Proceedings ArticleDOI
OntoSoft: Capturing Scientific Software Metadata
Yolanda Gil,Varun Ratnakar,Daniel Garijo +2 more
- pp 32
TLDR
OntoSoft, an ontology to describe metadata for scientific software, is presented, designed considering how scientists would approach the reuse and sharing of software.Abstract:
This paper presents OntoSoft, an ontology to describe metadata for scientific software. The ontology is designed considering how scientists would approach the reuse and sharing of software. This includes supporting a scientist to: 1) identify software, 2) understand and assess software, 3) execute software, 4) get support for the software, 5) do research with the software, and 6) update the software. The ontology is available in OWL and contains more than fifty terms. We are using OntoSoft to structure a software registry for geosciences, and to develop user interfaces to capture its metadata.read more
Citations
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Journal ArticleDOI
Software Citation Principles
Journal ArticleDOI
Towards FAIR principles for research software
Anna-Lena Lamprecht,Leyla Garcia,Mateusz Kuzak,Carlos Alberto Martinez,Ricardo Arcila,Eva Martin del Pico,Victoria Dominguez Del Angel,Stephanie van de Sandt,Jon Ison,Paula Martinez,Peter McQuilton,Alfonso Valencia,Alfonso Valencia,Jennifer Harrow,Fotis Psomopoulos,Josep Ll. Gelpi,Josep Ll. Gelpi,Neil Chue Hong,Carole Goble,Salvador Capella-Gutierrez +19 more
TL;DR: This position paper summarised and developed a basis for community discussion about what makes software different from data concerning the application of the FAIR principles, and which desired characteristics of research software go beyond FAIR.
Journal ArticleDOI
Most computational hydrology is not reproducible, so is it really science?
TL;DR: It is recommended that reuseable code and formal workflows, which unambiguously reproduce published scientific results, are made available for the community alongside data, so that the community can verify previous findings, and build directly from previous work.
Journal ArticleDOI
Toward the Geoscience Paper of the Future: Best practices for documenting and sharing research from data to software to provenance
Yolanda Gil,Cédric H. David,Ibrahim Demir,Bakinam T. Essawy,Robinson W. Fulweiler,Jonathan L. Goodall,Leif Karlstrom,Huikyo Lee,Heath J. Mills,Ji-Hyun Oh,Ji-Hyun Oh,Suzanne A. Pierce,Allen Pope,Allen Pope,Mimi W. Tzeng,Sandra R. Villamizar,Xuan Yu +16 more
TL;DR: The Geoscience Paper of the Future (GPF) as discussed by the authors is an approach to fully document, share, and cite all their research products including data, software, and computational provenance.
Semantic-Enhanced Training Data Augmentation Methods for Long-Tail Entity Recognition Models
TL;DR: This work aimed at showing how, by enhancing the size and quality of the training data using different techniques, it will be possible to improve the performance of Long-tail Entity Recognition (L-tER).
References
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