Research Data Management: Part 2, Practices

Managing Research Data Part 2

WHY – WHAT– WHO – WHEN & HOW

Planning Working

Finalizing Sharing Data

This work is licensed under a Creative Commons Attribution 4.0 International License.

WHY manage data -

WHAT research data are-

WHO manages research data -

WHEN & HOW data management is done -

Planning Working


Managing Research Data


This two-part course is a collaboration between CU Libraries/Information Services and the Office of Research Compliance & Training. The purpose of this course is to familiarize you with the various aspects of research data management (RDM) by taking

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56/ Managing Research Data

This course will guide you through these areas, offering in-depth details on each of them. Please refer to the top navigation to keep track of which area you are currently exploring.

•  Why RDM is both recommended and required

•  What research data are

•  Who is responsible for RDM

•  When RDM activities occur

•  How you can carry out RDM activities

Part 1:

Part 2:

Learning objectives: At the end of this training you will be able to: •  Identify at which research stages data management activities occur •  Understand practical details of research data management such as:

–  File naming –  File formats –  Spreadsheet structure –  Data preservation

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Links to many of the references and policies referred to in this course can be found on the final slides. Have Fun!

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When does Research Data Management happen? How is it done?

WHY –WHAT – WHO – WHEN & HOW

6 56/ Managing Research Data


Planning Working



Planning

Planning Working




When planning to manage data or writing a data management plan consider:

Planning



•  What data will be shared? •  Who will have access to the data? •  Where will the data to be shared be located? •  When will the data be shared? •  How will researchers locate and access the data?

CONSIDER: •  File format •  File sizes •  Changing rates of data production •  Anticipated size of project data •  Storage & Back-up •  Privacy / security requirements •  Data description •  Retention period •  Sharing requirements

Planning



Plan for the entire data life-cycle.

•  Non-proprietary •  Open, documented standard •  Standard representation (e.g., ASCII, Unicode) •  Common, or commonly used by the research community (e.g.

FITS, CIF) •  Unencrypted •  Uncompressed

Planning

Some commonly recognized formats to avoid for storage include: Word [.doc(x)], SPSS [.sav], Excel [.xls(x)], STATA [.dta], Access [.mdb, .accdb], JPEG [.jpg], .gif, QuickIme [.mov], SAS [.sas]

Some commonly recognized formats meeIng these criteria: ASCII [e.g., .csv, .txt], PDF [.pdf], FLAC, TIFF, JPEG2000 [.jp2], MPEG-‐4 [.mp4], XML [.xml, .odf, .rdf], R [.r]

11 http://www.data-archive.ac.uk/media/2894/managingsharing.pdf

http://www.digitalpreservation.gov/formats/index.shtml?PHPSESSID=c26c5e5101396d5f5ebacedb13cae6e3 56/ Managing Research Data


Storage file formats should be:

X ✓

Not sure about the extension? Check hYps://www.naIonalarchives.gov.uk/PRONOM/default.htm

Storage / Back-ups Planning

Lifespan of Storage Media: http://www.crashplan.com/medialifespan/



No storage medium lasts forever. Consider the following media life-spans:

When choosing storage and back-up options you should: •  Reduce the risk of damage or loss •  Use multiple locations (here, near, far) •  Create a back-up schedule •  Use reliable back-up media •  Test your back-up system (i.e., test file recovery, checksums)

Planning


WHY –WHAT – WHO – WHEN & HOW Storage / Back-ups

Remember to: •  Back up data frequently •  Make 3 copies –  Original (here) –  External/local (near) –  External/remote – different geographic area (far)

•  Verify recovery is possible –  Confirm that file has not been corrupted, e.g., checksum

validation –  Make sure you can reload the file, i.e., test file restore after

initial set-up –  Check file recovery periodically & systematically thereafter

Planning


WHY –WHAT – WHO – WHEN & HOW Storage / Back-ups

Consider physical, network, computer system and file security for: •  Intellectual Property –Trade secrets, commercial

information, confidential materials, restricted data •  Personally identifying information (PII) •  Personal health information (PHI) •  High-security data

Planning


WHY –WHAT – WHO – WHEN & HOW Privacy & Security

CU Information Security Charter: “Users are persons who use Information Resources. Users are responsible for ensuring that such Resources are used properly in compliance with the Columbia University Acceptable Usage of Information Resources Policyhttp://policylibrary.columbia.edu/acceptable-usage-information-resources-policy, information is not made available to unauthorized persons, and appropriate security controls are in place.”

Planning

16 http://policylibrary.columbia.edu/information-security-charter 56/ Managing Research Data


(Some) Best practices for handling sensitive data: •  Restrict physical access to computers, offices and storage media

•  Encrypt any device (mobile, laptop, desktop, tablet, removable media [e.g., USB flash drives, CDs, hard drives]) containing sensitive data

•  Store lab notebooks, research records, in locked cabinets

•  Keep confidential and sensitive data on computers not connected to the Internet

•  Don't send confidential data via e-mail or FTP (use encryption, if you must)

•  Use strong passwords on files and computers

•  Sanitize all systems before reusing, disposing, or donating

Planning



•  Lab notebooks •  Data descriptions / code book •  File naming

–  Consistency: Pick a system, write it down, & stick with it –  Identify necessary elements –  Create brief, understandable names –  Date: YYYY-MM-DD –  Version: v01, v02,…FINAL

In general, try to stay away from spaces in filenames as well as the following characters: .\ / : * ? “ < > | [ ] & $

•  File / directory structure •  Sometimes there is a community standard for data formatting &

description for sharing/integration (aka metadata schema) – Find yours!

Planning



Data Description / Documentation

Plan to keep your data according to: •  CU Data Retention Policy: at least 3 years •  Funder requirements: It varies – check them! •  Regulations •  Contract terms, for industry sponsored research •  The importance of the data, regardless of external

requirements

Planning


WHY –WHAT – WHO – WHEN & HOW Retention Period

Do you plan to share your data? Prepare to follow the requirements of your: •  Funder •  Journal •  Discipline •  Data repository

Planning


WHY –WHAT – WHO – WHEN & HOW Planning to share

Working

Planning Working




Review the data management plan: •  Are you following it? •  Did it survive first contact with the research? If not, –  Does it need to be revised? –  Take the opportunity to change it as necessary,

documenting the changes

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WHY –WHAT – WHO – WHEN & HOW Data Collection

Revisit your: •  File naming conventions –  Are they written down? –  Does everyone on the project know & follow them?

•  File structure / organization / tagging –  Is it easy to understand / logical? –  Is everyone on the project familiar with the organizational

practices so they can store and find files efficiently? •  Back-up processes –  Are they working? –  Are they being followed?

Working



Are you using someone else’s data as part of your research? You should probably cite it…

Consider a citation management software to keep track of it:

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hYp://library.columbia.edu/research/citaIon-‐management.html



Using a spreadsheet for your data? Structure your data so that it’s easily sortable & usable by other software/machines. Be consistent with your: •  Labels •  Types •  Formats •  Layout

(Alternatively, consider using a database for easier data management)

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Spreadsheet labels: Adopt a consistent style that indicates a cell contains a label rather than a value

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Date Instrument SoundLevel_R SoundLevel_L Amp_Se7ng

2013-‐12-‐22 BK-‐732A 84.6 86.0 3

2013-‐12-‐23 BK-‐732A 115.2 116.4 9

2013-‐12-‐24 BK-‐732A 128.7 130.0 11

Date: 12/22/2013 Instrument BK732A

Sound lev Right <85 Amplifier 3 (27%)

Lei 86.0

Date: Dec 23, 2013 Instrument Amp_Sekng

SoundLevel-‐R 115 BK_732-‐A 9

SoundLevel_L 116.4

J L



Spreadsheet types: Don’t mix text & number types in the same column

Working


2013-‐12-‐22 BK-‐732A 84.6 86.0 3

2013-‐12-‐23 BK-‐732A 115.2 116.4 9

2013-‐12-‐24 BK-‐732A 128.7 130.0 11



Lei 86.0



SoundLevel_L 116.4

J L



Spreadsheet formats: Do all of your dates or other variable values look the same?

Working


2013-‐12-‐22 BK-‐732A 84.6 86.0 3

2013-‐12-‐23 BK-‐732A 115.2 116.4 9

2013-‐12-‐24 BK-‐732A 128.7 130.0 11 J L



Lei 86.0



SoundLevel_L 116.4



Spreadsheet layout: Tables of similar data should be structured similarly

Working


2013-‐12-‐22 BK-‐732A 84.6 86.0 3

2013-‐12-‐23 BK-‐732A 115.2 116.4 9

2013-‐12-‐24 BK-‐732A 128.7 130.0 11 J L



Lei 86.0



SoundLevel_L 116.4



When it’s not a spreadsheet (it may be a database): Be consistent! •  Consistent process •  Consistent organization •  Consistent descriptions AND •  Consistently documenting everything that’s done

Working



(Some)Best practices for assuring quality data entry: •  System-limited value entry, i.e., hard code controlled lists of

values •  Check 5-10% of data records manually •  Check out-of-range values •  Check empty values / blank fields •  Consider using a data entry program or double entry keying

Working


WHY –WHAT – WHO – WHEN & HOW Quality Assurance / Control

•  Keep an untouched, “raw” copy of the data file – Make it Read Only

•  Save cleaned or analyzed data as new files (with good file names, as previously described) –  Take extensive notes of the actions taken or scripts used to

“clean” the data •  Use a scripted language (e.g., R, SAS, SPSS) to consistently

process data and create a record of data processing & analysis •  Document scripts / code with comments! •  Write a ReadMe.txt file as you go, rather than trying to

remember what you did later

Working


WHY –WHAT – WHO – WHEN & HOW Analysis

Create a file to document your project. Include: •  What data are being collected & why •  Names of project files (data & analysis) •  Project file naming and file organization conventions •  Data definitions (aka Code Book or Data Dictionary) – more

next slide •  Project standards •  Calibration, precision, accuracy & units of instruments or

measurements

Working


WHY –WHAT – WHO – WHEN & HOW Analysis - documenting

Code books / data dictionaries should include: •  Data codes or coding keys •  Missing value codes •  Field name / Column header / Data label

–  Definition e.g., Amp_setting | Dial setting of guitar amplifier –  Values – Possible values e.g., from 0 to 11, whole numbers –  Units – may be included in either Definitions or Values –  Type e.g., string, float, char, date [YYYYMMDD]

Working


WHY –WHAT – WHO – WHEN & HOW Analysis - documenting

Finalizing

Planning Working




•  Check requirements •  Are data useful/usable •  Select data for preservation •  Choose publication path •  Consider publishing negative data – Others may find it useful •  Repositories

Finalizing


WHY –WHAT – WHO – WHEN & HOW Preparing data for publication, sharing, storage, preservation:

Have you fulfilled the expectations of your: •  Funder •  Journal •  Discipline •  Repository •  Institution

Finalizing

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WHY –WHAT – WHO – WHEN & HOW Check requirements

Are data consistently: •  Formatted •  Named •  Organized •  Described / Documented

Are they in a file format that may be easily accessed and reused?

Finalizing


WHY –WHAT – WHO – WHEN & HOW Usability of data

You might consider long-term preservation if the answer to any of these questions is “Yes” •  Do the data support published research? •  Are the data difficult or expensive to regenerate? •  Are the data required for your research but from another source

(i.e. not your original research data)? –  If so, is the future availability of that data from the original source

uncertain? •  Do you plan to share your data, or are you required to per funder

agreement? •  Are the data historically significant? •  Are the data vulnerable to loss, corruption, endangerment, etc.?

Finalizing

https://lib.stanford.edu/data-services/preserve 39 56/ Managing Research Data

WHY –WHAT – WHO – WHEN & HOW Data preservation: Selection

Sharing

Planning Working




Honestly, the party most interested in the data you are producing today is probably:

Your future self But there are others to consider, too, so…

Sharing


WHY –WHAT – WHO – WHEN & HOW Who will you share with?

“By ‘final research data,’ we mean recorded factual material commonly accepted in the scientific community as necessary to validate research findings.”

NIH FAQ Data Sharing (3/03)

Guidelines will “be determined by the community of interest” and “may include…data, publications, samples, physical collections, software and models.”

Data Management and Sharing FAQ (11/10)

Sharing


WHY –WHAT – WHO – WHEN & HOW What to share

“Timely release and sharing’ is defined as no later than the acceptance for publication of the main findings from the final data set.”

Data Sharing Policy, Section II.8.2.3.1, NIH Grants Policy Statement (10/12)

“The expectation is that all data will be made available after a reasonable length of time….[which] will be determined by the community of interest…”

Data Management and Sharing FAQ (11/10)

Sharing


WHY –WHAT – WHO – WHEN & HOW When to share

There are many paths to publishing data: •  Data paper / Data journal •  Supplementary material •  Data repositories Wherever you publish, make sure people can find it, use it, and give you credit for it! (this usually requires a permanent identifier e.g., DOI) Do you have negative data? Others may find it useful – consider making it available!

Sharing


WHY –WHAT – WHO – WHEN & HOW Publishing data

Institutional repository Columbia’s repository accepts materials from faculty, students, and staff. It offers: •  Long-term preservation strategy •  Multiple back-ups (including off site) •  Quality content descriptions for increased discoverability •  Monthly usage reports •  Permanent URL & doi

Sharing


WHY –WHAT – WHO – WHEN & HOW Repositories

Disciplinary repository e.g., •  GenBank •  RCSB Protein Data Bank •  ICPSR

Sharing



Public access repository e.g., •  Figshare.com •  DataDryad.org •  ResearchCompendia •  Academic Commons

Sharing



•  How would you like your data cited •  Licensing •  Privacy/confidentiality/anonymization – Revisit IRB

commitments •  What to share •  When to share

Sharing



When you share your data, consider:

•  Publish your data, and make sure to cite it in your journal publication

•  When publishing your data, provide a preferred citation •  Did you use someone else’s data? –  Check the license for restrictions –  Provide the following minimum in your work’s citations:

•  Title •  Author/Creator name •  Publisher •  Publication year •  Unique identifier e.g., DOI

Sharing


WHY –WHAT – WHO – WHEN & HOW Data citation

In the USA, facts – which means most datasets – are outside the scope of copyright protection. Some researchers have adopted the practice of data licensing because of this. There are many different license types, with varied provisions for reuse and attribution. When thinking about licenses keep in mind: •  Funder requirements •  Institutional requirements •  Scientific and scholarly ethos of extending knowledge

Sharing


WHY –WHAT – WHO – WHEN & HOW Licensing

Revisit: •  IRB commitments •  Privacy Board requirements (HIPAA) •  Institutional requirements •  Ethical considerations Consider: •  Have direct identifiers been removed? •  Have indirect identifiers that could reveal identity when

combined been managed? •  Does relational or spatial data have the possibility of

identifying participants? Maintain the maximum amount of detail possible without compromising participants confidentiality.

Sharing


WHY –WHAT – WHO – WHEN & HOW Privacy / Anonymization

.

Planning Working


•  Managing research data takes place at every stage of the research and scholarly process –  Planning –  Working, where you

follow the plan, collecting and analyzing data

–  Finalizing, where you make sure you followed the plan –  Sharing, where you sigh with relief; it’s so simple, because you

followed your plan! •  Research data management can be complex, but there are resources

available


WHY –WHAT – WHO – WHEN & HOW Take-aways

à SEE NEXT PAGE!

Resources for Research Data Management ⬅⬅⬅links located to the left



Resources for Research Data Management:


Title URL

Scholarly Communications Program, Data Management http://scholcomm.columbia.edu/data-management/

Research and Data Integrity Program (ReaDI)

http://www.columbia.edu/cu/compliance/docs/ReaDI_Program/index.html

Data Management Plan Templates http://scholcomm.columbia.edu/data-management/data-management-plan-templates/

CUIT Research Computing Services http://rcs.columbia.edu

Academic Commons Archival Storage http://academiccommons.columbia.edu/about

Citation Management http://library.columbia.edu/research/citation-management.html

Managing Secure Information - Training http://columbia.sighttraining.com

Data Security Policies http://policylibrary.columbia.edu/category/computingtechnology


RESOURCES •  CU Data Policies & Procedures:

–  Faculty Handbook –  Sponsored Projects Handbook –  Clinical Research Handbook –  Administrative Policy Library, Security Policies

e.g., Electronic Information Resources Security, Data Classification Policy, Policy on Electronic Data Security Breach Reporting and Response

•  Scholarly Communications Program •  Office of Research Compliance and Training

54 56/ Managing Research Data This work is licensed under a Creative Commons

Attribution 4.0 International License.

RESOURCES •  Data Management Plans •  CUIT Active Storage options •  Academic Commons archival storage •  Citation management •  Executive Vice President’s Office of Research (EVPR) •  Training on managing Personal Health Information

(PHI) •  Research and Data Integrity Program (ReaDI)

55 56/ Managing Research Data This work is licensed under a Creative Commons

Attribution 4.0 International License.

REFERENCES •  ScoY, Mark, Boardman, Richard P., Reed, Philippa A.S. and Cox, Simon J. (2012)

Introducing research data. Southampton, GB, Univeristy of Southampton, 29pp. hYp://eprints.soton.ac.uk/338816/

•  Responsible research data management and the prevenIon of scienIfic misconduct www.knaw.nl/Content/Internet_KNAW/publicaIes/pdf/2013569.pdf

•  hYp://dmconsult.library.virginia.edu/

56 56/ Managing Research Data Created by: Amy Nurnberger, 2015-‐05-‐12


Research Data Management: Part 2, Practices

Data & Analytics

Transcript of Research Data Management: Part 2, Practices