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19 results for “Noisy data”
Interaction data for noisy tournament for the Axelrod Python project.
<p>This is the data from the tournament described here: http://axelrod-tournament.readthedocs.org/en/latest/noisy/strategies.html</p> <p>A description of the format is available here: http://axelrod.readthedocs.org/en/latest/tutorials/further_topics/reading_and_writing_interactions.html</p> <p> </p>
Data from: FMRI speech tracking in primary and non-primary auditory cortex while listening to noisy scenes
<p>This data set was analysed for the publication "FMRI speech tracking in primary and non-primary auditory cortex while listening to noisy scenes" by Hausfeld, Hamers, and Formisano (<em>Communications Biology</em>, 2024). Anatomical and functional MRI was acquired at 7 Tesla. Participants listened to speech of 1 or 2 (concurrent) audiobooks. To analyze fMRI-based speech tracking, participants were asked to listen to one speaker by performing a task. </p> <p>The dataset is arranged as follows:</p> <p>- MRI data [single particpant folders S1-15] (preprocessed) and individual speech tracking maps are contained in the participant-specific files S[participant_ID].zip in folder "MRI"</p> <p>- Stimulus descriptions (i.e., envelopes) are included in the folder "ENVELOPES"</p> <p>- Individual results (tracking map similarities and behavioral outcomes) are included in "INDIV_RESULTS"</p> <p>- Code to recreate figures is provided in folder "CODE" </p> <p>- the README contains information on the repository's content</p> <p> </p> <p>Please note additional information in the original publication</p> <p> </p> <p>Abstract of corresponding manuscript</p> <p>Invasive and non-invasive electrophysiological measurements during “cocktail-party”-like listening indicate that neural activity in the human auditory cortex (AC) “tracks” the envelope of relevant speech. However, due to limited coverage and/or spatial resolution, the distinct contribution of primary and non-primary areas remains unclear. Here, using 7-Tesla fMRI, we measured brain responses of participants attending to one speaker, in the presence and absence of another speaker. Through voxel-wise modeling, we observed envelope tracking in bilateral Heschl’s gyrus (HG), right middle superior temporal sulcus (mSTS) and left temporo-parietal junction (TPJ), despite the signal’s sluggish nature and slow temporal sampling. Neurovascular activity correlated positively (HG) or negatively (mSTS, TPJ) with the envelope. Further analyses comparing the similarity between spatial response patterns in the <em>single speaker </em>and<em> concurrent speakers</em> conditions and envelope decoding indicated that tracking in HG reflected both relevant and (to a lesser extent) non-relevant speech, while mSTS represented the relevant speech signal. Additionally, in mSTS, the similarity strength correlated with the comprehension of relevant speech. These results indicate that the fMRI signal tracks cortical responses and attention effects related to continuous speech and support the notion that primary and non-primary AC process ongoing speech in a push-pull of acoustic and linguistic information.</p> <p> </p> <p>Author contact: lars.hausfeld@maastrichtuniversity.nl</p> <p> </p>
Supplemental data for "Estimating the Jones polynomial for Ising anyons on noisy quantum computers"
<p>This data supports "Estimating the Jones polynomial for Ising anyons on noisy quantum computers" by Chris N. Self, Sofyan Iblisdir, Gavin K. Brennen, and Konstantinos Meichanetzidis https://arxiv.org/abs/2210.11127</p> <p>Related code can be found in the GitHub repository: (https://github.com/chris-n-self/Ising-anyons-Jones-polynomials-for-NISQ). The 'analysis' folder here can be dropped inside the code repository in order to view the data using the 'view...' notebooks. The experimental data folders 'ibmq_...' contain the individual sets of results and can be used to generate new zero-noise-extrapolation fits.</p>
Data and code from: Phenotypic memory drives population growth and extinction risk in a noisy environment
<p>Random environmental fluctuations pose major threats to wild populations. As patterns of environmental noise are themselves altered by global change, there is growing need to identify general mechanisms underlying their effects on population dynamics. This notably requires understanding and predicting population responses to the color of environmental noise, i.e. its temporal autocorrelation pattern. Here, we show experimentally that environmental autocorrelation has a large influence on population dynamics and extinction rates, which can be predicted accurately provided that a memory of past environment is accounted for. We exposed near to 1000 lines of the microalgae <em>Dunaliella salina</em> to randomly fluctuating salinity, with autocorrelation ranging from negative to highly positive. We found lower population growth, and twice as many extinctions, under lower autocorrelation. These responses closely matched predictions based on a tolerance curve with environmental memory, showing that non-genetic inheritance can be a major driver of population dynamics in randomly fluctuating environments. </p>
Data and code from: Phenotypic memory drives population growth and extinction risk in a noisy environment
Open the record for dataset details and reuse information.
Data from: Noisy neighbors can hamper the evolution of reproductive isolation by reinforcing selection
Reinforcement is the process by which selection against hybridization leads to an increase in reproductive isolation. The influence of reinforcing selection can be detected when sympatric individuals (those from areas of secondary contact) show a higher degree of prezygotic isolation than allopatric individuals (those from areas outside each other's range). In areas of secondary contact with Drosophila santomea, Drosophila yakuba females show reinforcement of gametic isolation but not behavioral isolation, despite the fact that both behavioral and gametic isolation evolve in D. yakuba in experimental sympatry. Using behavioral assays and experimental evolution, I studied how both gametic and behavioral isolation are affected by biotic factors that the two species encounter in their natural environment. I show that if D. yakuba females are in environments where D. yakuba, D. santomea, and males from other species coexist, these females cannot fully discern between conspecific and heterospecific males. In such complex environments, gametic but not behavioral isolation evolves. The presence of nonhybridizing species can constrain the effect of reinforcement on behavioral isolation.
Data from: The evolution of alternative adaptive strategies for effective communication in noisy environments
Animals communicating socially are expected to produce signals that are conspicuous within the habitats in which they live. The particular way in which a species adapts to its environment will depend on its ancestral condition and evolutionary history. At this point, it is unclear how properties of the environment and historical factors interact to shape communication. Tropical Anolis lizards advertise territorial ownership using visual displays in habitats where visual motion or 'noise' from windblown vegetation poses an acute problem for the detection of display movements. We studied eight Anolis species that live in similar noise environments, but belong to separate island radiations with divergent evolutionary histories. We found that species on Puerto Rico displayed at times when their signals were more likely to be detected by neighboring males and females (during periods of low noise). In contrast, species on Jamaica displayed irrespective of the level of environmental motion, apparently because these species have a display that is effective in a range of viewing conditions. Our findings appear to reflect a case of species originating from different evolutionary starting points evolving different signal strategies for effective communication in noisy environments.
TopasOpt: working with noisy data example
<p>TopasOpt is an optimisation wrapper for Topas Monte Carlo. This is the pre-run data described in this document:</p><p>https://image-x-institute.github.io/TopasOpt/NoisyOptimisation.html</p>
FLIGHTED: Inferring Fitness Landscapes from Noisy High-Throughput Experimental Data (Part 1)
<p>Data for FLIGHTED (Inferring Fitness Landscapes from Noisy High-Throughput Experimental Data). This data contains the model weights for FLIGHTED-Selection and FLIGHTED-DHARMA, the training data for both, and fits on the GB1 landscape. It does not contain anything related to the TEV protease landscape.</p> <p>The data is arranged in the following folders:</p> <ol> <li>DHARMA_Input: contains the input for the DHARMA models, with the canvas sequence, the DHARMA reads, and the FACS data.</li> <li>DHARMA_Models: contains the weights, hyperparameters, and model training history for the FLIGHTED-DHARMA model.</li> <li>Fitness_Landscapes: the GB1 landscape, with and without FLIGHTED, as well as splits published by FLIP.</li> <li>Landscape_Models: models trained on the GB1 landscape with and without FLIGHTED under the various FLIP splits. Each model folder contains hyperparameters, training history, and predictions on the test set which can be used to evaluate model performance. Raw model parameters are not provided for fine-tuned models due to size; contact us if you want them.</li> <li>FLIGHTED_Selection: contains the weights, hyperparameters, and model training history for the FLIGHTED-Selection model.</li> <li>Selection_Simulations: contains the simulated training data for the FLIGHTED-Selection model.</li> </ol>
FLIGHTED: Inferring Fitness Landscapes from Noisy High-Throughput Experimental Data (Part 2)
<p>Data for FLIGHTED (Inferring Fitness Landscapes from Noisy High-Throughput Experimental Data). This data contains the TEV protease landscape and models trained on it. All other FLIGHTED data is in the other Zenodo repository (refer to the paper for details).</p> <p>The data is arranged in the following folders:</p> <ol> <li>TEV_Landscape: contains the TEV landscape (in flighted_fitnesses.csv) and splits thereof in Splits/. The main files for model training are flighted_fitnesses.csv and the files labeled one_vs_rest, two_vs_rest, and three_vs_rest. The files labeled three_vs_rest_control within Splits/ and the read count CSV files refer to further information about the read count in the landscape; see the Supplement for details. The dictionary files are the original raw data prior to processing with FLIGHTED.</li> <li>TEV_Models: contains models trained on the TEV landscape under the various splits. Each model folder contains hyperparameters, training history, and predictions on the test set which can be used to evaluate model performance. Raw model parameters are not provided for fine-tuned models due to size; contact us if you want them. The control_run/ refers to the run described in the supplement on just read counts.</li> </ol>
simulated data for ASSE verification: noise-free and noisy data
<p>Simulation data of an ultralight aircraft.</p> <p>Data *.mat collects in MAT files all flight data and ASSE coefficients for three manoeuvres:</p> <ol> <li>stall: from 10 s to 40 s</li> <li>AoS sweep: from 80 s to 110 s</li> <li>3211 elevator: from 5 s to 40 s</li> </ol> <p>Data *_noise.mat are the same with uncertainty levels described in https://www.mdpi.com/1436482</p>
Datasets for a data-centric image classification benchmark for noisy and ambiguous label estimation
<p>This is the official data repository of the Data-Centric Image Classification (DCIC) Benchmark. The goal of this benchmark is to measure the impact of tuning the dataset instead of the model for a variety of image classification datasets. Full details about the collection process, the structure and automatic download at</p> <p>Paper: https://arxiv.org/abs/2207.06214</p> <p>Source Code: https://github.com/Emprime/dcic</p> <p>The license information is given below as download.</p> <p><strong>Citation</strong></p> <p>Please cite as</p> <pre><code>@article{schmarje2022benchmark, author = {Schmarje, Lars and Grossmann, Vasco and Zelenka, Claudius and Dippel, Sabine and Kiko, Rainer and Oszust, Mariusz and Pastell, Matti and Stracke, Jenny and Valros, Anna and Volkmann, Nina and Koch, Reinahrd}, journal = {36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks}, title = {{Is one annotation enough? A data-centric image classification benchmark for noisy and ambiguous label estimation}}, year = {2022} }</code></pre> <p>Please see the full details about the used datasets below, which should also be cited as part of the license.</p> <pre><code>@article{schoening2020Megafauna, author = {Schoening, T and Purser, A and Langenk{\"{a}}mper, D and Suck, I and Taylor, J and Cuvelier, D and Lins, L and Simon-Lled{\'{o}}, E and Marcon, Y and Jones, D O B and Nattkemper, T and K{\"{o}}ser, K and Zurowietz, M and Greinert, J and Gomes-Pereira, J}, doi = {10.5194/bg-17-3115-2020}, journal = {Biogeosciences}, number = {12}, pages = {3115--3133}, title = {{Megafauna community assessment of polymetallic-nodule fields with cameras: platform and methodology comparison}}, volume = {17}, year = {2020} } @article{Langenkamper2020GearStudy, author = {Langenk{\"{a}}mper, Daniel and van Kevelaer, Robin and Purser, Autun and Nattkemper, Tim W}, doi = {10.3389/fmars.2020.00506}, issn = {2296-7745}, journal = {Frontiers in Marine Science}, title = {{Gear-Induced Concept Drift in Marine Images and Its Effect on Deep Learning Classification}}, volume = {7}, year = {2020} } @article{peterson2019cifar10h, author = {Peterson, Joshua and Battleday, Ruairidh and Griffiths, Thomas and Russakovsky, Olga}, doi = {10.1109/ICCV.2019.00971}, issn = {15505499}, journal = {Proceedings of the IEEE International Conference on Computer Vision}, pages = {9616--9625}, title = {{Human uncertainty makes classification more robust}}, volume = {2019-Octob}, year = {2019} } @article{schmarje2019, author = {Schmarje, Lars and Zelenka, Claudius and Geisen, Ulf and Gl{\"{u}}er, Claus-C. and Koch, Reinhard}, doi = {10.1007/978-3-030-33676-9_26}, issn = {23318422}, journal = {DAGM German Conference of Pattern Regocnition}, number = {November}, pages = {374--386}, publisher = {Springer}, title = {{2D and 3D Segmentation of uncertain local collagen fiber orientations in SHG microscopy}}, volume = {11824 LNCS}, year = {2019} } @article{schmarje2021foc, author = {Schmarje, Lars and Br{\"{u}}nger, Johannes and Santarossa, Monty and Schr{\"{o}}der, Simon-Martin and Kiko, Rainer and Koch, Reinhard}, doi = {10.3390/s21196661}, issn = {1424-8220}, journal = {Sensors}, number = {19}, pages = {6661}, title = {{Fuzzy Overclustering: Semi-Supervised Classification of Fuzzy Labels with Overclustering and Inverse Cross-Entropy}}, volume = {21}, year = {2021} } @article{schmarje2022dc3, author = {Schmarje, Lars and Santarossa, Monty and Schr{\"{o}}der, Simon-Martin and Zelenka, Claudius and Kiko, Rainer and Stracke, Jenny and Volkmann, Nina and Koch, Reinhard}, journal = {Proceedings of the European Conference on Computer Vision (ECCV)}, title = {{A data-centric approach for improving ambiguous labels with combined semi-supervised classification and clustering}}, year = {2022} } @article{obuchowicz2020qualityMRI, author = {Obuchowicz, Rafal and Oszust, Mariusz and Piorkowski, Adam}, doi = {10.1186/s12880-020-00505-z}, issn = {1471-2342}, journal = {BMC Medical Imaging}, number = {1}, pages = {109}, title = {{Interobserver variability in quality assessment of magnetic resonance images}}, volume = {20}, year = {2020} } @article{stepien2021cnnQuality, author = {St{\c{e}}pie{\'{n}}, Igor and Obuchowicz, Rafa{\l} and Pi{\'{o}}rkowski, Adam and Oszust, Mariusz}, doi = {10.3390/s21041043}, issn = {1424-8220}, journal = {Sensors}, number = {4}, title = {{Fusion of Deep Convolutional Neural Networks for No-Reference Magnetic Resonance Image Quality Assessment}}, volume = {21}, year = {2021} } @article{volkmann2021turkeys, author = {Volkmann, Nina and Br{\"{u}}nger, Johannes and Stracke, Jenny and Zelenka, Claudius and Koch, Reinhard and Kemper, Nicole and Spindler, Birgit}, doi = {10.3390/ani11092655}, journal = {Animals 2021}, pages = {1--13}, title = {{Learn to train: Improving training data for a neural network to detect pecking injuries in turkeys}}, volume = {11}, year = {2021} } @article{volkmann2022keypoint, author = {Volkmann, Nina and Zelenka, Claudius and Devaraju, Archana Malavalli and Br{\"{u}}nger, Johannes and Stracke, Jenny and Spindler, Birgit and Kemper, Nicole and Koch, Reinhard}, doi = {10.3390/s22145188}, issn = {1424-8220}, journal = {Sensors}, number = {14}, pages = {5188}, title = {{Keypoint Detection for Injury Identification during Turkey Husbandry Using Neural Networks}}, volume = {22}, year = {2022} }</code></pre> <p>Addition: This repository also contains the original data from the paper "Annotating Ambiguous Images" (https://arxiv.org/abs/2306.12189). The data is created based on the original datasets and license from https://osf.io/t98fz/ and https://osf.io/nqjyw/</p>
Data and processing for "Multitone Microwave Frequency Locking to a Noisy Cavity via Real-Time Feedback"
<p>This folder contains all the code and data necessary to produce the figures of the paper titled "Multitone Microwave Frequency Locking to a Noisy Cavity via Real-Time Feedback" written by Jean-Paul van Soest, Clinton A. Potts, Sarwan Peiter, Adrián Sanz Mora and Gary A. Steele.</p>
Data from: The evolution of alternative adaptive strategies for effective communication in noisy environments
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Data from: Noisy neighbors can hamper the evolution of reproductive isolation by reinforcing selection
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Data from: Extra-pair paternity is not driven by inbreeding avoidance and does not affect provisioning rates in a cooperatively breeding bird, the noisy miner (Manorina melanocephala)
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Data from: Non-breeding European robins adjust their songs in noisy environments
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Data from: The evolution of cooperation by negotiation in a noisy world
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Data for the preprint of "Layer-by-layer unsupervised clustering of statistically relevant fluctuations in noisy time-series data of complex dynamical systems"
<p>README: description of the files. </p> <p>This Zenodo repository contains all the data and original code necessary to reproduce the results of the paper https://doi.org/10.48550/arXiv.2402.07786. The code (continuously mantained and updated) is available open-source as a Python package at https://pypi.org/project/onion-clustering/ and on GitHub (https://github.com/matteobecchi/timeseries_analysis). </p> <p>The repository contains the folders "Fig1", "Fig2" etc, which contain the corresponding Datasets, together with the code to reproduce the figures. Additionally, the folder "FigS1 contains code and data for FigS1. </p> <p>The repository also contains the Supplementary Movies S1 to S4, in .mp4 format. </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.