scholarly journals Data Reuse and the Social Capital of Open Science

2016 ◽  
Author(s):  
Bradly Alicea
Keyword(s):  
Game Theory ◽  
Social Capital ◽  
Data Sharing ◽  
Open Data ◽  
Open Science ◽  
Payoff Matrix ◽  
Reciprocal Relationship ◽  
Data Reuse ◽  
Data Set ◽  
Shared Data

ABSTRACTParticipation in open data initiatives require two semi-independent actions: the sharing of data produced by a researcher or group, and a consumer of shared data. Consumers of shared data range from people interested in validating the results of a given study to people who actively transform the available data. These data transformers are of particular interest because they add value to the shared data set through the discovery of new relationships and information which can in turn be shared with the same community. The complex and often reciprocal relationship between producers and consumers can be better understood using game theory, namely by using three variations of the Prisoners’ Dilemma (PD): a classical PD payoff matrix, a simulation of the PD n-person iterative model that tests three hypotheses, and an Ideological Game Theory (IGT) model used to formulate how sharing strategies might be implemented in a specific institutional culture. To motivate these analyses, data sharing is presented as a trade-off between economic and social payoffs. This is demonstrated as a series of payoff matrices describing situations ranging from ubiquitous acceptance of Open Science principles to a community standard of complete non-cooperation. Further context is provided through the IGT model, which allows from the modeling of cultural biases and beliefs that influence open science decision-making. A vision for building a CC-BY economy are then discussed using an approach called econosemantics, which complements the treatment of data sharing as a complex system of transactions enabled by social capital.

Open science: The open clinical trials data journey

2019 ◽  
Vol 16 (5) ◽  
pp. 539-546 ◽  
Author(s):  
Frank Rockhold ◽  
Christina Bromley ◽  
Erin K Wagner ◽  
Marc Buyse
Keyword(s):  
Data Sharing ◽  
Data Privacy ◽  
Resource Constraints ◽  
Open Data ◽  
Data Access ◽  
Open Science ◽  
Current Data ◽  
Shared Data ◽  
Privacy Issues ◽  
And Training

Open data sharing and access has the potential to promote transparency and reproducibility in research, contribute to education and training, and prompt innovative secondary research. Yet, there are many reasons why researchers don’t share their data. These include, among others, time and resource constraints, patient data privacy issues, lack of access to appropriate funding, insufficient recognition of the data originators’ contribution, and the concern that commercial or academic competitors may benefit from analyses based on shared data. Nevertheless, there is a positive interest within and across the research and patient communities to create shared data resources. In this perspective, we will try to highlight the spectrum of “openness” and “data access” that exists at present and highlight the strengths and weakness of current data access platforms, present current examples of data sharing platforms, and propose guidelines to revise current data sharing practices going forward.

scholarly journals Getting Started Creating Data Dictionaries: How to Create a Shareable Data Set

2021 ◽  
Vol 4 (1) ◽  
pp. 251524592092800
Author(s):  
Erin M. Buchanan ◽  
Sarah E. Crain ◽  
Ari L. Cunningham ◽  
Hannah R. Johnson ◽  
Hannah Stash ◽  
...  
Keyword(s):  
Data Collection ◽  
Data Sharing ◽  
Search Engine ◽  
Web Applications ◽  
Data Sets ◽  
Data Dictionary ◽  
Data Set ◽  
Entire Process ◽  
Shared Data ◽  
Source Data

As researchers embrace open and transparent data sharing, they will need to provide information about their data that effectively helps others understand their data sets’ contents. Without proper documentation, data stored in online repositories such as OSF will often be rendered unfindable and unreadable by other researchers and indexing search engines. Data dictionaries and codebooks provide a wealth of information about variables, data collection, and other important facets of a data set. This information, called metadata, provides key insights into how the data might be further used in research and facilitates search-engine indexing to reach a broader audience of interested parties. This Tutorial first explains terminology and standards relevant to data dictionaries and codebooks. Accompanying information on OSF presents a guided workflow of the entire process from source data (e.g., survey answers on Qualtrics) to an openly shared data set accompanied by a data dictionary or codebook that follows an agreed-upon standard. Finally, we discuss freely available Web applications to assist this process of ensuring that psychology data are findable, accessible, interoperable, and reusable.

scholarly journals Announcing the Journal of the Medical Library Association’s data sharing policy

2019 ◽  
Vol 107 (4) ◽  
Author(s):  
Katherine G. Akers ◽  
Kevin B. Read ◽  
Liz Amos ◽  
Lisa M. Federer ◽  
Ayaba Logan ◽  
...  
Keyword(s):  
Open Access ◽  
Data Sharing ◽  
Open Science ◽  
Data Reuse ◽  
Medical Library ◽  
Firm Data ◽  
Published Research

As librarians are generally advocates of open access and data sharing, it is a bit surprising that peer-reviewed journals in the field of librarianship have been slow to adopt data sharing policies. Starting October 1, 2019, the Journal of the Medical Library Association (JMLA) is taking a step forward and implementing a firm data sharing policy to increase the rigor and reproducibility of published research, enable data reuse, and promote open science. This editorial explains the data sharing policy, describes how compliance with the policy will fit into the journal’s workflow, and provides further guidance for preparing for data sharing.

scholarly journals Has open data arrived at the British Medical Journal (BMJ)? An observational study

BMJ Open ◽  
2016 ◽  
Vol 6 (10) ◽  
pp. e011784 ◽  
Author(s):  
Anisa Rowhani-Farid ◽  
Adrian G Barnett
Keyword(s):  
Medical Journal ◽  
British Medical Journal ◽  
Observational Study ◽  
Data Sharing ◽  
Meta Analysis ◽  
Open Data ◽  
Research Articles ◽  
Data Sets ◽  
Data Set ◽  
Article 50

ObjectiveTo quantify data sharing trends and data sharing policy compliance at the British Medical Journal (BMJ) by analysing the rate of data sharing practices, and investigate attitudes and examine barriers towards data sharing.DesignObservational study.SettingThe BMJ research archive.Participants160 randomly sampled BMJ research articles from 2009 to 2015, excluding meta-analysis and systematic reviews.Main outcome measuresPercentages of research articles that indicated the availability of their raw data sets in their data sharing statements, and those that easily made their data sets available on request.Results3 articles contained the data in the article. 50 out of 157 (32%) remaining articles indicated the availability of their data sets. 12 used publicly available data and the remaining 38 were sent email requests to access their data sets. Only 1 publicly available data set could be accessed and only 6 out of 38 shared their data via email. So only 7/157 research articles shared their data sets, 4.5% (95% CI 1.8% to 9%). For 21 clinical trials bound by the BMJ data sharing policy, the per cent shared was 24% (8% to 47%).ConclusionsDespite the BMJ's strong data sharing policy, sharing rates are low. Possible explanations for low data sharing rates could be: the wording of the BMJ data sharing policy, which leaves room for individual interpretation and possible loopholes; that our email requests ended up in researchers spam folders; and that researchers are not rewarded for sharing their data. It might be time for a more effective data sharing policy and better incentives for health and medical researchers to share their data.

scholarly journals Teaching Open Science: Published Data and Digital Literacy in Archaeology Classrooms

2018 ◽  
Vol 6 (2) ◽  
pp. 144-156 ◽  
Author(s):  
Katherine Cook ◽  
Canan Çakirlar ◽  
Timothy Goddard ◽  
Robert Carl DeMuth ◽  
Joshua Wells
Keyword(s):  
Digital Literacy ◽  
Open Data ◽  
Skills Training ◽  
The United States ◽  
Open Science ◽  
Data Reuse ◽  
Published Data ◽  
Skill Levels ◽  
And Training ◽  
Different Levels

ABSTRACTDigital literacy has been cited as one of the primary challenges to ensuring data reuse and increasing the value placed on open science. Incorporating published data into classrooms and training is at the core of tackling this issue. This article presents case studies in teaching with different published data platforms, in three different countries (the Netherlands, Canada, and the United States), to students at different levels and with differing skill levels. In outlining their approaches, successes, and failures in teaching with open data, it is argued that collaboration with data publishers is critical to improving data reuse and education. Moreover, increased opportunities for digital skills training and scaffolding across program curriculum are necessary for managing the learning curve and teaching students the values of open science.

scholarly journals Abordagens de reúso e a questão da reusabilidade dos dados científicos | Approaches for data reuse and the issue of scientific data reusability

2019 ◽  
Vol 15 (2) ◽  
Author(s):  
Renata Curty
Keyword(s):  
Data Sharing ◽  
Data Science ◽  
Meta Analysis ◽  
Science Research ◽  
Open Science ◽  
Scientific Data ◽  
Data Reuse ◽  
Data Repositories ◽  
Documentation Quality ◽  
Data Documentation

RESUMO As diretivas governamentais e institucionais em torno do compartilhamento de dados de pesquisas financiadas com dinheiro público têm impulsionado a rápida expansão de repositórios digitais de dados afim de disponibilizar esses ativos científicos para reutilização, com propósitos nem sempre antecipados, pelos pesquisadores que os produziram/coletaram. De modo contraditório, embora o argumento em torno do compartilhamento de dados seja fortemente sustentado no potencial de reúso e em suas consequentes contribuições para o avanço científico, esse tema permanece acessório às discussões em torno da ciência de dados e da ciência aberta. O presente artigo de revisão narrativa tem por objetivo lançar um olhar mais atento ao reúso de dados e explorar mais diretamente esse conceito, ao passo que propõe uma classificação inicial de cinco abordagens distintas para o reúso de dados de pesquisa (reaproveitamento, agregação, integração, metanálise e reanálise), com base em situações hipotéticas acompanhadas de casos de reúso de dados publicados na literatura científica. Também explora questões determinantes para a condição de reúso, relacionando a reusabilidade à qualidade da documentação que acompanha os dados. Oferece discussão sobre os desafios da documentação de dados, bem como algumas iniciativas e recomendações para que essas dificuldades sejam contornadas. Espera-se que os argumentos apresentados contribuam não somente para o avanço conceitual em torno do reúso e da reusabilidade de dados, mas também reverberem em ações relacionadas à documentação dos dados de modo a incrementar o potencial de reúso desses ativos científicos.Palavras-chave: Reúso de Dados; Reprodutibilidade Científica; Reusabilidade; Ciência Aberta; Dados de Pesquisa. ABSTRACT The availability of scientific assets through data repositories has been greatly increased as a result of government and institutional data sharing policies and mandates for publicly funded research, allowing data to be reused for purposes not always anticipated by primary researchers. Despite the fact that the argument favoring data sharing is strongly grounded in the possibilities of data reuse and its contributions to scientific advancement, this subject remains unobserved in discussions about data science and open science. This paper follows a narrative review method to take a closer look at data reuse in order to better conceptualize this term, while proposing an early classification of five distinct data reuse approaches (repurposing, aggregation, integration, meta-analysis and reanalysis) based on hypothetical cases and literature examples. It also explores the determinants of what constitutes reusable data, and the relationship between data reusability and documentation quality. It presents some challenges associated with data documentation and points out some initiatives and recommendations to overcome such problems. It expects to contribute not only for the conceptual advancement around the reusability and effective reuse of the data, but also to result in initiatives related to data documentation in order to increase the reuse potential of these scientific assets.Keywords:Data Reuse; Scientific Reproducibility; Reusability; Open Science; Research Data.  

scholarly journals A survey of researchers' needs and priorities for data sharing

2021 ◽  
Author(s):  
Iain Hrynaszkiewicz ◽  
James Harney ◽  
Lauren Cadwallader
Keyword(s):  
Data Sharing ◽  
Research Impact ◽  
Open Science ◽  
Research Data ◽  
Data Reuse ◽  
Data Availability ◽  
Data Repositories ◽  
Use Of Data ◽  
Share Data ◽  
Do So

PLOS has long supported Open Science. One of the ways in which we do so is via our stringent data availability policy established in 2014. Despite this policy, and more data sharing policies being introduced by other organizations, best practices for data sharing are adopted by a minority of researchers in their publications. Problems with effective research data sharing persist and these problems have been quantified by previous research as a lack of time, resources, incentives, and/or skills to share data. In this study we built on this research by investigating the importance of tasks associated with data sharing, and researchers’ satisfaction with their ability to complete these tasks. By investigating these factors we aimed to better understand opportunities for new or improved solutions for sharing data. In May-June 2020 we surveyed researchers from Europe and North America to rate tasks associated with data sharing on (i) their importance and (ii) their satisfaction with their ability to complete them. We received 728 completed and 667 partial responses. We calculated mean importance and satisfaction scores to highlight potential opportunities for new solutions to and compare different cohorts.Tasks relating to research impact, funder compliance, and credit had the highest importance scores. 52% of respondents reuse research data but the average satisfaction score for obtaining data for reuse was relatively low. Tasks associated with sharing data were rated somewhat important and respondents were reasonably well satisfied in their ability to accomplish them. Notably, this included tasks associated with best data sharing practice, such as use of data repositories. However, the most common method for sharing data was in fact via supplemental files with articles, which is not considered to be best practice.We presume that researchers are unlikely to seek new solutions to a problem or task that they are satisfied in their ability to accomplish, even if many do not attempt this task. This implies there are few opportunities for new solutions or tools to meet these researcher needs. Publishers can likely meet these needs for data sharing by working to seamlessly integrate existing solutions that reduce the effort or behaviour change involved in some tasks, and focusing on advocacy and education around the benefits of sharing data. There may however be opportunities - unmet researcher needs - in relation to better supporting data reuse, which could be met in part by strengthening data sharing policies of journals and publishers, and improving the discoverability of data associated with published articles.

Initiating FAIR geothermal data in Indonesia

2021 ◽  
Author(s):  
Dasapta Erwin Irawan
Keyword(s):  
Data Sharing ◽  
Data Exchange ◽  
Open Data ◽  
Data Reuse ◽  
Data Availability ◽  
Scientific Development ◽  
For Profit ◽  
Deep Well ◽  
Data Schema ◽  
Corporate Social

<p>One of the main keys to scientific development is data availability. Not only the data is easily discovered and downloaded, there's also needs for the data to be easily reused. Geothermal researchers, research institutions and industries are the three main stakeholders to foster data sharing and data reuse. Very expensive deep well datasets as well as advanced logging datasets are very important not only for exploitation purposes but also for the community involved eg: for regional planning or common environmental analyses. In data sharing, we have four principles of F.A.I.R data. Principle 1 Findable: data uploaded to open repository with proper data documentations and data schema, Principle 2 Accessible: removed access restrictions such as user id and password for easy downloads. In case of data from commercial entities, embargoed data is permitted with a clear embargo duration and data request procedure, Principle 3 Interoperable: all data must be prepared in a manner for straightforward data exchange between platforms, Principle 4 Reusable: all data must be submitted using common conventional file format, preferably text-based file (eg `csv` or `txt`) therefore it can be analyzed using various software and hardware. The fact that geothermal industries are packed with for-profit motivations and capital intensive would give even more reasons to embrace data sharing. It would be a good way for them to share their role in supporting society. The contributions from multiple stakeholders are the most essential part in science development. In the context of the commercial industry, data sharing is a form of corporate social responsibility (CSR). It shouldn't be defined only as giving out funding to support local communities.</p><p><strong>Keywords</strong>: open data, FAIR data, data sharing </p><p> </p>

scholarly journals Attitudes Toward Open Science and Public Data Sharing

2019 ◽  
Vol 50 (4) ◽  
pp. 252-260 ◽  
Author(s):  
Andrea E. Abele-Brehm ◽  
Mario Gollwitzer ◽  
Ulf Steinberg ◽  
Felix D. Schönbrodt
Keyword(s):  
Data Sharing ◽  
Structural Changes ◽  
Research Output ◽  
Open Data ◽  
Cost Benefit ◽  
Open Science ◽  
Early Career ◽  
Positive Attitudes ◽  
Public Data ◽  
Materials Used

Abstract. Central values of science are, among others, transparency, verifiability, replicability, and openness. The currently very prominent Open Science (OS) movement supports these values. Among its most important principles are open methodology (comprehensive and useful documentation of methods and materials used), open access to published research output, and open data (making collected data available for re-analyses). We here present a survey conducted among members of the German Psychological Society ( N = 337), in which we applied a mixed-methods approach (quantitative and qualitative data) to assess attitudes toward OS in general and toward data sharing more specifically. Attitudes toward OS were distinguished into positive expectations (“hopes”) and negative expectations (“fears”). These were un-correlated. There were generally more hopes associated with OS and data sharing than fears. Both hopes and fears were highest among early career researchers and lowest among professors. The analysis of the open answers revealed that generally positive attitudes toward data sharing (especially sharing of data related to a published article) are somewhat diminished by cost/benefit considerations. The results are discussed with respect to individual researchers’ behavior and with respect to structural changes in the research system.

scholarly journals Attitudes towards Open Science and Public Data Sharing: A Survey among Members of the German Psychological Society.

2019 ◽  
Author(s):  
Andrea Abele-Brehm ◽  
Mario Gollwitzer ◽  
Ulf Steinberg ◽  
Felix D. Schönbrodt
Keyword(s):  
Data Sharing ◽  
Structural Changes ◽  
Research Output ◽  
Open Data ◽  
Cost Benefit ◽  
Open Science ◽  
Early Career ◽  
Positive Attitudes ◽  
Public Data ◽  
Materials Used

Central values of science are, among others, transparency, verifiability, replicability and openness. The currently very prominent Open Science (OS) movement supports these values. Among its most important principles are open methodology (comprehensive and useful documentation of methods and materials used), open access to published research output, and open data (making collected data available for re-analyses). We here present a survey conducted among members of the German Psychological Society (N = 337), in which we applied a mixed-methods approach (quantitative and qualitative data) to assess attitudes towards OS in general and towards data sharing more specifically. Attitudes towards OS were distinguished into positive expectations (“hopes”) and negative expectations (“fears”). These were un-correlated. There were generally more hopes associated with OS and data sharing than fears. Both hopes and fears were highest among early career researchers and lowest among professors. The analysis of the open answers revealed that generally positive attitudes towards data sharing (especially sharing of data related to a published article) are somewhat diminished by cost/benefit considerations. The results are discussed with respect to individual researchers’ behavior and with respect to structural changes in the research system.

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