Dataset of seed characteristics of the main weeds on Reunion Island.

Characterizing seeds of tropical weeds is crucial for future research to understand their ability to disperse in space and time through their seeds, and building up seed banks. Seeds of 55 weed species occurring on Reunion Island were collected and characterized. Seeds were described by their shape, size, color, texture, hairiness, or particular markings.

Experimental dataset of sugarcane-cover crop intercropping trials to control weeds in Reunion Island.

Agroecological studies on sugarcane intercropping commonly generate complex datasets. A generic database (AEGIS - Agro-Ecological Global Information System) has been developed to facilitate the use of these datasets. The data described in this paper includes data from 8 experiments carried out on Reunion Island from 2012 to 2021 under three soil and climatic conditions to assess the capacity of cover crops in the sugarcane inter-row to control weed growth.

Experimental variables in sugarcane intercropping in Reunion Island for data matching.

This study aimed to link experimental data dealing with complex agroecological systems. For sharing and linking collected data with the generic AEGIS (Agro-Ecological Global Information System) database, our work described in this data paper consists in mapping researcher variables to the AEGIS dictionary variable for different tropical crops (sugarcane, rice, sorghum or cover crops). Additionally, this data paper presents a study case based on sugarcane intercropping systems for evaluating 3 matching measures of variables.

The Ontologies Community of Practice: A CGIAR Initiative for Big Data in Agrifood Systems.

Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture and agrifood systems requires quality data labeling or annotation in order to be interoperable. As recommended by the FAIR principles, data, labels, and metadata must use controlled vocabularies and ontologies that are popular in the knowledge domain and commonly used by the community. Despite the existence of robust ontologies in the Life Sciences, there is currently no comprehensive full set of ontologies recommended for data annotation across agricultural research disciplines.