scholarly journals Opening Pandora’s Box: Peeking inside Psychology’s data sharing practices, and seven recommendations for change

2020 ◽  
Author(s):  
John N. Towse ◽  
David A Ellis ◽  
Andrea S Towse
Keyword(s):  
Social Science ◽  
Data Sharing ◽  
Open Data ◽  
Research Papers ◽  
Research Project ◽  
Psychological Science ◽  
Funding Agencies ◽  
The Impact

Abstract Open data-sharing is a valuable practice that ought to enhance the impact, reach, and transparency of a research project. While widely advocated by many researchers and mandated by some journals and funding agencies, little is known about detailed practices across psychological science. In a pre-registered study, we show that overall, few research papers directly link to available data in many, though not all, journals. Most importantly, even where open data can be identified, the majority of these lacked completeness and reusability—conclusions that closely mirror those reported outside of Psychology. Exploring the reasons behind these findings, we offer seven specific recommendations for engineering and incentivizing improved practices, so that the potential of open data can be better realized across psychology and social science more generally.

scholarly journals Opening Pandora’s Box: Peeking inside psychology’s data sharing practices, and seven recommendations for change.

2019 ◽  
Author(s):  
John Towse ◽  
David Alexander Ellis ◽  
Andrea Towse
Keyword(s):  
Social Science ◽  
Data Sharing ◽  
Open Data ◽  
Research Papers ◽  
Research Project ◽  
Psychological Science ◽  
Funding Agencies ◽  
The Impact

Open data-sharing is a valuable practice that ought to enhance the impact, reach and transparency of a research project. While widely advocated by many researchers and mandated by some journals and funding agencies, little is known about detailed practices across psychological science. In a pre-registered study, we show that overall, few research papers directly link to available data in many, though not all, journals. Most importantly, even where open data can be identified, the majority of these lacked completeness and reusability - conclusions that closely mirror those reported outside of Psychology. Exploring the reasons behind these findings, we offer seven specific recommendations for engineering and incentivizing improved practices, so that the potential of open data can be better realized across psychology and social science more generally.

scholarly journals Archaeological documentation and data sharing: digital surveying and open data approach applied to archaeological fieldworks

2019 ◽  
Vol 10 (20) ◽  
pp. 17 ◽  
Author(s):  
Mattia Previtali ◽  
Riccardo Valente
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Data Sharing ◽  
Open Data ◽  
Geographic Information ◽  
Published Data ◽  
Archaeological Data ◽  
Disciplinary Expertise ◽  
Large Level ◽  
The Impact

<p>The open data paradigm is changing the research approach in many fields such as remote sensing and the social sciences. This is supported by governmental decisions and policies that are boosting the open data wave, and in this context archaeology is also affected by this new trend. In many countries, archaeological data are still protected or only limited access is allowed. However, the strong political and economic support for the publication of government data as open data will change the accessibility and disciplinary expertise in the archaeological field too. In order to maximize the impact of data, their technical openness is of primary importance. Indeed, since a spreadsheet is more usable than a PDF of a table, the availability of digital archaeological data, which is structured using standardised approaches, is of primary importance for the real usability of published data. In this context, the main aim of this paper is to present a workflow for archaeological data sharing as open data with a large level of technical usability and interoperability. Primary data is mainly acquired through the use of digital techniques (e.g. digital cameras and terrestrial laser scanning). The processing of this raw data is performed with commercial software for scan registration and image processing, allowing for a simple and semi-automated workflow. Outputs obtained from this step are then processed in modelling and drawing environments to generate digital models, both 2D and 3D. These crude geometrical data are then enriched with further information to generate a Geographic Information System (GIS) which is finally published as open data using Open Geospatial Consortium (OGC) standards to maximise interoperability.</p><p><strong>Highlights:</strong></p><ul><li><p>Open data will change the accessibility and disciplinary expertise in the archaeological field.</p></li><li><p>The main aim of this paper is to present a workflow for archaeological data sharing as open data with a large level of interoperability.</p></li><li><p>Digital acquisition techniques are used to document archaeological excavations and a Geographic Information System (GIS) is generated that is published as open data.</p></li></ul>

scholarly journals Financial industry under the background of digital industry: A Bibliometric Analysis of Related Research

2020 ◽  
Vol 214 ◽  
pp. 03010
Author(s):  
Chung-Lien Pan ◽  
Xianghui Chen ◽  
Mei Lin ◽  
Zhuocheng Cai ◽  
Xiaolin Wu
Keyword(s):  
Social Science ◽  
Computer Science ◽  
Digital Technology ◽  
Publishing Industry ◽  
Research Field ◽  
Future Research ◽  
Research Front ◽  
Financial Industry ◽  
Research Papers ◽  
The Impact

In recent years, the innovation and breakthrough of digital technology have brought great convenience to the economic development of various sectors and People’s daily life, especially in the field of financial services. To explore the impact of digital technology on the financial industry, this paper searched 285 papers based on Web of Science (WoS) and conducted a systematic scientific metrology and literature review, providing a research front for future research. According to the research papers published between 1984 and 2020, the analysis results of co-citation and co-cited by sources, disciplines, and keywords show that in recent years, the publishing industry in this field has developed rapidly in various countries, and the research field involves such disciplines as business economics, computer science, social science, and interdisciplinary application. According to the research papers published between 1984 and 2020, the analysis results of co-citation and co-cited by sources, disciplines, and keywords show that in recent years, the publishing industry in this field has developed rapidly in various countries, and the research field involves such disciplines as business, finance; economics; computer science; social science and interdisciplinary application. Besides, American, Chinese and British institutions are also good at hosting such interdisciplinary work. And different types of keywords present important interactions in the visualization: (a) digital-based innovation, (b) big data and regulation, (c) Internet finance and financial innovation, (d) financial inclusion, (e) digital finance and risk management, and (f) mobile payment.

scholarly journals The Evolution of Data Sharing Practices in the Psychological Literature

2021 ◽  
Author(s):  
Judith Neve ◽  
Guillaume A Rousselet
Keyword(s):  
Data Sharing ◽  
Linear Models ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Future Research ◽  
Science Data ◽  
Editorial Policies ◽  
Psychological Science ◽  
The Impact

Sharing data has many benefits. However, data sharing rates remain low, for the most part well below 50%. A variety of interventions encouraging data sharing have been proposed. We focus here on editorial policies. Kidwell et al. (2016) assessed the impact of the introduction of badges in Psychological Science; Hardwicke et al. (2018) assessed the impact of Cognition’s mandatory data sharing policy. Both studies found policies to improve data sharing practices, but only assessed the impact of the policy for up to 25 months after its implementation. We examined the effect of these policies over a longer term by reusing their data and collecting a follow-up sample including articles published up until December 31st, 2019. We fit generalized additive models as these allow for a flexible assessment of the effect of time, in particular to identify non-linear changes in the trend. These models were compared to generalized linear models to examine whether the non-linearity is needed. Descriptive results and the outputs from generalized additive and linear models were coherent with previous findings: following the policies in Cognition and Psychological Science, data sharing statement rates increased immediately and continued to increase beyond the timeframes examined previously, until reaching close to 100%. In Clinical Psychological Science, data sharing statement rates started to increase only two years following the implementation of badges. Reusability rates jumped from close to 0% to around 50% but did not show changes within the pre-policy nor the post-policy timeframes. Journals that did not implement a policy showed no change in data sharing rates or reusability over time. There was variability across journals in the levels of increase, so we suggest future research should examine a larger number of policies to draw conclusions about their efficacy. We also encourage future research to investigate the barriers to data sharing specific to psychology subfields to identify the best interventions to tackle them.

Reconciling stakeholder interests in the era of open data

2018 ◽  
Vol 82 (3) ◽  
pp. 253-259
Author(s):  
Alessandro S. De Nadai
Keyword(s):  
Mental Health ◽  
Data Analysis ◽  
Data Sharing ◽  
Health Research ◽  
Open Data ◽  
Secondary Data ◽  
Mental Health Research ◽  
Secondary Data Analysis ◽  
Stakeholder Interests ◽  
Funding Agencies

While there is great enthusiasm about new data sharing initiatives in mental health research, some concerns have recently been expressed that reflect tension between those who generate data and those who engage in secondary data analysis. While many aspects of data sharing have been considered, some of this tension has not been fully addressed. If this tension continues to go unresolved, enthusiasm for data sharing initiatives may be hindered. The author suggests solutions to these issues after carefully considering respective stakeholder interests (including those of patients, researchers, and funding agencies).

scholarly journals Journal Data Sharing Policies and Statistical Reporting Inconsistencies in Psychology

2017 ◽  
Author(s):  
Michele B. Nuijten ◽  
Jeroen Borghuis ◽  
Coosje Lisabet Sterre Veldkamp ◽  
Linda Dominguez Alvarez ◽  
Marcel A. L. M. van Assen ◽  
...  
Keyword(s):  
Decision Making ◽  
Data Sharing ◽  
Observational Studies ◽  
Statistical Significance ◽  
Open Data ◽  
Psychological Science ◽  
Statistical Errors ◽  
Share Data ◽  
Journal Policies

In this paper, we present three retrospective observational studies that investigate the relation between data sharing and statistical reporting inconsistencies. Previous research found that reluctance to share data was related to a higher prevalence of statistical errors, often in the direction of statistical significance (Wicherts, Bakker, &amp; Molenaar, 2011). We therefore hypothesized that journal policies about data sharing and data sharing itself would reduce these inconsistencies. In Study 1, we compared the prevalence of reporting inconsistencies in two similar journals on decision making with different data sharing policies. In Study 2, we compared reporting inconsistencies in psychology articles published in PLOS journals (with a data sharing policy) and Frontiers in Psychology (without a stipulated data sharing policy). In Study 3, we looked at papers published in the journal Psychological Science to check whether papers with or without an Open Practice Badge differed in the prevalence of reporting errors. Overall, we found no relationship between data sharing and reporting inconsistencies. We did find that journal policies on data sharing are extremely effective in promoting data sharing. We argue that open data is essential in improving the quality of psychological science, and we discuss ways to detect and reduce reporting inconsistencies in the literature.

scholarly journals Data availability, reusability, and analytic reproducibility: Evaluating the impact of a mandatory open data policy at the journal Cognition

2018 ◽  
Author(s):  
Tom Elis Hardwicke ◽  
Maya B Mathur ◽  
Kyle Earl MacDonald ◽  
Gustav Nilsonne ◽  
George Christopher Banks ◽  
...  
Keyword(s):  
Data Sharing ◽  
Open Data ◽  
Interrupted Time Series ◽  
Data Availability ◽  
Quality Of Data ◽  
Critical Feature ◽  
Target Values ◽  
Data Policy ◽  
Access To Data ◽  
The Impact

Access to data is a critical feature of an efficient, progressive, and ultimately self-correcting scientific ecosystem. But the extent to which in-principle benefits of data sharing are realized in practice is unclear. Crucially, it is largely unknown whether published findings can be reproduced by repeating reported analyses upon shared data (“analytic reproducibility”). To investigate, we conducted an observational evaluation of a mandatory open data policy introduced at the journal Cognition. Interrupted time-series analyses indicated a substantial post-policy increase in data available statements (104/417, 25% pre-policy to 136/174, 78% post-policy), although not all data appeared reusable (23/104, 22% pre-policy to 85/136, 62%, post-policy). For 35 of the articles determined to have reusable data, we attempted to reproduce 1324 target values. Ultimately, 64 values could not be reproduced within a 10% margin of error. For 22 articles all target values were reproduced, but 11 of these required author assistance. For 13 articles at least one value could not be reproduced despite author assistance. Importantly there were no clear indications that original conclusions were seriously impacted. Mandatory open data policies can increase the frequency and quality of data sharing. However, suboptimal data curation, unclear analysis specification, and reporting errors can impede analytic reproducibility, undermining the utility of data sharing and the credibility of scientific findings.

scholarly journals Open data repositories: Current risks and opportunities

2020 ◽  
Vol 81 (2) ◽  
pp. 62
Author(s):  
Abigail Goben ◽  
Robert J. Sandusky
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Open Data ◽  
Management Consulting ◽  
Academic Institutions ◽  
Data Repositories ◽  
Professional Societies ◽  
Funding Agencies ◽  
Academic Researchers

As data sharing has become a more familiar obligation for academic researchers, there has been a correlating increase in the roles that librarians play supporting open data repositories and providing data management consulting and services. These repositories are sponsored by governments, funding agencies, academic institutions, professional societies, and scholarly publishers.

scholarly journals Data availability, reusability, and analytic reproducibility: evaluating the impact of a mandatory open data policy at the journal Cognition

2018 ◽  
Vol 5 (8) ◽  
pp. 180448 ◽  
Author(s):  
Tom E. Hardwicke ◽  
Maya B. Mathur ◽  
Kyle MacDonald ◽  
Gustav Nilsonne ◽  
George C. Banks ◽  
...  
Keyword(s):  
Data Sharing ◽  
Open Data ◽  
Interrupted Time Series ◽  
Data Availability ◽  
Quality Of Data ◽  
Critical Feature ◽  
Target Values ◽  
Data Policy ◽  
Access To Data ◽  
The Impact

Access to data is a critical feature of an efficient, progressive and ultimately self-correcting scientific ecosystem. But the extent to which in-principle benefits of data sharing are realized in practice is unclear. Crucially, it is largely unknown whether published findings can be reproduced by repeating reported analyses upon shared data (‘analytic reproducibility’). To investigate this, we conducted an observational evaluation of a mandatory open data policy introduced at the journal Cognition . Interrupted time-series analyses indicated a substantial post-policy increase in data available statements (104/417, 25% pre-policy to 136/174, 78% post-policy), although not all data appeared reusable (23/104, 22% pre-policy to 85/136, 62%, post-policy). For 35 of the articles determined to have reusable data, we attempted to reproduce 1324 target values. Ultimately, 64 values could not be reproduced within a 10% margin of error. For 22 articles all target values were reproduced, but 11 of these required author assistance. For 13 articles at least one value could not be reproduced despite author assistance. Importantly, there were no clear indications that original conclusions were seriously impacted. Mandatory open data policies can increase the frequency and quality of data sharing. However, suboptimal data curation, unclear analysis specification and reporting errors can impede analytic reproducibility, undermining the utility of data sharing and the credibility of scientific findings.

scholarly journals Badges to Acknowledge Open Practices: A Simple, Low-Cost, Effective Method for Increasing Transparency

2016 ◽  
Author(s):  
Mallory Kidwell ◽  
Ljiljana B. Lazarevic ◽  
Erica Baranski ◽  
Tom Elis Hardwicke ◽  
Sarah Piechowski ◽  
...  
Keyword(s):  
Data Sharing ◽  
Low Cost ◽  
Open Data ◽  
Cost Effective ◽  
Change Over Time ◽  
Psychological Science ◽  
Cost Effective Method ◽  
Order Of Magnitude ◽  
Open Materials ◽  
Over Time

Beginning January 2014, Psychological Science gave authors the opportunity to signal open data and materials if they qualified for badges that accompanied published articles. Before badges, less than 3% of Psychological Science articles reported open data. After badges, 23% reported open data, with an accelerating trend; 39% reported open data in the first half of 2015, an increase of more than an order of magnitude from baseline. There was no change over time in the low rates of data sharing among comparison journals. Moreover, reporting openness does not guarantee openness. When badges were earned, reportedly available data were more likely to be actually available, correct, usable, and complete than when badges were not earned. Open materials also increased to a weaker degree, and there was more variability among comparison journals. Badges are simple, effective signals to promote open practices and improve preservation of data and materials by using independent repositories.

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