1.
P. Bryan Heidorn<br />University of Arizona and JRS Biodiversity Foundation<br />8 August 2011<br />Impacto de la informática en el conocimiento de la biodiversidad: actualidadyfuturo<br />Universidad Nacional de Colombia and Instituto de CienciasNaturales, Bogotá<br />Biodiversity Informatics: An Interdisciplinary Challenge<br />Adapted in part from 2010 KENYA’S INTERNATIONAL CONFERENCE ON BIODIVERSITY, LAND USE AND CLIMATE CHANGE<br />NAIROBI 15th to 17th September 2010<br />

2.
University of Arizona<br />

3.
Biodiversity Informatics<br />The development and use of information technology-based sociotechnical systems to document, understand and protect biological diversity particularly at the organismal level.<br />

4.
Main Themes<br />Cyberinfrastructure enabled science<br />Greater reuse of data<br />Mobilization of analog data<br />Data integration<br />Distributed collaborative research<br />Citizen science<br />High volume and high computation<br />

5.
Cyberinfrastructure Vision<br />“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.”<br />NSF Cyberinfrastructure Vision for 21st Century Discovery, Chapter 3<br />

6.
Recognition of need for data curation<br />“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.”<br />Long-Lived Digital Data Collections: Enabling Research and Education in the 21st Century, Recommendations<br />

7.
Interagency Working Group on Digital Data<br />Recognition of the importance of Information<br />Recognition of the need for education<br />New work roles within traditional institutions<br />

8.
Dark data is the data that we know is/was there but we can’t see it.<br />Hubble Space Telescope composite image "ring" of dark matter in the galaxy cluster Cl 0024+17<br />

9.
Does NSF’s Data Follow the Power Law?<br />I do not know but if $1 = X bytes…..<br />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 <br />Repositories: Current State and Future. Edited by Sarah Sheeves and Melissa Cragin. (http://hdl.handle.net/2142/9127).<br />

10.
The Future is all about Data<br />How do we get it?<br />How do we analyze it?<br />How do we disseminate it (Maps, charts tables..)?<br />How do we keep it? <br />Provenance, Storage, Weeding <br />How do we make it sustainable?<br />

11.
Data Repurposing<br />From: To stand the test of time: Long-term stewardship of of digital data sets in <br />science and engineering. Sept 26-27, 2006 Arlington VA <br />

12.
Where is your data now?<br />Is it doing good or is it sleeping or dead?<br />

13.
Cyberinfrastructure Needs<br />Storage<br />Access<br />Processing<br />Communication<br />Training<br />Institutions<br />

14.
The iPlant Collaborative Cyberinfrastructure to Support the Challenges of Modern Biology<br />Society for Experimental Biology, Glasgow, UK<br />July 3rd, 2011<br />Dan Stanzione<br />Co-PI and Cyberinfrastructure Lead, iPlant Collaborative<br />Deputy Director, Texas Advanced Computing Center<br />dan@tacc.utexas.edu<br />dan@iplantcollaborative.org<br />

15.
What is iPlant?<br />iPlant’s mission is to build the CI to support plant biology’s Grand Challenge solutions<br />Grand Challenges were not defined in advance, but identified through engagement with the community<br />A virtual organization with Grand Challenge teams relying on national cyberinfrastructure <br />Long term focus on sustainable food supply, climate change, biofuels, ecological stability, etc<br />Hundreds of participants globally… Working group members at >50 US institutions, USDA, DOE, etc.<br />

16.
Brief History<br />Funding by NSF – February 1st, 2008 <br />iPlant Kickoff Conference at CSHL – April 2008<br /><ul><li>~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</li></li></ul><li>iPlant’s Central Challenge<br />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. <br />

25.
Steve Goff, PI<br />U of Arizona<br />Dan Stanzione, coPI<br />Texas Advanced Computing Center<br />National Science Board<br />Update on Award Progress: DBI -0735191<br />Directorate for Biological Sciences<br />July 2011<br />

26.
What iPlant Offers<br />

27.
Grand Challenges in Plant Science<br />Genotype-to-Phenotype <br />To understand how DNA blueprints produce a plant’s characteristic traits and functions and to predict how traits change in response to complex environments<br />Requires ability to collect, query, interpret, and model high-throughput, genome-scale data sets<br />Tree of Life<br />To understand evolutionary <br /> relationships among green plants<br />Requires ability to create, display, and query information in very large phylogenetic trees<br />

28.
iPlant Progress<br />Science Planning (Year 1)<br />Community engagement<br />Grand Challenge selection<br />Cyberinfrastructure Design (Year 2)<br />Requirements generation<br />Technology evaluations<br />Prototyping<br />

29.
iPlant Progress<br />Release of CI deliverables (Year 3)<br />iPlant Discovery Environments and Tools<br />iPlant Genotype to Phenotype Tools<br />Processing and integration of high throughput data<br />Modeling and visualization of phenotypic expression<br />iPlant Tree of Life Tools<br />Assembly, Reconciliation and Viewing<br />Taxonomic Name Resolution Service<br />My-Plant social networking site<br />DNA Subway Tool for genome annotation / analysis<br />

30.
Taxonomic Name Resolution Service <br />

31.
Biodiversity: Development of new knowledge and tools to use knowledge<br />Progress on digitization of the world’s billion+ museum specimens <br />Distribution of digitized products through global networks (e.g. the Global Biodiversity Information Facility).<br />Digitization of hundreds of millions of pages of natural history text (begun with the Biodiversity Heritage Library)<br />Large online stores of information on species such as the Encyclopedia of Life<br />

32.
The Biodiversity Heritage Library has 34 million pages now <br />Long Citation Half-life<br />Critical use for Taxonomy<br />Ecology and Environmental History<br />Naming for genomics and metagenomics <br />Palaeontology, or, A systematic summary of extinct animals and their geological relations / by Richard Owen. Publication info:Edinburgh :A. and C. Black,1860.<br />

33.
The Rubiaceae of Colombia, by Paul C. Standley. Chicago,1930. <br />Chicago :Field Museum of Natural History,<br />

34.
Mobilizing Data Locked on Paper<br />Fine-Grained Semantic Markup of Descriptive Data for Knowledge Applications in Biodiversity Domains Hong Cui hongcui@email.arizona.edu (Principal Investigator) <br />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. <br />

35.
Semantic Markup System<br />Training Thursday for students<br />

36.
The Problem<br />It is difficult to find what is already known<br />Clone specimens may be stored in different museums around the world<br />DNA analysis may be conducted on one but not the other<br />Micrographs may be in a database<br />Taxonomic treatments or revisions may exist<br />

37.
Biological Science Collections (BiSciCol) Tracker <br />Nairobi National Museum<br />Gene Sequence<br />?<br />S1: KNM<br />?<br />?<br />GENBANK<br />?<br />Living Collection: Missouri Botanical Garden<br />?<br />Parasitism<br />?<br />Agave sisalana<br />S3: MBG<br />Muséum national d'histoire naturelle<br />?<br />Determination<br />S2: MNHN<br />

38.
BiSciCol Tracker<br />

39.
BiSciCol Design<br />Insert new design<br />

40.
NSF: Advanced Digitization of Biological Collections<br />iDigBio: The National Resource for Advancing Digitization of Biological Collections<br />

41.
Organization<br />National Hub (~$7.5M)<br />Title: A Collections Digitization Framework for the 21st Century<br />PI: Lawrence Page, University of Florida<br />Thematic Hub (~$2M each)<br />Title: InvertNet–An Integrative Platform for Research on Environmental Change, Species Discovery and Identification<br />PI: Christopher Dietrich, University of Illinois, Urbana-Champaign<br />Title: Plants, Herbivores and Parasitoids: A Model System for the Study of Tri-Trophic Associations<br />PI: Randall T. Schuh, American Museum of Natural History<br />Title: North American Lichens and Bryophytes: Sensitive Indicators of Environmental Quality and Change<br />PI (Principal Investigator): Corinna Gries, University of Wisconsin, Madison<br />

42.
Virtual Organization and Collaboration<br />VOSS: Next Steps in Articulating Success Factors for Distributed Collaborations. Gary Olson gary.olson@uci.edu (Principal Investigator) Judith Olson (Co-Principal Investigator) <br />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.<br />

43.
Example of Virtual Community in NanoTechnology<br />

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<br />

45.
Features of Virtual Organization<br />Common Goals<br />Geographic dispersal<br />Distributed strengths and capabilities<br />Need to multimedia collaboration<br />Non-residents to be treated as insiders<br />Document sharing, video and voice, workflow integration.<br />

46.
Interdisciplinary and high volume data<br />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) <br />Need to make order of magnitude improvements in rate of biodiversity study with 0 increase in cash.<br />Development of cyberinfrastructure (CI) supporting integrative research in biodiversity sciences.<br />

47.
Cloud Computing<br />Data-Intensive Science Workshops, to be held Sept. 19 to 20, 2010, Seattle, WA; and Mar 20 to 21, 2011, Washington DC <br />Needed for most modeling with large data sets including climate models<br />Needed for phylogenetic analysis<br />

48.
Occurrence Data Sharing<br />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) <br />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.<br />

49.
Agile Science<br />Disaster: RAPID: Gulf Coast Oil Spill Biodiversity Tracker. A Volunteer-based Observation Network Steven Kelling stk2@cornell.edu (Principal Investigator)<br />RAPID: Enhancement of Fishnet2 for Disaster Impact Assessment Henry Bart hank@museum.tulane.edu (Principal Investigator) <br />

50.
http://ebird.org/tools/oilspill/<br />

51.

52.
New Validation Models<br />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) <br />networked solution to enable annotation of distributed biological collection data and to share assertions about their quality or usability.<br />

53.
Improved collection management<br />Collaborative Biodiversity Collections Computing. James Beach beach@ku.edu (Principal Investigator)<br />http://digbiocol.wordpress.com/<br />

54.
Map of Life<br />An infrastructure for integrating and advancing global species distribution knowledge<br />Co-Pis: Walter Jetz (Yale)<br /> Rob Guralnick (CU Boulder)<br />

55.
Advancing species distribution knowledge<br />Species distributions<br />(Vertebrates)<br />Landcover<br />current<br />Landcover future<br />Topography<br />World<br />1996: GTOPO 30<br />2009: SRTMV V4<br />2003: GLC 2000<br />2009: GlobCover<br />1992:BIOME<br />2001:Image 2.2<br />Regional models<br />2006 WWF<br />2005-9: expert maps<br />?<br />Atlas data, surveys<br />Scale (Grain)<br />200km<br />50km<br />Knowledge Gap<br />1km<br />100m<br />Hurlbert and Jetz (PNAS 2007)<br />Jetz et al. (Conservation Biology 2008)<br />1m<br />

56.
Overcoming the “Wallacean shortfall”<br />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 <br />Narrowing the knowledge gap: <br />Data mobilization (Museums, NGOs, GBIF)<br />Focused sampling<br />Model-based data integration<br />‘Crowd-sourcing’<br />

57.
Map of Life<br />‘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. <br />

58.
Map of Life<br />An online workbench and knowledgebase to dynamically document, annotate, integrate, validate, advance, and analyze the disparate sources of global biodiversity distribution knowledge.<br />

59.

60.
Display, spatially explicit WIKI<br />Jetz, McPherson & Guralnick. in review<br />

61.
Cougar<br />

62.
Modeling Software Support<br />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)<br />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<br />

63.
Formalizing Location Data<br />Improving GEOLocate to Better Serve Biodiversity Informatics Henry Bart hank@museum.tulane.edu (Principal Investigator) Nelson Rios (Co-Principal Investigator)<br />a software tool for assigning latitude and longitude coordinates to text descriptions of locations where scientific collections were made (Georeferencing)<br />

64.
Collaborative Georeferencing<br />

65.
Grant Making: about $2M/yr<br />Animal Tracking in South Africa<br />Specimen Digitization in Ghana<br />Social Value of Conservation in Peru<br />Species Pages and BD Education in Costa Rica<br />Niche Modeling in Brazil<br />Travel Grants<br />Lake Victoria Data Library Project in Tanzania, Uganda and Kenya<br />Flora de Colombia en Línea<br />JRS Biodiversity Foundation<br />

66.
The Future is Collaboration and Data Sharing<br /><ul><li>NGO

67.
Private Land Holders

68.
Ranches

69.
Farms</li></ul>Libraries<br />Museums<br />Government<br />Universities<br />To bring the best data to the major problems and opportunities of our time and the future<br />