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591 results for “Regression”
Random forest regression for fuzzy grades
<p>The used dataset contained information about:</p> <ul> <li>students' gender (1 = male, 2 = female);</li> <li>schools' type (1 = scientific lyceums, 2 = other lyceums, 3 = technical schools, 4 = vocational schools);</li> <li>schools' macreregion (1 = Northwester Italy, 2 = Northeastern Italy, 3 = Central Italy, 4 = Southern Italy, 5 = Southern Italy and Isles);</li> <li>students' origin (1 = native Italian student, 2 = first-generation immigrant student, 3 = second-generation immigrant student);</li> <li>students' ESCS (continuous data);</li> <li>teacher-given grades in mathematics (from 1 to 10);</li> <li>students' achievements on the INVALSI mathematics test (continuous data);</li> <li>students' fuzzy grade, obtained as a combination of teacher-given grades and achievements in mathematics (from 1 to 10).</li> </ul>
Pion Energy Regression in High-Granularity Calorimeter Prototype
<p>The dataset consists of simulations of calibrated reconstructed hits produced by a pion passing through the HGCAL test beam prototype. For the simulations, Monte Carlo method is used to produce the pions with energy ranging from 10 to as high as 500 GeV. The dataset contains the coordinates of the calibrated reconstructed hits in the prototype along with the calibrated energy in units of MIP. The HDF5 files can be extracted from the gzip files.</p>
The role of fire in the carbon dynamics of the boreal forest I. - Response of area burned to changing climate in western boreal North America using a Multivariate Adaptive Regression Splines (MARS) approach (2003-2100).
The boreal forest contains large reserves of carbon, and across this region wildfire is a common occurrence. To improve the understanding of how wildfire influences the carbon dynamics of this region, methods were developed to incorporate the spatial and temporal effects of fire into the Terrestrial ecosystem Model (TEM). The historical role of fire on carbon dynamics of the boreal region was evaluated within the context of ecosystem responses to changing atmospheric CO2 and climate. These results show that the role of historical fire on boreal carbon dynamics resulted in a net carbon sink; however, fire plays a major role in the interannual and decadal scale variation of source/sink relationships. To estimate the effects of future fire on boreal carbondynamics, spatially and temporally explicit empirical relationships between climate andfire were quantified. Fuel moisture, monthly severity rating, and air temperature explained a significant proportion of observed variability in annual area burned. These relationships were used to estimate annual area burned for future scenarios of climate change and were coupled to TEM to evaluate the role of future fire on the carbon dynamics of the North American boreal region for the 21st Century. Simulations with TEM indicate that boreal North America is a carbon sink in response to CO2 fertilization, climate variability, and fire, but an increase in fire leads to a decrease in the sink strength. While this study highlights the importance of fire on carbon dynamics in the boreal region, there are uncertainties in the effects of fire in TEM simulations. These uncertainties are associated with sparse fire data for northern Eurasia, uncertainty in estimating carbon consumption, and difficulty in verifying assumptions about the representation of fires that occurred prior to the start of the historical fire record. Future studies should incorporate the role of dynamic vegetation to more accurately represent post-fire successional pr
RTPTorrent: An Open-source Dataset for Evaluating Regression Test Prioritization
<p>This dataset is designed to be used in evaluation studies of regression test prioritization techniques. It includes 20 open-source Java projects from GitHub and over 100,000 logs of real-world build logs from TravisCI. The projects span a wide range with regard to size, number of contributors, and maturity of open-source Java projects available on GitHub.</p> <p>Futher, the dataset includes the results of baseline approaches to ease the comparison of new techniques applied to the dataset.</p> <p>A readme file with a more detailed description of the structure of the dataset is included. For even more information see the corresponding MSR 2020 publication.</p> <p>Versions:</p> <ul> <li> 2020-09-23 (version 1.1) <ul> <li>Updated archived `deeplearning4j` repository with a fork that contains all of the original commits. Repository at the original GitHub location had been replaced. Defect identified by Daniel Elsner (Technische Universität München).</li> <li>Renamed root folder from MSR2 to rtp-torrent</li> </ul> </li> <li>2020-05-25 (version 1.0) <ul> <li>Initial release</li> </ul> </li> </ul> <p> </p>
Convolutional Neural Net (CNN) models for epigenomic landscapes in epidermal differentiation - Basset architecture, classification and regression
<p>Deep learning models trained on epigenomic landscapes in keratinocyte differentiation. The models are Basset convolutional neural networks (Kelley, et al 2016). The dataset used to train these models can be found at https://doi.org/10.5281/zenodo.4062509. The file `nn.ggr.models.basset.clf.tar.gz` contains 10 cross-validated models that were pretrained using ENCODE-Roadmap trained model weights as initialization weights and also 10 cross-validated models that were initialized with random weights. Similarly, the file `nn.ggr.models.basset.regr.tar.gz` contains 10 cross-validated models that were pretrained using the classification model weights as initialization weights and also 10 cross-validated models that were initialized with random weights.</p>
Grid Based Global Carbon Edge Regression Coefficents
<p>Grid cell based regression coefficients for predicting global biomass in the pantropics.</p> <p>To better account for the variability within a continent, we constructed 100-km grid cells throughout the pantropics. In grid cells where the majority of pixels were from forest biomes, we consider three candidate regression models to represent the relationship between biomass density and distance to forest edge. In particular, we consider:</p> <ol> <li>Asymptotic: <span class="math-tex">\(\mathrm{Biomass} = \theta_1-\theta_2\cdot\exp(-\theta_3\cdot\mathrm{Distance})\)</span>,</li> <li>Logarithmic: <span class="math-tex">\(\mathrm{Biomass}=\beta_0+\beta_1\ln\cdot(\mathrm{Distance})\)</span> , or</li> <li>Linear: <span class="math-tex">\(\mathrm{Biomass}=\eta_0+\eta_1\cdot Distance\)</span></li> </ol> <p>Then, for each grid cell, the candidate with the highest R<sup>2</sup> is used to best represent the relationship between density and distance to forest edge. Models (2) and (3) were deemed as suitable (and more simplistic) alternatives in cells where higher distances were generally not observed and as a result the forest core was not firmly established. We also note that in the vast majority of grid cells, model (1) was optimal. For each cell the magnitude and distance of the edge effect were again estimated. In cells using models (2) or (3) the forest core (<span class="math-tex">\(\theta_1\)</span>) was estimated as the average biomass density at the largest observed distance in the cell.</p>
Grid Based Global Carbon Edge Regression Coefficients and Aggregations
<p>Grid cell based regression coefficients for predicting global biomass in the pantropics.</p> <p>To better account for the variability within a continent, we constructed 100-km grid cells throughout the pantropics. In grid cells where the majority of pixels were from forest biomes, we consider three candidate regression models to represent the relationship between biomass density and distance to forest edge. In particular, we consider:</p> <ol> <li>Asymptotic: <span class="math-tex">Biomass=θ1−θ2⋅exp(−θ3⋅Distance)</span>,</li> <li>Logarithmic: <span class="math-tex">Biomass=β0+β1ln⋅(Distance)</span> , or</li> <li>Linear: <span class="math-tex">Biomass=η0+η1⋅Distance</span></li> </ol> <p>Then, for each grid cell, the candidate with the highest R2 is used to best represent the relationship between density and distance to forest edge. Models (2) and (3) were deemed as suitable (and more simplistic) alternatives in cells where higher distances were generally not observed and as a result the forest core was not firmly established. We also note that in the vast majority of grid cells, model (1) was optimal. For each cell the magnitude and distance of the edge effect were again estimated. In cells using models (2) or (3) the forest core (<span class="math-tex">θ1</span>) was estimated as the average biomass density at the largest observed distance in the cell.</p> <p>This dataset also contains a shapefile parameter analysis of both proportion and total area of all forest landcover types (1-5).</p>
abarbour/pbe-data: PBO BSM data, peak values, and regression biases -- updated with revised data and CRUST1.0 terms
<p>Two new tables are included here, each containing site and source classification terms from the CRUST1.0 global velocity model (see http://igppweb.ucsd.edu/~gabi/crust1.html)</p> <p>Dataset from Barbour and Crowell (2017), Dynamic Strains for Earthquake Source Characterization, <em>Seismol. Res. Lett.</em>, <strong>88</strong> (2A) 354-370; DOI: 10.1785/0220160155</p>
Data used in "Fast metabolite identification with Input Output Kernel Regression"
<p>This repository contains the data used in [1] to evaluate the performance for metabolite identification from tandem mass spectra. These data have been extracted and processed in [2]. We used a subset of 4138 MS/MS spectra extracted from the GNPS public spectral library (https://gnps.ucsd.edu/ProteoSAFe/libraries.jsp) for training and evaluation. For searching, we used molecular structures from PubChem as candidate sets.</p> <p>Please mention and cite GNPS when using these data.</p> <p>The implementation of the method proposed in [1] is available on: https://version.aalto.fi/gitlab/kepaco/Fast-metabolite-identification-with-IOKR</p> <p><strong>Files description:</strong></p> <ul> <li><em>spectra.txt</em>: informations about the MS/MS spectra (GNPS identifier, compound name and INCHI identifier)</li> <li><em>data_GNPS.mat</em>: contains the molecular fingerprints, molecular formula and InCHI corresponding to the MS/MS spectra</li> <li><em>cv_ind.txt</em>: indices of the cross-validation folds</li> <li><em>ind_eval.txt</em>: indices of the examples used for evaluation</li> <li><em>candidates</em>: fingerprints and INCHI for the different candidate sets</li> <li><em>input_kernels</em>: contains 24 input kernel matrices</li> </ul> <p><strong>References:</strong></p> <p>[1] Brouard, C., Shen, H., Dührkop, K., d'Alché-Buc, F., Böcker, S. and Rousu, J.: Fast metabolite identification with Input Output Kernel Regression. In the proceedings of ISMB 2016, Bioinformatics 32(12): i28-i36, 2016. DOI: https://doi.org/10.1093/bioinformatics/btw246</p> <p>[2] Dührkop, K., Shen, H., Meusel, M., Rousu, J. and Böcker, S.: Searching molecular structure databases with tandem mass spectra using CSI:FingerID. PNAS, 112(41), 12580-12585, 2015. doi:10.1073/pnas.1509788112</p> <p> </p> <p> </p>
Estimating the valence, arousal and balance of dyadic conversations using regression algorithms with autonomic nervous system responses
<p>This repository contains extracted data features and all questionnaires from our study "Estimating the valence, arousal and balance of dyadic conversations using regression algorithms with autonomic nervous system responses". </p><p> </p><p>Data_FinalFeatureSet.xlsx contains data for the 42 dyads who completed the study protocol. Rows represent individual participants, with the two participants in the same dyad always on consecutive rows. Columns consist of:</p><ul><li>Participant gender and age.</li><li>Group that dyads were assigned to. PosInit/NeutInit/NegInit represent positive, neutral or negative initial prompts. Devil1st/NoEmot1st represent which of the two secret prompts was presented first ("devil's advocate" or "no emotion").</li><li>A column stating which of the two participants was given the secret prompts (participant on left or right).</li><li>A column stating whether the participants had already known each other before the session (Y/N).</li><li>Extracted physiological features for 12 intervals: the first baseline (interval 1), 10 conversation intervals (intervals 2-11), and the second baseline (interval 12). Individual features are present for all individual participants while synchrony features exist for dyads (not individuals) and are thus present for only one row of a dyad.</li><li>Raw data from three personality questionnaires: the Brief Fear of Negative Evaluation Scale (BFNES), the Questionnaire of Cognitive and Affective Empathy (QCAE) and the Center for Epidemiologic Studies Depression Scale (CESD).</li><li>Self-reported results of the Self-Assessment Manikin (SAM) for the 10 conversation intervals, with the three columns in each interval corresponding to valence, arousal and balance.</li></ul><p>Note that one dyad's physiological data were corrupted and that dyad was not used for further analysis. Their demographics and questionnaire data are included, but no physiological features were calculated.</p><p> </p><p>Questionnaire files include the BFNES, QCAE and CESD as well as three versions of our modified SAM: one with no secret prompts, one with secret prompts for participants who saw the "devil's advocate" prompt first, and one with secret prompts for participants who saw the "no emotion" prompt first.</p>
Clockor2: Inferring global and local strict molecular clocks using root-to-tip regression
<p>Molecular sequence data from rapidly evolving organisms are often sampled at different points in time. Sampling times can then be used for molecular clock calibration. The root-to-tip (RTT) regression is an essential tool to assess the degree to which the data behave in a clock-like fashion. Here, we introduce Clockor2, a client-side web application for conducting RTT regression. Clockor2 uniquely allows users to quickly fit local and global molecular clocks, thus handling the increasing complexity of genomic datasets that sample beyond the assumption homogeneous host populations. Clockor2 is efficient, handling trees of up to the order of 10^4 tips, with significant speed increases compared to other RTT regression applications. Although clockor2 is written as a web application, all data processing happens on the client-side, meaning that data never leaves the user's computer. Clockor2 is freely available at https://clockor2.github.io/</p>
Regression-Test History Data for Flaky Test-Research, Dataset
<p>The dataset comprises developer test results of Maven projects with flaky tests across a range of consecutive commits from the projects' git commit histories. The Maven projects are a subset of those investigated in an <a href="https://doi.org/10.1145/3428270">OOPSLA 2020 paper</a>. The commit range for this dataset has been chosen as the flakiness-introducing commit (FIC) and iDFlakies-commit (see the OOPSLA paper for details). The commit hashes have been obtained from the <a href="https://github.com/TestingResearchIllinois/idoft/blob/main/tic-fic-data.csv">IDoFT dataset</a>.</p> <p>The dataset will be presented at the <a href="https://conf.researchr.org/home/icse-2024/ftw-2024">1st International Flaky Tests Workshop 2024 (FTW 2024)</a>. Please refer to <a href="https://doi.org/10.1145/3643656.3643901">our extended abstract</a> for more details about the motivation for and context of this dataset.</p> <p>The following table provides a summary of the data.</p> <table> <tbody> <tr> <td><strong>Slug (Module)</strong></td> <td><strong>FIC Hash</strong></td> <td><strong>Tests</strong></td> <td><strong>Commits</strong></td> <td><strong>Av. Commits/Test</strong></td> <td><strong>Flaky Tests</strong></td> <td><strong>Tests w/ Consistent Failures</strong></td> <td><strong>Total Distinct Histories</strong></td> </tr> <tr> <td>TooTallNate/Java-WebSocket</td> <td> 822d40</td> <td>146</td> <td> 75</td> <td> 75</td> <td>24</td> <td> 1</td> <td>2.6x10^9</td> </tr> <tr> <td>apereo/java-cas-client (cas-client-core)</td> <td> 5e3655</td> <td>157</td> <td> 65</td> <td>61.7</td> <td> 3</td> <td> 2</td> <td>1.0x10^7</td> </tr> <tr> <td>eclipse-ee4j/tyrus (tests/e2e/standard-config)</td> <td> ce3b8c</td> <td>185</td> <td> 16</td> <td> 16</td> <td>12</td> <td> 0</td> <td> 261</td> </tr> <tr> <td>feroult/yawp (yawp-testing/yawp-testing-appengine)</td> <td> abae17</td> <td> 1</td> <td>191</td> <td>191</td> <td> 1</td> <td> 1</td> <td> 8</td> </tr> <tr> <td>fluent/fluent-logger-java</td> <td> 5fd463</td> <td> 19</td> <td>131</td> <td>105.6</td> <td>11</td> <td> 2</td> <td>8.0x10^32</td> </tr> <tr> <td>fluent/fluent-logger-java</td> <td> 87e957</td> <td> 19</td> <td>160</td> <td>122.4</td> <td>11</td> <td> 3</td> <td>2.1x10^31</td> </tr> <tr> <td>javadelight/delight-nashorn-sandbox</td> <td> d0d651</td> <td> 81</td> <td>113</td> <td>100.6</td> <td> 2</td> <td> 5</td> <td>4.2x10^10</td> </tr> <tr> <td>javadelight/delight-nashorn-sandbox</td> <td> d19eee</td> <td> 81</td> <td> 93</td> <td>83.5</td> <td> 1</td> <td> 5</td> <td>2.6x10^9</td> </tr> <tr> <td>sonatype-nexus-community/nexus-repository-helm</td> <td> 5517c8</td> <td> 18</td> <td> 32</td> <td> 32</td> <td> 0</td> <td> 0</td> <td> 18</td> </tr> <tr> <td>spotify/helios (helios-services)</td> <td> 23260</td> <td>190</td> <td>448</td> <td>448</td> <td> 0</td> <td> 37</td> <td> 190</td> </tr> <tr> <td>spotify/helios (helios-testing)</td> <td> 78a864</td> <td> 43</td> <td>474</td> <td>474</td> <td> 0</td> <td> 7</td> <td> 43</td> </tr> </tbody> </table> <p> </p> <p>The columns are composed of the following variables:</p> <ul> <li><strong>Slug (Module):</strong> The project's GitHub slug (i.e., the project's URL is https://github.com/{Slug}) and, if specified, the module for which tests have been executed.</li> <li><strong>FIC Hash:</strong> The flakiness-introducing commit hash for a known flaky test as described in this OOPSLA 2020 paper. As different flaky tests have different FIC hashes, there may be multiple rows for the same slug/module with different FIC hashes. </li> <li><strong>Tests:</strong> The number of distinct test class and method combinations over the entire considered commit range.</li> <li><strong>Commits:</strong> The number of commits in the considered commit range</li> <li><strong>Av. Commits/Test:</strong> The average number of commits per test class and method combination in the considered commit range. The number of commits may vary for each test class, as some tests may be added or removed within the considered commit range.</li> <li><strong>Flaky Tests:</strong> The number of distinct test class and method combinations that have more than one test result (passed/skipped/error/failure + exception type, if any + assertion message, if any) across 30 repeated test suite executions on at least one commit in the considered commit range.</li> <li><strong>Tests w/ Consistent Failures:</strong> The number of distinct test class and method combinations that have the same error or failure result (error/failure + exception type, if any + assertion message, if any) across all 30 repeated test suite executions on at least one commit in the considered commit range.</li> <li><strong>Total Distinct Histories:</strong> The number of distinct test results (passed/skipped/error/failure + exception type, if any + assertion message, if any) for all test class and method combinations along all commits for that test in the considered commit range.</li> </ul>
Datasets for "Machine-Learning-Enhanced Symbolic Regression for Methane Storage Prediction in Covalent Organic Frameworks"
<p>This collection contains the datasets and associated files used in the research presented in the manuscript titled "Machine Learning-Enhanced Symbolic Regression for Methane Storage Prediction in Covalent Organic Frameworks". The datasets are critical for the development and validation of machine learning and symbolic regression models aiming to predict methane storage capacities in covalent organic frameworks (COFs).</p> <p><strong>Included Datasets:</strong></p> <ol> <li><code>COF_Data_for_ML.csv</code>: This dataset was utilized for the development of machine learning models.</li> <li><code>COF_Data_for_SISSO.csv</code>: This dataset was employed for the development of SISSO-based symbolic regression models.</li> <li><code>ML_vs_GCMC.xlsx</code>: This comparative dataset features GCMC-calculated results alongside machine learning predictions.</li> <li><code>Feature_Combination.xlsx</code>: This file contains data detailing all the feature combinations explored in the study.</li> <li><code>ML_SISSO_GCMC.xlsx</code>: This comparative dataset includes GCMC calculations, SISSO-based symbolic regression model predictions, and ML predictions.</li> <li><code>Crystallographic_Properties_of_535k_COFs.xlsx</code>: This consolidated dataset presents the crystallographic properties of 535,293 COFs.</li> </ol> <p><strong>Software Used:</strong></p> <ul> <li>Machine Learning Computations: Scikit-Learn (<a href="https://scikit-learn.org/stable/" target="_new">https://scikit-learn.org/stable/</a>)</li> <li>GCMC Simulations: RASPA2 (<a href="https://github.com/iRASPA/RASPA2" target="_new">https://github.com/iRASPA/RASPA2</a>)</li> <li>SISSO Calculations: SISSO toolkit (<a href="https://github.com/rouyang2017/SISSO" target="_new">https://github.com/rouyang2017/SISSO</a>)</li> <li>Crystallographic property calculations: Zeo++ (<a href="https://www.zeoplusplus.org/" target="_new">https://www.zeoplusplus.org/</a>)</li> </ul> <p>The datasets are provided to enable replication of the study's findings, encourage further research in the field, and facilitate the development of advanced predictive models by the scientific community. Researchers who use these datasets are requested to cite this Zenodo entry as well as the associated paper upon its publication.</p>
Data for publication: "Quantifying the relationship between observed variables that contain censored values using Bayesian error-in-variables regression"
<p>This archive contains the two datasets used in the publication: Vermeiren, Charles, Munoz: Quantifying the relationship between observed variables that contain censored values using Bayesian error-in-variables regression <br>Preprint: <a href="https://hal.science/hal-04764660" rel="nofollow">https://hal.science/hal-04764660</a><br><br>The first dataset is used to develop and test the model using cross-validation, the 2nd dataset is used as an independent, external dataset to test the model. For details, see the publication.</p> <p>The model code, combined with the data and outputs, are also available on GitHub: https://github.com/Peter-Vermeiren/EIVmodels </p>
GNN Models and results for the paper "Band-gap regression with architecture-optimized message-passing neural networks"
<p>Contains files with model parameters for random search and reference models, as well as the converted AFLOW dataset, in graphs form. Corresponds to results in <a href="https://arxiv.org/pdf/2309.06348.pdf">https://arxiv.org/pdf/2309.06348.pdf</a>.</p> <p>Model predictions along with AUID identifiers are located in result_combined.zip, band gap (egap) and formation energy (ef) predictions are from the PaiNN ensemble, band gap classification is done by MPEU model.</p> <p>New results include PaiNN NAS models.</p> <p>Compatible source code can be found at <a href="https://github.com/tisabe/jraph_MPEU/tree/v1.0.0">jraph_MPEU GitHub repository</a>.</p>
Code and Data to "Quantile regression for temporal streamflow modeling"
<p>This is the accompanying code to "Quantile regression for temporal streamflow modeling", which is part of the manuscript "The Role of Process Heterogeneity in Statistical Modeling", which was submitted to the Austrian Journal of Statistics. </p> <p> </p> <p>The data used in this publication is fully accessible through the <a href="https://doi.org/10.5194/essd-13-4529-2021" target="_blank" rel="noopener">LamaH-CE</a> dataset. The two scripts "functions_create_data.R" and "create_data.R" will create the final dataset used for modelling. </p> <p>"functions_modelling.R" provide the functions for tuning the XGBoost model and computing the SHAP values. An example script is also attached (calc_predictions_shap.R). "analyzing_results.R" and "error_metrics.R" will produce the final output used in the manuscript. Finally, two plots produced in the script are added as pdf. </p> <p>All data analysis was performed in R, and we want to acknowledge the following packages: <a href="https://dplyr.tidyverse.org/">dplyr</a>, <a href="https://tidyr.tidyverse.org/">tidyr</a>, <a href="https://www.jstatsoft.org/v40/i03/">lubridate</a>, <a href="https://purrr.tidyverse.org/">purrr</a>, <a href="https://doi.org/10.18637/jss.v033.i01">glmnet</a>, <a href="https://cran.r-project.org/web/packages/xgboost/index.html">xgboost</a>, <a href="https://CRAN.R-project.org/package=shapr">shapr</a>, <a href="https://CRAN.R-project.org/package=Metrics">Metrics</a>, <a href="https://CRAN.R-project.org/package=gridExtra" target="_blank" rel="noopener">gridExtra</a>, <a href="https://doi.org/10.18637/jss.v014.i06">zoo</a> and <a href="https://CRAN.R-project.org/package=wesanderson">wesanderson</a>. </p> <p> </p>
Global mapping of lunar refractory elements: multivariate regression vs. machine learning
<p>The quantitative estimation of elemental concentrations at the spatial resolution of hyperspectral near-infrared (NIR) images<br> of the lunar surface is an important tool for understanding the processes relevant for the origin and evolution of the Moon. The NIR reflectance of the lunar regolith is an integrated response to the presence of refractory elements and soil alteration processes. Our approach was to define a combination of spectral parameters that are robust with respect to the effects of soil maturity.<br> We calibrated the spectral parameters with respect to elemental abundances measured by the Lunar Prospector Gamma Ray Spectrometer (LP GRS) and the Kaguya GRS (KGRS). For this purpose, we compared a classical multivariate linear regression (MLR) approach and the machine learning based support vector regression (SVR) technique applied to M3 global observations. The M 3 -based global elemental maps are consistent in distribution and range with the LP GRS and KGRS elemental maps<br> and do not show artifacts in immature areas such as small fresh craters. The results derived using MLR and SVR are compared to<br> sample-based ground truth data of the Apollo and Luna sample-return sites, where the root-mean-square deviations obtained by the<br> two regression models are similar. The main advantage of the proposed new algorithm is its ability to minimize artifacts due to space-weathering effects. The elemental maps of Mg and Ca provide additional information and reveal structures not always visible in the Fe map. The global elemental abundance maps derived for the fully calibrated M 3 observations might thus serve as important tools to investigate the lunar geology and evolution.</p>
Going Above and Beyond: A Tenfold Gain in the Performance of Luminescence Thermometers Joining Multiparametric Sensing and Multiple Regression
<p>Dataset accompanying figures published in the publication DOI: https://doi.org/10.5281/zenodo.5930575</p>
Association of Body Index with Fecal Microbiome in Children Cohorts with Ethnic-Geographic Factor Interaction: Accurately Using a Bayesian Zero-inflated Negative Binomial Regression Model
<p>this dataset are “ssociation of Body Index with Fecal Microbiome in Children Cohorts with Ethnic-Geographic Factor Interaction: Accurately Using a Bayesian Zero-inflated Negative Binomial Regression Model” Supplementary Material.</p>
Fig. 2. Linear regression models showing the relationship between Aphis citricola and Harmonia axyridis abundance. A in Behavioral responses of Aphis citricola (Hemiptera: Aphididae) and its natural enemy Harmonia axyridis (Coleoptera: Coccinellidae) to non-host plant volatiles
Fig. 2. Linear regression models showing the relationship between Aphis citricola and Harmonia axyridis abundance. A: Catnip (Nepeta cataria) + French marigold (Tagetes patula), B: ageratum (Ageratum houstonianum) + French marigold, C: catnip + ageratum, and D: native vegetation.
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.