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356 results for “Machine learning models”
B3DB Dataset adapted for A Transparent Machine Learning Model To Understand Drugs Permeability Through the Blood Brain Barrier
<p>The B3DB dataset which we adapted for use for our paper "A Transparent Machine Learning Model To Understand Drugs Permeability Through the Blood Brain Barrier"</p>
Supplementary files: Machine Learning Insights into Türkiye's Climate Variability: Predictive Modelling and Spatial Analysis
<p>This dataset and python code were used in the study titled "Machine Learning Insights into Türkiye's Climate Variability: Predictive Modelling and Spatial Analysis".</p>
Automated Model Discovery for Tensional Homeostasis: Constitutive Machine Learning in Growth and Remodeling (Source code and data)
<p>This dataset contains</p> <ul> <li>the source code</li> <li>the data and examples</li> <li>the material subroutine with examples of uniaxial strain and stress</li> </ul> <p>of the inelastic Constitutive Artificial Neural Network (iCANN) enhanced by the concept of homeostatic surfaces to discover tensional homeostasis.</p> <p>The corresponding publication is:</p> <p>Holthusen, H., Brepols, T., Linka, K., & Kuhl, E..<em> </em></p> <p><em>Automated Model Discovery for Tensional Homeostasis: Constitutive Machine Learning in Growth and Remodeling.</em></p> <p> </p> <p><strong>Standalone_Materialroutine</strong></p> <ul> <li>00_Materialroutine: Contains the material subroutine implemented in FORTRAN</li> <li>01_uniaxial_strain: Example of the material subroutine in a uniaxial strain driven manner</li> <li>02_uniaxial_stress: Example of the material subroutine in a uniaxial stress driven manner</li> </ul> <p> </p> <p><strong>TensorFlow</strong></p> <ul> <li> <p>iCANN:</p> <ul> <li> <p>01_Biax/biax_l1: Keras/TensorFlow implementation of the iCANN. Example of the cross specimen with L1 (Lasso) regularization</p> </li> <li> <p>01_Biax/biax_l2: Keras/TensorFlow implementation of the iCANN. Example of the cross specimen with L2 (ridge) regularization</p> </li> <li> <p>02_Uniax/uniax_l1: Keras/TensorFlow implementation of the iCANN. Example of the stripe specimen with L1 (Lasso) regularization</p> </li> <li>02_Uniax/uniax_l1: Keras/TensorFlow implementation of the iCANN. Example of the stripe specimen with L2 (ridge) regularization</li> </ul> </li> <li> <p>iCANN_ABS_activation: Same four examples as above, however, with the absolute value activation function</p> </li> <li> <p>installed_packages: File containing a list of installed Python modules used to implement the iCANN</p> </li> </ul> <p>The TensorFlow implementations in all 01_Biax/ and 02_Uniax/ sub-directories are the same.</p> <p>The implementation in iCANN_ABS_activation is different with respect to the activation functions of the pseudo potential.</p> <p> </p> <p>The experimental data for the cross and stripe specimen are taken from the literature:</p> <p>Eichinger, J. F., Paukner, D., Szafron, J. M., Aydin, R. C., Humphrey, J. D., & Cyron, C. J. (2020).</p> <p>Computer-controlled biaxial bioreactor for investigating cell-mediated homeostasis in tissue equivalents. <em>Journal of biomechanical engineering</em>, <em>142</em>(7), 071011.</p> <p><a href="https://doi.org/10.1115/1.4046201">https://doi.org/10.1115/1.4046201</a></p>
Using machine learning to model nontraditional spatial dependence in occupancy data
<p>Spatial models for occupancy data are used to estimate and map the true presence of a species, which may depend on biotic and abiotic factors as well as spatial autocorrelation. Traditionally researchers have accounted for spatial autocorrelation in occupancy data by using a correlated normally distributed site-level random effect, which might be incapable of modeling nontraditional spatial dependence such as discontinuities and abrupt transitions. Machine learning approaches have the potential to model nontraditional spatial dependence, but these approaches do not account for observer errors such as false absences. By combining the flexibility of Bayesian hierarchal modeling and machine learning approaches, we present a general framework to model occupancy data that accounts for both traditional and nontraditional spatial dependence as well as false absences. We demonstrate our framework using six synthetic occupancy data sets and two real data sets. Our results demonstrate how to model both traditional and nontraditional spatial dependence in occupancy data which enables a broader class of spatial occupancy models that can be used to improve predictive accuracy and model adequacy.</p>
Dataset for the publication of "WRF model parameter calibration to improve the prediction of tropicalcyclones over the Bay of Bengal using Machine Learning-basedMultiobjective Optimization"
<p>The dataset consists of the modified WRF model software, that can be extracted and used in any Linux system with preinstalled required software.</p> <p>The namelists_file.zip consists of the namelist.input files that are used for the default and calibration simulations with different driving data namely, FNL files at 1deg with two nested domains, ERA files at 1deg with two nested domains, ERA files at 0.25deg with a single domain, and the ERA files at 0.25deg with two nested domains.</p>
Consensus machine-learning models for protein-ligand binding affinity estimation
<p><strong>Motivation:</strong> In structure-based virtual screening, machine learning based scoring function gained popularity in the last few years as they outperformed classical scoring function. The protein-ligand system can be encoded by a set of orthogonal descriptor spaces, which are then mined by machine learning algorithms to find a relationship with the binding affinity experimental value.</p> <p><strong> </strong></p> <p><strong>Results:</strong> In this work we propose our modelling approach to derive a new scoring function, derived from a combination of multiple descriptor spaces coupled with machine learning algorithms ensembled in consensus. The SF has been trained on the PDBbind v.2019 data and has been extensively internally and externally validated on a large set of complexes. When benchmarked on the PDBbind core set, it achieved better performance than state-of-the-art counterparts, scoring: R<sub>Pearson </sub>= 0.85-0.86 r<sup>2</sup> = 0.70-0.72 and RMSE = 1.15-1.21. As highlights: (i) an applicability domain definition has been implemented to delimit the SF’s application boundaries, and (ii) a mechanistic interpretation is proposed by investigating the contribution of each protein-ligand atom pairs in the prediction of the binding affinity, which could provide a support in the lead-optimization process.</p> <p><strong> </strong></p> <p><strong>Availability and implementation:</strong> Our scoring function is freely available through the webportal: <a href="https://predictor.exscalate.eu/">https://predictor.exscalate.eu/</a></p>
Dataset for "Comparative Analysis of Machine Learning Models to Forecast Flaring Capability of Solar Active Regions: A Parameter Based Approach"
<p>This CSV file contains the values of 14 selected magnetic features along with the active region class for all the regions used in our study. As discussed in the paper, these 14 magnetic features characterize the properties of active regions. All these magnetic features are obtained from the HMI SHARP data series which provides open-sourced vector magnetic field information of solar active regions. the column named 'AR_class' carries information about the class of active regions i.e., 1 for flaring regions and 0 for non-flaring regions.</p>
A machine learning based prediction model for life expectancy
<p>The social and financial systems of many nations throughout the world are significantly impacted by life expectancy (LE) models. Numerous studies have pointed out the crucial effects that life expectancy projections will have on societal issues and the administration of the global healthcare system. The computation of life expectancy has primarily entailed building an ordinary life table. However, the life table is limited by its long duration, the assumption of homogeneity of cohorts and censoring. As a result, a robust and more accurate approach is inevitable. In this study, a supervised machine learning model for estimating life expectancy rates is developed. The model takes into consideration health, socioeconomic, and behavioral characteristics by using the eXtreme Gradient Boosting (XGBoost) algorithm to data from 193 UN member states. The effectiveness of the model's prediction is compared to that of the Random Forest (RF) and Artificial Neural Network (ANN) regressors utilized in earlier research. XGBoost attains an MAE and an RMSE of 1.554 and 2.402, respectively outperforming the RF and ANN models that achieved MAE and RMSE values of 7.938 and 11.304, and 3.86 and 5.002, respectively. The overall results of this study support XGBoost as a reliable and efficient model for estimating life expectancy.</p>
Many-body machine learning models for water, acetonitrile, and methanol
<p><a href="https://keithgroup.github.io/mbGDML/">GDML</a>, <a href="https://libatoms.github.io/GAP/">GAP</a>, and <a href="https://schnetpack.readthedocs.io/en/stable/">SchNet</a> models trained on 1-, 2-, and 3-body energies and forces of water, acetonitrile, and methanol. Size-transferable <a href="https://github.com/mir-group/nequip">NequIPs</a> are trained on trimer data. Energies and forces were computed at the MP2/def2-TZVP level of theory in ORCA v4.2.0. Data sets, training scripts, and analyses of these potentials are available <a href="https://github.com/keithgroup/mbgdml-h2o-meoh-mecn">here</a>. Applications of these models on molecular dynamics simulations are found <a href="https://doi.org/10.5281/zenodo.7112198">here</a>.</p> <p><strong>Changelog</strong></p> <p>The format is based on <a href="https://keepachangelog.com/en/1.0.0/">Keep a Changelog</a>, and this project adheres to <a href="https://semver.org/spec/v2.0.0.html">Semantic Versioning.</a></p> <p>[0.0.2] - 2022-12-20</p> <p>Added</p> <ul> <li><a href="https://github.com/mir-group/nequip">NequIPs</a> trained for all solvents using 1000 trimers.</li> </ul> <p>[0.0.1] - 2022-09-25</p> <ul> <li>Initial release!</li> </ul> <p> </p>
Coupled Model Machine Learning Weights
<p>The coupled model weights file contains the necessary weights to the machine learning network to run our machine learning coupled model. The regridded ERA5 file contained a year's worth of initial conditions to be used for starting forecasts or climate simulations. </p>
[Replication package] Explainable Human-Machine Teaming using Model Checking and Interpretable Machine Learning
<p>Anonymized replication package of submission #1917: "Explainable Human-Machine Teaming using Model Checking and Interpretable Machine Learning".</p> <p>See README.md for further instructions.</p>
Data supporting "Machine Learning-based Modeling of Olfactory Receptors: Human OR51E2 as a Case Study"
<p>Simulation data and input files in support of the Manuscript:"Machine Learning-based Modeling of Olfactory Receptors: Human OR51E2 as a Case Study".<br> <br> The archive is organized in 6 different folders:</p> <p>1. <strong>7x7_rmsd</strong>, which contains a tcl script (to be run in VMD) to compute the 7x7 RMSD matrix (see Wang et al. J Struct. Bio, (2017)).<br> 2. <strong>a100_plumed</strong>, which contains PLUMED input files to compute A<sup>100 </sup>index on OR51E2 trajectories.<br> 3. <strong>initial_structures</strong>, which contains the 6 different conformation for hOR51E2 obtained by the different predictors in the pdb format.<br> 4. <strong>mdp_files</strong>, which contains the GROMACS mdp files to perform all the protocol described in the paper.<br> 5. <strong>topologies</strong>, which contains the 6 different topologies (in .top format) and the initial conformation (in .gro format) for the hOR51E2 embedded in the membrane and solvated.<br> 6. <strong>trajectories</strong>, which contains the 18 (6 systems, 3 replicas per system) different trajectories with the sodium in place close to D69<sup>2.50</sup> without solvent and ions (in .xtc format with a frame every 100 ps) and a reference conformation (in .gro format). Here we have also added the trajectory for the SwissModel-derived simulation without sodium ion in place, where we observed the ion binding. This last trajectory contains also solvent and ions, but with a lower printing frequency (1 ns).</p>
Development of a Machine Learning-Based Model to Determine the Optimum and Safe Restriping Timing of Thermoplastic Pavement Markings in Hot and Humid Climates
<p>Due to limited budget, most transportation agencies restripe their thermoplastic pavement markings based on a fixed schedule or based on visual inspection instead of monitoring the retroreflectivity and restriping when the retroreflectivity drops below a pre-determined threshold. These strategies are questionable in terms of efficiency and economy. Therefore, previous studies proposed degradation models to predict the retroreflectivity of thermoplastic markings based on key variables. Yet, most of these studies reported low R<sup>2</sup> (as low as 0.1), which placed little confidence in these models. Therefore, the objective of this study was to evaluate and predict the field performance of thermoplastics and to propose cost-effective restriping strategies for thermoplastics used in hot and humid climate service conditions. To achieve this objective, National Transportation Product Evaluation Program (NTPEP) data were mined and analyzed. Results indicated that the service life (SL) of thermoplastics ranged between 0.4 and 12.1 years (according to the initial retroreflectivity, traffic, and surface type) with an average value of 3.4 ± 0.2 years. Four regression models with relatively high accuracy were developed to predict the SL of thermoplastics based on key variables. In addition, the genetic algorithm was used to develop a model that predicts the future retroreflectivty of these pavement markings. The predicted values were compared against actual retroreflectivity measurements collected from a field experiment at Louisiana State University. The results of this study could be used to make effective decisions related to restriping scheduling. Using the proposed models in restriping scheduling can result in considerable cost savings (up to $8,212 per lane-mile), as compared to the conventional restriping strategy.</p>
Development of Distress Index Prediction Models for Rehabilitation Treatments in Louisiana Using Advanced Machine Learning Techniques
<p>Performance prediction models are used by state agencies to predict future trends in distress indices, hence, determining the required maintenance and/or rehabilitation treatment as well as the deterioration rate and remaining pavement service life. However, most of these models are based on a limited number of parameters and cannot predict the performance distress indices reliably. Such limitation resulted in having, most of the time, a maximum prediction period of five years. As a solution and coping with the ever-increasing size of pavement data, machine learning techniques have become a promising alternative. The objective of this study was to develop a machine-learning-based framework for states with a hot and humid climate that can predict the long-term field performance (for 11 years) of their asphalt (AC) overlays based on their key project conditions. Two machine learning algorithms were examined, namely Random Forest (RF) and CatBoost, and the one yielding a higher accuracy was considered. In this study, the well-known pavement condition index (PCI) was used as the pavement performance indicator. A total of 892 log miles of AC overlay data were obtained from the Louisiana Department of Transportation and Development (LaDOTD) Pavement Management System (PMS) database. Based on the collected data, six models were trained (for each algorithm) and validated to predict the future PCI of AC overlays for up to 11 years. Results indicated that the RF algorithm yielded higher accuracy than the CatBoost Algorithm and thus the RF-based models were considered in the proposed decision-making framework.</p>
Generative Machine Learning for Detector Response Modeling with a Conditional Normalizing Flow
<p>The samples are datasets used for testing in the scenarios: baseline (corr0.hdf5), correlation of 0.5 (corr50.hdf5), correlation of 1.0 (corr100.hdf5), and asymmetric detector responses (asymmetric.hdf5). Each file contains the conditional variables, generated and simulated detector responses, and generated and simulated reconstruction-level variables. </p>
Data sets and machine learning models for: Predicting critical properties and acentric factor of fluids using multi-task machine learning
<p>The experimental data sets, data splits, additional features, QM calculations, model predictions, and final machine learning models for the manuscript "Predicting Critical Properties and Acentric Factor of Fluids Using Multi-Task Machine Learning". <strong>Citation should refer directly to the manuscript:</strong></p> <ul> <li> <p>Biswas, S.; Chung, Y.; Ramirez, J.; Wu, H.; Green, W. H. Predicting Critical Properties and Acentric Factors of Fluids Using Multitask Machine Learning. <em>Journal of Chemical Information and Modeling.</em> <strong>2023</strong> <em>63</em> (15), 4574-4588. DOI: <a href="https://doi.org/10.1021/acs.jcim.3c00546">10.1021/acs.jcim.3c00546</a></p> </li> </ul> <p>To use the machine learning models, please refer to the sample files and instructions on <a href="https://github.com/yunsiechung/chemprop/tree/crit_prop">https://github.com/yunsiechung/chemprop/tree/crit_prop</a>. </p> <p>Detailed information can be found in README.md file.</p> <p> </p> <p><strong>Details on the properties considered</strong></p> <p>The data set includes the following 8 properties:</p> <ul> <li>Tc: critical temperature, in K</li> <li>Pc: critical pressure, in bar</li> <li>rhoc: critical density, in mol/L</li> <li>omega: acentric factor, unitless</li> <li>Tb: boiling point, in K</li> <li>Tm: melting point, in K</li> <li>dHvap: enthalpy of vaporization at boiling point, in kJ/mol</li> <li>dHfus: enthalpy of fusion at melting point, in kJ/mol</li> </ul> <p><strong>Details on the files</strong></p> <p>1. Data sets under CritProp_v1.1.0:</p> <ul> <li>all_data: includes the data sets used in this work. All data points are listed for each chemical compound as well as its corresponding data source. The details of the data sources can be found in the README.md file. The distribution of the data set is included in each folder. <ul> <li>estimated_data_for_pretraining: contains the estimated data from Yaws' handbook that are used to pre-train our machine learning (ML) model.</li> <li>experimental_data: contains the experimental data (references 1 - 15) used to fine-tune our final ML model.</li> </ul> </li> <li>additional_features: includes the additional features tested for the ML model. The Abraham features are generated for all data (references 1 - 15) while the acsf, qm, and rdkit features are only generated for the data from references 1 - 9. <ul> <li>abraham: Abraham solute parameters (E, S, A, B, L). Molecular features.</li> <li>acsf: ACSF (atom-centered symmetry functions). Atomic features that are coverted from the 3D coordinates of the compound</li> <li>qm_atom: QM (quantum chemical) atomic feature. </li> <li>qm_mol: QM molecular feature.</li> <li>rdkit: Selected RDKit 2D molecular features.</li> </ul> </li> <li>data_splits_and_model_predictions: contains the training and test sets used to evaluate the model. It also contains the predicted values from our final ML model for each test set. <ul> <li>random and scaffold splits: training and test sets that include the data from references 1 - 9.</li> <li>external test set: a test set that includes the data from only references 10 - 15.</li> </ul> </li> </ul> <p>2. Machine learning (ML) model files:</p> <ul> <li>CritProp_ML_model_files_with_abraham_feat.zip: contains the Chemprop ML model files that are trained using Abraham features as additional molecular features. This gives the best results.</li> <li>CritProp_ML_model_files_without_additional_feat.zip: contains the Chemprop ML model files that are trained without any additional features. This gives the second best results.</li> </ul> <p>To use these ML models, please refer to the sample files and instructions on <a href="https://github.com/yunsiechung/chemprop/tree/crit_prop">https://github.com/yunsiechung/chemprop/tree/crit_prop</a></p> <p>3. QM (quantum chemical) calculations:</p> <ul> <li>QM_calculations.zip: contains the results of the QM calculations that are performed to compute QM features.</li> </ul> <p> </p> <p> </p>
Hybrid Machine Learning Model for Ultra-Short-Term Wind Power Forecasting with Multi-Model Training Approach
<p>This is the core data code of the "<strong>Hybrid Machine Learning Model for Ultra-Short-Term Wind Power Forecasting with Multi-Model Training Approach".</strong></p>
Ensemble BLUP, Machine Learning, and Deep Learning Models Predict Maize Yield Better Than Each Model Alone.
<p>Data and scripts exploring ensembling strategies using the models developed in <a href="https://academic.oup.com/g3journal/advance-article/doi/10.1093/g3journal/jkad006/6982634">Kick et al., 2023</a> (see also <a href="https://zenodo.org/record/7401113">1</a>, <a href="https://zenodo.org/record/6916775">2</a>). Download all files to a single directory then run setup.sh or manually unzip using tar.</p> <p> </p> <table> <tbody> <tr> <td><strong>Filename</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>setup.sh</td> <td>Simple script that unzips zipped directories</td> </tr> <tr> <td>ext_data</td> <td>Reduced data from Kick et al. 2023</td> </tr> <tr> <td>ext_data_notebooks</td> <td>Contains python notebooks containing analysis and R markdown file containing visualization of results. Python and R data objects are written to allow results to be read in instead of re-generated.</td> </tr> <tr> <td>output</td> <td>Folder containing a placeholder file.</td> </tr> </tbody> </table> <p> </p> <p>This research used resources provided by the United States Department of Agriculture’s Agricultural Research Service (project number 5070-21000-041-000-D). The SCINet project of the USDA Agricultural Research Service (project number 0500-00093-001-00-D) was instrumental in the training of the models used in this work. In addition, we would like to acknowledge those presently and historically involved in generating data for the Genomes to Fields Initiative.</p> <p> </p> <p> </p> <p> </p>
Data sets and machine learning models for: Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates
<p>The datasets and final machine learning model files for the manuscript "Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates". Citation should refer directly to the manuscript:</p> <ul> <li>Chung, Y.; Green, W. H. Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates. <em>Chemical Science </em><strong>2024,</strong> doi: <a href="https://doi.org/10.1039/D3SC05353A">10.1039/D3SC05353A</a></li> </ul> <p>To use the machine learning models, please refer to the sample files and instructions on <a href="https://github.com/yunsiechung/chemprop/tree/RxnSolvKSE_ML">https://github.com/yunsiechung/chemprop/tree/RxnSolvKSE_ML</a>. </p> <p>Detailed information can be found in README.md file.</p> <p><br><strong>Details on the files</strong></p> <p>In the pretraining and finetuning set csv files, each column represents:</p> <ol> <li>rxn_smiles: atom-mapped reaction SMILES</li> <li>solvent_smiles: solvent SMILES</li> <li>ddGsolv: solvation free energy of activation of a reaction-solvent pair at 298K in kcal/mol (main prediction target)</li> <li>ddHsolv: solvation enthalpy of activation of a reaction-solvent pair at 298K in kcal/mol (main prediction target)</li> <li>dGsolv_reactant: solvation free energy of reactant(s) at 298K in kcal/mol (additional feature)</li> <li>dGsolv_product: solvation free energy of product(s) at 298K in kcal/mol (additional feature)</li> <li>dHsolv_reactant: solvation enthalpy of reactant(s) at 298K in kcal/mol (additional feature)</li> <li>dHsolv_product: solvation enthalpy of product(s) at 298K in kcal/mol (additional feature)</li> </ol> <p><strong>Data sets under 'RxnSolvKSE_dataset_v1.1.zip'</strong></p> <ul> <li>pretraining_set: contains the dataset used for pre-training <ul> <li>all_data: contains all calculated data <ul> <li>pretraining_rxn_solvent_ddGsolv_ddHsolv_with_features_all.csv: contains both main prediction targets and additional feature for reaction-solvent pairs</li> <li>pretraining_solvent_info.csv: list of all solvents</li> <li>pretraining_unique_rxn.csv: list of all reactions, both forward and reverse directions</li> </ul> </li> <li>chosen_500k_data: contains the chosen 500k data <ul> <li>pretraining_rxn_solvent_ddGsolv_ddHsolv_500k.csv: contains main prediction targets for reaction-solvent pairs</li> <li>pretraining_features_react_prod_dGsolv_dHsolv_500k.csv: contains additional features for reaction-solvent pairs</li> <li>train_test_split: contains the 5-fold random split training and test sets.</li> </ul> </li> </ul> </li> <li>finetuning_set: contains the dataset used for fine-tuning <ul> <li>all_data: contains all calculated data <ul> <li>finetuning_rxn_solvent_ddGsolv_ddHsolv_with_features_all.csv: constains both main prediction targets and additional features for reaction-solvent pairs. The rxn_key column indicates whether the reaction is bimolecular hydrogen abstraction (bihabs), unimolecular hydrogen migration (intrahabs), or radical addition to a multiple bond (raddition). The 'fwd' and 'rev' each indicate forward and reverse reactions.</li> <li>finetuning_solvent_info.csv: list of all solvents</li> <li>finetuning_unique_rxn.csv: list of all reactions, both forward and reverse directions</li> </ul> </li> <li>chosen_data: contains chosen data <ul> <li>finetuning_rxn_solvent_ddGsolv_ddHsolv_chosen.csv: contains main prediction targets for reaction-solvent pairs</li> <li>finetuning_features_react_prod_dGsolv_dHsolv_chosen.csv: contains additional features for reaction-solvent pairs</li> </ul> </li> </ul> </li> <li>experimental_set: contains the experimental rate constant data used to test the model. The original experimental data can be found at <a href="../record/7747557">https://zenodo.org/record/7747557</a>. <ul> <li> expt_rxn_atom_mapped_smiles.csv: contains the atom-mapped reaction SMILES used for the experimental data.</li> <li>expt_data_collected.xlsx: contains all experimental data and detailed information</li> <li>expt_rxn_solv_smiles_with_features_all.csv: contains the computed additional features for the experimental reaction-solvent pairs.</li> </ul> </li> </ul> <p><strong>Machine learning model files under 'RxnSolvKSE_ML_model_files.zip'</strong></p> <ul> <li>Contains the Chemprop machine learning model files for predicting ddGsolv and ddHsolv for a reaction-solvent pair. It takes atom-mapped reaction SMILES and solvent SMILES as inputs.</li> <li>To use these ML models, please refer to the sample files and instructions on <a href="https://github.com/yunsiechung/chemprop/tree/RxnSolvKSE_ML">https://github.com/yunsiechung/chemprop/tree/RxnSolvKSE_ML</a></li> </ul>
Spatial Modeling of Groundwater Potential in the North of Minas Gerais, Brazil: An Integrated Approach Using Machine Learning and Environmental Data
<p>This database is associated with the article published in the Revista Brasileira de Cartografia (RBC), entitled: Spatial Modeling of Groundwater Potential in the North of Minas Gerais, Brazil: An Integrated Approach Using Machine Learning and Environmental Data. This database contains the Groundwater flow rasters and the covariates used in spatial modeling. This database is associated with the article published in the Revista Brasileira de Cartografia (RBC), entitled: Spatial Modeling of Groundwater Potential in the North of Minas Gerais, Brazil: An Integrated Approach Using Machine Learning and Environmental Data. This database contains the Groundwater flow rasters and the covariates used in spatial modeling.</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.