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3,172 results for “Publications”
Dataset for the publication: Molecularly-controlled high swirl combustion system for ethanol/1-octanol dual fuel combustion
<p>This dataset contains the research data featured in the publication "Molecularly-controlled high swirl combustion system for ethanol/1-octanol dual fuel combustion" in Fuel (DOI: 10.1016/j.fuel.2023.128184)</p>
The PANORAMA Challenge: Public Training and Development Dataset (4)
<p>This dataset represents the <strong><a href="https://panorama.grand-challenge.org/" target="_blank" rel="noopener">PANORAMA</a>: Public Training and Development Dataset</strong>. It contains 2238 anonymized contrast-enhanced CT (CECT) scans acquired at two centers (Radboud University Medical Center, University Medical Center Groningen) based in The Netherlands. Additionally, it contains 194 cases from the <strong><a href="http://medicaldecathlon.com/" target="_blank" rel="noopener">Medical Segmentation Decathlon</a> </strong>dataset and 80 cases from<strong> <a href="https://www.cancerimagingarchive.net/collection/pancreas-ct/" target="_blank" rel="noopener">National Institutes of Health</a></strong>. For all updates/fixes regarding this dataset, please join the challenge and check out our <a href="https://grand-challenge.org/forums/forum/panorama-pancreatic-cancer-diagnosis-radiologists-meet-ai-711/topic/public-training-and-development-dataset-updates-and-fixes-2213/" target="_blank" rel="noopener">dedicated forum post</a> on this topic. The corresponding labels of the PANORAMA dataset can be found <a href="https://github.com/DIAGNijmegen/panorama_labels">here</a>. </p> <p>The PANORAMA challenge is an all-new grand challenge that aims to validate the diagnostic performance of artificial intelligence and radiologists at pancreatic ductal adenocarcinoma (PDAC) detection/diagnosis in CECT, with histopathology and follow-up (≥ 3 years) as the reference standard, in a retrospective setting in the hidden testing dataset. The study hypothesizes that state-of-the-art AI algorithms are non-inferior to radiologists reading CECT.</p> <p>Key aspects of the PANORAMA study design have been established in conjunction with an international scientific advisory board of 13 experts in AI and pancreas radiology as well as a patient representative —to unify and standardize present-day guidelines, and to ensure meaningful validation of pancreas AI towards clinical translation (<strong><a href="https://www.sciencedirect.com/science/article/pii/S2405456921001607">Reinke et al., 2021</a></strong>).</p> <p><em>This PANORAMA dataset contains: batch <strong>4</strong> <strong>out of 4</strong></em></p>
Supplementary dataset to publication: Approaching the complexity of Crimean-Congo hemorrhagic fever virus serology: a study in swine
<p>For the detection of anti-CCHFV antibodies in swine, we established a swine-specific in-house ELISA, indirect immunofluorescence assay and a virus neutralization test. Uploaded data contains sample performance in each test. Samples used in this study include swine serum samples from Germany and Spain.</p>
Dataset underlying the publication: "Organic contaminants in bio-based fertilizer treated soil: Target and suspect screening approaches" DOI: 10.1016/j.chemosphere.2023.139261
<p>Dataset underlying the publication "Organic contaminants in bio-based fertilizer treated soil: Target and suspect screening approaches" DOI: 10.1016/j.chemosphere.2023.139261.</p>
Dataset for publication "Scaling of planar sodium-metal chloride battery cells to 90 cm2 active area"
<p><span lang="EN-US">Sodium-metal chloride batteries; high-temperature ZEBRA batteries; molten-salt batteries; scalable stationary energy storage; alkali-metal anode</span></p>
Improving access to and reuse of research results, publications and data for scientific purposes - Stakeholders' consultations results
<p>The data sets were created via data collection effort for the Horizon Europe-funded "study to evaluate the effects of the EU copyright framework on research and the effects of potential interventions and to identify and present relevant provisions for research in EU data and digital legislation, with a focus on rights and obligations". The study was contacted by DG RTD. </p> <p>This research project supports Action 2 objectives of the European Research Area (ERA) Policy Agenda 2022-2024, which aims to propose an EU legislative and regulatory framework for copyright and data that is fit for research. The report provides a comprehensive analysis of barriers to the access and reuse of publicly funded research, including scientific publications and data. It assesses existing EU copyright legislation and EU data and digital legislation. It also assesses regulatory frameworks and national initiatives and identifies potential areas for improvement.</p> <p>Using a methodological, evidence-based approach (including the survey results posted in this repository), the study presents possible legislative and non-legislative measures to improve the current EU copyright and data framework and align it with the needs of scientific research and open research data principles. </p> <p>The data sets include the raw data of the three surveys (survey 1 targeted at researchers, survey 2 targeted at research-performing organisations, and survey 3 targeted at publishers). All surveys have two major parts: one concerning copyright legislation and another concerning data and digital legislation. In addition, we provide interview notes, they are also organised into two parts: one concerning copyright legislation and another concerning data and digital legislation. </p> <p>The data collection effort was partially supported by our colleagues from the Institute for Information Law (IVIR) and KU Leuven CiTIP. </p>
Data for a publication "Impact disruption of Bjurböle porous chondritic projectile"
<p>This archive contains raw and processed research data and photographic documentation for a publication "<em><strong>Impact disruption of Bjurböle porous chondritic projectile</strong></em>" by Kohout et al. The sample identifiers are consistent with the one used in the publication.</p> <p>Content:</p> <ol> <li><strong>Bjurböle photos.zip</strong>, <strong>Bjurböle photos II.zip</strong>, and <strong>Bjurböle photos III.zip</strong> - Photographs of studied Bjurböle meteorites from Finnish Museum of Natural History as well as from worldwide collections. Unless specified otherwise in the photographs (worldwide collections) the photo credits are Tomas Kohout, Assi-Johanna Soini, Arto Luttinen, University of Helsinki.</li> <li><strong>Meteoriteidentifier.ply</strong> - 3D shape reconstructions of studied Bjurböle and Chelyabinsk meteorites. By default a 3D mesh reconstruction with relaxed fitting was applied to partial aligned laser scans. In certain cases where multiple reconstruction techniques were applied (e.g. volume merge) this is indicated in file name.</li> <li><strong>All tables.xlsx</strong> - Spreadsheet file containing the tables presented in the publication</li> </ol>
Publication dates for ArXiv publication versions
<p>Lookup tables in plain JSON, mapping ArXiv publication version identifiers to their respective publications dates.</p> <p>The JSON files are archived in <em>arxiv-publication-dates-by-identifier-prefix.tar.gz</em>.<br>The archive contains files named after the date prefix of the ArXiv publication version identifiers they contain.<br>E.g., the file <em>1908.json</em> will contain the data for identifiers <em>1908.12345v1</em>, <em>1908.12345v2</em>, <em>1908.23456v1</em>, etc.<br>Publication dates are given in the format <em>YYYY-MM-DD</em>.</p> <h2>Reproducibility</h2> <p>The <a href="https://snakemake.readthedocs.io/">Snakemake</a> workflow that has produced this dataset has been archived and is available in <em>arxiv-publication-dates-workflow.tar.gz</em>.</p> <h3>Changes in version 1.2</h3> <p>Version 1.2 includes a JSON file that contains a JSON array with all file names included in the dataset: <em>file_names.json</em>.</p> <h3>Changes in version 1.1</h3> <p>For version 1.1, the dataset was extended manually to include a single missing date for <a href="https://arxiv.org/abs/0906.3421v3">arXiv:0906.3421v3</a>: <em>2010-02-02</em>. As of 2024-05-13, the date for the respective version had not been provided in the <em>arXivRaw</em> OAI-PMH data (<a href="http://export.arxiv.org/oai2?verb=GetRecord&identifier=oai:arXiv.org:0906.3421&metadataPrefix=arXivRaw">http://export.arxiv.org/oai2?verb=GetRecord&identifier=oai:arXiv.org:0906.3421&metadataPrefix=arXivRaw</a>).</p> <h3>Running the workflow</h3> <p>To reproduce the dataset on a Linux machine, you need a version of the <a href="https://conda-forge.org/"><em>conda</em></a> package manager installed on your system.</p> <p>Run the following:<br><br></p> <pre><code># Extract the archived workflow tar -xf my-workflow.tar.gz # Create conda environment from lock file conda env create -n arxiv-metadata --file conda-environment.lock.yaml # Activate the environment conda activate arxiv-metadata # Optionally, dry-run the workflow snakemake -n # Produce the output files snakemake --keep-storage-local-copies --software-deployment-method conda -c <NUMBER OF CORES TO USE></code><br><br>Then, append the file <em>0906.json</em> (included in the <em>tar.gz</em> output) with value <em>2010-02-02</em> for a new key <em>0906.3421v3</em>.</pre> <h2>Workflow</h2> <p>To adapt/change the workflow, clone it from <a href="https://github.com/sdruskat/arxiv-publication-metadata">https://github.com/sdruskat/arxiv-publication-metadata</a>.<br>The workflow version used to produce this dataset is available at <a href="https://doi.org/10.5281/zenodo.11507183">https://doi.org/10.5281/zenodo.11507183</a>. </p>
Data for a publication "Amino-Modified ZIF-8 for Enhanced CO2 Capture: Synthesis, Characterization and Performance Evaluation"
<p>Data for a publication "Amino-Modified ZIF-8 for Enhanced CO2 Capture: Synthesis, Characterization and Performance Evaluation".</p> <p><strong>Versions of dataset:</strong></p> <p><strong><span>V1: </span></strong><span>First dataset regarding the data used in the article.</span></p> <p><strong><span>V2:</span></strong><span> The dataset </span><span>was newly reorganized</span><span>, containing the </span><span>data,</span><span> that </span><span>were used</span><span> for the published article. </span><span>More information can be found</span><span> in the README file.</span></p> <p><strong>Article abstract</strong></p> <p>The urgent need for sustainable and innovative approaches to mitigate the increasing levels of atmospheric CO<sub>2</sub> necessitates the development of efficient methods for its removal. In this study, we focus on the new, innovative approach for synthesis and functionalization of metal-organic framework (MOF) ZIF-8 in one step at room temperature to enhance its capacity for CO<sub>2</sub> capture. Specifically, we investigated the impact of four amino-compounds, namely tetraethylenepentamine (TEPA), hexadecylamine (HDA), <a title="Learn more about ethanolamine from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/chemical-engineering/ethanolamine">ethanolamine</a> (ELA), and cyclopropylamine (CPA), on the <a title="Learn more about chemical structure from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/materials-science/structure-composition">chemical structure</a>, size, surface area and porosity, and CO<sub>2</sub> capturing of ZIF-8 powder. By varying concentrations of the amino-compounds, we examined their influence on the ZIF-8 properties. Our findings demonstrate that each amino-compound and its respective concentration exhibit distinct effects on the characteristics of ZIF-8. Notably, the ZIF-8 sample functionalized with the highest presented concentration of TEPA exhibited significant improvement in CO<sub>2</sub> trapping efficiency, with a 33.3% enhancement. Moreover, least concentrated samples with added HDA or CPA demonstrated notable improvements with enhancements of 46.6% and 18.6%, respectively. These results highlight the potential of simple synthesis and functionalization techniques for MOFs in enhancing their CO<sub>2</sub> capture capabilities. The findings from this study offer new opportunities for the development of strategies to mitigate CO<sub>2</sub> emissions using MOFs.</p>
Bioinformatics for public health microbiologists: Module 1 dataset (South African Salmonella)
<p>Module 1 (WGS) dataset (paired-end Illumina reads of <em>Salmonella enterica </em>strains isolated from animals and animal products in South Africa)</p> <ul> <li> <p>module1_dataset_ZAsalmonella.tar.gz: raw Illumina paired-end reads</p> </li> <li> <p>trimmed_reads.tar.gz: trimmed Illumina paired-end reads (i.e., trimmed via fastp v0.23.4)</p> </li> <li> <p>contigs.tar.gz: assembled genomes (i.e., trimmed reads assembled into contigs using SKESA v2.5.1)</p> </li> <li> <p>prokka.tar.gz: whole-genome annotation results (i.e., produced via Prokka v1.14.6)</p> </li> <li> <p>enterobase_salmonella.tar.gz: publicly available assembled genomes (downloaded via Enterobase; https://enterobase.warwick.ac.uk/, accessed 1 June 2024)</p> </li> <li> <p>snippy_input.tsv: input file used for Snippy (https://github.com/tseemann/snippy)</p> </li> <li> <p><span>snippy_final.tar.gz: output files produced by Snippy (https://github.com/tseemann/snippy), Gubbins (https://github.com/nickjcroucher/gubbins), and SNP-sites (https://github.com/sanger-pathogens/snp-sites)</span></p> </li> </ul>
Dataset for publication "Parallel experiments in electrochemical CO2 reduction enabled by standardized analytics"
<p>Dataset for the publication: "<strong>Parallel experiments in electrochemical CO<sub>2</sub> reduction </strong><strong>enabled by standardized analytics</strong>", https://doi.org/10.1038/s41929-024-01172-x,<strong> </strong>divided by paper Figure. The dataset contains data that are both raw and processed using the open-source software available at http://dgbowl.github.io </p>
iHelp public synthetic dataset
<p>The iHelp public synthetic dataset has been synthesized from primary and secondary data collected during the Medical University Plovdiv pilot implementing their cancer program. A detailed description can be found in the "iHelp public synthetic dataset.docx" file.</p>
Supplementary dataset to the publication "Ultraviolet C inactivation of Coxiella burnetii for production of a structurally preserved whole cell vaccine antigen"
<p>The dataset supplements the journal article "Ultraviolet C inactivation of <em>Coxiella burnetii </em>for production of a structurally preserved whole cell vaccine antigen" published by Katja Mertens-Scholz, Amira A. Moawad, Elisabeth M. Liebler-Tenorio, Andrea Helming, Jennifer Andrack, Peter Miethe, Heinrich Neubauer, Mathias W. Pletz and Ina-Gabriele Richter in the journal BMC Microbiology (https://doi.org/10.1186/s12866-024-03246-z). The file "NMII 100µW" contains all data regarding inactivation of <em>C. burnetii</em> Nine Mile phase II with 100µW in a time dependent manner. The file "NMI 100 and 250µW" contains all data regarding inactivation of <em>C. burnetii</em> Nine Mile phase I with 100µW and 250µW in a time dependent manner. The file "surviving fraction" contains all data regarding inactivation of <em>C. burnetii </em>Nine Mile phase I and II after UVC treatment. The file "serology" contains all data obtained from ELISA experiments. The file "diameter" contains all data regarding the bacterial diameter after UVC treatment.</p>
Data for a publication "Argon plasma-modified bacterial nanocellulose: Cell-specific differences in the interaction with fibroblasts and endothelial cells"
<p>A dataset containing data for the published article "Argon plasma-modified bacterial nanocellulose: Cell-specific differences in the interaction with fibroblasts and endothelial cells".</p> <p> </p> <p>For more details, please read the <strong>README - Description of data and analysis informations.txt</strong> file.</p> <p><strong>Dataset versions:</strong></p> <p><strong>V1:</strong> The first dataset containing a majority of the data.</p> <p><strong>V2:</strong> Dataset contains all the data mentioned in the article in the appropriate file formats for long-term preservation and accessibility.</p>
Supplementary dataset to the publication "Bi, S., and Hieronymi, M. (2024). Holistic optical water type classification for ocean, coastal, and inland waters. Limnology & Oceanography"
<p>The NetCDF data files contain the training dataset used to develop the Optical Water Type (OWT) framework proposed by Bi and Hieronymi (2024). The dataset is available in two spectral versions:</p> <p> 1. <code>owt_BH2024_training_data_hyper.nc</code>: This file includes training data with a spectral resolution of 2 nm, ranging from 400 to 900 nm.<br> 2. <code>owt_BH2024_training_data_olci.nc</code>: This file contains data formatted similarly to the hyperspectral version but aligned with the nominal Sentinel-3 OLCI wavebands.</p> <h2>Contents of the Dataset</h2> <p>For each version, the dataset includes spectral inherent and apparent optical properties such as:</p> <p> • Remote Sensing Reflectance (Rrs)<br> • Pure Water Absorption (aw)<br> • Absorption Coefficient of Detritus (ad)<br> • Total Absorption Coefficient without Pure Water (agp)<br> • Absorption Coefficient of Phytoplankton (aph)<br> • Backscattering Coefficient of Total Particulate Matter (bbp)<br> • Scattering Coefficient of Total Particulate Matter (bp)<br> • Scattering Coefficient of Pure Water (bw)</p> <p>Additionally, the dataset includes various environmental and biological parameters:</p> <p> • Chlorophyll a Concentration (Chl)<br> • Inorganic Suspended Matter Concentration (ISM)<br> • Colored Dissolved Organic Matter Absorption at 440 nm (ag440)<br> • Single-Scattering Albedo of Detritus at 550 nm (A_d)<br> • Power Law Exponent of Detritus Attenuation (G_d)<br> • Water Salinity (Sal)<br> • Water Temperature (Temp)<br> • Fraction for Diminished Coccolithophore Absorption (a_frac)<br> • Fraction of Coccolithophore Group (cocco_frac)</p> <h2>Optical Water Types</h2> <p>The training dataset includes 10 pre-defined optical water types, with 10,000 samples for each type. Detailed descriptions of these water types can be found in Table 1 of Bi and Hieronymi (2024) or as follows,</p> <table> <tbody> <tr> <td>OWT</td> <td>Desciption</td> </tr> <tr> <td>1</td> <td>Extremely clear and oligotrophic indigo-blue waters with high reflectance in the short visible wavelengths.</td> </tr> <tr> <td>2</td> <td>Blue waters with similar biomass level as OWT 1 but with slightly higher detritus and CDOM content.</td> </tr> <tr> <td>3a</td> <td>Turquoise waters with slightly higher phytoplankton, detritus, and CDOM compared to the first two types.</td> </tr> <tr> <td>3b</td> <td>A special case of OWT 3a with similar detritus and CDOM distribution but with strong scattering and little absorbing particles like in the case of Coccolithophore blooms. This type usually appears brighter and exhibits a remarkable ~490 nm reflectance peak.</td> </tr> <tr> <td>4a</td> <td>Greenish water found in coastal and inland environments, with higher biomass compared to the previous water types. Reflectance in short wavelengths is usually depressed by the absorption of particles and CDOM.</td> </tr> <tr> <td>4b</td> <td>A special case of OWT 4a, sharing similar detritus and CDOM distribution, exhibiting phytoplankton blooms with higher scattering coefficients, e.g., Coccolithophore bloom. The color of this type shows a very bright green.</td> </tr> <tr> <td>5a</td> <td>Green eutrophic water, with significantly higher phytoplankton biomass, exhibiting a bimodal reflectance shape with typical peaks at ~560 and ~709 nm.</td> </tr> <tr> <td>5b</td> <td>Green hyper-eutrophic water, with even higher biomass than that of OWT 5a (over several orders of magnitude), displaying a reflectance plateau in the Near Infrared Region, NIR (vegetation-like spectrum).</td> </tr> <tr> <td>6</td> <td>Bright brown water with high detritus concentrations, which has a high reflectance determined by scattering.</td> </tr> <tr> <td>7</td> <td>Dark brown to black water with very high CDOM concentration, which has low reflectance in the entire visible range and is dominated by absorption.</td> </tr> </tbody> </table> <h2>Additional Information</h2> <p>The detailed description of the data simulation can be found in the supporting information of Bi and Hieronymi (2024). The models used for simulating the data are available on GitHub:</p> <p> • Component IOP Model: <a href="https://github.com/bishun945/IOPmodel" target="_blank" rel="noopener">Bio-geo-optical modelling of natural waters by Bi, Hieronymi, and Röttgers (2023)</a><br> • OWT Package: <a href="https://github.com/bishun945/pyOWT" target="_blank" rel="noopener">pyOWT</a></p> <h2>References</h2> <p> 1. OWT Framework: Bi, S., and Hieronymi, M. (2024). Holistic optical water type classification for ocean, coastal, and inland waters. Limnology & Oceanography, lno.12606. doi: 10.1002/lno.12606<br> 2. Component IOP Model: Bi, S., Hieronymi, M., and Röttgers, R. (2023). Bio-geo-optical modelling of natural waters. Front. Mar. Sci. 10, 1196352. doi: 10.3389/fmars.2023.1196352<br> 3. Pure Water IOP Model: Röttgers, R., Doerffer, R., McKee, D., and Schönfeld, W. (2016). The Water Optical Properties Processor (WOPP): Pure Water Spectral Absorption, Scattering and Real Part of Refractive Index Model. Technical Report No WOPP-ATBD/WRD6. Available at: https://calvalportal.ceos.org/tools<br> 4. Rrs Model: Lee, Z., Du, K., Voss, K. J., Zibordi, G., Lubac, B., Arnone, R., et al. (2011). An inherent-optical-property-centered approach to correct the angular effects in water-leaving radiance. Appl. Opt. 50, 3155. doi: 10.1364/AO.50.003155</p> <h2>Authors and Contact</h2> <p> • Author: Shun Bi, Martin Hieronymi, Rüdiger Röttgers<br> • Creator: Shun Bi, Shun.Bi@hereon.de</p> <h2>Example Python Code to Read Data</h2> <p>Here is an example of how to read the NetCDF data using Python and the <code>xarray</code> library:</p> <pre><code>import xarray as xr # Load the dataset data_hyper = xr.open_dataset("path_to_your_file/owt_BH2024_training_data_hyper.nc") # Print the dataset to see its structure print(data_hyper) # Access a specific variable, e.g., remote sensing reflectance (Rrs) rrs = data_hyper['Rrs'] # Plot a sample of Rrs import matplotlib.pyplot as plt # Select a sample ID, for example the first sample sample_id = 0 plt.plot(data_hyper['wavelen'], rrs[sample_id, :]) plt.xlabel('Wavelength (nm)') plt.ylabel('Rrs (1/sr)') plt.title(f'Remote Sensing Reflectance for Sample ID {sample_id}') plt.show()</code></pre>
Supplementary dataset for the publication "Prevalence of tick-borne bacterial pathogens in Germany – has the situation changed after a decade?"
<p>The dataset supplements the journal article "Prevalence of tick-borne bacterial pathogens in Germany – has the situation changed after a decade?" published by Katja Mertens-Scholz, Bernd Hoffmann, Jörn M. Gethmann, Hanka Brangsch, Mathias W. Pletz and Christine Klaus in the journal mdpi microorganisms (DOI: <a href="https://doi.org/10.3389/fcimb.2024.1429667">https://doi.org/10.3389/fcimb.2024.1429667 </a><a>)</a></p> <p>The file "Tick samples" contains all data regarding time, location and detected pathogens". The files "Rickettsia sequences" and Borrelia sequences" contains all sequence information of positive samples. The file "Rickettsia reference genes" contains information of used reference genes for phylogenetic analysis.</p>
Dataset for the publication: Tactile Convolutional Networks for Online Slip and Rotation Detection
<p>Raw data recorded for the publication "Tactile Convolutional Networks for Online Slip and Rotation Detection" in rosbag format. Each .bag file contains recordings of two tactile sensors sampled with 1kHz as "sensor_msgs/Image" and a third channel for data labeling in the format "sr_robot_msgs/UBI0All". Detailed informations about the preprocessing and labeling are in the publication.</p>
Dataset: error characteristics of a class 0.2S current transformer for publication
<p>Data for demonstrating typical error characteristics of a current transformer of the class 0.2 S. Data are used in a publication.</p>
Data of publication All-optical control of long-lived nuclear spins in rare-earth doped nanoparticles
<p>Data corresponding to the figures of the publication "All-optical control of long-lived nuclear spins in rare-earth doped nanoparticles" by D. Serrano et al. (https://www.nature.com/articles/s41467-018-04509-w). A text file describes data in each compressed folder, please refer to the publication for more details. </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>
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.