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.
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…
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..
A. Schreiber, and R. Struminski. Universal Access in Human--Computer Interaction. Design and Development Approaches and Methods: 11th International Conference, UAHCI 2017, Held as Part of HCI International 2017, Vancouver, BC, Canada, July 9--14, 2017, Proceedings, Part I 11, page 444--455. Springer, (2017)