Glossary

(Step 3)

The processes and technologies used to manage and regulate who can view or use data stored in a computer system or network.

AGMES

Description, resource discovery, interoperability and data exchange of different types of information relevant to food production, nutrition and rural development. (CGIAR Recipes)

AGRIS

A metadata standard drawing on Dublin Core and AgMES. (CGIAR Recipes)

Agronomy Ontology

Describes agronomic practices, pest management, and soil fertility. (CGIAR Recipes)

AgrO CGIAR Agronomy Ontology

Ontology for standardising terms in agronomic research and field trials from CGIAR. (CGIAR Recipes)

AGROVOC

Multilingual agricultural vocabulary covering crops, diseases, techniques. (CGIAR Recipes)

Anatomical Entity Ontology

Ontology of anatomical structures. (CGIAR Recipes)

Animal Trait Ontology for Livestock

Phenotypes of livestock in their environment. (CGIAR Recipes)

(Step 6)

An Application Programming Interface (API) is a set of rules and protocols that allows one software application to interact with another. In this context, it can be thought of as the way in which a machine can access data directly from another machine. It defines the methods and data formats that applications can use to communicate with each other. APIs play a crucial role in enabling the integration of different software systems, allowing them to work together and share data.

A particular sector/theme e.g. Digital Solutions, Agriculture

The bottom-up approach encourages greater buy-in from team members because everyone is given the opportunity to influence decisions regardless of seniority. It also facilitates better relationships between colleagues by offering members of all seniority levels an equal opportunity to influence project outcomes. In doing so, this approach increases the likelihood that all members will be invested in the team’s success.

BrAPI
Standardises access to plant breeding data. (CGIAR Recipes)

BRENDA

Tissues, cell types and enzyme sources. (CGIAR Recipes)

CAB Thesaurus

Controlled vocabulary for agriculture, forestry, horticulture, soil science, entomology, mycology, parasitology, veterinary medicine, nutrition, rural studies. (CGIAR Recipes)

CEDAR

Describes the format of the generic metadata artifacts. (CGIAR Recipes)

Cell Behaviour Ontology

Multi-cell computational models. (CGIAR Recipes)

Cell Ontology

Cell types in animals. (CGIAR Recipes)

CG Core Metadata Schema

Minimum set of elements applicable for data and publication annotation and curation across CGIAR. (CGIAR Recipes)

ChEBI Ontology

Molecular functions, biological processes, cellular components.​ (CGIAR Recipes)

CKAN is an open-source DMS (data management system) for powering data hubs and data portals. CKAN makes it easy to publish, share and use data.

(Step 6)

Standardized, uniform data structure created to organize and describe data, enabling seamless interoperability and integration across different applications and domains.

A standard (or agreed-upon way of doing something that provides requirements, guidelines, specifications or characteristics) for metadata or data that is recognized and widely utilized within a community.

Comparative Data Analysis Ontology

Formalisation of concepts and relations relevant to evolutionary comparative analysis. (CGIAR Recipes)

Compositional Dietary Nutritional Ontology

Nutritional attributes contributing to human diet. (CGIAR Recipes)

(Step 3)

A controlled vocabulary is an organized arrangement of words and phrases used to index content and/or to retrieve content through browsing or searching. It typically includes preferred and variant terms and has a defined scope or describes a specific domain.

(Step 4)

A systematic process for evaluating the potential gains and losses associated with a proposed project or decision by comparing the costs involved with the expected benefits (in this case, the degree of FAIR to be achieved), helping decision-makers assess whether the benefits outweigh the costs and whether the investment or action is worthwhile.

Crop Ontology

Species-specific phenotypic plant traits. (CGIAR Recipes)

(Step 1)

The DCEC framework illustrates concepts that make up an interconnected “system”, with data being just the core within a larger ecosystem. Once data are collected, the question arises: can they be effectively utilized?

Capacities encompass the tools and methods necessary to transform these crumbs into actionable information, a process that will inevitably vary depending on the context in which the data was acquired. These capacities are possessed by an ecosystem of individuals who work with data, and also those who will be using the information gleaned from data processing.

Ultimately, all of these components exist within the superstructure of culture, which includes the set of incentives, expectations, and norms that arise from and shape the use of data-driven systems.

(Step 3)

Typically collections of data, each on a common topic, with consistent format and structure. These may include raw data, analyzed results or derived information.

(Step 1)

Data access protocols are the rules and procedures that govern how data is accessed, transmitted, and received between systems. These protocols ensure interoperability, security, and efficient data exchange.

(Step 2. Step 3)

Data, or collection of data, that holds significant value and contributes to the community’s strategic objectives. A valuable resource that can be leveraged to drive insights, make informed decisions, and improve processes and, ultimately, people’s lives.

Data Blockers  (Step 1, 2)

The issues identified that will stand in the way, or ‘block’, the data in the project from being made or used in line with FAIR principles and the FAIR-informed ideas discussed.

(Step 6)

A structured inventory of data assets within an organisation or for a particular project.

Data Custodian

Sometimes you will see ‘Data Owner’ when they mean the Data Custodian. We speak of a custodian as, if the data is FAIR and responsible, then to refer to a data ‘owner’ limits the role and can be ambiguous.

The Data Steward, while ultimately responsible for creating linkages, will not act alone in facilitating the flow of data to those who need access. Many of the individuals listed in the table below (Data Stewardship and Management) can be considered ‘Custodians’ who both manage the data and facilitate data access, including the Data Manager, Database Manager and Data Governance Manager.

(Step 2)

The interconnected network of people, processes, technologies and data flows that work together to enable the collection, storage, analysis and dissemination of data within an organization or across multiple organizations.

(Step 2)

A visual representation of the people, processes, relationships and data flows that make a specific Data Ecosystem.

(Step 2)

A technique for mapping the people, processes, relationships and data flows around a particular project or service. It helps to highlight where important value exchanges happen (such as transfers of funds, supply of data from one party to another, use of open data, etc). It can also help identify pain points for different actors, and the kind of strategies or support they may need.

(Step 5)

A structured set of guidelines, policies, roles and procedures that ensure the effective management of data within an investment. This is a comprehensive approach to managing data assets, ensuring that data is accurate, accessible, secure and compliant with regulations.

(Step 2)

This is made up of data assets, standards, technologies, policies and the organizations that steward and contribute to them.

(Step 1)

Data-related activities (or themes), likely involving the collection, access, use and sharing of data, which will help to conceptualize additional sub-steps or activities needed to effectively manage project data, services and stakeholders, and plan for FAIR implementation.

Data Levers
(Step 1, 2)

The specific tools, techniques or other solutions identified to overcome the ‘blockers’ to data being made or used in line with FAIR principles and the FAIR-informed ideas discussed. Levers are often elements within a complex system where a (seemingly) small shift in one area can produce a big impact on data outcomes.

(Step 3, 6)

The ways that data can be used, granting generalized permission to use and reuse data, provided the user complies with a specified set of conditions.

(DMAP) (Step 5)

A more operationally-focused plan that outlines the specific methods and procedures for handling data throughout the lifecycle of a project, from data collection to sharing and preservation.

(Step 6)

Practices that ensure personal and sensitive data is handled, stored, and processed securely to prevent unauthorized access or misuse.

(Step 1)

A problem statement that outlines the core challenge, its context, and why it matters to your project’s goals/investment goals.

(Step 3)

The legal rights and protections related to the collection, use, storage and sharing of data, particularly personal data about individuals.

(Step 3)

The practice of making data available to others for viewing or use, beyond the original collecting entity or individual.

(Step 6)

Conventions or rules that specify how data is structured, represented, and exchanged within a particular context or domain.

(Step 5)

Agreements between two or more entities that define the terms for how data will be shared and used. This ensures transparency, compliance, and proper data stewardship

An individual or entity with the ultimate responsibility and authority over a specific set of data within an organization, typically accountable for making decisions regarding the use, access, security and management of data. Data Stewards also ensure that data is handled appropriately and aligns with organizational policies and goals.

A unique alphanumeric string assigned to digital content, such as academic articles, datasets or multimedia files, providing a permanent and standardized means of identifying and accessing the content regardless of changes in web addresses or locations. DOIs enable reliable citation, linking and tracking of scholarly and creative works across various platforms and repositories.

DCAT

Data Catalog Vocabulary, used for describing datasets and data catalogues. (CGIAR Recipes)

Dublin Core

Metadata standard for describing various digital resources. (CGIAR Recipes)

Embrace Data and Methods Ontology

Comprehensive ontology for bioscientific data analysis and data management. (CGIAR Recipes)

(Step 2)

The Enabling Environment refers to the external factors and conditions that influence the success and effectiveness of data practices and interventions. This includes:

Policies and Regulatory Frameworks: The rules, regulations, and policies set by governments or other bodies that impact data management, sharing, and use.

Technical Infrastructure: The existing technological systems and platforms that support or hinder data access, sharing, and integration.

Cultural Norms: The values, attitudes, and practices within a community or organization that affect how data is perceived and used. Economic and

Societal Influences: The financial and social factors that can affect data practices, such as funding availability or societal priorities. Understanding the enabling environment helps in identifying how these factors interact with data practices and supports the development of strategies to address any challenges or leverage opportunities for effective data management.

(Step 1, 2)

• External factors and conditions that influence or inhibit the success and effectiveness of data practices and interventions. This includes:

   

  • Policies and regulatory frameworks: The rules, regulations and policies set by governments or other bodies that impact data management, sharing and use.
  • Technical infrastructure: The existing technological systems and platforms that support or hinder data access, sharing and integration.
  • Cultural norms: The values, attitudes and practices within a community or organization that affect how data is perceived and used.
  • Economic and societal influences: The financial and social factors that can affect data practices, such as funding availability or societal priorities.

  Understanding the enabling environment helps to identify how these factors interact with data practices, and supports the development of strategies to address any challenges or leverage opportunities for effective data management.

Describes environmental conditions like temperature, humidity, and rainfall. (CGIAR Recipes)

Pest-specific information. (CGIAR Recipes) Food Ontology – Food, fodder and food processes. (CGIAR Recipes)

Ethical data handling, while encompassing many principles of responsible data use, delves deeper into the moral implications of data practices. It considers not just whether data practices are lawful or efficient, but whether they are right and just.

Read more

Findable, accessible, interoperable and reusable data; improving the Findability, Accessibility, Interoperability, and Reuse of digital assets.

A vocabulary that is documented, resolvable and machine-understandable using globally persistent and unique identifiers.

FAIRification is the process of making data Findable, Accessible, Interoperable, and Reusable (FAIR). This concept was introduced in 2016 to improve the management and stewardship of scientific data.

(Step 6)

The FAIR Enabling Resource (FER) is a service, a specification or document that is essential to the operationalization of the FAIR Principles. It provides a list of digital tools across the FAIR Guiding Principles (subprinciples) to implement/operationalize FAIR.

(Step 6)

The FIP is a collection of FAIR implementation choices made by a community of practice for each of the FAIR Principles. Community specific FAIR Implementation Profiles are themselves captured as FAIR datasets and are made openly available to other communities for reuse. (reference: GO FAIR website).

A protocol (or method by which a user or machine can gain access to a resource) that can be used free of cost.

Represents places through their names using an ontological approach to promote semantic coherence. (CGIAR Recipes)

Molecular entities focused on ‘small’ chemical compounds. (CGIAR Recipes)

Covers all countries and contains over eleven million place names. (CGIAR Recipes)

General Data Protection Regulation (GDPR)

The GDPR defines an array of legal terms at length. Below are some of the most important ones that we refer to in this article:

Personal data — Personal data is any information that relates to an individual who can be directly or indirectly identified. Names and email addresses are obviously personal data. Location information, ethnicity, gender, biometric data, religious beliefs, web cookies, and political opinions can also be personal data. Pseudonymous data can also fall under the definition if it’s relatively easy to ID someone from it.

Data processing — Any action performed on data, whether automated or manual. The examples cited in the text include collecting, recording, organizing, structuring, storing, using, erasing etc.

Data subject — The person whose data is processed. These are your customers or site visitors.

Data controller — The person who decides why and how personal data will be processed. If you’re an owner or employee in your organization who handles data, this is you.

Data processor — A third party that processes personal data on behalf of a data controller. The GDPR has special rules for these individuals and organizations. These could include cloud servers, like Google Drive, Proton Drive, or Microsoft OneDrive, or email service providers, like Proton Mail.

A unique and permanent label assigned to digital objects such as datasets, publications or resources, ensuring their persistent and unambiguous identification across different systems, platforms and time periods. GPIs enable reliable referencing, citation, and linking of digital assets, enhancing discoverability, accessibility and interoperability in scholarly communication and data sharing.

Preliminary insights and information to a bigger initiative; baseline or foundation.

HIPAA (Health Insurance Portability and Accountability Act) is a U.S. law enacted in 1996 that establishes national standards for protecting sensitive patient health information, ensuring privacy, and facilitating the secure electronic exchange of healthcare data.

(Steps 1, 3, 5)

Input data assets refer to any data that is used as an input for processes, analyses, or decision-making within an organization. These data assets serve as the raw materials that feed into systems, models, or workflows to generate insights, support operations, or drive strategic initiatives. Input data can come from a variety of sources, both internal and external to the organization, and can be structured or unstructured in nature. Some examples include: surveys, market research reports, sensor data, financial transactions, social media feeds. (Step 3)

(Step 1)

A clear statement that connects your data goals to the broader objectives of the investment/project, establishing a shared purpose.

An intervention is an activity that generates data Intervention type — (Step 1, 3)

Investment, project and investment project is used interchangeably within the FAIR Process Framework.

(Step 2)

A structured and comprehensive catalog or repository that systematically documents and organizes the data assets of interest for a certain project, and serves as a foundational resource, enabling effective data management, governance and utilization.

(Step 2)

Combines data from multiple sources for Innovators and Researchers to develop data-driven services and products.

There is a difference between the definitions of metadata and (meta)data within and outside the context of FAIR principles.

In the context of FAIR Principles:

Metadata: Data providing information about other data. Essential for understanding, accessing and using primary data. It includes details like creator, date, format and source. It must be machine-readable to enable automated systems to find, access, interoperate and reuse data efficiently.

(Meta)data: Encompasses both primary data and its metadata. Ensures data and descriptive information adhere to standards. Must be findable, accessible, interoperable and reusable. Both data and metadata should be machine-readable to facilitate automated processes and enhance data sharing and long-term stewardship.

Outside the context of FAIR Principles:

Metadata: Data providing information about other data. Essential for understanding, accessing and using primary data. It includes details like creator, date, format and source. Machine readability can be beneficial but is not mandatory.

(Meta)data: Encompasses both primary data and its metadata. Highlights the importance of data and descriptive information. Emphasis on usability and integration, but not necessarily strict standards. Machine readability is less pronounced but can enhance usability.

Key Differences:

FAIR Context: Emphasizes machine readability and adherence to standards for findability, accessibility, interoperability and reusability. General Context: Less stringent on machine readability and FAIR attributes, focusing more on basic descriptive roles and data usability.

(Step 3)
There is a difference between the definitions of metadata and (meta)data within and outside the context of FAIR principles.

In the context of FAIR Principles:

Metadata: Data providing information about other data. Essential for understanding, accessing and using primary data. It includes details like creator, date, format and source. It must be machine-readable to enable automated systems to find, access, interoperate and reuse data efficiently.

(Meta)data: Encompasses both primary data and its metadata. Ensures data and descriptive information adhere to standards. Must be findable, accessible, interoperable and reusable. Both data and metadata should be machine-readable to facilitate automated processes and enhance data sharing and long-term stewardship.

Outside the context of FAIR Principles:

Metadata: Data providing information about other data. Essential for understanding, accessing and using primary data. It includes details like creator, date, format and source. Machine readability can be beneficial but is not mandatory.

(Meta)data: Encompasses both primary data and its metadata. Highlights the importance of data and descriptive information. Emphasis on usability and integration, but not necessarily strict standards. Machine readability is less pronounced but can enhance usability.

Key Differences:

FAIR Context: Emphasizes machine readability and adherence to standards for findability, accessibility, interoperability and reusability. General Context: Less stringent on machine readability and FAIR attributes, focusing more on basic descriptive roles and data usability.

M4M workshops are lightweight, fast-track events where policy and domain experts can build new, or make informed choices regarding the reuse of already existing metadata schema as part of their overall FAIRification efforts.

GO FAIR workshop in the technical implementation of FAIR practices (reference: GO FAIR website)

Reconciling data from plant phenotyping experiments. (CGIAR Recipes)

Mycological nomenclatural novelties. (CGIAR Recipes)

Online vocabulary tools English and Spanish to select controlled vocabulary terms for subject indexing of AGRICOLA, PubAg and other databases. (CGIAR Recipes)

Organisms’ classification and nomenclature. (CGIAR Recipes)

Open Data Kit (ODK) tools help people collect, manage, and use their data.

(Step 3)

The results or outputs generated from processing input data through an investment’s operational, analytical or decision-making processes. These assets are valuable for their insights, conclusions or actionable information that can inform future actions, strategies or operational adjustments. Output data assets often take the form of reports, dashboards, predictive models and strategic recommendations. Examples include: sales forecasts, customer segmentation reports, agricultural yield predictions, financial performance dashboards, and operational efficiency recommendations.

[definition as per the Foundation]

(Step 3)

The authorization granted to users, systems or applications to access, manipulate or manage data within a specific environment or framework.

(Step 2)

Personas are archetypes of different individuals encountered throughout the AgDev ecosystem, each with their own learning needs, barriers, motivations and goals.

(Step 3)

As defined by the United Nations, information relating to an identified or identifiable natural person.

Phenotypic qualities. (CGIAR Recipes)

Growth conditions used in plant experiments. (CGIAR Recipes)

Plant anatomy, morphology, growth and development and phenotypic traits. (CGIAR Recipes)

(step 3)
Data that is collected firsthand by a researcher, scientist or similar group, for a specific purpose. Primary data is original information that has not been published or analyzed before and was collected directly from the source or through a methodology. Examples of primary data include observations, survey results, interviews, experiments.

(Step 2)

Provides insights into the needs and goals of the private sector, beneficial for projects related to economic development.

(Step 1)

A statement that outlines the core challenge, its context, and why it matters to your project’s goals.

Protein-related entities. (CGIAR Recipes)

Ontology for provenance information of datasets and resources. (CGIAR Recipes)

Resource Description Framework, a standard for data interchange on the web. (CGIAR Recipes)

Registry of open, sustainable, usable, and unique identifiers for every research organization in the world. (CGIAR Recipes)

Responsible data describes the practices and policies implemented to ensure data is managed in a way that is accountable, transparent, and in compliance with legal and regulatory requirements. It encompasses a broad range of considerations, including data security, privacy protection, data accuracy, and the lawful use of data.
Read more

(Step 3)

The potential for loss, damage, or undesirable outcomes resulting from internal or external vulnerabilities within data management practices. This encompasses a wide range of issues, including but not limited to data breaches, unauthorized access, data corruption, loss of data integrity, privacy violations and non-compliance with data protection law.

A financial metric to evaluate the outcomes of an investment relative to its cost. It is calculated by dividing the outcomes (in this case, the degree of FAIR implementation achieved) generated by the investment by the initial cost of the investment, and is typically expressed as a percentage. A higher ROI indicates a more profitable investment relative to its cost.

(Step 3)

Assessing the potential for loss, damage, or undesirable outcomes resulting from internal or external vulnerabilities within data management practices. This encompasses a wide range of issues, including but not limited to data breaches, unauthorized access, data corruption, loss of data integrity, privacy violations and non-compliance with data protection law.

(step 3)

Secondary data is data already collected by another individual researcher or organisation. It includes previously published research, reports and existing datasets.

Ontology for agricultural household surveys. (CGIAR Recipes)

Features and attributes of biological sequence.​ (CGIAR Recipes)

Fisheries and aquaculture (under development). (CGIAR Recipes)

(Step 1)

Collaboration between actors often relies on the ability to share data and information.

(Step 4)

A universally agreed-upon set of rules and conventions for transmitting, formatting and interpreting data, ensuring consistency, interoperability and compatibility across different systems, applications or organizations.

(Step 6)

Methodologies and systems used to share data across different organizations. This process enables the verification, optimization, or building upon data. It includes both the technological platforms and the security measures implemented to protect data during the sharing process.

(Step 5)

The 3Ps Framework is a Data Governance Framework that highlights People, Processes and Platforms as three main components of an effective data governance framework. These have been adapted from the data governance industry-standard concept of ‘people, processes, and technology¹, to include ‘platform’ as a more specific component for the work you are undertaking.

According to this methodology: People are defined as the organizational structure of those involved in data governance, including the roles and responsibilities of those who own, collect, store, manage and use data. Processes refers to guidelines for using, protecting and managing data, ensuring consistency and compliance.

Platforms (or technology) is indicative of the tools and systems that support data governance, and can include security protocols or data management platforms. ¹A step-by-step guide to setting up a data governance program (ibm.com)

(Step 2)

Makes data available to the public or specific audiences, often managing central data repositories.

Ontology of units of measurements. (CGIAR Recipes)

(Step 4)

Specific codes or attributes assigned to individual entities or records, enabling their distinct identification and differentiation from others within a dataset, ensuring data integrity, facilitating data linkage across various sources, and supporting accurate referencing and retrieval processes.

(Step 2)

A value exchange represents the giving and receiving of data, information, resources or agreements between two (or more parties). Value exchanges work in conjunction with personas in that, by identifying the persona, you can also identify what they have to exchange, and how this exchange will benefit both them and your project.

Ontology for standardised, systematic description of effects, consequences and mechanisms of variations. (CGIAR Recipes)

Open-source software and an ontology for representing scholarship. (CGIAR Recipes)