled from centralized registries, identity providers, and certificate authorities. Specifically, while other parties might be used to help enable the discovery of information related to a DID, the design enables the controller of a DID to prove control over it without requiring permission from any other party. DIDs are URIs that associate a DID subject with a DID document allowing trustable interactions associated with that subject.
Annif is an open source toolkit for automated subject indexing. It integrates several machine learning and AI based algorithms for text classification.
M. Gärtner, U. Hahn, und S. Hermann. Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Seite 563--570. Paris, France, European Language Resources Association (ELRA), (Mai 2018)
D. Iglezakis. Dataset, https://doi.org/10.15770/darus-471, (2020)Related Publication: Schembera, B. & Iglezakis, D. (2020). EngMeta - Metadata for Computational Engineering. International Journal of Metadata, Semantics and Ontologies, 7 (9). p-122-156. (doi:10.23455/ijmso-12345).
D. Admin, und D. Iglezakis. Dataset, https://doi.org/10.15770/darus-470, (2020)related publication: Related Publication: Iglezakis, D., Seeland, A. (2020). Titel von Publikation. In: Titel von Sammelband (2020).p. 11-22. (doi:10.18324/324392034).
M. Gärtner, U. Hahn, und S. Hermann. Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan, European Language Resources Association (ELRA), (Mai 2018)
D. Dolzycka, K. Biernacka, K. Helbig, und P. Buchholz. Zenodo, (März 2019)Diese Publikation wurde im Rahmen des Verbundprojekts "FDMentor" vom Bundesministerium für Bildung und Forschung gefördert (Förderkennzeichen 16FDM010 und 16FDM011)..