nanoinformatics 2010 smirp-ons talk
DESCRIPTION
Jean-Claude Bradley presents on "The implications of Open Notebook Science and other new forms of scientific communication for Nanoinformatics" at the Nanoinformatics 2010 conference on November 3, 2010. The presentation first covers the use of the laboratory knowledge management system SMIRP for nanotechnology applications during the period of 1999-2001 at Drexel University. The exporting of single experiments from SMIRP and publication to the Chemistry Preprint Archive is then described followed by the evolution to Open Notebook Science in 2005. Abstraction of semantic structure from ONS projects in the areas of drug discovery and solubility is then detailed as an efficient mechanism to provide web services and machine readable data feeds.TRANSCRIPT
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The implications of Open Notebook Science and other new forms of
scientific communication for Nanoinformatics
Jean-Claude Bradley
November 3, 2010
Nanoinformatics 2010
Associate Professor of ChemistryDrexel University
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LIMS CENS
Single Instrument Automation
Laboratory Information Management Systems
Collaborative Electronic Notebook Systems
Human /Autonomous Agent Hybrid Systems
Human ManagedFully AutonomousScientific Research Systems
TODAY
SMIRP bridge
The Evolution of Automation in Scientific Research
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StandardModularIntegratedResearchProtocols
Capturing semantic structure in research
at the point of data entry
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HumanAgent
AutonomousAgent
SMIRP
(Bot)
Browser
Excel
The SMIRP model for a hybrid Human/Autonomous Agent System
Anthropomimetic Design
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Approaches to Collaborative Electronic Notebooks
rigid
SMIRPcompromise:Rigid information representationFlexible linking of modules
flexible
• Structured• Generallydomainspecific
• Adaptable• Unstructured
http://smirp.drexel.edu
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Fundamental Information Representation in SMIRP
Module 1 Module 2
Parameter 1
Parameter 2
Parameter 4
Parameter 5
instance
Record 1
instance
Record 2
(People)
(Name)
(Employee of)
(Company)
(Name)
Parameter 3(email)
(Address)
Bill Gates Microsoft
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Two approaches to the development of databases
Communicateanticipated need
Designdatabase structure
Let database structureevolvethrough useSMIRP
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Case-study: Evolution of SMIRP structure in a nanoscience laboratory
Location Drexel UniversityDepartment of Chemistry
Users faculty, undergraduate students, graduatestudents, librarians and other university personnel
Period Feb 1999 – April 2001, with a detailed focus onlast 7 months (Sept 2000-April 2001)
Total accounts (last 7 months) 78
Active Accounts (added records) 50
Administrators (changed database structure)
9
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HumanResource Management 13%
Maintenance1%
Knowledge Processing 72%
Most Active Module Categories (9/00 – 4/01)
Labwork14%
118 modules 1/3 account for 98% of activity
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Activity Analysis by Category over Time
20
00
-10
-3
20
00
-10
-17
20
00
-10
-30
20
00
-11
-12
20
00
-11
-25
20
00
-12
-8
20
00
-12
-21
20
01
-1-3
20
01
-1-1
6
20
01
-1-3
0
20
01
-2-1
2
20
01
-2-2
5
20
01
-3-1
0
20
01
-3-2
3
20
01
-4-5
20
01
-4-1
8
Maintenance
Human Resource Management
Laboratory Work
Knowledge Processing0
1000
2000
3000
4000
5000
6000
7000
8000
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Recruitment events 2%
ProjectManager 5%Errors
5%
Productivity Tracking 14%
People 28%
Workstudy hours reporting 46%
Most Active Human Resource Management Modules
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Most Active Maintenance Modules
SMIRPProblems22%
Orders 19%
Invoice (TEM/SEM and other instrument charges) 19%
Laboratorymaterials16%
Vendor15%
Orderforms9%
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Most Active Knowledge Processing Modules
Journal 9%
Knowledge Filter 3%
ReformatReference requests 20%Find
Reference 66%
PublisherDocument ProductionReference ProcessingParameter CorrelationData source filesExperimental Conclusion GenerationKnowledge consolidation
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Seamless Integration of Human and Autonomous Agents in Workflows
Real-Time Workflow Designs
Automated
Human(default)
State A State B
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Workflow for Extraction of Article information and URL
Queries Web and extracts information
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Most Active Laboratory Modules
Preparation of Silver rods for SCBETEM Micrographs Of Pd on CSCBE on membranesHydrogenation of Crotonaldehyde using Pd CatalystsReduction of Methylene blue by Pd Metal Particles in a Field
Electrodeposition of Pd on Graphite 29%
Protocol Prototyping25%
Pd onto Carbon Nanofibers17%
Electroless plating on Membranes9%
Synthesis of Pd catalysts by Bipolar electrochemistry5%
TEM Micrographs Of Pd on C3%
Pd particle size analysis using TEM 3%
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Keyword Search Results: example “nanotube”
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From Keyword to Orders
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From Keyword to Article
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From Keyword to Knowledge Filter
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From Keyword to Protocol Prototyping
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Sharing results semi-automatically: SMIRP Knowledge Product
•Single Experiment•Full Context•Supporting Data•Not suitable for traditional peer-reviewed publications
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Non-traditional publication options in 2003
(Elsevier)
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To Cite or Not to Cite?
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“I would never consider a claim made in a patent as blocking an author's claim of novelty.” Langmuir Editor
What is a Scientific Precedent in Academia?
What is a Scientific Precedent in Patent Law?
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What is Scholarship?*also indexed in Chemical Abstracts!
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The UsefulChem Project (2005)
What would happen if a chemistry project was completely transparent
in real time?
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Motivation: Faster Science, Better Science
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TRUST
PROOF
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First record then abstract structure
In order to be discoverable use Google friendly formats (simple HTML, no
login) In order to be replicable use free hosted tools (Wikispaces, Google
Spreadsheets)
Strategy for an Open Notebook:
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UsefulChem Project: Open Primary Research in Drug Design using Web2.0
tools
Docking
Synthesis
Testing
Rajarshi GuhaIndiana U
JC BradleyDrexel U
Phil RosenthalUCSF
(malaria)
Dan ZaharevitzNCI
(tumors)
Tsu-Soo TanNanyang Inst.
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Malaria Target: falcipain-2 involved in hemoglobin metabolism
Dana.org
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Outcome of Guha-Bradley-Rosenthal collaboration
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The Ugi reaction: can we predict precipitation?
Can we predict solubility in organic solvents?
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Crowdsourcing Solubility Data
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ONS Challenge Judges
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ONS Submeta Award Winners
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Data provenance: From Wikipedia to…
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…the lab notebook and raw data
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• Concentration (0.4, 0.2, 0.07 M)• Solvent (methanol, ethanol, acetonitrile, THF)• Excess of some reagents (1.2 eq.)
How does Open Notebook Science fit with traditional publication?
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Paper written on Wiki
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References to papers, blog posts, lab notebook pages, raw
data
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Paper on Journal of Visualized Experiments (JoVE)
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Pre-print on Nature Precedings
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ONSArchive: Semi-Automated Snapshot of the Entire Scientific Record
Automated Download of
Spreadsheets and Parsing of
Web Pages
Manual Backup of Spectral Data Files
Manual
Export of
Wikispace
s
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Lulu.com Data Disks
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Interactive NMR spectra using JSpecView and JCAMP-DX
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Raw Data As Images
Splatter?
Some liquid
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YouTube for demonstrating experimental set-up
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The importance of raw data availability
Missed in a prior publication on
solubility for this compound
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The Intersection of Open Notebooks (Bradley/Todd) and IP implications
Open Notebook could have blocked patent
if done earlier
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Convenient web services for solubility measurement and
prediction
(Andrew Lang)
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Other Web Services…
(Andrew Lang)
General Transparent Solubility Prediction
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Semi-Automated Measurement of solubility via
web service analysis of JCAMP-DX files
(Andy Lang)
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Integration of Multiple Web Services to Recommend Solvents
for Reactions
(Andrew Lang)
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Reaction Attempts Book
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Reaction Attempts Book: Reactants listed Alphabetically
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For all Formats of ONS Projects
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Dynamic links to private tagged Mendeley collections
(Andrew Lang)
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Conclusions
• Open Notebook Science can provide an additional channel to communicate useful scientific information
• Recording first for human consumption followed by abstracting the semantics later works but the format will be field specific
• As long as proof is valued over trust there is no limit to what useful forms of scientific communication will emerge.