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461,131 results for “FIGURES”
LTER-Italy site Appennino centrale Velino-Duchessa figure
<p>Geographical representation of the LTER-Italy site Appennino centrale Velino-Duchessa (LTER_EU_IT_025) - DEIMS-ID <a href="https://deims.org/12c79ecb-7890-4b75-9655-0883dacd8a29">https://deims.org/12c79ecb-7890-4b75-9655-0883dacd8a29</a></p>
LTER-Italy site Sacca di Goro figure
<p>Geographical representation of the LTER-Italy site Sacca di Goro (LTER_EU_IT_040) - DEIMS-ID <a href="https://deims.org/b7869194-b220-473a-b035-feeadfa21aba">https://deims.org/b7869194-b220-473a-b035-feeadfa21aba</a></p>
LTER-Italy site Valli di Comacchio figure
<p>Geographical representation of the LTER-Italy site Valli di Comacchio (LTER_EU_IT_041) - DEIMS-ID <a href="https://deims.org/70e1bc05-a03d-40fc-993d-0c61e524b177">https://deims.org/70e1bc05-a03d-40fc-993d-0c61e524b177</a></p>
LTER-Italy site Lago di Garda figure
<p>Geographical representation of the LTER-Italy site Lago di Garda (LTER_EU_IT_044) - DEIMS-ID <a href="https://deims.org/c713db56-373c-46cc-8828-ce8cadc4f3bb">https://deims.org/c713db56-373c-46cc-8828-ce8cadc4f3bb</a></p>
LTER-Italy site Lago di Orta figure
<p>Geographical representation of the LTER-Italy site Lago di Orta (LTER_EU_IT_042) - DEIMS-ID <a href="https://deims.org/8bd7d2f8-421a-48bd-b212-04bc1e9f31d5">https://deims.org/8bd7d2f8-421a-48bd-b212-04bc1e9f31d5</a></p>
LTER-Italy site Val Masino LOM1 figure
<p>Geographical representation of the LTER-Italy site Val Masino LOM1 (LTER_EU_IT_028) - DEIMS-ID <a href="https://deims.org/68a5673c-9172-48cc-88e5-b9408b203309">https://deims.org/68a5673c-9172-48cc-88e5-b9408b203309</a></p>
Long-Term Trends in Seagrass Metabolism Data from Figures Covering 2007-2018
This dataset contains the data used to create figures 2, 4 and 5 in: Berger, A.C., Berg, P., McGlathery, K.J. and Delgard, M.L. (2020), Long-term trends and resilience of seagrass metabolism: A decadal aquatic eddy covariance study. Limnol Oceanogr, 65: 1423-1438. https://doi.org/10.1002/lno.11397
Figures and videos for: Patterned invagination prevents mechanical instability during gastrulation
<p>This repository contains the high-resolution figures and videos for the paper:</p> <p>Vellutini, B. C., Cuenca, M. B., Krishna, A., Szałapak, A., Modes, C. D. & Tomancak, P. Patterned invagination prevents mechanical instability during gastrulation. <em>Nature</em> (2025). doi:<a href="https://10.0.4.14/s41586-025-09480-3">10.1038/s41586-025-09480-3</a></p> <p>Please refer to the main repository for more information: <a href="https://doi.org/10.5281/zenodo.7781947">https://doi.org/10.5281/zenodo.7781947</a></p>
Data files for figures in "Sensitivity of extreme precipitation to climate change inferred using artificial intelligence shows high spatial variability" by Bird et al.
<p>The data files for figures in <i>Sensitivity of extreme precipitation to climate change inferred using artificial intelligence shows high spatial variability</i> by Bird, Bodeker and Clem. The files required to create each figure in the paper and in the supplementary material are described in a readme.txt file which is also provided below:</p><p><strong>Figure 1</strong></p><p>The background image was obtained from the 'NaturalEarthFeature' function of the python Cartopy library (Figure1_background.png). The data required to generate the plots shown in Figure 1 are provided in the Figure1.nc file:</p><ul><li>The latitudes and longitudes for the 10,000 training sites are provided in Training_location_latitudes and Training_location_longitudes variables. </li><li>The latitudes and longitudes for the 8 sites used to demonstrate the ability of the CNN to generalise spatially are provided in the Validation_location_latitudes and Validation_location_longitudes variables. </li><li>The block maxima at each of the 8 sites are provided in the Location_1year_block_maxima variable.</li><li>The GEV fits at 0°C are provided in the GEV_fit_at_0.0C variable.</li><li>The GEV fits at 1.5°C are provided in the GEV_fit_at_1.5C variable.</li></ul><p><strong>Figure 2</strong></p><ul><li>The 1-in-100 year precipitation fields for values of T'Global from 0.0°C to 2.0°C in 0.1°C increments are provided in netCDF files named Figure2_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The ratios of the 1-in-100 year precipitation depths with respect to the long-term (20 years or more) average of the annual maximum 1-day precipitation are provided in text files named Figure2_Precipitation_mean_block_max_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li></ul><p><strong>Figure 3</strong></p><ul><li>The cumulative distribution functions (CDFs) shown in the lower four panels are provided as text files listing the ARI in years and the daily total precipitation depth in mm. These files are named CDF_<lat>_<long>.dat where <lat> is the latitude and <long> is the longitude. Files for each region are zipped into .7z files named Figure3_<region>.7z where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The latitudes and longitudes for the upper panels can be inferred from the file names for each region.</li></ul><p><strong>Figure 4</strong></p><p>The data for each panel are provided in a netCDF file named Figure4_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</p><p><strong>Figure 5</strong></p><p>The data are provided as text files named Figure5_<region> where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the sensitivities at ARIs of 10, 20, 50, 100, and 200 years.</p><p><strong>Figure 6</strong></p><p>The data are provided as text files named Figure6_<region> where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the precipitation depths and the sensitivities at ARIs of 10, 20, 50, 100, and 200 years. </p><p><strong>Figure 7</strong></p><p>The data for each panel are provided in a netCDF file named Figure7_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</p><p><strong>Figure 8</strong></p><p>The data are provided as text files named Figure8_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the global surface temperature anomaly (°C) and the average negative log likelihood.</p><p><strong>Figure S1 and Figure S3</strong></p><p>The data are provided as text files named Figure_S1_and_S3_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. A header at the top of each file describes its contents.</p><p><strong>Figure S2</strong></p><ul><li>The 1-in-20 year precipitation fields for values of T'Global from 0.0°C to 2.0°C in 0.1°C increments are provided in netCDF files named FigureS2_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The ratios of the 1-in-20 year precipitation depths with respect to the long-term (20 years or more) average of the annual maximum 1-day precipitation are provided in text files named FigureS2_Precipitation_mean_block_max_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li></ul><p><strong>Figure S4</strong></p><ul><li>The block maxima for each site are provided in text files named FigureS4_blockmaxima_siteA.txt and FigureS4_blockmaxima_siteB.txt. A header at the top of each column described the column contents. </li><li>The GEV-derived curves for each site are provided in text files named FigureS4_gevcurves_siteA.txt and FigureS4_gevcurves_siteB.txt. A header at the top of each column described the column contents. </li></ul><p><strong>Figure S5</strong></p><p>There are no data associated with this figure. This figure was made using Microsoft Powerpoint.</p><p><strong>Figure S6</strong></p><p>The data are provided as text files named FigureS6_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. A header at the top of each file describes the files contents.</p>
Figure data and code used in Inconsistent definitions of GDP: Implications for estimates of decoupling
<p>Figure code in R and underlying data to reproduce all figures in the article "Inconsistent definitions of GDP: Implications for estimates of decoupling".</p>
Figure 2: Direct and indirect paths of knowledge transfer to New Zealand to manage sand drifting in the nineteenth and twentieth centuries.
<p>Figure 2 of article: Managing Coastal Sand Drift in the Anthropocene: A Case Study of the Manawatū-Whanganui Dune Field, New Zealand, 1800s–2020s</p> <p>DOI zenodo: 10.5281/zenodo.5075980</p>
Figure data and model used in Stranded fossil-fuel assets translate to major losses for investors in advanced economies
<p>The package contains i) the figure code and underlying data to create all figures in the main paper and supplementary information of the journal article and ii) the network and imputation model used to calculate the shock calculation.</p>
Data access for figures of Chen, Ginoux, Wyart, Mora & Walczak
<p>README: </p> <p><br> Pandas DataFrame</p> <p>to load: <br> import pickle<br> pickle_filename = 'YOUR_DATA_PATH/df_name.pkl' # change accordingly<br> with open(pickle_filename, 'rb') as pickle_in:<br> df_name = pickle.load(pickle_in)</p> <p><br> Motorneuron data:<br> Fish 3 Trial 1 and Fish 5 Trial 2 for Figure 3.<br> Fish 5 Trial 2 for figure 4.</p> <p>Columns:<br> - Fish: fish index<br> - Trial: trial index<br> - fluo: fluorescence traces [n_cells x n_timesteps]<br> - fluo_type: 'dff' or 'f_smooth', respectively before and after smoothing procedure<br> - n_cells: number of cells in the plane (only those kept for analysis, "bad" cells removed)<br> - mid: middle cell, to split left vs right neurons (left until index mid-1, right from index mid and on)<br> - cell_centers: x and y position of the cell center [n_cells x 2]<br> - multivariate: boolean to indicate bivariate (False) or multivariate (True) GC<br> - GC: Granger causality matrix results [n_cells x n_cells]<br> - GC_sig: Granger causality matrix results, significant with original threshold (where Fstat > threshold_F) [n_cells x n_cells]<br> - GC_sig_new_thresh: Granger causality matrix results, significant with new threshold (where Fstat > new_threshold_F) [n_cells x n_cells]<br> - Fstat: F-statistics matrix [n_cells x n_cells]<br> - threshold_F: original threshold for the F-statistics significance<br> - new_threshold_F: new threshold for the F-statistics after the whole pipeline is applied</p> <p><br> Hindbrain data<br> Fish 6 Trial 07</p> <p>Columns:<br> - fluo: fluorescence traces [n_cells x n_timesteps]<br> - cell_centers: x and y position of the cell center [n_cells x 2]<br> - background: plane background for plotting [249 x 512]<br> - n_cells: number of cells in the plane<br> - tail_angle: array of angle of the tail [75000,] - 75000 timesteps: higher frequency than calcium imaging recording<br> - tail_angle_regressor: tail angle convolved to calcium decay function [75000,] <br> - is_swim: boolean array whether each cell in correlated to swim activity (True if pearson correlation between cell's fluorescence trace and tail_angle_regressor > 0.6) [n_cells,]<br> - swim_neurons: indices of swim-correlated neurons [n_swim_cells,]<br> - medial_neurons: indices of swim-correlated neurons [n_medial_cells,]<br> - SNR: signal-to-noise ratio for each cell [n_cells,]</p> <p>- BV_GC_medial: original bivariate (BV) Granger causality results matrix [n_medial_cells,n_medial_cells]<br> - BV_Fstat_medial: original BV F-statistics matrix [n_medial_cells,n_medial_cells]<br> - BV_threshold_F_ori: original threshold for the BV F-statistics significance<br> - BV_threshold_F_new_mat_medial: new threshold customized for each pair of neurons (BV) [n_medial_cells,n_medial_cells]<br> - BV_Fstat_normalized_medial: new BV F-statistics matrix normalized by customized threshold [n_medial_cells,n_medial_cells]<br> - BV_GC_normalized_medial: new BV GC results matrix normalized by customized threshold [n_medial_cells,n_medial_cells]</p> <p>- MV_GC_medial: original multivariate (MV) Granger causality results matrix [n_medial_cells,n_medial_cells]<br> - MV_Fstat_medial: original MV F-statistics matrix [n_medial_cells,n_medial_cells]<br> - MV_threshold_F_ori_medial: original threshold for the MV F-statistics significance<br> - MV_threshold_F_new_mat_medial: new MV F-statistics matrix normalized by customized threshold [n_medial_cells,n_medial_cells]<br> - MV_Fstat_normalized_medial: new MV F-statistics matrix normalized by customized threshold [n_medial_cells,n_medial_cells]<br> - MV_GC_normalized_medial: new MV GC results matrix normalized by customized threshold [n_medial_cells,n_medial_cells]</p>
IPBES Assessment of the diverse values and valuation of nature - Figures presented in the summary for policymakers
<p>These figures are an integral part of the Summary for policymakers of the Methodological assessment of the diverse values and valuation of nature of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. To see the full document visit the related links. </p>
Moderate associations between the use of levonorgestrel-releasing intrauterine device and metabolomics profile; Supplementary Figures
<p>Supplementary Material for the article "Moderate associations between the use of levonorgestrel-releasing intrauterine device and metabolomics profile", in the Journal of Clinical Endocrinology and Metabolism</p>
Supplemental Figures for "On the comparative utility of entropic learning versus deep learning for long-range ENSO prediction"
<p>Supplemental figures for the paper "On the comparative utility of entropic learning versus deep learning for long-range ENSO prediction".</p>
Data and code for figures: Intrinsic Kerr amplification for microwave electromechanics
<p>This directory contains the datasets and code for generating the figures in the research article "Intrinsic Kerr amplification for microwave electromechanics", <em>Appl. Phys. Lett.</em> 124, 243503 (2024).</p>
Figures in Scientific Open Access Publications - Underlying Data
<p>This publication contains data for a statistical analysis of an OA article corpus. The underlying dataset consists of over 1 million open access articles from different publishers (Copernicus: 9592; Springer:78418; Hindawi: 147848; Frontiers: 57621; PMC (aggregator): 747839)</p>
Equation-of-Motion Coupled-Cluster Theory based on the 4-component Dirac–Coulomb(–Gaunt) Hamiltonian. Energies for single electron detachment, attachment and electronically excited states: Figures
<p>This entry contains the figures included in the paper titled "Equation-of-Motion Coupled-Cluster Theory based on the 4-component Dirac--Coulomb(--Gaunt) Hamiltonian. Energies for single electron detachment, attachment and electronically excited states", by Avijit Shee, Trond Saue, Lucas Visscher and Andre Severo Pereira Gomes.</p> <p>It accompanies the dataset found at the DOI: 10.5281/zenodo.1320320</p> <p>There are three figures that use the (original) png files included in <a href="https://zenodo.org/api/files/7bda2e2b-ac69-41aa-a21e-821e88bfb973/original-figures.tar.bz2">original-figures.tar.bz2 </a>:</p> <p>figure 1: Potential energy curves of the spin-orbit split X<sup>2</sup>Π and A<sup>2</sup>Π states of the XO molecules, obtained with EOM-IP and the <sup>2</sup>DCG<sup>M</sup> Hamiltonian.</p> <p>figure 2: Internuclear distances (in Angstrom), harmonic vibrational frequencies (in cm<sup>−1</sup>) and the vertical Ω = 3/2 − 1/2 energy difference (in eV) for the X<sup>2</sup>Π and A<sup>2</sup>Π states of the XO molecules, obtained with EOM-IP and the <sup>2</sup>DCG<sup>M</sup> Hamiltonian.</p> <p>figure 3: SO-ZORA/QZ4P/Hartree-Fock (ADF) spinor magnetization plots (isosurfaces at 0.03 a.u.) and energies (in Eh) for the valence spinors of the XO<sup>−</sup> species (from left to right: X = Cl, Br, I, At, Ts).</p>
Predictive simulations of ionization energies of solvated halide ions with relativistic embedded Equation of Motion Coupled-Cluster Theory: Figures
<p>This entry contains the sources for the figures included in the body of the paper titled "Predictive simulations of ionization energies of solvated halide ions with relativistic embedded Equation of Motion Coupled-Cluster Theory", by Yassine Bouchafra, Avijit Shee, Florent Réal, Valérie Vallet and André Severo Pereira Gomes, as well as those found in the supplementary information.</p> <p>It accompanies the dataset found at the DOI: 10.5281/zenodo.1477004</p> <p> </p> <p> </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.