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120 results for “data augmentation”
Data archive for paper "Copula-based synthetic data augmentation for machine-learning emulators"
<p><strong>Overview</strong></p> <p>This is the data archive for paper "<a href="https://doi.org/10.5194/gmd-14-5205-2021">Copula-based synthetic data augmentation for machine-learning emulators</a>". It contains the paper’s data archive with model outputs (see <code>results</code> folder) and the Singularity image for (optionally) re-running experiments.</p> <p>For the Python tool used to generate synthetic data, please refer to <a href="https://github.com/dmey/synthia">Synthia</a>.</p> <p><strong>Requirements</strong></p> <ul> <li><a href="https://sylabs.io/singularity/">Singularity</a> >= 3</li> <li><a href="https://en.wikipedia.org/wiki/Portable_Batch_System">Portable Batch System</a> (PBS) job scheduler*</li> <li>Today's high-performance computer (e.g. ~ 32 CPUs @ 2 500 MHz with 64 GB of RAM )</li> </ul> <p>*Although PBS in not a strict requirement, it is required to run all helper scripts as included in this repository. Please note that depending on your specific system settings and resource availability, you may need to modify PBS parameters at the top of submit scripts stored in the <code>hpc</code> directory (e.g. <code>#PBS -lwalltime=72:00:00</code>).</p> <p><strong>Usage</strong></p> <p>To reproduce the results from the experiments described in the paper, first fit all copula models to the reduced NWP-SAF dataset with:</p> <pre><code>qsub hpc/fit.sh</code></pre> <p>then, to generate synthetic data, run all machine learning model configurations, and compute the relevant statistics use:</p> <pre><code>qsub hpc/stats.sh qsub hpc/ml_control.sh qsub hpc/ml_synth.sh</code></pre> <p>Finally, to plot all artifacts included in the paper use:</p> <pre><code>qsub hpc/plot.sh</code></pre> <p><strong>Licence</strong></p> <p>Code released under <a href="./LICENSE.txt">MIT license</a>. Data from the reduced NWP-SAF dataset released under <a href="./data/LICENSE.txt">CC BY 4.0</a>.</p>
Use of Augmented Reality in the preservation of architectural heritage: case of aqueduct Kuru Kopru _Data set
<p>Architectural preservation embeds all the activities dealing with the physical sustainability of the built heritage<strong>,</strong> its diffusion and comprehension by a wide scope public. Representation and diffusion of heritage take a core place in that process. Nowadays, Augmented Reality (AR) is one of the most used digital tools in the diffusion of architectural heritage. This dataset embeds data used for the modeling of reconstruction models of the Roman aqueduct Kuru Kopru from the Roman-Byzantıne period to the year 2017 and those used for the development of two AR applications for the diffusion of the aforementioned aqueduct.</p>
Replication Data for: ``Toward machine learning-augmented, bathymetry-aware parameterizations of mesoscale eddy buoyancy fluxes across upwelling slope fronts''
<p>This dataset contains the Python scripts for training the Artificial Neural Networks (ANNs), the trained ANNs, configuration files for the reference 2D MITgcm simulations, and model outputs used in the paper.</p>
Raw data supporting: Augmenting the Performance of Hydrogenase for Aerobic Photocatalytic Hydrogen Evolution via Solvent Tuning
<p>Raw experimental data supporting the article "Augmenting the Performance of Hydrogenase for Aerobic Photocatalytic Hydrogen Evolution <em>via</em> Solvent Tuning"</p>
Data from: Ready species one: exploring the use of augmented reality to enhance systematic biology with a revision of Fijian Strumigenys (Hymenoptera: Formicidae)
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Data from: Mitigation of pollen limitation in the lowbush blueberry agroecosystem: effect of augmenting natural pollinators
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Image-based automated species identification: Can virtual data augmentation overcome problems of insufficient sampling?
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Data from: Abundance estimation with sightability data: a Bayesian data augmentation approach
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Data from: Kin selection, not group augmentation, predicts helping in an obligate cooperatively breeding bird
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Fast and accurate estimation of species-specific diversification rates using data augmentation
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Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning: W+jet training/test dataset
<p>W+jet events at generator and reconstruction level, used to train analysis-specific generative models.</p> <p>Events are represented as an array of relevant high-level features. Reco objects are matched to Gen objects and a minimal selection is applied to define the generator support in the N-dim space identified by the input features.</p> <p>About 2M events, used for training/validation/testing</p> <p>Details in https://arxiv.org/abs/2010.01835</p>
Data from: Intestinal Ralstonia pickettii augments glucose intolerance in obesity
An altered intestinal microbiota composition has been implicated in the pathogenesis of metabolic disease including obesity and type 2 diabetes mellitus (T2DM). Low grade inflammation, potentially initiated by the intestinal microbiota, has been suggested to be a driving force in the development of insulin resistance in obesity. Here, we report that bacterial DNA is present in mesenteric adipose tissue of obese but otherwise healthy human subjects. Pyrosequencing of bacterial 16S rRNA genes revealed that DNA from the Gram-negative species Ralstonia was most prevalent. Interestingly, fecal abundance of Ralstonia pickettii was increased in obese subjects with pre-diabetes and T2DM. To assess if R. pickettii was causally involved in development of obesity and T2DM, we performed a proof-of-concept study in diet-induced obese (DIO) mice. Compared to vehicle-treated control mice, R. pickettii-treated DIO mice had reduced glucose tolerance. In addition, circulating levels of endotoxin were increased in R. pickettii-treated mice. In conclusion, this study suggests that intestinal Ralstonia is increased in obese human subjects with T2DM and reciprocally worsens glucose tolerance in DIO mice.
Phylogenetic Augmentation Data
<p>This entry contains input files used for the analysis from the paper "Improving the performance of supervised deep learning for regulatory genomics using phylogenetic augmentation" by Andrew G Duncan, Jennifer A Mitchell, and Alan M Moses. The code to run the analysis can be found at "<a href="http://github.com/agduncan94/phylogenetic_augmentation_paper">https://github.com/agduncan94/phylogenetic_augmentation_paper</a>"</p>
MySQL dump for finding optimal parameters for data augmentation techniques in publication "Leveraging Data Augmentation for Process Information Extraction"
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Supported data for manuscript "Can LLM-Augmented autonomous agents cooperate?, An evaluation of their cooperative capabilities through Melting Pot"
<p>The repository data corresponds partially to the manuscript titled <strong>"Can LLM-Augmented Autonomous Agents Cooperate? An Evaluation of Their Cooperative Capabilities through Melting Pot,"</strong> submitted to <em>IEEE Transactions on Artificial Intelligence</em>. The dataset comprises experiments conducted with <strong>Large Language Model-Augmented Autonomous Agents (LAAs)</strong>, as implemented in the ["Cooperative Agents" repository](https://github.com/Cooperative-IA/CooperativeGPT/tree/main), using substrates from the Melting Pot framework.</p> <h3>Dataset Scope</h3> <p>This dataset is divided into two main experiment categories:</p> <ol> <li> <p><strong>Personality__experiments</strong>:</p> <ul> <li>These focus on a single scenario (<strong>Commons Harvest</strong>) to assess various agent personalities and their cooperative dynamics.</li> </ul> </li> <li> <p><strong>Comparison_baselines__experiments</strong>:</p> <ul> <li>These experiments include three distinct scenarios designed by Melting Pot: <ul> <li><strong>Commons Harvest Open</strong></li> <li><strong>Externally Mushrooms</strong></li> <li><strong>Coins</strong></li> </ul> </li> </ul> </li> </ol> <p>These scenarios evaluate different cooperative and competitive behaviors among agents and are used to compare <strong>decision-making architectures</strong> of LAAs against reinforcement learning (RL) baselines. Unlike the <strong>Personality__experiments</strong>, these comparisons do not involve bots but exclusively analyze RL and LAA architectures.</p> <h3>Scenarios and Metrics</h3> <p>The metrics and indicators extracted from the experiments depend on the scenario being evaluated:</p> <ol> <li> <p><strong>Commons Harvest Open</strong>:</p> <ul> <li>Focus: <strong>Resource consumption</strong> and <strong>environmental impact</strong>.</li> <li>Metrics include: <ul> <li>Number of apples consumed.</li> <li>Devastation of trees (i.e., depletion of resources).</li> </ul> </li> </ul> </li> <li> <p><strong>Externally Mushrooms</strong>:</p> <ul> <li>Focus: <strong>Self-interest vs. collective benefit</strong>.</li> <li>Agents consume mushrooms with different outcomes: <ul> <li>Mushrooms that benefit the individual.</li> <li>Mushrooms that benefit everyone.</li> <li>Mushrooms that benefit only others.</li> <li>Mushrooms that benefit the individual but penalize others.</li> </ul> </li> <li>Metrics evaluate trade-offs between individual gain and collective welfare.</li> </ul> </li> <li> <p><strong>Coins</strong>:</p> <ul> <li>Focus: <strong>Reciprocity and fairness</strong>.</li> <li>Agents collect coins with two options: <ul> <li>Collect their own color coin for a reward.</li> <li>Collect a different color coin, which grants a reward to the agent but penalizes the other.</li> </ul> </li> <li>Metrics include reciprocity rates and the balance of mutual benefits.</li> </ul> </li> </ol> <h3>Objectives of Comparison Experiments</h3> <p>The <strong>Comparison_baselines__experiments</strong> aim to:</p> <ol> <li>Assess how LAAs compare to RL baselines in cooperative and competitive tasks across diverse scenarios.</li> <li>Compare <strong>decision-making architectures</strong> within LAAs, including chain-of-thought and generative approaches.</li> </ol> <p>These experiments help evaluate the robustness of LAAs in scenarios with varying complexity and social dilemmas, providing insights into their potential applications in real-world cooperative systems.</p> <h3>Simulation Details (Applicable to All Experiments)</h3> <p>In each simulation:</p> <ol> <li> <p><strong>Participants</strong>:</p> <ul> <li>Experiments involve predefined numbers of LAAs or RL agents.</li> <li>No bots are included in <strong>Comparison_baselines__experiments</strong>.</li> </ul> </li> <li> <p><strong>Action Dynamics</strong>:</p> <ul> <li>Each agent performs high-level actions sequentially.</li> <li>Simulations conclude either after reaching a <strong>preset maximum number of rounds</strong> (typically 100) or prematurely if the scenario's resources are fully depleted.</li> </ul> </li> <li> <p><strong>Metrics and Indicators</strong>:</p> <ul> <li>Extracted metrics depend on the scenario and include measures of individual performance, collective outcomes, and agent reciprocity.</li> </ul> </li> </ol> <p>This repository enables reproducibility and serves as a benchmark for advancing research into cooperative and competitive behaviors in LLM-based agents.</p>
data source file to factors affecting augmented reality head mounted device performance in real OR
<p>Excel spread sheet of data generated durgin the experiments and later used for statistical analysis</p>
GANDALF: Generative AttentioN based Data Augmentation and predictive modeLing Framework for personalized cancer treatment
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PDBscreen with multiple data augmentation strategies suitable for training protein-ligand interaction prediction methods
<p>PDBscreen with multiple data augmentation strategies suitable for training protein-ligand interaction prediction methods.</p> <p>PDBscreen is the training dataset for EquiScore.</p>
Augmented Street-Level Imagery with Points of Interest (Data and Resources Track)
<p>Augmented Street-Level Imagery with Points of Interest (Data and Resources Track)</p>
Data from: Augmented reality powers a cognitive assistant for the blind
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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.