data management, information management, data, big data, personal organization, organization, file management, scientific research, research, project management, data security, file naming conventions, data management plan,

2. DATA MANAGEMENT
July 7, 2014

3. Hello there!

4. 1 | Data definitions
2 | Dealing with Data
3 | The Real World

5. 1 |Data definitions

6. asdf
Data can be complex

7. Data can be amazing

8. Data are about discovery

9. But…

10. Data does not speak for itself…

11. YOU speak for YOUR data

12. and you need to manage it

13. But, even more fundamentally…

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

17. Data means different things
to different people

18. TraditionalData Types
Observational
Experimental
Simulation data
Derived or compiled data

19. Welcome to the 21st Century

21. Data is not static

26. The data timeline
1. BrilliantIdea!
2. DesignExperiment
3. Do Experiment
4. Collectdata
5. Compileand Analyze
6. Publish
7. Fame,Fortune

27. 1. BrilliantIdea!
2. Design Experiment
3. Do Experiment
4. Collect data
5. Compileand Analyze
6. Publish
7. Fame,Fortune
The data timeline:
What people think

28. The data timeline:
What Happens
Idea!
design
experiment
Compile&Cleandata
Publish
Try #2
Failure!!
#896
coffee
#896!!!!
Analyzingdata
Other
People’s
data

29. The data cycle:
WhatReally Happens

31. 2 |dealing with data

32. Hello there!
Why should I care?

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

34. Hello there!
Do you get frustrated with…

35. 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 (versioning)
i. Creating metadata (what’s metadata?)
j. Protecting intellectual property rights
k. Ensuring appropriate professional credit/citation is given

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

37. naming

38. Naming: File Names

39. File naming

40. File naming
This is fake…

41. File naming
This is real!

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

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

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

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

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. Recommended File Formats
Type Recommended Meh
Tabular data CSV, TSV, SPSS portable Excel
Text
Plain text, HTML, RTF
PDF/A only if layout matters
Word
Media
Sound: FLAC, Ogg
Video: MP4
Sound: MP3, WAV, AIFF
Video: .mj2
Images TIFF, JPEG2000, PNG GIF, JPG, PDF
Structured data XML, RDF RDBMS

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

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. Data presentation
CTSAconnect presentation
Monarch presentation
Presentations
SPARC CTSAconnect Monarch

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

58. Mindmapping Old School
RESOURCES

59. Oldie but goodie…

60. Naming: Version Control

62. 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

66. Version Control
RESOURCES
Electronic Lab Notebooks

67. Naming: Backups

68. Which of the following do you do?

69. 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

70. 3 | copies (you,lab,other)
2 | 2 different forms
1 | remote location

71. ETHICS

72. Computingin the cloud

73. Ownership

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

75. Metadata/ Controled Vocab/Ontologies

76. You speak for your data

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

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

79. Controlled vocabularies
metadata
Whatit is
Whatit takesto do it
Relevantvariables
definitions
grouping
classification
connection
Controlled vocab
ontologies

80. Hello there!
What is metadata,really?

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

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

83. Title
Author
Call number
Publisher
ISBN

86. - AnneGilliland
Your metadatashould
make your data
understandable to
others withoutyour
involvement
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

87. - Jackie
Your metadatashould
make your data
understandable to
your mother
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

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

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

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

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

94. Craigslist search: Chaise
Craigslist search: Fainting couch

97. = acetominophen

98. PubMed indexes articles with
MeSH Terms

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

102. Hello there!
Ummmm….So what?!?

104. naming|metadata |standards | tools
How?

105. standards

106. Why? The Methods Section…

107. Meet the Urban Lab
Meet the Urban Lab

108. A+ organization!
The Urban Lab Antibodies

109. 0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
Commerical Ab
identifiable
Catalog number
reported
Source organism
reported
Target uniquely
identifiable
Of 14 antibodies published in 45 articles,
only 38% were identifiable
Percentidentifiable

111. AntibodyRegistry.org
RESOURCES

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

113. How do these standards work?

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

115. RESOURCES
Minimum Information for Biological and Biomedical Investigations

116. www.force11.org/node/4463 biosharing.org/bsg-000532
Reporting Standards
RESOURCES
http://www.biosharing.org/standards/mibbi www.cdisc.org

117. naming|metadata |standards | tools
How?

118. tools

119. RESOURCES
www.wf4ever-project.org runmycode.org
galaxyproject.org/
Workflow analysis platforms
www.pegasus.isi.edu/

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

121. RESOURCES
www.labtove.org www.openwetware.org

123. What types of data will be created?
Who will own / access / be responsible?
Where will data be stored during and after?
What info is necessary for my mom to get it?

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

126. Uniquely identifying data

127. 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

128. RESOURCES
Repository Map

129. RESOURCES
Data Sharing Repositories

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

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

132. *special topics in data management*
How do I not go crazy?

133. Raw Science Small publications Alt Publishing
Datasets
Code
Experimental
design
Argument or
passage
Blogging
Social Media
Comments &
Reviews
Annotations
Single figure
publications
Nanopublications
Beyond the Traditional Journal Article

134. “Research Products”

135. You are unique, too!

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

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

140. 3 |the Real World

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

142. So, what does this mean for day to day research?
How do I not go crazy?

144. Gummy Bear:
the Groundbreaking Paper

145. Your Data: Gummy Bear Raw Data
Haribo Gummi Bears Sugar Free 5lb Bag
Bounces Amplitude Color
15 4 blue
43 3 red
58 9 green
75 82 purple

146. Your task: Create a Figure for Nature
Figure with the following:
Image of Gummy skeleton only with belly
button annotated
Chart of springiness per color of bear
Figure legend
Methods section

147. Figure 1. A) Gummy skeleton with belly button annotated
with red arrow B) Springiness by sample color.
Methods Section: Haribo Gummi Bears (Sugar Free) were purchased from
Amazon.com (UPC: 422384500110). Gummy bears were placed in the
SpringOMatic 3000 (ICanPickleThat, Portland OR) according to the manufactures
instructions. The Gummy Anatomy (Jason Freeny) image was cropped in PPT
(Microsoft) and annotate to highlight the bellybutton.
0
2
4
6
8
10
12
14
16
blue red green purple
Springiness(bounces/length)
Sample Color
A B
Final Figure Example

148. Gummy Bear ChallengeFinal Figure
Group 1

149. Gummy Bear Final Data
0
2
4
6
8
10
12
14
16
blue red green purple
4 3 9 82
15 43 58 75
Springiness (Bounces/Amplitude)
15 4 blue
43 3 red
58 9 green
75 82 purple
Methods:
A schematic of a Gummi Bear was cropped to indicate where the belly button is located
(Fig. 1). At this point, raw experimental data showing the bounce, amplitude, and color
were analyzed and the springiness calculated for each color of bear. This was
accomplished by dividing the bounce by the amplitude and plotting this against bear
color.
Fig. 1
Belly button of
Haribo Sugar Free
Gummi Bear

150. Gummy Bear ChallengeFinal Figure
Group 2

151. Figure 1. A) Gummy skeleton with belly button annotated
with red arrow B) Springiness by sample color.
Methods Section: Haribo Gummi Bears (Sugar Free) were
purchased from Amazon.com (UPC: 422384500110).
Gummy bears were placed in the SpringOMatic 3000
(ICanPickleThat, Portland OR) according to the
manufactures instructions.
Gummy Bear Final Data
0
2
4
6
8
10
12
14
16
blue red green purple
Springiness(bounces/length)
Sample Color
A B
B

152. Gummy Bear ChallengeFinal Figure
Group 3

153. 0
2
4
6
8
10
12
14
16
purple blue green red
Figure 1: Haribo Gummi Bear (Ursus gummius
hariboensis) springiness as a measure of
bounces/amplitude, by color (n = 1).
Springiness(Bounces/Amplitude)
Figure 2: Schematic depiction of
Haribo Gummi Bear umbilical
skeletal anatomy.
Methods & Materials
Gummi Bears were obtained through Amazon in 3 kg bags. Lot and temperature during transport
data were not made available. Bears were housed in a plastic bowl in accordance with IACUC
policy and national standards for gummi bear care. They were housed at room temperature on a
natural light cycle.
Food and water were provided ad libitum (consumption was not monitored)
Each bear was sampled only once to reduce costs

154. Methods & Materials
• Gummi Bears were obtained through Amazon in 3 kg bags. Lot and
temperature during transport data were not made available. Bears were
housed in a plastic bowl in accordance with IACUC policy and national
standards for gummi bear care. They were housed at room temperature
on a natural light cycle.
• Food and water were provided ad libitum (consumption was not
monitored)
• Each bear was sampled only once to reduce costs

155. Gummy Bear ChallengeFinal Figure
Group 4

156. Belly Button
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
blue red green purpleSpringiness(bounces/amplitude)
Gummy Bear Color
(a) (b)
Fig. 1. (a) schematic of the anatomy of a gummy bear (adapted from 1). (b)
springiness of bear by color using spring-o-matic.

157. Methods:
Insert the sample of interest, specifically a colored
gummy bear (Haribo, Japan). Position the probe
above the sample. Press "Tickle" and the
SpringOMatic (ICanPickleThat, Portland) will poke
the belly button a standard depth of 1 cm. Record
the number of bounces and the amplitude of the
largest bounce in cm. From these values, the
springiness can be calculated (bounce/amplitude).

159. Take the Challenge!

160. 1 | Data definitions
2 | Dealing with Data
3 | The Real World

161. wirzj@ohsu.edu

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

163. Thank you!

164. Questions?

165. “We are Drowning in
Informationbut
Starved for
Knowledge”
John Naisbitt

166. Hello there!
“We are Drowning in
data but
Starved for
Knowledge”
Jackie’s badparaphraseofJohn Naisbitt