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Research data management workshop april12 2016
Rebecca Raworth presented a workshop on research data management. The presentation covered: - Why research data management plans are important, such as satisfying funder requirements and increasing research efficiency. - Current requirements for data management plans in Canada. - Tools for research data management, including Portage for creating data management plans and Dataverse for data storage and access. - Best practices for organizing, documenting, storing and sharing research data, including using metadata standards, file naming conventions, and choosing appropriate data repositories.
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Research data management workshop april12 2016
- 1. Research Data Management Workshop http://libguides.uvic.ca/rdmp RebeccaRaworth, MLIS April 12, 2015
- 2. Declaration of Conflictof Interest or Relationship Speaker: Rebecca Raworth I have no conflicts of interest to disclose with regard to the subject matter of this presentation.
- 3. Learning objectives By theend of this session you should: • Understand why research data management plans are important • Be able to identify the benefits and challenges of data management • Be able to complete an initial plan for managing your own research data
- 4. Outline • Why? – RDMtoday, in Canada • What is a Research Data Management Plan? • What you can do today – Portage – RDM – Dataverse – data storage & access • Where to get help
- 5. Why manage yourdata? • Satisfy grant & journal requirements • Find your files • Keep track of different versions of your data • Organize and compile information at the end of a project • Reproduce your work (if required for a journal or patent) • Pass on your work to another researcher • Share your work • Satisfy research ethics board requirements • Increase research efficiency • Promote collaboration & maximize transparency • Ensure data access & longevity • Journal articles with published datasets get more citations • Publishing data could prove you were first
- 6. Current RDM Requirementsin Canada
- 7.
- 9. CANADIAN UNIVERSITY RESEARCH DATAMANAGEMENT STATEMENT OF PRINCIPLES Research data means: • Data that are used as primary sources to support technical or scientific enquiry, research, scholarship, or artistic activity, and that are used as evidence in the research process and/or are commonly accepted in the research community as necessary to validate research findings and results. All other digital and non-digital content have the potential of becoming research data. Research data may be experimental data, observational data, operational data, third party data, public sector data, monitoring data, processed data, or repurposed data.
- 10. Journal mandates Ross JS,Lehman R, Gross CP. The importance of clinical trial data sharing toward more open science. Circulation: Cardiovascular Quality and Outcomes. 2012 Mar 1;5(2):238-40. Moorthy VS, Roth C, Olliaro P, Dyed C, Kienyb MP. Best practices for sharing information through data platforms: establishing the principles. Bulletin of the World Health Organization. 2016 Apr 1;94(4):234.
- 11. Video Data Sharing andManagement Snafu in 3 Short Acts http://youtu.be/N2zK3sAtr-4 11 https://www.youtube.com/watch?v=N2zK3sAtr-4, By Karen Hanson, Alisa Surkis & Karen Yacobucci, NYU Health Sciences Library
- 12. Take a momentto think about your research • Talk with your neighbour and discuss the types of data you generate, where your data is stored and how it is organized. • If you were asked to share your data with another researcher would they be able to make sense of your data? • If you needed to locate your data files from 5 years ago, how easy would they be to find and use?
- 13.
- 14. Data management plans •Describe how data is collected, formatted, preserved and shared, as well as how existing datasets will be used and what new data will be created • FAIR Data Guiding Principles: – Data should be findable – Data should be accessible – Data should be interoperable – Data should be re-usable
- 15. The Data ManagementPlan: Common Misconceptions • Does not require that all data must be shared – Sensitive information/patient privacy – Intellectual property rights and commercial value • Sharing can take many forms • Funders recognize that different disciplines have different “cultures” of data sharing • Sharing “at no more than incremental costs and within a reasonable time.”
- 16. Best practices indata documentation • At the very least you should document the following in a readme.txt file stored alongside your data: – context of data collection (the goal of your research) – data collection methods (protocols, sampling, instruments, coverage…) – structure of files – sources used – quality assurance (data validation, checking) – data modifications – confidentiality and permissions – names of labels and variables – explanations of codes and classifications
- 17.
- 18. Metadata standards • Manydisciplines have created their own metadata standards to ensure that data records can be interpreted and compared across projects and fields • List of standards in your field - http://www.dcc.ac.uk/resources/metadata-standards • A few examples: – CARMEN A virtual laboratory for neurophysiology, enabling sharing and collaborative exploitation of data, analysis, code and expertise. Metadata must include the MIBBI-registered MINI recommendations. – MIBBI – Minimum Information for Biological and Biomedical Investigations – Genome Metadata
- 19. Metadata standards -neuroimaging Poldrack RA, Gorgolewski KJ. Making big data open: data sharing in neuroimaging. Nature neuroscience. 2014 Nov 1;17(11):1510-7. CARMEN: Code Analysis, Repository & Modelling for E-Neuroscience, http://www.carmen.org.uk/
- 20. Also, remember to… •Avoid jargon & symbols – use keywords • Define terms and acronyms • State limitations • Use descriptive titles • Be specific and quantify • Use keywords • Make it machine readable (avoid symbols)
- 21. File naming • Keepfile names under 32 characters • Classify broad types of files (transcript, photo, etc.) • Avoid spaces and special characters • Use underscores instead of periods or spaces • Make sure that file names are descriptive outside of their folders (in case they are misplaced or change locations); i.e., the file name should include all necessary descriptive information • Include dates and format them consistently (international standard for date notation is YYYY_MM_DD or YYYYMMDD) • Include a version number to track multiple versions of a docume • Use non-proprietary file formats (e.g. .docs, bmp/tif) • Be consistent!
- 22. File types • Chosea format that is: – Non-proprietary – Open, with documented standards – Used by your community – Encoded using standard character encoding – uncompressed
- 23. Data storage, security& backup
- 24. How to sharedata • Find a home for your data – Subject specific repository/archive (see re3data.org) – Institutional repository/archive – Journal website – Project website • License your data (e.g. Creative Commons or Open Data Commons) • Provide suggested data citation
- 25.
- 26. Ethics & legalcompliance • How will you ensure sensitive data is securely managed & accessible only to approved members of the project? • What strategies are there for secondary use of sensitive data
- 27. Sharing and re-use •What data will you be sharing and in what form? – Raw – Processed – Analyzed – Final • What type of end-user license for your data? – Creative Commons – Open Data Image: http://www.sdw-uk.de/2013/03/open-data-26-28-april-in-edinburgh/
- 28.
- 29. Digital Object Identifiers(DOIs) “Publication & citation are accepted by the scholarly community as the principal way of acknowledging the value and impact of a researcher’s contributions to the development of knowledge in their field.” (https://www.elsevier.com/connect/data-citation-is-becoming-real-with-force11-and-elsevier) “Data are to be considered as first-class scholarly objects, and treated similarly in many ways to cited and archived scientific and scholarly literature.” (Starr J, Castro E, Crosas M, Dumontier M, Downs RR, Duerr R, Haak LL, Haendel M, Herman I, Hodson S, Hourclé J, Kratz JE, Lin J, Nielsen LH, Nurnberger A, Proell S, Rauber A, Sacchi S, Smith A, Taylor M, Clark T. (2015) Achieving human and machine accessibility of cited data in scholarly publications. PeerJ Computer Science1:e1 https://doi.org/10.7717/peerj-cs.1)
- 30. Portage, Canada’s DMPassistant
- 31. Dataverse – nowavailable at UVic
- 32. Resources to helpyou… – UK Data Archive Checklist: http://data- archive.ac.uk/create-manage/planning- forsharing/data-management-checklist – Canada’s DMP Assistant: https://portagenetwork.ca/ – In USA (for NSF, NIH, etc.) – Data Management Planning Tool: https://dmptool.org/ – UK Digital Curation Centre Curation Checklists – http://www.dcc.ac.uk/sites/default/files/Curation %20Checklists.pdf
- 33. UVic Library’s ResearchData Subject Guide http://libguides.uvic.ca/rdmp
- 34.
- 35. Acknowledgements • Best Practicesfor Research Data PPT, Dec. 2014 by Eugene Barsky, Sally Taylor & Jennifer Abel, UBC Library • UBC Library DataGuide, (version 3.1, October 2015) by Eugene Barskey, UBC Research Data Librarian
- 36.
Editor's Notes
- #6 Mark Walport and Paul Brest of the Wellcome Trust in England said in a 2011 article in the Lancet, that Funders “need to ensure that research outputs are used to maximise knowledge and potential health benefits.” (Walport M, Brest P. Sharing research data to improve public health. The Lancet. 2011 Dec 2;377(9765):537-9.) Grantees are required to deposit their data in publically accessible repositories by… Canadian Institutes of Health Research (CIHR) Canadian Social Sciences and Humanities Research Council (SSHRC) NIH NSF Consider future preservation and context, not just what is needed right now. It’s good research practice to manage your research data. RDM is about evidencing your research about trust, transparency, accountability and the contract to the research participants. RDM is linked to your research reputation.
- #7 Why research data management plans?
- #8 Data management plans are encouraged and listed as an upcoming requirement in the July 2015 policy document - Draft Tri-Agency Statement of Principles on Digital Data Management - http://www.science.gc.ca/default.asp?lang=En&n=83F7624E-1 To promote excellence in digital data management practices and data stewardship in Agency-funded research. To advance knowledge, avoid research duplication and encourage reuse, to maximize access to the results of federally-funded research to encourage great collaboration and engagement with the scientific community, the private sector and the public. To validate research findings and results. The Agencies believe that research data collected with the use of public funds belong to the fullest extent possible in the public domain. “Data should be managed in accordance with the most appropriate and relevant standards and best practices… Responsibilities of researchers include: Incorporating data management best practices into their research, and developing data management plans to guide the responsible collection, formatting, preservation and sharing of their data throughout the entire lifecycle of a research project and beyond; Following the requirements of applicable institutional policies and professional or disciplinary standards; Acknowledge and cite datasets that contribute to their research; Staying abreast of standards and expectations of their disciplinary community. Responsibilities of research communities include: Developing data management standards, or promoting existing standards, and working collaboratively to review and improve these standards; Recognizing data as an important research output and fostering excellence in data management within their research community; Identifying and encouraging the use of specific repositories and platforms. Also, many American funding agencies including the National Science Foundation & the NIH already require data management plans, so it is likely only a matter of time before they are a requirement for Canadian federal funds. We recommend that you save yourself a headache later and work on your data management plan now. http://www.science.gc.ca/default.asp?lang=En&n=83F7624E-1
- #11 -As a condition of consideration for publication of a clinical trial report in our member journals, the ICMJE proposes to require authors to share with others the de-identified individual-patient data (IPD) underlying the results presented in the article (including tables, figures, and appendices or supplementary material) no later than 6 months after publication. The data underlying the results are defined as the IPD required to reproduce the article’s findings, including necessary metadata. This requirement will go into effect for clinical trials that begin to enroll participants beginning 1 year after the ICMJE adopts its data-sharing requirements. (The ICMJE plans to adopt data-sharing requirements after considering feedback received to the proposals made here.) -The ICMJE also proposes to require that authors include a plan for data sharing as a component of clinical trial registration. This plan must include where the researchers will house the data and, if not in a public repository, the mechanism by which they will provide others access to the data, as well as other data-sharing plan elements outlined in the 2015 Institute of Medicine report (eg, whether data will be freely available to anyone upon request or only after application to and approval by a learned intermediary, whether a data use agreement will be required).1ClinicalTrials.gov has added an element to its registration platform to collect data-sharing plans. (http://jama.jamanetwork.com/article.aspx?articleid=2483008) The BMJ now requires researchers to share the underlying data that forms the basis of their clinical trials. At the same time, some prominent medical researchers are questioning whether the NEJM is slipping in relevancy and reputation as it is lagging behind others in an industry-wide push for more openness, including open data, in medical research. The NEJM’s current Editor-in-Chief, Dr. Jeffrey M. Drazen, and a deputy, used, in a widely derided editorial on data sharing (http://www.nejm.org/doi/full/10.1056/NEJMe1516564) in January, the term “research parasites” to describe researchers who seek others’ data to analyze or replicate their studies, and was critical of current efforts to share data on clinical research so that others can assess the findings and perhaps replicate the analyses. The journal Science then published an editoria entitled “I am a research parasite” saying “No more excuses: let’s step up to data sharing.” Drazen and his co-workers also called out Dr. Eric Topol, director of the Scripps Translational Science Institute, and another cardiologist (https://www.propublica.org/article/amid-public-feuds-a-venerated-medical-journal-finds-itself-under-attack) after they wrote an opinion piece in the New York Times saying that the data behind a groundbreaking study about blood pressure treatment should be made available to doctors right away, not dealyed for journal publication.
- #13 Interview transcripts/textual Observations/occurrences Photographs/images Lab notebooks Slides, artifacts, specimens, samples Think about the types of data you collect or generate in your research? What file formats do you use? What conventions will you use to structure your data files? What constitutes a distinct dataset? Location, occurrences, time period, etc.
- #14 I contacted Dr. Harding and she uses her own blog, LabScribbles, as a lab notebook but she’s not yet storing her data in a repository and hasn’t yet made a data management plan
- #15 Force11, a research communications and e-scholarship community came up with the FAIR data guiding principles: https://www.force11.org/group/fairgroup/fairprinciples
- #16 RDM “consists of a number of difference activities and processes associated with the data lifecycle, involving the design and creation of data, storage, security, preservation, retrieval, sharing and reuse, all taking into account technical capabilities, ethical considerations, legal issues and governance frameworks.” (Cox AM, Pinfield S. Research data management and libraries: Current activities and future priorities. Journal of Librarianship and Information Science. 2014 Dec 1;46(4):299-316.) Some disciplines share their data using institutional or subject repositories, others share data person to person, some share in data journals
- #18 Often described as “data about data”, it helps answer the questions who, what, when, where and why Metadata helps researchers share their data, publicize their data and locate and retrieve data sets from others
- #19 -you may have heard of the metadata standards Dublin Core
- #20 Other common metadata standards include Dublin Core and Darwin Core for the biological sciences
- #21 Finally, don’t wait to document your data, start documenting your data at the beginning of your project
- #22 Large research projects can generate hundreds of data files. Proper file names and organization can make these files easier to locate and navigate. We recommend choosing a file naming convention and sticking to it.
- #23 File types are an essential consideration in data storage. Software and data storage technology changes quickly, and files can easily become obsolete or difficult to access. In general it is recommended that data files are copied to new media if technology changes or if files begin to degrade (every 2-5 years). The UK data archive has a chart (see ppt slide) of recommended file formats for a variety of data types
- #24 Remember that data can be lost for a number of reasons: Hardware failures, Software failures, Viruses or hacking, Power failures, Natural disasters, Human error At the beginning of any project researchers should map out what data they will be generating and how they plan on storing it. In deciding where to store your data ensure that you understand your organization’s policies and infrastructure for data storage and backups. A best practice is to have three copies stored in at least two locations (in case of a failure at one location). Think about who has access to the network? Are there firewalls? Computer security – anti-virus software? Power surges? Passwords and firewalls? Data encryption? Data storage secure?
- #25 -ubc data citation help -also, Digital Curation Centre guide to licensing research data at http://www.dcc.ac.uk/resources/how-guides/license-research-data Guiding Principles for Sharing Clinical Trial Data The ultimate goal of data sharing should be to increase scientific knowledge, leading to better therapies for patients. With this goal in mind, the IOM committee presents the following guiding principles for responsible sharing of clinical trial data: • Maximize the benefits of clinical trials while minimizing the risks of data sharing. • Respect individual participants whose data are shared. Data sharing could advance scientific discovery and improve clinical care by maximizing the knowledge gained from data collected in trials, stimulating new ideas for research, and avoiding unnecessarily duplicative trials. REPORT BRIEF JANUARY 2015 2 • Increase public trust in clinical trials and the sharing of trial data. • Conduct the sharing of trial data in a fair manner. These principles should be balanced in the context of specific trials and stakeholder needs, including concerns about the potential harms and costs of data sharing.(http://iom.nationalacademies.org/~/media/Files/Report%20Files/2015/SharingData/DataSharingReportBrief.pdf)
- #26 Indeed, in the March 28 issue of the New Yorker this year, Siddhartha Mukherjee writes an Annals of Science article called “Runs in the Family; new findings about schizophrenia rekindle old questions about genes and identity” which mentions, in passing, a central repository, the NIMH repository and Genomics Resource that contains data from more than 64,000 schizophrenia investigators in 22 countries that schizophrenia researchers can access remotely, which was used by Harvard researchers to determine that the risk of schizophrenia correlated powerfully with the inheritance of the the C4A gene variant.
- #29 Take steps to let the research community know your data exists? Get a DOI, tweet the link to your data Why cite data? Because that facilitates the re-use and verification of data, it enables the impact of your data to be tracked, is creating a scholarly structure that recognizes and rewards data producers DataCite Canada –get DOIs for data sets here
- #30 For your data sets We can help you obtain DOIs for your data sets Why cite data? To facilitate reuse and verification of data, to enable impact of data to be tracked, to create a scholarly structure that recognises and rewards data producers (https://www.datacite.org/services/cite-your-data.html) “Elsevier is having an important impact on the movement to promote sound, reproducible scholarship,” said Dr. Tim Clark, Assistant Professor at Harvard Medical School and leader of the workshop. “Their contribution towards implementing data citation and data metrics is particularly welcomed.” (https://www.elsevier.com/connect/data-citation-is-becoming-real-with-force11-and-elsevier) Elsevier has been among the first organisations to embrace research data as a crucial research output of researchers and thus also to contribute to the development of community-supported data citation principles, having first endorsed the Joint Declaration of Data Citation Principles in 2015. Here we briefly review the most current and widely agreed set of principle-based recommendations for scholarly data citation, the Joint Declaration of Data Citation Principles (JDDCP). We then present a framework for operationalizing the JDDCP; and a set of initial recommendations on identifier schemes, identifier resolution behavior, required metadata elements, and best practices for realizing programmatic machine actionability of cited data. The main target audience for the common implementation guidelines in this article consists of publishers, scholarly organizations, and persistent data repositories, including technical staff members in these organizations. But ordinary researchers can also benefit from these recommendations. The guidance provided here is intended to help achieve widespread, uniform human and machine accessibility of deposited data, in support of significantly improved verification, validation, reproducibility and re-use of scholarly/scientific data. (Starr J, Castro E, Crosas M, Dumontier M, Downs RR, Duerr R, Haak LL, Haendel M, Herman I, Hodson S, Hourclé J, Kratz JE, Lin J, Nielsen LH, Nurnberger A, Proell S, Rauber A, Sacchi S, Smith A, Taylor M, Clark T. (2015) Achieving human and machine accessibility of cited data in scholarly publications. PeerJ Computer Science1:e1 https://doi.org/10.7717/peerj-cs.1)
- #31 Portage is a national initiative to support Research Data Management. It’s bilingual There are several questions to answer in each section Sign up for the DMP Assistant! It was adapted from the UK Digital Curation Centre’s DMPOnline tool and is hosted at the Univ of Alberta. (the usa data management planning tool, https://dmptool.org can help, too)
- #32 The library can help -Dataverse is an open source web application created at Harvard University to share, preserve, cite, explore and analyze research data. It facilitates making data available to others, and allows you to replicate others' work. Researchers, data authors, publishers, data distributors, and affiliated institutions all receive appropriate credit. -host and manage your data in Dataverse, http://dvn.library.ubc.ca/dvn/ (Uvic’s Research data Repository hosted at UBC) -digital preservation and access for your data within UVicSpace (http://uvic.ca/library/featured/uvicspace/index.php)
- #33 Uvic Libraries can host & manage your data in DataVerse, http://dvn.library.ubc.ca/dvn/ (Uvic’s research data repository hosted at UBC), and can provide digital preservation & access for your data within UvicSpace, http://www.uvic.ca/library/featured/uvicspace/index.php
- #35 Free online course for those who manage digital data