AgroFIMS: A tool to enable digital collection of standards-compliant FAIR data
Agricultural research has been traditionally driven by linear approaches dictated by hypothesis-testing. With the advent of powerful data science capabilities, predictive, empirical approaches are possible that operate over large data pools to discern patterns. Such data pools need to contain well-d...
| Autores principales: | , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
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Frontiers Media
2021
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| Acceso en línea: | https://hdl.handle.net/10568/115543 |
| _version_ | 1855515667109773312 |
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| author | Devare, Medha Aubert, Céline Benites Alfaro, Omar Eduardo Pérez Masias, Ivan Omar Laporte, Marie-Angélique |
| author_browse | Aubert, Céline Benites Alfaro, Omar Eduardo Devare, Medha Laporte, Marie-Angélique Pérez Masias, Ivan Omar |
| author_facet | Devare, Medha Aubert, Céline Benites Alfaro, Omar Eduardo Pérez Masias, Ivan Omar Laporte, Marie-Angélique |
| author_sort | Devare, Medha |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Agricultural research has been traditionally driven by linear approaches dictated by hypothesis-testing. With the advent of powerful data science capabilities, predictive, empirical approaches are possible that operate over large data pools to discern patterns. Such data pools need to contain well-described, machine-interpretable, and openly available data (represented by high-scoring Findable, Accessible, Interoperable, and Reusable—or FAIR—resources). CGIAR's Platform for Big Data in Agriculture has developed several solutions to help researchers generate open and FAIR outputs, determine their FAIRness in quantitative terms1, and to create high-value data products drawing on these outputs. By accelerating the speed and efficiency of research, these approaches facilitate innovation, allowing the agricultural sector to respond agilely to farmer challenges. In this paper, we describe the Agronomy Field Information Management System or AgroFIMS, a web-based, open-source tool that helps generate data that is “born FAIRer” by addressing data interoperability to enable aggregation and easier value derivation from data. Although license choice to determine accessibility is at the discretion of the user, AgroFIMS provides consistent and rich metadata helping users more easily comply with institutional, founder and publisher FAIR mandates. The tool enables the creation of fieldbooks through a user-friendly interface that allows the entry of metadata tied to the Dublin Core standard schema, and trial details via picklists or autocomplete that are based on semantic standards like the Agronomy Ontology (AgrO). Choices are organized by field operations or measurements of relevance to an agronomist, with specific terms drawn from ontologies. Once the user has stepped through required fields and desired modules to describe their trial management practices and measurement parameters, they can download the fieldbook to use as a standalone Excel-driven file, or employ via free Android-based KDSmart, Fieldbook, or ODK applications for digital data collection. Collected data can be imported back to AgroFIMS for statistical analysis and reports. Development plans for 2021 include new features such ability to clone fieldbooks and the creation of agronomic questionnaires. AgroFIMS will also allow archiving of FAIR data after collection and analysis from a database and to repository platforms for wider sharing. |
| format | Journal Article |
| id | CGSpace115543 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Frontiers Media |
| publisherStr | Frontiers Media |
| record_format | dspace |
| spelling | CGSpace1155432025-11-12T04:46:24Z AgroFIMS: A tool to enable digital collection of standards-compliant FAIR data Devare, Medha Aubert, Céline Benites Alfaro, Omar Eduardo Pérez Masias, Ivan Omar Laporte, Marie-Angélique data agriculture data collection standards digital records interoperability agricultura normas colección de datos interoperabilidad horticulture ecology food science Agricultural research has been traditionally driven by linear approaches dictated by hypothesis-testing. With the advent of powerful data science capabilities, predictive, empirical approaches are possible that operate over large data pools to discern patterns. Such data pools need to contain well-described, machine-interpretable, and openly available data (represented by high-scoring Findable, Accessible, Interoperable, and Reusable—or FAIR—resources). CGIAR's Platform for Big Data in Agriculture has developed several solutions to help researchers generate open and FAIR outputs, determine their FAIRness in quantitative terms1, and to create high-value data products drawing on these outputs. By accelerating the speed and efficiency of research, these approaches facilitate innovation, allowing the agricultural sector to respond agilely to farmer challenges. In this paper, we describe the Agronomy Field Information Management System or AgroFIMS, a web-based, open-source tool that helps generate data that is “born FAIRer” by addressing data interoperability to enable aggregation and easier value derivation from data. Although license choice to determine accessibility is at the discretion of the user, AgroFIMS provides consistent and rich metadata helping users more easily comply with institutional, founder and publisher FAIR mandates. The tool enables the creation of fieldbooks through a user-friendly interface that allows the entry of metadata tied to the Dublin Core standard schema, and trial details via picklists or autocomplete that are based on semantic standards like the Agronomy Ontology (AgrO). Choices are organized by field operations or measurements of relevance to an agronomist, with specific terms drawn from ontologies. Once the user has stepped through required fields and desired modules to describe their trial management practices and measurement parameters, they can download the fieldbook to use as a standalone Excel-driven file, or employ via free Android-based KDSmart, Fieldbook, or ODK applications for digital data collection. Collected data can be imported back to AgroFIMS for statistical analysis and reports. Development plans for 2021 include new features such ability to clone fieldbooks and the creation of agronomic questionnaires. AgroFIMS will also allow archiving of FAIR data after collection and analysis from a database and to repository platforms for wider sharing. 2021-10 2021-10-20T13:22:37Z 2021-10-20T13:22:37Z Journal Article https://hdl.handle.net/10568/115543 en Open Access application/pdf Frontiers Media Devare, M.; Aubert, C.; Benites Alfaro, O.E.; Perez Masias, I.O.; Laporte, M-A. (2021) AgroFIMS: A tool to enable digital collection of standards-compliant FAIR data. Frontiers in Sustainable Food Systems 5:726646. ISSN: 2571-581X |
| spellingShingle | data agriculture data collection standards digital records interoperability agricultura normas colección de datos interoperabilidad horticulture ecology food science Devare, Medha Aubert, Céline Benites Alfaro, Omar Eduardo Pérez Masias, Ivan Omar Laporte, Marie-Angélique AgroFIMS: A tool to enable digital collection of standards-compliant FAIR data |
| title | AgroFIMS: A tool to enable digital collection of standards-compliant FAIR data |
| title_full | AgroFIMS: A tool to enable digital collection of standards-compliant FAIR data |
| title_fullStr | AgroFIMS: A tool to enable digital collection of standards-compliant FAIR data |
| title_full_unstemmed | AgroFIMS: A tool to enable digital collection of standards-compliant FAIR data |
| title_short | AgroFIMS: A tool to enable digital collection of standards-compliant FAIR data |
| title_sort | agrofims a tool to enable digital collection of standards compliant fair data |
| topic | data agriculture data collection standards digital records interoperability agricultura normas colección de datos interoperabilidad horticulture ecology food science |
| url | https://hdl.handle.net/10568/115543 |
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