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Development of a database for organizing papers collected to understand relationships between indoor environmental factor and indoor microbial ecology.

Development of a database for organizing papers collected to understand relationships between indoor environmental factor and indoor microbial ecology.

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  • How can we detect patterns in microbial diversity and identify the mechanisms that shape them?
  • Electronic information resources, Chapter 4 in Biodiversity of Fungi: Inventory and Monitoring Methods
  • Mendeley is a free desktop and web program for Managing and sharing research papers Discovering research data Collaborating online It combines Mendeley Desktop, a PDF and reference management application (available for Windows, Mac and Linux) with Mendeley Web, an online social network for researchers MBE: Catalogue publications and other information about microbiological studies of the built environment Curated in part by microBEnet (
  • Sources of and influences on indoor microorganisms
  • Microorganisms are the focal pieces of information, which are linked to the details in the other tables to identify relationships
  • Electronic information resources, Chapter 4 in Biodiversity of Fungi: Inventory and Monitoring Methods Focus on what information is needed for the project; have reasonable expectations Avoid being stalled by being overzealous at the beginning The biggest culprit is the effort required to collate and enter information**
  • Different sets of data stored in separate tables Establish relationships among the tables Software uses the relationships to find requested data Data entry persons consult reference tables to ensure entry of standardized data, e.g., geographic names (states) or taxonomic nomenclature One of our first steps will be to collect “lookup/authority” lists from as many sources as possible to develop those for this project**
  • 545 papers in Mendeley database 226 scanned to date (41%) 95 included environmental samples (42% of read papers, 17% of total) 50 residences (53% of samples, 22% of read papers) 18 office buildings (19% of samples, 8% of read papers) Think of indoor environment from a hungry microbe’s point of view (“The other end of the microscope,” Elmer Koneman, 2002)
  • Question for audience: How much detail?
  • Data entry will become very laborious (and error prone) if each item from tables or graphs must be entered manually Data reporting inconsistent Detected but not quantified Quantified as fraction of total samples or total organisms Quantified as air concentration (organisms m -3 ), dust concentration (organisms g -1 ), surface load (organisms m -2 ), liquid (organisms L -1 )
  • Ideally we would have as detailed information about sampled buildings as was collected for this and other large studies Typically, research studies not as comprehensive and consistent
  • Are readily collected indicators of indoor dampness associated with one another and with fungal concentrations?


  • 1. Development of a Databaseto Manage and Analyze Publications on the Microbial Ecology of Indoor Environments Hal Levin Building Ecology Research Group Janet Macher California Department of Public Health
  • 2. What environmental factors affect theindoor microbial ecology? Where is the data How is the information recorded Can the information be extracted for analysis and comparison Who would contribute to a publicly available database or an information-sharing repository
  • 3.
  • 4. Database Design (Farr and Farr, 2004) Specimen database • Document the occurrence of an organism • In a particular place • At a given time • Design from the top down • Purpose • Subjects • Specific data bits
  • 5. Mendeley —Microbiology of the Built Environment Bibliographic database (545 papers, 48 members) • Journal citations • Abstracts • Related research • Searchable • built-environment/
  • 6. Occupants Building interior Furnishings Activities IndoorBuilding envelope microbiome Outdoors Ventilation
  • 7. Database Design Tables • Citation • Building type • Occupants • Environmental samples • Sample analyses • Microorganisms
  • 8. Database Design (Farr and Farr, 2004) Fields • Describe the subject • Comprised of the smallest logical unit • Evolve as new pieces of information are identified • Consistent format and content
  • 9. Database Design (Farr and Farr, 2004) Tables • Only fields that pertain to the same subject • Avoid fields most often blank • Duplicate data only as necessary to establish relationships • Include “lookup” or “authority” tables
  • 10. Extraction of Information Occupant demographics • Number • Gender • Age • Time / activity patterns • Include animals and plants
  • 11. Extraction of Information (Mendeley database) Buildings (N = 95) • Time of year • Type • Age • 50 (53%) Residences • Ventilation • 18 (8%) Offices • Schools, commercial, • Nutrients transportation • Water • Location (outdoor • Temperature environment) • pH
  • 12. Extraction of Information Environmental samples • Type • 59 (62%) Air • 27 (28%) Surface (wipe or house dust) • Locations • Number • Frequency
  • 13. Extraction of Information Sample analysis • Type • 64 (67%) Culture • 25 (26%) PCR • How much detail needed?
  • 14. Extraction of Information Microorganisms • Identification • General (group) • Genus • Species •… • Quantification
  • 15. Descriptors ofBuildings, Occupants, Environments U.S. Environmental Protection Agency • Building Assessment Survey and Evaluation (BASE) • 100, randomly selected office buildings • Building and ventilation system characteristics • Building occupant demographics, symptoms, and perceptions • Environmental and comfort measurements • IAQ Building Education and Assessment Model (I-BEAM) • Computer software for indoor air quality in commercial buildings
  • 16. Program Considerations Data format • Text • Digit Transferability • Import and export
  • 17.  Data entry • Read and highlight information • Enter into tables • Consistent terms • Home, house, apartment, condo = Residence • Check / audit Sorting and searching Ideally, electronic publications of the future will provide data in formats that can be reformatted for multiple uses
  • 18. Example — Dampness and Mold Inspector-reported indicators  Air samples • Visible water damage or mold • Time of year • Musty odor • Location • Moisture meter measurement  Fungi • Rotting wood • Types • Peeling paint • Concentrations • Leaks
  • 19. Acknowledgements SloanFoundation Indoor Environment Program: grant to UC Davis for microbenet project – Janet Macher for advice and preparation of the abstract and slides Any of you who can send references to papers of interest, comments on our collection on Mendeley
  • 20. Thank you in advance for your help Hal Levin