Supporting successful data sharing practices in earthquake engineering
| Published date | 18 November 2019 |
| Pages | 764-780 |
| Date | 18 November 2019 |
| DOI | https://doi.org/10.1108/LHT-03-2019-0058 |
| Author | Shuheng Wu,Adam Worrall |
| Subject Matter | Library & information science |
Supporting successful
data sharing practices in
earthquake engineering
Shuheng Wu
Graduate School of Library and Information Studies, Queens College,
Queens, New York, USA, and
Adam Worrall
School of Library and Information Studies,
University of Alberta, Edmonton, Canada
Abstract
Purpose –Prior studies identified a need for further comparison of data-sharing practices across
different disciplines and communities. Toward addressing this need, the purpose of this paper is to examine
the data-sharing practices of the earthquake engineering (EE) community, which could help inform
data-sharing policies in EE and provide different stakeholders of the EE community with suggestions
regarding data management and curation.
Design/methodology/approach –This study conducted qualitative semi-structured interviews with
16 EE researchers to gain an understanding of which data might be shared, with whom, under what
conditions and why; and their perceptions of data ownership.
Findings –This study identified 29 data-sharing factors categorized into five groups. Requirements from
funding agencies and academic genealogy were frequent impacts on EE researchers’data-sharing practices.
EE researchers were uncertain of data ownership and their perceptions varied.
Originality/value –Based on the findings, this study provides funding agencies, research institutions, data
repositories and other stakeholders of the EE community with suggestions, such as allowing researchers to
adjust the timeframe they can withhold data based on project size and the amount of experimental data
generated; expanding the types and states of data required to share; defining data ownership in grant
requirements; integrating data sharing and curation into curriculum; and collaborating with library and
information schools for curriculum development.
Keywords Data sharing, Data ownership, Data practices, Data repositories, Earthquake engineering
Paper type Research paper
1. Introduction
Modern scientific practices are characterized by computational technologies generating data
at a rate beyond researchers’abilities to process, analyze, use, manage and share them.
Many government agency and university-based initiatives have aimed at the challenge of
scientific data management, bringing together librarians, archivists, scientists and system
developers to reach specific scientific communities, study their disciplinary activities and
address their data management and sharing problems (Borgman, 2012; Witt et al., 2009).
Funding agencies believe data sharing can enhance scientific research. For example, in the
USA the National Endowment for the Humanities (NEH, 2019), the National Science
Foundation (NSF, 2011) and the National Institutes of Health (NIH, 2003) require applicants
to submit data management plans specifying how they will disseminate and provide access
to their data. Journals (e.g. Nature,Science,PLoS One) have implemented data sharing or
depositing policies, requiring authors to make data underlying their findings available.
To allow for data sharing in earthquake engineering (EE), NSF founded the George E.
Brown, Jr Network for Earthquake Engineering Simulation (NEES), developing a
Library Hi Tech
Vol. 37 No. 4, 2019
pp. 764-780
© Emerald PublishingLimited
0737-8831
DOI 10.1108/LHT-03-2019-0058
Received 4 March 2019
Revised 23 May 2019
14 June 2019
Accepted 14 June 2019
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0737-8831.htm
The authors would like to express gratitude to Dr Besiki Stvilia, Dr Roberta Brody, and Dr Kwong-Bor
Ng for their helpful suggestions and to the reviewers for useful feedback.
764
LHT
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cyberinfrastructure platform named NEEShub to provide experimental facilities, data
curation services and open access to experimental data and documentation (Pejša and
Hacker, 2013; Pejšaet al., 2014). NEES required NSF-funded research projects to submit
corrected data with necessary documentation to NEEShub within six months after an
experiment ends. The data would become public at NEEShub 12 months after completing
the experiment. To broaden the support for other natural hazards (e.g. windstorms,
tsunamis, coastal flooding) engineering research, NSF recently founded the National
Hazards Engineering Research Infrastructure (NHERI) to replace NEES, while continuing
its emphasis on the EE research previously supported by NEES (Rathje et al., 2017).
To succeed NEEShub, NHERI built a new cyberinfrastructure platform named DesignSafe
consisting of experimental facilities located at eight universities in the USA. At the heart of
DesignSafe is a central open data repository named Data Depot that now hosts NEEShub-
published data and supports the full lifecycle of research in natural hazards engineering,
from data creation and analysis to curation and publication. Unlike NEES’s 12-month
requirement, NHERI does not set firm deadlines for research projects performed at NHERI’s
experimental facilities to publish data in Data Depot, but recommends timelines for
publishing different data types (DesignSafe, n.d.a).
Data sharing can be defined as “the release of research data for use by others”(Borgman,
2012, p. 4). Besides releasing data in open repositories like Data Depot, data sharing in
EE may include private exchanges between researchers; publishing data in journals,
websites, wikis or blogs; and presenting data in conferences. Data-sharing practices vary by
individuals, and within and across teams, disciplines, institutions and communities. Prior
research (Borgman, 2012; Cragin et al., 2010; Faniel and Zimmerman, 2011; Fecher et al.,
2015; Kowalczyk and Shankar, 2011; Tenopir et al., 2015, 2018) has identified a need for
further study and comparison of data-sharing practices of different science and engineering
disciplines, including identification of discipline-specific enablers and barriers for data
sharing that are of ever-increasing importance due to the requirements from funding
agencies and publications.
Toward addressing this need, this study examined the data-sharing practices of the
EE community, gaining an understanding of which data might be shared, with whom, under
what conditions and why alongside their responses to the data-sharing policies by funding
agencies. Answers to these questions can inform the formulation of data sharing and
curation policies, the further development and maintenance of cyberinfrastructure
platforms, the delivery of services by data or institutional repositories and the education
of data producers, curators and users.
2. Literature review
Data sharing is a sociotechnical practice (Kowalczyk and Shankar, 2011; Van House, 2003).
It relies on the technical infrastructure (e.g. data repository, metadata schema, management
software) to ensure the persistence, longevity, security and quality of data. From the data
curation perspective, the US National Science Board (NSB, 2005) categorizes three types of
infrastructure supporting the collection, curation, analysis and sharing of digital data: first,
research data collections produced from individual researchers or research projects, second,
community data collections serving a specific science or engineering community and third,
reference data collections serving a diverse set of communities (e.g. students, scientists or
educators). King (2007) introduced the Dataverse Network as an infrastructure for data
sharing within scientific communities to meet their requirements for recognition, public
distribution, authorization, validation, persistence, ease of use and legal protection.
Besides technical infrastructure, data-sharing practices are influenced by complex social,
organizational, cultural and ethical factors such as research ethics, institutional policies and
disciplinary norms (Borgman, 2012; Fecher et al., 2015; Kowalczyk and Shankar, 2011;
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