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DMPTool and Data Management Basics. Hannah Norton July 29, 2014. Image modified from : http://www.flickr.com/photos/blprnt/3642742876/in/photostream /. Background: the Data Lifecycle. Data Archiving. Data Analysis. Data Collection. Data Distribution. Data Analysis. Study Concept.

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DMPTool and Data Management Basics

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Dmptool and data management basics

DMPTool and Data Management Basics

Hannah Norton

July 29, 2014

Image modified from : http://www.flickr.com/photos/blprnt/3642742876/in/photostream/


Background the data lifecycle

Background: the Data Lifecycle

Data Archiving

Data Analysis

Data Collection

Data Distribution

Data Analysis

Study Concept

Data Processing

Data Discovery

Repurposing

Data Management Planning

* Based on Data Documentation Initiative (DDI) version 3.0 Combined Life Cycle Model


What is a data management plan dmp

What is a data management plan (DMP)?

  • A clear description of how you plan to address data management issues in your research.

  • A way to communicate your data management efforts to members of your team and others (especially funders).

    A data management plan gives a concise description of the who, what, where, and when of your data throughout its life cycle.


Why do researchers need a data management plan dmp

Why do researchers need a Data Management Plan (DMP)?

For all the same reasons you should take care of your data…

To ensure that valuable data resources will be accessible in the future to members of the research team and the broader community.

To make life easier – by planning ahead and documenting data throughout its life cycle, researchers can save time and focus on research.

To increase the visibility of research.

To satisfy funders’ requirements.


Components of a dmp

Components of a DMP

  • Project description

  • Data collection:

    • Types of data

    • Data and metadata standards to be used

  • Legal and ethical issues:

    • Privacy and confidentiality

    • Intellectual property rights

  • Policies for data sharing and re-use

  • Data preservation (long-term)

  • Who is responsible for data management


Http dmptool org

http://dmptool.org


Log in to dmptool with gatorlink

Log in to DMPTool with Gatorlink


Funders with dmptool templates

Funders with DMPTool Templates

  • Alfred P. Sloan Foundation

  • Gordon and Betty Moore Foundation

  • Gulf of Mexico Research Initiative

  • Institute of Education Sciences (US Dept of Education)

  • Institute of Museum and Library Services

  • Joint Fire Science Program

  • National Institutes of Health

  • National Endowment for the Humanities – Office of Digital Humanities

  • National Science Foundation (General and 11 Directorates)

  • U.S. Geological Survey


Http library ufl edu datamgmt

http://library.ufl.edu/datamgmt


Http guides uflib ufl edu datamanagement

http://guides.uflib.ufl.edu/datamanagement


Sample dmps from uf

Sample DMPs from UF

  • Example text in the [email protected]: http://ufdc.ufl.edu/AA00014694/00001/

  • Research Computing guidance on Data Management Plans (includes links to UF College of Engineering and Department of Astronomy guides): http://www.hpc.ufl.edu/research/proposal-support/data-management-plan/


Components of a dmp1

Components of a DMP

  • Project description

  • Data collection

  • Legal and ethical issues

  • Policies for data sharing and re-use

  • Data preservation (long-term)

  • Who is responsible for data management


Example data collection questions

Example data collection questions

  • What file formats will you use for your data, and why? What metadata/documentation will be submitted alongside the data? (NIH)

  • Describe the data to be collected (actual observations) during your research including amount (if known). Name the type of data, the instrument or collection approach, and how the data will be sampled. (NSF-BIO)

  • Give a short description of the data, including amount (estimated amount or known amount) and content. Data types could include XML spreadsheets, interview transcripts, text files, historical documents, diaries, field notes, geospatial data, citations, software code, algorithms, etc. (NEH)


Data generated throughout the lifecycle has different needs

Data generated throughout the lifecycle has different needs

  • Raw data - some must be kept forever, others can be discarded after the project is complete

  • Intermediate data for analyzing and processing - can be often be discarded at the end of the computation, but computational methods should be kept for reproducibility

  • Final data - should be made available indefinitely to the community


File formats

File formats

Formats with the following characteristics are considered relatively stable and better for long-term preservation:

  • open documentation

  • support across a range of software platforms

  • wide adoption

  • no compression (or lossless compression)

  • no embedded files or embedded programs/scripts

  • non-proprietary format

    See the following for preferred and accepted file formats for the [email protected]: http://ufdc.ufl.edu/AA00017119/00011


What exactly i s metadata again

What exactly is metadata again?

  • Descriptive information that helps you and others understand your data

  • “Data about data” that acts as a surrogate for your datawhen you or others are trying to:

    • Find the data later

    • Know what the data is later

    • Share the data later


Metadata across the disciplines

Metadata across the disciplines

Basic information to keep:

  • Descriptive

    • What is it about?

    • Title, time, author, keywords

    • Relations to other data objects

  • Administrative

    • Ownership and use permissions

  • Provenance

    • Where does it come from?

    • History of changes to the data, versions

      More specific information varies by discipline


Components of a dmp2

Components of a DMP

  • Project description

  • Data collection

  • Legal and ethical issues

  • Policies for data sharing and re-use

  • Data preservation (long-term)

  • Who is responsible for data management


Example legal ethical questions

Example legal/ethical questions

  • Procedures for managing and for maintaining the confidentiality of the data to be shared (IES)

  • Will any permission restrictions need to be placed on the data? (NSF-BIO)

  • Policies for public access and sharing should be described, including provisions for appropriate protection of privacy, confidentiality, security, intellectual property, or other rights or requirements. (NEH)


Components of a dmp3

Components of a DMP

  • Project description

  • Data collection

  • Legal and ethical issues

  • Policies for data sharing and re-use

  • Data preservation (long-term)

  • Who is responsible for data management


Example data sharing questions

Example data sharing questions

  • Will you share data via a repository, handle requests directly or use another mechanism? (IES)

  • What transformations will be necessary to prepare data for preservation/data sharing? (NIH)

  • How long will the original data collector/creator/principal investigator retain the right to use the data before opening it up to wider use? (NEH)


Example data preservation archiving questions

Example data preservation/archiving questions

  • If your method of sharing is with an archive, which archive/repository/database have you identified as a place to deposit data? (IES)

  • What is the long-term strategy for maintaining, curating and archiving the data? (NSF-BIO)

  • The Data Management Plan should describe physical and cyber resources and facilities that will be used for the effective preservation and storage of research data. These can include third party facilities and repositories. (NEH)


Finding a home for your data

Finding a home for your data

  • Data storage, both short-term and long-term, can take place in 3 types of places:

    • Locally, within the lab or research environment

    • Within the institution

    • Within a national/discipline-based repository

      See the following guide to find discipline-based repositories: http://guides.uflib.ufl.edu/datasets


Http www hpc ufl edu

http://www.hpc.ufl.edu/


Repositories

Repositories

Advantages of an institutional repository:

  • Linked to your institution – intellectual capital of the institution in one place

  • You can put all your datasets together

  • Some guarantee of support from the university

  • Some domain repositories may “go out of business” once their funding ends

Advantages of a domain repository:

  • Your data will stored with similar datasets

  • Researchers in your discipline will may find your data more easily

  • The repository will understand what your data needs in terms of storage, archiving and preservation

  • Computational tools may be developed to crunch a critical mass of data of a certain kind

Adapted from: http://libraries.mit.edu/guides/subjects/data-management/Managing%20Research%20Data%20101.pdf


Benefits of sharing data

Benefits of sharing data

  • Data can be used by other researchers with different objectives

  • Accelerate the time of discovery by building upon previous research

  • Results can be reproduced more easily and accurately

  • Researchers receive the credit they’re due

  • Data producers have a new channel by which to promote their work (increase impact of research)


Components of a dmp4

Components of a DMP

  • Project description

  • Data collection

  • Legal and ethical issues

  • Policies for data sharing and re-use

  • Data preservation (long-term)

  • Who is responsible for data management


Example data management responsibility questions

Example data management responsibility questions

  • Roles and responsibilities of project or institutional staff in the management and retention of research data (IES)

  • Who will be responsible for data management and for monitoring the data management plan? How will adherence to this data management plan be checked or demonstrated? (NSF-BIO)

  • Who will have responsibility over time for decisions about the data once the original personnel are no longer available? (NEH)


A cautionary tale

A cautionary tale…

From NYU Health Science Center Libraries: http://youtu.be/N2zK3sAtr-4


Questions

Questions?

Feel free to contact the Data Management/Curation Task Force: [email protected]

Or me: Hannah Norton, [email protected], 352-273-8412


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