Riding the wave - Paradigm shifts in information access

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Jan Brase's keynote at eResearch Australiasia 2011 conference.

Jan Brase's keynote at eResearch Australiasia 2011 conference.

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  • Ein Paper aus der Chemie enthält chemische Informationen in unterschiedlichsten Facetten und Formen. So finden sich im Text chemischen Namen, Namen von Reaktionen, Verweise auf Strukturformel in Bilder. Es gibt Tabellen mit Text und chemischer Strukturinformation und graphische Darstellungen von chemischen Strukturen oder Reaktionsschemata. Für eine inhaltliche Erschließung müssen daher sowohl Text als auch nicht-textuelle Elemente ausgewertet werden damit chemische Suchportale sowohl Text- als auch Struktursuchen anbieten können. Dies ist ein arbeits- und kostenintensiver Prozeß, der überwiegend von kommerziellen Datenbankanbieter durchgeführt wird. Die Kommerzialisierung der Suchportale konterkariert allerdings die Idee von Open Access Was nützen Open Access Zeitschriften, wenn sie über kostenpflichtige Suchportal recherchiert werden müssen?
  • Gerade Forschungsdatensätze von Spektren aller Art stellen eine potentielle Quelle von Referenzdaten dar. Aber wie kann man später solche Forschungsdatensätze wiederfinden? Aktuelle Spektrendatenbanken wie die frei verfügbare Spectral Database for Organic Compounds erlauben die Suche nach ausgewählten Metadaten oder eine inhaltsbasierte Suche durch Angaben von numerischen Peaks Aber was wäre, wenn man die relevanten Teile eines Spektrums, das man suchen möchte, einfach zeichnen kann? Man müßte nicht mühselig irgendwelche Zahlenbereiche von Peaks o.ä. angeben.
  • In our current research project „Visual access to research data“ the TIB, the Interactive Graphics Group at the Technical University of Darmstadt and the Fraunhofer IGD we are working on a concept for visual retrieval process. To start a query you just need to sketch line or curve or you can upload similiar datasets. I have mentioned these two query input types as Query-by-Example and Query-by-Sketch before. The search algorithm will then search a special index, based on the original research data. As a result of your visual search you will retrieve a so called Visual Catalog shown here on the slide, where you can study similar curve progressions found in the database. Colormaps indicate the similiarity between your query and the found curve progression. If you find some interesting curve in the visual catalog you just click on the cell to access the underlying datasets.
  • Knowledge, as published through scientific literature, is the last step in a process originating from primary scientific data. (Click) These data are analysed, synthesised, interpreted (Click) and we gain information. (Click) The outcome of this process is published (Click) as a scientific article, which is the classical shape of knowledge.(Click) We can access the article in an easy way (Click) We can trace the information in the article (Click) But in today’s practice data are primarily stored in private files, not in secure institutional repositories, and effectively are lost.
  • The approach that DataCite is taking – using DOIs - has some important social benefits. Researchers, authors, publishers are comfortable, understand, and know how to use them. They put datasets on a level playing field with articles.
  • Started in 2004 with a DFG funded project lead by TIB and including three World Data Centres (STD-DOI Scientific Technical Data with DOIs)
  • Schauen wir von außen nach innen. Zunächst die äußeren Rahmenbedingungen: Informationszuwachs (Beispiel chemische Strukturen CAS - 2000: 20 mio Substanzen 2008: 40 mio Substanzen 2009: 50 mio Substanzen, 2010: 56.2 mio. Substanzen Diversifizierung digitaler Wissensobjekte, vernetztes und interdisziplinäres Arbeiten. All dies führt zu Informationsflut und zum Information Overload beim Benutzer, d. h. er kann aus den zur Verfügung gestellten Informationen nicht mehr den optimalen Nutzen ziehen.

Transcript

  • 1. Riding the Wave - Paradigm shifts in Information Access Jan Brase, DataCite eResearch Australiasia Conference November 9th Melbourne
  • 2.
    • Thousand years ago: science was empirical
      • describing natural phenomena
    • Last few hundred years: theoretical branch
      • using models, generalizations
    • Last few decades: a computational branch
      • simulating complex phenomena
    • Today: data exploration (eScience)
      • unify theory, experiment, and simulation
    • Jim Gray , eScience Group, Microsoft Research
    Science Paradigms
  • 3.
    • Scientific Information is more than a published article or a book
    • Libraries should open their cataolgues to this non-textual information
    • The catalogue of the future is NOT ONLY a window to the library‘s holding, but
    • A portal in a net of trusted providers of scientific content
    Consequences for Libraries
  • 4. We do not have it BUT We know where you can find And here is the link to it!
  • 5.
    • German National Library of Science and Technology
      • Architecture
      • Chemistry
      • Computer Science
      • Mathematics
      • Physics
      • Engineering technology
    • Global Supplier for scientific and technical documents
    • Financed by Federal Government and all Federal States
      • € 18 mio. annual acquisition budget
      • 18,500 journal subscriptions
      • 7,0 mio. items
    TIB
  • 6. Including non-textual content Simulation Scientific Films 3D Objects Text Research Data Software
  • 7.
    • https://getinfo.de/app
    • Query for „SAFOD“
    • „ Potter peninsula“
  • 8.
    • Make more scientific and technical content searchable
    • Develop tools to address each type of scientific and technical Information
      •  Present systems are designed to handle text formats
    Move beyond text - Tools
  • 9. Example from architecture
  • 10. Indexing and search content based indexing visual search
  • 11. classification of floor types  machine learning
    • content based indexing
    • visual search
    segmentation with form-primitives extraction of room connectivity graphs 3D sketch attributed graph result visualization How it works
  • 12. Table with reaction scheme Reaction scheme Extracting the information from the text 2a-i: Derivates from the reaction Chemical structure Chemical Names Linked entities from the table
  • 13. Chemical search
  • 14. Content based search
  • 15.
      • Visual Search in Time series
      • Query-by-Example, Query-by-Sketch
      • Visual Catalog as result list
      • Colormaps for the indication of similarity
    Visual search approch
  • 16.
    • Data
  • 17. Problem with data: The research trajectory analysed synthesised interpreted are become Information is published becomes Knowledge Publication … is accessible … is traceable … is lost! Data
  • 18. DOI names for data
    • Digital Object Identifiers (DOI names) offer a solution
    • Mostly widely used identifier for scientific articles
    • Researchers, authors, publishers know how to use them
    • Put datasets on the same playing field as articles
      • Dataset
      • Yancheva et al (2007). Analyses on sediment of Lake Maar. PANGAEA.
      • doi:10.1594/PANGAEA.587840
    • URLs are not persistent
    • (e.g. Wren JD: URL decay in MEDLINE- a 4-year follow-up study . Bioinformatics. 2008, Jun 1;24(11):1381-5).
     
  • 19.
    • High visability of the data
    • Easy re-use and verification of the data sets.
    • Scientific reputation for the collection and documentation of data (Citation Index)
    • Encouraging the Brussels declaration on STM publishing
    • Avoiding duplications
    • Motivation for new research
    What if data would be citable?
  • 20. How to achive this?
    • Science is global
        • it needs global standards
        • Global workflows
        • Cooperation of global players
    • Science is carried out locally
        • By local scientist
        • Beeing part of local infrastrucures
        • Having local funders
  • 21.
    • Global consortium carried by local institutions
    • focused on improving the scholarly infrastructure around datasets and other non-textual information
    • focused on working with data centres and organisations that hold data
    • Providing standards, workflows and best-practice
    • Initially, but not exclusivly based on the DOI system
    • Founded December 1st 2009 in London
    DataCite
  • 22.
      • TIB begins to issue DOI names for datasets
    History
      • Paris Memo-randum
      • DataCite Asso-ciation founded in London
      • 7 members
      • 12 members
      • All members assigned DOIs
      • Over 800,000 items registered
      • Pilot projects with Data Centres
    06. 11 05 03. 09 06. 10 12. 09
      • 15 members
      • Over 1,2 million DOI names
      • Metadata store
    03
      • DFG funded project with German WDCs
  • 23.
    • Technische Informationsbibliothek (TIB)
    • Canada Institute for Scientific and Technical Information (CISTI),
    • California Digital Library, USA
    • Purdue University, USA
    • Office of Scientific and Technical
    • Information ( OSTI), USA
    • Library of TU Delft,
    • The Netherlands
    • Technical Information
    • Center of Denmark
    • The British Library
    • ZB Med, Germany
    • ZBW, Germany
    • Gesis, Germany
    • Library of ETH Zürich
    • L’Institut de l’Information Scientifique
    • et Technique (INIST), France
    • Swedish National Data Service (SND)
    • Australian National Data Service (ANDS)
    • Affiliated members:
    • Digital Curation Center (UK)
    • Microsoft Research
    • Interuniversity Consortium for Political and Social Research (ICPSR)
    • Korea Institute of Science and Technology Information (KISTI)
    DataCite members
  • 24. DataCite structure Carries International DOI Foundation DataCite … Works with Managing Agent (TIB) Member Associate Stakeholder Member Institution Data Centre Data Centre Data Centre Member Institution Data Centre Data Centre Data Centre
  • 25.
    • Act as DOI registration agency
    • Actively involved in developing standards and workflows
    • CODATA-TG, STM, ICSTI, Data citation index
    • Central portal allowing access to the metadata from all registered objects. (OAI)
    • ISI, Scopus, Microsoft Academic search
    • Community for exchange of all relevant stakeholders in the area access to and linking of data (data centers, publishers, libraries, research organisation, science unions, funders)
    DataCite‘s main goals
  • 26.
    • Over 1,300,000 DOI names registered so far
    • DataCite Metadata schema published (in cooperation with all members) http:// schema.datacite.org
    • DataCite MetadataStore
    • http://search.datacite.org
    DataCite in 2011
  • 27. DataCite Content Service
    • Service for displaying DataCite metadata
    • Different formats (BibTeX, RIS, RDF, etc.)
    • Content Negotation (through MIME-Typ)
      • Access through DOI proxy ( http://dx.doi.org )
      • First implemented by CNRI and CrossRef:
    • Alpha available:
    • http://data.datacite.org
  • 28. Examples
    • curl -L -H "Accept: application/x-datacite+text" "http://dx.doi.org/10.5524/100005"
    • Li, j; Zhang, G; Lambert, D; Wang, J (2011): Genomic data from Emperor penguin. GigaScience. http://dx.doi.org/10.5524/100005
    • curl -L -H "Accept: application/rdf+xml" http://dx.doi.org/10.5524/100005
    • RDF-file
    • curl -L -H "Accept: application/raw" http://dx.doi.org/10.5524/100005
    • => ?
  • 29.
    • Earth quake events => doi:10.1594/GFZ.GEOFON.gfz2009kciu
    • Climate models => doi:10.1594/WDCC/dphase_mpeps
    • Sea bed photos => doi:10.1594/PANGAEA.757741
    • Distributes samples => doi:10.1594/ PANGAEA .51749
    • Medical case studies => doi:10.1594/eaacinet2007/CR/5-270407
    • Computational model => doi:10.4225/02/4E9F69C011BC8
    • Audio record => doi:10.1594/PANGAEA.339110
    • Videos => doi:10.3207/2959859860
    What type of data are we talking about? Anything that is the foundation of further reserach is research data Data is evidence
  • 30. Citation
      • The dataset:
      • Storz, D et al. (2009):
      • Planktic foraminiferal flux and faunal composition of sediment trap L1_K276 in the northeastern Atlantic .
      • http://dx.doi.org/10.1594/PANGAEA.724325
      • Is supplement to the article:
      • Storz, David; Schulz, Hartmut; Waniek, Joanna J; Schulz-Bull, Detlef; Kucera, Michal (2009): Seasonal and interannual variability of the planktic foraminiferal flux in the vicinity of the Azores Current.
      • Deep-Sea Research Part I-Oceanographic Research Papers, 56(1), 107-124,
      • http://dx.doi.org/10.1016/j.dsr.2008.08.009
  • 31.
    • We don’t use the Web.
    • Berners-Lee created the Web as a scholarly communication tool.
    • Today the Web has changed everything but scholarly communication.
    • Online journals are essentially paper journals, delivered by faster horses.
    • But journals and citation are technology of the 18th century
    Beyond citation
  • 32. Future
    • Try to measure various kinds of use:
    • Resolution
    • Downloads
    • Mentions
    • Citations
    • Other types of linking
  • 33. The wave Growth of Information – Diversity of media types and formats User requirements – e. g. : Science 2.0, collaborative networks, social media
  • 34. A threat?
    • Information overload is only a problem for manual curation.
    • Google is not complaining about data deluge—they’re constantly trying to get more data.
    • The more data you throw, the better the filter gets.
    • Don’t turn off the taps, build boats.
  • 35.
    • It is not only a challenge …
    • … it is an opportunity
    • Let us ride the wave together…