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| 1
Anita de Waard 0000-0002-9034-4119
VP Research Data Collaborations
Elsevier RDM Services
a.dewaard@elsevier.com
Big Data PI Meeting
March 16, 2016
Real-World Data
Challenges:
Moving Towards
Richer Data Ecosystems
| 2
ESGF-
VL
ESGF
ESG-
CET
ESG-II
ESG-I
Usable
capabilities
Future
capabilities
Prototype
capabilities
1999-2001
2001-2006
2006-2011
2011-2020
2020-
Planned Earth System Grid System Evolution
Planned Earth System Grid System Data Archival
Model
Intercomparison
Projects
Remote Sensing,
In Situ, Climatology,
Diagnostics, Ecosystem,
Hydrology, Biology,
Etc.
Petabytes (1015) Exabytes (1018)
1999 20222017
Centralized Archive Distributed Data Ecosystem Virtual Laboratory
Source: Dean Williams, Lawrence Livermore/ESGF, March 1st 2017
Trend # 1: Repositories are becoming virtual labs
| 3
Trend # 2: Scientists are Moving ‘Beyond Downloads’
| 4
Trend # 3: Computers are scientists, too!
“intelligent systems for computer-aided
discovery can complement and integrate
into the insight generation loop in
scalable ways…”
http://ieeexplore.ieee.org/abstract/document/7515118/: Computer-Aided Discovery: Toward Scientific Insight Generation with Machine Support
“This work combines time series Principal
Component Analysis with InSAR to constrain
the space of possible model explanations on
current empirical data sets and achieve a better
identification of deformation patterns”
| 5
Raising many technical/organisational/policy questions:
• Is Long-Tail Data + Semantics = Big Data?
• Is Data Science a field, or a skill? (A department, or a class?)
• Are supercomputing centers research departments or bits of infrastructure? (And if
infrastructure, are they part of IT? (“Oh, no, anything but that!”)
• Are repositories places to store outputs, or places where science is conducted?
• If so, how are repositories and HPC’s recognised and rewarded?
• How can we keep track of (micro)provenance of parts of data sets?
• Should we explore Blockchain technology for this? (“Oh no, anything but that!”)
• Is a piece of software part of the University’s Research Outputs?
• If so, how do we reward brilliant coders who blog, but don’t write?
• How do we reward (virtual) collaboration?
• Why won’t those damn scientists share their data?
• Who will own the Data Science Cloud: Amazon? Or the joint HPC’s (NDS??) Is NIH
Data Commons the Model? Or is this a free for all? What is the role of commercial
parties?
• Is data curation/stewardship a part of science, or a glorified administrator's job?
• What is the role of libraries, in all this?
• And why the hell is a publisher talking about it?
| 6 6
Inst. Data
Repositorie(s)
Lab
ELN(s)
Data
Journal
Data search
Link to article
Journal
Find
Topic
Identify
gaps
Plan &
Fund
Discover data, people,
methods & protocols
Collect, analyze &
vizualize
Store, preserve
& share
Publish
Prepare, reproduce,
re-use & benchmark
Domain-specific
Repositories
General search
Faculty
LIMS
Data
center
Inst. Data
Repositorie(s)
Lab
ELN(s)
Data
Journal
Data search
Data Management
Plans
Metadata, methods &
protocols ready for
preservation and publishing
Link to article
Journal
Publish data
(under embargo)
Secure
discoverability
in & outside
the institution
Plan each step from
experiment to publish
Domain-specific
Repositories
General search
What Elsevier is Interested in: Supporting RDM Networks
| 7
Biological Pathways extracted via
semantic text mining
A upregulates B
B upregulates C
C increases disease D
Normalizing vocabularies required: proteins, diseases, drugs, chemicals
A  B  C  D
Bioactivities
through text analysis
IC50 6.3nM, kinase binding assay
10mM concentration
Chemical Structures
And Properties
InChi,
Name
NCBI,
Uniprot
EMTREE
ReaxysTree,
Structures
What Elsevier is Interested in: Knowledge Graphs in Life
Science
| 8
What Elsevier is Interested in: Knowledgegraphs in Research
| 9
Thank you!
Links to things we’re involved with:
• https://www.elsevier.com/connect/10-aspects-of-highly-effective-research-data
• https://www.elsevier.com/about/open-science/research-data
• https://www.hivebench.com
• https://data.mendeley.com/
• https://datasearch.elsevier.com/
• https://www.elsevier.com/books-and-journals/content-innovation/data-base-
linking
• http://www.journals.elsevier.com/softwarex/
• https://www.elsevier.com/physical-sciences/earth-and-planetary-sciences/the-
2015-international-data-rescue-award-in-the-geosciences
• https://rd-alliance.org/groups/rdawds-publishing-data-services-wg.html
• https://www.force11.org/
• http://www.nationaldataservice.org/
• https://rd-alliance.org/
Anita de Waard, a.dewaard@elsevier.com

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Real-World Data Challenges: Moving Towards Richer Data Ecosystems

  • 1. | 1 Anita de Waard 0000-0002-9034-4119 VP Research Data Collaborations Elsevier RDM Services a.dewaard@elsevier.com Big Data PI Meeting March 16, 2016 Real-World Data Challenges: Moving Towards Richer Data Ecosystems
  • 2. | 2 ESGF- VL ESGF ESG- CET ESG-II ESG-I Usable capabilities Future capabilities Prototype capabilities 1999-2001 2001-2006 2006-2011 2011-2020 2020- Planned Earth System Grid System Evolution Planned Earth System Grid System Data Archival Model Intercomparison Projects Remote Sensing, In Situ, Climatology, Diagnostics, Ecosystem, Hydrology, Biology, Etc. Petabytes (1015) Exabytes (1018) 1999 20222017 Centralized Archive Distributed Data Ecosystem Virtual Laboratory Source: Dean Williams, Lawrence Livermore/ESGF, March 1st 2017 Trend # 1: Repositories are becoming virtual labs
  • 3. | 3 Trend # 2: Scientists are Moving ‘Beyond Downloads’
  • 4. | 4 Trend # 3: Computers are scientists, too! “intelligent systems for computer-aided discovery can complement and integrate into the insight generation loop in scalable ways…” http://ieeexplore.ieee.org/abstract/document/7515118/: Computer-Aided Discovery: Toward Scientific Insight Generation with Machine Support “This work combines time series Principal Component Analysis with InSAR to constrain the space of possible model explanations on current empirical data sets and achieve a better identification of deformation patterns”
  • 5. | 5 Raising many technical/organisational/policy questions: • Is Long-Tail Data + Semantics = Big Data? • Is Data Science a field, or a skill? (A department, or a class?) • Are supercomputing centers research departments or bits of infrastructure? (And if infrastructure, are they part of IT? (“Oh, no, anything but that!”) • Are repositories places to store outputs, or places where science is conducted? • If so, how are repositories and HPC’s recognised and rewarded? • How can we keep track of (micro)provenance of parts of data sets? • Should we explore Blockchain technology for this? (“Oh no, anything but that!”) • Is a piece of software part of the University’s Research Outputs? • If so, how do we reward brilliant coders who blog, but don’t write? • How do we reward (virtual) collaboration? • Why won’t those damn scientists share their data? • Who will own the Data Science Cloud: Amazon? Or the joint HPC’s (NDS??) Is NIH Data Commons the Model? Or is this a free for all? What is the role of commercial parties? • Is data curation/stewardship a part of science, or a glorified administrator's job? • What is the role of libraries, in all this? • And why the hell is a publisher talking about it?
  • 6. | 6 6 Inst. Data Repositorie(s) Lab ELN(s) Data Journal Data search Link to article Journal Find Topic Identify gaps Plan & Fund Discover data, people, methods & protocols Collect, analyze & vizualize Store, preserve & share Publish Prepare, reproduce, re-use & benchmark Domain-specific Repositories General search Faculty LIMS Data center Inst. Data Repositorie(s) Lab ELN(s) Data Journal Data search Data Management Plans Metadata, methods & protocols ready for preservation and publishing Link to article Journal Publish data (under embargo) Secure discoverability in & outside the institution Plan each step from experiment to publish Domain-specific Repositories General search What Elsevier is Interested in: Supporting RDM Networks
  • 7. | 7 Biological Pathways extracted via semantic text mining A upregulates B B upregulates C C increases disease D Normalizing vocabularies required: proteins, diseases, drugs, chemicals A  B  C  D Bioactivities through text analysis IC50 6.3nM, kinase binding assay 10mM concentration Chemical Structures And Properties InChi, Name NCBI, Uniprot EMTREE ReaxysTree, Structures What Elsevier is Interested in: Knowledge Graphs in Life Science
  • 8. | 8 What Elsevier is Interested in: Knowledgegraphs in Research
  • 9. | 9 Thank you! Links to things we’re involved with: • https://www.elsevier.com/connect/10-aspects-of-highly-effective-research-data • https://www.elsevier.com/about/open-science/research-data • https://www.hivebench.com • https://data.mendeley.com/ • https://datasearch.elsevier.com/ • https://www.elsevier.com/books-and-journals/content-innovation/data-base- linking • http://www.journals.elsevier.com/softwarex/ • https://www.elsevier.com/physical-sciences/earth-and-planetary-sciences/the- 2015-international-data-rescue-award-in-the-geosciences • https://rd-alliance.org/groups/rdawds-publishing-data-services-wg.html • https://www.force11.org/ • http://www.nationaldataservice.org/ • https://rd-alliance.org/ Anita de Waard, a.dewaard@elsevier.com

Notas do Editor

  1. Outline: Some Trends Some Questions What Elsevier is interested in, and doing
  2. Example – your eln being able to publish protocols directly - easing the resaerchers burden