GIS Data Curation in Libraries

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  • Public or private?Check websitesCall and askLand grant mission (Lincoln University, University of MO)
  • GIS Data Curation in Libraries

    1. 1. GIS Data Curation inLibrariesA panel will explore the future of GIS data curation in libraries.Speakers will address traditional ways libraries incorporate GISservices, how researchers use GIS data through the life cycle & finallythe potential/challenge of GIS data curation.
    2. 2. Michael ElliotAssistant Professor of Biostatistics at SLUKaren HogenboomNumeric and Spatial Data Librarian at UIUCCynthia HudsonDigital Data Outreach Librarian at WUSTLJennifer MooreGIS / Anthropology Librarian at WUSTLChris FreelandAssociate University Librarian at WUSTL
    3. 3. Case Study Data Digital in the Curation in Assets Research Libraries Management Lifecycle (Michael) (Cynthia) Systems (Chris) GIS in Curating Discussion Libraries GIS Data and (Karen) (Jennifer) Questions
    4. 4. My Experience as a Public Health Faculty Member Using GIS Data Michael B. Elliott, Ph.D. Assistant Professor
    5. 5. Public Health has a long history with spatialdata: 19th century London John Snow
    6. 6. Obesity Trends* Among U.S. AdultsBRFSS, 1985 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14%
    7. 7. Obesity Trends* Among U.S. AdultsBRFSS, 1986 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14%
    8. 8. Obesity Trends* Among U.S. AdultsBRFSS, 1987 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14%
    9. 9. Obesity Trends* Among U.S. AdultsBRFSS, 1988 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14%
    10. 10. Obesity Trends* Among U.S. AdultsBRFSS, 1989 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14%
    11. 11. Obesity Trends* Among U.S. AdultsBRFSS, 1990 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14%
    12. 12. Obesity Trends* Among U.S. AdultsBRFSS, 1991 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19%
    13. 13. Obesity Trends* Among U.S. AdultsBRFSS, 1992 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19%
    14. 14. Obesity Trends* Among U.S. AdultsBRFSS, 1993 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19%
    15. 15. Obesity Trends* Among U.S. AdultsBRFSS, 1994 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19%
    16. 16. Obesity Trends* Among U.S. AdultsBRFSS, 1995 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19%
    17. 17. Obesity Trends* Among U.S. AdultsBRFSS, 1996 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19%
    18. 18. Obesity Trends* Among U.S. AdultsBRFSS, 1997 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19% ≥20%
    19. 19. Obesity Trends* Among U.S. AdultsBRFSS, 1998 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19% ≥20%
    20. 20. Obesity Trends* Among U.S. AdultsBRFSS, 1999 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19% ≥20%
    21. 21. Obesity Trends* Among U.S. AdultsBRFSS, 2000 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19% ≥20%
    22. 22. Obesity Trends* Among U.S. AdultsBRFSS, 2001 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19% 20%–24% ≥25%
    23. 23. Obesity Trends* Among U.S. Adults BRFSS, 2002 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)No Data <10% 10%–14% 15%–19% 20%–24% ≥25%
    24. 24. Obesity Trends* Among U.S. AdultsBRFSS, 2003 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19% 20%–24% ≥25%
    25. 25. Obesity Trends* Among U.S. AdultsBRFSS, 2004 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19% 20%–24% ≥25%
    26. 26. Obesity Trends* Among U.S. AdultsBRFSS, 2005 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%
    27. 27. Obesity Trends* Among U.S. AdultsBRFSS, 2006 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%
    28. 28. Obesity Trends* Among U.S. AdultsBRFSS, 2007 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%
    29. 29. Obesity Trends* Among U.S. AdultsBRFSS, 2008 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%
    30. 30. Obesity Trends* Among U.S. AdultsBRFSS, 2009 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%
    31. 31. Obesity Trends* Among U.S. AdultsBRFSS, 2010 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%
    32. 32. How I’ve used GIS data in myresearch Associate aspects of the neighborhood (built environment) with behaviors and chronic disease (diabetes)
    33. 33. Start with diabetes mortality rate
    34. 34. Look at poverty status
    35. 35. Look at location of parks
    36. 36. Look at location of fast food chains
    37. 37. Look at location of convenience stores
    38. 38. Look at location of grocery stores
    39. 39. When You Put it all together
    40. 40. What takes the most time? Finding data Modifying / Limiting shape files Re-finding data
    41. 41. Where do I try to find my data? Census Bureau MSDIS (Missouri Spatial Data Information Service) City/County Departments of Planning CDC Various pay sources
    42. 42. Where to store all this data?
    43. 43. What a mess!
    44. 44. Problems Trying to locate where I stored files for different projects Trying to remember what I named the files (especially when I accepted ArcMap’s default names) Trying to remember how I changed the files Concerns about quality of the files Lack of access to colleagues files across department, college, university, (city? Etc.) Lack of normalization of shape file projections Lack of metadata Disrupted linkages if switching computers or changing file structure or updating software Using Dropbox as a collaborative temporary solution does not fix problem
    45. 45. Possibilities for the future…
    46. 46. Possibilities for the future…
    47. 47. GISServices inAcademicLibrariesKaren HogenboomNumeric and Spatial DataLibrarianUniversity of Illinois atUrbana-Champaignhogenboo@illinois.edu
    48. 48. Consultations with GIS Users  Finding data  Help with choosing or using software  Data management (and curation)  Metadata  Database design  Etc…
    49. 49. Providing Access to Data Compilations of trusted sources http://www.library.illinois.edu/sc/datagis Geo-portals: http://geodata.tufts.edu Subscriptions to data sources  SimplyMap  Social Explorer  Geolytics  Small topical data sets (countrydata.com, UNIDO Industrial Statistics)
    50. 50. http://www.library.illinois.edu/sc/datagis
    51. 51. http://www.library.illinois.edu/sc/datagis
    52. 52. Providing Access to Data Compilations of trusted sources http://www.library.illinois.edu/sc/datagis Geo-portals: http://geodata.tufts.edu Subscriptions to data sources  SimplyMap  Social Explorer  Geolytics  Small topical data sets (countrydata.com, UNIDO Industrial Statistics)
    53. 53. geodata.tufts.edu
    54. 54. geodata.tufts.edu
    55. 55. geodata.tufts.edu
    56. 56. Providing Access to Data Compilations of trusted sources http://www.library.illinois.edu/sc/datagis Geo-portals: http://geodata.tufts.edu Subscriptions to data sources  SimplyMap  Social Explorer  Geolytics  Small topical data sets (countrydata.com, UNIDO Industrial Statistics)
    57. 57. (Geo)Data Literacy Data literate students must “be able to access, assess, manipulate, summarize, and present data.”1  Workshops (geographic concepts and software, finding data)  Sessions with classes/groups  Online guides: http://libguides.com 1 MiloSchield, “Information Literacy, Statistical Literacy, and Data Literacy,” IASSIST Quarterly (Summer/Fall 2004): 7-11.
    58. 58. http://www.libguides.com
    59. 59. http://www.libguides.com
    60. 60. Accessing Academic LibraryGIS Services
    61. 61. Data Curation in Libraries The model and existing tools to get you there...Cynthia HudsonDigital Data Outreach LibrarianWashington University in St. LouisAdapted from: Dorothea Salo “Librarians love data”
    62. 62. DCC Curation Lifecycle Model http://www.dcc.ac.uk/resour ces/curation-lifecycle-model
    63. 63. CONCEPTUALIZE
    64. 64. CREATE OR RECEIVE
    65. 65. APPRAISE & SELECT
    66. 66. INGEST
    67. 67. PRESERVATION ACTION
    68. 68. STORE
    69. 69. ACCESS, USE & REUSE
    70. 70. TRANSFORM
    71. 71. GIS Data Curation: Challenges & Potential Jennifer MooreJennifer Moore | GIS Outreach & Anthropology Librarian | Washington University Libraries | j.moore@wustl.edu | @anthrolibrarian
    72. 72. Curation Lifecycle Model as a Guide for GIS Data http://www.dcc.ac.uk/resources/curation-lifecycle-model Curation Lifecycle from the DCCJennifer Moore | GIS Outreach & Anthropology Librarian | Washington University Libraries | j.moore@wustl.edu | @anthrolibrarian
    73. 73. Provenance Photo by Silver Stack http://www.flickr.com/photos/silverstack/7163871656/ Two issues: Who/when/how/where Collection? was it originally collected Licensed? Where/when/how did Purchased? the researcher get it? Public Domain? CREATE/RECIEVE PRESERVE STOREJennifer Moore | GIS Outreach & Anthropology Librarian | Washington University Libraries | j.moore@wustl.edu | @anthrolibrarian
    74. 74. Authoritative? What does authoritative mean Photo from woodlywonder works http://www.flickr.com/photos/wwworks/2222523486/ for GIS data? Original, raw data? Confirmed by local Sources? Quality? Centuries long problem for cartographers Now there are many collectors of GIS data; some argue this makes the question of CREATE/RECEIVE quality harder to answerJennifer Moore | GIS Outreach & Anthropology Librarian | Washington University Libraries | j.moore@wustl.edu | @anthrolibrarian
    75. 75. Derivatives Derivatives Derivatives Derivatives Derivatives http://www.flickr.com/photos/luzbonita/2353227140/ Photo by Luz Authority Accuracy Currency APPRAISE/SELECT PRESERVE TRANSFORM Versioning ACCESS/REUSEJennifer Moore | GIS Outreach & Anthropology Librarian | Washington University Libraries | j.moore@wustl.edu | @anthrolibrarian
    76. 76. Data Complexity Photos by Doug88888 http://www.flickr.com/photos/doug88888/3220357081/ Diverse Structured Layered Needs AttributionJennifer Moore | GIS Outreach & Anthropology Librarian | Washington University Libraries | j.moore@wustl.edu | @anthrolibrarian
    77. 77. Data management File size Robust Photo by Artform Canado http://www.flickr.com/photos/artform/3266013003/ Formats Obsolete Proprietary Versatile Best practices Naming conventions metadata CONCEPTUALIZEJennifer Moore | GIS Outreach & Anthropology Librarian | Washington University Libraries | j.moore@wustl.edu | @anthrolibrarian
    78. 78. Data that informs us about the data. Necessary for data management, preservation and discovery. Data curators say it is often a challenge that But, researchers don’t want researchers do to learn a metadata standard not accurately to make the data useful; they document their just want to fill in a form. data. metadataJennifer Moore | GIS Outreach & Anthropology Librarian | Washington University Libraries | j.moore@wustl.edu | @anthrolibrarian
    79. 79. FGDC metadata. I mean, really. FGDC is RIDICULOUSLY complex, and tool support for it is therefore nonexistent. Who thought this would work, and have they been fired yet?metadata - Dorothea SaloISO 19115? Geographic Markup Language (GML)?Jennifer Moore | GIS Outreach & Anthropology Librarian | Washington University Libraries | j.moore@wustl.edu | @anthrolibrarian
    80. 80. Photo by Davewing68 http://www.flickr.com/photos/davewing68/2834143854/ CONCEPUTALIZATION Data Access and Support ACCESS/REUSEJennifer Moore | GIS Outreach & Anthropology Librarian | Washington University Libraries | j.moore@wustl.edu | @anthrolibrarian
    81. 81. Good Examples http://cugir.mannlib.cornell.edu/ http://inside.uidaho.edu/ http://www.geomapp.net/Jennifer Moore | GIS Outreach & Anthropology Librarian | Washington University Libraries | j.moore@wustl.edu | @anthrolibrarian
    82. 82. Steps Forward Create a Geospatial Data Collection Policy (model NGDA) Develop relationship with other institutions Establish GeoPortal with OAIS standard guidelinesJennifer Moore | GIS Outreach & Anthropology Librarian | Washington University Libraries | j.moore@wustl.edu | @anthrolibrarian
    83. 83. Bibliography Bethune, Alec, Butch Lazorchak, and Zsolt Nagy. 2009. “GeoMAPP: A Geospatial Multistate Archive and Preservation Partnership.” Journal of Map & Geography Libraries 6 (1): 45–56. doi:10.1080/15420350903432630. Bose, Rajendra, and Femke Reitsma. 2006. “Advancing Geospatial Data Curation.” http://www.era.lib.ed.ac.uk/handle/1842/1074. Downs, Robert R., and Robert S. Chen. "Organizational needs for managing and preserving geospatial data and related electronic records." Data Science Journal 4, no. 0 (2005): 255-271. Erwin, Tracey, and Julie Sweetkind-Singer. 2009. “The National Geospatial Digital Archive: A Collaborative Project to Archive Geospatial Data.” Journal of Map & Geography Libraries 6 (1): 6–25. doi:10.1080/15420350903432440. Gold, Anna K. "Cyberinfrastructure, data, and libraries, part 2: Libraries and the data challenge: Roles and actions for libraries." Office of the Dean (Library) (2007): 17. Jenkins, Keith. 2013. “Expert Feedback on Geospatial Data Curation.” http://guides.library.cornell.edu/profile.php?uid=1097 Kenyon, Jeremy. 2012. “Geospatial Data Curation at the University of Idaho.”Journal of Web Librarianship 6 (4): 251–262. Salo, Dorothea. 2013. “Expert Feedback on Geospatial Data Curation.” http://dsalo.info/ Shaon, Arif, and Andrew Woolf. 2011. “Long-term Preservation for Spatial Data Infrastructures: a Metadata Framework and Geo-portal Implementation.” D-Lib Magazine 17 (9): 1–. Steinhart, Gail. 2006. “Libraries as Distributors of Geospatial Data: Data Management Policies as Tools for Managing Partnerships.” Edited by Gail Steinhart. Library Trends 55 (2): 264–284. Stonltenberg, Jaime. 2013. “Expert Feedback on Geospatial Data Curation.” http://www.library.wisc.edu/directory/staff/Jaime- Stoltenberg Sweetkind, Julie, Mary Lynette Larsgaard, and Tracey Erwin. 2006. “Digital Preservation of Geospatial Data.” Library Trends 55 (2): 304–314. Xia, Jingfeng. 2012. “Metrics to Measure Open Geospatial Data Quality.” Issues in Science & Technology Librarianship (68): 7.Jennifer Moore | GIS Outreach & Anthropology Librarian | Washington University Libraries | j.moore@wustl.edu | @anthrolibrarian
    84. 84. GIS & Digital AssetManagement Systems(DAMS)Chris FreelandAssociate University Librarian Twitter: @chrisfreeland
    85. 85. What is a Digital AssetManagement System? Combination of hardware & software used to store and access digital objects  Documents  Images / Photos  Video  Audio  Datasets
    86. 86. UIs / APIs: DAMS • Add/Edit/Delete • Access controlMetadata Files DB SAN
    87. 87. Kinds of DAMSEnterpriseInstitutionalPersonal
    88. 88. Connecting GIS & DAMS…little to no native support, requires custom programming
    89. 89. Putting it all togetherTropicos: http://www.tropicos.orgMissouri Botanical Garden’s botanical information system  4 million+ specimen records  1.2 million plant names  98,000 collectors / authors  140,000 imagesMaps via ESRI tools & other technologies…  ArcIMS in 2000, only recently taken offline  ArcGIS Server 9.3 & JavaScript API in 2010Digital Asset Management via Fedora Commons
    90. 90. UI / API ASP.NET (C#) ArcGIS API for JavaScript App ArcGIS Server Fedora Commons djatoka DBSpatial Data MySQL Image Metadata SQL Server File System Images GIS DAMS
    91. 91. GIS & DAMS: Conclusions Libraries have invested in DAMS for media storage & delivery Opportunities for use with custom GIS apps, but requires customization / tradeoffs  It DOES work  It IS NOT simple Move towards community-supported research data portals will probably win
    92. 92. Case Study Data Digital in the Curation in Assets Research Libraries Management Lifecycle (Michael) (Cynthia) Systems (Chris) GIS in Curating Discussion Libraries GIS Data and (Karen) (Jennifer) Questions Thank you!

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