FAIR Maturity Matrix : resources and lexicon
- Giovanni Nisato
Table : Maturity Matrix resources
Short | URL | Why is this relevant what can we do, learn from resource. |
ARDC FAIR Self assessment tool | ARDC FAIR Self assessment tool | |
Change management stakeholders | Addresses stakeholders affecting change management for FAIR | |
EDISON at Roche, from FAIRtoolkit | Prospective FAIRification of Data on the EDISON platform – Roche | The EDISON tool for FAIR data. |
EDMC-DCAM model | DCAM is an industrial maturity model for data and its goverances developed and maintained by the EDM Council. | |
F-UJI |
| A web service to programatically assess FAIRness of research data objects at the dataset level based on the FAIRsFAIR Data Object Assessment Metrics |
FAIR Checker | A tool aimed at assessing FAIR principles and empowering data provider to enhance the quality of their digital resources. | |
FAIR Cookbook | An online, open resource for the Life Sciences with recipes that help you to make and keep data Findable, Accessible, Interoperable and Reusable; in one word FAIR. | |
FAIR Cookbook code repository | FAIR Cookbook code repository | |
FAIR data maturity model | https://www.rd-alliance.org/groups/fair-data-maturity-model-wg | FAIR data maturity model from the Research Data Alliance |
FAIR dataset maturity DSM tool | FAIR dataset maturity DSM maturity assessment tool | |
FAIR Evaluation services | FAIR Evalulation services | |
FAIR toolkit case: “FAIRifying” data | FAIR toolkit case: creating FAIR data from from real world data | |
FAIR-Decide framework for pharmaceutical R&D | https://pubmed.ncbi.nlm.nih.gov/36716952/
| A FAIR-Decide framework for pharmaceutical R&D: FAIR data cost-benefit assessment; 2023 |
FAIRplus Data-Maturity - code repository | GitHub - FAIRplus/Data-Maturity: FAIR Dataset Maturity model | FAIR data maturity framework from the FAIRplus EU project / concluded in 2022 - Github repository |
FAIRplus Data-Maturity | FAIR data maturity framework from the FAIRplus EU project concluded in 2022 | |
FAIRshake | FAIRshake is a system to evaluate the FAIRness of Digital Objects | |
FIP Ontology | FAIR Implementation Profiles Ontology | |
FIP Wizard | FAIR Implementation Profiles Wizard | |
Identifier Policy at AstraZeneca, from FAIRtoolkit | First steps for PID identifiers. | |
Interpretation of the FAIR data principles | Paper on the practical interpretation of the FAIR data principles, by some of the authors of the original FAIR paper. This still seems to represents the views of GO FAIR foundation in 2023 | |
Ontology catalogue and FAIR “tools” | Ontology catalogue, FAIR “tools" | |
Process for FAIR from FAIRtoolkit | https://fairtoolkit.pistoiaalliance.org/use-cases/sharing-fair-data-about-healthcare-partners-bayer/ | Process for FAIR: a 4 step data asset implementation cycle. |
Readiness for change | https://fairtoolkit.pistoiaalliance.org/methods/readiness-for-change/ | Addresses change management for FAIR |
Research Data Framework (RDaF) | https://www.nist.gov/news-events/news/2024/02/nist-releases-version-20-research-data-framework-rdaf | Research Data Framework (RDaF) developed at NIST |
Search engine, FAIR Connect | Open Access publishing platform for the development and dissemination of good practices for professional FAIR-Data stewardship. | |
Data Capability Maturity Model | https://fairtoolkit.pistoiaalliance.org/methods/data-capability-maturity-model/ | Capability Maturity Model applied to data helps an organisation to determine the capability for making data assets FAIR. |
Table: FAIR maturity matrix lexicon.
Term | Definition |
C level leader | In an organization, personnel holding roles such as CEO, CFO, CTO, COO with executive and strategy level decision- making capacities |
Citizen data scientist | Individual within an organization who, despite lacking formal training in data science, utilizes data analysis tools and techniques to extract insights from data. These individuals often come from various backgrounds, such as business, marketing, or operations, and possess domain-specific expertise. Citizen Data Scientists leverage self-service analytics platforms, intuitive data visualization tools, and automated machine learning algorithms to explore datasets, generate reports, and uncover patterns or trends relevant to their roles. |
Community Manager | Person who organizes groups of people around shared aims - see https://the-turing-way.netlify.app/collaboration/research-infrastructure-roles/community-manager.html |
Community of Practice | A community of practice (CoP) is a group of people who share a common interest, profession, or expertise and come together to learn from each other, share knowledge, and collaborate on solving common problems. |
Community of Practice : CSCCE Participation Model | |
Community of Practice Organiser | An individual or group responsible for facilitating and managing a community of practice (CoP). The organizer's role involves coordinating meetings, events, and activities for the community, creating channels for communication and knowledge sharing, and fostering a supportive and inclusive environment where members feel comfortable exchanging ideas and experiences. They may also be involved in recruiting new members, setting goals and objectives for the community, and ensuring that it remains vibrant and relevant to its members' needs and interests. |
Data actionability | Synonymous with machine actionable data - a property of the data that enables it to be acted upon by machines, presumably by being identifiable in some way by the agent as being of a specific type, for which defined actions/process are expected to be enabled |
Data architect | A role in an organization focusing on processes, models and frameworks that manage the generation, use and governance of data in the most optimal form |
Data Contract | The manifestation of expected properties for particular 'data' with respect to format compliance, internally consistent, and probably specification of the 'type' of the data, which enables defined services or operations to be performed on it |
Data mesh | An approach to address the problems of scaling data in large organizations, which promotes treating data as a product and organizing data architecture around business needs rather than technical or functional boundaries. |
Data scientist | See also “researcher” . Data Scientists find, compile, clean, preprocess and analyze complex datasets. They interpret data trends, develop predictive models, and communicate findings to inform strategic decision-making. Data Scientists also design experiments, possess expertise in statistical methods, programming, data visualization, database management, and may have domain-specific knowledge. |
Data steward | A data steward refers to the lead role in a data governance project. Data Stewards take ownership of the data and work with the business to define the programme's objectives. The role of a Data Steward is specifically tasked with maintaining data control in data governance and master data management initiatives on a day-to-day basis. Data Stewardship is required for data implementation and data management to succeed. An example of what they may do to achieve this is drafting the data quality rules which their data is measured against. |
Domain | A domain is typically a broad area of study, for example BioSciences, Life Sciences. These may be divided into ‘sub-domains’ which are more specific, for example microbial genetics, plant phenotyping |
Enterprise | A large-scale commercial entity engaged in economic activities, such as production, distribution, or services, with the primary goal of generating profit. Enterprises vary widely in size, structure, and scope, ranging from small businesses to multinational corporations. They often involve complex organizational structures, such as departments, divisions, and subsidiaries, and may operate in multiple industries or geographic locations. Enterprises typically employ a significant number of people and utilize diverse resources, including capital, technology, and human expertise, to achieve their business objectives and meet the needs of customers or clients. |
FAIR | An established acronym for the aspirational principles defined elsewhere (ref), indicating Findable, Accessible, Interoperable, Reusable. |
FAIR architect | a role in an organization specifically tasks with developing processes to deliver FAIR data and services |
FAIR Digital Object | A FDO is an information entity composed by a persistent identifier (PID) such as a DOI resolving to a PID Record that gives the object a type along with a mechanism to retrieve its bit sequences, metadata and references to possible operations according to the FAIR principles. |
Fake FAIR | Refers to instances in which the term FAIR is used but the underlying principles (or their semantic meaning) are ignored or deliberately misinterpreted, |
FIP | FAIR Implementation Profile |
Governance | Governance is a key element of community building, providing a means for conflict resolution and reconciliation. |
GUPRI | globally unique persistent resolvable identifier. |
KG / Knowledge Graph | knowledge graph: a type of data structure defined by nodes and edges, where nodes represent entities and edges, represent relationships between entities. |
Knowledge enabled citizen | Organization member who is empowered with access to information, possesses critical thinking skills, and is digitally literate, enabling them to contribute effectively to the organization's goals and objectives. |
Machine actionable data | Data is machine actionable when machines can perform automated processing based on the object type information about the supported operations that can be applied to the object. |
Machine interpretable data | Data is machine interpretable when described with semantic artefacts to interpret the nature of the digital object. |
Machine readable data | Data is machine-readable if it is structured using knowledge representation languages (such as JSON, JSON-LD, RDF, XML) |
Machine-ready data | Machine-ready data adheres to specific data standards, such as consistent formatting, well-defined data types, and clear labeling, making it readily interpretable by automated systems. It includes metadata that provides contextual information about the dataset, further enhancing its usability for machine-based analysis. |
MDM | Master Data Management |
MVP | Minimum viable product is a version of a product with just enough features to be usable by early customers who can then provide feedback for future product development. A focus on releasing an MVP means that developers potentially avoid lengthy and unnecessary work. MVP represents a more entreprise relevant outcome compared to POV/POC, which may not be pursued or supported further. |
Ontologist | An expert in knowledge representation and ontology building. |
Operating Model | The framework or blueprint that defines how an organization delivers value to its stakeholders, executes its strategies, and manages its resources to achieve its objectives. It encompasses the processes, structures, systems, and capabilities that are necessary to support the organization's business activities and functions effectively. The operating model provides a detailed description of how different parts of the organization interact, collaborate, and operate to fulfill its mission and vision. It outlines the allocation of resources, roles and responsibilities, decision-making processes, and performance metrics that govern the organization's day-to-day operations. |
PID | Persistent Identifiers |
POV/POC | proof of value/proof of concept correspond to exploratory work to demonstrate feasibility and potential value. these are prerequisite to decide on future investments, prioritization efforts and scaling |
RDM | Reference Data Management (NB: also stands for Research Data Management). |
Researcher | A person engaged in conducting research, possibly recognized as an occupation by a formal job title. While a researchers produces and uses data, they are not necessarily data scientists (see: data scientist). |
RFP | Request for Proposal: a formal process requesting suppliers for an offering regarding one or several capabilities |
Subject matter expert | A subject matter expert (SME) is a person with deep domain knowledge, often as a practitioner of the area of knowledge. SME are often consulted by knowledge engineers and ontologists in what is known as 'knowledge elicitation exercice' to express use cases and concepts which are then organized and modeled in semantic artifact by knowledge engineers. (NB: also stands for Small to Medium Enterprise). |
Table illustration credits
Level | Nickname | Marketplace metaphor | Image credit | URL | Formatted image |
0 | Life is unFAIR | “Junkyard” | jumble, detail of photo by Beth Macdonald on Unsplash | https://unsplash.com/photos/brown-wooden-table-and-chairs-a1O67ZQmaYc |  |
1 | Started the FAIR journey | "Flea market" | yard sale, detail of photo by Nikola Duza on Unsplash | https://unsplash.com/photos/man-in-jacket-sitting-on-floor-while-smoking-CLaojy0IG1E |  |
2 | Getting FAIR | "Street Market" | market, detail of photo by Toa Heftiba on Unsplash
| https://unsplash.com/photos/man-sitting-on-brown-chair-beside-rack-R_bySyREwUY | |
3 | Pretty FAIR | "Specialized Local Markets” | store, detail of photo by big dodzy on Unsplash | ||
4 | Really FAIR | "Hyper Market" | super center, detail of photo by Sangga Rima on Unsplash | https://unsplash.com/photos/people-inside-building-RUA9K_rEzq4 | |
5 | FAIRest of them all | "Digital Online Store"
| Online shopping, detail of photo by Campaign Creators on Unsplash | https://unsplash.com/photos/person-using-macbook-pro-OGOWDVLbMSc |