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382 results for “Climate impacts”
Radiative and climatic impacts of coarse aerosols scattering in the long-wave spectrum
<p>The data deposited on the site concern the output of the ARPEGE climate model at monthly resolution. This concerns the different variables used and analysed in the article. They include simulations with LW aerosol scattering (LWAS) and without aerosol scattering (NOLWAS). The data description, resolution and information on fields outputs are available in each NetCDF file submitted.</p>
Impact of host climate model on contrail cirrus effective radiative forcing estimates
<p>Data to reproduce the figures in the article 'Impact of host climate model on contrail cirrus effective radiative forcing estimates'.</p>
Enhanced Convective Microphysics Scheme and Its Impacts on Mean Climate Simulation in E3SM
<ol> <li>Simulation data of E3SM with an enhanced convective microphysics scheme.</li> <li>Source code of E3SM with an enhanced convective microphysics scheme.</li> </ol>
Human impact, climate and dispersal strategies determine plant invasion on islands
<p><span><strong>Aim:</strong></span></p> <p><span>Biological invasions are likely determined by species dispersal strategies as well as environmental characteristics of a recipient region, especially climate and human impact. However, the contribution of climatic factors, human impact and dispersal strategies in driving invasion processes is still controversial and not well embedded in the existing theoretical considerations. Here, we study how climate, species dispersal strategies and human impact determine plant invasion processes on islands distributed in all major oceans in the context of directional ecological filtering. </span></p> <p><span><strong>Location:</strong></span></p> <p><span>Six mountainous, tropical and subtropical islands in three major oceans: Island of Hawai'i and Maui (Pacific), Tenerife and La Palma (Atlantic), La Réunion and Socotra (Indian Ocean).</span></p> <p><span><strong>Taxon:</strong></span></p> <p><span>Vascular Plants.</span></p> <p><span><strong>Methods:</strong></span></p> <p><span>We recorded 360 non-native species in 218 plots along roadside elevational transects covering the major temperature, precipitation and human impact (i.e. road density) gradients of the islands. We collected dispersal strategies for a majority of the recorded species and calculated the environmental niche per species using a hypervolume approach. </span></p> <p><span><strong>Results:</strong></span></p> <p><span>Non-native species' generalism (i.e., mean community niche width) increased with precipitation, elevation and human impact but showed no relationship with temperature. Increasing precipitation led to environmental filtering of non-native species resulting in more generalist species under high precipitation conditions. We found no directional filtering for temperature but an optimum range of most species between 10 and 20°C. Niche widths of non-native species increased with the prevalence of certain dispersal strategies, particularly anemochory and anthropochory. </span></p> <p><span><strong>Main conclusions:</strong></span></p> <p><span>Plant invasion on tropical and subtropical islands seems to be mainly driven by precipitation and human impact, while temperature seems to be of little importance. Furthermore, anemochory and anthropochory are dispersal strategies associated with large niche widths of non-native species. Our study allows a more detailed look at the mechanisms behind directional ecological filtering of non-native plant species in non-temperature limited ecosystems. </span></p>
ARISE-SAI-1.5: Assessing Responses and Impacts of Solar climate intervention on the Earth system with Stratospheric Aerosol Injection, with cooling to 1.5C
<p>Assessing Responses and Impacts of Solar climate intervention on the Earth system with Stratospheric Aerosol Injection (ARISE-SAI) is a set of simulations carried out with the Community Earth System Model, version 2 with the Whole Atmosphere Community Climate Model, version 6 (CESM2(WACCM6)) that aims at simulating a plausible deployment of solar climate intervention of stratospheric aerosol injection to enable community assessment of responses of the Earth system. This first set of simulations introduce stratospheric aerosol injection at ~ 21 km in simulated year 2035, called ARISE-SAI-1.5, utilize the middle-of-the-road SSP2-4.5 emission scenario,, and keep global mean surface air temperature near 1.5°C above the pre-industrial value. Sulfur dioxide injections in the ARISE-SAI-1.5 simulations are placed at four injection locations (15°S, 15°N, 30°S, 30°N) into one grid box at 180° longitude, and midpoint altitude of 21.6 km. The injection amount at each latitude is specified annually by a “controller” algorithm. This strategy ensures that the global mean surface temperature (T0), north-south temperature gradient (T1), and equator-to-pole temperature gradient (T2) remain close to ~ 1.5°C above the pre-industrial value throughout the simulation.</p> <p> </p> <p>The files contained here contain output of surface temperature (TREFHT), total precipitation (PRECT), SO4, and controller log files with amounts of SO2 injection. </p>
FOCI model output used in the study by Ivanciu et al. - On the ridging of the South Atlantic Anticyclone over South Africa: the impact of Rossby wave breaking and of climate change
<p>This dataset contains the model output used in the analysis presented in the study by Ivanciu et al., 2022 - On the ridging of the South Atlantic Anticyclone over South Africa: the impact of Rossby wave breaking and of climate change. Four ensembles of three simulations each were performed with the global coupled climate model FOCI (Flexible Ocean and Climate Infrastructure, Matthes et al., 2020). Details about the ensembles can be found in the above-mentioned publication. The files containing "past" in their name belong to the ensemble "PAST", the files containing "future" in their name belong to the ensemble "FUTURE", the files containing "future_GHG" in their name belong to the ensemble "GHG" and the files containing "future_Ozone" in their name belong to the ensemble "OZONE" from the publication.</p>
Climate Impacts of Parameterizing Subgrid Partitioning of Land Surface Heat Fluxes to the Atmosphere with the NCAR CESM1.2
<p>The modified code as well as the CAM5 output for all the simulations in this study (V0 for the CTL run, CON1 for the EXP run, and PCON1R for EXP_COR run).</p> <p>The CESM1.2.1-CAM5.3 source code can be downloaded through the CESM official website https://www.cesm.ucar.edu/models/cesm1.2/cesm/doc/usersguide/x290.html#download_ccsm_code. Its output files are named in V0*.nc.</p> <p>The modified code for the EXP run in the study is in CON1.tar, with its CAM5 output files named in CON1*.nc.</p> <p>The modified code for the EXP_COR run in the study is in PCON1R.tar, with its CAM5 output files named in PCON1R*.nc</p>
Impact of mountains in Southern China on the Eocene climates of East Asia
<p>These files contain the model outputs from Noresm1-F that are used to draw the main parts of Figures in our paper entitled "Impact of mountains in Southern China on the Eocene climates of East Asia".</p>
Multidecadal, continent-level analysis indicates agricultural practices impact wheat aphid loads more than climate change
<p><span>Temperature has a large influence on insect abundances, thus under climate change, identifying major drivers affecting pest insect populations is critical to world food security and agricultural ecosystem health. Here, we conducted a meta-analysis with data obtained from 120 studies across China and Europe from 1970 to 2017 to reveal how climate and agricultural practices affect populations of wheat aphids. H</span><span>ere</span><span> we showed that aphid loads on wheat had distinct patterns between these two regions, with a significant increase in China but a decrease in Europe over this time period. Although temperature increased over this period in both regions, we found no evidence showing climate warming affected aphid loads. Rather, differences in pesticide use, fertilization, land use, and natural enemies between China and Europe may be key factors accounting for differences in aphid pest populations. These long-term data suggest that agricultural practices impact wheat aphid loads more than climate warming. </span></p>
Data and code of Land use scenario for 'Development of common socio-economic scenarios for climate change impact assessments in Japan'
<p>Land use scenario calculation: Executable files, source code files and data files<br> This dataset contains program codes and input data used for reproducing land use scenarios explained in Chapter 5.2 in Yoshikawa et al. (submitted to GMDD).</p> <p>We found a few fatal errors in the following code.<br> These code were fixed from version 2 (http://dx.doi.org/10.5281/zenodo.7090670).<br> /Step3/a01_calc_land_use.py<br> /Step3/a01_calc_land_use_std.py<br> /Step3/a01_calc_land_use_rate.py<br> /Step3/run03.bat</p>
Data from: Choice of prioritization method impacts recommendations for climate-informed bird conservation in the United States
<p class="MsoNormal">Climate-informed spatial planning is urgently needed to guide initiatives aimed at both conserving biodiversity as a whole (e.g., protection of 30% of lands and waters by 2030) and recovering North American avifauna in particular. Various methods for prioritizing conservation areas exist, yet alternative methods may direct managers to different lands for protection and thus varying recommendations for meeting area-based targets. Here, we used bird species distribution models and landcover projections to systematically evaluate two widely-used methods for prioritizing areas most likely to facilitate the persistence of multiple species under climate change: (1) <em>in situ </em>macrorefugia, identified as areas of high predicted species retention; and (2) complementarity-based optimizations, identified using the Zonation conservation planning software. For 17 biogeographical groups in the continental United States, we compared priority areas for bird conservation derived from these two alternatives with respect to their spatial distributions and consensus (i.e., overlap), expected conservation outcomes (e.g., species and functional diversity), predicted climate change exposure, habitat characteristics, landscape configurations, and degree of formal protection. Spatial distributions of priority areas differed by biogeographical group and method, with 40.5% consensus on average across groups. Consensus was extensive within mountainous and coastal regions and limited at high latitudes (e.g., Alaska) and in flat, interior regions (e.g., grasslands). As expected, complementarity-based optimizations more efficiently represented species than retention-based <em>in situ </em>macrorefugia, especially for forest groups, and had greater overall biodiversity value and better habitat condition. Conversely, <em>in situ </em>macrorefugia encompassed higher elevations and larger contiguous patches and were expected to experience less winter-season warming. Formal protection averaged <50% across biogeographical groups, regardless of prioritization method. Our findings illustrate the value of complementarity-based optimizations for bird conservation under climate change. More broadly, comparing approaches for prioritizing areas for long-term species persistence can reveal critical tradeoffs in recommendations for climate-informed protected area planning.</p>
Montreal Protocol's impact on the ozone layer and climate
<p>These are the datasets used for the paper: Montreal Protocol's impact on the ozone layer and climate</p>
Meteorological and Ecosystem Flux Data for: Climate Change Impacts on Net Ecosystem Productivity in a Subtropical Scrubland of Northwestern México
<p>This dataset accompains the paper: Climate Change Impacts on Net Ecosystem Productivity in a Subtropical Scrubland of Northwestern México which is submitted for publication at the Journal of Geophysical Research - Biogeosciences </p> <p>With this data set, we calibrate and validate an ecohydrological and a soil carbon model to assess climate change impacts in a subtropical scrubland located in northwest México. Model calibration and validation is performed using five continuous years of water, energy and carbon flux measurements from an eddy covariance tower and remotely-sensed vegetation indices. </p>
Wave climate simulations for Denmark - for paper 'Coinciding storm surge and wave setup: A regional assessment of sea level rise impact'
<p>This wave climate dataset are the results for the paper titled 'Coinciding storm surge and wave setup: A regional assessment of sea level rise impact'.</p> <p>The operational wave forecasting service provided by DMI-WAM uses the WAM Cycle version 4.5.4, a third-generation spectral wave model. DMI-WAM is used for the wave climate simulations. The meteorological forcing used in this study was obtained from the regional climate model DMI-HIRHAM, developed by the Danish Meteorological Institute (DMI). It is a component of the CORDEX (Coordinated Regional Climate Downscaling Experiment) ensemble in Europe. Regarding the selection of the time frame and IPCC scenarios in our study, we adhered to the recommendations provided by municipalities. Municipalities are keenly interested in obtaining near-future wind wave data for the specific purpose of using them for risk management. Therefore, the examination of forthcoming weather extremes in the near future within the context of the high greenhouse gas emission scenario (RCP8.5 scenario) is of significance within this investigation. We conduct simulations that encompass two distinct time periods: the historical period spanning from 1976 to 2005, and the near-future period from 2041 to 2070. We analyse the WAM model results for wave climate under both present climate conditions (1976-2005) and future climate scenarios (2041-2070) under the RCP8.5 scenario. Furthermore, note that while our wave climate simulations provide valuable insights into the dynamics of wind-induced waves, the mean SLR is not explicitly taken into account. The mean SLR component is considered in the storm surge simulations.</p> <p>Description of files:</p> <p><a href="../api/records/11052226/draft/files/sla.swh.slope.hist.final.max.nc/content" target="_blank" rel="noopener noreferrer">sla.swh.slope.hist.final.max.nc</a> - Maximum sea level, significant wave height, wave length and slope for the historical period.</p> <p><a href="../api/records/11052226/draft/files/sla.swh.slope.rcp85.final.max.MSLR35.nc/content" target="_blank" rel="noopener noreferrer">sla.swh.slope.rcp85.final.max.MSLR35.nc</a> - Maximum sea level, significant wave height, wave length and slope for the RCP8.5 period.</p> <p><a href="../api/records/11052226/draft/files/wavesetup.hist.final.max.nc/content" target="_blank" rel="noopener noreferrer">wavesetup.hist.final.max.nc</a> - Maximum wave setup for the historical period.</p> <p><a href="../api/records/11052226/draft/files/wavesetup.rcp85.final.max.MSLR35.nc/content" target="_blank" rel="noopener noreferrer">wavesetup.rcp85.final.max.MSLR35.nc</a> - Maximum wave setup for the RCP8.5 period.</p> <p><a href="../api/records/11052226/draft/files/wam.grib.his.swh.98p.nc/content" target="_blank" rel="noopener noreferrer">wam.grib.his.swh.98p.nc</a> - 2% exceedence of significant wave height for the historical period.</p> <p><a href="../api/records/11052226/draft/files/wam.grib.rcp8.swh.98p.nc/content" target="_blank" rel="noopener noreferrer">wam.grib.rcp8.swh.98p.nc</a> - 2% exceedence of significant wave height for the RCP8.5 period.</p>
Constructing visualization tools and training resources to assess climate impacts on the channel islands national marine sanctuary NetCDF files
<p>The Channel Islands Marine Sanctuary (CINMS) comprises 1,470 square miles surrounding the Northern Channel Islands: Anacapa, Santa Cruz, Santa Rosa, San Miguel, and Santa Barbara, protecting various species and habitats. However, these sensitive habitats are highly susceptible to climate-driven 'shock' events which are associated with extreme values of temperature, pH, or ocean nutrient levels. A particularly devastating example was seen in 2014-16, when extreme temperatures and changes in nutrient conditions off the California coast led to large-scale die-offs of marine organisms. Global climate models are the best tool available to predict how these shocks may respond to climate change. To better understand the drivers and statistics of climate-driven ecosystem shocks, a 'large ensemble' of simulations run with multiple climate models will be used. The objective of this project is to develop a Python-based web application to visualize ecologically significant climate variables near the CINMS. The web application will be used by researchers from the University of California, Santa Barbara (UCSB) to analyze climate model output, and by CINMS staff to develop new indicators of shocks to marine ecosystems.</p>
Data and Code for "Climate impacts and adaptation in US dairy systems 1981-2018"
<p>This data and code archive provides all the files that are necessary to replicate the empirical analyses that are presented in the paper "Climate impacts and adaptation in US dairy systems 1981-2018" authored by Maria Gisbert-Queral, Arne Henningsen, Bo Markussen, Meredith T. Niles, Ermias Kebreab, Angela J. Rigden, and Nathaniel D. Mueller and published in 'Nature Food' (2021, DOI: <a href="https://doi.org/10.1038/s43016-021-00372-z">10.1038/s43016-021-00372-z</a>). The empirical analyses are entirely conducted with the "R" statistical software using the add-on packages "car", "data.table", "dplyr", "ggplot2", "grid", "gridExtra", "lmtest", "lubridate", "magrittr", "nlme", "OneR", "plyr", "pracma", "quadprog", "readxl", "sandwich", "tidyr", "usfertilizer", and "usmap". The R code was written by Maria Gisbert-Queral and Arne Henningsen with assistance from Bo Markussen. Some parts of the data preparation and the analyses require substantial amounts of memory (RAM) and computational power (CPU). Running the entire analysis (all R scripts consecutively) on a laptop computer with 32 GB physical memory (RAM), 16 GB swap memory, an 8-core Intel Xeon CPU E3-1505M @ 3.00 GHz, and a GNU/Linux/Ubuntu operating system takes around 11 hours. Running some parts in parallel can speed up the computations but bears the risk that the computations terminate when two or more memory-demanding computations are executed at the same time.</p> <p>This data and code archive contains the following files and folders:</p> <p>* README<br> Description: text file with this description</p> <p>* flowchart.pdf<br> Description: a PDF file with a flow chart that illustrates how R scripts transform the raw data files to files that contain generated data sets and intermediate results and, finally, to the tables and figures that are presented in the paper.</p> <p>* runAll.sh<br> Description: a (bash) shell script that runs all R scripts in this data and code archive sequentially and in a suitable order (on computers with a "bash" shell such as most computers with MacOS, GNU/Linux, or Unix operating systems)</p> <p>* Folder "DataRaw"<br> Description: folder for raw data files<br> This folder contains the following files:</p> <p>- DataRaw/COWS.xlsx<br> Description: MS-Excel file with the number of cows per county<br> Source: USDA NASS Quickstats<br> Observations: All available counties and years from 2002 to 2012</p> <p>- DataRaw/milk_state.xlsx<br> Description: MS-Excel file with average monthly milk yields per cow<br> Source: USDA NASS Quickstats<br> Observations: All available states from 1981 to 2018</p> <p>- DataRaw/TMAX.csv<br> Description: CSV file with daily maximum temperatures<br> Source: PRISM Climate Group (spatially averaged)<br> Observations: All counties from 1981 to 2018</p> <p>- DataRaw/VPD.csv<br> Description: CSV file with daily maximum vapor pressure deficits<br> Source: PRISM Climate Group (spatially averaged)<br> Observations: All counties from 1981 to 2018</p> <p>- DataRaw/countynamesandID.csv<br> Description: CSV file with county names, state FIPS codes, and county FIPS codes<br> Source: US Census Bureau<br> Observations: All counties</p> <p>- DataRaw/statecentroids.csv<br> Descriptions: CSV file with latitudes and longitudes of state centroids<br> Source: Generated by Nathan Mueller from Matlab state shapefiles using the Matlab "centroid" function<br> Observations: All states</p> <p>* Folder "DataGenerated"<br> Description: folder for data sets that are generated by the R scripts in this data and code archive. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these generated data files so that parts of the analysis can be replicated (e.g., on computers with insufficient memory to run all parts of the analysis).</p> <p>* Folder "Results"<br> Description: folder for intermediate results that are generated by the R scripts in this data and code archive. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these intermediate results so that parts of the analysis can be replicated (e.g., on computers with insufficient memory to run all parts of the analysis).</p> <p>* Folder "Figures"<br> Description: folder for the figures that are generated by the R scripts in this data and code archive and that are presented in our paper. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these figures so that people who replicate our analysis can more easily compare the figures that they get with the figures that are presented in our paper. Additionally, this folder contains CSV files with the data that are required to reproduce the figures.</p> <p>* Folder "Tables"<br> Description: folder for the tables that are generated by the R scripts in this data and code archive and that are presented in our paper. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these tables so that people who replicate our analysis can more easily compare the tables that they get with the tables that are presented in our paper.</p> <p>* Folder "logFiles"<br> Description: the shell script runAll.sh writes the output of each R script that it runs into this folder. We provide these log files so that people who replicate our analysis can more easily compare the R output that they get with the R output that we got.</p> <p>* PrepareCowsData.R<br> Description: R script that imports the raw data set COWS.xlsx and prepares it for the further analyses</p> <p>* PrepareWeatherData.R<br> Description: R script that imports the raw data sets TMAX.csv, VPD.csv, and countynamesandID.csv, merges these three data sets, and prepares the data for the further analyses</p> <p>* PrepareMilkData.R<br> Description: R script that imports the raw data set milk_state.xlsx and prepares it for the further analyses</p> <p>* CalcFrequenciesTHI_Temp.R<br> Description: R script that calculates the frequencies of days with the different THI bins and the different temperature bins in each month for each state</p> <p>* CalcAvgTHI.R<br> Description: R script that calculates the average THI in each state</p> <p>* PreparePanelTHI.R<br> Description: R script that creates a state-month panel/longitudinal data set with exposure to the different THI bins</p> <p>* PreparePanelTemp.R<br> Description: R script that creates a state-month panel/longitudinal data set with exposure to the different temperature bins</p> <p>* PreparePanelFinal.R<br> Description: R script that creates the state-month panel/longitudinal data set with all variables (e.g., THI bins, temperature bins, milk yield) that are used in our statistical analyses</p> <p>* EstimateTrendsTHI.R<br> Description: R script that estimates the trends of the frequencies of the different THI bins within our sampling period for each state in our data set</p> <p>* EstimateModels.R<br> Description: R script that estimates all model specifications that are used for generating results that are presented in the paper or for comparing or testing different model specifications</p> <p>* CalcCoefStateYear.R<br> Description: R script that calculates the effects of each THI bin on the milk yield for all combinations of states and years based on our 'final' model specification</p> <p>* SearchWeightMonths.R<br> Description: R script that estimates our 'final' model specification with different values of the weight of the temporal component relative to the weight of the spatial component in the temporally and spatially correlated error term</p> <p>* TestModelSpec.R<br> Description: R script that applies Wald tests and Likelihood-Ratio tests to compare different model specifications and creates Table S10</p> <p>* CreateFigure1a.R<br> Description: R script that creates subfigure a of Figure 1</p> <p>* CreateFigure1b.R<br> Description: R script that creates subfigure b of Figure 1</p> <p>* CreateFigure2a.R<br> Description: R script that creates subfigure a of Figure 2</p> <p>* CreateFigure2b.R<br> Description: R script that creates subfigure b of Figure 2</p> <p>* CreateFigure2c.R<br> Description: R script that creates subfigure c of Figure 2</p> <p>* CreateFigure3.R<br> Description: R script that creates the subfigures of Figure 3</p> <p>* CreateFigure4.R<br> Description: R script that creates the subfigures of Figure 4</p> <p>* CreateFigure5_TableS6.R<br> Description: R script that creates the subfigures of Figure 5 and Table S6</p> <p>* CreateFigureS1.R<br> Description: R script that creates Figure S1</p> <p>* CreateFigureS2.R<br> Description: R script that creates Figure S2</p> <p>* CreateTableS2_S3_S7.R<br> Description: R script that creates Tables S2, S3, and S7</p> <p>* CreateTableS4_S5.R<br> Description: R script that creates Tables S4 and S5</p> <p>* CreateTableS8.R<br> Description: R script that creates Table S8</p> <p>* CreateTableS9.R<br> Description: R script that creates Table S9<br> </p>
Coupled PPE model output - land parameter impacts on the mean climate state
<p>Terrestrial processes influence the atmosphere by controlling land-to-atmosphere fluxes of energy, water, and carbon. Prior research has demonstrated that parameter uncertainty drives uncertainty in land surface fluxes. However, the influence of land process uncertainty on the climate system remains underexplored. Here, we quantify how assumptions about land processes impact climate using a perturbed parameter ensemble for 18 land parameters in the Community Earth System Model (CESM2) under preindustrial conditions. We find that an observationally-informed range of land parameters generate biogeophysical feedbacks that significantly influence the mean climate state, largely by modifying evapotranspiration. Global mean land surface temperature ranges by 2.2°C across our ensemble (standard deviation = 0.5°C) and precipitation changes were significant and spatially variable. Our analysis demonstrates that the impacts of land parameter uncertainty on surface fluxes propagates to the entire Earth system, and provides insights into where and how land process uncertainty influences climate.</p>
Output data for: Flammable Futures – Storylines of climatic impacts on wildfire events and palm oil plantations in Indonesia
<p>This repository contains the output data associated with the publication "Flammable Futures – Storylines of climatic impacts on wildfire events and palm oil plantations in Indonesia". It contains the FLAM modeled burned area and the GLOBIOM output, as well as the a downscaling grid.</p> <p>Descriptions of the results can be found in the publication (DOI will follow).</p>
Data for Tropical Cyclones flood hazards and impacts in Beira for study "Exploring coastal climate adaptation through storylines: Insights from Cyclone Idai in Beira, Mozambique"
<p>Data for Tropical Cyclones flood hazards and impacts in Beira for study "Exploring coastal climate adaptation through storylines: Insights from Cyclone Idai in Beira, Mozambique"<br><br><span><a href="../api/records/12664900/draft/files/hmax_idai_ifs_rebuild_bc_hist_rain_surge_noadapt.tiff/content" target="_blank" rel="noopener noreferrer">hmax_idai_ifs_rebuild*</a> -> Flood maps<br><a href="../api/records/12664900/draft/files/spatial_idai_ifs_rebuild_bc_3c-hightide_rain_surge_retreat.gpkg/content" target="_blank" rel="noopener noreferrer">spatial_idai_ifs_rebuild*</a> -> Impacts<br></span></p>
Impact Degassing of H2 on Early Mars and Its Effect on the Climate System
<p>Supporting information contains IDL saveset files to reproduce figures, and IDL source code to run the model, and an excel spreadsheet of crater statistics. </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.