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Dataset results
195 results for “publication models”
Wind measurement data from the publication: "Development of a load model validation framework applied to synthetic turbulent wind field evaluation"
<h3>Dataset description:</h3> <p>This datasat represents supplementary material used in the contribution "Development of a load model validation framework applied to<br>synthetic turbulent wind field evaluation" by Meyer, Huhn and Gottschall.</p> <p>Wind measurements from the Testfeld BHV are made available. For installation details, see the mentioned reference.</p> <p> </p> <h3>File description:</h3> <ul> <li>Lidar_HWS.nc - Horizontal wind speed measurements (10 min averages) from a WindCube V2 vertical profiler for one day with a low-level jet occurrence ( <div> <div>2021-04-20)</div> </div> </li> <li>Cups_HWS.nc - Horizontal wind speed measurements (10 min averages) from cup anemometer installed on a met mast for the same day</li> <li>Ensemble_averaged_Spectra.nc - Ensemble averaged spectra for neutral and near neutral situations from a Gill Windmaster at 110m above ground level, used to fit the Mann and KSEC model parameters</li> </ul> <h3> </h3> <h3>Referencing:</h3> <p>When used, please cite like the following:</p> <p>Meyer, Paul J., Matthias L. Huhn, and Julia Gottschall. 2024. "Development of a Load Model Validation Framework Applied to Synthetic Turbulent Wind Field Evaluation" <em>Energies</em> 17, no. 4: 797. https://doi.org/10.3390/en17040797</p> <p> </p> <p> </p>
Data for paper publication "Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol-climate model ECHAM6.3–HAM2.3"
<p>The dataset presented here is related to the article by Leon-Marcos et al. 2025: "Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol-climate model ECHAM6.3–HAM2.3" accepted for publication in GMD. It comprises global fields of the FESOM2.1-REcoM3 biogeochemistry model tracers employed to calculate the ocean biomolecule concentration that serve as input data for the aerosol model. Additionally, the ECHAM6.3–HAM2.3 code of the marine aerosol implementation and the required scripts to run the model experiments are provided here. The aerosol-climate model simulation results of the marine aerosol emission, as well as the evaluation of the model results compared to observations, are also included. For further information, please refer to the attached data description. </p> <p> </p> <h2> </h2>
Dataset for the publication "The TACS Model: Understanding Teachers' Adoption of Computer Science Pedagogical Content in Primary School"
<p>This dataset contains the quantitative teacher data used to analyse an in service teacher training program for Computer Science that took place from September 2019 to March 2020 in the Canton Vaud in Switzerland. Approximately 180 teachers from the the 5th and 6th grade in primary school (ages 9-11) participated in 3 days of training sessions. At the end of each training session, teachers were asked to fill in a web-based questionnaire providing information relating to their perception of the training sessions and adoption of the computer science activities. The surveys were analysed from three perspectives which are detailed in the corresponding article (the professional development program's perspective, the activities' perspective, the teacher's perspective). The present repository thus contains three csv files, one per analysis. A README is included and provides additional information regarding :</p> <p>- the requirements for re-use. </p> <p>- the survey instrument used</p> <p>- the specific content of the 3 csv files</p>
Modelling of ready biodegradability based on combined public and industrial data sources
<p>The European REACH (Registration, Evaluation, Authorization and restriction of Chemicals) Regulation, requires marketed chemicals to be evaluated for Ready Biodegradability (RB). In-silico prediction is a valid alternative to expensive and time-consuming experimental testing. However, currently available models may not be relevant to predict compounds of industrial interest, due to accuracy and applicability domain restriction issues.</p> <p>In this work we present a new and extended RB dataset (2830 compounds), issued by the merging of several public data sources. It was used to train classification models, which were externally validated and benchmarked against already-existing tools on a set of 316 compounds coming from the industrial context. New models showed good performances in terms of predictive power (BA = 0.74 – 0.79) and data coverage (83 – 91 %).</p> <p>The Generative Topographic Mapping approach was employed to compare the chemical space of the various data sources: several chemotypes and structural motifs unique to the industrial dataset were identified, highlighting for which chemical classes currently available models may have less reliable predictions.</p> <p>Finally, public and industrial data were merged into Global dataset containing 3146 compounds and including a significant subset of compounds coming from the industrial context. This is the biggest dataset reported in the literature so far which covers some chemotypes absent in the public data. Thus, predictive model developed on the Global dataset has much larger applicability domain than related models built on publicly available data. The developed model is available for the user on the Laboratory of Chemoinformatics website.</p> <p>This dataset is only the "All-Public" set, since the industrial compounds cannot be disclosed.</p> <p>This update contains additional entries from [J. Chem. Inf. Model. 52 (2012), pp. 655–669] and [J. Chem. Inf. Model. 53 (2013), pp. 867–878]</p>
TDA4ContextualEmbeddings - Public - Debug Data for the codebase of the publication "Local Topology Measures of Contextual Language Model Latent Spaces With Applications to Dialogue Term Extraction"
<p>Debug dataset for testing the <a href="https://gitlab.cs.uni-duesseldorf.de/general/dsml/tda4contextualembeddings-public">codebase</a> of the paper <a href="https://doi.org/10.18653/v1/2024.sigdial-1.31">“Local Topology Measures of Contextual Language Model Latent Spaces With Applications to Dialogue Term Extraction”</a> published at the 25th Meeting of the Special Interest Group on Discourse and Dialogue, Kyoto, Japan (SIGDIAL 2024).</p>
Data for figures in the Publication "The importance of mixed-phase and ice clouds for climate sensitivity in the global aerosol–climate model ECHAM6-HAM2"
<p>This repository contains the data to produce figures for the paper:</p> <p>"Lohmann, U. and Neubauer, D.: The importance of mixed-phase and ice clouds for climate sensitivity in the global aerosol–climate model ECHAM6-HAM2, Atmos. Chem. Phys., 18, 8807–8828, https://doi.org/10.5194/acp-18-8807-2018, 2018."</p> <p>Note that the scripts are to be found in the accompanying package (https://doi.org/10.5281/zenodo.8183412)</p>
Dataset of publication "Derivation and validation of a reference data-based real gas model for hydrogen"
<p>In this repository, a new real gas model for hydrogen based on the Reference Fluid Thermodynamic and Transport Properties Database (REFPROP) v10.0 is provided for the use in the simulation software OpenFOAM v2012. The model is valid in a temperature and pressure range of 150-400 K and 0.1-1000 bar, respectively. Usage beyond this range is not recommended as it may lead to unrealistic results.</p>
PISM model output data from Garbe et al. (Nature, 2020) publication
<p>This dataset contains the <a href="https://www.pism.io">PISM</a> model output data of the Antarctic Ice Sheet hysteresis simulations published and discussed in</p><p><a href="https://doi.org/10.1038/s41586-020-2727-5">Garbe, J., Albrecht, T., Levermann, A., Donges, J. F., and Winkelmann, R. The hysteresis of the Antarctic Ice Sheet. <i>Nature</i><strong> 585</strong>(7826), 2020.</a></p><p>A detailed description of the individual file contents is given in `README.txt` below. The corresponding PISM model code used for these simulations is archived <a href="https://doi.org/10.5281/zenodo.3956431">here</a>.</p><p>In case of questions, feel free to contact me at <a href="mailto:julius.garbe@pik-potsdam.de">julius.garbe@pik-potsdam.de</a>.</p>
Data supporting the publication "Many-body quantum sign structures as non-glassy Ising models"
<p>This repository contains all raw data that were used to draw conclusions and generate figures for the paper:</p> <p><strong>"Many-body quantum sign structures as non-glassy Ising models"</strong><br> by Westerhout, T., Katsnelson, M. I., & Bagrov, A. A.</p> <p><em>Abstract:</em> The non-trivial phase structure of the eigenstates of many-body quantum systems severely limits the applicability of quantum Monte Carlo, variational, and machine learning methods. Here, we study real-valued signful ground-state wave functions of frustrated quantum spin systems and, assuming that the tasks of finding wave function amplitudes and signs can be separated, show that the signs can be easily bootstrapped from the amplitudes. We map the problem of finding the sign structure to an auxiliary classical Ising model defined on a subset of the Hilbert space basis. We show that the Ising model does not exhibit significant frustrations even for highly frustrated parental quantum systems, and is solvable with a fully deterministic O(K log K)-time combinatorial algorithm (where K is the Ising model size). Given the ground state amplitudes, we reconstruct the signs of the ground states of several frustrated quantum models, thereby revealing the hidden simplicity of many-body sign structures.</p>
Deep learning models predicting gene functions and pathways using public DRKG knowledge graph and graph neural network
<p>The attached dataset contains pretrained link prediction models, as described in our paper 'Morphological Map of Under- and Over-Expression of Genes in Human Cells'.</p>
Data for publication "Statistical characteristics of extreme daily precipitation during 1501 BCE - 1849 CE in the Community Earth System Model".
<p>Here, the data used in Kim, W. M., Blender, R., Sigl, M., Messmer, M., & Raible, C. C. (2021). "Statistical characteristics of extreme daily precipitation during 1501 BCE–1849 CE in the Community Earth System Model" in <em>Climate of the Past </em>(<a href="https://doi.org/10.5194/cp-2021-61">https://doi.org/10.5194/cp-2021-6</a>) are provided.</p> <p>Two simulations covering the period 1501 BCE - 2008 CE are performed with CESM 1.2.2: the orbital-only and the full-forcing simulations. The full-forcing transient simulation includes the new long record of volcanic eruptions (<a href="https://doi.org/10.1594/PANGAEA.928646">https://doi.org/10.1594/PANGAEA.928646</a>) that covers the last 3500 years. The output from the simulations is used to examine the long-term variability and characteristics of daily extreme precipitation during 1501BCE-1849 CE.</p> <p>The following files are provided:</p> <ul> <li> <strong>CESM122.transient.PRECT.anom.above99th.1501BCE-1849CE_I and II</strong>: Daily precipitation anomalies above the 99th percentiles relative to 1501BCE-1849CE from the full forcing simulation. The file is split into two parts, with the first file containing the first 50% of extremes (I) and the second file containing the rest 50% (II).</li> <li><strong>CESM122.orbital.PRECT.anom.above99th.1501BCE-1849CE I and II:</strong> Daily precipitation anomalies above the 99th percentiles relative to 1501BCE-1849CE from the orbital-only simulation.</li> <li> <strong>CESM122.transient.variables.mon.1979-2008CE:</strong> monthly precipitation, temperature, and geopotential height at 500 hPa for 1979-2008CE from the full-forcing simulation.</li> <li><strong>CESM122.trans.variable_names.years:</strong> Monthly variables from the full-forcing simulation. The simulation starts from the model year 1, which corresponds to the actual year 1501BCE. The variables are solar insolation (SOLIN), clear-sky net surface shortwave radiation (FSNSC), geopotential height at 500hPa (Z500), and surface temperature (TS).</li> <li><strong>CESM122.orbital.variable_names.years:</strong> Monthly variables from the orbital-only simulation. The simulation starts from the model year 1, which corresponds to the actual year 1501BCE.</li> <li><strong>CESM122.*.log-likelihood-GPDmodel-ExtForcing</strong>: Negative log-likelihood for the stationary and non-stationary Generalized Pareto Distribution models for external forcings.</li> <li> <strong>CESM122.*.log-likelihood-GPDmodel-ModesVar</strong>: Negative log-likelihood for the stationary and non-stationary Generalized Pareto Distribution models for modes of variability.</li> <li><strong>Evolk_EVA_distribution_1501BCE-2015CE</strong>: Distribution of volcanic aerosol for CAM5, produced based on Kim et al. (2021).</li> </ul> <p>If you use this dataset, please cite:</p> <p><em>Kim, W. M., Blender, R., Sigl, M., Messmer, M., & Raible, C. C. (2021). Statistical characteristics of extreme daily precipitation during 1501 BCE–1849 CE in the Community Earth System Model. Climate of the Past Discussions, 1-38. <a href="https://doi.org/10.5194/cp-2021-61">https://doi.org/10.5194/cp-2021-61</a></em></p>
Data to publication: Fibre optic measurements and model uncertainty quantification for Fe-SMA strengthened concrete structures
<p>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.</p>
Waveform data for centroid moment tensor solutions presented in publication "Bayesian seismic source inversion with a 3-D Earth model of the Japanese islands"
<p>The dataset includes waveform data for centroid moment tensor solutions inferred using Hamiltonian Monte Carlo and a 3-D Earth model in the Japanese islands. The data are provided as Green's strains at the maximum-likelihood location (indicated in the title of each text file) for all study events inverted at different periods. Inversion period is also indicated in the title. All the data are filtered between 15 s and 80 s. Additionally we provide a Python code to obtain displacement from strains given a moment tensor.</p>
A Corpus of Publicly Available Simulink Models for Model-based Empirical Studies
<p>Abstract: Recent years have seen many empirical studies of model-based cyber-physical systems and commercial CPS development tool chains such as Matlab/Simulink. To benefit such research, this paper presents the by-far largest corpus of freely available Simulink models to date, containing over 1,000 models.</p> <p>Surprising findings based on this corpus include that (a) tool support for metric collection is not adequate and (b) users do not reuse model components as they would in object-oriented programs.</p> <p>The paper both confirms and contradicts earlier findings that are based on significantly fewer models, suggesting the utility of the corpus for future research. While others have not yet leveraged this model corpus, we hope that our freely available corpus and infrastructure will benefit future model-based empirical research and tool development efforts, by reducing the model-collection overhead and thus easing evaluation.</p> <p>Please see our paper "A curated corpus of Simulink models for model-based empirical studies" In Proc. 4th International Workshop on Software Engineering for Smart Cyber-Physical Systems (SEsCPS)</p> <p>See https://github.com/verivital/slsf_randgen/wiki for more details including information related to downloading the Simulink models.</p> <p>http://ranger.uta.edu/~csallner/csallner_bib.html#Chowdhury18Curated</p>
Data for the publication "The global aerosol-climate model ECHAM6.3-HAM2.3 – Part 2: Cloud evaluation, aerosol radiative forcing and climate sensitivity"
<p>This repository contains the data for the paper:</p> <p>"Neubauer, D., Ferrachat, S., Siegenthaler-Le Drian, C., Stier, P. Partridge, D. G., Tegen, I., Bey, I., Stanelle, T., Kokkola, H., and Lohmann, U.: The global aerosol-climate model ECHAM6.3-HAM2.3 – Part 2: Cloud evaluation, aerosol radiative forcing and climate sensitivity, Geosci. Mod. Dev., https://doi.org/10.5194/gmd-2018-307, 2019."</p> <p>Each tar-file contains the data (or instructions how to obtain the data) to reproduce a figure or table in our paper.</p> <p>Note that the scripts to plot this data are to be found in the accompanying package (http://dx.doi.org/10.5281/zenodo.2553891)</p> <p> </p>
Data for the publication "Incorporation of inline warm rain diagnostics into the COSP2 satellite simulator for process-oriented model evaluation"
<p>Michibata et al. (2019), currently under peer-review for publication in <em>Geoscientific Model Development</em>, incorporated a diagnostic tool for warm rain microphysics into the CFMIP Observation Simulator Package (COSP; Bodas-Salcedo et al. 2011; Swales et al., 2018), designed to evaluate model representations of aerosol–cloud–precipitation interactions at a fundamental process-level. The tool automatically generates two diagnostics related to warm rain microphysics during COSP execution in a host model. One is the contoured frequency by optical depth diagram (CFODD), which visualizes a cloud-to-rain microphysical vertical structure (Suzuki et al., 2015). The other diagnostic is a global map of warm rain fraction classified as non-precipitating clouds (< –15 dBZ<sub>e</sub>), drizzling clouds (–15 < dBZ<sub>e</sub>< 0), and precipitating clouds (0 < dBZ<sub>e</sub>).</p> <p>This repository contains the MIROC6/COSP2 input data and A-Train satellite statistics used in Michibata et al. (2019). A sample of the post-processing scripts for visualization using the GrADS software is also included in this repository.</p>
Data for the publication "Addressing complexity in global aerosol climate model cloud microphysics"
<p>This repository contains the data for the paper:</p> <p>Authors: Ulrike Proske, Sylvaine Ferrachat, and Ulrike Lohmann<br> Titel: Addressing complexity in global climate model cloud microphysics<br> Date: 2022</p> <p>Note that the scripts can be found in the accompanying package (https://doi.org/10.5281/zenodo.7375978).</p>
Model agreement and trend analysis data associated to the publication: "Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050"
<p>This dataset is associated with the following publication:</p> <p>Haslebacher, C., Demory, M.-E., Demory, B.-O., Sarazin, M., and Vidale, P. L., “Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050. Projected increase in temperature and humidity leads to poorer astronomical observing conditions”, <em>Astronomy and Astrophysics</em>, vol. 665, 2022. doi:10.1051/0004-6361/202142493.</p> <p>In the folder 'model_agreement', there are pickle files from which a python dictionary can be extracted with:</p> <pre><code>with open('mypklfile.pkl', 'rb') as myfile: dload = pickle.load(myfile)</code></pre> <p>Pickle files ending with '_d_obs_ERA5.pkl' contain in situ data and ERA5 data. Pickle files ending with 'd_model.pkl' contain PRIMAVERA model data. A few explanations:<br> - 'ds_sel': contains monthly timeseries of selected intersecting data<br> - 'ds_taylor': contains data used for the Taylor diagram (Figs. 4-10)<br> - 'ds_mean_month': contains seasonal cycle for plotting (Figs. 4-10)<br> - 'ds_mean_year': contains yearly timeseries for plotting (Figs. 4-10) </p> <p>The subfolder 'median_nc_u_v_t' contains NETCDF files with the median and interquartile range of the wind speed in u and v direction, the temperature and geopotential height. This was used for Figs. G1-G8 and to calculate the refractive index structure constant Cn2.</p> <p>The subfolder 'skill_score_classification' contains csv files with the sorted skill score classifications. The column headers are: model_name, skill score, correlation coefficient, standard deviation, centred root mean square error.</p> <p>The folder 'trend_analysis' contains for each variable csv files of ERA5 and PRIMAVERA monthly time series used for trend analysis, pdf files of analysis summaries, csv files of Bayesian analysis results and png files of longitude-latitude maps of trends (analysed with linear regression). Additionally, there is a csv file of averaged in situ pressures.</p> <p>Code that generated and used this data is available on github: <a href="https://github.com/CarolineHaslebacher/Astroclimate-future-project">https://github.com/CarolineHaslebacher/Astroclimate-future-project</a> </p> <p> </p>
Processed data for "Model identification of neural encoding (MINE)" publication
<p>This dataset contains mouse and zebrafish data processed by MINE. These datafiles were used to generate the publication figures for the mouse cortical dataset [m<em>usall.hdf5</em>] (Figure 5) and the zebrafish whole-brain [<em>main_analysis.hdf5</em>] (Figures 6 and 7) and reticulospinal datasets [r<em>s_analysis.hdf5</em>] (Figure 6).</p> <p> </p> <p><em>Musall.hdf5 </em>contains reordered data from "Musall, S., Kaufman, M.T., Juavinett, A.L. <em>et al.</em> Single-trial neural dynamics are dominated by richly varied movements. <em>Nat Neurosci</em> <strong>22</strong>, 1677–1686 (2019)."</p> <p>The contents of each dataset are described in <em>DataContent_xxx.pdf</em></p>
Data for ECHAM-HAM in the Publication "Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions"
<p>This repository contains 3-hourly data of the ECHAM-HAM experiment in the paper:</p> <p>"Saponaro, G., Sporre, M. K., Neubauer, D., Kokkola, H., Kolmonen, P., Sogacheva, L., Arola, A., de Leeuw, G., Karset, I. H. H., Laaksonen, A., and Lohmann, U.: Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions, Atmos. Chem. Phys., 20, 1607–1626, https://doi.org/10.5194/acp-20-1607-2020, 2020."</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.