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356 results for “Machine learning models”
Dataset Nucleation Patterns of Polymer Crystals Analyzed by Machine Learning Models
<p>This dataset contains the raw data (01_raw_data), processed data (02_processed_data), and plotting scripts (03_figures) related to the paper:</p> <p>"Nucleation Patterns of Polymer Crystals Analyzed by Machine Learning Models"<br>Atmika Bhardwaj, Jens-Uwe Sommer, Marco Werner</p> <p>Macromolecules <strong>2024</strong>; DOI: <a href="10.1021/acs.macromol.4c00920">10.1021/acs.macromol.4c00920</a></p> <p>Please refer to the README.md files in their respective folders.</p>
Using machine learning to integrate genetic and environmental data to model genotype-by-environment interactions
<p>Files generated from the study described in <a href="https://doi.org/10.1101/2024.02.08.579534">Fernandes et. al (2024)</a> .</p> <p>The file "cvs_h2s.csv" comprises the coefficient of variation and the Cullis heritability for each environment.</p> <p>The file "all_predictions.csv" contains the predictions from all the models evaluated, in different cross-validation (CV) scenarios.</p> <p>The file "coincidence_index.csv" has the Coincidence Index (CI) for each CV and models evaluated in our study.</p> <p>Our study used the multi-environment maize yield trials data from the Genomes to Fields 2022 initiative (<a href="https://doi.org/10.1186/s13104-023-06421-z">Lima et. al 2024</a>).</p>
Improving machine-learning models in materials science through large datasets
<p>1. Image of the <a href="https://alexandria.icams.rub.de/"><strong>Alexandria database </strong></a> state corresponding to the paper "<strong>Improving machine-learning models in materials science through large datasets</strong>".</p> <ul> <li>Static pbe calculations for 1D, 2D, 3D compounds can be found in 1D_pbe.tar.gz, 2D_pbe.tar.gz, 3D_pbe.tar.gz in batches of 100k materials. The latter also contains a separate convex hull pickle with all compounds on the pbe convex hull (convex_hull_pbe_2023.12.29.json.bz2) and a list of prototypes in the database (prototypes.json.bz2). The systematic 3D calculations performed for the article <strong>Improving machine-learning models in materials science through large datasets </strong>(in the paper referred to as round 2 and 3) can be found by the location keyword in the data dictionary of each ComputedStructureEntry containing "<strong>cgat_comp/quaternaries</strong>" (round 2) and "<strong>cgat_comp2/</strong>" (round 3). Round 1 (10.1002/adma.202210788) can be found under "cgat_comp/ternaries", ""cgat_comp/binaries".</li> <li>Static pbesol calculations for 3D compounds can be found in 3D_ps.tar (still zip compressed) in batches of 100k materials. The folder also contains a separate convex hull pickle with all compounds on the pbesol convex hull (convex_hull_ps_2023.12.29.json.bz2). </li> <li>Static scan calculations for 3D compounds can be found in 3D_scan.tar (still zip compressed) in batches of 100k materials. The folder also contains a separate convex hull pickle with all compounds on the scan convex hull (convex_hull_scan_2023.12.29.json.bz2). </li> <li>Geometry relaxation curves for 1D and 2D and 3D compounds calculated with PBE can be found in geo_opt_1D.tar.gz, geo_opt_2D.tar.gz. and geo_opt_3D.tar. Each file in each folder contains a batch of up to 10k relaxation trajectories.</li> <li>PBESOL relaxation trajectories for 3D compounds can be found in geo_opt_ps.tar</li> </ul> <p>2. Crystal graph attention networks to predict the volume (<a href="https://zenodo.org/api/records/12582650/draft/files/volume_round_3.tar.gz/content" target="_blank" rel="noopener noreferrer">volume_round_3.tar.gz</a>) and distance to the convex hull (<a href="https://zenodo.org/api/records/12582650/draft/files/e_above_hull_round_3.tar.gz/content" target="_blank" rel="noopener noreferrer">e_above_hull_round_3.tar.gz</a>) trained for the paper "Improving machine-learning models in materials science through large datasets".</p> <p>Can be used with the code at https://github.com/hyllios/CGAT/tree/main/CGAT.<br><strong>Note will predict the distance to the convex hull not normalized per atom when using the code on the github.<br></strong></p> <p>3. Alignn models as well as m3gnet and mace models corresponding to the publication can be found in <a href="https://zenodo.org/api/records/12582650/draft/files/alexandria_v2.tar.gz/content" target="_blank" rel="noopener noreferrer">alexandria_v2.tar.gz</a></p> <p>4. scripts.tar.gz Some scripts used for generating CGAT input data/ performing parallel predictions and for relaxations with m3gnet/mace force fields</p>
Dataset for Machine Learning Framework for Modeling Exciton-Polaritons in Molecular Materials
<p>The data consists of several NumPy arrays saved in the binary format (npy files) with a total size of 108 MB. The details of these files are listed below.</p> <table> <tbody> <tr> <td> <p><strong>Filename and path</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>training/azo_R.npy</p> </td> <td> <p>Coordinates for training</p> </td> </tr> <tr> <td> <p>training/azo_Z.npy</p> </td> <td> <p>Atomic indices for training</p> </td> </tr> <tr> <td> <p>training/azo_E.npy</p> </td> <td> <p>Molecular energies for training</p> </td> </tr> <tr> <td> <p>training/azo_D.npy</p> </td> <td> <p>Transition dipoles for training</p> </td> </tr> <tr> <td> <p>training/azo_ScaledNACR.npy</p> </td> <td> <p>Non-adiabatic coupling vectors scaled by energy difference for training</p> </td> </tr> <tr> <td> <p>scan/azo_R.npy</p> </td> <td> <p>Coordinates for PES scan</p> </td> </tr> <tr> <td> <p>scan/azo_Z.npy</p> </td> <td> <p>Atomic indices for PES scan</p> </td> </tr> <tr> <td> <p>scan/azo_E.npy</p> </td> <td> <p>Molecular energies for PES scan</p> </td> </tr> <tr> <td> <p>scan/azo_D.npy</p> </td> <td> <p>Transition dipoles for PES scan</p> </td> </tr> <tr> <td> <p>scan/azo_ScaledNACR.npy</p> </td> <td> <p>Non-adiabatic coupling vectors scaled by energy difference for PES scan</p> </td> </tr> <tr> <td> <p>spectrum/azo_R.npy</p> </td> <td> <p>Coordinates for spectrum calculations</p> </td> </tr> <tr> <td> <p>spectrum/azo_Z.npy</p> </td> <td> <p>Atomic indices for spectrum calculations</p> </td> </tr> <tr> <td> <p>spectrum/azo_E.npy</p> </td> <td> <p>Molecular energies for spectrum calculations</p> </td> </tr> <tr> <td> <p>spectrum/azo_D.npy</p> </td> <td> <p>Transition dipoles for spectrum calculation</p> </td> </tr> </tbody> </table>
Stiffness Moduli Modelling and Prediction in Four-Point Bending of Asphalt Mixtures: A Machine Learning-Based Framework within Weave-UNISONO 2021 project, NCN project No 2021/03/Y/ST8/00079, and GACR project GA22-04047K
<div><strong>Summary:</strong></div> <div>Two selected mixtures were thoroughly investigated in an experimental trial carried out by means of a four-point bending test (4PBT) apparatus. The mixtures were prepared using spilite aggregate, a conventional 50/70 penetration grade bitumen, and limestone filler. Their stiffness moduli (SM) were determined while samples were exposed to 11 loading frequencies (from 0.1 to 50 Hz) and 4 testing temperatures (from 0 to 30 °C). Observations were recorded and used to develop a machine learning (ML) model. The main scope was the prediction of the stiffness moduli based on the volumetric properties and testing conditions of the corresponding mixtures, which would provide the advantage of reducing the laboratory efforts required to determine them.</div> <div> </div> <div><strong>The dataset includes:</strong></div> <div>Characteristics of bituminous binder, CSV raw data</div> <div> <ul> <li>bituminous binder.csv</li> </ul> </div> <div>Grading curves of tested asphalt mixtures</div> <ul> <li>AML16 Grading curves.csv</li> <li>AMP22 Grading curves.csv</li> </ul> <div>Volumetric characterizations of AML16 and AMP22 mixtures</div> <ul> <li>AML16 Volumetric characterizations.csv</li> <li>AMP22 Volumetric characterizations.csv</li> </ul> <div>Outcomes of the 4PBT experimental trial carried out on AML16 and AMP22 mixtures</div> <ul> <li>AML16 Stiffness Modulus 4PB.csv</li> <li>AMP22 Stiffness Modulus 4PB.csv</li> </ul>
Machine learning predicts earthquakes in the continuum model of a rate-and-state fault with frictional heterogeneities
<p>Numerical data used to make Figures in the manuscript entitled "Machine learning predicts earthquakes in the continuum model of a rate-and-state fault with frictional heterogeneities". We provide the data to create Figures 1 to 4 from the main text and Figures S1 to S9 from the supplementary information. We also provide Python scripts to plot them.</p>
3d Transition Metal K-edge XANES Dataset for Machine Learning Models
<p><strong>Data</strong><br><br>This dataset contains machine learning data for K-edge X-ray Absorption Near-Edge Structure (XANES) prediction models for eight 3d transition metals (Ti -Cu).</p> <ul> <li><strong>features_and_spectra:</strong> Material features (X) and corresponding XAS spectra (y) for each dataset split: training (train), validation (val), and test.</li> <li><strong> material_id_and_site:</strong> Material identifiers and site indices (according to <a href="https://github.com/AI-multimodal/Lightshow">Lightshow</a>) for each dataset split. </li> </ul> <p><strong>Funding</strong><br><br>This research is based upon work supported by the U.S. Department of Energy, Office of Science, Office Basic Energy Sciences, under Award Number FWP PS-030. This research also used theory and computational resources of the Center for Functional Nanomaterials, which is a U.S. Department of Energy Office of Science User Facility, and the Scientific Data and Computing Center, at Brookhaven National Laboratory under Contract No. DE-SC0012704 and by Brookhaven National Laboratory (BNL), Laboratory Directed Research and Development (LDRD) grant no. 24-004.</p> <p> </p>
Reference Mean and Low Streamflow for all Brazilian Catchments Generated Using Machine Learning Models
<p>This dataset provides comprehensive hydrological information for river networks in Brazil, focusing on reference streamflows, specifically long-term mean flows (qm) and low flows exceeded 95% of the time (q95). Covering over 400,000 ungauged river points, the dataset was developed using advanced machine learning models trained on environmental descriptors and validated against data from 1,069 gauging stations spread across the country. The machine learning pipeline evaluated six regression models to achieve high predictive accuracy (R² > 0.8 for qm and > 0.7 for q95). The 62 environmental descriptors - encompassing climate, topography, land cover, lithology, and water storage characteristics - that were used as features for the models are also included.</p> <p>Key features:</p> <ul> <li><strong>Spatial Coverage:</strong> Brazilian territory and the Amazon River basin, based on the <a href="https://metadados.snirh.gov.br/geonetwork/srv/api/records/f7b1fc91-f5bc-4d0d-9f4f-f4e5061e5d8f" target="_blank" rel="noopener">BHO 5k</a> dataset of officially adopted river networks.</li> <li><strong>Outputs:</strong> Predicted qm and q95 values for each river stretch, with 90% and 75% confidence intervals to account for prediction uncertainty.</li> <li><strong>Environmental Descriptors:</strong> Aggregated from upstream catchment area.</li> </ul> <p> </p>
Data supplement: Spatial autocorrelation in machine learning for modelling soil organic carbon
<p>Spatial autocorrelation in machine learning for modelling soil organic carbon: Data supplement</p> <p><br>Alexander Kmoch, Clay Taylor Harrison, Jeonghwan Choi, Evelyn Uuemaa</p> <p>Spatial autocorrelation, the relationship between nearby samples of a spatial<br>random variable, is often overlooked in machine learning models, leading to<br>biased results. This study investigates various methods to account for spa-<br>tial autocorrelation when predicting soil organic carbon (SOC) using random<br>forest models. Five models incorporating spatial structure were compared<br>against baseline models that did not have any added spatial components.<br>Cross-validation showed slight improvements in accuracy for models consid-<br>ering spatial autocorrelation, while Shapley Additive Explanations confirmed<br>the importance of spatial variables. However, no decrease in spatial autocor-<br>relation of residuals was observed. Raster-based models exhibited enhanced<br>prediction detail, but high-resolution validation data availability limited thor-<br>ough validation. The findings emphasize the value of incorporating spatial<br>autocorrelation for improved SOC prediction in machine learning models.<br>Considerations such as the distribution of predictions and computational<br>complexity should help guide the selection of suitable approaches for specific<br>spatial modelling tasks.</p>
Supplementary material for "Towards a metagenomics machine learning interpretable model for understanding the transition from adenoma to colorectal cancer"
<p>Supplementary files for "Towards a metagenomics machine learning interpretable model for understanding the transition from adenoma to colorectal cancer".</p>
ΔvapHm-VOC: Standard Molar Vaporization Enthalpy Database for Machine Learning Prediction Models
<p>We present the full database of the article "Data-Driven, Explainable Machine Learning Model for Predicting Volatile Organic Compounds’ Standard Vaporization Enthalpy".</p> <p>This is the database used for data driven, explainable supervised ML model to predict Δ<sub>vap</sub><em>H</em><sub>m</sub>° of VOCs. The model was built on an established experimental database of 2410 unique molecules and 223 VOCs categorized by chemical groups. Using supervised ML regression algorithms, the Random Forest successfully predicted VOCs’ Δ<sub>vap</sub><em>H</em><sub>m</sub>° with a mean absolute error of 3.02 kJ mol<sup>-1</sup> and a 94% test score. The model was successfully validated through the prediction of Δ<sub>vap</sub><em>H</em><sub>m</sub>° for a known database of VOCs and through molecular group hold-out tests.</p> <div> <div> <div> <div> <p>The model's database was built with a variety of molecules from diverse chemical families with known experimental Δ<sub>vap</sub><em>H</em><sub>m</sub>° values. Entries were collected from <a href="https://doi.org/10.1063/1.3309507" target="_blank" rel="noopener">Acree and Chickos’ 2010 compilation</a>, curated by <a href="https://doi.org/10.1016/j.fluid.2013.09.021" target="_blank" rel="noopener">Gharagheizi (2013)</a>, with experimental vaporization enthalpy at the standard temperature of 298.15 K. This database was selected as it is an open-access repository, generally presenting experimental values with low uncertainties and corrected for the real-to-ideal behavior of the gas phase. We introduced a routine to convert and present each chemical entry into a SMILES string, along with chemical family categorization. For VOCs, we built a specific database of compounds documented in a VOC regulatory environmental guideline (<a href="https://www.s-t-a.org/Files%20Public%20Area/Documents/The%20Categorisation%20of%20Volatile%20Organic%20Compounds%20HMIP%20(1996).pdf">Marlowe <em>et al</em>., 1995</a>), and we used our web-scrapping routine to gather experimental Δ<sub>vap</sub><em>H</em><sub>m</sub>° values. The external dataset for validation studies was also collected from <a href="https://doi.org/10.1016/j.fluid.2013.09.021" target="_blank" rel="noopener">Gharagheizi (2013)</a>.</p> <p>Along with Δ<sub>vap</sub><em>H</em><sub>m</sub>° experimental values, each molecule is represented by its CAS number, SMILES string and InChlKey. We generated 106 chemical descriptors for every molecule in the database, using <a href="http://http//www.rdkit.org/">RDKit</a> software version 2022.09.4, running on top of Python 3.9. Descriptors were calculated from the “MolFromSmiles” function in “RDKIT.Chem” as descriptors with non-numerical values were removed. The descriptors encode significant chemical information and are used to present physicochemical characteristics of compounds, building a relationship between structure and Δ<sub>vap</sub><em>H</em><sub>m</sub>°.</p> </div> </div> </div> </div> <p>Through chemical feature importance analysis, the explainable model revealed that VOC polarizability, connectivity indexes and electrotopological state are key for the model’s prediction accuracy. We thus present a replicable and explainable model, which can be further expanded towards the prediction of other thermodynamic properties of VOCs.</p>
Results: Towards Realistic SATD Identification Through Machine Learning Models: Ongoing Research and Preliminary Results
<p>Automated identification of self-admitted technical debt (SATD) has been crucial for advancements in managing such debt. <br>However, state-of-the-arts studies often overlook chronological factors, leading to experiments that do not faithfully replicate the conditions developers face in their daily routines.<br>This study initiates a chronological analysis of SATD identification through machine learning models, emphasizing the significance of temporal factors in automated SATD detection. <br>The research is in its preliminary phase, divided into two stages: evaluating model performance trained on historical data and tested in prospective contexts, and examining model generalization across various projects. Preliminary results reveal that the chronological factor can positively or negatively influence model performance and that some models are not sufficiently general when trained and tested on different projects.</p>
Quantifying the basic reproduction number and the under-estimated fraction of mpox cases around the world at the onset of the outbreak: a mathematical modeling and machine learning- based study
<p><span>In 2022, there was a global resurgence of mpox, with different clinico-epidemiological features compared with previous</span><br><span>outbreaks. During this resurgence, sexual contact was hypothesized as the primary transmission route, with the community</span><br><span>of men having sex with men (MSM) being disproportionately affected. Because of the stigma associated with sexually</span><br><span>transmitted infections, especially those impacting MSM, the real burden of mpox could be masked.</span><br><span>We quantified the basic reproduction number (R</span><span>0</span><span>) and the under-estimated fraction of mpox cases in 16 countries, from the</span><br><span>onset of the outbreak until early September 2022, using Bayesian inference and a compartmentalized, risk-structured (high-</span><br><span>and low-risk populations), two-route (sexual and non-sexual transmission) mathematical model. Machine learning (ML) was</span><br><span>leveraged to identify under-estimation determinants.</span><br><span>Estimated R</span><span>0</span><span> </span><span>ranged between 1·37 (Canada) and 3·68 (Germany). The under-estimation rates for the high- and low-risk</span><br><span>populations varied between 25-93% and 65-85%, respectively. The estimated total number of mpox cases, relative to the</span><br><span>reported cases, is highest in Colombia (3·60) and lowest in Canada (1·08). In the ML analysis, two clusters of countries could</span><br><span>be identified, differing in terms of attitudes towards the 2SLGBTQIAP+ community and importance of religion.</span><br><span>Given the substantial mpox under-estimation, surveillance should be enhanced and campaigns against the stigmatization of</span><br><span>MSM should be organized. Countries have different social characteristics, potentially explaining the various degrees of under-</span><br><span>reporting in mpox cases, which should be considered by studies assessing the effectiveness of community-based</span><br><span>interventions.</span></p>
Modeling Autonomic Pupillary Responses from External Stimuli Using Machine Learning - Dataset
<p>This page contains the data collected for the paper: <em><strong>Modeling Autonomic Pupillary Responses from External Stimuli Using Machine Learning </strong></em>(<a href="https://doi.org/10.26717/BJSTR.2019.20.003446">DOI:10.26717/BJSTR.2019.20.003446</a>). The dataset consists of spectral and pupillometric data collected during three outdoor/indoor walks. The folders “raw”, “merged”, and “cleaned” contain data collected by the Konica Minolta CL-500A Illuminance Spectrophotometer and Tobii Pro Glasses 2 at three different stages in the data preparation process. The “raw” folder contains uncleaned and unsynchronized .csv/.json files. The “merged” folder contains uncleaned, but synchronized light and ocular data in .csv format. The “cleaned” folder contains a single .csv of cleaned and synchronized data with the derived variables: average pupil diameter and pupil diameter difference. </p> <p>The best choice of data files will depend on desired analysis. More guidance on how to handle this data can be found in the readMe files located in each subsequent folder. More information on the sensing devices used here can be found at the Minolta and Tobii information links below. </p> <p><strong>Minolta Information</strong>: <a href="https://sensing.konicaminolta.us/uploads/cl-500a_instruction217a_eng-250cl60686.pdf">https://sensing.konicaminolta.us/uploads/cl-500a_instruction217a_eng-250cl60686.pdf</a></p> <p><strong>Tobii Information</strong>: <a href="https://www.tobiipro.com/siteassets/tobii-pro/user-manuals/tobii-pro-glasses-2-user-manual.pdf/?v=1.1.3">https://www.tobiipro.com/siteassets/tobii-pro/user-manuals/tobii-pro-glasses-2-user-manual.pdf/?v=1.1.3</a></p> <p>The codes used to prepare, analyze, and visualize this data is available in the LightOcular GitHub Repository linked below. </p> <p><strong>LightOcular GitHub Repo</strong>: <a href="https://github.com/mi3nts/LightOcular">https://github.com/mi3nts/LightOcular</a></p>
Dataset of "Comprehensive Machine Learning Approaches for Modelling the State of Charge of Lithium-ion Batteries"
<p>This paper evaluates three ML approaches for SOC modeling in LIBs: the multilayer perceptron (MLP), long short-term memory (LSTM), and the nonlinear autoregressive with exogenous input (NARX) neural network architectures. These models were tested using an experimental dataset with multiple input variables, including electrochemical impedance spectroscopy (EIS) data, voltage, and capacity readings for commercial LIB cells. Results indicate that MLP and LSTM are more adaptable with a smaller training dataset (14 samples), while the NARX model required more than 34 out of 67 samples to achieve reasonable accuracy. Additionally, the NARX model is more sensitive to changes in the learning rate (α) and exhibits larger output error deviations. The MLP and LSTM models consistently performed well across various hidden layer sizes, showing no upper bound constraints, whereas the NARX model’s performance deteriorated with certain hidden layer configurations.</p>
InChI to IUPAC name machine learning model
<p>This is a machine learning model that predicts IUPAC names from InChI. It was trained on a dump of PubChem's database, and has a transformer encoder-decoder architecture.</p> <p><strong>Instructions</strong></p> <p>Requires:</p> <ul> <li>Python >= 3.6</li> <li>PyTorch == 1.6.0</li> </ul> <p>1. Install <a href="https://github.com/OpenNMT/OpenNMT-py/tree/2.0.0">OpenNMT-py</a> version 2.0.0:</p> <pre><code class="language-bash">pip install OpenNMT-py==2.0.0</code></pre> <p>2. Prepare InChI to be translated by splitting into individual characters separated by whitespace and saving in a text file. You can predict multiple IUPAC names by having one InChI per line (see example.inchi for reference).</p> <p>3. Perform the prediction with the supplied model file:</p> <pre><code class="language-bash">onmt_translate --beam_size 10 --length_penalty wu --alpha 1.0 --model inchi2iupac_step_259200.pt --src <infile> --max_length 300 --output <outfile></code></pre> <p> </p>
Code and data for "KGML-ag: A Modeling Framework of Knowledge-Guided Machine Learning to Simulate Agroecosystems: A Case Study of Estimating N2O Emission using Data from Mesocosm Experiments "
<p>This is code and data for manuscript: <br> "KGML-ag: A Modeling Framework of Knowledge-Guided Machine Learning to Simulate Agroecosystems: <br> A Case Study of Estimating N<sub>2</sub>O Emission using Data from Mesocosm Experiments"<br> Licheng Liu, Shaoming Xu, Zhenong Jin*, Jinyun Tang, Kaiyu Guan, Timothy J. Griffis, <br> Matt D. Erickson, Alexander L. Frie, Xiaowei Jia, Taegon Kim, Lee T. Miller, Bin Peng, Shaowei Wu, Yufeng Yang, Wang Zhou, Vipin Kumar</p> <p>All the files belong to Prof. Zhenong Jin, University of Minnesota, UA. jinzn@umn.edu<br> "code" foler includes code for data processing, model training, and results plotting.<br> "trained_model_saved" includes all trained model so you can use to reproduce the results showed in the study;<br> "data" includes all data presented in the study. Finetuning data is refering to Miller, L.T. , Griffis, T. J., Erickson, M. D., Turner, P. A., Deventer, M. J., Chen, Z., Yu, Z., Venterea, R.T., Baker, J. M., and Frie, A. L. (2021). Response of nitrous oxide emissions to future changes in precipitation and individual rain events. Journal of Environmental Quality, In review</p>
Predicting Survival of Tongue Cancer Patients by Machine Learning Models
<p>This repository contains the dataset used in the paper "Predicting Survival of Tongue Cancer Patients by Machine Learning Models." The dataset contains information on 1712 tongue cancer curative surgery recipients. Each row represents one patient. The meaning of each variable is summarized here:</p> <ul> <li>id: patient identifier</li> <li>gender: patient sex</li> <li>survival: patient survival status at follow-up; 0: survival, 1: death</li> <li>follow_time: length of follow-up period in days</li> <li>part: site of operation</li> <li>stage: tumor stage; 0: very small, no spreading, 1: small, no spreading, 2: some growth, spreading, 3: large, spreading to surrounding tissue or lymph nodes, 4A/4B/4C: larger, metastasis to at least one other organ</li> <li>op: operation status; 1: complete</li> <li>rt: radiation therapy status; 0: not received, 1: received</li> <li>ct: chemotherapy status; 0: not received, 1: received</li> <li>t_stage: tumor size; 1 (small) to 4 (large)</li> <li>n_stage: metastasis to lymph nodes; 0 (no metastasis) to 3 (metastasis to multiple lymph nodes)</li> <li>grade: tumor grade; 1 (no proliferation) to 3 (aggressive proliferation)</li> </ul>
Development of a machine learning model for river bedload - Data, Model, and Scripts
<p>This repository for “Development of a machine learning model for river bedload” Hosseiny et. Al (in review at Earth Surface Dynamics) contains the following assets. These assets may need to be modified for your purposes. You are responsible for inspecting these assets and making adjustments as necessary.</p> <p>Assets:</p> <p>1) The trained ANN model described in Hosseiny et al. (in review) as a .zip file named ‘Hosseiny_et_al_trained_ANN.zip’. This contains a folder (BEDLOAD_MODEL_FINAL) which contains the trained ANN Model (saved_model.pb) and associated information related to the input variables and variable weights.</p> <p>2) An accompanying Jupyter notebook named “bedload_ann_example.ipynb” that provides a step-by-step guide for implemented the trained ANN model (Asset 1).</p> <p>3) A .xls file named “Hosseiny et al_Supplemental_Data_Tables.xlsx” which provides the original observations that the model was trained and tested on, the summary statistics of the original input data as a compilation and for individual sites, the model errors associated with training and validation steps, the bedload calculations from the four uncalibrated existing bedload transport models described in the original study and for the ANN for the test data population, associated summary statistics with model output, and additional site-specific calculations of model error.</p>
Supplementary datasets for the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" - Part 2
<p>Supplementary files containing datasets needed to reproduce the results of the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" by S. Choudhury et al.</p> <p>The code to use with these data and reproduce the manuscript results is available at https://github.com/EPFL-LCSB/renaissance and https://gitlab.com/EPFL-LCSB/renaissance. The execution of parts of this code is dependent on the SkimPy toolbox (https://github.com/EPFL-LCSB/skimpy). Refer to the readme files on the RENAISSANCE code repositories for more details.</p> <p>The dataset contains the following files:</p> <p>1. param_fixing.zip - self-explanatory (Figure 4 & 5); contains an explanatory note for this part (experiment_details.txt), and the file containing Km values fetched from the BRENDA database (Km_database.csv).</p> <p>2. scripts.zip - scripts to generate figure 2-5 on toy data</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.