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18,658 results for “impacts”
Data from: Elevated human impact on islands increases the introduction and extinction status of native insular reptiles
<p>In the Anthropocene, the ranges of introduced species are expanding, while extinction-prone species are contracting. Introductions and extinctions are caused by how species respond to human impacts, but it is unknown why the ranges of some species expand and some contract. Here, we test whether this opposite response of human impact is due to introduced and extinction-prone species falling at opposite ends of geographic, evolutionary, or ecological trait continua. We constructed a database of native range maps, traits, phylogenetic relationships, and the introduction and extinction-prone status of squamate reptiles with ranges native to the Western Hemisphere. Across >3,000 snake and lizard species (88% of known native squamates), 142 had been introduced elsewhere and 483 were extinction-prone (i.e., vulnerable, endangered, critically endangered, extinct in the wild, extinct). To explain variation in status, we first tested if the same human-impacted regions in the Americas contained the native ranges of species of either status. Second, we tested for phylogenetic signal in species status. Finally, we tested the explanatory power of multiple trait continua. The native ranges of introduced and extinction-prone reptiles were clustered in island regions with high human impact vs. mainland regions with lower human impact. Phylogenetic signal was weak for status, but introduced and extinction-prone species were clustered in different clades. All geographic and ecological traits that explained each status supported the opposite ends hypothesis. Introduced species had larger, edgier ranges, while extinction-prone species had smaller, simpler ranges. Introduced species were mostly herbivorous/omnivorous, while extinction-prone species were mostly carnivorous. Introduced species produced larger clutches, while extinction-prone species had smaller body sizes. In the Anthropocene, the naive ranges of introduced and extinction-prone species are in the same human-impacted regions where trait continua, having opposite effects, determine whether species ranges expand or contract in the continuing face of global change.</p>
Coastal Upwelling Modulates Winds and Air-Sea Fluxes, Impacting Offshore Wind Energy
<p>Model Output supporting the paper "Coastal Upwelling Modulates Winds and Air-Sea Fluxes, Impacting Offshore Wind Energy"</p> <p>The dataset includes four WRF runs, with upwelling (labeled 'operational') and with upwelling removed (labeled 'experimental'). Two of the runs have parameterized wind turbines, labeled "Fitch". </p> <p>This work was supported by NJ Board of Public Utilities. </p> <p> </p>
Invasive water hyacinth impacts dataset
Open the record for dataset details and reuse information.
Quantifying Salt Crystallization Impact on Evaporation Dynamics from Porous Surfaces
<p>The file contains cumulative evaporative mass losses during evaporation from sand columns saturated with 10%, 15%, and 20% NaCl solutions (mass basis).</p>
Dataset of "Impact-driven oxidation of organics explains chondrite shock metamorphism dichotomy"
<p>This is a dataset of the manuscript entitled "<strong><span>Impact-driven oxidation of organics explains chondrite shock metamorphism dichotomy</span></strong>".</p>
Evaluating the impact of filler size and filler content on the stiffness, strength, and toughness of polymer nanocomposites using coarse-grained molecular dynamics: dataset
<div><strong>Abstract:</strong></div> <div>(from [1])</div> <div>Their great versatility makes polymer nanocomposites an important class of engineering materials. In order to gain detailed insights into the nanoscale mechanisms underlying their macroscopic mechanical properties, molecular dynamics (MD) simulations are a valuable tool to complement experimental studies. In this work, we modify the analytical potential functions of an efficient bead-spring model representing a generic polymer nanocomposite to account for the breaking of covalent bonds. We perform uniaxial tensile simulations of double-notched specimens and validate the model using experimental trends for overall stiffness, strength, and toughness. First, we study the effects of sample size, notch geometry, strain rate, temperature, and molar mass for the pure thermoplastic matrix material. Second, we analyze the influence of filler size and filler content on the mechanical behavior of the polymer nanocomposite. With this study, we show that in both the development of new materials and the optimization of established materials, it is possible to gain important preliminary insights into the effects of pertinent material characteristics with a simple MD setup, which can then be further refined by increasing the complexity of the material description and the boundary conditions. </div> <div> </div> <div> </div> <div><strong>Contact:</strong></div> <div>Felix Weber</div> <div>Institute of Applied Mechanics</div> <div>Friedrich-Alexander-Universität Erlangen-Nürnberg</div> <div>Egerlandstr. 5</div> <div>91058 Erlangen</div> <div>Germany</div> <div> </div> <div> </div> <div><strong>Software:</strong></div> <div>All simulations were performed with LAMMPS [2,3] (version 23 June 2022, patch_23Jun2022_update3) </div> <div> </div> <div>Compiler: GNU C++ 11.2.0 with OpenMP not enabled</div> <div>C++ standard: C++11</div> <div> </div> <div>Active compile time flags:</div> <div>-DLAMMPS_GZIP</div> <div>-DLAMMPS_SMALLBIG</div> <div> </div> <div>Installed packages:</div> <div>BPM CLASS2 DPD-BASIC EXTRA-DUMP EXTRA-FIX EXTRA-MOLECULE INTEL KSPACE MANYBODY </div> <div>MC MISC MOLECULE MOLFILE MPIIO NETCDF OPT </div> <div> </div> <div>Moreover, we employ a self-avoiding random walker [4,5] implemented in MATLAB [6] for the initial positioning of the polymer chains and nanoparticles.</div> <div> </div> <div> </div> <div><strong>License:</strong></div> <div>Creative Commons Attribution 4.0 International</div> <div> </div> <div> </div> <div><strong>Context:</strong></div> <div>This dataset contains the results presented in [1] and the necessary data to obtain those.</div> <div> </div> <div> </div> <div><strong>Content:</strong></div> <div>Throughout this data set, LAMMPS lj units are used. The files to reproduce our simulations and their results are structured as follows:</div> <div>- 01_neat: Neat polymer systems</div> <div> - 01_EQU: Equilibration simulations</div> <div> - 02_UT: Uniaxial tensile simulations, including the notch insertion (token "initcrack")</div> <div> - 1.1: Simulations for different sample sizes/numbers of chains (token "chains") at constant molar mass/number of beads per chain</div> <div> - 1.3: Simulations for different widths of the Dirichlet boundary (token "diri")</div> <div> - 2.1: Simulations for different critical bond lengths (token "bondcrit")</div> <div> - 2.2: Simulations for different bond breaking probabilities (token "bondcprob")</div> <div> - 3.1: Simulations for different crack widths (token "crackwidth")</div> <div> - 3.2: Simulations for different crack lengths (token "crackdepth")</div> <div> - 4: Simulations for different strain rates (token "strainrate")</div> <div> - 5: Simulations for different temperatures (token "tem")</div> <div> - 6: Simulations for different molar masses/numbers of beads per chain (token "chain-len")</div> <div>- 02_PNC: Polymer nanocomposite (PNC) systems </div> <div> - 01_EQU: Equilibration simulations</div> <div> - 02_UT: Uniaxial tensile simulations for different filler radii (token "rF") and filler contents/numbers (token "nF"), including the notch insertion (token "initcrack")</div> <div>- parameter_study: Postprocessing of the MD results </div> <div> - parameter_study.xlsx: Overview of the simulations with their respective parameters and statistical analysis of stiffness, strength, and toughness from filtered stress-strain curves (Savitzky-Golay filter applying a linear polynomial and frame length 21)</div> <div> - .csv files of the single sheets of parameter_study.xlsx:</div> <div> - samples.csv: Individual specimens</div> <div> - averages.csv: Statistical analysis of the different samples corresponding to one batch</div> <div> </div> <div>Each simulation directory contains:</div> <div>- LAMMPS input script (*.in) of the simulation</div> <div>- input.prm: Input parameters of the simulation (read by the input script)</div> <div>- LAMMPS data file (*.data, molecular style) of the investigated sample</div> <div>- LAMMPS_out: Resulting LAMMPS data files, log files and simulation results in tabulated form</div> <div> - additional files for the tensile tests: </div> <div> - brokenbonds.dat: Fix print output for fix brokenbondsprint (step time brokenbondsPerStep brokenbondsSum)</div> <div> - stressstrain.dat: Time-averaged data for fix dumpOpt (step v_strain_xx v_OBSstrain_xx v_Piola_xx) with the local strain at the crack tip v_OBSstrain_xx</div> <div> - thermo_out.Dat: Thermodynamic output in condensed tabulated form</div> <div> - thermo_out_SG.Dat: Thermodynamic output in condensed tabulated form, filtered by a Savitzky-Golay filter (linear polynomial, frame length 21)</div> <div> - thermo_out_STD.Dat: Standard deviation between the filtered and unfiltered data</div> <div>- job.out: Simulation log file</div> <div>- meta.info: Meta data of the simulation run</div> <div> </div> <div>Naming convention:</div> <div>- 01_neat: GTPm-[number of chains]_chains-[number of beads per chain]_chain_len-[temperature]_tem-[parameter value]_[parameter]-[sample]</div> <div> - [parameter]: Parameter studied, i.e. diri/bondcrit/bondcprob/crackwidth/crackdepth/strainrate/tem (see above)</div> <div> - [parameter value]: Value of the parameter studied</div> <div> - [sample]: Sample ID</div> <div>- 02_PNC: GTPm_rF-[filler radius]_nF-[number of fillers]_[sample]</div> <div> - [sample]: Sample ID</div> <div> </div> <div>Output quantities (columns of *.Dat files):</div> <div>- Step: time step</div> <div>- Time: time</div> <div>- TotEng: total energy</div> <div>- PotEng: potential energy</div> <div>- KinEng: kinetic energy</div> <div>- E_pair: pair energy</div> <div>- E_bond: bond energy</div> <div>- E_angle: angle energy</div> <div>- E_dihed: dihedral energy</div> <div>- Temp: temperature</div> <div>- Press: hydrostatic pressure</div> <div>- Pxx: xx component of pressure tensor</div> <div>- Pyy: yy component of pressure tensor</div> <div>- Pzz: zz component of pressure tensor</div> <div>- Pxy: xy component of pressure tensor</div> <div>- Pxz: xz component of pressure tensor</div> <div>- Pyz: yz component of pressure tensor</div> <div>- Volume: volume of simulation box</div> <div>- Lx: box length in x direction</div> <div>- Ly: box length in y direction</div> <div>- Lz: box length in z direction</div> <div>- Density: mass density</div> <div>- c_RG: radius of gyration</div> <div>- c_RG[1]: squared radius of gyration tensor (xx component)</div> <div>- c_RG[2]: squared radius of gyration tensor (yy component)</div> <div>- c_RG[3]: squared radius of gyration tensor (zz component)</div> <div>- c_RG[4]: squared radius of gyration tensor (xy component)</div> <div>- c_RG[5]: squared radius of gyration tensor (xz component)</div> <div>- c_RG[6]: squared radius of gyration tensor (yz component)</div> <div>- c_bondave[1]: bond energy averaged over all atoms</div> <div>- c_bondave[2]: bond distance averaged over all atoms</div> <div>- c_bondave[3]: squared bond distance averaged over all atoms</div> <div>- c_angleave[1]: angle energy averaged over all atoms</div> <div>- c_angleave[2]: angle averaged over all atoms degree</div> <div>- c_angleave[3]: cosine of angle</div> <div>- c_angleave[4]: squared cosine of angle</div> <div>- c_MSD[1]: mean squared displacement x-direction</div> <div>- c_MSD[2]: mean squared displacement y-direction</div> <div>- c_MSD[3]: mean squared displacement z-direction</div> <div>- c_MSD[4]: total mean squared displacement</div> <div>- c_COM[1]: x coordinate of center of mass</div> <div>- c_COM[2]: y coordinate of center of mass</div> <div>- c_COM[3]: z coordinate of center of mass</div> <div>- v_strain_xx: xx component of engineering strain tensor </div> <div>- v_strain_yy: yy component of engineering strain tensor </div> <div>- v_strain_zz: zz component of engineering strain tensor </div> <div>- v_vMisesequivstress: von Mises equivalent stress</div> <div>- v_Piola_xx: xx component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_yy: yy component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_zz: zz component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_xy: xy component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_xz: xz component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_yz: yz component of the virial stress tensor normalized by the initial volume</div> <div>- v_strain_xy: xy component of engineering strain tensor </div> <div>- v_strain_xz: xz component of engineering strain tensor </div> <div>- v_strain_yz: yz component of engineering strain tensor </div> <div> </div> <div> </div> <div><strong>References:</strong></div> <div>[1] F. Weber, V. Dötschel, P. Steinmann, S. Pfaller, M. Ries, "Evaluating the impact of filler size and filler content on the stiffness, strength, and toughness of polymer nanocomposites using coarse-grained molecular dynamics", Engineering Fracture Mechanics, vol. 307, p. 110270, 2024.</div> <div>[2] S. Plimpton, "Fast parallel algorithms for short-range molecular dynamics", Journal of computational physics, vol. 117, no. 1, pp. 1-19, 1995.</div> <div>[3] A. P. Thompson, H. M. Aktulga, R. Berger, D. S. Bolintineanu, W. M. Brown, P. S. Crozier, P. J. in 't Veld, A. Kohlmeyer, S. G. Moore, T. D. Nguyen, R. Shan, M. J. Stevens, J. Tranchida, C. Trott, S. J. Plimpton, "LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales", Computer Physics Communications, vol. 271, p. 108171, 2022.</div> <div>[4] V. Dötschel, S. Pfaller, and M. Ries, "Studying the mechanical behavior of a generic thermoplastic by means of a fast coarse-grained molecular dynamics model", Polymers and Polymer Composites, vol. 31, pp. 1–11, 2023.</div> <div>[5] M. Ries, V. Dötschel, J. Seibert, and S. Pfaller, A self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites, Zenodo, 2022, https://doi.org/10.5281/zenodo.6245699.</div> <div>[6] The MathWorks, Inc., "Matlab. the language of technical computing", https://de.mathworks.com/help/matlab/.</div> <div> </div> <div> </div> <div><strong>Funding:</strong></div> <div>The authors gratefully acknowledge funding by various sources:</div> <div>The overall research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 377472739/GRK 2423/2-2023. Sebastian Pfaller is furthermore funded by the DFG projects 396414850 (Individual Research Grant 'Identifikation von Interphaseneigenschaften in Nanokompositen') and 505866713 together with the Agence nationale de la recherché (ANR, French Research Agency) – ANR-22-CE92-0049 (Individuel Research Grant 'BIO ART'). In addition, scientific support and HPC resources have been provided by the Erlangen National High Performance Computing Center (NHR@FAU) of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) under the NHR project b136dc. NHR funding is provided by federal and Bavarian state authorities. NHR@FAU hardware is partially funded by the DFG project 440719683.</div>
Data for impacts of topography-based subgrid scheme and downscaling of atmospheric forcing on modeling land surface processes in the conterminous US
<p>The effects of small-scale topography-induced land surface heterogeneity are not well represented in current Earth System Models (ESMs). A topography-based subgrid structure and methods of downscaling of atmospheric forcing from the atmospheric grid to the subgrids of the land model grid (TGUs) have been implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) to improve representation of the effects of small-scale topography-induced land surface heterogeneity on land surface processes. This study evaluates the impacts of the topography-based subgrid structure and downscaling of atmospheric forcing on modeling land surface processes in E3SM over the conterminous United States (CONUS). For this purpose, ELM simulations are performed using two configurations without (NoD ELM) and with (D ELM) downscaling, both using TGUs derived for the 0.5-degree grids and the same land surface parameters. Simulations using the two ELM configurations are compared over the CONUS domain, regional levels, and at observational sites (e.g., SNOTEL). The CONUS-level results suggest that D ELM simulates more snowfall and snow water equivalent (SWE), higher runoff, and less ET during spring and summer. Regional-level results suggest more pronounced impacts of downscaling over regions dominated by higher elevation TGUs and regions with maximum precipitation occurring during cool seasons. Results at the SNOTEL sites suggest that D ELM has superior capability of reproducing the observed SWE at 83% of the sites, with more pronounced performance over topographically heterogeneous TGUs with their maximum precipitation occurring during cool seasons. The results highlight the importance of improving representation of small-scale surface heterogeneity in ESMs and motivate future research to understand their effects on land-atmosphere interactions, streamflow, and water resources management over mountainous regions.</p> <p>The data utilized to evaluate effects of the topography-based subgrid structure and downscaling of atmospheric forcing in land surface modeling include a TGU level land surface data file, atmospheric forcing to drive the land model, ELM user name list configuration parameters, regionalization variables (topographic regions, snow fraction regions, water versus energy limited regions, and regions of season of maximum precipitation), model restart files for both ELM configurations, and model outputs (grid and subgrid levels), model outputs aggregated to TGUs and grid levels.</p> <p>The data files include:</p> <ol> <li><a href="../api/files/12bc9f1c-be98-4721-8c92-6e23845be441/daily_prism_precip.zip?versionId=be97ca8d-182a-4f1e-9ae3-9da3f2b87e24">DELM.zip</a>: Directory containing the following files relevant to the D ELM configuration and model output files.</li> <ol> <li>Restart file: 202201289.tgu_all_disag_yr1850surfdata.ielm.r05_r05.compy.elm.r.2005-01-01-00000.nc</li> <li>Configuration file: user_nl_elm</li> <li>Aggregated grid-level monthly output file: grd_level_output_disag_mnly_run_11_new_20221109.nc</li> <li>Aggregated grid-level daily output file: grd_level_output_disag_daily_20220512.nc</li> <li>TGU-level monthly output file: tgu_level_output_all_disag_20221109.nc</li> </ol> <li> <a href="../api/files/12bc9f1c-be98-4721-8c92-6e23845be441/dem_4km4.nc">NoDELM.zip</a>: Directory containing the following files relevant to the NoD ELM configuration and model output files.</li> <ol> <li>Restart file: 202201289.tgu_no_disag_yr1850surfdata.ielm.r05_r05.compy.elm.r.2005-01-01-00000.nc</li> <li>Configuration file: user_nl_elm</li> <li>Aggregated grid-level monthly output file: grd_level_output_nodisag_mnly_run_11_new_20221109.nc</li> <li>Aggregated grid-level daily output file: grd_level_output_nodisag_daily_20220512.nc </li> <li>TGU-level monthly output file: tgu_level_output_no_disag_20221109.nc</li> </ol> <li><a href="../api/files/12bc9f1c-be98-4721-8c92-6e23845be441/fr_number.zip?versionId=eacb5b60-9561-47f9-97c6-cd91e96afa1f">shared.zip</a>: Directory containing the following files relevant to both the D ELM and NoD ELM configurations.</li> <ol> <li>Subgrid-based surface data file: MASKED.half_degree_merge.surfdata_0.5x0.5_simyr1850_c200924.pft17.10262022v2.nc</li> <li>Regionalization file used to generate regions based on snow fraction, water versus energy limited state, and seasons of maximum precipitation: half_deg_budyko_curve_analysis_20230104_disag.nc</li> <li>Topographic ratio file used to generate topography-based regions: grd_level_output_nodisag_run_11_new_20221109.nc</li> <li>TGU-level surface elevation data file where surface elevation data are derived from high resolution surface elevation data (90 m) obtained from HydroSHEDS [Lehner et al. 2008, Lehner and Grill 2013]: half_deg_subgrids_with_PFTs_and_stat_20210403.nc</li> </ol> <li><a href="../api/files/12bc9f1c-be98-4721-8c92-6e23845be441/fr_number.zip?versionId=eacb5b60-9561-47f9-97c6-cd91e96afa1f">SNOTEL_files.zip</a>: Directory containing the following SNOTEL data related files used to evaluate model performance.</li> <ol> <li> SNOTEL list of stations file: SNOTEL_halfdegree_intersect4.csv</li> <li>SNOTEL data files/folders: csv</li> </ol> </ol> <p> </p> <p><strong>References</strong></p> <p>Lehner, B., et al. (2008). "New Global Hydrography Derived From Spaceborne Elevation Data." Eos, Transactions American Geophysical Union <strong>89</strong>(10): 93-94.</p> <p>Lehner, B. and G. Grill (2013). "Global river hydrography and network routing: baseline data and new approaches to study the world's large river systems." Hydrological Processes <strong>27</strong>(15): 2171-2186.</p> <p> </p>
EU Biotransform - Repository on existing impact assessment methodologies
<p>A thorough review of already existing methodologies to assess environmental/social/economic impacts of fossil/bio-based and linear/circular economies, as well as their transitions, e.g. impact forecasting through theory of change, full or simplified LCAs, material flow analyses, and outlook like<br>automated data extraction with AI, at macro level (country or regional level).</p>
Data for irrigation impacts on urban heat stress in North America
<p>Includes all model simulation results, summaries by urban clusters, and evaluation results of the study. <br><br>The netcdf files are for various model variables and simulations (noURB for no urban simulation, IRR for irrigation simulation, and CTRL for the urban with no irrigation simulation, which is treated as the baseline), the csv files are the summaries for various cases (by urban cluster, and by world and climate zone for model evaluations), and the geotiff files are the raster images for key variables shown in the manuscript.<br><br>The python notebook has all the scripts to estimate the heat stress metrics from the netcdf files.</p>
Figure 1 in Ecological and economic impacts of exotic fish species on fisheries in the Pearl River basin
Figure 1. Locations of the 10 sampling sites in the Pearl River basin, China.
Codes and data for the article: Biodiversity on the Line: Life Cycle Impact Assessment of Power Lines on Birds and Mammals in Norway
<p>This repository contains all input data required to run the habitat conversion, collision, and electrocution LCIA models and reproduce the results, as well as all output data generated in various formats. The models are described in the paper "Biodiversity on the Line: Life Cycle Impact Assessment of Power Lines on Birds and Mammals in Norway" (https://doi.org/10.1088/2634-4505/ad5bfd).</p> <p>"The files "01_Get_GBIF_points.R, "02_SDMs_maxent.R" describe how to create the species distribution maps.</p> <p>"03_Data_preparation.R", "04_Pylon_cleaning.py" are to prepare and modify the raw data for the analysis. The raw data are not provided, yet links to the sources are provided either in the R codes or the paper.</p> <p>To run the models, run the "05_SHR_modelling.R" and "06_Collision_electrocution_models.R" files.</p> <p>To calculate characterization factors, run the "07_Characterisation_factors.R" file.</p> <p>To export the tables in the Supporting Information 1, run the file "08_Supporting_Information.R".</p> <p>Finally, the file "09_Sensitivity_analysis.R" performs the sensitivity analyses.</p>
Fig. 6 in The Impact Of Forest Harvesting Machines On Understorey Vegetation In Hylocomiosa Forest Type In Zemgale, Latvia
Fig. 6. The proportion of evergreen and summer green plant species in the herbaceous' layer.
Fig. 4 in The Impact Of Forest Harvesting Machines On Understorey Vegetation In Hylocomiosa Forest Type In Zemgale, Latvia
Fig. 4. The projective coverage of herbaceous' and moss' layers (mean values are shown ± SE).
Fig. 3 in The Impact Of Forest Harvesting Machines On Understorey Vegetation In Hylocomiosa Forest Type In Zemgale, Latvia
Fig. 3. The number of species of herbaceous and moss layers (mean values are shown ± SE).
Fig. 1 in Short-Term Impact Of Nitrogen Fertilizer And Wood Ash On Forest Ground Vegetation
Fig. 1. Sample plot design in vegetation field surveyed.
Fig. 7 in Urban Green Areas, Recreational Use And Health Impact Of Victory Gardens (Córdoba - Spain)
Fig. 7. Principal pollination pediod for ornamental flora in the studied gardens.
Fig. 3 in Urban Green Areas, Recreational Use And Health Impact Of Victory Gardens (Córdoba - Spain)
Fig. 3. Origin for ornamental flora in the studied gardens.
Fig. 4 in Urban Green Areas, Recreational Use And Health Impact Of Victory Gardens (Córdoba - Spain)
Fig. 4. Pollination strategy for ornamental flora in the studied gardens.
Fig. 1 in Urban Green Areas, Recreational Use And Health Impact Of Victory Gardens (Córdoba - Spain)
Fig. 1. Map showing the location of Córdoba city.
Heatwave ontogenetic timing impacts on potato crop-pest dynamics
<p>Heat waves – brief periods of unusually high temperatures – are damaging to agroecosystems and are increasing in frequency and intensity due to climate change. Despite growing appreciation for the threat that heat waves pose pose to agricultural sustainability, we have a poor understanding of what determines their impact on agroecological interactions in the field. Here we report a field experiment that examined how heat waves and their timing interact with crop pest resistance to influence the interactions between potato (<em>Solanum tuberosum</em>) and its most damaging pest, the Colorado potato beetle (CPB; <em>Leptinotarsa decemlineata</em>). We used open-top chambers and ceramic heaters to generate heat wave conditions in field plots with pest-resistant (Atlantic) and -susceptible (King Harry) potato varieties at four CPB developmental stages (neonate, late-larval, pupa, and adult stages). We then assessed CPB performance at larval and adult stages, leaf herbivory, and tuber yield.</p>
ScienceDex guides
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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.