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DMPTool Webinar 10: More Extensive DMPs
The document outlines logistics for a webinar series on data management planning, including audio dial-in information and a schedule of topics related to the DMPTool, data management plans, and institutional requirements. Goals of the webinars include reviewing funder requirements and operational data management. It also discusses data management best practices, responsibilities, and the importance of data archiving and sharing.
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DMPTool Webinar 10: More Extensive DMPs
- 1. Logistics for Webinar Youmust call in for audio: 866-740-1260 access code 9870179# Participants muted Ask questions in chat any time 20 minutes for Q&A Recording & slides, schedule of webinars: blog.dmptool.org/webinar-series DMPToolWebinar Series 10: More Extensive DMPs Sponsored by IMLS 1 October 2013
- 2. 28 May Introductionto the DMPTool 4 June Learning about data management: Resources, tools, materials 18 June Customizing the DMPTool for your institution 25 June Environmental Scan:Who's important at your campus 9 July Promoting institutional services; EZID Outreach Made Simple! 16 July Health Sciences & DMPTool - Lisa Federer, UCLA 23 July Digital humanities and the DMPTool - Miriam Posner, UCLA 13 Aug Data curation profiles and the DMPTool – Jake Carlson, Purdue 27 Aug Talking Points for Meeting with Institutional Stakeholders 1 Oct Beyond Funder Requirements: More Extensive DMPs 15 Oct Tools and resources that complement the DMPTool 5 Nov Case studies – Librarians successfully supporting data blog.dmptool.org/webinar-series
- 3. Data Management: Beyond Funder Requirements SherryLake | @shlakeuva Data ManagementConsultant, University ofVirginia Library DMPToolWebinar Series 10: More Extensive DMPs Sponsored by IMLS 1 October 2013 FromFlickrbyjdretro
- 4. Roadmap https://www.facebook.com/charlottesvillevirginia: Photo Instagrammerihugtrees05 Goals of theWebinar Review DMP Funder Requirements Operational Data Management Data ManagementWorkbook Preview of DMPTool2: Institutional Requirements
- 5. • Differentiate between FunderData Management Plan and Operational Data Management • Understand the parts of a Data Management Workbook • Introduce the institutional data management requirements in DMPTool2 PersonalCollection:SherryLake Webinar Goals
- 6. Roadmap https://www.facebook.com/charlottesvillevirginia: Photo Instagrammerihugtrees05 Goals of theWebinar Review DMP Funder Requirements Operational Data Management Data ManagementWorkbook Preview of DMPTool2: Institutional Requirements
- 7. Parts of a(Generic) NSF Data Management Plan I. Products of the Research:The types of data, samples, physical collections, software, curriculum materials, and other materials to be produced in the course of the project. II. Data Formats:The standards to be used for data and metadata format and content (where existing standards are absent or deemed inadequate, this should be documented along with any proposed solutions or remedies). III. Access to Data and Data Sharing Practices and Policies: Policies for access and sharing including provisions for appropriate protection of privacy, confidentiality, security, intellectual property, or other rights or requirements. IV. Policies for Re-Use, Re-Distribution, and Production of Derivatives. V. Archiving of Data: Plans for archiving data, samples, and other research products, and for preservation of access to them. 7 Grant Proposal Guide (GPG) Chapter II.C.2.j http://www.nsf.gov/pubs/policydocs/pappguide/nsf13001/gpg_2.jsp#dmp
- 8. Specific Requirements • Rolesand Responsibilities – outline the rights and obligations of all parties • Project Data Management – how data is managed and maintained during project “lifetime” From Flickr by jesseFewell
- 9. Funder Data ManagementPlan Obligations • Plan monitored via annual and final report • Outcomes reported in subsequent proposals – Under “Results of Prior NSF support” FromFlickrbystarheadboy
- 10. Roadmap https://www.facebook.com/charlottesvillevirginia: Photo Instagrammerihugtrees05 Goals of theWebinar Review DMP Funder Requirements Operational Data Management Data ManagementWorkbook Preview of DMPTool2: Institutional Requirements
- 11. What is aData Management Plan? • Brief description of how you will comply with funder’s data sharing policy • Reviewed as part of a grant application AND • A comprehensive plan of how you will manage your research data throughout the lifecycle of your research project
- 12. Lifecycle Data Management DataLife Cycle Re- Purpose Re- Use Deposit Data Collection Data Analysis Data Sharing Proposal Planning Writing Data Discovery End of Project Data Archive Project Start Up Research Lifecycle: Uva Library Data ManagementConsultingGroup http://dmconsult.library.virginia.edu/lifecycle/
- 13. Operational Data Management •Appoint Data Manager Contact • Describe data to be collected and methodology • Include guidelines on data documentation • Plan security and backup procedures • Plan sharing of data for public use • Include preservation plans • Document copyright and intellectual property rights Ted Nguyens USA Blog: http://www.tednguyenusa.com
- 14. Methods of DataManagement • Data Organization – Working more efficiently with data • DataAdministration – Protecting data and ensuring quality • DataArchiving and Sharing – Archiving for preservation and sharing to promote re-use
- 15. Roadmap https://www.facebook.com/charlottesvillevirginia Photo Instagrammerihugtrees05 Goals of theWebinar Review DMP Funder Requirements Operational Data Management Data ManagementWorkbook Preview of DMPTool2: Institutional Requirements
- 16. Parts of theWorkbook 1.Project description 2. Survey of existing data 3. Data to be created i. Data organization methods 4. Data administration issues: i. Funding and institutional requirements ii. Data owners and stakeholders iii. Access and security iv. Privacy/Confidentiality v. Backups 5. Data sharing and archiving 6. Responsibilities 7. Data documentation and metadata 8. Budget
- 17. Data to beCreated
- 18. Data to beCreated File Naming ConventionsFileVersion Control • Via file naming conventions • Software forVersion Control • NetworkVersion Control
- 19. DataAdministration • Funding &Institutional Policies • Access (working storage) & Security • Backups
- 20. Data Sharing &Archiving
- 21. Other Key Sections Budget •Responsibilities • Data Documentation and Metadata • Budget
- 22. When a PlanComesTogether • Consistent Data Management • Can be used for training • Use parts for Data Management Reports • Easy to record changes (i.e., policy) Rich Murmane Blog: http://richmurnane.blogspot.com/
- 23. Roadmap https://www.facebook.com/charlottesvillevirginia Photo Instagrammerihugtrees05 Goals of theWebinar Review DMP Funder Requirements Operational Data Management Data ManagementWorkbook Preview of DMPTool2: Institutional Requirements
- 24. Institutional Requirements • Institutionscan create local data management requirements • Can reflect institution data management policies Flickrby:jackson_lo
- 26.
- 27. ANU Data ManagementManual: Managing Digital Research Data at theAustralian NationalUniversity (6th Edition): http://tinyurl.com/ANU- DMP Managing and Sharing Data: Best Practices for Researchers (3rd Edition): http://www.data- archive.ac.uk/media/2894/managing sharing.pdf University ofVirginia Data Management PlanningWorkbook, e- mail: shlake@virginia.edu For More Information Flickrby:SpacemanSpiff,intergalacticexplorer!
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- 29. DMP: Beyond the2-Pager James Gibeaut, PhD GRIIDC Director and Endowed Chair for Geospatial Sciences Harte Research Institute for Gulf of Mexico Studies Texas A&M University – Corpus Christi, TX (email: james.gibeaut@tamucc.edu) Felimon Gayanilo Systems Architect Harte Research Institute for Gulf of Mexico Studies Texas A&M University – Corpus Christi, TX (email: felimon.gayanilo@tamucc.edu)
- 30. Mission The mission ofGulf of Mexico Research Initiative (GoMRI) is to implement an independent research program that will (1) Study the effects of the Deepwater Horizon incident and the potential associated impact of this and similar incidents on the environment and public health and (2) Develop improvements for spill mitigation, oil detection and characterization, and advanced remediation technologies. Presenter: J. Gibeaut
- 31. Goal The ultimate goalof the GoMRI is be to improve society’s ability to understand, respond to and mitigate the impacts of petroleum pollution and related stressors of the marine and coastal ecosystems, with an emphasis on conditions found in the Gulf of Mexico. Knowledge accrued will be applied to restoration and to improving the long-term environmental health of the Gulf of Mexico. Presenter: J. Gibeaut
- 32. Research Themes 1. Physicaldistribution and ultimate fate of contaminants associated with the Deepwater Horizon incident; 2. Chemical evolution and biological degradation of the contaminants; 3. Environmental effects of the contaminants on Gulf of Mexico ecosystems, and the science of ecosystem recovery; 4. Technology developments for improved detection, characterization, mitigation, and remediation of offshore oil spills; and 5. Impacts of oil spills on public health. Presenter: J. Gibeaut
- 33. Presenter: J. Gibeaut GoMRIFunds Awarded as of December 31, 2011
- 34. Data Management Policy GuidingPrinciples • Timely submission of data and model outputs to GRIIDC and to national repositories • Use of existing data standards and management systems as much as practicable • Employ best practices for data policy and data management as elucidated by NSF and NOAA or other agency appropriate for the topic of study • A strong commitment to data management by each PI Presenter: J. Gibeaut
- 35. Request for Proposaland the DMP • RFPs are drafted by the GoMRI Research Board and announced through the GoMRI website (see http://gulfresearchinitiative.org/request-for-proposals/rfp-iv-save- the-date/ for the next RFP) • Includes pre-proposal and full proposal requirement with abbreviated DMP. Post-Award: Revisiting the DMP • GoMRI/GRIIDCTemplate (http://data.gulfresearchinitiative.org/docs/DMP/Gulf-of- Mexico-Research-Initiative-Data-Management-Plan- Template.pdf) • DMPTool (https://dmp.cdlib.org/; GoMRI template is also available) • Example DMPs are also posted on GRIIDC site Presenter: J. Gibeaut
- 36. Dataset: Concept toRegistration Presenter:F.Gayanilo
- 37. Dataset: Concept toRegistration Presenter:F.Gayanilo
- 38.
- 39. Dataset: Concept toRegistration Presenter:F.Gayanilo
- 40. Dataset Information Form(DIF) • Inform the community of what is planned to be collected, and • Inform GRIIDC of what is expected to come. Presenter:F.Gayanilo
- 41. Dataset: Concept toRegistration Presenter:F.Gayanilo
- 42.
- 43. Dataset: Concept toRegistration Presenter:F.Gayanilo
- 44.
- 45. ISO 19115/19115-2 MetadataEditor Presenter:F.Gayanilo
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- 48. Thank you! DMP: Beyondthe 2-Pager James Gibeaut, PhD GRIIDC Director and Endowed Chair for Geospatial Sciences Harte Research Institute for Gulf of Mexico Studies Texas A&M University – Corpus Christi, TX (email: james.gibeaut@tamucc.edu) Felimon Gayanilo Systems Architect Harte Research Institute for Gulf of Mexico Studies Texas A&M University – Corpus Christi, TX (email: felimon.gayanilo@tamucc.edu)
- 49. blog.dmptool.org/ webinar-series From Flickr byJeff Keacher In 2 weeks: ComplementaryTools Presenters: Carly Strasser, Perry Willett, Marisa Strong, Center for Open Science Tuesday 15 Oct @ 10am PT
- 50.
- 51. blog.dmptool.org/webinar-series 28 May Introductionto the DMPTool 4 June Learning about data management: Resources, tools, materials 18 June Customizing the DMPTool for your institution 25 June Environmental Scan:Who's important at your campus 9 July Promoting institutional services; EZID Outreach Made Simple! 16 July Health Sciences & DMPTool - Lisa Federer, UCLA 23 July Digital humanities and the DMPTool - Miriam Posner, UCLA 13 Aug Data curation profiles and the DMPTool – Jake Carlson, Purdue 27 Aug Talking Points for Meeting with Institutional Stakeholders 1 Oct Beyond Funder Requirements: More Extensive DMPs 15 Oct Tools and resources that complement the DMPTool 5 Nov Case studies – Librarians successfully supporting data
- 52.
Editor's Notes
- #2 Change Title, webinar #, and date in green text at top
- #8 Hopefully you are familiar with the NSF DMP requirement. This is the funder that I’m going to focus on in this 1st section. These are the parts that “may” be included in an NSF Data Management Plan.NSF defines a Data Management Plan as: Plans for data management and sharing of the products of research. …….This supplement should describe how the proposal will conform to NSF policy on the dissemination and sharing of research results
- #9 Divisions and specific directorates in the NSF have refined the requirements and have asked researchers to include more information in those specific data management plans. Roles (ENG, SBE, CISE, BIO others)CISE (Directorate for Computer & Information Science & Engineering) Roles:This should include time allocations, project management of technical aspects, training requirements, and contributions of non-project staff—individuals should be named where possible.EHR (Directorate for Education & Human Resources): Specifies to: Identify your methods for collecting data. with how data is managed and maintained during the project's lifetime until it is "shared.”
- #10 So you can’t just write a DM plan and be over it. You have to report on your data management (plan). You will have to report on your data management plan (and state changes) in your annual reports. Then at the end of your funding, you will have to report how and where you have shared your data per NSF requirements.We haven’t seen this in action yet, but in order to get future NSF funding, you need to list your shared data (where it is archived, what you archived) under “Results of Prior NSF support”.
- #11 Next I’m going to talk about a new concept, maybe to some. Operational Data Management, how that differs from the Funder DMP requirement.
- #12 Want to step back and ask the “Big” question? What is a Data Management Plan?We have already seen that it is…..[read 1st 2 bullets]AND now I’m going to talk about “THE” or “BIG” Data Management Plan …. [read last bullet]
- #13 Data Management is managing the lifecycle of data including: collection, formatting, organizing, documenting, security, updating access & sharing (during and after), quality control, transformations and destruction.That means managing how it's collected in one or many systems and how it's represented and arranged in database systems.It also means managing how these bits of information are thoroughly documented, backed up safely, monitored over time.Protected from unauthorized access or changes shared with other people or systems, updated with new information.Checked for quality and corrected if errors are found. How it's converted for different uses. And finally how it's destroyed.Remember: Managing Data in a research project is a process that runs throughout the project. Good data management is the foundation for good research. Especially if you are going to share your data. This is the “main” reason for the funder requirements: to share data that has been funded with public money.Good management is essential to ensure that data can be preserved and remain accessible in the long-term, so it can be re-used and understood by other researchers. When managed and preserved properly research data can be successfully used for future scientific purposes.
- #14 So a data management plan is not just for the proposal. It is a plan that a research project should implement during the project – here I call it “Operational” to differentiate it from the “data management plan”. An operational plan provides:To the “team” information about the plan (specifics) so everyone will know what is required of each other.Consistency, to ensure that all “operations” parts are done the same way w/in the team/lab.Documented methodology and guides lines that can be used for training when new team members join.To serve as a training document for teaching users about a process To serve as an historical record of the how, why and when of steps in a process for use when modifications are made to that process To serve as an explanation of steps in a process that can be reviewed and included in annual and final reports.
- #15 The overall Operational Data Management can be divided into 3 main sections.[read bullets]
- #16 Now let’s put some specific Operational Data Management specifics in a “workbook”.
- #17 We, at Uva have created a Data Management Workbook, (or Notebook, or Manual). We are still in debate as to what we should call it.To create our workbook, we used the Australian National University’s Data Management Manual as a model. This is a great resource and I have the link at the end of my slides. As you can see it contains more than the 3 Operational Data Management sections on a previous slide. Remember the purpose of the Workbook is to document all parts of the project from funding (Budget) to archiving.Remember that a data management plan is a living document and should be reviewed and updated regularly, especially if unforeseen data is collected. Use the section in the workbook to document the management of your data.
- #18 For each section, the researcher is going to “write” in the book (that’s why we are calling it a workbook – notebook), something that is to be changed (and will) over time.We have added a table like this in our book.You should list all the data that will be created during the project. The remainder of the DMP then deals with how each item of data will be managed.
- #19 Also in the “Data to be Created” section, directory and file name conventions can be detailed. What file version will the lab be using (via filenames or w/ software)?Organization methodsDirectory structureFile naming conventionsFile Version Controlrecords changes to a file or set of files over time so that you can recall specific versions later.
- #20 For the Data Administration section, this should include specifics about access to the data, how will it be protected if needed, specific information on backups – who’s responsible. How (if) will data be shared during the project. What media will the group be used.Here I show a couple of pages from our workbook. For security, they can “check” which security(s) will be required. We also have room in the workbook to write about the specific policies. Same for the backup.
- #21 Data sharing and archiving part of the Data Management Workbook might not get filled out until near the end of the project. Here researchers can list the data that they are planning to share and list the formats of the files. We offer tips on sharing and archving.The workbook lets us include local policies and information. Here we would include URLs to local storage options, local policies on data retention and archiving options.
- #22 There are other key sections to complete a Data Management Workbook: Responsibilities (who’s in charge of what during the project). A nice chart listing roles and people would be great to include in this section.The Data Documentation and Metadata will be a HUGE section in the workbook. In fact this is not a section in the Australian National University’s manual. The creation of this is its own webinar. I included it here to make sure it gets attention and not forgotten, or left for the last minute. Data documentation & metadata needs to be included with the data that you are archiving and sharing. In our workbook, we include a sample “Readme” file from our Institutional Repository’s readme template. Budget is the last part of the manual:Now that the data management methods and responsibilities have been established, you can estimate the costs of data management for your project. Often the time involved in documenting, writing metadata, and archiving are underestimated. Make note of any costs associated with using data management services or purchasing equipment (such as fileservers, backup media, software, etc.) used for data management.
- #23 So how do I get started managing data? Why is a Data Management Plan beyond the funder requirements needed?I hope I have enlightened you on the necessity and the contents on a DM Workbook.This is at the project (overall) levelBest Time to develop your data management plan is at the beginning of your research. Take that Plan you created for your proposal and expand upon that.The plan can be used for training new people.
- #25 Do you know if your institution has local data management requirements – policies?If so, or if your institution is considering such policies or requirements, you will be able to include them in DMPTool, Version 2.Let me show you how……
- #26 In DMPTool2 we have new roles – one is the Institutional –administrator who can customize the DMPTool with local customizations (resource links, local help, etc.) AND a new customization, can create institutional DMP templates.Here’s a screen shot of DMPTool2. It’s not the final look-N-feel, but I want to focus on the functionality of creating Institutional Requirements. The institutional-administrator can also assign others to be DMP template editors.
- #27 This next page is where the template is created.(per local institution).Follow the development of DMPTool here on the DMPTool2 Project page:https://bitbucket.org/dmptool/main/wiki/DMPTool2Project
- #50 http://www.flickr.com/photos/13261823@N05/6622322535/