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Outline

Title
Abstract
Figures
Limitations
Data Underlying This Whitepaper
The Data Sharing Landscape
Data Sharing Norms and Challenges by Subject Area
Subject Influences on Regional Behaviour
Discussion and Springer Nature Perspectives
Future Research
References

Whitepaper: Practical challenges for researchers in data sharing

Profile image of Mathias AstellMathias Astell

2018

https://doi.org/10.6084/M9.FIGSHARE.5975011.V1
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17 pages

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Abstract

In one of the largest surveys of researchers about research data (with over 7,700 respondents), Springer Nature finds widespread data sharing associated with published works and a desire from researchers that their data are discoverable.<br><br>This whitepaper examines the results of this survey and discusses the challenges that researchers face in sharing their data. The whitepaper looks at data sharing attitudes globally, as well as in relation to region, subject and seniority.<br><br>Infographic: https://doi.org/10.6084/m9.figshare.5996786

Figures (17)
This work is licensed under a Creative Commons Attribution International License (CC BY 4.0)
This work is licensed under a Creative Commons Attribution International License (CC BY 4.0)
There are also differences between subjects in the way the data are shared when publishing research articles. In the Earth sciences and the biological sciences a greater proportion of datasets are shared as supplementary information files, whereas in the physical and medical sciences a greater proportion of data were shared in a repository.
There are also differences between subjects in the way the data are shared when publishing research articles. In the Earth sciences and the biological sciences a greater proportion of datasets are shared as supplementary information files, whereas in the physical and medical sciences a greater proportion of data were shared in a repository.
Subject area differences in the challenges in sharing data
Subject area differences in the challenges in sharing data
Challenges in organising data varied from 57% in the physical sciences to 40% in the medical sciences, copyright and licensing ranged from 44% in the medical sciences to 31% in the physical sciences, and not knowing which repository to use ranged from 37% in the medical sciences to 27% in the physical sciences. These variations between physical and medical sciences imply that the barriers researchers face in data sharing are linked to the expectation of data usage in each area e.g. medical data are more strictly regulated by data protection rules and issues arising from data reuse and so there is a greater concern around copyright, licensing and access through repositories. Lack of awareness of community mandates/repositories is prevalent even in disciplines where data sharing is common and there are established mandates, notably the biological sciences.
Challenges in organising data varied from 57% in the physical sciences to 40% in the medical sciences, copyright and licensing ranged from 44% in the medical sciences to 31% in the physical sciences, and not knowing which repository to use ranged from 37% in the medical sciences to 27% in the physical sciences. These variations between physical and medical sciences imply that the barriers researchers face in data sharing are linked to the expectation of data usage in each area e.g. medical data are more strictly regulated by data protection rules and issues arising from data reuse and so there is a greater concern around copyright, licensing and access through repositories. Lack of awareness of community mandates/repositories is prevalent even in disciplines where data sharing is common and there are established mandates, notably the biological sciences.
Of the four subject areas, the problems facing researchers in the medical sciences differ the most from other areas. The most often cited issue was being unsure about copyright and licensing (44%), followed by organising data (40%), not knowing which repository to use (37%), lack of time (25%) and costs of sharing data (21%). Of the 2,683 respondents that reported that their data were in the medical sciences, 39% shared data neither as supplementary information nor in a repository when submitting a manuscript. This is a far higher percentage than the 25% in the biological sciences. The importance ascribed to the discoverability of data was also lower in the medical sciences (7.2) than both the biological sciences (7.8) and the Earth sciences (7.7).
Of the four subject areas, the problems facing researchers in the medical sciences differ the most from other areas. The most often cited issue was being unsure about copyright and licensing (44%), followed by organising data (40%), not knowing which repository to use (37%), lack of time (25%) and costs of sharing data (21%). Of the 2,683 respondents that reported that their data were in the medical sciences, 39% shared data neither as supplementary information nor in a repository when submitting a manuscript. This is a far higher percentage than the 25% in the biological sciences. The importance ascribed to the discoverability of data was also lower in the medical sciences (7.2) than both the biological sciences (7.8) and the Earth sciences (7.7).
Medical sciences were the only subject area for which organising data was not the most often cited issue. More often than any other subject area, respondents in the medical sciences selected that they were “unsure about copyright and licensing”, did not know which repository to use, and were concerned about the costs of sharing data. They selected the problems of organising data and a lack of time less often than any other subject area. Although organising data in a presentable and useful way is identified as an issue by 40% of respondents in the medical sciences, this is far less than the 57%
Medical sciences were the only subject area for which organising data was not the most often cited issue. More often than any other subject area, respondents in the medical sciences selected that they were “unsure about copyright and licensing”, did not know which repository to use, and were concerned about the costs of sharing data. They selected the problems of organising data and a lack of time less often than any other subject area. Although organising data in a presentable and useful way is identified as an issue by 40% of respondents in the medical sciences, this is far less than the 57%
Figure 12: Problems in sharing data in the physical sciences (n=487) The low prevalence of data sharing in the physical sciences may be partly applicable to the lower importance ascribed to the discoverability of data (6.6 out of 10), but there are also more practical barriers. ‘Organising data’ was the most often identified problem for data sharing in the physical sciences, mentioned by 57% of respondents. The second most mentioned issue was being unsure about copyright and licensing (31%), followed by lack of time (27%), not knowing which repository to use (27%), and the cost of sharing data (15%). The 19 classified ‘Other’ comments were spread across al categories, with four related to organisational policies (“Employer restrictions”), four to technical issues (“Too big to share”) and four to data issues (“raw data are of no use for people outside our collaboration.”). In high energy physics, data may not be publicly share-able as they are too large — generated at large central facilities. While at CERN, for example, there is a strong data sharing policy and repository," this is not common across this field. In these cases it is not practical to share data even on request, as researchers might visit facilities for short periods to carry out research projects, and may not have access to the aggregated or raw data themselves. Of the four subject areas, data sharing alongside publication was least prevalent within the physical sciences. Of the 487 respondents, 41% stated they neither shared data as supplementary information nor in repositories when submitting a research manuscript. Such a proportion is even higher than in the medical sciences, for all its concerns about intellectual property rights and the sensitivity of information.
Figure 12: Problems in sharing data in the physical sciences (n=487) The low prevalence of data sharing in the physical sciences may be partly applicable to the lower importance ascribed to the discoverability of data (6.6 out of 10), but there are also more practical barriers. ‘Organising data’ was the most often identified problem for data sharing in the physical sciences, mentioned by 57% of respondents. The second most mentioned issue was being unsure about copyright and licensing (31%), followed by lack of time (27%), not knowing which repository to use (27%), and the cost of sharing data (15%). The 19 classified ‘Other’ comments were spread across al categories, with four related to organisational policies (“Employer restrictions”), four to technical issues (“Too big to share”) and four to data issues (“raw data are of no use for people outside our collaboration.”). In high energy physics, data may not be publicly share-able as they are too large — generated at large central facilities. While at CERN, for example, there is a strong data sharing policy and repository," this is not common across this field. In these cases it is not practical to share data even on request, as researchers might visit facilities for short periods to carry out research projects, and may not have access to the aggregated or raw data themselves. Of the four subject areas, data sharing alongside publication was least prevalent within the physical sciences. Of the 487 respondents, 41% stated they neither shared data as supplementary information nor in repositories when submitting a research manuscript. Such a proportion is even higher than in the medical sciences, for all its concerns about intellectual property rights and the sensitivity of information.
AS previously noted, care should be taken with these findings as sample sizes in Asia and South America were small (n = 359 and n = 137, respectively) in comparison to the population and known levels of research in these regions, and the sample sizes from North America (n = 2,215) and Europe (n = 4,692). Respondents from Asia and South America may represent a self-selecting, data-interested group.
AS previously noted, care should be taken with these findings as sample sizes in Asia and South America were small (n = 359 and n = 137, respectively) in comparison to the population and known levels of research in these regions, and the sample sizes from North America (n = 2,215) and Europe (n = 4,692). Respondents from Asia and South America may represent a self-selecting, data-interested group.
There is also a Lot of variation between countries within the same region, although outside of Australasia, North America and Europe, country-level analysis is limited by the number of respondents. Australasia, North America and Europe are also the regions that rate the importance of discoverability least highly: Australasia, 6.9 out of 10; North America, 7.2; Europe, 7.3; Asia, 7.6; and South America, 7.7. Figure 14 shows distribution by region of the importance that data are discoverable, grouped in ranges of two.
There is also a Lot of variation between countries within the same region, although outside of Australasia, North America and Europe, country-level analysis is limited by the number of respondents. Australasia, North America and Europe are also the regions that rate the importance of discoverability least highly: Australasia, 6.9 out of 10; North America, 7.2; Europe, 7.3; Asia, 7.6; and South America, 7.7. Figure 14 shows distribution by region of the importance that data are discoverable, grouped in ranges of two.
Table 1: Percentages of respondents sharing data through a repository, as supplementary information files, or both, in countries with >100 respondents There is a need for further research, especially amongst large producers of research such as China, Japan, and India, where sample sizes were too small for analysis by country.
Table 1: Percentages of respondents sharing data through a repository, as supplementary information files, or both, in countries with >100 respondents There is a need for further research, especially amongst large producers of research such as China, Japan, and India, where sample sizes were too small for analysis by country.
Applying an equal weighting to different subjects in each region (Figure 16), a similar distribution occurs as Figure 13 with Australasia, North America and Europe sharing less. This does not necessarily mean there is an underlying cultural difference between the regions, however, the sample sizes from Asia and South America are relatively small and there may be a self-selection bias toward those in favour of data sharing. This is an area for further analysis of the results of this survey, and for future research. This work is licensed under a Creative Commons Attribution International License (CC BY 4.0)
Applying an equal weighting to different subjects in each region (Figure 16), a similar distribution occurs as Figure 13 with Australasia, North America and Europe sharing less. This does not necessarily mean there is an underlying cultural difference between the regions, however, the sample sizes from Asia and South America are relatively small and there may be a self-selection bias toward those in favour of data sharing. This is an area for further analysis of the results of this survey, and for future research. This work is licensed under a Creative Commons Attribution International License (CC BY 4.0)
Figure 17: Reasons for not sharing data by region (n=7,654) - including % of highest and lowest responding regions
Figure 17: Reasons for not sharing data by region (n=7,654) - including % of highest and lowest responding regions
Of the 7,686 that stated the subject area of their data, 5,323 were in either the medical or biological sciences (See Figure 19). ‘Other sciences’ covers many disparate fields, including the social sciences, computer science, humanities and mathematics.
Of the 7,686 that stated the subject area of their data, 5,323 were in either the medical or biological sciences (See Figure 19). ‘Other sciences’ covers many disparate fields, including the social sciences, computer science, humanities and mathematics.

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Kajar Köster

Scientific Data

Data sharing is one of the cornerstones of modern science that enables large-scale analyses and reproducibility. We evaluated data availability in research articles across nine disciplines in Nature and Science magazines and recorded corresponding authors’ concerns, requests and reasons for declining data sharing. Although data sharing has improved in the last decade and particularly in recent years, data availability and willingness to share data still differ greatly among disciplines. We observed that statements of data availability upon (reasonable) request are inefficient and should not be allowed by journals. To improve data sharing at the time of manuscript acceptance, researchers should be better motivated to release their data with real benefits such as recognition, or bonus points in grant and job applications. We recommend that data management costs should be covered by funding agencies; publicly available research data ought to be included in the evaluation of application...

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Exploring the determinants of scientific data sharing: Understanding the motivation to publish research data
Djoko Sayogo

Government Information Quarterly, 2013

The research community is working to create new capabilities to share data and to deal with issues of data quality, standards, and protection, and ethical and responsible use of shared data. These issues have been found to influence the willingness of researchers to publish data created during the course of their research. We use the results of a survey conducted by the working groups of the DataONE project to present a new understanding of challenges to the development of global data collections and preservation by systematically examining the determinants of the researchers' likelihood to openly publish research data. This study found two key determinants affecting researchers' willingness to publish their data. First is data management in terms of data management skills and organization support. Second is the acknowledgement of the data set's originator in terms of appreciation and legal and policy requirements. This study also found that the impact of the significant determinants is contingent on the amount of data to be published. Finally, this study calls for further investigation to ascertain the relationship of data management and data quality, and systematic investigation on the roles and responsibility of government within these global data preservations.

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Identifying the Complex Position of Research Data and Data Sharing Among Researchers in Natural Science
keiko kurata

SAGE Open

This article aims to provide an overview of researchers' practices and perceptions on data use and sharing. Semistructured interviews were conducted with 23 Japanese researchers in the natural sciences to identify their research practices and data use, including data sharing. We divided the interview scripts into meaningful phrases as a unit of analysis. Next, we focused on 406 statements on research data and reanalyzed them based on four aspects: stance on research data, practices and perceptions of data use, range of data sharing, and data type. A cluster analysis identified 14 clusters, which were divided into five groups: open access for data, restricted access for data, data interpretation, data processing and preservation, and data infrastructure. Our results reveal the complexity and diversity of the relationship between data and research practices. That is, the practice of research data sharing is heterogeneous, with no "one size fits all" between and among researchers.

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Cited by

Open Data Commons for Preclinical Traumatic Brain Injury Research: Empowering Data Sharing and Big Data Analytics
Susanna Rosi

2021

Traumatic brain injury (TBI) is a major unsolved public health problem worldwide with considerable preclinical research dedicated to recapitulating clinical TBI, deciphering the underlying pathophysiology, and developing therapeutics. However, the heterogeneity of clinical TBI and correspondingly in preclinical studies have made translation from bench to bedside difficult. Here, we present the potential of data sharing, data aggregation, and multivariate analytics to integrate heterogeneity and empower researchers. We introduce the Open Data Commons for Traumatic Brain Injury (ODC-TBI.org) as a user-centered web platform and cloudbased repository focused on preclinical TBI research that enables data citation with persistent identifiers, promotes data element harmonization, and follows FAIR data sharing principles. Importantly, the ODC-TBI implements data sharing at the level of individual subjects, thus enabling data reuse for granular big data analytics and data-hungry machine lear...

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