What’s wrong with this Picture?….

This is the result of a search on one of Wolfram Alpha’s “highly curated” data sets: more chemical disinformation for the world. Can I go in and correct it? No.

‘Nuff said for now.

UPDATE: The picture I alluded to is there, but for some reason doesn’t show up in certain feedreaders. Please click through to the main blog to see it.

Open Innovation in Science and health Panel Discussion @ NESTA Open Innovation Workshop

Again live blogged….and hence public notes of the meeting…

Open Innovation in Science and health Panel Discussion

===Panel Membership===
Prof Sir John Sulston
Dr Davd Brown
John Wilbanks
Dr Adams heathfield (Pfizer– Director of Science Policy)

===Member Statements===

David Brown
Three anrena for science commons
Academia
Non-Proft
For Profit

The Global health Field is the low hanging fruit for open innovation
Used to be Head of Research for Roche
Had Analysis why resarch failed
poor target validation
poor quality chemical leads
poor toxicity
poor clinical trials
All of those can be addressed by information sharing
Tox Databases need to be shared – non sharing leads to death of phrama industry

6 Million under 5s die of preventable diseases if we apply ourselves properly – we have the technology
Only 300 people dealing with this problem worldwide…..all of them in Switzerland and US – none of the UK people involved in this….use science commons to get information sharing and therefore participation in these diseases
Cultural Barriers is one reason for non-participation….need to enable the commons to help the scientists in these countries to work on these diseases

John Sulston
UK Citizens are demanding too much innovation
No harm in consumer goods etc, though the increasing consumer market is a bad thing….but more serious in health….rather give people drugs than counselling them
Human Genome Project was constructed as an Open Structure to make resulting data available – the open databases have been infectious for setting standards
Dismay at the horrendous drive at inequality in medicine motivated by greed
needs to neglect of preventable diseases and focus on pointless new innovations
Can the Commons be used to benefit the many or only the few?
Asks NESTA to temper its ambition to create wealth with making this country take part in global share responsibility

John Wilbanks
The COmmons allows the many vs the few – there is no limitation on who uses the commons
Therefore design for aggregation of CONTENT by anyone

Adam Heathfield
Pfizer is no 1 or 2 in global pharma
7 billion dollars on R&D Alzheimers/Oncology
Commons can help in drug R&D
Drug R&D is under more and mor pressure to provide more and more DATA
Regulation
Marketin
Tracking
Difficult to see how to use the Commons approach to tackle the challenges that pharma is having
HOWEVER
CC can be used on screening approaches, trial design etc….COMMONS in the precompetitive stage
need more experiments how to collaborate and how to compete
NO of industry collaborations are going up: 3-fold at Pfizer

===Questions===

1. Clinical Trial(shared) Data Mining and Sharing…has that any influence? Clinical Trials.gov…..Pfizer sees this as opening up new possibilities but more research needed
not sure whether the info is used in a way the info is useful
Issues with consent governanace and clinical study data sharing
Overall Adam Heathfield is very sceptical overalll with commons approaches in live science..

2. Glyn Moody Prizes for Developing Drugs with Results being in public domain?
Brown: for western dieseases the money is there and these models not neeeded…just need better data sharing
for rarer diseases
Wilbanks: use Commons for Drug R&D in precompetityve biology
Brown: Drug Discovery cannot be done in Academia – not enough experience, too difficult
Sulston: Sure about that? Is it just a function of availability of money and some re-tooling?

John Wilbanks @ NESTA Open Innovation Meeting

John Wilbanks – SCIENCE COMMONS

Starts of with talking about network effects: resources become more valuable the more connected they are
What was it that allowed to turn the ARPANET into the Internet
it was open transmiossion protocols…anyone who could build a compatible computer could connect
same for the www
the ability to make copies and derivatives – this is at odds with copyright
ignoring a law does not scale

Science KNowledge
Science has not been disrupted by the web – journals in HTML same as Journals in paper
Incrementalism – distributing pdfs of science papers is an earhorn
we do not have the ability to hyperlink knowledge – cannot “compatibly communicate” and then we wonder why we do not get network effects
Open Innovation (see Henry Chesborough)
depends on the quantity, quality, legal availability and tech usability of open innovation
Computers – tcp/ip
domuments – html/http
knowledge – commons

Literature: Open Access under CC
Neurocommons: open scientific data, difficulty with integrating databases – different licencing schemes significantly hinder preparation of derivative data products
CC0 1.0 Universal licence

The comons is technical NOT just legal…

CC workin gon commons to hook up physical objects with the web
CC now working on developing a fully virtual drug discovery environment, glued together by CC….for data exchange etc and giving freedom to operate
Nike and CC patent exchange via CC

Open Innovation
purposive inflows and outflows of knowledge
expnd the capacity of the external market to generate internally useful knowledge
the business model is at the centre of value creation and capture

COST
in software
frr as in speech
free as beer
==>free as in puppy (not really free..have to maintain the dog)
YOU ARE BUYING A DOG, not beer)

James Boyle @ NESTA Open Innovation Meeting

I am live-blogging this…hence the sketchiness and the typos…
James Boyle – Prof of Law Duke Law School

Starts off by both apploauding NESTA and Welcome Trust

Open Innovation in Culture and Science
Cultural Agoraphobia…..fear of open methods to develop innovation and science
e.g. if someone had proposed Wikipedia as a business plan –>no funding
e.g. IBM making more money from open innovation than closed innovation

Are there new methods for achieving open innovation? Purpose of the workshop to elucidate that question.

Creative Commons
as a way to distribute creative work in the face of copyright law
make open sources searchable and findable
INITIAL EXPECTATION: CC licences allows the distibution of copyrighted works
NOT EXPECTED: people can actually make money from CC licenced works (CC licences not a panacea – what makes this work)
What is it that allows people to make money from CC licenced works??
Think of licence components as switches – turn some of them on and off(e.g. remixing, commercial expolitation etc….) how can these be used most productively
Need structured research into how these tools can be used

Science is different from culture
CC knows to little about science to map CC ont science
Reasons for starting Science Commons –> illustrated by the development of the WWW by a SCIENTISTS
WWW works better for porn, sales etc than for science
most of the into on the WWW is wrong –>idiots…
why does resarch on the web still work?
search engines bring us the place where knowledgeable people think there is value e.g. amount of linking
THE VALUE IS IN THE METADATA – it’s the second layer of linkage that makes the web useful
That second layer of lnkage is not available for science….becuase scientific knowledge is bound up behind firewalls…we cannot get the second layer of linkage done
other example: get from a revoew f the book to a book on Amazon
same doesn’t work for science: e.g. no linking from the components of an xperimental to the compnents….
BARRIERS to scientific innovation on the web have not been thought through….we can make science work better on the internet
“It is a crime how our government funders etc have failed to understand how innovation depends on infrastructure”
“The network is not the cable along which innovation travels – the network is the cables along which the innovation travels”
Is it good to publicly fund research and then lock it down behind firewalls
Is it good to not fund infrastructure?
We need experimentation. Suggestions:
Admit that we know less than we thought. DO EXPERIMENTS WITH OPEN INNOVATION.
Academia has been much less innovative to make its materials open than many commercial entities, particularly the media.
Research: we need to move away from well-founded assumptions to solid research about what works and what doesn’t.

Capturing process: In silico, in laboratorio and all the messy in-betweens – Cameron Neylon @ the Unilever Centre

I am not very good at live-blogging, but Cameron Neylon is at the Unilever Centre and giving a talk about capturing the scientific process. This is important stuff and so I shall give it a go.

He starts off by making the point that to capture the scientific process we need to capture the information about the objects we are investigating as well as the process how we get there.

Journals not enough – the journal article is static but knowledge is dynamic. Can solutions come from software development? Yes to a certain extent….

e.g. source control/versioning systems – captures snapshots of development over time, date stamping etc.
Unit testing – continuous tests as part of the science/knowledge testing
Solid-replication…distributed version control

Branching and merging: data integration. However, commits are free text..unstructured knowledge…no relationships between objects – what Cameron really wants to say is NO ONTOLOGIES, NO LINKED DATA.

Need linked data, need ontologies: towards a linked web of data.

Data is nice and well…but how about the stuff that goes on in the lab? Objects, data spread over multiple silos – recording much harder: we need to worry about the lab notebook.

“Lab notebook is pretty much an episodic journal” – which is not too dissimilar to a blog. Similarities are striking: descriptions of stuff happening, date stamping, categorisation, tagging, accessibility…and not of much interest to most people…;-). But problem with blogs is still information retrieval – same as lab notbook…

Now showing a blog of one of his students recording lab work…software built by Jeremy Frey’s group….blog IS the primary record: blog is a production system…2GB of data. At first glance lab-log similar to conventional blog: dates, tags etc….BUT fundamental difference is that data is marked up and linked to other relevant resources…now showing video demo of capturing provanance, date, linking of resources, versioning, etc: data is linked to experiment/procedure, procedure is linked to sample, sample is linked to material….etc….

Proposes that his blog system is a system for capturing both objects and processes….a web of objects…now showing a visualisation of resources in the notbook and demonstrates that the visualisation of the connectedness of the resources can indicate problems in the science or recording of science etc….and says it is only the linking/networking effect that allows you to do this. BUT…no semantics in the system yet (tags yes…no PROPER semantics).

Initial labblog used hand-coded markup: scientists needed to know how to hand code markup…and hated it…..this led to a desire for templates….templates create posts and associate controlled vocab and specify the metadata that needs to be recorded for a given procedure….in effect they are metadata frameworks….templates can be preconfigured for procedures and experiments….metadata frameworks map onto ontologies quite well….

Bio-ontologies…sometimes convolute process and object….says there is no particularly good ontology of experiments….I think the OBI and EXPO people might disagree….

So how about the future?

Important thing is: capture at source IN CONTEXT

Capture as much as possible automatically. Try and take human out of the equation as much as possible.

In the lab capture each object as it is created and capture the plan and track the execution step by step

Data repositories as easy as Flickr – repos specific for a data type and then link artefacts together across repos..e.g. the Periodic Table of Videos on YouTube, embedding of chemical structures into pages from ChemSpider…

More natural interfaces to interact with these records…better visualisation etc…

Trust and Provenance and cutting through the noise: which objects/people/literature will I trust and pay attention to? Managing people and reputation of people creating the objects: SEMANTIC SOCIAL WEB (now shows FriendFeed as an example: subscription as a measure of trust in people, but people discussing objects) “Data finds the data, then people find the people”..Social network with objects at the Centre…

Connecting with people only works if the objects are OPEN

Connected research changes the playing field – again resources are key

OUCH controversy: communicate first, standardize second….but at least he ackowledges that it will be messy….

UPDATE: Cameron’s slides of the talk are here:

Capturing Process

View more presentations from Cameron Neylon.

ChemAxiom: An Ontology for Chemistry 4. ChemAxiomChemDomain

Obligations to our funders and some publishers have delayed me in continuing this series of blog post and participation in the discussion on the Google Group for a few days, but I hope I can catch up on either now. In my previous blogpost, I have summarised all of the ChemAxiom modules briefly: now is the time to delve into some more detail. First up then: ChemAxiomChemDomain.

ChemAxiomChemDomain is, at the moment, a rather small, but nevertheless important ontology, which clarifies some fundamental domain concepts in chemistry, namely the relationship between platonic molecules, platonic bulk substances, instances of either and roles.  

First oof all, let’s turn to some fundamental concepts. The classes “ChemicalElement”, “MolecularEntity”, and “ChemicalSpecies”are all subclasses of “snap:Object”. The class “Object” in the BFO is defined as a “material entity [snap:MaterialEntity] that is spatially extended, maximally self-connected and self-contained (the parts of a substance are not separated from each other by spatial gaps) and possesses an internal unity. The identity of substantial object [snap:Object] entities is independent of that of other entities and can be maintained through time.” Various disjoint axioms specify the fact that “MolecularEntities” are not the same as “ChemicalSpecies”, thus addessing some of fundamental issues about the relationship between molecules and substances etc.

Further axioms on these classes specify other necessary parthood relationships: “ChemicalSpecies” are composed of molecules or other ChemicalSpecies (thus giving recursion and allowing the modeling of formulations) or BulkChemicalElements.:

ChemistryOntology:ChemicalSpecies
      a       owl:Class ;
      rdfs:comment “An ensemble of chemically identical molecular entities that can explore the same set of molecular energy levels on the time scale of the experiment.”@en ;
      rdfs:subClassOf snap:Object ;
      rdfs:subClassOf
              [ a       owl:Class ;
                owl:unionOf ([ a       owl:Restriction ;
                            owl:onProperty ChemistryOntology:hasPart ;
                            owl:someValuesFrom ChemistryOntology:MolecularEntity
                          ] [ a       owl:Restriction ;
                            owl:onProperty ChemistryOntology:hasPart ;
                            owl:someValuesFrom ChemistryOntology:ChemicalSpecies
                          ] [ a       owl:Restriction ;
                            owl:hasValue ChemistryOntology:BulkChemicalElement ;
                            owl:onProperty ChemistryOntology:hasPart
                          ])
              ] ;
      rdfs:subClassOf
              [ a       owl:Restriction ;
                owl:onProperty ChemistryOntology:preseentInAmount ;
                owl:someValuesFrom xsd:string
              ] ;
      rdfs:subClassOf
              [ a       owl:Restriction ;
                owl:onProperty ChemAxiomProp:hasProperty ;
                owl:someValuesFrom ChemAxiomProp:Property
              ] ;
      owl:disjointWith ChemistryOntology:ChemicalElement , ChemistryOntology:MolecularEntity

When intengrated with ChemAxiomProp (as has been done in ChemAxiomComtinuants), ChemicalSpecies can be connected up to their properties and other statements which one might wish to make about chemical species.

Another part of ChemAxiomChemDomain is the definition of roles: generic types of ChemicalSpecies, such as solvents, acids, catalysts, can be defined in terms of roles: no molecule is ever only just a solvent or an acid or a catalyst. Rather, these categories are realisable entities; a molecular species or a chemical entity behaves as a catalyst, nucleophile or a solvent under certain circumstances

ChemistryOntology:NucleophileMolecule
      a       owl:Class ;
      rdfs:subClassOf ChemistryOntology:MolecularEntity ;
      owl:disjointWith ChemistryOntology:ElectrophileMolecule ;
      owl:equivalentClass
              [ a       owl:Class ;
                owl:intersectionOf (ChemistryOntology:MolecularEntity [ a       owl:Restriction ;
                            owl:onProperty ChemistryOntology:hasRole ;
                            owl:someValuesFrom ChemistryOntology:NucleophileRole
                          ])
              ] .

Furthemore, roles in combination with MolecularEntity or ChemicalSpecies allow the definition of generic molecules or substances, such as acids (hydrochloric acid) and acids (proton donor), catalysts, solvents etc. At the moment, the number of axio
ms is small, however, as the body of axioms grows in the future, it can be expected, that  ChemAxiom will become more and more useful for the disambiguation of concepts: while it would make sense for a chemical species, which is an acid, to talk about a pH-Value, it would not make sense to speak of “molecular acids” in the same terms.

Finally, OWL’s model of classes as collections of instances models the things we need to model really well: the class “ChemicalSpecies” and “MolecularEntitiy” and thweir respective subclasses can be thought of as rpreesentinmg the platonic ideals of molecules or substances, whereas instances of these classes can be thought of as representing “real” samples of both molecules (e.g. a single molecule, in for example, matrix isolation) and substances (100 ml of HCl in a flask).

So much for ChamAxiomChemDomain fo rnow. It is the beginning of a domain model and very much driven by the use-case I ourtlined in a prewvious blog post. Obviously, we would like to expand the scope of this particular ontology to be morwe universally useful in the future., However, I believe that rather to do this via random ontological engineering, this should be driven by use-cases. So therefore, if you have use-cases in mind, please be in touch and let’s discuss how we can collaborate.

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