Search

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: χ 2 (1) = 5.77, p

Data marketplaces are expected to play a crucial role in tomorrow’s data economy, but such marketplaces are seldom commercially viable. Currently, there is no clear understanding of the knowledge gaps in data marketplace research, especially not of neglected research topics that may advance such marketplaces toward commercialization. This study provides an overview of the state-of-the-art of data marketplace research. We employ a Systematic Literature Review (SLR) approach to examine 133 academic articles and structure our analysis using the Service-Technology-Organization-Finance (STOF) model. We find that the extant data marketplace literature is primarily dominated by technical research, such as discussions about computational pricing and architecture. To move past the first stage of the platform’s lifecycle (i.e., platform design) to the second stage (i.e., platform adoption), we call for empirical research in non-technological areas, such as customer expected value and market segmentation.

Current open data systems lag behind in their promised value creation and sustainability. The objective of the current study is twofold: 1) to investigate whether existing open data systems meet the requirements of open data ecosystems, and 2) to develop a research agenda that discusses the gaps between current open data systems on the one hand and participatory, value-creating, sustainable open data ecosystems on the other hand. The literature reveals that the main characteristics of value-creating, sustainable open data ecosystems are user-drivenness, inclusiveness, circularity, and skill-based. Our comparative case study of five open data systems in various application domains and countries highlighted that none of these systems are real open data ecosystems: they often do not balance open data supply and demand, exclude specific user groups and domains, are linear, and lack skill-training. We elaborate on a research agenda that discusses how research should address the challenge of making open data ecosystems more value-generating and sustainable.

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.

National governments often expect municipalities to develop toward open cities and be equally motivated to open up municipal data, yet municipalities have different characteristics influencing their motivations. This paper aims to reveal how municipality size influences municipalities’ motivation perspectives on opening up municipality data. To this end, Q-methodology is used, which is a method that is suited to objectify people’s frames of mind on a particular topic. By applying this method to 37 municipalities in the Netherlands, we elicited the motivation perspectives of three main groups of municipalities: (1) advocating municipalities, (2) careful municipalities, and (3) conservative municipalities. We found that advocating municipalities are mainly large-sized municipalities (>65,000 inhabitants) and a few small-sized municipalities (

There is a lack of research concerning the factors influencing the success or failure of open data initiatives. Based on the results of two workshops, we provide a list of 47 success factors for open data publication and 18 success factors for open data use. We further use three case studies (ENGAGE, Open NY, and Open Vienna) to examine how the criticality of factors varies depending on the geographical level and other characteristics of the open data initiative. The cases, representing open data initiatives at city, regional and transnational levels, point at different categories of critical success factors. Our key conclusions are that 1) the criticality of the factors depends considerably on the context of the open data initiative; 2) a number of success factors appear to be more universally applicable than others; 3) the factors that are critical to all three cases are derived from many different success factor categories, which suggests that open data initiatives should adopt an interdisciplinary approach, and 4) further work is needed to detail the success factors for open data publication and use in other contexts.

Public and private organizations increasingly release their data to gain benefits such as transparency and economic growth. The use of these open data can be supported and stimulated by providing considerable metadata (data about the data), including discovery, contextual and detailed metadata. In this paper we argue that metadata are key enablers for the effective use of Linked Open Data (LOD). We illustrate the potential of metadata by 1) presenting an overview of advantages and disadvantages of metadata derived from literature, 2) presenting metadata requirements for LOD architectures derived from literature, workshops and a questionnaire, 3) describing a LOD metadata architecture that meets the requirements and 4) showing examples of the application of this architecture in the ENGAGE project. The paper shows that using metadata with the appropriate metadata architecture can yield considerable benefits for LOD publication and use, including improving find ability, accessibility, storing, preservation, analysing, comparing, reproducing, finding inconsistencies, correct interpretation, visualizing, linking data, assessing and ranking the quality of data and avoiding unnecessary duplication of data. The Common European Research Information Format (CERIF) can be used to build the metadata architecture and achieve the advantages.