Big Data Standards - Workshop, ExpBio, Boston, 2015

!

!

Big Data Standards: how to set the bar?!!

!

Susanna-Assunta Sansone, PhD!

!

@biosharing!@isatools!

!

Experimental Biology, Big Data Workshop, 28 March, 2015

Data Consultant, Honorary Academic Editor

Associate Director, Principal Investigator

http://www.slideshare.net/SusannaSansone

https://projects.ac/blog/five-top-reasons-to-protect-your-data-and-practise-safe-science/

Credit to:

A community mobilization for “openness”

Is open data understandable, reusable?

“Reproducing the method took several months of effort, and required using new versions and new software that posed challenges to reconstructing and validating the results”

Is open data understandable, reusable? Not always…but why?

•  Outputs are multi-dimensional, diverse, not always well cited / stored

•  Software, codes, workflows etc.; hard(er) to get hold of

•  Data often distributed and fragmented to fit (siloed) databases

o  Not contain enough information for others to understand it

•  Uneven level of details and annotation across different databases

o  Specialized, generalist, public and institutional

•  Data curation activities are perceived as time consuming

o  Collection and harmonization of detailed methods and experimental

steps is done/rushed at publication stage

Not just open, but FAIR data

Responsibilities lie across several stakeholder groups

Understand the benefits of sharing FAIR datasets and enact them

Engage and assist researchers to enable them to share FAIR datasets

Release or endorse practices and polices, but also incentive

and credit mechanisms for researchers, curators and

developers

Rise of a data-centric enterprise, e.g.:

Not just data, but FAIR digital research objects

•  We need to report sufficient information to reuse the dataset

•  We must strike a balance between depth and breadth of information

Without context data is meaningless

Information intensive experiments

•  Not too much •  Not too little •  But just right

And conversely….

LS1_C2_LD_TP2_P1! file1.gz!

…how not to report the experimental information!

•  L!S1 ! !liver sample 1!•  C2 ! !compound 2!•  LD ! !low dose!•  TP2 ! !time point 2!

•  P1 ! !protocol 1!•  file1.gz ! !compressed data file with !! ! !phenotypic and other information ! ! !on this sample!

Sample name (?!)! Data file!

LS1_C2_LD_TP2_P1! file1.gz!

The International Conference on Systems Biology (ICSB), 22-28 August, 2008 Susanna-Assunta Sansone www.ebi.ac.uk/net-project

14

•  make annotation explicit and discoverable

•  structure the descriptions for consistency

•  ensure/regulate access

•  deposit and publish •  etc….

•  To make any dataset ‘FAIR’, one must have standards, tools and best practices to: §  report sufficient details §  capture all salient features of

the experimental workflow

The International Conference on Systems Biology (ICSB), 22-28 August, 2008 Susanna-Assunta Sansone www.ebi.ac.uk/net-project

15

…breadth and depth !of the experimental context!

…is pivotal !

…and has to be both human and machine

readable!

nature.com/scientificdata

A new category of publication that provides detailed descriptors of scientifically valuable

datasets. They are a highly effective link between traditional research articles and data repositories

Introducing the Data Descriptor

Res

earc

h pa

pers

D

ata

reco

rds

Dat

a D

escr

ipto

rs

To add value to research articles and data records

!!!

Experimental metadata or !structured component!

(in-house curated, machine-readable format)!

Article or !narrative component!

(PDF and HTML) !

Data Description narrative and structured components

19

A curated, structured component - why?

•  Supplements the scientific discourse!o  natural language has a degree of ambiguity!

•  Brings clarity in reporting research methods and procedures!o  no trimming, no cooking!o  clear samples to data files links and relation to methods!

•  Provides the basis for search and discovery features!

SciData DD

Structured content SciData DD

Structured content

SciData DD

Structured content

SciData DD

Structured content

SciData DD

Structured content

SciData DD

Structured content

SciData DD

Structured content

SciData DD

Structured content

SciData DD

Structured content

SciData DD

Structured content

Same tissue

Same organism

Same assay

Community Data

Repositories

Seven week old C57BL/6N mice were treated with low-fat diet.

Liver was dissected out, hepatocytes prepared…

From natural language to ‘computable’ concepts

Data Curation Editor

Responsible for creating the structured component, ensuring that the most appropriate metadata is being captured.

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 …

From natural language to ‘computable’ concepts

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 …

From natural language to ‘computable’ concepts

Type of protocol – cell preparation

Type of protocol - sample treatment

Type of protocol – liver preparation

Including minimum information reporting requirements, or checklists to report the same core, essential information

Including controlled vocabularies, taxonomies, thesauri, ontologies etc. to use the same word and refer to the same ‘thing’

Including conceptual model, conceptual schema from which an exchange format is derived to allow data to flow from one system to another

Community-developed content standards To structure and enrich the description of datasets, facilitating

understanding, sharing and reuse!

de jure de facto

grass-roots groups

standard organizations

Community mobilization, some examples

•  Structural and operational differences §  organization types (open, close to members, society, WG etc.) §  standards development (how to formulate, conduct and maintain) §  adoption, uptake, outreach (link to journals, funders and commercial sector) §  funds (sponsors, memberships, grants, volunteering)

~ 156

~ 70

~ 334

miame!MIAPA!

MIRIAM!MIQAS!MIX!

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!

In the life sciences…..almost 600!

Databases, !annotation,!

curation !tools !

implementing !standards!

A web-based, curated and searchable registry ensuring that standards are registered, informative and discoverable; monitoring their

development and evolution and their use in databases, and the adoption of both in data policies.

Launched Jan 2011

The International Conference on Systems Biology (ICSB), 22-28 August, 2008 Susanna-Assunta Sansone www.ebi.ac.uk/net-project

Core functionalities: •  search and filtering, e.g. by

funder •  submissions forms to add

new records •  “claim” functionality of

existing records •  person’s profile (as

maintainer of records) associated to the ORCID profile (for credit, as incentive)

•  visualization and views of content

Search, filter, claim, view and more

Assists users to make informed decisions

Advisory Board and Working Group - core members and adopters

Operational Team

The relationship among popular standard formats for pathway information. !

Demir, et al., The BioPAX community standard for pathway data sharing, Nat Biotech. 2010.

Standards as an area of research - still a lot to do! E.g.:

1. Create relation or “usage maps and guides”, e.g.:

2. Metrics of maturity, usability and popularity

3. Embed in the ecosystem of complementary registries

31

Technologically-delineated views of the world !

Biologically-delineated views of the world!

Generic features (‘common core’)!- description of source biomaterial!- experimental design components!

Arrays!

Scanning! Arrays &Scanning!

Columns!

Gels!MS! MS!

FTIR!

NMR!

Columns!

transcriptomics proteomics metabolomics

plant biology epidemiology microbiology

To compare and integrate data we need interoperable standards

How do we address fragmentation, duplications gaps?

Global alliances are needed, e.g.:

biocaddie.org

metadatacenter.org

•  Most researchers understand the value of standardized descriptions, when using third-party datasets!

!

•  But when asked to structure their datasets, they view requests for even “minimal” information as burdensome!

re is an urgent need to lower the bar for authoring good metadata!

Researchers hate standards!

•  Most researchers understand the value of standardized descriptions, when using third-party datasets!

!

•  But when asked to structure their datasets, they view requests for even “minimal” information as burdensome!

!

Ø  There is an urgent need to lower the bar for authoring good metadata!

Researchers hate standards!