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Automated FAIR tools have well-documented blind spots. In peer review
of a COVID-19 FAIR-assessment study, the reviewer (Melissa Haendel)
noted that such tools reward the presence of a license, an
identifier, or a metadata field without checking whether the data is
actually reusable, legitimately restricted, or properly identified.
rfair adds checks for exactly these.
Detecting that a license exists says nothing about whether you may
reuse the data. license_reuse() classifies the actual
permissions, and maps each license to the six-category taxonomy of the
(Re)usable Data Project (Carbon
et al. 2019).
license_reuse("https://creativecommons.org/licenses/by/4.0/")[c("category", "rdp_category", "facilitates_reuse")]
#> $category
#> [1] "open (attribution)"
#>
#> $rdp_category
#> [1] "permissive"
#>
#> $facilitates_reuse
#> [1] TRUE
license_reuse("https://creativecommons.org/licenses/by-nc-nd/4.0/")[c("category", "rdp_category", "facilitates_reuse")]
#> $category
#> [1] "restrictive (non-commercial, no-derivatives)"
#>
#> $rdp_category
#> [1] "restrictive"
#>
#> $facilitates_reuse
#> [1] FALSEOnly permissive licenses facilitate reuse without negotiation; CC-BY-NC-ND is present and standard, yet restrictive.
Data behind a data-use agreement (e.g. human/clinical data) is
legitimately restricted; it should be judged on metadata richness, not
open download. classify_access() flags this, drawing on the
(Re)usable Data Project curations.
Layered identifiers (an identifier minted on top of another) and non-persistent identifiers reduce interoperability.
identifier_hygiene("RRID:MGI:5577054")$issues
#> [1] "Compound/layered identifier: an identifier minted on top of another (e.g. RRID:MGI:...) reduces interoperability; prefer the underlying source PID."
#> [2] "Identifier scheme not recognized; may not follow identifier best practices."
identifier_hygiene("https://doi.org/10.5281/zenodo.8347772")$hygiene_ok
#> [1] TRUEThe reviewer’s own framework extends FAIR with three principles (Haendel et al., FAIR+):
data should be Traceable (provenance, attribution),
Licensed (clearly and reusably), and
Connected (qualified links to related entities).
fair_tlc() computes these from an assessment.
a <- assess_fair("https://doi.org/10.5281/zenodo.8347772")
fair_tlc(a)
#> dimension indicator met
#> 1 Traceable T1 Provenance TRUE
#> 2 Traceable T2 Attribution TRUE
#> 3 Licensed L1 Documented & minimally restrictive TRUE
#> 4 Licensed L2 Flowthrough transparency TRUE
#> 5 Connected C1 Connectedness TRUEFor reference, the authoritative principle definitions (from the FAIR-nanopubs vocabulary used by go-fair.org):
head(fair_principles(), 4)
#> id label category
#> 1 F1 F1 F
#> 2 F2 F2 F
#> 3 F3 F3 F
#> 4 F4 F4 F
#> definition
#> 1 (meta)data are assigned a globally unique and persistent identifier
#> 2 data are described with rich metadata (defined by R1 below)
#> 3 metadata clearly and explicitly include the identifier of the data it describes
#> 4 (meta)data are registered or indexed in a searchable resource
#> uri
#> 1 https://w3id.org/fair/principles/terms/F1
#> 2 https://w3id.org/fair/principles/terms/F2
#> 3 https://w3id.org/fair/principles/terms/F3
#> 4 https://w3id.org/fair/principles/terms/F4These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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