Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
676
datasets available to search
ShareScore release 0.7.1
Dataset results
676 results for “population density”
Effects of population density on static allometry between horn length and body mass in mountain ungulates
<p class="MsoNoSpacing">Little is known about the effects of environmental variation on allometric relationships of condition-dependent traits, especially in wild populations. We estimated sex-specific static allometry between horn length and body mass in four populations of mountain ungulates that experienced periods of contrasting density over the course of the study. These species displayed contrasting sexual dimorphism in horn size; high dimorphism in <i>Capra ibex</i> and <i>Ovis canadensis</i> and low dimorphism in <i>Rupicapra rupicapra</i> and <i>Oreamnos americanus</i>. The effects of density on static allometric slopes were weak and inconsistent while allometric intercepts were generally lower at high density, especially in males from species with high sexual dimorphism in horn length. These results confirm that static allometric slopes are more canalized than allometric intercepts against environmental variation induced by changes in population density, particularly when traits appear more costly to produce and maintain.</p>
Agricultural grasslands buffer density effects in red deer populations
<p>Data for</p> <p>Agricultural grasslands buffer density effects in red deer populations</p> <p>Initially accepted in Journal of Wildlife Management</p>
Supplementary material for Metabolic constraints on the body size scaling of extreme population densities
<p>Data for the analysis of the paper entitled</p> <p>Metabolic constraints on the body size scaling of extreme population densities. Ecology letters.</p> <p><a href="https://zenodo.org/api/files/a60c335d-e6ca-4bd9-a17f-fc98ca9703e2/air_sea_outputs_L4_nsw.txt?versionId=1416dceb-47d0-45b1-99a8-baa833df8f76">air_sea_outputs_L4_nsw.txt </a></p> <p>This data set contains 8693 rows and 13 variables</p> <p>From colum 1 to 13 the following names apply:</p> <p>Julian- Day of the year (1rst January =1)</p> <p>day- Day number</p> <p>month- Month number</p> <p>year- Year number</p> <p>lat- Latitude</p> <p>lon- Longitude</p> <p>wind_speed_m_s- 1m wind speed in meters per second </p> <p>presure_mB- Air pressure in milibars</p> <p>Temp_°C- Air temperature in degrees Celsius </p> <p>Temp_algo- Other temperature</p> <p>Cloud_cover%- Percentage of cloud cover</p> <p>SST_°C- sea surface temperature in degree Celsius</p> <p>SW_Radiation_W_m2- Short wave radiation in Watts per square meter.</p> <p><a href="https://zenodo.org/api/files/a60c335d-e6ca-4bd9-a17f-fc98ca9703e2/SurfaceTemperature_L4_Merged.csv?versionId=9738e9d6-53ed-49c4-ab0d-136b74ddb16e">SurfaceTemperature_L4_Merged.csv </a></p> <p>This data set has 1064 rows and two columns</p> <p>Fechas_tempAll is the date of measurement formated as (Year-Month-Day Hour)</p> <p>e.g. 2013-05-20 09:00:00</p> <p>TEMP_EC- Surface water Temperature in degree Celsius</p> <p><a href="https://zenodo.org/record/7374166/files/Nutrients.csv?download=1">Nutrients.csv</a></p> <p>The data set sas 445 rows and 8 Variables</p> <p>Sample.Date- Date when surface water sample was taken.</p> <p>NITRITE.µM, - Nitrite (NO2) concentration in micromolar</p> <p>NITRATE.NITRITE.µM"- Nitrate (NO3) concentration in micromolar</p> <p>AMMONIA.µM, - Ammonia (NH4) concentration in micromolar</p> <p>SILICATE.µM, - Silica (Si) concentration in micromolar</p> <p>PHOSPHATE.µM- - Posphate (PO4) concentration in micromolar<br> FLAG- Quality flag (TRUE/FALSE).</p> <p> </p> <p> </p>
Sentinel2 RGB chips over Colombia (NE) with JRC GHSL Population Density 2015 for Learning with Label Proportions
<p><strong>Region of Interest (ROI) is comprised of the east - northeast region of Colombia covering<br> parts of Santander, Norte de Santander, Boyacá, Bolívar, Antioquia and Cundinamarca.</strong></p> <p>We use the communes administrative division defined by DANE (Departamento Administrativo<br> Nacional de Estadística) under "municipios" in the MGN2021 at <br> <a href="https://geoportal.dane.gov.co/geovisores/territorio/mgn-marco-geoestadistico-nacional/">https://geoportal.dane.gov.co/geovisores/territorio/mgn-marco-geoestadistico-nacional/</a></p> <p><strong>images: Sentinel2 RGB from 2020-01-01 to 2020-31-12</strong><br> filtered out pixels with clouds during the observation period according to QA60 band following the example<br> given in GEE dataset info page, and took the median of the resulting pixels</p> <p> see <a href="https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED">https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED</a></p> <p> see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py</a></p> <p><strong>labels: Global Human Settlement Layers, Population Grid 2015</strong></p> <p> labels range from 0 to 31, with the following meaning:<br> label value original value in GEE dataset<br> 0 0<br> 1 1-10<br> 2 11-20<br> 3 21-30<br> ...<br> 31 >=291 </p> <p> see <a href="https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2016_POP_GPW_GLOBE_V1">https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2016_POP_GPW_GLOBE_V1</a></p> <p> see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/humanpop2015.py</a></p> <pre><code>_aschips.geojson the image chips geometries along with label proportions for easy visualization with QGIS, GeoPandas, etc. _communes.geojson the communes geometries with their label prortions for easy visualization with QGIS, GeoPandas, etc. splits.csv contains two splits of image chips in train, test, val - with geographical bands at 45° angles in nw-se direction - the same as above reorganized to that all chips within the same commune fall within the same split. data/ a pickle file for each image chip containing a dict with - the 100x100 RGB sentinel 2 chip image - the 100x100 chip level lavels - the label proportions of the chip - the aggregated label proportions of the commune the chip belongs to </code></pre> <p> </p>
Sentinel2 RGB chips over BENELUX with JRC GHSL Population Density 2015 for Learning with Label Proportions
<p>Region of Interest (ROI) is comprised of the Belgium, the Netherlands and Luxembourg</p> <p>We use the communes adminitrative division which is standardized across Europe by EUROSTAT at:<br> <a href="https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units">https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units</a><br> This is roughly equivalent to the notion municipalities in most countries.</p> <p>From the link above, communes definition are taken from COMM_RG_01M_2016_4326.shp and country borders<br> are taken from NUTS_RG_01M_2021_3035.shp.</p> <p><strong>images: Sentinel2 RGB from 2020-01-01 to 2020-31-12</strong><br> filtered out pixels with clouds acoording to QA60 band following the example<br> given in GEE dataset info page at:<br> see <a href="https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED">https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED</a></p> <p> see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py</a></p> <p><strong>labels: Global Human Settlement Layers, Population Grid 2015</strong></p> <p> labels range from 0 to 31, with the following meaning:<br> label value original value in GEE dataset<br> 0 0<br> 1 1-10<br> 2 11-20<br> 3 21-30<br> ...<br> 31 >=291 </p> <p> see <a href="https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2016_POP_GPW_GLOBE_V1">https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2016_POP_GPW_GLOBE_V1</a></p> <p> see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/humanpop2015.py</a><br> </p> <pre><code>_aschips.geojson the image chips geometries along with label proportions for easy visualization with QGIS, GeoPandas, etc. _communes.geojson the communes geometries with their label prortions for easy visualization with QGIS, GeoPandas, etc. splits.csv contains two splits of image chips in train, test, val - with geographical bands at 45° angles in nw-se direction - the same as above reorganized to that all chips within the same commune fall within the same split. data/ a pickle file for each image chip containing a dict with - the 100x100 RGB sentinel 2 chip image - the 100x100 chip level lavels - the label proportions of the chip - the aggregated label proportions of the commune the chip belongs to </code></pre>
Distribution, population density and behavior of dwarf galagos (Paragalago sp.) in Taita Hills, Kenya
<p>We studied habitat preferences and behavior of dwarf galagos (<em>Paragalago </em>sp.), recently rediscovered from the Taita Hills, Kenya. Small populations of Taita dwarf galagos survive in the two largest remnants of moist montane forest. Inspection of several smaller forest fragments failed to provide evidence of additional survivors. Acoustic data on the two remaining populations were obtained with AudioMoths, and analyzed in relation to forest structure data obtained by airborne lidar and by ground-level observations. A Zero-inflated negative binomial GLMM was implemented with calls per hour as the response variable and indicator of relative population density. Our results demonstrate that Taita dwarf galagos prefer dense canopy coverage and avoid forest edges. Regarding forest height, they prefer lower 20–30 m tall forest. Forest size also significantly affects Taita dwarf galago population size. Mbololo forest (185 ha) has a relatively viable population, whereas in Ngangao forest (120 ha) dwarf galagos are nearly extinct. The calls of Taita dwarf galagos resemble calls of Kenya coast dwarf galagos (<em>Paragalago cocos</em>). However, some differences exist between the Taita animals and those recently recorded by us at the Kenyan coast, and even between the two remaining populations in the Taita Hills. In addition to other data, we present the first ever photographs of the Taita dwarf galagos from the Mbololo forest and compare them to those from Ngangao forest and the Kenya coast from Diani and Shimba Hills. We conclude that DNA studies are urgently needed to resolve the taxonomic status of both surviving populations of dwarf galagos in the Taita Hills.</p>
Data for 'Population density affects sexual selection in an insect model'
<p>Data set (.csv file), analysis code (.R file) and readme (.txt file giving details for dataset and code) accompanying the publication 'Population density affects sexual selection in an insect model' (Winkler L, Eilhardt R, Janicke T, 2023).</p>
Effects of local density dependence and temperature on the spatial synchrony of marine fish populations
<ol> <li><span>Disentangling empirically the many processes affecting spatial population synchrony is a challenge in population ecology. Two processes that could have major effects on the spatial synchrony of wild population dynamics are density dependence and variation in environmental conditions like temperature. Understanding these effects is crucial for predicting the effects of climate change on local and regional population dynamics.</span></li> <li><span>We quantified the direct contribution of local temperature and density dependence to spatial synchrony in the population dynamics of nine fish species inhabiting the Barents Sea. First, we estimated the degree to which the annual spatial autocorrelations in density are influenced by temperature. Second, we estimated and mapped the local effects of temperature and strength of density dependence on annual changes in density. Finally, we measured the relative effects of temperature and density dependence on the spatial synchrony in changes in density. </span></li> <li><span>Temperature influenced the annual spatial autocorrelation in density more in species with greater affinities to the benthos and to warmer waters. Temperature correlated positively with changes in density in the eastern Barents Sea for most species. Temperature had a weak synchronising effect on density dynamics, while increasing strength of density dependence consistently desynchronised the dynamics. </span></li> <li><span>Quantifying the relative effects of different processes affecting population synchrony is important to better predict how population dynamics might change when environmental conditions change. Here, high degrees of spatial synchrony in the population dynamics remained unexplained by local temperature and density dependence, confirming the presence of additional synchronizing drivers, such as trophic interactions or harvesting. </span></li> </ol>
Asymmetric density-dependent competition does not contribute to the maintenance of sex in a mixed population of sexual and asexual Potamopyrgus antipodarum
Open the record for dataset details and reuse information.
Data from: Ecological tradeoffs drive a power-law relationship between group size and population density in social foragers
Open the record for dataset details and reuse information.
Data from: Effects of age, breeding strategy, population density, and number of neighbors on territory size and shape in Savannah Sparrows
Open the record for dataset details and reuse information.
Varying genetic imprints of roads and human density in North American mammal populations
Open the record for dataset details and reuse information.
Data and R code from: Relics of beavers past: time and population density drive scale-dependent patterns of ecosystem engineering
Open the record for dataset details and reuse information.
Effects of population density on static allometry between horn length and body mass in mountain ungulates
Open the record for dataset details and reuse information.
French Guianan mammal and bird population densities with spatial-capture recapture, line transect distance sampling, and 'unmarked' density models
Open the record for dataset details and reuse information.
Effects of local density dependence and temperature on the spatial synchrony of marine fish populations
Open the record for dataset details and reuse information.
Data from: One-stage spatial mark-resight analysis reveals an increasing grizzly bear population with declining density near roads
Open the record for dataset details and reuse information.
Evolution under pH stress and high population densities leads to increased density-dependent fitness in the protist Tetrahymena thermophila
Open the record for dataset details and reuse information.
Population density and timing of breeding mediate effects of early life conditions on recruitment
Open the record for dataset details and reuse information.
Data from: An open spatial capture–recapture model for estimating density, movement, and population dynamics from line-transect surveys
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.