{"title":"Making Australian Drought Monitor dataset findable, accessible, interoperable and reusable","authors":"","doi":"10.1016/j.compag.2024.109381","DOIUrl":null,"url":null,"abstract":"<div><p>Making agricultural research datasets Findable, Accessible, Interoperable, and Reusable (FAIR) is an evolving priority for research organisations in Australia. Indigenous data governance standards, described in the CARE (Collective benefit, Authority to control, Responsibility and Ethics) principles complement FAIR principles when managing research datasets. Agricultural research data have traditionally been difficult to publicly access and share due in part to conflicting interests in ownership, commerce, multiparty contracts, and diverse research practices.</p><p>As part of an agriculture digital research platform development project (AgReFed Platform project), we develop here a workflow that applies the FAIR data and CARE principles to the Australian Drought Monitor dataset, a product developed as part of the Northern Australia Climate Program (NACP), a joint project funded by Meat and Livestock Australia, the Queensland Drought and Climate Adaptation Program and the University of Southern Queensland (UniSQ). We present here a complete process on how to apply the FAIR principles to the Australian Drought Monitor dataset, including a digital infrastructure development to enable its re-use in the AgReFed Platform project.</p></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7000,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0168169924007725/pdfft?md5=031553ad1ed3c90adad4814fa02c11aa&pid=1-s2.0-S0168169924007725-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers and Electronics in Agriculture","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0168169924007725","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Making agricultural research datasets Findable, Accessible, Interoperable, and Reusable (FAIR) is an evolving priority for research organisations in Australia. Indigenous data governance standards, described in the CARE (Collective benefit, Authority to control, Responsibility and Ethics) principles complement FAIR principles when managing research datasets. Agricultural research data have traditionally been difficult to publicly access and share due in part to conflicting interests in ownership, commerce, multiparty contracts, and diverse research practices.
As part of an agriculture digital research platform development project (AgReFed Platform project), we develop here a workflow that applies the FAIR data and CARE principles to the Australian Drought Monitor dataset, a product developed as part of the Northern Australia Climate Program (NACP), a joint project funded by Meat and Livestock Australia, the Queensland Drought and Climate Adaptation Program and the University of Southern Queensland (UniSQ). We present here a complete process on how to apply the FAIR principles to the Australian Drought Monitor dataset, including a digital infrastructure development to enable its re-use in the AgReFed Platform project.
期刊介绍:
Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.