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.
419
datasets available to search
ShareScore release 0.9.0
Dataset results
419 results for “airborn”
RGB and Thermal Integral Image dataset for Search and Rescue with Airborne Optical Sectioning.
<p>The `Integral Images` folder contains labels and augmented AOS integral images (both RGB and Thermal) used for training, validation and testing (`data`).</p> <p>The integral images are computed using the complete data that were recorded during 18 flights at 6 different sites over 10 different days.</p> <p> </p> <p>The dataset mirrors [YOLO (8GB)](https://zenodo.org/record/3894774/files/YOLO.zip?download=1) (`data`) for integral (`SARAOS/AOS`) images, however, now additionally contain corresponding RGB integral images in addition to corresponding thermal integral images.</p>
Supplementary material for: AIRBORNE LASER SCANNING CHANGE DETECTION FOR QUANTIFYING GEOMORPHOLOGICAL PROCESSES IN HIGH MOUNTAIN REGIONS
<p>Supplementary material for: TCII/10-AIRBORNE LASER SCANNING CHANGE DETECTION FOR QUANTIFYING GEOMORPHOLOGICAL PROCESSES IN HIGH MOUNTAIN REGIONS</p> <p>For submission of the full paper to: International Society for Photogrammetry and Remote Sensing (ISPRS) / <strong>XXIV ISPRS Congress 2022 Nice, France, 6 - 11 June 2022</strong>.</p> <p><strong>ADDITIONAL FIGURES AND MAPS</strong><br> Supplementary materials:<br> Figure S1: Classification preparation and decision tree.<br> Figure S2: 3D distance change and change offset overall map.<br> Figure S3: Geomorphological map inventoried by manual mapping.<br> Figure S4: Classified real changes of the valley area between 2006 and 2017.<br> Figure S5: Classified real changes between 2006 and 2017.</p> <p> </p> <p> </p> <p> </p>
Greenland mass trends from airborne and satellite altimetry during 2011–2020
<p><span>We use satellite and airborne altimetry to estimate </span><span>annual</span><span> mass changes of the Greenland Ice Sheet. We estimate ice loss corresponding to a sea-level rise of 6.9±0.4 millimeters from April 2011 to April 2020, with the highest annual </span><span>ice loss rate of 1.4 mm/yr </span><span>sea-level equivalent</span><span> from </span><span>April 2019 to April 2020</span><span>. On a regional scale, our annual mass loss timeseries reveals 10-15 m/yr dynamic thickening at the terminus of Jakobshavn Isbræ from April 2016 to April 2018, followed by a return to dynamic </span><span>thinning. </span><span>We observe contrasting patterns of mass loss acceleration in different basins across the ice sheet. Our gridded satellite altimetry data and surface mass balance (SMB), along with corrections due to firn compaction are available for download. Here, we provide:</span></p> <p><span>(1) Annual (April to April) elevation change rates of the Greenland Ice Sheet from April 2011 to April 2020 from CryoSat-2, ICESat-2 and NASA's ATM flights. 1x1 km grid.</span></p> <p><span>(2) Annual (April to April) elevation change rates due to SMB anomalies. 1x1 km grid.</span></p> <p><span>(3) Ice-sheet wide annual corrections due to firn compaction.</span></p>
Dataset- Barking up the right tree: Using tree bark to track airborne particles in school environment and link science to society
<p>Experimental data regarding magnetic properties measured in bio-sensors and air filters.</p> <p>Bio-sensors include tree bark samples, either collected in situ (courtyard tree bark) and exposed indoors-outdoors (bio-sensors) in the schools. Bio-sensors had their magnetic properties measured.</p> <p> PM1, EC and OC concentrations and SIRM measured on air filters.</p>
Airborne spectral reflectance dataset of submerged plastic targets in a coastal environment (SUPPLEMENTARY DATA FILES)
<p>SUPPLEMENTARY DATA FILES for the paper entitled: "Airborne spectral reflectance dataset of submerged plastic targets in a coastal environment." Data are organized in three categories:</p> <p>- orthophoto maps (date in YYYY,MM,DD e.g. 20210615_SPOTS_Ortho_UTM_35N)</p> <p>- Hyper Cubes UTM_35N NO Normalized (date in YYYY,MM,DD_Flight number_Height_UTM zone e.g. 20210615_F47_H25_UTM_35N)</p> <p>Hyper Cubes UTM_35N Normalized (date in YYYY,MM,DD_Flight number_Height_UTM zone_NORM e.g. 20210615_F47_H25_UTM_35N_NORM)</p>
Methane plumes from airborne surveys
<p>Plume_attribution_july2020_aug2021 -> plume list with source IDs, source attribution from July 2020 to Aug 2021</p> <p>Plume_attribution_sep2021_dec2021 -> plume list with source IDs, source attribution from September 2020 to December 2021</p> <p>Plumes_jan2022_may2022 -> plume list with source IDs for Jan-May 2022 </p> <p>Source_List_2019_2021 -> source list with source IDs that match the 2020-2021 data</p> <p>Source_jan2022_may2022 -> source IDs that match the 2022 data</p>
Supplementary material related to the thesis "Perception of airborne sounds and vibrations in crocodiles"
<p>This repository contains all datasets, statistical codes and videos examples for the 4 different studies conducted during my thesis. </p>
On modelling airborne infection risk
<div> <div> <div> <p>Airborne infection risk analysis is usually performed for enclosed spaces where susceptible indi- viduals are exposed to infectious airborne respiratory droplets by inhalation. It is usually based on exponential, dose-response models of which a widely used variant is the Wells-Riley (WR) model. We revisit this infection-risk estimate and extend it to the population level. We use an epidemiolog- ical model where the mode of pathogen transmission, airborne or contact, is explicitly considered. We illustrate the link between epidemiological models and the WR and the Gammaitoni and Nucci models. We argue that airborne infection quanta are, up to an overall density, airborne infectious respiratory droplets modified by a parameter that depends on biological properties of the pathogen, physical properties of the droplet, and behavioural parameters of the individual. We calculate the time-dependent risk to be infected for two scenarios. We show how the epidemic infection risk de- pends on the viral latent period and the event time, the time infection occurs. Infection risk follows the dynamics of the infected population. As the latency period decreases, infection risk increases. The longer a susceptible is present in the epidemic, the higher its risk of infection for equal exposure time to the mode of transmission is.</p> </div> </div> </div>
Supporting data for: Ozone Formation Sensitivity to Precursors and Lightning in the Tropical Troposphere Based on Airborne Observations
<p>We provide here the supporting data for our study on ozone formation sensitivity in the upper tropical troposphere based on in situ observations. </p>
Dataset: Direct insertion of NASA Airborne Snow Observatory-derived snow depth time-series into the iSnobal energy balance snow model
<p>This dataset is a companion to the submitted WRR publication entitled ‘Direct insertion of NASA Airborne Snow Observatory-derived snow depth time-series into the iSnobal energy balance snow model’. The file structure is organized as follows:</p> <ul> <li><strong>ASO_50m_depth_surfaces</strong> - This folder contains the Airborne Snow Observatory lidar-derived snow depth products aggregated to 50m gridded spatial resolution. Each file is titled with a date such as ‘TB<em>YYYYMMDD</em>_SUPERsnow_depth.asc’. The coordinates are in UTM zone 11N and use the WGS84 coordinate system.</li> <li><strong>static_grids</strong> <ul> <li>Static grids are used in each of the subsequent folders and are not changed between years.</li> <li>init0000.ipw <ul> <li>Initialization file to begin the model run. Contains the digital elevation model in band 1, surface roughness raster in band 2, and zeroed images of snow properties in bands 3-7.</li> </ul> </li> <li>maxus.nc <ul> <li>netCDF file of 72 separate images of maximum upwind slope for all upwind directions from 0 (north) to 355 degrees in 5-degree increments. Derived using Adam Winstral’s Sx algorithm.</li> </ul> </li> <li>tuolx_dem_50m.ipw <ul> <li>Digital elevation model from ASO snow-free acquisition aggregated to 50m gridded spatial resolution. Same information as band 1 in the init0000.ipw file.</li> </ul> </li> <li>tuolx_hetchy_mask_50m.ipw <ul> <li>Basin mask of the Tuolumne River Basin above Hetch Hetchy Reservoir. Out-of-basin cells denoted as 0, and in-basin cells denoted as 1.</li> </ul> </li> <li>tuolx_vegheight_50m.ipw <ul> <li>Vegetation height raster in meters. Derived from NLCD dataset of vegetation type..</li> </ul> </li> <li>tuolx_vegk_50m.ipw <ul> <li>Emissivity of the vegetation canopy. Derived from NLCD dataset of vegetation type.</li> </ul> </li> <li>tuolx_vegnlcd_50m.ipw <ul> <li>Vegetation type from the National Land Cover Database.</li> </ul> </li> <li>tuolx_vegtau_50m.ipw <ul> <li>Fractional transmissivity of the vegetation canopy. Derived from NLCD dataset of vegetation type.</li> </ul> </li> </ul> </li> <li><strong>level1_raw_data</strong> <ul> <li>{Hourly data interpolated to nearest hour from downloaded raw data (CDEC/MesoWest)}</li> <li>air_temp_level1.csv</li> <li>precip_accum_level1.csv</li> <li>relative_humidity_level1.csv</li> <li>solar_radiation_level1.csv</li> <li>wind_direction_level1.csv</li> <li>wind_speed_level1.csv</li> </ul> </li> </ul> <p>The directories for each water year contain the configuration file for that year along with the vector meteorological data from measurement sites and site metadata in .csv format.</p> <ul> <li><strong>wy2013</strong></li> <li><strong>wy2014</strong></li> <li><strong>wy2015</strong></li> <li><strong>wy2016</strong> <ul> <li> <ul> <li>backup_config.ini {Initialization file used to distribute station data over a regular grid for each water year.}</li> <li>air_temp.csv</li> <li>cloud_factor.csv</li> <li>metadata.csv</li> <li>precip.csv</li> <li>vapor_pressure.csv</li> <li>wind_direction.csv</li> <li>wind_speed.csv</li> <li><strong>data/</strong> <ul> <li>[subdirectory containing all future created forcing grid files]</li> </ul> </li> <li><strong>runs/</strong> <ul> <li>[subdirectory containing all <em>iSnobal</em> output files in addition to reinitialization scripts for ASO snow depth updates]</li> </ul> </li> </ul> </li> </ul> </li> </ul>
Airborne microbial transport limitation to isolated Antarctic soil habitats
<p>Supplementary Figures 2-4 and 4-6 for Manuscript "Airborne microbial transport limitation to isolated Antarctic soil habitats".</p> <p> </p> <p> </p>
Data and code repository for the research "Assessing the use of Airborne Electromagnetic Data for nitrate vulnerability assessment in the Central Valley, California"
<p>Data and code for "Assessing the use of Airborne Electromagnetic Data for nitrate vulnerability assessment in the Central Valley, California". This repository contains the analysis and post-processing code for generating results and figures used in the manuscript.</p>
Data from: Urban street lighting differentially affects community attributes of airborne and ground-dwelling invertebrate assemblages
<p><span>The introduction of artificial light at night (ALAN) into natural and urbanised landscapes is a known and highly pervasive disruptor of invertebrate communities. However, the effect of variation in intensity and spectra of ALAN on invertebrate communities inhabiting different spatial niches is little understood. Further, the remarkable ability of ALAN to continue to disrupt biodiversity even in chronically-illuminated urban landscapes is not often acknowledged. </span></p> <p><span>Here, we simultaneously sampled airborne and ground-dwelling invertebrate assemblages under and between urban streetlights to explore the effects on community composition and abundance of a) proximity to decadal (i.e long-illuminated) nocturnal street lighting and b) variation in the spectral output of light. </span></p> <p><span>The two assemblages responded differently. For airborne invertebrates, night-time abundance doubled, and night-time assemblage composition was significantly different for traps under, compared with between, streetlights. These differences in abundance were not affected by streetlight intensity, and were absent in day samples, suggesting that even weak ALAN may be causing short-term redistribution of nocturnal invertebrates. Further, the abundance (but not composition) effects of ALAN on airborne invertebrates increased when the streetlights emitted a higher proportion of short-wavelength light. </span></p> <p><span>In contrast, for ground-dwelling invertebrates, we found only marginal effects of proximity and spectrum of lighting on abundance and no effect on assemblage composition. However, more intense streetlighting reduced abundance and altered composition at traps both under and between lights. </span></p> <p><span><i>Synthesis and Applications: </i>Public lighting managers must consider ALAN impacts on invertebrate communities not only when introducing ALAN to naïve environments, but also when changing lighting in areas that are highly urbanised and exposed to decades of artificial light at night. Further, lighting proposals and environmental monitoring of invertebrate communities must take into account effects on both ground-dwelling and airborne assemblages, as these may respond very differently to the presence, intensity and spectrum of ALAN. </span></p>
Data from dissertation: 'Landscape and Aboveground Biomass Dynamics of Brazilian Savanna using airborne LIDAR and MapBiomas datasets : case study of Rio Vermelho Watershed, Brazil'
<p>This dissertation was submitted to University of Manchester as part of MSc GIS program</p> <p>This repository contains:</p> <p>1) Contains the R language code used in the dissertation (CHM_&_LiDAR_metric.R; Landscape_metric.R; Generalized_Linear_Model.R; Random_Forest_Model.R).</p> <p><br> 2) Canopy Height Model (CHM) and 56 LiDAR metric raster files with a resolution of 1m (CHM_&_LiDAR_metric_2014.zip; CHM_&_LiDAR_metric_2018.zip), the original LiDAR data come from Brazil project supported by the Brazilian Agricultural Research Corporation (EMBRAPA), the US Forest Service, USAID, and the US Department of State.</p> <p><br> 3) AGB raster files with a resolution of 10m (AGB_2014.tif; AGB_2018.tif; AGB_dynamic.tif), field plots used for AGB estimation come from Sabrinado Couto de Miranda from University of Goiás State (UEG), Brazil, and her team.</p> <p><br> 4) Landscape metric interpolation raster file (SHDI.tif; SHEI.tif; AREA_CV.tif; CIRCLE_MN.tif; SHAPE_MN.tif) with a resolution of 10m, land cover map Map come from Biomas team for landscape metric calculation.</p>
Data for: Characterizing individual tree-level snags using airborne lidar-derived forest canopy gaps within closed-canopy conifer forests
<p><span>1. Airborne lidar is often used to calculate forest metrics about trees but it may also provide a wealth of information about the space between trees. Forest canopy gaps are defined by the absence of vegetative structure and serve important roles for wildlife, such as facilitating animal movement. Forest canopy gaps also occur around snags, keystone structures that provide important substrates to wildlife species for breeding, roosting, and foraging.</span></p> <p><span>2. We wanted to test a method for quantifying canopy gaps around individual snags and live trees, with the working hypothesis that snags would have more gaps surrounding them overall than live trees. We evaluated canopy gaps around individual snags (n=270) and live trees (n=2186) and evaluated correlations between canopy structure and snag occurrence in dense conifer stands of the Idaho Panhandle National Forest, USA. We paired airborne lidar with ground reference data collected at fixed-radius plots (n=53) to evaluate local gap structure. The R package ForestGapR was used to quantify canopy gaps throughout the canopy to determine where the differences were greatest. A canopy space profile was created for each tree by mapping gaps (a) vertically every 2 m in height (2–50 m above ground), and (b) horizontally across small (16 m<sup>2</sup>), medium (36 m<sup>2</sup>), and large (64 m<sup>2</sup>) footprint sizes.</span></p> <p><span>3. Our results suggest this method is robust for quantifying canopy gaps around individual trees. The canopy space profiles were distinctly different for snags and live trees, with more canopy gaps within the area surrounding snags relative to live trees. The greatest differences occurred at mid-canopy heights (~20 m above ground) and at the smallest footprint size (16 m<sup>2</sup>).</span></p> <p><span>4. These results show potential to improve understanding of gap dynamics in closed-canopy conifer forests, and we suggest snag modeling could be improved by incorporating lidar-derived canopy gap analyses alongside existing methodologies.</span></p>
EverWatch airborne bird imagery dataset for detection and species classification of Everglades wading birds
<p>Training and evaluation data to support the development of wading bird species detection models in the Everglades.</p> <div> <h1>Dataset contents</h1> <ul> <li>Training data: 4438 images with 45069 annotations</li> <li>Validation data: 493 images with 5422 annotations</li> <li>Test data: 196 images with 4113 annotations</li> </ul> </div> <div> <h1>Dataset structure</h1> All data is combined in everwatch_dataset.zip. This archive contains image crops (stored as PNG files) and their associated annotations in CSV format (stored in train_annotations.csv, validation_annotations.csv, and test_annotations.csv) and COCO format (stored in train_annotations.json, validation_annotations.json, and test_annotations.json). The CSV files with the annotations contain the following columns.</div> <ul> <li>image_path - basename of the image</li> <li>label - Species label</li> <li>xmin, xmax, ymin, ymax - coordinates for the ground truth bounding box on the image</li> </ul> <p>COCO formatted data follows COCO conventions so labels are in “categories” and “category_id”, image paths are in “file_name”, and bounding boxes are stored as “bbox” in the default “ltwh” format [left, top, width, height].</p> <p>Data on the annotation type for each image is stored in annotation_types.csv, which contains the following columns:</p> <ul> <li>image_path - basename of the image</li> <li>dataset - which data split the image is part of (train, validation, or test)</li> <li>annotation type - approach used for annotating the data (crop_level or colony_level) </li> </ul> <div> <h2>Labels</h2> <ul> <li>'Great Egret'</li> <li>'Great Blue Heron'</li> <li>'White Ibis'</li> <li>'Roseate Spoonbill'</li> <li>'Wood Stork'</li> <li>'Snowy Egret'</li> <li>'Anhinga'</li> <li>'Unknown' (test only)</li> </ul> <h1>Annotation methods</h1> <p>To provide data for training and testing computer vision models we used a pragmatic combination of labels from a variety of different approaches. Seven species were identified when labeling: White Ibis (<em>Eudocimus albus</em>), Great Egret (<em>Ardea alba</em>), Great Blue Heron (<em>Ardea herodias</em>), Snowy Egret (<em>Egretta thula</em>), Wood Stork (<em>Mycteria americana</em>), Roseate Spoonbill (<em>Platalea ajaja</em>), and Anhinga (<em>Anhinga anhinga</em>). Anhinga have not traditionally been included in airborne counts because they are difficult to see from the air, but were included in our labeling as a nuisance class because they were occasionally detected by the models and classified as Great Blue Herons.</p> <h2>Crop level annotations</h2> <p>Our primary approach labeled birds in 3145 1500×1500 pixels crops from surveys conducted in 2020 and 2021. Crops were automatically extracted from the orthomosaics for each survey. Labeling was conducted using a private Zooniverse project and every bird in each crop was annotated as a point location (the center of the bird) with a species label. The point-based labels were converted into bounding boxes for model training and evaluation by centering a bounding box on each point using a 50x50 pixel box for larger species (Great Egret, Great Blue Heron, Wood Stork, Roseate Spoonbill, Anhinga) and a 36x36 pixel for smaller species (White Ibis, Snowy Egret).</p> <p>We combined these point-based data with explicit bounding box-based labels created using a combination of QGIS and LabelStudio. We selected imagery for this labeling to help address small sample sizes for some species in the initial training datasets. Imagery was selected using a combination of expert field knowledge of where less common species were nesting and automated detection of likely rare species. For automated detection we ran a preliminary model based on the crop data over large amounts of survey imagery and identified crops that the model thought included less common species. These crops were then provided to annotators for labeling. This type of “active learning”, where an existing model is used to identify images for labeling is increasingly recognized as a valuable approach for rapidly producing improved machine learning models (Kellenberger et al. 2019, 2021, Norouzzadeh et al. 2021).</p> <p>In combination these crop-based annotations included 2948 of 1500x1500m crops, including 41173 birds, that were used for model training. The remaining 197 crops, including 4113 birds, were reserved for model evaluation. These annotations are indicated by 8 digit numeric file names.</p> <h2>Colony level annotations</h2> <p>We combined the crop-based labels with full colony counts from 2022 conducted as part of reporting maximum counts. This labeling was conducted using Photoshop to label every bird in an entire colony as a point location with a species label, but due to the scale of labeling the point locations are not placed carefully on the center of the bird. To convert these point locations into bounding boxes needed for model training and evaluation, we used the bird detector module in the DeepForest Python package (Weinstein et al. 2022) to predict the location of birds within the image. We then associated the species label from the photoshop points with the overlapping bounding box prediction. For the minority of boxes that did not overlap a predicted bird, we drew a 1m buffer around the point. We used data from 13 full colony counts, totaling 9318 birds, which were cropped into 1983 1500x1500 pixel crops. Full colony counts did not overlap with existing training and testing data from the 1500×1500 pixels crops because they were from different years. These annotations are indicated by variable length file names that start with a site name, followed by a date, followed by a number.</p> <h1>Difficult to distinguish species</h1> <p>Model training for most object detection algorithms requires that all birds in an image are labeled. Therefore, for all labeling approaches, the annotator’s best estimate of the species label was included even in cases where the species identity could not be conclusively determined from the imagery. The most common difficulty for annotators in distinguishing between species is White Ibis and Snowy Egrets. These birds both have white feathers, are of similar size, and appear similar in airborne imagery. As a result, annotators often use information beyond what is visibly apparent from individual birds to distinguish between these species, including what is known from the field about which species are present in the associated colonies and patterns in how the species aggregate in space.</p> <h1>Cleaning of evalutation dataset</h1> <p>Test data for evaluating the performance of the model was based exclusively on the crop-based labels. To ensure that the test data was as accurate as possible, all labels in the test set were checked and adjusted if necessary by the lead of the labeling effort (Lindsey Garner). This included both checking the species identification, adjusting any bounding boxes that did not properly contain the associated bird, and adding any labels that were missing entirely. To allow for understanding when it was not possible for a human to label a bird confidently to species, the labels for birds in the test set that could not be identified by the labeling lead were changed to indicate that the species was unknown (n = 94 birds out of 4113 test labels).</p> </div>
Airborne cues accelerate flowering and promote photosynthesis in Brassica rapa
<p>1. Volatile cues can induce and/or prime plant defences, but it is less well known if plants also respond by altering growth and reproduction-related parameters.</p> <p>2. Here we evaluated whether plant volatile cues can elicit changes in growth, flower production, net photosynthesis rate and defences in receiver plants and whether those responses are similar to those elicited in response to direct herbivore-feeding.</p> <p>3. Our results demonstrate that exposure to volatiles emitted from damaged neighbours accelerated the time to first flowering, increased the number of flowers produced and the net photosynthesis rate, but did not alter the fresh harvested plant biomass and volatile defences, compared to non-exposed plants. Earlier flowering was also observed for plants exposed to volatiles from undamaged plants.</p> <p>4. These responses differed from those of plants exposed to actual herbivore-feeding for which feeding enhanced net photosynthesis rate but reduced growth and had no effect on reproduction and defence in response to subsequent herbivory.</p> <p>5. Synthesis. These findings document that plant airborne cues can influence floral traits of receiver plants. In particular, the flower phenology is modulated, and photosynthesis is enhanced, suggesting that carbon allocation could be directed toward reproduction.</p>
Airborne and drone from Asiago (Italy) forest area before and after logging activities (ZIPPED archive version)
<ul> <li>lidar DSM and DTM (tif format) from airborne laser scanning before logging activities</li> <li>drone DSM (tif format) from photogrammetry after logging activities</li> <li><a href="https://zenodo.org/api/files/58082214-481c-44ce-9d3f-3dfdb311aa70/tarif_forest_parcels.gpkg">tarif_forest_parcels.gpkg</a> : tarif parcels polygons in geopackage format</li> <li>trees32632.csv: tree positions in delimited text format</li> <li>tarifVolumeTables.xlsx: tarif look-up-tables</li> <li><a href="https://zenodo.org/api/files/58082214-481c-44ce-9d3f-3dfdb311aa70/treeExtractionFromDSM_DTM_version02Dec2022.R">treeExtractionFromDSM_DTM_version02Dec2022.R</a> - R script to extract tree positions from a CHM</li> </ul>
Airborne eDNA documents a diverse and ecologically complex tropical bat and other mammal community
<p><span>Environmental (e)DNA has rapidly become a powerful biomonitoring tool, particularly in aquatic ecosystems. This approach has not been as widely adopted in terrestrial communities where the methods of vertebrate eDNA collection have varied from the use of secondary collectors such as blood-feeding parasites and spider webs to washing surfaces of leaves and soil sampling. Recent studies have demonstrated the potential of direct collection of eDNA from air sampling, but none have tested how effective airborne eDNA sampling might be in a </span><span>biodiverse environment.</span> <span>We used three prototype samplers to actively sample a mixed neotropical bat community in a partially controlled environment. We assess whether airborne eDNA can accurately characterize a high-diversity community with skewed abundances and to determine if filter design impacts DNA collection and taxonomic recovery. Our study provides evidence for the accuracy of airborne eDNA as a detection tool and highlights its potential for monitoring high-density, diverse assemblages such as </span><span>bat roosts. </span><span>Analysis of air samples recovered >91% of the species present and some limited relationship between species abundance and read count. Our data suggest this method can accurately depict a diverse mixed mammal community, particularly when the location is contained (e.g., a roost, den or burrow) but also highlights the potential for secondary transfer of eDNA material on clothing and equipment. Our results also demonstrate that simple, inexpensive, battery-operated homemade air samplers can collect an abundance of eDNA from the air, opening the opportunity for sampling in remote environments. </span></p>
A Comparison of Passive and Active Dust Sampling Methods for Measuring Airborne MRSA in Pig Farms
<p>Study Abstract:</p> <p>Authors: Anne Rittscher, Abel A. Vlasblom, Birgitta Duim, Peter Scherpenisse, Isabella van Schothorst, Inge M. Wouters, Liese Van Gompel, Lidwien A.M. Smit</p> <p> Methicillin resistant strains of <em>Staphylococcus aureus </em>(MRSA) are resistant to most b-lactam antibiotics. Pigs are an important reservoir of livestock associated MRSA (LA-MRSA), which is genetically distinct from both hospital and community acquired MRSA. Occupational exposure to pigs on farms can lead to LA-MRSA carriage by workers. There is a growing body of research on MRSA found in the farm environment, the airborne route of transmission, and its implication on human health. This study aims to compare two sampling methods used to measure airborne MRSA in the farm environment; passive dust sampling with electrostatic dust fall collectors (EDCs), and active inhalable dust sampling using stationary air pumps with Teflon filters.</p> <p>Paired dust samples using EDCs and GSP samplers, totaling 87 samples, were taken from seven Dutch pig farms, in multiple compartments housing pigs of varying ages. Total nucleic acids of both types of dust samples were extracted and targets indicating MRSA (<em>femA, nuc, mecA</em>) and total bacterial count (16S rRNA) were quantified using quantitative Real Time PCRs.</p> <p>MRSA could be measured in all stationary pump samples and in 94% of the EDCs, additionally MRSA was present on every farm sampled. There was a strong positive relationship between the paired MRSA levels found in EDCs and those measured on filters (Normalized by 16S rRNA; Pearson’s correlation coefficient r=0.94, Not Normalized; Pearson’s correlation coefficient r=0.84).</p> <p>This study suggests that EDCs can be used as an affordable and easily standardized method for quantifying airborne MRSA levels in the pig farm setting.</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.