Little science confronts the data deluge: habitat ecology, embedded sensor networks, and digital libraries

Abstract

Abstracte-Science promises to increase the pace of science via fast, distributed access to computational resources, analytical tools, and digital libraries. "Big science" fields such as physics and astronomy that collaborate around expensive instrumentation have constructed shared digital libraries to manage their data and documents, while "little science" research areas that gather data through hand-crafted fieldwork continue to manage their data locally. As habitat ecology researchers begin to deploy embedded sensor networks, they are confronting an array of challenges in capturing, organizing, and managing large amounts of data. The scientists and their partners in computer science and engineering make use of common datasets but interpret the data differently. Studies of this field in transition offer insights into the role of digital libraries in e-Science, how data practices evolve as science becomes more instrumented, and how scientists, computer scientists, and engineers collaborate around data. Among the lessons learned are that data on the same variables are gathered by multiple means, that data exist in many states and in many places, and that publication practices often drive data collection practices. Data sharing is embraced in principle but little sharing actually occurs, due to interrelated factors such as lack of demand, lack of standards, and concerns about publication, ownership, data quality, and ethics. We explore the implications of these findings for data policy and digital library architecture. Research reported here is affiliated with the Center for Embedded Networked Sensing.

Data Information Literacy : Librarians, Data and the Education of a New Generation of Researchers

“…Data practices in research teams are often not standardized (Borgman, Wallis, & Enyedy, 2007) and vary from one person to another even within research teams under a common faculty member .…”

Role of communication in data reuse

“…Because data reuse practices are influenced by different disciplinary cultures and the processes for creating various types of data, the majority of this research has investigated data reuse practices and reusers' behaviors within specific disciplines, including cancer epidemiology research (Rolland & Lee, 2013), ecology (Borgman, Wallis, & Enyedy, 2007;Zimmerman, 2003), biological science (Chin & Lansing, 2004), environmental science (Van House, Butler, & Schiff, 1998), astronomy (Sands, Borgman, Wynholds, & Traweek, 2012), and earthquake engineering (Faniel & Jacobsen, 2010). Data reuse practices can be distinctive, depending on the types of data being reused, and some research has focused on certain types of data for reuse, such as quantitative (Faniel, Kriesberg, & Yakel, 2016) or qualitative data (e.g., Broom, Cheshire, & Emmison, 2009;Moore, 2007;Yoon, 2014).…”

A research data sharing game

“…This means that the measures taken for each scientific community to make 340 sharing worthwhile will have to differ in their focus between them [Borgman et al, 2007; 341 Acord and Harley, 2013]. For instance, standardization of data and metadata is easier in 342 some disciplines, such as genomics, then it is in others [Acord and Harley, 2013].…”