From the field to the cloud: A review of three approaches to sharing historical data from field stations using principles from data science
- 1Environmental Science, Policy and Management Department, University of California, Berkeley, United States
- 2School of Information, University of California, Berkeley, United States
- 3Department of History, Simon Fraser University, Canada
- 4Department of History and Department of Environmental Studies, University of California, Santa Barbara, United States
- 5Statewide Program in Informatics and GIS, Division of Agriculture and Natural Resources, University of California, United States
Historical data play an important role in our understanding of environmental change and ecosystem dynamics. By lengthening the temporal scale of scientific inquiry, historical data reveal insights into the dynamic nature of ecosystems. However, most historical data have yet to make a full contribution, remaining dark and out of reach to the broader scientific community. This article responds to several calls stressing the importance of empirical historical materials and urges their preservation and accessibility. Despite the importance of historical data collections, few standards have emerged to integrate historical dark data into the larger digital data landscape. To encourage greater use of historical data across scientific disciplines it is vital to make data findable, accessible, interoperable, and reusable (e.g. the FAIR principles). In this paper, we discuss the potential of historical dark data to contribute to the modern digital ecological data landscape. We do this by focusing on three cases from the University of California field and research stations and the groups that have worked to make historical dark data discoverable. Despite the common goal of maximizing the potential use of these data collections, each case and the methods employed are unique and showcase varying levels of success in achieving the FAIR principles and shepherding historical data into the 21st century.
Keywords: Dark data, data science, historical data, field stations, Open Data
Received: 17 May 2018;
Accepted: 20 Jul 2018.
Edited by:Peng Liu, Institute of Remote Sensing and Digital Earth (CAS), China
Reviewed by:Clive R. McMahon, Sydney Institute of Marine Science, Australia
Americo Montiel, University of Magallanes, Chile
Copyright: © 2018 Easterday, Paulson, DasMohapatra, Alagona, Feirer and Kelly. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Prof. Maggi Kelly, University of California, Berkeley, Environmental Science, Policy and Management Department, 134 Mulford Hall, UC Berkeley, Mail #3114, Berkeley, 94720, CA, United States, email@example.com