Biodiversity Informatics: An Interdisciplinary Challenge

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"Impacto de la Informática en el Conocimiento de la Biodiversidad: Actualidad y Futuro” at Universidad Nacional de Colombia on August 12, 2011. …

"Impacto de la Informática en el Conocimiento de la Biodiversidad: Actualidad y Futuro” at Universidad Nacional de Colombia on August 12, 2011. https://sites.google.com/site/simposioinformaticaicn/home

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  • Government staff, scientists, researchers, land manager spend to much time looking for data and getting it into a shape that is usefulIt is too difficult for data gatherers to make their data available in a useful format.
  • BIEN: Biological information and ecology networkNCEA: Nation center for ecological analysis and sythesis

Transcript

  • 1. P. Bryan Heidorn
    University of Arizona and JRS Biodiversity Foundation
    8 August 2011
    Impacto de la informática en el conocimiento de la biodiversidad: actualidadyfuturo
    Universidad Nacional de Colombia and Instituto de CienciasNaturales, Bogotá
    Biodiversity Informatics: An Interdisciplinary Challenge
    Adapted in part from 2010 KENYA’S INTERNATIONAL CONFERENCE ON BIODIVERSITY, LAND USE AND CLIMATE CHANGE
    NAIROBI 15th to 17th September 2010
  • 2. University of Arizona
  • 3. Biodiversity Informatics
    The development and use of information technology-based sociotechnical systems to document, understand and protect biological diversity particularly at the organismal level.
  • 4. Main Themes
    Cyberinfrastructure enabled science
    Greater reuse of data
    Mobilization of analog data
    Data integration
    Distributed collaborative research
    Citizen science
    High volume and high computation
  • 5. Cyberinfrastructure Vision
    “The anticipated growth in both the production and repurposing of digital data raises complex issues not only of scale and heterogeneity, but also of stewardship, curation and long-term access.”
    NSF Cyberinfrastructure Vision for 21st Century Discovery, Chapter 3
  • 6. Recognition of need for data curation
    “Recommendation 6: The NSF, working in partnership with collection managers and the community at large, should act to develop and mature the career path for data scientists and to ensure that the research enterprise includes a sufficient number of high-quality data scientists.”
    Long-Lived Digital Data Collections: Enabling Research and Education in the 21st Century, Recommendations
  • 7. Interagency Working Group on Digital Data
    Recognition of the importance of Information
    Recognition of the need for education
    New work roles within traditional institutions
  • 8. Dark data is the data that we know is/was there but we can’t see it.
    Hubble Space Telescope composite image "ring" of dark matter in the galaxy cluster Cl 0024+17
  • 9. Does NSF’s Data Follow the Power Law?
    I do not know but if $1 = X bytes…..
    Heidorn, P. Bryan (2008). Shedding Light on the Dark Data in the Long Tail of Science. Library Trends 57(2) Fall 2008 . Institutional Repositories: Institutional
    Repositories: Current State and Future. Edited by Sarah Sheeves and Melissa Cragin. (http://hdl.handle.net/2142/9127).
  • 10. The Future is all about Data
    How do we get it?
    How do we analyze it?
    How do we disseminate it (Maps, charts tables..)?
    How do we keep it?
    Provenance, Storage, Weeding
    How do we make it sustainable?
  • 11. Data Repurposing
    From: To stand the test of time: Long-term stewardship of of digital data sets in
    science and engineering. Sept 26-27, 2006 Arlington VA
  • 12. Where is your data now?
    Is it doing good or is it sleeping or dead?
  • 13. Cyberinfrastructure Needs
    Storage
    Access
    Processing
    Communication
    Training
    Institutions
  • 14. The iPlant Collaborative Cyberinfrastructure to Support the Challenges of Modern Biology
    Society for Experimental Biology, Glasgow, UK
    July 3rd, 2011
    Dan Stanzione
    Co-PI and Cyberinfrastructure Lead, iPlant Collaborative
    Deputy Director, Texas Advanced Computing Center
    dan@tacc.utexas.edu
    dan@iplantcollaborative.org
  • 15. What is iPlant?
    iPlant’s mission is to build the CI to support plant biology’s Grand Challenge solutions
    Grand Challenges were not defined in advance, but identified through engagement with the community
    A virtual organization with Grand Challenge teams relying on national cyberinfrastructure
    Long term focus on sustainable food supply, climate change, biofuels, ecological stability, etc
    Hundreds of participants globally… Working group members at >50 US institutions, USDA, DOE, etc.
  • 16. Brief History
    Funding by NSF – February 1st, 2008
    iPlant Kickoff Conference at CSHL – April 2008
    • ~200 participants
    • 17. Grand Challenge Workshops – Sept-Dec 2008
    • 18. CI workshop – Jan 2009
    • 19. Grand Challenge White Paper Review – March 2009
    • 20. Project Recommendations – March 2009
    • 21. Project Kickoffs – May 2009 & August 2009
    • 22. Start of software development; September 2009
    • 23. First prototypes to public: April 2010
    • 24. First release with user-driven tool integration: July 2011
  • iPlant’s Central Challenge
    To define what it means to build a lasting, community driven Cyberinfrastructure for the Grand Challenges of Plant Science, to get community buy-in of this vision, and to execute this vision.
  • 25. Steve Goff, PI
    U of Arizona
    Dan Stanzione, coPI
    Texas Advanced Computing Center
    National Science Board
    Update on Award Progress: DBI -0735191
    Directorate for Biological Sciences
    July 2011
  • 26. What iPlant Offers
  • 27. Grand Challenges in Plant Science
    Genotype-to-Phenotype
    To understand how DNA blueprints produce a plant’s characteristic traits and functions and to predict how traits change in response to complex environments
    Requires ability to collect, query, interpret, and model high-throughput, genome-scale data sets
    Tree of Life
    To understand evolutionary
    relationships among green plants
    Requires ability to create, display, and query information in very large phylogenetic trees
  • 28. iPlant Progress
    Science Planning (Year 1)
    Community engagement
    Grand Challenge selection
    Cyberinfrastructure Design (Year 2)
    Requirements generation
    Technology evaluations
    Prototyping
  • 29. iPlant Progress
    Release of CI deliverables (Year 3)
    iPlant Discovery Environments and Tools
    iPlant Genotype to Phenotype Tools
    Processing and integration of high throughput data
    Modeling and visualization of phenotypic expression
    iPlant Tree of Life Tools
    Assembly, Reconciliation and Viewing
    Taxonomic Name Resolution Service
    My-Plant social networking site
    DNA Subway Tool for genome annotation / analysis
  • 30. Taxonomic Name Resolution Service
  • 31. Biodiversity: Development of new knowledge and tools to use knowledge
    Progress on digitization of the world’s billion+ museum specimens
    Distribution of digitized products through global networks (e.g. the Global Biodiversity Information Facility).
    Digitization of hundreds of millions of pages of natural history text (begun with the Biodiversity Heritage Library)
    Large online stores of information on species such as the Encyclopedia of Life
  • 32. The Biodiversity Heritage Library has 34 million pages now
    Long Citation Half-life
    Critical use for Taxonomy
    Ecology and Environmental History
    Naming for genomics and metagenomics
    Palaeontology, or, A systematic summary of extinct animals and their geological relations / by Richard Owen. Publication info:Edinburgh :A. and C. Black,1860.
  • 33. The Rubiaceae of Colombia, by Paul C. Standley. Chicago,1930.
    Chicago :Field Museum of Natural History,
  • 34. Mobilizing Data Locked on Paper
    Fine-Grained Semantic Markup of Descriptive Data for Knowledge Applications in Biodiversity Domains Hong Cui hongcui@email.arizona.edu (Principal Investigator)
    The University of Arizona is awarded a grant to develop and evaluate a set of algorithms/software to help computers to read and “understand” taxonomic descriptions of plants, animals, and other living or fossil organisms. The major functions of the algorithms/software include 1) annotate large sets of text descriptions in a machine-readable way to support various knowledge applications, including producing character matrices and identification keys for various taxon groups.
  • 35. Semantic Markup System
    Training Thursday for students
  • 36. The Problem
    It is difficult to find what is already known
    Clone specimens may be stored in different museums around the world
    DNA analysis may be conducted on one but not the other
    Micrographs may be in a database
    Taxonomic treatments or revisions may exist
  • 37. Biological Science Collections (BiSciCol) Tracker
    Nairobi National Museum
    Gene Sequence
    ?
    S1: KNM
    ?
    ?
    GENBANK
    ?
    Living Collection: Missouri Botanical Garden
    ?
    Parasitism
    ?
    Agave sisalana
    S3: MBG
    Muséum national d'histoire naturelle
    ?
    Determination
    S2: MNHN
  • 38. BiSciCol Tracker
  • 39. BiSciCol Design
    Insert new design
  • 40. NSF: Advanced Digitization of Biological Collections
    iDigBio: The National Resource for Advancing Digitization of Biological Collections
  • 41. Organization
    National Hub (~$7.5M)
    Title: A Collections Digitization Framework for the 21st Century
    PI: Lawrence Page, University of Florida
    Thematic Hub (~$2M each)
    Title: InvertNet–An Integrative Platform for Research on Environmental Change, Species Discovery and Identification
    PI: Christopher Dietrich, University of Illinois, Urbana-Champaign
    Title: Plants, Herbivores and Parasitoids: A Model System for the Study of Tri-Trophic Associations
    PI: Randall T. Schuh, American Museum of Natural History
    Title: North American Lichens and Bryophytes: Sensitive Indicators of Environmental Quality and Change
    PI (Principal Investigator): Corinna Gries, University of Wisconsin, Madison
  • 42. Virtual Organization and Collaboration
    VOSS: Next Steps in Articulating Success Factors for Distributed Collaborations. Gary Olson gary.olson@uci.edu (Principal Investigator) Judith Olson (Co-Principal Investigator)
    Theory of Remote Collaboration. Evaluation A prototype online Collaboration Success Wizard will be developed for those engaged in collaboration or planning to collaborate to assess their strengths and weaknesses.
  • 43. Example of Virtual Community in NanoTechnology
  • 44. Three of the pioneers behind novel light-scattering techniques to detect certain early stage cancers joined an outside expert on biophotonics in a call-in program to discuss new research results that were presented in the Aug. 1, 2007, edition of Clinical Cancer Research. Richard McCourt (right), of NSF's Directorate for Biological Sciences, was the moderator.Credit: National Science Foundation
  • 45. Features of Virtual Organization
    Common Goals
    Geographic dispersal
    Distributed strengths and capabilities
    Need to multimedia collaboration
    Non-residents to be treated as insiders
    Document sharing, video and voice, workflow integration.
  • 46. Interdisciplinary and high volume data
    Cyberinfrastructure and the Dimensions in Biodiversity - Planning for Success -Madison, WI - Oct 13-15, 2010 Corinna Gries cgries@wisc.edu (Principal Investigator) Matthew Jones (Co-Principal Investigator)David Vieglais (Co-Principal Investigator)
    Need to make order of magnitude improvements in rate of biodiversity study with 0 increase in cash.
    Development of cyberinfrastructure (CI) supporting integrative research in biodiversity sciences.
  • 47. Cloud Computing
    Data-Intensive Science Workshops, to be held Sept. 19 to 20, 2010, Seattle, WA; and Mar 20 to 21, 2011, Washington DC
    Needed for most modeling with large data sets including climate models
    Needed for phylogenetic analysis
  • 48. Occurrence Data Sharing
    SilverLining: A highly scalable cloud-based platform for data distribution and user collaboration. David Vieglais vieglais@ku.edu (Principal Investigator) Eileen Lacey (Co-Principal Investigator)
    Potential for leveraging a cloud-based Platform as a Service (PaaS) for data publication to address myriad challenges currently faced by existing distributed data service architectures such as Distributed Generic Information Retrieval (DiGIR) and TDWG Access Protocol for Information Retrieval (TAPIR). Specific goals are to 1) simplify and reduce the ongoing cost of publishing data, 2) improve data quality at the source, 3) provide scalable, effective access to published data, 4) stimulate innovation by creating a simple, highly scalable platform for new applications for data interaction, and 5) develop a suite of reference applications demonstrating capacities of the new architecture.
  • 49. Agile Science
    Disaster: RAPID: Gulf Coast Oil Spill Biodiversity Tracker. A Volunteer-based Observation Network Steven Kelling stk2@cornell.edu (Principal Investigator)
    RAPID: Enhancement of Fishnet2 for Disaster Impact Assessment Henry Bart hank@museum.tulane.edu (Principal Investigator)
  • 50. http://ebird.org/tools/oilspill/
  • 51.
  • 52. New Validation Models
    Filtered Push: Continuous Quality Control for Distributed Collections and Other Species-Occurrence Data. James Macklin james.macklin@agr.gc.ca (Principal Investigator) Bertram Ludaescher (Co-Principal Investigator)
    networked solution to enable annotation of distributed biological collection data and to share assertions about their quality or usability.
  • 53. Improved collection management
    Collaborative Biodiversity Collections Computing. James Beach beach@ku.edu (Principal Investigator)
    http://digbiocol.wordpress.com/
  • 54. Map of Life
    An infrastructure for integrating and advancing global species distribution knowledge
    Co-Pis: Walter Jetz (Yale)
    Rob Guralnick (CU Boulder)
  • 55. Advancing species distribution knowledge
    Species distributions
    (Vertebrates)
    Landcover
    current
    Landcover future
    Topography
    World
    1996: GTOPO 30
    2009: SRTMV V4
    2003: GLC 2000
    2009: GlobCover
    1992:BIOME
    2001:Image 2.2
    Regional models
    2006 WWF
    2005-9: expert maps
    ?
    Atlas data, surveys
    Scale (Grain)
    200km
    50km
    Knowledge Gap
    1km
    100m
    Hurlbert and Jetz (PNAS 2007)
    Jetz et al. (Conservation Biology 2008)
    1m
  • 56. Overcoming the “Wallacean shortfall”
    The “Wallacean shortfall”, i.e. the geographic bias and coarseness of our species distribution knowledge is a (the?) major impediment for biodiversity science and our understanding of global change impacts on biodiversity
    Narrowing the knowledge gap:
    Data mobilization (Museums, NGOs, GBIF)
    Focused sampling
    Model-based data integration
    ‘Crowd-sourcing’
  • 57. Map of Life
    ‘Map of Life’ aims to build on and complement the spatial biodiversity aspects of these and other efforts. By addressing key storage, query, visualization and modeling challenges common to all, and by providing mapping and data integration services, the platform is expected to empower region- and taxon-specific efforts, freeing their resources for investment in core competencies, including quality control or specific user-community needs.
  • 58. Map of Life
    An online workbench and knowledgebase to dynamically document, annotate, integrate, validate, advance, and analyze the disparate sources of global biodiversity distribution knowledge.
  • 59.
  • 60. Display, spatially explicit WIKI
    Jetz, McPherson & Guralnick. in review
  • 61. Cougar
  • 62. Modeling Software Support
    Development of a Data Assimilation Capability Towards Ecological Forecasting in a Data-Rich Era. Yiqi Luo yluo@ou.edu (Principal Investigator) S Lakshmivarahan (Co-Principal Investigator)
    Powerful eco-informatics tool that assimilate data from measurement sensor networks and to generate data products that will be useful for policy making on resource management and climate change mitigation. Ecological Platform for Assimilation of Data (EcoPAD) for data assimilation and forecasting in ecology. EcoPAD will include components of (1) core computational algorithms (e.g., ecological models) that are specifically designed to solve ecological issues, (2) a variety of optimization techniques for data assimilation, (3) various data bases that will feed into EcoPAD, and (4) diverse functions of EcoPAD
  • 63. Formalizing Location Data
    Improving GEOLocate to Better Serve Biodiversity Informatics Henry Bart hank@museum.tulane.edu (Principal Investigator) Nelson Rios (Co-Principal Investigator)
    a software tool for assigning latitude and longitude coordinates to text descriptions of locations where scientific collections were made (Georeferencing)
  • 64. Collaborative Georeferencing
  • 65. Grant Making: about $2M/yr
    Animal Tracking in South Africa
    Specimen Digitization in Ghana
    Social Value of Conservation in Peru
    Species Pages and BD Education in Costa Rica
    Niche Modeling in Brazil
    Travel Grants
    Lake Victoria Data Library Project in Tanzania, Uganda and Kenya
    Flora de Colombia en Línea
    JRS Biodiversity Foundation
  • 66. The Future is Collaboration and Data Sharing
    Libraries
    Museums
    Government
    Universities
    To bring the best data to the major problems and opportunities of our time and the future