c(
person("Bruno", "Ribeiro", , "ribeiro.brr@gmail.com", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-7755-6715")),
person("Santiago", "Velazco", , "sjevelazco@gmail.com", role = "aut",
comment = c(ORCID = "0000-0002-7527-0967")),
person("Karlo", "Guidoni-Martins", , "kguidonimartins@gmail.com", role = "aut",
comment = c(ORCID = "0000-0002-8458-8467")),
person("Geiziane", "Tessarolo", , "geites@gmail.com", role = "aut",
comment = c(ORCID = "0000-0003-1361-0062")),
person("Lucas", "Jardim", , "lucas.ljardim9@gmail.com", role = "aut",
comment = c(ORCID = "0000-0003-2602-5575")),
person("Steven", "Bachman", , "S.Bachman@kew.org ", role = "ctb",
comment = c(ORCID = "0000-0003-1085-6075")),
person("Rafael", "Loyola", , "rdiasloyola@gmail.com", role = "ctb",
comment = c(ORCID = "0000-0001-5323-2735"))
)
The bdc package brings together several aspects of
biodiversity data-cleaning in one place. It is organized in thematic
modules related to different biodiversity dimensions, including 1)
Merge datasets: standardization and integration of different datasets; 2) Pre-filter:
flagging and removal of invalid or non-interpretable information,
followed by data amendments; 3) Taxonomy: cleaning, parsing, and
harmonization of scientific names from several taxonomic groups
against taxonomic databases locally stored through the application of
exact and partial matching algorithms; 4) Space: flagging of
erroneous, suspect, and low-precision geographic coordinates; and 5)
Time: flagging and, whenever possible, correction of inconsistent
collection date. In addition, the package contains features to
visualize, document, and report data quality – which is essential for
making data quality assessment transparent and reproducible.
contentid (>= 0.0.15), countrycode, covr, cowplot, DBI,
doParallel, duckdb (>= 0.3.2), knitr, maps, markdown,
rangeBuilder, rappdirs, raster, remotes, rmarkdown,
rnaturalearthdata, rvest, sp, testthat (>= 3.0.0), xml2, rlang
(>= 1.0.1)

It brings together several aspects of biodiversity
data-cleaning in one place. 'bdc' is organized in thematic modules
related to different biodiversity dimensions, including 1) Merge
datasets: standardization and integration of different datasets; 2)
Pre-filter: flagging and removal of invalid or non-interpretable
information, followed by data amendments; 3) Taxonomy: cleaning,
parsing, and harmonization of scientific names from several taxonomic
groups against taxonomic databases locally stored through the
application of exact and partial matching algorithms; 4) Space:
flagging of erroneous, suspect, and low-precision geographic
coordinates; and 5) Time: flagging and, whenever possible, correction
of inconsistent collection date. In addition, it contains
features to visualize, document, and report data quality – which is
essential for making data quality assessment transparent and
reproducible. The reference for the methodology is Bruno et al. (2022)
<doi:10.1111/2041-210X.13868>.
contentid (>= 0.0.15), countrycode, covr, cowplot, DBI,
doParallel, duckdb (>= 0.3.2), knitr (>= 1.31), maps, markdown,
rangeBuilder, rappdirs, raster, remotes, rlang (>= 1.0.1),
rmarkdown, rnaturalearthdata, rvest, sp, testthat (>= 3.0.0),
xml2