My short statement on the (close) debate on Big Data: http://www.bdebate.org/en/forum/big-data-biomedicine-challenges-and-opportunities

1. !
What is Big Data in Biomedicine?!
Data Types to be considered!
!
Susanna-Assunta Sansone, PhD!
!
@biosharing!
@isatools!
@scientificdata!
!
B-DEBATE: Big Data in Biomedicine. Challenges and Opportunities, 11 Nov, 2014
Data Consultant,
Honorary Academic Editor
Associate Director,
Principal Investigator

2. Let’s not forget the long tail of research data
• Big science efforts represent only a small proportion!
o often featuring homogenous and well-organized data!
!
• There is a large proportion of small independent research efforts!
o a rich variety of specialty data sets!

3. Let’s not forget the long tail of research data
• Small independent research efforts fall in the long-tail of the distribution!
o Most of this (such as as siloed databases, null findings) is
unpublished!
o These dark data hold a potential wealth of knowledge!

4. Plagued by selective reporting of data and methods
• Over 50% of completed studies in
biomedicine do not appear in the
published literature!
!
• Instead reside in file drawers
personal and hard drives!
!
• Often because results do not
conform to author's hypotheses!
“Only half the health-related
studies funded by the European
Union between 1998 and 2006 -
an expenditure of €6 billion - led
to identifiable reports”!

5. Role of data papers and data journals
• Incentive, credit for sharing!
o Big and small data!
o Unpublished data!
o Long tail of data!
o Curated aggregation !
• Peer review focus!
• Value of data vs. analysis!
• Discoverability and reusability!
o Complementing community
databases!
• Narrative/context!

6. Role of data papers and data journals
• The power of “small data” are in their aggregation and integration
with other datasets!
• There is value in all well-curated, validated and reusable data – big
and small!

7. Adding value to research articles and data records
Research
articles
Descriptors
Data
Data
records

8. Adding value to research articles and data records
Research
articles
Descriptors
Data
Data
records
Credit for sharing
your data
Focused on reuse
and reproducibility
Peer reviewed,
curated
Open Access
Promoting
community
data and code
repositories

9. Progressively refine guidance to authors and reviewers
~ 156
~ 70
~ 334
Source: BioPortal
Databases !
implementing !
standards!
miame!
MIAPA!
MIRIAM!
MIX!MIQAS!
MIGEN!
MIAPE!
CIMR!
MIASE!
REMARK!
MIQE!
CONSORT!
MISFISHIE….!
MAGE-Tab!
GCDML!
SRAxml!
SOFT! FASTA!
DICOM!
MzML!
SBRML!
CML!
GELML!
SEDML…!
MITAB!
ISA-Tab!
AAO!
CHEBI!
OBI!
PATO! ENVO!
MOD!
TEDDY!
BTO!
IDO…!
XAO!
PRO!
DO
VO!

10. Mapping the landscape of standards and databases

11. Mapping the landscape of standards and databases

12. Help stakeholders to make informed decisions
Researchers, developers and curators lack support and guidance on how to best navigate and
select content standards, understand their maturity, or find databases that implement them;
Funders, journals and librarians do not have enough information to make informed decisions
on which content standards or database to recommended in policies, or funded or implemented

13. Summarizing
• Selective reporting of data and methods is still an issue
• Let’s not forget the potential value of the long-tail of data
• Data papers and journals can provide incentive and
credit to share more data - big and small
• Content standards do help - but the current wealth of
options is an obstacle