Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
617
datasets available to search
ShareScore release 0.7.1
Dataset results
617 results for “Climate models”
Data from: Microclimate-based species distribution models in complex terrain indicate widespread cryptic refugia under climate change
<p class="MsoNoSpacing"><i>Aim: </i>Species' climatic niches may be poorly predicted by regional climate estimates used in species distribution models (SDMs) due to microclimatic buffering of local conditions. Here, we compare SDMs generated using a locally validated below-canopy microclimate model to those based on interpolated weather station data at two spatial scales to determine the effects of scale, topography, and forest cover on potential future ground-level warming and species distributions.</p> <p class="MsoNoSpacing"><i>Location:</i> Great Smoky Mountains National Park (2090 km<sup>2</sup>; NC, TN, USA)</p> <p class="MsoNoSpacing"><i>Time period: </i>1970 – 2006</p> <p class="MsoNoSpacing"><i>Major taxa:</i> Vascular plant species of the Southern Appalachians</p> <p class="MsoNoSpacing"><i>Methods:</i> We compared the fit and predictions of SDMs generated using a database of plant occurrences and three climate models: macroclimate (1 km, WorldClim), fine-scale (30 m) interpolation of macroclimate with elevation, and fine-scale below-canopy microclimate from a ground-level sensor network.</p> <p class="MsoNoSpacing"><i>Results: </i>We found that, although SDM fit was similar across models, microclimate-derived SDMs predicted substantially greater species persistence with 4 °C of regional warming, with a difference of 50% of the species pool in some areas. Microclimate SDMs predicted higher stability of mid-elevation species, particularly in thermally buffered areas near streams, and critically, less change in species composition at high elevation. In contrast, predictions of macroclimate and interpolation models were similar despite improved resolution.</p> <p class="MsoNoSpacing"><i>Main conclusions:</i> Our results demonstrate that careful selection of climate drivers, including local near-ground validation rather than interpolation, is critical for projecting distributions. They also suggest that some species at risk from climate change might persist, even with 4 °C of macroclimate warming, in cryptic refugia buffered by microclimate, pointing to the roles of forest cover and topography in explaining slower-than-expected changes in understory communities. However, certain species, such as those currently occurring on low-elevation ridges that are sensitive to atmospheric changes, may be at more risk than macroclimate or interpolated SDMs suggest.</p> <p class="MsoNoSpacing"> </p>
Last glacial cycle simulations forced by PMIP3 climate with a matrix and index method using a 3D thermodynamical ice-sheet model IMAU-ICE
<p>IMAU-ICE 2.0 model output of the ice evolution during the last glacial cycle at a 10 ka temporal resolution, as described in Scherrenberg at al., 2023.</p>
Multi-model ensemble bias-corrected precipitation dataset for historical and future climate (1961–2099) in China
<p>本文基于耦合模式比较项目第六阶段(CMIP6)的27个全球气候模式(GCM),采用随机森林(RF)模型和EQM方法整合27个大气监测模型的降水模拟数据,进一步修正中国综合月降水数据。修正后的降水资料在月降水量和极端降水量方面均明显优于原GCM降水资料。数据以 GeoTIFF 格式,其中嵌入了具有 1° 空间分辨率的地理配准信息。LST在GeoTIFF中的单位是mm。压缩文件被命名为历史文件.zip、SSP126.zip、SSP245.zip 和 SSP585.zip。压缩文件中的每个文件都命名为“yyyymm.tif”,其中“yyyy”和“mm”分别表示年份和月份。例如,文件“196101.tif”存储了 1961 年 1 月中国每月降水量。</p>
Data and GrADS scripts for "Changes in March mean snow water equivalent since the mid-twentieth century and the contributing factors in reanalyses and CMIP6 climate models", submitted to The Cryosphere
<p>Data and GrADS (Grid Analysis and Display System) scripts for reproducing the figures and numerical results included in the manuscript "Changes in March mean snow water equivalent since the mid-twentieth century and the contributing factors in reanalyses and CMIP6 climate models". Revised for The Cryosphere in March 2023.</p> <p>In addition to the README file, there are two zipped archives:</p> <p>swe_trends.zip (2.3 GB) includes both the data (mostly as GrADS binaries), the GrADS data descriptor files and the scripts.</p> <p>swe_trends_no_data.zip (74 kB) includes just the scripts and the data descriptor files.</p> <p>Please see the README file for further details on the content and use of the archives.</p>
Bayesian spatiotemporal modelling of wildfire occurrences and sizes for projections under climate change (Data)
<p>This repository contains the data necessary to reproduce the study developed in Legrand et al. (2023) "Bayesian spatiotemporal modelling of wildfire occurrences and sizes for projections under climate change"</p>
Ensembles of climate model parameters of Mars and significance values
<p>Data set containing an ensemble of model parameters for each of the candidate climate models in Table 1 of Izquierdo et al., (2023). These ensembles are stored as Python objects using the Pickle module. Files for each candidate model are named based on the accumulation and lag sub models and the number of steps used in the Markov chain Monte Carlo algorithm. From each Python object, it can be extracted the distribution of accumulation and retreat rates with time following the scripts and notebooks of the repository referenced in the open research section of the paper. </p> <p>The folders in this repository refer to the ensembles of all candidate models dependent of insolation values (insolation), ensembles of all candidate models dependent on obliquity values (obliquity), and the mean likelihood and Bayes factor of each candidate model (bayesFactor). </p>
A New GFSv15 based Climate Model Large Ensemble and Its Application to Understanding Climate Variability, and Predictability
<p>Data and analysis scripts for figures of Journal article (A New GFSv15 based Climate Model Large Ensemble and Its Application to Understanding Climate Variability, and Predictability)</p>
Effects of winter wheat irrigation on local climate and extreme events over the North China by using the high resolution non-hydrostatic regional climate model
<p>The control and irrigation simulation dataset from RegCM4.7.</p>
Developing a Physics-informed Deep Learning Model to Simulate Runoff Response to Climate Change in Alpine Catchments
<p>This data archive includes the source code of EXP-HYDRO, standard DL, hybrid-J, and hybrid-Z models, as well as simulated daily runoff (mm/d) of all five models in the paper at the three subbasins in the source region of the Yellow River. For more details please see the publication.</p> <p>Please cite the paper as follows:</p> <p>Zhong, L., Lei, H., & Gao, B. (2023). Developing a physics-informed deep learning model to simulate runoff response to climate change in Alpine catchments. Water Resources Research, 59, e2022WR034118. https://doi. org/10.1029/2022WR034118</p> <p> </p>
Data from "Connecting large-scale meteorological patterns to extratropical cyclones in CMIP6 climate models using self-organizing maps"
<p>The following files were used as data and analysis in the article "Connecting large-scale meteorological patterns to extratropical cyclones in CMIP6 climate models using self-organizing maps" (<a href="https://doi.org/10.1029/2022EF003211">https://doi.org/10.1029/2022EF003211</a>). In the study, we applied self-organizing maps (SOMs) as an automated machine-learning approach to characterize the large-scale meteorological patterns (LSMP) and associated frequency and intensity of discrete extratropical cyclone (ETC) events over the northeastern U.S. The dominant patterns of geopotential height variability are identified through SOM analysis of five reanalysis products during 1980 - 2019. ETC events are tracked using TempestExtremes and are integrated with SOMs to classify the accumulated cyclone activity associated with each pattern. We then evaluate the skill of CMIP6 historical experiments in simulating the LSMP and ETC events identified in the SOM. Please see the published paper for more details. Here we have archived: </p> <p>- data pre-processing scripts</p> <p>- code to run the self-organizing map analysis</p> <p>- code to calculate the SOM and ETC statistics</p> <p>- composites of 500-hPa geopotential height for each dataset as organized by the SOM</p> <p>- ETC tracking script and tracking output for each dataset</p> <p>- SOM output for each dataset </p>
Assessment of equilibrium climate sensitivity of the Community Earth System Model version 2 through simulation of the Last Glacial Maximum
<p>Simulation data (TS, FSNT, and FLNT) and apap cloud feedback analysis for CESM2 LGM simulation</p> <p><strong>Simulation boundary condition files in 1-degree resolution: boundary_condition_files.zip</strong></p> <p><strong>Please cite: </strong></p> <p>Zhu, J., Otto-Bliesner, B. L., Brady, E. C., Poulsen, C. J., Tierney, J. E., Lofverstrom, M., & DiNezio, P. (2021). Assessment of equilibrium climate sensitivity of the Community Earth System Model version 2 through simulation of the Last Glacial Maximum. <em>Geophysical Research Letters</em>, <em>n/a</em>(n/a), e2020GL091220. https://doi.org/10.1029/2020GL091220</p>
Urban Heat: Forward-Looking Climate Modeling for Gaza City
<p>We produced actionable data on heat stress in cities to inform analysis and client dialogue on the part of World Bank teams. We applied an urban-scale climate modeling framework to generate datasets describing modeled heat stress exposure for present-day and future conditions under selected climate scenarios. The study domain focuses on Gaza City.</p> <p>More details about the dataset: </p> <ul> <li>The dataset includes calculations for each indicator across three scenarios (<strong>present, SSP1-1.9, SSP3-7.0</strong>) and three twenty-year periods (<strong>2001-2020, 2021-2040, and 2041-2060</strong>). The present period refers to 2001-2020, while the other two periods correspond to the two SSP scenarios.</li> <li>All indicators are available in both <strong>NetCDF</strong> and <strong>GeoTiff</strong> formats.</li> <li>The indicators are calculated at a resolution of <strong>100 m</strong>, consistent with the UrbClim and WBGT simulations. Additionally, downscaled versions of the indicators are provided at a resolution of <strong>30 m</strong>.</li> <li>The UrbClim and WBGT simulations, as well as the postprocessing, are conducted using the regional projection <strong>E</strong><strong>PSG 32636</strong>. The NetCDF and GeoTiff data also adopt this projection. Furthermore, a GeoTiff data file with <strong>EPSG 4326</strong> projection is included.</li> <li>All indicators are calculated as <strong>yearly averages</strong>. Some indicators also have additional calculations for <strong>seasonal averages</strong>, including Spring (MAM), Summer (JJA), Autumn (SON), and Winter (DJF).</li> <li>Ten representative locations within the study domain have been selected to retrieve the WBGT profile on a chosen date (2017-07-11). The results and the locations are stored in wbgt_profile.xlsx.</li> <li>Images for <strong>quick viewing</strong> <strong>in</strong> <strong>png</strong> format visualizing the results for each indicator. Present denotes the period 2001-2020; 2030 denotes the period 2021-2040; & 2050 denotes the period 2041-2060.</li> <li>The NetCDF and GeoTiff data can be found in the data.zip; The png files for quick viewing can be found in quickview.zip; more information about the dataset, including the methodology, all available data list, contact information, etc. can be found in the Technical_Annex_Gaza.docx</li> </ul>
Model outputs and species-level data for "Functional traits and climate drive interspecific differences in disturbance-induced tree mortality".V2
<p>A minor coding error was found in the pre-formatted data of <a href="https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.16630">Barrere et al. (2023)</a>. This error did not affect the main results of the paper, but led to minor change in the value of the posterior estimates, stored in data/sensitivity/jags_dominance.Rdata. This repository contains the new version of the parameters. </p>
Data from: Phylogeography in continuous space: coupling species distribution models and circuit theory to assess the effect of contiguous migration at different climatic periods on genetic differentiation in Busseola fusca (Lepidoptera: Noctuidae)
Open the record for dataset details and reuse information.
Data from: Limited alpine climatic warming and modeled phenology advancement for three alpine species in the Northeast United States
Open the record for dataset details and reuse information.
Data from: Cross-scale interactions among bark beetles, climate change and wind disturbances a landscape modeling approach
Open the record for dataset details and reuse information.
Climate change modelling indicates extensive range contractions for a scarce southern African endemic and minimal protected area network within its future climatically suitable range
Open the record for dataset details and reuse information.
Data from: Can impacts of climate change and agricultural adaptation strategies be accurately quantified if crop models are annually re-initialized?
Open the record for dataset details and reuse information.
Data from: Tests of species-specific models reveal the importance of drought in postglacial range shifts of a Mediterranean-climate tree: insights from integrative distributional, demographic and coalescent modelling and ABC model selection
Open the record for dataset details and reuse information.
Genetic data improves niche model discrimination and alters the direction and magnitude of climate change forecasts
Open the record for dataset details and reuse information.
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.