The 2022 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some states and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census and beyond, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

This statistic shows the number of SPECT-CT units per million population in Canada in 2019/2020, by province. In that year, the province of Newfoundland and Labrador had **** SPECT-CT units per every million of its population.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Harvard Forest (HFR) contains human population density measurements in numberPerKilometerSquared units and were aggregated to a yearly timescale.

This statistic shows the number of PET-CT units per million population in Canada in 2019/2020, by province. PET-CT stands for positron emission tomography–computed tomography. In that year, the province of New Brunswick had *** PET-CT units per every million of its population.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Harvard Forest (HFR) contains percent urban population measurements in percent units and were aggregated to a yearly timescale.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities.

Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office.

The following dataset from Harvard Forest (HFR) contains human population density measurements in numberPerKilometerSquared units and were aggregated to a yearly timescale.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities.

Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office.

The following dataset from Harvard Forest (HFR) contains human population density measurements in numberPerKilometerSquared units and were aggregated to a yearly timescale.

This shapefile contains landscape factors representing human disturbances summarized to local and network catchments of river reaches for the state of Connecticut. This dataset is the result of clipping the feature class 'NFHAP 2010 HCI Scores and Human Disturbance Data for the Conterminous United States linked to NHDPLUSV1.gdb' to the state boundary of Connecticut. Landscape factors include land uses, population density, roads, dams, mines, and point-source pollution sites. The source datasets that were compiled and attributed to catchments were identified as being: (1) meaningful for assessing fish habitat; (2) consistent across the entire study area in the way that they were assembled; (3) representative of conditions in the past 10 years, and (4) of sufficient spatial resolution that they could be used to make valid comparisons among local catchment units. In this data set, these variables are linked to the catchments of the National Hydrography Dataset Plus Version 1 (NHDPlusV1) using the COMID identifier. They can also be linked to the reaches of the NHDPlusV1 using the COMID identifier. Catchment attributes are available for both local catchments (defined as the land area draining directly to a reach; attributes begin with "L_" prefix) and network catchments (defined by all upstream contributing catchments to the reach's outlet, including the reach's own local catchment; attributes begin with "N_" prefix). This shapefile also includes habitat condition scores created based on responsiveness of biological metrics to anthropogenic landscape disturbances throughout ecoregions. Separate scores were created by considering disturbances within local catchments, network catchments, and a cumulative score that accounted for the most limiting disturbance operating on a given biological metric in either local or network catchments. This assessment only scored reaches representing streams and rivers (see the process section for more details). Please use the following citation: Esselman, P., D.M. Infante, L. Wang, W. Taylor, W. Daniel, R. Tingley, J. Fenner, A. Cooper, D. Wieferich, D. Thornbrugh and J. Ross. (April 2011) National Fish Habitat Action Plan (NFHAP) 2010 HCI Scores and Human Disturbance Data (linked to NHDPLUSV1) for Connecticut. National Fish Habitat Partnership Data System. http://dx.doi.org/doi:10.5066/F79021SW

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities.

Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office.

The following dataset from Harvard Forest (HFR) contains human population density measurements in numberPerKilometerSquared units and were aggregated to a yearly timescale.

The 2015 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. The records in this file allow users to map the parts of Urban Areas that overlap a particular county. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the "urban footprint." There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2010.

Computer tomography (CT) scanners are vital medical technology used in the diagnosis and monitoring of various medical conditions. CT scanner utilize x-ray technology to make images of bones, vessels and other internal organs. As of 2024, Japan had the largest density of CT scanners with almost 120 scanners per million people. The country with the second most scanners at that time was Australia with over 74 scanners per million people. Diagnostic imaging Diagnostic imaging is a branch of medical technology that aims to use advanced technologies to create images of the human body for the purposes of diagnosing and monitoring medical conditions. There are several kinds of imaging available. Magnetic resonance imaging (MRI) is another type of medical imaging common in developed countries. As of 2024, Japan and Greece had the largest number of MRI units per million population. Usage of medical imaging also varies significantly among countries with Germany and Austria having the highest rates of examinations by MRI in recent years. Medical technology market globally The medical technology market has been an ever-expanding industry. With segments in diagnostic imaging, cardiology and optometry there is ample opportunities for new technologies to be utilized. The top medical technology segment based on market share was in vitro diagnostics, followed by cardiology and diagnostic imaging. Among medical technology companies Medtronic and Johnson & Johnson were the top two based on worldwide revenue in 2023.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Harvard Forest (HFR) contains percent urban population measurements in percent units and were aggregated to a yearly timescale.

Data from Statistics Canada [26].*p-Value from Wilcoxon-Mann-Whitney test;**p-Value from t-test;¶ People 15 years of age and over; SD = Standard Deviation.Sociodemographic characteristics of the rural small towns in which the EDs were located.

This is a copy of the statewide Census Tract GIS Tiger file. It is used to determine if a census tract (CT) is EDA or not by adding ACS (American Community Survey) Median Household Income (MHI) and Population Density data at the CT level. The IRWM web based DAC mapping tool uses this GIS layer. Every year this table gets updated after ACS publishes their updated estimates. Created by joining 2018 EDA table to 2010 Census Tracts feature class. The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2010 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

The wood frog (Rana sylvatica = Lithobates sylvaticus) is a common, early-spring breeding anuran species in the United States and Canada. Females typically lay their egg masses in concentrated areas of a few meters over several days. Most female wood frogs mature after two years. Each female lays one egg mass in a given year, and most show high (~100%) site fidelity after first breeding, although a small portion of juveniles disperse up to 2000 m away from their natal site before their first breeding season. The lifespan of wood frogs depends on latitude, but they rarely live longer than five years. From 2000 to 2020 we conducted wood frog egg mass counts in 64 freshwater nonpermanent wetlands in the 3212 hectare Yale-Myers Forest in northeastern Connecticut, USA. The wetlands varied in surface area (average = 2642 m2, range = 24–41361 m2, CV = 252), canopy closure (i.e., global site factor; average = 52%, range = 0–98%, CV = 68), depth (average = 52 cm, range = 22–118, CV = 46), and egg mass counts (average = 71, range = 0–1113, CV = 130). As each female only lays one egg mass per year (i.e., only produces one clutch) and site fidelity is high, egg mass counts offer an accurate proxy for the number of breeding females within a pond in a given year. Previous work indicates egg mass counts are an accurate and precise technique for monitoring wood frog populations. Methods Pond-level variables Attributes of ponds in our models included maximum pond depth and canopy closure. Depth was recorded at the time of egg mass surveys. Most ponds have a permanent depth gauge so measurements are standardized across years, otherwise depth was recorded as the deepest point in the pond. Pond canopy closure was measured as in Arietta et al. (2020) by using five hemispherical photographs taken along the shore at each cardinal point and at the center of each pond during leaf-off and leaf-on seasons. We estimated average leaf-on and leaf-off global site factor (GSF; the ratio of above-canopy radiation to under-canopy radiation) (Anderson 1964) and used a weighted GSF value integrated over the duration of wood frog embryonic and larval life cycle (Halverson et al. 2003). GSF is scaled between 0–1, and we report it here as a percentage. Regional-level variables We included air temperature and Palmer Drought Severity Index as regional-scale variables. Here we define regional factors as those affecting multiple breeding populations simultaneously. We downloaded daily temperature records from the National Climatic Data Center of the National Oceanic and Atmospheric Administration (NOAA) observing station at the Windham Airport in Willimantic, Connecticut, approximately 19 km south of the study area. We estimated winter thaw as the number of days between 1 October and 30 March above freezing in the winter prior to breeding (i.e., winter thaw(t-1)). This date range gives an estimate of the fall and winter conditions for juveniles and adults and aligns winter temperature with the hydrologic water year that begins 1 October each year. The Palmer Drought Severity Index (hereafter drought severity) uses temperature, precipitation, and soil information to estimate the departure of moisture supply from the norm. We downloaded historical monthly drought severity data for Connecticut from the National Centers for Environmental Information division of NOAA (available at https://www.ncdc.noaa.gov/temp-and-precip/drought/historical-palmers/psi/200011-202010). Drought severity typically ranges between -4 and 4, although more extreme values are possible. A drought severity value around zero indicates normal conditions, whereas a value ≤ -4 indicates extreme drought and a value ≥ 4 extremely wet conditions. We used an average monthly drought severity value from 1 March to 30 September to represent the moisture conditions that breeding adults, tadpoles, and new metamorphs would experience from the highest pond levels (early spring) to lowest levels in late summer and early fall. To test if larval and juvenile conditions affected females during their first breeding year, we also tested a two-year lag in drought severity (i.e., drought severity(t-2)) corresponding to the first year of maturity. Estimating density dependence measures Females typically take two years to reach sexual maturity, so the effect of larval intraspecific competition within a focal pond on a breeding female was defined as the density of egg masses (i.e., egg masses / pond area) two years prior. The effect of neighboring ponds (a proxy for terrestrial density dependence) was estimated using a summed function of egg mass data from neighboring ponds (i.e., within 500 m) weighted by inverse distance. Closer ponds are given greater weight in generating the estimate. Population growth rate We defined population growth rate (hereafter growth rate) as the number of breeding females over a generation time of two years: ln((egg massest + 1)/egg masses(t-2) +1))/2 Where t is the number of egg masses in the survey year, t-2 is the number egg masses two years prior, and the entire function is divided by the number of years between measurements. This approach normalizes high and low values for better comparison across ponds.

** The proportions were calculated for 23 EDs because of 3 missing value.*** The proportions were calculated for 61 EDs because of 1 missing value.*p-Value from Fisher exact test.This is the Table 5. Comparison of 24/7 local access to equipment in rural EDs.

Accurate and precise assessment of population density plays a critical role in effective wildlife management, but reliable estimates are often difficult to obtain. Camera traps have emerged as valuable non-invasive tools for studying elusive species, offering cost-effective solutions for both marked and unmarked populations. We evaluated the consistency of badger (Meles meles) density estimates obtained from the random encounter model (REM) and camera trap distance sampling (CT-DS) with independent estimates from spatial mark-resight (SMR) models and quantified the bias in CT-DS arising from animals reacting to camera traps. Six camera trap surveys were conducted in Cornwall, UK, in 2019 and 2021, and data were used to estimate badger density using the REM and CT-DS. Four sites were included in a badger vaccination research project, providing an opportunity to mark badgers with uniquely identifiable fur clips to facilitate resighting within an SMR framework. We found consistency in the ..., Data collection Data were collected from six camera trap surveys at five sites in Cornwall, UK, in 2019 and 2021. Data Analysis Badger density was estimated using three methods: The Random Encounter Model (REM), Camera trap Distance Sampling (CT-DS), and Spatially Explicit Mark Resight (SEMR). Details of each method are given below. REM Density Estimation

Density estimates were calculated from encounter rates using an equation involving variables like the number of independent badger encounters (y), temporal survey effort (t), and camera detection zone parameters (r and θ). Model parameters were estimated from camera images, including badger position data, speed, activity level, and detection zone dimensions. Density estimates were obtained using the 'camtools' package, including a nonparametric bootstrap of trap rate errors. Where badgers showed reactive behaviour, 'reactive' sequences were removed from the estimation of animal speed and the camera detection zone.

CT-DS Density Esti..., , # Camera-based badger density estimation using the REM, CT-DS, and SMR

The data and code are provided for three methods used to estimate badger density - the Random Encounter Model (REM), Camera-Trap Distance Sampling (CT-DS), and Spatially-Explicit Mark Resight (SMR).

Description of the data and file structure

For each method, data are organised into separate files representing the different sites (numbered 1-5). Any data containing location information has been omitted in line with privacy-sharing agreements so that participating landholders remain anonymous. As such, we have not included the shapefiles to generate the habitat mask for SMR or the coordinates of camera locations.

We have also included the code for the simulation of animal density using SMR across a range of pID values, reflecting the proportion of identifiable individuals. The values provided are similar to the observed detection conditions of the full dataset. Â

Below we have outlined the methodology...

I studied how breeding and wintering forest bird communities across Connecticut responded to variation in habitat characteristics and particularly such landscape attributes as forest fragmentation. I surveyed birds at 1,815 points along 121 transects that traversed ca. 400 km of forest. I also made 12705 habitat measurements at survey points and computed areas of forest, non-forest, core forest and perimeter/area ratios of forest for 31,550 ha of study area. I computed sampled species richness and community density as well as individual species’ population densities for each transect. Moreover, I classified species encountered as to their nest site selection, macrohabitat use, microhabitat use, migratory strategy and trophic affiliation. Based on observations of 36,702 summering individuals of 123 species and 13,742 wintering individuals of 63 species, declines in community density occurred with increasing fragmentation although species richness was often more closely associated with ha..., I established 121 bird survey transects, with each traversing 3.2−4 km of forest depending upon terrain and other local conditions. Each transect had 15 survey points—the maximum I could visit during the peak of morning bird activity. Routes began at first light (05:15 in summer, 07:00 in winter) and lasted 3.5−4 hr.  I used the Variable Circular Plot (VCP) technique to survey. I computed population densities with Distance 7.3 software. I visually evaluated habitat to a 70 m radius from each sampling station for: 1) forest type, 2) moisture regime, 3) diameter of canopy trees at breast height (dbh), 4) canopy cover, 5) understory density and 6) elevation at the location of the survey point. I also summed canopy and understory measures to provide a measure of 7) vertical vegetation complexity. To characterize the landscapes within which the survey transects were situated, I employed QGIS 3.16 geographic information systems software to analyze aerial orthophotos. I plotted the survey po..., , # The comparative effects of landscape-level forest fragmentation, forest area and local habitat measures on Connecticut bird communities

https://doi.org/10.5061/dryad.tht76hf5v

The dataset contains summer and winter results of variable plot surveys of birds. Â Column heads for survey data are: Global =Â state location; Region = NECT (northeast CT), SECT (southeast CT), CECT (central CT), NWCT (northwest CT), SWCT (southwest CT); an additional S = summer and an additional W = winter; Season = summer, winter; Transect = transect number; Station = station number along transect; Species = U.S. Fish and Wildlife Service species code; Observation = distance from survey point in m; Flock = flock size for flocking species, otherwise = 1; Forest = 1 (deciduous), 2 mixed, 3 (conifer); Moisture = 1 (hydric, 2 (mesic), 3 (xeric); dbh = 1 (< 15 cm), 2 (15-45 cm), 3 (> 45 cm); Canopy = 1 (< 40% cover), 2 (50-60% cover), 3 (> 70% cover); Underst...

1Average annual ED patient visits and standard deviation;2 Average number of stretchers and standard deviation. ICU = intensive care unit; ED = emergency department.*p-Value from t-test;**p-Value from Wilcoxon-Mann-Whitney test;***p-Value from fisher exact test.Comparison of rural hospitals general characteristics.

标题：全球CT成像设备密度分析报告 数据内容： 本数据集记录了全球范围内计算机断层扫描（CT）成像设备的密度数据，具体包括每百万人口对应的CT成像设备数量。数据集包含以下字段： - 实体（Entity）：表示国家或地区。 - 代码（Code）：表示国家或地区的唯一标识码。 - 年份（Year）：表示数据对应的年份。 - CT密度（Total density per million population, computed tomography units）：表示每百万人口对应的CT成像设备数量。 数据来源： 互联网公开数据 数据用途： 该数据集可用于多个行业的研究与分析，例如： 1. 医疗健康行业：评估不同地区CT设备的覆盖率，优化医疗资源分配。 2. 公共卫生行业：研究CT设备密度与疾病预防、诊断能力的关系。 3. 政策制定行业：为医疗政策的制定提供数据支持，确保资源公平分配。 4. 智慧城市行业：分析CT设备密度与城市化进程的关系，优化医疗设施布局。 标签：CT成像设备, 医疗资源密度, 全球健康, 医疗数据分析, 公共卫生政策, 医疗设备分布， 行业分类： 1. 医疗健康行业 2. 公共卫生行业 3. 政策制定行业 4. 智慧城市行业