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UBC Research Data Management Survey : Health Sciences : Report Barsky, Eugene; Brown, Helen; Ellis, Ursula; Ishida, Mayu; Janke, Robert; Menzies, Erin; Miller, Katherine; Mitchell, Marjorie; Vis-Dunbar, Mathew May 31, 2017

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UBC Research Data Management Survey: Health Sciences – Report May 2017 Eugene Barsky, Helen Brown, Ursula Ellis, Mayu Ishida, Robert Janke, Erin Menzies, Katherine Miller, Marjorie Mitchell, and Mathew Vis-Dunbar UBC Library Contact – eugene.barsky@ubc.ca   Executive Summary  Background  In 2016, Canadian federal funding agencies introduced the Tri-Agency Statement of Principles on Digital Data Management, which advocates for developing data management plans (DMPs) and making data available for future research. A data management plan addresses questions about: research data types and formats, metadata standards, ethics and legal compliance, data storage and reuse, assignment of data management responsibilities, and resource requirements. DMPs are likely to be increasingly required in grants applications as has happened in the US, the UK, and the EU. In preparation for this, librarians at University of British Columbia surveyed researchers about their RDM practices and needs in three phases, each of which targets different disciplines: (1) the Sciences and Engineering (fall 2015), (2) the Social Sciences and Humanities (fall 2016), and (3) the Health Sciences (spring 2017). These surveys illuminate disciplinary differences in RDM, and will inform the University in developing infrastructure and services to support researchers in RDM. This report describes findings from the third survey at UBC targeting researchers in the Health Sciences.   Key Findings  The total of 89 respondents completed the survey, most of whom were faculty members (assistant/associate/full professors, clinical faculty).  Types of Data Over 70% of the respondents use textual and numerical data in their research (80.9% and 71.9%, respectively), and 49.4% use multimedia data files that include images from MRI, functional MRI, and electron microscopes (Q8).   Storage and Retention of Data Most researchers (62.9%) estimate using less than 50 GB in their average research project, while 9% are big data users consuming more than 1 TB in their average research (Q7). Just over half (51.7%) of the respondents keep processed data underlying publications for more than 10 years after project completion (Q13).   Data Management Plans When asked, 87.6% of the respondents either preferred or stated a need for assistance to draft a data management plan as part of a grant application (Q21). Almost 90% of the respondents were interested or very interested in a service that would provide assistance preparing data management plans to meet funding requirements, or assistance creating formal or documented data management policies (Q24).   Data Sharing Issues When asked about privacy restrictions and embargoes, 69.7% of respondents noted that privacy, confidentiality or ethics considerations may prevent sharing their research data (Q16). One respondent commented that consent forms may not have included information for research participants about data sharing. Another notable concern was the need to publish data before sharing, cited by 38.2% of respondents. 87% of the respondents were interested or very interested in assistance with issues associated with data preservation and/or sharing (Q24).   Vulnerable Data Storage To store research data from their current project(s), 36% of the respondents use Cloud-based solutions (e.g., Dropbox, Google Drive, Amazon Cloud, Microsoft Cloud) and 33.7% use flash drives (Q10). These data storage options are considered vulnerable and data security risks. 46.1% of respondents also retain their data in the physical or paper form and store them in boxes and cabinets. Only 3.4% use external data repositories (e.g., Dryad, Protein Data Bank, GenBank). When asked to rate their interest in research data management service, 79% of the respondents were interested or very interested in data storage and backup during active research projects (Q24). In their comments (Q25), some respondents urged the university to provide access to secure cloud storage and data collection tools for the researchers.       Disciplinary and institutional repositories Responses to questions about repositories demonstrated limited knowledge and confusion about repositories generally.  When asked about their awareness of any discipline-specific research data repositories related to their field, 44 of the 62 respondents (64%) said they were not aware of any discipline-specific research data (Q20). Some respondents mentioned sources of data or statistics rather than repositories to which researchers may upload their own data. This suggests there may be confusion between sources of statistics or administrative data, and research data generated by the respondent. 73% of the respondents were interested or very interested in assistance with depositing research data in appropriate disciplinary or other external data repositories, and 87% were interested or very interested in an institutional repository for long-term access and preservation of research data (Q24).   Data management education 80% of the respondents were interested or very interested in workshops on best practices in data management for faculty, and 85% were interested or very interested in personalized consultation on data management practices for specific research groups or projects (Q24). Comments stated that data management workshops target specific groups, research areas, or methodologies, and that a series of modules be offered to cover more details (Q25). Moreover, 90% were interested or very interested in communication about information about funding requirements and journal requirements regarding research data (Q24).    Conclusions:  The results of this study have demonstrated a clear need for UBC institutional support for RDM services. Federal funders in Canada will soon be requiring data management plans as part of grant applications. Just above 50% of the survey respondents feel they have the appropriate documentation for their data to be understood and used. Respondents expressed reasons for not sharing their data which have existing solutions currently available - an educational opportunity exists to easily remove these barriers. Researchers also expressed an overwhelming interest in institutional programming to inform them of research data management best practices. Opportunities for UBC to comprehensively and sustainably manage research data, itself a valuable asset, are present and now is the time to implement these services.   Recommendations  This group recommends that UBC stakeholders - Faculties, Advanced Research Computing, Library, Research Ethics Board, Office of Research Services, University-Industry Liaison Office, Chief Data Officer, and PopDataBC come together and work on an institutional approach to Research Data Management that will include: ● Sustainable support for an institutional repository to provide long-term data access and digital preservation ● A systematic program of assistance with the development of research data management plans all through the data life cycle ● Personalized consultation on data management practices to meet grant application requirements and recognized data management best practices ● Workshops on data management for faculty members ● Workshops on data management for graduate students    Research Data  Research data, survey instrument, and data dictionary are available at:   Barsky, Eugene; Brown, Helen; Ellis, Ursula; Ishida, Mayu; Janke, Robert; Menzies, Erin; Miller, Katherine; Mitchell, Marjorie; Vis-Dunbar, Mathew, 2017-May, "UBC Research Data Management Survey: Health Sciences", http://hdl.handle.net/11272/10491 V3 [Version]                   Introduction  An increasing number of funding agencies and publishers are developing policies over research data management (RDM) and requiring researchers to share and preserve data underlying their findings. In 2016, the Canadian federal funding agencies introduced the Tri-Agency Statement of Principles on Digital Data Management, which advocates for developing data management plans (DMPs) and making data available for future research. Canada is expected to follow the direction of the United States and United Kingdom where funding agencies mandate DMPs as part of grant applications. These developments may influence researchers’ RDM practices and lead to increasing demand for RDM support.   As part of the CARL Portage Canadian RDM Survey Consortium, librarians at University of British Columbia surveyed researchers about their RDM practices and needs in three phases, each of which targets different disciplines: (1) the Sciences and Engineering (fall 2015), (2) the Social Sciences and Humanities (fall 2016), and (3) the Health Sciences (spring 2017). The surveys aim to: ● Determine baseline current RDM practices ● Gather researchers’ requirements for RDM ● Raise awareness for the prospective service and gauge interest levels for the proposed library role in RDM    The surveys illuminate disciplinary differences in RDM, and inform the University in developing infrastructure and services to support researchers in RDM. This report describes findings from the third survey at UBC targeting researchers in the Health Sciences.   Methodology  The survey questions were created by the CARL Portage Canadian RDM Survey Consortium. The consortium currently consists of 14 Canadian research libraries including the University of British Columbia, and has conducted a series of surveys to learn about the data management practices and needs in the natural sciences, engineering, social sciences, humanities, and health sciences. The consortium is doing a comparative analysis across disciplines, institutions, regions, and the country.   Following is the list of participating institutions: ● Dalhousie University ● McGill University ● McMaster University ● Queen’s University ● Ryerson University ● University of Alberta ● University of British Columbia ● University of Ontario Institute of Technology ● University of Ottawa ● University of Toronto ● University of Victoria ● University of Waterloo ● University of Windsor ● Western University   An invitation to the survey was distributed through the UBC Health Council. Chaired by the Associate-Provost Health, the Council consists of 1 representative from each of the health and human service programs at UBC, 1 representative from Woodward Library, 2 patient community representatives, and 2 student representatives. Each Council member was asked to forward the survey invitation to researchers in their areas through appropriate communication channels (e.g., listservs, newsletters). Health sciences librarians at UBC Vancouver and UBC Okanagan also promoted the survey by speaking with individual researchers in their liaison areas and posting the survey information in their LibGuides and email signatures. The survey targeted all ranks of faculty, graduate students, postdoctoral fellows, residents, and research staff in Faculties of Dentistry, Land and Food Systems, Medicine, and Pharmaceutical Sciences, and School of Nursing, affiliated research institutes, and clinical academic campuses.   The survey was available online at https://survey.ubc.ca/s/RDMHealth/ from Monday, February 6, 2017 to Tuesday, March 7, 2017. The survey was voluntary and accessible online. At the end of the survey, the respondent was given an option to provide their email address so that the library could follow up with data management assistance. In total, 25 questions were included in the survey, covering demographic information, data types, storage options, metadata and data documentation, data sharing, data management plans, research data in teaching, and support for data management. The survey results are described in details below.    Results  Participants:  A total of 89 respondents completed the survey, the majority of whom were faculty members. As shown below, 44 respondents (49.4%) identified as Assistant, Associate, Full Professor, or Clinical Colleagues. The survey did not include an option specifically for staff, but 13 respondents (14.6%) identified themselves as staff in the “other” field and described their positions as research staff, research managers, research coordinators, and scientists. The importance of staff in managing research data was further emphasized in later comments and fits with research practices in the health sciences. 15 postdoctoral fellows and 13 graduate students also responded in significant numbers, with participation rates of 16.8% and 14.6%.    Q1. Please indicate your rank at UBC:   Departments, Research Institutes, and Centres:  Of the 89 respondents who specified their home department at UBC, the majority, 67.4%, came from the Faculty of Medicine’s 15 Departments and 2 Schools, including the Department of Pediatrics which had the highest response at 16.9% overall. The School of Nursing had the next highest response rate at 14.6%, followed by the Faculty of Pharmaceutical Sciences at 7.9%, the School of Population and Public Health and the Department of Physical Therapy at 6.7%, and the Department of Occupational Science and Occupational Therapy at 5.6%. 3.4% of respondents came from the Department of Land and Food Systems, which includes the Human Nutrition graduate program. The figure below shows departments with the largest number of responses (please see the full dataset for the entire list).    While the above survey question asked respondents to select the University Department that they are most closely associated with, further questions allowed respondents to detail multiple affiliations. Many respondents had more than one departmental or institutional affiliation, with most naming other UBC Faculties, Departments, Schools, and affiliated Research Institutes, and Centres.   Secondary departmental affiliations varied considerably, but 5 respondents listed the School of Population and Public Health and 3 listed Experimental Medicine as cross discipline affiliations.   Most respondents came from the BC Children’s Hospital Research Institute (34 respondents) and the Vancouver Coastal Health Research Institute (14 respondents). Additional affiliations included: 5 respondents from the Djavad Mowafaghian Centre for Brain Research, 4 from the Women's Health Research Institute (WHRI), 4 from the Life Sciences Institute, and 4 from the Health Services and Policy Research Centre (CHSPR).   41 respondents identified as belonging to a UBC clinical academic campus. The clinical academic campuses with the highest number of responses were BC Children’s Hospital and Sunny Hill Health Centre for Children at 26, followed by BC Women’s Hospital and Health Centre at 10, Vancouver General Hospital at 6, and St. Paul’s Hospital and the BC Cancer Agency which were both listed by 4 respondents.  Number of Research Projects per Researcher:  Overall, 78.6% of the respondents collaborated on 3 or more research projects in the past year, with 40.4% working on between 3-5 research projects, and 38.2% collaborating on 5 or more research projects in the past year.    Funding/Grants:  The majority of survey respondents used multiple funding sources in the past 5 years. The Canadian Institutes of Health Research (CIHR) was by far the most common source of funding, with 79.8% of respondents using CIHR funding within the past 5 years. The Michael Smith Foundation was the next most common source of funding, providing funding to 36% of respondents through Michael Smith Foundation funding programs, including the BC Nursing Research Initiative (listed separately as another funding source in the survey responses). The most common funding sources are listed below (please see the dataset for a complete list).      Size of Data:  The majority of the respondents (62.9%) use less than 50 GB of data storage in an average research project, with 47.2% using less than 10 GB. At the other end of the spectrum, 9% of the respondents are big data users, and consume more than 1 TB of data storage.      Types of Data and Devices for Data Capture/Analysis:  Over 70% of the respondents use textual and numerical data in their research (80.9% and 71.9%, respectively). 49.4% use multimedia data which include images from MRI, functional MRI, and electron microscopes. There was a variety of additional data types used showing the complexity of data capture for health research.    There is a wide variety of devices used to capture and analyze data: computers, laptops, mobile phones, lab equipment such as microscopes. See Appendix A for a complete list of software and hardware reported in the survey.   Data Storage Options:  The variety of data storage options is used among the respondents to store research data from their current project(s). 68.5% of the respondents used the shared drive on the university or departmental server, and 49.4% used the local hard drive on their computer. These were the two most popular data storage options. A significant number of the respondents, 46.1%, retained their data in the physical or paper form and store them in boxes and cabinets. 40.4% used the laptop hard drive and 36% used the external hard drive.   36% of the respondents used Cloud-based solutions (e.g., Dropbox, Google Drive, Amazon Cloud, Microsoft Cloud) and 33.7% used flash drives. These data storage options are considered vulnerable and data security risks. Only 3.4% used external data repositories (e.g., Dryad, Protein Data Bank, GenBank).   Among the data storage options the respondents specified as “Other” are REDCap (4 respondents / 4.5%) and UBC Workspace 2.0 (5 respondents / 5.6%).     When asked how long they keep (1) raw data, (2) working data, and (3) processed data for publication, 100% of the respondents would retain raw data and process data for at least 3 years while 6.7% would not keep working data longer than 3 years. The majority of the respondents would keep all three types of data for at least 5 years (raw data: 85.4%, working data: 74.1%, processed data: 89.9). 51.7% would keep processed data for more than 10 years.      Metadata/Data Documentation:  When asked about the quality of metadata and data documentation, 51.7 % of the respondents felt confident that they provided sufficient description (e.g., variable and field definitions, data dictionaries, scripts used for analysis) so that their data are understandable and reusable while 48.3% did not. Moreover, 50.6% of the respondents felt confident that they provided sufficient description so that their methodologies could be replicated while 49.4% did not.         Data Sharing:  Current Data Sharing Practices    The majority of the respondents (66.3%) are currently sharing data in some way, mainly via personal request or through online systems which enable restricted access. Including data as a supplementary file to a journal article is also a common practice.  Respondents mentioned the following repositories that they are either using now, or plan to use for one of their current projects:  ● Enigma (http://enigma.usc.edu/)  ● Figshare (https://figshare.com/)  ● GenBank (https://www.ncbi.nlm.nih.gov/genbank/)  ● NCBI Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra)  ● Protein Data Bank (http://www.rcsb.org/pdb/home/home.do)  ● UBC Workspace (restricted access) ● NVivo team server (restricted access) ● PopData BC (prospective, not yet shared; restricted access) (https://www.popdata.bc.ca/)  ● ICPSR/CLDR (prospective, not yet shared) (https://rehabsciences.utmb.edu/cldr/)  Willingness to Share Data and Barriers to Sharing Data    Even if not currently sharing data, most respondents are willing to do so if feasible. Specific repositories mentioned in response to Question 15 include:  ● Enigma (http://enigma.usc.edu/)  ● GenBank (https://www.ncbi.nlm.nih.gov/genbank/) ● NCBI GEO (https://www.ncbi.nlm.nih.gov/geo/)  ● Protein Data Bank (http://www.rcsb.org/pdb/home/home.do)  ● Secure FTP transfer for personal requests  One respondent elaborated that choice of repository depends on many factors: which system works best, costs, ethical protections, and how well the data can be managed and its quality assured.    Question 16 asked about privacy restrictions and embargoes. 69.7% of respondents noted that privacy, confidentiality or ethics considerations may prevent sharing their research data. A respondent commented that consent forms may not have included information for research participants about data sharing. Another notable concern was the need to publish data before sharing, cited by 38.2% of respondents.  Questions 17 and 18 further examined barriers to sharing data.     Nearly 30% of respondents would be willing to share their data with anyone, and a majority of respondents would share with their collaborators and/or colleagues in their field.   Some major barriers to sharing are; privacy, legal or security issues (47.2%); incomplete data (34.8%); a wish to derive further value from the data (32.6%); and data being used without proper citation and acknowledgement (27%). Barriers which the Library could potentially help with are insufficient time (by providing training, tools and staff to streamline the process of sharing), no place to put them (by providing access to an institutional data repository), and lack of standards.     Question 19 asked why researchers would be motivated to share data. Clearly, respondents see numerous benefits to sharing data if the barriers can be overcome.      Awareness of Subject Repositories  Question 20 asked: “Are you aware of any discipline-specific research data repositories related to your field? Please list. If you are not aware of any discipline-specific data repositories related to your field please say ‘none’.” A total of 62 responses were received for this question, 44 of which were “none.” One person mentioned they are only aware of journal-specific repositories; and one person responded yes without listing repositories.     Some respondents mentioned sources of data or statistics rather than repositories to which researchers may upload their own data. This suggests there may be confusion between sources of statistics or administrative data, and research data generated by the respondent. One respondent commented that access to Statistics Canada data is a barrier due to restrictions imposed by the Statistics Canada Act. Other data sources, such as CIHI and PopDataBC, also impose restrictions on data access and reuse.   Public Repositories Other Data Repositories or Sources of Data Dryad (datadryad.org/)  Enigma (enigma.usc.edu/)  GitHub (github.com/)  NCBI GEO (www.ncbi.nlm.nih.gov/geo/)  NCBI Sequence Read Archive (www.ncbi.nlm.nih.gov/sra)  OpenfMRI (openfmri.org/)  PhysioNet (www.physionet.org/)  OpenfMRI (openfmri.org/)  PhysioNet (www.physionet.org/)  Protein Data Bank (www.rcsb.org/pdb/home/home.do)  Saccaromyces Genome Database (www.yeastgenome.org/)  ADNI (adni.loni.usc.edu/)  BC data on dental-related issues (BCDA) (bcdental.org/)  Canadian Health Measures Survey (CHMS) (www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&Id=148760)  Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP) (www.phac-aspc.gc.ca/injury-bles/chirpp/index-eng.php)  Canadian Institute for Health Information (www.cihi.ca/en)  Canadian Longitudinal Study on Aging (CLSA) (www.clsa-elcv.ca/)  Canadian National Trauma Registry (CaNTR) (www.cihi.ca/en/national-trauma-registry-metadata)  Canadian Primary Care Sentinel Surveillance Network data--EMR data from primary care (cpcssn.ca/)  Cleft Q data  Manitoba Centre for Health Policy (umanitoba.ca/faculties/health_sciences/medicine/units/chs/departmental_units/mchp/resources/repository/index.html)   National Survey of Family Growth (www.cdc.gov/nchs/nsfg/)  NINDS Common Data Elements NITRC (www.nitrc.org/)  OASIS (www.oasis-brains.org/)  PopData BC (www.popdata.bc.ca/)  Various American data sources  Data Management Plans:  A data management plan addresses questions about: research data types and formats, metadata standards, ethics and legal compliance, data storage and reuse, assignment of data management responsibilities, and resource requirements. When asked whether the researcher would be able to draft a data management plan as part of a grant application, 87.6% expressed either preference or need for assistance while 12.4% felt confident that they could draft a data management plan on their own.      RDM and Teaching Practice:    Over 70% of the respondents do not teach RDM topics. A small percentage (11-20%) is teaching topics such as data ethics, data privacy, data retention, data documentation, and backup. The smallest percentage (3-7%) teaches about data version control, data archiving and data sharing.    35.2% of the respondents indicated they use their own research data in their teaching practice. However, if we remove the 44.3% that indicated “Not applicable”, we see a majority, 63.3% of the respondents indicated they do use their own research data in their teaching practice.     RDM Support:  Question 24 asks the respondents to rate their interest in RDM services.   Data management plans 89% of the respondents were interested or very interested in assistance preparing data management plans to meet funding requirements, or assistance creating formal or documented data management policies.  Issues associated with data sharing 87% were interested or very interested in assistance with issues associated with data preservation and/or sharing.  Data storage  79% were interested or very interested in data storage and backup during active research projects. In their comments (Q25), some respondents urged the university to provide access to secure cloud storage and data collection tools for the researchers.      Disciplinary and institutional repositories 73% were interested or very interested in assistance with depositing research data in appropriate disciplinary or other external data repositories, and 87% were interested or very interested in an institutional repository for long-term access and preservation of research data. Data management education 80% were interested or very interested in workshops on best practices in data management for faculty, and 85% were interested or very interested in personalized consultation on data management practices for specific research groups or projects. Some respondents suggested in their comments (Q25) that data management workshops target specific groups, research areas, or methodologies, and that a series of modules be offered to cover more details. Moreover, 90% were interested or very interested in communication about information about funding requirements and journal requirements regarding research data.  Other services Question 25 asked, “If there are other services you would like to see offered, please specify.” As well, a final survey question asked for feedback on the survey content or general thoughts on research data management practices. Responses to these questions brought up several concerns:  ● requests for usable cloud storage ● request that existing repository used by research group be able to support data sharing ● comment that some data requires proprietary software to be usable, and UBC does not have site licenses for these programs ● comments that the survey does not address issues relevant to prevention, population-level, or complex intervention research ● comment that accountability for data management is complex and there are institutional barriers ● comment about the need for time, funding and more specific training on research data management to simplify sharing  Conclusion  The results presented here are from the third in a series of surveys conducted by librarians at UBC to determine the current practices and needs of researchers with respect to research data management. As the Tri-Council granting bodies prepare to include Research Data Management Plans as requirements for grant proposals, the information gathered provides a solid base from which to develop a suite of services supporting the complete management of research data, from the planning and grant application phase through to preserving valuable data for long term access. As one respondent said “I like the idea of making data available, but we need to make this process as simple as possible to facilitate this practice (e.g., funded as part of the grant applications).”              Appendix A: Question 9 - List of software and hardware used for collection, analysis, or manipulation of research data  In addition to Custom Programs / In-house developed data collection software, the following pieces of software were identified.  3D MD Genome alignment tools REDCap AFNI graphpad Saber Arc GIS ImageXpress SAS Asana JMP Seahorse BASH KinArm / KinArm robotics shell scripts Biorep LabChart Simulink Cisco VPN Client LabView Softmax Pro Confocal imaging Mass Cytometer Spike Dacima MATLAB SPM Dexterit-E MetaXpress SPSS DNA sequencer Microsoft Access STATA DTIStudio Microsoft Excel Survey Monkey EEG Microsoft Kinnect Tableau Electron Microscope Images Microsoft Word Transcranial Doppler ESHA Millenium Transcranial Magnetic Stimulation Epi Data MPlus Txt FASTQC MRI / Functional MRI UCINet FileMaker Pro Netdraw wizard Finapres NVivo X2 Impact Monitoring Software Flexivent physiological lung function PowerLab X2 xPatch Flow Cytometer PL/SQL Developer  Flowjo and flow cytometers Prism  FMRIB-FSL Python  GasAnalyzer R   Hardware identified as being used for collection, analysis, or manipulation of research data included:  computers, laptops, iPads, and digital voice recorders.   

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