scholarly journals Game theory and research data sharing

2018 ◽  
Keyword(s):  
Game Theory ◽  
Data Sharing ◽  
Research Data

scholarly journals A Research data Sharing Game

2014 ◽  
Author(s):  
Tessa E Pronk ◽  
Paulien H Wiersma ◽  
Anne van Weerden
Keyword(s):  
Game Theory ◽  
Decision Theory ◽  
Data Sharing ◽  
Scientific Community ◽  
Data Retrieval ◽  
Research Data ◽  
Top Down ◽  
Average Efficiency ◽  
Natural Tendency ◽  
Share Data

While reusing research data has evident benefits for the scientific community as a whole, decisions to archive and share these data are primarily made by individual researchers. For individuals, it is less obvious that the benefits of sharing data outweigh the associated costs, i.e. time and money. In this sense the problem of data sharing resembles a typical game in interactive decision theory, more commonly known as game theory. Within this framework we analyse in this paper how different measures to promote sharing and reuse of research data affect sharing and not sharing individuals. We find that the scientific community can benefit from top-down policies to enhance sharing data even when the act of sharing itself implies a cost. Namely, if (almost) everyone shares, many individuals can gain a higher efficiency as datasets can be reused. Additionally, measures to ensure better data retrieval and quality can compensate for sharing costs by enabling reuse. Nevertheless, an individual researcher who decides not to share omits the costs of sharing. Assuming that the natural tendency will be to use a strategy that will lead to maximisation of individual efficiency it is seen that, as more individuals decide not to share, there is a point at which average efficiency for both sharing and non-sharing researchers becomes lower than was originally the case and scientific community efficiency steadily drops. With this in mind, we conclude that the key to motivate the researcher to share data lies in reducing the costs associated with sharing, or even better, turning it into a benefit.

scholarly journals A research data sharing game

2014 ◽  
Author(s):  
Tessa E Pronk ◽  
Paulien H Wiersma ◽  
Anne van Weerden
Keyword(s):  
Game Theory ◽  
Decision Theory ◽  
Data Sharing ◽  
Scientific Community ◽  
Data Retrieval ◽  
Research Data ◽  
Top Down ◽  
Individual Researcher ◽  
Natural Tendency ◽  
Share Data

While reusing research data has evident benefits for the scientific community as a whole, decisions to archive and share these data are primarily made by individual researchers. For individuals, it is less obvious that the benefits of sharing data outweigh the associated costs, for example time and money. In this sense the problem of data sharing resembles a typical game in interactive decision theory, more commonly known as game theory. Within this framework we analyse how measures to promote sharing and reuse of research data affect individuals who do and do not share data. We find that the scientific community can benefit from top-down policies to enhance sharing data even when the act of sharing itself implies a cost. Namely, if (almost) everyone shares, many individuals receive benefits, as datasets in our model can be reused to achieve a higher efficiency (i.e. more publications, higher quality papers). Surprisingly, as sharing implies a cost, even sharing individuals themselves, in a community in which sharing is common, can gain a higher efficiency than individuals who do not share, in a community in which sharing is not common. In addition to these findings, we find that measures to ensure better data retrieval and quality can compensate for sharing costs by further enabling reuse. Nevertheless, an individual researcher who decides not to share omits the costs of sharing. Assuming that the natural tendency will be to use a strategy that will lead to maximisation of individual efficiency, we see the average scientific community efficiency in our model steadily drop as more individuals decide not to share. With this in mind, we conclude that the key to motivate the researcher to share data lies in reducing the costs associated with sharing, or even better, turning it into a benefit.

scholarly journals Challenges arising when seeking broad consent for health research data sharing: a qualitative study of perspectives in Thailand

2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Phaik Yeong Cheah ◽  
Nattapat Jatupornpimol ◽  
Borimas Hanboonkunupakarn ◽  
Napat Khirikoekkong ◽  
Podjanee Jittamala ◽  
...  
Keyword(s):  
Qualitative Study ◽  
Data Sharing ◽  
Health Research ◽  
Research Data ◽  
Broad Consent

scholarly journals Tutkimusaineiston avoin jakaminen – tutkimusorganisaatioiden jäsenten käsityksiä, kokemuksia ja mielipiteitä

2018 ◽  
Vol 37 (4) ◽  
Author(s):  
Heidi Enwald
Keyword(s):  
Data Sharing ◽  
Data Storage ◽  
Online Survey ◽  
Open Science ◽  
Research Data ◽  
Research Field ◽  
Open Research ◽  
Research Organizations ◽  
Data Collection Instrument ◽  
In The Beginning

Open research data is data that is free to access, reuse, and redistribute. This study focuses on the perceptions, opinions and experiences of staff and researchers of research institutes on topics related to open research data. Furthermore, the differences across gender, role in the research organization and research field were investigated. An international questionnaire survey, translated into Finnish and Swedish, was used as the data collection instrument. An online survey was distributed through an open science related network to Finnish research organizations. In the end, 469 responded to all 24 questions of the survey. Findings indicate that many are still unaware or uncertain about issues related to data sharing and long-term data storage. Women as well as staff and researchers of medical and health sciences were most concerned about the possible problems associated with data sharing. Those in the beginning of their scientific careers, hesitated about sharing their data.

scholarly journals Práticas e percepções dos pesquisadores brasileiros sobre serviços de acesso aberto a dados de pesquisa | Practices and perceptions of Brazilian researchers on open access to research data

2019 ◽  
Vol 15 (2) ◽  
Author(s):  
Sonia Elisa Caregnato ◽  
Samile Andrea de Souza Vanz ◽  
Caterina Groposo Pavão ◽  
Paula Caroline Jardim Schifino Passos ◽  
Eduardo Borges ◽  
...  
Keyword(s):  
Open Access ◽  
Data Sharing ◽  
Research Data ◽  
Exploratory Analysis ◽  
Data Reuse ◽  
Institutional Repositories ◽  
Open Research

RESUMO O artigo apresenta análise exploratória das práticas e das percepções a respeito do acesso aberto a dados de pesquisa embasada em dados coletados por meio de survey, realizada com pesquisadores brasileiros. As 4.676 respostas obtidas demonstram que, apesar do grande interesse pelo tema, evidenciado pela prevalência de variáveis relacionadas ao compartilhamento e ao uso de dados e aos repositórios institucionais, não há clareza por parte dos sujeitos sobre os principais tópicos relacionados. Conclui-se que, apesar da maioria dos pesquisadores afirmar que compartilha dados de pesquisa, a disponibilização desses dados de forma aberta e irrestrita ainda não é amplamente aceita.Palavras-chave: Dados Abertos de Pesquisa; Compartilhamento de Dados; Reuso de Dados.ABSTRACT This article presents an exploratory analysis of the practices and perceptions regarding open access to research data based on information collected by a survey with Brazilian researchers. The 4,676 responses show that, despite the great interest in the topic, evidenced by the prevalence of variables related to data sharing and use and to institutional repositories, there is no clarity on the part of the subjects on the main related topics. We conclude that, although the majority of the researchers share research data, the availability of this data in an open and unrestricted way is not yet widely accepted.Keywords: Open Research Data; Data Sharing; Data Reuse.

scholarly journals PsychData – Experiences from 12 Years of Research Data Archiving

2015 ◽  
Author(s):  
Peter Weiland ◽  
Ina Dehnhard
Keyword(s):  
Data Sharing ◽  
Large Scale ◽  
Research Data ◽  
Data Reuse ◽  
German Research Foundation ◽  
Cross Sectional ◽  
Data Archiving ◽  
Wide Range ◽  
Domain Specific Knowledge ◽  
Meta Analyses

See video of the presentation.The benefits of making research data permanently accessible through data archives is widely recognized: costs can be reduced by reusing existing data, research results can be compared and validated with results from archived studies, fraud can be more easily detected, and meta-analyses can be conducted. Apart from that, authors may gain recognition and reputation for producing the datasets. Since 2003, the accredited research data center PsychData (part of the Leibniz Institute for Psychology Information in Trier, Germany) documents and archives research data from all areas of psychology and related fields. In the beginning, the main focus was on datasets that provide a high potential for reuse, e.g. longitudinal studies, large-scale cross sectional studies, or studies that were conducted during historically unique conditions. Presently, more and more journal publishers and project funding agencies require researchers to archive their data and make them accessible for the scientific community. Therefore, PsychData also has to serve this need.In this presentation we report on our experiences in operating a discipline-specific research data archive in a domain where data sharing is met with considerable resistance. We will focus on the challenges for data sharing and data reuse in psychology, e.g.large amount of domain-specific knowledge necessary for data curationhigh costs for documenting the data because of a wide range on non-standardized measuressmall teams and little established infrastructures compared with the "big data" disciplinesstudies in psychology not designed for reuse (in contrast to the social sciences)data protectionresistance to sharing dataAt the end of the presentation, we will provide a brief outlook on DataWiz, a new project funded by the German Research Foundation (DFG). In this project, tools will be developed to support researchers in documenting their data during the research phase.

scholarly journals Making Research Data Accessible

2021 ◽  
Author(s):  
Diana Kapiszewski ◽  
Sebastian Karcher
Keyword(s):  
Data Sharing ◽  
Research Method ◽  
Secondary Analysis ◽  
Research Data ◽  
Data Generation ◽  
Shared Data

This chapter argues that the benefits of data sharing will accrue more quickly, and will be more significant and more enduring, if researchers make their data “meaningfully accessible.” Data are meaningfully accessible when they can be interpreted and analyzed by scholars far beyond those who generated them. Making data meaningfully accessible requires that scholars take the appropriate steps to prepare their data for sharing, and avail themselves of the increasingly sophisticated infrastructure for publishing and preserving research data. The better other researchers can understand shared data and the more researchers who can access them, the more those data will be re-used for secondary analysis, producing knowledge. Likewise, the richer an understanding an instructor and her students can gain of the shared data being used to teach and learn a particular research method, the more useful those data are for that pedagogical purpose. And the more a scholar who is evaluating the work of another can learn about the evidence that underpins its claims and conclusions, the better their ability to identify problems and biases in data generation and analysis, and the better informed and thus stronger an endorsement of the work they can offer.

scholarly journals DataStaR: Using the Semantic Web approach for Data Curation

2011 ◽  
Vol 6 (2) ◽  
pp. 209-221 ◽  
Author(s):  
Huda Khan ◽  
Brian Caruso ◽  
Jon Corson-Rikert ◽  
Dianne Dietrich ◽  
Brian Lowe ◽  
...  
Keyword(s):  
Social Sciences ◽  
Semantic Web ◽  
Data Sharing ◽  
Research Data ◽  
Data Curation ◽  
Data Staging ◽  
Domain Specific ◽  
Digital Infrastructure

In disciplines as varied as medicine, social sciences, and economics, data and their analyses are essential parts of researchers’ contributions to their respective fields. While sharing research data for review and analysis presents new opportunities for furthering research, capturing these data in digital forms and providing the digital infrastructure for sharing data and metadata pose several challenges. This paper reviews the motivations behind and design of the Data Staging Repository (DataStaR) platform that targets specific portions of the research data curation lifecycle: data and metadata capture and sharing prior to publication, and publication to permanent archival repositories. The goal of DataStaR is to support both the sharing and publishing of data while at the same time enabling metadata creation without imposing additional overheads for researchers and librarians. Furthermore, DataStaR is intended to provide cross-disciplinary support by being able to integrate different domain-specific metadata schemas according to researchers’ needs. DataStaR’s strategy of a usable interface coupled with metadata flexibility allows for a more scaleable solution for data sharing, publication, and metadata reuse.

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