In May-June 2020 PLOS surveyed researchers from Europe and North America to rate tasks associated with data sharing on (i) their importance to researchers and (ii) researchers' satisfaction with their ability to complete those tasks. Researchers were recruited via direct email campaigns, promoted Facebook and Twitter posts, a post on the PLOS Blog, and emails to industry contacts who distributed the survey on our behalf. Participation was incentivized with 3 random prize draws, which were managed separately to maintain anonymity.This dataset consists of:1) The survey sent to researchers (pdf).2) The anonymised data export of survey results (xlsx).The data export has been processed to retain the anonymity of participants. The comments left in the final question of the survey (question 17) have been removed. Answers to questions 12 to 16 have been recoded to give each answer a numerical value (see 'Scores' tab of spreadsheet). The counts, means, standard deviations and confidence intervals used in the associated manuscript for each factor are given in rows 619-622.Version 2 contains only the completed responses. Completed responses in the version 2 dataset refer to those who answered all the questions in the survey. The version 1 dataset contains a higher number of responses categorised as 'completed' but this has been reviewed for version 2.Version 1 data was used for the preprint: https://doi.org/10.31219/osf.io/njr5u.

The rapidly growing number of digital research data and developing scientific methods has challenged science policy makers and research funders to seek tools for increasing openness and sharing of research data. In some countries, academic research funders have adopted formal data policies to guide data management and data sharing in new research projects. The International Federation of Data Organizations for Social Science (IFDO) conducted an international web-based survey of data professionals to gather information on the institutional policies of social sciences and humanities in different countries and to collect and locate sources for a more detailed analysis.

Data sharing is crucial to the advancement of science because it facilitates collaboration, transparency, reproducibility, criticism, and re-analysis. Publishers are well-positioned to promote sharing of research data by implementing data sharing policies. While there is an increasing trend toward requiring data sharing, not all journals mandate that data be shared at the time of publication. In this study, we extended previous work to analyze the data sharing policies of 447 journals across several scientific disciplines, including biology, clinical sciences, mathematics, physics, and social sciences. Our results showed that only a small percentage of journals require data sharing as a condition of publication, and that this varies across disciplines and Impact Factors. Both Impact Factors and discipline are associated with the presence of a data sharing policy. Our results suggest that journals with higher Impact Factors are more likely to have data sharing policies; use shared data in peer review; require deposit of specific data types into publicly available data banks; and refer to reproducibility as a rationale for sharing data. Biological science journals are more likely than social science and mathematics journals to require data sharing.

This dataset provides the data underlying the scientific article "Researchers’ willingness and ability to openly share their research data: a survey of COVID-19 pandemic-related factors". The abstract of the article is as follows: While previous studies show that the drivers and inhibitors for openly sharing research data are diverse and complex, there is a lack of studies empirically examining the influence of the COVID-19 pandemic on researchers’ open data sharing behavior. Using a questionnaire (n=135), this study investigates the influence of COVID-19 pandemic-related factors on researchers’ willingness and ability to openly share their research data. Fifty-one respondents (37.8%) stated that factors related to the COVID-19 pandemic increased their willingness and ability to openly share their research data, while 80 (59.3%) reported that various pandemic-related factors did not influence their willingness and ability in this way. As one of the possible influencing factors, this study finds a significant association between the COVID-19-relatedness of researchers’ research discipline and whether or not the COVID-19 pandemic led to a change in their willingness and ability to share their research data openly: χ2 (1) = 5.77, p < .05. Social influences on open data sharing behavior, institutional support for open data sharing, and the fear of potential negative consequences of open data sharing were nearly similar for the respondents who were and were not involved in COVID-19-related research. This study contributes scientifically by going beyond conceptual studies as it provides empirically-based insights concerning the influence of COVID-19 pandemic-related factors on researchers’ willingness and ability to openly share their data. As a practical contribution, this study discusses recommendations that policymakers can use to sustainably support open research data sharing in post-COVID-19 times.

This is the slides and the speaking manuscript of a presentation given at the event Figshare fest in Amsterdam, Netherlands on June 28th 2017. The presentation is meant to show a case study of organizing research data management in a university both from the management and user perspectives. The presentation describes how the Stockholm University Library team is currently (June 2017) working to manage research data at Stockholm University across different administrative departments. The presentation also includes an overview of the current research data management activities at the Stockholm University with a deeper insight on working with a knowledge hub for research data management based on user feedback.

Introduction: During the coronavirus pandemic, changes in the way science is done and shared occurred, which motivates meta-research to help understand science communication in crises and improve its effectiveness. Objective: To study how many Spanish scientific papers on COVID-19 published during 2020 share their research data. Methodology: Qualitative and descriptive study applying nine attributes: (1) availability, (2) accessibility, (3) format, (4) licensing, (5) linkage, (6) funding, (7) editorial policy, (8) content and (9) statistics. Results: We analyzed 1340 papers, 1173 (87.5%) did not have research data. 12.5% share their research data of which 2.1% share their data in repositories, 5% share their data through a simple request, 0.2% do not have permission to share their data and 5.2% share their data as supplementary material. Conclusions: There is a small percentage that shares their research data, however it demonstrates the researchers' poor knowledge on how to properly share their research data and their lack of knowledge on what is research data.

Instructions and guidance materials on how to prepare your research data for sharing and long-term access and how to deposit your research data in the University of Guelph Research Data Repositories (Data Repositories).How the Data Repositories work: Upon request, depositors are given dataset creator access to a collection in the Data Repositories allowing them to create new draft dataset records and submit their draft datasets for review. Repository staff review all submitted datasets for alignment with repository policies and data deposit guidelines. Repository staff will work with depositors to make any required changes to the metadata, data files, and/or supplemental documentation to improve the FAIRness (findability, accessibility, interoperability, and reusability) of the dataset. When the dataset is ready, repository staff will make the dataset publicly available in the repository on behalf of the depositor. How to start the deposit process:First time depositor?: Create a repository account using your U of G credentials by going to the repository log in page. On this page, under the ‘Your Institution’ section, select University of Guelph from the drop-down menu and click Continue. Follow the instructions to link the repository to your U of G central login credentials.Complete the U of G Research Data Repositories Data Deposit Intake Form online survey.Already have dataset creator access to your home department's collection? Simply log in using your U of G credentials and begin a new deposit.

Researchers across the country and around the world expend tremendous resources to gather and analyze vast stores of data and populate models to better understand the process they are studying. Each of those researchers has limited money, time, computational capacity, data storage, and ability to put that data to productive use. What if they could combine their efforts to make collaboration easier? What if those collected data sets and processed model outputs could be used collaboratively to help advance knowledge beyond their original purpose? It is these questions that are motivating the movement towards open data, better data management and collaboration and sharing in the use of data and models. In short, researchers are relying more on teamwork to tackle the big problems of the day. This seminar will describe research being done at Utah State University and other collaborating organizations developing a system, called HydroShare, to address these questions in the context of water data and models. HydroShare is advancing hydrologic science by enabling the scientific community to more easily and freely share products resulting from their research, not just the scientific publication summarizing a study, but also the data and models used to create the scientific publication. This capability is necessary for community model development, execution, and evaluation and to improve reproducibility and community trust in scientific findings through transparency. As a platform for collaboration and running models on advanced computational infrastructure, HydroShare enhances the capability for data intensive research in hydrology and other aligned sciences. This seminar will provide information for you on the data management resources available to you at Utah State University and how you could take advantage of HydroShare in your own work.

This dataset tallies researcher responses to a question on why they would be motivated to share their research data. Each researcher was asked to tick four of the motivations. The first 19 motivations were taken from 'If We Share Data, Will Anyone Use Them? Data Sharing and Reuse in the Long Tail of Science and Technology'. The additional motivations were added by some of the researchers.

This is a hands-on workshop on the management of qualitative social science data, with a focus on data sharing and transparency. While the workshop addresses data management throughout the lifecycle – from data management plan to data sharing – its focus is on the particular challenges in sharing qualitative data and in making qualitative research transparent. One set of challenges concerns the ethical and legal concerns in sharing qualitative data. We will consider obtaining permissions for sharing qualitative data from human participants, strategies for (and limits of) de-identifying qualitative data, and options for restricting access to sensitive qualitative data. We will also briefly look at copyright and licensing and how they can inhibit the public sharing of qualitative data.

A second set of challenges concerns the lack of standardized guidelines for making qualitative research processes transparent. Following on some of the themes touched on in the talk, we will jointly explore some cutting edge approaches for making qualitative research transparent and discuss their potentials as well as shortcomings for different forms of research.

Monash Q Project's Q Behavioural Insights share discussions on key behaviours and thinking cues.

The focused of this piece is on on the themes of "sharing research effectively."

This codebook was used to analyze the interview data (from 11 interviews) in the master thesis project titled "Enhancing Open Research Data Sharing and Reuse via Infrastructural and Institutional Instruments: a Case Study in Epidemiology" which is openly available on TU Delft Education Repository.

Scientists_Second_FollowUpAGUSurvey of geophysicists from the American Geophysical Union on data management attitudes and practices

Did you know the UR has its own repository where you can share your research? Called the University of Rochester Research Repository (URRR), students, faculty, and staff can share their research, data, code, posters, articles, videos, etc.! Learn tips on how to share your research, tour URRR and try it out!This workshop is part of the Data Skills series, and co-taught by UR Libraries Data Services and UR Libraries Open Publishing Librarian. Feel free to contact us with any data or publishing questions you have! To receive the zoom link for this workshop, please register.This webinar was presented on April, 30th, 2025 via Zoom.

The spreadsheet in the present dataset (CSV format) includes the anonymised thematic coding that has been applied to our interview and literature review findings to inform the preparation of the report: From intent to impact: Investigating the effects of open sharing commitments.

The thematic coding has been applied by using NVivo, a professional qualitative analysis software, and then exported in spreadsheet form for public sharing.

Find out more about this project in our dedicated Zenodo project community.

This study sought to provide in-depth insight about the complex interaction of factors influencing motivations for sharing and re-using open research data within a single discipline, namely astrophysics. We identified the following research questions.
1. What discipline-specific characteristics influence motivation for sharing and re-using open research data?
2. What factors influence researcher’s motivations to openly share their data?
3. What factors influence researcher’s motivations to re-use open research data shared by others?
4. How can researchers in disciplines with low rates of open data sharing and re-use be encouraged to share and re-use more?
These research questions are addressed through a case study consisting of nine in-depth interviews with astrophysics researchers and through observations of researchers in this discipline. With permission of the interviewees, all interviews were recorded, and these recordings were transcribed. The checked transcripts were imported into the ATLAS.ti software. ATLAS.TI software was used for open, axial, and selective coding.

Presentation to CUAHSI staff on 4/20/17 as a high level overview of HydroShare to orient new staff on how their work fits into the big picture of HydroShare.

GENERAL INFORMATION

Title of Dataset: A dataset from a survey investigating disciplinary differences in data citation

Date of data collection: January to March 2022

Collection instrument: SurveyMonkey

Funding: Alfred P. Sloan Foundation

SHARING/ACCESS INFORMATION

Licenses/restrictions placed on the data: These data are available under a CC BY 4.0 license

Links to publications that cite or use the data:

Gregory, K., Ninkov, A., Ripp, C., Peters, I., & Haustein, S. (2022). Surveying practices of data citation and reuse across disciplines. Proceedings of the 26th International Conference on Science and Technology Indicators. International Conference on Science and Technology Indicators, Granada, Spain. https://doi.org/10.5281/ZENODO.6951437

Gregory, K., Ninkov, A., Ripp, C., Roblin, E., Peters, I., & Haustein, S. (2023). Tracing data:
A survey investigating disciplinary differences in data citation. Zenodo. https://doi.org/10.5281/zenodo.7555266

DATA & FILE OVERVIEW

File List

• Filename: MDCDatacitationReuse2021Codebookv2.pdf
  Codebook
• Filename: MDCDataCitationReuse2021surveydatav2.csv
  Dataset format in csv
• Filename: MDCDataCitationReuse2021surveydatav2.sav
  Dataset format in SPSS
• Filename: MDCDataCitationReuseSurvey2021QNR.pdf
  Questionnaire

Additional related data collected that was not included in the current data package: Open ended questions asked to respondents

METHODOLOGICAL INFORMATION

Description of methods used for collection/generation of data:

The development of the questionnaire (Gregory et al., 2022) was centered around the creation of two main branches of questions for the primary groups of interest in our study: researchers that reuse data (33 questions in total) and researchers that do not reuse data (16 questions in total). The population of interest for this survey consists of researchers from all disciplines and countries, sampled from the corresponding authors of papers indexed in the Web of Science (WoS) between 2016 and 2020.

Received 3,632 responses, 2,509 of which were completed, representing a completion rate of 68.6%. Incomplete responses were excluded from the dataset. The final total contains 2,492 complete responses and an uncorrected response rate of 1.57%. Controlling for invalid emails, bounced emails and opt-outs (n=5,201) produced a response rate of 1.62%, similar to surveys using comparable recruitment methods (Gregory et al., 2020).

Methods for processing the data:

Results were downloaded from SurveyMonkey in CSV format and were prepared for analysis using Excel and SPSS by recoding ordinal and multiple choice questions and by removing missing values.

Instrument- or software-specific information needed to interpret the data:

The dataset is provided in SPSS format, which requires IBM SPSS Statistics. The dataset is also available in a coded format in CSV. The Codebook is required to interpret to values.

DATA-SPECIFIC INFORMATION FOR: MDCDataCitationReuse2021surveydata

Number of variables: 95

Number of cases/rows: 2,492

Missing data codes: 999 Not asked

Refer to MDCDatacitationReuse2021Codebook.pdf for detailed variable information.

Raw data belonged to the study: "The sharing of research data facing the COVID-19 pandemic"

The primary data collection element of this project related to observational based fieldwork at four universities in Kenya and South Africa undertaken by Louise Bezuidenhout (hereafter ‘LB’) as the award researcher. The award team selected fieldsites through a series of strategic decisions. First, it was decided that all fieldsites would be in Africa, as this continent is largely missing from discussions about Open Science. Second, two countries were selected – one in southern (South Africa) and one in eastern Africa (Kenya) – based on the existence of the robust national research programs in these countries compared to elsewhere on the continent. As country background, Kenya has 22 public universities, many of whom conduct research. It also has a robust history of international research collaboration – a prime example being the long-standing KEMRI-Wellcome Trust partnership. While the government encourages research, financial support for it remains limited and the focus of national universities is primarily on undergraduate teaching. South Africa has 25 public universities, all of whom conduct research. As a country, South Africa has a long history of academic research, one which continues to be actively supported by the government.

Third, in order to speak to conditions of research in Africa, we sought examples of vibrant, “homegrown” research. While some of the researchers at the sites visited collaborated with others in Europe and North America, by design none of the fieldsites were formally affiliated to large internationally funded research consortia or networks. Fourth, within these two countries four departments or research groups in academic institutions were selected for inclusion based on their common discipline (chemistry/biochemistry) and research interests (medicinal chemistry). These decisions were to ensure that the differences in data sharing practices and perceptions between disciplines noted in previous studies would be minimized.

Within Kenya, site 1 (KY1) and Site 2 (KY2) were both chemistry departments of well-established universities. Both departments had over 15 full time faculty members, however faculty to student ratios were high and the teaching loads considerable. KY1 had a large number of MSc and PhD candidates, the majority of whom were full-time and a number of whom had financial assistance. In contrast, KY2 had a very high number of MSc students, the majority of whom were self-funded and part-time (and thus conducted their laboratory work during holidays). In both departments space in laboratories was at a premium and students shared space and equipment. Neither department had any postdoctoral researchers.

Within South Africa, site 1 (SA1) was a research group within the large chemistry department of a well-established and comparatively well-resourced university with a tradition of research. Site 2 (SA2) was the chemistry/biochemistry department of a university that had previously been designated a university for marginalized population groups under the Apartheid system. Both sites were the recipients of numerous national and international grants. SA2 had one postdoctoral researcher at the time, while SA1 had none.

Empirical data was gathered using a combination of qualitative methods including embedded laboratory observations and semi-structured interviews. Each site visit took between three and six weeks, during which time LB participated in departmental activities, interviewed faculty and postgraduate students, and observed social and physical working environments in the departments and laboratories. Data collection was undertaken over a period of five months between November 2014 and March 2015, with 56 semi-structured interviews in total conducted with faculty and graduate students. Follow-on visits to each site were made in late 2015 by LB and Brian Rappert to solicit feedback on our analysis.