1. Data management.
NicoleVasilevsky, NCNM, OHSU
JackieWirz, OHSU
Melissa Haendel, OHSU

3. Outline
• Introduction
• Why do we need good data management?
• Good data management
• Databases and tools
• Sharing your data

4. Who are we?
• NicoleVasilevsky, PhD
– Assistant Professor, Helfgott Research Institute, NCNM
– Project Manager, Ontology Development Group, OHSU
• JackieWirz, PhD
– Assistant Professor, Bioinformation Specialist, OHSU library
• Melissa Haendel, PhD
– Assistant Professor, Department Head, Ontology Development
Group, OHSU

5. What does data mean to you?

6. Do you have any training in data management?

7. Do you know what metadata is?
a. Philosophy
b. describes data
c. dating site
d. data

8. What is data?
• Clinical data
• Experimental data
• School related data
• Personal data
• Social data

9. So much data

10. Why?
Personal organization
Credit where
credit is due
Reproducibility of science
and medicine
Accelerates scientific and
clinical discovery
Efficiency

11. Do you get frustrated with any of the following
in your personal or professional life?
a. Storing data
b. Backing up data
c. Analyzing/manipulating data
d. Finding data produced by other researchers/clinicians
e. Ensuring data are secure
f. Making data accessible to other researchers
g. Controlling access to data
h. Tracking updates to data (ie versioning)
i. Creating metadata (ie describing the data to be more useful at a later
time or by others)
j. Protecting intellectual property rights
k. Ensuring appropriate professional credit/citation is given to data
sets/generated

12. http://davidmichaelangelosilva.wordpress.com/2012/01/29/organize-your-messy-desktop-with-fences/
Messy Desktop?

13. Which of the following do you do?
a. Save copies of data on a disk, USB drive, tape, or computer hard drive
b. Save copies of data on a local server
c. Save copies of data on a central campus server
d. Save copies of data on a web-based or cloud server
e. Store data in a repository or archives
f. Automatically backup files
g. Manually generate backup
h. Restrict access to files

14. Credit where credit is due
Data collection
& Analysis
Authoring
Storage,
Archiving, &
Preservation
Publication &
Dissemination
The scholarly
communicatio
n cycle

15. Reproducibility of science
• Lack of information
makes it difficult to
reproduce experiments
• Retraction rates are on
the rise
• Difficulty identifying
resources in the
published literature
Cokol et al. EMBO reports (2008) 9, 2
0%
25%
50%
75%
100%
Antibodies Cell lines Constructs Knockdown
reagents
Organisms

16. Sharing can be advantageous
http://www.flickr.com/photos/eltonl/107582334/sizes/l/in/photostream/

17. Why share your data?
• Data sharing
mandates
– NIH public access
policy
– NIH/NSF data
sharing plan for
new applications
• Further science and
and medicine
• Build collaborations
• Enable new
discoveries with
your data
• Can be required at
time of publication

18. Efficiency
http://hbr.org/2012/10/big-data-the-management-revolution
https://upload.wikimedia.org/wikipedia/commons/b/ba/HMS_Surprise_at_s
unset_with_airplane.jpg

19. How?
• File naming and data storage
• Metadata
• Controlled vocabularies and Ontologies
• Databases andTools
• Data accessibility

20. File naming

21. Informative file names
Will I remember what
this file is in a month
from now?

22. Naming conventions
Project_instrument_location_YYYYMMDDhhm
mss_extra.ext
Index/grant conditions Leading zero!
s/n, variable Retain
order

23. Directory Structure
Sticking with a directory structure can be hard
Files:
SPARC presentation
CTSAconnect presentation
Monarch presentation
Presentations
SPARC CTSAconnect Monarch

24. Versioning
DataManagement_SPARC_050313_final_NV
• Save a copy of every version of a data file
• Follow a file naming convention
• Version control software
– Dropbox
– Google docs
– GIT
– SMART SVN

25. Dropbox
www.dropbox.com

26. Google docs

27. Remember to backup your
data!
• Recommended to back up three copies!
– 1 on your local workstation
– 1 local/remove, such as external hard drive
– 1 remote, such as on a cloud server*
*Depending on the type of data, as cloud servers are not always secure
http://libraries.mit.edu/guides/subjects/data-management/Managing%20Research%20Data%20101.pdf

28. Organizing your IRB application
Created by Heather Schiffke
See:
http://libguides.ohsu.edu/data

29. File renaming applications
• Bulk Rename Utility (Windows)
• Renamer (Mac)
• PSRenamer

30. Metadata

31. What is Metadata?
Title
Author
Call number
Publisher
ISBN

34. File name File type
Who created the
data
Title
Date created

37. Using structured phenotype data to identify genetic
basis of disease
Human Disease:
HADZISELIMOVIC
SYNDROME
Most similar
mouse model:
b2b1035Clo
(aka Blue Meanie)
tricuspid
valve atresia
MP:0006123
prenatal growth
retardation
MP:0010865
persistent truncus
arteriosis
MP:0002633
cleft palate
MP:0000111
Ventricular
hypertrophy
HP:0001714
High-arched
palate
HP:0000156
Failure to thrive
HP:0001508
Pulmonary
artery atresia
HP:0004935
Renal
hypoplasia
HP:0000089
abnormal
kidney
morphology
abnormal
palate
morphology
growth
deficiency
Malformation
of the heart
and great
vessels
abnormal
heart and
great artery
attachment
duplex kidney
MP:0004017
Phenotypes in
common
(UBEROpheno)

38. Metadata standards:
Controlled vocabularies and
ontologies

39. Controlled vocabularies

40. MeSH

41. MeSH
acetominophen

42. What is an Ontology?
1. Hierarchical terms are
defined textually and
logically
2. Relationships between
the terms are defined
3. Expressed in a language
that can be reasoned
across by computers
4. Data can be reused and
can be easily linked
together

43. Commonly Used Ontologies
• GeneOntology
• LinnaeanTaxonomy
• SNOMED

44. Why are CVs and Ontologies useful?
• Can be used to structure your metadata
• Are often used to structure information in
databases

45. Structured data helps with
searching
Craigslist search: Chaise
Craigslist matches on strings only
Craigslist search: Fainting couch

46. Structured data helps with
searching
PubMed indexes articles with
MeSHTerms

47. In Summary:
Structured Metadata = good
How can I create structured metadata?
http://www.flickr.com/photos/san_drino/1454922072/

48. and Tools…
(to make your life easier)
(s)
http://farm4.static.flickr.com/3560/3332644561_c9d5041d02.jpg

49. Data Management tools and
repositories
• Purpose: Software where you can
organize, store and/or share data
• Often contain metadata to assist with data
entry and create structured data

50. Tools for data management

51. Data Sharing Repositories
http://www.nlm.nih.gov/NIHbmic/nih_data_sharing_repositories.html

52. Repositories use Unique IDs
• Document Object Identifier (DOI)
• Example: DOIs for publications
– doi: 10.1371/journal.pbio.1001339
• Unique resource identifier (URI)
• A URI will resolve to a single location on the web
• URIs for people

53. People Disambiguation

54. • Example:
• John L Campbell, Research Ecologist, Oregon State University, Corvallis
OR
• John L Campbell, Research Ecologist, Center for Research on
Ecosystem Change, Durham, NC

56. Tools for personal data
management
• Google drive
• Dropbox
• Evernote
• Task Paper
• Diigo- bookmarking websites
• Mendeley, EndNote, Zotero- citation manager
• Sound Gecko
http://blogs.scientificamerican.com/information-culture/2012/12/10/managing-personal-knowledge-data-and-information/

57. URLs to resources
Go to:
http://libguides.ohsu.edu/data

58. Data Sharing and Management Snafu
in 3 short acts