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870 results for “Model development”
FIGURE 9. Maximum entropy model developed for D in A new species of Desmopachria Babington (Coleoptera: Dytiscidae) from Cuba with a prediction of its geographic distribution and notes on other Cuban species of the genus
FIGURE 9. Maximum entropy model developed for D. andreae sp. n. in Cuba. Values range from high (red areas) to low environmental suitability (blue areas).
Products developed through the "What About Model Data?, Determining Best Practices for Preservation and Replicability, EarthCube Research Coordination Network" project
This dataset includes products developed through the "What About Model Data? Determining Best Practices for Preservation and Replicability, EarthCube Research Coordination Network (RCN)" project. Products include: 1) a rubric worksheet to assist researchers in deciding what simulation output needs to be preserved in a trusted, community repository to communicate knowledge and satisfy publisher and funder requirements, 2) instructions on how to use the rubric worksheet, which include reference use cases, and 3) outputs and presentations from the three project workshops.
Dataset of 'Development of a total variation diminishing (TVD) Sea ice transport scheme and its application in in an ocean (SCHISM v5.11) and sea ice (Icepack v1.3.4) coupled model on unstructured grids'
<p>As a dataset of the paper "Development of a total variation diminishing (TVD) Sea ice transport scheme and its application in in an ocean (SCHISM v5.11) and sea ice (Icepack v1.3.4) coupled model on unstructured grids" , this dataset includes all configuration files of the idealized case and realistic test on the Arctic Ocean and the source code.</p>
Datasets for the paper "Advancing Scanning Probe Microscopy Simulations: A Decade of Development in Probe-Particle Models"
<p>Input data for probe-particle (PP) atomic force microscopy (AFM), scanning tunnelling microscopy (STM), and Kelvin probe microscopy (KPFM) simulations in the paper "Advancing Scanning Probe Microscopy Simulations: A Decade of Development in Probe-Particle Models".</p> <p>Included files:</p> <ul> <li>CO_tip_densities.tar.gz: the total and delta electron densities for a CO tip.</li> <li>hartree-density.tar.gz: the hartree potential (LOCPOT.xsf), electron density (CHGCAR.xsf), and xyz geometry with point charges (mol.xyz) for 6 example molecules: C60 fullerene, formic acid dimer (FAD), 4-(4-(2,3,4,5,6- pentafluorophenylethynyl)-2,3,5,6- tetrafluorophenylethynyl) phenylethynylbenzene (FFPB), pentacene, phtalocyanine, perylene carboxylic anhydride (PTCDA).</li> <li>dft-afm.tar.gz: DFT-calculated forces for the CO-tip for all of the example molecules. Saved as numpy npz files that containt the force arrays under the key 'force' and the physical extent of the region in Ångströms under the key 'scan_window'.</li> <li>Fig_4_STM_data.tar.gz: Hartree potential (cube_001_hartree_potential.cube) and eigenvectors (KS_eigenvectors.band_1.kpt_1.out) of PTCDA for PPSTM simulations.</li> <li>KPFM_Hartree.tar.gz: Hartree potentials of FFPB with a bias voltage for KPFM simulations.</li> </ul> <p>Reproducing the figures in the manuscript:</p> <ul> <li>Fig. 2 (comparison of force-field models): Install ppafm with the [opencl] option, and run the example script in the ppafm repository at examples/paper_figure/run_simulation.py. The image is saved into the same folder with the script.</li> <li>Fig. 3 (KPFM LCPD map): Install ppafm, navigate to examples/FFPB_KPFM, and run the script ./run.sh. After running the script, the LCPD map of Fig. 3(a) can be found in LCPD_atoms_cbar_018.png and the AFM map of Fig. 3(b) in Q-0.10K0.25V0.00/Amp0.50/df_atoms_cbar_018.png.</li> <li>Fig. 4 (STM dI/dV map): Download and compile the PPSTM code (<a href="https://github.com/Probe-Particle/PPSTM">https://github.com/Probe-Particle/PPSTM</a>). After that navigate to tests/PTCDA_mol and use the run_all.sh script.</li> <li>Fig. 5 (IETS): Install ppafm, navigate to examples/FePc_Au-IETS, and run the script run_ppafm-iets.sh. The IETS image can be found in the Q0.00K0.24 folder.</li> </ul> <p>The ppafm version 0.3.1, and PPSTM version 1.0.2 (<a href="https://doi.org/10.5281/zenodo.10669867" rel="nofollow">https://doi.org/10.5281/zenodo.10669867</a>) was used when producing the images for the manuscript.</p>
Development of equivalent circuit model for state of power estimation of NMC-based Li-ion cell
Open the record for dataset details and reuse information.
Model Development for State-of-Power Estimation of Large-Capacity Nickel-Manganese-Cobalt Oxide-Based Lithium-Ion Cell Validated Using a Real-Life Profile
Open the record for dataset details and reuse information.
Time-series analysis of rhenium(I) organometallic covalent binding to a model protein for drug development: Raw Diffraction Images (38 week soak). Zenodo
<p>The synchrotron raw diffraction images obtained at 38 weeks and wavelength 0.976 Å, illustrates the covalent coordination of the rhenium(I) tricarbonyl fragment to the His and Asp amino acid residues as well as movement along the solvent channels as described in the publication titled "Time-series analysis of rhenium(I) organometallic covalent binding to a model protein for drug development", written by Jacobs, Helliwell & Brink,<em> IUCrJ</em>, 2024, https://doi.org/10.1107/S2052252524002598.</p> <p>The raw diffraction images for the DLS data sets are made available at the Zenodo research data archive, as specified in the publication.</p>
Field and Lab_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Field and Lab observations_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Meteorological data_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Meteorological data_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Depressions and Boundary_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Depressions and Boundary_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Aerial Images_Part 3_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Aerial Images_Part 3_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Orthomosaic_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Orthomosaic of the aerial images_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
DSM_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>DSM_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Processed dataset used for developing tsunami inundation emulators(Part-I Training Dataset and Model Weights)
<p>This dataset contains some of the processed training datasets covering tsunami inputs and outputs parameters (for offshore waveforms, local deformation fields and inundation depths used in developing a machine learning emulator discussed in the preprint article -<strong> Towards Using Machine Learning Emulation for Probabilistic Inundation Mapping: Multiple Earthquake Sources and Near-field Effects</strong> with project repo - <a href="https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators.">https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators.</a></p> <p>####Code####</p> <p>Source code from the above github project : <strong>ML4SicilyTsunami-NPJ2024.tar.gz</strong></p> <p>The post-processed numpy dat files for the two test location of <strong>Catania(CT) and Siracusa(SR)</strong> are provided for each variable retrieved from data/processed folder of the project as compressed gzip files(.gz):</p> <p>####Model Checkpoints#### <br>ML Model checkpoints from all training experiments.<br><strong>- outSR.tar.gz (Siracusa)</strong><br><strong>- outCT.tar.gz (Catania)</strong></p> <p>####Training Datasets####<br><strong>- t.tar.gz: </strong>Offshore waveforms for gauge sites in a 3D numpy file<br><strong>- dZ.tar.gz: </strong>Local deformation fields in 2D numpy files or reduced 1D flat files on flooded grids<br><strong>- d.tar.gz: </strong>Maximum inundation depth in 2D numpy files or reduced 1D flat files</p> <p>####Flood Mask####<br>- Identifies floodable and non-floodable areas in the domain.<br><strong> - zero_mask_SR_961.npy.gz</strong><br><strong> - zero_mask_CT_892.npy.gz</strong></p> <p>####Flood Indices####<br>Links between 2D and 1D numpy files:<br>- Indices connecting 2D (events x m x n) numpy files to flattened 1D (events x m) numpy files:<br><strong> - zero_indices_SR_961.npy.gz</strong><br><strong> - zero_indices_CT_892.npy.gz</strong><br>- Indices connecting latitude-longitude coordinates to 1D (events x m) numpy files:<br><strong> - lat_lon_idx_CT_892.npy.gz</strong><br><strong> - lat_lon_idx_SR_961.npy.gz</strong></p> <p>The filenames follow nomenclature representing <strong>{parameter}_{site}_{size}</strong></p> <ol> <li>The first var represents the parameter of the file. <ul> <li>d - 2 dimensional file for inundation depth (<strong>events </strong>x <strong>m </strong>x <strong>n</strong>)</li> <li>dZ - 2 dimensional file for local deformation( <strong>events </strong>x <strong>m</strong> x <strong>n</strong>)</li> <li>dflat - 1 dimensional flat file for inundation ( <strong>events </strong>x <strong>locations</strong>)</li> <li>dZflat - 1 dimensional flat file for local deformation( <strong>events </strong>x <strong>locations</strong>)</li> <li>t - 3 dimension offshore waveform for (<strong>events </strong>x <strong>gauges </strong>x <strong>timesteps</strong>)</li> <li>lat_lon_idx - index file for matching lat long coordinate with location indices(<strong>locations x lat x lon</strong>)</li> </ul> </li> <li>The second var represents site of the file. <ul> <li>CT - Catania</li> <li>SR - Siracusa</li> </ul> </li> <li>The third var represents number of events in the file or the selection size.<br> <ul> <li>892 - represents the training events for the file, of 4 different sizes for Catania and Siracusa each</li> </ul> </li> </ol> <div>More information on the attached readme.</div>
Netzero2040: Reaching climate neutrality in Austria by 2040: engaging stakeholders for model-supported scenario development. Data repository
<p><span>This repository contains scenario results in pyam format, qualitative scenario narratives and drivers identified by stakeholders for the NetZero2040 project.</span></p> <p><span>NetZero2040 developed the first independent scenarios achieving climate-neutrality in Austria by 2040. We improve on previous analyses by employing a structured co-creation process involving stakeholders and modellers, simultaneously modelling the whole energy system and the electricity system in great detail, creating shared visions of a climate-neutral future. Results are openly available and have been broadly disseminated in the scientific community and to the public. Our scenarios are differentiated by assumptions on energy demand and imports of energy carriers. They show that a rapid electrification of transport and heating, in combination with a build out rate of renewable energies which is well above historical maxima in the Austrian power system, allow significant emission reductions until 2030, and that these measures are consistently required in all scenarios. However, after 2030 scenarios diverge and uncertainty about the most cost-efficient transformation measures prevail. </span></p>
Evaluating Large Language Models in Summarizing Developer Chat Conversations: A Linguistic Perspective
<p>This is a replication package that includes:</p> <ul> <li>GoldenSet: contains the best summaries by participants for each conversation and the corresponding LLM generated summaries)</li> <li>LinguisticAnalysis_HumanGenerated: linguistic analysis such as speech tags, entities, etc. for summaries created by the Mturk participants (golden set)</li> <li>LinguisticAnalysis_LLMGenerated: linguistic analysis such as speech tags, entities etc. for summaries generated by the large language models</li> </ul> <p> </p>
Data underlying research paper "Developing an open data intermediation business model: insights from the case of Esri"
<p><strong>Data underlying research paper “Developing an open data intermediation business model: insights from the case of Esri” </strong></p> <p>by Ashraf Shaharudin, Bastiaan van Loenen, and Marijn Janssen from Delft University of Technology (TU Delft), the Netherlands.</p> <p>This folder contains data underlying the research paper “Developing an open data intermediation business model: insights from the case of Esri”. It consists of:</p> <p>1. De-identified interview transcripts</p> <p>2. Informed consent form template</p> <p><strong>Note about the de-identified interview transcripts:</strong></p> <p>The de-identified interview transcripts should be read in the context of the research on open data ecosystem and the role of Esri as open data intermediaries.</p> <p>The 27 interviews, involving 29 interviewees, were conducted between April 2023 and April 2024 based on the semi-structured approach. We shared the tentative interview questions with the interviewees in advance (for the majority, at least three working days prior). Since they are semi-structured interviews, the ultimate interview questions may differ from the tentative questions.</p> <p>We removed personally identifiable information from the transcripts. Some interviewees may risk being identifiable if their organization is known. Hence, we removed the organization and country information from all transcripts. </p> <p>With verbal communication, some sentences may be less incomprehensible in writing. Thus, we did minimal edits when transcribing to improve the comprehensibility where necessary, but the main objective was to keep the transcripts as close to verbatim as possible. </p> <p><strong>Note about the informed consent form template:</strong></p> <p>We sent the informed consent form to every interviewee in advance and requested that they return it to us before or during the interview. </p> <p>All interviewees whose interview transcripts are recorded in this document give permission for the anonymized transcript of their interview, with personally identifiable information redacted, to be shared in 4TU.ResearchData repository so it can be used for future research and learning.</p> <p><strong>Acknowledgement:</strong></p> <p>This research is part of the 'Towards a Sustainable Open Data ECOsystem' (ODECO) project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 955569. The opinions expressed in this document reflect only the author’s view and in no way reflect the European Commission’s opinions. The European Commission is not responsible for any use that may be made of the information it contains.</p>
Dataset for Learning in Continuous Action Space for Developing High Dimensional Potential Energy Models
<p>The NN potentials developed in this study and the other available MLIP methods such as GAP, SNAP, qSNAP, and MEGNET used for benchmarking.</p>
Scripts and data for the PARASO Geoscientific Model Development paper figures
<p>Scripts and data for reproducing all figures from the PARASO model description paper.</p> <ul> <li> <p><code>*.tex</code> sources generate Tikz pdf figures calling <code>pdflatex <input.tex></code>. they require a working LaTeX installation with some standard libraries (e.g. Tikz and others).</p> </li> <li> <p><code>*.py</code> are python scripts generating figures. They require standard python libraires such as matplotlib, numpy, cartopy...</p> </li> </ul> <p><strong>Model description</strong>: Pelletier, C., Fichefet, T., Goosse, H., Haubner, K., Helsen, S., Huot, P.-V., Kittel, C., Klein, F., Le clec'h, S., van Lipzig, N. P. M., Marchi, S., Massonnet, F., Mathiot, P., Moravveji, E., Moreno-Chamarro, E., Ortega, P., Pattyn, F., Souverijns, N., Van Achter, G., Vanden Broucke, S., Vanhulle, A., Verfaillie, D., and Zipf, L.: PARASO, a circum-Antarctic fully coupled ice-sheet–ocean–sea-ice–atmosphere–land model involving f.ETISh1.7, NEMO3.6, LIM3.6, COSMO5.0 and CLM4.5, Geosci. Model Dev., 15, 553–594, <a href="https://doi.org/10.5194/gmd-15-553-2022">10.5194/gmd-15-553-2022</a>, 2022.</p> <p><strong>Source code (no COSMO)</strong>: Pelletier, Charles, Klein, François, Zipf, Lars, Haubner, Konstanze, Mathiot, Pierre, Pattyn, Frank, Moravveji, Ehsan, & Vanden Broucke, Sam. (2021). PARASO source code (no COSMO) (v1.4.3). Zenodo. <a href="https://doi.org/10.5281/zenodo.5576201">10.5281/zenodo.5576201</a></p> <p><strong>Input data: </strong>Pelletier, Charles, Klein, François, Zipf, Lars, Vanden Broucke, Sam, Haubner, Konstanze, & Helsen, Samuel. (2021). Input data for PARASO, a circum-Antarctic fully-coupled 5-component model (v1.4.3) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.5588468">10.5281/zenodo.5588468</a></p> <p><strong>Forcings</strong>: Pelletier, Charles, & Helsen, Samuel. (2021). PARASO ERA5 forcings (1.4.3) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.5590053">10.5281/zenodo.5590053</a></p> <p><strong>Acknowledgements</strong></p> <ol> <li> <p>The ERA5 data (Hersbach, 2019) was downloaded on 01-09-2019 from the Copernicus Climate Change Service (C3S) Climate Data Store. The results contain modified Copernicus Climate Change Service information 2020. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.</p> <p><strong>Reference</strong>: Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2018): ERA5 hourly data on single levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on 01-SEP-2019), <a href="https://doi.org/10.24381/cds.adbb2d47">10.24381/cds.adbb2d47</a>.</p> </li> <li> <p>The sea-ice index (NSIDC-G02315) was downloaded on 01-09-2019 from the National Snow & Ice Data Center.</p> <p><strong>Reference</strong>: Fetterer, F., K. Knowles, W. N. Meier, M. Savoie, and A. K. Windnagel. 2017, updated daily. Sea Ice Index, Version 3. Daily Antarctic. Boulder, Colorado USA. NSIDC: National Snow and Ice Data Center. <a href="https://doi.org/10.7265/N5K072F8">10.7265/N5K072F8</a>. Accessed on 01-SEP-2019.</p> </li> <li> <p>The World Ocean Atlas 2018 (WOA18):</p> <p><strong>Reference: </strong>Boyer, Tim P.; Garcia, Hernan E.; Locarnini, Ricardo A.; Zweng, Melissa M.; Mishonov, Alexey V.; Reagan, James R.; Weathers, Katharine A.; Baranova, Olga K.; Seidov, Dan; Smolyar, Igor V. (2018). World Ocean Atlas 2018. Statistical means of temperature and salinity on $0.25^\circ$ grid.. NOAA National Centers for Environmental Information. Dataset. <a href="https://accession.nodc.noaa.gov/NCEI-WOA18">https://accession.nodc.noaa.gov/NCEI-WOA18</a>. Accessed 01-SEP-2020.</p> </li> <li> <p>Ice-shelf melt rates observations:</p> <p><strong>Reference: </strong>Rignot, E., Jacobs, S., Mouginot, J., & Scheuchl, B. (2013). Ice-shelf melting around Antarctica (Supplementary Material) Science, 341(6143), 266-270. <a href="https://doi.org/10.1126/science.1235798">10.1126/science.1235798</a>.</p> <p><strong>Reference: </strong>Adusumilli, Susheel; Fricker, Helen A.; Medley, Brooke C.; Padman, Laurie; Siegfried, Matthew R. (2020). Data from: Interannual variations in meltwater input to the Southern Ocean from Antarctic ice shelves. UC San Diego Library Digital Collections. <a href="https://doi.org/10.6075/J04Q7SHT">10.6075/J04Q7SHT</a></p> </li> <li> <p>JRA-55 reanalysis:</p> <p><strong>Reference: </strong>Kobayashi, S., Y. Ota, Y. Harada, A. Ebita, M. Moriya, H. Onoda, K. Onogi, H. Kamahori, C. Kobayashi, H. Endo, K. Miyaoka, and K. Takahashi , 2015: The JRA-55 Reanalysis: General specifications and basic characteristics. J. Meteor. Soc. Japan, 93, 5-48, <a href="https://doi.org/10.2151/jmsj.2015-001">10.2151/jmsj.2015-001</a></p> </li> <li> <p>BedMachine Antarctic topography:</p> <p><strong>Reference</strong>: Morlighem, M., Rignot, E., Binder, T. et al. Deep glacial troughs and stabilizing ridges unveiled beneath the margins of the Antarctic ice sheet. Nature Geoscience 13, 132–137 (2020). <a href="https://doi.org/10.1038/s41561-019-0510-8">10.1038/s41561-019-0510-8</a></p> <p><strong>Reference: </strong>Morlighem, M. 2020. MEaSUREs BedMachine Antarctica, Version 2. Ice-sheet thickness, surface elevation and mask. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: <a href="https://doi.org/10.5067/E1QL9HFQ7A8M">10.5067/E1QL9HFQ7A8M </a>(accessed 01-FEB-2020).</p> </li> </ol>
Dataset associated with the manuscript " Locally developed models improve the accuracy of remotely assessed metrics as a rapid tool to classify sandy beach morphodynamics"
<p>Raw dataset associated with the manuscript " Locally developed models improve the accuracy of remotely assessed metrics as a rapid tool to classify sandy beach morphodynamics"</p>
ScienceDex guides
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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.