Research Data Management

Data Management for Research

Aaron Collie, MSU LibrariesLisa Schmidt, University Archives

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/

http://tiny.cc/msudataseminar

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

Even more consequential, 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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So, things can get a little messy.

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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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The Research Depth Chart

Scientific Method

Research Design

Research Method

Research Tasks Mor

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Problem Identification

Study Concept

Literature Review

Environmental Scan

Funding & Proposal

Research Design

Research Methodolog

y

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

Upfront Decisions for Researchers How are the data described and organized? Who are the expected and potential audiences for

the datasets? What publications or discoveries have resulted from

the datasets? How should the data be made accessible? How might the data be used, reused, and

repurposed?

Upfront Decisions for Researchers What is the expected lifespan of the data? Besides the researcher(s) on the project, who else

should be given access to the data? Does the dataset include any sensitive information? Who owns or controls the research data? Should any restrictions be placed on the dataset? How are the data stored and preserved?

• 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

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

Impetus: NSF Data Management Plan

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

Impetus: NSF Data Management Plan

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

Effect: 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”

Effect: More is on the way

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.

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.

Effect: Local Policy

University Research Council Best PracticesResearch 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

Response: Changing Data Landscape

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

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/

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





Agenda

Research Data Management Fundamentals

File Organization Documentation Storage & Backup Data Publishing, Sharing,

& Reuse Protecting Data

& Responsible Reuse

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

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

Design a File Plan

Choose a sortable directory hierarchy Example 1: Investigator, Process, Date

CollieTEI_Encoding20110117

Example 2: Instrument, Date, Sample Usability Survey

20120430Sample 1

Design a File Plan

Example documentation of Directory Hierarchy: /[Project]/[Grant Number]/[Event]/[Investigator/Date]

Use File Naming Conventions

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

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


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


To create consistent file names, specify a template such as:

[investigator]_[descriptor]_[YYYYMMDD].[ext]


This Not ThissharpeW_krillMicrograph_backscatter3_20110117.tif KrillData2011.tif

This Not ThisborgesJ_collocation_20080414.xml Borges_Textbase.xml

Choose Appropriate File Formats

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

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

Documentation Practices: Overview

Even researchers require proper documentation to decipher or reuse their datasets

Documentation = accessible, intelligible datasets

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

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

“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

Use Metadata Standards

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

Use Metadata Standards 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/collect.jsp

http://www.lib.msu.edu/about/diginfo/collect.jsp

http://www.lib.msu.edu/about/diginfo/collect.jsp

Use In-Line Code Commentary

Example of R code commentary

# Cumulative normal densitypnorm(c(-1.96,0,1.96))

If applicable, in-line code commentary helps explain code

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

Avoid Single Points of FailureA 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

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

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

$$$

Understand Common Types of Storage

• Optical Media• Portable Flash Media• Commercial Hard Drives• Commercial NAS• Cloud Storage• Enterprise Network Storage• Trusted Archival Storage

Understand Common Types of Storage

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

Understand Common Types of 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 ✗ ✗ ✗ ✔ ✔ ✔

Understand Common Types of StorageMedia Storage @ MSU

Optical Media MSU Computer Store—Sells Optical Media and hardware accessoriesUAHC Media Storage Service—Offers physical lock-box like storage for MSU

Flash Media MSU Computer Store—Sells Optical Media and hardware accessoriesUAHC Media Storage Service—Offers physical lock-box like storage for MSU

Commercial Hard Drives

MSU Computer Store—Sells Optical Media and hardware accessories.UAHC Media Storage Service—Offers physical lock-box like storage for MSU

Enterprise Cloud Storage

Angel—Free. Ideal for collaboration; not storage space. Phase out 2015Desire2Learn—Free. Ideal for collaboration; not storage space. Replaces AngelGoogleApps—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. accountIT Services Individual, Mid-Tier and Enterprise Storage—Fee basedHPCC 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.

Data Publishing, Sharing, Reuse

1. Time-intensive, with potentially high return on investment

2. Publish data in several data publication venues to morebroadly share results of research

Research datasets on par with peer-reviewed journal articles as first-class scholarly contributions

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

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

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

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

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

©

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

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

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





Agenda

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



ContactLisa M. SchmidtElectronic Records ArchivistUniversity Archives & Historical [email protected]

Aaron CollieDigital Curation LibrarianMSU [email protected]

mailto:[email protected]

mailto:[email protected]

Research Data Management

Technology

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