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13,064 results for “predictions”
Dataset 2 "Aerosol optical properties within the atmospheric boundary layer predicted from ground-based observations compared to Raman lidar retrievals during RITA-2021"
<p>This dataset provides additional profiles for the manuscript titled "Aerosol optical properties within the atmospheric boundary layer predicted from ground-based observations compared to Raman lidar retrievals during RITA-2021". It is available for those who are interested.</p>
Training data for predicting PS from deep learning
<p>In each panel, the left half picture is the lable (PS ADCIGs) and the right one is the input (PP ADCIGs).</p>
Investigating Reproducibility in Deep Learning-Based Software Fault Prediction
<p>Over the past few years, increasingly complex machine learning methods have been applied for various Software Engineering (SE) tasks, particularly for the important task of automated fault prediction and localization. It, however, becomes much more difficult for scholars to reproduce the results that are reported in the literature, especially when the applied deep learning models and the evaluation methodology are not properly documented and when code and data are not shared. Given some recent---and very worrying---findings regarding reproducibility and progress in other areas of applied machine learning, this study aims to analyze to what extent the field of software engineering, in particular in the area of software fault prediction, is plagued by similar problems. We have therefore conducted a systematic review of the current literature and examined the level of reproducibility of 56 research articles that were published between 2019 and 2022 in top-tier software engineering conferences. Our analysis revealed that scholars are apparently largely aware of the reproducibility problem, and about two-thirds of the papers provide code for their proposed deep-learning models. However, it turned out that in the vast majority of cases, crucial elements for reproducibility are missing, such as the code of the compared baselines, code for data pre-processing, or code for hyperparameter tuning. In these cases, it, therefore, remains challenging to reproduce the results in the current research literature exactly. Overall, our meta-analysis, therefore, calls for improved research practices to ensure the reproducibility of machine-learning-based research.</p>
Dataset: Predicting T Cell Receptor Functionality against Mutant Epitopes
<p>This upload contains the Datasets and Supplementary Material for "Predicting T Cell Receptor Functionality against Mutant Epitopes" (DOI: https://doi.org/10.1101/2023.05.10.540189) by Felix Drost, Emilio Dorigatti, Adrian Straub, Philipp Hilgendorf, Karolin I. Wagner, Kersten Heyer, Marta López Montes, Bernd Bischl, Dirk H. Busch, Kilian Schober, and Benjamin Schubert.</p>
Raw data for 'Prediction of Photodynamics of 200 nm Excited Cyclobutanone with Linear Response Electronic Structure and Ab Initio Multiple Spawning'
<p>Raw data from AIMS simulations and scripts for image generation for the paper "Prediction of Photodynamics of 200 nm Excited Cyclobutanone with Linear Response Electronic Structure and Ab Initio Multiple Spawning": J. Chem. Phys. 2024.</p>
Proteomic and Metabolomic Profiling of Plasma Predict Immune-related Adverse Events in Older Patients with Advanced Non-small Cell Lung Cancer
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TCRBuilder2+ predictions of the Observed T-cell receptor Space (OTS)
<p>TCRBuilder2+ predictions of over 1.5 million TCR structures from the Observed T-cell receptor Space database. </p>
E-Commerce Price Prediction Challenge
<h2>Problem Statement</h2> <p>Akshat is new to the market and is unaware of the prices of multiple products. he wants to be assured of the price before making any purchase! Help Akshat out in predicting the prices for Products and derive some conclusive evidences!</p> <h2>What's in there!?</h2> <p>In the dataset, there are products/images/dimensions/prices/ratings and much more for Feature Engineering.</p> <h2>What is the Inspiration from this!?</h2> <p>This type of business problem is typical for any new products being launched into the market and sites like Trivago/Policy Bazaar compare same product over multiple sites to reach a conclusive rate. Can you achieve the same!?</p>
Synthetic dataset and prediction files for the paper "Denoising of Geodetic Time Series Using Spatiotemporal Graph Neural Networks: Application to Slow Slip Event Extraction", by Costantino et al. (2024)
<p>Synthetic database used for training and evaluation of SSEdenoiser</p>
PHACTboost and PHACT predictions
<p>Most algorithms that are used to predict the effects of variants rely on evolutionary conservation. However, a majority of such techniques compute evolutionary conservation by solely using the alignment of multiple sequences while overlooking the evolutionary context of substitution events. We had introduced PHACT, a scoring-based pathogenicity predictor for missense mutations that can leverage phylogenetic trees, in our previous study. By building on this foundation, we now propose PHACTboost, a gradient boosting tree-based classifier that combines PHACT scores with information from multiple sequence alignments, phylogenetic trees, and ancestral reconstruction. The results of comprehensive experiments on carefully constructed sets of variants demonstrated that PHACTboost can outperform 40 prevalent pathogenicity predictors reported in the dbNSFP, including conventional tools, meta-predictors, and deep learning-based approaches as well as state-of-the-art tools, AlphaMissense, EVE, and CPT-1. The superiority of PHACTboost over these methods was particularly evident in case of hard variants for which different pathogenicity predictors offered conflicting results. We provide predictions of 215 million amino acid alterations over 20,191 proteins. PHACTboost can improve our understanding of genetic diseases and facilitate more accurate diagnoses.</p>
Dataset: Predicting relative agronomic efficiency of phosphorus-rich organic residues
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Pulsatile electrical stimulation creates predictable, correctable disruptions in neural firing
<p>Raw data from Pulsatile electrical stimulation creates predictable, correctable disruptions in neural firing Nat Comm (2024) . Afferent files include raw experimental recordings from vestibular afferents (mitchel_data_all). Simulation files from the variety of experiments described in the text (optimization_data_andruns -original irregular afferent runs and additional_simulation_experiment_runs - jitter experiment, diff thresholds, etc.). The associated Github also shows how to use each of these experimental outputs to create figures. </p>
ML models for dengue prediction using Orange
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A strategy to validate protein function predictions in vitro - accompanying data
<p>This repository contains the ProteinCartography analyses that accompany the pub "<a href="https://doi.org/10.57844/arcadia-cae9-96c4">A strategy to validate protein function predictions <em>in vitro</em></a><a href="https://doi.org/10.57844/arcadia-cae9-96c4">."</a> These proteins were selected from a list of the 200 most studied human proteins in the Protein Data Bank (PDB) which was published in <a href="https://onlinelibrary.wiley.com/doi/10.1002/pro.4038">Liu and Buck, 2021</a>. </p> <p>Each analysis was done using <a href="https://github.com/Arcadia-Science/ProteinCartography/releases/tag/v0.5.0">v0.5.0</a> of ProteinCartography using the standard configuration parameters. Each analysis is in its own zipped file corresponding to the protein name. The zipped files contain inputs, configuration files, all the structures, and all ProteinCartography results. Additionally, for a handful of the proteins, we ran a scaled-up version of ProteinCartography asking for 10,000 hit proteins instead of the standard. These are also included with the file prefix "Scaled-up_". </p> <p>Finally, we've decided to move forward with 2 protein families for validation of our tool, ProteinCartography. These protein families are <a href="https://doi.org/10.57844/arcadia-1e5d-e272">deoxycytidine kinase</a> and <a href="https://doi.org/10.57844/arcadia-74ad-345f">Ras GTPase</a>. For more about these two protein families, visit the mentioned pubs! </p>
Bayesian Uncertainty Quantification and Optimization of Jet Grout Column Diameter Prediction
<p><span>This dataset includes the jet grout data compiled from published case histories for Bayesian Uncertainty Quantification and Optimization of Jet Grout Column Diameter Prediction.</span></p>
Data associated with "The importance of terrain and climate for predicting soil organic carbon is highly variable across local to continental scales"
<p>The zipped folder contains the processed soil datasets including covariates, soil depths, and SOC concentrations for training the deep learning models described in the paper "The importance of terrain and climate for predicting soil organic carbon is highly variable across local to continental scales". </p> <p>"soil_profile/" contains a table including the geolocations of all the soil profiles in this study. "patch_data/" and "point_data/" contain the covariates to feed the models with patch input and point input respectively. "depth/" contains the upper and lower depths of the soil samples. "y/" contains the target variable - SOC concentration of the soil samples. The data files with suffix "_1" is a small subset of their counterparts without "_1" (10 % in sample size) used for model hyperparameters tuning.</p>
Chemperium database for: Geometric Deep Learning for Molecular Property Predictions with Chemical Accuracy Across Chemical Space
<p>The dataset and trained models for the submitted manuscript "Geometric Deep Learning for Molecular Property Prediction with Chemical Accuracy Across Chemical Space"</p> <p>The trained models can be used in combination with the predict module in github.com/mrodobbe/chemperium. More information in README.md.</p> <p><em>When using these datasets, refer directly to the manuscript: https://doi.org/10.1186/s13321-024-00895-0 </em></p>
PhysicsGen - Can Generative Models Learn from Images to Predict Complex Physical Relations?
<p>This dataset comprises 300,000 pairs of images designed for the advancement of generative model applications in physical simulations. Each pair consists of an input image and its corresponding output image that represents a physical simulation. The dataset aims to facilitate research into whether generative models can effectively learn and reproduce complex physical dynamics from visual data, potentially replacing traditional differential equation-based methods with significant computational speedups.</p> <p>Data, baseline models and evaluation code: <a href="https://www.physics-gen.org">https://www.physics-gen.org</a></p>
F-DATA: A Fugaku Workload Dataset for Job-centric Predictive Modelling in HPC Systems
<p>F-DATA is a novel workload dataset containing the data of around 24 million jobs executed on <a href="https://www.r-ccs.riken.jp/en/fugaku/">Supercomputer Fugaku</a>, over the three years of public system usage (March 2021-April 2024). Each job data contains an extensive set of features, such as exit code, duration, power consumption and performance metrics (e.g. #flops, memory bandwidth, operational intensity and memory/compute bound label), which allows for a multitude of job characteristics prediction. The full list of features can be found in the file <code>feature_list.csv</code>.</p> <p>The sensitive data appears both in anonymized and encoded versions. The encoding is based on a Natural Language Processing model and retains sensitive but useful job information for prediction purposes, without violating data privacy. The scripts used to generate the dataset are available in the<a href="https://github.com/francescoantici/F-DATA"> F-DATA GitHub repository</a>, along with a series of plots and instruction on how to load the data.</p> <p>F-DATA is composed of 38 files, with each <code>YY_MM.parquet</code> file containing the data of the jobs submitted in the month MM of the year YY. </p> <div> <div>The files of F-DATA are saved as <code>.parquet</code> files. It is possible to load such files as dataframes by leveraging the <code>pandas</code> APIs, after installing <code>pyarrow</code> (<code>pip install pyarrow</code>). A single file can be read with the following <code>Python</code> instrcutions:</div> <br> <blockquote> <div><code># Importing pandas library</code></div> <div><code>import pandas as pd</code></div> <div> </div> <div><code># Read the 21_01.parquet file in a dataframe format</code></div> <div><code>df = pd.read_parquet("21_01.parquet")</code></div> <div><code>df.head()</code></div> </blockquote> <div> </div> <div>Please cite this work as:<br><br> <div> <div>@article{antici2025fdata,</div> <div>title={F-DATA: A Fugaku Workload Dataset for Job-centric Predictive Modelling in HPC Systems},</div> <div>author={Antici, Francesco and Bartolini, Andrea and Domke, Jens and Kiziltan, Zeynep and Yamamoto, Keiji},</div> <div>journal = {Scientific Data},</div> <div>volume={12},</div> <div>pages={1321},</div> <div>year={2025},</div> <div>publisher={Nature Publishing Group},</div> <div>doi={https://doi.org/10.1038/s41597-025-05633-1}</div> <div>}</div> </div> </div> </div>
Fluorination increases the electron affinity of phenoxy in a predictable fashion
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ScienceDex guides
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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.