Towards Incidental Collaboratories; Research Data Services

Research Data Services: Towards a Framework for Incidental Collaboratories

Anita de Waard VP Research Data Collabora@ons, Elsevier RDS

Jericho, VT, USA

Brief bio: •  Background: –  Low-‐temperature physics (Leiden & Moscow) –  Joined Elsevier in 1988 as publisher in solid state physics –  1991: ArXiV => publishers will go out of business very soon!

•  1997-‐ now: Disrup@ve Technologies Director, focus on beXer representa@on of scien@fic knowledge: –  Iden@fying key knowledge elements in ar@cles (linguis@cs thesis) –  Building claim-‐evidence networks (through collabora@ons) –  Help build communi@es to accelerate rate of change (Force11)

•  Star@ng 1/1/2013: VP Research Data Collabora@ons -‐ why? –  Douglas Engelbart’s thinking: connect minds! – My (non-‐biologists) understanding of biology:

The big problem in biology:

hXp://en.wikipedia.org/wiki/File:Duck_of_Vaucanson.jpg

Interspecies variability: A specimen is not a species Gene expression variability: Knowing genes is not knowing how they are expressed Microbiome: An animal is an ecosystem Systems biology: A whole is more than the sum of its parts Reduc@onist science doesn’t work for living systems!

Sta@s@cs to the rescue! With enough observa@ons, trends and anomalies can be detected: •  “Here we present resources from a popula@on of 242 healthy adults

sampled at 15 or 18 body sites up to three @mes, which have generated 5,177 microbial taxonomic profiles from 16S ribosomal RNA genes and over 3.5 terabases of metagenomic sequence so far.”

The Human Microbiome Project Consor@um, Structure, func@on and diversity of the healthy human microbiome, Nature 486, 207–214 (14 June 2012) doi:10.1038/nature11234

•  “The large sample size — 4,298 North Americans of European descent and 2,217 African Americans — has enabled the researchers to mine down into the human genome.”

Nidhi Subbaraman, Nature News, 28 November 2012, High-‐resolu@on sequencing study emphasizes importance of rare variants in disease.

•  “A profile unique for a DNA sample source is obtained … a series of numbers are generated which can be used as a bar code for that DNA source. A registry of bar codes would make it easy to compare DNA samples”

Roland M. Nardone, Ph.D., Eradica@on of Cross-‐Contaminated Cell Lines: A Call for Ac@on, hXp://www.sivb.org/[email protected]

•  Collect: store data at the level of the experiment: – Accessible through a single interface – Add enough metadata to know what was done/seen

•  Connect: allow analyses over: –  Similar experiment types –  Experiments done with/on similar biological ‘things’:

•  Species, strains, systems, cells •  Anatomical components (e.g. spleen, hypothalamus) •  An@bodies, biomarkers, bioac@ve chemicals, etc

•  Keep: –  Long-‐term preserva@on of data and sosware (Olive) –  Fulfill Data Management Plan requirements – Allow gated access, if needed

Enable ‘incidental collaboratories’:

Problem: biological research is quite insular •  Biology is small: because objects/

equipment are 10^-‐5 – 10^2 m, you can work alone (‘King’ and ‘subjects’).

•  Biology is messy: it doesn’t happen behind a terminal.

•  Biology is compe@@ve: different people with similar skill sets, vying for the same grants.

•  In summary: it does not promote inherent collabora@on (vs., for instance, big physics or astronomy).

Prepare

Observe

Analyze

Ponder

Communicate

Try to pop the ‘lab bubble’!

Prepare

Analyze Communicate Think

Prepare

Analyze Communicate

Prepare

Analyze Communicate

Observa@ons

Observa@ons

Observa@ons

Labs go from being informa@on islands, to being ‘sensors in a network’.

Some objec@ons, and rebuXals: Objec&on: Rebu-al:

“But our lab notebooks are all on paper”

Develop smart phone/tablet apps for data input

“I need to see a direct benefit from something I spend my @me on”

Develop ‘data manipula@on dashboard’ for PI to allow beXer access to full experimental output for his/her lab

“I am afraid other people might scoop my discoveries”

Develop intra-‐lab data communica@on systems first and allow @med/granular data export

“I want things to be peer reviewed before I expose them”

Allow reviewers access to experimental database before publica@on (of data or paper)

“I don’t really trust anyone else’s data – well, except for the guys I went to Grad School with…”

Add a social networking component to this data repository so you know who (to the individual) created that data point.

Elsevier Research Data Services: Goals

1.  Help increase the amount of data shared from the lab, enabling incidental collaboratories

2.  Help increase the value of the data shared by increasing annota@on, normaliza@on, provenance enabling enhanced interoperability

3.  Help measure and deliver credit for shared data, the researchers, the ins@tute, and the funding body, enabling more sustainable pla;orms

RDS Guiding Principles: •  In principle, all open data stays open and URLs, front end etc. stay where they are (i.e. with repository)

•  Collabora@on is tailored to data repositories’ unique needs/interests and of a ‘service-‐model’ type: – Aspects where collabora@on is needed are discussed – A collabora@on plan is drawn up using a Service-‐Level Agreement: agree on @me, condi@ons, etc.

– All communica@on, finance, IPR etc. is completely transparent at all @mes.

•  Very small (2/3 people) department; immediate communica@on; instant deployment of ideas

RDS Approach:

•  Collaborate and build on rela@onships with data repositories (life science, earth science, others)

•  Integrate with other content sources, if possible •  Build annota@on and standardisa@on tools and processes to implement this

•  Develop next-‐genera@on infrastructure solu@ons for back-‐end integra@on

•  Explore crea@ve revenue opportuni@es

NIF An@body Registry: Problem: •  95 an@bodies were iden@fied in 8 papers •  52 did not contain enough informa@on

to determine the an@body used •  Some provided details in another paper •  Failed to give species, vendor, catalog # Solu@on # 1: •  Journals ask authors to provide

an@body catalog nr •  Link to NIF Registry from manufacturers/

vendors’ sites

Solu@on #2: •  Pilot with a lab:

Let’s start with the Urban Lab

•  Geyng an@bodies •  And messy bits •  From the notebook •  Into Nathan Urban’s command center

•  By providing – 7” Tablets – Links to IgorPro – A dashboard UI

My ques@ons to you: •  Thoughts on this approach: –  In principle? –  In prac@ce?

•  Do you see serious hurdles: – Are we overlapping with other ini@a@ves; if so, are we complementary?

– How does this connect to libraries/local repositories? – Are there sensi@vi@es/pain points we are overlooking?

•  Where to start: – How to collaborate? – Who to talk to – funding agencies, socie@es: who else? –  Thoughts on data repositories/plazorms to connect to?

Your ques@ons to me?

[email protected] hXp://elsatglabs.com/labs/anita/

hXp://www.slideshare.net/anitawaard

Thanks go to: •  Anita Bandrowski and Maryann Martone, NIF •  Nathan Urban, Shreejoy Tripathy, CMU •  David Marques, SVP RDS

Towards Incidental Collaboratories; Research Data Services

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