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13,064 results for “predictions”
Towards Dynamical Annual To Decadal Climate Prediction Using the IAP-CAS Model
<p>We execute a set of Annual to Decadal (A2D) hindcast experiments using the IAP-CAS model (FGOALS-f2), which integrates 129 months of each prediction initialized from 1981 to 2015 annually, and evaluate the results of this experiment. The IAP-CAS model output associated with this work is stored here.</p>
varCADD: large sets of standing genetic variation enable genome-wide pathogenicity prediction
<p>Data and trained models for the manuscript <em>varCADD: large sets of standing genetic variation enable genome-wide pathogenicity prediction.</em></p>
Assessing interaction recovery of predicted protein-ligand poses
<p>We provide the following data:</p> <ol> <li>PDB files containing protein structures from the PoseBusters dataset prepared with OpenEye's SPRUCE protein preparation software</li> <li>SDF files containing the corresponding PoseBusters ligand in its crystal pose</li> </ol> <p>This data is provided for the 256 PoseBuster targets used in our paper "Assessing interaction recovery of predicted protein-ligand poses" [1] with associated code at https://github.com/Exscientia/plif_validity.</p> <p> </p> <h3>References</h3> <p>[1] Errington D, Schneider C, Bouysset C, Dreyer FA, Assessing interaction recovery of predicted protein-ligand poses, arXiv; 2024. Available from: https://arxiv.org/abs/2409.20227 </p>
Predicted structures of the periplasmic adaptor protein, CmeA
<p>Predicted structures for CmeA, sequence alignments, and a fully assembled model for CmeABC (Chapter 6 of Kahlan Newman's Doctoral Thesis). </p>
Prediction of equatorial plasma bubble formation using ionosonde observations from India
<p>The given dataset has been used for the analysis in the aricle "Prediction of equatorial plasma bubble formation using ionosonde observations from India". It contains day-to-day observations/predictions of events from Gadanki, Trivandrum, and Sriharikota. It aslo contains ionosonde data (Time vs h'F) from Gadanki on 13-03-2015, 14-03-2015, 18-03-2015, 19-03-2015, 20-03-2015 and 21-03-2015 used in the study. All data are given in the .txt format.</p>
Evaluation of Spectral Light Simulation Tools For Prediction of ipRGC-influenced Light Responses in Field Conditions
<p><span>Spectral light simulation tools can be used to optimize lighting design in built environments for occupants’ ipRGC-influenced light (IIL) responses. The accuracy of these tools has been previously tested against measurements in controlled, neutral-colored spaces. This study aims to evaluate the accuracy of ALFA and Lark spectral light simulation tools in spectrally and geometrically complex environments. The methods consist of comparison of spectral irradiance, three IIL response metrics, and photopic illuminance in two office rooms in a university building. Measurements and simulations are performed in daylight, electric light, and combination of daylight and electric light. The dataset presented here documents this research.</span></p>
Source data for Figures and Tables in "Photoinduced hydrogen dissociation in thymine predicted by coupled cluster theory"
<p>Source data for figures and tables in "Unexpected hydrogen dissociation in thymine predicted by coupled cluster theory".<br><br>This work has received funding from the Norwegian Research Council through FRINATEK project 275506, the European Research Council (ERC)<br>under the European Union’s Horizon 2020 Research and Innovation Program<br>(Grant No.~101020016), the AMOS program within the U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences, Chemical Sciences, Geosciences, and Biosciences Division. <br>We acknowledge computing resources through UNINETT Sigma2--the National Infrastructure for High Performance Computing and Data Storage in Norway, project NN2962k.</p>
Predicting Equatorial Spread F at JICAMARCA Sector via Supervised Machine Learning
<p>Dataset used for ESF prediction model</p> <p> </p>
Reliability of plastid and mitochondrial localisation prediction declines rapidly with the evolutionary distance to the training set increasing
<p>Raw data and scripts for 'Reliability of plastid and mitochondrial localisation prediction declines rapidly with the evolutionary distance to the training set increasing' submitted to PLOS Computational Biology</p>
DMGAT: Predicting ncRNA-Drug resistance associations based on diffusion map and heterogeneous graph attention network
<p>Dataset for the paper: DMGAT: Predicting ncRNA-Drug resistance associations based on diffusion map and heterogeneous graph attention network</p>
Samples and 4-class predictions of agroforest-forest frontiers mapping in Peru
<p>This is a database for the study of agroforest-forest frontiers mapping conducted by Wanting Yang, etc.</p> <p>The data contained in the repository includes two parts.</p> <p>1. the training samples(polygons and rectagle_roi) annotated with Google Earth as reference. We used Google Earth Pro to make the date of Google Earth imagery match with PlanetScope imagery.</p> <p>2. the prediction from the 4-class model.</p> <p>Users can visualize the data with QGIS or other GIS software.</p>
Using AI Algorithms for Predictive Analysis in Personalized Medicine
<p><strong><span>This study explored the factors influencing patients' willingness to adopt AI-powered personalized medicine. This research found the problems. Integrating AI and personalized medicine has the potential to revolutionize healthcare. However, public trust in AI for healthcare applications remains a challenge. This research examines the factors determining people's views toward using artificial intelligence for predictive analytics in personalized medicine. A cross-sectional design was employed through a survey distributed via Google Forms in April 2024 using purposive sampling. The target respondents included residents of the Jabodetabek area (Jakarta, Bogor, Depok, Tangerang, Bekasi- cities in Indonesia) with prior experience seeking medical consultation or checkups. A total of 267 responses were collected after removing outliers. The study used a Partial Least Squares Structural Equation Modeling (PLS-SEM) approach to analyze the data. </span></strong><strong><span>The study considered six independent variables: AI knowledge, trust in AI, attitude towards data privacy, personalized medicine expectations, personalized medicine understanding, and perceived risk of discrimination in AI. The dependent variable was the intention to use AI in personalized medicine. It found five of six hypotheses have significant impact. </span></strong></p>
Data of Spatio-Temporal deep learning model for regional EPB irregularities short-term Prediction
<p>Using the dense ground-based GNSS receiver network and ionosonde data from East and Southeast Asia during 2010-2021, a novel Spatio-Temporal deep learning model for regional EPB irregularities short-term Prediction (STEP) was developed. The model integrates the convolutional neural network (CNN) and long short-term memory (LSTM) network, together with attention mechanisms, to capture both spatial and temporal features of regional ionospheric irregularities.<br>This dataset includes both the model and the results generated by STEP. The parameters provided are: UT (hours), Latitude (°), Longitude (°), Date, Y_pred (TECU/min), and Y_true (TECU/min). The dimensions of Y_pred and Y_true are 10812 x 610, where 10812 represents the product of the number of date and the number of UT (minus 18), and 610 corresponds to the product of the number of Latitude and Longitude. The model with a .pth extension can be loaded using PyTorch.</p>
Google Trend Enhanced Deep Learning Dataset for Renewable Energy Asset Price Prediction
<h3>Overview</h3> <p>This dataset accompanies the research paper titled <strong>“<a href="https://doi.org/10.1016/j.knosys.2024.112733">A Google Trend Enhanced Deep Learning Model for the Prediction of Renewable Energy Asset Price</a>”</strong> by Dr. Nachiketa Mishra, Dr. Lalatendu Mishra, Balaji Dinesh, P M Kavyassree . The study investigates the predictive efficiency of various forecasting models using oil prices and investor sentiment for renewable energy assets, specifically focusing on renewable energy ETFs such as ICLN, PBD, and QCLN.</p> <p>The dataset contains the processed inputs and raw data used in the analysis, including sentiment indices derived from Google Trends and traditional financial indices.</p> <h3>Citation :</h3> <p>Please cite this dataset as:</p> <ul> <li>Mishra, L., Dinesh, B., Kavyassree, P.M. and Mishra, N., 2024. A Google Trend enhanced deep learning model for the prediction of renewable energy asset price. <em>Knowledge-Based Systems</em>, p.112733.</li> </ul> <pre><code>@bibtex<br><br>@article{MISHRA2025112733,<br>title = {A Google Trend enhanced deep learning model for the prediction of renewable energy asset price},<br>journal = {Knowledge-Based Systems},<br>volume = {308},<br>pages = {112733},<br>year = {2025},<br>issn = {0950-7051},<br>doi = {https://doi.org/10.1016/j.knosys.2024.112733},<br>url = {https://www.sciencedirect.com/science/article/pii/S0950705124013674},<br>author = {Lalatendu Mishra and Balaji Dinesh and P.M. Kavyassree and Nachiketa Mishra},<br>}</code><code><br></code></pre> <h2>Code : </h2> <p>Refer Repository URL provided</p> <h2>Directory Structure and Description</h2> <pre><code>📦 data ├── 📂 etf-data │ ├── 📜 ICLN_INPUT.csv # Input data for ICLN │ ├── 📜 PBD_INPUT.csv # Input data for PBD │ ├── 📜 QCLN_INPUT.csv # Input data for QCLN │ └── 📂 raw-data # Original unprocessed data │ ├── 📂 market-data # ETF market prices and oil volatility (OVX) │ ├── 📂 navs # Net Asset Value (NAV) data │ └── 📂 volatility # Volatility data (GARCH and Moving Average models) ├── 📂 google-trends │ ├── 📜 keys.txt # Keywords for Google Trends search │ ├── 📂 trends │ ├── 📂 first-principal-components # Final Google Trend Index (PCA) │ ├── 📂 formatted-trends # Cleaned trends data │ └── 📂 raw-google-trends # Raw fetched Google Trends data</code></pre> <pre>Key Files</pre> <ul> <li><strong>ICLN_INPUT.csv</strong>, <strong>PBD_INPUT.csv</strong>, <strong>QCLN_INPUT.csv</strong>: Processed inputs for the prediction models of each ETF.</li> <li><strong>raw-data</strong>: Contains original data for market prices, NAVs, and volatility measures (GARCH, Moving Average).</li> <li><strong>google-trends</strong>: Data related to Google search trends, including raw, formatted, and the final index derived using Principal Component Analysis (PCA).</li> </ul> <h3>Usage Notes</h3> <ol> <li><strong>Google Trends Data</strong>: The Google Trend Index constructed from the keywords can be found in the <code>first-principal-components</code> folder. This index was a key input in the predictive models and used to construct modified indices in data>*_INPUT.csv’s.</li> <li><strong>Reproducibility</strong>: For reproducing the results from the study, you can directly use the inputs provided under <code>/data</code> to build predictive models.</li> <li><strong>Modifications</strong>: If you aim to modify or extend the dataset, be cautious of the index construction process, particularly around Principal Component Analysis (PCA) in the Google Trends data.</li> </ol> <h2>License</h2> <p>This dataset is released under the <strong>Creative Commons Attribution 4.0 International (CC BY 4.0)</strong> license. You are free to share and adapt the data, provided appropriate credit is given.</p> <h2>Contact Information</h2> <p>For any questions or further information, please contact:</p> <ul> <li><strong>Dr. Nachiketa Mishra</strong>: Department of Mathematics, Indian Institute of Information Technology Design and Manufacturing Kancheepuram, India</li> <li><strong>Dr. Lalatendu Mishra</strong>: Department of Management Sciences, Indian Institute of Technology Kanpur, India</li> <li><strong>Balaji Dinesh</strong>: Department of Computer Science, Indian Institute of Information Technology Design and Manufacturing Kancheepuram, India. email : <a href="mailto:balajidinesh918@gmail.com">balajidinesh918@gmail.com</a></li> </ul>
Data and code for manuscript: The performance and potential of deep learning for predicting species distributions
<p>This repository contains the (preprocessed) data and code for the publication titled "The performance and potential of deep learning for predicting species distributions".</p>
Battery Pack Temperature Change Prediction for Running BLDC 1500 W Motor using Artificial Neural Network
<p><span>The study explores the prediction of battery temperature using an artificial neural network (ANN) model, trained with experimental data from a brushless DC (BLDC) motor setup. The ANN model, with a 15-14-1 architecture, successfully predicted battery temperature change based on various input parameters, including RPM, load and voltage change of thirteen series of battery. The ANN predictions aligned closely with experimental results, demonstrating the model's effectiveness in capturing the nonlinear behavior of battery temperature changes. These findings highlight the potential of deep learning techniques to improve real-time thermal management in BMS, offering a promising approach for extending battery life and optimizing performance in electric vehicles and energy storage systems.</span></p>
Pathogen-induced alterations in fine-scale movement behaviour predict impaired reproductive success
<p><strong>This R-Project contains the following code files (in folder R):</strong></p> <p><em>01_movement_data_cleaning: Code to clean the raw ATLAS tracking data</em></p> <p><em>02_nestbox_data_cleaning: Code to clean nest box control sheets</em></p> <p><em>03_pathogen_data_cleaning</em>: <em>Code to clean the laboratory results for blood parasites PCRs</em></p> <p><em>04_ctmm_models</em>: Code to prepare data, applying continuous time movement models (incl. model selection) on movement data. Moreover, track reconstruction is performed</p> <p><em>05_Neural_Network: Code to perform Neural Networks for classification of behavioural states</em></p> <p><em>06_behavioural classification_analyses: Code to analyse Neural Network output</em></p> <p><em>07_HSF: Code for Habitat Selection Functions (HSF)</em></p> <p><em>08_ISSF: : Code for integrated Step Selection Functions (iSSF)</em></p> <p><em>09_breeding_success_HSF: Multivariate model to perform on HSF estimates</em></p> <p><em>10_breeding_success_ISSF: Multivariate model to perform on iSSF estimates</em></p> <p><strong>This repository contains the following data files (in folder data-raw)</strong> :</p> <p><em>Nestboxes_2021: Nest box monitoring sheets 2021</em></p> <p><em><em>Nestboxes_2022: Nest box monitoring sheets 2022</em></em></p> <p><em><em>Nestboxes_2023: Nest box monitoring sheets 2023</em></em></p> <p><em>Starlings_ring: Starling ringing data</em></p> <p><em>pathogen_data</em>: Blood parasite infection data </p> <p> </p>
Predictive factors of inpatient rehabilitation stay and post-discharge care burden after joint replacement for hip and knee osteoarthritis: a retrospective study on 1,678 patients.
<p>Dataset of the study entitled "<span>Predictive factors of inpatient rehabilitation stay and post-discharge care burden after joint replacement for hip and knee osteoarthritis: a retrospective study on 1,678 patients"</span></p>
ANALYSIS OF GIRLS' HEALTH PROBLEMS IN ADOLESCENCE AND PREDICTION OF PCOS
<p><span>The relevance of the study of reproductive health and risk assessment of polycystic ovary syndrome (PCOS) is related to the fact that this disease is a common cause of infertility, and is also a global somatic health problem, including disorders of the endocrine-metabolic status, cardiovascular diseases and has a high oncological risk. The aim of the study is to evaluate the results of clinical and anamnestic indicators that contribute to the development of PCOS in adolescent girls. The study included 182 adolescent girls from 13 to 17 years old, who were interviewed with questionnaires that reflected such health issues as the characteristics of menstrual function, metabolic disorders, genetic predisposition to diabetes mellitus and arterial hypertension. The degrees of development of the mammary glands were also assessed according to the Tanner scale and the degree of hirsutism according to the Ferriman-Gallwey scale. Thus, according to the results of the study, it is clear that half of the adolescent girls in Tashkent suffer from algomenorrhea syndrome. Our region is also prone to anemia due to the monthly loss of large amounts of blood. It should be noted that almost half of the girls have intermenstrual bleeding, which may be associated with an unsettled menstrual cycle, but this is observed in every second girl. As is known, theoretically, thelarche occurs a year earlier than menarche. Our studies have confirmed the results of previous studies. More than half of the girls assess their skin condition as unsatisfactory, noting acne, which is considered a physiological norm, but this is not satisfactory and leads to depression. Of particular note is the burden of diabetes mellitus and arterial hypertension in relatives. Most girls suffer from hirsutism. The onset of puberty is a critical moment in the development of PCOS and early intervention is, in the opinion of researchers in recent years, mandatory.</span></p> <p><strong><span>Key words:</span></strong><span> PCOS, hyperandrogenemia, acne, anovulation, insulin resistance, obesity, body mass index.</span></p>
PREDICTING OUTCOMES OF NEPHROTIC GLOMERULONEPHRITIS SYNDROME IN YOUNG CHILDREN
<p><span>В развитии хронической почечной недостаточности у детей по-прежнему значительна доля гломерулонефрита. Выявление хронической почки патологии на начальной стадии имеет важное значение для повышения эффективности профилактических и лечебных мероприятий, направленных на замедление течения прогрессирование заболевания. Обследованы больные с<span> </span>острым ГН (74) с нефротическим синдромом в возрасте от 2-х до 5 лет, из них 30 мальчиков и 44 девочек. Больные были разделены на 2 группы. Первая группа включала 35 больных, у которых не отмечалось рецидивов заболевания в течение двух и более лет, вторая группа больных (39) у которых полная ремиссия не была достигнута и развилась хроническая форма<span> </span>заболевания (ХГН). Разработка алгоритма прогноза осуществлялась методом последовательного анализа Вальда. В развитии хронической почечной недостаточности во все возрастные периоды по прежнему значителен<span> </span>удельный вес гломерулонефрита (ГН). </span></p>
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.