| Table of Contents | ||
|---|---|---|
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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}Table level 1 summary.
Summary Profile: Level 1 “Started the FAIR journey”
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
Level 1 - Questions to ask
Are there Globally Universal Identifiers (GUID) for key data and their metadata? Are they unique?
(WIP input needed): questions that speak to other dimensions
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.
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.
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,...).
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.
Level 1 - Processes for FAIR
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.
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).
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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