While previous studies show that the drivers and inhibitors for openly sharing research data are diverse and complex, there is a lack of studies empirically examining the influence of the COVID-19 ...pandemic on researchers’ open data sharing behavior. Using a questionnaire ( n = 135), this study investigates the influence of COVID-19 pandemic-related factors on researchers’ willingness and ability to openly share their research data. Fifty-one respondents (37.8%) stated that factors related to the COVID-19 pandemic increased their willingness and ability to openly share their research data, while 80 (59.3%) reported that various pandemic-related factors did not influence their willingness and ability in this way. As one of the possible influencing factors, this study finds a significant association between the COVID-19-relatedness of researchers’ research discipline and whether or not the COVID-19 pandemic led to a change in their willingness and ability to share their research data openly: χ2 (1) = 5.77, p < .05. Social influences on open data sharing behavior, institutional support for open data sharing, and the fear of potential negative consequences of open data sharing were nearly similar for the respondents who were and were not involved in COVID-19-related research. This study contributes scientifically by going beyond conceptual studies as it provides empirical insights concerning the influence of COVID-19 pandemic-related factors on researchers’ willingness and ability to openly share their data. As a practical contribution, this study discusses recommendations that policymakers can use to sustainably support open research data sharing in post-COVID-19 times.
Both sharing and using open research data have the revolutionary potentials for forwarding scientific advancement. Although previous research gives insight into researchers' drivers and inhibitors ...for sharing and using open research data, both these drivers and inhibitors have not yet been integrated via a thematic analysis and a theoretical argument is lacking. This study's purpose is to systematically review the literature on individual researchers' drivers and inhibitors for sharing and using open research data. This study systematically analyzed 32 open data studies (published between 2004 and 2019 inclusively) and elicited drivers plus inhibitors for both open research data sharing and use in eleven categories total that are: 'the researcher's background', 'requirements and formal obligations', 'personal drivers and intrinsic motivations', 'facilitating conditions', 'trust', 'expected performance', 'social influence and affiliation', 'effort', 'the researcher's experience and skills', 'legislation and regulation', and 'data characteristics.' This study extensively discusses these categories, along with argues how such categories and factors are connected using a thematic analysis. Also, this study discusses several opportunities for altogether applying, extending, using, and testing theories in open research data studies. With such discussions, an overview of identified categories and factors can be further applied to examine both researchers' drivers and inhibitors in different research disciplines, such as those with low rates of data sharing and use versus disciplines with high rates of data sharing plus use. What's more, this study serves as a first vital step towards developing effective incentives for both open data sharing and use behavior.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Many public organizations are opening their data to the general public and embracing social media in order to stimulate innovation. These developments have resulted in the rise of new, infomediary ...business models, positioned between open data providers and users. Yet the variation among types of infomediary business models is little understood. The aim of this article is to contribute to the understanding of the diversity of existing infomediary business models that are driven by open data and social media. Cases presenting different modes of open data utilization in the Netherlands are investigated and compared. Six types of business models are identified: single-purpose apps, interactive apps, information aggregators, comparison models, open data repositories, and service platforms. The investigated cases differ in their levels of access to raw data and in how much they stimulate dialogue between different stakeholders involved in open data publication and use. Apps often are easy to use and provide predefined views on data, whereas service platforms provide comprehensive functionality but are more difficult to use. In the various business models, social media is sometimes used for rating and discussion purposes, but it is rarely used for stimulating dialogue or as input to policy making. Hybrid business models were identified in which both public and private organizations contribute to value creation. Distinguishing between different types of open data users was found to be critical in explaining different business models.
Government policies focused on Open Government Data (OGD) often aim to stimulate the provision of public, interoperable data towards any user, including lay citizens, through online portals. However, ...these OGD portals are mostly developed for expert users. This hinders the realization of critical values such as transparency, empowerment, and equality of access. Following a Design Science Research approach, this study aims to examine how gamification can help tailor OGD portals for lay citizens. As a pre-condition to this goal, we identify requirements toward OGD portals through twenty interviews with experts and lay citizens. Compared to expert users, lay citizens expect an OGD portal with a more playful interface, vulgarized content, customized visualizations, and transparency-related datasets in a human-readable format. Second, we develop our research artifact, the OGD portal prototype, implementing fifteen design propositions using gamification theory to address lay citizens’ requirements. Third, the evaluation with ten lay citizens reveals the perceived usefulness of the design propositions. Badges were evaluated as most useful to highlight portal relevance. This study contributes to OGD theory development by identifying lay citizens' requirements towards OGD use. Furthermore, this study is the first to reveal the usefulness of implementing notions from gamification theory into OGD portal design. Finally, practitioners can use our findings to make OGD portals more inclusive and thus contribute to attaining key OGD policy objectives.
•Open Government Data (OGD) portals are tailored for experts and not for lay citizens.•We identify 15 lay citizens’ requirements towards OGD portals.•5 lay citizens’ requirements are in conflict with experts’ requirements.•15 gamification design propositions are suggested to tailor OGD portals to lay citizens.•Badges are the most useful to highlight OGD portal relevance to lay citizens.
This study investigates which combination of institutional and infrastructural arrangements positively impact research data sharing and reuse in a specific case. We conducted a qualitative case study ...of the institutional and infrastructural arrangements implemented at Delft University of Technology in the Netherlands. In the examined case, it was fundamental to change the mindset of researchers and to make them aware of the benefits of sharing data. Therefore, arrangements should be designed bottom-up and used as a “carrot” rather than as a “stick.” Moreover, support offered to researchers should cover at least legal, financial, administrative, and practical issues of research data management and should be informal in nature. Previous research describes generic institutional and infrastructural instruments that can stimulate open research data sharing and reuse. This study is among the first to analyze what and how infrastructural and institutional arrangements work in a particular context. It provides the basis for other scholars to study such arrangements in different contexts. Open data policymakers, universities, and open data infrastructure providers can use our findings to stimulate data sharing and reuse in practice, adapted to the contextual situation. Our study focused on a single case and a particular part of the university. We recommend repeating this research in other contexts, that is, at other universities, faculties, and involving other research data infrastructure providers.
To understand how open research data sharing and reuse can be further improved in the field of Epidemiology, this study explores the facilitating role that infrastructural and institutional ...arrangements play in this research discipline. It addresses two research questions: 1) What influence do infrastructural and institutional arrangements have on open research data sharing and reuse practices in the field of Epidemiology? And 2) how could infrastructural and institutional instruments used in Epidemiology potentially be useful to other research disciplines? First, based on a systematic literature review, a conceptual framework of infrastructural and institutional instruments for open research data facilitation is developed. Second, the conceptual framework is applied in interviews with Epidemiology researchers. The interviews show that two infrastructural and institutional instruments have a very high influence on open research data sharing and reuse practices in the field of Epidemiology, namely (a) access to a powerful search engine that meets open data search needs and (b) support by data stewards and data managers. Third, infrastructural and institutional instruments with a medium, high, or very high influence were discussed in a research workshop involving data stewards and research data officers from different research fields. This workshop suggests that none of the influential instruments identified in the interviews are specific to Epidemiology. Some of our findings thus seem to apply to multiple other disciplines. This study contributes to Science by identifying field-specific facilitators and challenges for open research data in Epidemiology, while at the same time revealing that none of the identified influential infrastructural and institutional instruments were specific to this field. Practically, this implies that open data infrastructure developers, policymakers, and research funding organizations may apply certain infrastructural and institutional arrangements to multiple research disciplines to facilitate and enhance open research data sharing and reuse.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
7.
Editorial 15(1) Edelmann, Noella; Zuiderwijk-van Eijk, Anneke
EJournal of eDemocracy and open government,
09/2023, Letnik:
15, Številka:
1
Journal Article
In developing open data policies, governments aim to stimulate and guide the publication of government data and to gain advantages from its use. Currently there is a multiplicity of open data ...policies at various levels of government, whereas very little systematic and structured research has been done on the issues that are covered by open data policies, their intent and actual impact. Furthermore, no suitable framework for comparing open data policies is available, as open data is a recent phenomenon and is thus in an early stage of development. In order to help bring about a better understanding of the common and differentiating elements in the policies and to identify the factors affecting the variation in policies, this paper develops a framework for comparing open data policies. The framework includes the factors of environment and context, policy content, performance indicators and public values. Using this framework, seven Dutch governmental policies at different government levels are compared. The comparison shows both similarities and differences among open data policies, providing opportunities to learn from each other's policies. The findings suggest that current policies are rather inward looking, open data policies can be improved by collaborating with other organizations, focusing on the impact of the policy, stimulating the use of open data and looking at the need to create a culture in which publicizing data is incorporated in daily working processes. The findings could contribute to the development of new open data policies and the improvement of existing open data policies.
•A framework is developed which contains key elements for comparing open data policies.•Policies are context-dependent, and a variety of policy implementations exist.•Public organizations can learn much from each other's policies.•A gap between political ambitions and organizational realities is identified.•Open data policies require a trade-off between openness and risk.
•Sharing and re-using open research data is more common in some disciplines than others.•Disciplines with low open data sharing and re-use might learn from those where these practices are ...common.•Various solutions exist to overcome a lack of motivation.•Some motivations relate to typical characteristics of astrophysics research.•We derive lessons for disciplines with a culture of low open data sharing and re-use.
Open data sharing and re-use is currently more common in some academic disciplines than others. Although each discipline has unique challenges and characteristics which can influence data sharing and re-use behavior, it may be possible to gain transferable insight from disciplines where these practices are more common. Several studies of the motivations underlying data sharing and re-use have been conducted, however these studies often remain at a high level of abstraction rather than providing in-depth insight about discipline-specific challenges and opportunities. This study sought to provide in-depth insight about the complex interaction of factors influencing motivations for sharing and re-using open research data within a single discipline, namely astrophysics. We focused on this discipline due to its well-developed tradition of free and open access to research data. Eight factors were found to influence researchers’ motivations for sharing data openly, including the researcher’s background, personal drivers, experience, legislation, regulation and policy, data characteristics, performance expectancy, usability, and collaboration. We identified six factors that influence researchers’ motivations to re-use open research data, including the researcher’s background, facilitating conditions, expected performance, social and affiliation factors, effort and experience. Finally, we discuss how data sharing and re-use can be encouraged within the context of astrophysics research, and we discuss how these insights may be transferred to disciplines with low rates of data sharing and re-use.
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