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.
The Compliance Assessment Toolkit will support the EOSC PID policy with services to encode, record, and query compliance with the policy. To do so, a wide range of compliance requirements ( TRUST, FAIR, PID Policy, Reproducibility, GDPR, Licences) will be evaluated as use cases for definition of a conceptual model. At the same time, vocabularies, concepts, and designs are intended to be re-usable for other compliance needs: TRUST, FAIR, POSI, CARE, Data Commons.
The CEOS System Engineering Office (SEO) worked with the CEOS Working Group on Information Systems and Services (WGISS) to gather and organize key information on data policies, data access portals and interoperability protocols.
CEOS is currently operating and planning hundreds of Earth observation satellites. The information contained in this portal will improve the efficiency and effectiveness of gaining access to space-based Earth observation data to support many global intiatives with vast societal impact.
Calcyte is (will be) a toolkit for managing metadata for collections of content
via automatically generated spreadsheets and for creating static HTML repositories.
Calcyte targets the Draft DataCrate Packaging format v0.2.
At this stage Calcyte does not Bag content, it jsut creates Working DataCrates.
This document specifies a method of organising file-based data with associated metadata, known as DataCrate in both human and machine readable formats, based on the schema.org linked-data vocabularly, supplemented with terms from the SPAR ontologies and [PCDM] where schema.org does not have coverage. The motivation for this work comes from the research domain.
A DataCrate is a dataset a set of files contained in a single directory. There are two ways of organizing a DataCrate.
For working data or data that does not need to be distributed with checksums, a Working DataCrate is a plain-old directory containing payload data files, with two metadata files at the root; one for humans and one for machines.
For distribution, or archiving; where integrity is important, a Bagged DataCrate is a BagIt bag conforming to the DataCrate BagIt profile with the payload files in the /data directory. A Bagged DataCrate has a clear separation between metadata and payload, and can be integrity-checked using the checksums in the BagIt manifest.
D. Admin, and 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).
D. Almeida, G. Murphy, G. Wilson, and M. Hoye. Proceedings of the 25th International Conference on Program Comprehension, page 1--11. Piscataway, NJ, USA, IEEE Press, (2017)