The puzzle of sharing scientific data

Priego, Laia Pujol; Wareham, Jonathan; Romasanta, Angelo

doi:10.1080/13662716.2022.2033178

Cited by 6 publications

(9 citation statements)

References 80 publications

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“…In addition, Kajüter et al (2022) identify 35 barriers, grouped into six categories, that affect coordinated digital interaction and data sharing among healthcare stakeholders. Data sharing is tangent to various disciplines and extant literature reveals extensive research on data sharing barriers in an academic and governmental context (e.g., Otjacques et al, 2007;Priego & Wareham, 2020).…”

Section: Foundations and Related Workmentioning

confidence: 99%

“…Despite its potential to create additional value by combining data from different sources, multiple barriers impede the sharing of data among organizations, which is why many firms are reluctant to share their data (Enders et al, 2021;Heinz et al, 2022b;Krämer et al, 2021). While extant literature has primarily focused on examining data sharing in scientific and governmental contexts for the last two decades (e.g., Otjacques et al, 2007;Priego & Wareham, 2020), research on data sharing among private sector organizations has gathered momentum only within the last few years (Richter & Slowinski, 2019). As a result, knowledge of impeding and promoting factors for successful implementations of data sharing -especially in private sector organizations -is still sparse and highly fragmented.…”

Section: Introductionmentioning

confidence: 99%

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Barriers to Data Sharing among Private Sector Organizations

Fassnacht,

Benz,

Heinz

et al. 2023

Proceedings of the Annual Hawaii International Conference on System Sciences

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In today's digital world, sharing data among private sector organizations to realm mutual benefits, such as innovation and value co-creation, is considered a promising yet barely explored and realized approach. Although private sector organizations are pursuing data sharing, successful real-world examples are sparse due to a multitude of barriers. However, knowledge on barriers to data sharing among private sector organizations is scarcely existent in scientific literature. Therefore, we apply an exploratory research approach by triangulating insights from fourteen expert interviews and a systematic literature review to identify barriers which we group along five distinct perspectives. By exploring the multi-faceted barriers to data sharing among private sector organizations, our work contributes to a better understanding of data sharing in this field and lays the foundation for future studies. For practitioners, we identify key challenges to successful data sharing among private sector organizations and, hence call for additional endeavors in data sharing.

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Section: Foundations and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Barriers to Data Sharing among Private Sector Organizations

Fassnacht,

Benz,

Heinz

et al. 2023

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…In our analysis, we identified two relevant dimensions, i.e., data marketplace and data sharing platform. For example, Abbas et al (2021) and Agahari et al (2021) focus on the need for data marketplaces to mediate when sharing data to different domains (Pujol Priego & Wareham, 2020). Other authors like Vesselkov et al (2019) and Kalkman et al (2019) mention platforms or data sharing platforms as necessary infrastructure.…”

Section: Characteristics Of Data Sharingmentioning

confidence: 99%

“…In addition, the literature reviewed also mentions the possibility of data sharing between governments (G2G), between governments and businesses (G2B), or individuals (G2C) as a potential implementation of data sharing (Hao et al, 2020;Omar et al, 2014;Susha et al, 2017). This shows that many actors and segments can engage in data sharing across domains (Gelhaar & Otto, 2020;Pujol Priego & Wareham, 2020;Zhong et al, 2011).…”

Section: Characteristics Of Data Sharingmentioning

confidence: 99%

Data Sharing Fundamentals: Definition and Characteristics

Jussen,

Schweihoff,

Dahms

et al. 2023

Proceedings of the Annual Hawaii International Conference on System Sciences

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The importance of data as a key resource is a universal theme dominating social and business life. In this regard, inter-organizational data sharing shines in a new light prompting businesses to leverage the potential of their own data. However, it is still unclear what data sharing actually entails, i.e., what it means, what its potentials are, and what barriers organizations must overcome. In short, it lacks conceptual clarity and a clear description of its characteristics. The conceptual ambiguity and the synonymous use with data exchange in the literature are particularly problematic, which prevents a targeted conceptualization and use. The paper starts precisely at this point as it proposes a unifying definition and characteristics of data sharing. We report on a systematic literature review characterizing data sharing and delineating it from data exchange.

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“…In their paper, Murray-Rust and Rzepa (2004) contend that “Most publicly funded scientific information is never fully published and decays rapidly.” Wilkinson et al (2016) point to structural conditions, arguing that “Unfortunately, the existing digital ecosystem surrounding scholarly data publication prevents us from extracting maximum benefit from our research investments.” Similarly, there are those who highlight slower innovation in the absence of data sharing. Work studying researchers’ attitudes towards data sharing argues that “Recent studies have estimated the annual financial cost of not sharing FAIR [Findable, Accessible, Interoperable, and Reusable] data to be at least €10.2bn for the European economy, while the impact of FAIR on potential economic annual growth is estimated to be €16bn annually” ( Pujol Priego et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

A dataset without a code book: ethnography and open science

Khan,

Hirsch,

Zeltzer-Zubida

2024

Front. Sociol.

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This paper reflects upon calls for “open data” in ethnography, drawing on our experiences doing research on sexual violence. The core claim of this paper is not that open data is undesirable; it is that there is a lot we must know before we presume its benefits apply to ethnographic research. The epistemic and ontological foundation of open data is grounded in a logic that is not always consistent with that of ethnographic practice. We begin by identifying three logics of open data—epistemic, political-economic, and regulatory—which each address a perceived problem with knowledge production and point to open science as the solution. We then evaluate these logics in the context of the practice of ethnographic research. Claims that open data would improve data quality are, in our assessment, potentially reversed: in our own ethnographic work, open data practices would likely have compromised our data quality. And protecting subject identities would have meant creating accessible data that would not allow for replication. For ethnographic work, open data would be like having the data set without the codebook. Before we adopt open data to improve the quality of science, we need to answer a series of questions about what open data does to data quality. Rather than blindly make a normative commitment to a principle, we need empirical work on the impact of such practices – work which must be done with respect to the different epistemic cultures’ modes of inquiry. Ethnographers, as well as the institutions that fund and regulate ethnographic research, should only embrace open data after the subject has been researched and evaluated within our own epistemic community.

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The puzzle of sharing scientific data

Cited by 6 publications

References 80 publications

Barriers to Data Sharing among Private Sector Organizations

Barriers to Data Sharing among Private Sector Organizations

Data Sharing Fundamentals: Definition and Characteristics

A dataset without a code book: ethnography and open science

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