Do You Still Have Your Data?

• What if your hard drive crashes?• What if you are accused of fraud?• What if your collaborator abruptly quits?• What if the building burns down?• What if you need to use your old data?• What if your backup fails?• What if your computer gets stolen?• What if…

Data Management &Data Management Plans

Responsible Conduct of Research22 November 2013

Kristin Briney & Brad Houston

Why Data Management?

• Don’t lose data• Find data more easily– Especially if you need older data

• Easier to analyze organized, documented data• Avoid accusations of fraud & misconduct• Get credit for your data• Don’t drown in irrelevant data

For each minute of planning at beginning of a project, you will save 10 minutes of headache later

What Are Data?

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What Are Data?

• “Research data is defined as the recorded factual material commonly accepted in the scientific community as necessary to validate research findings”– OMB Circular A-110

http://www.whitehouse.gov/omb/circulars_a110

What Are Data?

• Observational– Sensor data, telemetry, survey data, sample data, images

• Experimental– Gene sequences, chromatograms, toroid magnetic field

data• Simulation– Climate models, economic models

• Derived or compiled– Text and data mining, compiled database, 3D models,

data gathered from public documents

General Data Management Considerations

Brad Houston, University Records OfficerResponsible Conduct of Research

November 22, 2013

Two Words:

Source: Jim Linwood

Your Data Management Plan should come *last*.

First consider:◦ Information about

your data◦ Information about

your audience◦ Obligations to

funders and others

You need to have a plan

Source: Sam Howzit

What kind of data is it?◦ (See Kristin’s slide on the 4 categories)

What are the key characteristics of the data? ◦ (File Format? Size? Programs needed to access it?)

Can I recreate the data, if needed? What infrastructure is available to manage it?

◦ On-campus and off-campus– don’t limit yourself Is the data intelligible to people other than

me?◦ If the answer to this one is “no”, that’s something

you should probably fix

Questions to ask about data

In order of amount of documentation you’ll need:◦ Future You (reference use only)◦ Colleagues within your discipline, in your lab or

elsewhere◦ Colleagues in related disciplines◦ General Public/The World!

The question to ask: is my data described well enough to be usable by my audience?

Who is this data for?

Rights shared with collaborators◦ Decide who’s

responsible for the official copy of data

Information Security Access Provisions

◦ NIH: Public Access policy

◦ NSF: Directorate access policy

◦ Others? (OMB A-110)

Obligations to Others

Often attached to funding.

Your data management plan (DMP) should contain 5 key components:◦ Expected Data◦ Standards for format and content◦ Policies for Access and sharing◦ Policies for Reuse and distribution◦ Plans for archiving data and preserving access

Note: These are minimum requirements.◦ Specific agencies or directorates may ask for

more– check their application sites!

Write away!

In short: What kind of data will be produced by your research processes?

Keep in mind:◦ File formats of complete data sets◦ Any software or code that will be

needed/produced◦ Physical samples or other individual data points

Some divisions require retention of physical samples; consult your Program Officer

1) Expected Data

In short: how will you organize your data within datasets to make it widely accessible, and how will you make data sets identifiable?

Keep in mind:◦ Any data formatting standards for your particular

discipline◦ Any metadata (author, date, subject, etc.) that

your program attaches automatically, and what you will need to attach manually

◦ How will you find your data for later consultation? How will others find it?

2) Data Standards and Metadata

In short: How will you allow other researchers to find and use your data?

Keep in mind:◦ How will other

researchers find your data?

◦ How will you provide access to your data?

◦ How will you prepare your data for sharing?

3) Policies for Access/Sharing

In short: How will researchers obtain permission to use your data?

Keep in mind:◦ Will you grant blanket

permission or case-by-case?

◦ What responsibilities will users of your data have re: privacy, intellectual property, etc.?

◦ What if a provision is violated?

4) Policies for Re-Use

In short: How will you make sure your data stays available?

Keep in Mind:◦ What are your retention

requirements? Is this a permanent data set?

◦ What storage media will you use? Are you prepared to migrate as needed?

◦ Do you have a data backup plan?

5) Archiving your Data

Above: Not A Good Way to archive your data.

You also need to keep track of supplementary research records:◦ Documentation on funding/expenditures◦ Copies of IRB/Animal Care research protocols◦ Hazardous Materials documentation◦ Invention Disclosure/Tech Transfer documentation◦ Conflict of Interest reports

Every institution has a different retention requirement– ask your records officer!◦ For UWM: almost all of this is “End of Grant + 3

years”

“So I’m all set, right?” Well…

Document Everything!◦ Information about the data and your methods◦ Information about where/how you’re keeping the

data (short-term and long-term)◦ What is needed to access the data◦ What security/privacy policies apply◦ Any collaborators outside the institution and their

rights◦ Any supplementary files or forms needed to

document use of funding

If you take nothing else away…

PRACTICAL DATA MANAGEMENTA Crash Course in

Storage and Backups

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Storage and Backups

• Library motto: Lots of Copies Keeps Stuff Safe!• Rule of 3: 2 onsite, 1 offsite

• Any backup is better than none• Automatic backup is better than manual• Your research is only as safe as your backup

plan– Periodically test restore from backup!

Storage and Backups

• Library motto: Lots of Copies Keeps Stuff Safe!• Rule of 3: 2 onsite, 1 offsite

• Any backup is better than none• Automatic backup is better than manual• Your research is only as safe as your backup

plan– Periodically test restore from backup!

Example

• I keep my data– On my computer– Backed up manually on shared drive• I set a weekly reminder to do this

– Backed up automatically via SpiderOak cloud storage

• A note on cloud storage…

Consistency

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Consistency

• Consistent file naming– Make it easier to find files– Avoid many duplicates– Make it easier to wrap up a project

• Names descriptive but short (<25 characters)• Avoid “ / \ : * ? ‘ < > [ ] & $ and spaces• Date convention: YYYY-MM-DD

Examples

• DataManagement_v6.pptx• 20090923_spctrm_trans_03.csv• SLAposter_FINAL.ai• BlogPost-2011-11-12.docx

• Find a system that works for you

Consistency

• Consistent documentation– Record all necessary information– Keep information in one place– Easier to search and use later

• Take 5 minutes before starting a project• Create a list of information to record– Don’t forget to record the units!

Example

• For my experiment, I need to collect:– Date– Experiment– Scan number– Powers– Wavelengths– Concentration (or sample weight)– Calibration factors, like timing and beam size

Recording Your Conventions

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Recording Your Conventions

• What if someone needs to find your data?• Eventually will hand off data to your PI

• Record your naming conventions• Record your documentation schemes• Record overall project information– Contact info, grant #, project summary, etc.

Examples

• Print out near computer/experiment area– Document conventions

• In front of research/lab notebook– Page 1: Project information– Page 2: Conventions and abbreviations– Page 3-X: Index of experiments

• README.txt in data folder– Top-level folder: project information– Lower-level folder: what’s in this folder?

Planning for the Future

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Planning for the Future

• Get help for sensitive data!– HIPAA, FERPA, FISMA, IRB, etc.

• UWM Information Security Office– Visit: www.uwm.edu/itsecurity/– Email: infosec@uwm.edu

• Policy pages– www.uwm.edu/legal/hipaa/index.cfm– www.uwm.edu/academics/ferpa.cfm

Planning for the Future

• We can’t open files from 10 years ago

• Proprietary file types– Convert to open file format• .doc .txt• .xls .csv• .jpg .tif

– Preserve software if no open file format• Periodically move data to new media

Don’t Stress Over Data

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More Data Management

• Data Services– www.uwm.edu/libraries/dataservices/

• Data Management Plans– dataplan.uwm.edu

• Kristin Briney, Data Services Librarian• Brad Houston, University Records Officer

Thank You

• The content of this presentation is licensed under a Creative Commons Attribution 3.0 Unported License (CC BY)– Image licenses as marked