2020 Convention session – Machine Learning and Crop Modeling: A Modern Affair?
This session on machine learning and crop modeling aired live at the virtual 2020 CGIAR Convention on Big Data in Agriculture.
by Hannah Craig | Nov 4, 2020 | Crop Modeling CoP, Video, Webinar
This session on machine learning and crop modeling aired live at the virtual 2020 CGIAR Convention on Big Data in Agriculture.
by International Maize and Wheat Improvement Center (CIMMYT) | Oct 30, 2020 | CGIAR Updates, Crop Modeling CoP
Improving global coordination of crop modeling efforts.
by Hannah Craig | Oct 28, 2020 | Crop Modeling CoP, Video, Webinar
This session by the Crop Modeling Community of Practice aired live at the virtual 2020 CGIAR Convention on Big Data in Agriculture.
by Hannah Craig | Oct 28, 2020 | Crop Modeling CoP, Video, Webinar
This session by the Crop Modeling Community of Practice aired live at the virtual 2020 CGIAR Convention on Big Data in Agriculture.
by Hannah Craig | Jul 29, 2020 | Communities of Practice, Crop Modeling CoP, News
A new publication by scientists from the International Potato Center (CIP) highlights the usefulness of combining crop growth model, remote sensing, and plant ecophysiological tools to assess genetic efficiencies in potato landraces.
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)