Rec. 25: Facilitate automated processing

[sjDCC]

Automated processing should be supported and facilitated by FAIR components. This means that machines should be able to interact with each other through the system, as well as with other components of the system, at multiple levels and across disciplines.

• Automated workflows between the various components of the FAIR data ecosystem should be developed by means of coordinated activities and testbeds.
  Stakeholders: Data services; Standards bodies.

• Metadata standards should be adopted and used consistently in order to enable machines to discover, assess and utilise data at scale.
  Stakeholders: Data services; Research communities.

• Structured discoverability and profile matching mechanisms need to be developed and tested to broker requests and mediate metadata, rights, usage licences and costs.
  Stakeholders: Data services.

[ghost]

4TU.Centre for Research Data position: This in relation to creating a FAIR data ecosystem requires more resources to realize it. Institutions and funders as stakeholders in terms of financial support should be added.

[katerbow]

DFG position: Comments to Recommendation 24 apply in a comparable way to Recommendation 25.

[Eefkesmit]

Contribution on behalf of the International Association of STM Publishers (STM):
As mentioned under several related recommendations, we see 4 cornerstone components in a machine-actionable eco system for FAIR Data; in the following list, components 2 and 3 would benefit from automated processes supporting them, components 1 and 4 will facilitate automated processes if embedded in machine readable metadata:

1. Data Availability policies and statements -- design and implement standardised research data policies for scholarly publications, including Data Availability statements in published articles, preferably with the Research Data Alliance,
2. Aligning the submission of data and publications -- promote and enable the use of trusted data repositories for datasets supporting publications, in conjunction with submission of manuscripts where appropriate – via recommended repository lists, services to help deposit data alongside the submission of manuscripts, and technological integrations between scholarly infrastructure, eg by means of API-standards.
3. Arrange for universal linking between datasets and publications, bi-directionally-- support adoption and implementation of a SCHOLIX-framework, see www.scholix.org .
4. Data Citation standards -- Promote and implement data citation rules and standards according to the recommendations of FORCE11, to provide credit for good data practice.”

(see also our suggestions under recommendation 4)

[pkdoorn]

I do not disagree, but find this less of a priority and something that cannot be realized in the short run for the majority of research data anyway.

[mromanie]

ESO position
It is not clear what goal this recommendation aims at achieving.

[npch]

SSI position:

We believe that this is a valuable long-term goal. However it is also an extremely hard task, and should not mean that the community fails to implement other recommendations because this one is mandated in the way that it is currently structured. I would prefer to see this set out in differing levels of ability, with a minimum recommended level (around the use of open metadata standards) and higher levels which can be aspired to.

[mark-cox]

euroCRIS position:

As noted in the response to rec. 24 the CERIF-XML protocol allows interoperability between systems, and includes a rich semantic layer. This layer, as well as accommodating human-readable terms, is capable of allowing automated processing through the inclusion of appropriate technological language.

[gtoneill]

Fully support developing automated processing. There is overlap with previous recommendations including Recommendations 3, 7, 8, and 24 related to interoperability and standards. Perhaps merge?