2021-10-06_SIAgro_IGAD-Webinar.pdf (3.48 MB)
An information system to support agrobiodiversity conservation
Presentation for the Research Data Alliance Interest Group for Agricultural Data (IGAD) Coffee Break Webinar series.
Developing an information system to support agrobiodiversity conservation in Mexico
CONABIO is a governmental agency which coordinates biodiversity data collection and analysis in support of the sustainable management of natural resources in Mexico. The Agrobiodiversity Information System (SIAgroBD) at CONABIO is a comprehensive project that is part of the Mexican Agrobiodiversity GEF project, together with the Food and Agriculture Organization of the United Nations (FAO) as the implementing agency. SIAgroBD’s objective is to produce and systematize data in order to generate knowledge that enables the conservation of traditional agricultural practices and native crops which have a large global importance.
SIAgroBD is in a late development stage, and is currently testing with users the publication of the first datasets. During this webinar, we discussed lessons learned while integrating new and pre-existing datasets on specimen records, nutritional information, qualitative and quantitative agronomic data, and qualitative assessments of local use of agrobiodiversity. We also discussed how we are implementing a workflow that facilitates FAIRness across the data lifecycle, from collection to publication. This includes the adoption of field data collection tools, vocabulary standards, reproducible methods for data cleaning, open data training for participants, and the development of a custom data integration platform.
SIAgroBD is in a late development stage, and is currently testing with users the publication of the first datasets. During this webinar, we discussed lessons learned while integrating new and pre-existing datasets on specimen records, nutritional information, qualitative and quantitative agronomic data, and qualitative assessments of local use of agrobiodiversity. We also discussed how we are implementing a workflow that facilitates FAIRness across the data lifecycle, from collection to publication. This includes the adoption of field data collection tools, vocabulary standards, reproducible methods for data cleaning, open data training for participants, and the development of a custom data integration platform.
