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.
2,395
datasets available to search
ShareScore release 0.9.0
Dataset results
2,395 results for “Contrast”
On the effect of precession for magnetic differential phase contrast imaging
<p>4D-STEM datasets and processing scripts used for the journal publication "<strong>On the effect of precession for magnetic differential phase contrast imaging</strong>".</p> <p>DOI link to publication: <a href="https://doi.org/10.1016/j.micron.2024.103761">10.1016/j.micron.2024.103761</a></p> <p> </p> <p><strong>Prerequisites</strong></p> <p>The scripts presented below were created using open-source Python packages with specific versions:</p> <ul> <li>hyperspy 1.7.1</li> <li>pyxem 0.14.2</li> <li>fpd 0.2.5 (with scikit-image 0.18)</li> <li>jupyterlab 4.0.7</li> </ul> <p>There is no guarantee that other versions of the listed packages will run the code.</p> <p><strong>Data files</strong></p> <p>In total five zipped (.7z extension) files are included in this repository. The files contain data from the different samples studied with different precession electron diffraction calibrations where applicable. The three samples studied are LSMO, and the corresponding 4D-STEM datasets taken away from a high-symmetry zone axis (<em>LSMO_LZ_data.7z</em>) and on the zone axis (<em>LSMO_HZ_data.7z</em>), FeAl sample (<em>FeAl_data.7z</em>), and scans from an Au TEM alignment grid (<em>AuGrid_data.7z</em>). The <em>precession_azimuths.7z</em> files contains the precession azimuthal circles from the three precession calibrations performed.</p> <p><strong>Processing scripts</strong></p> <p>Three scripts are included in the IPython notebook format (.ipynb extension). The <em>Precession_azimuth_plot.ipynb</em> script plots the precession azimuth for comparison. The <em>Segmentation_script.ipynb</em> is used to process the precession path segmented 4D-STEM datasets, and the <em>DPC_processing.ipynb</em> includes code to perform center of mass and phase correlation processing on the 4D-STEM data to extract differential phase contrast signals.</p>
Multimodal contrastive learning for spatial gene expression prediction using histology images
<p>we employed two human breast cancer datasets and one human cutaneous squamous cell carcinoma (cSCC) dataset.</p>
Comparison of the nutrient resorption stoichiometry of Quercus variabilis Blume growing in two sites contrasting in soil phosphorus content
<p>The databases provided here support the statistical analyses of the manuscript “Comparison of the nutrient resorption stoichiometry of <em>Quercus variabilis</em> Blume growing in two sites contrasting in soil phosphorus content” (Annals of Forest Science). The dataset is provided in Microsoft Excel format. The dataset (2015 data) contains information about concentrations of N, C, P, K, Ca, Mg, Fe, Mn, Zn, Cu, Al, and Na (mg g<sup>-1</sup>), ratios of C:N, N:P, and C:P on molar basis in soils, green leaves, and senesced leaves, and their resorption efficiencies and resorption efficiency ratios calculated as log<sub>e</sub>(C<sub>RE</sub>/N<sub>RE</sub>), log<sub>e</sub>(N<sub>RE</sub>/P<sub>RE</sub>) and log<sub>e</sub>(C<sub>RE</sub>/P<sub>RE</sub>) by leaves of <em>Q. variabilis</em>, and also leaf mass per area (mg cm<sup>-2</sup>), total height (m) and diameter at breast height (cm) of the<em> Q. variabilis</em> at P-rich and P-deficient sites, in subtropical China. These data were used to assess differences in nutrient concentrations and the C:N, N:P, and C:P molar ratios of green leaves, senesced leaves, and soils, as well as variations in foliar nutrient resorption efficiencies and resorption efficiency ratios between P-rich and -deficient sites, and to assess the relationship between resorption, and soil or leaf chemistry, and the relationship between LMA and nutrient concentrations or resorption, across P-rich and -deficient sites.</p>
A Tale of Two Retinal Domains: Near-Optimal Sampling of Achromatic Contrasts in Natural Scenes through Asymmetric Photoreceptor Distribution
<p>Spectral image data of an early spring daytime forest scene recorded using a ‘‘hyperspectral scanner’’. For details, see<a href="https://www.ncbi.nlm.nih.gov/pubmed/24314730"> Baden, Schubert et al. (2013)</a> doi: 10.1016/j.neuron.2013.09.030. For a script to access the data, see <a href="https://github.com/eulerlab/published_data">github.com/eulerlab/published_data</a>.</p>
Diurnal regulation of photosynthetic light absorption, electron transport and carbon fixation in two contrasting oceanic environments
<p>Dataset accompanying the publication Schuback N & Tortell PD, Diurnal regulation of photosynthetic light absorption, electron transport and carbon fixation in two contrasting oceanic environments, Biogeosciences, 2019. https://doi.org/10.5194/bg-2018-524.</p>
Effectiveness of agri-environmental management on pollinators is moderated more by ecological contrast than by landscape structure or land-use intensity
<p>Study dataset</p>
Reconstructed spatial resolution and contrast recovery with Bayesian penalized likelihood reconstruction (Q.Clear) for FDG-PET compared to time-of-flight (TOF) with point spread function (PSF)
<p>DICOM data and SPSS datasets with all derived measures (SUV, recovery coefficients, spatial resolution, SNR) that are the basis for the publication.</p>
Fig. 4 in Genetic and morphological divergence among Gravel Bank Grasshoppers, Chorthippus pullus (Acrididae), from contrasting environments
Fig. 4 Discriminant function plot (group centroids ± SD for the first two discriminant func- tions) for the analysis presented in Table 7. Dark grey centroids: lowland heath populations; light grey centroids: alpine gravel bank populations
Fig. 4 in Turtle and tortoise mitogenomes under contrasting positive selection pressure
Fig. 4 Graphical representation denoting the distribution of PSS in Testudinoidean lineages across the OXPHOS system. All the 12 mitochondrial PCGs encoding the OXPHOS system were projected onto homologous protein structures and are represented in different colours. Grey structures represent the entire OXPHOS complex and no solved crystallized structures were available for the ATP8 gene. The NAD3 gene (lemon green) represents sites under positive selection across all Testudinoidean lineages as detected by random sites analysis. The rest of the genes, NAD1 (hot pink), NAD2 (yellow),
Fig. 2 in Turtle and tortoise mitogenomes under contrasting positive selection pressure
Fig. 2 Gene-wise estimates of dN/dS for the concatenated data set of 12 PCGs using M8a-M8 nested random site models. The y-axis denotes BEB posterior mean estimate of dN/dS for each site, with
Figure 4 in Body size variation in a lineage of spur-thighed tortoises (Testudo graeca whitei) contrasts with that expected from the species level
Figure 4. Differences in body size (Straight Carapace Length; SCL) observed between the Testudo graeca whitei ranges in North Africa and SE Spain. The violin plot represents these differences, with its width indicating the frequency of a particular body size.
Figure 2 in Body size variation in a lineage of spur-thighed tortoises (Testudo graeca whitei) contrasts with that expected from the species level
Figure 2. Density contour plot illustrating the environmental conditions inhabited by Testudo graeca whitei in North Africa (pink dots) and SE Spain (green dots). The analysis considered two bioclimatic variables from WorldClim, namely Annual Precipitation and Annual Mean Temperature (BIO1 and BIO12). The total range of T. g. whitei was defined based on the minimum convex polygon, which encompasses all the previously sampled specimens, as outlined in Anadón et al. (2015). The color scale indicates frequency of occurrence, with lighter colors denoting more frequent bioclimate conditions. Site numbers are referenced in supplementary table S1.
Figure 6 in Body size variation in a lineage of spur-thighed tortoises (Testudo graeca whitei) contrasts with that expected from the species level
Figure 6. Effect of latitude on the adult size (Straight Carapace Length; SCL) of Testudo graeca whitei. N = 119 and N =
Figure 1 in Body size variation in a lineage of spur-thighed tortoises (Testudo graeca whitei) contrasts with that expected from the species level
Figure 1. Sampling of Testudo graeca whitei in North Africa and SE Spain according to the present-day map (1980-2016)
Predicting the pro-longevity or anti-longevity effect of model organism genes with enhanced Gaussian noise augmentation-based contrastive learning on protein-protein interaction networks
<p>The datasets used to evaluate Enhanced Gaussian noise augmentation-based contrastive learning (EGsCL) against predicting the pro-longevity or anti-longevity effect of model organism gene. This repo also includes the pretrained encoders that obtained the best predictive performance for each organism (see Table 2).</p>
Dataset underying the publication titled: Warming-induced contrasts in snow depth drive the future trajectory of soil carbon loss across the Arctic-Boreal region
<p>LPJ-GUESS model outputs underlying the figures published in the article "Warming-induced contrasts in snow depth drive the future trajectory of soil carbon loss across the Arctic-Boreal region".</p> <p> </p>
OsO2 as the Contrast-Generating Chemical Species of Osmium-Stained Biological Tissues in Electron Microscopy
Open the record for dataset details and reuse information.
Figure 13 in A new Antarctic species of Orchomenella G.O. Sars, 1890 (Amphipoda: Lysianassoidea: Tryphosidae): is phase-contrast micro-tomography a mature technique for digital holotypes?
Figure 13. Rooted maximum likelihood tree of the Orchomene s.l. complex COI. Bootstrap support is shown for each branch. Hirondellea gigas was used as the outgroup. Ŋe position of Orchomenella rinamontiae is shown in yellow.
Figure 11 in A new Antarctic species of Orchomenella G.O. Sars, 1890 (Amphipoda: Lysianassoidea: Tryphosidae): is phase-contrast micro-tomography a mature technique for digital holotypes?
Figure 11. Orchomenella rinamontiae. Holotype, ♀, 24.4 mm. Less uropods (U1, U2, and U3) and telson (T) in dorsal view. Scale bars: 0.5 mm.
Figure 12 in A new Antarctic species of Orchomenella G.O. Sars, 1890 (Amphipoda: Lysianassoidea: Tryphosidae): is phase-contrast micro-tomography a mature technique for digital holotypes?
Figure 12. Unrooted non-redundant maximum likelihood tree of the Lysianassoidea COI. Leaves represent individual specimens, coloured by genus where this information was available in the NCBI database. Our samples of Orchomenella rinamontiae, from both RNA sequencing and DNA sequencing, are coloured in red. Ŋe area of the tree illustrated in Figure 13 is enclosed by a grey doưed line. A complete list of sequences in each genus group can be found in the Supporting Information (Table S1).
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.