Research Data Management 101

The Lifecycle of a Dataset

Source: MITLibraries, https://ocw.mit.edu/resources/res-str-002-data-management-spring-2016/workshop-materials/MITRES_STR002S16_IntroDM.pdf
Workshop Materials PDF

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Today's session: Topics

Today’s session: the research data lifecycle

Research data lifecycle phases

What do we mean by data?

A case study

Pre-project: planning

Where do you start?

1. Consider your goals.

• Part of ongoing research
• Compliance with funder or publisher requirements:
• Dissemination for others' use

1. Consider your goals.
2. What are you collecting?
3. What are you keeping?

1. Consider your goals.
2. What are you collecting?
3. What are you keeping?
4. Where do you want to keep it?

1. Consider your goals.
2. What are you collecting?
3. What are you keeping?
4. Where do you want to keep it?
5. What do you need to be able to use it & share it later?

Funder requirements

• Funding agencies increasingly requiring people to share outputs of research, including data (and publications)
• Their motivation: extend the impact of their research farther
• What funders require data sharing?
• Most federal agencies and many private funders as well
• What do funders require?
• Data management plan (DMP) submitted with grant application
• Can request funds for data management in the grant
• Sharing of final data produced
• See: http://libraries.mit.edu/oarequirements
• In our case study, my research is funded by NSF; I have to:
• Share all of the data generated in my project
• Include a DMP in my grant application

What can help me write my DMP? DMPTool!

• New tool to help you create your data management plan
• Guides you through writing a DMP specific to a funder's requirements using templates 

Data Documentation (AKA Metadata)

Metadata: why does it matter?

Metadata: why does it matter?

Data is not self-describing.

• Finding the data later
• Understanding what the data is later
• Sharing the data (both with collaborators and future secondary data users)
• Consider it an investment of time that will save you trouble later several-fold

Metadata standards

Examples:

• FGDC (Federal Geographic Data Committee)
• DDI (Data Documentation Initiative)
• Dublin Core
• Darwin Core
• ABCD (Access to Biological Collections Data)
• AVMS (Astronomy Visualization Metadata Standard)
• CSDGM (Content Standard for Digital Geospatial Metadata)

Advantages:

• Ensure you have a complete, standard set of information about each part of your data
• Enable your dataset to be organized with other datasets 

Metadata

Do what works for you!

Document and describe your data

in whatever way works for you.

Better "good enough" than doing nothing

Metadata: our case study

Possible metadata options:

1. Dublin Core

• General metadata standard
• Widely applicable
• Used in many different repositories

2. Darwin Core

• For biological diversity
• Emphasizes taxonomy, which I don't care about
• Frequently used in biodiversity databases 

Metadata: our case study

Our directory: sam_monarch_wing_20150415

Metadata for this directory:

• Creator: Katherine McNeill
• Subject: monarch butterfly wing
• Description: this directory contains Sashimi ESEM images of a monarch butterfly wing I took after finding a butterfly floating by the Charles River near MIT
• Contributor: Mark Clemente helped me with these images
• Date: 20151015
• Original Format: Sashimi Microscope format (.sam)
• Relation: this is a directory that will contain multiple files
• Type: image
• Coverage: By the Charles River in Cambridge, MA, MIT side
• Rights: Monarch Butterfly Research Foundation (funder) owns the data (grant number: 00213)

Metadata: our case study

Metadata for this image:

• Title:

• Source:

• Relation:

Metadata: capturing it

In a filename

In a readme file

In a spreadsheet

In an XML file

Into a database

Active phase of research

Storing data

What are your goals?

• To find a place to store your data?
• To appropriately back up your data so that it's not lost? 

Backing up data: storing during active phase

Ideally, keep three copies of your data:

1. local/working copy
   e.g. on your workstation or in shared workspace
2. remote copy
   e.g. on a managed backup system
3. other copy in another remote location, or local/external
   e.g., external hard drive* (CDs and DVDs not built to last)

File Organization & File Formats

File organization: naming conventions

Naming conventions make life easier!

Naming conventions should be:

• Descriptive

• Consistent

File organization: naming conventions

Naming conventions make life easier!

Naming conventions should be:

• Descriptive

• Consistent

For our case study: 

File organization: file structure

For our case study:

File organization: versioning

Track versions of either:

• Analysis/program/script files, while keep the original version of the data file the same; OR
• Data files themselves

In some cases, it may make sense to log the changes so that

you can quickly assess and access the versions.

It's good to document:

• What was changed?
• Who is responsible?
• When did it happen?
• Why?

File formats

In the best case, your data files are both:

• Non-proprietary (also known as open), and
• Unencrypted and uncompressed

File formats: preferred examples

File formats: converting considerations

Information can be lost when converting file formats.

To mitigate the risk of lost information when converting:

– Note the conversion steps you take

– If possible, keep the original file as well as the converted ones 

File formats: our case study

• My data is in "Sashimi Environmental Scanning Electron

Microscope (ESEM)" format!

– Problem: This is a proprietary format. How can I convert it to a more open-non proprietary format that will be usable in the future?

– Solution: I found Bio-Formats, a standalone Java library for reading and writing life sciences image file formats into alternative open formats.

Sharing & Preserving Data

Sharing data: why?

• Further science as a whole
• Further your research/reputation
• Enable new discoveries with your data
• Comply with funder/publisher data sharing requirements

Share Informally:

Posting on a web site, sending via email upon request

Share Formally:

Via a repository, which may also provide preservation and makes your data more accessible and citable

Preserving your data

What happens to your data when…

• the software you use to render it changes or becomes obsolete?
• the platform on which you manipulate it changes?
• the hardware you created it on becomes obsolete?

Preservation means storing your data in a place where it will be:

• Stored
• Backed up
• Discoverable
• Accessible for the future (as much as possible)

Preservation means that it is a particular person's job to make every effort to make the data usable in the future.

Preservation = Long-term access

Some repositories ensure preservation of data over time

Storing data: repositories

Overall advantages:

• May preserve your data for the future
• Provides a metadata structure
• Serves as a backup vehicle for your data
• Makes sharing your data accessible and citable by others
• May provide some computational/online analysis tools for people to use your data
• Gives your dataset a unique persistent identifier, e.g., DOI

As a researcher, you still need to:

• Keep thorough documentation
• Keep at least one copy of your data in an open, nonproprietary format 

Storing data: repository options

1. Domain repositories

Advantages:

• Data is stored with similar datasets (by subject, format, or both)
• Other researchers will find your data easily
• The repository will understand what your data needs in terms of storage, archiving, and preservation
• Computational/online analysis tools may be available tailored to analyzing that particular kind of data Examples:
• GenBank (for genome data)
• ICPSR (for numeric social science data)

2. Institutional repositories

Advantages:

• Linked to your institution
• You can put all your datasets together (and collocate them with publications)
• University guarantees support of Institutional Repositories
• Some Domain Repositories may "go out of business" once their funding ends

Example:

• DSpace@MIT

Storing data: our case study

Repository decision:

Put our Monarch Wing Scan Data in DSpace@MIT

Reasoning:

• Will be co-located with our other materials
• TIFF format is supported
• No alternative domain repository

Additional considerations:

• Must have faculty sponsorship
• Each file cannot exceed 2.5 GB

Sharing Data: additional considerations

Intellectual Property & Confidentiality

• Data is not copyrightable, but an expression of data can be.
• MIT or the funder may own your data (consult with the Technology Licensing Office)
• You can share your data if you, in fact, own it
• You can license data to limit what others can do with it (e.g., require attribution)
• It's incumbent upon you to police usage of your data
• Look at Creative Commons Licenses, including the CC0 Declaration to emphatically put it in the
• public domain
• Confidential and sensitive data requires additional consideration and planning

Journal requirements

• Many journals require that underlying data accompany published articles, usually found in "instructions for authors"
• More resources at http://libraries.mit.edu/datamanagement/share/journal-requirements/

Using other people's data

• Make sure that data doesn't have a license agreement that prevents you from sharing the data
• Most databases to which the MIT Libraries subscribe are licensed and carry restrictions on use, but many do allow for educational and research use, which allows for sharing limited portions of data. 

Measure Twice, Cut Once

Research data management is an ongoing, but beneficial activity.

Things you want to check and re-check over time:

• Is the data still stable and retrievable?
• Is the metadata still available and understandable?
• Are the formats still usable?
  • Is the software still available?
  • Is any specialized hardware still available?
  • Is the data still in the correct location?
    • Are my backups working as I expect?