Find research datasets worth reusing

Experimental Seismic Data Obtained Using a 3D-Printed Model of the Los Angeles Basin Structure

These data were obtained and analyzed by Park et al., (2022) "Seismic wave simulation using a 3D printed model of the Los Angeles Basin" (doi:10.1038/s41598-022-08732-w).

opencc-by-4.0Aug 2021View details →

Supplementary data - Modelling the role of dynamic topography and eustasy in the evolution of the Great Artesian Basin

This data repository contains the supplementary data for the paper: Modelling the role of dynamic topography and eustasy in the evolution of the Great Artesian Basin. Carmen Braz1, Sabin Zahirovic1, Tristan Salles1, Nicolas Flament2, Lauren Harrington1, R. Dietmar Müller1 1 EarthByte Group, School of Geosciences, The University of Sydney, Sydney, Australia 2 GeoQuEST Research Centre, School of Earth and Environmental Sciences, University of Wollongong, Wollongong, NSW, Australia Basin Research, https://doi.org/10.1111/bre.12606 Included in this supplement are: <ul> <li>    All input files required for running Badlands models M1-M4</li> <li>    Badlands digital output for preferred model M4</li> <li>    Animations of topography and erosion-deposition through time for all four models presented in the paper </li> <li>    Sediment layers for all time steps for preferred model M4 provided as netcdf grids.</li> </ul>

opencc-by-4.0Apr 2021View details →

Data to publication: Fibre optic measurements and model uncertainty quantification for Fe-SMA strengthened concrete structures

This dataset contains the results of an experimental campaign, presented in the publication "Fibre optic measurements and model uncertainty quantification for Fe-SMA strengthened concrete structures". The publication covers fibre optic measurements inside large-scale specimens subjected to external load. The specimens comprised reinforced concrete slabs, strengthened with reinforcement bars made from iron-based shape memory alloy.

Carbonyl Sulfide (OCS) TOMCAT Model Data

These data are monthly mean carbonyl sulfide (OCS) model mixing ratios between 2004 and 2018. We use the TOMCAT/SLIMCAT 3-D off-line chemical transport model (Chipperfield, 2006). The model reads in 6-hourly fields of temperature, humidity, vorticity, divergence and surface pressure from ECMWF ERA-INTERIM meteorological (re)analyses. Horizontal resolution of 2.8° × 2.8° with 60 hybrid σ-pressure levels from the surface to ~60 km. The model dataset TOMCATOCS is defined in the files as 'OCS_SOIL_2_5_mm'. We recommend using this variable for any reproductions of the publication plots. TOMCATCON and TOMCATSOTA available upon request.

Data sets and models for the Deep API Learning Revisited paper

Training and test data for the machine learning experiments described in the paper Deep API Learning Revisited paper.  Trained models are also included. Deep API Learning Revisited paper:  https://doi.org/10.1145/3524610.3527872 GitHub repository:  https://github.com/hapsby/deepAPIRevisited

openepl-2.0Mar 2022View details →

Geosci. Model Dev. paper data for Flipo et al., "Regional coupled surface-subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency-domain discharge data"

Data and associated user guide, as part of the paper : Flipo N., Gallois N., Schuite J. Regional coupled surface-subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency-domain discharge data, Geoscientific Model Development. In consistency with the “Code and data availability” sub-section of the paper, all data necessary for the reproduction of Figs. 7, 8c, 8d, 9, 10 and 11 are here provided.

Experimental data and scripts used for the paper "Experiments and low-order modelling of intermittent transitions between clockwise and anticlockwise spinning thermoacoustic modes in annular combustors"

The folder contains the experimental data, the scripts an the instructions to generate the figures of the paper. Because of difficulties for uploading large files on zenodo, the heaviest files, which are the acoustic measurement files (.TDMS format), are not included in the zip file, but are put aside of it. For the scripts to work correctly, all the tdms files should be moved in the folder Faure-BeaulieuA_StochasticTransitionsAzimuthalMode_PROCI_20200713/01_input_data/

Industry 5.0 Data Model

Industry 5.0 Data Model

Data from: Estimation in the multinomial reencounter model - Where do migrating animals go and how do they survive in their destination area?

Abstract Spatial variation in survival has individual fitness consequences and influences population dynamics. Which space animals use during the annual cycle determines how they are affected by this spatial variability. Therefore, knowing spatial patterns of survival and space use is crucial to understand demography of migrating animals. Extracting information on survival and space use from observation data, in particular dead recovery data, requires explicitly identifying the observation process. We build a fully stochastic model for animals marked in populations of origin, which were found dead in spatially discrete destination areas. It acts on the population level and includes parameters for use of space, survival and recovery probability. The model is based on the division coefficient and the multinomial reencounter model. We use a likelihood-based approach, derive Restricted Maximum Likelihood-like estimates for all parameters and prove their existence and uniqueness. In a simulation study we demonstrate the performance of the model by using Bayesian estimators derived by the Markov chain Monte Carlo method. We obtain unbiased estimates for survival and recovery probability if the sample size is large enough. Moreover, we apply the model to real-world data of European robins Erithacus rubecula ringed at a stopover site. We obtain annual survival estimates for different spatially discrete non-breeding areas. Additionally, we can reproduce already known patterns of use of space for this species. We would like to thank the Greifswalder Oie Bird Observatory of the Verein Jordsand, Ahrensburg, and the Hiddensee Bird Ringing Centre, Güstrow, for providing the robin data.

Supplementary data release for "Cosmology and modified gravitational wave propagation from binary black hole population models"

We release the data products associated to the paper <a href="https://arxiv.org/abs/2112.05728">"Cosmology and modified gravitational wave propagation from binary black hole population models", </a><a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.105.064030">Phys.Rev.D 105 (2022) 6 </a>. The data can be used in conjunction with the code <a href="https://github.com/CosmoStatGW/MGCosmoPop">MGCosmoPop</a> to reproduce the results of the paper.  The data product contains the following folders: * injections_GWTC3: injections used to analyze the GWTC3 catalog, generated with the code <a href="https://github.com/CosmoStatGW/MGCosmoPop">MGCosmoPop</a> . Injections are available separately for O1-O2, O3a, O3b for minimum SNR of 10, 11, 12 (folder names are self-explicative). Each folder contains a file named selected.h5 with the injections. For loading them, refer to the tutorial of the code <a href="https://github.com/CosmoStatGW/MGCosmoPop">MGCosmoPop</a> . * mock_BPL_5yr_GR : mock data for 5 years of aLIGO observations, with fiducial cosmological model set to General Relativity (see the paper for details) * mock_BPL_5yr_MG : mock data for 5 years of aLIGO observations, with fiducial cosmological model set to a modified gravity model with modified gravitational-wave propagation (see the paper for details) * injections_mock : injections for analyzing the mock datasets above

Sensitivity experiment data using the CHASER chemical transport model for investigation of lower-tropospheric spring ozone enhancement over Hanoi

This is the data from the numerical model experiment for investigating the relative importance of different emission source regions on the spring ozone enhancement in the lower troposphere over Hanoi, Vietnam. The details of the investigation are written in the paper by Ogino et al. (2022, Journal of Geophysical Research, Atmosphere, in revision). Experiment description We performed sensitivity experiments using the global chemical-transport model, CHASER (Sudo et al., 2002) with T42 horizontal resolution (approximately 2.8 degrees longitude × 2.8 degrees latitude) and 32 vertical layers from the surface up to 10 hPa in sigma coordinate. The two-hourly model outputs interpolated onto the constant pressure levels at 1000, 990, 970, 930, 870, 790, 700, 610, 530, 460, 400, 350, 300, 260, 230, 200, 176, 153, 133, 116, and 100 hPa were used in this study. Note that the updated model, MIROC-Chem (Miyazaki et al., 2017; Watanabe et al., 2011), includes more detailed chemical processes for both troposphere and stratosphere. Nevertheless, CHASER already includes the most important chemical processes in the NOx-CO-Ozone reactions and can be used to evaluate the impact of NOx emissions on ozone productions. In addition, the simulated ozone performance, as well as ozone response to NOx emissions, are comparable between CHASER and MIROC-Chem (Miyazaki et al., 2020). Thus, the results should not be sensitive to the choice of model. The surface emissions of major ozone precursors, such as carbon monoxide (CO), nitrogen oxide (NOx), and nonmethane hydrocarbons, were included in the model based on the published emission inventories (the Emission Database for Global Atmospheric Research (EDGAR) version 4.2 (EC-JRC/PBL, 2011), the monthly Global Fire Emissions Database (GFED) version 3.1 (van der Werf et al., 2010), and monthly mean Global Emissions Inventory Activity (GEIA) (Graedel et al., 1993)). We employed daily NOx and CO emissions that were optimized using the assimilation of satellite NO2 and CO measurements, where the a priori emissions were constructed based upon bottom-up emission inventories (Miyazaki et al., 2015; 2017). These emissions, including both anthropogenic and biomass burning components, used were obtained from the Tropospheric Chemistry Reanalysis version 1 (TCR-1, Miyazaki et al., 2015) and enabled us to evaluate the emission impacts for individual sources. In the sensitivity experiments, we eliminated the emissions of ozone precursors from the following three source regions: the Indian subcontinent, the northern Indochina Peninsula, and southern China. We conducted spin-up calculations with the optimized emissions for all regions (i.e., standard emissions) from January 1st to the end of February in each year for 10 years from 2005 to 2014. Then, we performed four types of experiments from March 1st to 21st: the control experiment with the standard emissions, and the three sensitivity experiments with the elimination of emission from the above-mentioned three regions, namely the Indian subcontinent, the northern Indochina, the southern China experiments. Because of the non-linear chemistry, the cumulative response from the sensitivity calculations can be different from the total ozone response in the control simulation to some extent as shown by the HTAP modeling works (Turnock et al., 2018; Wild et al., 2012). Nevertheless, they provided important information on the relative contributions of emission sources from different regions. The results of the sensitivity experiments will be compared with the control experiment to investigate the relative contributions of individual emission sources to the ozone enhancement over Hanoi. Files <ul> <li>O3_Fullyear_[YYYY].nc: The 2-hourly data of ozone mixing ratio obtained in the control experiment from January 1 to December 31 in year [YYYY] from 2005 to 2014.</li> <li>[Param]_March_[YYYY].nc: The 2-hourly data obtained in the sensitivity experiment from Mar 1 to 21 in every year [YYYY] from 2005 to 2014. [Param] is one of  the following: <ul> <li>O3_Control: Ozone mixing ratio in the control experiment</li> <li>O3_IndianSubcontinent: Ozone mixing ratio in the Indian Subcontinent experiment</li> <li>O3_NorthernIndochina: Ozone mixing ratio in the northern Indochina experiment</li> <li>O3_SouthernChina: Ozone mixing ratio in the southern China experiment</li> <li>CO: Carbon monoxide</li> <li>T: Temperature</li> <li>U: Zonal wind</li> </ul> </li> <li>CO_Emission.nc and NOx_Emission.nc: The monthly mean CO and NOx emissions from the surface used in the model experiments.</li> </ul> Contact Shin-Ya Ogino Japan Agency for Marine-Earth Science and Technology (JAMSTEC) E-mail: ogino-sy@jamstec.go.jp

Data set for: Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models

This data set contains the simulations and data analysis files used in the publication: "Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models", by D. Cortés-Ortuño, K. Fabian and L. V. de Groot. The data set includes: <ul> <li>Scripts and output files from MERRILL simulations</li> <li>Jupyter notebooks with data analysis</li> <li>Figures</li> </ul> A preprint of this work can be found in: David Cortés-Ortuño, Karl Fabian and Lennart V. de Groot. Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models. DOI: 10.1002/essoar.10510574.1. Earth and Space Science Open Archive. <a href="https://doi.org/10.1002/essoar.10510574.1">https://doi.org/10.1002/essoar.10510574.1</a> The README file in this dataset (in markdown format) contains full details about the simulations. The dataset also contains pre-computed data files to calculate the inversions and produce the figures and analyze the inversion data without processing the vbox files. To cite this dataset you can use the following bibtex entry: <pre><code>@Misc{Cortes2022, author = {Cortés-Ortuño, David and Fabian, Karl and de Groot, Lennart V.}, title = {{Data set for: Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models}}, publisher = {Zenodo}, year = {2022}, doi = {10.5281/zenodo.6501818}, url = {https://doi.org/10.5281/zenodo.6501818}, } </code></pre>

opencc-by-4.0May 2022View details →

Data and code for: A conceptual model-based sediment connectivity assessment for patchy agricultural catchments

Authors: Pedro V G Batista, Peter Fiener, Simon Scheper, Christine Alewell ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Abstract The accelerated sediment supply from agricultural soils to riverine and lacustrine environments leads to negative off-site consequences. In particular, the sediment connectivity from agricultural land to surface waters is strongly affected by landscape patchiness and the linear structures that separate field parcels (e.g. roads, tracks, hedges, and grass buffer strips). Understanding the interactions between these structures and sediment transfer is therefore crucial for minimising off-site erosion impacts. Although soil erosion models can be used to understand lateral sediment transport patterns, model-based connectivity assessments are hindered by the uncertainty in model structures and input data. In specific, the representation of linear landscape features in numerical soil redistribution models is often compromised by the spatial resolution of the input data and the quality of the process descriptions. Here we adapted the WaTEM/SEDEM model using high resolution spatial data (2 m x 2 m) to analyse the sediment connectivity in a very patchy mesoscale catchment (73 km2) of the Swiss Plateau. We used a global sensitivity analysis to explore model structural assumptions about how linear landscape features (dis)connect the sediment cascade, which allowed us to investigate the uncertainty in the model structure. Furthermore, we compared model simulations of hillslope sediment yields from five sub-catchments to tributary sediment loads, which were calculated with long-term water discharge and suspended sediment measurements. The sensitivity analysis revealed that the assumptions about how the road network (dis)connects the sediment transfer from field blocks to water courses had a much higher impact on modelled sediment yields than the uncertainty in model parameters. Moreover, model simulations showed a higher agreement with tributary sediment loads when the road network was assumed to directly connect sediments from hillslopes to water courses. Our results ultimately illustrate how a high-density road network combined with an effective drainage system increases sediment connectivity from hillslopes to surface waters in agricultural landscapes. This further highlights the importance of considering linear landscape features and model structural uncertainty in soil erosion and sediment connectivity research.   ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Metainformation This dataset includes: 1 - The input data used for running the WaTEM/SEDEM model in the Baldegg catchment. 2 - The discharge and sediment concentration data used for producing the sediment rating curves for the tributaries of the Lake Baldegg. 3 - The model and sediment rating curve output data. 4 - The R scripts for running the WaTEM/SEDEM model in the Baldegg catchment, the code for producing the sediment rating curves, and the code for summarising and analysing the model output data. ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- The sediment concentration and water discharge data were supplied by Robert Lovas, from the Department of Environment and Energy of the Canton of Lucerne. The model input data were adapted from freely available ©swisstopo geodata products: Swisstopo. SwissALTI3D. Das hoch aufgelöste Terrainmodell der Schweiz, 2014. Swisstopo. Swiss Map Vector 25 Beta, Das digitale Landschaftsmodell der Schweiz. 2018. Swisstopo. SwissTLM3D. Das grossmassstäbliche Topografische Landschaftsmodell der Schweiz, 2020. ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- For further information we refer to our preprint: https://doi.org/10.5194/hess-2021-231

opencc-by-4.0May 2022View details →

Auxiliary Euro-Calliope datasets: QTDIAN storyline-specific spatial data to represent a European energy system model at several spatial resolutions

Custom output generated with the <a href="https://github.com/brynpickering/possibility-for-electricity-autarky/tree/custom-regions">custom-region possibility-for-electricity-autarky</a> workflow. This output provides similar data to <a href="https://zenodo.org/record/6600619">https://zenodo.org/record/6600619</a> (technically eligible land area for renewables and other spatially disaggregated energy system data), but with three additional land area scenarios. These scenarios are in line with three storylines from the <a href="https://zenodo.org/record/5834010">QTDIAN toolbox</a> and are based on updating the `possibility-for-electricity-autarky` workflow configuration to include the following parameters (also included in `config.yaml`):   <pre><code> scenarios: people-powered: use-of-protected areas: false pv-on-farmland: true share-farmland-used: 0.2 # agro pv share-forest-areas-used: 0.1 share-other-land-used: 1.0 share-offshore-used: 0.1 share-rooftop-used: 1.0 government-directed: use-of-protected areas: false pv-on-farmland: true share-farmland-used: 1.0 share-forest-areas-used: 0.1 share-other-land-used: 1.0 share-offshore-used: 1.0 share-rooftop-used: 1.0 market-driven: use-of-protected areas: true pv-on-farmland: true share-farmland-used: 1.0 share-forest-areas-used: 1.0 share-other-land-used: 1.0 share-offshore-used: 1.0 share-rooftop-used: 1.0</code></pre>   This dataset includes different spatial resolutions of land availability. For more information on the `ehighways` resolution, see <a href="https://zenodo.org/record/6600619">https://zenodo.org/record/6600619</a>. This dataset is used as an input to the <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-Coupled Euro-Calliope workflow</a>.

opencc-by-4.0Dec 2020View details →

FAIR raw data and heat maps of ARAP deposition modeling

FAIR Supplementary Information and Raw Data for <a href="https://www.plus.ac.at/biowissenschaften/der-fachbereich/arbeitsgruppen/duschl/members/martin-himly/list-of-publications/">Hofer S. et al., 2021, SARS-CoV-2-Laden Respiratory Aerosol Deposition in the Lung Alveolar-Interstitial Region Is a Potential Risk Factor for Severe Disease: A Modeling Study, Journal of Personalized Medicine 11(5):431</a>, DOI: <a href="https://doi.org/10.3390/jpm11050431">https://doi.org/10.3390/jpm11050431</a> 1. pdf/A of deposition heat maps (incl probability values) for 5 different ARAP modes 2. xls-formatted file of MPPD v3.04-derived deposition raw data sets for 5 different ARAP modes 3.-7. rpt-formatted MPPD v3.04 files of deposition raw data sets for 5 different ARAP modes 8.-12. csv-formatted files of MPPD v3.04-derived deposition raw data sets for 5 different ARAP modes 13. pdf/A of deposition heat maps (incl probability values) for 5 different ERAP modes (upon rehydration of ARAPs) 14. txt-formatted README file for Hofer et al 2021

opencc-by-4.0Jun 2022View details →

"Chronomodel" Bayesian chronological models for East Borneo, based on data from the Liang Abu and Kimanis sites

Bayesian chronological models generated using the <a href="https://chronomodel.com">ChronoModel</a> software East Borneo (Indonesia), based on data from the Liang Abu and Kimanis (Arifin, 2017) archaeological sites. Two models were generated: <ul> <li> a “conservative” model, observing the Bayesian approach and the distinction between prior and posterior information;</li> <li>a “restricted”  model: excluding possible outliers and without application of a “Fresh- water reservoir effect” correction.</li> </ul> Four files are provided: <ul> <li>abu-kimanis-conservative-model.chr: model specification for the “conservative” model</li> <li>abu-kimanis-conservative-model_synthetic-stats-table.csv: results for the “conservative” model</li> <li>abu-kimanis-restricted-model.chr: model specification for the “restricted” model</li> <li>abu-kimanis_restricted-model_synthetic-stats-table.csv: results for the “restricted” model</li> </ul> The .chr files can be open and edited using the ChronoModel software.

Dataset: Stochastic data-driven parameterization of unresolved eddy effects in a baroclinic quasi-geostrophic model

This dataset is used to reproduce the figures in the manuscript "Stochastic data-driven parameterization of unresolved eddy effects in a baroclinic quasi-geostrophic model" The figures can be created with the following Python scripts: .

Global agricultural ammonia emissions simulated with the ORCHIDEE land surface model : Model Ouput Data

This upload includes data associated with the manuscript "Global agricultural ammonia emissions simulated with the ORCHIDEE land surface model : )" submitted to Geoscientific Model Development. The dataset includes an output file with the simulated ammonia emissions for the agricultural sector. The emissions (manure management and soil), manure production and soil ammonium concentrations are monthly fields from the simulation for 2007-2015. Additional information is given in the readme file

Southern African Power Pool GridPath Model Input Data

This data repository holds GridPath model input data for the paper Chowdhury, A.K., Deshmukh, R., Wu, G., Uppal, A., Mileva, A., Curry, T., Armstrong, L., Galelli, S., and Kudakwashe, N. (2022) “Enabling a low-carbon electricity system for Southern Africa”, Joule. See Readme for more details.

GFDL hurricane model track data associated with "Dynamical downscaling projections of late 21st century U.S. landfalling hurricane activity"

These data include North Atlantic tropical cyclone track and intensity for control and projected late 21st century simulation from the GFDL hurricane model used in a Climatic Change manuscript:  Knutson, T., J. Sirutis, M. Bender, R. Tuleya, and B. Schenkel, 2022: Dynamical downscaling projections of late 21st century U.S. landfalling hurricane activity. Clim. Change, 171, 1–23. A readme file included below describes the variables and format of the tropical cyclone track data.  Questions about the dataset may be directed to Ben Schenkel (<a href="mailto:benschenkel@gmail.com">benschenkel@gmail.com</a>) and Tom Knutson (<a href="mailto:tom.knutson@noaa.gov">tom.knutson@noaa.gov</a>).