Results 11 to 20 of about 2,911,636 (265)

FAIR + FIT: Guiding Principles and Functional Metrics for Linked Open Data (LOD) KOS Products

Journal of Data and Information Science, 2020
To develop a set of metrics and identify criteria for assessing the functionality of LOD KOS products while providing common guiding principles that can be used by LOD KOS producers and users to maximize the functions and usages of LOD KOS products.
Zeng Marcia Lei, Clunis Julaine
doaj   +3 more sources

Verbesserung der Messdatenqualität durch Anwendung der FAIR Guiding Principles Leveraging measurement data quality by adoption of the FAIR guiding principles

tm - Technisches Messen
441453The analysis and reuse of measured process data are enablers for sustainable and resilient manufacturing in the future. Maintaining high measurement data quality is vital for maximising the usage and value of the data at hand.
Sanders, Mark P., Hamann, Tobias, Moser, Mario, Abdelrazeq, Anas, Bodenbenner, Matthias, Schmitt, Robert H.   +5 more
core   +2 more sources

FAIR 2.0: Extending the FAIR Guiding Principles to Address Semantic Interoperability [PDF]

CoRR
FAIR data presupposes their successful communication between machines and humans while preserving their meaning and reference, requiring all parties involved to share the same background knowledge.
Vogt, Lars, Strömert, Philip, Baum, Roman, Prinz, Manuel, Konrad, Marcel, Matentzoglu, Nicolas, Karam, Naouel   +6 more
core   +3 more sources

Aligning restricted access data with FAIR: a systematic review [PDF]

PeerJ Computer Science, 2022
Understanding the complexity of restricted research data is vitally important in the current new era of Open Science. While the FAIR Guiding Principles have been introduced to help researchers to make data Findable, Accessible, Interoperable and Reusable,
Margherita Martorana, Tobias Kuhn, Ronald Siebes, Jacco van Ossenbruggen   +3 more
doaj   +2 more sources

Assessing the use of HL7 FHIR for implementing the FAIR guiding principles: a case study of the MIMIC-IV Emergency Department module. [PDF]

JAMIA Open
Abstract Objectives To provide a real-world example on how and to what extent Health Level Seven Fast Healthcare Interoperability Resources (FHIR) implements the Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles for scientific data.
van Damme P, Löbe M, Benis N, de Keizer NF, Cornet R.   +4 more
europepmc   +4 more sources

An interpretation of the FAIR principles to guide implementations in the HMC digital ecosystem [PDF]

, 2022
Findable, accessible, interoperable and reusable (FAIR) set principles that determine best practice for managing the dissemination and ensuring longevity of digital resources. The Helmholtz Metadata Collaboration (HMC) provides guidance on metadata and related topics to those working in the Helmholtz ecosystem.
Buttigieg, Pier Luigi, Curdt, Constanze, Ihsan, Ahmad Z., Jejkal, Thomas, Kubin, Markus, Mannix, Oonagh, Mohr, Daniel P., Pirogov, Anton, Port, Barbara, Stucky, Karl-Uwe   +9 more
openaire   +2 more sources

FAIR Digital Twins for Data-Intensive Research

Frontiers in Big Data, 2022
Although all the technical components supporting fully orchestrated Digital Twins (DT) currently exist, what remains missing is a conceptual clarification and analysis of a more generalized concept of a DT that is made FAIR, that is, universally machine ...
Erik Schultes, Erik Schultes, Marco Roos, Marco Roos, Luiz Olavo Bonino da Silva Santos, Giancarlo Guizzardi, Giancarlo Guizzardi, Jildau Bouwman, Jildau Bouwman, Thomas Hankemeier, Arie Baak, Barend Mons, Barend Mons, Barend Mons, Barend Mons   +14 more
doaj   +1 more source

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   +2 more sources

The FAIR Principles [PDF]

, 2021
<p>According to the <a href="https://www.go-fair.org/fair-principles/">FAIR Principles</a>, research objects should be findable, accessible, interoperable and re-usable.
Sanchez Solis, Barbara, Jaber, Samah
core   +1 more source

FAIR for AI: An interdisciplinary and international community building perspective

Scientific Data, 2023
A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data.
E. A. Huerta, Ben Blaiszik, L. Catherine Brinson, Kristofer E. Bouchard, Daniel Diaz, Caterina Doglioni, Javier M. Duarte, Murali Emani, Ian Foster, Geoffrey Fox, Philip Harris, Lukas Heinrich, Shantenu Jha, Daniel S. Katz, Volodymyr Kindratenko, Christine R. Kirkpatrick, Kati Lassila-Perini, Ravi K. Madduri, Mark S. Neubauer, Fotis E. Psomopoulos, Avik Roy, Oliver Rübel, Zhizhen Zhao, Ruike Zhu   +23 more
doaj   +1 more source