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Dataset results
104 results for “State estimation”
ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation - Training Set - Corner Clamp Part 2 - Geared Caliper Base
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ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation - Training Set - NanoVise part 1
<p>@article{schieber2024asdf,<br> title={ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation},<br> author={Schieber, Hannah and Li, Shiyu and Corell, Niklas and Beckerle, Philipp and Kreimeier, Julian and Roth, Daniel},<br> journal={arXiv preprint arXiv:2403.16400},<br> year={2024}<br>}</p>
ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation - Training Set - NanoVise part 2
<p>@article{schieber2024asdf,<br> title={ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation},<br> author={Schieber, Hannah and Li, Shiyu and Corell, Niklas and Beckerle, Philipp and Kreimeier, Julian and Roth, Daniel},<br> journal={arXiv preprint arXiv:2403.16400},<br> year={2024}<br>}</p>
Dataset for "Real-Time Hydraulic Interval State Estimation for Water Transport Networks: a Case Study"
<p>The dataset (EPANET file) which accompanies the publication </p> <p>Vrachimis, S. G., Eliades, D. G., and Polycarpou, M. M.: Real-Time Hydraulic Interval State Estimation for Water Transport Networks: a Case Study, Drink. Water Eng. Sci., 2018</p>
ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation - Training Set - Hand Screw Clamp
<p>@article{schieber2024asdf,<br> title={ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation},<br> author={Schieber, Hannah and Li, Shiyu and Corell, Niklas and Beckerle, Philipp and Kreimeier, Julian and Roth, Daniel},<br> journal={arXiv preprint arXiv:2403.16400},<br> year={2024}<br>}</p>
Power grid attack detection and state estimation with machine learning
<p>Detecting attacks and estimating states of power grids from partial observations with machine learning. A manuscript submitted to PRX Energy.</p>
Models and data for: State estimation of a physical system without governing equations
<p>Contains the accompanying data and models for the paper State estimation of a physical system with unknown governing equations. Accompanying code can be found here: https://github.com/coursekevin/svise</p> <p> </p>
Solid State vs. Balloon Esophageal Catheter for Estimation of Pleural Pressure
ClinicalTrials.gov study NCT05817968. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Joint estimation of survival and breeding probability in female dolphins and calves with uncertainty in state assignment
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Effect of data source on estimates of regional bird richness in northeastern United States
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Data from: Estimating range expansion of wildlife in heterogeneous landscapes: a spatially explicit state-space matrix model coupled with an improved numerical integration technique
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A multi-state occupancy modeling framework for robust estimation of disease prevalence in multi-tissue disease systems
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State estimation of surface and deep flows from sparse SSH observations of geostrophic ocean turbulence using Deep Learning
<p>This is a data repository in support of the publication "State estimation of surface and deep flows from sparse SSH observations of geostrophic ocean turbulence using Deep Learning" by Manucharyan et al. (2020), Journal of Advances in Modeling Earth Systems. The zipped file contains 10-day-separated snapshots of surface and deep ocean streamfunctions from the two-layer quasigeostrophic model of ocean turbulence. The included Python scripts demonstrate the efficacy of Deep Learning in temporal interpolation and state estimation given partial observations of the surface ocean turbulence.</p> <p> </p>
Data from: Estimating parent-specific QTL effects through cumulating linked identity-by-state SNP effects in multiparental populations
The emergence of multiparental mapping populations enabled plant geneticists to gain deeper insights into the genetic architecture of major agronomic traits and to map quantitative trait loci (QTLs) controlling the expression of these traits. Although the investigated mapping populations are similar, one open question is whether genotype data should be modelled as identical by state (IBS) or identical by descent (IBD). Whereas IBS simply makes use of raw genotype scores to distinguish alleles, IBD data are derived from parental offspring information. We report on comparing IBS and IBD by applying two multiple regression models on four traits studied in the barley nested association mapping (NAM) population HEB-25. We observed that modelling parent-specific IBD genotypes produced a lower number of significant QTLs with increased prediction abilities compared with modelling IBS genotypes. However, at lower trait heritabilities the IBS model produced higher prediction abilities. We developed a method to estimate multiallelic QTL effects in multiparental populations from simple biallelic IBS data. This method is based on cumulating IBS-derived single-nucleotide polymorphism (SNP) effect estimates in a defined genetic region surrounding a QTL. Comparing the resulting parent-specific QTL effects with those obtained from IBD approaches revealed high accordance that could be confirmed through simulations. The method turned out to be also applicable to a barley multiparent advanced generation inter-cross (MAGIC) population. The 'cumulation method' represents a universal approach to differentiate parent-specific QTL effects in multiparental populations, even if no IBD information is available. In future, the method could further benefit from the availability of much denser SNP maps.
Data from: A multi-state dynamic occupancy model to estimate local colonization-extinction rates and patterns of co-occurrence between two or more interacting species
1. Although ecology is rife with theory that explores how multiple species co-occur through space and time, the field lacks robust statistical models to parameterize this theory with empirical data, particularly when species are detected imperfectly and data are collected as a time-series. 2. We address this need by developing an occupancy model that estimates local colonization and extinction rates for two or more interacting species when data are collected across multiple sampling occasions. This model estimates how community composition at a site may change across sampling occasions by assuming the latent occupancy state is a categorical random variable. We used a multinomial-logit model to parameterize species-specific parameters and pairwise interactions between species, both of which can be made a function of covariates. These transition probabilities between community states can then be converted to occupancy or co-occurrence probabilities to determine how community composition varies along an environmental gradient or through time. 3. As an example, we estimate patterns of co-occurrence between coyote (Canis latrans), Virginia opossum (Didelphis virginiana), and raccoon (Procyon lotor) in Chicago, Illinois, USA with data from a multi-year camera trapping study. Models with pairwise interactions between species greatly out performed models that assumed independence between species. Opossum and raccoon, for example, were far less likely to go extinct in habitat patches where coyotes were present. 4. Community composition at a site depends on species interactions and the local environment. Our model can separate such effects by estimating the underlying processes that define species occurrence patterns. As a result, our model can more explicitly quantify a wide range of ecological dynamics and therefore be used to empirically test ecological theory, such as estimating priority effects at a site or turnover rates between species, both of which can be made to vary as a function of covariates.
Power Quality State Estimation for Distribution Grids based on Physics-Aware Neural Networks - Harmonic State Estimation
<p>Data set for the paper "Power Quality State Estimation for Distribution Grids based on Physics-Aware Neural Networks - Harmonic State Estimation"</p> <p>This upload contains</p> <ul> <li>Training set</li> <li>Validation set</li> <li>Test set</li> <li>Admittance matrices per frequency</li> </ul> <p>used for the paper as pickle files and weights of trained models as zip files.</p> <p>Weights represent the model with the best validation loss recorded within the first 3000 Epochs of training.</p> <p>Code for reading in the data sets, preprocessing and state estimation is available in the linked repository.</p> <p>To replicate the results of the paper follow these steps:</p> <ol> <li>clone the linked repository</li> <li>save the provided pickle files in the data folder of the linked repository</li> <li>optional: unzip weights and save them in the data folder, otherwise train a model yourself instead</li> </ol> <p><strong>Version 1.1:</strong></p> <p>Added data and model weights for the IEEE33 grid to improve comparability.</p> <p>For the IEEE33 grid, all data (train, test, validation) is saved in one pickle file; see the release tag 1.1.0 in the accompanying GitHub repository for details on the data format. Moreover, the training set size of the new grid was increased from 35040 to 131400 samples to incorporate simulation results that capture a broader range of system states. </p> <p>The code was slightly updated to account for inclusion of the IEEE33 grid. Therefore, model weights and input data are now expected in either `cigrelv` or `ieee33` subfolder.</p> <p>Added Transformer and CNN model weights for IEEE33 and CNN weights for the CIGRE grid. The Transformer model is trained with a smaller batch size since the model did not fit into GPU memory using the same batch size as in other models. This change results in more gradient updates and significantly longer training times, thus the amount of epochs was reduced to achieve a fairer comparison (batch sized reduced from 16384 to 1024, epochs reduced from 3000 to 375, total amount of gradient updates increased from 27000<em> </em>to 48375). The training of the PANN model over 3000 Epochs is significantly faster than that of the Transformer model trained over 375 epochs (approximately 2.5 hours vs 12.5 hours).</p>
ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation - Training Set - Hand Screw Clamp Part 2
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Figure 8 from: DeWalt R, Cao Y, Tweddale T, Grubbs S, Hinz L, Pessino M, Robinson J (2012) Ohio USA stoneflies (Insecta, Plecoptera): species richness estimation, distribution of functional niche traits, drainage affiliations, and relationships to other states. ZooKeys 178: 1-26. https://doi.org/10.3897/zookeys.178.2616
Figure 8 - Non–parametric Multi–Dimensional Scaling of Ohio Plecoptera assemblages associated with HUC6 drainages. Axis 1 vs Axis 3.
Figure 2 from: DeWalt R, Cao Y, Tweddale T, Grubbs S, Hinz L, Pessino M, Robinson J (2012) Ohio USA stoneflies (Insecta, Plecoptera): species richness estimation, distribution of functional niche traits, drainage affiliations, and relationships to other states. ZooKeys 178: 1-26. https://doi.org/10.3897/zookeys.178.2616
Figure 2 - Pre-European settlement vegetation percentage cover for Ohio (from Ohio Department of Natural Resources 2003).
Figure 7 from: DeWalt R, Cao Y, Tweddale T, Grubbs S, Hinz L, Pessino M, Robinson J (2012) Ohio USA stoneflies (Insecta, Plecoptera): species richness estimation, distribution of functional niche traits, drainage affiliations, and relationships to other states. ZooKeys 178: 1-26. https://doi.org/10.3897/zookeys.178.2616
Figure 7 - A–B Sampling intensity, drainage area, unique locations, and species richness relationships for HUC6 drainages A Sampling intensity for HUC6 drainages B Species richness vs. number of unique locations in HUC6 drainage areas.
ScienceDex guides
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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.