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.
533
datasets available to search
ShareScore release 0.9.0
Dataset results
533 results for “Aerial”
Figure 7 in Aquatic insects in the forest canopy: a new genus of moth flies (Diptera: Psychodidae) developing in slime on aerial roots
Figure 7. Mucomyia browni adult male and female. (a) Male head, frontal view; (b) wing; (c) apical flagellomeres of antenna; (d) male terminalia, dorsal view; (e) female terminalia, ventral view. Scale bars = 125 µm (a), 50 µm (c, d), 100 µm (e).
Figure 6 in Aquatic insects in the forest canopy: a new genus of moth flies (Diptera: Psychodidae) developing in slime on aerial roots
Figure 6. Scanning electron micrographs of larval and pupal Mucomyia emersa. (a) Antenna of larva, dorsal view; (b) anal division of larva, dorsal view; (c) segment VII of larva, dorsolateral view; (d) detail of posterior spiracles of larva, dorsal view; (e) respiratory organ of pupa, anterodorsal view; (f) mesonotum of pupa, anterodorsal view; (g) partial thorax and abdomen of pupa, anterodorsal view; (h) segment IX of pupa, dorsal view. Scale bars = 10 µm (a–f), 100 µm (g, h). Abbreviations: m = mesotergite; me = mushroom element; mt = microtrichia; p = protergite; pr = pores; ps = posterior spiracles; st = setae; t = metatergite.
Figure 5 in Aquatic insects in the forest canopy: a new genus of moth flies (Diptera: Psychodidae) developing in slime on aerial roots
Figure 5. Mucomyia emersa adult male. (a) Male head, frontal view, (b) male terminalia, dorsal view. Scale bars = 0.1 mm. Abbreviations: ae = aedeagus; ea = ejaculatory apodeme; ep = epandrium; gc = gonocoxal condyle; gs = gonostyle; gx = gonocoxite; ha = hypandrium; ho = hypoproct; pm = paramere; ss = surstylus; tn = tenaculum.
Figure 1 in Aquatic insects in the forest canopy: a new genus of moth flies (Diptera: Psychodidae) developing in slime on aerial roots
Figure 1. Araceae plant from which Mucomyia emersa larvae were collected. (a) Habitus of plant, (b) detail of plant mucilage inhabited by larvae.
Evolution of chain migration in an aerial insectivorous bird, the common swift Apus apus
Spectacular long-distance migration has evolved repeatedly in animals enabling exploration of resources separated in time and space. In birds, these patterns are largely driven by seasonality, cost of migration, and asymmetries in competition leading most often to leap-frog migration, where northern breeding populations winter furthest to the south. Here we show that the highly aerial common swift Apus apus, spending the non-breeding period on the wing, instead exhibits a rarely-found chain migration pattern, where the most southern breeding populations in Europe migrate to wintering areas furthest to the south in Africa, while the northern populations winter to the north. The swifts concentrated in three major areas in sub-Saharan Africa during the non-breeding period, with substantial overlap for nearby breeding populations. We found that the southern breeding swifts were larger, raised more young, and arrived to the wintering areas with higher seasonal variation in greenness (Normalized Difference Vegetation Index, NDVI) earlier than the northern breeding swifts. This unusual chain migration pattern in common swifts is largely driven by differential annual timing and we suggest it evolves by prior occupancy and dominance by size in the breeding quarters and by prior occupancy combined with diffuse competition in the winter.
An ArcGIS Pro workflow to extract vegetation indices from aerial imagery of small‐plot turfgrass research
<p>Collection of multispectral imagery from an aerial sensor is a means to obtain plot-level vegetation index (VI) values; however, post-capture image processing and analysis remain a challenge for small-plot researchers. An ArcGIS Pro workflow of two task items was developed with established routines and commands to extract plot-level VI values (Normalized Difference VI, Ratio VI, and Chlorophyll Index-Red Edge) from multispectral aerial imagery of small-plot turfgrass experiments. Users can access and download task item(s) from the ArcGIS Online platform for use in ArcGIS Pro. The workflow standardizes the processing of aerial imagery to ensure repeatability between sampling dates and across site locations. A guided workflow saves time with assigned commands, ultimately allowing users to obtain a table with plot descriptions and index values within a .csv file for statistical analysis. The workflow was used to analyze aerial imagery from a small-plot turfgrass research study evaluating herbicide effects on St. Augustinegrass [<em>Stenotaphrum secundatum</em> (Walt.) Kuntze] grow-in. To compare methods, index values were extracted from the same aerial imagery by TurfScout, LLC and were obtained by handheld sensor. Index values from the three methods were correlated with visual percentage cover to determine the sensitivity (i.e., the ability to detect differences) of the different methodologies.</p>
A global data set of realized treelines sampled from Google Earth aerial images
<div> <span>We </span><span>sampled</span><span> Google Earth aerial images</span><span> to get a representative and globally distributed dataset of treeline locations</span><span>. </span><span>Google Earth images</span><span> are available to everyone, but may not be automatically downloaded and processed according to Google's license terms. Since we only wanted to detect tree individuals, we evaluated the aerial images manually by hand.</span> </div> <div> </div> <div> <span>Doing so, we scaled Google Earth's GUI interface to a buffer size of approximately 6000 m from a perspective of 100 m (+/- 20 m) above Earth's surface. Within this buffer zone, we took coordinates and elevation of the highest </span><span>realized </span><span>treeline locations. In some remote areas of Russia and Canada, individual trees were not identifiable due to insufficient image resolution. If this was the case, no treeline was sampled, unless we detected another visible treeline within the 6,000 m buffer and took this next highest treeline</span><span>. We did not ap</span><span>p</span><span>ly an automated image processing approach. </span><span>We calculated mass elevation effect as the distance to the nearest mountain chain limits. Continentality was assessed by the distance to the nearest coastline. Isolation was calculated by the nearest distance of a mountain chain to another mountain chain within a comparable elevational band. </span> </div>
Our processed LoveDA dataset for "LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images"
<p>Our processed LoveDA dataset is used for the paper "<a href="https://doi.org/10.7717/peerj-cs.1467">LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images</a>"</p>
Our processed CITY_OSM dataset for "LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images"
<p>Our processed CITY_OSM dataset is used for the paper "<a href="https://doi.org/10.7717/peerj-cs.1467">LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images</a>".</p>
A dataset of aerial images taken by UAV that we collected for "LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images"
<p>Our private dataset of UAV aerial imagery for paper "<a href="https://doi.org/10.7717/peerj-cs.1467">LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images</a>".</p>
FIG. 4 in Small scale habitat preferences of Myotis daubentonii, Pipistrellus pipistrellus, and potential aerial prey in an upland river valley
FIG. 4. Relationship between aerial insect number (pooled for altitude and all habitat types) with air temperature (r² = 0.092)
F. 7 in Small scale habitat preferences of Myotis daubentonii, Pipistrellus pipistrellus, and potential aerial prey in an upland river valley
F. 7. Bat activity expressed as 0 ± SD bat passes hr-1 in relation to habitat type (water state) of both M. daubentonii and IG P. pipistrellus, pooled for all four altitudes
FIG. 6 in Small scale habitat preferences of Myotis daubentonii, Pipistrellus pipistrellus, and potential aerial prey in an upland river valley
FIG. 6. Percentage of most prevalent aerial insects found during the 40 nights of sweep net sampling along river Wharfe. Samples been pooled for altitude and habitat type. Insect families represented by <0.1 % not shown
FIG. 3 in Small scale habitat preferences of Myotis daubentonii, Pipistrellus pipistrellus, and potential aerial prey in an upland river valley
FIG. 3. Mean ± SD of number of aerial insects caught with sweep net above water surface (and over grass = control), pooled for all four altitudes (n = 40 nights)
FIG. 5 in Small scale habitat preferences of Myotis daubentonii, Pipistrellus pipistrellus, and potential aerial prey in an upland river valley
FIG. 5. Relationship between numbers of aerial insects caught with a sweep net (pooled for all altitudes and habitat types) above the water surface with minimum wind speed
FIG. 2 in Small scale habitat preferences of Myotis daubentonii, Pipistrellus pipistrellus, and potential aerial prey in an upland river valley
FIG. 2. Wind direction (degrees) expressed as windroses at for 10 nights from highest (a) to lowest (d) altitudes along river Wharfe
PSMNet-FusionX3: LiDAR-Guided Deep Learning Stereo Dense Matching on Aerial Images
<p>We release two datasets we used in the paper "PSMNet-FusionX3: LiDAR-Guided Deep Learning Stereo Dense Matching on Aerial Images". These two datasets are from aerial images and LiDAR, the detailed information can be found on Github : https://github.com/whuwuteng/PSMNet-FusionX3. These datasets can be also used for Deep learning stereo-dense matching.</p>
Fig. 9. Compounds 4 and 5 in Eremophilane-type and xanthanolide-type sesquiterpenes from the aerial parts of Xanthium sibiricum and their anti-inflammatory activities
Fig. 9. Compounds 4 and 5 inhibited the activation of the PI3K/AKT/mTOR signaling pathway in LPS induced RAW264.7 cells. Compound 4 (A) influenced the expression of relative proteins related to the PI3K/AKT/mTOR signaling pathway and the relative levels of P-mTOR/mTOR (B) and P-AKT/AKT (C) were quantified. Compound 5 (D) influenced the expression of relative proteins related to the PI3K/AKT/mTOR signaling pathway and the relative levels of P-mTOR/mTOR (E) and PAKT/AKT (F) were quantified. Data are presented as the mean ± SD (n = 3). ##p <0.01 and ###p <0.001, compared with the group untreated with LPS; *p <0.05, **p <0.01, and ***p <0.001 compared with the group treated with LPS.
Fig. 7 in Eremophilane-type and xanthanolide-type sesquiterpenes from the aerial parts of Xanthium sibiricum and their anti-inflammatory activities
Fig. 7. HPLC separation chromatograms of 4, 4a, and 4b. Comparison of the experimental and calculated ECD spectra of 4a and 4b.
Fig. 8 in Eremophilane-type and xanthanolide-type sesquiterpenes from the aerial parts of Xanthium sibiricum and their anti-inflammatory activities
Fig. 8. Inhibitory effect of the isolated compounds (1–13) at concentrations of 10 μM on the mRNA levels of Tnf-α (A), Il-1β (B), and Il-6 (C) in LPS stimulated RAW264.7 cells. Dexamethasone was used as a positive control drug. Data are presented as the mean ± SD (n = 3). ###p <0.001 compared with the group untreated with LPS; **p <0.01 and ***p <0.001 compared with the group treated with LPS.
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.