Biodiversity Informatics, Training Modules, 13, 2018, pp. 49-50

49

SAMPLE DATA AND TRAINING MODULES FOR CLEANING 
BIODIVERSITY INFORMATION

Marlon E. Cobos, Laura Jiménez, Claudia Nuñez-Penichet,
Daniel Romero-Alvarez, Marianna Simões

Department of Ecology and Evolutionary Biology and Biodiversity Institute,
University of Kansas, Lawrence, KS, USA

Abstract.—Large-scale biodiversity databases have become crucial information sources in many 
analyses in biogeography, macroecology, and conservation biology, often involving development of 
empirical models of species’ ecological niches and predictions of their geographic distributions. These 
analyses, however, can be impaired by the presence of errors, particularly as regards taxonomic identi-
fications and accurate geographic coordinates. Here, we present an introductory data-cleaning exercise 
based on two contrasting datasets; we link these example data with a step-by-step guide to overcoming 
these problems and improving data quality for analyses based on these data.

Key words: accuracy, data cleaning, error, redundancy, precision, primary biodiversity data

The availability and management of large on-
line biodiversity databases has become an exciting, 
although challenging, step in the development of an 
authoritative and comprehensive basis for biodiver-
sity knowledge. Thanks to improvements in technol-
ogy and data collection, steps that previously took 
years can now be accomplished in seconds (Robin 
2012; Musa et al. 2013). The temptation to use this 
information and interpret analyses quickly, howev-
er, often undermines the fundamental necessity of 
checking data and addressing data quality carefully. 
This tension is a perpetual caveat to use of modern, 
open datasets, and has been particularly problematic 
as regards use of georeferenced, primary biodiversi-
ty data. Still, these data are the building blocks for 
many interesting analyses, perhaps most prominent-
ly, species distribution and ecological niche models 
(Peterson et al. 2011; Anderson 2015).

Primary biodiversity data have become much 
more accessible in recent decades, with a major tran-
sition from recalcitrance (Graves 2000) to enthusi-
asm. Many institutions (e.g., museums, herbaria, 
observational data initiatives) have digitized data 
associated with their work, and increasing numbers 
now make these data available via the Internet (So-
berón and Peterson 2004). The Global Biodiversity 
Information Facility (GBIF), the Botanical Informa-
tion and Ecology Network (BIEN), and the Distrib-
uted Information System for Biological Collections 
(speciesLink), are a few examples of repositories 
that provide online open access biodiversity infor-
mation, now providing access to over a billion in-
dividual records. The benefit of these initiatives is 

clear; for instance, in 2016, 438 articles were pub-
lished using data from GBIF alone in multiple fields 
including data management, evolution, biogeogra-
phy, biodiversity, and public health (GBIF 2018). 
However, data quantity is compromised by frequent 
low data quality; occurrence data from these sources 
suffer from diverse errors that should be identified, 
assessed, and minimized before performing any anal-
ysis. Low accuracy, low precision, occurrences from 
outside species’ ranges, abundant duplicate records, 
and taxonomic misidentifications, are just a few of 
the common errors found in these data (Rahm and 
Do 2000). 

To present a means of building and assessing ca-
pacity to handle such problems, we provide a hands-
on exercise for data cleaning, with two worked exam-
ples, coupled with recommendations and suggestions 
on how to identify and overcome the most common 
errors. The goal, of course, is to obtain a cleaned 
database that will be robust and reliable in different 
downstream analyses (Peterson et al. 2011). We have 
assembled two example datasets, one small (960 re-
cords) and one large (36,574 records), using records 
from GBIF—in each case, we have cleaned the data 
extensively, and then re-populated the dataset with 
typical classes of errors—we then provide a step-by-
step guide to the process of cleaning and improving 
them. The databases and the data-cleaning manual 
are available at: http://hdl.handle.net/1808/26512.

These data files and the associated manual are not 
intended as a detailed treatment of biodiversity data 
quality, or to offer automated methods with which to 
solve these problems (Chapman 2005; Hijmans and 

http://hdl.handle.net/1808/26512


Marlon E. Cobos et al. – Training Modules

50

Elith 2013; Maldonado et al. 2015). Rather, we fo-
cus on individualized, hands-on, user assessment of 
occurrence datasets, and providing detailed training 
materials with which to build capacity to make such 
assessments possible. This publication can be used 
as a tool for educational purposes, as an exercise 
for a broader audience that is starting to explore the 
field, and/or as a reminder of this often-overlooked 
step (Anderson 2015). We are aware of the time-con-
suming nature of manual data cleaning, but, to avoid 
a ‘garbage in, garbage out’ situation, high-quality 
data should always be the goal, to allow researchers 
to develop informative experiments and operational 
models. 

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