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| Registros recuperados : 8 | |
| 3. |  | CRUZ, S. M. S. da; NASCIMENTO, J. A. P. do. SisGExp: Rethinking long-tail agronomic experiments. In: MATOSO, M.; GLAVIC, B. (Ed.). Provenance and annotation of data and processes. Berlin: Springer, 2016. Proceedings of the 6th International Provenance and Annotation Workshop, IPAW 2016, held in McLean, VA, USA, in June 2016.| Biblioteca(s): Embrapa Agrobiologia. |
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| 5. | | DRUCKER, D. P.; CRUZ, S. M. S. da; SARAIVA, A. M.; FORTALEZA, J. M.; BERTIN, P. R. B.; SIMAO, V. P. M.; TELLES, M. A.; SILVA, A. R. da; SANTOS, P. S. S.; MACARIO, C. G. do N. Implantação da Rede Temática GO-FAIR Agro Brasil: primeiros passos. In: CONGRESSO BRASILEIRO DE AGROINFORMÁTICA, 13., 2021, Bagé. Anais [...]. Bagé: Unipampa, 2021. p. 164-171. 2177-9724 Organizado por Ana Paula Lüdtke Ferreira.| Biblioteca(s): Embrapa Meio Ambiente; Embrapa Unidades Centrais. |
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| 6. | | DRUCKER, D. P.; CRUZ, S. M. S. da; SARAIVA, A. M.; FORTALEZA, J. M.; BERTIN, P. R. B.; SIMAO, V. P. M.; TELLES, M. A.; SILVA, A. R. da; SANTOS, P. S. S.; MACARIO, C. G. do N. Implantação da Rede Temática GO-FAIR Agro Brasil: primeiros passos. In: CONGRESSO BRASILEIRO DE AGROINFORMÁTICA, 13., 2021, Bagé. Anais [...]. Bagé: Unipampa, 2021. p. 164-171. 2177-9724 Organizado por Ana Paula Lüdtke Ferreira. SBIAgro 2021.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 7. | | DRUCKER, D. P.; CRUZ, S. M. S. da; TELLES, M. A.; FERREIRA, A. P. L.; CORRÊA, F. E.; BERTIN, P. R. B.; MARASSI, L.; AQUINO, K.; BEZERRA, G.; CRUZ, P. V.; SOARES, F. M. Desdobramentos da implementacão da Rede GO FAIR Agro Brasil no biênio 2021-2023. In: CONGRESSO BRASILEIRO DE AGROINFORMÁTICA, 14., 2023, Natal. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023. p. 286-293. SBIAgro 2023.| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Agroenergia; Embrapa Unidades Centrais. |
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| 8. | | SOARES, F. M.; BERGIER, I.; CORADINI, M. C.; FERREIRA, A. P. L.; TELLES, M. A.; MACULAN, B. C. M. dos S.; ALENCAR, M. de C. F.; SIMAO, V. P. M.; ALMEIDA, B. T. de; DRUCKER, D. P.; VIEIRA, M. dos S. M.; CRUZ, S. M. S. da. Unveiling knowledge organization systems' artifacts for digital agriculture with lexical network analysis. In: INTERNATIONAL WORKSHOP ON CONCEPTUAL MODELING FOR LIFE SCIENCES, 4.; WORKSHOP ON CONCEPTUAL MODELING, ONTOLOGIES AND (META)DATA MANAGEMENT FOR FINDABLE, ACCESSIBLE, INTEROPERABLE AND REUSABLE (FAIR) DATA, 3.; INTERNATIONAL WORKSHOP ON EMPIRICAL METHODS IN CONCEPTUAL MODELING, 6.; INTERNATIONAL WORKSHOP ON DIGITAL JUSTICE, DIGITAL LAW AND CONCEPTUAL MODELING, 2.; INTERNATIONAL WORKSHOP ON ONTOLOGIES AND CONCEPTUAL MODELING. 9.; INTERNATIONAL WORKSHOP ON QUALITY AND MEASUREMENT OF MODEL-DRIVEN SOFTWARE DEVELOPMENT, 4.; WORKSHOP ON CONTROLLED VOCABULARIES AND DATA PLATFORMS FOR SMART FOOD SYSTEMS, 1., 2023, Lisbon. Advances in conceptual modeling: proceedings. Cham: Springer, 2023. p. 299-311. (Lecture notes in computer science, 14319). Editors: Tiago Prince Sales, João Araújo, José Borbinha, Giancarlo Guizzardi. ER 2023 Workshops.| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Territorial; Embrapa Unidades Centrais. |
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| Registros recuperados : 8 | |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agrobiologia. Para informações adicionais entre em contato com cnpab.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Agrobiologia. |
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Data corrente: |
30/05/2019 |
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Data da última atualização: |
19/11/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
CRUZ, S. M. S. da; NASCIMENTO, J. A. P. do. |
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Afiliação: |
Sérgio Manuel Serra da Cruz; JOSE ANTONIO PIRES DO NASCIMENTO, CNPAB. |
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Título: |
Towards integration of data-driven agronomic experiments with data provenance. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Computers and Electronics in Agriculture, v. 161, p. 14-28, 2019. |
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DOI: |
https://doi.org/10.1016/j.compag.2019.01.044 |
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Idioma: |
Inglês |
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Conteúdo: |
With improvements in computing and communications, the amount of scientific data in agriculture has been exploding. Thus, researchers must rely on computational simulations to model the data-driven in silico agronomic experiments, the in silico experiments are those that are completely executed by using computer models. Reproducibility, transparency, independent verification are major features of Science. However, even agricultural research of exemplary quality may have irreproducible empirical findings because of random or systematic error. Funding agencies, researchers, and reviewers are demanding improved processes and the use of open data to increase reproducibility of those experiments. Currently, there are no scientific criteria to evaluate the integration of data-driven agronomic experiments with data provenance. We propose RFlow, a framework that aid researchers to manage, share, and enact the scientific in silico experiments of research projects that use reusable R scripts. The framework uses open data standards and transparently captures provenance of the agronomic experiments. RFlow is non-intrusive, can be connected to workflow systems and does not require researchers to change their working way. Our computational experiments show that the framework can collect provenance metadata and enrich a scientific project. This study shows how RFlow can serve as the primary integration platform for statistical systems, like R, with implications for other data and compute-intensive agronomic projects. As a proof of concept, we show the concrete effectiveness and expressive power of the RFlow which was evaluated through a set of data-driven agronomic in silico experiments and provenance SQL queries that exemplifies what kind of information was gathered. MenosWith improvements in computing and communications, the amount of scientific data in agriculture has been exploding. Thus, researchers must rely on computational simulations to model the data-driven in silico agronomic experiments, the in silico experiments are those that are completely executed by using computer models. Reproducibility, transparency, independent verification are major features of Science. However, even agricultural research of exemplary quality may have irreproducible empirical findings because of random or systematic error. Funding agencies, researchers, and reviewers are demanding improved processes and the use of open data to increase reproducibility of those experiments. Currently, there are no scientific criteria to evaluate the integration of data-driven agronomic experiments with data provenance. We propose RFlow, a framework that aid researchers to manage, share, and enact the scientific in silico experiments of research projects that use reusable R scripts. The framework uses open data standards and transparently captures provenance of the agronomic experiments. RFlow is non-intrusive, can be connected to workflow systems and does not require researchers to change their working way. Our computational experiments show that the framework can collect provenance metadata and enrich a scientific project. This study shows how RFlow can serve as the primary integration platform for statistical systems, like R, with implications for other data and compute-i... Mostrar Tudo |
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Palavras-Chave: |
Reproducible research; Scientific workflows. |
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Thesaurus NAL: |
Agriculture; Data analysis; Provenance. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
|
Marc: |
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024 7 $ahttps://doi.org/10.1016/j.compag.2019.01.044$2DOI
100 1 $aCRUZ, S. M. S. da
245 $aTowards integration of data-driven agronomic experiments with data provenance.$h[electronic resource]
260 $c2019
520 $aWith improvements in computing and communications, the amount of scientific data in agriculture has been exploding. Thus, researchers must rely on computational simulations to model the data-driven in silico agronomic experiments, the in silico experiments are those that are completely executed by using computer models. Reproducibility, transparency, independent verification are major features of Science. However, even agricultural research of exemplary quality may have irreproducible empirical findings because of random or systematic error. Funding agencies, researchers, and reviewers are demanding improved processes and the use of open data to increase reproducibility of those experiments. Currently, there are no scientific criteria to evaluate the integration of data-driven agronomic experiments with data provenance. We propose RFlow, a framework that aid researchers to manage, share, and enact the scientific in silico experiments of research projects that use reusable R scripts. The framework uses open data standards and transparently captures provenance of the agronomic experiments. RFlow is non-intrusive, can be connected to workflow systems and does not require researchers to change their working way. Our computational experiments show that the framework can collect provenance metadata and enrich a scientific project. This study shows how RFlow can serve as the primary integration platform for statistical systems, like R, with implications for other data and compute-intensive agronomic projects. As a proof of concept, we show the concrete effectiveness and expressive power of the RFlow which was evaluated through a set of data-driven agronomic in silico experiments and provenance SQL queries that exemplifies what kind of information was gathered.
650 $aAgriculture
650 $aData analysis
650 $aProvenance
653 $aReproducible research
653 $aScientific workflows
700 1 $aNASCIMENTO, J. A. P. do
773 $tComputers and Electronics in Agriculture$gv. 161, p. 14-28, 2019.
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