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DMPTool Webinar 10: More Extensive DMPs
 

DMPTool Webinar 10: More Extensive DMPs

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Data management plans existed long before the NSF started requiring them. DMPs have inherent value despite their being relatively unknown to researchers until now. Proper, thorough data management ...

Data management plans existed long before the NSF started requiring them. DMPs have inherent value despite their being relatively unknown to researchers until now. Proper, thorough data management plans are potentially a major time saver and a huge asset for the project. In this webinar, we will cover how to go beyond funder requirements and develop more thorough data DMPs The Gulf of Mexico Research Initiative requires an extensive data management plan for projects it funds; we will hear about their efforts and how they are planning to use the DMPTool going forward.

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  • Change Title, webinar #, and date in green text at top
  • 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
  • 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.”
  • 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”.
  • Next I’m going to talk about a new concept, maybe to some. Operational Data Management, how that differs from the Funder DMP requirement.
  • 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]
  • 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.
  • 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.
  • The overall Operational Data Management can be divided into 3 main sections.[read bullets]
  • Now let’s put some specific Operational Data Management specifics in a “workbook”.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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……
  • 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.
  • 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
  • http://www.flickr.com/photos/13261823@N05/6622322535/

DMPTool Webinar 10: More Extensive DMPs DMPTool Webinar 10: More Extensive DMPs Presentation Transcript