scholarly journals Data Sharing in Psychology

Psychology ◽  
2021 ◽  
Author(s):  
Joy Kennedy
Keyword(s):  
Data Sharing ◽  
Scientific Progress ◽  
Secondary Data ◽  
Open Science ◽  
Primary Data ◽  
Institutional Review Boards ◽  
Data Repositories ◽  
Replication Studies ◽  
Explicit Consent ◽  
Meta Analyses

Narrowly defined, data sharing is the practice of making scientific research data available to other researchers. However, the term is often used to include a variety of open-science practices, including making data, methodology (e.g., coding scheme), analytic syntax, and other research materials available to other researchers, as well as the reuse of those resources by others. There are multiple avenues for data sharing, for example data repositories (either subscription-based or free) or direct request to the researcher. Data sharing is a fairly common practice in the life and earth sciences. Excepting a handful of longitudinal projects, psychology lacks this robust historical precedent for sharing data. In fact, in the not-so-distant past, institutional review boards typically required that data be destroyed after a preset period in order to protect participants’ privacy—and some still do. And many researchers still do not take the first step—modifying their informed consent procedures to include explicit consent to share. Although still not frequent, data sharing in psychology is becoming more common. In part, this trend is being driven by the requirements set by publications and funding agencies. For publications, data sharing is intrinsic to transparency and replication of study findings. For funders, data sharing ensures greater return on investment—that expensive and time-consuming primary data collection does not wind up sitting on a dusty shelf, but rather can be reused for secondary data analysis to answer new questions. In psychology as in other fields, technological improvements in storage capacity and computing power have also facilitated data sharing and reuse. While many psychologists are still concerned that data sharing will result in being “scooped” or found in error, there is increasing recognition of the benefits of data sharing. First, data repositories ensure that data are archived, and that the burden of preservation does not fall on the researcher or the researcher’s institution. Sharing also increases the pace of scientific progress, as researchers can build on each other’s work. For example, researchers can learn how other experts approached measurement or coding of a given outcome. In replication studies, inconsistent findings can point to contextual variations in the construct under study, rather than researcher error. And in a field where null findings are often difficult to publish, sharing allows these data to be included in meta-analyses across studies to examine broader impacts. Most importantly, data sharing enhances transparency, a key ingredient in the scientific process.

scholarly journals Emerging Privacy Issues in Times of Open Science

2021 ◽  
Author(s):  
Benjamin Erb ◽  
Christoph Bösch ◽  
Cornelia Herbert ◽  
Frank Kargl ◽  
Christian Montag
Keyword(s):  
Research Process ◽  
Open Science ◽  
Primary Data ◽  
Future Research ◽  
Data Sets ◽  
Replication Studies ◽  
Replication Crisis ◽  
Important Challenge ◽  
Meta Analyses ◽  
Privacy Issues

The open science movement has taken up the important challenge to increase transparency of statistical analyses, to facilitate reproducibility of studies, and to enhance reusability of data sets. To counter the replication crisis in the psychological and related sciences, the movement also urges researchers to publish their primary data sets alongside their articles. While such data publications represent a desirable improvement in terms of transparency and are also helpful for future research (e.g., subsequent meta-analyses or replication studies), we argue that such a procedure can worsen existing privacy issues that are insufficiently considered so far in this context. Recent advances in de-anonymization and re-identification techniques render privacy protection increasingly difficult, as prevalent anonymization mechanisms for handling participants' data might no longer be adequate. When exploiting publicly shared primary data sets, data from multiple studies can be linked with contextual data and eventually, participants can be de-anonymized. Such attacks can either re-identify specific individuals of interest, or they can be used to de-anonymize entire participant cohorts. The threat of de-anonymization attacks can endanger the perceived confidentiality of responses by participants, and ultimately, lower the overall trust of potential participants into the research process due to privacy concerns.

scholarly journals Open-access policy and data-sharing practice in UK academia

2019 ◽  
Vol 46 (1) ◽  
pp. 41-52 ◽  
Author(s):  
Yimei Zhu
Keyword(s):  
Open Access ◽  
Data Sharing ◽  
Secondary Data ◽  
Open Science ◽  
Research Data ◽  
Academic Disciplines ◽  
Free Access ◽  
Online Publishing ◽  
Institutional Repositories ◽  
Access To Data

Data sharing can be defined as the release of research data that can be used by others. With the recent open-science movement, there has been a call for free access to data, tools and methods in academia. In recent years, subject-based and institutional repositories and data centres have emerged along with online publishing. Many scientific records, including published articles and data, have been made available via new platforms. In the United Kingdom, most major research funders had a data policy and require researchers to include a ‘data-sharing plan’ when applying for funding. However, there are a number of barriers to the full-scale adoption of data sharing. Those barriers are not only technical, but also psychological and social. A survey was conducted with over 1800 UK-based academics to explore the extent of support of data sharing and the characteristics and factors associated with data-sharing practice. It found that while most academics recognised the importance of sharing research data, most of them had never shared or reused research data. There were differences in the extent of data sharing between different gender, academic disciplines, age and seniority. It also found that the awareness of Research Council UK’s (RCUK) Open-Access (OA) policy, experience of Gold and Green OA publishing, attitudes towards the importance of data sharing and experience of using secondary data were associated with the practice of data sharing. A small group of researchers used social media such as Twitter, blogs and Facebook to promote the research data they had shared online. Our findings contribute to the knowledge and understanding of open science and offer recommendations to academic institutions, journals and funding agencies.

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.

scholarly journals The Spectrum of Data sharing Policies in Neuroimaging Data Repositories

2021 ◽  
Author(s):  
Anita Jwa ◽  
Russell Poldrack
Keyword(s):  
Data Sharing ◽  
Data Privacy ◽  
Public Investment ◽  
Science Inquiry ◽  
Open Science ◽  
Machine Learning Techniques ◽  
Data Repositories ◽  
Learning Techniques ◽  
Neuroimaging Data ◽  
Regulatory Analysis

Sharing data is a scientific imperative that accelerates scientific discoveries, reinforces open science inquiry, and allows for efficient use of public investment and research resources. Considering these benefits, data sharing has been widely promoted in diverse fields and neuroscience has been no exception to this movement. For all its promise, however, the sharing of human neuroimaging data raises critical ethical and legal issues, such as data privacy. Recently, the heightened risks to data privacy posed by the exponential development in artificial intelligence and machine learning techniques has made data sharing more challenging; the regulatory landscape around data sharing has also been evolving rapidly. Here we present an in-depth ethical and regulatory analysis that will examine how neuroimaging data are currently shared against the backdrop of the relevant regulations and policies and how advanced software tools and algorithms might undermine subjects’ privacy in neuroimaging data sharing. This analysis will inform researchers on responsible practice of neuroimaging data sharing and shed light on a regulatory framework to provide adequate protection of neuroimaging data while maximizing the benefits of data sharing.

scholarly journals Openness and Transparency Promotion With Existing Longitudinal Data: A Worked Example of a Coordinated Analysis

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 568-568
Author(s):  
Daniel Mroczek ◽  
Eileen Graham ◽  
Emily Willroth
Keyword(s):  
Longitudinal Data ◽  
Data Sharing ◽  
Mortality Risk ◽  
Large Scale ◽  
Secondary Data ◽  
Open Science ◽  
Worked Example ◽  
Study Designs

Abstract The application of openness and transparency principles is challenging when using existing or ongoing long-term longitudinal data. One technique that promotes replicability and also is consistent with openness and transparency principles is coordinated analysis. Such analyses, especially when done with a large number of extant longitudinal datasets, tend to draw upon values of data sharing, revelation of code and scripts, and pre-registration. Thus coordinated analyses often provide good examples of how multiple transparency and openness values can come together. We will demonstrate this by presenting two recent large-scale coordinated analyses. One was a 15-study investigation of personality and mortality risk (Graham et al., 2017). The second is a new 16-study investigation of personality trajectories (Graham et al., under revision). We show how multi-study designs are congruent with open science and transparency ideas in the context of longitudinal and other secondary data.

scholarly journals Enforcing Human Subject Regulations using Blockchain and Smart Contracts

2018 ◽  
Author(s):  
Olivia Choudhury ◽  
Hillol Sarker ◽  
Nolan Rudolph ◽  
Morgan Foreman ◽  
Nicholas Fay ◽  
...  
Keyword(s):  
Data Sharing ◽  
Data Exchange ◽  
Human Subjects ◽  
Secondary Data ◽  
Institutional Review Boards ◽  
Sensitive Information ◽  
Smart Contracts ◽  
Blockchain Technology ◽  
Research Activities ◽  
Genomic Study

Recent changes to the Common Rule, which govern Institutional Review Boards (IRB), require implementing new policies to strengthen research protocols involving human subjects. A major challenge in implementing such policies is an inability to automatically and consistently meet these ethical rules while securing sensitive information collected during the study. In this paper, we propose a novel framework, based on blockchain technology, to enforce IRB regulations on data collection. We demonstrate how to design smart contracts and a ledger to meet the requirements of an IRB protocol, including subject recruitment, informed consent management, secondary data sharing, monitoring risks, and generating automated assessments for continuous review. Furthermore, we show how we can employ the immutable transaction log in the blockchain to embed security in research activities by detecting malicious activities and robustly tracking subject involvement. We evaluate our approach by assessing its ability to enforce IRB guidelines in different types of human subjects studies, including a genomic study, a drug trial, and a wearable sensor monitoring study. Keywords: Blockchain, Data Sharing, Data Exchange, EHR, electronic health record, Ethereum, interplanetary filesystem, IPFS

Implications of the Credibility Revolution for Productivity, Creativity, and Progress

2018 ◽  
Vol 13 (4) ◽  
pp. 411-417 ◽  
Author(s):  
Simine Vazire
Keyword(s):  
Best Practices ◽  
Data Sharing ◽  
Subjective Experience ◽  
Scientific Progress ◽  
Replication Studies ◽  
Psychological Science ◽  
Shared Goal ◽  
Standards Of Evidence ◽  
Crisis In Psychology ◽  
Scientific Claims

The credibility revolution (sometimes referred to as the “replicability crisis”) in psychology has brought about many changes in the standards by which psychological science is evaluated. These changes include (a) greater emphasis on transparency and openness, (b) a move toward preregistration of research, (c) more direct-replication studies, and (d) higher standards for the quality and quantity of evidence needed to make strong scientific claims. What are the implications of these changes for productivity, creativity, and progress in psychological science? These questions can and should be studied empirically, and I present my predictions here. The productivity of individual researchers is likely to decline, although some changes (e.g., greater collaboration, data sharing) may mitigate this effect. The effects of these changes on creativity are likely to be mixed: Researchers will be less likely to pursue risky questions; more likely to use a broad range of methods, designs, and populations; and less free to define their own best practices and standards of evidence. Finally, the rate of scientific progress—the most important shared goal of scientists—is likely to increase as a result of these changes, although one’s subjective experience of making progress will likely become rarer.

scholarly journals Data Management and Data Sharing in Psychological Science: Revision of the DGPs Recommendations

2020 ◽  
Author(s):  
Mario Gollwitzer ◽  
Andrea Abele-Brehm ◽  
Christian Fiebach ◽  
Roland Ramthun ◽  
Anne M. Scheel ◽  
...  
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Data Protection ◽  
Scientific Practice ◽  
Data Access ◽  
Scientific Data ◽  
Research Data ◽  
Primary Data ◽  
Practical Implementation ◽  
Data Repositories

Providing access to research data collected as part of scientific publications and publicly funded research projects is now regarded as a central aspect of an open and transparent scientific practice and is increasingly being called for by funding institutions and scientific journals. To this end, researchers should strive to comply with the so-called FAIR principles (of scientific data management), that is, research data should be findable, accessible, interoperable, and reusable. Systematic data management supports these goals and, at the same time, makes it possible to achieve them efficiently. With these revised recommendations on data management and data sharing, which also draw on feedback from a 2018 survey of its members, the German Psychological Society (Deutsche Gesellschaft für Psychologie; DGPs) specifies important basic principles of data management in psychology. Initially, based on discipline-specific definitions of raw data, primary data, secondary data, and metadata, we provide recommendations on the degree of data processing necessary when publishing data. We then discuss data protection as well as aspects of copyright and data usage before defining the qualitative requirements for trustworthy research data repositories. This is followed by a detailed discussion of pragmatic aspects of data sharing, such as the differences between Type 1 and Type 2 data publications, restrictions on use (embargo period), the definition of "scientific use" by secondary users of shared data, and recommendations on how to resolve potential disputes. Particularly noteworthy is the new recommendation of distinct "access categories" for data, each with different requirements in terms of data protection or research ethics. These range from completely open data without usage restrictions ("access category 0") to data shared under a set of standardized conditions (e.g., reuse restricted to scientific purposes; "access category 1"), individualized usage agreements ("access category 2"), and secure data access under strictly controlled conditions (e.g., in a research data center; “access category 3"). The practical implementation of this important innovation, however, will require data repositories to provide the necessary technical functionalities. In summary, the revised recommendations aim to present pragmatic guidelines for researchers to handle psychological research data in an open and transparent manner, while addressing structural challenges to data sharing solutions that are beneficial for all involved parties.

scholarly journals Is There a Social Life in Open Data? The Case of Open Data Practices in Educational Technology Research

Publications ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 9 ◽  
Author(s):  
Juliana Raffaghelli ◽  
Stefania Manca
Keyword(s):  
Educational Technology ◽  
Data Sharing ◽  
Social Life ◽  
Social Activity ◽  
Open Data ◽  
Open Science ◽  
Future Research ◽  
Data Repositories ◽  
Research Perspectives ◽  
Open Datasets

In the landscape of Open Science, Open Data (OD) plays a crucial role as data are one of the most basic components of research, despite their diverse formats across scientific disciplines. Opening up data is a recent concern for policy makers and researchers, as the basis for good Open Science practices. The common factor underlying these new practices—the relevance of promoting Open Data circulation and reuse—is mostly a social form of knowledge sharing and construction. However, while data sharing is being strongly promoted by policy making and is becoming a frequent practice in some disciplinary fields, Open Data sharing is much less developed in Social Sciences and in educational research. In this study, practices of OD publication and sharing in the field of Educational Technology are explored. The aim is to investigate Open Data sharing in a selection of Open Data repositories, as well as in the academic social network site ResearchGate. The 23 Open Datasets selected across five OD platforms were analysed in terms of (a) the metrics offered by the platforms and the affordances for social activity; (b) the type of OD published; (c) the FAIR (Findability, Accessibility, Interoperability, and Reusability) data principles compliance; and (d) the extent of presence and related social activity on ResearchGate. The results show a very low social activity in the platforms and very few correspondences in ResearchGate that highlight a limited social life surrounding Open Datasets. Future research perspectives as well as limitations of the study are interpreted in the discussion.

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