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.
53
datasets available to search
ShareScore release 0.9.0
Dataset results
53 results for “Earth system data”
The INMCM-4.8 Earth system model data used in the paper by Guryanov V.V. et al. entitled ''The present-day and future lightning frequency as simulated by four CMIP6 models'
<p>The INMCM-4.8 Earth system model data used in the paper by Guryanov V.V. et al. entitled ''The present-day and future lightning frequency as simulated by four CMIP6 models'</p>
Demo-Dataset for publication "FAIR workflows in Earth system modelling: a use case with semantic data management"
<p>This demodataset is intended to be used to test the workflow described in the publication by Lennartz & Schlemmer "FAIR workflows in Earth System modelling: a use case with semantic data management". It contains example model output for an arbitrary biogeochemical model tracer (here: dissolved organic carbon, DOC) from an ocean model as a 4-dimensional dataset (latitude, longitude, depth, time), the corresponding grid point locations as well as a textfile specifying parameter inputs for the model. The file structure is adapted for seamless integration into the workflow described in Lennartz & Schlemmer, which builds on the open source semantic research data management system LinkAhead. The dataset contains the following structure: The folder DataAnalysis stores data required for data analysis, such as the grid point locations in the file TMM_grid_v2018a.mat. The folder SimulationData stores model output in the folder 2022_TMM, containing the parameter input file nl_in.txt and the model output TR_monthly.mat. Related instructions can be accessed here: https://gitlab.com/salexan/fairworkflows-demodataset .</p>
Model data and code supporting "Updated Isoprene and Terpene Emission Factors for the Interactive BVOC Emission Scheme (iBVOC) in the United Kingdom Earth System Model (UKESM1.0) "
<p>Model data and analysis code supporting the Geoscientific Model Development manuscript "Updated Isoprene and Terpene Emission Factors for the Interactive BVOC Emission Scheme (iBVOC) in the United Kingdom Earth System Model (UKESM1.0) "</p> <p> </p> <p> </p>
Data for: Improvements in the Land and Crop Modeling over Flooded Rice Fields by Incorporating the Shallow Paddy Water (Submitting to the Journal of Advances in Modeling Earth Systems)
<p>We incorporated the shallow paddy surface water layer into the Noah-MP land surface model to improve its performance of surface heat fluxes over flooded rice paddies. Field measurements from two crop sites, i.e., SAITO (early rice) and SAGA (late rice), were used to initialize and evaluate the modified Noah-MP model (Maruyama, 2021). Additionally, we investigated the roles of some key parameters in the land and crop modeling. </p> <p>Note that, all numerical experiments in this study were conducted at the field scale using the offline version of Noah-MP (Niu et al., 2011) running within the High-Resolution Land Data Assimilation System (HRLDAS v3.9; Chen et al., 2007). Please refer to the official HRLDAS/Noah-MP unified Github repository (<a href="https://github.com/NCAR/hrldas">https://github.com/NCAR/hrldas-release</a>) for the original model codes.</p> <p>The related model code modifications and model outputs were included in this dataset. Surface observations for the nearest AMeDAS or meteorological observatory stations were obtained from the Japan Meteorological Agency website (<a href="https://www.jma.go.jp/jma/indexe.html">https://www.jma.go.jp/jma/indexe.html</a>), and were also provided in this dataset.</p> <p> </p> <p>References</p> <p>Chen, F., Manning, K. W., LeMone, M. A., Trier, S. B., Alfieri, J. G., Roberts, R. D., et al. (2007). Description and evaluation of the characteristics of the NCAR high‐resolution land data assimilation system. <em>Journal of Applied Meteorology and Climatology</em>, 46(6), 694-713. <a href="https://doi.org/10.1175/JAM2463.1">https://doi.org/10.1175/JAM2463.1</a></p> <p>Maruyama, A. (2021). Data for: Coupling land surface and crop models to estimate the effects of changes in the growing season on energy balance and water use of rice paddies (version 2) [Data set]. Mendeley Data. <a href="https://doi.org/10.17632/tv23z95r5g.2">https://doi.org/10.17632/tv23z95r5g.2</a></p> <p>Niu, G., Yang, Z., Mitchell, K., Chen, F., Ek, M., Barlage, M., et al. (2011). The community Noah land surface model with multiparameterization options (Noah‐MP): 1. Model description and evaluation with local‐scale measurements. <em>Journal of Geophysical Research, </em>116, D12109. <a href="https://doi.org/10.1029/2010JD015139">https://doi.org/10.1029/2010JD015139</a></p>
Supplementary data for the study "Unveiling the rheological control of magmatic systems on volcano deformation: the interplay of poroviscoelastic magma-mush and thermo-viscoelastic crust ", submitted in the Journal of Geophysical Research: Solid Earth
<p>Supplementary data for the study "Unveiling the rheological control of magmatic systems on volcano deformation: the interplay of poroviscoelastic magma-mush and thermo-viscoelastic crust ", submitted in the Journal of Geophysical Research: Solid Earth</p>
Data for "Benchmarking GOCART-2G in the Goddard Earth Observing System (GEOS)"
<p>This tar file contains the data produced for the manuscript "Benchmarking GOCART-2G in the Goddard Earth Observing System (GEOS)". The contents include model data from the simulation presented in the paper, a four-year benchmark simulation using version 10.23.0 of GEOS with GOCART2G, spanning the period of 2016 through 2019. Aerosol mass budget terms including emissions, production, and deposition are included as well as the model data sampled according to the observational data sets for MODIS, AERONET, OMPS-LP, CALIOP, IMPROVE, and EMEP.</p>
Data from: Gene trees, species trees and Earth history combine to shed light on the evolution of migration in a model avian system
The evolution of migration in birds has fascinated biologists for centuries. In this study, we performed phylogenetic-based analyses of Catharus thrushes, a model genus in the study of avian migration, and their close relatives. For these analyses, we used both mitochondrial and nuclear genes, and the resulting phylogenies were used to trace migratory traits and biogeographic patterns. Our results provide the first robust assessment of relationships within Catharus and relatives and indicate that both mitochondrial and autosomal genes contribute to overall support of the phylogeny. Measures of phylogenetic informativeness indicated that mitochondrial genes provided more signal within Catharus than did nuclear genes, whereas nuclear loci provided more signal for relationships between Catharus and close relatives than did mitochondrial genes. Insertion and deletion events also contributed important support across the phylogeny. Across all taxa included in the study, and for Catharus, possession of long-distance migration is reconstructed as the ancestral condition, and a North American (north of Mexico) ancestral area is inferred. Within Catharus, sedentary behaviour evolved after the first speciation event in the genus and is geographically and temporally correlated with Central American distributions and the final closure of the Central American Seaway. Migratory behaviour subsequently evolved twice in Catharus and is geographically and temporally correlated with a recolonization of North America in the late Pleistocene. By temporally linking speciation events with changes in migratory condition and events in Earth history, we are able to show support for several competing hypotheses relating to the geographic origin of migration.
Model data for "The GERB Obs4MIPs Radiative Flux Dataset: A new tool for climate model evaluation", submitted to Earth System Science Data
<p>© Crown Copyright, Met Office</p><p>The E1hrClimMon files contain the monthly mean diurnal cycles of TOA radiative fluxes (all-sky and clear-sky) for amip experiment of two configurations of HadGEM3: GC3.1 and GC5.0. The monthly mean diurnal cycle is constructed by averaging each UTC hourly mean over the entire month. The HadGEM3 OLR diagnostics used in this study differ from those submitted to CFMIP3. The OLR diagnostics submitted to CFMIP3 contain a correction that accounts for the surface temperature adjustment by the boundary layer scheme in model time steps between radiation time steps. This OLR diagnostic adjustment is introduced to conserve energy, but it significantly distorts the diurnal cycle of OLR. For comparison with the GERB obs4MIPs products, the OLR without this correction is recommended.</p><p>The COSP file contains the average monthly climatologies for the variables cfadLidarsr532 and clisccp for the amip simulations of GC3.1 and GC5.0.</p>
The evaluation data and source codes of a new conceptual coupled Earth system model and the MOC box model.
<p>The dataset contains the results of a conceptual Atmosphere-Ocean-Ice-Land coupled Earth system model and a MOC box model and the evaluation data of their.</p>
Data and Codes of A Deep Learning-Based Consistency Test for Earth System Models on Heterogeneous Many-Core Systems
<p>These are the supporting information to verify the results in the paper, including input data, model outputs, the postprocessing scripts and the source codes.</p>
Data for Past terrestrial hydroclimate sensitivity controlled by Earth System Feedbacks
<p>This folder contains PlioMIP2 ensemble data and CESM2 simulations used in Feng et al., Past terrestrial hydroclimate sensitivity controlled by Earth System Feedbacks (2022). Here is some useful information:</p> <p>1. Experiment IDs of CESM2 experiments are described in the Method section of the manuscript. The first dimension of variables in ensemble files reflects model IDs : "CCSM4", "CESM1", "CESM2", "COSMOS", "IPSL-CM6", "MIROC4m", "NorESM1-F", "HadCM3", "EC-Earth3.3", "IPSL-CM5", "IPSL-CM5A2", "HadGEM3", "GISS-E2-1G".</p> <p>2. The naming convention of variables in ensemble files follows CMIP6 convention. The naming convention of CESM2 variables follows the convention of the model.</p> <p>3. The script pe_budget_season_new.ncl produces moisture budget decomposition. The results are shown in Fig. 4 of the manuscript.</p>
Data and Codes of Characterizing Uncertainties of Earth System Modeling with Heterogeneous Many-core Architecture Computing
<p>These are the supporting information to verify the results in the paper, including input data, model outputs, the postprocessing scripts and the source codes.</p>
Code and data for results and figures of the manuscript "Multi-million year cycles in modelled δ13C as a response to astronomical forcing of organic matter fluxes." submitted to Earth System Dynamics
<p>This dataset contains the code of the model used in the manuscript submitted to Earth System Dynamics "Multi-million year cycles in modelled δ13C as a response to astronomical forcing of organic matter fluxes.". It also contains some model outputs and code to draw the figures.</p>
Raw Data for Publication "Earth observations reveal impacts of climate variability on maize cropping systems in Sub-Saharan Africa"
<p>Phenological metrics extracted for all agricultural fields used in the study. Data also includes the coordinates of the fields.</p>
Data and analysis for "Fldgen v1.0: An Emulator with Internal Variability and Space-Time Correlation for Earth System Models"
<p>This is an archive of the raw data and analysis source code for the paper "Fldgen v1.0: An Emulator with Internal Variability and Space-Time Correlation for Earth System Models". The archive contains:</p> <ul> <li><strong>devel.Rmd : </strong>Source code for the worksheet that contains the early development and figures for the paper.</li> <li><strong>devel.html</strong> : HTML rendering of devel.Rmd</li> <li><strong>lg-ensemble-stats.Rmd </strong>: Source code for the worksheet that contains the statistical analysis described in the paper.</li> <li><strong>lg-ensemble-stats.html</strong> : HTML rendering of lg-ensemble-stats.Rmd</li> <li><strong>cc-analysis.Rmd </strong>: Analysis of the compromise conjecture raised by some readers of the paper</li> <li><strong>cc-analysis.nb.html</strong> : HTML rendering of cc-analysis.Rmd</li> <li><strong>data.tar.bz2 </strong>: Input data for the analyses above.</li> </ul> <p>The source code in this archive is written in R and requires the R runtime environment. It also uses the fldgen package, version 1.0.0, which is available at <a href="https://github.com/JGCRI/fldgen">https://github.com/JGCRI/fldgen</a></p> <p> </p>
CESM1.2 simulation data for "Quantifying the cloud particle-size feedback in an Earth system model"
<p>CESM1.2-CAM5 simulation data for "Quantifying the cloud particle-size feedback in an Earth system model"</p> <p><strong>Citation: </strong>Zhu, J., & Poulsen, C. J. (2019). Quantifying the cloud particle-size feedback in an Earth system model. <em>Geophysical Research Letters</em>, <em>46</em>, 10910–10917. <a href="https://doi.org/10.1029/2019GL083829">https://doi.org/10.1029/2019GL083829</a></p> <p>Data include:</p> <p>(1) cloud liquid particle size for liquid (AREL) and ice (AREI), grid box averaged cloud liquid (CLDLIQ) and ice (CLDICE), fractional occurrence of liquid (FREQL) and ice (FREQI), and surface temperature (TS) in the preindustrial and 2xCO2 experiments; and<br> (2) the cloud feedback (lam_CLDTOT) and cloud particle-size feedback (lam_CLDEFR3L) from our PRP-based method.</p>
NOAA Open Data Dissemination: Petabyte scale Earth System Data in the Cloud
<p>NOAA Open Data Dissemination (NODD) makes NOAA environmental data publicly and freely available on Amazon Web Services (AWS), Microsoft Azure (Azure), and Google Cloud Platform (GCP). These data can be accessed by anyone with an internet connection and span key datasets across the Earth system including satellite imagery, radar, weather models and observations, ocean databases, and climate data records. NODD works closely with NOAA stakeholders to make these data available consistently and performantly, often with near-real-time latency. Since its inception, NODD has grown to provide public access to more than 24PB of NOAA data and can support billions of requests and petabytes of access daily. NODD growth is driven by use; stake- holders routinely access more than 5PB of NODD data every month. NODD continues to grow to support open petabyte-scale Earth system data science in the cloud by investing in onboarding additional NOAA data, performant data formats, and streaming data platforms. Here, we document how this pro- gram works with a focus on data provenance, discuss the key datasets available through NODD, show how these data are being used, and highlight ways these data can be accessed with the goal of accelerating use of NOAA resources in the cloud.</p>
Supporting data to reproduce figures and anaylisis presented in Bruciaferri et al. 2023 - submitted to Journal of Advances in Modeling Earth Systems (JAMES)
<p>Data for reproducing figures and the analysis of</p> <p>Diego Bruciaferri, Catherine Guiavarc’h, Helene T. Hewitt, James Harle, Mattia Almansi and Pierre Mathiot. Localised general vertical coordinates for quasi-Eulerian ocean models: the Nordic overflows test-case, submitted to JAMES.</p> <p>Data includes (© Crown copyright Met Office):</p> <p>1) models_geometry: bathymetry, horizontal grid and domain files needed to run the models and analyse their results.</p> <p>2) hpge: output data from HPG error idealised test.</p> <p>3) ideal_ovf: output data from the idealised overflow experiment</p> <p>4) realistic: output data from the realistic simulations</p>
Supporting data for manuscript: "Global-scale evaluation of coastal ocean alkalinity enhancement in a fully-coupled Earth system model"
<p>Supporting data for manuscript: "Global-scale evaluation of coastal<br> ocean alkalinity enhancement in a fully-coupled Earth system model"</p> <p>Authors: Julien Palmieri and Andrew Yool</p> <p>Institute: National Oceanography Centre, European Way, Southampton<br> SO14 3ZH, UK</p> <p>This repository consists of four main sets of files:</p> <p>1. Matlab scripts used for analysis, figure plotting and table<br> preparation</p> <p> Filenames of the format: Figure_??.m</p> <p>2. Raw netCDF output files from UKESM1 for six model experiments</p> <p> Filenames of the format: medusa_c*.nc</p> <p>3. BGCVal processed timeseries shelve files</p> <p> Filenames of the format: u-c*.shelve.txt</p> <p>4. CMM2 processed timeseries netCDF files</p> <p> Filenames of the format: c*_global.nc</p> <p>File sets 2-4 are read and processed by script files in file set 1<br> </p>
Arraylake: A Cloud-Native Data Lake Platform for Earth System Science
<p>The vast amount of earth system data available today is an incredible resource for understanding our planet and confronting the challenge of climate change. Traditionally, a few large organizations have provided most of the data, and users have downloaded data to local computers. This way of working is becoming increasingly infeasible as data volumes grow and as AI-based methods demand direct access to full-scale data archives. With essentially infinite compute and storage capacity, cloud computing has the potential to revolutionize our interaction with weather and climate data, allowing everyone to bring their own compute workloads to bear against a single shared copy of the data. Over the past years, via our work in the Pangeo project, we have prototyped a cloud-native approach to weather and climate data in the cloud, combining scalable computing technologies such as Xarray and Dask with analysis-ready, cloud-optimized data in formats like Zarr. While these tools show great potential, they remain difficult to deploy and use in an operational context for many scientists and institutions.</p> <p>Motivated by this challenge, we founded Earthmover, a company aimed at democratizing access to state-of-the-art cloud-native data analytics, and built Arraylake, a data platform which enables teams of any size to manage and analyze weather and climate data in the cloud. Arraylake users can access high-quality public datasets alongside their own private data, all via the high-performance Zarr data standard. This talk describes Arraylake’s architecture, novel version control system for data, and approach to supporting all common climate data formats (NetCDF, HDF5, Grib, Tiff, Zarr) via a single, user-friendly interface. Via a short demo, we illustrate how Arraylake helps overcome common data management challenges that have henceforth limited widespread adoption of cloud computing in earth system science.</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.