The 'German Network for Bioinformatics Infrastructure – de.NBI' is a national, academic and non-profit infrastructure supported by the Federal Ministry of Education and Research providing bioinformatics services to users in life sciences research and biomedicine in Germany and Europe. The partners organize training events, courses and summer schools on tools, standards and compute services provided by de.NBI to assist researchers to more effectively exploit their data.
Im Onlineportal finden Nutzerinnen und Nutzer zunächst die bereits bekannten Leitlinien und ihre Erläuterungen. Neu hinzu kommen nun allgemeine und fachspezifische Kommentierungen, Fallbeispiele, eine Übersicht über häufig gestellte Fragen, Verweise auf Gesetze und andere Normen, zugehörige DFG-Stellungnahmen sowie externe Quellen. Für die Nutzerinnen und Nutzer des Portals existieren verschiedene Such- und Zugangsmodi. Eine englische Fassung soll 2021 freigeschaltet werden.
Slides give an overview over the actual situation of AAI and the used technologies (SAML, OAuth) and implementations (Shibboleth, ...) and outlook to a possible future
Annif is an open source toolkit for automated subject indexing. It integrates several machine learning and AI based algorithms for text classification.
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
Home page for Library of Congress Recommended Formats Statement (RFS). RFS identifies analog and digital formats suitable for the large-scale acquisition of and long-term access to library collections. The identified formats have been selected to maximize the preservation and accessibility of creative content into the future.
PRONOM is an online technical registry providing impartial and definitive information about file formats, software products and other technical components required to support long-term access of electronic records.
Data obtained from the open survey developed by the LEARN project (http://www.learn-rdm.eu/) as a self-assessment tool to assist institutions discover how ready they are for managing research data. This dataset replaces the first one published at http://doi.org/10.5281/zenodo.61903. The survey is based on the issues posed to institutions by the LERU Roadmap for Research Data published at the end of 2013, and available at: http://www.learn-rdm.eu/material/leru_roadmap_for_research_data The survey has thirteen questions addressing the main elements to be taken into account in developing an institutional strategy for research data management. Each question has three possible answers representing green, yellow or red light. The more ‘green light’ responses recorded, the readier an institution probably is for managing its research data. The survey is available in English at http://learn-rdm.eu/en/rdm-readiness-survey/ and in Spanish at http://learn-rdm.eu/encuesta-rdm/
DataCite, CrossRef und mEDRA lieferen auf Anforderung Metadaten zu einer DOI in verschiedenen Formaten zurück: RDF XML (application/rdf+xml), RDF Turtle (text/turtle), Citeproc JSON (application/vnd.citationstyles.csl+json), Formatted text citation (text/x-bibliography), BibTeX (application/x-bibtex),
DataCite und CrossRef: RIS (application/x-research-info-systems)
Nur DataCite: Schema.org in JSON-LD (application/vnd.schemaorg.ld+json), DataCite XML (application/vnd.datacite.datacite+xml)
Nur CrossRef: CrossRef Unixref XML (application/vnd.crossref.unixref+xml)
Nur mEDRA: ONIX for DOI (application/vnd.medra.onixdoi+xml)
Ask biological questions, get computational answers, choose the right tool to analyze your data. OMICtools bridges the gap between life science and computational biology.
This paper introduces application profiles as a type of metadata schema. We use application profiles as a way of making sense of the differing relationship that implementors and namespace managers have towards metadata schema, and the different ways they use and develop schema. The idea of application profiles grew out of UKOLN's work on the DESIRE project (1), and since then has proved so helpful to us in our discussions of schemas and registries that we want to throw it out for wider discussion in the run-up to the DC8 Workshop in Ottawa in October.
The WAND Engineering Taxonomy includes over 1,200 terms and 165 synonyms.
Top Level terms include Engineering Design Process, Engineering Documents, Engineering Drawings, Engineering Fields, Engineering Materials, as well as Engineering Standards and Codes Organizations.
The WAND Engineering Taxonomy provides a strong foundation for any enterprise that needs to tag and organize documents related to the field of engineering.
BioSchemas relies and extends from schema.org and aims to reuse existing standards and reach consensus among a wide number of life sciences organizations and communities.
This document describes RML, a generic mapping language, based on and extending [R2RML]. The RDF Mapping language (RML) is a mapping language defined to express customized mapping rules from heterogeneous data structures and serializations to the RDF [RDF-CONCEPTS] data model. RML is defined as a superset of the W3C-standardized mapping language [R2RML], aiming to extend its applicability and broaden its scope, adding support for data in other structured formats. [R2RML] is the W3C standard to express customized mappings from relational databases to RDF. RML follows exactly the same syntax as R2RML; therefore, RML mappings are themselves RDF graphs. The present document describes the RML language and its concepts through definitions and examples.