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.
29
datasets available to search
ShareScore release 0.9.0
Dataset results
29 results for “Climate index”
Predicting time series of vegetation leaf area index across North America based on climate variables for land surface modeling using attention-enhanced LSTM
<p>We developed an attention-enhanced long and short memory (AELSTM) model for predicting vegetation LAI time series based on climatic data. The developed AELSTM model establishes the relationships between the time series of vegetation LAI and climatic variables. </p>
Mediterranean Climatic Indexes
<p>Climatic indexes for the Mediterranean Sea (-6.25W–36.5E, 30N–46N) from 1950 to 2015. The file "Med_Climatic_Indexes.tar.gz" contains : a) an ascii file named “inventory.lst” which lists all the available indexes, and b) a directory named “Med_Climatic_Indexes” with all the available indexes under the following structure:</p> <ul> <li>Anomalies/Annual_Decadal/Variable_decade_allmonths_z1_z2.nc</li> <li>Anomalies/Seasonal_Decadal/Variable_decade_season_z1_z2.nc</li> <li>ArealDensity/Annual_Decadal/Variable_decade_allmonths_z1z2.nc</li> <li>ArealDensity/Seasonal_Decadal/ Variable_decade_season_z1z2.nc</li> <li>Climashift_30yrs/Anomalies/Annual/Variable_decade2_decade1_allmonths_z1_z2.nc</li> <li>Climashift_30yrs/Anomalies/Seasonal/ Variable_decade2_decade1_season_z1_z2.nc</li> <li>Climashift_30yrs/Vertical_Averages/Annual/Variable_decade2_decade1_allmonths_z1z2.nc</li> <li>Climashift_30yrs/Vertical_Averages/Seasonal/Variable_decade2_decade1_season_z1z2.nc</li> <li>LinearTrends/Annual/Variable_period_allmonths_z1z2.nc</li> <li>LinearTrends/Seasonal/ Variable_period_season_z1z2.nc</li> <li>TimeSeries/Annual/Variable_period_allmonths_z1z2.dat</li> <li>TimeSeries/Seasonal/ Variable_period_season_z1z2.dat</li> <li>VerticalAverages/Annual_Decadal/Variable_decade_allmonths_z1z2.nc</li> <li>VerticalAverages/Seasonal_Decadal/ Variable_decade_season_z1z2.nc</li> </ul> <p>The Variable naming is:</p> <ul> <li>Tanom: Temperature anomaly</li> <li>Sanom: Salinity anomaly</li> <li>Tanomvavg: Vertically averaged temperature anomaly</li> <li>Sanomvavg: Vertically averaged salinity anomaly</li> <li>OHCad: areal density Ocean Heat Content anomaly</li> <li>OSCad: areal density Ocean Salt Content anomaly</li> <li>Tanomclimashift: Temperature anomaly difference between decade2 and decade1</li> <li>Sanomclimashift: Temperature anomaly difference between decade2 and decade1 at</li> <li>Tclimashift: Vertically averaged temperature anomaly difference between decade2 and decade1</li> <li>Sclimashift: Vertically averaged salinity anomaly difference between decade2 and decade1</li> <li>OHCclimashift: Ocean Heat Content anomaly difference between decade2 and decade1</li> <li>OSCclimashift: Ocean Salt Content anomaly difference between decade2 and decade1</li> <li>Tlineartrend: Vertically averaged temperature anomaly linear trend</li> <li>Slineartrend: Vertically averaged salinity anomaly linear trend</li> <li>OHClineartrends: Ocean Heat Content anomaly linear trend</li> <li>OSClineartrends: Ocean Salt Content anomaly linear trend</li> </ul> <p>Other naming:</p> <ul> <li>allmonths: 0112 (all months from January to December)</li> <li>seasons: 0103 for winter, 0406 for spring, 0709for summer, 1012 for autumn</li> <li>decade1: stands for 19501979</li> <li>decade2: stands for 19802015</li> <li>period: stands for 19502015</li> <li>z1z2: vertical layer between z1 and z2 depths</li> <li>z1, z2: standard depth levels</li> </ul> <p>Examples:</p> <ol> <li>OHC_19502015_0103_0150.dat, is the winter Ocean Heat Content anomaly for the period 1950 to 2015, at 0-150 m</li> <li>Tanomvavg_20062015_1012_6004000.nc, is the autumn vertically averaged temperature anomaly, for the decade 2006-2015, at 600 – 4000 m</li> <li>Sanomclimashift_19802015_19501979_0112_5_4000.nc, is the annual salinity anomaly difference between 1980-2015 and 1950-1970 from 5 to 4000 m.</li> </ol> <p> </p>
Climate risk index for Italy
<p>We describe a climate risk index that has been developed to inform national climate adaptation planning in Italy and that is further elaborated in this paper. The index supports national authorities in designing adaptation policies and plans, guides the initial problem formulation phase, and identifies administrative areas with higher propensity to being adversely affected by climate change. The index combines (i) climate change-amplified hazards; (ii) high-resolution indicators of exposure of chosen economic, social, natural and built- or manufactured capital (MC) assets and (iii) vulnerability, which comprises both present sensitivity to climateinduced hazards and adaptive capacity. We use standardized anomalies of selected extreme climate indices derived from high-resolution regional climate model simulations of the EURO-CORDEX initiative as proxies of climate change-altered weather and climate-related hazards. The exposure and sensitivity assessment is based on indicators of manufactured, natural, social and economic capital assets exposed to and adversely affected by climate-related hazards. The MC refers to material goods or fixed assets which support the production process (e.g. industrial machines and buildings); Natural Capital comprises natural resources and processes (renewable and non-renewable) producing goods and services for well-being; Social Capital (SC) addressed factors at the individual (people’s health, knowledge, skills) and collective (institutional) level (e.g. families, communities, organizations and schools); and Economic Capital (EC) includes owned and traded goods and services. The results of the climate risk analysis are used to rank the subnational administrative and statistical units according to the climate risk challenges, and possibly for financial resource allocation for climate adaptation. This article is part of the theme issue ‘Advances in risk assessment for climate change adaptation policy’.</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>
GloUTCI-M: A Global Monthly 1 km Universal Thermal Climate Index Dataset from 2000 to 2022
<p>The GloUTCI-M comprises global monthly UTCI data at a spatial resolution of 1km, spanning from March 2000 to October 2022. The dataset is expressed in degrees Celsius (°C) and is stored as an integer type (Int16). To utilize it appropriately, one must divide the values by 100.</p>
Genetic Diversity, Ecological Niches, and Climate Change Vulnerability of Aspens in the Upper Midwest:Leaf osmotic potential and stomatal pore index
Quaking aspen (Populus tremuloides) is the most cosmopolitan tree species in North America and an important native at Cedar Creek and across the Midwest. Aspen stands are quite common through eastern, central, and northern Minnesota, and occur sporadically in cool, wet microclimates across the Great Plains. Currently, these stands are in decline, are poorly reproducing in the wild, and are suffering from a range of stresses. Climate change associated phenomena, drought and altered freeze-thaw cycles, have contributed to massive aspen dieback, especially in the American West. We have received funding from the National Park Service to assess the genetic diversity and hybrid status, age structure and health, ecological niche and historical rate of range contraction, and drought and freezing tolerance physiology of an aspen stand of interest at the Niobrara National Scenic River (NNSR) in northern Nebraska. As part of this project, we are also studying genetic diversity and physiological vulnerability to climate change in quaking and bigtooth (P. grandidentata) aspen populations in Minnesota, Wisconsin, Iowa, South Dakota, and Nebraska. We will use genetic markers to identify genetically unique stands and compare growth and survival of these to populations of the parent species under different drought and freeze-thaw conditions. This study will allow us to better pinpoint the causes of decline in the NNSR aspen stands and aspen stands across the upper Midwest, and potentially provide guidance to managers on the prioritization of particular stands for conservation or in identifying genetic sources for any ex situ conservation or assisted migration.
Nonlinear sensitivity of glacier-mass balance to climate attested by temperature-index models; synthetic data
<p>Synthetic data and results of the PDD model used in the paper <a href="https://doi.org/10.5194/tc-2022-210">https://doi.org/10.5194/tc-2022-210</a></p>
Open data for "Predicting dominant terrestrial biomes at a Global Scale: Assessments of machine learning algorithms, climate variables indexing, and extreme climate"
<p>______________________________________________________<br> This page contains public-domain data required to reconstruct simulation results in the manuscript "Predicting dominant terrestrial biomes at a Global Scale: Assessments of machine learning algorithms, climate variables indexing, and extreme climate," submitted by the following author.</p> <p>Author: Hisashi SATO (JAMSTEC) <br> email : hsatoscb_(at)_gmail.com</p> <p>______________________________________________________<br> 1. Folder "Code"<br> Detailed descriptions are available on the code. </p> <p>1-1. MachineLearningComparison.R<br> Machine learning programs using random forest (RF), naive Bayes classifier (NV), and support vector machine (SVM) algorithms.</p> <p>1-2. Analyse_MapSimilarity.R<br> Calculate coincidences of simulated potential natural vegetation (PNV) maps simulated by different models.</p> <p>1-3. Visualize_VCE.R<br> Generating VCE (Visualize Climate Image) for training CNN models.</p> <p>1-4. Visualize_Maps.R<br> Visualizing global PNV maps.</p> <p>1-5. Visualize_ClimateHistgrams.R<br> Visualizing histograms of climate datasets.</p> <p>______________________________________________________<br> 2. Folder "Input"</p> <p>2-1. Unified_BIOCLIM_WorldClim.csv<br> Input data for the current climate.<br> This file contains the following variables.<br> lon Longitude at the center of the grid<br> lat Latitude at the center of the grid<br> bio1~19 Average climate indices from BIOCLIM (AveI)<br> CDD~WSDI Extreme climate indices (CEI)<br> c1~c16 Fraction of PNV from MODIS data<br> tavg01~tavg12 Monthly mean air temperature from January to December (Ave)<br> prec01~prec12 Monthly precipitation from January to December (Ave)</p> <p>2-2. Unified_BIOCLIM_WorldClimFutureRCP85.csv<br> Input data for future climate (@RCP8.5)<br> Including variables are the same as Unified_BIOCLIM_WorldClim.csv</p> <p>2-3. BIOCLIM_RefNo.csv<br> This CSV file contains the following information for each grid.<br> lat: Latitude at the center of the grid<br> lon: Longitude at the center of the grid<br> latNo: Latitude number corresponding to the image file name<br> lonNo: Longitude number corresponding to the image file name<br> lineNo: No use. Don't mind.<br> vegNo: Most dominant PNV based on the Unified_BIOCLIM_WorldClim.csv</p> <p>______________________________________________________<br> 3. Folder "Output"</p> <p>3-1. PNV_sim<br> 3-2. PNV_sim_RCP85.csv<br> Current and future PNV maps from various models. These files are the main output files from the code MachineLearningComparison.R. For PNV maps from CNN models (m4p1~6) were supplemented. Detailed methods to build CNN models, please refer to the following manuscript.<br> Sato, H. & T. Ise (2022). "Predicting global terrestrial biomes with the LeNet convolutional neural network." Geoscientific Model Development 15(7): 3121-3132.</p> <p>Labels indicate combinations of machine-learning-algorithm and dataset for training the model. For example, In case of "m1p1", that column shows the simulation result of models trained with randomForest (RF) algorithm and Ave dataset.<br> m1: randomForest (RF)<br> m2: Support vector machine (SVM)<br> m3: Naive Bayes (NB)<br> m4: Convolutional Neural Network (CNN), which is NOT analysed in this code<br> p1: Ave<br> p2: Ave + CEI<br> p3: Ave + CEIpart<br> p4: AveI <br> p5: AveI + CEI<br> p6: AveI + CEIpart</p>
Global Leaf Area Index, and Climatic Variables, 1982-2015
<p>This dataset contains a group of global, half-degree, monthly climate covariates, and leaf area index records, for the period of 1982-2015.</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.