create and recieve scientific data

a centre of expertise in data curation and preservation

Digital Curation 101, October 6th-10th, 2008, NeSC, Edinburgh

Funded by:This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 UK: Scotland License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/2.5/scotland/ ; or, (b) send a letter to Creative Commons, 543 Howard Street, 5th Floor, San Francisco, California, 94105, USA.

Create or Receive Scientific data

Dr. Frank Gibson and Dr. Phillip [email protected]@newcastle.ac.uk


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“In the standard model, one collects data, publishes a paper or papers and then gradually loses the original dataset.”

- Geoffrey Bowker


Create or ReceiveSlide by Cameron Neylon http://www.slideshare.net/CameronNeylon


Create or Receivehttp://flickr.com/photos/nicmcphee/2756494307/

If we have a paper who cares about the data?


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A paper = a claim (or claims)

The full record that supports that claim should be available for detailed

examination and critique

Slide by Cameron Neylon http://www.slideshare.net/CameronNeylon


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1000+Databases


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Biocuration: Databases


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Biocuration: Wiki


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Funders

http://flickr.com/photos/luismimunoznajar/2093185804/


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Create or

Receive


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Curation aims

AmenablePreservableOwnableAccessibleCitable


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Content

Syntax

Semantics

Significant Properties of Data


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Content


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Title

Creator

Type

Source

Date

Identifier

Publisher

Rights


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Simple Dublin Core

Title Creator Subject Description Publisher Contributor Date

Type Format

Identifier Source

Language Relation

Coverage Rights


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Content:Domain Specific


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Syntax


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Choosing a Syntax• Openness

• -is there an open, publicly available specification for the format; are its specifications in the public domain; is it unencrypted?

• Portability • -is the format independent of hardware, operating system, of

other software; is it independent of particular institutions, groups, or events; is it in widespread current use; does it contain little or no built-in functionality?

• Quality • -is it robust; simple; highly tested; loss-free?


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Semantics


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Semantics can be complex

One semantic = many wordsMany words = one semantic


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• Excel data example – do I need it?

•Zeeberg et al. BMC Bioinformatics 2004 5:80 doi:10.1186/1471-2105-5-80 •Zeeberg et al. BMC Bioinformatics 2004 5:80 doi:10.1186/1471-2105-5-80


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What is fly?

•http://en.wikipedia.org/wiki/Image:Air_india_b747-400_vt-esn_arp.jpg

•http://en.wikipedia.org/wiki/Image:MuscuDomestica.jpg

•http://en.wikipedia.org/wiki/Image:Green_Highlander_salmon_fly.jpg

•http://en.wikipedia.org/wiki/Image:Fly_poster.jpg

•Fly

•Fly•Fly

•Fly


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Ontology• A controlled vocabulary is an association

between formal names (identifiers) and their definitions.

• An ontology is a controlled vocabulary augmented with logical constraints that describe their interrelationships.


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Ontologies for Life science• Emergence has occurred for two reasons• Consistent annotation of data• To add meaning and understanding that can

be interpreted computationally• Bio-ontologies registered on the OBO foundry


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Application of Significant PropertiesInProteomics


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Minimum Information about a Proteomics Experiment (MIAPE)• Sufficiency.

• The MIAPE guidelines should require sufficient information abouta dataset and its experimental context to allow a reader to understand and critically evaluate the interpretation and conclusions, and to support their experimental corroboration.

• Practicability. • Achieving compliance with MIAPE should not be so burdensome

as to prohibit its widespread use.


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Minimum reporting guidelines• Describe content• Implementation

independent

• Impacts • Publication• Syntax• Semantics


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Syntax for proteomics• The content in MIAPE GE needs to be structured to

facilitate • dissemination • transfer• storage

• A community development process to agree on a syntax • building upon the FuGE data model• A pre-existing community developed representation of

scientific experiments• Interoperable


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FuGE• Model of common components in science investigations, such

as materials, data, protocols, equipment and software. • Provides a framework for capturing complete laboratory

workflows, enabling the integration of pre-existing data formats.


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UML/XML/RDBMS• UML gives structure (but not syntax)

• Very abstract, very general• XML provides a concrete syntax

• Meta language is interoperable, checkable, viable and has basic metadata support (language, character coding and so on).

• Tends toward the verbose. Not (very) searchable for itself.• Therefore, transfer and archive format.

• RDBMS• SQL is (sort of) a standard• Highly computationally amenable form; v. good for searching• Conversion from XML is possible, but in a number of ways. • Hard work – nice to have an off-the-shelf implementation.


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GelML


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Semanticsfor

Gels


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Semantics for science


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Curation of Gel experiments

MAIPEGE

MAIPEGI

LaboratoryPublic repositoriesData entry and transfer

I) GelML data entry tools

GelML

II) Direct database submission

III) Automated export of GelInfoML

sepCV


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Discoverability and reuse

•Persistent Identifiers•Rights management


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Persistent Identifiers• a name for a resource which will remain the same

regardless of where the resource is located • In biology typically assigned to data upon publication• Type of identifier dependent on publication method

• Description and Representation Information provides more information about persistent identifiers


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Rights management• Difficult to determine • Lots of legal issues• In biology/bioinformatics

tends to be open access

•Creative commons


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Receiving data for curation

ContentSyntaxSemantics


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Route map

Route mapWho will receive it?

What are their policies on: Content, Syntax, Semantics

Plan your experiment to conform to Content, Syntax, Semantics

Implement experiment to;Collect appropriate ContentStructure in appropriate SyntaxEnsure Semantics are preserved

Curate…


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Meta Route Map• How to build the map if you don’t have one

yet.


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Appraise and Select• Investigates the evaluation and selection of

data for longterm curation and preservation


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Acknowledgments• The CARMEN project

• www.carmen.org.uk• The Proteomics Standards Initiative (PSI)

• http://psidev.info• Colleagues at Newcastle University

• Phillip Lord, Anil Wipat, Allyson Lister

http://www.carmen.org.uk/

http://psidev.info/


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create and recieve scientific data

Technology

data curation

preservation curation

papera centre of expertise

fundersa centre of expertise

preservation http

digital curation

preservation content

preservation syntaxcreate