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18,658 results for “impacts”
Supporting Datasets for 'SWOT Satellite Reveals Devastating Flood Impact in Rio Grande do Sul, Brazil'
<p>This dataset contains multiple sources of geospatial and environmental data used for flood extension and volume analysis in the study of the May 2023 extreme flood that hit south Brazil. The data integrates satellite remote sensing, atmospheric reanalysis, hydrological measurements, terrain data, and socioeconomic indicators to evaluate flooding impacts and their connection to climate variability.</p> <p>The dataset include:</p> <ol> <li> <p><strong>Sentinel-2 Satellite Data</strong></p> </li> <li> <p><strong>SWOT Mission High-Density Pixel Cloud Data</strong></p> </li> <li> <p><strong>HIDROWEB Water Level Data</strong></p> </li> <li> <p><strong>Forest And Buildings Removed Copernicus Digital Elevation Model (FABDEM)</strong></p> </li> <li> <p><strong>Modern-Era Retrospective Analysis for Research and Applications 2 (MERRA-2)</strong></p> </li> <li> <p><strong>Integrated Multi-satellite Retrievals for Global Precipitation Measurements (IMERG)</strong></p> </li> <li> <p><strong>Brazilian Meteorological Database (BDMEP)</strong></p> </li> <li> <p><strong>Institute of Hydraulic Research (IPH) Flood Extent Map</strong></p> </li> <li> <p><strong>Socioeconomic Data from the Social Vulnerability Atlas</strong></p> </li> </ol> <p> </p> <p><strong>Disclaimer:</strong></p> <p>This dataset is a compilation of various data sources, each of which may be subject to its own specific licensing terms and conditions. Users of this dataset are strongly advised to review the license associated with each individual sub-dataset before using, modifying, or redistributing the data. While we have provided the dataset under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, certain sub-datasets may have additional restrictions or requirements, such as attribution, non-commercial use, or limitations on derivative works. It is the responsibility of the user to ensure compliance with all applicable licenses. Please refer to the original data sources and their respective licenses for detailed information.</p>
The Impact of Generative AI on Student Learning Outcomes: A Statistical Analytical Approach - Dataset
Open the record for dataset details and reuse information.
An Analysis of the Coastal Evolution of the South Coast of County Wexford and the Impact of Wind
<p>An assessment of coastal change along the south coast of County Wexford in conjunction with the GSI using orthophoto sets from 3 different years spanning between 2014 and 2019 (2014, 2016, and 2019) using the ArcGIS add-in tool DSAS to calculate the changes. The study area was the coastline between Carnsore Point and Hook Head.</p> <p>The key findings of the study were that between 2014 and 2019, just under 59% of the coastline was eroding and the average rate of erosion of these areas was 46 cm per year. </p> <p>Wind speed and direction data for County Wexford provided by the GSI was used to observe the correlation between wind speed, mean height of waves (SWH) and coastal erosion. There was a strong correlation between wind speed and mean wave height in both periods 2014-2016 and 2016-2019. Average wind speed and mean wave height were both lower during 2014-2016 than during 2016-2019. The average rate of erosion between 2016-2019 was higher than during 2014-2016 which points to a connection between wind and coastal erosion as average wind speed and wave height were higher during the 2016-2019 period. Irish wind data available on the Met Éireann (Ireland's National Meteorological Service) website was also used to highlight the stilling phenomenon of wind observable in Ireland during the last century.</p> <p> </p>
Illustrations for 'Disturbances in the evergreen boreal forest and their impact on 21st century vegetation and climate dynamics - A stochastic modeling approach' (Doctoral thesis)
<p>This repository contains all the original illustrations I created for my doctoral thesis at the Technical University of Munich. This work is published under a Creative Commons CC-BY-SA license, which means that you are free to use and adapt this work under the same license for commercial and non-commercial applications as long as you credit the original work. To credit, please cite this repository as well as my doctoral thesis.</p> <p> </p> <p> </p>
Impact of water models on structure and dynamics of enzyme tunnels
<ul> <li>1-initial_topologies_coordinates.tar.gz <ul> <li>primary input coordinates and parameter-topology files of all initial systems (LinBwt, LinB32, and Linb86 variants of haloalkane dehalogenase) in OPC and TIP3P water models</li> <li>prepared with the tleap module of AMBER18 package</li> <li>parm7 and crd formatted</li> </ul> </li> <li>2-cap_domain_gate_distances.tar.gz <ul> <li>datasets with minimum distance calculation between Asp146 and Leu176</li> <li>calculated by CPPTRAJ module of AMBER 18 for each performed simulation</li> <li>plain text formatted</li> </ul> </li> <li>3-protein_trajectories-linbwt.tar.gz <ul> <li>three replicated 400 ns (20,000 frames, i.e., every second frame) dry production phase trajectories of LinBwt in OPC and Tip3P water models with corresponding parameter-topology files</li> <li>produced by pmemd.cuda module of AMBER 18</li> <li>netcdf and parm7 formatted</li> </ul> </li> <li>3-protein_trajectories-linb32-closed.tar.gz <ul> <li>two replicated 400 ns (20,000 frames, i.e., every second frame) dry production phase trajectories of closed state LinB32 in OPC and Tip3P water models with corresponding parameter-topology files</li> <li>produced by pmemd.cuda module of AMBER 18</li> <li>netcdf and parm7 formatted</li> </ul> </li> <li>3-protein_trajectories-linb32-open.tar.gz <ul> <li>two replicated 400 ns (20,000 frames, i.e., every second frame) dry production phase trajectories of open state LinB32 in OPC and Tip3P water models with corresponding parameter-topology files</li> <li>produced by pmemd.cuda module of AMBER 18</li> <li>netcdf and parm7 formatted</li> </ul> </li> <li>3-protein_trajectories-linb86-closed.tar.gz <ul> <li> two replicated 400 ns (20,000 frames, i.e., every second frame) dry production phase trajectories of closed state LinB86 in OPC and Tip3P water models with corresponding parameter-topology files</li> <li>produced by pmemd.cuda module of AMBER 18</li> <li>netcdf and parm7 formatted</li> </ul> </li> <li>3-protein_trajectories-linb86-open.tar.gz <ul> <li>two replicated 400 ns (20,000 frames, i.e., every second frame) dry production phase trajectories of open state LinB86 in OPC and Tip3P water models with corresponding parameter-topology files</li> <li>produced by pmemd.cuda module of AMBER 18</li> <li>netcdf and parm7 formatted</li> </ul> </li> <li>4-basic_analyses.tar.gz <ul> <li>datasets on RMSF, RMSD, RoG, and RDF from the CPPTRAJ module of AMBER 18</li> <li>for selected replicas 3x LinBwt, 2x LinB32-closed, 2x LinB32-open, 2x LinB86-closed and 2x LinB86-open</li> <li>plain text and PDB formatted</li> </ul> </li> <li>5-caver_analyses.tar.gz <ul> <li>results of tunnel analyses for two replicas of open & closed state each for LinB32 & LinB86 in OPC and TIP3P, and three replicas of LinBWT in OPC and TIP3P</li> <li>generated by CAVER 3.0 using "Divide-and-conquer approach" (MethodsX, 10, 2023, 101968)</li> <li>comprising csv and pdb formatted: tunnel_profiles.csv and bottlenecks.csv, stripped_system.10001.pdb, v_origins.pdb</li> <li>For this and following analyses, the names of the trajectories were modified as follows: <ul> <li>linbwt_opc1_2 = md1_opc_linbwt; linbwt_opc2_2 = md2_opc_linbwt; linbwt_opc3_2 = md3_opc_linbwt;</li> <li>linbwt_tip3p1_2 = md1_tip3p_linbwt; linbwt_tip3p2_2 = md2_tip3p_linbwt; linbwt_tip3p3_2 = md3_tip3p_linbwt;</li> <li>linb32-closed_opc1_2 = md1_closed_opc_linb32; linb32-closed_opc2_2 = md2_closed_opc_linb32;</li> <li>linb32-open_opc1_2 = md1_open_opc_linb32; linb32-open_opc2_2 = md2_open_opc_linb32;</li> <li>linb32-closed_tip3p1_2 = md1_closed_tip3p_linb32; linb32-closed_tip3p2_2 = md2_closed_tip3p_linb32;</li> <li>linb32-open_tip3p1_2 = md1_open_tip3p_linb32; linb32-open_tip3p2_2 = md2_open_tip3p_linb32;</li> <li>linb86-closed_opc1_2 = md1_closed_opc_linb86; linb86-closed_opc2_2 = md2_closed_opc_linb86;</li> <li>linb86-open_opc1_2 = md1_open_opc_linb86; linb86-open_opc2_2 = md2_open_opc_linb86;</li> <li>linb86-closed_tip3p1_2 = md1_closed_tip3p_linb86; linb86-closed_tip3p2_2 = md2_closed_tip3p_linb86;</li> <li>linb86-open_tip3p1_2 = md1_open_tip3p_linb86; linb86-open_tip3p2_2 = md2_open_tip3p_linb86.</li> </ul> </li> </ul> </li> <li>6-transport_tools_analyses.tar.gz <ul> <li>results of comparative analyses for all simulations generated in 5-caver_analyses.tar.gz</li> <li>generated by TransportTools 0.9.3</li> <li>comprising csv, pdb, plain text and py formatted: configuration file (config_TT.ini), tunnel_profiles (data folder) for all filtered tunnels and bottlenecks (data folder) for all filtered tunnels, statistics (statistics folder) and visualization (visualization folder)<br> </li> </ul> </li> </ul> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Data output from Projecting future climate change impacts on the distribution of pelagic squid in the Southern Ocean
<p>Data output from Projecting future climate change impacts on the distribution of pelagic squid in the Southern Ocean:<br>Rasters, R models and scripts</p>
Software sustainability of global impact models (Dataset and analysis script)
<p><strong><em>slocount.py</em></strong>: This script calculates the number of comment lines, total lines of code (TLOC) and source lines of code (SLOC). It uses a code line counter developed by Ben Boyter, which must be installed (https://github.com/boyter/scc.). The source code links to the global impact models (GIMs) can be found in the 'ISIMIP_models.xlsx' file.</p> <p><strong><em>active_dev.py</em></strong>: This script plots the number of active developers for each GIM across 10 sectors. It utilizes data from the 'active_dev.csv' file, which lists the GIMs and their respective number of developers.</p> <p><strong><em>cocomo.py</em></strong>: This script estimates the effort required for software development using the methodology proposed by Sachan et al. 2016 (https://doi.org/10.1016/j.procs.2016.06.107). It also generates plots for these estimates.</p> <p><strong><em>comment_density_modularity.py</em></strong>: This script calculates the comment density and evaluates the modularity of the modules. It also produces plots for these metrics.</p> <p><strong><em>code_standard.py</em></strong>: This script uses Pylint (<a href="https://pylint.readthedocs.io/en/latest/user_guide/usage/output.html">https://pylint.readthedocs.io/en/latest/user_guide/usage/output.html</a>) to check if the source code, either in part or in its entirety, adheres to the PEP8 coding standard. It also generates lint scores for the source code.</p> <p><strong><em>line_count.zip</em></strong>: This file contains the results of counting the number of comment lines, TLOC and SLOC for each GIM.</p> <p><strong><em>lint_score.zip</em></strong>: This file contains the results of running pylint on GIMs that include Python in their source code. Results also include lint score per GIM</p>
Data Supporting "Air-Sea Momentum Exchange with Explicit Wind-Wave-Current Coupling and Effects on Hurricane Structure and Impacts"
<p>This dataset produced the figures for the Air-Sea Momentum Exchange with Explicit Wind-Wave-Current Coupling and Effects on Hurricane Structure and Impacts Manusrcipt.</p>
Table 1 in Mitochondrial DNA reveals the impact of Pleistocene glaciations on a widespread palearctic bat species
<p><b>Table 1</b> Average values of detected genetic p-distances (±SD) in percentage per lineage of <i>H. savii</i> s.l. according to used marker set</p><table><tbody><tr><th></th><th></th><th>A</th><th>B</th><th>C</th><th>D</th><th>E</th></tr></tbody><tbody><tr><th>A</th><td>ND1:</td><td>0.46± 0.12</td><td></td><td></td><td></td><td></td></tr><tr><td>CytB:</td><td>0.32± 0.21</td><td></td><td></td><td></td><td></td></tr><tr><td>COI:</td><td>0.28± 0.24</td><td></td><td></td><td></td><td></td></tr><tr><td>16 S:</td><td>0.13± 0.11</td><td></td><td></td><td></td><td></td></tr><tr><th>B</th><td>ND1:</td><td>9.14± 0.28</td><td>0.47± 0.57</td><td></td><td></td><td></td></tr><tr><td>CytB:</td><td>8.03± 1.31</td><td>1.68± 0.96</td><td></td><td></td><td></td></tr><tr><td>COI:</td><td>7.90± 0.24</td><td>0.18± 0.19</td><td></td><td></td><td></td></tr><tr><td>16 S:</td><td>3.90± 0.27</td><td>0.34± 0.41</td><td></td><td></td><td></td></tr><tr><th>C</th><td>ND1:</td><td>6.79± 0.51</td><td>8.46± 0.35</td><td>0.62 ±0.68</td><td></td><td></td></tr><tr><td>CytB:</td><td>8.27± 0.68</td><td>9.01± 1.06</td><td>1.44 ±0.71</td><td></td><td></td></tr><tr><td>COI:</td><td>7.36± 0.25</td><td>8.92± 0.24</td><td>0.83 ±0.61</td><td></td><td></td></tr><tr><td>16 S:</td><td>/</td><td>/</td><td>/</td><td></td><td></td></tr><tr><th>D</th><td>ND1:</td><td>9.19± 0.30</td><td>9.12± 0.24</td><td>8.55 ±0.64</td><td>0.42±0.54</td><td></td></tr><tr><td>CytB:</td><td>/</td><td>/</td><td>/</td><td>/</td><td></td></tr><tr><td>COI:</td><td>8.91± 0.18</td><td>7.36± 0.28</td><td>10.15 ±0.38</td><td>0</td><td></td></tr><tr><td>16 S:</td><td>/</td><td>/</td><td>/</td><td>/</td><td></td></tr><tr><th>E</th><td>ND1:</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td></tr><tr><td>CytB:</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td></tr><tr><td>COI:</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td></tr><tr><td>16 S:</td><td>12.8± 0.65</td><td>11.81 ±0.89</td><td>/</td><td>/</td><td>0.59± 0.30</td></tr><tr><th>X</th><td>ND1:</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td></tr><tr><td>CytB:</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td></tr><tr><td>COI:</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td></tr><tr><td>16 S:</td><td>4.62± 0.23</td><td>5.12± 0.29</td><td>/</td><td>/</td><td>9.97± 0.31</td></tr></tbody></table>
Table 2 in Mitochondrial DNA reveals the impact of Pleistocene glaciations on a widespread palearctic bat species
<p><b>Table 2</b> Estimated divergence times (in MYa) between mitochondrial lineages, obtained by BI analysis of the different marker datasets. Node heights (mean ages) are given, with 95% HPD intervals in square brackets. FIS indicates the first internal split within what we consider <i>H. savii</i> s.l. (including all four lineages – A, B, C and D), while SNN denotes the split from its nearest neighbour in the tree (most commonly <i>H. alaschanicus</i>). In the case of 16 S dataset, SNN does not include lineage E, as it is placed significantly more basal (see Fig. 2d). E is also excluded from <i>H. savii</i>, alongside X (Fig. 1c), in case of concatenated dataset. (C1, C2) refers to dating of the split within lineage C. Hstu – <i>H. stubbei</i>, Hara – <i>H. arabicus</i>, sc – sister clade</p><table><tbody><tr><th></th><th>ND1</th><th>CytB</th><th>COI</th><th>16 S</th><th>Conc.</th></tr></tbody><tbody><tr><th>(A, C)</th><td>2.17 [1.62–2.70]</td><td>2.16 [1.75–2.60]</td><td>2.58 [1.94–3.30]</td><td></td><td>3.21 [2.56–3.89]</td></tr><tr><th>(A, B)</th><td></td><td></td><td></td><td>3.31 [2.13–4.52]</td><td></td></tr><tr><th>(B, D/Hstu)</th><td>2.38 [1.89–2.92]</td><td></td><td>2.54 [1.87–3.24]</td><td></td><td>3.12 [2.46–3.82]</td></tr><tr><th>(B, (A, C))</th><td></td><td>2.50 [2.08–2.94]</td><td></td><td></td><td></td></tr><tr><th>(C1,C2)</th><td>0.51 [0.32–0.70]</td><td>0.54 [0.36–0.73]</td><td>0.44 [0.24–0.66]</td><td></td><td>0.64 [0.43–0.85]</td></tr><tr><th>(E, sc)</th><td></td><td></td><td></td><td>11.18 [8.15–14.39]</td><td>9.93 [7.79–12.19]</td></tr><tr><th>(X, (A, B))</th><td></td><td></td><td></td><td>4.31 [2.97–5.76]</td><td></td></tr><tr><th>(X, Hara)</th><td></td><td></td><td></td><td></td><td>2.47 [0.00-5.44]</td></tr><tr><th>FIS</th><td>2.79 [2.32–3.28]</td><td></td><td>3.29 [2.67–3.95]</td><td></td><td>3.89 [3.22–4.62]</td></tr><tr><th>SNN</th><td>3.12 [2.58–3.67]</td><td>3.36 [2.81–3.93]</td><td>3.61 [2.95–4.34]</td><td>5.96 [4.68–7.37]</td><td>4.51 [3.72–5.30]</td></tr></tbody></table>
Novel Endpoint Characterization Factors for Life Cycle Impact Assessment of Terrestrial Acidification
<p>This repository contains Excel files with the characterization factorsand the soil response factors for the publication entitled "Novel Endpoint Characterization Factors for Life Cycle Impact Assessment of Terrestrial Acidification", published in the "Journal of Ecological Indicators" .<br><br></p> <p>Content:</p> <p><strong>Datasets.zip</strong> is an folder containing the following Excel files: </p> <ul> <li><strong>CF_Terrestrial_Acidification_2024-08-01.xlsx</strong> with the following sheets <ul> <li><em>Dataframe</em> gathering terrestrial acidification marginal endpoint CF [PDF.yr/kg_emitted] values at country level (with the world average value), for 3 acidifying substances (NOx, NHx, SOx) at global and regional impact scales (with and without the inclusion of the Global Extinction Probability - GEP - respectively)</li> <li><em>Calc Info</em> summing up calculation informations</li> <li><em>Calc Table</em> registering input parameters used to run the calculations leading to the <em>Dataframe</em> sheet (substance used, original and adapted resolutions for emission and deposition compartments, resolutions for each CF components and spatial transformations, GEP normalization method)</li> </ul> </li> <li><strong>RF_NO3_2024-03-27.xlsx</strong> with the following sheet:<br> <ul> <li><em>RFs</em> containing the soil response values [(molH+ / L).(yr / kg_dep)] at ecoregion level (referred to as "idTarget") for a marginal increase of 10% in NO3 deposition rate. It also gathers the intermediate results leading the final RFs (sustances' deposition rates, reference and post-deposition increase pHs) </li> </ul> </li> <li><strong>RF_NH4_2024-03-25.xlsx</strong> with the following sheet:<br> <ul> <li><em>RFs</em> containing the soil response values [(molH+ / L).(yr / kg_dep)] at ecoregion level (referred to as "idTarget") for a marginal increase of 10% in NH4 deposition rate. It also gathers the intermediate results leading the final RFs (sustances' deposition rates, reference and post-deposition increase pHs)</li> </ul> </li> <li><strong>RF_SO4_2024-03-25.xlsx</strong> with the following sheet:<br> <ul> <li><em>RFs</em> containing the soil response values [(molH+ / L).(yr / kg_dep)] at ecoregion level (referred to as "idTarget") for a marginal increase of 10% in SO4 deposition rate. It also gathers the intermediate results leading the final RFs (sustances' deposition rates, reference and post-deposition increase pHs)</li> </ul> </li> </ul>
Data from "Examination of how properties of a fissioning system impact isomeric yield ratios of the fragments"
<p>Data from PRC article "Examination of how properties of a fissioning system impact isomeric yield ratios of the fragments"<br>https://doi.org/10.1103/PhysRevC.108.064602</p>
Impact of a Bimodal Dust Distribution on the 2018 Martian Global Dust Storm with the NASA/Ames Mars Global Climate Model
<p>This dataset is simulation data from the NASA Ames Mars GCM, produced for the research article titled "Impact of a Bimodal Dust Distribution on the 2018 Martian Global Dust Storm with the NASA/Ames Mars Global Climate Model." It is presented in NetCDF format.</p>
Impact of Interval Censoring on Data Accuracy and Machine Learning Performance in Biological High-Throughput Screening
<div> <h2>Overview</h2> <div>Data and Results used in the publication entitled "Impact of Interval Censoring on Data Accuracy and Machine Learning Performance in Biological High-Throughput Screening"</div> </div> <div> <h3><strong>Data</strong></h3> <div>This folder contains the raw data used during this work.</div> <div>`EvoEF.csv` contains information on the library used (sequences, number of mutations, etc.) and the fitness (energy) used as continuous mean values. `mut.csv` contains the information about the combinatorial scaling (N vs N_norm), the number of mutations (m) and the probability of each variant using different distributions (uniform and binomial) at different $p_{WT}$.</div> <div>For further details on how the fitness values were calculated and how the combinatorial scale works, please refer to our prevoius [Paper](https://arxiv.org/abs/2405.05167).</div> <div> </div> <h3><strong>Results</strong></h3> <div> <div>This folder contains the results (outputs) of all scripts used. Such results are included in the form of `.npy` and `.npz` files. To load such files with numpy you should include the option `allow_pickle=True`.</div> </div> </div>
Climate change impacts pair-bond dynamics in a long-lived monogamous species
<p>Manuscript title: Climate change impacts pair-bond dynamics in a long-lived monogamous species</p> <p><br>Author(s):<br>Ruijiao Sun<br>Woods Hole Oceanographic Institution<br>Massachusetts Institute of Technology<br>University of California, Santa Barbara, USA<br>ruijiaos@ucsb.edu<br>ruijiaos@icloud.com</p> <p>Christophe Barbraud<br>Centre d’Etudes Biologiques de Chizé, <br>CNRS-La Rochelle University UMR7372, <br>79360 Villiers en Bois, France<br>christophe.barbraud@cebc.cnrs.fr </p> <p>Stéphanie Jenouvrier<br>Biology Department, <br>Woods Hole Oceanographic Institution, <br>Woods Hole, MA 02543, USA<br>sjenouvrier@whoi.edu</p> <p>Karine Delord<br>Centre d’Etudes Biologiques de Chizé, <br>CNRS-La Rochelle University UMR7372, <br>79360 Villiers en Bois, France</p> <p>Kristen Krumhardt<br>Climate and Global Dynamics, <br>NSF National Center for Atmospheric Research (NCAR), <br>Boulder, Colorado, USA</p> <p>Rémi Fay<br>Laboratoire de Biom´etrie et Biologie Évolutive, <br>UMR 5558, Université Claude Bernard Lyon 1, <br>Villeurbanne, France</p> <p>Francesco Ventura<br>Biology Department, <br>Woods Hole Oceanographic Institution, <br>Woods Hole, MA 02543, USA<br>sjenouvrier@whoi.edu</p> <p>Bilgecan Şen<br>Center for Environmental Science,<br>University of Maryland,<br>Cambridge, MD 21613,USA</p> <p> </p> <p>Abstract:<br>Climate change can influence populations of monogamous species by affecting pair-bond dynamics. This study examined the impact of climate on widowhood and divorce, and the subsequent effects on individual vital rates and life-history outcomes over 54 years in a snow petrel (Pagodroma nivea) population. We found that environmental conditions can affect pair-bond dynamics both directly and indirectly. Divorce was adaptive, occurring more frequently after breeding failure and leading to improved breeding success. Divorce probabilities also increased under severe climatic conditions, regardless of prior breeding success, supporting the "Habitat-mediated” mechanisms. Generally, pair-bond disruptions reduced subsequent vital rates and lifetime outcomes. Climate forecasts from an Atmosphere-Ocean General Circulation Model projected increased male widowhood rates due to decreased sea ice negatively affecting female survival, despite considerable uncertainty. These findings highlight the importance of environmentally induced changes in demographic and pair-bond disruption rates as crucial factors shaping demographic responses to climate change. </p> <p>Funding information: <br>This work was supported by the National Science Foundation (OPP 1840058) to SJ and RS. Field data has been collected since 1963 on Ile des Pétrels as part of ORNITHOECO Project 109 "Seabirds and marine mammals as sentinels of global changes in the Southern Ocean”, funded by the French Polar Institute Paul-Emile Victor (IPEV; PI C. Barbraud). Institut Polaire Françis, Paul-Emile Victor (IPEV), Terres Australes et Antarctiques Françaises, and Zone Atelier Antarctique et Terres Australes (LTSER France, CNRS-INEE) provided logistical and financial support. This study is part of the long-term Studies in Ecology and Evolution (SEE-Life) program of the CNRS and a contribution to Project SENSEI (Sentinels of the sea ice) funded by Fondation BNP Paribas.</p> <p>Data location:<br>Ile des Pétrels, Pointe Géologie Archipelago (66°40'S, 140°01'E), Terre Adélie, Antarctica</p> <p>File folder list:<br>Forecast<br>JAGS_models<br>LHO</p> <p>Software:<br>Bayesian multi-state capture-mark-recapture analyses were conducted using JAGS through R. Life history analyses and pair-bond disruption forecasts were performed using MATLAB. All figures were produced in MATLAB.</p> <p>Description:<br>These files contain all the data, code, and model outputs needed to reproduce the results and figures in our study. <br>The data comes from a long-term monitoring project of snow petrels breeds at Ile des Pétrels, Pointe Géologie Archipelago (66°40'S, 140°01'E), Terre Adélie, Antarctica. Since 1963, an annual long-term monitoring study has been conducted. Adults and chicks were leg-banded with stainless-steel bands, with adult sex determined by vocalization and relative size. Nest surveys during incubation and fledging periods determined breeding success and pair identities.</p> <p>There are three main subfolders included: <br>(1) JAGS_models: JAGS code and R code to perform our Multi-state capture-mark-recapture models and corresponding model output; <br>(2) LHO: Code for life-history outcome analysis; <br>(3) Forecast: Climate projection data and code for pair-bond disruption forecast.<br>Each subfolder includes a README.txt file that describes its contents.</p> <p> </p>
Projections of Precipitation and Temperatures in Greenland and the Impact of Spatially Uniform Anomalies on the Evolution of the Ice Sheet
<p>Supplementary data used in the article <em>Projections of Precipitation and Temperatures in Greenland and the Impact of Spatially Uniform Anomalies on the Evolution of the Ice Sheet</em> submitted to The Cryosphere.</p> <p>Please cite the corresponding paper if you use this data. </p> <p>We supply</p> <ol> <li>the regridded CMIP6 precipitation (pr) and temperature (tas) model output for Greenland.</li> <li>local precipitation-near surface temperature sensitivities for each model in the folder <em>local_slopes_precipitation</em></li> <li>The monthly anomalies for near-surface temperature and precipitation as well as the local change in precipitation (compare to the climatology, in %) with respect to the climatology (year 1980-200) in the folder <em>2100_anomalies</em></li> <li>The CMIP6 monthly climatology from the years 1980-2000 (precipitation & near-surface temperature) </li> <li>The monthly precipitation and near-surface temperature anomalies from 2015-2100 in the folder <em>2100_85years</em></li> </ol> <p> </p> <p>License of CMIP6 model output: </p> <p>CMIP6 model data produced is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0; https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file). The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</p>
Data from: Heterogeneous zonal impacts of climate change on a wide hyperendemic area of human and animal fascioliasis assessed within a One Health action for prevention and control
Open the record for dataset details and reuse information.
The impact of social complexity on the efficacy of natural selection in termites
<p>This repository contains the dataset and markdown files necessary to reproduce the analysis conducted for the article entitled <em>Social evolution in termites reduces natural selection efficacy</em> (NEW TITLE: <em>The impact of social complexity on the efficacy of natural selection in termites</em>). It includes both raw and processed data, as well as all scripts used in the analysis, organized by the order of execution. Instructions for setting up the environment and running the scripts are provided to facilitate replication of the results.</p> <p>Here, we look at the distribution of dN/dS within Blattodea and Isoptera in order to test for a positive relationship with eusociality.</p> <p>The file <strong>analysis.html</strong> is a R-markdown file describing step by step the analysis of the submitted manuscript. To reproduce the analysis, all files have to be downloaded within the same directory.</p> <p>For any further information or questions, please contact camille.roux@univ-lille.fr or jonathan.romiguier@umontpellier.fr.</p> <p>Please cite this repository if you use these materials in your research.</p>
Data analysis & code: Quantifying the impact of climate change and forest management on Swedish forest ecosystems using the dynamic vegetation model LPJ-GUESS
<p><span>This file contains code to optimize the allometric parameters, to plot the figures, and details of the underlying data analysis in "Quantifying the impact of climate change and forest management on Swedish forest ecosystems using the dynamic vegetation model LPJ-GUESS" (Bergkvist et al.). <br></span></p>
Data and codes for "The impact, costs, and cost-effectiveness of tuberculosis outbreak investigations in the United States: a model-based analysis"
<p>Data and codes for the manuscript entitled:</p> <p>"<strong>The impact, costs, and cost-effectiveness of tuberculosis outbreak investigations in the United States: a model-based analysis"</strong></p> <p> </p> <p><strong>Please see Readme.txt for details</strong></p>
ScienceDex guides
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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.