New e-Science Edinburgh Late Edition
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New e-Science Edinburgh Late Edition

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Definition of the New e-Science in 10 points. This follows up a talk in Edinburgh in November 2007 which had an 8 point definition.

Definition of the New e-Science in 10 points. This follows up a talk in Edinburgh in November 2007 which had an 8 point definition.

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New e-Science Edinburgh Late Edition Presentation Transcript

  • 1. Edinburgh Late Edition
  • 2. In November 2007 I presented a talk in the e-Science Institute about the New e-Science . 10 months later I’m back... with a new 10 point definition of how research will be conducted in the future.
  • 3.
    • Due to the complexity of the software and the backend infrastructural requirements, e-Science projects usually involve large teams managed and developed by research laboratories, large universities or governments.
    e-Science is about global collaboration in key areas of science, and the next generation of infrastructure that will enable it.
  • 4. How do we move from heroic scientists doing heroic science with heroic infrastructure to everyday scientists doing science they couldn’t do before? humanists archaeologists geographers musicologists ... researchers! research It’s the democratisation of e-Research 
  • 5.
    • Decreasing cost of entry into digital research means more people, data, tools and methods.
    • Anyone can participate: researchers in labs, archaeologists in digs or schoolchildren designing antimalarial drugs. Citizen science!
    • Improved capabilities of digital research (e.g. increasing automation, ease of collaboration) incentivises this participation.
    • "You're letting the oiks in!" people cry, but peer review benefits from scale of participation too.
    • “ Long Tail Science”
    Increasing scale and diversity of participation 1 VERA
  • 6.
    • Deluge due to new experimental methods (microarrays, combinatorial chemistry, sensor networks, earth observation, ...) and also (1).
    • Increasing scale, diversity and complexity of digital material, processed separately and in combination .
    • New digital artefacts like workflows, provenance, ontologies and lab books.
    • Context and provenance essential for re-use, quality and trust.
    • Digital Curation challenge!
    Increasing scale and diversity of data 2 Taverna workflow
  • 7.
    • Anyone can play and they can play together.
    • Anyone can be a publisher as well as a consumer – everyone’s a first class citizen.
    • Science has always been a social process, but now we're using new social tools for it.
    • Evidenced by use of wikis, blogs, instant messaging.
    • The lifecycle goes faster, we accelerate research and reduce time-to-experiment.
    Sharing 3 Open Wetware
  • 8.
    • Increasing participation means network effects through community intelligence: tagging, reviewing, discussion.
    • Recommendation based on usage.
    • This is in fact the only significant breakthrough in distributed systems in the last 30 years.
    • Community curation: combat workflow decay!
    Collective Intelligence 4 myExperiment
  • 9.
    • Publicly available data but also the open services and software tools of open science.
    • Increasing adoption of Science Commons, open access journals, open data and linked data*, PLoS, ...
    • Open notebook science
    • * formerly known as Semantic Web
    Open Research 5 arXiv, Science Commons, UsefulChem
  • 10.
    • Scripts, workflows, experimental plans, statistical models, ...
    • Makes research repeatable, reproducible and reusable.
    • Propagates expertise.
    • Builds reputation.
    • See Usefulchem, myExperiment.
    Sharing methods 6 JC Bradley, experimental plan
  • 11.
    • Increasing facility with new tools puts the researchers in control – of their software/data apparatus and their experiments.
    • Empowerment enables creativity and creation of new, sharable methods.
    • Tools that take away autonomy will be resisted.
    • Beware accidental disempowerment! Ultimately automation frees the researcher to do what they're best at, but can also be disempowering.
    Empowering researchers 7 Cameron Neylon, Blogging the lab
  • 12.
    • Researchers will choose tools that are better than what they had before but not necessarily perfect.
    • This force encourages bottom-up innovation in the practice of research.
    • It opposes the adoption of over-engineered computer science solutions to problems researchers don't know they have and perhaps never will.
    Better not Perfect 8 Carole Goble
  • 13.
    • Increasingly rich intersection between the physical and digital worlds through devices and instruments.
    • Web-based interfaces not software downloads.
    • Shift towards devices and the cloud.
    • REST architecture coupling components that transcend their application.
    Pervasive deployment 9 Geoffrey Fox
  • 14.
    • e-Science is now enabling researchers to do some completely new stuff!
    • As the pieces become easy to use, researchers can bring them together in new ways and ask new questions.
    • Boundaries are shifting, practice is changing.
    • Ease of assembly and automation is essential.
    Standing on the shoulders of giants 10 Allen Brain Atlas
  • 15. People say it’s difficult to program the Grid because distributed systems are fundamentally difficult. But the Web works well! What can we learn?
  • 16. use Web 2.0 here? Usability layer Grid
  • 17. use Web 2.0 here? Grid
  • 18. use Web 2.0 here Coupling layer Grid Grid cloud HPC
  • 19. www.myexperiment.org
  • 20.
    • “ Facebook for Scientists”...but different to Facebook!
    • A community social network
    • A gateway to other publishing environments
    • Federated, public & private
    • A platform for launching workflows
    • Publishing Scientific Research Objects
    • Foundation of the e-Laboratory
    • Started March 2007
    • Closed beta since July 2007
    • Open beta November 2007
    myExperiment currently has 1120 users, 95 groups, 339 workflows, 121 files and 26 packs myExperiment.org is…
  • 21.
    • Increasing scale and diversity of data
      • Workflows, social network, tags, ...
    • Sharing
      • Fine control over ownership, sharing, permissions
      • Credit, attribution, licensing
    • Collective Intelligence
      • Tags, reviews, comments, favourites, ...
    • Open Research
      • Public site, open source, open API
    The experiment that is myExperiment
    • Sharing methods
      • Workflows, experimental plans, scripts, ...
    • Empowering researchers
      • Familiar interfaces
      • Sharing workflows
    • Better not Perfect
      • Perpetual beta web site
    • Pervasive deployment
      • RESTful, mobile
    • Standing on the shoulders of giants
      • Functionality mashups, Linked Data, open notebook science
  • 22.
    • We need to understand how research will be done. It’s a social story.
    • Balancing the bottom-up forces in the e-Research ecosystem with the top-down. How much coordination do we really need for the ecosystem to flourish?
    • We have some insights into why the Web has worked as a pervasively adopted distributed application platform and the Grid hasn’t.
    • myExperiment is a Social Virtual Research Environment
    • CS challenge: Ease of assembly and automation is essential
    Closing comments
  • 23.
    • Contact
    • David De Roure
    • dder@ecs.soton.ac.uk
    • Acknowledgements
    • Carole Goble
    • Jeremy Frey
    • Some Reading
    • http://usefulchem.wikispaces.com/
    • http://michaelnielsen.org/blog/?p=448
    • http://wiki.myexperiment.org