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.

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 The reproducibility challenge – what researchers need

2017 ◽  
Author(s):  
Federica Rosetta
Keyword(s):  
Scientific Discovery ◽  
Open Data ◽  
Science Research ◽  
Open Science ◽  
Research Data ◽  
Data Sets ◽  
Scientific Practices ◽  
Data Citation ◽  
Materials Used ◽  
High Level

Watch the VIDEO here.Within the Open Science discussions, the current call for “reproducibility” comes from the raising awareness that results as presented in research papers are not as easily reproducible as expected, or even contradicted those original results in some reproduction efforts. In this context, transparency and openness are seen as key components to facilitate good scientific practices, as well as scientific discovery. As a result, many funding agencies now require the deposit of research data sets, institutions improve the training on the application of statistical methods, and journals begin to mandate a high level of detail on the methods and materials used. How can researchers be supported and encouraged to provide that level of transparency? An important component is the underlying research data, which is currently often only partly available within the article. At Elsevier we have therefore been working on journal data guidelines which clearly explain to researchers when and how they are expected to make their research data available. Simultaneously, we have also developed the corresponding infrastructure to make it as easy as possible for researchers to share their data in a way that is appropriate in their field. To ensure researchers get credit for the work they do on managing and sharing data, all our journals support data citation in line with the FORCE11 data citation principles – a key step in the direction of ensuring that we address the lack of credits and incentives which emerged from the Open Data analysis (Open Data - the Researcher Perspective https://www.elsevier.com/about/open-science/research-data/open-data-report ) recently carried out by Elsevier together with CWTS. Finally, the presentation will also touch upon a number of initiatives to ensure the reproducibility of software, protocols and methods. With STAR methods, for instance, methods are submitted in a Structured, Transparent, Accessible Reporting format; this approach promotes rigor and robustness, and makes reporting easier for the author and replication easier for the reader.

Support for Open Science Practices in Emotion Science: A Survey Study

2020 ◽  
Author(s):  
Olmo Van den Akker ◽  
Laura Danielle Scherer ◽  
Jelte M. Wicherts ◽  
Sander Koole
Keyword(s):  
Open Data ◽  
Open Science ◽  
High Impact ◽  
Survey Study ◽  
Science Practices ◽  
Replication Studies ◽  
Positive Attitudes ◽  
Methodological Rigor ◽  
Negative Findings ◽  
Larger Sample

So-called “open science practices” seek to improve research transparency and methodological rigor. What do emotion researchers think about these practices? To address this question, we surveyed active emotion researchers (N= 144) in October 2019 about their attitudes toward several open science practices. Overall, the majority of emotion researchers had positive attitudes toward open science practices and expressed a willingness to engage in such practices. Emotion researchers on average believed that replicability would improve by publishing more negative findings, by requiring open data and materials, and by conducting studies with larger sample sizes. Direct replications, multi-lab studies, and preregistration were all seen as beneficial to the replicability of emotion research. Emotion researchers believed that more direct replications would be conducted if replication studies would receive increased funding, more citations, and easier publication in high impact journals. Emotion researchers believed that preregistration would be stimulated by providing researchers with more information about its benefits and more guidance on its effective application. Overall, these findings point to considerable momentum with regard to open science among emotion researchers. This momentum may be leveraged to achieve a more robust emotion science.

scholarly journals Individual costs and benefits of sharing open data in ecology and evolution

2020 ◽  
Author(s):  
Sandrine Soeharjono ◽  
Dominique Roche
Keyword(s):  
Empirical Evidence ◽  
Data Sharing ◽  
Scientific Discovery ◽  
Open Data ◽  
Early Career ◽  
Faculty Members ◽  
Career Stage ◽  
Costs And Benefits ◽  
Early Career Researchers ◽  
And Gender

Open data facilitate reproducibility and accelerate scientific discovery but are hindered by perceptions that researchers bear costs and gain few benefits from publicly sharing their data, with limited empirical evidence to the contrary. We surveyed 140 faculty members working in ecology and evolution across Canada’s top 20-ranked universities and found that more researchers report benefits (47.9%) and neutral outcomes (43.6%) than costs (21.4%) from sharing data. Benefits were independent of career stage and gender, but men and early career researchers were more likely to report costs. We outline proposed mechanisms to reduce individual costs of data sharing and increase benefits.

scholarly journals Improving Impact Metrics of Open and Free Biodiversity Data through Linked Metadata and Academic Outreach

2019 ◽  
Vol 3 ◽  
Author(s):  
Daniel Noesgaard
Keyword(s):  
Data Sharing ◽  
Citation Count ◽  
Open Data ◽  
Scientific Paper ◽  
Open Science ◽  
Digital Object Identifier ◽  
Biodiversity Data ◽  
Global Biodiversity Information Facility ◽  
Scientific Publications ◽  
Creative Commons

The work required to collect, clean and publish biodiversity datasets is significant, and those who do it deserve recognition for their efforts. Researchers publish studies using open biodiversity data available from GBIF—the Global Biodiversity Information Facility—at a rate of about two papers a day. These studies cover areas such as macroecology, evolution, climate change, and invasive alien species, relying on data sharing by hundreds of publishing institutions and the curatorial work of thousands of individual contributors. With more than 90 per cent of these datasets licensed under Creative Commons Attribution licenses (CC BY and CC BY-NC), data users are required to credit the dataset providers. For GBIF, it is crucial to link these scientific uses to the underlying data as one means of demonstrating the value and impact of open science, while seeking to ensure attribution of individual, organizational and national contributions to the global pool of open data about biodiversity. Every single authenticated download of occurrence records from GBIF.org is issued a unique Digital Object Identifier (DOI). These DOIs each resolve to a landing page that contains details of the search parameters used to generate the download a quantitative map of the underlying datasets that contributed to the download a simple citation to be included in works that rely on the data the search parameters used to generate the download a quantitative map of the underlying datasets that contributed to the download a simple citation to be included in works that rely on the data When used properly by authors and deposited correctly by journals in the article metadata, the DOI citation establishes a direct link between a scientific paper and the underlying data. Crossref—the main DOI Registration Agency for academic literature— exposes such links in Event Data, which can be consumed programmatically to report direct use of individual datasets. GBIF also records these links, permanently preserving the download archives while exposing a citation count on download landing pages that is also summarized on the landing pages of each contributing datasets and publishers. The citation counts can be expanded to produce lists of all papers unambiguously linked to use of specific datasets. In 2018, just 15 per cent of papers based on GBIF-mediated data used DOIs to cite or acknowledge the datasets used in the studies. To promote crediting of data publishers and digital recognition of data sharing, the GBIF Secretariat has been reaching out systematically to authors and publishers since April 2018 whenever a paper fails to include a proper data citation. While publishing lags may hinder immediate effects, preliminary findings suggest that uptake is improving—as the number of papers with DOI data citations during the first part of 2019 is up more than 60 per cent compared to 2018. Focusing on the value of linking scientific publications and data, this presentation will explore the potential for establishing automatic linkage through DOI metadata while demonstrating efforts to improve metrics of data use and attribution of data providers through outreach campaigns to authors and journal publishers.

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.

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.

scholarly journals The importance of adherence to international standards for depositing open data in public repositories

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Diego A. Forero ◽  
Walter H. Curioso ◽  
George P. Patrinos
Keyword(s):  
Open Access ◽  
Data Sharing ◽  
Meta Analysis ◽  
Open Data ◽  
Open Science ◽  
International Standards ◽  
Raw Data ◽  
The World ◽  
Public Repositories ◽  
Evaluation Of Evidence

AbstractThere has been an important global interest in Open Science, which include open data and methods, in addition to open access publications. It has been proposed that public availability of raw data increases the value and the possibility of confirmation of scientific findings, in addition to the potential of reducing research waste. Availability of raw data in open repositories facilitates the adequate development of meta-analysis and the cumulative evaluation of evidence for specific topics. In this commentary, we discuss key elements about data sharing in open repositories and we invite researchers around the world to deposit their data in them.

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.

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