Data collection and analysis tools for food security and nutrition

participation of data contributors in the data 
cycle (Carroll 
et al., 2020).
As the underlying problem with regard to 
protecting individual and collective rights in 
data collection and use appears to be a lack of 
sufficient trust in the way data and information 
are collected, maintained and shared, an 
interesting avenue to explore is for the CFS 
to take the lead in establishing 
data trusts 
(Hardinges, 2018, 2020) for FSN.
As defined by the Open Data Initiative, a data 
trust is:
 […] a legal structure that provides 
independent stewardship of some data for the 
benefit of a group of organisations or people. 
That benefit might be to create new businesses, 
help research a medical disease, or empower a 
community of workers, consumers or citizens. 
In a data trust, the trustors may include 
individuals and organisations that hold data. 
The trustors grant some of the rights they have 
to control the data to a set of trustees, who then 
make decisions about the data – such as who 
has access to it and for what purposes. The 
beneficiaries of the data trust include those who 
are provided with access to the data (such as 

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DATA COLLECTION AND ANALYSIS TOOLS FOR FOOD SECURITY AND NUTRITION
researchers and developers) and the people who 
benefit from what they create from the data. The 
trustees take on a legally binding duty to make 
decisions about the data in the best interests of 
the beneficiaries. This is sometimes referred to 
as a fiduciary duty. Proponents of data trusts 
suggest this duty would help to increase the 
trust that individuals and organisations have 
in the way data is used.” (Open Data Initiative, 
2018). 
Spearheaded in the context of personal data 
protection (see, for example, 
https://datatrusts.
uk/
) similar initiatives to data trusts might be 
extended to food security and nutrition data. 
This might be an effective way to promote the 
establishment of viable data collaboratives 
among public and private entities involved in the 
generation, storage, and dissemination of FSN-
relevant data.
RELEVANT RECENT 
INITIATIVES ON DATA 
GOVERNANCE FOR FSN
This section reviews recent international 
initiatives concerning FSN data that address data 
governance and transparency.
WORLD BANK OPEN DATA
The World Bank data portal (
https://data.
worldbank.org/
) provides access to FSN datasets 
and disseminates anonymised microdata from 
sample surveys, censuses and administrative 
systems under its open data policy (
http://
microdata.worldbank.org
). Datasets are generated 
by the World Bank or by third parties, including 
member states, international organizations, 
and regional agencies. The World Development 
Report 2021 is dedicated to data issues, with 
many insights and recommendations that concern 
directly FSN (World Bank 2021).
OPEN SCIENCE INITIATIVES AND THE 
FAIR AND CARE DATA PRINCIPLES
Open science initiatives are developing rapidly in all 
research areas, including FSN and are considered 
very promising. They are based on international 
collaboration and contribute to the deployment of 
cloud-based services and other collaborative tools 
that facilitate data access, sharing, interoperability 
and reuse (see, for example the REDCap example 
in 
BOX 12
). The openness of data and research 
output facilitates timely and universal access to 
information on food system developments. Open 
access standards can promote the use of official 
statistics in research by balancing the usability and 
confidentiality of primary data (microdata).
The FAIR (findable, accessible, interoperable, 
reusable) data principles (
SEE TABLE 1
) provide 
international guidelines for organising research 
outputs, so that they can be easily found, accessed, 
understood and integrated in other applications 
or different settings (Wilkinson 
et al., 2016). Major 
research funding bodies, including the European 
Commission, are adopting the FAIR data principles 
to optimise the integrity and impact of research 
outputs.

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INSTITUTIONS AND GOVERNANCE FOR FSN DATA COLLECTION, ANALYSIS, AND USE
TABLE 1:
FAIR DATA PRINCIPLES
FAIR PRINCIPLES
COMPLIANCE INDICATORS
Findable
Metadata and data should be 
easy to find for both humans 
and computers.
F1. (meta)data are assigned a globally unique and persistent identifier
F2. data are described with rich metadata (defined by R1 below)
F3. metadata clearly and explicitly include the identifier of the data they 
describe
F4. (meta)data are registered or indexed in a searchable resource
Accessible
The exact conditions under 
which the data are accessible 
should be provided in such a 
way that humans and machines 
can understand them.
A1. (meta)data are retrievable by their identifier, using a standardized 
communications protocol
A1.1 the protocol is open, free and universally implementable
A1.2 the protocol allows for an authentication and authorization procedure, 
where necessary
A2. metadata are accessible, even when the data are no longer available
Interoperable
The (meta)data should be based 
on standardized vocabularies, 
ontologies, thesauri etc. so 
that they integrate with existing 
applications or workflows.
I1. (meta)data use a formal, accessible, shared and broadly applicable 
language for knowledge representation
I2. (meta)data use vocabularies that follow FAIR principles
I3. (meta)data include qualified references to other (meta)data
Reusable
Metadata and data should be 
well-described so that they can 
be replicated or combined in 
different settings.
R1. meta(data) are richly described with a plurality of accurate and relevant 
attributes
R1.1. (meta)data are released with a clear and accessible data usage license
R1.2. (meta)data are associated with detailed provenance
R1.3. (meta)data meet domain-relevant community standards
SOURCE: AUTHOR’S OWN ELABORATION BASED ON WILKINSON 
ET AL. (2016)
The FAIR principles are often applied in conjunction 
with the CARE (collective benefit, authority to 
control, responsibility and ethics) principles (
SEE 
ANNEX TABLE 4
), which are more people-oriented 
and reflect the importance of 
data sovereignty
in advancing Indigenous innovation and self-
determination (Research Data Alliance International 
Indigenous Data Sovereignty Interest Group, 2019).
One good example of making data open access 
comes from the International Food Policy 
Research Institute (IFPRI), which views the 
products of its research, including research 
datasets, as global public goods, and is committed 
to enabling their widespread distribution and use. 
They do so by depositing their data at Harvard 
Dataverse,
30
an open-access repository for 
31 The access policy is available at: 
https://www.ifpri.org/cdmref/
p15738coll2/id/133308/filename/133517.pdf
.
32 For more information, see: 
https://cgspace.cgiar.org/bitstream/
handle/10947/4488/Open%20Access%20Data%20Management%20
Policy.pdf
.
30 Visit the Dataverse at: 
https://dataverse.harvard.edu/
.
research data, keeping with the IFPRI Research 
Data Management and Open Access (RDMOA) 
Policy
31
and the CGIAR Open Access and Data 
Management Policy.
32
Another example is SIAgroBD, a collaborative 
initiative to inform food security and 
agrobiodiversity conservation policies in Mexico. 
SIAgroBD focuses on integrating data on native 
crops of global importance, food composition 
and nutritional data, qualitative and quantitative 
agronomic data and qualitative assessments of 
local agrobiodiversity, among other data. These 

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DATA COLLECTION AND ANALYSIS TOOLS FOR FOOD SECURITY AND NUTRITION
data are often collected in collaboration with local 
communities (
https://siagro.conabio.gob.mx/
).
SIAgroBD implements a workflow for open and 
FAIR data, including the adoption of digital field 
data collection tools, vocabulary standards, 
reproducible practices, open data training for