Recruitment

Our recruitment plan utilized a wide range of channels to reach researchers. This strategy leveraged (a) direct email campaigns, (b) promoted Facebook and Twitter posts, (c) a post on the PLOS Blog, and (d) emails to industry contacts who distributed the survey on our behalf. URL variables were assigned to track the efficacy of each recruitment channel.

Participation was incentivized with 3 random prize draws, each with a $200 prize. The prize draw was managed via a separate survey to maintain anonymity, and 559 of 728 eligible participants entered the prize draw.

The survey received 617 completed responses although 1477 people responded to some of the survey.

The effectiveness of these recruiting methods varied widely. Of the participants who completed the survey, nearly 80% were recruited via direct email campaigns, with the overwhelming majority of these coming in response to a dedicated message about the survey.

Given the importance of our direct email campaigns, it is unsurprising that our cohort was composed largely of former PLOS authors, accounting for 82% of the users who completed the survey.

Survey Instrument

The individual factors associated with data sharing tasks were recast into outcome statements, which represent a researcher’s hypothetical success measures for completing a task. Statements were identified by considering the policies, procedures and tasks associated with various aspects of research data management and publishing, the solutions and support currently available to researchers to complete these tasks, and were further informed by conversations with researchers to develop the survey. The outcome statements were constructed with a standard syntax, to ensure that they could be usefully compared to each other.

Typically, a desired outcome statement is composed of a direction of change, a metric of change, an object of change, and an optional context. For example,

“Spend less time creating a data availability statement”

Where, “spend less” is the direction, “time” is the metric, and “data availability statement” is the object. The context in this case is provided in the associated survey question phrase, “when submitting your research for peer review”.

In some cases we have forgone the direction when the context is sufficient to define the goal the researcher is trying to achieve such as “My research data has its own Digital Object Identifier (DOI)” (context from associated question: “when preserving or archiving your data”). While the resulting statements diverge from standard practice, it should not impact how well they can be tested in survey work, or their usefulness in identifying researcher needs.

Using these statements, we constructed a survey in SurveyGizmo, now known as Alchemer, which measured how important a researcher thought the task was, and their level of satisfaction with being able to complete it. The survey was tested by individuals not involved with the study, and who have scientific backgrounds, before deployment to ensure it is understandable.

Importance was measured using a five-point unipolar scale, which was later mapped to a value from 0 to 100:

1. Not at all important: 0
2. Slightly important: 25
3. Moderately important: 50
4. Very important: 75
5. Extremely important: 100

Since satisfaction can be expressed in negative terms, it needs a different scale that can account for this. Therefore, satisfaction was measured using a seven-point bipolar scale, also mapped to a value from 0 to 100:

1. Completely dissatisfied: 0
2. Mostly dissatisfied: 16.7
3. Somewhat dissatisfied: 33.3
4. Neither satisfied nor dissatisfied: 50
5. Somewhat satisfied: 66.7
6. Mostly satisfied: 83.3
7. Completely satisfied: 100

Data Analysis Process

We are most likely to see a need for a new solution when we identify user needs that are both important and underserved, measured by importance and satisfaction scores. We can see the relationship of importance and satisfaction by mapping the mean importance and satisfaction scores for each factor on a scatter plot, with importance on the y-axis, and satisfaction on the x-axis (Figure 1). On each axis, a neutral response is mapped to 50. Viewed in this way, the factors that map to the upper-left quadrant indicate opportunities for new solutions, as they are generally regarded by researchers as both important and underserved.

Survey participants

The survey received 617 completed responses. The distribution of respondents by discipline and career stage is very similar when comparing the completed and whole cohort (completed and partial responses). Respondents self-identified their career stage in question 5 of the survey. Responses from the 617 individuals who have completed the survey —those who answered all questions in the survey— are used in our analysis (Table 1).

Over half of the respondents were from Biology and Life Sciences or Medicine and Health Sciences disciplines. The cohorts from Physical Sciences, Engineering and Technology, Earth Sciences are small (with a maximum of 18 completed responses) (Figure 2). The largest proportion of survey respondents self-identified as Early Career researchers (45%), followed by Mid-Career researchers (36%) and Late-Career (18%). The majority of survey respondents were from North America (79%).

Current data sharing practices

Data sharing approaches

Respondents were asked to select all the methods of data sharing that they had previously used. Sharing data as supplemental files alongside a research paper was the most common method for all career levels (67%), followed by deposition in a public repository (59%) and sharing privately on request (49%). Only 10% of respondents reported that they had never shared their research data – the largest proportion of whom (42%) work in Medicine and Health Science disciplines. Sharing data privately, upon request was more common for more experienced researchers (Figure 3).

Figure 3 

The most common method for sharing research data in the past is as supplemental files.

Importance and satisfaction scores of data sharing tasks

Respondents were asked to rate the importance and their satisfaction for 36 factors related to sharing or reusing data. These answers have been turned into importance and satisfaction scores (see Methods section for details). A score of 0 indicates that researchers do not find the factor at all important or they are completely dissatisfied with their ability to carry out the task. A score of 100 indicates that they regard the factor as of the highest importance or they are completely satisfied with their ability to undertake the task. The mean importance scores ranged from 37.8 to 85.0 and the mean satisfaction scores ranged from 41.4 to 69.1. Tasks related to data sharing and reuse have been grouped according to the section of the research lifecycle that they primarily fall in (Table 2). The following groupings were used in our analysis: data preparation, policy requirements, data publishing, and data reuse.

Career stage and disciplinary differences

Limitations

The survey focused on researchers from North America and Europe, a high proportion of whom have published with PLOS. This scope limitation was to help ensure a sufficiently large sample of researchers in certain regions to draw meaningful conclusions. Further, the survey was written only in English, and we assumed that this impacts response rates in some regions.

Some of the disciplinary samples (Earth Sciences, Engineering, and Physical Sciences) were too small to be considered representative of the corresponding research community. The high proportion of PLOS authors could impact our results, as their experience with data sharing requirements – PLOS’s strong data availability policy relative to most other publishers – may differ from the non-PLOS cohort.

The factors that the survey asked about do not cover all aspects of data sharing, as they were derived in part from our assumptions about which tasks researchers might find problematic and where there might be opportunities for new solutions. They were also intended to be discipline-agnostic. For example, issues specific to certain types of data, such as sensitive data, are not included. There may be important and underserved needs around data sharing that were not tested in our survey. For example, we did not include factors relating to technical problems or the quality of the data that is being shared.

Impact of career stage

There is a general tendency from Early- to Mid- to Late-Career researchers, and researchers who have published more articles, of declining importance scores, although the differences are mostly not statistically significant (p < 0.5). More experienced researchers and authors rate the importance of the majority of factors lower on average, although there are fewer differences with levels of satisfaction with existing tools, as satisfaction numbers remain more stable across these segments. This suggests that ECRs regard these factors as more important, as opposed to having not yet mastered the tools needed to effectively share and reuse data. However, the factors with the greatest mean importance scores when comparing early and late career researchers can be considered those that are more likely to be relevant to junior researchers, for example “Increase my co-authorship opportunities” and “Spend less time making individual requests for datasets” both of which have p value < 0.001.

Comparison with previous research

Multiple surveys have quantified how common researchers’ problems or concerns are with data sharing (Allagnat et al. 2019; Borghi et al. 2018; Lucraft et al. 2019; Science et al. 2017; 2018; 2019; Tenopir et al. 2011; Wiley Open Science Researcher Survey 2016). Our findings suggest that while many factors (problems) associated with sharing research data are important to researchers, on average, researchers are reasonably satisfied with their ability to share data, from their perspective. Overall our findings are additive to previous research, providing additional context as to why solutions such as data repositories are still used by a minority of researchers, despite data repositories, ostensibly, being available for most types of research data. If researchers are generally satisfied with their ability to complete a task associated with data sharing, this suggests that researchers will be unlikely to be motivated to seek (new) solutions to that problem, no matter how common it is.

For example, our finding that the ‘ability to control who can use my dataset’ was slightly important (39.1 importance) extends previous findings that researchers’ concerns relating to misuse of their data is very common (Science et al. 2019). If this concern is common yet not very important to the average researcher, then it may be viewed as “low stakes”, and not a motivator for action. The ability to ‘trust the researchers who request my data’ may also relate to potential for misuse of researchers’ data but was rated as moderately important, not viewed as “low stakes”, (58.3) as was ‘choose an appropriate license for my data’ (54.4). Both of these factors, associated with reuse of researchers’ own data, were somewhat satisfied, however.

The highest average score for importance was found for ‘Increase the likelihood that my research benefits science’ (85.0 importance; 54.9 satisfaction). This supports previous research, where increasing the benefit to science or society of research is commonly amongst the top reasons or motivations for sharing research data (Science et al. 2019). The second most important factor overall related to compliance with funder policies on data sharing (73.8 importance ; 69.1 satisfaction), ranking it more highly than in previous research exploring factors that motivate data sharing (Science et al. 2019). The third most important factor related to increasing the likelihood that researchers’ papers are cited (70.0 importance; 52.5 satisfaction). This reputational factor, citations, is consistent with increased impact of research and desire for greater credit (recognition) for data sharing found by previous research. It may also offer opportunities for promotion of the potential benefits of sharing research data by tool and service providers. Sharing research data is associated with increased citations to researchers’ papers (Colavizza et al. 2020; Piwowar et al. 2013).

Potential opportunities relating to data reuse

While most of the factors we assessed appear to be reasonably well satisfied from the researchers’ perspective, a small number of factors suggest potential opportunities for new or better solutions. Around half of survey respondents indicated that they have reused research data in the past, consistent with findings from other surveys (Science et al. 2018; 2019). These factors feature in the upper left quadrant (Figure 4) — albeit moderately so — and relate to reuse of other researchers’ data:

• – Spend less time searching for articles with reusable datasets (61.4 importance; 41.4 satisfaction)
• – Spend less time making individual requests for datasets (58.9 importance; 43.0 satisfaction)

Both these factors are relevant to scholarly publishers, who can influence the accessibility and availability of research data associated with publications — with research data policies (Vines et al. 2013) and associated workflows. Making research data available in repositories that enable compliance with the FAIR Data principles, and creating prominent and visible links to those data in journal articles, might be a simple solution to the first factor.

Researchers’ dissatisfaction with obtaining research data from individual requests to other researchers has policy implications for journals and publishers who wish to further support open science and open research. While many journals now have policies on sharing research data, and many peer-reviewed papers include statements about the availability of data supporting publications, many of those statements state that data are “available on [reasonable] request”. Multiple studies (Rowhani-Farid et al. 2016; Savage & Vickers 2009; Vanpaemel et al. 2015; Wicherts et al. 2006) have found that researchers have been unable to obtain data supporting publications when those data are ‘available on request’, consistent with the dissatisfaction amongst our survey respondents. Since PLOS introduced its data availability policy in 2014, “data available on request” has not been permitted when publishing in PLOS journals. Publishers can potentially meet this data reuse need by strengthening their policies on data sharing – requiring all data supporting publications to be publicly available unless legal or ethical restrictions apply, and working to eliminate “data available on request” as an acceptable policy. And, in such cases where data must be available under restricted access, requiring information on conditions and procedures for data access and reuse.

Opportunities to better support data sharing

The relative unimportance of some factors associated with best data publishing practice, such as deposition of data in repositories, suggests the need for more advocacy to researchers and education of the benefits, or for data repositories to be more integrated with the traditional publishing experience in such a way that researchers do not need to change their behaviour in order to use them. Amongst PLOS authors and the survey respondents, the most common method for sharing data is via supplemental (supporting information) files with their publications. More than half our survey respondents indicated they had shared data in a repository in the past but when published articles are analysed, data repositories are used by around a quarter of authors publishing with PLOS. At PLOS this proportion has been slowly growing each year, from 18% of authors in 2015 (Colavizza et al. 2020).

While part of our motivation for this research was to explore opportunities for new products or services to support researchers in sharing, discovering and managing research data, the results imply that, amongst the PLOS author community in particular, researcher needs and better support for FAIR data can likely be met by working with existing solutions. This includes tactics such as more closely partnering with established data repositories and improving the linking of research data and publications, as well as maintaining, or enhancing where appropriate, stringent journal data sharing policies.