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461,131 results for “FIGURES”
Variation and overlaps in non-breeding regions of three less-studied breeding populations of the Eurasian Reed Warbler: Figures and datasheet
<p>Non-breeding regions of Eurasian Reed Warbler, <em>Acrocephalus scirpaceus</em> breeding in Finland, Jordan, and Kazakhstan as predicted with hydrogen stable isotope analysis.</p> <p>file name corresponds to the following figure titles:</p> <p>"Fig 2": <strong><span>Figure 2:</span></strong><span> The predicted non-breeding regions of Eurasian Reed Warblers breeding in Finland, Jordan, and Kazakhstan.</span></p> <p><span><span>"Fig 3": <strong><span>Figure </span></strong><span>3: Predicted non-breeding regions for Eurasian Reed Warblers sampled at a breeding site in Finland assigned to isotopic clusters A and </span><span><span>B.</span></span></span></span></p> <p><span><span></span></span><span></span><span><span><span>"Fig 4": </span></span></span><strong><span>Figure 4:</span></strong><span> Predicted within population average non-breeding regions for Eurasian Reed Warblers assigned to clusters A-F breeding in Jordan </span><span>(see Table S1 for details).</span></p> <p><span>Also uploaded is a csv file containing the isotopic signature of the birds. The file name is "data_Acrocephalus_scirpaceus"</span></p>
LTER-Italy Antarctic sites figure
<p>Geographical representation of the LTER-Italy Antarctic sites Macrosito Baia di Terranova (MBT - LTER_EU_IT_086), Mooring A: Southwestern Ross Sea, Ross Island (LTER_EU_IT_069), Mooring B: North Central Ross Sea, Joides Basin (LTER_EU_IT_070, Mooring D: Western Ross Sea, TerraNova Bay Polynya (LTER_EU_IT_071) and Mooring H: Central Ross Sea (LTER_EU_IT_072) - DEIMS-iDs <a href="https://deims.org/7fb8e2c6-b11f-41a7-b494-44ceeb3bed2d">https://deims.org/7fb8e2c6-b11f-41a7-b494-44ceeb3bed2d</a>, <a href="https://deims.org/86b6465c-b604-4efa-9145-0805f62216f4">https://deims.org/86b6465c-b604-4efa-9145-0805f62216f4</a>, <a href="https://deims.org/1fb62b9c-4d5c-4f1f-8882-807032337de7">https://deims.org/1fb62b9c-4d5c-4f1f-8882-807032337de7</a>, <a href="https://deims.org/b4121cd7-8b02-4872-b1d2-516d1c02056a">https://deims.org/b4121cd7-8b02-4872-b1d2-516d1c02056a</a> and <a href="https://deims.org/63a444a3-22e1-44fe-a7e3-7982366a2c1b">https://deims.org/63a444a3-22e1-44fe-a7e3-7982366a2c1b</a>.</p>
LTER-Italy Gulf of Naples sites figure
<p>Geographical representation of the LTER-Italy Gulf of Naples sites LTER Lacco Ameno (LTER_EU_IT_060) and LTER Marechiara (LTER_EU_IT_061) - DEIMS-ID <a href="https://deims.org/4a05a2fb-0015-4310-96d5-a94c019bda58">https://deims.org/4a05a2fb-0015-4310-96d5-a94c019bda58</a> and h<a href="http://ttps://deims.org/0b87459a-da3c-45af-a3e1-cb1508519411">ttps://deims.org/0b87459a-da3c-45af-a3e1-cb1508519411</a></p>
Data and code for figures: Kinetic Inductive Electromechanical Transduction for Nanoscale Force Sensing
<p>This directory contains the datasets and code (if applicable) for generating the figures in the research article "Kinetic Inductive Electromechanical Transduction for Nanoscale Force Sensing", Physical Review Applied 20, 024022 (2023).</p>
Dataset regarding the « Reasons for concern » about climate change from figures in IPCC and related publications
<p>This data corresponds to the 'burning ember' diagrams from IPCC reports and related publications (IPCC TAR, Smith et al. 2009 for AR4-related embers, AR5 and SR15). It was used to build figure 3 of Zommers et al. 2020 (<em>Burning Embers: Towards more transparent and robust climate change risk assessments</em>. Accepted for publication in Nature Reviews Earth & Environment). The data provided here is the result of extraction of information from the original figures, as presented in the related technical document <a href="https://doi.org/10.5281/zenodo.3992856">10.5281/zenodo.3992856</a>. As explained in the Supplementary Information of Zommers et al. 2020 and the technical document, this is not data from the IPCC. The provided values are approximations of the global mean temperature increase corresponding to each change in risk in the original diagrams. The rigour of the preparation process and the limitations of the dataset are explained in the technical document.</p>
Figure data and code used in Technical comment on "Fairness considerations in global mitigation investments"
<p>The package contains the data and code to create the figure in the associated technical comment in Science published at <a href="https://www.science.org/doi/10.1126/science.adg5893">https://www.science.org/doi/10.1126/science.adg5893</a></p>
Data for Figure 8 of Publication "Additional global climate cooling by clouds due to ice crystal complexity"
<p>This repository contains the data to produce Figure 8 in the paper:</p> <p>"Järvinen, E., Jourdan, O., Neubauer, D., Yao, B., Liu, C., Andreae, M. O., Lohmann, U., Wendisch, M., McFarquhar, G. M., Leisner, T., and Schnaiter, M.: Additional global climate cooling by clouds due to ice crystal complexity, Atmos. Chem. Phys., 18, 15767–15781, https://doi.org/10.5194/acp-18-15767-2018, 2018."</p> <p>Note that the scripts are to be found in the accompanying package (http://dx.doi.org/10.5281/zenodo.8095469)</p>
Data for Figures 4, A-F and Table J of Publication "Blue skies over China: The effect of pollution-control on solar power generation and revenues"
<p>This repository contains the data to produce Figures 4, A-F and Table J and emission data in the paper:</p> <p>"Labordena M, Neubauer D, Folini D, Patt A, Lilliestam J (2018) Blue skies over China: The effect of pollution-control on solar power generation and revenues. PLoS ONE 13(11): e0207028. https://doi.org/10.1371/journal.pone.0207028"</p> <p>Note that the scripts are to be found in the accompanying package (https://doi.org/10.5281/zenodo.8130726)</p>
Data for figures in the Publication "The importance of mixed-phase and ice clouds for climate sensitivity in the global aerosol–climate model ECHAM6-HAM2"
<p>This repository contains the data to produce figures for the paper:</p> <p>"Lohmann, U. and Neubauer, D.: The importance of mixed-phase and ice clouds for climate sensitivity in the global aerosol–climate model ECHAM6-HAM2, Atmos. Chem. Phys., 18, 8807–8828, https://doi.org/10.5194/acp-18-8807-2018, 2018."</p> <p>Note that the scripts are to be found in the accompanying package (https://doi.org/10.5281/zenodo.8183412)</p>
Pathways to enhance electrochemical CO2 reduction identified through direct pore-level modeling (data for figures)
<p>This is the data used to create the figures in the article "Pathways to enhance electrochemical CO2 reduction identified through direct pore-level modeling".</p> <p>Published in EES Catalysis</p> <p>DOI: 10.1039/d3ey00122a<br> Evan Johnson<br> Etienne Boutin<br> Shuo Liu<br> Sophia Haussener</p> <p><br> Additional notes are given in the "ReadMe.txt" file.</p>
Coastal marine megaripple fields are metabolic hotspots - Data for figures and tables
This dataset contains the data used to create figures 2-5 in the paper: Berg, P. in press. Coastal marine megaripple fields are metabolic hotspots with highly dynamic oxygen exchange. L&O Letters (publication expected in 2023). Details on the nature of the data and how it was collected are included in the paper.
FIGURE 1 from conference paper AIP Proceedings 2145 "Solid state synthesis of CdS quantum dots through laser direct writing"
<p>FIGURE 1 of the conference paper "Solid state synthesis of CdS quantum dots through laser direct writing"</p>
FIGURE 4 from conference paper AIP Proceedings 2145 "Solid state synthesis of CdS quantum dots through laser direct writing"
<p>The dataset includes two files: the word file describe the type of sample and the procedures used to pick up the data; the excel file includes the raw data used to obtain the plots of figure 4.</p> <p>in the following is reported the description of figure 4 as wrote in the paper.</p> <p>PL emission of CdDBX QDs synthetized with single pulses at 355 nm, 10 ps and pulse energy varied between 4.67 to 7.6 μJ. (a), (b) and (c) are respectively the emission in blue, green and red range as defined in methodology. Graph (d) shows the change of the ratio between the red and green emission from the same points.</p>
FIGURE 5 from conference paper AIP Proceedings 2145 "Solid state synthesis of CdS quantum dots through laser direct writing"
<p>The dataset contains two files: the word file describe the type of sample examined and the procedure used to obtain the DOIs used to obtain the plots. The second file is the excel file that includes the raw data used to obtain the plots reported in the figure 5 of the paper.</p> <p>In the following is reported the description of figure 5 as written in the paper:</p> <p>PL emission of CdDBX QDs synthetized with scanlines at 355 nm, 10 ps overlapping pulse of about 99.5% and</p> <p>pulse energy varied between 4.67 to 7.6 μJ. (a), (b) and (c) are respectively the emission in blue, green and red range as defined</p> <p>in methodology. Graph (d) shows the change of the ratio between the red and green emission from the same point. Point “X” is</p> <p>censored and not used to fit the model.</p>
Figure data for "Detection of tar brown carbon with the single particle soot photometer (SP2)"
<p>Data contained in Figures 2 and 4 of Corbin and Gysel-Beer 2019. https://doi.org/10.5194/acp-2019-568</p>
Figure 2. Comparison bar of statistic results SC 2015 – 2017
<p>The theme of the Summer Reading and Creativity Campaign 2018, in which 148 members of the GLN participated, focused on “Favorite Data”. From June 20th to September 7th, children were called on to observe, record, comprehend, and report data through 46 different workshops tailored accordingly to innovative methodological approaches to pedagogy. </p>
HCP-YA Tractography Atlas (Figures)
<p>Yeh, F. C., Panesar, S., Fernandes, D., Meola, A., Yoshino, M., Fernandez-Miranda, J. C., ... & Verstynen, T. (2018). Population-averaged atlas of the macroscale human structural connectome and its network topology. NeuroImage, 178, 57-68.</p>
figure data for "Subsurface radiation environment of Mars and its implication for shielding protection of future habitats" by L.Röstel, J.Guo et al. 2020
<pre>This data of dose rates at different elevations above and below the Martian surface was modeled using the GEANT4-based AtRIS toolkit. Please refer to the following paper for reference and a detailed description of the model and scaling: „Subsurface radiation environment of Mars and its implication for shielding protection of future habitats“, L.Röstel, J.Guo et al. 2020 JGR: planets. List of files: AbsorbedDosePrimariesAR.txt - figures 2 in the paper EquivalentDosePrimariesAR.txt - figure 3 AbsorbedDoseSiliconSlabScenarios.txt - figure 4 AbsorbedDoseWaterSphereScenarios.txt - figure 5 EquivalentDoseWaterSphereScenarios.txt - figure 6 NeutronFlux.txt - figure 7 RequiredShieldingDepth.txt - figure 8</pre>
Figures S1-S7. SMR-HEIDI analysis results for 8q24.21 locus between BP and selected phenotypes.
<p><strong>Supplementary Figures S1-S7. </strong><strong>SMR-HEIDI analysis results for rs6651255 between BP and selected phenotypes.</strong></p> <p>This project contains the following figures:</p> <ul> <li> <p>Figure S1. SMR-HEIDI analysis results for rs6651255 between BP and LDH.</p> </li> <li> <p>Figure S2. SMR-HEIDI analysis results for rs6651255 between BP and <em>GSDMC</em> expression in skeletal muscle (GTEx v6).</p> </li> <li> <p>Figure S3. SMR-HEIDI analysis results for rs6651255 between BP and <em>FAM49B</em> expression in Brain anterior cingulate cortex BA24 (GTEx v6).</p> </li> <li> <p>Figure S4. SMR-HEIDI analysis results for rs6651255 between BP and <em>FAM49B</em> expression in CD8 cell line (CEDAR).</p> </li> <li> <p>Figure S5. SMR-HEIDI analysis results for rs6651255 between BP and heel bone mineral density (UKBB).</p> </li> <li> <p>Figure S6. SMR-HEIDI analysis results for rs6651255 between BP and disc problem phenotype (UKBB)</p> </li> <li> <p>Figure S7. SMR-HEIDI analysis results for rs6651255 between BP and height (UKBB).</p> </li> </ul> <p> </p> <p><strong>Figures legend:</strong></p> <p>Each figure consists of four parts (1 – top left; 2- top right; 3- bottom left; 4 – bottom right):</p> <ol> <li> <p>Regional association plots for GWAS-1 (in our case BP GWAS) and GWAS-2 (expression or complex trait). Blue triangles represent SNPs used to calculate HEIDI test. Crossed triangle is leading SNP for which SMR test was computed.</p> </li> <li> <p>Z-Z plot (GWAS-1 on y-axis and GWAS-2 on x-axis).</p> </li> <li> <p>Visualization of LD matrix for SNPs used in calculation of HEIDI test.</p> </li> <li> <p>Plot of SMR regression coefficient estimates. The plot visualizes the heterogeneity of SMR coefficient. Blue color represents SNPs used to calculate HEIDI test.</p> </li> </ol>
figure data for "Radiation environment and doses on Mars at Oxia Planum and Mawrth Vallis: support for exploration at sites with biosignature preservation potential", by F. Da Pieve, G. Gronoff, J. Guo et al (2020)
<p>The data are the tabular format of the plots in figures 2-7 of the paper submitted.</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.