Open Science aims at transforming science through ICT tools, networks and media, to make research more open, global, collaborative, creative and closer to society.
Data Citation Synthesis Group: Joint Declaration of Data Citation Principles. Martone M. (ed.) San Diego CA: FORCE11; 2014 [/datacitation]. Endorsement List
It is important to ensure that different copies or versions of files, files held in different formats or locations, and information that is cross-referenced between files are all subject to version control.
Data Observation Network for Earth (DataONE) is the foundation of new innovative environmental science through a distributed framework and sustainable cyberinfrastructure that meets the needs of science and society for open, persistent, robust, and secure access to well-described and easily discovered Earth observational data.
Im Januar 2013 hat der Wissenschaftsrat Empfehlungen zu einem Kerndatensatz Forschung verabschiedet. Der Kerndatensatz ist ein Angebot an Hochschulen und außeruniversitäre Forschungseinrichtungen, um bereits bestehende Aktivitäten bei der informationstechnischen Erfassung ihrer Forschungsaktivitäten zu unterstützen. Er stellt einen Standard zur Eigenverwaltung dieser Daten bereit, eine zentrale Datensammlung erfolgt nicht.
The Research Data Alliance (RDA) builds the social and technical bridges that enable open sharing of data.
The RDA vision is researchers and innovators openly sharing data across technologies, disciplines, and countries to address the grand challenges of society.
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.
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
Forschungsdatenmanagement ist eine im wissenschaftlichen Bereich auf den Umgang mit Forschungsdaten ausgerichtete Form des Projektmanagements, der Arbeitsorganisation und -steuerung. Es geht darum, die eigenen Arbeitsprozesse, die die Erzeugung von und den Umgang mit Forschungsdaten betreffen, möglichst effizient und zielorientiert zu organisieren und fortlaufend zu steuern. Den Nutzen und die Vorteile, welche dies mit sich bringt, soll an den nachfolgenden Modulen aufgezeigt werden.
Progress in scientific research is dependent on the quality and accessibility of software at all levels and it is now critical to address many new challenges related to the development, deployment, and maintenance of reusable software. In addition, it is essential that scientists, researchers, and students are able to learn and adopt a new set of software-related skills and methodologies.
Starting in January 2016 and funded for three years by the German Research Foundation (DFG), project CONQUAIRE – Continuous Quality Control for Research Data to Ensure Reproducibility will focus on reproducibility and quality control during the research process in an institutional setting.
Opening Reproducible Research is a DFG-funded research project by Institute for Geoinformatics (ifgi) and University and Regional Library (ULB), University of Münster, Germany
T. Wissik, und M. Ďurčo. Selected Papers from the CLARIN Annual Conference 2015, October 14–16, 2015, Wroclaw, Poland, 123, Seite 94-107. Linköping University Electronic Press, Linköpings universitet, (2015)