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Assess the FAIRness of a batch of identifiers

Source: R/assess_data_code.R
assess_fair_batch.Rd

Runs assess_fair() over a vector of identifiers and returns one tidy row per identifier (deduplicated). Failures are captured in an error column rather than aborting the batch.

Usage

assess_fair_batch(
  ids,
  metric_version = "0.8",
  quiet = FALSE,
  workers = 1L,
  keep = FALSE,
  previous = NULL,
  ...
)

Arguments

ids

Character vector of DOIs, PIDs, URLs, or identifiers.org codes.

metric_version

Metric version (see rfair_metric_versions()).

quiet

If FALSE (default), print per-identifier progress.

workers

Number of identifiers to assess at once. Values above 1 fork worker processes with parallel::mclapply(), which is not available on Windows (there the batch runs serially with a warning). HTTP requests to one host stay rate limited per process (see options(rfair.rate_per_host)).

keep

If TRUE, keep the full fair_assessment objects, named by identifier, in the "assessments" attribute of the result.

previous

Optional result of an earlier assess_fair_batch() call. Identifiers it already scored without an error (for the same metric version) are reused instead of assessed again, so an interrupted batch can be resumed.

...

Passed to assess_fair().

Value

A data frame with one row per unique identifier: identifier, metric_version, scheme, is_persistent, resolved (did the identifier resolve; NA when resolve = FALSE), http_status, resolved_url, fair_percent, F, A, I, R, maturity, n_pass, n_metrics, error.

See also

assess_data_code(), assess_fair()

Examples

# \donttest{
res <- assess_fair_batch(c("https://doi.org/10.5281/zenodo.8347772", "geo:GSE12345"),
                         keep = TRUE)
#> [1/2] assessing https://doi.org/10.5281/zenodo.8347772
#> [2/2] assessing geo:GSE12345
attr(res, "assessments")[[1]]
#> <fair_assessment> https://doi.org/10.5281/zenodo.8347772
#>   resolved: https://zenodo.org/records/8347772
#>   metrics: v0.8 (17 metrics)
#> 
#>   FAIR     earned  percent  maturity
#>   F           7/7   100.0%         3
#>   A           7/7   100.0%         3
#>   I           4/6    66.7%         2
#>   R           5/6    83.3%         2
#>   FAIR      23/26    88.5%       2.5
#> 
#>   reuse:    custom/unknown; open (software, permissive)
# }