The document discusses the key features and functions that an ideal scientific data management system should have. It should manage users, instruments, biological samples, experiments and related workflows. It should support standards, ontologies, data models and data exchange formats. The system should be accessible from any device and integrate with other software and external resources. It should support the full lifecycle of information, enable collaboration and knowledge generation from documents and data. It should also be prepared to handle large increases in data volumes.

1. scientific information management
Alberto Labarga, Feb 13th 2009

2. LIMS: Laboratory Information Management System
ELN: Electronic Laboratory Notebook
SDMS: Scientific Data Management System

3. ideal
Your system should …

4. users
manage your

6. instruments
manage your

8. store
manage your biological

9. Samples
Cell lines
Tissues

10. experiments
manage your

11. requests schedule execute results collaborative analysis

12. Workflows: protein identification
Gel
Liquid
Spots
sample
Picking
Digestion and Digestion and
deposition concentation
MALDI LC-MS 2LC-MS
.wiff .wiff
Data processing (MASCOT, SEQUEST)

13. standards
Support

14. CONTENT: Minimal/Core Information
-> MIBBI (http://www.mibbi.org)
SEMANTIC: Terminology used, ontologies
-> OBI (http://obi-ontology.org)
SYNTAX: Data Model, Data Exchange
->Fuge (http://fuge.sourceforge.net/)

15. MIBBI: Standard Content

16. MIBBI: Standard Content

17. OBO: Ontologies and
controlled vocabulart

18. any device
Be accessible from

20. other software
be accessible from

21. Your LIMS goes here

22. external resources
integrate

23. submission
curation
ws ws ws ws ws
dataflow workflow

24. paper labboks
support your

26. infrastructure
integrate with your

28. architecture
a pluggeable

29. functional genomics platform
web application
document management system
Service Provider Interface
LDAP iggLIMS BASE GEMS

30. protocols
reports
publications
requests
raw data

31. visualization

32. data analysis

33. further, it should
But going a little bit

34. life cycle
cover the full of information

36. collaboration
support

37. co-creation

38. ideation

39. visualization

40. documents
mine your

42. knowledge
Help to generate new

43. Antileukoproteinase, Secretory leukocyte protease inhibitor, P03973
uniprot: http://www.uniprot.org/uniprot/P03973
genecards: http://www.genecards.org/cgi-bin/carddisp.pl?id=P03973
dasty: http://www.ebi.ac.uk/dasty/client/ebi.php?q=P03973
>sp|P03973|SLPI_HUMAN Antileukoproteinase OS=Homo sapiens GN=SLPI
MKSSGLFPFLVLLALGTLAPWAVEGSGKSFKAGVCPPKKSAQCLRYKKPECQSDWQCPGK
KRCCPDTCGIKCLDPVDTPNPTRRKPGKCPVTYGQCLMLNPPNFCEMDGQCKRDLKCCMG
MCGKSCVSPVKA

44. performance
evaluate your

45. Scientific overviews (institutions, groups)
Citation analysis
Scientific impact and excellence assessment
Measurement of the impact of funding and
programs on scientific production
Identification of scientific experts for
conferences, workshops and peer panels
organizations.

47. data deluge!
And be prepared for the

49. Illumina / Solexa
Genetic Analyzer
Applied Biosystems Roche / 454 Applied Biosystems
Genome Sequencer SOLiD
ABI 3730XL
3000 Mb/run
100 Mb/run
1 Mb/day

50. 3000x

51. In 2010
available
scientific
information
will double
every 72 hours