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424 results for “drift”
Data Models for Dataset Drift Controls in Machine Learning With Optical Images - Datasets
<p>This dataset accompanies the paper titled</p> <p><em>Data Models for Dataset Drift Controls in Machine Learning with Images</em><br> <br> that appeared in the Transactions on Machine Learning Research<br> <br> <a href="https://openreview.net/forum?id=I4IkGmgFJz">https://openreview.net/forum?id=I4IkGmgFJz</a><br> </p> <pre><code>@article{ oala2023data, title={Data Models for Dataset Drift Controls in Machine Learning With Optical Images}, author={Luis Oala and Marco Aversa and Gabriel Nobis and Kurt Willis and Yoan Neuenschwander and Mich{\`e}le Buck and Christian Matek and Jerome Extermann and Enrico Pomarico and Wojciech Samek and Roderick Murray-Smith and Christoph Clausen and Bruno Sanguinetti}, journal={Transactions on Machine Learning Research}, issn={2835-8856}, year={2023}, url={https://openreview.net/forum?id=I4IkGmgFJz}, note={} }</code></pre> <p>We make available two datasets.</p> <p><strong>Raw-Microscopy:</strong></p> <ul> <li><strong>940 raw bright-field microscopy images</strong> of human blood smear slides for leukocyte classification (microscopy/images/raw_scale100) with corresponding labels (microscopy/labels).</li> <li><strong>5,640 variations measured at six additional different intensities </strong>(microscopy/images/raw_scale001-raw_scale0075)</li> <li><strong>11,280 images of the raw sensor data processed through twelve different pipelines</strong> (microscopy/images/processed_views)</li> </ul> <p><strong>Raw-Drone:</strong></p> <ul> <li><strong>548 raw drone camera images for car segmentation</strong> (drone/images_tiles_256/raw_scale100) with corresponding binary segmentation mask (drone/masks_tiles_256). The images and the masks are cropped from 12 raw drone camera images (drone/images_full/raw_scale100) and 12 masks (drone/masks_full) of size 3648 by 5472.</li> <li><strong>3,288 variations measured at six additional different intensities</strong> (drone/images_tiles_256/raw_scale001-raw_scale075).</li> <li><strong>6,576 images of the raw sensor data processed through twelve different pipelines</strong> (drone/images_tiles_256/processed_views).</li> </ul> <p>Detailed datasheets for the two datasets can be found in the appendices of the TMLR paper.</p> <p>The code repository for this project can be found at <a href="https://github.com/aiaudit-org/raw2logit">https://github.com/aiaudit-org/raw2logit</a></p> <p> </p>
Measurements of nearshore waves through coherent arrays of free-drifting wave buoys
<p>Surface gravity wave breaking occurs along coastlines in complex spatial and temporal patterns that significantly impact erosion, scalar transport, and flooding. Numerical models are used to predict these processes, but many models lack sufficient evaluation with observations during storm events. To fill the need for more nearshore wave measurements during extreme conditions, we deployed coherent arrays of small-scale, free-drifting wave buoys named microSWIFTs. The result is a large dataset covering a range of conditions. The microSWIFT is a small wave buoy with a GPS module, and Inertial Measurement Unit (IMU) used to directly measure the buoy's global position, horizontal velocities, rotation rates, accelerations, and heading. We use an Attitude and Heading Reference System (AHRS), 9 degrees-of-freedom Kalman filter to rotate the measured accelerations from the reference frame of the buoy to the Earth reference frame. We then use the corrected accelerations to compute the vertical velocity and sea surface elevation. The measurements were collected over a 27-day field experiment in October 2021 at the US Army Corps of Engineers Field Research Facility in Duck, NC. The microSWIFTs were deployed as a series of coherent arrays. They all sampled simultaneously with a common time reference, leading to a robust spatial and temporal dataset during each deployment. We evaluate wave spectral energy density estimates from individual microSWIFTs by comparing them with a nearby acoustic waves and currents (AWAC) sensor. We also compare significant wave height estimates from the coherent arrays with the nearby AWAC estimates. A zero crossing algorithm is applied to each buoy time series of sea surface elevation to extract realizations of measured surface gravity waves, yielding 116,307 wave realizations throughout the experiment. These measurements spanned offshore significant wave heights ranging from 0.5 meters to 3 meters and peak wave periods ranging from 5 to 15 seconds over the entire experiment. </p>
The evolution of lexical semantics dynamics, directionality, and drift: S4
<p>Supplementary Material (S4) for the study "The evolution of lexical semantics dynamics, directionality, and drift", for Frontiers in Communication, Special Issue "<a href="https://www.frontiersin.org/research-topics/38650/the-evolution-of-meaning-challenges-in-quantitative-lexical-typology?fbclid=IwAR3AeXx_11P-8CZG0UavlOgqvbVo6MlMxX8AejtPiREAKmeLyy3LIB3Ux24">The Evolution of Meaning: Challenges in Quantitative Lexical Typology</a>", ed. Gerd Carling & Annemarie Verkerk</p>
Fig. 2 in A coalescent-based estimator of genetic drift, and acoustic divergence in the Pteronotus parnellii species complex
Fig. 2 Echolocation call frequency by island by sex and its relation to mass on the call frequency (F(1, 49) = 0.435, P value = 0.512). The body dimensions. a Boxplots of echolocation frequency (summarizing 95% HPD of population differences in means for body mass was 10 calls/individual). Bayesian 95% high-probability density (HPD) of 0.89–2.29 g. c Call frequency as a function of forearm length. Anathe difference in call frequency means between Puerto Rico and Hislyses of covariance support little influence of forearm length on the call paniola was 5.2–6.0 kHz. b Call frequency as a function of body mass. frequency (F(1, 52) = 2.851, P value = 0.097). The 95% HPD of Analyses of covariance support very different call frequency for island population differences in means for forearm lengths was −1.82, groups (F(1, 49) = 704.260, P value = 0.000), but no influence of body 0.422 mm
Fig. 1 Results from IMa2 in A coalescent-based estimator of genetic drift, and acoustic divergence in the Pteronotus parnellii species complex
Fig. 1 Results from IMa2 analyses of Pteronotus parnellii s.l. populations. a Joint posterior density of Ne estimates for island populations. b Divergence time estimates between Puerto Rican and Hispaniolan populations in thousands of years (Ka)
Fig. 3 in A coalescent-based estimator of genetic drift, and acoustic divergence in the Pteronotus parnellii species complex
Fig. 3 Densities of Bayesian posteriors for FST based on betweenpopulation migration rates, and PST for relevant phenotypic variables (Brommer et al. 2014). The lines show the 95th percentile for the corresponding FST, and the 5% percentile for the PST. The overlap between PST body mass and FST Hispaniola was 0.023, for FST Puerto Rico it was 0.084; between PST call frequency and FST Hispaniola was <0.001, for FST Puerto Rico it was 0.003; and between PST forearm length and FST Hispaniola was 0.049, for FST Puerto Rico it was 0.125
DrIFT 2 Study: Displacement in Feeding Tubes
ClinicalTrials.gov study NCT06239610. IPD Sharing: YES. Countries: 1. Publications: 12.
Stimulus-dependent representational drift in primary visual cortex
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Data from: Towards drift-free high-throughput nanoscopy through adaptive intersection maximization
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Genetic diversity, structure, and demography of Pandanus boninensis(Pandanaceae) with sea drifted seeds, endemic to the Ogasawara Islands of Japan: Comparison between young and old islands
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Data for: Harvest and decimation affect genetic drift and the effective population size in wild reindeer
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Data from: Off-target drift of the herbicide dicamba disrupts plant-pollinator interactions via novel pathways
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Data for: ‘Drifting’ Buchnera genomes track the microevolutionary trajectories of their aphid hosts
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Measurements of nearshore waves through coherent arrays of free-drifting wave buoys
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Machine learning reveals that climate, geography, and cultural drift all predict bird song variation in coastal Zonotrichia leucophrys
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Data from: Drift happens: molecular genetic diversity and differentiation among populations of jewelweed (Impatiens capensis Meerb.) reflect fragmentation of floodplain forests
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Detecting and Tracking Drift in Quantum Information Processors
<p>This is supplemental data and code for:<strong> </strong>T. Proctor et al, <a href="https://www.nature.com/articles/s41467-020-19074-4"><em>Detecting and tracking drift in quantum information processors</em></a>, Nat. Comm. 11, 5396 (2020).</p> <p>Please direct any questions to Timothy Proctor (tjproct@sandia.gov).</p> <p>This folder contains all the data and the analysis code to generate the results presented in that paper. The core data analysis routines use PyGSTi, which can be found at <a href="https://github.com/pyGSTio/pyGSTi">https://github.com/pyGSTio/pyGSTi</a>.</p> <p>The analysis was run using pyGSTi commit 7c6ddd1de209b795ea39bfb69d010b687e812d07. This code does <em>not</em> work on the latest full release of pyGSTi (0.9.9). It is anticipated that it will work with the next full release of pyGSTi (0.9.10).</p> <p>Below is a basic guide to navigating this SI:</p> <p><strong>Time-resolved Ramsey tomography on experimental data.</strong></p> <p><em>Directory: ramsey/experiment</em></p> <p>This folder contains the data and analysis code for the time-resolved Ramsey experiment, the results of which are presented in Figure 1 of the paper. The folder contains a single Jupyter notebook, which runs all of the data analysis.</p> <p><strong>Time-resolved randomized benchmarking (RB) on simulated data.</strong></p> <p><em>Directory: rb/simulation</em></p> <p>This folder contains the data and analysis code for the simulation of time-resolved RB, the results of which are presented in Figure 2 of the paper. The folder contains a single Jupyter notebook, which runs all of the data analysis on the simulated data, and which can be used to run new simulations with the same noise model.</p> <p><strong>Time-resolved gate set tomography (GST) on simulated data.</strong></p> <p><em>Directory: gst/simulation</em></p> <p>This contains the data and analysis code for the simulation of time-resolved GST, the results of which are presented in Figure 2 of the paper. The raw simulated data is contained in the "data" folder. All the code is contained in the "analysis" folder. This contains the following code files:</p> <ul> <li>create_simulated_data.py : this generates the simulated data. This was run using MPI on 20 cores.</li> <li>drift.ipynb : this contains the general circuit-agnostic drift analysis.</li> <li>trgst_fit.py : this contains the TR-GST model-fitting code. This was run using MPI on 20 cores.</li> <li>tdmodel.py : encodes the general time-dependent model that the data is fit to.</li> </ul> <p><strong>Time-resolved gate set tomography (GST) on experimental data.</strong></p> <p><em>Directory: gst/experiments</em></p> <p>This folder contains the data and analysis code for the two time-resolved GST experiments, the results of which are presented in Figure 3 of the paper. The raw data is contained in the two folders "data/1" and "data/2", corresponding to the first and second experiment, respectively. All analysis code is contained in the "analysis" folder. This contains the following code files:</p> <ul> <li>drift.ipynb : this contains the general circuit-agnostic drift analysis.</li> <li>gst.ipynb : this contains the standard GST analysis, used to inform the TR-GST analysis.</li> <li>trgst_fit.py : this contains the TR-GST model-fitting code. This was run using MPI on 20 cores.</li> <li>trgst_plotting.ipnyb : this contains code that analyzes the results of the TR-GST fit.</li> <li>tdmodel.py : encodes the general time-dependent model that the data is fit to.</li> </ul>
Data from: Selfing ability and drift load evolve with range expansion
Colonization at expanding range edges often involves few founders, reducing effective population size. This process can promote the evolution of self-fertilization, but implicating historical processes as drivers of trait evolution is often difficult and requires an explicit model of biogeographic history. In plants, contemporary limits to outcrossing are often invoked as evolutionary drivers of self-fertilization, but historical expansions may shape mating system diversity, with leading-edge populations evolving elevated selfing ability. In a widespread plant, Campanula americana, we identified a glacial refugium in the southern Appalachian Mountains from spatial patterns of genetic drift among 24 populations. Populations farther from this refugium have smaller effective sizes and fewer rare alleles. They also displayed elevated heterosis in among-population crosses, reflecting the accumulation of deleterious mutations during range expansion. While populations with elevated heterosis had reduced segregating mutation load, the magnitude of inbreeding depression lacked geographic pattern. The ability to self-fertilize was strongly positively correlated with the distance from the refugium and mutation accumulation—a pattern that contrasts sharply with contemporary mate and pollinator limitation. In this and other species, diversity in sexual systems may reflect the legacy of evolution in small, colonizing populations, with little or no relation to the ecology of modern populations.
Reduced drift kinetic neoclassical tearing mode (RDK-NTM) solver and drift kinetic neoclassical tearing mode (DK-NTM) solver benchmarking
<p>A 4D drift kinetic non-linear code, DK-NTM, has been developed to describe the plasma response to the neoclassical tearing mode (NTM) magnetic perturbation. Employing the drift island formalism allows the dimensionality reduction from 4D to 3D (the RDK-NTM approach). This simplifies the numerical task and efficiently resolves the collisional boundary layer across the trapped-passing boundary. The benchmarking DK- and RDK-NTM data is presented. </p>
Young frigatebirds learn how to compensate for wind-drift
<p><span><span><span><span><span><span><span><span><span><span><span>Compensating for wind drift can improve goalward flight efficiency in animal taxa, especially amongst those that rely on thermal soaring to travel large distances. Little is known, however, about how animals acquire this ability. The great frigatebird (<i>Fregata minor</i>) exemplifies the challenges of wind drift compensation because it lives a highly pelagic lifestyle, traveling very long distances over the open ocean but without the ability to land on water. Using GPS tracks from fledgling frigatebirds, we followed young frigatebirds from the moment of fledging to investigate whether wind drift compensation was learnt and, if so, what sensory inputs underpinned it. We found that the effect of wind drift reduced significantly with both experience and access to visual landmark cues. Further, we found that the effect of experience on wind drift compensation was more pronounced when birds were out-of-sight of land. Our results suggest that improvement in wind drift compensation is not solely the product of either physical maturation or general improvements in flight control. Instead, we believe it is likely that they reflect how frigatebirds learn to process sensory information so as to reduce wind drift and maintain a constant course during goalward movement. </span></span></span></span></span></span></span></span></span></span></span></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.