College of Information Studies
Permanent URI for this communityhttp://hdl.handle.net/1903/1631
The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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Item DATA SHARING ACROSS RESEARCH AND PUBLIC COMMUNITIES(2016) He, Yurong; Preece, Jennifer; History/Library & Information Systems; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)For several decades, the intensifying trend of researchers to believe that sharing research data is “good” has overshadowed the belief that sharing data is “bad.” However, sharing data is difficult even though an impressive effort has been made to solve data sharing issues within the research community, but relatively little is known about data sharing beyond the research community. This dissertation aims to address this gap by investigating how data are shared effectively across research and public communities. The practices of sharing data with both researchers and non-professionals in two comparative case studies, Encyclopedia of Life and CyberSEES, were examined by triangulating multiple qualitative data sources (i.e., artifacts, documentation, participant observation, and interviews). The two cases represent the creation of biodiversity data, the beginning of the data sharing process in a home repository, and the end of the data sharing process in an aggregator repository. Three research questions are asked in each case: • Who are the data providers? • Who are the data sharing mediators? • What are the data sharing processes? The findings reveal the data sharing contexts and processes across research and public communities. Data sharing contexts are reflected by the cross-level data providers and human mediators rooted in different groups, whereas data sharing processes are reflected by the dynamic and sustainable collaborative efforts made by different levels of human mediators with the support of technology mediators. This dissertation provides theoretical and practical contributions. Its findings refine and develop a new data sharing framework of knowledge infrastructure for different-level data sharing across different communities. Both human and technology infrastructure are made visible in the framework. The findings also provide insight for data sharing practitioners (i.e., data providers, data mediators, data managers, and data contributors) and information system developers and designers to better conduct and support open and sustainable data sharing across research and public communities.Item Community-as-a-Service: Data Validation in Citizen Science(METHOD 2015 workshop, 2015-10-11) He, Yurong; Wiggins, AndreaCurrently, most citizen science projects that adopt a crowdsourcing model focus primarily on collecting and analyzing data. As yet, few of them leverage community interactions for effective data validation yet, which would have significant impact on improving the quality of the increasing volume of citizen science data. In this paper, we introduce an exploratory pilot study focused on understanding how an established online community can be leveraged to create a “community as a service” structure to support collaborative citizen science data validation.Item Community-based Data Validation Practices in Citizen Science(Association for Computing Machinery, 2016-03-02) Wiggins, Andrea; He, YurongTechnology-supported citizen science has created huge volumes of data with increasing potential to facilitate scientific progress, however, verifying data quality is still a substantial hurdle due to the limitations of existing data quality mechanisms. In this study, we adopted a mixed methods approach to investigate community-based data validation practices and the characteristics of records of wildlife species observations that affected the outcomes of collaborative data quality management in an online community where people record what they see in the nature. The findings describe the processes that both relied upon and added to information provenance through information stewardship behaviors, which led to improved reliability and informativity. The likelihood of community-based validation interactions were predicted by several factors, including the types of organisms observed and whether the data were submitted from a mobile device. We conclude with implications for technology design, citizen science practices, and research.