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13,064
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ShareScore release 0.9.0
Dataset results
13,064 results for “predictions”
Predicting the failure of dental implants using supervised learning techniques
<p>A total of 747 fixtures from patients who completed their prosthodontics treatments. The dependent variable is dental implant failure; a total of 20 independent variables were collected, including age, gender, factors of missing, systemic disease, tobacco smoking, alcohol consumption, betel nut chewing, department of surgeon, surgeon experience, location of implant, bone density, ridge augmentation, Maxillary sinus augmentation, implant system, fixture length, fixture width, types of prosthesis, angle of abutment, and prosthesis fixation.</p>
Tools for prediction of tumor heterogeneity by a machine learning approach
<p>The package includes R codes and datasets. We applied three classification algorithms: Support Vector Machine, Random Forest, and Naïve Bayes. Datasets include tab-delimited files of mutation and gene expression profile for stomach cancer. </p>
yaleemmlc/admissionprediction: Predicting hospital admission at emergency department triage using machine learning - Data and Scripts
<p>First release for PLOS One. Please cite original paper for all research using this dataset.</p>
Aeroelastic Prediction Workshop PUMI meshes and EnGPar graphs
<p>PUMI meshes and EnGPar graphs from the Aeroelastic Prediction Workshop.</p>
The replication page for paper: Revisiting Supervised and Unsupervised Models for Effort-Aware Just-in-Time Defect Prediction
<p>The replication page for the paper submitted to EMSE: Revisiting Supervised and Unsupervised Models for Effort-Aware Just-in-Time Defect Prediction.</p> <p>There are four java files in the package named "model". Each file corresponds to one specific model (CBS and CBS+ are in the same file). Each file has a main function and you will get the experiment results when running the main function.</p> <p> </p>
Interpretable semi-supervised population prediction and disaggregation using ancillary data: processed dataset
<p>Dataset ready for use for the tool "cross_validator.py", that runs the experiments.</p> <p> </p> <p>The data cannot be redistributed as-is as it includes non-redistributable work. Contact authors for access.</p>
Interpretable semi-supervised population prediction and disaggregation using ancillary data: experiment results
<p>Result of the experiments for the paper "Interpretable semi-supervised population prediction and disaggregation using ancillary data"</p>
Base data for "Interpretable semi-supervised population prediction and disaggregation using ancillary data"
<p>Dataset used for experiments for the paper "Interpretable semi-supervised population prediction and disaggregation using ancillary data".</p> <p>The data comes from various sources, some of them cannot be legally redistributed; in any case, the provider of the data is clearly indicated in each folder.</p>
Inducibility of plant secondary metabolites predicts genetic variation in resistance against a key insect herbivore in maritime pine
<p>SNP dataset in maritime pine (<em>Pinus pinaster</em>) together with the population structure (<em>Q</em>) and kinship (<em>K</em>) matrices.</p>
Prediction shapes peripheral appearance
<p>Dataset related to the paper "Prediction shapes peripheral appearance" to appear in the Journal of Vision.</p>
Gene predictions in GFF format for tuatara genome assembly QEPC00000000.1
<p><strong>Maker gene predictions for Sphenodon punctatus (tuatara) isolate: mauimua-1 </strong> <br> <br> These GFF files correspond to the genome assembly in GenBank ID QEPC00000000.1<br> https://www.ncbi.nlm.nih.gov/nuccore/QEPC00000000.1</p> <p>Files:</p> <p> <strong>MASKED_Tuatara.DEVO.annot15102.gff.gz</strong><br> - gene and transcript predictions only<br> <br> <strong>20160427.tuatara.maker.raw.gff3.gz</strong><br> - raw Maker output including gene and transcript predictions, alignment results and output of individual gene prediction algorithms (SNAP and Augustus).</p> <p><br> <br> </p>
FIGURE 2. Predicted secondary structures for 22 in The complete mitochondrial genome of the jumping grasshopper Sinopodisma pieli (Orthoptera: Acrididae) and the phylogenetic analysis of Melanoplinae
FIGURE 2. Predicted secondary structures for 22 tRNA genes of the S. pieli mitogenome. The tRNAs are labeled with the abbreviations of their corresponding amino acids. The minus sign (-) indicates Watson-Crick base pairing and plus sign (.) indicates G-U base pairing.
Supplementary Movies (Humans use Predictive Gaze Strategies to Target Waypoints During Steering)
<p>Supplementary movies for the article Humans use Predictive Gaze Strategies to Target Waypoints During Steering</p>
IMPS: Interactive Musical Prediction System: Demo Video
<p>IMPS is a system for predicting musical control data in live performance. It uses a mixture density recurrent neural network (MDRNN) to observe control inputs over multiple time steps, predicting the next value of each step, and the time that expects the next value to occur. It provides an input and output interface over OSC and can work with musical interfaces with any number of real-valued inputs (we’ve tried from 1-8). Several interactive paradigms are supported for call-response improvisation, as well as independent operation, and “filtering” of the performer’s input. Whenever you use IMPS, your input data is logged to build up a training corpus and a script is provided to train new versions of your model.</p> <p>This video shows some demo musical interface designs with the IMPS controller.</p>
Raw data for MSI prediction from HE
<ul> <li><strong>Network_comparison.xlsx</strong> contains raw data for the comparison of different neural network models on the tumor detection benchmark task. This is related to Extended Data Figure 1 in our manuscript "Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer"</li> <li><strong>TCGA_GI_Test_set_predictions.xlsx</strong> contains predicted MSIness for test set patients in TCGA-CRC-KR, TCGA-CRC-DX, TCGA-STAD. This is related to Extended Data Figure 2 in our manuscript "Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer"</li> <li><strong>DACHS_predictions.xlsx</strong> contains predicted MSIness for DACHS patients. This is related to Extended Data Figure 2 in our manuscript "Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer"</li> <li><strong>DACHS_MSIness_in_geneticMSS.xlsx </strong>contains MSIness raw data for genetic MSS DACHS patients. This is related to Extended Data Figure 2 in our manuscript "Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer"</li> </ul> <p>in all files, each line is one patient.</p>
Raw Data from - Speech Auditory Brainstem Responses in Adult Hearing Aid Users: Effects of Aiding and Background Noise, and Prediction of Behavioral Measures
<p><em><strong>Folder and Data Description for dataset of:</strong></em></p> <p><strong>Speech Auditory Brainstem Responses in Adult Hearing Aid Users: Effects of Aiding and Background Noise, and Prediction of Behavioral Measures</strong></p> <p>Ghada BinKhamis, Antonio Elia Forte, Tobias Reichenbach, Martin O’Driscoll, and Karolina Kluk</p> <p><strong>Please site the paper when using this dataset</strong> (DOI: 10.1177/2331216519848297)</p> <p> </p> <p><strong>Shared dataset is as follows:</strong></p> <ul> <li><strong>Behavioral data is in the excel spread sheet entitled:</strong> “BinKhamis_et_al_behavioral_data .xlsx”<br> </li> <li><strong>Speech-ABRs (raw EEG (speech-ABR) data) are contained within the five 'zip' folders.</strong></li> </ul> <p><strong>Description of the “Speech-ABRs” folders, subfolders, and raw EEG files:</strong></p> <p><strong>“Speech-ABRs” Folder Information:</strong></p> <ul> <li><strong>Each Speech_ABR folder</strong> contains subfolders from a subset of participants (e.g. Speech_ABR_1_20.zip contains data from participant number 1 to participant number 20)</li> <li><strong>Subfolders:</strong> <ul> <li>Each subfolder starts with the participant code: e.g. HA1, HA2, HA3, HA4, HA5, …, HA98</li> <li>Next is the background condition: noise or quiet</li> <li>Next is whether recordings were: aided or unaided</li> </ul> </li> <li><strong>Example subfolder names:</strong> <ul> <li><strong><em>HA1 noise aided:</em></strong> participant number 1, aided speech-ABRs in background noise</li> <li><strong><em>HA4 noise unaided:</em></strong> participant number 4, unaided speech-ABRs in background noise</li> <li><strong><em>HA55 quiet aided:</em></strong> participant number 55, aided speech-ABRs in quiet</li> <li><strong><em>HA97 quiet unaided</em></strong>: participant number 97, unaided speech-ABRs in quiet</li> </ul> </li> <li>Each participant has 4 subfolders for the four recording conditions (aided quiet, aided noise, unaided quiet, unaided noise) <ul> <li><strong>Each subfolder contains four ‘.mat’ files, ‘.mat’ file names:</strong> <ul> <li>Each ‘.mat’ file starts with the participant code: e.g. HA1, HA2, HA3, HA4, HA5, …, HA98</li> <li>Next is the stimulus: 40 da</li> <li>Next is ‘unaided’ only if recordings were without HA</li> <li>Next is ‘noise’ only if the background condition was noise</li> <li>Next is the stimulus polarity: <ul> <li>‘Pos’ for positive/standard</li> <li>‘Neg’ for negative (reversed polarity stimulus)</li> </ul> </li> <li>And finally the test ear and recording number for that polarity <ul> <li>R1 is the first recording from the right ear, R2 is the second recording from the right ear</li> <li>L1 is the first recording from the left ear, L2 is the second recording from the left ear</li> </ul> </li> <li><strong>Example ‘.mat’ file name:</strong> <ul> <li><strong><em>HA1 40 da Neg Noise R1.mat: </em></strong>participant number 1, aided speech-ABR in response to the 40 ms [da], reversed stimulus polarity, in background noise, right ear recording number 1.</li> <li><strong><em>HA4 40 da unaided Pos Noise L2.mat:</em></strong> participant number 1, unaided speech-ABR in response to the 40 ms [da], standard stimulus polarity, left ear recording number 2.</li> <li><strong><em>HA7 40 da Neg R2.mat:</em></strong> participant number 7, aided speech-ABR in response to the 40 ms [da], reversed stimulus polarity, right ear recording number 2.</li> <li><strong><em>HA10 40 da unaided Pos Noise L1.mat:</em></strong> participant number 10, unaided speech-ABR in response to the 40 ms [da], standard stimulus polarity, in background noise, left ear recording number 1.</li> </ul> </li> </ul> </li> </ul> </li> </ul> <p><strong>File Information:</strong></p> <p><strong>Description of ‘.mat’ files that can be accessed and processed using MATLAB (MathWorks):</strong></p> <p>Each ‘.mat’ file is a structure that contains the following fields:</p> <ul> <li>The first nine fields are informational, for example:</li> <li><strong><em>xunits</em></strong>: ‘s’ indicates that the recording time window is in seconds, conversion to milliseconds would be required to plot the data in milliseconds</li> <li><strong><em>start: </em></strong>‘0’ indicates that both stimulus and recording start at 0 seconds</li> <li><strong><em>points:</em></strong> <strong>2200</strong> is the number of sample points</li> <li><strong><em>chans:</em></strong> 2 is the number of channels <ul> <li><em>Right ear:</em> channel 2, <em>Left ear:</em> channel 1</li> </ul> </li> <li><strong><em>frames:</em></strong> 2500 is the number of epochs</li> <li>The last filed <strong>‘values’</strong> is what contains the raw EEG data (2200x2x2500) <ul> <li><strong>2200 </strong>is the number of samples</li> <li><strong>2 </strong>is the number of channels (channel one is recorded from the left ear lobe (A1) and channel two is from the right ear lobe (A2))</li> <li><strong>2500 </strong>is the number of epochs <ul> <li>Stimulus starts at 0 seconds per epoch, pre-stimulus baseline may be extracted from the end of each epoch (i.e. before the next stimulus).</li> <li>Data are in Volts; conversion to <strong>μVolts </strong>(multiply by 1000) is required.</li> </ul> </li> </ul> </li> </ul> <p><strong>Date of data collection: </strong>October 2017 to July 2018</p>
PATTERNS OF RICHNESS OF FRESHWATER MOLLUSCS FROM CHILE: PREDICTIONS OF ITS DISTRIBUTION BASED ON NULL MODELS
<p>Script and data for GLM analysis and co-occurrence analysis. </p>
Interpretable semi-supervised population prediction and disaggregation using ancillary data - Supplementary Material
<p>Interpretable semi-supervised population prediction and disaggregation using ancillary data - Supplementary Material</p> <p>(details about the datasets + access to datasets + access to source code)</p>
Dataset and results for paper: Predicting missing links in directed networks: An investment-profit index
<p>Dataset and raw results for paper: Predicting missing links in directed networks: An investment-profit index. File "All_indices_12_dataset.mat" contains results of all 9 indices in 12 datasets. File "IP_sigma_12_dataset.mat" contains AUC and precision values when parameter sigma changes in IP index.</p>
Fueling ab initio folding with oceanic metagenomics enables structure and function predictions of new protein families
<p>Code and protein sequence database to construct multiple sequence alignment from Tara Ocean 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.