Results 31 to 40 of about 27,284,541 (286)

FAIR Principles for Research Software (FAIR4RS Principles) [PDF]

, 2021
Chue Hong NP, Katz DS, Barker M, et al. FAIR Principles for Research Software (FAIR4RS Principles). 2021.Research software is a fundamental and vital part of research worldwide, yet there remain significant challenges to software productivity, quality ...
Stevens, Sarah, Niehues, Anna, Gruenpeter, Morane, WG, FAIR4RS, Williams, Alan, Bakker, Tom, Cuesta, Sergio Martinez, Fox, Merc, van Werkhoven, Ben, Leng, Joanna, Lee, Allen, Sesink, Laurents, Yehudi, Yo, Jones, Catherine, Loewe, Axel, Sinha, Manodeep, Martinez, Carlos, Peroni, Silvio, Harrow, Jen, Plomp, Esther, Servillat, Mathieu, Todorov, Ilian, Castro, Leyla Jael, van Werkhove, Ben, Sochat, Vanessa, Hwang, Lorraine, Sandström, Malin, Islam, Sharif, Rabemanantsoa, Tovo, Tovani-Palone, Marcos Roberto, Zhang, Qian, RDA FAIR4RS WG, Shanahan, Hugh, Pollard, Tom, Ranguelova, Elena, Gray, Charles, Werkhoven, Ben van, Lavanchy, Paula Martinez, Tartarini, Daniele, McNally, James, Martinez, Paula Andrea, Liffers, Matthias, Czerniak, Andreas, Richard, Céline, Atmojo, Udayanto Dwi, Hellström, Maggie, Struck, Alexander, Nüst, Daniel, Miljković, Nadica, Martinez Lavanchy, Paula, Sansone, Susanna-Assunta, Kuzak, Mateusz, Barker, Michelle, Moldon, Javier, Genova, Francoise, Martinez Cuesta, Sergio, Gesing, Sandra, Lamprecht, Anna-Lena, Garijo, Daniel, Chue Hong, Neil P., Herterich, Patricia, Lynch, Nick, Wilkinson, Mark D., Soiland-Reyes, Stian, Erdmann, Chris, Burnett, Jessica, Garrido-Sánchez, Julián, Psomopoulos, Fotis E., Desinghu, Bala, Hong, Neil P. Chue, Czerniak, Andreas ; https://orcid.org/, Fouilloux, Anne Claire, Anzt, Hartwig, Goble, Carole, Honeyman, Tom, Katz, Daniel S., Belhajjame, Khalid, Soiland-Reyes, Stian; id_orcid, Martinez PA, Barker M, Loewe A, Chue Hong, Neil P; id_orcid, Psomopoulos F E, Katz D, Martinez C, Lamprecht A-L, Harrow J, Honeyman T, Castro LJ, Lee A, Jones CM, Struck A, Chue Hong N P, Gruenpeter M, van Werkhoven, B   +94 more
core   +1 more source

FAIR Data Maturity Model. Specification and Guidelines

, 2020
Findability, Accessibility, Interoperability and Reusability – the FAIR principles – intend to define a minimal set of related but independent and separable guiding principles and practices that enable both machines and humans to find, access ...
FAIR Data Maturity Model Working Group
core   +1 more source

Position paper on management of personal data in environment and health research in Europe

Environment International, 2022
Management of datasets that include health information and other sensitive personal information of European study participants has to be compliant with the General Data Protection Regulation (GDPR, Regulation (EU) 2016/679).
Govarts Eva, Gilles Liese, Bopp Stephanie, Holub Petr, Matalonga Leslie, Vermeulen Roel, Vrijheid Martine, Beltran Sergi, Hartlev Mette, Jones Sarah, Rodriguez Martin Laura, Standaert Arnout, Swertz Morris A., Theunis Jan, Trier Xenia, Vogel Nina, Van Espen Koert, Remy Sylvie, Schoeters Greet   +18 more
doaj   +1 more source

FAIR Principles for Phase-Field Practitioners | CHiMaD Phase-field XIII

, 2022
FAIR Principles for Phase-Field Practitioners was delivered at the CHiMaD Phase-Field Workshop XIII in May ...
Trevor Keller, Andrea Medina-Smith
core   +1 more source

ARDC FAIR Data 101 self-guided

, 2021
FAIR Data 101 v3.0 is a self-guided course covering the FAIR Data principles The FAIR Data 101 virtual course was designed and delivered by the ARDC Skilled Workforce Program twice in 2020 and has now been reworked as a self-guided course.
Brady, Catherine, Burton, Nichola, Liffers, Matthias, Russell, Keith, Simons, Natasha, Ferrers, Richard, McCafferty, Siobhann, Brownlee, Rowan, Stokes, Liz, Honeyman, Tom, Quiroga, Maria del Mar, Martinez, Paula A., Barlow, Melanie, McEachern, Steve   +13 more
core   +1 more source

tkphd/fair-phase-field-data: CHiMaD Phase-field XIII

, 2022
FAIR Principles for Phase-Field ...
Trevor Keller
core   +1 more source

Work Plans: A Tool for Suggesting Implementation Strategies to Improve the FAIRness of Data Repositories

Data Science Journal
Improving the findability, accessibility, interoperability, and reusability (or ‘FAIRness’) of digital resources has become increasingly important since the publication of the ‘FAIR Principles’ in 2016.
Nancy J. Hoebelheinrich, Ismael Kherroubi Garcia, Christopher Erdmann, Julianne Christopher, Lisa M. Mayer, Darya Pokutnaya, Christine R. Kirkpatrick   +6 more
doaj   +1 more source

Designing a data infrastructure for catalysis science aligned to FAIR data principles

Catalysis Communications, 2022
The UK Catalysis Hub (UKCH) is designing and implementing an infrastructure to facilitate the management of research data produced by researchers, the Catalysis Data Infrastructure (CDI).
Abraham Nieva de la Hidalga, Josephine Goodall, Corinne Anyika, Brian Matthews, C. Richard A. Catlow   +4 more
doaj   +1 more source

Commitment Statement to Enabling FAIR Data in the Earth, Space, and Environmental Sciences

, 2018
<p>As a part of the Enabling FAIR Data project, a community-driven project led by the American Geophysical Union, developed a commitment statement defining for each stakeholder group their role in making data open and FAIR. In specific, moving
Enabling FAIR Data Community (8223777), Enabling FAIR Data Community   +1 more
core   +1 more source

Ontology Engineering and the FAIR principles: A Gap Analysis toward a FAIR-by-design methodology

International audienceOntologies and vocabularies play a key role when standardising, organizing and integrating data from heterogeneous data sources into Knowledge Graphs.
Jonquet, Clement, Poveda-Villalon, María, Le Franc, Yann, Garijo, Daniel, Gonzalez-Beltran, Alejandra   +4 more
core   +4 more sources