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35 results for “degradation models”
Beyond the Four-Level Model: Dark and Hot States in Quantum Dots Degrade Photonic Entanglement
<p><strong>Dataset for "Beyond the four-level model: Dark and hot states in quantum dots degrade photonic entanglement"</strong></p> <p><em>Nano Lett.</em> 2023, 23, 4, 1409–1415<br> Publication Date: February 6, 2023<br> <a href="https://doi.org/10.1021/acs.nanolett.2c04734">https://doi.org/10.1021/acs.nanolett.2c04734</a></p> <p>A description of the dataset is found in the <strong>readme.md</strong> file (markdown markup language).</p> <p><strong>Data reuse</strong><br> Please cite B.U. Lehner et al., <em>Nano Lett.</em> 2023, 23, 4, 1409–1415 (2023) in publications that reuse this data and if possible inform the corresponding authors.</p>
Data and Code for: Phospholipase-catalyzed Degradation Drives Domain Morphology and Rheology Transitions in Model Lung Surfactant Monolayers
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Data for "A stochastic model of geomorphic risk due to episodic river aggradation and degradation"
<p>The code and the dataset can be read/run by using Matlab. The description as follows:<br>1. Dataset of riverbed measurement (long profile and water level gauge data), carbon dating data, and rainfall record in the Laonong River (Taiwan). The dataset are used for the model calibration and the model application. <br>2. The developed riverbed stochastic processing model and the maximum likelihood calibration model. </p> <p>Note: this new version includes the corrected Monte Carlo simulation code and a required Matlab function (fminsearchbnd.m) that was missing in the first version.</p>
Project - Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis
<p>Here are the datasets for our publication entitled "<a href="https://www.nature.com/articles/s41467-024-48779-z">Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis</a>" published in Nature Communications. </p> <p>The object of this experiment is the 18650 nickel-cobalt-manganese (NCM) lithium-ion battery manufactured by "LISHEN". The chemical composition is LiNi<sub>0.5</sub>Co<sub>0.2</sub>Mn<sub>0.3</sub>O<sub>2</sub>. The nominal capacity of the battery is 2000 mAh, and the nominal voltage is 3.6 V. The charging cut-off voltage and discharging cut-off voltage are 4.2 V and 2.5 V, respectively. The whole experiment was conducted at room temperature. A total of 55 batteries were included in this experiment, conducted under 6 different charging and discharging strategies. The charging and discharging platform is ACTS-5V10A-GGS-D, and the sampling frequency for all data is 1Hz.</p> <p>Other details can be found in "Data Introduction.pdf" file.</p> <p>The <strong>Python Code</strong> for reading and preprocessing this dataset is available at: <a href="https://github.com/wang-fujin/Battery-dataset-preprocessing-code-library">https://github.com/wang-fujin/Battery-dataset-preprocessing-code-library</a></p> <p>Summary of articles using the this dataset: <a href="https://github.com/wang-fujin/XJTU-Battery-Dataset-Papers-Summary">https://github.com/wang-fujin/XJTU-Battery-Dataset-Papers-Summary</a></p> <p> </p> <p>If you find this data helpful, please consider citing our paper:</p> <p>Wang, F., Zhai, Z., Zhao, Z. <em>et al.</em> Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis. <em>Nat Commun</em> <strong>15</strong>, 4332 (2024). https://doi.org/10.1038/s41467-024-48779-z</p>
Rapid and specific degradation of endogenous proteins in mouse models using auxin-inducible degrons
<p>Auxin-inducible degrons are a chemical genetic tool for targeted protein degradation and are widely used to study protein function in cultured mammalian cells. Here we develop CRISPR-engineered mouse lines that enable rapid and highly specific degradation of tagged endogenous proteins <em>in vivo</em>. Most but not all cell types are competent for degradation. By combining ligand titrations with genetic crosses to generate animals with different allelic combinations, we show that degradation kinetics depend upon the dose of the tagged protein, ligand, and the E3 ligase substrate receptor TIR1. Rapid degradation of condensin I and condensin II – two essential regulators of mitotic chromosome structure - revealed that both complexes are individually required for cell division in precursor lymphocytes, but not in their differentiated peripheral lymphocyte derivatives. This generalisable approach provides unprecedented temporal control over the dose of endogenous proteins in mouse models, with implications for studying essential biological pathways and modeling drug activity in mammalian tissues.</p>
Contaminations on Lidar Sensor Covers: Performance Degradation including Fault Detection and Modeling as Potential Applications
<p><strong>Data description of contamination measurements with lidar sensors RIEGL LD05-A20 and Ouster OS1-64</strong></p> <p><em><strong>Photos of the measurement setup</strong></em></p> <p>We provide photos of the measurement setup and the contaminations applied in the folder "/photos".</p> <p> </p> <p><em><strong>Riegl LD05-A20 data</strong></em></p> <p>The data of the Riegl LD05-A20 can be found in the folder "riegl_LD05-A20_full_waveforms" - one file per experiment. An example notebook for reading the data is provided in "/notebooks/example_riegl_LD05-A20.ipynb". Note that the files contain only the prominent peaks of the full waveform calculated by the V08Wave software provided by RIEGL. If you are interested in the entire full waveforms, please contact the authors.</p> <p> </p> <p><em><strong>Ouster OS1-64</strong></em></p> <p>The data of the Ouster OS1-64 can be found in the folder "ouster_OS1-64_point_clouds" - one folder per experiment. An example notebook for reading the data is provided in "/notebooks/example_ouster_OS1-64.ipynb". The python package <strong><em>pointcloudset</em></strong> (https://github.com/virtual-vehicle/pointcloudset) and its documentation is suggested for further data analytics of the point cloud data.</p> <p> </p>
Simulation results with the EULAG research model for the publication: "Large eddy simulations of the interaction between the Atmospheric Boundary Layer and degrading Arctic permafrost"
<p>Supplementary material for the publication</p> <ul> <li>Mark Schlutow, Tobias Stacke, Tom Doerffel, et al. Large eddy simulations of the interaction between the Atmospheric Boundary Layer and degrading Arctic permafrost. ESS Open Archive . January 24, 2024. <a href="https://doi.org/10.22541/essoar.170612558.81370785/v1">https://doi.org/10.22541/essoar.170612558.81370785/v1</a></li> </ul> <p>The material contains all simulation results and raw outputs that are necessary to reproduce the figures and statistics of the publication. </p>
Electromagnetic Wave Dataset for Strength Degradation Detection in Reinforced Concrete Structures Using RFID Measurements and CNN Model
<p>This dataset comprises 1,800 electromagnetic wave (EM-wave) images collected from three different reinforced concrete beams subjected to varying levels of corrosion. Each image is classified into 'normal' or 'reduced strength' categories based on the beam's structural integrity. Generated through a non-destructive RFID-based monitoring technique, this dataset integrates advanced analyses like 2-D Fourier transforms and fractal dimensions. It is specifically designed to train and validate Convolutional Neural Networks (CNNs) for detecting strength degradation in reinforced concrete structures.</p>
Dataset for Symptoms of performance degradation during multi-annual drought: a large-sample, multi-model study
<p>This dataset contains the data described in the journal article "Symptoms of performance degradation during multi-annual drought: a large-sample, multi-model study" (Trotter et al., 2023, <strong>DOI: </strong>10.1029/2021WR031845)</p>
Rapid and specific degradation of endogenous proteins in mouse models using auxin-inducible degrons
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Predictive Modeling of Bearing Degradation: LSTM Neural Networks for Uncertainty Quantification
<p>These MATLAB codes are part of a research project focused on predicting bearing degradation through vibration measurements. The codes implement LSTM (Long Short-Term Memory) neural network models trained under different objectives, including uncertainty quantification and RMSE (Root Mean Square Error) minimization. The objective of the research is to compare the performance of these models in predicting bearing health and assessing the associated uncertainty.</p> <p><strong>Note:</strong> The current codes are under embargo access as the corresponding paper has been submitted to the ESCA 11 conference. The codes will be made openly accessible upon acceptance of the paper and during the presentation dates. Please cite our paper when using these codes.</p>
Modeling bacterial interactions uncovers the importance of outliers in the coastal lignin degrading consortium
<h3> FT-ICR-MS spectrometry data.</h3> <ul> <li>LD: LD consortium 1.zip, LD consortium 2.zip, LD consortium 3.zip;</li> <li>P: Pluralibacter gergoviae 1.zip, Pluralibacter gergoviae 2.zip, Pluralibacter gergoviae 3.zip;</li> <li>Blank: Blank 1.zip, Blank 2.zip, Blank 3.zip</li> </ul>
Diagnosis of lithium-ion batteries degradation with P2D model parameters identification: a case study on low temperature charging
<p>Dataset and code of the equilibrium model. They refer to publication <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.fub.2024.100006" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.fub.2024.100006</span></span></a></p>
Degraded Pastures in Brasil: dataset used in econometric models
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Figure 3 from: Schwerk A, Szyszko J (2011) Model of succession in degraded areas based on carabid beetles (Coleoptera, Carabidae). ZooKeys 100: 319-332. https://doi.org/10.3897/zookeys.100.1534
Figure 3 - Model graphs based on the parameters in Table 1 for the relationship between MIB values (mg) and age of stands for the first 60 years in A planted stands on forest soil, B naturally regenerated stands on post-agricultural soil, C planted stands on post-agricultural soil, D stands on ash heap, and E stands on mining heap (Open circles indicate that MIB was calculated from less than 25 individuals; broken lines indicate that the respective part of the graph cannot be verified due to a lack of data).
Figure 2 from: Schwerk A, Szyszko J (2011) Model of succession in degraded areas based on carabid beetles (Coleoptera, Carabidae). ZooKeys 100: 319-332. https://doi.org/10.3897/zookeys.100.1534
Figure 2 - Schematic illustration of the model. Succession starts from an 'initial degradation level', followed by a facultative 'delay phase' (characterized by the time of 'delay'). The optional 'delay phase' is followed by an 'increase phase' (characterized by the 'increase rate'), and a 'stagnation phase' in which the succession process runs towards a 'recovery level'. The type of area, origin of vegetation, and landscape-related aspects are assumed to influence the trajectory of succession.
Figure 1 from: Schwerk A, Szyszko J (2011) Model of succession in degraded areas based on carabid beetles (Coleoptera, Carabidae). ZooKeys 100: 319-332. https://doi.org/10.3897/zookeys.100.1534
Figure 1 - Relationship between MIB values (mg) and age of stands in A planted stands on forest soil (r = 0.343, p <0.05), B naturally regenerated stands on post-agricultural soil (r = 0.677, p <0.001), C planted stands on post-agricultural soil (r = 0.238, p <0.001), D stands on ash heap (r = 0.025, n.s.) and E stands on mining heap (r = 0.839, p <0.001) (Open circles indicate that MIB was calculated from less than 25 individuals).
Multi-omics Analysis Unveils Metabolic Inflexibility Manifesting as Impaired Branched Amino Acid and Enhanced Fatty Acid Degradation in a Mouse Model of Diabetic Cardiomyopathy [RNA-seq]
GEO Series GSE274500. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
A selective WDR5 degrader inhibits acute myeloid leukemia in patient-derived mouse models [ChIP-seq]
GEO Series GSE150552. Homo sapiens. 9 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
A selective WDR5 degrader inhibits acute myeloid leukemia in patient-derived mouse models[RNA-seq]
GEO Series GSE150554. Homo sapiens. 26 samples. Type: Expression profiling by high throughput sequencing.
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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)
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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.