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 Cross-Domain Interoperability Framework (CDIF) is a set of guidelines and practice for using domain-agnostic standards to support the interoperability and reusability of FAIR data, especially across domain and institutional boundaries. It is being developed in response to the need for agreements on the use of standards in FAIR
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.
This website is for information related to the CESAER Taskforce on Open Science, and in particular on its sub-group looking at how the Technical Universities in Europe deal with Engineering and Research Data Management. The group is working on two tasks Task 1 - FAIR Data in Engineering (2018-19) Read - Summary of First Findings on…
Status: Recognised & Endorsed The Metadata IG will concern itself with all aspects of metadata for research data. In particular it will attempt to coordinate the efforts of the WGs concerned with metadata to produce a coherent approach to metadata covering metadata modalities of description, restriction, navigation, provenance, preservation and the use of metadata for the purposes discovery, contextualisation, validation, analytical processing, simulation, visualisation and interoperation. It will also liaise with the other WGs especially Data Foundation and Terminology, PIDs, Standardisation of data categories and codes and Data Citation. This IG activity relates to data management policies and plans of research organisations and researchers, and to policies and standards of research funders and of research communities which may or may not be official standards.
The Library of Congress and its digital preservation partners from the federal, library, creative, publishing, technology, and copyright communities are working to develop a national strategy to collect, archive, and preserve digital content.
Ein bedeutendes neues Handlungsfeld der Forschung, welches im Zuge der Digitalisierung entstanden ist, ist das Management von digitalen Forschungsdaten. Wissenschaftler*innen benötigen für ein nachhaltiges Forschungsdatenmanagement (FDM) neben Kenntnissen und Fähigkeiten im fachlichen Bereich zusätzliche Kompetenzen im Umgang mit digitalen Daten. Die Vermittlung dieser Kenntnisse sollte idealerweise bereits im Studium erfolgen. Zudem besteht ein steigender Bedarf an forschungsunterstützendem Personal, z.B. in Form von Data Stewards, der nur über geeignete Aus- und Weiterbildungsmaßnahmen gedeckt werden kann. Die vorliegende Lernzielmatrix fasst für das FDM relevante Vermittlungsinhalte sowie zugehörige Lernziele auf den Qualifikationsstufen Bachelor, Master, PhD und Data Steward aus einer Reihe von nationalen wie internationalen Projekten und Fortbildungskonzepten zum Themenbereich FDM in einheitlicher Form zusammen und bietet Nachnutzenden eine Orientierungshilfe für die Identifikation von relevanten Inhaltsaspekten sowie eine Arbeitsgrundlage, etwa für eine erweiterte fach- oder veranstaltungsspezifische Ausgestaltung. Die Lernzielmatrix entstand im Rahmen der DINI/nestor AG Forschungsdaten UAG Schulungen/Fortbildungen unter Einbezug externer Kolleg*innen.
European Research Community on Flow, Turbulence and Combustion Database. This classic collection of test cases for validation of turbulence models started as an EU / ERCOFTAC project led by Pr. W. Rodi in 1995. It is maintained by Dr. T. Craft at Manchester since 1999. Initialy limited to experimental data, computational results, and results and conclusions drawn from the ERCOFTAC Workshops on Refined Turbulence Modelling (SIG15).
METS: An Overview & Tutorial: Metadata Encoding and Transmission Standard (METS) Official Web Site. The METS schema is a standard for encoding descriptive, administrative, and structural metadata regarding objects within a digital library, expressed using the XML schema language of the World Wide Web Consortium. The standard is maintained in the Network Development and MARC Standards Office of the Library of Congress, and is being developed as an initiative of the Digital Library Federation.
L. Käde. Datenrecht und neue Technologien Nomos, Baden-Baden, 1 edition, (2021)Die interdisziplinäre Analyse nimmt konkreten Bezug zu in derKI-Entwicklung eingesetzten Machine Learning (ML)-Frameworks und gibtpraxisrelevante Antworten auf damit zusammenhängendeurheberrechtliche Fragen. Insbesondere der Datenbank(werk)schutz fürML-Modelle steht dabei im Fokus. Die Arbeit bietet außerdem eineEinschätzung der Relevanz von Text und Data Mining-Schranken imKI-Kontext. Mit Blick auf die Erzeugung von Werken durch bzw. mithilfevon ML wird die Zurechnungsproblematik erörtert, eine Lösungvorgeschlagen und eine Hilfestellung zur Ermittlung eines Urhebersangeboten. Darüber hinaus erfolgt hinsichtlich etwaiger KI-Autonomieeine Einführung in die Zusammenhänge von Intelligenz, Kreativitätund Computational Creativity..
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. 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).
A. Kesper, V. Wenz, and G. Taentzer. (2020)cite arxiv:2007.11298Comment: 28 pages. This paper is an extended version of a paper to be published in "ACM/IEEE 23rd International Conference on Model Driven Engineering Languages and Systems (MODELS '20)". Added subtitle.