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Developing data services: a tale from two Oregon universities

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While the generation or collection of large, complex research datasets is becoming easier and less expensive all the time, researchers often lack the knowledge and skills that are necessary to …

While the generation or collection of large, complex research datasets is becoming easier and less expensive all the time, researchers often lack the knowledge and skills that are necessary to properly manage them. Having these skills is paramount in ensuring data quality, integrity, discoverability, integration, reproducibility, and reuse over time. Librarians have been preserving, managing and disseminating information for thousands of years. As scholarly research is increasingly carried out digitally, and products of research have expanded from primarily text-based manuscripts to include datasets, metadata, maps, software code etc., it is a natural expansion of scope for libraries to be involved in the stewardship of these materials as well. This kind of evolution requires that libraries bring in faculty with new skills and collaborate more intimately with researchers during the research data lifecycle, and this is exactly what is happening in academic libraries across the country. In this webinar, two researchers-turned-data-specialists, both based in academic libraries, will share their experiences and perspectives on the development of research data services at their respective institutions. Each will share their perspective on the important role that libraries can play in helping researchers manage, preserve, and share their data.

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  • National Network of Libraries of Medicine, Pacific Northwest Region
    PNR Rendezvous

    Here is the link to the recording of the presentation: https://webmeeting.nih.gov/p8swadmbzpo/ and to our PNR Rendezvous webpage where the recording is posted: http://nnlm.gov/pnr/training/RMLrendezvous.html

    Talk abstract: “While the generation or collection of large, complex research datasets is becoming easier and less expensive all the time, researchers often lack the knowledge and skills that are necessary to properly manage them. Having these skills is paramount in ensuring data quality, integrity, discoverability, integration, reproducibility, and reuse over time. Librarians have been preserving, managing and disseminating information for thousands of years. As scholarly research is increasingly carried out digitally, and products of research have expanded from primarily text-based manuscripts to include datasets, metadata, maps, software code etc., it is a natural expansion of scope for libraries to be involved in the stewardship of these materials as well. This kind of evolution requires that libraries bring in faculty with new skills and collaborate more intimately with researchers during the research data lifecycle, and this is exactly what is happening in academic libraries across the country. In this webinar, two researchers-turned-data-specialists, both based in academic libraries, will share their experiences and perspectives on the development of research data services at their respective institutions. Each will share their perspective on the important role that libraries can play in helping researchers manage, preserve, and share their data.”
  • Adobe Connect instant polling to poll attendees (N=37).

    Responses:
    45% - yes, have data-related tasks or duties;
    90 % - metadata plays a big role in the modern research cycle
  • Does not include, “any of the following: preliminary analyses, drafts of scientific papers, plans for future research, peer reviews, or communications with colleagues. This "recorded" material excludes physical objects (e.g., laboratory samples).” This narrow definition mostly takes a retrospective view of your dataset, in that it does not account for raw and intermediate that may be critical to the research process but that don’t become part of the ’final’ dataset.

    Data could be:
    Observational
    Experimental
    Simulated
    Derived



  • Does not include, “any of the following: preliminary analyses, drafts of scientific papers, plans for future research, peer reviews, or communications with colleagues. This "recorded" material excludes physical objects (e.g., laboratory samples).” This narrow definition mostly takes a retrospective view of your dataset, in that it does not account for raw and intermediate that may be critical to the research process but that don’t become part of the ’final’ dataset.

    Data could be:
    Observational
    Experimental
    Simulated
    Derived



  • Data management is a verb – it involves intentional effort and activity.
    The main goals of DM are preservation and reuse, for you and for others.
    Covers all aspects of the data lifecycle from planning digital data capture methods, whittling down, ingestion to databases, providing for access and reuse, to transformation.

  • image: Microsoft clipart
  • Let’s look at one important area of scientific inquiry: climate change. What scale of data integration is necessary to study global trends over geologic timescales?

    Slide credit: DataONE Education Module 1. http://www.dataone.org/education-modules
  • Data are being generated in massive quantities daily. Improvements in technology enable higher precision and coverage in data acquisition and makes higher capacity systems store and migrate more data –increasing the importance of managing, integrating, and re-using data. In order to integrate these diverse datasets to answer questions of global significance, the data have to be well organized, well documented and described, preserved and accessible. It all depends of effective management of the data.

    Slide credit: DataONE Education Module 1. http://www.dataone.org/education-modules
  • Slide from: Heather Coates, Data Management Lab: Session 1 Slides
    http://www.slideshare.net/goldenphizzwizards/data-mgmtlab-spr14mod1slides201403245
  • 22 February 2013: The Office of Science and Technology Policy in the White House released a memorandum about expanding pubic access to the results of federally funded research. In addition to scholarly publications, federal agencies are making serious efforts to increase the sharing of research data.

    All federal agencies with more than $100M in R&D expenditures are subject to this memo.

    http://www.whitehouse.gov/sites/default/files/microsites/ostp/ostp_public_access_memo_2013.pdf

  • This is going to place huge additional demands on faculty who submit and review proposals – they overwhelmingly have NO IDEA what constitutes a good DMP.
  • “PLOS is now releasing a revised Data Policy that will come into effect on March 1, 2014, in which authors will be required to include a data availability statement in all research articles published by PLOS journals … {policy language: PLOS journals require authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. When submitting a manuscript online, authors must provide a Data Availability Statement describing compliance with PLOS’s policy. The data availability statement will be published with the article if accepted.}”

    Since the policy was updated in March 2014: “…more than 16,000 sets of authors have included information about data availability with their submission. We have had fewer than 10 enquiries per week to data@plos.org from authors who need advice about ‘edge cases’ of data handling and availability – fewer than 1% of authors.”

    http://blogs.plos.org/biologue/2014/05/30/plos-data-policy-update/
  • Citations on statement that accumulation of β amyloid “precedes” other abnormalities in inclusion body myositis muscle.
    Statement as fact is supported through citation to papers that only state it as hypothesis
  • Four most authoritative papers were from same lab, two had potentially the same data, and all lacked quantitative data as to how many affected muscle fibres were seen and a specificity of reagents for distinguishing β amyloid protein from β amyloid precursor protein.
  • MH - notes
  • We are working on determining how to deal with this longer term- is this a new data citation that goes alongside the paper. Needs to be in the keywords do it is mineable. Trying to figure out to deal with this in the long run.
  • “ When an official at America’s National Institutes of Health (NIH) reckons, despairingly, that researchers would find it hard to reproduce at least three-quarters of all published biomedical findings, the public part of the process seems to have failed.”
  • Give background about the reproducibility initiatve.
    Talk about example of replication- scientific reproducibility experiment with leishmania and it being a different strain, different amidation, etc.
  • Most research projects use and create multiple data types & formats, and produce many, many files. My own dissertation work included the generation or use of all of the data types shown here (which might help to explain why it took me 7 years to earn a Ph.D.). http://hdl.handle.net/1957/9088 This data was collected over the course of 5 years, at locations all over the Pacific and Atlantic Oceans. I never received ANY formal training in how to organize and manage all of this data. Where is all of this data now? On an external hard drive sitting in my desk.

    Image credit: Document by Piotrek Chuchla from The Noun Project
  • Librarians have been preserving, managing and disseminating information for thousands of years, going all the way back to Alexandria.

    As scholarly research is increasingly carried out digitally, and products of research have expanded from primarily text-based manuscripts to include datasets, metadata, maps, software code etc., it is a natural expansion of scope for libraries to be involved in the stewardship of these materials, too.

    This kind of evolution requires that libraries bring in faculty with new skills, and that’s exactly what’s happening in academic libraries across the country.
  • Data management is something that faculty all over campus have become aware of.

    As a neutral entity, the library is well positioned to address campus-wide needs, like data management. It makes sense, under the economy of scale, for a centralized unit to address a campus-wide need.

    We recognize that individual colleges and departments have computer support personnel and resources, and we aim to complement those resources (not duplicate them).

    (Switzerland metaphor swiped from the incomparable Jackie Wirz at OHSU)
  • We aren’t here to replace the external resources that already exist to support you – we are here to act as a conduit to these resources.

    Our goal is to help you effectively discover, navigate and utilize these resources where appropriate, in the same way that the library has been providing this kind of support for decades.
  • SO, what’s going on with academic libraries and data services? This ACRL white paper (2012) provides some context.
  • I spent my first year here getting my feet under me:
    Participating in the DuraSpace/ARL/DLF E-Science Institute, which involved doing an environmental scan and engaging faculty and administrators in interviews. Strengthened an existing relationship with campus Information Services (IS). This experience resulted in the creation of our Strategic Agenda for Research Data Services, which really laid out my priority tasks and areas of emphasis. http://hdl.handle.net/1957/38794
    Submitting an IMLS National Leadership Grant with 4 co-PIs
    Developing a collaboration with the Graduate School to create a credit-bearing course for graduate students in research data management (http://bit.ly/GRAD521)
    Trying (with limited success) to advertise the existence of library-based data services for faculty & grad students
    Creating a data services web site (via LibGuides, http://bit.ly/OSUData)
    Curating the limited number of datasets in our IR; updating metadata practices

    The first ¼ of 2014:
    All GRAD 521, all the time
    And, some grant stuff.
  • Response rate was 23%, 451 completed surveys across all colleges and ranks surveyed. The goal was to get a feel for how much and what types of data are being produced on campus, what faculty are doing with it, and figure out where they need more support.
  • Example question and responses to the OSU faculty data stewardship survey (figure created in R).

    What do faculty find more difficult: metadata creation, version control, finding and accessing data created by others, long-term storage, and sharing their own data.

    What am I going to do with the survey results? I’m working on a report, which I will share with faculty and OSU administration. Am hoping that it leads to a campus-wide conversation about data stewardship.

  • The OSUL&P Research Data Services model.

    Data planning & consultation
    DMPs/Planning
    Storage & backup
    File organization & naming
    Documentation & metadata
    Legal/ethical considerations
    Sharing & reuse
    Archiving & preservation

    Data access & preservation infrastructure
    Data curation in our IR
    We offer DOIs for datasets via membership in EZID (CDL)
    Recommend using ORCID iDs but haven’t had much traction on this yet. NIH mandate will change this.

    Data management training
    90-minute workshops, mostly grad students, some faculty
    2-credit course launched in January 2014. GRAD 521. http://bit.ly/GRAD521
    presentations at faculty/staff mtgs;
    invited lectures in classes

    Open data consortia & collaborations
    CUAHSI – implemented, in parntership with OSU faculty in CEOAS and Institute for Natural Resources
    DataONE & DataFOUR are under consideration or development


  • Periodic surveys can be used to identify service needs on campus, but depend on useful response rates. We suggest that regular reviews of DMPs can also be a legitimate source of information regarding what researchers are up to, and where they may need support. This project aims to provide a tool for librarians to facilitate consistent, quality reviews of DMPs.

    Project in a nutshell:
    Develop a rubric for consistent evaluation of NSF DMPs
    Multi-university study of DMPs
    Identify common gaps in knowledge, skills and practice
    Target data support services to ameliorate gaps

    Website with more info. is under development. Contact Amanda or DMPResearch@oregonstate.edu with questions.
  • Graduate students (like to meet in person) or faculty (most prefer email)

    Generally project or task-specific
    Examples:
    coming up with a file-naming convention and data organization strategy for a project
    reviewing a data management plan for a grant proposal
    how to share data in support of a submitted manuscript
  • Midterm assignment: a scaled-back Data Curation Profile
    Final assignment: a data management plan

    First cohort: 11 students, including 3 faculty members; degree ranges from non-thesis MS to PhD; many disciplines

    Whitmire, Amanda (2014): GRAD 521 Research Data Management Syllabus and Lesson Plans. figshare. http://dx.doi.org/10.6084/m9.figshare.1003834
    Whitmire, Amanda (2014): GRAD 521 Research Data Management Course Assignments. figshare. http://dx.doi.org/10.6084/m9.figshare.1003852
    Whitmire, Amanda (2014): GRAD 521 Research Data Management Lectures. figshare. http://dx.doi.org/10.6084/m9.figshare.1003835

Transcript

  • 1. Developing data services A tale from two Oregon universities NN/LM, Pacific Northwest Region PNR Rendezvous | 18 June 2014 Melissa Haendel OHSU Library Amanda Whitmire OSU Libraries
  • 2. B.S. in Aquatic Biology, 2000 Worked in a bioluminescence laboratory Ph.D. in Oceanography, emphasis in biological oceanography, 2008 Dissertation study area: bio-optics; using optical tools to study ocean ecology (N. California Current) Post-doc in Oceanography, emphasis in biological oceanography, 2008-2012 Study area: bio-optics; using optical tools to study ocean ecology in low oxygen zones (N. Chile) Assistant Professor, Data Management Specialist, Sept. 2012 - present About Amanda… Not a librarian.
  • 3. B.A. in Chemistry, 1990 Modeled drug-receptor ligand binding Ph.D. in Neuroscience, 1999, Dissertation study area: Identification of novel genes involved in neural development in the mouse Post-doc, 2002-2004 Study area: Toxic effects of biocides in zebrafish and salmon Assistant Professor, Library, 2010 – present Lead semantic research team About Melissa… Not a librarian. Post-doc, 2000-2002, Study area: Role of thyroid hormone during neural cell death in zebrafish Post-doc, 2002-2004 Study area: Ontologies, data models, gene nomenclature, biocuration ?
  • 4. Do you have any data-related tasks or responsibilities in your job description or duties? [Yes/No] What role do you believe metadata plays in the modern research cycle? [big, small, none, other] Questions
  • 5. Why data management? The researcher perspective Why libraries? Why bring in non-librarians? Amanda & Melissa share their experiences Wrap-up image credit: http://www.flickr.com/photos/54803625@N08/8296296949/
  • 6. “…the recorded factual material commonly accepted in the scientific community as necessary to validate research findings.” Research data is: U.S. Office of Management and Budget, Circular A-110 6
  • 7. “Unlike other types of information, research data are collected, observed, or created, for the purposes of analysis to produce and validate original research results.” What is research data? University of Edinburgh MANTRA Research Data Management Training, ‘Research Data Explained’ 7
  • 8. Actions that contribute to effective storage, use, preservation, and reuse of data and documentation throughout the research lifecycle. Data management:
  • 9. Why data management?
  • 10. Images collected by DataONE.org
  • 11. Photocourtesyofwww.carboafrica.net Data is collected from sensors, sensor networks, remote sensing, observations, and more - this calls for increased attention to data management and stewardship Data deluge Photocourtesyof http://modis.gsfc.nasa.gov/ Photocourtesyof http://www.futurlec.com CCimagebytajaionFlickr CCimagebyCIMMYTonFlickr ImagecollectedbyVivHutchinson Slide credit: http://www.dataone.org/education-modules
  • 12. Federal movement toward open data 1985: National Research Council 1999: OMB Circular A-110 revisions 2003: NIH Data Sharing Policy 2008: NIH Public Access Policy 2011: NSF DMP requirement 2012: NEH, Office of Digital Humanities DMP requirement 2013: NSF bio- sketch change 2013: OSTP memo on public access to results of federally funded data
  • 13. More funder mandates are coming 22 Feb. 2013
  • 14. The memorandum states that, “digitally formatted scientific data resulting from unclassified research supported wholly or in part by Federal funding should be stored and publicly accessible to search, retrieve, and analyze.” To this end, federal agencies must create a public access plan that includes the following mandates: • Maximize public access to data while protecting personal privacy and confidentiality, intellectual property, and balancing costs with long-term benefits; • Ensure that investigators create data management plans that describe strategies for long-term preservation of and access to data; • Costs of data management are included in proposal budgets; • Ensure that the merits of data management plans are properly evaluated; • Implement mechanisms to ensure that investigators comply with their data management plans and policies; • Promote deposition of data into publicly accessible repositories; • Encourage private and public cooperation to improve data access and interoperability; • Develop and standardize approaches to data citation/attribution; • Support training in data management best practices; • Assess needs and strategies for the long-term preservation of data.
  • 15. Journal data policies
  • 16. Information propagation tales: The researcher’s perspective
  • 17. Data isn’t always what it seems
  • 18. Assertion: “β amyloid, known for its role in injuring brain in Alzheimer’s disease, is also produced by and injures skeletal muscle fibres in the muscle disease sporadic inclusion body myositis.” Greenberg 2009
  • 19. BMJ 2009;339:b2680 doi:10.1136/bmj.b2680 All 242 papers point to 4 from same lab, and very few to the ones with negative results Greenberg, 2009
  • 20. How do we believe what we think we know?  Is it true or do we just believe it because everyone else does?  How do we transcend “follow the leader”? What tools can we build to help us?
  • 21. How reproducible is science? Let’s start simple. Do we know what the ingredients were?
  • 22. Journal guidelines for methods are often poor and space is limited “All companies from which materials were obtained should be listed.” - A well-known journal Reproducibility is dependent at a minimum, on using the same resources. But…
  • 23. How identifiable are resources in the published literature? An experiment in reproducibility Gather journal articles 5 domains: Immunology Cell biology Neuroscience Developmental biology General biology 3 impact factors: High Medium Low 84 Journals 248 papers 707 antibodies 104 cell lines 258 constructs 210 knockdown reagents 437 model organisms
  • 24. Only ~50% of resources were identifiable Vasilevsky et al, 2013, PeerJ
  • 25. There is no correlation between impact factor and resource identification Journal Impact Factor 0 10 20 30 40 Fractionofresourcesidentified 0.0 0.2 0.4 0.6 0.8 1.0 Antibodies Cell Lines Constructs Knockdown reagents Organisms
  • 26. Maybe labs are just disorganized?
  • 27. Meet the Urban Lab Meet the Urban Lab
  • 28. A+ organization! The Urban lab antibodies
  • 29. Of 9 antibodies published in 5 articles, only 44% were identifiable Percentidentifiable 0% 25% 50% 75% 100% Commerical Ab identifiable Catalog number reported Source organism reported Target uniquely identifiable
  • 30. Resource information is not adequately getting into the literature, EVEN THOUGH IT IS READILY AVAILABLE The problem is a lack of standards, review, and tools LIBRARIES CAN HELP!!!!!!
  • 31. http://www.force11.org/Resource_Identification_Initiative Numerous endorsers https://www.force11.org/RII/SignUp Implementation of the new standard http://biosharing.org/bsg-000532
  • 32. Sample citation: Polyclonal rabbit anti- MAPK3 antibody, Abgent, Cat# AP7251E, RRID:AB_2140114 1. Research er submits a manuscri pt for publicatio n 2. Editor or Publisher OR LIBRARIA N! asks for inclusion of RRID 3. Author goes to Research Identification Portal to locate RRID 4. RRID is included in Methods section and as Keyword Publishing Workflow
  • 33. http://www.economist.com/news/briefing/21588057-scientists-think-science-self-correcting-alarming- degree-it-not-trouble
  • 34. $1.3 million grant from the Laura and John Arnold Foundation to validate 50 landmark cancer biology studies Partnership between Science Exchange, PLoS, FigShare, Mendelay, and some of us scientists
  • 35. Librarians can help researchers understand:  How to be critical of data and where it came from  Data provenance and meeting data standards  That there is a need to reinterpret data when new information comes to light  That reproducibility depends on many things, including very basic things  Why both retrospective and prospective efforts are needed to ensure data quality, consistency, and utility
  • 36. Amanda’s dissertation The spectral backscattering properties of marine particles Observations ship-based sampling & moored instruments Simulation results scattering & absorption of light Experimental optical properties of phytoplankton cultures Derived variables endless things Compiled observations global oceanic bio- optical observations [self + from peers] Reference global oceanic bio- optical observations [NASA]
  • 37. Why libraries? OSU Libraries Digital Collections | http://oregondigital.org/u?/archives,31
  • 38. image: http://www.beautiful-libraries.com/7200-1.html
  • 39. Agricultural Sciences Engineering Education Business Liberal Arts Public Health & Human Sciences Veterinary Medicine Science Pharmacy Forestry Earth, Ocean & Atmospheric Sci. Libraries
  • 40. Libraries
  • 41. http://www.ala.org/acrl/sites/ala.org.acrl/files/content/publications/whitepapers/Tenopir_Birch_Allard.pdf “Only a small minority of academic libraries in the United States and Canada currently offer research data services (RDS), but a quarter to a third of all academic libraries are planning to offer some services within the next two years.” “Few academic libraries are responsible for developing research data policies. Being able to serve as a clearinghouse of ideas and to provide expertise to build these policies is an opportunity for libraries to be members of the knowledge creation process.” “Reassigning existing library staff is the most common tactic for offering RDS.”
  • 42. Our experiences http://clubads.com/photos/custom/fish-OutOfWAter.jpg
  • 43. Timeline of data services at OSU UL & library admin. recognize need for role of RDS on campus that requires a dedicated FTE late 2011 Sept. 2012 Data Management Specialist starts Oct. 2013 Data survey launches Strategic Agenda in place* Jan. 2013 GRAD 521 launches Jan. 2014 *Sutton, Shan; Barber, David; Whitmire, Amanda L. (2013): Oregon State University Libraries and Press Strategic Agenda for Research Data Services. Oregon State University Libraries. http://hdl.handle.net/1957/38794. ESI
  • 44. OSU Data stewardship survey Interview by Sarah Abraham from The Noun Project
  • 45. Responses to the question, “Please indicate whether or not you generate each of the following data format(s) as a part of your research process. Select Yes or No for each.” Color scale indicates what percentage of respondents in each college or unit selected ‘Yes’ for each data type. The number in each tile shows the number of faculty responses for that data type and college/unit.
  • 46. Scope of Data Services at OSU
  • 47. Research Analysis of data management plans as a means to inform and empower academic librarians in providing research data support. National Leadership Grant LG-07-13-0328, Oct 2014 – Sept 2015 Data management plans As a Research Tool The DART Project
  • 48. Consultations
  • 49. Teaching: GRAD 521 Logistical Details • http://bit.ly/GRAD521 • All course materials on figshare • 2 credits • Discipline-agnostic • Offered annually, winter quarter Topics covered • Overview of RDM • Types, formats & stages of data • RDM planning • Storage, backup & security • Documentation & metadata • Legal & ethical considerations • Sharing & reuse • Archive and preservation
  • 50. Timeline of data activities at OHSU OHSU library awarded eagle-i late 2009 Sept. 2012 Monarch Initiative awarded Oct. 2013 Data survey launches Beyond the PDF 1K challenge award April 2013 OHSU hiring CRIO position Now ESI NIH BD2K program
  • 51. OHSU Data stewardship survey Interview by Sarah Abraham from The Noun Project
  • 52. 0% 10% 20% 30% 40% 50% 60% Specific Uniform Resource Identifier (URI) or other URL where data is held Contact information of the data steward Reference to a public repository where the data is held Provide supplementary data to the journal SPARQL endpoint and/or Linked Open Data Digital Object Identifier (DOI) I don't know Other (please specify) How do you reference your data when you publish, either in the context of a journal publication, or by direct publication of data sets?
  • 53. Are there any professional community standards in your research area regarding data management, sharing, storage, archiving, and/or producing metadata or other descriptive information that would apply to your research data? Answer Instructor Assistant Professor, Research Assistant Professor, or Assistant Scientist Associate Professor or Associate Scientist Professor or Senior Scientist Director, Division Head, Department Head PostDoc/ ResAssoc/ PhD Yes 1 9 5 16 6 13 No 1 8 9 15 1 10 I don't know 1 19 13 14 4 19
  • 54. Scope of Data Services at OHSU Open houses, Lib Guides, NIH proposals to improve data education, hosting fellows New IR, research profiling tools Participation in national efforts: BD2K, Force11, Galaxy, Biocuration Society Data consults, collaborations
  • 55. Consultations
  • 56. NIH Big Data to Knowledge Initiative http://bd2k.nih.gov/
  • 57. 1 | Can facilitate the creation of a smarter body of literature for future research 2 | Train researchers to utilize metadata standards to enable data reuse 3 | Facilitate researchers understanding of available resources Libraries, in summary…
  • 58. Members from: Oregon Health & Science University Oregon State University University of Oregon University of Idaho University of Washington Portland State University Reed College Join us @ bit.ly/pnwdatalibs Also we need a logo: Free data science training for good suggestions! PNW Research Data Geeks Group http://commons.wikimedia.org/wiki/File:DARPA_Big_Data.jpg
  • 59. How do you think libraries can best facilitate best practices in data management?