Geospatial Data

Community of Practice

Welcome!

The Community of Practice on Geospatial data is led by IFPRI and has been launched as a part of the CGIAR Platform for Big Data in Agriculture.

This community facilitates CGIAR’s research using geospatial data and analysis, undertaking activities to bring spatial scientists together through coordinated communication, community-developed products, and the convening of members at various events to represent CGIAR in the geospatial domain of expertise.

This space can be used as a discussion area, share and request relevant information and contribute towards building the community as a whole.

Community Partners
We work closely with our external partners in academia, donors, and public/private sectors – including ITC, Esri, Digital Globe, and Geospatial and Farming Systems Research Consortium.

Community Members
Members of CGIAR-CSI include all geospatial scientists across CGIAR. Community-wide activities are coordinately by the Global Coordinator. Center-level activities are coordinated by the Center Representative. Currently (as of October 2017), CGIAR-CSI reaches to 90 members across all 15 CGIAR Centers.

If you use geospatial data and analyses in CGIAR, join our community! For external party interest, please sign up for updates or email us direct.

Get in touch

CoP Lead

Jawoo Koo | Email
The CSI-CoP formed in 2017 and is led by Agricultural Engineer and Senior Research Fellow Jawoo Koo at the International Food Policy Research Institute (IFPRI) in Washington D.C, U.S.

Administration

Michelle Fotsy | Email
At the International Food Policy Research Institute (IFPRI) in Washington D.C, U.S

Ivy Romero | Email
At the International Food Policy Research Institute (IFPRI) in Washington D.C, U.S

Twitter | Facebook | Email

Visit the CGIAR CSI website

Engage with the Community

BE INFORMED

Tune in to the latest CoP updates by subscribing to our newsletter.

ENGAGE

Interact with community members through our LinkedIn Group.

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CONTRIBUTE

Share your expertise in our working groups.

Thank you for your interest in joining the GeoSpatial Community of Practice

Members of CGIAR-CSI include all geospatial scientists across CGIAR. Community-wide activities are coordinately by the Global Coordinator. Center-level activities are coordinated by the Center Representative. Currently, CGIAR-CSI reaches to 90 members across all 15 CGIAR Centers. The following spaces are available for direct collaboration and communication on community projects.

SharePoint:
CGIAR MEMBERS ONLY: Hosted and managed by IFPRI, authenticated access leads to a discussion forum, CoP resources, events, tasks, contact information and working groups. It’s a private and secure location where CGIAR CoP members can share working documents, optimize workflows, and brainstorm. Join here.

Community News

Articles in this section were originally posted on the CGIAR Consortium for Spatial Information (CGIAR-CSI) website.

High-Resolution and Bias-Corrected CMIP5 Climate Projections

January 21, 2020 / CGIAR-CSI

A new Data Descriptor article published in the Nature Scientific Data describes CGIAR’s effort to downscale global climate projection data ...

Mapping Spatial Variation in Fertilizer Prices

January 21, 2020 / CGIAR-CSI

A new study developed by UC Davis, CIMMYT, and IFPRI is the first major attempt to systematically describe the spatial ...

Mapping Cropland in South Asia using Landsat data on Google Earth Engine

December 11, 2019 / CGIAR-CSI

Three ICRISAT scientists contributed to a study mapping the extent of agricultural cropland and areas in six countries in South ...

CGIAR-CSI at the AfricaGIS 2019 conference in Kigali, Rwanda

December 6, 2019 / CGIAR-CSI

As a Conference Partner of AfricaGIS 2019 in Kigali, Rwanda, 19-21 November, CGIAR-CSI organized a session on the use of ...

Simplifying Soil Data Management in Sub-Saharan Africa

December 4, 2019 / CGIAR-CSI

The Global Soil Data Manager – Sub-Saharan Africa is an example of how we can combine outcomes from big data and ...

Mapping Rice Yields using Synthetic Aperture Radar (SAR) and the ORYZA Model

September 20, 2019 / CGIAR-CSI

Data on rice production is a fundamental component of the economy in many Asian countries, yet the existing national accounting ...

Resources

2017 Work Plan – Detailed description of expected outcomes and deliverables for 2017

2018 Work Plan – Detailed description of expected outcomes and deliverables for 2018

2019 Work Plan – Detailed description of expected outcomes and deliverables for 2019

Events

[WEBINAR] FAIR Guidelines and Assessment in GARDIAN

June 25, 2019 – June 25, 2019 | Online

[WEBINAR] Ask Gerrit and Cheryl Anything about Decision Support System for Agrotechnology Transfer

August 29, 2019 – August 29, 2019 | Online

[WEBINAR] Semantic Annotation of Images in the FAIR Data Era

September 3, 2019 – September 3, 2019 | Online

Communities

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Interested in joining our community of practice?

Become a youth in data partner

AgroFIMS: Your new companion for easy standardization of data collection and description

The Agronomy Field Information Management System (AgroFIMS) has been developed on CGIAR’s HiDAP (Highly-interactive Data Analysis Platform created by CGIAR’s International Potato Center, CIP). AgroFIMS draws fully on ontologies, particularly the Agronomy Ontology and the Crop Ontology. It consists of modules that represent the typical cycle of operations in agronomic trial management, and enables the creation of data collection sheets and digital data collection using the same ontology-based set of variables, terminology, units and protocols. AgroFIMS therefore:

Standardizes data collection and description for easy aggregation and inter-linking across disparate datasets;
Allows easy integration with HiDAP breeding data, or any other ontology-based datasets;
Functions as a data staging repository, allowing data uploads with view/edit permissions;
Enables data quality checks, statistical analysis of the data collected, and the generation of sophisticated statistics reports;
Aligns a priori with CGIAR’s CG Core metadata schema;
Enables easy upload to the institutional repositories, and much more.

Funding for AgroFIMS was provided by the Bill and Melinda Gates Foundation’s Open Access, Open Data Initiative, and the CGIAR Big Data Platform. Two user-testing workshops have been organized, with the most recent in March, with 14 participants including agronomists, crop modelers and developers from CGIAR centers (CIP, IITA, CIMMYT, Bioversity International, IFPRI), the University of Florida, and Rothamsted Research. A beta version will be released in summer for field testing and digital data collection using the KDSmart application developed in collaboration with Diversity Arrays Technology

Go to AGROFIMS →

Responsible Data Management Guidelines to protect privacy

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.

Check the guidelines →

REUSE / TRANSFER

Ensure consistency with the DMP-PII and the purpose for which prior informed consent has been obtained
Revaluate likelihood of (re-)identification and risk of harm, particularly if it involves a public data-set containing PII (as above)
Ensure PII is stored securely to protect privacy (as above)
Minimize use of PII and risk of disclosure through pro-privacy access controls and analytical tools (as above)

Don’t transfer data containing PII unless have explicit consent
Don’t transfer data containing PII in the absence of a data sharing agreement identifying aspects such as purpose and scope of use, privacy protections measures, confidentiality and any limitations)
Don’t reuse or transfer PII until any inconsistencies with the DMP-PII and/or purpose compatibility have been resolved (e.g. through updated ethics review or consent from participant)

ARCHIVING / DISCARDING

Plan for archiving or data destruction early in the process. Destroying data can be more secure, however, archiving can be beneficial if the data has ongoing evidentiary, scientific or cultural value. If archiving, identify where and how, the budget require
Ensure DMP-PII and purpose compatibility (as above)
Ensure adequate security measures to protect privacy (as above)

Don’t wait until the end of the project to assess archiving needs when time and resources may be limited
Don’t assume the longevity of a particular format, future-proof your archives data
Don’t forget to budget for archiving data, this should be done as part of your Data Management Plan

PUBLISHING AND DISCOVERY

Ensure DMP-PII and purpose compatibility (as above)
Revaluate likelihood of (re-)identification and risk of harm, particularly if it involves a public data-set containing PII
Indicate in metadata the availability of raw data or minimized data containing PII, if available bilaterally
Minimize use of PII and risk of disclosure through pro-privacy access controls and analytical tools

Don’t include PII in public datasets unless absolutely necessary to preserve the data’s analytic potential, scientific utility or benefit to the participant (and subject to participants informed consent and a rigorous risk assessment)

STORAGE AND ANALYSIS

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)

encryption for the storage and transmission of PII
access control measures to limited access to PII
two-factor or multifactor authentication
cloud services & back-end security

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)

COLLECTION

Ensure compatibility with the DMP-PII
De-identify data to anonymize by default unless it will impair the data’s analytic potential, scientific utility or benefit to the participant,
If you cannot anonymize, minimize the PII and pseudonymize to reduce the disclosure risk
Provide research participants sufficient information to use reasoned judgment to decide whether or not they wish to participate in the project
Ensure informed consent is designed to address the following elements:
- competence, comprehension, full disclosure, voluntariness
- legitimate scientific purpose for which the PII is collected and scope of use (e.g. stored, transferred, published and whether as anonymized, minimized or raw data)
- foreseeable risk of privacy loss and consequences
- meaningful alternatives including opt-in protection/anonymization
- safeguards to protect privacy, conditions on which PII may be shared and any limitations on reuse or third- party access and use of PII
- permission to follow-up or contact the participant and for what purpose (including by third- parties)
- participant’s right to withdraw and rights regarding their data (e.g. to be informed; to access; to rectify; to object; to erase)
- inclusion of physical, phone and/or electronic contact (at least two forms of contact) that participant can reach to exert her/rights
- explicit consent and participant’s acknowledgement of understanding
- if written, provide the participant a copy of processed informed consent
Use plain language and adapt informed consent to meet the needs of vulnerable populations (e.g. obtain orally or in local language)

Don’t collect PII unless you have a Data Management Plan and any necessary approvals in place, including the recorded approval of the potential participant
Don’t collect PII unless you absolutely need it
Don’t assume that removal of direct identifiers is sufficient to anonymize data or that all de-identification techniques will result in anonymized data. Consider the risk of re-identification of a research participant, particularly if datasets are combined. If there is a reasonable risk of re-identification the information should be handled as PII (i.e. undertake risk analysis, evaluate stronger anonymization techniques, seek informed consent for the disclosure of data and explain its possible consequences)
Don’t include vulnerable participants or communities if their ability or capacity to provide voluntary informed consent is genuinely in question
Don’t underestimate the potential of quasi or indirect identifiers to identify an individual, particularly the inherent ability of location-based data to identify participants and their communities, and the increased risk of harm this may pose to potentially vulnerable individuals/communities
Avoid seeking overly broad consent that may call into question transparency or a research participant’s understanding regarding the use of their PII, be specific regarding the activities, purpose and limitations associated with PII so that the participant can make a genuinely informed decision and downstream users can evaluate purpose compatibility and seek fresh consent if needed

PLANNING AND APPROVAL

Develop a Data Management Plan which governs the handling of PII in the research project and beyond (DMP-PII). It should address:
- the type and nature of PII
- compliance requirements (including necessary forms for obtaining consent, and ethics clearance, if applicable)
- legitimate research objectives that will be advanced by the PII
- foreseeable risks and consequences if participants are identified from the data
- privacy protection measures (or lack thereof) for collection, storage, transfer and publishing
- process for obtaining informed consent
- timeframe or trigger for archiving or deletion of PII
Employ stricter standards for research involving vulnerable populations such as children or illiterate participants or sensitive data such as ethnicity or religious beliefs
Undertake due-diligence of datasets previously collected by you or third parties to ensure you are entitled/permitted to use for your research project
Consult the legal, IRB or ethics clearance committee or any other relevant institutional group for specific institutional, local, regional or national policies and regulatory frameworks that may apply to PII in the context of your work

Don’t leave the handling of PII and privacy protection as an after-thought, plan ahead!
Don’t forget to check local laws and donor or third-party requirements in addition to institutional policies governing research ethics and privacy protection (seek expert support if unsure!)
Don’t ignore ethical practices/standards, if your institution does not have an ethics framework or clearance process in place self-assess!
In assessing whether information is capable of identifying someone (i.e. PII) don’t limit your focus to direct identifiers, also consider indirect/quasi identifiers. Appreciate this will depend on the context of the research project, the data in question and external data which is or may become otherwise available (i.e. there is no exhaustive list).
In assessing risk of harm don’t forget to consider potential harm to the participant’s community or groups of individuals that can otherwise be identified or associated with the participant

Geospatial Data

Welcome!

Get in touch

CoP Lead

Administration

Engage with the Community

BE INFORMED

ENGAGE

CONTRIBUTE

Thank you for your interest in joining the GeoSpatial Community of Practice

Community News

Resources

Events

Communities

Interested in joining our community of practice?

Become a youth in data partner

AgroFIMS: Your new companion for easy standardization of data collection and description

Responsible Data Management Guidelines to protect privacy

<img class="wp-image-93311 aligncenter" src="https://bigdata.cgiar.org/wp-content/uploads/2019/01/REUSE_arrow.png" alt="" width="100" height="100" />

REUSE / TRANSFER

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ARCHIVING / DISCARDING

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PUBLISHING AND DISCOVERY

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STORAGE AND ANALYSIS

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<img class=" wp-image-93249 aligncenter" src="https://bigdata.cgiar.org/wp-content/uploads/2019/01/data-collection-icon.png" alt="" width="100" height="75" />

COLLECTION

<img class="alignnone size-full wp-image-92805 aligncenter" src="https://bigdata.cgiar.org/wp-content/uploads/2019/01/tips-icon-orange-100px.png" alt="" width="100" height="100" />

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<img class=" wp-image-93217 aligncenter" src="https://bigdata.cgiar.org/wp-content/uploads/2019/01/planning-icon.png" alt="" width="100" height="114" />

PLANNING AND APPROVAL

<img class="alignnone size-full wp-image-92805 aligncenter" src="https://bigdata.cgiar.org/wp-content/uploads/2019/01/tips-icon-orange-100px.png" alt="" width="100" height="100" />

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