Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Alejandra N. Gonzalez Beltran"'
Publikováno v:
Data Science Journal, Vol 23, Pp 4-4 (2024)
In this paper, a framework and a system architecture are presented to support researchers in DMP creation and execution, with a focus on the generation of FAIR data. Using the research data lifecycle within Photon and Neutron analytical facilities as
Externí odkaz:
https://doaj.org/article/9d4932966c7645198c6ab7cd5c6d8f96
Autor:
Riccardo Albertoni, David Browning, Simon Cox, Alejandra N. Gonzalez-Beltran, Andrea Perego, Peter Winstanley
Publikováno v:
Data Intelligence, Vol 6, Iss 2 (2024)
Externí odkaz:
https://doaj.org/article/f938ee847d8e4a2688717df1d575dc49
Publikováno v:
PLoS Computational Biology, Vol 17, Iss 6, p e1009041 (2021)
We present ten simple rules that support converting a legacy vocabulary-a list of terms available in a print-based glossary or in a table not accessible using web standards-into a FAIR vocabulary. Various pathways may be followed to publish the FAIR
Externí odkaz:
https://doaj.org/article/147795f0dc32426ba9af38b653cd5352
Autor:
Sonia Natalie Mitchell, Andrew Lahiff, Nathan Cummings, Jonathan Hollocombe, Bram Boskamp, Ryan Field, Dennis Reddyhoff, Kristian Zarebski, Antony Wilson, Bruno Viola, Martin Burke, Blair Archibald, Paul Bessell, Richard Blackwell, Lisa A. Boden, Alys Brett, Sam Brett, Ruth Dundas, Jessica Enright, Alejandra N. Gonzalez-Beltran, Claire Harris, Ian Hinder, Christopher David Hughes, Martin Knight, Vino Mano, Ciaran McMonagle, Dominic Mellor, Sibylle Mohr, Glenn Marion, Louise Matthews, Iain J. McKendrick, Christopher Mark Pooley, Thibaud Porphyre, Aaron Reeves, Edward Townsend, Robert Turner, Jeremy Walton, Richard Reeve
Publikováno v:
Mitchell, S N, Lahiff, A, Cummings, N, Hollocombe, J, Boskamp, B, Field, R, Reddyhoff, D, Zarebski, K, Wilson, A, Viola, B, Burke, M, Archibald, B, Bessell, P, Blackwell, R, Boden, L A, Brett, A, Brett, S, Dundas, R, Enright, J, Gonzalez-Beltran, A N, Harris, C, Hinder, I, David Hughes, C, Knight, M, Mano, V, McMonagle, C, Mellor, D, Mohr, S, Marion, G, Matthews, L, McKendrick, I J, Mark Pooley, C, Porphyre, T, Reeves, A, Townsend, E, Turner, R, Walton, J & Reeve, R 2022, ' FAIR data pipeline : provenance-driven data management for traceable scientific workflows ', Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 380, no. 2233, 20210300 . https://doi.org/10.1098/rsta.2021.0300
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2022, 380 (2233), ⟨10.1098/rsta.2021.0300⟩
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2022, 380 (2233), ⟨10.1098/rsta.2021.0300⟩
Modern epidemiological analyses to understand and combat the spread of disease depend critically on access to, and use of, data. Rapidly evolving data, such as data streams changing during a disease outbreak, are particularly challenging. Data manage
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::16a702da9a11b8817950011b5202a3cd
https://doi.org/10.1098/rsta.2021.0300
https://doi.org/10.1098/rsta.2021.0300
Autor:
Sonia Natalie, Mitchell, Andrew, Lahiff, Nathan, Cummings, Jonathan, Hollocombe, Bram, Boskamp, Ryan, Field, Dennis, Reddyhoff, Kristian, Zarebski, Antony, Wilson, Bruno, Viola, Martin, Burke, Blair, Archibald, Paul, Bessell, Richard, Blackwell, Lisa A, Boden, Alys, Brett, Sam, Brett, Ruth, Dundas, Jessica, Enright, Alejandra N., Gonzalez-Beltran, Claire, Harris, Ian, Hinder, Christopher David, Hughes, Martin, Knight, Vino, Mano, Ciaran, Mcmonagle, Dominic, Mellor, Sibylle, Mohr, Glenn, Marion, Louise, Matthews, Iain J., Mckendrick, Christopher Mark, Pooley, Thibaud, Porphyre, Aaron, Reeves, Edward, Townsend, Robert, Turner, Jeremy, Walton, Richard, Reeve
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______165::adec1354d48482c59dae9643f6e648da
https://hal.science/hal-03747524
https://hal.science/hal-03747524
Autor:
Michel Dumontier, Alasdair J.G. Gray, M. Scott Marshall, Vladimir Alexiev, Peter Ansell, Gary Bader, Joachim Baran, Jerven T. Bolleman, Alison Callahan, José Cruz-Toledo, Pascale Gaudet, Erich A. Gombocz, Alejandra N. Gonzalez-Beltran, Paul Groth, Melissa Haendel, Maori Ito, Simon Jupp, Nick Juty, Toshiaki Katayama, Norio Kobayashi, Kalpana Krishnaswami, Camille Laibe, Nicolas Le Novère, Simon Lin, James Malone, Michael Miller, Christopher J. Mungall, Laurens Rietveld, Sarala M. Wimalaratne, Atsuko Yamaguchi
Publikováno v:
PeerJ, Vol 4, p e2331 (2016)
Access to consistent, high-quality metadata is critical to finding, understanding, and reusing scientific data. However, while there are many relevant vocabularies for the annotation of a dataset, none sufficiently captures all the necessary metadata
Externí odkaz:
https://doaj.org/article/87a6908c1991464f8c104ef7ffc724a9
Shared terminology is key for data sharing, harmonisation of datasets within and across disciplines and cross-domain data integration. Many organizations and disciplines have a tradition of curating lists of terms. These lists come in many forms, fro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::af2b6e555c2e790790a1c1bf4e7b4ce9