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.
14
datasets available to search
ShareScore release 0.7.1
Dataset results
14 results for “Earth System Science”
Data/ codes used in the the Natural Hazards and Earth System Sciences (NHESS) publication titled "Wind-Wave Characteristics and extremes along the Emilia-Romagna coast" by Pranavam Ayyappan Pillai et al. (2022)
<p>The archive contains datasets and codes used in the manuscript titled "Wind-Wave Characteristics and extremes along the Emilia-Romagna coast", and published in the journal <em>Natural Hazards and Earth System Sciences</em> (<em>NHESS</em>) by Pranavam Ayyappan Pillai et al., 2022.</p> <p>Pranavam Ayyappan Pillai, U., Pinardi, N., Federico, I., Causio, S., Trotta, F., Unguendoli, S., and Valentini, A.: Wind-Wave Characteristics and extremes along the Emilia-Romagna coast, Nat. Hazards Earth Syst. Sci. Discuss. https://doi.org/10.5194/nhess-2022-103, 2022.</p>
Winter Precipitation-Type Models for "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications"
<p>This contains trained model weights, scalers, and evaluation metrics for the winter precipitation-type models trained as part of the paper "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications". </p>
Data for: High-Speed 3D Imaging of Multiphase Systems: Applying SCAPE Microscopy to Analogue Experiments in Volcanology and Earth Sciences
<p>Microscale processes in three-phase suspensions (mixtures of gas, liquids, and solids) can affect the macroscale behavior of the whole suspension. To visualize these small-scale processes at high speed and in 3D, we use a recently developed imaging system: Swept Confocally-Aligned Planar Excitation (SCAPE) microscopy. This dataset contains 3D videos taken with SCAPE microscopy of experiments where different phases interact with each other. Each zipped folder contains raw data and processed data for a single experiment. "Case 1" experiments show CO2 bubbles growing on PMMA (acrylic) particles in sparkling water. The "Case 2" experiment shows water droplets suspended in canola oil and flowing through a porous medium made of packed PMMA particles. "Case 3" experiments show growth of injected air bubbles in particle suspensions (either glass beads in immersion oil, or PMMA particles in a refractive index matched liquid).</p> <p>All scaling parameters are provided in Table 1. "info.txt" files contain metadata for the processed hyperstacks.</p> <p>The experiments provided here are discussed in the following publication:<br> Oppenheimer, J.*, Patel, K.*, Lindoo, A., Hillman, E. M. C., and Lev, E.: High-Speed 3D Imaging of Multiphase Systems: Applying SCAPE Microscopy to Analogue Experiments in Volcanology and Earth Sciences. <em>Geochemistry, Geophysics, Geosystems.</em> (In press, 12/2020)</p> <p><br> </p>
Benefits of Ontologies in Earth System Science
<p>The exponential growth of data due to technological developments along with an increased recognition of research data as relevant research output during the last decades substantiates fundamental challenges in terms of interoperability, reproducibility and reuse of scientific information. Being cross-disciplinary at its core, research in Earth System Science comprises divergent domains such as Paleontology, Marine Science, Atmospheric Sciences and Molecular Biology in addition to different types of data such as observation and simulation data. Within the various disciplines, distinct methods and terms for indexing, cataloguing, describing and finding scientific data have been developed, resulting in several controlled Vocabularies, Taxonomies and Thesauri. However, given the semantic heterogeneity across scientific domains, effective utilisation and (re)use of data is impeded while the importance of enhanced and improved interoperability across research areas will increase even further, considering the global impact of Climate Change to literally all aspects of everyday life. There is thus a clear need to harmonise practices around the development and usage of semantics in representing and describing information and knowledge. For a beneficial usage of semantic artefacts, sustainability is the key: any kind of terminology service must be maintained to guarantee that terms and relations are offered in a persistent way. But if they are, Vocabularies, Taxonomies, Thesauri and Ontologies can serve as a driving force for improved visibility and findability of research output within and across different research areas. Why Ontologies matter, what they are, and how they can be used will be depicted on our Poster in an easy-to-understand way.</p> <p> </p>
Datasets used in "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications"
<p>The precipitation type (p-type) dataset (ptype.parquet) comprises observational weather reports sourced from the Meteorological Phenomena Identification Near the Ground (mPING) project, combined with corresponding numerical weather prediction data from the NOAA Rapid Refresh (RAP) model. These crowd-sourced mPING reports offer precipitation type labels (rain, snow, sleet, and freezing rain) across North America, while the RAP model provides atmospheric data, including temperature, humidity, and wind profiles, on pressure levels.</p> <p> </p> <p>The RAP data covers the contiguous United States (CONUS) from 2015 to 2022 on an hourly 13km grid. The mPING observations are matched to the nearest RAP grid cell and hour, allowing the two data sources to be merged into a labeled dataset suitable for classification tasks. </p> <p> </p> <p>The surface layer flux dataset (surface_layer.csv) contains high-frequency meteorological observations spanning from 2013 to 2015, collected at the Cabauw Experimental Site in the Netherlands. It includes measurements of various variables such as temperature, humidity, wind, radiation, and soil moisture, recorded every 10 minutes. The target output encompasses friction velocity, sensible heat, and latent heat.</p> <p><br> The code used for processing the datasets and training neural network models is available in the Miles-Guess repository (<a href="https://github.com/ai2es/miles-guess">https://github.com/ai2es/miles-guess</a>).</p>
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>
Trained Surface Layer Models and Metrics for "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications"
Open the record for dataset details and reuse information.
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>
VIC-Res Upper Mekong -- 2005-2020 -- Vu et al., 2023, Hydrology and Earth System Sciences
<p>This data repository holds VIC-Res model input / output data for the paper "Vu, D.T., Dang, T. D., Pianosi, F., & Galelli, S. Calibrating macroscale hydrological models in poorly gauged and heavily regulated basins. Hydrol. Earth Syst. Sci., 27, 3485–3504, 2023"</p><p>The model can be used to simulate hydrological processes and streamflow routing in the Upper Mekong River basin for the period 2005-2020. The streamflow routing scheme can be run with and without water reservoirs.</p>
Cross-track Infrared Sounder (CrIS) Level 2 Earth System Science Profiling Algorithm Ammonia Retrieval Algorithm (ESSPA-NH3) V1 (SNDRSNIL2ESPNH3)
The objective of this limited edition data collection is to examine the ammonia products generated by the ESSPA (Earth System Science Profiling Algorithm) algorithm from the Cross-track Infrared Sounder (CrIS) instruments. The CrIS instrument used for this product is deployed on board the Suomi National Polar-orbiting Partnership (SNPP) platform and uses the Normal Spectral Resolution (NSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 1305 NSR infrared sounding channels covering the longwave (655-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2155-2550 cm-1) spectral regions.The NH3 L2 ammonia algorithm is based on an AER (Atmospheric and Environmental Research, Inc.) program initially developed to process TES (Tropospheric Emissions Spectrometer) trace gas products. This version runs within the ESSPA software framework: it uses the AER OSS forward model and an optimal estimation approach with a Levenberg-Marquardt algorithm. Temperature and water profiles are obtained from the CLIMCAPS Field of Regard (FOR) products, as are initial guesses for surface temperature and emissivity. The algorithm consists of a two-step sequential retrieval the first step retrieves surface temperature and emissivity and the second step an ammonia profile. The algorithm produces ammonia retrievals on every field of view (FOV) in each FOR.A level 2 granule has been set as 6 minutes of data, 30 footprints crosstrack by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.
All data for: Megaherbivore impacts on ecosystem and Earth system functioning: The current state of the science
<p>Megaherbivores (adult body mass >1000 kg) are suggested to disproportionately shape ecosystem and Earth system functioning. We systematically reviewed the empirical basis for this general thesis and for the more specific hypotheses that (i) megaherbivores have disproportionately larger effects on Earth system functioning than their smaller counterparts, (ii) this is true for all extant megaherbivore species and (iii) their effects vary along environmental gradients. We furthermore explored possible biases in our understanding of megaherbivore impacts. We found that there are too few studies to quantitatively evaluate the general thesis or any of the hypotheses for all but the African savanna elephant. Following this finding, we performed a qualitative vote counting analysis. Our synthesis of this analysis suggests that megaherbivores can elicit strong impacts on e.g. vegetation structure, and biodiversity and all the elephant species promote seed dispersal. We were however unable to evaluate whether these effects are disproportionate to smaller large herbivores. Although environmental conditions can mediate megaherbivore impact, few studies quantified the effect of rainfall or soil fertility on megaherbivore impacts, precluding prediction of megaherbivore effects on the Earth system, particularly under future climates. Moreover, our review highlights major taxonomic, thematic and geographic biases in our understanding of megaherbivore effects. Most of the studies focused on African savanna elephant impacts on vegetation structure and biodiversity, with other megaherbivores and Earth system functions comparatively neglected. Studies were also biased towards semi-arid and relatively fertile systems, with the arid, high-rainfall and/or nutrient-poor parts of the megaherbivores' distribution ranges largely unrepresented. Our findings highlight that the empirical basis of our understanding of the ecological effects of extant megaherbivores is still limited for all species, except African savanna elephant, and that our current understanding is biased towards certain environmental and geographic areas. We further outline a detailed, urgently needed avenue for future research.</p>
All data for: Megaherbivore impacts on ecosystem and Earth system functioning: The current state of the science
Open the record for dataset details and reuse information.
Earth Science Mission Control Center Systems Study Project
Earth Science Mission Control Center Systems Study Project
Pinterest as a facilitator of Transactive Memory Systems for Citizen Engagement in Earth Science
<p>This is a test to see how Zenodo works.</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.