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This survey-based study aims to explore the research data dissemination and requesting practices of Arab researchers. It investigates the reasons, types, methods, barriers and motivations associated with data sharing and requesting in the Arab research community.

A cross-sectional survey was conducted with 205 Arab researchers representing various disciplines and career stages. Descriptive statistics were used for data analysis.

The study found that 91.2% of Arab researchers share data, while 56.6% access data from others. Reasons for sharing include promoting transparency and collaboration while requesting data is driven by the need to validate findings and explore new research questions. Processed/analysed data and survey/questionnaire data are the most commonly shared and requested types.

This study contributes to the literature by examining data sharing and requesting practices in the Arab research community. It provides original insights into the motivations, barriers and data types shared and requested by Arab researchers. This can inform future research and initiatives to promote regional data sharing.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-06-2023-0283

This paper adopts a multi-tier perspective and aims to explore challenges of small and medium-sized enterprises (SMEs) in collaborative manufacturing amid the emergence of dedicated B2B platforms. Original equipment manufacturers welcome formation of demand-driven collaborations between SME suppliers to facilitate ramp-up of production capacity. While being potentially beneficial to suppliers, such collaborations face various barriers.

An exploratory study of 17 suppliers within the European Union’s aerospace industry was undertaken. The study comprised two stages. In the first stage, suppliers’ answers to self-administered interviews were analysed using thematic analysis. In the second stage, interactions between the barriers were determined through interviews with experienced SME collaboration facilitators. The authors apply system dynamics modelling to analyse the links between barriers and identify re-enforcing and balancing loops of other factors.

The authors establish five major groups of barriers to collaboration impeding: market transparency, access to orders, partner trust, contracting and (e) data sharing and coordination. The authors model application of four enablers that facilitate barrier removal for technology-enabled supply chains: digital platforms, supplier development, smart contracts and Industry 4.0.

The study is limited by the data collection from the aerospace industry; validation of the models in other low-volume high-variability manufacturing sectors is needed.

The reader will learn about the barriers which impede demand-driven SME collaboration within manufacturing supply chains, interrelationships between these barriers and suggestions about how to remove them. SME cluster managers will find managerial implications particularly interesting as they will help them to overcome collaboration concerns and better prepare cluster members for Industry 4.0.

The models developed within this study can be used to explore the effects of intervening at critical points in the model to create virtuous improvement cycles between key barriers and related variables in the model. This can help decision-making and policymaking in the area of supply chain integration.

There is currently a lack of studies about how the existing barriers amplify and de-amplify themselves and what the managerial approaches to tackle the barriers are. It is unclear how far companies will go in terms of information sharing, given the trust levels, power dynamics and governance structures evident in supply chains. This study contributes by explaining the reinforcing interaction between the barriers and showing ways to overcome these using enablers.

The global food industry is faced with the dilemma of finding a balance between food wastage and food shortage. Approximately one-third of food produced globally goes to waste, while about 800 million people suffer from undernourishment. Given this context, the purpose of this study is to investigate the unresolved challenges related to enhancing transparency associated with products of high perishability and low shelf life.

The authors conducted 25 interviews with global agri-food supply chains (AFSCs) experts to ask what impedes the progress of the current technologies, such as blockchain, to enable transparency and traceability (T&T) in AFSCs.

The findings indicate barriers at the individual, firm and supply chain levels. Based on these barriers, the authors propose an interconnected framework to explain technologically-driven T&T and guide on barrier removal from AFSCs. The authors conclude that by applying technology (i.e. blockchain) the authors can resolve the tension of supporting T&T in AFSCs. This can enable the efficient and transparent tracking of goods, reduction of food waste and loss, as well as promotion of the use of recyclable packaging and further sustainable practices and materials, all of which are aligned with a range of UN Sustainable Development Goals (2, 8, 10 and 12). Moreover, the authors see that some factors are interrelated. Based on these factors, the authors build an interconnected framework to guide on barrier removal from AFSCs. Managers in AFSC would find the findings especially relevant.

Drawing on industrial network theory and signalling theory, the authors propose an interconnected framework for explaining barriers (challenges) and potential solutions (opportunities) to T&T in AFSCs. This framework is developed by examining the interconnections of barriers at micro, meso and macro levels and applying signalling theory to explain how solutions address these barriers. The specific contributions of this study are: the list of barriers that impede the implementation of technological solutions for T&T in AFSCs; and a three-stage framework that explains how to remove the barriers for T&T. The study is limited by the focus on blockchain, which calls for future research once the next decentralised technology becomes available.

Open data and data sharing should improve transparency of research. The purpose of this paper is to investigate how different institutional and individual factors affect the data sharing behavior of authors of research articles in sociology and political science.

Desktop research analyzed attributes of sociology and political science journals (n=262) from their websites. A second data set of articles (n=1,011; published 2012-2014) was derived from ten of the main journals (five from each discipline) and stated data sharing was examined. A survey of the authors used the Theory of Planned Behavior to examine motivations, behavioral control, and perceived norms for sharing data. Statistical tests (Spearman’s ρ, χ²) examined correlations and associations.

Although many journals have a data policy for their authors (78 percent in sociology, 44 percent in political science), only around half of the empirical articles stated that the data were available, and for only 37 percent of the articles could the data be accessed. Journals with higher impact factors, those with a stated data policy, and younger journals were more likely to offer data availability. Of the authors surveyed, 446 responded (44 percent). Statistical analysis indicated that authors’ attitudes, reported past behavior, social norms, and perceived behavioral control affected their intentions to share data.

Less than 50 percent of the authors contacted provided responses to the survey. Results indicate that data sharing would improve if journals had explicit data sharing policies but authors also need support from other institutions (their universities, funding councils, and professional associations) to improve data management skills and infrastructures.

This paper builds on previous similar research in sociology and political science and explains some of the barriers to data sharing in social sciences by combining journal policies, published articles, and authors’ responses to a survey.

The purpose of this paper is to report a study of how research literature addresses researchers' attitudes toward data repository use. In particular, the authors are interested in how the term data sharing is defined, how data repository use is reported and whether there is need for greater clarity and specificity of terminology.

To study how the literature addresses researcher data repository use, relevant studies were identified by searching Library Information Science and Technology Abstracts, Library and Information Science Source, Thomas Reuters' Web of Science Core Collection and Scopus. A total of 62 studies were identified for inclusion in this meta-evaluation.

The study shows a need for greater clarity and consistency in the use of the term data sharing in future studies to better understand the phenomenon and allow for cross-study comparisons. Furthermore, most studies did not address data repository use specifically. In most analyzed studies, it was not possible to segregate results relating to sharing via public data repositories from other types of sharing. When sharing in public repositories was mentioned, the prevalence of repository use varied significantly.

Researchers' data sharing is of great interest to library and information science research and practice to inform academic libraries that are implementing data services to support these researchers. This study explores how the literature approaches this issue, especially the use of data repositories, the use of which is strongly encouraged. This paper identifies the potential for additional study focused on this area.

The purpose of this paper is twofold: first, to further develop Paul Edwards’ concept of “data friction” by examining the socio-material forces that are shaping data movements in the cases of research data and online communications data, second, to articulate a politics of data friction, identifying the interrelated infrastructural, socio-cultural and regulatory dynamics of data friction, and how these are contributing to the constitution of social relations.

The paper develops a hermeneutic review of the literature on socio-material factors influencing the movement of digital data between social actors in the cases of research data sharing and online communications data. Parallels between the two cases are identified and used to further develop understanding of the politics of “data friction” beyond the concept’s current usage within the Science Studies literature.

A number of overarching parallels are identified relating to the ways in which new data flows and the frictions that shape them bring social actors into new forms of relation with one another, the platformisation of infrastructures for data circulation, and state action to influence the dynamics of data movement. Moments and sites of “data friction” are identified as deeply political – resulting from the collective decisions of human actors who experience significantly different levels of empowerment with regard to shaping the overall outcome.

The paper further develops Paul Edwards’ concept of “data friction” beyond its current application in Science Studies. Analysis of the broader dynamics of data friction across different cases identifies a number of parallels that require further empirical examination and theorisation.

The observation that sites of data friction are deeply political has significant implications for all engaged in the practice and management of digital data production, circulation and use.

It is argued that the concept of “data friction” can help social actors identify, examine and act upon some of the complex socio-material dynamics shaping emergent data movements across a variety of domains, and inform deliberation at all levels – from everyday practice to international regulation – about how such frictions can be collectively shaped towards the creation of more equitable and just societies.

The paper makes an original contribution to the literature on friction in the dynamics of digital data movement, arguing that in many cases data friction may be something to enable and foster, rather than overcome. It also brings together literature from diverse disciplinary fields to examine these frictional dynamics within two cases that have not previously been examined in relation to one another.

It is widely recognized that sharing data is beneficial not only for science but also for the common good, and researchers are increasingly expected to share their data. However, many researchers are still not making their data available, one of the reasons being that this activity is not adequately recognized in the current reward system of science. Since the attribution of data sets to individual researchers is necessary if we are to include them in research evaluation processes, the purpose of this paper is to explore the feasibility of linking data set records from DataCite to the authors of articles indexed in the Web of Science.

DataCite and WoS records are linked together based on the similarity between the names of the data sets’ creators and the articles’ authors, as well as the similarity between the noun phrases in the titles of the data sets and the titles and abstract of the articles.

The authors report that a large number of DataCite records can be attributed to specific authors in WoS, and the authors demonstrate that the prevalence of data sharing varies greatly depending on the research discipline.

It is yet unclear how data sharing can provide adequate recognition for individual researchers. Bibliometric indicators are commonly used for research evaluation, but to date no large-scale assessment of individual researchers’ data sharing activities has been carried out.

The purpose of this paper is to investigate how scientists’ prior data-reuse experience affects their data-sharing intention by updating diverse attitudinal, control and normative beliefs about data sharing.

This paper used a survey method and the research model was evaluated by applying structural equation modelling to 476 survey responses from biological scientists in the USA.

The results show that prior data-reuse experience significantly increases the perceived community and career benefits and subjective norms of data sharing and significantly decreases the perceived risk and effort involved in data sharing. The perceived community benefits and subjective norms of data sharing positively influence scientists’ data-sharing intention, whereas the perceived risk and effort negatively influence scientists’ data-sharing intention.

Based on the theory of planned behaviour, the research model was developed by connecting scientists’ prior data-reuse experience and data-sharing intention mediated through diverse attitudinal, control and normative perceptions of data sharing.

This research suggests that to facilitate scientists’ data-sharing behaviours, data reuse needs to be encouraged. Data sharing and reuse are interconnected, so scientists’ data sharing can be better promoted by providing them with data-reuse experience.

This is one of the initial studies examining the relationship between data-reuse experience and data-sharing behaviour, and it considered the following mediating factors: perceived community benefit, career benefit, career risk, effort and subjective norm of data sharing. This research provides an advanced investigation of data-sharing behaviour in the relationship with data-reuse experience and suggests significant implications for fostering data-sharing behaviour.

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.

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.

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.

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.

Artificial intelligence (AI) reasoning is fuelled by high-quality, detailed behavioural data. These can usually be obtained by the biometrical sensors embedded in smart devices. The currently used data collecting approach, where data ownership and property rights are taken by the data scientists, designers of a device or a related application, delivers multiple ethical, sociological and governance concerns. In this paper, the author is opening a systemic examination of a data sharing concept in which data producers execute their data property rights.

Since data sharing concept delivers a substantially different alternative, it needs to be thoroughly examined from multiple perspectives, among them: the ethical, social and feasibility. At this stage, theoretical examination modes in the form of literature analysis and mental model development are being performed.

Data sharing concepts, framework, mechanisms and swift viability are examined. The author determined that data sharing could lead to virtuous data science by augmenting data producers' capacity to govern their data and regulators' capacity to interact in the process. Truly interdisciplinary research is proposed to follow up on this research.

Since the research proposal is theoretical, the proposal may not provide direct applicative value but is largely focussed on fuelling the research directions.

For the researchers, data sharing concepts will provide an alternative approach and help resolve multiple ethical considerations related to the internet of things (IoT) data collecting approach. For the practitioners in data science, it will provide numerous new challenges, such as distributed data storing, distributed data analysis and intelligent data sharing protocols.

Data sharing may post significant implications in research and development. Since ethical, legislative moral and trust-related issues are managed in the negotiation process, data can be shared freely, which in a practical sense expands the data pool for virtuous research in social sciences.

The paper opens new research directions of data sharing concepts and space for a new field of research.