bdc_scientificName_empty

Flags records with empty or not interpretable scientific names.

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 Ribeiro and colleagues (2022)
<doi:10.1111/2041-210X.13868>.

Bruno Ribeiro

Biodiversity Data Cleaning

Santiago Velazco

Karlo Guidoni-Martins

Geiziane Tessarolo

Lucas Jardim

Steven Bachman

Rafael Loyola

bdc_scientificName_empty function

<dl><dt>data</dt>
<dd>data.frame. Containing the species scientific names.</dd>
<dt>sci_names</dt>
<dd>character string. The column name with the species
scientific name. Default = "scientificName".</dd></dl>

Arguments

Identify records with empty scientific names — bdc_scientificName_empty

<dl>

<dt>data</dt>
<dd>data.frame. Containing the species scientific names.</dd>


<dt>sci_names</dt>
<dd>character string. The column name with the species
scientific name. Default = "scientificName".</dd>

</dl>

bdc_scientificName_empty: Identify records with empty scientific names

Description

Usage

Value

Arguments

Details

See Also

Examples