Dark Data In the Long Tail of Science:   Examples in Biology

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  • Dark Data In the Long Tail of Science:   Examples in Biology

    1. 1. Dark Data In the Long Tail of Science:   Examples in Biology September 2, 2009 National Institute of Standards and Technology P. Bryan Heidorn NSF University of Illinois University of Arizona
    2. 2. Introduction <ul><li>Program Manager, Division of Biological Infrastructure, National Science Foundation </li></ul><ul><li>Associate Professor, Graduate School of Library and Information Science, University of Illinois </li></ul><ul><li>Director School of Information Resources and Library Science, University of Arizona </li></ul><ul><li>JRS Biodiversity Foundation Board of Directors </li></ul>
    3. 3. Cyberinfrastructure Vision <ul><li>“ 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 . ” </li></ul><ul><ul><li>NSF Cyberinfrastructure Vision for 21st Century Discovery, Chapter 3 </li></ul></ul>
    4. 4. Recognition of need for data curation <ul><li>“ 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.” </li></ul><ul><li>Long-Lived Digital Data Collections: Enabling Research and Education in the 21 st Century, Recommendations </li></ul>
    5. 5. <ul><li>Recognition of the importance of Information </li></ul><ul><li>Recognition of the need for education </li></ul><ul><li>New work roles within traditional institutions </li></ul>Interagency Working Group on Digital Data
    6. 6. New Information Disciplines <ul><li>Digital Curator : an expert knowledgeable of and with responsibility for the content of a digital collection(s) </li></ul><ul><li>Digital Archivist : an expert competent to appraise, acquire, authenticate, preserve, and provide access to records in digital form </li></ul><ul><li>Data Scientists : the information and computer scientists, database and software engineers and programmers, disciplinary experts, expert annotators, and others, who are crucial to the successful management of a digital data collection </li></ul><ul><li>(Long Long-Lived Digital Data Collections: Enabling Research and Education in the 21st Century, report of the National Science Board, September, 2005) </li></ul>
    7. 7. Library Skills
    8. 8. Economics of the long tail <ul><li>The Long Tail , By Chris Anderson. Wired Magizine.12.10, 2004. ( http://www.wired.com/wired/archive/12.10/tail_pr.html ) </li></ul><ul><li>NetFlix versus BlockBuster </li></ul><ul><li>Genbank versus Joe’s Lab </li></ul><ul><li>Big Science versus New Science </li></ul>
    9. 9. Naive View of Science Data GenBank PDB f ( x )= ax k + o ( x k ) Power Law of Science Data f ( x )= ax k + o ( x k )| X<.20 Data Volume Science Projects and Initiatives
    10. 10. Does NSF’s Data Follow the Power Law? I do not know but if $1 = X bytes…..
    11. 11. 20-80 Rule The small are big! $350,000- $831 $6,892,810-$350,000 Range $938,548,595 $1,199,088,125 Total Dollars 7478 1869 Number Grants 80% 20% 9347 $2,137,636,716 Total Grants
    12. 12. <ul><li>Dark data is the data that we know is/was there but we can’t see it. </li></ul>Hubble Space Telescope composite image &quot;ring&quot; of dark matter in the galaxy cluster Cl 0024+17
    13. 13. Related Ideas <ul><li>John Porter: </li></ul><ul><ul><li>Deep verses Wide databases </li></ul></ul><ul><li>Swanson: </li></ul><ul><ul><li>Undiscovered Public Knowledge </li></ul></ul><ul><li>Science Commons: </li></ul><ul><ul><li>Big Verses Small science </li></ul></ul>
    14. 14. Why is the tail also important <ul><li>Valuable science data is in the tail </li></ul><ul><li>Many scientists could use the tail data </li></ul><ul><li>Unpublished observations of flowing time in Concord by Alfred Hosmer from 1888 to 1902 </li></ul><ul><li>Photographs of Flowers </li></ul><ul><li>Blue Hill Observatory meteorological data </li></ul><ul><li>Richard B. Primack, Abraham J. Miller-Rushing, Daniel Primack, and Sharda Mukunda (2007). Using Photographs to Show the Effects of Climate Change on Flowing Time. Arnoldia 65(1), p2-9. </li></ul><ul><li>Valuable science data is in the tail </li></ul><ul><li>Many scientists could use the tail data </li></ul><ul><li>Science innovation occurs in the long tail </li></ul><ul><li>Unpublished negative results / aka dark data </li></ul><ul><li>We know very little about the tail </li></ul><ul><li>Transformative science happens in the tail </li></ul><ul><li>Computational thinking needed to free the tail </li></ul><ul><li>NSF Current investments in the tail </li></ul><ul><li>OECD Principles and Guidelines for Access to Research Data from Public Funding </li></ul>
    15. 15. Technical Solutions: Move the tail to the head (increase k) <ul><li>Data standards </li></ul><ul><ul><li>e.g. Environmental Markup Language (EML) </li></ul></ul><ul><ul><li>e.g. TaxonX - taXMLit </li></ul></ul><ul><li>Metadata </li></ul><ul><ul><li>Darwin Core (DwC) </li></ul></ul><ul><ul><li>Access to Biological Collection Data (ABCD) </li></ul></ul><ul><li>Protocols </li></ul><ul><ul><li>TAPIR </li></ul></ul>
    16. 16. Solutions <ul><li>Controlled Vocabularies </li></ul><ul><ul><li>MeSH, ZooBank, IPNI, ITIS </li></ul></ul><ul><li>Ontologies </li></ul><ul><ul><li>Gene Ontology (GO) </li></ul></ul><ul><ul><li>Science Environment for Ecological Knowledge (SEEK) </li></ul></ul><ul><ul><li>EcoGrid </li></ul></ul><ul><ul><li>Leopold Semi-Automated ontology generation for Amphibian Morphology DBI-0640053 </li></ul></ul><ul><li>(Semantic) web software </li></ul><ul><li>DataNet </li></ul>
    17. 17. Institutional Solutions <ul><li>Well Paid Librarians </li></ul><ul><li>Well-heeled Museums </li></ul><ul><li>Professional Societies </li></ul><ul><li>Generous Publishers </li></ul>Library director John Hanson told the Associated Press that a couple of dozen people are cited each year for failure to return materials or pay fines. The incident cost Dalibor about $30 for the two overdue paperbacks. It cost her mother $172 to free her. Book and Bake Sale at the Mary E. Tippitt Memorial Library in Townsend. Sailing Yacht Maltese Falco owned by Tom Perkins
    18. 18. Organizational Solutions <ul><li>LTER, NEON, GBIF, TDWG </li></ul><ul><li>National Center for Ecological Analysis and Synthesis (NCEAS) </li></ul><ul><li>National Evolutionary Synthesis Center (NESCent) </li></ul><ul><li>European Union Networks of Excellence (NoE) </li></ul><ul><li>European Distributed Institute of Taxonomy (EDIT) </li></ul><ul><li>Digital Curation Centre (UK) </li></ul>
    19. 19. Questions about the long-tail <ul><li>How long is the tail? </li></ul><ul><li>What is the area under the tail? </li></ul><ul><li>How steep is the back of science data? </li></ul><ul><li>How valuable could the tail be? </li></ul><ul><li>What is different between tail-science and head-science? </li></ul><ul><li>What is the differential distribution of sciences? </li></ul>
    20. 20. Barriers <ul><li>Lack of professional reward structure </li></ul><ul><li>Lack of education in data curation </li></ul><ul><li>Intellectual property rights (IPR) </li></ul><ul><li>Lack of technology </li></ul><ul><li>Lack of financial reward structure </li></ul><ul><li>Under valuation / lack of investment </li></ul><ul><li>Cost of infrastructure creation </li></ul><ul><li>Cost of infrastructure maintenance </li></ul><ul><li>PDF, excel, MS word, arcview, floppy disks </li></ul>
    21. 21. My Solutions <ul><li>Research </li></ul><ul><ul><li>HERBIS </li></ul></ul><ul><ul><li>Biogeomancer </li></ul></ul><ul><ul><li>Next - Biodiversity Retrieval Evaluation Conference (BREC) </li></ul></ul><ul><li>Education </li></ul><ul><ul><li>Biological Informatics Masters </li></ul></ul><ul><ul><li>Data Curation </li></ul></ul><ul><li>Service </li></ul><ul><ul><li>JRS Biodiversity Foundation </li></ul></ul><ul><ul><li>National Science Foundation </li></ul></ul><ul><ul><li>Taxonomic Database Working Group </li></ul></ul>
    22. 22. Automatic Metadata Extraction (Darwin Core) From Museum Specimen Labels 2008 Dublin Core Conference P. Bryan Heidorn, Qin Wei University of Illinois at Urbana-Champaign … <co> Curtis, </co><hdlc> North American Pl </hdlc><cnl> No.</cnl><cn> 503*</cn> <gn> Polygala</gn><sp> ambigua,</sp><sa> Nutt.,</sa><val> var.</val> <hb> Coral soil,</hb><lc> Cudjoe Key, South Florida. </lc><col> Legit</col><co> A. H. Curtiss.</co><dt>February</dt>…
    23. 23. The problem <ul><li>>1 Billion Natural History Specimens </li></ul><ul><li>Collected over 250 years / many languages </li></ul><ul><li>No publishing standards </li></ul><ul><li>Near infinite classes </li></ul><ul><ul><li>Your high school teacher lied </li></ul></ul><ul><li>6 min / label * 1B labels = 100M hours </li></ul><ul><li>Saving 1 min = 16.7 Million hours </li></ul><ul><li>$10/hr = $167,000,000 </li></ul><ul><li>1/4790 of U.S. deregulation financial bailout </li></ul>
    24. 24. Why care about the specimens? <ul><li>Largest extinction in Cretaceous period </li></ul><ul><li>Rapid Environmental Change </li></ul>
    25. 25. http://www.ncdc.noaa.gov/img/climate/globalwarming/ar4-fig-3-9.gif
    26. 26. Why care <ul><li>Largest mass extinction in millions of years </li></ul><ul><li>Rapid Environmental Change </li></ul><ul><li>Historic distribution of species </li></ul><ul><li>Ecological niche modeling (invasiveness, crop hardiness, pest potential) </li></ul><ul><li>Projections of the impact of climate change </li></ul><ul><li>Where did Herbert Lang and James Chapin go on the Congo Expedition? ( 1909-1915) </li></ul><ul><li>Will I see a Kirkland Warbler here? </li></ul><ul><li>Are some potato species resistant to potato blight? </li></ul><ul><li>When did Linden trees bloom before the industrial revolution? </li></ul>
    27. 27. A real-life example: Baronia brevicornis and its single food plant, Acacia cochliacantha (Soberon)
    28. 28. B. brevicornis Abiotic Niche using BS Garp
    29. 29. Natural History Specimens
    30. 30. S ample records
    31. 31. Sample OCR Output <ul><li>Yale University Herbarium </li></ul><ul><li>~r-^&quot;&quot;&quot; r-n------- </li></ul><ul><li>YU.001300 </li></ul><ul><li>Curtisb, North American Pl </li></ul><ul><li>C^o.nr r^-n </li></ul><ul><li>ANTS, </li></ul><ul><li>No. 503* &quot;^ </li></ul><ul><li>Polygala ambigna, Nntt., var. </li></ul><ul><li>Coral soil, Cudjoe Key, South Florida. </li></ul><ul><li>Legit A. H. Curtiss. </li></ul>
    32. 32. Label Labels <ul><li>bc - barcode </li></ul><ul><li>bt - barcode text </li></ul><ul><li>cm - common/colloquial name </li></ul><ul><li>cn - collection number </li></ul><ul><li>co - collector </li></ul><ul><li>cd - collection date </li></ul><ul><li>fm - family name </li></ul><ul><li>ft - footer info </li></ul>
    33. 33. Label Labels <ul><li>gn - genus name </li></ul><ul><li>hd - header info </li></ul><ul><li>in - infra name </li></ul><ul><li>ina - infra name author </li></ul><ul><li>lc - location </li></ul><ul><li>pd - plant description </li></ul><ul><li>sa - scientific name author </li></ul><ul><li>sp - species name </li></ul>
    34. 34. Example Training Record <ul><li><?xml version=&quot;1.0&quot; encoding=&quot;UTF-8&quot;?> </li></ul><ul><li><?oxygen RNGSchema=&quot;http://www3.isrl.uiuc.edu/~TeleNature/Herbis/semanticrelax.rng&quot; type=&quot;xml&quot;?> </li></ul><ul><li><labeldata> </li></ul><ul><li><bt>Yale University Herbarium </li></ul><ul><li></bt><ns> ~r-^&quot;&quot;&quot; r-n------</ns><bc> YU.001300 </li></ul><ul><li></bc><co cc=&quot;Curtiss&quot; > Curtisb, </co><hdlc cc=&quot;North American Plants&quot; > North American Pl </li></ul><ul><li></hdlc><ns>C^o.nr r^-n </li></ul><ul><li>ANTS,</ns> </li></ul><ul><li><cnl> No.</cnl><cn> 503*</cn><ns> &quot;^</ns> </li></ul><ul><li><gn> Polygala</gn><sp> ambigna,</sp><sa> Nntt.,</sa><val> var.</val> </li></ul><ul><li><hb> Coral soil,</hb><lc> Cudjoe Key, South Florida. </li></ul><ul><li></lc><col> Legit</col><co> A. H. Curtiss.</co> </li></ul><ul><li></labeldata> </li></ul>
    35. 35. Supervised Learning Framework Gold Classified Labels Training Phase Application Phase Machine Learner Unclassified Labels Segmented Text Silver Classified Labels Segmentation Machine Classifier Unclassified Labels Human Editing Trained Model
    36. 36. Herbis Experimental Data <ul><li>295 marked up records </li></ul><ul><li>74 label states </li></ul><ul><li>5-fold cross-validation </li></ul>
    37. 37. Performances of NB and HMM
    38. 38. Element Identifiers
    39. 39. Improved Performance With Field Element Identifiers
    40. 41. Learning w/ pre categorization Gold Labels Machine Learner Model n Classified Labels Class 1 Labels Categor- ization Class 2 Labels Class n Labels Machine Learner Machine Learner Model 2 Model 1 Class 1 Labels Categor- ization Class 2 Labels Class n Labels Machine Classification Machine Classification Machine Classification Classified Labels Classified Labels Unclassified Labels
    41. 42. FIG. 5. Improved Performance of Specialist Model Specialist100 Curtiss VS 100 General
    42. 43. P. Bryan Heidorn 1 , Hong Zhang 1 , Eugene Chung 2 and BGWG 1 Graduate School of Library and Information Science, 2 Linguistics, University of Illinois Machine Learning in BioGeomancer’s Locality Specification SPNHC & NSCA 2006
    43. 44. BioGeomancer Working Group (BGWG) http://203.202.1.217/bgwebsite/index.html <ul><li>Worldwide collaboration of natural history and geospatial data experts </li></ul><ul><li>Maximize the quality and quantity of biodiversity data that can be mapped </li></ul><ul><li>Support of scientific research, planning, conservation, and management </li></ul><ul><li>Promotes discussion, manages geospatial data and data standards, and develops software tools in support of this mission </li></ul>
    44. 45. Participants
    45. 46. Example Locality Types F; NF; FS Seward Peninsula; vic. Bluff, S coast 204 FPOH 0.4 mi N Collinston on LA 138 181 FOO WALTMAN, 9 MI N, 2.5 MI W OF 160 P; FOH; NP TIESMA RD, 1.5 MI NW EDGEWATER; OFF LAKE MICHIGAN R 109 P; POH INDIAN CREEK, 11 MI. W HWY 160 100 NF; FH near Aleutian Islands; S of Amukta Pass 86 FOH; F dario 7 mi wnw of; RIO VIEJO 43 Locality Type Specification of Location Record #
    46. 48. <ul><ul><li>JOH : offset from a junction at heading </li></ul></ul><ul><ul><li>e.g. 0.5 mi. W Sandhill and Hagadorn Roads </li></ul></ul><ul><ul><li>[ FEATURE [ CITY = Sandhill ]] </li></ul></ul><ul><ul><li>[ FEATURE [ ROAD= Hagadorn Roads ]] </li></ul></ul><ul><ul><li>OFFSET VALUE = 0.5 </li></ul></ul><ul><ul><li> DIRECTION= W </li></ul></ul><ul><ul><li> UNIT = mile </li></ul></ul><ul><ul><li> JUNCITON [ FEATURE [ CITY = Sandhill ]] </li></ul></ul><ul><ul><li> [ FEATURE [ ROAD= Hagadorn Roads ]] </li></ul></ul>FRAME
    47. 49. Xiaoya Tang and P. Bryan Heidorn <ul><li>Different vocabularies in queries and documents </li></ul>Long leaves … ... Leaves 20–75, many-ranked, spreading and recurved, not twisted, gray-green (rarely variegated with linear cream stripes), to 1 m  1.5–3.5 cm, ……... Inflorescences: ……. spikes very laxly 6–11-flowered, erect to spreading, 2–3-pinnate, ……. User query Description of leaf Length in texts
    48. 50. Information Extraction From FNA Templates for useful information Extraction Rules Structured information Leaf_Shape obovate Leaf_Shape orbiculate Blade_Dimension 3—9 x 3—8 cm ………… .. ………… .. Original documents ……… .. Leaf blade obovate to nearly orbiculate, 3--9 × 3--8 cm, leathery, base obtuse to broadly cuneate, margins flat, coarsely and often irregularly doubly serrate to nearly dentate, . ……………… Knowledge bases … .. PartBlade: Leaf blade Blades blade …… Pattern:: * <PartBlade> ' ' <leafShape> * ( <leafShape> ) ',' * Output:: leaf {leafShape $1} Pattern:: * <PartBlade> * ', ' ( <Range> ' ' * <LengUnit> ) * <PartBase> Output:: leaf {bladeDimension $1} User log analysis Leaf_Shape Leaf_Margin Leaf_Apex    Leaf_Base Blade_Dimension … .. … .. 
    49. 51. Results – System Performance NT: number of tasks accomplished in total NTH: number of tasks accomplished per hour TSR: task success rate SSR: search success rate NSST: number of searches to accomplish a task TST: time spent to accomplish a task NDVST: number of documents viewed to accomplish a task 0.162 14.75 11.16 NDVST 0.72 435.2 338.8 TST 0.000 9.584 4.779 NSST 0.053 0.568 3.598 4.50 SEARF 0.011 0.000 0.005 0.005 Sig.(ANOVA) 0.210 0.860 8.078 6.75 SEARFA SSR TSR NTH NT Group
    50. 52. Education Programs <ul><li>Biological Information Specialist </li></ul><ul><li>Concentration in Data Curation (MSLIS) </li></ul><ul><li>Certificate of Advanced Study in Data Curation </li></ul><ul><li>Information and professional education in biodiversity informatics </li></ul>
    51. 53. Biological Information Specialists <ul><li>At present: </li></ul><ul><li>Biologists at all degree levels self-trained in information technology </li></ul><ul><li>Information technologists at all degree levels self-trained in biology </li></ul><ul><ul><ul><li>(both with gaps in knowledge for many months, years) </li></ul></ul></ul><ul><li>Differing roles of BIS in large and small </li></ul>
    52. 54. Master of Science in Biological Informatics <ul><li>Degree Program began September 2007 </li></ul><ul><li>Part of campus-wide bioinformatics masters program </li></ul><ul><li>NSF/CISE/IIS, Education Research and Curriculum Development, 0534567 (Palmer, PI) </li></ul><ul><li>Combines Biology, Bioinformatics, Computer Science core with LIS courses </li></ul>
    53. 55. What does a BIS need to know? <ul><li>Biological training and interest in solving biological research problems </li></ul><ul><li>Information skills </li></ul><ul><li>Evaluation and implementation of information systems: user based assessment and continual quality improvement for the development of tools that work and are used. </li></ul><ul><li>Information acquisition, management, and dissemination: development of digital libraries, data archives, institutional repositories, and related tools. </li></ul><ul><li>Information organization and integration: ontology development, structuring information for optimal use and sharing, and standards development. </li></ul>
    54. 56. UIUC bioinformatics core coursework <ul><li>Cross-disciplinary course distribution requirement </li></ul><ul><ul><ul><li>Bioinformatics: Computing in Molecular Biology Algorithms in Bioinformatics Principles of Systematics </li></ul></ul></ul><ul><ul><ul><li>Computer Science: Algorithms Database Systems </li></ul></ul></ul><ul><ul><ul><li>Biology: Human Genetics Introductory Biochemistry Macromolecular Modeling </li></ul></ul></ul>
    55. 57. Sample of existing LIS courses <ul><li>Information Organization and Knowledge Representation </li></ul><ul><li>LIS 551 Interfaces to Information Systems </li></ul><ul><li>LIS 590DM Document Modeling </li></ul><ul><li>LIS 590RO Representing and Organizing Information Resources </li></ul><ul><li>LIS590ON Ontologies in Natural Science </li></ul><ul><li>Information Resources, Uses and users </li></ul><ul><li>LIS 503 Use and Users of Information </li></ul><ul><li>LIS 522 Information Sources in the Sciences </li></ul><ul><li>LIS 590TR Information Transfer and Collaboration in Science </li></ul><ul><li>Information Systems </li></ul><ul><li>LIS 456 Information Storage and Retrieval </li></ul><ul><li>LIS 509 Building Digital Libraries </li></ul><ul><li>LIS 566 Architecture of Network Information Systems </li></ul><ul><li>LIS 590EP Electronic Publishing </li></ul><ul><li>Disciplinary Focus </li></ul><ul><li>LIS 530B Health Sciences Information Services and Resources </li></ul><ul><li>LIS 590HI Healthcare Informatics (Healthcare Infrastructure) </li></ul><ul><li>LIS 590EI/BDI Ecological Informatics (Biodiversity Informatics) </li></ul>
    56. 58. MSLIS Data Curation Concentration <ul><li>Data Curation Educational Program (DCEP) </li></ul><ul><ul><li>IMLS – Laura Bush 21 st Century Librarian Program, </li></ul></ul><ul><ul><ul><li>RE-05-06-0036-06 (Heidorn, PI) </li></ul></ul></ul><ul><li>Students with the DC concentration will be trained to add value to data and promote sharing across labs and disciplinary specializations </li></ul>
    57. 59. New research directions <ul><li>Focus on integration and scale </li></ul><ul><li>Informatics infrastructure as competitive edge </li></ul><ul><ul><li>Sample areas of development </li></ul></ul><ul><ul><li>Landinformatics Group </li></ul></ul><ul><ul><ul><li>Atmospheric science, hydrology, nutrient balance, carbon cycle, ecology, agronomy </li></ul></ul></ul><ul><li>BREC </li></ul><ul><li>Focus on data integration problems across larger range of sciences </li></ul>
    58. 60. Example Service <ul><li>JRS Biodiversity Foundation </li></ul><ul><li>National Science Foundation </li></ul><ul><li>Taxonomic Database Working Group </li></ul>
    59. 61. JRS Biodiversity Foundation <ul><li>History: The J.R.S. Biodiversity Foundation was created in January 2004 when the nonprofit publishing company, BIOSIS was sold to Thomson Scientific. The proceeds from that sale were applied to fund an endowment and create a new grant-making foundation. </li></ul><ul><li>Mission: The Foundation defined a mission within the field of biodiversity: To enhance knowledge and promote the understanding of biological diversity for the benefit and sustainability of life on earth. </li></ul><ul><li>JRS Biodiversity Foundation </li></ul>
    60. 62. JRS Biodiversity Foundation <ul><li>Scope: To further advance the Foundation’s mission a scope was developed as: Interdisciplinary activities primarily carried out via collaborations in developing countries and economies in transition. The Foundation Board of Trustees has expressed a particular interest in focusing its grant-making in Africa. </li></ul><ul><li>Strategic Interest: Within those bounds a considered course has been chosen to: Advance projects, or parts of biodiversity projects that focus on: (1) collecting data, (2) aggregating, synthesizing, publishing data, and making it more widely available to potential end users, and (3) interpreting and gaining insight from data to inform policy-makers </li></ul>
    61. 63. <ul><li>Grant Making: about $2M/yr </li></ul><ul><ul><li>Animal Tracking in South Africa </li></ul></ul><ul><ul><li>Specimen Digitization in Ghana </li></ul></ul><ul><ul><li>Social Value of Conservation in Peru </li></ul></ul><ul><ul><li>Species Pages and BD Education in Costa Rica </li></ul></ul><ul><ul><li>Niche Modeling in Brazil </li></ul></ul><ul><ul><li>Travel Grants </li></ul></ul><ul><ul><li>Lake Victoria Data Library Project in Tanzania, Uganda and Kenya </li></ul></ul><ul><li>e-Biosphere ‘09 </li></ul>JRS Biodiversity Foundation
    62. 64. National Science Foundation <ul><li>Advances in Biological Informatics </li></ul><ul><li>Data Working Group </li></ul><ul><li>Plant Science Cyberinfrastructure Center (iPlant) </li></ul><ul><li>Cyber-enabled Discovery and Innovation </li></ul><ul><li>Hiring Committees </li></ul><ul><li>Division of Biological Infrastructure Planning </li></ul>
    63. 65. Taxonomic Database Working Group <ul><li>Structure of Descriptive Data </li></ul><ul><li>Education Initiative </li></ul><ul><li>HERBIS </li></ul><ul><li>Taxonomic Name Identification </li></ul>

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