Presentation on Data management @ UCC

5. DATA MANAGEMENT 101
October 11, 2013

6. Hello there!

7. 1 | Data definitions
2 | Dealing with Data
3 | The Next Steps

8. 1 |Data definitions

9. asdf
Data can be complex

10. Data can be amazing

11. Data are about discovery

12. But…

13. Data does not speak for itself…

14. YOU speak for YOUR data

15. and you need to manage it

16. But, even more fundamentally…

20. tomaytoe
PANTONE
1795 C
tomahto
Solanum
lycopersicum
tdTomato
554ex 581em
$64

21. Data means different things
to different people

22. Data is not static

27. 1. Brilliant Idea!
2. Design Experiment
3. Do Experiment
The data timeline
4. Collect data
5. Compile and Analyze
6. Publish
7. Fame, Fortune

28. 1. Brilliant Idea!
2. Design Experiment
3. Do Experiment
4. Collect data
The data timeline:
What people think
5. Compile and Analyze
6. Publish
7. Fame, Fortune

29. Idea!
Analyzing data
The data timeline:
What Happens
experiment
Compile data
design
Other
People’s
data
Try #2
Failure!!
beer
#896
#896
6. Publish

30. The data cycle:
What Really Happens

32. 2 |dealing with data

33. Why should I care?
Hello there!

34. So you won’t go crazy
Efficiency
Accelerates scientific discovery
Reproducibility of science
Credit where credit is due
Personal organization

35. Do you get frustrated with…
Hello there!

36. a. Storing data
b. Backing up data
c. Analyzing/manipulating data
d. Finding data produced by other researchers
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
j. Protecting intellectual property rights
k. Ensuring appropriate professional credit/citation is given

37. How do I not go crazy?
naming|metadata |standards | tools

38. naming

39. Naming: File Names

40. File naming

41. File naming

42. Naming conventions
s/n, variable
Retain order
Project_instrument_location_YYYYMMDDhhmmss_extra.ext
Index/grant
conditions
Leading zero!

43. Experiment: stem cells on fibrin to damaged heart
Lamar Soutter Library UMMS

44. ‐2 days: Incubate stem cells with markers
‐1 day: Stem cells in solution with biological suture
0 day: #1 Surgery: infarct/delivery of stem cells to damaged heart tissue
Variable days: #2 Surgery: examination, high speed imaging/LVPs,
isolate heart and place it in freezer
Post days +: Section heart, tissues on slides, staining, images of tissues,
tracking particles on heart
Collective data from experiment

45. ‐2 days: Incubate stem cells with markers
‐1 day: Stem cells in solution with biological suture
0 day: #1 Surgery: infarct/delivery of stem cells to damaged heart tissue
TIME | TYPE | USE
Variable days: #2 Surgery: examination, high speed imaging/LVPs,
isolate heart and place it in freezer
Post days +: Section heart, tissues on slides, staining, images of tissues,
tracking particles on heart
Collective data from experiment

46. Data
File Format
Images
Machine dependent
Ventricular pressure
measurements
Proprietary
Home made software
MATLAB or C
Histology sections
Slides and images
Contextual
Project, Experiment, Animal
Many different file types

47. Data
File Format
Name
Images
Machine
dependent
Scope_Date_Var
Ventricular
pressure
measurements
Proprietary
M_Date_Var.raw
Home made
software
MATLAB or C
Script_Date_Var
Histology
sections
Slides and images
Anat_Date_Stain
Separate Nomenclature

48. Data
File Format
Name
Images (1)
Machine
dependent
E_1_Date_var
Ventricular
pressure
measurements
(2)
Proprietary
E_2_Date_Var
Home made
software (3)
MATLAB or C
E_3_Script_Var
Histology
sections (4)
Slides and images
E_4_Date_Stain
Unified Nomenclature

49. Type
Recommended
Avoid for data sharing
Tabular data
CSV, TSV, SPSS portable
Excel
Text
Plain text, HTML, RTF
PDF/A only if layout matters
Word
Media
Container: MP4, Ogg
Codec: Theora, Dirac, FLAC
Quicktime
H264
Images
TIFF, JPEG2000, PNG
GIF, JPG
Structured data
XML, RDF
RDBMS
Recommended File Formats

50. RESOURCES
• Bulk Rename Utility (Windows)
• Renamer (Mac)
• PSRenamer
• Mendeley
Bulk File Renaming Tools

51. Naming conventions
Grant_Project_experiment_instrument_location_weather_catsname_i
cecreamflavor_collaborator_owner_zodiacsign_mousemodel_address
_painscalerating_favoritecolor_ssn_shoesize_sex_eyecolor_tattoos_
scars_votingrecord_YYYYMMDDhhmmss_extra.ext

52. Naming: Directory Structure

54. Presentations
Data presentation
CTSAconnect presentation
Monarch presentation
SPARC
CTSAconnect
Monarch

56. RESOURCES
www.coggle.it
http://ftp.ihmc.us/
www.mindjet.com
Mindmapping Software

57. Oldie but goodie…

58. Naming: Version Control

60. DataManagement@UPR_seminars_101113_JW
DataManagement@UPR_data_101113_JW
DataManagement_dataship_100313_NV_JW_MH_RC
Data101_dataship_091113_FINAL_JW
Data101_dataship_091013_v04_JW
DataManagement_dataship_091013_v03_JW
DataManagement_dataship_090913_v02_JW
DataManagement_dataship_090913_v01_JW
DataManagement_SPARC_082013_FINAL_NV
DataManagement_SPARC_052013_v8

64. RESOURCES
Dropbox | Google docs
GIT | SMART SVN
Version Control

65. Naming: Backups

66. Which of the following do you do?

67. a. Save copies of data on a disk, USB drive, 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

68. 3 | copies (you, lab, other)
2 | 2 different forms
1 | remote location

69. ETHICS

70. Computing in the cloud

71. How do I not go crazy?
naming|metadata |standards | tools

72. Metadata/ Controled Vocab/Ontologies

73. You speak for your data

74. How do you speak for your data
when you are not around?

75. Metadata
Controlled
Vocabularies
How do you speak for your data
ontologies
when you are not around?

76. What it is
Controlled vocabularies
metadata
What it takes to do it
Relevant variables
Controlled vocab
definitions
grouping
ontologies
classification
connection

77. What is metadata, really?
Hello there!

78. a. a philosophy
b. describes data
c. dating site
d. data

79. Title
Author
Call number
Publisher
ISBN

82. Metadata
Metadata
Metadata
Metadata
Metadata
Metadata
Metadata
Metadata
Metadata
Metadata
Metadata
Metadata
Metadata
Metadata
metadata
Metadata
Metadata
Metadata
Metadata
Metadata
Metadata
Metadata
Metadata
Metadata
Metadata
Metadata
Metadata
Metadata
Metadata
metadata
Metadata
Metadata
Metadata
Metadata
Metadata
Metadata
Metadata
Metadata
Metadata
Metadata
Metadata
Metadata
Metadata
Metadata
metadata
Your metadata should
make your data
understandable to
others without your
involvement
- Anne Gilliland

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

86. RESOURCES
http://www.dlib.indiana.edu/~jenlrile/metadatamap/
Metadata standards

87. RESOURCES
http://rs.tdwg.org/dwc/
Metadata standards

88. What is controlled vocab, really?
Hello there!

89. Craigslist search: Chaise
Craigslist search: Fainting couch

92. acetominophen

93. PubMed indexes articles with
MeSH Terms

94. What is an ontology, really?
Hello there!

97. Human Disease:
PFEIFFER
SYNDROME
Coronal
craniosynostosis
HP:0004440
Hypoplasia of
the maxilla
HP:0000327
Cross-species
Phenotype
premature
suture
closure
Most similar
mouse model:
CD1.Cg-Fgfr2tm4Lni/H
premature
suture closure
MP:0000081
maxilla
hypoplasia
short maxilla
MP:0000097
malocclusion
Dental crowding
HP:0000678
Brachyturricephaly
HP:0000244
Hypertelorism
HP:0000316
malocclusion
MP:0000120
shortened
head
ocular
hypertelorism
shortened
head
MP:0000435
ocular
hypertelorism
MP:0001300

98. How?
naming|metadata |standards | tools

99. standards

101. Meet the Urban Lab
Meet the Urban Lab

102. The Urban Lab Antibodies
A+ organization!

103. Percent identifiable
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Commerical Ab Catalog number Source organism Target uniquely
identifiable
reported
reported
identifiable
Of 14 antibodies published in 45 articles,
only 38% were identifiable

105. RESOURCES
AntibodyRegistry.org

107. Are you aware of data standards in
your field?
@OHSU, 72% said no or didn’t know!

108. Data standards are the rules by which data are
described and recorded. In order to share, exchange,
and understand data, we must standardize the format
as well as the meaning.
www.usgs.gov/datamanagement/plan/datastandards.php

109. Many microarray transcriptomics standards
JAMIA:sea‐of‐standards

110. Reporting
guidelines
Terminology
structure
(Interoperability)
Exchange
Formats

111. How do these standards work?

112. Metadata
Controlled
Vocabularies
How do you speak for your data
ontologies
when you are not around?

113. RESOURCES
Minimum Information for Biological and Biomedical Investigations

114. RESOURCES
www.cdisc.org

115. RESOURCES
www.force11.org/node/4463
biosharing.org/bsg-000532
Minimum Information for
Biological and Biomedical Investigations
http://www.biosharing.org/standards/mibbi
Reporting Standards

116. How?
naming|metadata |standards | tools

117. tools

118. RESOURCES
runmycode.org
www.wf4ever‐project.org
galaxyproject.org/
Workflow analysis platforms

119. RESOURCES
http://opus.bath.ac.uk/32296
www.labarchives.com
www.labguru.com

121. RESOURCES
https://dmp.cdlib.org/

122. Uniquely identifying data
www.flickr.com/photos/pmeimon

123. Digital Object Identifier (DOI)
Example: 10.1371/journal.pbio.1001339
Unique resource identifier (URI)
A URI will resolve to a single location on the web
URIs for people
Repositories use Unique IDs

124. RESOURCES
Repository Map

125. RESOURCES
Data Sharing Repositories

126. 3 |next steps

127. Thinking Beyond the PDF
Raw Science
Small publications
Self-publishing
Datasets
Nanopublications
Blogging
Code
Argument or
passage
Social Media
Experimental
design
Single figure
publications
Comments &
Reviews
Annotations

128. Research Products

129. RESOURCES
figshare.com
datadryad.org
thedata.org
www.dataone.org
data.rutgers.edu/
v
n2t.net/ezid
nature.com/scientificdata/
F1000.com/
Data publishing and sharing

130. You are unique, too

131. John L Campbell, Research Ecologist, Oregon State
University, Corvallis OR
John L Campbell, Research Ecologist, Center for
Research on Ecosystem Change, Durham, NC

133. RESOURCES
Impact.Story
impactstory.org
www.plumanalytics.com
http://myidp.sciencecareers.org/
orcid.org
Yes, you are an individual!

135. 1 | Data definitions
2 | Dealing with Data
3 | The Next Step

136. wirzj@ohsu.edu

137. http://libguides.ohsu.edu/data

138. Melissa Haendel
Nicole Vasilevsky
Robin Champieux

139. Thank you!

140. Questions?

141. what happens
between publications?

143. “We are Drowning in
Information but
Starved for
Knowledge”
John Naisbitt

144. “We are Drowning in
data but
Starved for
Knowledge”
Hello there!
Jackie’s bad paraphrase of John Naisbitt

145. Dr. Sawyer
Dr. Finn
Connected?
University
Collegeville College
University of Badass
No
Journals
Journal of Information
Journal of Data
No
Grant Title
Protein G is Important
Protein H is Important
No

146. Dr. Sawyer
Dr. Finn
Connected?
Machines Used
Alpha, Gamma, Theta,
Sigma
Gamma, Beta, Kappa, Theta
Yes!
Reagents Used
Cyan, Orange, Green,
Mauve, Beige
Mauve, Chartreuse, Cyan,
Green, Taupe
Yes!
Genes Referenced
bz3d14.2, bz3c13.1,
bz3d,98.1
bz3c13.1
Yes!
Proteins Referenced
Eng1a, Ntl, Ncdq
Ndrw, Eng1a, Brs
Yes!
People Affiliated with
Resources
Harry, Neville, Ron
Harry, Ron, Hermione
Yes!