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.
133
datasets available to search
ShareScore release 0.9.0
Dataset results
133 results for “density estimation”
Point-Quarter Distance and Dimension Measurements to Calculate Shrub Density and Estimate Shrub ANPP in a Chihuahuan Desert Creosote Shrubland at the Sevilleta National Wildlife Refuge, New Mexico
In an effort to better quantify NPP of Creosotebush in the Five-Points region, it was decided to test the Point-Quarter method against the standard 1-m2 quadrat method that has been in use since 1998. Transects were laid out across the 5 mammal trapping webs as well as across burned and unburned plots of the Mixed Shrub site (MS). Repeated measures of the same bushes are performed seasonally. Whole shrubs of various size classes are collected, sorted, and weighed to develop regressions for biomass. Purpose: Data was collected initially to determine density and dimensions of creosote bush in the Five points area on core rodent webs and on burned and unburned plots following the 2003 prescribed burn. It was decided to expand the project by continuing measurements through time to quantify the change in shrub size and with simultaneous harvest of shrubs to measure NPP.
Supporting data for "Estimating animal density for a community of species using information obtained only from camera-traps"
<p>Data underlying a paper published in Methods in Ecology and Evolution (<a href="https://doi.org/10.1111/2041-210X.13930">https://doi.org/10.1111/2041-210X.13930</a>).</p> <p>These data are suitable for estimating animal density using the Random Encounter Model and include: i) detection counts for 35 species across 510 camera-trap locations; ii) movement speeds (estimated by tracking animal movements in camera-trap image sequences), iii) activity times (filtered so that records of the same species at the same location are > 60 minutes apart), and iv) measurements of the angular and radial distance from camera-traps for animals that were detected.</p>
End-condition for solution small angle X-ray scattering measurements by kernel density estimation
<p>The set of python scripts and some datasets for estimating the minimum X-ray exposure time for X-ray solution scattering experiments using statistical and mathematical approaches.</p> <p>We apply a statistical inequality to estimate the kernel density estimation (KDE) method’s error to determine the minimum X-ray exposure time.</p> <p>Please refer to the following article, </p> <p>End-condition for solution small angle X-ray scattering measurements by kernel density estimation<br> Science and Technology of Advanced Materials: Methods, Volume 2 Issue 1, pages 426-434 (2022)<br> DOI: 10.1080/27660400.2022.2140021<br> <a href="https://doi.org/10.1080/27660400.2022.2140021">https://doi.org/10.1080/27660400.2022.2140021</a></p>
Figure reproduction for "Accelerating small angle scattering experiments on anisotropic samples using kernel density estimation"
<p>These datasets and a Jupyter notebook reproduce figures in <a href="https://www.nature.com/articles/s41598-018-37345-5">a publication by Saito et al in Scientific Reports</a>. The notebook also serves as a demo for kernel density estimation (smoothing) of 2D data using Python. Details are described in the notebook. If you have no idea about ipynb format, please see HTML version with your web browser instead. It contains exactly the same codes and results as ipynb version.</p>
Data and code from: Evaluating spatially explicit density estimates of unmarked wildlife detected by remote cameras.
<p>Detection data from American black bears and code used in "Evaluating spatially explicit density estimates of unmarked wildlife detected by remote cameras" published in the Journal of Applied Ecology (Evans & Rittenhouse 2018). Unmarked detection data was collected using remote cameras in northwest Connecticut in 2014, and individual detection data was determined from unique genotypes obtained from non-invasive hair snares constructed at camera sampling locations.</p> <p>EN14.rds contains detection data as an R list:</p> <p>$y (num): J (sites) x K (occasions) matrix containing detection counts</p> <p>$X (int): 2 x J matrix of site coordinates</p> <p>$xlims (num): bounding x-coordinates</p> <p>$ylims (num): bounding y-coordinates</p> <p>$M (int): upper bound for super population of individuals</p> <p>$nTraps (int): number of sampling sites (J)</p> <p>$nReps (int): number of MCMC interations</p> <p>$forest (num): vector of site-specific covariates</p> <p>$mark (int): K x I matrix storing site numbers at which individual (i) was detected on occasion k</p> <p>FullModel.R provides functions used to fit constant density models to unmarked detections incorporating covariates of detection probability.</p> <p>partialID.R provides functions and code used to estimate density from mixtures of marked and unmarked detection data</p> <p>VariableDensity.R provides functions and code used to fit variable density models to unmarked detection data incorporating spatial covariates of density.</p> <p> </p>
Pest Sticky Traps: a dataset for Whitefly Pest Population Density Estimation in Chromotropic Sticky Traps
<p><strong>The dataset<br></strong></p> <p>The Pest Sticky Traps (PST) dataset is a collection of yellow chromotropic sticky trap pictures specifically designed for training/testing deep learning models to automatically count insects and estimate pest populations.</p> <p>Images were manually annotated by some experts of the Department of Agriculture, Food and Environment of the University of Pisa (Italy) by putting a dot over the centroids of each identified insect. Specifically, we labeled insects as belonging to the category “whitefly” considering two different species, i.e., the sweet potato whitefly (<em>Bemisia tabaci</em>) (Gennadius) and the greenhouse whitefly (<em>Trialeurodes vaporariorum</em>) (Westwood).</p> <p>The dataset comprises two subsets:<br>- a subset we suggest using for the training/validation phases (contained in the `train/` folder)<br>- a subset we suggest using for the test phase (contained in the `test/` folder)</p> <p>Annotations of the two subsets are contained in `train/annotations.csv` and `test/annotations.csv`, respectively. They have the following columns:<br>- *imageName* - filename of the image containing the whiteflies,<br>- *X,Y* - 2D coordinates of the whitefly in the image space,<br>- *class* - class index of the insect (always 0 in this dataset).</p> <p> </p> <p><strong>Citing our work</strong></p> <p>If you found this dataset useful, please cite the following paper</p> <blockquote> <pre>@inproceedings{CIAMPI2023102384,<br> title = {A deep learning-based pipeline for whitefly pest abundance estimation on chromotropic sticky traps},<br> journal = {Ecological Informatics},<br> volume = {78},<br> pages = {102384},<br> year = {2023},<br> issn = {1574-9541}, doi = {10.1016/j.ecoinf.2023.102384}, url = {https://www.sciencedirect.com/science/article/pii/S1574954123004132}, year = 2023, author = {Luca Ciampi and Valeria Zeni and Luca Incrocci and Angelo Canale and Giovanni Benelli and Fabrizio Falchi and Giuseppe Amato and Stefano Chessa}, } </pre> </blockquote> <p>and this Zenodo Dataset</p> <blockquote> <pre>@dataset{ciampi_2023_7801239, author = {Luca Ciampi and Valeria Zeni and Luca Incrocci and Angelo Canale and Giovanni Benelli and Fabrizio Falchi and Giuseppe Amato and Stefano Chessa}, title = {Pest Sticky Traps: a dataset for Whitefly Pest Population Density Estimation in Chromotropic Sticky Traps}}, month = apr, year = 2023, publisher = {Zenodo}, version = {1.0.0}, doi = {10.5281/zenodo.7801239}, url = {<a href="https://doi.org/10.5281/zenodo.7801239">https://doi.org/10.5281/zenodo.6560823</a>} } </pre> </blockquote> <p> </p> <p><strong>Contact Information</strong></p> <p>If you would like further information about the dataset or if you experience any issues downloading files, please contact us at <a href="mailto:mobdrone@isti.cnr.it">luca.ciampi@isti.cnr.it</a></p> <p> </p> <p> </p>
Dalton and Nenana study site data including: invasive plant density estimates, invasive plant density, soil data, seedling estimates for dominant tree species and ground cover estimates for sites
This dataset contains invasive plant and stand level data for study sites along the Dalton and Parks highways in interior Alaska in the summer of 2012. Study sites were situated in burned and mature black spruce forests to compare invasive plant colonization patterns. Invasive plant density estimates along the road adjacent to each site are included, as well as invasive plant density within study sites. Other data includes ground cover estimates for dominant ground cover types, estimates of seedling abundance for dominant tree species, soil paramters (mineral soil pH and mineral soil moisture, residual organic layer/ organic layer depths, and active layer depths).
Point count surveys in order to gain density estimates of birds
Recent evidence suggests that the southern Appalachian Mountains are experiencing increasing spatial and temporal variability in hydroclimate. Specifically, the region is experiencing both increasing frequency of extreme precipitation and a reduced frequency of more moderate precipitation. Moreover, heterogeneity in precipitation among subwatersheds has resulted in neighboring watersheds experiencing large differences in precipitation levels over the past 30 years. This extreme spatial variability creates an opportunity to examine how differences in precipitation relate to the abundances of regional flora or fauna. Breeding bird abundance was measured on areas of intact forest in Macon County, North Carolina, USA that have varied in precipitation over the past 30 years. I hypothesized that long term changes in moisture regimes would cause terrestrial vertebrates to persist in an area where their species specific environmental requirements were being met. Focal passerines of this study have variable life history traits and therefore I hypothesized that vegetation that promotes their respective habitat would be a main factor determining their abundance; furthermore, vegetation is proximally associated with precipitation, therefore some measurable differences among sites may exist. All dry sites in this study have higher estimated avian densities, Black-throated Blue Warblers were found exclusively on dry sites and Black-throated Green Warblers were found almost exclusively on wet sites. These results suggest that a correlation exist between microclimate and avifauna surveyed in this study.
Interim model output of estimated bare peat/erosion density in Aberdeenshire and Angus at 1m resolution.
<p>This dataset contains the estimated unit density of peat erosion accross Aberdeenshire and Angus at 1m resolution. These outputs are derived from high resolution aerial imagery using the PeatNet pipeline for predicting the location and extent of peatland degradation features. This output was created to estimate the particulate organic carbon (POC) emissions from eroding peat based on multiple scenarios.</p>
Estimating wolf density from cameras
<p>DNA recapture data used to estimate wolf density in three study areas in Idaho, USA 2016-2018. </p>
Improving estimates of the ionosphere during geomagnetic storm conditions through assimilation of thermospheric mass density
<p>Swarm A/B/C neutral mass density normalized to 400 km to be assimilated by the CTIPe physics based model coupled with the thermosphere ionosphere data assimilation scheme (TIDA). Swarm-A is assimilated and B/C are used for validation purposes. The selected period is March 2015 that contains the St. Patrick's Day storm 2015 between 16-18 of that month.</p>
On the estimation of landslide intensity, hazard and density via data-driven models.
<p>The geographic prediction of landslide occurrence is undertaken by assessing whether a slope may be stable or unstable. In other words, current practices treat slopes where a single landslide occurred in the same way as slopes where many landslides occurred. At the slope scale, this procedure inevitably underestimates the effect of multiple landslides.<br> Here we model the number of landslides per slope instead. Then, thanks to the close relation that the number of failures shows with respect to landslide size, we convert the estimated number of landslides into estimated landslide areas. Ultimately, we also estimate the expected proportion of a slope affected by landslides. This framework is more informative than the stable/unstable paradigm and may help landslide risk mitigation strategies.</p>
A comparison of density estimation methods for monitoring marked and unmarked animal populations
<p>These data were generated to compare different methods of estimating population density from marked and unmarked animal populations. We compare conventional live trapping with two more modern, non-invasive field methods of population estimation: genetic fingerprinting from hair-tube sampling and camera trapping for the European pine marten (Martes martes). We used arrays of camera traps, live traps, and hair tubes to collect the relevant data in the Ring of Gullion in Northern Ireland. We apply marked spatial capture-recapture models to the genetic and live trapping data where individuals were identifiable, and unmarked spatial capture-recapture (uSCR), distance sampling (CT-DS), and random encounter models (REM) to the camera trap data where individual ID was not possible. All five approaches produced plausible and relatively consistent point estimates (0.41 – 0.99 animals per km<sup>2</sup>), despite differences in precision, cost, and effort being apparent.</p> <p>In addition to the data, we provide novel code for running unmarked spatial capture-recapture (uSCR) and random encounter models (REM) to the camera trap data where individual ID was not possible. </p>
Data belonging to the article: Estimating pre-harvest density, adult sex ratio and fecundity of white-tailed deer using wildlife cameras
<p>Adult sex ratio and fecundity (juveniles per female) are key population parameters in sustainable wildlife management, but inferring these requires abundance estimates of at least three age/sex classes of the population (male and female adults and juveniles). Prior to harvest, we used an array of 36 wildlife camera traps during 2 and 3 weeks in the early autumn of 2016 and 2017 respectively. We recorded white-tailed deer adult males, adult females and fawns from the pictures. Simultaneously, we collected fecal DNA (fDNA) from 92 20mx20m plots placed in 23 clusters of four plots between the camera traps. We identified individuals from fDNA samples with microsatellite markers and estimated the total sex ratio and population density using Spatial Capture Recapture (SCR). The fDNA-SCR analysis concluded equal sex ratio in the first year and female bias in the second year, and no difference in space use between sexes (fawns and adults combined). Camera information was analyzed in a Spatial Capture (SC) framework assuming an informative prior for animals' space use, either (1) as estimated by fDNA-SCR (same for all age/sex classes), (2) as assumed from the literature (space use of adult males larger than adult females and fawns), (3) by inferring adult male space use from individually-identified males from the camera pictures. These various SC approaches produced plausible inferences on fecundity, but also inferred total density to be lower than the estimate provided by fDNA-SCR in one of the study years. SC approaches where adult male and female were allowed to differ in their space use suggested the population had a female-biased adult sex ratio. In conclusion, SC approaches allowed estimating the pre-harvest population parameters of interest and provided conservative density estimates.</p>
Рис. 6. Графики Зависимости оценок варианс (S2) от средней плотности (D) популЯций наЗемных моллюсков B. cylindrica (А) и M. cartusiana (В): 1 – участок № 1, 2010 г.; 2 – участок № 2, 2011 г.; 3 – участок № 4, 2012 г.; 4 – участок № 5, 2012 г. Fig. 6. Variance estimation (S2) and average density (D) of the land snail B. cylindrica (А) and M. cartusiana (В) population scatter plots: 1 – site 1, 2010; 2 – site 2, 2011; 3 – site 4, 2012; 4 – site 5, 2012. in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 6. Графики Зависимости оценок варианс (S2) от средней плотности (D) популЯций наЗемных моллюсков B. cylindrica (А) и M. cartusiana (В): 1 – участок № 1, 2010 г.; 2 – участок № 2, 2011 г.; 3 – участок № 4, 2012 г.; 4 – участок № 5, 2012 г. Fig. 6. Variance estimation (S2) and average density (D) of the land snail B. cylindrica (А) and M. cartusiana (В) population scatter plots: 1 – site 1, 2010; 2 – site 2, 2011; 3 – site 4, 2012; 4 – site 5, 2012.
Fig. 2 in Assessment Of Census Techniques For Estimating Density And Biomass Of Gibbons (Primates: Hylobatidae)
Fig. 2. Cumulative number of groups of Müller's gibbon, Hylobates muelleri, as observed during range mapping in Kayan Mentarang National Park (KMNP) and Sungai Wain protection forest (SWPF), East Kalimantan, Indonesia.
Fig. 1 in Assessment Of Census Techniques For Estimating Density And Biomass Of Gibbons (Primates: Hylobatidae)
Fig. 1. The island of Borneo showing the location of the two study areas. Grey shading indicates the area in which range mapping of all gibbon groups was executed, the straight lines indicate the transects, and the triangles indicate the listening positions from which the fixed point counts were made.
Forest density estimates in the vicinity of the Ilirney lake system region, Chukotka, Russia
<p>Forest density estimates visually determined by describing the amount of present trees satellite imagery from Esri basemap (Esri) at an area of ~30x30 m qualitatively for 6515 stratified sampled locations at an equal number of locations based on elevation, aspect and slope angle. The density was categorized ranging from 1: single trees to 4: dense tree stands present, and contains a 0: no trees present.</p> <p>Format: ESRI shapefile, points; projection UTM58N; extent: 642085.1, 654775.1, 7462263, 7492833 m (xmin, xmax, ymin, ymax)</p>
DTM files from wildlife–vehicle collisions using kernel density estimation (KDE)
<p>21 CSV files that contain the Digital Terrain Model (DTM) from wildlife–vehicle collisions (WVC) hotspots using kernel density estimation (KDE) in Spain between 2016 and 2021. Data source of each WVC record is the Spanish General Directorate of Traffic (DGT).</p> <p>The context is the Final Master's Degree Project 'Analysis and Predictive Modelling of Wildlife–Vehicle Collision on Interurban Roads in Spain' (Data Science Master’s Degree of Universitat Oberta de Catalunya - UOC).</p> <p>This dataset is the output of the KDE analysis and the <a href="https://github.com/alba620/analisis-prediccion-accidentes-trafico-animales">code repository</a> is available on GitHub.</p>
Counting animals in aerial images with a density map estimation model
<p>Animal abundance estimation is increasingly based on drone or aerial survey photography. Manual post-processing has been used extensively, however, volumes of such data are increasing, necessitating some level of automation, either for complete counting or as a labour-saving tool. Any automated processing can be challenging when using such tools on species that nest in close formation such as <em>Pygoscelis</em> penguins. We present here a customized CNN-based density map estimation method for counting of penguins from low-resolution aerial photography. Our model, an indirect regression algorithm, performed significantly better in terms of counting accuracy than standard detection algorithm (Faster RCNN) when counting small objects from low-resolution images and gave an error rate of only 0.8 percent. Density map estimation methods as demonstrated here can vastly improve our ability to count animals in tight aggregations, and demonstrably improve monitoring efforts from aerial imagery. </p>
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.