Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,943
datasets available to search
ShareScore release 0.9.0
Dataset results
1,943 results for “machine learning”
GMSK Spectrum Sensing for Machine Learning
<p>This file contains measurement results achievied in the following scenario:<br>Single PC with GNU Radio and connected USRP transmits GMSK signal at center frequncy 2100 MHz with different amplifier gain.<br>Single PC with GNU Radio and connected USRP receives signal with central frequency 2100 MHz and bandwidth 40 MHz (treated like 40 x 1 MHz channels). It uses local osciallator offset equal 10 MHz and due to non-linear characteristics of amplifiers and filters in USRP the extreme 12 (on both sides) are removed. However to create (this) dataset only one channel where signal was transmitted were included.<br><br>Data that can be found in the files is stored in CSV format to be easly analyzed in ML models. </p><p>Data collected at during this experiment contains:<br>the first column of average received power in the analyzed channel (in dBm)<br>the second column of autocorrelation function skewness in the analyzed channel (in linear scale)<br>the third column of autocorrelation function kurtosis in the analyzed channel (in linear scale)<br>the fourth column with information about signal transmission (label; 0 - noise, 1 - signal transmitted)</p>
Acoustic Data for Endotracheal Intubation Simulation with Machine Learning Feedback
<p>This dataset contains raw acoustic data collected during endotracheal intubation simulations, utilized for developing a machine learning-based performance feedback system. The data includes .wav audio recordings sampled at 192 kHz, organized by buzzer and microphone location and intubation states.</p><p>The data is associated with the following paper:</p><p>Steffensen, T. L., Bartnes, B., Fuglstad, M. L., Auflem, M., & Steinert, M. (2023). Playing the pipes: Acoustic sensing and machine learning for performance feedback during endotracheal intubation simulation. <i>Frontiers in Robotics and AI</i>, <i>10–2023</i>. https://doi.org/10.3389/frobt.2023.1218174</p>
Blair et al. 2020: Machine learning identification of ground beetles (repackaging of occurrences published by the NEON Biorepository Data Portal)
Blair, J.; Weiser, M. D.; Kaspari, M.; Miller, M.; Siler, C.; Marshall, K. E. 2020. Robust and simplified machine learning identification of pitfall trap-collected ground beetles at the continental scale. Ecology and Evolution 10 (23): 13143-13153. https://doi.org/10.1002/ece3.6905 Additional NEON samples (not yet archived at the Biorepository) were used in this research: full list of occurrences used.
Sensor data associated with Lucius et al. 2020 – Using machine learning to correct for nonphotochemical quenching in high-frequency in vivo fluorometer data.
This document describes a dataset used to produce Using machine learning to correct for nonphotochemical quenching in high-frequency, in vivo fluorometer data, as reported in: Lucius, M.A., Johnston, K.E., Eichler, L.W., Farrell, J.L., Moriarty, V.W. and Relyea, R.A. (2020), Using machine learning to correct for nonphotochemical quenching in high‐frequency, in vivo fluorometer data. Limnol Oceanogr Methods, 18: 477-494. https://doi.org/10.1002/lom3.10378 The dataset consists of high-frequency water quality and meterological sensor data collected from two autonomous vertical profiling platforms deployed on Lake George, NY during the ice-free months of 2017-2019. Water quality data include depth-referenced measurements of chlorophyll fluorescence, water temperature and dissolved oxygen. Meteorological data include surface-incident total radiation as well as two derived values: solar azimuth and 1-hr rolling average of total radiation. Finally, using interpolated data from regularly collected subsurface profiles of photosynthetically active radiation, estimates of subsurface total radiation were estimated and included in this dataset. This dataset does not include raw data. The data used were subjected to quality control procedures of the Jefferson Project, as well as additional outlier removal measures and the creation of derived data (as previously described and described in detail in Lucius et al. 2020).
Training dataset used in the magazine paper entitled "A Flexible Machine Learning-Aware Architecture for Future WLANs"
<p><a href="https://arxiv.org/pdf/1910.03510.pdf"><strong>A Flexible Machine Learning-Aware Architecture for Future WLANs</strong></a></p> <p><strong>Authors: </strong>Francesc Wilhelmi, Sergio Barrachina-Muñoz, Boris Bellalta, Cristina Cano, Anders Jonsson & Vishnu Ram.</p> <p><strong>Abstract: </strong>Lots of hopes have been placed in Machine Learning (ML) as a key enabler of future wireless networks. By taking advantage of the large volumes of data generated by networks, ML is expected to deal with the ever-increasing complexity of networking problems. Unfortunately, current networking systems are not yet prepared for supporting the ensuing requirements of ML-based applications, especially for enabling procedures related to data collection, processing, and output distribution. This article points out the architectural requirements that are needed to pervasively include ML as part of future wireless networks operation. To this aim, we propose to adopt the International Telecommunications Union (ITU) unified architecture for 5G and beyond. Specifically, we look into Wireless Local Area Networks (WLANs), which, due to their nature, can be found in multiple forms, ranging from cloud-based to edge-computing-like deployments. Based on ITU's architecture, we provide insights on the main requirements and the major challenges of introducing ML to the multiple modalities of WLANs.</p> <p><strong>Dataset description: </strong>This is the dataset generated for training a Neural Network (NN) in the Access Point (AP) (re)association problem in IEEE 802.11 Wireless Local Area Networks (WLANs). </p> <p>In particular, the NN is meant to output a prediction function of the throughput that a given station (STA) can obtain from a given Access Point (AP) after association. The features included in the dataset are:</p> <ol> <li>Identifier of the AP to which the STA has been associated.</li> <li>RSSI obtained from the AP to which the STA has been associated.</li> <li>Data rate in bits per second (bps) that the STA is allowed to use for the selected AP.</li> <li>Load in packets per second (pkt/s) that the STA generates.</li> <li>Percentage of data that the AP is able to serve before the user association is done.</li> <li>Amount of traffic load in pkt/s handled by the AP before the user association is done.</li> <li>Airtime in % that the AP enjoys before the user association is done.</li> <li>Throughput in pkt/s that the STA receives after the user association is done.</li> </ol> <p>The dataset has been generated through random simulations, based on the model provided in <a href="https://github.com/toniadame/WiFi_AP_Selection_Framework">https://github.com/toniadame/WiFi_AP_Selection_Framework</a>. More details regarding the dataset generation have been provided in <a href="https://github.com/fwilhelmi/machine_learning_aware_architecture_wlans">https://github.com/fwilhelmi/machine_learning_aware_architecture_wlans</a>.</p>
Druggability Assessment in TRAPP using Machine Learning Approaches
<p>This archive contains data sets and software codes used for building ML models reported in the paper "Druggability Assessment in TRAPP using Machine Learning Approaches"; <em>J. Chem. Inf. Model.</em> 2020, 60, 3, 1685–1699; <a href="https://doi.org/10.1021/acs.jcim.9b01185">https://doi.org/10.1021/acs.jcim.9b01185</a></p>
An information theory-based machine learning approach to detecting functionally conserved and coordinated protein dynamics
<p>The application of machine learning classification to the molecular dynamics of the functional states of protein allows for application of an information theoretic framework familiar to traditional bioinformatics. The functional states of proteins involving binding interactions with partners comprised of protein, DNA or small molecules can first be defined in a binary fashion (i.e. bound vs unbound), subsequently simulated in molecular dynamics software, and then employed as a comparative training set for a binary machine learning classifier capable of discerning the complex dynamical consequences of binding interaction. This learner can subsequently be deployed on new simulations of the functionally bound state to validate its ability to recognize the molecular motions that are supporting binding function. Regions of proteins with functionally conserved dynamics will induce significant local correlations in learning performance across independent validation runs. Through case studies of Rbp subunit 4/7 interaction in RNA Pol II and DNA-protein interactions of TATA binding protein, we demonstrate this method of detecting functionally conserved protein dynamics. We also demonstrate how Shannon information, relative entropy and mutual information can be applied to these binary classification states of dynamic simulations in order to compare dynamics and identify concerted motions involved in dynamic interactions across sites.</p>
A global flood risk modeling framework built with climate models and machine learning - Submission - Data Supplement
<p>This contribution contains data, fitted statistical models, and an analysis script for the submitted manuscript "A global flood risk modeling framework built with climate models and machine learning" by David A. Carozza and Mathieu Boudreault.</p>
Raw data for "Examining the effects of transcranial direct current stimulation on human episodic memory with machine learning"
<p>This is the raw dataset for "Examining the effects of transcranial direct current stimulation on human episodic memory with machine learning". Each .xlsx file represents an experimental results of a single participant.</p> <p>Directory description:</p> <p>Eng_sham - the results from experiments without stimulation on English sample from Medvedeva, 2019.</p> <p>Eng_vlPFC - the results from experiments with vLPFC stimulation on English sample from Medvedeva, 2019.</p> <p>enc_off_new - the results from experiments with dLPFC offline encoding stimulation on Russian sample.</p> <p>enc_on_new - the results from experiments with dLPFC online encoding stimulation on Russian sample.</p> <p>sham_no stimulation - the results from experiments without stimulation offline encoding stimulation on Russian sample.</p> <p>vlPFC_stimulation - the results from experiments with vLPFC stimulation on Russian sample.</p> <p>Age.xlsx - the ages of the participants</p> <p> </p>
Appendix: Data Analysis and Machine Learning Experiments
<p>The plots and statistics generated for the data analysis are given in this data set.<br> </p> <p>Furthermore, this data set contains the models, feature sets, scaler, prediction results and visualizations for the machine learning experiments conducted.</p> <ol> <li>Reproduction Experiment</li> <li>Multiple Commit Thresholds Experiment</li> <li>Imbalanced Training Experiment</li> </ol>
Released Experimental Dataset for Sampled Automated Machine Learning
<p>Released Experimental Dataset of "Doing More with Less: Characterizing Dataset Downsampling for AutoML"</p> <p> </p> <p>Experiments were run for 5 and 60 minutes on 16 datasets:<br> 4 small: < 10.000<br> 5 medium: < 100.000<br> 7 large: > 100.000<br> </p>
Covid-19 automated diagnosis and risk assessment through Metabolomics and Machine Learning
<p>COVID-19 plasma samples spectrometry datasets for machine learning input. Used in the work of article Covid-19 automated diagnosis and risk assessment through Metabolomics and Machine Learning, currently under submittion.</p> <p>Abstract:</p> <p>COVID-19 is still placing a heavy health and financial burden worldwide. Impairments in patient screening and risk management play a fundamental role on how governments and authorities are directing resources, planning reopening, as well as sanitary countermeasures, especially in regions where poverty is a major component in the equation. An efficient diagnostic method must be highly accurate, while having a cost-effective profile. We combined a machine learning-based algorithm with mass spectrometry to create an expeditious platform that discriminate COVID-19 in plasma samples within minutes, while also providing tools for risk assessment, to assist healthcare professionals in patient management and decision-making. A cross-sectional study with 815 patients (442 COVID-19, 350 controls and 23 COVID-19 suspicious) was enrolled from three Brazilian epicenters from April to July 2020. We were able to elect and identify 19 molecules that are related to the disease’s pathophysiology and several discriminating features to patient’s health-related outcomes. The method applied for COVID-19 diagnosis showed specificity >96% and sensitivity >83%, and specificity >80% and sensitivity >85% during risk assessment, both from blinded data. Our method introduced a new approach for COVID-19 screening, providing the indirect detection of infection through metabolites and contextualizing the findings the disease’s pathophysiology. The pairwise analysis of biomarkers brought robustness to the model developed using Machine Learning algorithms, transforming this screening approach in a tool with great potential for real-world application. </p>
Data set of the article: Using Machine Learning for Web Page Classification in Search Engine Optimization
<p>Data of investigation published in the article: "Using Machine Learning for Web Page Classification in Search Engine Optimization"</p> <p>Abstract of the article:</p> <p>This paper presents a novel approach of using machine learning algorithms based on experts’ knowledge to classify web pages into three predefined classes according to the degree of content adjustment to the search engine optimization (SEO) recommendations. In this study, classifiers were built and trained to classify an unknown sample (web page) into one of the three predefined classes and to identify important factors that affect the degree of page adjustment. The data in the training set are manually labeled by domain experts. The experimental results show that machine learning can be used for predicting the degree of adjustment of web pages to the SEO recommendations—classifier accuracy ranges from 54.59% to 69.67%, which is higher than the baseline accuracy of classification of samples in the majority class (48.83%). Practical significance of the proposed approach is in providing the core for building software agents and expert systems to automatically detect web pages, or parts of web pages, that need improvement to comply with the SEO guidelines and, therefore, potentially gain higher rankings by search engines. Also, the results of this study contribute to the field of detecting optimal values of ranking factors that search engines use to rank web pages. Experiments in this paper suggest that important factors to be taken into consideration when preparing a web page are page title, meta description, H1 tag (heading), and body text—which is aligned with the findings of previous research. Another result of this research is a new data set of manually labeled web pages that can be used in further research. </p>
Sign-specific stimulation "hot" and "cold" spots in Parkinson's disease validated with machine learning
<p><strong>Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has become a standard therapy for Parkinson’s disease (PD). Despite extensive experience, however, the precise target of optimal stimulation and the relationship between site of stimulation and alleviation of individual signs remains unclear. We examined whether machine learning could predict the benefits in specific parkinsonian signs when informed by precise locations of stimulation.</strong></p> <p> </p> <p><strong>We studied 275 PD patients who underwent STN-DBS between 2003 and 2018. We selected pre-DBS and best available post-DBS scores from motor items of the Unified Parkinson's Disease Rating Scale (UPDRS-III) to discern sign-specific changes attributable to DBS. Volumes of tissue activated (VTAs) were computed and weighted by i) tremor, ii) rigidity, iii) bradykinesia, and iv) axial signs changes. Then, sign-specific sites of optimal (“hot spots”) and suboptimal efficacy (“cold spots”) were defined. These areas were subsequently validated using machine learning prediction of sign-specific outcomes with in-sample and out-of-sample data (n=51 STN-DBS patients from another institution).</strong></p> <p><strong> </strong></p> <p><strong>Tremor and rigidity hot spots were largely located outside and dorsolateral to STN whereas hot spots for bradykinesia and axial signs had larger overlap with STN. Using VTA overlap with sign-specific hot and cold spots, support vector machine (SVM) classified patients into quartiles of efficacy with ≥92% accuracy. The accuracy remained high (68-98%) when only considering VTA overlap with hot spots but was markedly lower (41-72%) when only using cold spots. The model also performed poorly (44-48%) when using only stimulation voltage, irrespective of stimulation location. Out-of-sample validation accuracy was ≥96% when using VTA overlap with the sign-specific hot and cold spots.</strong></p> <p><br> <strong>In two independent datasets, distinct brain areas could predict sign-specific clinical changes in PD patients with STN-DBS. With future prospective validation, these findings could individualize stimulation delivery to optimize quality of life improvement. </strong></p> <p><strong>Hot and cold spots for each sign are publicly available as binary labels in NIfTI format. </strong></p>
2DUV Machine Learning Protocol Code
<p>Simulation data and code of ML protocol for 2DUV spectra of proteins.</p> <p>Any researchers who interested in protein spectroscopy can use our ML protocol online service: <a href="http://www.dcaiku.com:13000">http://www.dcaiku.com:13000</a></p> <p>For the machine learning protocol source code written in Python and Bash language which including:</p> <ul> <li>2duv_simulation folder <ul> <li>2DUV folder <ul> <li>0_parse_traj.py: Parse the MD trajectory in pdb format.</li> <li>1_bench_genH.sh: PBS script for generating the excition Hamiltonian,and E/M dipoles.</li> <li>1_cal_Hamil.py: Python script for submitting a large number of PBS script at once.</li> <li>2_extract_struc.py: Extract mode information of secondary structure segments from pdb file.</li> <li>3_extract_Hamil.py: Extract Hamiltonian, E/M dipoles information of secondary structure segments.</li> <li>4_cal_spectra.py: Python script for calculate the 2DUV spectra.</li> <li>4_run_calspectra.py: Python script for submitting a large number of PBS script for calculating 2DUV spectra at once.</li> <li>4_sub_calspectra.pbs: PBS script for runing python script of calculating 2DUV spectra.</li> <li>inputs folder: Spectron main input files.</li> </ul> </li> <li>GramacsFile: Gromacs main input files.</li> </ul> </li> <li>web-api folder: Source code of our ML protocol online service.</li> </ul>
Machine learning and multi-layer molecular network-assisted screening uncovers unknown compounds in the fentanyl family
<p>These LC-HRMS data was collected in study of Fentanyl-Hunte. All source codes along with a user manual are available for scientific research purposes at https://github.com/FangLabNTU/Fentanyl-Hunter.</p>
Comprehensive Datasets for RNA Design, Machine Learning and Beyond
<p>This repository contains a comprehensive collection of RNA multi-loops extracted from major RNA databases, along with benchmark results for various RNA design algorithms. The resource is intended to facilitate research and development in RNA design, particularly for multi-loop structures.</p>
Data accompanying publication: "General Chemically Intuitive Atom-Level DFT Descriptors for Machine Learning Approaches to Reaction Condition Prediction"
<p>Embeddings and raw files to complement the paper "General Chemically Intuitive Atom-Level DFT Descriptors for Machine Learning Approaches to Reaction Condition Prediction". The embeddings should be all the data needed for full reproducibility of the results published. The GitHub repo GeneralDFT (https://github.com/moleculebits/GeneralDFT) contains the python scripts required to make use of the data, along with some basic plotting functionalities.</p>
XIS: A daily spatiotemporal machine-learning model for environmental exposures in the contiguous United States
<p>These Parquet files contain the outputs used for many analyses and plots in the linked papers. For temperature and humidity, the full sets of observations for cross-validation aren't included because we used restricted-use MADIS data.</p>
ecDNA machine learning modeling
<h3><strong>1. Today (2024-06-27), we discovered an issue with the labeling of sample groups in one of the supplementary figures (Supplementary Figure 14c) in our published article. We have corrected the figure and present it here, and we extend our apologies to all readers for any confusion this may have caused (although no report received).</strong></h3> <h3><strong>2. The source data of supplementary figure 13 in the accompanying article table has been found to have issues, which were identified as a result of improper Excel operation. Here, we have uploaded the correct data table</strong></h3> <p>--------------------------------------------------</p> <p> </p> <p>1. ecDNA_cargo_gene_modeling_data.csv.gz</p> <p>The dataset contains features from 386 TCGA tumors for modeling ecDNA cargo gene prediction. It was converted from R data format with the following code. NOTE: columns 'sample' and 'gene_id' are not used for actual modeling but for identifying, and sampling purposes.</p> <p>library(data.table)</p> <p>data = readRDS("~/../Downloads/ecDNA_cargo_gene_modeling_data.rds")</p> <p>colnames(data)[3] = "total_cn"</p> <p>data.table::fwrite(data, file = "~/../Downloads/ecDNA_cargo_gene_modeling_data.csv.gz", sep = ",")</p> <p> </p> <p>2. gcap_pcawg_WGS_result.tar.gz</p> <p>GCAP analysis results for PCAWG allele-specific copy number profiles derived from WGS.</p> <p> </p> <p>3. gcap_tcga_snp6_result.tar.gz</p> <p>GCAP analysis results for TCGA allele-specific copy number profiles derived from SNP6 array.</p> <p> </p> <p>4. gcap_Changkang_WES_result.tar.gz</p> <p>GCAP analysis results for SYSUCC Changkang allele-specific copy number profiles derived from tumor-normal paired WES.</p> <p> </p> <p>5. tcga_overlap_gene_wgs.rds, tcga_overlap_gene_snp.rds and tcga_overlap_gene_wes.rds</p> <p>These datasets contain TCGA gene-level copy number results in R data format from overlapping samples (dataset above). WGS from PCAWG, SNP array, and WES from GDC portal.</p> <p> </p> <p>6. cellline-batch1.zip & cellline-batch1.zip</p> <p> </p> <p>GCAP results of cell line batch 1 and batch 2.</p> <p> </p> <p>7. AA_cellline_wgs.zip</p> <p>AA software results for cell line batch 1.</p> <p> </p> <p>8. Batch2_AA_summary.xlsx</p> <p>AA software results for cell line batch 2.</p> <p> </p> <p>9. FISH-for-supp-file.zip</p> <p>Extended raw FISH images from 12 CRC samples.</p> <p> </p> <p>10. SNU216.zip</p> <p>Extended AA and GCAP analysis on SNU216.</p> <p> </p> <p>11. aa_ffpe.zip and AA_summary_table_of_6_erbb2_ffpe_samples.xlsx</p> <p>Extended AA running files (all results) and result summary data for 6 GCAP predicted ERBB2 amp clinical samples.</p> <p> </p> <p>12. source data of fig.4</p> <p> </p> <p>13. source data of supp fig.2 subplots</p> <p> </p> <p>13. source data of supp fig.15</p> <p> </p> <p>14. GCAP result data objects for three ICB cohorts. Both gene-level and sample-level data included.</p> <p> </p> <p>15. PDX-P68: processed (AA and CNV) data of P68 from WGS and WES data.</p> <p> </p> <p>16. source data of supp fig.13</p> <p> </p> <p>17. updated supplementary figure 14</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.