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Makx –
I have now had time to re-read all the material from the last RDA Plenary session (unfortunately I had a clash with another meeting). I would like to make a few comments.
1. Beyond FAIR. I believe the items here are all covered by the existing FAIR principles, however not necessarily one for one. (This raises another question about the intersections of concepts in the FAIR principles – see below). To take the Beyond FAIR list from the GitHub:
* Data Repository: a dataset may reside in a repository but not necessarily. Consider streamed data from sensors or satellite. Some colleagues get excited about repository quality or ‘trusted repositories’; from m experience the only real criteria for FAIR use by a user concern the relevance and quality of the dataset for their purpose at that time (assuming no limitations on (re-)use) – and this can only be judged from the rich metadata describing the dataset.
* Curation and maintenance: the DCC lifecycle (and associated DMP) is useful here. Curation involves difficult decisions based on the value of the dataset and the cost of curation. The real problem is the lack of economic models that deal with (potentially) infinite time. However, the FAIR requirement for an eternally persistent ID for the dataset implies curation (if a decision is taken to assign (manually or automatically) a persistent identifier an implicit decision is taken to preserve the dataset. Curation implies a DMP, repository (possibly more than one), appropriate metadata, management responsibility, ownership responsibility, licensing, usage rules/constraints… all largely covered by what I understand by rich metadata.
* Open Data: this is a very difficult term to define. Many conflate open and free and confuse with open access. Open data strictly concerns government data made available to citizens although the term is used more widely and is generally used meaning open data, open access, free access (possibly subject to licence conditions such as acknowledgement). I believe the open data concept is covered by the FAIR principles.
* Data Quality: as I suggest under (a) in my experience quality is determined by the end-user in the sense of appropriate quality for the current purpose. I suggest there is no absolute measure but only relative (to the purpose). Data quality is then determined by the intersection of the user requirement and the dataset quality as described by rich metadata. This is likely to include properties/attributes such as precision and accuracy although in some sciences the reputation of the experimental team is sufficient. The equipment used for data collection and the data collection/correction/summarisation method may also be relevant.
* Others: this is of course not yet defined. I would hope that all could be accommodated by the existing FAIR principles because they are relatively abstract; as always the ‘devil is in the detail’ and this will be specified by interpretation towards concreteness of the FAIR principles.
I am hopeful that as other potential ‘beyond FAIR’ concepts arise they will increase our understanding of the several more concrete interpretations of each of the FAIR principles.
1. Intersections in FAIR principles:
* I believe there is some confusion in the FAIR principles concerning data and metadata. Many of the principles start with (meta)data. While I subscribe to the principle that metadata is also data (library catalog cards are metadata to a researcher finding a particular paper but data to a librarian counting papers on the human genome) I fear the FAIR principles are not clear on what should be a property of the thing referred to(data) and what should be a property of the referring thing (metadata). The obvious example is persistent identifier (I prefer UUPID): while both a dataset and the metadata describing it should have a UUPID, A1 is relevant for data (where the UUPID is an attribute in the metadata as specified in F4) but not really for metadata where the retrieval is usually by user values for metadata attributes.
* I believe F2 and R1 are – for metadata – really the same principle and although different sets of attributes may be used for F and R there is likely to be a large intersection. For example R1.2 provenance metadata may well be highly relevant for a user finding appropriate (relevance, quality) datasets.
* It seems to me that I2 and I3 concern aspects of I1 and could equally be I1.1 (a formal language should have its semantics defined) and I1.2 (a formal language should support qualified references, this is, for example, the advantage of RDF over XML).
I raise these concerns now because – as we define progressively the metrics for assessing FAIRness – we have to be clear on exactly what is being measured.
Thanks for your patience!
Keith G Jeffery Consultants
Prof Keith G Jeffery
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Subject: [fair_maturity] Workshop #2 Report
Dear members of the RDA FAIR Data Maturity Model Working Group,
We would like to thank you for attending the meeting of the Working Group in Philadelphia on 3 April 2019 and hope you found the meeting useful.
The report of the meeting is now available for download from the WG page on the RDA site at
We are currently finalising a Google spreadsheet for your contributions to the development of the indicators for the FAIR principles following the approach presented at the meeting in Philadelphia, and we plan to share the spreadsheet with the Working Group in the coming days.
Many thanks!
Makx Dekkers
Editorial team