eResearch: Digital Service Infrastructures for Collaboration, Information, and Data Management in Joint Research Projects in Ecology—An Example

eResearch: Digital Service Infrastructures for Collaboration, Information, and Data Management in Joint Research Projects in Ecology—An Example

Jan C. Thiele (Georg-August University of Göttingen, Göttingen, Germany)
Copyright: © 2018 |Pages: 20
DOI: 10.4018/IJeC.2018100103

Abstract

Joint research projects in ecology typically aim to integrate scientific knowledge from various disciplines. This raises the request for collaboration technologies. As ecological research is data-intensive, it requires the management and exchange of large datasets, often with spatial reference. The demand for collaboration, data, and information management tools in science is addressed by the creation of digital service infrastructures, so-called eResearch Infrastructures, which are collections of typically web-based software systems. Here, an example eResearch infrastructure implemented for a joint research project is presented. It is described by the user stories, the derived functional requirements, and their implementation in software systems. This infrastructure followed an open-source paradigm with only two exceptions. Based on the lessons learned, recommendations for the future development of eResearch infrastructures and their embedment in an organizational, project, and scientific framework are derived.
Article Preview
Top

Introduction

Joint research projects in Ecology, as in other field study and data-intensive research areas, have manifold use cases and requirements for information, collaboration, and data management. Some examples are:

  • The recording of action logs for irrigation and other occurences on the experimental plots

  • The coordination of joint measurement campaigns

  • The management of carpools for driving to research fields

  • The spread of information by means of project coordination

  • The provision of base data for further data analysis

  • The exchange and management of measurement data

  • The validation of automatically recorded measurement data

  • The joint writing of publication manuscripts and project reports

The need for eResearch Infrastructures for those requirements especially increases when the research teams are geographically dispersed (Siemens, 2010). With the help of IT systems, the exchange of information and data can be made independently from time and/or location. Therefore, eResearch Infrastructures comprising collaboration, information, and data management systems can deliver a substantial contribution to an efficient information and data flow in joint research projects (see, e.g., Markauskaite, Kennan, Richardson, Aditomo, & Hellmers, 2012; Martin, 2014; Thomas, 2011).

Particularly, the task of the management, provision and preservation of research data gained increasing attention ever since several years ago (Akers, Sferdean, Nicholls, & Green, 2014; Androulakis et al., 2009; Pinfield, Cox, & Smith, 2014; Pryor, 2013). Even the Organization for Economic Co-operation and Development (OECD) promotes the internet-based accessibility and preservation of research data from public funding as a key element of the research infrastructure (OECD, 2007).

In recent years, several scientific libraries picked up the topic and are now planning to build up or are already operating public data repositories (Corrall, Kennan, & Afzal, 2013; Cox, Kennan, Lyon, & Pinfield, 2017; Cox & Pinfield, 2014; Tenopir, Sandusky, Allard, & Birch, 2014; Wittenberg & Elings, 2017).

Moreover, some research funders explicitly request strategies for data management for joint research projects (German Research Foundation, 2009; National Science Foundation, 2017). This requirement resulted in the development of several further research data repositories— either for a specific research project (Curdt & Hoffmeister, 2015; Engelhardt, 2013; Willmes, Kürner, & Bareth, 2014) or as public available repositories such as Pangea (Diepenbroek et al., 2002; Grobe, Diepenbroek, Dittert, Reinke, & Sieger, 2006), DataONE (Michener et al., 2012), and Dryad (Miller, 2016; Vision, 2010). Furthermore, in the context of biodiversity research, a microcosm of sophisticated data repositories have been set up (see, e.g., Bendix, Nieschulze, & Michener, 2012). Nevertheless, as stated by Bach et al. (2012), many of these systems have been build up from scratch without the reuse of existing open-source software for data management and without the support of existing data exchange interfaces—so-called harvesting interfaces, such as OIA-PMH (Open Archives Initiative, 2015) or CSW (Open Geospatial Consortium, 2016)—for the interconnection of repositories in order to build up a data network.

Complete Article List

Search this Journal:
Reset
Open Access Articles
Volume 16: 4 Issues (2020): 2 Released, 2 Forthcoming
Volume 15: 4 Issues (2019)
Volume 14: 4 Issues (2018)
Volume 13: 4 Issues (2017)
Volume 12: 4 Issues (2016)
Volume 11: 4 Issues (2015)
Volume 10: 4 Issues (2014)
Volume 9: 4 Issues (2013)
Volume 8: 4 Issues (2012)
Volume 7: 4 Issues (2011)
Volume 6: 4 Issues (2010)
Volume 5: 4 Issues (2009)
Volume 4: 4 Issues (2008)
Volume 3: 4 Issues (2007)
Volume 2: 4 Issues (2006)
Volume 1: 4 Issues (2005)
View Complete Journal Contents Listing