Database Commons
Database Commons

a catalog of worldwide biological databases

Database Profile

AgBioData

General information

URL: https://www.agbiodata.org
Full name:
Description: AgBioData, is a consortium of people working at agricultural biological databases, data archives and knowledgbases who strive to identify common issues in database development, curation and management, with the goal of creating database products that are more Findable, Accessible, Interoperable and Reusable.
Year founded: 2018
Last update:
Version:
Accessibility:
Accessible
Country/Region: United States

Contact information

University/Institution: Washington State University
Address: Corn Insects and Crop Genetics Research Unit, USDA-ARS, Ames, IA, USA
City: Ames
Province/State:
Country/Region: United States
Contact name (PI/Team): Lisa Harper
Contact email (PI/Helpdesk): lisa.harper@ars.usda.gov

Publications

38079567
Genotype and phenotype data standardization, utilization and integration in the big data era for agricultural sciences. [PMID: 38079567]
Cecilia H Deng, Sushma Naithani, Sunita Kumari, Irene Cobo-Simón, Elsa H Quezada-Rodríguez, Maria Skrabisova, Nick Gladman, Melanie J Correll, Akeem Babatunde Sikiru, Olusola O Afuwape, Annarita Marrano, Ines Rebollo, Wentao Zhang, Sook Jung

Large-scale genotype and phenotype data have been increasingly generated to identify genetic markers, understand gene function and evolution and facilitate genomic selection. These datasets hold immense value for both current and future studies, as they are vital for crop breeding, yield improvement and overall agricultural sustainability. However, integrating these datasets from heterogeneous sources presents significant challenges and hinders their effective utilization. We established the Genotype-Phenotype Working Group in November 2021 as a part of the AgBioData Consortium (https://www.agbiodata.org) to review current data types and resources that support archiving, analysis and visualization of genotype and phenotype data to understand the needs and challenges of the plant genomic research community. For 2021-22, we identified different types of datasets and examined metadata annotations related to experimental design/methods/sample collection, etc. Furthermore, we thoroughly reviewed publicly funded repositories for raw and processed data as well as secondary databases and knowledgebases that enable the integration of heterogeneous data in the context of the genome browser, pathway networks and tissue-specific gene expression. Based on our survey, we recommend a need for (i) additional infrastructural support for archiving many new data types, (ii) development of community standards for data annotation and formatting, (iii) resources for biocuration and (iv) analysis and visualization tools to connect genotype data with phenotype data to enhance knowledge synthesis and to foster translational research. Although this paper only covers the data and resources relevant to the plant research community, we expect that similar issues and needs are shared by researchers working on animals. Database URL: https://www.agbiodata.org.

Database (Oxford). 2023:2023() | 10 Citations (from Europe PMC, 2025-12-20)
37971715
Data sharing and ontology use among agricultural genetics, genomics, and breeding databases and resources of the Agbiodata Consortium. [PMID: 37971715]
Jennifer L Clarke, Laurel D Cooper, Monica F Poelchau, Tanya Z Berardini, Justin Elser, Andrew D Farmer, Stephen Ficklin, Sunita Kumari, Marie-Angélique Laporte, Rex T Nelson, Rie Sadohara, Peter Selby, Anne E Thessen, Brandon Whitehead, Taner Z Sen

Over the last couple of decades, there has been a rapid growth in the number and scope of agricultural genetics, genomics and breeding databases and resources. The AgBioData Consortium (https://www.agbiodata.org/) currently represents 44 databases and resources (https://www.agbiodata.org/databases) covering model or crop plant and animal GGB data, ontologies, pathways, genetic variation and breeding platforms (referred to as 'databases' throughout). One of the goals of the Consortium is to facilitate FAIR (Findable, Accessible, Interoperable, and Reusable) data management and the integration of datasets which requires data sharing, along with structured vocabularies and/or ontologies. Two AgBioData working groups, focused on Data Sharing and Ontologies, respectively, conducted a Consortium-wide survey to assess the current status and future needs of the members in those areas. A total of 33 researchers responded to the survey, representing 37 databases. Results suggest that data-sharing practices by AgBioData databases are in a fairly healthy state, but it is not clear whether this is true for all metadata and data types across all databases; and that, ontology use has not substantially changed since a similar survey was conducted in 2017. Based on our evaluation of the survey results, we recommend (i) providing training for database personnel in a specific data-sharing techniques, as well as in ontology use; (ii) further study on what metadata is shared, and how well it is shared among databases; (iii) promoting an understanding of data sharing and ontologies in the stakeholder community; (iv) improving data sharing and ontologies for specific phenotypic data types and formats; and (v) lowering specific barriers to data sharing and ontology use, by identifying sustainability solutions, and the identification, promotion, or development of data standards. Combined, these improvements are likely to help AgBioData databases increase development efforts towards improved ontology use, and data sharing via programmatic means. Database URL  https://www.agbiodata.org/databases.

Database (Oxford). 2023:2023() | 4 Citations (from Europe PMC, 2025-12-20)
30239679
AgBioData consortium recommendations for sustainable genomics and genetics databases for agriculture. [PMID: 30239679]
Lisa Harper, Jacqueline Campbell, Ethalinda K S Cannon, Sook Jung, Monica Poelchau, Ramona Walls, Carson Andorf, Elizabeth Arnaud, Tanya Z Berardini, Clayton Birkett, Steve Cannon, James Carson, Bradford Condon, Laurel Cooper, Nathan Dunn, Christine G Elsik, Andrew Farmer, Stephen P Ficklin, David Grant, Emily Grau, Nic Herndon, Zhi-Liang Hu, Jodi Humann, Pankaj Jaiswal, Clement Jonquet, Marie-Angélique Laporte, Pierre Larmande, Gerard Lazo, Fiona McCarthy, Naama Menda, Christopher J Mungall, Monica C Munoz-Torres, Sushma Naithani, Rex Nelson, Daureen Nesdill, Carissa Park, James Reecy, Leonore Reiser, Lacey-Anne Sanderson, Taner Z Sen, Margaret Staton, Sabarinath Subramaniam, Marcela Karey Tello-Ruiz, Victor Unda, Deepak Unni, Liya Wang, Doreen Ware, Jill Wegrzyn, Jason Williams, Margaret Woodhouse, Jing Yu, Doreen Main

The future of agricultural research depends on data. The sheer volume of agricultural biological data being produced today makes excellent data management essential. Governmental agencies, publishers and science funders require data management plans for publicly funded research. Furthermore, the value of data increases exponentially when they are properly stored, described, integrated and shared, so that they can be easily utilized in future analyses. AgBioData (https://www.agbiodata.org) is a consortium of people working at agricultural biological databases, data archives and knowledgbases who strive to identify common issues in database development, curation and management, with the goal of creating database products that are more Findable, Accessible, Interoperable and Reusable. We strive to promote authentic, detailed, accurate and explicit communication between all parties involved in scientific data. As a step toward this goal, we present the current state of biocuration, ontologies, metadata and persistence, database platforms, programmatic (machine) access to data, communication and sustainability with regard to data curation. Each section describes challenges and opportunities for these topics, along with recommendations and best practices.

Database (Oxford). 2018:2018() | 46 Citations (from Europe PMC, 2025-12-20)

Ranking

All databases:
1637/6895 (76.273%)
Gene genome and annotation:
528/2021 (73.924%)
Genotype phenotype and variation:
244/1005 (75.821%)
Expression:
328/1347 (75.724%)
Interaction:
326/1194 (72.781%)
Standard ontology and nomenclature:
76/238 (68.487%)
Metadata:
156/719 (78.442%)
1637
Total Rank
58
Citations
8.286
z-index

Community reviews

Not Rated
Data quality & quantity:
Content organization & presentation
System accessibility & reliability:

Word cloud

Related Databases

Citing
Cited by

Record metadata

Created on: 2019-10-27
Curated by:
Haochen Liu [2024-07-16]
zheng luo [2024-07-15]
Shoaib Saleem [2019-11-25]
irfan Hussain [2019-10-27]