1.
BioSharing.org – mapping the
landscape of standards in the
life sciences
Peter McQuilton, PhD (@drosophilic)
BioSharing content lead
On behalf of the BioSharing team (@biosharing)

2.
Outline
• Standards, databases and policies in the life
sciences
• BioSharing – an informative and educational
resource
• What it is
• How to use it
• How it can help you

3.
A growth in data, a growth in
databases
Number of databases in the NAR database issue, up to
2015 (from @AlexBateman1)

4.
Credit: ttps://projects.ac/blog/five-top-reasons-to-protect-your-data-and-practise-safe-science/ 2014
Better data = better science

5.
The FAIR Principles

6.
But in all fairness, not all data is
FAIR!

7.
A B C D E
1 Group1 Group2
2 Day 0
3 Sodium 139 142
4 Potassium 3.3 4.8
5 Chloride 100 108
6 BUN 18 18
7 Creatine 1.2 1.2
8 Uric acid 5.5* 6.2*
9 Day 7
10 Sodium 140 146
11 Potassium 3.4 5.1
12 Chloride 97 108
S1Sh.cuo
Credit to: Iain Hrynaszkiewicz
Sharing starts with good metadata…

8.
A B C D E
1 Group1 Group2
2 Day 0
3 Sodium 139 142
4 Potassium 3.3 4.8
5 Chloride 100 108
6 BUN 18 18
7 Creatine 1.2 1.2
8 Uric acid 5.5* 6.2*
9 Day 7
10 Sodium 140 146
11 Potassium 3.4 5.1
12 Chloride 97 108
S1Sh.cuo Meaningless
column titles
Special characters can
cause text mining
errors
No units
Unhelpful
document name
Undefined
abbreviation
Formatting for
information that
should be in
metadata
Credit to: Iain Hrynaszkiewicz
…. which this isn’t...

9.
A B C D E F
1 Parameter Day Control Treated Units P
2 Sodium 0 139 142 mEq/l 0.82
3 Sodium 7 140 146 mEq/l 0.70
4 Sodium 14 140 158 mEq/l 0.03
5 Sodium 21 143 160 mEq/l 0.02
6 Potassium 0 3.3 4.8 mEq/l 0.06
7 Potassium 7 3.4 5.1 mEq/l 0.07
8 Potassium 14 3.7 4.7 mEq/l 0.10
9 Potassium 21 3.1 3.6 mEq/l 0.52
10 Chloride 0 100 108 mEq/l 0.56
11 Chloride 7 97 108 mEq/l 0.68
12 Chloride 14 101 106 mEq/l 0.79
Table_S1_Shanghai_blood.xls
Credit to: Iain Hrynaszkiewicz
…. This is much clearer!

10.
Seven week old C57BL/6N mice were treated
with low-fat diet.
Liver was dissected out, hepatocytes prepared…
From natural language to
structured data

11.
Age value
Unit
Strain name
Subject of the experiment
Type of diet and
experimental condition
Anatomy part
Seven week old C57BL/6N mice were treated
with low-fat diet.
Liver was dissected out, hepatocytes prepared …
Type of protocol – cell preparation
Type of protocol - sample treatment
Type of protocol – liver preparation
From natural language to
structured data

12.
• Data/content standards:
• Structure, enrich and report the description of the
datasets and the experimental context under which they
were produced
• Facilitate the discovery, sharing, understanding and
reuse of datasets
Data has to be structured for sharing
– we need standards

13.
de jure de facto
grass-roots
groups
standard
organizations
Nanotechnology Working Group
Community mobilisation to develop
content standards
Formats Terminologies Guidelines

14.
Guidelines = Minimum information
reporting requirements, checklists
o Report the same core, essential
information
o e.g. ARRIVE guidelines
Terminologies = Controlled
vocabularies, taxonomies,
thesauri, ontologies etc.
o Use the same word and
refer to the same ‘thing’
o e.g. Gene Ontology
Models/Formats = Conceptual
model, conceptual schema,
exchange formats
o Allow data to flow from one
system to another
o e.g. FASTA
Enablers: to better describe, share
and query data
Formats Terminologies Guidelines

15.
193
85
346
miame
MIAPA
MIRIAM
MIQASMIX
MIGEN
ARRIVE
MIAPE
MIASE
MIQE
MISFISHIE….
REMARK
CONSORT
MAGE-Tab
GCDML
SRAxml
SOFT FASTA
DICOM
MzML
SBRML
SEDML…
GELML
ISA-Tab
CML
MITAB
AAO
CHEBI
OBI
PATO ENVO
MOD
BTO
IDO…
TEDDY
PRO
XAO
DO
VO
There are over 600 standards in
the life sciences
Formats Terminologies Guidelines

16.
Data policies
(30)
Databases
(763)
data/metadata standards
(652)
A complex and evolving landscape
Formats Terminologies Guidelines

17.
Is there a database, implementing
standards, where to deposit my
metagenomics dataset?
My funder’s data sharing policy
recommends the use of
established standards, but which
ones are widely endorsedand
applicable to my toxicological
and clinical data?
Am I using the most up-to-date
version of this terminology to
annotate cell-based assays?
I understand this format has been
deprecated; what has been replaced by
and how is leading the work?
Are there databases implementing this
exchange format, whose development
we have funded?
What are the mature
standardsand
standards-compliant
databases we should
recommend to our
authors?
Helping people make the right
decision

18.
Introducing BioSharing

19.
Mapping the landscape of ‘standards’ in the life, environmental and
biomedical sciences
Mapping the landscape of ‘standards’ in the life, environmental and
biomedical sciences
1,400 records and growing
What is BioSharing?
A web-based, curated and searchable portal that monitors the development
and evolution of standards, their use in databases and the adoption of both in
data policies, to inform and educate the user community.

20.
Mapping the landscape of ‘standards’ in the life, environmental and
biomedical sciences
Mapping the landscape of ‘standards’ in the life, environmental and
biomedical sciences
What is BioSharing?
Launched in 2011, as an evolution of the MIBBI portal (2008-2011)
Manually curated
Community driven
Growing userbase and visibility
1,400 records and growing

21.
also operates as a WG in Run at is also an Resource that
The BioSharing community
1,400 records and growing

22.
How do we describe
standards?

23.
23
Criteria for evaluating standards

24.
Linking standards,
databases and policies

25.
Model/format formalizing reporting guideline -->
<-- Reporting guideline used by model/format
Cross-linking standards to
standards and databases

26.
Model/format formalizing reporting guideline -->
<-- Reporting guideline used by model/format
Cross-linking standards to
standards and databases

27.
Indicators of life cycle status
Ready for use, implementation, or recommendation
In development
Status uncertain
Deprecated as subsumed or superseded
Manually curated, approved by the community

28.
An informative and
educational resource
Simple and advanced searches, ask our wizard or
view journal recommendations

29.
The International Conference on Systems Biology (ICSB), 22-28 August,2008 Susanna-Assunta
Sansone www.ebi.ac.uk/net-project
Search, filter, and refine using our faceted
search
Search, filter, and refine using our faceted
search

30.
Collections group together
one or more types of resource
by domain, project or
organization.
Recommendations are a core-
set of resources that are
selected and recommended
by a funder or journal data
policy.

31.
Standards and databases
recommended by journal data
policies
Standards and databases
recommended by journal data
policies

32.
The wizard:
• Guides users through the data
• Will grow in functionality and
complexity, based on user
feedback
• Powered by curated
descriptions of each standard
and database, and their
relations

33.
Is there a database, implementing
standards, where to deposit my
metagenomics dataset?
My funder’s data sharing policy
recommends the use of
established standards, but which
ones are widely endorsedand
applicable to my toxicological
and clinical data?
Am I using the most up-to-date
version of this terminology to
annotate cell-based assays?
I understand this format has been
deprecated; what has been replaced by
and how is leading the work?
Are there databases implementing this
exchange format, whose development
we have funded?
What are the mature
standardsand
standards-compliant
databases we should
recommend to our
authors?
Helping people make the right decision

34.
BioSharing – what we do
Inform – what’s out there, which databases use
which standards. Map the landscape.
Educate– what databases are recommended by your
funder, or journal of choice, which standards should
you be using, which standards and databases should
you recommend? Explore the landscape.

35.
Acknowledgements
Eamonn
Maguire, DPhil
Software Engineer
(contractor)
Philippe
Rocca-Serra, PhD
Senior Research Lecturer
Alejandra
Gonzalez-Beltran, PhD
Research Lecturer
Milo
Thurston, DPhil
Research SW Engineer
Massimiliano
Izzo, PhD
Research SW Engineer
Peter
McQuilton, PhD
Knowledge Engineer
Allyson
Lister, PhD
Knowledge Engineer
David
Johnson, PhD
Research SW Engineer
Susanna-Assunta Sansone, PhD
Centre’s Associate Director, Principal Investigator
and
Springer Nature’s Consultant for Scientific Data