OpenRefine (formerly Google Refine) is a powerful tool for working with messy data: cleaning it; transforming it from one format into another; and extending it with web services and external data.
Das übergreifende Ziel des Vorhabens ist es, praktikable, einfach nutzbare und umfassende Provenance-Techniken und -Methoden für spezielle Anwendungsdomänen zu entwickeln, die einen minimalen Overhead und geringe Benutzeranforderungen für die Erstellung von Provenance-Informationen aufweisen. Insbesondere sollen nicht nur unkommentierte Datentransformationsabläufe und Interaktionssequenzen aufgezeichnet, sondern Überlegungen und Argumentationen zu Hypothesen, Einsichten, Entscheidungen und Interaktionen in ihrem Kontext dokumentiert und zugänglich gemacht werden. Provenance ist eine zentrale Maßnahme zur Vertrauensbildung für digitale Informationen auf der Basis von Nachvollziehbarkeit und Reproduzierbarkeit. Unser Vorhaben „Provenance Analytics“ schafft das Bewusstsein für diesen wichtigen Aspekt des digitalen Strukturwandels, einerseits durch Ausbildung und Forschung an den beteiligten Universitäten und Fachhochschulen und andererseits durch intensiven Austausch mit lokalen KMUs im Rahmen von Workshops und Tutorials.
This paper proposes improved ways of indicating open and restricted access, as well as license information, for remote online resources through coordinated changes to the MARC 21 fields 506 (Restrictions on Access Note), 540 (Terms Governing Use and Reproduction Note), and 856 (Electronic Location and Access). (Network Development and MARC Standards Office, Library of Congress)
This paper proposes improved ways of indicating open and restricted access, as well as license information, for remote online resources through coordinated changes to the MARC 21 fields 506 (Restrictions on Access Note), 540 (Terms Governing Use and Reproduction Note), and 856 (Electronic Location and Access). (Network Development and MARC Standards Office, Library of Congress)
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.
Schema.org is a collaborative, community activity with a mission to create, maintain, and promote schemas for structured data on the Internet, on web pages, in email messages, and beyond.
The I-ADOPT framework: The I-ADOPT framework is based on the I-ADOPT ontology designed to be used as a semantic broker between existing variable description models (including ontologies, taxonomies, and structured controlled vocabularies).
VIMMP provides an easily accessible, user-friendly hub to access all tangible and intangible components, such as information, knowledge, services and tools to support the efficient decision making, uptake and effective use of materials. At the core of VIMMP will be a metadata enriched data environment that eases the tasks of all actors. In particular it will facilitate the translation of a scientific problem into modelling workflows, ready for simulation using a range of software tools integrated into an open simulation platform and deployed on cloud services. The VIMMP platform is open, so that any provider can easily integrate and deploy their software codes as well as services.
T. Richter, P. Grube, and D. Zutin. 2012 IEEE International Symposium on Multimedia ISM 2012, page 451 - 456. Irvine, California, USA, IEEE, IEEE, (December 2012)
