Presentation at the National Library of Medicine, in a Symposium organized by the National Data Stewardship Residency, funded by the Library of Congress and the Institute of Museum and Library Services, on "Digital Frenemies: Closing the Gap in Born-Digital and Made-Digital Curation”. https://ndsr2016.wordpress.com/

1. The Rise of Data Publishing
in the Digital World
(and how Dataverse and DataTags help)
Mercè Crosas, Ph.D.
Chief Data Science and Technology Officer
Institute for Quantitive Social Science
Harvard University
@mercecrosas
NDSR 2016 Symposium

2. From 1665 to late 20th century:
A steady increase in size and
complexity of research output

3. The number of journals doubles every 20 years
since 1750s, with growth of number of scientists
1665 1765 1865 1965
100
10000
Mabe, 2003

4. The number of journals doubles every 20 years
since 1750s, with growth of number of scientists
1700: 3 journals
1665 1765 1865 1965
100
10000
Mabe, 2003

5. The number of journals doubles every 20 years
since 1750s, with growth of number of scientists
1700: 3 journals
1800: ~10 journals
1665 1765 1865 1965
100
10000
Mabe, 2003

6. The number of journals doubles every 20 years
since 1750s, with growth of number of scientists
1700: 3 journals
1800: ~10 journals
1900: ~400 journals
1665 1765 1865 1965
100
10000
Mabe, 2003

7. The number of journals doubles every 20 years
since 1750s, with growth of number of scientists
1700: 3 journals
1800: ~10 journals
1900: ~400 journals
2000: ~14,000 journals
(peer-reviewed)
1665 1765 1865 1965
100
10000
Mabe, 2003

8. 1665 1765 1865 1965
100
10000

9. Data Tables andVisuals Become Increasingly
Common, and part of the Scientific Argument
1665 1765 1865 1965
100
10000

10. Data Tables andVisuals Become Increasingly
Common, and part of the Scientific Argument
a few tables &
visuals, as part of
the text
1665 1765 1865 1965
100
10000

11. Data Tables andVisuals Become Increasingly
Common, and part of the Scientific Argument
a few tables &
visuals, as part of
the text 50% cite previous
work
1665 1765 1865 1965
100
10000

12. Data Tables andVisuals Become Increasingly
Common, and part of the Scientific Argument
a few tables &
visuals, as part of
the text 50% cite previous
work
First Line Graphs
and bar charts
(Playfair, 1786)
1665 1765 1865 1965
100
10000

13. Data Tables andVisuals Become Increasingly
Common, and part of the Scientific Argument
a few tables &
visuals, as part of
the text
50% of articles have
tables & figures
50% cite previous
work
First Line Graphs
and bar charts
(Playfair, 1786)
1665 1765 1865 1965
100
10000

14. Data Tables andVisuals Become Increasingly
Common, and part of the Scientific Argument
a few tables &
visuals, as part of
the text
50% of articles have
tables & figures
50% cite previous
work
method sections
appear
First Line Graphs
and bar charts
(Playfair, 1786)
1665 1765 1865 1965
100
10000

15. Data Tables andVisuals Become Increasingly
Common, and part of the Scientific Argument
a few tables &
visuals, as part of
the text
50% of articles have
tables & figures
50% cite previous
work
method sections
appear
First Line Graphs
and bar charts
(Playfair, 1786)
First Scatterplots
(Hershel,1833;
Galton 1896)
1665 1765 1865 1965
100
10000

16. Data Tables andVisuals Become Increasingly
Common, and part of the Scientific Argument
a few tables &
visuals, as part of
the text
50% of articles have
tables & figures
most articles
have tables &
figures, often
standalone
50% cite previous
work
method sections
appear
First Line Graphs
and bar charts
(Playfair, 1786)
First Scatterplots
(Hershel,1833;
Galton 1896)
1665 1765 1865 1965
100
10000

17. Data Tables andVisuals Become Increasingly
Common, and part of the Scientific Argument
a few tables &
visuals, as part of
the text
50% of articles have
tables & figures
most articles
have tables &
figures, often
standalone
50% cite previous
work
100% with citations
(1 per 100 words)
part of scholarly credit
method sections
appear
First Line Graphs
and bar charts
(Playfair, 1786)
First Scatterplots
(Hershel,1833;
Galton 1896)
1665 1765 1865 1965
100
10000

18. Scholarly Publishing Adapts to the
Increase of Cognitive Complexity (Gross et al 2001)

19. Scholarly Publishing Adapts to the
Increase of Cognitive Complexity (Gross et al 2001)
• 18th century:

20. Scholarly Publishing Adapts to the
Increase of Cognitive Complexity (Gross et al 2001)
• 18th century:
• formal components appear in articles (introduction,
conclusions, table, figures, citations)

21. Scholarly Publishing Adapts to the
Increase of Cognitive Complexity (Gross et al 2001)
• 18th century:
• formal components appear in articles (introduction,
conclusions, table, figures, citations)
• 19th century:

22. Scholarly Publishing Adapts to the
Increase of Cognitive Complexity (Gross et al 2001)
• 18th century:
• formal components appear in articles (introduction,
conclusions, table, figures, citations)
• 19th century:
• explain data instead of establish observations of facts

23. Scholarly Publishing Adapts to the
Increase of Cognitive Complexity (Gross et al 2001)
• 18th century:
• formal components appear in articles (introduction,
conclusions, table, figures, citations)
• 19th century:
• explain data instead of establish observations of facts
• wide use of visuals, high citation density, methods section

24. Scholarly Publishing Adapts to the
Increase of Cognitive Complexity (Gross et al 2001)
• 18th century:
• formal components appear in articles (introduction,
conclusions, table, figures, citations)
• 19th century:
• explain data instead of establish observations of facts
• wide use of visuals, high citation density, methods section
• 20th century:

25. Scholarly Publishing Adapts to the
Increase of Cognitive Complexity (Gross et al 2001)
• 18th century:
• formal components appear in articles (introduction,
conclusions, table, figures, citations)
• 19th century:
• explain data instead of establish observations of facts
• wide use of visuals, high citation density, methods section
• 20th century:
• structured quantitative data with increased use of statistics

26. Scholarly Publishing Adapts to the
Increase of Cognitive Complexity (Gross et al 2001)
• 18th century:
• formal components appear in articles (introduction,
conclusions, table, figures, citations)
• 19th century:
• explain data instead of establish observations of facts
• wide use of visuals, high citation density, methods section
• 20th century:
• structured quantitative data with increased use of statistics
• wide range of data types with new technologies

27. Scholarly Publishing Adapts to the
Increase of Cognitive Complexity (Gross et al 2001)
• 18th century:
• formal components appear in articles (introduction,
conclusions, table, figures, citations)
• 19th century:
• explain data instead of establish observations of facts
• wide use of visuals, high citation density, methods section
• 20th century:
• structured quantitative data with increased use of statistics
• wide range of data types with new technologies
• Number of scientists increases from 100s to a few millions

28. Scholarly Publishing Adapts to the
Increase of Cognitive Complexity (Gross et al 2001)
• 18th century:
• formal components appear in articles (introduction,
conclusions, table, figures, citations)
• 19th century:
• explain data instead of establish observations of facts
• wide use of visuals, high citation density, methods section
• 20th century:
• structured quantitative data with increased use of statistics
• wide range of data types with new technologies
• Number of scientists increases from 100s to a few millions
• Science becomes extremely specialized:

29. Scholarly Publishing Adapts to the
Increase of Cognitive Complexity (Gross et al 2001)
• 18th century:
• formal components appear in articles (introduction,
conclusions, table, figures, citations)
• 19th century:
• explain data instead of establish observations of facts
• wide use of visuals, high citation density, methods section
• 20th century:
• structured quantitative data with increased use of statistics
• wide range of data types with new technologies
• Number of scientists increases from 100s to a few millions
• Science becomes extremely specialized:
• from 1 journal to 14,000 peer-reviewed journals

30. Scholarly Publishing Adapts to the
Increase of Cognitive Complexity (Gross et al 2001)
• 18th century:
• formal components appear in articles (introduction,
conclusions, table, figures, citations)
• 19th century:
• explain data instead of establish observations of facts
• wide use of visuals, high citation density, methods section
• 20th century:
• structured quantitative data with increased use of statistics
• wide range of data types with new technologies
• Number of scientists increases from 100s to a few millions
• Science becomes extremely specialized:
• from 1 journal to 14,000 peer-reviewed journals
• one new journal for each 150 authors, read by 500

31. In the last decades, more
and more publications
and data

32. A Steeper Growth of Scholarly Output
Since 1950, the total number of journals doubles every ~15 years
2010: 80,000 journals
2010: 33,000 peer-reviewed

33. An Outburst of Research Data and Specialization,
Results into > 1000 Community Repositories

34. An Outburst of Research Data and Specialization,
Results into > 1000 Community Repositories
1920 - 1950s

35. An Outburst of Research Data and Specialization,
Results into > 1000 Community Repositories
First Social Science
Data Archives
(ODUM, ICPSR, ...)
1920 - 1950s

36. An Outburst of Research Data and Specialization,
Results into > 1000 Community Repositories
First Social Science
Data Archives
(ODUM, ICPSR, ...)
1920 - 1950s 1970 - 1980s

37. An Outburst of Research Data and Specialization,
Results into > 1000 Community Repositories
First Social Science
Data Archives
(ODUM, ICPSR, ...)
First Biomedical
Databases
(PDB, GenBank, ...)
1920 - 1950s 1970 - 1980s

38. An Outburst of Research Data and Specialization,
Results into > 1000 Community Repositories
First Social Science
Data Archives
(ODUM, ICPSR, ...)
First Biomedical
Databases
(PDB, GenBank, ...)
1920 - 1950s 1970 - 1980s 2016

39. An Outburst of Research Data and Specialization,
Results into > 1000 Community Repositories
First Social Science
Data Archives
(ODUM, ICPSR, ...)
A wide range of
Research Data
Repositories
First Biomedical
Databases
(PDB, GenBank, ...)
1920 - 1950s 1970 - 1980s 2016

40. An Outburst of Research Data and Specialization,
Results into > 1000 Community Repositories
First Social Science
Data Archives
(ODUM, ICPSR, ...)
A wide range of
Research Data
Repositories
First Biomedical
Databases
(PDB, GenBank, ...)
1500 repositories listed in re3data.org
1920 - 1950s 1970 - 1980s 2016

41. Data Publishing Emerges as the Union of
Scholarly Publishing and Data Archiving

42. Data Publishing Emerges as the Union of
Scholarly Publishing and Data Archiving
Scholarly publishing:
Distribute research output

43. Data Publishing Emerges as the Union of
Scholarly Publishing and Data Archiving
Scholarly publishing:
Distribute research output
• Attribution and credit

44. Data Publishing Emerges as the Union of
Scholarly Publishing and Data Archiving
Scholarly publishing:
Distribute research output
• Attribution and credit
• Dissemination

45. Data Publishing Emerges as the Union of
Scholarly Publishing and Data Archiving
Scholarly publishing:
Distribute research output
• Attribution and credit
• Dissemination
• Finding & Reuse

46. Data Publishing Emerges as the Union of
Scholarly Publishing and Data Archiving
Scholarly publishing:
Distribute research output
• Attribution and credit
• Dissemination
• Finding & Reuse
Data Archiving:
Long-term access to data

47. Data Publishing Emerges as the Union of
Scholarly Publishing and Data Archiving
Scholarly publishing:
Distribute research output
• Attribution and credit
• Dissemination
• Finding & Reuse
Data Archiving:
Long-term access to data
• Accessibility

48. Data Publishing Emerges as the Union of
Scholarly Publishing and Data Archiving
Scholarly publishing:
Distribute research output
• Attribution and credit
• Dissemination
• Finding & Reuse
Data Archiving:
Long-term access to data
• Accessibility
• Preservation

49. Data Publishing Emerges as the Union of
Scholarly Publishing and Data Archiving
Scholarly publishing:
Distribute research output
• Attribution and credit
• Dissemination
• Finding & Reuse
Data Archiving:
Long-term access to data
• Accessibility
• Preservation
• Finding & Reuse

50. Why Data Publishing now?

51. Why Data Publishing now?
Extending Gross et al. thesis, data publishing accommodates the
complexity of research input and output in the digital world.

52. Why Data Publishing now?
Extending Gross et al. thesis, data publishing accommodates the
complexity of research input and output in the digital world.

53. Why Data Publishing now?
• Data (and software) have become common input and
output of research
Extending Gross et al. thesis, data publishing accommodates the
complexity of research input and output in the digital world.

54. Why Data Publishing now?
• Data (and software) have become common input and
output of research
• A scholarly article cannot hold or describe accurately these
vast amounts of data and software
Extending Gross et al. thesis, data publishing accommodates the
complexity of research input and output in the digital world.

55. Why Data Publishing now?
• Data (and software) have become common input and
output of research
• A scholarly article cannot hold or describe accurately these
vast amounts of data and software
• As input and output of research, data must be citable and
accessible to enable validation and reuse, with attribution
Extending Gross et al. thesis, data publishing accommodates the
complexity of research input and output in the digital world.

56. What is needed for FAIR Data Publishing
FAIR = Findable Accessible Interoperable Reusable

57. What is needed for FAIR Data Publishing
Data Citation
FAIR = Findable Accessible Interoperable Reusable

58. What is needed for FAIR Data Publishing
Data Citation
• Persistent id to
reference data uniquely
FAIR = Findable Accessible Interoperable Reusable

59. What is needed for FAIR Data Publishing
Data Citation
• Persistent id to
reference data uniquely
• Support for versions
and fixity
FAIR = Findable Accessible Interoperable Reusable

60. What is needed for FAIR Data Publishing
Data Citation
• Persistent id to
reference data uniquely
• Support for versions
and fixity
• Attribution to authors
and repository
FAIR = Findable Accessible Interoperable Reusable

61. What is needed for FAIR Data Publishing
Data Citation
• Persistent id to
reference data uniquely
• Support for versions
and fixity
• Attribution to authors
and repository
Metadata
FAIR = Findable Accessible Interoperable Reusable

62. What is needed for FAIR Data Publishing
Data Citation
• Persistent id to
reference data uniquely
• Support for versions
and fixity
• Attribution to authors
and repository
Metadata
• Catalog to discover and
locate the data
FAIR = Findable Accessible Interoperable Reusable

63. What is needed for FAIR Data Publishing
Data Citation
• Persistent id to
reference data uniquely
• Support for versions
and fixity
• Attribution to authors
and repository
Metadata
• Catalog to discover and
locate the data
• Sufficient information to
understand and reuse the
data
FAIR = Findable Accessible Interoperable Reusable

64. What is needed for FAIR Data Publishing
Data Citation
• Persistent id to
reference data uniquely
• Support for versions
and fixity
• Attribution to authors
and repository
Metadata
• Catalog to discover and
locate the data
• Sufficient information to
understand and reuse the
data
Repository
FAIR = Findable Accessible Interoperable Reusable

65. What is needed for FAIR Data Publishing
Data Citation
• Persistent id to
reference data uniquely
• Support for versions
and fixity
• Attribution to authors
and repository
Metadata
• Catalog to discover and
locate the data
• Sufficient information to
understand and reuse the
data
Repository
• Digital access to metadata
and data
FAIR = Findable Accessible Interoperable Reusable

66. What is needed for FAIR Data Publishing
Data Citation
• Persistent id to
reference data uniquely
• Support for versions
and fixity
• Attribution to authors
and repository
Metadata
• Catalog to discover and
locate the data
• Sufficient information to
understand and reuse the
data
Repository
• Digital access to metadata
and data
• Archive and preservation for
long-term access
FAIR = Findable Accessible Interoperable Reusable

67. What is needed for FAIR Data Publishing
Data Citation
• Persistent id to
reference data uniquely
• Support for versions
and fixity
• Attribution to authors
and repository
Metadata
• Catalog to discover and
locate the data
• Sufficient information to
understand and reuse the
data
Repository
• Digital access to metadata
and data
• Archive and preservation for
long-term access
• Interoperability through
standards and APIs
FAIR = Findable Accessible Interoperable Reusable

69. A data repository system that serves as a
solution for publishing FAIR research data

70. Around the World
Dataverse repositories serve a community, an institution, an archive, ...

71. Around the World
Harvard Dataverse:
Generic data repository open
to researchers world wide
Dataverse repositories serve a community, an institution, an archive, ...

72. Dataverses contain datasets,
datasets contain metadata and data files

73. Data Citation in Dataverse

74. Data Citation in Dataverse
Published
Year
Dataset
Title
Global
Persistent
Identifier
Repository
= Data Publisher
Version (or
time range)
Authors

75. Data Citation Basics
Force11, Joint Declaration of Data Citation Principles; Starr et al, 2015

76. Data Citation Basics
Force11, Joint Declaration of Data Citation Principles; Starr et al, 2015
The dataset landing page is accessible and guaranteed by the repository
(or data publisher), even when data are restricted or deaccessioned

77. Metadata In Dataverse

78. Metadata In Dataverse
Citation Metadata
author, title, repository,
year published, version,
etc
• Dublin Core
• DataCite
Domain-specific
Metadata
data collection info
(methods, organism,
observation, survey,
experiment, etc)
• DDI (social sciences)
• ISA-Tab BioCaddie (biomed)
• Virtual Observatory (astro)
• + Custom metadata blocks
File-level Metadata
metadata inside the data
file (variables, instrument
details, geospatial info,
etc)
• DDI (for variables),
• + more to be determined
Fields StandardsMetadata Level

79. Metadata In Dataverse
Citation Metadata
author, title, repository,
year published, version,
etc
• Dublin Core
• DataCite
Domain-specific
Metadata
data collection info
(methods, organism,
observation, survey,
experiment, etc)
• DDI (social sciences)
• ISA-Tab BioCaddie (biomed)
• Virtual Observatory (astro)
• + Custom metadata blocks
File-level Metadata
metadata inside the data
file (variables, instrument
details, geospatial info,
etc)
• DDI (for variables),
• + more to be determined
Fields StandardsMetadata Level

80. Metadata In Dataverse
Citation Metadata
author, title, repository,
year published, version,
etc
• Dublin Core
• DataCite
Domain-specific
Metadata
data collection info
(methods, organism,
observation, survey,
experiment, etc)
• DDI (social sciences)
• ISA-Tab BioCaddie (biomed)
• Virtual Observatory (astro)
• + Custom metadata blocks
File-level Metadata
metadata inside the data
file (variables, instrument
details, geospatial info,
etc)
• DDI (for variables),
• + more to be determined
Fields StandardsMetadata Level

81. Metadata In Dataverse
Citation Metadata
author, title, repository,
year published, version,
etc
• Dublin Core
• DataCite
Domain-specific
Metadata
data collection info
(methods, organism,
observation, survey,
experiment, etc)
• DDI (social sciences)
• ISA-Tab BioCaddie (biomed)
• Virtual Observatory (astro)
• + Custom metadata blocks
File-level Metadata
metadata inside the data
file (variables, instrument
details, geospatial info,
etc)
• DDI (for variables),
• + more to be determined
Fields StandardsMetadata Level
DataverseJSONSchema

82. Information Extraction:Tabular Files

83. Information Extraction:Tabular Files
RData
Stata
SPSS
Excel
CSV
var 1 var 2 var 3
obs 1 2 a 0
obs 2 4 c 0
obs 3 6 b 1
obs 4 1 e 0
obs 5 2 a 1
obs 6 3 b 1

84. Information Extraction:Tabular Files
RData
Stata
SPSS
Excel
CSV
var 1 var 2 var 3
obs 1 2 a 0
obs 2 4 c 0
obs 3 6 b 1
obs 4 1 e 0
obs 5 2 a 1
obs 6 3 b 1
Variable Metadata:
Variable name, label,
type, stats, geospatial
coordinates

85. Information Extraction:Tabular Files
RData
Stata
SPSS
Excel
CSV
var 1 var 2 var 3
obs 1 2 a 0
obs 2 4 c 0
obs 3 6 b 1
obs 4 1 e 0
obs 5 2 a 1
obs 6 3 b 1
Variable Metadata:
Variable name, label,
type, stats, geospatial
coordinates
2 a 0
4 c 0
6 b 1
1 e 0
2 a 1
3 b 1
DataValues:
Independent of format

86. Information Extraction:Tabular Files
RData
Stata
SPSS
Excel
CSV
var 1 var 2 var 3
obs 1 2 a 0
obs 2 4 c 0
obs 3 6 b 1
obs 4 1 e 0
obs 5 2 a 1
obs 6 3 b 1
Variable Metadata:
Variable name, label,
type, stats, geospatial
coordinates
2 a 0
4 c 0
6 b 1
1 e 0
2 a 1
3 b 1
DataValues:
Independent of format
Universal Numerical Fingerprint (UNF):
checksum on data values, from canonical format

87. Information Extraction: FITS (astro) Files

88. Information Extraction: FITS (astro) Files

89. Information Extraction: FITS (astro) Files
Header Metadata:
coordinates (R.A.,
declination),
photometric info, ...

90. Information Extraction: FITS (astro) Files
Header Metadata:
coordinates (R.A.,
declination),
photometric info, ...
Data Objects:
•Image Files
•Spectra
•Data cubes
•Tables
•...

91. In addition to data citation and
metadata features, Dataverse
has a rich set of features that
facilitate data publishing

92. Tiered Access

93. Tiered Access
Open (default):
CC0
Open Open Click to Download
GuestBook Open Open
Fill in guestbook before
download
Terms of Use Open Open
Click through terms of
use before download
Data Restricted Open Restricted Request Access via
click through
Data Restricted Open Restricted
Request Access via
application
Metadata Files How to Access

94. Tiered Access
Open (default):
CC0
Open Open Click to Download
GuestBook Open Open
Fill in guestbook before
download
Terms of Use Open Open
Click through terms of
use before download
Data Restricted Open Restricted Request Access via
click through
Data Restricted Open Restricted
Request Access via
application
Metadata Files How to Access

95. Tiered Access
Open (default):
CC0
Open Open Click to Download
GuestBook Open Open
Fill in guestbook before
download
Terms of Use Open Open
Click through terms of
use before download
Data Restricted Open Restricted Request Access via
click through
Data Restricted Open Restricted
Request Access via
application
Metadata Files How to Access

96. Tiered Access
Open (default):
CC0
Open Open Click to Download
GuestBook Open Open
Fill in guestbook before
download
Terms of Use Open Open
Click through terms of
use before download
Data Restricted Open Restricted Request Access via
click through
Data Restricted Open Restricted
Request Access via
application
Metadata Files How to Access

97. Tiered Access
Open (default):
CC0
Open Open Click to Download
GuestBook Open Open
Fill in guestbook before
download
Terms of Use Open Open
Click through terms of
use before download
Data Restricted Open Restricted Request Access via
click through
Data Restricted Open Restricted
Request Access via
application
Metadata Files How to Access

98. Data Publishing Workflows

99. Data Publishing Workflows
Create Dataset
(landing page
restricted)

100. Data Publishing Workflows
Create Dataset
(landing page
restricted)
Review
(collaborators or
anonymous reviewers)

101. Data Publishing Workflows
Create Dataset
(landing page
restricted)
Publish v. 1
Review
(collaborators or
anonymous reviewers)

102. Data Publishing Workflows
Create Dataset
(landing page
restricted)
Publish v. 1
Review
(collaborators or
anonymous reviewers)
Minor change
(metadata only)

103. Data Publishing Workflows
Create Dataset
(landing page
restricted)
Publish v. 1
Review
(collaborators or
anonymous reviewers)
Minor change
(metadata only)

104. Data Publishing Workflows
Create Dataset
(landing page
restricted)
Publish v. 1
Review
(collaborators or
anonymous reviewers)
Minor change
(metadata only)
Publish v. 1.1

105. Data Publishing Workflows
Create Dataset
(landing page
restricted)
Publish v. 1
Review
(collaborators or
anonymous reviewers)
Minor change
(metadata only)
Publish v. 1.1
Major change
(might include new
data file)

106. Data Publishing Workflows
Create Dataset
(landing page
restricted)
Publish v. 1
Review
(collaborators or
anonymous reviewers)
Minor change
(metadata only)
Publish v. 1.1
Major change
(might include new
data file)

107. Data Publishing Workflows
Create Dataset
(landing page
restricted)
Publish v. 1
Review
(collaborators or
anonymous reviewers)
Minor change
(metadata only)
Publish v. 1.1
Major change
(might include new
data file)
Publish v. 2

108. And more at dataverse.org guides ...

109. Biomedical Dataverse addresses data
publication of large files: SBGridData

110. The Biomedical Dataverse at Harvard Medical School -
also tested as a persistent repository for LINCS data
(NIH Library of Integrated Network based Cellular Signatures)
Collaboration with Piotr Sliz and Caroline Shamu (HMS)
(NIH Library of Integrated Network-based Cellular Signatures)

111. The Biomedical Dataverse at Harvard Medical School -
also tested as a persistent repository for LINCS data
(NIH Library of Integrated Network based Cellular Signatures)
Collaboration with Piotr Sliz and Caroline Shamu (HMS)
(NIH Library of Integrated Network-based Cellular Signatures)

112. An additional challenge
for data publishing:
Sensitive Data

113. “User
Uploads
must
be
void
of
all
iden4fiable
informa4on,
such
that
re-­‐iden4fica4on
of
any
subjects
from
the
amalgama4on
of
the
informa4on
available
from
all
of
the
materials
(across
datasets
and
dataverses)
uploaded
under
any
one
author
and/or
user
should
not
be
possible.”

114. “SubmiCer
represents
and
warrants
that
the
Content
does
not
contain
any
informa4on
(i)
which
iden4fies,
or
which
can
be
used
in
conjunc4on
with
other
publicly
available
informa4on
to
personally
iden4fy,
any
individual;”

115. “If
you
are
submiHng
human
sequences
to
GenBank,
do
not
include
any
data
that
could
reveal
the
personal
iden4ty
of
the
source.
It
is
our
assump4on
that
you
have
received
any
necessary
informed
consent
authoriza4ons
that
your
organiza4ons
require
prior
to
submiHng
your
sequences.”
GenBank

116. How can we maximize
publishing sensitive data while
being mindful of privacy?

117. Sweeney
L,
Crosas
M,
Bar-­‐Sinai
M.
Sharing
Sensi4ve
Data
with
Confidence:
The
DataTags
System.
Technology
Science.
2015101601.
October
16,
2015.
hCp://techscience.org/a/2015101601
The DataTags System

119. A datatag is a set of security features and access
requirements for file handling

120. A datatag is a set of security features and access
requirements for file handling
A datatags repository is one that stores and shares
data files in accordance with a standardized and
ordered levels of security and access requirements

121. Datatags&Levels&
Tag$Type$ Descrip-on$ Security$Features$ Access$Requirements$
Blue$ Public& Clear&storage&
Clear&transmission&
&
Open&
Green$ Controlled$
public&
Clear&storage&
Clear&transmission&
Email,&OAuth&verified&
registra:on&
Yellow$ Accountable& Clear&storage&
Encrypted&transmit&
Password,&Registered&,&
Approval,&Click&DUA&
Orange$ More$
accountable&
Encrypted&storage&
Encrypted&transmit&
Password,&Registered,&
Approval,&Signed&DUA&
Red$ Fully$
accountable&
Encrypted&storage&
Encrypted&transmit&
TwoDfactor&authen:ca:on,&
Approval,&Signed&DUA&
Crimson$ Maximally$
restricted&
Mul:Encrypt&store&
Encrypted&transmit&
TwoDfactor&authen:ca:on,&
Approval,&Signed&DUA&

122. DataTags Workflow in a Dataverse Repository
(under development)
Data$File$
Inges-on$
Sensi-ve$
Dataset$
Direct$
Access$
Privacy$
Preserving$
Access$
Automa-c$
Interview$$
Review$Board$
Approval$
hCp://datatags.org
hCp://privacytools.seas.harvard.edu
Two-­‐factor
Authen4ca4on;
Signed
DUA

123. Example of DataTags Interview

124. Example of DataTags Interview

125. Example of DataTags Interview

126. Example of DataTags Interview

127. Example of DataTags Interview

128. Example of DataTags Interview

129. Thanks!
And join us to this year’s
Dataverse Community Meeting

130. References
• http://dataverse.org
• http://dataverse.harvard.edu
• http://datatags.org
• Sweeney L, Crosas M, Bar-Sinai M. 2015, Sharing
Sensitive Data with Confidence:The DataTags System.
Technology Science, hCp://techscience.org/a/2015101601
• Gross Harmon, Reidy, 2001, Communicating Science
• Mabe,
2003,
The
Growth
and
Number
of
Journals
• Friendly,
2006,
A
Brief
History
of
Data
Visualiza4on