Open AccessA data management workflow of biodiversity data from the field to data users
Author(s) -
Hackett Rachel A.,
Belitz Michael W.,
Gilbert Edward E.,
Monfils Anna K.
Publication year - 2019
Publication title -
applications in plant sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.64
H-Index - 23
ISSN - 2168-0450
DOI - 10.1002/aps3.11310
Subject(s) - biology , workflow , biodiversity , data management , field (mathematics) , citizen science , data science , environmental resource management , database , ecology , computer science , mathematics , botany , environmental science , pure mathematics
Premise Heterogeneity of biodiversity data from the collections, research, and management communities presents challenges for data findability, accessibility, interoperability, and reusability. Workflows designed with data collection, standards, dissemination, and reuse in mind will generate better information across geopolitical, administrative, and institutional boundaries. Here, we present our data workflow as a case study of how we collected, shared, and used data from multiple sources. Methods In 2012, we initiated the collection of biodiversity data relating to Michigan prairie fens, including data on plant communities and the federally endangered Poweshiek skipperling ( Oarisma poweshiek ). Results Over 23,000 occurrence records were compiled in a database following Darwin Core standards. The records were linked with media and biological, chemical, and geometric measurements. We published the data as Global Biodiversity Information Facility data sets and in Symbiota SEIN et portals. Discussion We highlight data collection techniques that optimized transcription time, including the use of predetermined and controlled vocabulary, Darwin Core terms, and data dictionaries. The validity and longevity of our data were supported by voucher specimens, metadata with measurement records, and published manuscripts detailing methods and data sets. Key to our data dissemination was cooperation among partners and the utilization of dynamic tools. To increase data interoperability, we need flexible and customizable data collection templates, coding, and enhanced communication among communities using biodiversity data.