New Knowledge from Old Data

Abstract

This article analyzes the experiences of ecologists who used data they did not collect themselves. Specifically, the author examines the processes by which ecologists understand and assess the quality of the data they reuse, and investigates the role that standard methods of data collection play in these processes. Standardization is one means by which scientific knowledge is transported from local to public spheres. While standards can be helpful, the results show that knowledge of the local context is critical to ecologists' reuse of data. Yet, this information is often left behind as data move from the private to the public world. The knowledge that ecologists acquire through fieldwork enables them to recover the local details that are so critical to their comprehension of data collected by others. Social processes also play a role in ecologists' efforts to judge the quality of data they reuse.

Social scientists' data reuse behaviors: Exploring the roles of attitudinal beliefs, attitudes, norms, and data repositories

“…Discovering relevant data may be challenging for scientists across disciplines (e.g., Faniel & Jacobsen, 2010;Zimmerman, 2008), but it is especially difficult for social scientists because data are distributed among various sources and systems (Yoon, 2015). Easy access to data was one of the most influential factors in determining social scientists' satisfaction with data reuse (Faniel, Kriesberg, & Yakel, 2016).…”

“…Social scientists are also concerned with choosing good quality, trustworthy data and avoiding data with errors (Yoon, 2014a(Yoon, , 2016(Yoon, , 2017. Assessing data for each of these qualities requires different criteria; some important assessment factors which have been identified include data producers' ability to generate trustworthy data, other reusers' positive experiences using the data, and soundness of methodology used to produce data (Faniel & Jacobsen, 2010;Faniel, Kansa, Kansa, Barrera-Gomez, & Yakel, 2013;Yoon, 2017;Zimmerman, 2008).…”

“…"Perceived effort", rather than "perceived ease of use," emphasizes the effort expectancy involved in data reuse as compared to the effortlessness or ease of data reuse that a "perceived ease of use" construct would imply (Venkatesh, Morris, Davis, & Davis, 2003). Data reuse literature suggests that when data reusers spend significant time actually using existing data (Rolland & Lee, 2013;Zimmerman, 2008), and less time acquiring and processing data, their level of satisfaction with data reuse improves (Faniel et al, 2016). Thus, it is likely that a greater perceived effort to reuse data would negatively influence social scientists' attitudes toward data reuse and their intention to reuse other scientists' data.…”

“…Although many research studies support the use of intentions as proxies for actual behaviors (Chen & Chen, 2009;Kuo & Young, 2008;Tsai & Cheng, 2010), there is a possibility that data reuse intentions may not explain social scientists' actual data reuse behaviors. Because data reuse is a complex and ongoing process in which unexpected challenges may arise (Zimmerman, 2008), expert and novice data reusers' perceptions may vary. Thus, future studies should measure actual behaviors of data reuse rather than intentions to reuse data, to build on the groundwork laid here and provide a richer understanding of data reuse in the social sciences.…”

Looting hoards of gold and poaching spotted owls: Data confidentiality among archaeologists & zoologists

“…Dallmeier-Tiessen et al, 2014;Zimmerman, 2008), data confidentiality issues present a particular challenge. Researchers are increasingly dependent on funding from sources that require them to share their data (e.g.…”