The Future of Research (Science and Technology)
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The Future of Research (Science and Technology)

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Talk by Carole Goble at the British Library Board Awayday 23rd September 2008

Talk by Carole Goble at the British Library Board Awayday 23rd September 2008

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16 of 6 Post a comment

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  • Great presentation......The introduction is well defined and each and every page is well established...Looks cool to understand.......
    Thanks for sharing
    http://www.proboxinggear.com/
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  • There is much more and few things went beyond our expectation....But here you have presented a detailed post about the growth of technology and the science and how people and the nature react on those changes.....

    great post .....much needed for the scientific world.....
    http://www.tracknfieldgear.com/
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  • I found good Research informative slide presentation. Great presentation, nice information i like this 'Archaeological Excavation Report' presentation slide.


    our archaeology related blogspot is http://archaeologyexcavations.blogspot.com/
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  • yes Leobard - I am
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  • are these slides creative commons? I don't think so, they don't say so.
    you uploaded them as cc-by-sa, is Carole Goble ok with this?
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  • This presentation was made to the British Library Board Awayday on 22-24 September. as part of a discussion on their vision for 2020 The board are mostly not technical and many are not familiar with scientific research practice. The speakers before me were Kate Worlock and Charlie Leadbeater who had both spoken about the web 2.0 social and collective intelligence aspects of the Web and publishing. Charlie called this commons based production. http://www.bl.uk/ FUTURE OF PUBLISHING / SCHOLARLY COMMUNICATION Presentation and discussion led by Kate Worlock. SOCIAL IMPACT OF THE INTERNET IN THE 21ST CENTURY Presentation and discussion led by Charlie Leadbeater.

The Future of Research (Science and Technology) The Future of Research (Science and Technology) Presentation Transcript

  • The Future of Research (Science and Technology) Carole Goble [email_address] University of Manchester, UK OMII-UK British Library Board Awayday 23rd September 2008
  •  
  • Acknowledgements
    • David De Roure
    • Michael McLennan
    • Noshir Contractor
    • Christine Borgman
    • Tony Linde
    • Cameron Neylon
    • Duncan Hull
    • Geoffery Fox
    • Malcolm Atkinson
    • Jean Claude Bradley
    • Anne Trefethen
    • Graham Cameron
    • Phil Bourne
    • Bertram Ludaescher
    • Tim Wess
    • Roger Barga
    • Paul Fisher
    • Jane Hunter
    • Jeremy Frey
    • Tony Hey
    • Jim Hendler
    • Bob Jones
    • Liz Lyon
    • Juliana Friere
    • Domenico Talia
    • Michael Nielsen
    • Marco Roos
    • Doug Kell
    • Anthony Finkelstein
    • Peter Murray-Rust
    • Robert Tansley
    • Michael Wilson
    • Rob Tansley
  • Trypanosomiasis in Africa http://www.genomics.liv.ac.uk/tryps/trypsindex.html Andy Brass Steve Kemp Paul Fisher
  • Hypothesis driven research Now we add Data driven Simulation / prediction driven Automated experiments Open “as you go” communication Team research New types of research output
  • Data intensive Science Data from observations Data from predictions through simulations and computer models Industrialised science
  • 1070 databases, Nucleic Acids Research Jan 2008 (96 in Jan 2001)
    • Proteomics
    • Genomics
    • Transcriptomics
    • Protein sequence prediction
    • Phenotypic studies
    • Phylogeny
    • Sequence analysis
    • Protein Structure prediction
    • Protein-protein interaction
    • Metabolomics
    • Model organism collections
    • Systems Biology
    • Epidemiology ….
  • Growth of data, regardless of discipline
    • Raw, predicted, derived, combined, aggregated
    • Curated to be annotated and enriched manually or automagically
    • Interlinked
  • Large Hadron Collidor [Norbert Neumeister]
  • Why Data intensive Science?
    • New high throughput experimental methods (microarrays, combinatorial chemistry, sensor networks, earth observation, sky surveys, heroic experiments ….)
    • Increasing scale, diversity and complexity of digital material processed separately and in combination.
    • Commons based production
    • über accessibility
    • Heterogeneous, Autonomous and Volatile
  • Why Data Intensive Science?
    • Small data.
    • Spreadsheets.
    • Personal lab books.
    • Privately held.
    • Increasingly publicly shared.
    • Through the web
    • Millions of them.
    • Born digital.
  • Raw and Interpretive Data
    • What is fact?
    • Revision is constantly occurring. Even primary data can be revised.
    • Science is interpretation
    • Much of scientific data is secondary datasets of interpretative, information.
    Primary Data Primary Data Primary Data Secondary Curated Data Processed Data Secondary Curated Data Secondary Data Integrated data Processing details Capture details Update revise Update revise revise revise
  •  
  • Data collection management
    • Large scale community-wide global data centres – EBI, DDBJ, NCBI, NCI, CERN
    • Institutional data centres and labs and individuals – precarious and uncertain.
    • Role for data stewardship and preservation on behalf of the community
    • Cloud data
    • [email_address]
  • Not the end of theory!
    • The prevalence of data and the rise of data intensive science and data driven science adds to the pool of hypothesis driven and theory driven research.
    • It doesn’t replace it.
    Data Theory Prediction Hypothesis
  • 200 Genotype Phenotype Metabolic pathways Literature [Paul Fisher]
    • Large scale data collection from multiple sites throughout the world.
    • The team’s own data and personal data sets.
    • Analytical pipelines and automated workflows with intelligent intervention.
    • Literature auto found and mined
    • If manual: its logged
    • If automated: faster, systematic, repeatable, reduced bias, auto-logged, explicit, shareable
    • Born digital
  • Automated processing of library content
    • PubMed contains ~17,787,763 articles to date
    • Manually searching is tedious and frustrating
    • Can be hard finding links between data and articles
    • Conclusion? Machines will be reading the library.
    • Link between cholesterol , patient trauma and parasite resistance in cattle revealed.
    http://www.myexperiment.org/workflows/172 Paul Fisher
  • Data driven research
    • Was:
    • Hypothesis to experiment to analyse the data
    • Now:
    • start with the data. There is so much data that is accessible.
    Ideas Data Synthesis / Induction Hypothesis Analysis / Deduction [Kell and Oliver]
  • Published. Eventually.
  • Reproducible, or rather “fully supported” Transparent science, Composite research components Methods Lab Books Preprints Data Video Blogs Podcasts Codes Algorithms Models Presentations Ontologies Intermediate Results Related Articles Comments & Reviews Plans Models
  • Reproducible, or rather “fully supported” Transparent science, Composite research components Methods Lab Books Preprints Data Video Blogs Podcasts Codes Algorithms Models Presentations Ontologies Intermediate Results Related Articles Comments & Reviews
  • Reproducible Science means context, quality, trust means easy access to the sources
  • Methods are Scientific commodities
    • Scripts, workflows, simulations, experimental plans statistical models, ...
    • Repeatable, reproducible, comparable and reusable research.
    • Sharing to propagates expertise and build reputation.
    , http://myexperiment.org
  • 120 Simulation tools 1,200 Seminars, podcasts, etc. 77,000 Users worldwide 550 Contributors Developed by the NSF Network for Computational Nanotechnology Online since October 2002 [Michael McLennan] http://nanoHUB.org
  • [Jean-Claude Bradley] http://usefulchem.wikispaces.com/
    • BioLit
    • Seamless integration between data and publications
    • From the Public Library of Science people.
    1. A link brings up figures from the paper 0. Full text of PLoS papers stored in a database 2. Clicking the paper figure retrieves data from the PDB which is analyzed 3. A composite view of journal and database content results 4. The composite view has links to pertinent blocks of literature text and back to the PDB 1. 2. 3. 4. The Knowledge and Data Cycle http://biolit.ucsd.edu [Phil Bourne]
  • ICTP Trieste, December 10, 2007 [Phil Bourne]
  • [Phil Bourne]
  • The reproducible and interactive research documents* Mixed stewardship research documents The recombinant, compound research documents The virtual research document Multi-versioned, dynamic research document *Papers, Books, whatever. 2020
  • Data, image, model, process, workflow, podcast, slideset* Finding, citation, peer review, preservation, identity, versioning, security, privacy, copyright management, format authority Authority on metadata descriptions Propagation of descriptions * Insert new research commodity type here 2020
  • What does this mean for library services? Seamless interlinking of data, literature and other research commodities Integrated search across external resources Selective quality curation Hell is other people’s (lack of) semantic metadata 2020
  • Supporting Paul, The Scientist Search/Discover Serendipitous Finding Collaborative Searching Structural Search Keeping Current Gather Collecting Manage Organizing Create Annotating Review & Rate Describe Write Share Publish Sharing Rights Integrated search Automatic paper download Continual queries Paper recommendation Alert Project and Personal Internal search Refereed and Grey literature Tag, annotate, rate Templates Multi-author authoring Bibliography management Version management Copyright tools (CC and SC) Linking up data, models and other components [Roger Barga]
  • Collaboration
  • (Virtual) Team Research
    • Research increasingly team-based
    • Teams produce more highly cited research
    • Team science is increasingly composed of co-authors located at different universities.
    • “ virtual communities of scholars” produce higher impact work than comparable co-located teams or solo scientists.
    • True for all fields and team sizes.
    Studies of 19.9 million research articles over 5 decades as recorded in the Web of Science database, and an additional 2.1 million patent records from 1975-2005. Using the Web of Science database to analyze the collaboration arrangements of over 4,000,000 papers over a 30 year period Sources: Wuchty, Jones, and Uzzi Noshir Contractor
  • Distributed [Helen Hulme]
  • Distributed and Collaborative .....skills-rich and time-poor Biologists, Geneticists, Bioinformaticians, Immunologists, Microarray specialists, Computer Scientists, Mathematicians, Physicists..... [Helen Hulme]
    • Personal: log books and spreadsheets, file stores
    • Group: shared data, methods, protocols, information, failures, insights, observations, know-how
    • Born digital but not very digitally processable.
    [Helen Hulme]
  • Virtual Research Environments 1 Collaboration Environments Science Gateways to data and computing grids Multi-authored document preparation
  • Multi-disciplinary
    • Proteomics
    Classical Genetics / QTL studies Animal Experts Transcriptomics Parasite Experts Statistical modelling Text Analysis Image analysis Health Epidemiology
  • Crossing boundaries Interdisciplinary Support
    • Expert finding
    • Complementary experts swarming around a problem
    • Transferring data, methods and know-how from one discipline to another
    • e.g. astronomy image analysis applied to cancer tissue microarrays
    • How do you find relevant material that uses a different jargon in a different discipline organised to only suit its experts?
    • Overlay and virtual journals are few and far between – e.g. the Virtual Journal of Quantum Information.
    • Where is the overlay library?
  • Virtual Research Environments 2 Social Professional Networking Expert finding
  • [Roger Barga]
  • The BL’s Research Information Centre
  • Open Science Collective Intelligence Researcher participation Commons based production Sharing Accelerated dissemination Embedded in the researchers environment and work practices
  • “ Long Tail” Science. “Hypo” Science
    • Increased scale and diversity of scientific participation
      • The small research team.
      • Niche experts.
      • The citizen.
    • Easier to work with, and get hold of, digital output.
      • Better tools.
    • Scaling effects of peer review, social working and community curation.
  • Open content, services and software. Social tools for the social process of science.
    • Publicly available data
    • Open services and software tools.
    • Science Commons, open access journals, open data and linked data*, PLoS, ...
    • Open notebook science
    • Recent US funding agencies declarations on open access
    * formerly known as Semantic Web
    • Anyone can be a publisher as well as a consumer.
    • Social tools for the social process of science.
    • Wikis, (micro)blogs, instant messaging.
    • Accelerate research and reduce time-to-experiment.
    • Community collective intelligence network effects
    • Share information and know-how
    • Tag resources for finding
    • Socially curate resources
    • Openly review and debate
    • Recommendations based on usage and opinion.
  • http://www.wikipathways.org/
  • [Duncan Hull]
  • Growth of open access scientists digital natives, always online, hybrids catalysts for change [Phil Bourne]
  • Cameron Neylon’s chemistry notebook
  • Sharing reusable methods Paul Jo
  • Competitive advantage. Academic vanity. Reputation. Adoption. Scrutiny. Being scooped. Misinterpretation. New Reward Schemes Rewards Fears
  • What is the role of the library? Trusted curator Trusted data manager Quality arbiter Knowledge disseminator Format authority Add value content provider Metadata / controlled vocabulary provider Add value service provider 2020
  • Services Embedding into the Researchers Workflow The Cloud
  • Personal Scientist-centric tooling
    • We don’t come to the library, it comes to us.
    • We don’t use just one library or one source.
    • We don’t use just one tool!
    • Library services embedded in our toolkits, workbenches, browsers, authoring tools.
    Zotero Firefox plug-in
  • Hypothesis Construction from the Literature Marco Roos, Scott Marshall , University of Amsterdam
  • Towards Automated Science Inspired by Jean-Claude Bradley Human Human Human Machine Machine Machine Quality Trust Ease Ubiquity We are here
  • Combining information on the web
    • DIY mash ups
    • As the pieces become easy to use, researchers bring them together in new ways and ask new questions.
    • Boundaries are shifting, practice is changing.
    • Based on the ease of assembly and automation.
    Allen Brain Atlas
  • http://info.scopus.com/scsearchapi/geoCitations/index.html
    • Take a PubMed search result
    • Combine it with a Google Scholar search for number of citations
    • Mash the results into the PubMed results with a link to Google Scholar
  • What does this mean for library services? With not For Opening up to researcher’s tools and research environments for discovery, management and curation of research commodities Enabling and encouraging new services and new content to add new value Remove obstacles to interoperate and share Collaborate, don’t control
    • Give researchers tools and access to content
      • They control their own software/data apparatus and their experiments.
      • They are creative
    Pervasive devices and the mixing up of virtual and real worlds
    • Prior to leaving home Paul, a Manchester graduate student, syncs his IPhone with the latest papers, delivered overnight by the library via a news syndication feed. On the bus he reviews the stream, selecting a paper close to his interest in HIV-1 proteases.
    • The data shows apparent anomalies with his own work, and the method, an automated script, looks suspect.
    • Being on-line he notices that a colleague in Madrid has also discovered the same paper through a blog discussion and they Instant Message, annotating the results together.
    • By the time the bus stops he has recomputed the results, proven the anomaly, made a rebuttal in the form of a pubcast to the Journal Editor, sent it to the journal and annotated the article with a comment and the pubcast.
    Based on an original idea by Phil Bourne
  • Questions? http://research.microsoft.com/towards2020science/
  • Extras
  • Other References
    • Duncan Hull, Steve Pettifer, Doug Kell, Defrosting the digital library: bibliographic tools for the next generation web to appear in PLoS Computational Biology
    • Michael Nielsen, The Future of Science http://michaelnielsen.org/blog/?p=448
    • Philip Bourne Will a biological database be different from a biological journal, PLOS Computational Biology 1(3) www.ploscompbiol.org
    • James A. Evans Electronic Publication and the Narrowing of Science and Scholarship Science 18 July 2008: Vol. 321. no. 5887, pp. 395 - 399 http://www.sciencemag.org/cgi/content/abstract/321/5887/395
    • James Hendler Reinventing Academic Publishing, Editorials for IEEE Intelligent Systems http://www.mindswap.org/blog/2007/08/14/reinventing-academic-publishing-%E2%80%93-part-i/ http://www.mindswap.org/blog/2007/11/23/reinventing-academic-publishing-%E2%80%93-part-ii/ http://www.mindswap.org/blog/2008/01/03/reinventing-academic-publishing-%E2%80%93-part-iii/
    • Cameron’s suggested open science blogs
    • http://www.earlham.edu/~peters/fos/2008/07/online-researchers-have-acces s-to-more.html
    • http://scienceblogs.com/clock/2008/07/electronic_publication_and_the.php
    • http://www.sennoma.net/main/archives/2008/07/an_open_access_partisans_vi ew.php
    • http://openwetware.org/wiki/Science_2.0/Brainstorming
    • http://sciencex2.org/en/user/113/track
  • http://research.microsoft.com/towards2020science/