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1,068 results for “Demographics”
Land Conservation and Human Demographics by Census Tract in New England 2014-2018
This dataset summarizes land protection, conservation prioritization layer scores, and human demographics within New England communities, defined as census tracts. This dataset was created to identify disparities in land protection according to metrics of social marginalization and assess how incorporating environmental justice criteria into land conservation prioritization systems might change conservation priorities.
Row data for the experiment: "Clinical, psychosocial and demographic factors affect decisions in SLE people".
<p>These datasets correspond to the article titled: “Clinical, psychosocial and demographic factors affect decisions in SLE people”, which can be found at <a href="https://www.medrxiv.org/content/10.1101/2024.03.25.24304643v1.full.pdf">https://www.medrxiv.org/content/10.1101/2024.03.25.24304643v1.full.pdf</a></p> <p>Analysis scripts, and an explanation of variables, can be found at: <a href="https://github.com/NeuroGenomicsMX/Factors_affecting_decisions_in_SLE">https://github.com/NeuroGenomicsMX/Factors_affecting_decisions_in_SLE</a></p> <p>Abstract</p> <p><span>Neurological and psychiatric manifestations affect most lupus individuals and include depression, anxiety, mood disorders, and cognitive dysfunction. Although there is evidence supporting suboptimal decision-making in lupus and its association with glucocorticoids consumption, it is not clear what variables impact such decisions. The aim of this study is to explore how social, clinical, psychological, and demographic factors impact social and temporal decision-making in people with lupus. Through a within-subjects experimental-design, our participants responded to social, clinical, psychological, and demographic electronic questionnaires. Then, they participated in two behavioral economics experiments: the third-party dictator game, and the delay discounting task. Our results show that hostility, and age are essential predictors of social decisions, whereas obsessive-compulsiveness and anxiety better predict temporal decisions. These variables behave as expected, but anxiety shows unexpected results: most anxious people act patiently and prefer delayed but bigger rewards. Finally, clinical factors are critical decision predictors for social and temporal decisions. When people are in remission, they tend to impose higher punishment on those who violate the social norm, and they also tend to prefer immediate rewards. When taking glucocorticoids, they also prefer immediate rewards, and as the dosage of glucocorticoids intake increases, they tend to impose higher punishment on norm violators. Clinicians, researchers, and practitioners must consider the side effects of glucocorticoids on decision-making.</span></p>
Long-term demographic dataset for Cladonia perforata, including fine-scale cover, occupancy, and subpopulation area data, 2011-2024
This dataset includes all data pertaining to a long-term demographic study of Cladonia perforata (perforate reindeer lichen), a federally endangered lichen endemic to Florida, including fine-scale cover, occupancy, and population area data, conducted by the Archbold Biological Station Plant Ecology Program. This includes 13 years of data (2011-2024) from nine subpopulation (including seven at Archbold Biological Station, and two at the Lake Wales Ridge Wildlife and Environmental Area, Royce Unit), all located in rosemary scrub habitat within the Lake Wales Ridge metapopulation. This study sought to characterize the fire ecology and long-term population trends for the species, and thus also includes data on prescribed burn severity and time since fire. Data were collected using a stratified random plot design, with occupancy plots (presence/absence within 1.5 meter radius) throughout the subpopulation and a subset of these designated as cover plots only, with this cover data collected as point intercept hits within a 48x48cm area. Cover data also includes microhabitat data – canopy cover in densiometer reading and dominant ground cover. Cover and occupancy data were taken every 3 years for each subpopulation (subpopulations were on different yearly schedules). Subpopulation area was mapped using a submeter GPS unit every 6 years. Subpopulations were resampled for all metrics as soon as possible following a fire, and the sampling schedule was then reset.
Demographic census data for four perennial plants under experimental pollination treatments
These demographic data were collected to measure the effects of manipulated pollination treatments on the population dynamics of four iteroparous perennial plant species: Delphinium nuttallianum (Ranunculaceae), Hydrophyllum fendleri (Boraginaceae [Hydrophyllaceae]), Erigeron speciosus (Asteraceae), and Potentilla pulcherrima (Roseaceae). The pollination treatments consisted of Control corresponding to ambient pollination, Reduced for which 50% of open flowers on each individual were enclosed in mesh to exclude pollinators, Supplemented for which all receptive flowers were hand pollinated with outcross pollen, and Variable for which individuals received the Reduced treatment in ca. 50% of years. The full life cycle was characterized from at least four annual demographic censuses of tagged plants between 2017-2022, germination rates estimated in seed addition plots, and soil seed bank survival estimated from buried seed bags.
Small Mammal Demographic Data at the Sevilleta National Wildlife Refuge, New Mexico
This file contains mark/recapture trapping data collected from 2013-present on permanently established small mammal trapping webs in the creosote-shrubland ecotone on the Sevilleta National Wildlife Refuge in central New Mexico. The two trapping webs are sampled for 3 consecutive nights once per month following the new moon. Each trapping web consists of 145 rebar stakes, 12 spokes originating from a central rebar point, each containing 12 rebars each. The first 4 stakes of the spoke are 5m apart, with the rest being 10m apart for a total of 100m per spoke (200m diameter). Demographic data is collected from each captured animal including age, sex, species, trap location, and reproductive status. Each animal is marked with a unique ear or radio frequency identification (RFID) tag and tissue samples (hair, whiskers, blood and fecal) are collected from each individual once per month. Demographic Findings: From 2013–2023, the program captured an average of 432 unique individuals per year across >10 species from two rodent families, Heteromyidae and Cricetidae. Heteromyids are the most abundant (~77% of captures), with Perognathus flavus (51%), Dipodomys merriami (11%), D. ordii (11%), and D. spectabilis (4%) dominating the community. Recapture rates for these species are >75%, generating longitudinal data on survival.
Demographic, seed ecology, and range wide survey datasets for Chrysopsis highlandsensis 1999-2022
Chrysopsis highlandsensis (Highlands Goldenaster; Asteraceae) is a state endangered herb found primarily within pyrogenic scrub communities in south-central Florida. These datasets span 24 yrs of demographic monitoring across ten populations, 7 seed ecology experiments, and a repeated range wide survey conducted every 5 yr from 2005-2020.
Demographic data from long-term symbiont removal experiments with grasses and Epichloë fungal endophytes
This project was designed to understand the demographic effects of vertically transmitted fungal endophytes (Epichloë spp.) on their grass hosts. The experiment includes seven host-symbiont taxonomic pairs: Agrostis perennans - E. amarillans, Elymus villosus - E. elymi, Elymus virginicus - E. elymi or EviTG-1, Festuca subverticillata - E. starrii, Poa alsodes - E. alsodes, Poa sylvestris - E. PsyTG-1, Schedonorus arundinaceus - E. coenophiala. Experimental plots were established at the Indiana University Lilly-Dickey Woods Research and Teaching Preserve in south-central Indiana, USA in 2007. For each species, 5-10 plots were planted with naturally symbiotic (S+) hosts, and 5-10 plots were plated with hosts that were disinfected of fungal endophytes by heat treatment (S-). Over 15 years (2007-2022) we collected demographic data on the survival, growth, reproduction, and recruitment of all plants in all plots. Beginning in 2018 we also collected data on the locations of all plants in every plot.
Hybrid gridded demographic data for the world, 1950-2020
<p>This is a hybrid gridded dataset of demographic data for the world, given as 5-year population bands at a 0.5 degree grid resolution.</p> <p>This dataset combines the NASA SEDAC Gridded Population of the World version 4 (GPWv4) with the ISIMIP Histsoc gridded population data and the United Nations World Population Program (WPP) demographic modelling data.</p> <p>Demographic fractions are given for the time period covered by the UN WPP model (1950-2050) while demographic totals are given for the time period covered by the combination of GPWv4 and Histsoc (1950-2020)</p> <p><strong>Method - demographic fractions</strong></p> <p>Demographic breakdown of country population by grid cell is calculated by combining the GPWv4 demographic data given for 2010 with the yearly country breakdowns from the UN WPP. This combines the spatial distribution of demographics from GPWv4 with the temporal trends from the UN WPP. This makes it possible to calculate exposure trends from 1980 to the present day.</p> <p>To combine the UN WPP demographics with the GPWv4 demographics, we calculate for each country the proportional change in fraction of demographic in each age band relative to 2010 as:</p> <p><span class="math-tex">\(\delta_{year,\ country,age}^{\text{wpp}} = f_{year,\ country,age}^{\text{wpp}}/f_{2010,country,age}^{\text{wpp}}\)</span></p> <p> </p> <p>Where:</p> <p>- <span class="math-tex">\(\delta_{year,\ country,age}^{\text{wpp}}\)</span> is the ratio of change in demographic for a given age and and country from the UN WPP dataset.</p> <p>- <span class="math-tex">\(f_{year,\ country,age}^{\text{wpp}}\)</span> is the fraction of population in the UN WPP dataset for a given age band, country, and year.</p> <p>- <span class="math-tex">\(f_{2010,country,age}^{\text{wpp}}\)</span> is the fraction of population in the UN WPP dataset for a given age band, country for the year 2020.</p> <p> </p> <p>The gridded demographic fraction is then calculated relative to the 2010 demographic data given by GPWv4.</p> <p>For each subset of cells corresponding to a given country <em>c</em>, the fraction of population in a given age band is calculated as:</p> <p><span class="math-tex">\(f_{year,c,age}^{\text{gpw}} = \delta_{year,\ country,age}^{\text{wpp}}*f_{2010,c,\text{age}}^{\text{gpw}}\)</span></p> <p>Where:</p> <p>- <span class="math-tex">\(f_{year,c,age}^{\text{gpw}}\)</span> is the fraction of the population in a given age band for given year, for the grid cell <em>c</em>.</p> <p>- <span class="math-tex">\(f_{2010,c,age}^{\text{gpw}}\)</span> is the fraction of the population in a given age band for 2010, for the grid cell <em>c</em>.</p> <p>The matching between grid cells and country codes is performed using the GPWv4 gridded country code lookup data and country name lookup table. The final dataset is assembled by combining the cells from all countries into a single gridded time series. This time series covers the whole period from 1950-2050, corresponding to the data available in the UN WPP model.</p> <p> </p> <p><strong>Method - demographic totals</strong></p> <p>Total population data from 1950 to 1999 is drawn from ISIMIP Histsoc, while data from 2000-2020 is drawn from GPWv4. These two gridded time series are simply joined at the cut-over date to give a single dataset covering 1950-2020.</p> <p>The total population per age band per cell is calculated by multiplying the population fractions by the population totals per grid cell.</p> <p>Note that as the total population data only covers until 2020, the time span covered by the demographic population totals data is 1950-2020 (not 1950-2050).</p> <p> </p> <p><strong>Disclaimer</strong></p> <p>This dataset is a hybrid of different datasets with independent methodologies. No guarantees are made about the spatial or temporal consistency across dataset boundaries. The dataset may contain outlier points (e.g single cells with demographic fractions >1). This dataset is produced on a 'best effort' basis and has been found to be broadly consistent with other approaches, but may contain inconsistencies which not been identified.</p> <p> </p>
PALEODEM/Late Glacial and Early Holocene human demographic responses to climatic and environmental change in Atlantic Iberia
<p>This data files and R markdown scripts have been used in the meta-analysis of chronological and subsistence patterns of Atlantic hunter-gatherer groups between Late Glacial and Early Holocene in Atlantic Iberia.</p> <p>They correspond to the following reference: </p> <p>McLaughlin, T.R., Gómez-Puche, M., Cascalheira, J., Bicho, N.F., Fernández-López de Pablo, J. 2020. Late Glacial and Early Holocene human demographic responses to climatic and environmental change in Atlantic Iberia. <em>Phil. Trans. R. Soc. B. </em>(revised submitted version 29/04/2020)</p> <p>We specify the content of each file further down:</p> <ol> <li>Analysis_markdown.Rmd – R markdown file with the scripts to reproduce the analyses.</li> <li>Analysis_markdown.pdf – R markdown file in pdf format to reproduce the analyses.</li> <li>database_references.docx –A separate text file that comprises the extended bibliographic references used as source of the archaeological radiocarbon archaeological and isotopic data sets analyzed.</li> <li>Datelist.csv – spreadsheet that contains the 371 radiocarbon dates used as raw data to run the scripts. The last column of the table includes the bibliographical reference of the archaeological data compiled.</li> <li>ngrip.csv – NGRIP GICC05 paleotemperature record based on oxygen isotope series from Rasmussen SO <em>et al.</em>2006 A new Greenland ice core chronology for the last glacial termination. <em>J. Geophys. Res. Atmos.</em><strong>111</strong>. (doi:10.1029/2005JD006079) and Andersen KK <em>et al.</em>2006 The Greenland Ice Core Chronology 2005, 15–42ka. Part 1: constructing the time scale. <em>Quat. Sci. Rev.</em>25, 3246–3257. </li> <li>Pailler_and_Bard_42.csv­­ – Sea surface temperature data of the Atlantic margin of Iberia based on the paper: Pailler D, Bard E. 2002 High frequency palaeoceanographic changes during the past 140 000 yr recorded by the organic matter in sediments of the Iberian Margin. <em>Palaeogeogr. Palaeoclimatol. Palaeoecol.</em>181, 431–452. (doi:https://doi.org/10.1016/S0031-0182(01)00444-8)</li> <li>Paleodiet.csv – spreadsheet containing the published palaeodietary isotopic information of the human remains considered in this study.</li> <li>src.r – source r code of custom functions called upon this analysis by the R.markdown files. </li> </ol> <p>To reproduce analyses reported in the McLaughlin et al Phil Trans paper, donwload R_scripts and csv_files into the same folder. Open the *.rmd scripts in RStudio (https://www.rstudio.com), and run the scripts. </p> <p>The csv files can also be imported into R and used by the scripts. </p> <p> </p> <p> </p> <p> </p>
MCMC chains for demographic fits presented in "NICMOS Kernel-Phase Interferometry II: Demographics of Nearby Brown Dwarfs"
<p>These files are the data behind the figure for Figure 3 (and the corresponding Figure Set) as well as other fits presented in Table 5. They are saved in <a href="https://numpy.org/doc/stable/reference/generated/numpy.lib.format.html">npy</a> format which can be read into python using numpy according to the code snippet below.</p> <p>The files are flattened and trimmed MCMC chains produced by running emcee (Foreman-Mackey et al. 2013) using 64 walkers for 10,000 steps. The first 1,000 steps were trimmed for burn in and the remaining chains were thinned by 40 steps.</p> <p>The files are named according to the following convention: flatSamples<malm cor><age><prior>.npy where:</p> <p><malm cor> is either 'Malm' or '' (nothing) if the model population was or was not corrected for Malmquist bias (before comparing to the observed population while fitting).</p> <p><age> is '0p9', '1p2', '1p5', '1p9', '2p4', or '3p1' according to that assumed field age (in Gyr).</p> <p><prior> is 'U' or 'I' for uninformed or informed (incorporating the information from Blake et al. 2010 on the unresolved population).</p> <p>The true underlying population corresponds to the flatSamplesMalm<age>I.npy files while the others are included for context and comparison to populations fit to the observed (not Malmquist corrected) population. The uninformed prior chains are dominated by a significant population of unresolved companions which is not consistent with previous RV studies.</p> <p>The files can be read into python using:</p> <pre><code class="language-python">import numpy as np flat_samples0p9I = np.load('flatSamples0p9I.npy') </code></pre> <p>which produces an array with shape 14400 x 4. The rows are the samples and the four columns are the parameters <span class="math-tex">\(F, \gamma, \overline{\log(\rho)}\)</span>, and <span class="math-tex">\(\sigma_{\log(\rho)}\)</span>, respectively.</p>
Neighborhood Socioeconomic and demographic changes in Baltimore's (BES) Neighborhoods: 1930 to 2010
This dataset was created primarily to map and track socioeconomic and demographic variables from the US Census Bureau from year 1940 to year 2010, by decade, within the City of Baltimore's Mayor's Office of Information Technology (MOIT) year 2010 neighborhood boundaries. The socioeconomic and demographic variables include the percent White, percent African American, percent owner occupied homes, percent vacant homes, the percentage of age 25 and older people with a high school education or greater, and the percentage of age 25 and older people with a college education or greater. Percent White and percent African American are also provided for year 1930. Each of the the year 2010 neighborhood boundaries were also attributed with the 1937 Home Owners' Loan Corporation (HOLC) definition of neighborhoods via spatial overlay. HOLC rated neighborhoods as A, B, C, D or Undefined. HOLC categorized the perceived safety and risk of mortgage refinance lending in metropolitan areas using a hierarchical grading scale of A, B, C, and D. A and B areas were considered the safest areas for federal investment due to their newer housing as well as higher earning and racially homogenous households. In contrast, C and D graded areas were viewed to be in a state of inevitable decline, depreciation, and decay, and thus risky for federal investment, due to their older housing stock and racial and ethnic composition. This policy was inherently a racist practice. Places were graded based on who lived there; poor areas with people of color were labeled as lower and less-than. HOLC's 1937 neighborhoods do not cover the entire extent of the year 2010 neighborhood boundaries. The neighborhood boundaries were also augmented to include which of the year 2017 Housing Market Typology (HMT) the 2010 neighborhoods fall within. Finally, the neighborhood boundaries were also augmented to include tree canopy and tree canopy change year 2007 to year 2015.
DEM01 Demographic studies of four forb species at Konza Prairie
Plant survival, growth, reproduction, and recruitment of 4 forb species (Amorpha canescens, Echinacea angustifolia, Aster oblongifolius, Kuhnia eupatorioides) were estimated annually within permanent transects in 20 watersheds, starting in 2020.
Potentilla demographic and environmental data for Rocky Mountains of Colorado (Niwot LTER & RMBL), 2018 - 2020.
To understand parent-hybrid dynamics in cinquefoil (Potentilla) species in the Colorado Rocky Mountains, I am estimating environmental overlap among parents and hybrids, interbreeding among parents and hybrids, and hybrid population growth in multiple natural populations at NWT and the Rocky Mountain Biological Laboratory (RMBL). This data was collected to test broad hypotheses about hybrid-parent dynamics in changing montane environments.
Skin-blubber biopsy samples and associated demographic data collected from cetaceans encountered along the Western Antarctic Peninsula (WAP), 2010 – 2024
Baleen whale populations in the Southern Ocean are recovering after intense commercial whaling in the 20th century. Along the Western Antarctic Peninsula (WAP), this recovery is occurring in one of the planet's most rapidly changing marine ecosystems. Understanding how climate-driven changes influence the population dynamics of whales in this region is critical for understanding what conservation and management actions must be prioritized to maintain the structure and function of this marine ecosystem. To begin understanding the dynamics of whale recovery under continued environmental change, we need to study these whales' demography and population dynamics. As part of our annual sampling surveys for cetaceans along the WAP through the PAL LTER program, we actively collect remote non-lethal skin-blubber biopsy samples and have developed one of the most extensive tissue archives in the Southern Ocean. With these samples, we conduct a series of demographic and physiological measurements. Using the skin portion of the biopsy sample, we isolate nuclear and mitochondrial DNA (mtDNA) to develop a DNA profile for each sample, including genetic sex, a microsatellite genotype, and a mtDNA haplotype. These profiles are used to compare sex ratios of the population, determine individual recaptures through genotype analysis, and better understand population mixing. Using the blubber portion of the biopsy sample, we isolate endocrine markers (e.g., progesterone and cortisol) to monitor population pregnancy rates and stress levels. This data represents some of the first non-lethal quantitative observations of the demography and population dynamics of recovering whale populations in the Antarctic and provides a critical reference point for future work as the Antarctic climate continues to change and populations continue to recover from whaling.
Demographics of high marsh consumers from Breder trap transect collections in tidal creeks associated with long term fertilization experiments, Rowley, MA
At PIE, mummichog (Fundulus heteroclitus) use the spring-cycle high tides to access the flooded high marsh platform and eat invertebrate prey, coupling the high marsh and aquatic creek food webs by gathering energy produced on the high marsh and making it available to the aquatic food web. Changes in the geomorphology of saltmarsh creek edges greatly influence the survival, biomass, and resource use of mummichog populations. Here, we capture animals using Breder traps to quantify the communities accessing the high marsh at night during one of these high tides in July 2018 across 3 PIE creeks known to present different geomorphologic patterns in their low marsh zones. These data can be used for the assessment of the impact of low marsh geomorphology on consumer communities in PIE marshes. Mummichog captured in these Breder traps were further analyzed for gut content (LTE-TIDE-BrederTrap-GutContents). These data were included in part of the study “Habitat decoupling via saltmarsh creek geomorphology alters connection between spatially-coupled food webs” (Lesser et al. 2020) and were a portion of an MBL REU project.
Investigating dynamics between energy use and socio-demographic characteristics in spatial modeling of residential energy consumption
<p>Files represent datasets (2017 Residential Building Stock Assessment and American Community Survey 2012-2017 5-year estimate) and R-code associated with the analysis. </p>
Resilient farm demographics withstand, adapt, or transform in the face of competitive pressure, technological change, and the expected lifestyles of future generations
<p>Farm demographics has been recognized as an important driver of structural change in European agriculture. Focus groups and computer simulations on farm demographic change were used to better understand its role for the case study regions of the Altmark in the eastern part of Germany and Flanders in the northern part of Belgium. According to these analyses, many potential agricultural entrants are deterred by what they view as a poor quality of life that farming offers. This applies to farm successors as well as hired workers. For higher attractiveness of agriculture, policy objectives should address the social image of farming as well as revitalize rural areas. Increasingly critical is the demand for skilled hired labour. However, policies dealing with farm demographic change ignore these needs and focus almost exclusively on farm succession. Particularly, the direct payment system, including additional support for small farms and young farmers, must be re-evaluated for its effectiveness. The analyses provide evidence that this system constrains European agricultural development more than assists it; ultimately preventing farms from adapting and transforming.</p>
Supplementary material for "Decomposing fecundity and evaluating demographic influence of multiple broods in a migratory bird"
<p>Data and code files used in the paper. The four data files are provided in ASCII format (marr_S.TXT, broods_S.TXT, marr_G.TXT, broods_G.TXT). The code file (script_IPM_wryneck_Tenan_etal.txt) is a space delineated text file. The code file is written for R, but models are run in NIMBLE from R. The code file also contains the description of the data files and code for data management.</p>
Data from "The first ALMA survey of protoplanetary discs at 3 mm: demographics of grain growth in the Lupus region"
<p>Table 1 and Table 2 from Tazzari et al., 2021, "The first ALMA survey of protoplanetary discs at 3 mm: demographics of grain growth in the Lupus region", Monthly Notices of the Royal Astronomical Society, arXiv:2010.02248</p> <p>Both tables are available in IPAC format, which is in human- and machine-readable:</p> <pre><code class="language-python">from astropy.io import ascii tb = ascii.read('Table1.txt', format='ipac')</code></pre> <p>Table comments (stored at the beginning of the ASCII file as lines starting with "/") can be read as:</p> <pre><code class="language-python">tb.meta['comments'] </code></pre> <p> </p>
Unique demographic history and population substructure among the Coorgs of Southern India
<p>Quality filtered GSA data of the individuals analysed in Mukhopadhyay et al., 2024 from Coorg, Karnataka, India.</p> <p> </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.