A presentation of our approach to organizing the information we are collecting to understand better the influence of indoor environmental factors on the microbial ecology of the indoor environment.
Cascading Style Sheet(CSS) PDF Notes by Apna College
Levin Development of a Database to Manage and Analyze Publications
1. Development of a Database
to 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 the
indoor 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
• www.mendeley.com/groups/844031/microbiology-of-the-built-en
6. Occupants
Building interior
Indoor
Building envelope Furnishings
microbiome Activities
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
http://phil.cdc.gov/phil/details.asp
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
www.epa.gov/iaq/iaqhouse.html
12. Extraction of Information
Environmental samples
• Type
• 59 (62%) Air
• 27 (28%) Surface (wipe or house dust)
• Locations
• Number
• Frequency
www.epa.gov/iaq/base/
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 of
Buildings, 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
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
Sloan Foundation Indoor Environment
Program: grant to UC Davis for
microbenet project – www.microbe.net
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
hlevin6@gmail.com
Editor's Notes
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 (http://microbe.net)
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?
