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Dataset results
558 results for “Training Data”
Training data for ship track detection machine learning algorithms
<p>The training data and labels used to train the linked machine learning algorithm</p>
Blinded Predictions and Post-hoc Analysis of the Second Solubility Challenge Data: Exploring Training Data and Feature Set Selection for Machine and Deep Learning Models
<p>Training and test datasets and scripts for training models.</p>
Processed data for the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data"
<p>This is the data used to reproduce the results from "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data".</p>
Scatter plots for the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data"
<p>This file contains the test-score-vs-metric plots generated by the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data".</p>
Generalization metrics for the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data"
<p>This file contains all the generalization metrics that can be used to reproduce the results of "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data".</p>
Rank correlation results for the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data"
<p>This file contains the rank correlation results from the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data".</p>
Training Data Incunabula Reichenau
<p>This data set contains the training data for the following three published Transkribus models:</p> <ol> <li><a href="https://readcoop.eu/model/german-incunabula-reichenau/" target="_blank" rel="noopener">German Incunabula (Reichenau)</a></li> <li><a href="https://readcoop.eu/model/latin-incunabula-reichenau/" target="_blank" rel="noopener">Latin Incunabula (Reichenau)</a></li> <li><a href="https://readcoop.eu/model/latin-german-bilingual-incunabula-reichenau/" target="_blank" rel="noopener">Latin/German Bilingual Incunabula (Reichenau)</a></li> </ol> <p>This model is trained to recognize the Gothic and Antiqua typefaces found in Latin incunabula and early prints. The Ground Truth used to train and evaluate this models is based on a collection of incunabula and post-incunabula of the former Reichenau monastery, now held at the Badische Landesbibliothek in Karlsruhe.</p> <p>It was developed by the project Digitalisierung und Volltexterkennung der ehemals Reichenauer Inkunabeln at the Badische Landesbibliothek, which was funded by the Stiftung Kulturgut Baden-Württemberg.</p>
Research data of variational operator learning: A unified paradigm marrying training neural operators and solving partial differential equations
<ul> <li>This is the repository for the paper "<a href="https://www.sciencedirect.com/science/article/pii/S0022509624001807">Variational operator learning: A unified paradigm marrying training neural operators and solving partial differential equations</a>" (accepted by <a href="https://www.sciencedirect.com/journal/journal-of-the-mechanics-and-physics-of-solids">Journal of the Mechanics and Physics of Solids</a>). Please refer <code>README.md</code> for detailed instructions to reproduce our experiments.</li> <li> <div>This work was supported by National Key Research and Development Program of China (2023YFB3306700 and 2021YFF0306404), and National Natural Science Foundation of China (U2341233 and U21A20429).</div> </li> <li>If you have any technical problems to report or interesting ideas to share, feel free to drop a issue at <a href="https://github.com/BraveDrXuTF/VOL/issues">this issue board.</a> <a href="https://bravedrxutf.github.io/">The first author of this work</a> is now actively seeking full-time internship opportunities and a doctoral position in AI4science, and if you are interested in collaborating with him, kindly consider <a href="mailto:3285935860@qq.com">drop him an email.</a></li> <li>Note: all data and codes in this repository are distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">CC BY-NC-SA 4.0,</a> and related algorithms are patent-protected. If you want to use materials in this repository for commercial purposes, please contact <a href="mailto:haopeng@dlut.edu.cn">haopeng@dlut.edu.cn</a>.</li> </ul>
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>
Refugee Camp Flood Risk - NASA ARSET Training Data
<p>This upload contains data and documentation to complete the practical exercise in <em>Part 1 - Assessing Flood Risk in Refugee Camp Settings</em> of the NASA ARSET <em>Earth Observations for Humanitarian Applications </em>training programme. Further details can be found at http://appliedsciences.nasa.gov/get-involved/training/english/arset-earth-observations-humanitarian-applications</p>
Data for "Improving semantic video retrieval models by training with a relevance-aware online mining strategy"
<p>This repository contains all the data available for the publication:</p> <p><a href="https://doi.org/10.1016/j.cviu.2024.104035">Alex Falcon, Giuseppe Serra, and Oswald Lanz. <em>Improving semantic video retrieval models by training with a relevance-aware online mining strategy</em>. <strong>Computer Vision and Image Understanding</strong>. 2024.</a></p> <p>Code is available at: <a href="https://github.com/aranciokov/ranp/">https://github.com/aranciokov/ranp/</a></p> <p>The data includes:</p> <ul> <li>pre-extracted features (ordered_feature_*.zip files)</li> <li>annotations, such as pre-extracted semantic graphs, glove checkpoints, class annotations, etc (annotations_*.zip files)</li> <li>train/val/test, when available, split information (public_split_*.zip) files</li> <li>pretrained models for HGR and EAO (details in the github repo)</li> </ul>
Test and Train data for the retrieval experiment in "A molecule generation-oriented lead compound optimization architecture: discovery of potent, selective, oral NLRP3 inflammasome inhibitors"
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Trained Random Forest model and scaler parameters on new physical and tsfel features from seismic data of 150s length.
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Data from: Captive birds exhibit greater foraging efficiency and vigilance after anti-predator training
<p>Rearing animals in captivity for conservation translocation is a complex undertaking that demands interdisciplinary management tactics. The maladapted behaviors that captive animals can develop create unique problems for wildlife managers seeking to release these animals into the wild. Often, released captive animals show decreased survival due to predation and their inability to display appropriate anti-predator, vigilance, and risk-analysis behaviors. Additionally, released animals may have poor foraging skills, further increasing their vulnerability to predation. Often conservation translocation programs use anti-predator training to ameliorate these maladapted behaviors before release but find mixed results in behavioral responses. The behavioral scope of analyzing the effect of anti-predator trainings is frequently narrow; the effect of this training on an animal's risk-analysis competency, or ability to assess the predation risk of a foraging patch and subsequently adjust its behavior, remains unstudied. Using a captive reared passerine species, the American robin (<em>Turdus migratorius</em>) (46 individuals), we applied an experimental giving up density test (GUD) to analyze the effect of anti-predator training on the robins' vigilance/risk-analysis behaviors, patch choice, and the GUD of food left behind after one foraging session. Robins moved and foraged freely between three foraging patches of differing predation risk before and after a hawk silhouette was presented for one minute. Results indicate that after anti-predator training, robins displayed increased vigilance across most foraging patches and better foraging efficiency (higher vigilance and latency to forage with simultaneous lower GUD) in the safest patch. These results can have positive survival implications post-release, however, more research on this training is needed because anti-predator training has the potential to elicit indiscriminate increased vigilance to the detriment of foraging gains. Further research is required to standardize GUD's application in translocation programs with multigenerational captive-bred animals to fully comprehend its effectiveness in identifying and correcting maladaptive behaviors. GUD tests combined with behavioral analysis should be used by conservation translocation managers to examine the need for anti-predator and foraging trainings, the effects of trainings, and a group's suitability for release.</p>
Training data for building a machine learning wildfire model over the CONUS
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Galaxy Training Tutorial: "Divers and Adaptable Visualisations of Metabarcoding Data Using ampvis2"
<p><span>This tutorial teaches you how to filter data for significant information, visualise it effectively, and adapt plots to your needs. You will explore multiple visualisation methods to gain deeper insights from your data.</span></p> <p><a href="https://training.galaxyproject.org/training-material/"><span>Galaxy Training Material Website</span></a></p>
Data for manuscript : Effect of spatial training on space-number mapping: A situated cognition account
<p><span>From an embodied perspective of cognition, sensorimotor mechanisms play a crucial role in abstract processing, such as the understanding of Arabic numerals. For instance, spatial cognition can influence number processing. These </span><span>spatial–numerical</span><span> associations (SNAs) have been thoroughly investigated since the pioneering </span><span>spatial–numerical association of response codes (SNARC) effect</span><span>, which demonstrates faster left/right responses to small/large numbers, respectively. While there </span><span>has been</span><span> no systematic assessment of SNAs on other planes in three-dimensional space in the literature, recent primary </span><span>evidence has</span><span> revealed that SNAs along </span><span>the transverse and sagittal planes </span><span>are</span><span> mutually exclusive </span><span>with respect</span><span> </span><span>to the required spatial reference frames used by the participant.</span><strong><span> </span></strong><span>Specifically, under </span><span>egocentric</span><span> spatial reference frames</span><span>,</span><span> SNAs have been observed only along the sagittal plane, </span><span>whereas</span><span> under</span><span> allocentric reference </span><span>frames, </span><span>the </span><span>reverse</span><span> pattern has been observed</span><span>,</span><span> with SNAs present exclusively along the transverse plane of the body. Given </span><span>this</span><span> empirical </span><span>evidence</span><span>, we have hypothesized that the subject's ability to switch spatial reference frames to match that of another person could significantly </span><span>influence</span><span> the occurrence of SNAs according to the processed plane. Therefore, this study has two aims. The first is to replicate </span><span>previous</span><span> seminal findings. The second is to investigate how referential </span><span>frame</span><span> switching (RFS) training can affect this organization. </span><span>While the results of</span><span> the two experiments reveal a general replication, more importantly, we find that RFS training enables </span><span>the development of</span><span> new situated cognition strategies </span><span>from</span><span> egocentric perspectives and </span><span>the generalization of</span><span> transverse SNAs to other spatial planes </span><span>from</span><span> allocentric perspectives.</span></p>
Abundance Trend Indicator - Models, Prediction, Stacked Environmental Data and Training Set Similarity
<p># Readme</p> <p>These trained models can be used to predict the abundance trends of New Zealand's forest species and can be used together with the code in https://github.com/lnilya/abundance-trend-indicator</p> <p>Since the process of using the models requires coding expertise and some setting up, please make sure to reach out to ilya.shabanov@vuw.ac.nz for any questions. All files will require the code in the repository to be read and used. </p> <p>If you want to explore the results generated with these models, please visit https://ati-nz-predictions-7e6f3d514735.herokuapp.com/ for a user-friendly, interactive UI.</p> <p>## Contents</p> <p>_models: Contains the trained models (Artificial Neural Network (ANN), Random Forest (RF), SVMW (Support vector machine) and GLM (logistic regression)) at different degrees of noise filtering, different datasets and variable sets. The model files also contain test and training scores. To load the files please refer to the readme in the code repository: ttps://github.com/lnilya/abundance-trend-indicator</p> <p><br>_predictions/_environment: Contains the predictor variables for the study area (New Zealand, 1950-2019) that are needed by the models to make predictions. </p> <p>_predictions/_similarity: Contains the masks of areas that can be predicted by models and are similar to the training set.</p> <p>_predictions/_ati: Contain the predicted results for the abundance trend. These can be explored on https://ati-nz-predictions-7e6f3d514735.herokuapp.com/ </p> <p> </p>
One Hand project - Transfer of prosthesis control skill after training in VR, data set pre-test post-test
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Calculating α and β diversity from microbiome taxonomic data - Galaxy Training Material
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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.