Research Data Management

MSU Libraries
Research Data Management
Research Data
Management
Aaron Collie
collie@msu.edu
@aaroncollie

MSU Libraries
Introductions
• Please tell us your
name and department
• A brief description of
your primary research
area
• What do you consider
to be your research
data
• Experience and/or
comfort level with
managing research
data?
cc http://www.flickr.com/photos/quinnanya/

MSU Libraries
• Introduction
• Background
• The Impetus: NSF Data Management Plan Mandate
• The Effect: Policy to Practice
• The Response: Changing Data Landscape
• Fundamentals Practices
• File Organization
• Data Documentation
• Reliable Backup
• Data Publishing, Sharing, & Reuse
• Protecting Data & Responsible Reuse
• Data Lifecycle Resources
Agenda

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Data Management. Isn’t that…
trivial?
• Not so much. Data is a primary output of research; it is
very expensive to produce high quality data. Data may
be collected in nanoseconds, but it takes the expert
application of research protocol and design to generate
data.
CC-BY-SA-3.0 Rob Lavinsky CC-BY-SA-3.0 Rob

MSU Libraries
Data is the input of a process that generates
higher orders of understanding.
Wisdom
Knowledge
Information
Data
Understanding is
hierarchical!
Russell Ackoff

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This is the engine of the academic
industry…

MSU Libraries

MSU Libraries
The scientific method “is often
misrepresented as a fixed
sequence of steps,” rather than
being seen for what it truly is,
“a highly variable and creative
process” (AAAS 2000:18).
Gauch, Hugh G. Scientific Method in Practice. New York: Cambridge University Press, 2010. Print. (Emphasis added)

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And, things get a little messy.

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May you live in interesting times

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Public Trust of Science : The Crisis Tri-
force
• Scholarly Communication crisis
– Challenge: Increasing costs of
access
– Opportunity: Open Access
• Reproducibility crisis
– Challenge: Failure-to-replicate
rates
– Opportunity: Open Science
• Higher Education Crisis
– Challenge: Value of education
– Opportunity: Open Education

MSU Libraries
Crisis, Part 1:
Scholarly Communication “The
cage”
• https://www.lib.msu.
edu/about/collections
/scholcomm/more/
• http://www.arl.org/sto
rage/documents/mo
nograph-serial-
costs.pdf

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Crisis, Part 2:
Replication Crisis “The Canary”
• Ioannidis JPA (2005) Why Most Published
Research Findings Are False. PLoS Med
2(8): e124.
doi:10.1371/journal.pmed.0020124
• "Estimating the reproducibility of
psychological science". Science 349
(6251). August 28, 2015.
doi:10.1126/science.aac4716. Retrieved
September 12, 2015.

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Crisis, Part 3:
Higher Education “The coal mine”
http://news.bbc.co.uk/onthisday/hi/dates/stories/december/30/newsid_2547000/2547587.stm

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The Research Depth Chart
Scientific Method
Research Design
Research Method
Research Tasks
MoreDomainSpecificMoreGeneric

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Problem
Identification
Study Concept
Literature
Review
Environmental
Scan
Funding &
Proposal
Research
Design
Research
Methodology
Research
Workflow
Hypothesis
Formation
Design
Validation
Research
Activity
Data
Management
Data
Organization
Data
Storage
Data
Description
Data Sharing
Scholarly
Communication
Report
Findings
Publish
Peer Review

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Data Management
• The process of
planning for and
implementing a
system of care for
your research data
before, during, and
after a research
project in order to
ensure a (re)usable
resource.

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So why are we here?
Good science!
Government and Research
Funder Mandates

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But why are we really here?
• Impetus: NSF has mandated that all grant applications
submitted after January 18th, 2011 must include a
supplemental “Data Management Plan”
• Effect: The original NSF mandate has had a domino
effect, and many funders now require or state guidelines
for data management of grant funded research
• Response: Data management has not traditionally
received a full treatment in (many) graduate and doctoral
curricula; intervention is necessary

MSU Libraries
Positive reinforcement….
• National Science Foundation Data
Management Plan mandate (January 18,
2011)
• Presidential Memorandum on Managing
Government Records (August 24, 2012)
– Managing Government Records Directive: All
permanent electronic records in Federal
agencies will be managed electronically to the
fullest extent possible for eventual transfer
and accessioning by NARA in an electronic
format.

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Positive reinforcement… (cont.)
• White House policy memo (February 22,
2013)
– Increasing Access to the Results of Federally Funded Scientific
Research: Federal agencies with more than $100M in R&D
expenditures must develop plans to make the published results
of federally funded research freely available to the public within
one year of publication.
• OSTP policy memo (March 20, 2014)
– Improving the Management of and Access to Scientific
Collections: directs each Federal agency that owns, maintains,
or otherwise financially supports permanent scientific collections
to develop a draft scientific-collections management and access
policy within six months.

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Positive reinforcement… (cont. w/
teeth!)
• AHRQ = “…all AHRQ-funded researchers will be
required to include a data management plan for
sharing final research data in digital format, or state
why data sharing is not possible.
• NASA = This plan extends NASA’s culture of open
data access to all NASA-funded research.”
• USDA = Phased approach beginning with DMP
• More: http://www.arl.org/focus-areas/public-access-
policies/federally-funded-research/2696-white-house-
directive-on-public-access-to-federally-funded-
research-and-data#agency-policies

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1980
Forsham v.
Harris
•Research data
not subject to
FOIA
1999
Data Access
Act of 1999
•OMB Circular A-
110 revised:
data produced
with Federal
monies subject
to FOIA
2003
NIH Data
Sharing
Plans
•Grants over
$500,000
require plans
2010
America
Competes
Reauthorization
Act
•OSTP must coordinate
policies for
dissemination and
stewardship of scholarly
publications and data
produced with Federal
funds
2011
NSF Data
Management
Plans
•Grants require
supplementary DMP
2013
OSTP memo:
Increasing Access
to the Results of
Federally Funded
Research
•Requires agencies to
develop plans for public
access to publications and
data
Federally funded research data
Fischer, E. A. (2013). Public Access to Data from Federally Funded Research: Provisions in OMB Circular A-110 (Congressional Research Service). Retrieved from
HTTP://congressional.proquest.com.proxy2.cl.msu.edu/congressional/docview/t21.d22.crs-2013-rsi-0116?accountid=12598

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Funder Policies
NASA “promotes the full and open sharing of all data”
“requires that data…be submitted to and archived by
designated national data centers.”
“expects the timely release and sharing of final research data"
"IMLS encourages sharing of research data."
“…should describe how the project team will manage and
disseminate data generated by the project”

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 Policies for re-use, re-distribution, and creation of
derivatives
 Plans for archiving data, samples, and other research
outcomes, maintaining access
 Types of data, samples, physical collections, software
generated
• Standards for data and metadata format and content
• Access and sharing policies, with stipulations for
privacy, confidentiality, security, intellectual property, or
other rights or requirements

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• NSF will not evaluate any proposal
missing a DMP
• PI may state that project will not generate
data
• DMP is reviewed as part of intellectual
merit or broader impacts of application, or
both
• Costs to implement DMP may be included
in proposal’s budget
• May be up to two pages long

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• Investigators seeking $500,000 or more in direct costs in any year
should include a description of how final research data will be
shared, or explain why data sharing is not possible.
• The precise content of the data-sharing plan will vary, depending on
the data being collected and how the investigator is planning to
share the data.
• More stringent data management and sharing requirements may be
required in specific NIH Funding Opportunity Announcements.
Principal Investigators must discuss how these requirements will be
met in their Data Sharing Plans.

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 Roles and responsibilities
 Expected Data
 Period of data retention
• Data formats and dissemination
• Data storage and preservation of access

MSU Libraries
Local Policy
University Research Council Best Practices:
https://rio.msu.edu/research-data
Research Data: Management, Control, and
Access
– To assure that research data are appropriately
recorded, archived for a reasonable period of
time, and available for review under the
appropriate circumstances.
• Ownership = MSU
• “Stewardship” = You
• Period of Retention = 3 years
• Transfer of Responsibility = Written Request

MSU Libraries
Broader Response: Changing
Data Landscapes
• Data Management Competencies
– Standards & Best Practices
– Discipline Specific Discourse
• Data sharing and open data
– Data sets as publications
– Data journals
– Citations for data (e.g., used in secondary
analysis)
– Data as supplementary materials to traditional
articles
– Data repositories and archives

MSU Libraries
Science Paradigms
• Thousand years ago:
science was empirical
describing natural phenomena
• Last few hundred years:
theoretical branch
using models, generalizations
• Last few decades:
a computational branch
simulating complex phenomena
• Today:
data exploration (eScience)
unify theory, experiment, and simulation
– Data captured by instruments
Or generated by simulator
– Processed by software
– Information/Knowledge stored in computer
– Scientist analyzes database / files
using data management and statistics
2
2
2
.
3
4
a
cG
a
a












Slide credit: Gray, J. & Szalay, A. (11 January 2007). eScience Talk at NRC-CSTB meeting. http://research.microsoft.com/en-us/um/people/gray/talks/NRC-CSTB_eScience.ppt

MSU Libraries
Curation responsibilities (Carlson, The Chronicle, 2006)
“Data from Big Science is … easier to handle, understand and archive.
Small Science is horribly heterogeneous and far more vast. In time Small
Science will generate 2-3 times more data than Big Science.”
big science
data
small science data
institution?
domain?
MacColl, John (2010). The Role of libraries in data curation. RLG Partnership Annual Meeting, Chicago. June 2010

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What’s in it for me?
• Better organization = less headaches
– Course management
– Bibliographic management
– File management
– Research
• Career advancement
– Publish datasets and list on your CV
– Data management is an “unnamed practice” –
name it for yourself and your students!

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Data Sharing Impacts
• Reinforces open
scientific inquiry
• Encourages diversity
of analysis and opinion
• Promotes new
research, testing of
new or alternative
hypotheses and
methods of analysis
• Supports studies on
data collection
methods and
measurement
Cc http://www.flickr.com/photos/pinchof_10/

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Data Sharing Impacts
• Facilitates education
of new researchers
• Enables exploration
of topics not
envisioned by initial
investigators
• Permits creation of
new datasets by
combining data from
multiple sources

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Data as
Publication
Figure 1. To be
published, datasets are
typically deposited in a
repository to make them
available, documented
to support reproduction
and reuse, and assigned
an identifier to facilitate
citation.
Kratz J and Strasser C (2014) [version 3] Data publication consensus and controversies.
F1000Research 3:94 (doi: 10.12688/f1000research.3979.3)

MSU Libraries
What could come next?
• Increasing emphasis on making data available over long time
periods from institutionally maintained repositories.
– The end of project or PI based data repositories
• Increasing pressure for rapid release of data and project
transparency
– The annual reports maybe used to dictate data release in the
near future
• How these challenges are resolved is a core institutional
responsibility. It is important that MSU be a leader in this
initiative.
– Institutions are broadly competing to establish these data
repositories, and I suspect these will be a competitive indicator
for proposal success in the future.
Slide from Messina, Joseph. (2015) Data Plans in SBE and Geography. CI Forum October 22, 2015

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http://retractionwatch.com/2014/01/07/doing-the-right-thing-authors-retract-brain-paper-with-systematic-human-error-in-coding/

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Fundamentals
• Documentation
• File Organization
• Storage & Backup
• Data Publishing, Sharing,
& Reuse
• Protecting Data
& Responsible Reuse

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Documentation Practices:
Overview
• Researchers benefit from proper
documentation to decipher or reuse their
datasets – even prior to thinking about
sharing
• Think “downstream”

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Documentation Practices: Overview
1. At minimum create a
README file that you can
use to document your
project
2. Utilize standards for
describing data including
Metadata Standards
3. If applicable, use in-line
code commentary to
explain code
(cc) Will Scullin

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Create a README file
• At minimum, store documentation in
readme.txt file or equivalent, with data
– What data consists of
– How it was collected
– Restrictions to distribution or use
– Other descriptive information

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• “Data about data”
• Standardized way of describing data
• Explains who, what, where, when of data
creation and methods of use
• Data more easily found
• Data more easily compared to other data sets
Use Metadata Standards

MSU Libraries
Basic project metadata:
• Title • Language • File Formats
• Creator • Dates • File Structure
• Identifier • Location • Variable List
• Subject • Methodology • Code Lists
• Funders • Data Processing • Versions
• Rights • Sources • Checksums
• Access
Information
• List of File Names

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• Dublin Core: Commonly-used descriptive
metadata format facilitates dataset discovery
across the Web.
• Data Documentation Initiative (DDI): Defines
metadata content, presentation, transport, and
preservation for the social and behavioral
sciences.
• ISO 19115:2003: Describes geographic data such
as maps and charts.
• More
examples:http://www.lib.msu.edu/about/diginfo/coll
ect.jsp

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Use In-Line Code Commentary
Example of R code commentary
# Cumulative normal density
pnorm(c(-1.96,0,1.96))
• If applicable, in-line code commentary helps
explain code

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File Organization Practices:
Overview
1. Design a file plan
for your research
project
2. Use file naming
conventions that
work for your project
3. Choose file formats
to maximize
usefulness
“When I was a
freshmen I named
my assignments
Paper Paperr
Paperrr Paperrrr”
-Undergrad

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Design a File Plan
• File structure is the framework
• Classification system makes it easier to
locate folders/files
• Benefits:
– Simple organization intuitive to team
members and colleagues
– Reduces duplicate copies in personal drives
and e-mail attachments

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Design a File Plan
Choose a sortable directory hierarchy
• Example 1: Investigator, Process, Date
Collie
TEI_Encoding
20110117
• Example 2: Instrument, Date, Sample
Usability Survey
2012043
sample_1

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Design a File Plan
Example documentation of Directory Hierarchy:
/[Project]/[Grant Number]/[Event]/[Investigator/Date]

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Use File Naming Conventions
– Enable better access/retrieval of files
– Create logical sequences for file sorting
– More easily identify what you’re searching for

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• Meaningful but short—255 character limit
• Use alphanumeric characters
– Example: abc123
• Capital letters or underscores differentiate
between words
• Surname first followed by initials of first name

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• Year-month-day format for dates, with or
without hyphens
Example 1: 2006-03-13
Example 2: 20060313
• Decide on a simple versioning method
Example: file_v001

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• To create consistent file names, specify a
template such as:
[investigator]_[descriptor]_[YYYYMMDD].[ex
t]
This Not This
sharpeW_krillMicrograph_backscatter3_20110117.tif KrillData2011.tif
This Not This
borgesJ_collocation_20080414.xml Borges_Textbase.xml

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Choose Appropriate File Formats
• Non-proprietary
• Open, documented standard
• Common usage by research community
• Standard representation (ASCII, Unicode)
• Unencrypted
• Uncompressed

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Choose Appropriate File Formats
Format Genre Optimal Standards
TEXT .txt; .odt; .xml; .html
AUDIO .flac; .wav,
VIDEO .mp2/.mp4; .mkv
IMAGE .tif; .png; .svg; .jpg
DATA .sql; .csv

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Storage & Backup Practices
1. Avoid single points of
failure
2. Ensure data redundancy &
replication
3. Understand common
types of storage
(cc) George Ornbo
Data at significant risk of loss without storage
and backup plan

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Avoid Single Points of Failure
A single point of failure occurs when it
would only take one event to destroy all
data on a device
• Use managed networked storage when
possible
• Move data off of portable media
• Never rely on one copy of data
• Do not rely on CD or DVD copies to be
readable
• Be wary of software lifespans

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Ensure Data Redundancy
• Effective data storage plan provides for 3
copies:
– Primary authoritative copy
– Secondary local backup
– Tertiary remote backup
• Geographically distribute and secure
– Local vs. remote, depending on needed recovery
time
• Personal computer, external hard drives,
departmental, or university servers may be
used

MSU Libraries
Ensure Data Redundancy
• Cloud storage
– Amazon s3
– Google
– MS Azure
– DuraCloud
– Rackspace
– Glacier
Note that many enterprise
cloud storage services
include a charge for in/out of
data transfers
$$$

MSU Libraries
Understand Common Types of
Storage
• Optical Media
• Portable Flash Media
• Commercial Hard Drives
• Commercial NAS
• Cloud Storage
• Enterprise Network Storage
• Trusted Archival Storage

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Storage
• Features of storage types:
• Portable data transfers
• Short-term storage
• Project term storage
• Networked data transfer
• Long-term storage
• Reliable backup option

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StoragePortable
Data
Transfer
Short
Term
Storage
Project
Term
Storage
Networked
Data Transfer
Long
Term
Storage
Reliable
Backup
Option
Optical Media ✔ ✗ ✗ ✗ ✗ ✗
Portable Flash
Media
✔ ✔ ✗ ✗ ✗ ✗
Commercial Hard
Drives
✔ ✔ ✔ ✗ ✗ ✗
Commercial NAS ✗ ✔ ✔ ✔ ✗ ✗
Cloud Storage ✗ ✔ ✔ ✔ ✗ ✗
Enterprise Network
Storage
✗ ✔ ✔ ✔ ✔ ✔
Trusted Archival
Storage
✗ ✗ ✗ ✔ ✔ ✔

MSU Libraries
Storage
Media Storage @ MSU
Optical Media MSU Computer Store—Sells Optical Media and hardware accessories
UAHC Media Storage Service—Offers physical lock-box like storage for MSU
Flash Media MSU Computer Store—Sells Optical Media and hardware accessories
Commercial Hard
Drives
MSU Computer Store—Sells Optical Media and hardware accessories.
Enterprise Cloud
Storage
Angel—Free. Ideal for collaboration; not storage space. Phase out 2015
Desire2Learn—Free. Ideal for collaboration; not storage space. Replaces Angel
GoogleApps—Free. Ideal for collaboration; not intended as storage space
Enterprise
Network Storage
AFS Space—Free to 1GB, add’l space can be purchased w/dept. account
IT Services Individual, Mid-Tier and Enterprise Storage—Fee based
HPCC Home or Research—Free up to 1TB. Fee based additions available
Trusted Archival
Storage
Disciplinary Repositories – Disciplinary repositories offer archival services for
pertinent research data.

MSU Libraries
Data Publishing, Sharing, Reuse
1. Time-intensive, with potentially
high return on investment
2. Publish data in several data
publication venues to more
broadly share results of research
Research datasets on par with peer-reviewed
journal articles as first-class scholarly contributions

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Sharing & Publishing Data
• Data preparation for sharing and publication
is a time-intensive process
• Potential positive outcomes:
• Increased research impact and citations
• Enable additional scientific inquiry
• Opportunities for co-authorship and
collaboration
• Enhance your grant proposal’s
competitiveness

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Data Publication Venues
• Multiple ways to publish research data
• Faculty or project website
• Journal supplementary materials
• Disciplinary data repository (data archive)
• Varying levels of support for indexing, access
controls, and long-term curation

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Data Publication Venues
• Disciplinary Data Repository
• Securely share data, ensure long-term access
• High visibility
• Often offer persistent citations
• Availability varies across domains
• Databib.org directory

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Protecting Data & Responsible Reuse
1. Consider how to protect
data and intellectual
property rights while
encouraging reuse
2. Keep in mind ethical
concerns when sharing
data
(cc) Will Scullin

MSU Libraries
Intellectual Property
• IP refers to exclusive rights of creators of
works
• Individual data cannot be protected by US
copyright
• Organization of data such as database,
creative work produced by data, and research
instruments used may be protected
©

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Intellectual Property
• Principal investigator’s institution holds IP
rights
• Provide clearly stated license for producing
derivatives, reusing, and redistributing
datasets
• License under Creative Commons
• State if any restrictions or embargos on use
• Provide example of how work should be cited
to encourage proper attribution on reuse
• Document any IP / copyright issues

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Ethics & Data Sharing
• Keep in mind the following ethical concerns
when sharing your data:
• Privacy
• Confidentiality
• Security and integrity of the data
• For data involving human subjects, obtain
written permission or consent stating how the
data may be reused

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Best Practices = High Impact Data
• File organization ensures easier access and
retrieval of data
• Documentation makes datasets accessible
and intelligible to users
• Storage and backup safeguards data
• Data publishing and sharing encourages the
most widespread reuse of data
• Data protection ensures responsible reuse

MSU Libraries
Volunstrordinaries!
Aaron
Collie
Devin Higgins Brandon
Locke
Ranti Junus Judy
Matthews
Tina Qin

MSU Libraries
We teach people about RDM
Librarianship
Training
Assessment
Consultation
Ad-hoc
6-12 new clients per semester
100% satisfied / 100% would use
again
71% of new clients are referrals
60% requested additional services
15% through NFO, 14% through
website

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http://www.lib.msu.edu/rdmg

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RDM@MSU 101
• Who: You, as the designated steward
• What: “the data”
• When: Minimum 3 years after
publ./degree
• Where: Managed networked storage
• Why: Legal, Ethical, Scholarly
• How: With fidelity and documentation
sufficient to reproduce the research

MSU Libraries
Contact
Aaron Collie
collie@msu.edu
@aaroncollie
http://www.lib.msu.edu/rdmg

Research Data Management

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