An occupancy model makes use of data that are structured as sets of repeated visits to each of many sites, in order estimate the actual probability of occupancy (i.e., proportion of occupied sites) after correcting for imperfect detection using the information contained in the sets of repeated observations. We explore the conditions under which preexisting, volunteer-collected data from the citizen science project eBird can be used for fitting occupancy models. The data archived here are used to explore two ways in which the single-visit records could be used in occupancy models. First, we use empirical data contained within this archive to assess the potential for space-for-time substitution: aggregating single-visit records from different locations within a region into pseudo-repeat visits. The archived data are used to illustrate that the locations chosen for data collection by observers were not always representative of the habitat in the surrounding area, which would lead to biased estimates of occupancy probabilities when using space-for-time substitution. Second, create a large set of simulated data (output from the simulations contained in this archive) that we used to explore the utility of including data from single-visit records to supplement sets of repeated-visit data.

AbstractHistorical museum records provide potentially useful data for identifying drivers of change in species occupancy. However, because museum records are typically obtained via many collection methods, methodological developments are needed in order to enable robust inferences. Occupancy-detection models, a relatively new and powerful suite of statistical methods, are a potentially promising avenue because they can account for changes in collection effort through space and time. We use simulated datasets to identify how and when patterns in data and/or modelling decisions can bias inference. We focus primarily on the consequences of contrasting methodological approaches for dealing with species' ranges and inferring species' non-detections in both space and time. We find that not all datasets are suitable for occupancy-detection analysis but, under the right conditions (namely, datasets that are broken into more time periods for occupancy inference and that contain a high fraction of community-wide collections, or collection events that focus on communities of organisms), models can accurately estimate trends. Finally, we present a case-study on eastern North American odonates where we calculate long-term trends of occupancy by using our most robust workflow. These results indicate that occupancy-detection models are a suitable framework for some research cases and expand the suite of available tools for macroecological analysis available to researchers, especially where structured datasets are unavailable. MethodsWe simulated multiple unstructured datasets to test the behavior of occupancy-detection models when applied to natural history museum data. Also included are data from the Global Biodiversity Information Facility for eastern North American odonates. Usage notesWe strongly recommend using a computing cluster to reproduce this analysis.

The once-a-decade decennial census was conducted in April 2010 by the U.S. Census Bureau. This count of every resident in the United States was mandated by Article I, Section 2 of the Constitution and all households in the U.S. and individuals living in group quarters were required by law to respond to the 2010 Census questionnaire. The data collected by the decennial census determine the number of seats each state has in the U.S. House of Representatives and is also used to distribute billions in federal funds to local communities. The questionnaire consisted of a limited number of questions but allowed for the collection of information on the number of people in the household and their relationship to the householder, an individual's age, sex, race and Hispanic ethnicity, the number of housing units and whether those units are owner- or renter-occupied, or vacant. Results for sub-state geographic areas in New Mexico were released in a series of data products. The first wave of results was released on March 15, 2011, through the Redistricting Data (PL94-171) Summary File. This batch of data covers the state, counties, places (both incorporated and unincorporated communities), tribal lands, school districts, neighborhoods (census tracts and block groups), individual census blocks, and other areas. The Redistricting products provide counts by race and Hispanic ethnicity for the total population and the population 18 years and over, and housing unit counts by occupancy status. The 2010 Census Redistricting Data Summary File can be used to redraw federal, state and local legislative districts under Public Law 94-171. This is an important purpose of the file and, indeed, state officials use the Redistricting Data to realign congressional and state legislative districts in their states, taking into account population shifts since the 2000 Census. More detailed population and housing characteristics were released in the summer of 2011. The data in these particular RGIS Clearinghouse tables are for Torrance County and all census block groups within Torrance County. There are two data tables. One provides total counts of housing units, ocupied housing units and vacant housing units, while the other provides counts of total housing uings along with proportions of occupied and vacant housing units. These files, along with file-specific descriptions (in Word and text formats) are available in a single zip file.

Occupant satisfaction surveys are widely used in laboratory and field research studies of indoor environmental quality. Field studies pose several challenges because researchers usually have no control over the indoor environments experienced by building occupants, it is difficult to recruit and retain participants, and data collection methods can be cumbersome. With this in mind, we developed a survey platform that uses real-time feedback to send targeted occupant surveys (TOS) at specific indoor environmental conditions and stops sending survey requests when collected responses reach the maximum surveys required. We performed a pilot study of the TOS platform with occupants of a radiant heated and cooled building to target survey responses at 16 radiant slab surface (infrared) temperatures evenly distributed from 15 to 30 °C. We developed metrics and ideal datasets to compare the TOS platform against other occupant survey distribution methods. The results show that this novel method has a higher approximation to characteristics of an ideal dataset; 41% compared to 23%, 19%, and 12% of other datasets in previous field studies. Our TOS method minimizes the number of times occupants are surveyed and ensures a more complete and balanced dataset. This allows researchers to more efficiently and reliably collect subjective data for occupant satisfaction studies.

The digitization of museum collections as well as an explosion in citizen science initiatives has resulted in a wealth of data that can be useful for understanding the global distribution of biodiversity, provided that the well-documented biases inherent in unstructured opportunistic data are accounted for. While traditionally used to model imperfect detection using structured data from systematic surveys of wildlife, occupancy models provide a framework for modelling the imperfect collection process that results in digital specimen data. In this study, we explore methods for adapting occupancy models for use with biased opportunistic occurrence data from museum specimens and citizen science platforms using 7 species of Anacardiaceae in Florida as a case study. We explored two methods of incorporating information about collection effort to inform our uncertainty around species presence: (1) filtering the data to exclude collectors unlikely to collect the focal species and (2) incorporating collection covariates (collection type, time of collection, and history of previous detections) into a model of collection probability. We found that the best models incorporated both the background data filtration step as well as collector covariates. Month, method of collection and whether a collector had previously collected the focal species were important predictors of collection probability. Efforts to standardize meta-data associated with data collection will improve efforts for modeling the spatial distribution of a variety of species.

Context

This data were collected and disseminated according to this publication: https://www.nature.com/articles/s41597-019-0273-5

All descriptors below are taken from this publication and are copyright of the authors.

Abstract

Adaptive interactions between building occupants and their surrounding environments affect both energy use and environmental quality, as demonstrated by a large body of modeling research that quantifies the impacts of occupant behavior on building operations. Yet, available occupant field data are insufficient to explore the mechanisms that drive this interaction. This paper introduces data from a one year study of 24 U.S. office occupants that recorded a comprehensive set of possible exogenous and endogenous drivers of personal comfort and behavior over time. The longitudinal data collection protocol merges individual thermal comfort, preference, and behavior information from online daily surveys with datalogger readings of occupants’ local thermal environments and control states, yielding 2503 survey responses alongside tens of thousands of concurrent behavior and environment measurements. These data have been used to uncover links between the built environment, personal variables, and adaptive actions, and the data contribute to international research collaborations focused on understanding the human-building interaction.

Background

Humans interact with the built environment in a variety of ways that contribute to both building energy use and environmental quality and thus warrant significant attention in the building design, operation, and retrofit processes. Occupants’ thermally adaptive behaviours such as adjusting thermostats and clothing, opening and closing windows and doors, operating personal heating and cooling devices, are strongly tied to total site energy consumed in residential and commercial buildings in the United States (U.S.). This dataset introduces longitudinal data from a one-year study of occupant thermal comfort and several related behavioural adaptations in an air-conditioned office setting in U.S. Offices. The primary objective of the data collection approach was to record a comprehensive range of exogenous and endogenous factors that may drive personal comfort and behaviour outcomes over time.

Methods

Longitudinal data on building occupant behavior, comfort, and environmental conditions were collected between July 2012 and August 2013 at the Friends Center office building in Center City Philadelphia, Pennsylvania, United States. Data collection proceeded in three stages:

1. Semi-structured interviews Semi-structured interviews identify aspects of behavior that are not yet well known or understood and provide a rich qualitative context for developing and interpreting responses from structured survey instruments. 32 interviews about thermal comfort and related behaviours were first conducted with office occupants from 7 air-conditioned buildings, ranging from aging to recently renovated, around the Philadelphia region

2. Site selection and subject recruitment for the longitudinal study Subject recruitment was initiated through an e-mail message sent to all employees in the Friends Center by its Executive Director. The following question areas were included: (a) demographic information, (b) office characteristics, (c) thermal comfort and preferences, (d) control options, (e) personal values, and f) typical work schedule (arrival, lunch, departure times).

3. Longitudinal survey and datalogger measurements. The final occupant sample participated in a series of subjective and objective measurements of thermal comfort, adaptive behavior, and related items. These measurements were carried via longitudinal online surveys, as well as through parallel datalogger and BAS measurements of the local environment and behavioural actions.

Technical Validation

Several measures were taken to ensure the validity of the collected data, following data collection guidance included in the final report for International Energy Agency Annex 66: Definition and Simulation of Occupant Behavior in Buildings. These measures include survey preparation phase, encourage high response rates, pilot studies, quality control, redundancy and comparison against expected conditions. Details for these measures can be found in the paper.

Usage Notes

• NaNs for certain variables do not generally imply missing data; rather, they indicate that the variable was not being measured for a given occupant at a given time stamp
• The variable “Occupant Number” refers to an occupant ID (e.g., a value between 1 and the 24 occupants who participated in the study); it does not refer to an occupancy (presence/absence) measurement.
• The variable “Occupancy 1” is an expected occupancy (presence/absence) value calculated across all time steps in the study based on the occupant’s responses about typical periods of occupancy on the background survey conducted before the start of the longitudinal measurements. The variable “Occupancy 2” is an expected occupancy state calculated across all time steps that fall under the two week daily surveying window, based on the time of arrival reported in the daily morning survey and recent departures from the office reported in the daily mid-day and afternoon surveys, as well as periods of prolonged absence reported on the retrospective surveys conducted after each two week period.

This data collection provides information on the characteristics of a national sample of housing units, including apartments, single-family homes, mobile homes, and vacant housing units. Unlike previous years, the data are presented in nine separate parts: Part 1, Work Done Record (Replacement or Additions to the House), Part 2, Housing Unit Record (Main Record), Part 3, Worker Record, Part 4, Mortgages (Owners Only), Part 5, Manager and Owner Record (Renters Only), Part 6, Person Record, Part 7, Mover Group Record, Part 8, Recodes (One Record per Housing Unit), and Part 9, Weights. Data include year the structure was built, type and number of living quarters, occupancy status, access, number of rooms, presence of commercial establishments on the property, and property value. Additional data focus on kitchen and plumbing facilities, types of heating fuel used, source of water, sewage disposal, heating and air-conditioning equipment, and major additions, alterations, or repairs to the property. Information provided on housing expenses includes monthly mortgage or rent payments, cost of services such as utilities, garbage collection, and property insurance, and amount of real estate taxes paid in the previous year. Also included is information on whether the household received government assistance to help pay heating or cooling costs or for other energy-related services. Similar data are provided for housing units previously occupied by respondents who had recently moved. Additionally, indicators of housing and neighborhood quality are supplied. Housing quality variables include privacy of bedrooms, condition of kitchen facilities, basement or roof leakage, breakdowns of plumbing facilities and equipment, and overall opinion of the structure. For quality of neighborhood, variables include use of exterminator services, existence of boarded-up buildings, and overall quality of the neighborhood. In addition to housing characteristics, some demographic data are provided on household members, such as age, sex, race, marital status, income, and relationship to householder. Additional data provided on the householder include years of school completed, Spanish origin, length of residence, and length of occupancy. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR -- https://doi.org/10.3886/ICPSR02912.v2. We highly recommend using the ICPSR version as they made this dataset available in multiple data formats.

The once-a-decade decennial census was conducted in April 2010 by the U.S. Census Bureau. This count of every resident in the United States was mandated by Article I, Section 2 of the Constitution and all households in the U.S. and individuals living in group quarters were required by law to respond to the 2010 Census questionnaire. The data collected by the decennial census determine the number of seats each state has in the U.S. House of Representatives and is also used to distribute billions in federal funds to local communities. The questionnaire consisted of a limited number of questions but allowed for the collection of information on the number of people in the household and their relationship to the householder, an individual's age, sex, race and Hispanic ethnicity, the number of housing units and whether those units are owner- or renter-occupied, or vacant. Results for sub-state geographic areas in New Mexico were released in a series of data products. These data come from the Demographic Profile 1 (DP-1) Summary File. The geographic coverage for DP-1 SF includes the state, counties, places (both incorporated and unincorporated communities), tribal lands, school districts, census tracts, and other areas. More detailed population and housing characteristics will be released in the summer of 2011 in the Summary File 1 data product. The data in these particular RGIS Clearinghouse tables are for New Mexico and all counties. There are two data tables. One provides total counts of housing units, ocupied housing units and vacant housing units, while the other provides counts of total housing uings along with proportions of occupied and vacant housing units. These files, along with file-specific descriptions (in Word and text formats) are available in a single zip file.

The once-a-decade decennial census was conducted in April 2010 by the U.S. Census Bureau. This count of every resident in the United States was mandated by Article I, Section 2 of the Constitution and all households in the U.S. and individuals living in group quarters were required by law to respond to the 2010 Census questionnaire. The data collected by the decennial census determine the number of seats each state has in the U.S. House of Representatives and is also used to distribute billions in federal funds to local communities. The questionnaire consisted of a limited number of questions but allowed for the collection of information on the number of people in the household and their relationship to the householder, an individual's age, sex, race and Hispanic ethnicity, the number of housing units and whether those units are owner- or renter-occupied, or vacant. Results for sub-state geographic areas in New Mexico were released in a series of data products. The first wave of results was released on March 15, 2011, through the Redistricting Data (PL94-171) Summary File. This batch of data covers the state, counties, places (both incorporated and unincorporated communities), tribal lands, school districts, neighborhoods (census tracts and block groups), individual census blocks, and other areas. The Redistricting products provide counts by race and Hispanic ethnicity for the total population and the population 18 years and over, and housing unit counts by occupancy status. The 2010 Census Redistricting Data Summary File can be used to redraw federal, state and local legislative districts under Public Law 94-171. This is an important purpose of the file and, indeed, state officials use the Redistricting Data to realign congressional and state legislative districts in their states, taking into account population shifts since the 2000 Census. More detailed population and housing characteristics were released in the summer of 2011. The data in this particular RGIS Clearinghouse table are for each block in Dona Ana County and the county as a whole. The data table provides total counts of housing units, ocupied housing units and vacant housing units. This file, along with file-specific descriptions (in Word and text formats) are available in a single zip file.

The once-a-decade decennial census was conducted in April 2010 by the U.S. Census Bureau. This count of every resident in the United States was mandated by Article I, Section 2 of the Constitution and all households in the U.S. and individuals living in group quarters were required by law to respond to the 2010 Census questionnaire. The data collected by the decennial census determine the number of seats each state has in the U.S. House of Representatives and is also used to distribute billions in federal funds to local communities. The questionnaire consisted of a limited number of questions but allowed for the collection of information on the number of people in the household and their relationship to the householder, an individual's age, sex, race and Hispanic ethnicity, the number of housing units and whether those units are owner- or renter-occupied, or vacant. Results for sub-state geographic areas in New Mexico were released in a series of data products. The first wave of results was released on March 15, 2011, through the Redistricting Data (PL94-171) Summary File. This batch of data covers the state, counties, places (both incorporated and unincorporated communities), tribal lands, school districts, neighborhoods (census tracts and block groups), individual census blocks, and other areas. The Redistricting products provide counts by race and Hispanic ethnicity for the total population and the population 18 years and over, and housing unit counts by occupancy status. The 2010 Census Redistricting Data Summary File can be used to redraw federal, state and local legislative districts under Public Law 94-171. This is an important purpose of the file and, indeed, state officials use the Redistricting Data to realign congressional and state legislative districts in their states, taking into account population shifts since the 2000 Census. More detailed population and housing characteristics were released in the summer of 2011. The data in this particular RGIS Clearinghouse table are for each block in Otero County and the county as a whole. The data table provides total counts of housing units, ocupied housing units and vacant housing units. This file, along with file-specific descriptions (in Word and text formats) are available in a single zip file.

The once-a-decade decennial census was conducted in April 2010 by the U.S. Census Bureau. This count of every resident in the United States was mandated by Article I, Section 2 of the Constitution and all households in the U.S. and individuals living in group quarters were required by law to respond to the 2010 Census questionnaire. The data collected by the decennial census determine the number of seats each state has in the U.S. House of Representatives and is also used to distribute billions in federal funds to local communities. The questionnaire consisted of a limited number of questions but allowed for the collection of information on the number of people in the household and their relationship to the householder, an individual's age, sex, race and Hispanic ethnicity, the number of housing units and whether those units are owner- or renter-occupied, or vacant. Results for sub-state geographic areas in New Mexico were released in a series of data products. The first wave of results was released on March 15, 2011, through the Redistricting Data (PL94-171) Summary File. This batch of data covers the state, counties, places (both incorporated and unincorporated communities), tribal lands, school districts, neighborhoods (census tracts and block groups), individual census blocks, and other areas. The Redistricting products provide counts by race and Hispanic ethnicity for the total population and the population 18 years and over, and housing unit counts by occupancy status. The 2010 Census Redistricting Data Summary File can be used to redraw federal, state and local legislative districts under Public Law 94-171. This is an important purpose of the file and, indeed, state officials use the Redistricting Data to realign congressional and state legislative districts in their states, taking into account population shifts since the 2000 Census. More detailed population and housing characteristics were released in the summer of 2011. The data in this particular RGIS Clearinghouse table are for each block in Grant County and the county as a whole. The data table provides total counts of housing units, ocupied housing units and vacant housing units. This file, along with file-specific descriptions (in Word and text formats) are available in a single zip file.

The data collection and processing are described in the published study. The code for analysis is included with the files.

Historical museum records provide potentially useful data for identifying drivers of change in species occupancy. However, because museum records are typically obtained via many collection methods, methodological developments are needed in order to enable robust inferences. Occupancy-detection models, a relatively new and powerful suite of statistical methods, are a potentially promising avenue because they can account for changes in collection effort through space and time.

We use simulated datasets to identify how and when patterns in data and/or modelling decisions can bias inference. We focus primarily on the consequences of contrasting methodological approaches for dealing with species' ranges and inferring species' non-detections in both space and time.

We find that not all datasets are suitable for occupancy-detection analysis but, under the right conditions (namely, datasets that are broken into more time periods for occupancy inference and that contain a high fraction of community-wide collections, or collection events that focus on communities of organisms), models can accurately estimate trends. Finally, we present a case-study on eastern North American odonates where we calculate long-term trends of occupancy by using our most robust workflow.

These results indicate that occupancy-detection models are a suitable framework for some research cases and expand the suite of available tools for macroecological analysis available to researchers, especially where structured datasets are unavailable.

The dataset is a combination of (1) data on the detection and non-detection of black-backed woodpeckers and (2) woodboring beetle surveys at points located within fires in the Sierra Nevada, California. All details on data collection are provided within the associated mansucript. Very limited pre-processing was done to data prior to their inclusion in the attached files. Primarily, data have been re-formatted from database flat-files into R-based data objects that faciliate analysis. The included code provides additional processing and formatting, as well as running the primary JAGS model analysis, as described in the manuscript.

The once-a-decade decennial census was conducted in April 2010 by the U.S. Census Bureau. This count of every resident in the United States was mandated by Article I, Section 2 of the Constitution and all households in the U.S. and individuals living in group quarters were required by law to respond to the 2010 Census questionnaire. The data collected by the decennial census determine the number of seats each state has in the U.S. House of Representatives and is also used to distribute billions in federal funds to local communities. The questionnaire consisted of a limited number of questions but allowed for the collection of information on the number of people in the household and their relationship to the householder, an individual's age, sex, race and Hispanic ethnicity, the number of housing units and whether those units are owner- or renter-occupied, or vacant. Results for sub-state geographic areas in New Mexico were released in a series of data products. The first wave of results was released on March 15, 2011, through the Redistricting Data (PL94-171) Summary File. This batch of data covers the state, counties, places (both incorporated and unincorporated communities), tribal lands, school districts, neighborhoods (census tracts and block groups), individual census blocks, and other areas. The Redistricting products provide counts by race and Hispanic ethnicity for the total population and the population 18 years and over, and housing unit counts by occupancy status. The 2010 Census Redistricting Data Summary File can be used to redraw federal, state and local legislative districts under Public Law 94-171. This is an important purpose of the file and, indeed, state officials use the Redistricting Data to realign congressional and state legislative districts in their states, taking into account population shifts since the 2000 Census. More detailed population and housing characteristics were released in the summer of 2011. The data in these particular RGIS Clearinghouse tables are for Los Alamos County and all census block groups within Los Alamos County. There are two data tables. One provides total counts of housing units, ocupied housing units and vacant housing units, while the other provides counts of total housing uings along with proportions of occupied and vacant housing units. These files, along with file-specific descriptions (in Word and text formats) are available in a single zip file.

Note: For information on data collection, confidentiality protection, nonsampling error, subject definitions, and guidance on using the data, visit the 2020 Census Demographic and Housing Characteristics File (DHC) Technical Documentation webpage..To protect respondent confidentiality, data have undergone disclosure avoidance methods which add "statistical noise" - small, random additions or subtractions - to the data so that no one can reliably link the published data to a specific person or household. The Census Bureau encourages data users to aggregate small populations and geographies to improve accuracy and diminish implausible results..Source: U.S. Census Bureau, 2020 Census Demographic and Housing Characteristics File (DHC)

The once-a-decade decennial census was conducted in April 2010 by the U.S. Census Bureau. This count of every resident in the United States was mandated by Article I, Section 2 of the Constitution and all households in the U.S. and individuals living in group quarters were required by law to respond to the 2010 Census questionnaire. The data collected by the decennial census determine the number of seats each state has in the U.S. House of Representatives and is also used to distribute billions in federal funds to local communities. The questionnaire consisted of a limited number of questions but allowed for the collection of information on the number of people in the household and their relationship to the householder, an individual's age, sex, race and Hispanic ethnicity, the number of housing units and whether those units are owner- or renter-occupied, or vacant. Results for sub-state geographic areas in New Mexico were released in a series of data products. The first wave of results was released on March 15, 2011, through the Redistricting Data (PL94-171) Summary File. This batch of data covers the state, counties, places (both incorporated and unincorporated communities), tribal lands, school districts, neighborhoods (census tracts and block groups), individual census blocks, and other areas. The Redistricting products provide counts by race and Hispanic ethnicity for the total population and the population 18 years and over, and housing unit counts by occupancy status. The 2010 Census Redistricting Data Summary File can be used to redraw federal, state and local legislative districts under Public Law 94-171. This is an important purpose of the file and, indeed, state officials use the Redistricting Data to realign congressional and state legislative districts in their states, taking into account population shifts since the 2000 Census. More detailed population and housing characteristics were released in the summer of 2011. The data in these particular RGIS Clearinghouse tables are for Valencia County and all census block groups within Valencia County. There are two data tables. One provides total counts of housing units, ocupied housing units and vacant housing units, while the other provides counts of total housing uings along with proportions of occupied and vacant housing units. These files, along with file-specific descriptions (in Word and text formats) are available in a single zip file.