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Level | Nickname | Marketplace metaphor | Key Features | Picture |
1 | "Started the FAIR journey." | "Flea market" | Awareness started, and the first pilots for implementation. |
{table}
Summary Profile: Level 1 “Started the FAIR journey”
Level - Capabilities
Level - Business value
Level - Questions to ask
...
Table level 1 summary.
Level 1 - Capabilities
The “F” principle is in focus, especially with the establishment and implementation of unique identifiers for key data sets, and the implementation of metadata catalogs.
Level 1 - Business value
Speed and cost are the main benefits. Humans can more efficiently locate important data assets; for example, researchers take much less time to find data. There is also a reduction in redundant data collection. Data is recognised as a Strategic Asset.
Level 1 - Questions to ask
Are there Globally Universal Identifiers (GUID) for key data and their metadata? Are they unique?
How does heterogeneity manifest in the organization's data landscape, and what efforts are being made to address this?
What steps are being taken to deploy identifiers and metadata within the organization?
How are top-down and bottom-up leadership approaches contributing to FAIR data initiatives within the organization?
What initial steps are being taken to craft a vision and plan for FAIR data within the organization?
What emerging roles are being recognized within the organization's FAIR data initiatives?
How is the organization determining which data should be made FAIR retrospectively, and what processes are being established for this purpose?
How are individuals connecting with external communities of practice to further their understanding of FAIR data?
How are existing IT practices being enhanced to accommodate data and metadata creation and curation?
Level 1 - FAIR data
Some data is cataloged and hosted in a data lake, and there is governance for accessibility; however, data is generally "unconformed" (meaning that no domain model is used to constrain information).
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Heterogeneity is a feature of this stage: some data may be unstructured, while pockets of structured data may be in the organization. There is an emerging awareness and application of metadata. Scoping starts with making data FAIR. The deployment of Identifiers and metadata is beginning. Multiple types of identifiers (not necessarily GUPRI) may exist.
(Back to the FAIR Matrix).
Level 1 - FAIR leadership
Leadership Awareness of FAIR data starts. NB "Leadership" typically implies persons with responsibility and budget for financial and human resources. (Cf. FAIR roles dimension and lexicon).
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There can be situations where FAIR implementation is driven “top-down”. However, the strategy and means to implement may be vague as “C-suite” leaders may lack the specific knowledge to develop those. Leadership may have some buy-in to train the first people to make data FAIR.
(Back to the FAIR Matrix).
Level 1 - FAIR strategy
Awareness of FAIR data started. A vision and plan for FAIR data begins to emerge. Different strategies are possible and are embryonic.
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Strategic considerations and approaches emerge towards more "closed" (internal creation of FAIR tools and process) or more "open-source" FAIR resources (e.g. Controlled Vocabularies, ontologies,...).
(Back to the FAIR Matrix).
Level 1 - FAIR roles
Those who work with data start to use FAIR Processes.
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Organization members are sent to external workshops or training (e.g. GO FAIR Foundation) to lead the first internal projects.
(Back to the FAIR Matrix).
Level 1 -
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FAIR processes
In recognizing FAIR data's potential (business) value, there are processes to consider and determine which data we should make FAIR retrospectively. Efforts are underway to establish processes that provide guidelines for creating FAIR data, including the policy development for globally unique identifiers to enable the "F" principle.
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Data domains will have a data discovery, profiling, and optimization phase or project.
(Back to the FAIR Matrix).
Level 1 - FAIR knowledge
Awareness of FAIR data starts with first workshops and training by external experts. People are sent to external workshops or training to understand FAIR better. Higher-level leadership are sent the First whitepapers, proposals or plans for buy-in.
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Community of practice: not yet established "internally", but some individuals connect with external communities of practice (e.g. academia, Public-Private Partnerships, Pistoia Alliance, ELIXIR).
(Back to the FAIR Matrix).
Level 1 - FAIR tools and infrastructures
At this stage, there is an understanding of the data challenges and a recognition of the importance of FAIR data. Still, the organization needs to learn how to make it happen. Initial plans for tooling and infrastructure for POCs start to take shape, along with organizational buy-in.
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The organization starts thinking about and implementing "Findability" with Uniform Resource Identifier (URI) / Globally Unique, Persistent, Resolvable Identifier (GUPRI) services, published metadata catalogs, controlled vocabularies, Master Data Management (MDM) system (What needs to be "found"? Who needs to find what?). At least one policy on unique resource locators.
(Back to the FAIR Matrix).
Findability:
Tool(s) or infrastructure component which contributes, enhances or enriches Findability:
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