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Deliverable 1.1.1.1 BEL-Float project | Dataset containing the results of numerical simulations (motions, forces) of the operational performance analysis - Part 3: Operational scenario with 3.0 m significant wave height
<p>This dataset contains the results of OpenFAST simulations performed on the DeepCwind OC4 semi-submersible combined with the 5MW NREL turbine for various wind and wave conditions. The basis of the OpenFAST input files are taken from <a href="https://github.com/OpenFAST/r-test/tree/main/glue-codes/openfast/5MW_OC4Semi_WSt_WavesWN">OpenFAST r-test GitHub repository (5MW_OC4Semi_WSt_WavesWN)</a> and adapted to simulate various wind and wave conditions. The turbulent wind field as the input to the InflowWind module is generated using <a href="https://www.nrel.gov/wind/nwtc/turbsim.html">TurbSim</a>. The simulations are performed on a modified version of OpenFAST v3.5.3 to which adaptation to the code is made to extract additional Morison drag output up to 16 cylindrical members. This adapted code is <a href="https://github.com/abkpribadi/openfast/tree/Morison_additional_output">uploaded on GitHub as a branch from a forked OpenFAST repository</a>. In total there are 1152 simulation results consists of 768 irregular waves and 384 regular waves cases. The complete dataset is divided into 9 sub-datasets to which this is part number 3. A report describing this dataset is available on the BEL-Float project website: https://www.owi-lab.be/bel-float.</p>
Results of carbapenemases determination
<p>The production of carbapenemases was assessed using phenotypic and genetic methods. T<span>he phenotypic determination of </span><span>ca</span><span>r</span><span>bapenemases</span><span> was performed using the disk-diffusion method</span><span>. </span><span>DDST</span><span>-</span><span>EDTA (double-disk </span><span>synergy</span> <span>test) for</span><span> MBL, </span><span>CDT(</span><span>combined disc test) for KPC</span><span>,</span><span> and </span><span>TEMfor</span><span> OXA-48 were performed. The other phenotypic method were <span><span>NG-TEST CARBA 5 assay, CIM test, the <span>Carba</span> tube assay, the RAPIDEC CARBA NP assay and the <span>Carba's</span> CHROM ID medium. The PCR was used as a genetic method.</span></span></span></p>
Linked collectors and determiners for: The Plecoptera of Panama. III. The genus Anacroneuria (Plecoptera: Perlidae) in Panama's national parks: 2017 survey results.
Natural history specimen data linked to collectors and determiners held within, "The Plecoptera of Panama. III. The genus Anacroneuria (Plecoptera: Perlidae) in Panama's national parks: 2017 survey results". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/3b6f03c1-2954-473a-af9e-05e6ed66f807">https://bionomia.net/dataset/3b6f03c1-2954-473a-af9e-05e6ed66f807</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/3b6f03c1-2954-473a-af9e-05e6ed66f807">https://gbif.org/dataset/3b6f03c1-2954-473a-af9e-05e6ed66f807</a>. Formatted as a Frictionless Data package.
Sonic Boom Propagation Results for Case Study 1 of the MORE&LESS Project
<p>This dataset provides the output of sonic boom simulations under various flight conditions, including predictions using Carlson’s method and higher-fidelity nonlinear propagation simulations (using propaBoom). The data is categorized by flight phase (climb, cruise, descent) and atmosphere models (ISA, SBPW3). Each flight phase includes ray tracing results and sonic boom signatures for multiple azimuth angles.</p> <p>The dataset includes:</p> <ul> <li><strong>Carlson Method Predictions</strong>: Peak overpressure and signal duration for various off-track angles.</li> <li><strong>Nonlinear Propagation Simulations</strong>: Ray tracing data (ground intersections and angles) and shock wave signatures at the beginning of the non-linear shock wave propagation and at ground level.</li> </ul> <p>Files are provided in CSV format for easy analysis using standard tools (e.g., Excel, pandas). The dataset contains simulation results for different azimuth angles, e.g., acoustic pressure, and ray intersections for thorough investigation of sonic boom propagation effects.</p> <p><strong>Folder Structure</strong>:</p> <ul> <li><code>carlson_predictions.zip</code>: Carlson method results for climb, cruise, and descent operating conditions.</li> <li><code>nonlinear_propagation_predictions.zip</code>: High-fidelity simulation outputs including ray tracing and propagated ground signatures.</li> <li><code>readme.md</code>: Readme file with information on the datasets.</li> </ul> <p><strong>Usage</strong>: Researchers can use these datasets to compare sonic boom prediction methods under the effects of flight conditions and atmospheric variations on sonic boom ground signatures.</p> <p><strong>Contact</strong>: For any questions or inquiries regarding this dataset, please contact Jacob Jäschke (jacob.jaeschke[at]tuhh.de) (<a title="Orcid Profile of Jacob Jäschke" href="https://orcid.org/0000-0002-5155-4877" target="_blank" rel="noopener">https://orcid.org/0000-0002-5155-4877</a>).</p>
Wisdom of Crowds for Supporting the Safety Evaluation of Nanomaterials - Data and results
<p>This repository contains the input data and the generated results utilized as part of the research article titled <em>"Wisdom of Crowds for Supporting the Safety Evaluation of Nanomaterials"</em> by Saarimäki & Fratello et al.</p> <h1>Data</h1> <p>This dataset includes anonymized responses of a panel of experts to a questionnaire focused on nanomaterials safety.</p> <h2>Additional Data Sources</h2> <p>This entry further includes additional data from the following sources:</p> <ul> <li><strong>Saarimäki et al. (2021)</strong>: Preprocessed data and primary physicochemical characteristics are available on <a href="https://doi.org/10.5281/zenodo.6425445">Zenodo</a>.</li> <li><strong>Gallud et al. (2020)</strong>: Data is available from the NCBI Gene Expression Omnibus (GEO) under accession number <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE148705">GSE148705</a>.</li> <li><strong>del Giudice et al. (2023)</strong>: The advanced descriptors are accessible through the associated <a href="https://doi.org/10.5281/zenodo.7674574">Zenodo repository</a>.</li> <li><strong>Labouta et al. (2019)</strong>: The harmonized dataset of ENMs cell viability assays is available as <a href="https://pubs.acs.org/doi/suppl/10.1021/acsnano.8b07562/suppl_file/nn8b07562_si_001.xlsx">supporting information of the reasearch article</a>.</li> </ul> <h1>Outputs</h1> <ul> <li><strong>Statistical modeling:</strong> The inferred parameters of the statistical model developed to analyze the expert responses and identify the consensus among the experts.</li> <li><strong>Machine Learning classifiers:</strong> The performances of several machine learning classifiers trained on the consensus responses to learn models that can predict safety concerns of new nanomaterials based on transcriptomics and physicochemical descriptors data.</li> <li><strong>Feature Importance:</strong> The relevant features extracted from the models analyzed to understand which aspects of the molecular responses to ENMs and which physicochemical properties are most important in driving the predictions.</li> </ul> <h1>Contents</h1> <p><code>data/Combined_cleaned_responses_anon.xlsx</code>: Contains the anonymized responses from the expert panel.</p> <p><code>data/enms_grouping.txt</code>: Contains a categorization of the ENMs based on their core material.</p> <p><code>data/expert_bibliographies_anon.pickle</code>: Contains the anonymized bibliography of the experts to assess the multidisciplinarity of the panel assembled.</p> <p><code>data/gex.csc.gz</code>: Contains the gene expression after exposure to the ENMs.</p> <p><code>data/phenodata.txt</code>: Contains the meta-data of the experiments</p> <p><code>data/physicochemical_descriptors.txt</code>: Contains the physicochemical descriptors of the ENMs.</p> <p><code>data/external/nn8b07562_si_001.xlsx</code>: Contains a panel of harmonized ENMs cytotoxicity assays.</p> <p><code>outputs/concern_scores.csv</code>: Contains the consensus scores inferred by the statistical model.</p> <p><code>outputs/cross_validation/</code>: Contains the logs and performances of all the machine learning classification runs .</p> <p><code>outputs/important_{genes,physchem}_weighted.xlsx</code>: Feature relevance scores of both views.</p> <p><code>outputs/model_inference_anon.nc</code>: Inferred parameters of the statistical model.</p>
Results of inter-laboratory testing on COSC-1 cores and reference samples (density, porosity, thermal properties)
<p>The data files contain the results of comparative measurements of thermal properties of four widely used rock references and nine core samples originating from the ICDP COSC-1 borehole, using a steady-state and a transient divided-bar device, a transient plane source device, a modified Angstrom device, as well as two optical thermal conductivity scanners. In addition, the Dewar method provided benchmark values for specific heat capacity. </p> <p>participating institutions and methods (acronyms used in file headers)</p> <p>Chalmers University (CU) transient plane source (TPS)<br>Geological Survey of Sweden (SGU) optical scanning (TCS) <br>Ruhr-Universit ̈at Bochum (RUB) Dewar method, modified Angstrom (mAng), optical scanning (TCS)<br>Aarhus University (AU) transient divided bar (TDB)<br>University College Dublin (UCD) divided bar (DB)</p>
Detailed analytical results for archaeological textiles from the Uden–Slabroekse Heide elite burial (Early Iron Age, Netherlands)
<p>The dataset contains 3D volume of the mineralised textile, showing four superimposed layers. The textile layer was segmented on the basis of the fibre orientation used for the 3D rendering of the vanished colour pattern.</p>
Evolution of fault reactivation potential in the greater Ruhr region (Germany). Results from analytical and numerical studies.
<p>This dataset presents results of analytical-probabilistic and numerical studies on the evolution of fault reactivation potential in the greater Ruhr region in western Germany. The dataset includes shapefile of major faults in the greater Ruhr region, with their specific dip angles, strike values, slip tendencies, dilation tendencies, fracture susceptibilities, and reduced-risk dilation tendencies. The results of long-term coupled thermo-hydro-mechanical simulations on two conceptual geothermal systems based on the two most prevailing fault architectures in the greater Ruhr region are also included in the dataset. The model results are saved with the COMSOL Multiphysics software format, where the simulations were carried out.</p> <p>Please check the README files for a detailed explanation of both datasets.</p> <p>_________________________________</p> <p>Update on 06/11/2024</p> <p>New version includes only one updated fully coupled thermo-hydro-mechanical model with a NW-SE-striking fault and a finer mesh.</p>
Results of the ROSCon24 Workshop Survey 'Hands-on with ROS2 Deliberation Technologies'
<p><span>Results of the ROSCon24 Deliberation Workshop Survey:</span></p> <p><span>The members of the ROS deliberation community group (ros-wg-delib) hosted a 'Hands-on with ROS2 Deliberation Technologies' workshop at ROSCon24 in Odense. The workshop aimed to showcase the diversity of deliberation technologies, including behaviour trees, finite-state machines, task planning, and verification and validation systems. Participants had the opportunity to tackle predefined tasks using deliberation tools such as BehaviorTree.CPP, FlexBE, ros_bt_py, SkiROS2, and the AS2FM tool by CONVINCE. Guided by the tools' creators, they explored how different technologies can be applied to solve various robotic challenges.</span></p> <p><span>A survey was conducted at the end of the workshop to gather feedback from participants about the tools they explored. The survey results highlight the strengths and limitations of each tool, offering insights into areas for improvement, and are particularly valuable for the newly introduced tools developed by the CONVINCE consortium.</span></p>
Quantitative results of the analysis of two types of bone particles corresponding to the work "A comprehensive analysis of two types of xenogeneic bone particles for use in maxillofacial bone regeneration therapies"
<p>This dataset corresponds to the quantitative data generated in the work entitled "A comprehensive analysis of two types of xenogeneic bone particles for use in maxillofacial bone regeneration therapies".</p> <p>Regeneration of maxillofacial bone structures is challenging. One of the strategies applied to bone damage repair is the use of bone filler particles, and different types of these particles have been tested. In this work, we analyzed the regenerative potential of deproteinized bone particles (DP) and collagen-based bone particles (CP) to determine the potential of each biomaterial in bone repair. Results of the structural analysis using scanning electron microscopy and 3D scanning showed that DP and CP were structurally similar, and consisted of a heterogeneous mixture of bone particles of different sizes and shapes. Then, ex vivo analyses using morphological evaluation, LIVE & DEAD and quantification of DNA released to the medium demonstrated that CP and DP were highly biocompatible when used in direct and in indirect contact with human cells, at 24, 48 and 72h of follow-up. Then, when both particles were grafted for 2 months on Wistar rats in which a critical defect had been generated at the mandible bone. Results of the computed tomography analysis showed a significant reduction of the bone defect in the CP group, but not in the DP group, as compared with negative controls devoid of any bone particles. Histological analysis of the graft area revealed that both particles were biocompatible in vivo, and a regenerative tissue with collagen fibers and mineralized spots was found in CP and DP, with higher number of mineralized spots in DP. Histochemistry and immunohistochemistry analyses confirmed the presence of collagen, proteoglycans and osteocalcin at the regeneration area of CP and DP. In general, these results confirm the biocompatibility of both types of particles and that both were able to induce maxillofacial bone regeneration, especially in the case of CP. Future studies should determine their clinical usefulness in patients with cleft palate, mandibular damage and other maxillofacial applications.</p>
The relationship between drugs consumption and dating app use: results from an Italian survey
<p>Open-access dataset regarding the survey "The relationship between drugs consumption and dating app use: results from an Italian survey" by L.Flesia, V.Fietta, C.Foresta and M.Monaro</p>
Science ready spectra, their best-fitting models and results of Jeans axisymmetric modelling described in the research paper "Transforming gas-rich low-mass discy galaxies into ultra-diffuse galaxies by ram pressure" by Grishin, Chilingarian, Afanasiev et al.
<p>This package contains data presented in the paper "Transforming gas-rich low-mass discy galaxies into ultra-diffuse galaxies by ram pressure" by Grishin, Chilingarian, Afanasiev et al. (2021 Nature Astronomy in press). The dataset can be used to reproduce Figures 3 and 4 from the main manuscript and Extended Data Figures 1, 2, 4, 5 from the Supplementary Information.</p> <p>(1) Python scripts and data points required to reproduce Figure 4 in the manuscript and Extended Data Figure 5 from the Supplementary Information. The data and scripts are presented in a combined .zip archive for both figures.</p> <p>(2) One-dimensional spectra extracted within 1 half-light radius from long-slit Binospec spectra and multi-wavelength far-UV-to-near-IR broadband spectral energy distributions (SEDs) assembled from the photometric measurements extracted within the same aperture for 11 galaxies from the main sample (9 in the Coma cluster and 2 in the Abell 2147 cluster) and 5 galaxies from the supplementary (auxiliary) list. The spectra and SEDs are accompanied with their best-fitting stellar population models and parameters determined by the NBursts+phot algorithm: radial velocity, velocity dispersion, truncation age, final stellar metallicity. The templates are MILES-based models with self-consistent chemical evolution presented in Grishin et al. 2019 (https://ui.adsabs.harvard.edu/abs/2019arXiv190913460G/abstract). The filenames contain the coefficient for galactic winds and the mass fraction of stars in the final starburst, e.g. _l15_60 means lambda=1.5, SSP_frac=60 per cent. The files are presented as binary FITS tables with the fields annotated using unified content descriptors (UCDs) from the list established by the International Virtual Observatory Alliance and physical units where applicable.</p> <p>(3) Two-dimensional profiles of internal kinematics (radial velocity and velocity dispersion) and stellar population properties (truncation age and final stellar metallicity) derived from the analysis of long-slit Binospec spectra for 12 galaxies after adaptive binning, 11 from the main sample and GMP3016 in the Coma cluster from the supplementary sample; best-fitting Jeans axisymmetric models without adaptive binning, i.e. full profiles along the slit. The data are presented in binary FITS tables in the two FITS extensions, one for the profiles derived from the corresponding spectra and the second one for dynamical models.</p>
Low-Range FMCW Insect Radar - Lab Experiments Data, Results, and Data Processing Software
<p>This research was funded by the Hessian State Ministry for Higher Education, Research and the Arts, Germany, as part of the LOEWE priority project Nature 4.0—Sensing Biodiversity.</p>
Survey questionnaire and results on user needs for energy models for the European energy transition, related to Süsser et al. (2021)
<p>The online survey was designed and conducted in the framework of the EU H2020 project SENTINEL in collaboration with the project openENTRANCE. The aim of the survey was to identify needs by modellers and model result users across Europe for energy modelling. We developed it as an explorative, semi-quantitative, self-completion online questionnaire, using the online tool “LimeSurvey”. We performed the online survey among different stakeholders from academia, policy, NGO’s and energy industry. </p> <p>The study by Süsser <em>et al.</em> (2021) investigates the differences between energy model improvements and adjustments as perceived by modellers, and the actual needs of users of model results. If you use this questionnaire in an academic publication, please cite the corresponding article:</p> <p><em>Süsser, D., Gaschnig, H., Ceglarz, A., Stavrakas, V., Flamos, A. & Lilliestam, J. (under review). Better suited or just more complex? </em><em>On the fit between user needs and modeller-driven improvements of energy system models. Energy.</em></p>
Complete results of Procedure section in manuscript "PathML: A unified framework for whole-slide image analysis with deep learning"
<p>The results from a complete run of the Procedure section of the paper "PathML: A unified framework for whole-slide image analysis with deep learning".</p>
Trash to Treasure: How the Renewable Fuel Standard can use garbage to pay for electric vehicles - summary results
<p>Summary results for submitted article: Trash to Treasure: How the Renewable Fuel Standard can use garbage to pay for electric vehicles</p> <p> </p>
Data for From principles to practices: Open Science at European Universities. 2020-2021 EUA Open Science Survey Results
<p>This database refers to the data collected by the European University Association (EUA) for its 2020-2021 EUA Open Science Survey, which gathered responses from universities and higher education institutions across Europe. The full report published by the association is available at <a href="https://www.eua.eu/resources/publications/976:from-principles-to-practices-open-science-at-europe%E2%80%99s-universities-2020-2021-eua-open-science-survey-results.html">https://www.eua.eu/resources/publications/976:from-principles-to-practices-open-science-at-europe%E2%80%99s-universities-2020-2021-eua-open-science-survey-results.html</a> (<a href="http://doi.org/10.5281/zenodo.5062982">http://doi.org/10.5281/zenodo.5062982</a>).</p> <p>All information that could lead to the identification of individual universities and higher education institutions was removed from the database. The following files are available:</p> <ul> <li>2020-2021 EUA Open Science Survey</li> <li>Database in the following formats: .xlsx (Microsoft Excel) and .sav (IBM SPSS)</li> <li>Survey Codebook: includes information on all the variables and their coding (.xlsx)</li> <li>Data Management Plan.</li> </ul>
Accurate annotation of protein coding sequences with IDTAXA - classification results
<p>Raw classification results generated by IDTAXA, BLAST, and HMMER on a training set scraped from KEGG of > 1.5M sequences. HMMER and BLAST results are also converted into IDTAXA like objects for ease of comparison.</p>
Collaborative Program Comprehension based on Augmented Reality (Evaluation Results of Master's Thesis)
<p>The dataset contains feedback generated through a survey for an augmented reality approach in the ExplorViz project.</p> <p>The dataset includes the results for a pilot study with two probands and the results for a case study with 20 probands.</p>
Simulation Results Data for 'Modelling new insecticide-treated bed nets for malaria-vector control: How to strategically manage resistance?'
<p>GENERAL INFORMATION</p> <p>1. Title of Dataset: Simulation Results Data for 'Modelling new insecticide-treated bed-nets for malaria-vector control: How to strategically manage resistance?'</p> <p>2. Author Information<br> A. Investigator Contact Information<br> Name: Philip G. Madgwick<br> Institution: Syngenta <br> Address: Jealott’s Hill International Research Centre, Bracknell, RG42 6EY, UK<br> Email: philip.madgwick@syngenta.com</p> <p> B. Investigator Contact Information<br> Name: Ricardo Kanitz<br> Institution: Syngenta <br> Address: Syngenta Crop Protection, Rosentalstrasse 67, CH-4058 Basel, Switzerland<br> Email: ricardo.kanitz@syngenta.com</p> <p><br> 3. Date of data collection (single date, range, approximate date): 2021-01-13 to 2021-02-01 </p> <p>4. Geographic location of data collection: UK </p> <p>5. Information about funding sources that supported the collection of the data: </p> <p>This work was conducted during a postdoctoral research position for PGM funded by the Innovative Vector Control Consortium (IVCC).</p> <p><br> SHARING/ACCESS INFORMATION</p> <p>1. Licenses/restrictions placed on the data: NA</p> <p>2. Links to publications that cite or use the data: [UPDATE]</p> <p>3. Links to other publicly accessible locations of the data: NA</p> <p>4. Links/relationships to ancillary data sets: NA</p> <p>5. Was data derived from another source? No</p> <p>6. Recommended citation for this dataset: [UPDATE]</p> <p><br> DATA & FILE OVERVIEW</p> <p>1. File List: <br> PSData_random6.csv - 10^6 random samples of each of the 17 parameters in the model, where rows are samples and columns are parameters (with column names corresponding to the parameters identified in the rows of Table 1 of the manuscript; see also DATA-SPECIFIC INFORMATION)<br> Data_random6_maxpsMixture_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k=1; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_maxpsMixture_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k=1; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_maxpsMixture_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k=1; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Mixture_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_Mixture_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Mixture_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Mosaic_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used at 50% frequency each as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_Mosaic_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used at 50% frequency each as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Mosaic_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used at 50% frequency each as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Rotation_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_Rotation_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Rotation_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Rotation_Fixed_revmm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_Rotation_Fixed_revmn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Rotation_Fixed_revnn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_SoloA_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_SoloA_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_SoloA_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_SoloB_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_SoloB_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_SoloB_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance </p> <p>2. Relationship between files, if important: </p> <p>Files are named in accordance with the variables that describe each simulation setup, as described above. Each simulation dataset has 10^6 runs that correspond to the 10^6 random samples of each of the 17 parameters in the model in 'PSData_random6.csv'.</p> <p>3. Additional related data collected that was not included in the current data package: NA</p> <p>4. Are there multiple versions of the dataset? No</p> <p><br> METHODOLOGICAL INFORMATION</p> <p>1. Description of methods used for collection/generation of data: Data were collected using Simulator.R, which is in the vignettes of the 'detsims' R package that accompanies the manuscript. </p> <p>2. Methods for processing the data: Data were processed using Figures.R, which is in the vignettes of the 'detsims' R package that accompanies the manuscript. </p> <p>3. Instrument- or software-specific information needed to interpret the data: Analysis was conducted in R version 4.0.3 (2020-10-10), using R packages identified in Figures.R, which is in the vignettes of the 'detsims' R package that accompanies the manuscript. </p> <p>4. Standards and calibration information, if appropriate: NA</p> <p>5. Environmental/experimental conditions: NA</p> <p>6. Describe any quality-assurance procedures performed on the data: NA</p> <p>7. People involved with sample collection, processing, analysis and/or submission: NA </p> <p><br> DATA-SPECIFIC INFORMATION FOR: PSData_random6.csv</p> <p>1. Number of variables: </p> <p>17 variables with column names that have the following parameter meanings (see Table 1 in the manuscript): <br> Population Size = N = starting population size (and carrying capacity in logistic model); random sample range on log-scale: 10^2 - 10^9<br> Intrinsic Birth Rate = b = % population growth rate (in logistic model); random sample following a standard log-normal distribution with mean=0 and sd=1<br> Intrinsic Death Rate = d = % breeding mosquitoes that die into next generation; random sample range: 0 - 1<br> Female Exposure = x_[female-symbol] = % female mosquitoes that receive a dose; random sample range: 0 - 1<br> Male Exposure x_[male-symbol] = % male mosquitoes that receive a dose; random sample range: 0 - 1<br> Initial Frequency A = f_0,A = starting frequency of allele A; random sample range on log-scale: 10^-9 - 10^-2, limited to be within the range 1/N - N/100 where N is Population Size<br> Effectiveness 1 = m_1 = % dosed mosquitoes that die from insecticide 1; random sample range: 0 - 1<br> Resistance Restoration A = r_A = % return to baseline fitness with resistance allele A; random sample range: 0 - 1<br> Dominance of Resistance Restoration A = h^r_A = % resistance restoration in heterozygote with allele A; random sample range: 0 - 1<br> Resistance Cost A = c_A = % non-dosed mosquitoes that die from carrying allele A; random sample range on log-scale: 10^-3 - 10^-0.5<br> Dominance of Resistance Cost A = h^c_A = % resistance cost in heterozygote with allele A; random sample range: 0 - 1<br> Initial Frequency B = f_0,B = starting frequency of allele B; random sample range on log-scale: 10^-9 - 10^-2, limited to be within the range 1/N - N/100 where N is Population Size<br> Effectiveness 2 = m_2 = % dosed mosquitoes that die from insecticide 2; random sample range: 0 - 1<br> Resistance Restoration B = r_B = % return to baseline fitness with resistance allele B; random sample range: 0 - 1<br> Dominance of Resistance Restoration B = h^r_B = % resistance restoration in heterozygote with allele B; random sample range: 0 - 1<br> Resistance Cost B = c_B = % non-dosed mosquitoes that die from carrying allele B; random sample range on log-scale: 10^-3 - 10^-0.5<br> Dominance of Resistance Cost B = h^c_B = % resistance cost in heterozygote with allele B; random sample range: 0 - 1</p> <p>2. Number of cases/rows: </p> <p>10^6, corresponding to the number of random samples </p> <p>3. Variable List: NA </p> <p>4. Missing data codes: NA</p> <p>5. Specialized formats or other abbreviations used: NA</p> <p><br> DATA-SPECIFIC INFORMATION FOR: all other dataset files (e.g. Data_random6_maxpsMixture_Fixed_mm.csv) </p> <p>1. Number of variables: </p> <p>10 variables with column names that have the following meanings:<br> A_t_50% = the recorded number of generations that it takes for resistance allele A to reach >50% frequency; 0 means that resistance allele A never reaches >50% frequency <br> A_f_250 = the frequency of resistance allele A at the 250th generation <br> A_f_bar = the mean frequency of resistance allele A over the first 250 generations <br> B_t_50% = the recorded number of generations that it takes for resistance allele B to reach >50% frequency; 0 means that resistance allele B never reaches >50% frequency <br> B_f_250 = the frequency of resistance allele B at the 250th generation <br> B_f_bar = the mean frequency of resistance allele B over the first 250 generations <br> nf_80% = the recorded number of generations that it takes for the female population size to recover to >80% of its original size in the 0th generation; 0 means that the female population size never reaches >80% recovery; 1 means that the female population size never drops below 80% of its original size in the 1st generation<br> nf_250 = the female population size at the 250th generation<br> nf_bar = the mean female population size over the first 250 generations <br> nf_ext = the recorded number of generations that it takes for the female population size to drop below 1 (i.e. population extinction); 0 means that female population size never reaches <1</p> <p>2. Number of cases/rows: </p> <p>10^6, corresponding to the number of random samples </p> <p>3. Variable List: NA</p> <p>4. Missing data codes: all missing data is recorded as 0 </p> <p>5. Specialized formats or other abbreviations used: NA</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)
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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.