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.
10,356
datasets available to search
ShareScore release 0.9.0
Dataset results
10,356 results for “Severity”
Fig. 4 in First report of a severe nasopulmonary acariasis caused by Orthohalarachne diminuata Doetschman, 1944 (Acari: Halarachnidae) in a captive South American sea lion (Otaria flavescens Shaw, 1800)
Fig. 4. ML tree based on 16S rDNA sequences. I. ricinus and D. reticulatus were used as outgroups. Bootstrap values of>70% are shown.
Fig. 3 in First report of a severe nasopulmonary acariasis caused by Orthohalarachne diminuata Doetschman, 1944 (Acari: Halarachnidae) in a captive South American sea lion (Otaria flavescens Shaw, 1800)
Fig. 3. Retrospective evaluation of ante mortem examinations. (A) Dorsal and (B) cranial view of computed tomography (CT) of lung field, revealing marginal emphysematous bullae potentially associated with O. diminuata infestations (arrows). (C) Macroscopically visible mite and mucous in bronchoalveolar lavage (BAL) fluid.
Fig. 2 in First report of a severe nasopulmonary acariasis caused by Orthohalarachne diminuata Doetschman, 1944 (Acari: Halarachnidae) in a captive South American sea lion (Otaria flavescens Shaw, 1800)
Fig. 2. Morphological identification of Orthohalarachne diminuata. Light microscopy of (A) hexapod larval stage and (B) adult female. (C, D, E) Scanning electron microscope (SEM) images of (C) a larval stage, revealing (D) the anterior end of a pedipalp and (E) claw of first leg, showing hair-like sensillae. Scale bars: (A, B, C) 200 μm, (D) 10 μm, (E) 20 μm.
Fig. 1 in First report of a severe nasopulmonary acariasis caused by Orthohalarachne diminuata Doetschman, 1944 (Acari: Halarachnidae) in a captive South American sea lion (Otaria flavescens Shaw, 1800)
Fig. 1. Severe Orthohalarachne diminuata infestation in a 2-year-old South American sea lion. (A) Bifurcatio tracheae reveals various adult mites migrating out of bronchial system. (B) Hyperemic tracheal vessels and adult mites. (C) Trepanation of nasal cavity and sinus paranasalis. (D, E) Multiple clusters of larval mites infesting turbinate mucosa. (F, G) Larval mites burying their pedipalpes and legs into the turbinate mucosa and causing multiple petechial hemorrhages and hyperemia of mucosa. (H) Histological cross-section of turbinate mucosa, showing larval mite (ha), surrounded by saniserous exsudate (hb), and epithelial exfoliation (hc). Legend: White arrows = O. diminuata. Scale bars: (F, G) 2 mm, (H) 200 μm.
Detection of novel P. falciparum haplotypes under treatment pressure in pediatric severe malaria
<p>The dataset contains Amplicon sequencing data targeting three <em>Plasmodium falciparum</em> polymorphic markers—<em>cpmp</em>, <em>cpp</em>, and <em>ama1</em>. These markers were analyzed to investigate the association between parasite clearance rate and the multiplicity of infection in Beninese children undergoing treatment for severe malaria.</p>
FIGURE 5 in Structure and composition of ichthyofauna associated with cage fish farming and compared to a control area after severe drought in a Neotropical reservoir
FIGURE 5 | Redundancy analysis (RDA). Relation of ichthyofauna to the significant environmental variable (quota) in the drought and wet seasons in an area without the influence of fish farming (Control) at the Ilha Solteira reservoir, Upper Paraná River, São Paulo, Brazil. Ala = Acestrorhynchus lacustris, Acr = Astronotus crassipinnis, Cke = Cichla kelberi, Cpi = Cichla piquiti, Cbr = Cyphocharax gillii, Cgi = Crenicichla britskii, Gsv = Geophagus sveni, Hma = Hoplias aff. malabaricus, Mac = Megalancistrus parananus, Mli = Metynnis lippincottianus, Oni = Oreochromis niloticus, Ppl = Pimelodus platicirris, Ppi = Pinirampus pirinampu, Psq = Plagioscion squamosissimus, Pam = Pterygoplichthys ambrosettii, Rvu = Rhaphiodon vulpinus, Rde = Roeboides descalvadensis, Spa = Satanoperca pappaterra, Sin = Steindachnerina insculpta, Sit = Schizodon intermedius, Sna = Schizodon nasutus, Sma = Serrasalmus maculatus, Smg = Serrasalmus marginatus, Hbu = Heterotilapia buttikoferi, and Tne = Triportheus nematurus.
FIGURE 4 in Structure and composition of ichthyofauna associated with cage fish farming and compared to a control area after severe drought in a Neotropical reservoir
FIGURE 4 | Beta diversity (PERMDISP) in the drought and wet seasons in the control and fish farm areas at the Ilha Solteira reservoir, Upper Paraná River, São Paulo, Brazil. Average distances of centroids with presence/absence of data. Asterisks indicates significant upper value between seasons within each area.
FIGURE 3 in Structure and composition of ichthyofauna associated with cage fish farming and compared to a control area after severe drought in a Neotropical reservoir
FIGURE 3 | Box plots (minimum and maximum value = vertical line ends, standard error = box, and mean = horizontal line) of the ichthyofauna attributes in the control area and fish farm area, in the drought and wet seasons at the Ilha Solteira reservoir, Upper Paraná River, SP, Brazil. A. Total abundance; B. Species richness; C. Shannon index; D. Pielou evenness. Asterisks indicates significant upper value between seasons within each area. Hash indicates significant upper value between areas within each season.
FIGURE 2 in Structure and composition of ichthyofauna associated with cage fish farming and compared to a control area after severe drought in a Neotropical reservoir
FIGURE 2 | Historical series of the variation in quota (m) between 1999 and 2019 in the Ilha Solteira reservoir, Upper Paraná River, São Paulo, Brazil. The dots indicate the sampling period.
FIGURE 1 in Structure and composition of ichthyofauna associated with cage fish farming and compared to a control area after severe drought in a Neotropical reservoir
FIGURE 1 | Map of South America showing the Ilha Solteira reservoir, with an indication of the sample areas (black circles). Adapted from Kliemann et al. (2018).
FIGURE 4 in Fish biodiversity of a tropical estuary under severe anthropic pressure (Doce River, Brazil)
FIGURE 4 | Main human-driven impacts after the 19th century and their consequences on the Doce River basin. Photos by A. Villela (Exotic species) and E. Nascimento (Mining dam collapse); the other pictures belong to the public domain (Tab. S2).
FIGURE 2 in Fish biodiversity of a tropical estuary under severe anthropic pressure (Doce River, Brazil)
FIGURE 2 | Number of species, genera and families for fish orders in the Doce River estuary. The following orders were represented by a single species and were omitted in the figure: Anguilliformes, Atheriniformes, Aulopiformes, Beloniformes, Blenniiformes, Centrarchiformes, Cyprinodontiformes, Elopiformes, Scombriformes, Spariformes, Symbranchiformes and Syngnathiformes (illustrative taxa images from Phylopic.org).
FIGURE 1 in Fish biodiversity of a tropical estuary under severe anthropic pressure (Doce River, Brazil)
FIGURE 1 | Location of the study area in Eastern Brazil with some features detailed: A. Sandy banks; B. Marginal creek; and C. Main channel of the Doce River estuary (DRE). Photos: Helder C. Guabiroba (A; C), Alexandre Villela (B). Aerial image source: (GEOBASES, 2015).
FIGURE 3 in Fish biodiversity of a tropical estuary under severe anthropic pressure (Doce River, Brazil)
FIGURE 3 | Examples of fishes recorded for the first time in the Doce River estuary. A. Cichlasoma dimerus; B. Pygocentrus nattereri; C. Coptodon rendalli; D. Butis koilomatodon; E. Lutjanus cyanopterus; F. Genidens barbus; G. Pimelodus maculatus; H. Catathyridium garmani. Photos: Helder C. Guabiroba (A, B, C, E, H), Flávio T. Szablak (D, F, G).
Data archive for 'Convolutional neural networks facilitate river barrier detection and evidence severe habitat fragmentation in the Mekong River biodiversity hotspot'
<p>This repository contains the code and databases used in the paper 'Convolutional neural networks facilitate river barrier detection and evidence severe habitat fragmentation in the Mekong River biodiversity hotspot'. </p> <p>The 'Mekong River Barrier Database (MRBD)' folder contains the basin-scale barrier database developed in this study. This database contains more than 13,000 unique barriers, which were identified by using the convolutional neural networks-based object detection method from Google Earth’s satellite imagery.</p> <p>The 'FCOS' folder contains the barrier detection model (FCOS ResNext-101-FPN), trained for detecting river barriers from remotely sensed images within the MMDetection framework. The 'FCOS_x101_v2' folder contains the enhanced FCOS model.</p> <p>The 'R_script' folder contains R files used in the paper. Coordinate.R was used to extract coordinates from bounding boxes in each TIF image. CAFI.R was used to calculate the CAFI index in each sub-catchment.</p> <p>The 'Barrier image training set' folder contains over 10,000 river barrier satellite images and their associated JSON files, forming the 'training, validation, and test datasets' used during the model training process. This dataset is made available to the user community in raw, in the hope that others will contribute to its future development, thereby enhancing its use and utility.</p> <p>For more information on the MMDetection framework, refer to the following GitHub repository: <a href="https://github.com/open-mmlab/mmdetection">https://github.com/open-mmlab/mmdetection</a></p>
Linked collectors and determiners for: New records and range extensions of several species of native bees (Hymenoptera: Apoidea) from Mississippi.
Natural history specimen data linked to collectors and determiners held within, "New records and range extensions of several species of native bees (Hymenoptera: Apoidea) from Mississippi". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/6bccbb3f-e095-4f54-b1f2-a1be8837e69c">https://bionomia.net/dataset/6bccbb3f-e095-4f54-b1f2-a1be8837e69c</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/6bccbb3f-e095-4f54-b1f2-a1be8837e69c">https://gbif.org/dataset/6bccbb3f-e095-4f54-b1f2-a1be8837e69c</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: BSBI data from several English counties, up to 2015.
Natural history specimen data linked to collectors and determiners held within, "BSBI data from several English counties, up to 2015". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/36555a0c-8361-42c4-9f78-805816266f2e">https://bionomia.net/dataset/36555a0c-8361-42c4-9f78-805816266f2e</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/36555a0c-8361-42c4-9f78-805816266f2e">https://gbif.org/dataset/36555a0c-8361-42c4-9f78-805816266f2e</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Review of the genus Meganola Dyar, 1898 of Ivory Coast and adjacent areas with descriptions of 5 new species and several taxonomic updates (Lepidoptera Nolidae, Nolinae) - Taxonomic studies on West African Nolinae I..
Natural history specimen data linked to collectors and determiners held within, "Review of the genus Meganola Dyar, 1898 of Ivory Coast and adjacent areas with descriptions of 5 new species and several taxonomic updates (Lepidoptera Nolidae, Nolinae) - Taxonomic studies on West African Nolinae I.". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/fbc6b44f-4c5e-481a-8073-8a809b4a3819">https://bionomia.net/dataset/fbc6b44f-4c5e-481a-8073-8a809b4a3819</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/fbc6b44f-4c5e-481a-8073-8a809b4a3819">https://gbif.org/dataset/fbc6b44f-4c5e-481a-8073-8a809b4a3819</a>. Formatted as a Frictionless Data package.
Electrochemical activity of several compositions in the system Ag-Pd-Pt-Ru for the oxygen reduction reaction in 0.05 M KOH solution pH 12.5
<p>The dataset comprises electrochemical screening data of several compositions from three different thin film materials libraries in the system Ag-Pd-Pt-Ru. Given are three csv files that contain the measurement coordinates, the chemical composition in atomic percent and the electrochemical current that was evaluated from linear sweep voltammograms at a potential of 0.85V vs. RHE. </p> <p><strong>Experimental description</strong></p> <p>Materials libraries were fabricated by co-sputtering thin films on 100 mm diameter sapphire wafers (c-plane) from 4 elemental tar-gets. The targets were confocally aligned to a 100 mm substrate (target-substrate distance approx. 12 cm). Target materials had a purity of 99.99,%. Ar (99.9999 %) was used as a sputter gas. The deposition pressure was 0.667 Pa. The film thickness was 100 - 150 nm. The chemical composition of the materials libraries was measured by energy dispersive X-ray spectroscopy (EDX) with an acceleration voltage of 20 kV. 81 measurements were done on a regular grid of 9x9 (8.5 mm spacing) on each library. Linear regression was used to interpolate the composition over the 342 measurement areas of a 4.5 mm grid that was used in the scanning droplet cell experiments. Electrochemical measurements were conducted with the use of a high-throughput scanning droplet cell (SDC). The SDC head incorporates counter (Pt wire) and reference (Ag|AgCl|3 M KCl) electrodes and a teflon tip with 1 mm diameter. The materials library is connected as working electrode, e.g. the surface of the investigated sample in every spot where the tip touches the sample. The electrolyte was replaced for every measurement area. Linear sweep voltammograms were measured in 0.05 M KOH, pH 12.5, with a scan rate of 10 mV/s. All potentials are reported versus the RHE according to the following equation: URHE (V) = U(Ag|AgCl|3 M KCl) + 0.210 + (0.059 * pH), where U(Ag|AgCl|3 M KCl) is the potential measured versus Ag|AgCl|3 M KCl reference electrode, 0.210 V is the standard potential of the Ag|AgCl|3 M KCl reference electrode at 25◦C. Note that 0.059 is the result of (RT )· (nF)−1, where R is the gas constant, T is the temperature (298 K), F is the Faraday constant, and n is the number of electrons transferred during the reaction.</p> <p> </p>
A Data-Driven Epigenetic Characterization of Morning Fatigue Severity in Oncology Patients Receiving Chemotherapy: Associations with Epigenetic Age Acceleration, Blood Cell Types, and Expression-Associated Methylation
<p>This dataset contains supplementary materials including the eCpG mapping analysis results and annotation. The manuscript has been accepted for publication at Cancer Medicine. Please cite both the paper as well as the DOI of this dataset if you make use of the data.</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.