Scientists develop an early warning system that delivers wheat rust predictions directly to farmers’ phones
New research describes a revolutionary early warning system that can predict and mitigate wheat rust diseases in Ethiopia.
by International Maize and Wheat Improvement Center (CIMMYT) | Nov 4, 2019 | CGIAR Updates, Inspire news
New research describes a revolutionary early warning system that can predict and mitigate wheat rust diseases in Ethiopia.
by Hannah Craig | Aug 13, 2019 | Inspire Challenge, Inspire news, News
A new paper published in BMC Biology by the MARPLE diagnostics team, a 2018 Inspire Challenge Scale-up winner, shows how the research partnership reduced the speed of diagnostics from many months in high-end labs, to just two days from the side of an Ethiopian field.
by CGIAR-CSI | May 28, 2019 | Geospatial CoP
CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) released an integrated modeling framework, CRAFT, developed for running gridded crop modeling simulations.
by CGIAR-CSI | May 23, 2019 | Geospatial CoP
Matthew Reynolds and Zhonghu He from CIMMYT co-authored a paper, A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral unmanned aerial vehicle (UAV) platform, investigating the potential of using UAV-based NDVI measurement in the phenotyping of wheat varieties in the breeding program.
by CGIAR-CSI | May 22, 2019 | Geospatial CoP
Awais Rasheed and Zhonghu He from CIMMYT co-authored a research paper on the assessment of plant height measurement using unmanned aerial vehicles.
CGIAR Platform for Big Data in Agriculture advocates open data for agricultural research for development. It considers that opening up research data for scrutiny and reuse confers significant benefits to society.
However, the Platform appreciates that not all research data can be open and that a broad range of legitimate circumstances may require data to be restricted.
As an integral component of its advocacy for open data, the Platform promotes responsible data management through the entire research data lifecycle from planning, collecting, storing, disclosing or publishing, transferring, discovery and archiving.
These guidelines were created from information collected from: review on best and emerging practices across various sectors in the fast changing landscape of privacy and ethics (130 external resources); privacy and ethic materials sourced from seven CGIAR centers; first draft was circulated for input and feedback across CGIAR and incorporated into this edition. It’s important to note that this is an evolving document, the next stage is to consult externally for further input.
These Guidelines are intended to assist agricultural researchers handle privacy and personally identifiable information (PII) in the research project data lifecycle.
Ensure compatibility with the DMP-PII (as above) and also the purpose for which prior informed consent has been obtained
Ensure PII is stored securely to protect privacy, through organizational or project specific safeguards to prevent unauthorized access, accidental disclosure or breach of data (physical & technical)
Don’t store data in unsecured locations or on unsecured devices or servers
Don’t store encrypted data and encryption keys in locations where they can be easily accessed simultaneously
Don’t underestimate the importance and value of administrative safeguards to standardize practices (i.e. organizational policies, procedures and maintenance of security measures that are designed to protect private information, data and access)