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Proceedings of TDWG : Conference Abstract
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Tomer Gueta‡, Vijay Barve§, Ashwin Agrawal|, Thiloshon Nagarajah¶, Yohay Carmel‡
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Corresponding author: Tomer Gueta (tomer.gu@gmail.com), Vijay Barve (vijay.barve@gmail.com)
Received: 15 Aug 2017 | Published: 15 Aug 2017
© 2017 Tomer Gueta, Vijay Barve, Ashwin Agrawal, Thiloshon Nagarajah, Yohay Carmel
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation: Gueta T, Barve V, Agrawal A, Nagarajah T, Carmel Y (2017) Towards a comprehensive workflow for biodiversity data in R. Proceedings of TDWG 1: e20311. https://doi.org/10.3897/tdwgproceedings.1.20311
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Abstract
Increasing number of scientists are using R for their data analyses, however, proficiency required to manage biodiversity data in R is considerably rarer. Since, users need to retrieve, manage and assess high-volume data with inherent complex structure (Darwin Core standard, DwC), various R packages dealing with biodiversity data and specifically data cleaning have been published. Though numerous new procedures are now available, implementing them require users to provide a great deal of efforts in exploring and learning each R package. For the common users, this task can be daunting. In order to truly facilitate data cleaning using R, there is an urgent need for a package that will fully integrate functionality of existing packages, enhance their functionality, and simplify its implementation. Furthermore, it is also necessary to identify and develop missing crucial functionalities.
We are attempting to address these issues by developing two projects under Google Summer of Code (GSoC)-- an international annual program that matches up students with open source organizations to develop code during their summer break. The first project is dealing with the integration challenge by developing a taxonomic cleaning workflow; standardizing various spatial and temporal data quality checks; and enhancing different data retrieval and data management techniques. The second project aims at advancing new and exciting features, such as establishing a flagging system (HashMap-like) in R, an innovative set of DwC summary tables, and developing new techniques for outliers analysis.
The products of these projects lay down crucial infrastructure for data quality assessment in R. Obviously this is a work in progress and needs further inputs. By developing a comprehensive framework for handling biodiversity data, we can fully harness the synergetic quality of R, and hopefully supply more holistic and agile solutions for the user.
Keywords
R, user-level, data quality assessment, Google Summer of Code, biodiversity data
Presenting author
Tomer Gueta and Vijay Barve
Grant title
This research is supported by the Israel Science Foundation (ISF) and the Google Summer of Code program