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Raritas: a program for counting high diversity categorical data with highly unequal abundances
Author(s) -
David Lazarus,
Johan Renaudie,
Dorina Lenz,
Patrick Diver,
Jens Klump
Publication year - 2018
Publication title -
peerj
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.927
H-Index - 70
ISSN - 2167-8359
DOI - 10.7717/peerj.5453
Subject(s) - python (programming language) , categorical variable , computer science , metadata , source code , sample (material) , biodiversity , data mining , file format , data type , information retrieval , database , programming language , world wide web , ecology , biology , machine learning , chemistry , chromatography
Acquiring data on the occurrences of many types of difficult to identify objects are often still made by human observation, for example, in biodiversity and paleontologic research. Existing computer counting programs used to record such data have various limitations, including inflexibility and cost. We describe a new open-source program for this purpose—Raritas. Raritas is written in Python and can be run as a standalone app for recent versions of either MacOS or Windows, or from the command line as easily customized source code. The program explicitly supports a rare category count mode which makes it easier to collect quantitative data on rare categories, for example, rare species which are important in biodiversity surveys. Lastly, we describe the file format used by Raritas and propose it as a standard for storing geologic biodiversity data. ‘Stratigraphic occurrence data’ file format combines extensive sample metadata and a flexible structure for recording occurrence data of species or other categories in a series of samples.

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