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36 results for “erasure”
Quantifying the erasure of earthquake surface ruptures from desert landscapes: Implications for seismic hazard assessment
<p><strong>Original Landscapes</strong></p> <p>DEMs of ~120x140m landscapes clipped from:</p> <p>R1-10 = 2019 M7.1 Ridgecrest earthquake, 2019 lidar (Hudnut et al., 2020), and </p> <p>E1-10 = 2010 M7.2 El Mayor-Cucapah earthquake, 2010 lidar (OpenTopography, 2010).</p> <p>Example: "E5.asc"</p> <p> </p> <p><strong>Degraded Landscapes</strong></p> <p>Linearly diffused using <em>Landlab </em>(Hobley et al., 2017; Barnhart et al., 2020) at timesteps (100, 1000, 5000, 10000 yr) using a <em>k</em> of 1 m^2/kyr.</p> <p>Example: "e5_1000_001_eroded.asc"</p> <p> </p> <p><strong>Mapped Faults Shapefiles </strong>- E1_10_shps & R1_10_shps</p> <p>Faults mapped on each degraded landscape using a systematic mapping process (Scott et al., 2023; Adam, 2023)</p> <p> </p> <p><strong>Ridgecrest DEM</strong> - rc_7_1_0424_utm.tif</p> <p>0.014 m/pix DEM of a portion of the 2019 M7.1 Ridgecrest earthquake rupture, from 6 April 2024. Created from Structure from Motion using drone images. </p> <p> </p> <p><strong>Degradation and analysis python code</strong> - landscape_evolution_earthquake_ruptures-main.zip</p> <p>A set of scripts to simulate the effect of surface processes on surface ruptures and quantify the information loss associated with landscape evolution over time. Includes options to simulate surface processes with linear and non-linear diffusion, implemented using open-access code landlab.</p> <p> </p> <p><strong>References</strong></p> <p>Adam, R. (2023). Evaluation of remote mapping of active fault traces. Arizona State University.</p> <p>Barnhart, K.R., Hutton, E.W.H., Tucker, G.E., Gasparini, NM., Istanbulluoglu, E., Hobley, D.E.J., Lyons, N.J., Mouchene, M., Nudurupati, S.S., Adams, J.M., Bandarogoda, C., 2020, Short communication: Landlab v2.0: A software package for Earth surface dynamics: Earth Surface Dynamics Discussions, doi: 10.5194/esurf-2020-12.</p> <p>Hobley, D.E.J., Adams, J.M., Nudurupati, S.S., Hutton, E.W.H. Gasparini, N.M., Istanbulluoglu, E., and Tucker, G.E., 2017, Creative computing with Landlab: an open-source toolkit for building, coupling, and exploring two-dimensional numerical models of Earth-surface dynamics: Earth Surface Dynamics, v. 5, n. 1, p. 21-46, doi: 10.5194/esurf-5-21-2017.</p> <p>Hudnut, K.W., B. Brooks, K. Scharer, J.L. Hernandez, T.E. Dawson, M.E. Oskin, R. Arrowsmith, C.A. Goulet, K. Blake, M.L. Boggs, S. Bork, C.L. Glennie, J.C. Fernandez-Diaz, A. Singhania, D. Hauser, S. Sorhus (2020). 2019 Ridgecrest, CA Post-Earthquake Lidar Collection. National Center for Airborne Laser Mapping (NCALM). Distributed by OpenTopography. https://doi.org/10.5069/G9W0942Z.. Accessed: 2024-11-25 </p> <p>Opentopography; El Mayor-Cucapah Earthquake (4 April 2010) Rupture LiDAR Scan. Distributed by OpenTopography. https://doi.org/10.5069/G9TD9V7D . Accessed: 2024-11-25</p> <p>Scott, C., Adam, R., Arrowsmith, R., Madugo, C., Powell, J., Ford, J., Gray, B., Koehler, R., Thompson, S., Sarmiento, A., Dawson, T., Kottke, A., Young, E., Williams, A., Kozaci, O., Oskin, M., Burgette, R., Streig, A., Seitz, G., … Ingersoll, S. (2023). Evaluating how well active fault mapping predicts earthquake surface-rupture locations. Geosphere. https://doi.org/10.1130/GES02611.1</p>
Datasets for "A superconducting dual-rail cavity qubit with erasure-detected logical measurements"
<p>Title of Dataset: Demonstrating a superconducting dual-rail cavity qubit with erasure-detected logical measurements<br>---</p> <p>Included are data shown in Figures 1-4 of the main text and Extended Data Figures 2 and 3 in the Methods.<br>Data was collected by running quantum programs on hardware deployed at Quantum Circuits, Inc. The results of these experiments are decoded and assigned an appropriate label (discussed below and in the manuscript). We provide various levels of processing: number of counts, fraction of counts, and the logical assignment. Full labeled shot-by-shot outcomes can be provided upon request.</p> <p>## Description of the data and file structure</p> <p>Description of column labels<br>- xs: sweep variable (if applicable)<br>- Counts:<br> - 00_counts, 01_counts, 10_counts, 11_counts: Number of runs with outcome labeled "00", "01", "10", "11", respectively<br> - A_counts: Number of runs with outcome labeled as ambiguous outcome<br> - FSP_counts: Number of runs with outcome labeled as a failed state preparation<br> - all_shots: Total number of runs<br> - total_counts: Number of runs with a successful state preparation (e.g. all_shots - B_counts)<br>- Outcome fraction<br> - 00, 01, 10, 11, A, FSP: Fraction of counts normalized by total_counts<br> - 00_err, 01_err, 10_err, 11_err, A_err, FSP_err: Standard error for all above<br>- Logical outcomes<br> - 0L, 1L, erasures, Z_L: Computed logical dual-rail outcome for "0_L", "1_L", erasures, and expectation value of logical sigma_z <Z>, respectively<br> - 0L_err, 1L_err, erasures_err, Z_L_err: Standard error for all above</p> <p>State assignment datasets:<br>- Fig2_state_assignment_1msmts.csv: Data for Figure 2<br>- ExtendedDataFig2_state_assignment_2msmts.csv: Data for Extended Data Figure 2</p> <p>Bit-flip datasets: <br>- Each row corresponds to a different delay specified in the xs column in units of microseconds<br>- Counts and Outcome fraction are plotted in Figure 3A<br>- Logical outcomes are plotted in Figure 3B<br>- Detail on datasets:<br> - Fig3_bit_flip_0L_1ms.csv: Data for Figure 3, left panel<br> - Fig3_bit_flip_0L_20us.csv: Data for Figure 3, left panel inset<br> - Fig3_bit_flip_1L_1ms.csv: Data for Figure 3, right panel<br> - Fig3_bit_flip_1L_20us.csv: Data for Figure 3, right panel inset</p> <p>Phase error datasets: <br>- Each row corresponds to a different delay specified in the xs column in units of microseconds<br>- Counts and Outcome fraction are plotted in the top panels for Figure 4A and 4B<br>- Logical outcomes are plotted in the bottom panel for Figure 4A and 4B<br>- Short time data are shown in the inset of bottom panel for Figure 4A and 4B. <br>- We provide additional detail for the short time data in Extended Data Figure 3 for short-time Ramsey<br>- Detail on ramsey_short_time.csv: Data includes an additional dimension where the Ramsey phase angle is swept<br> - values in radians are enumerated in ExtendedDataFig3_ramsey_short_time_phases.csv<br>- Detail on datasets:<br> - Fig4_ramsey_long_time.csv: Data for Figure 4A<br> - ExtendedDataFig3_ramsey_short_time.csv, ExtendedDataFig3_ramsey_short_time_phases.csv: Data for Figure 4A, inset (bottom panel); Data for Extended Data Figure 3<br> - Fig4_echo_long_time.csv: Data for Figure 4B<br> - Fig4_echo_short_time.csv: Data for Figure 4B, inset (bottom panel)</p>
DuckDuckGo: 'Erasure poem' query
<p>A query of "erasure poem" on the search engine DuckDuckGo, on November 18, 2020.</p>
Data-set for "Reliability and operation cost of underdamped memories during cyclic erasures" (Research Article, No. apxr.202300074) to Advanced Physics Research.
<p><strong>Dataset for the article: "Reliability and operation cost of underdamped memories during cyclic erasures" (Research Article, No. apxr.202300074) to Advanced Physics Research. </strong></p> <ul> <li><strong>FigX.fig: </strong>Matlab format figure source used to draw all of the plots of the article, embedding the all data.</li> <li><strong>evolution_K.m:</strong> Matlab code file that implement the Repeated Erasure model and compute the success rate of the erasure from the temperature evolution computed from the model.</li> <li><strong>Model_Landauer_enchaine.m:</strong> Matlab code file. Function which uses the SE Model to calculate the evolution of all the energy quantities (average) during step 1 and step 2 starting at an initial temperature CI, and also renders the final temperature conditions to be used iteratively in the RE Model.</li> <li><strong>Toy_Model.m: </strong>Theoretical toy model for explaning the Landauer repeted erasures temperature evolution.</li> <li><strong>Analysis_repeated_erasure.m:</strong> Matlab code file to treat raw data .mat files of 45 repeated erasures to compute the energy, work and heat evolution. The average is done on the full raw data samples corresponding to thousands of 45 repeated erasures.</li> <li><strong>XmsRawData.zip:</strong> Raw data of 45 repeated erasures of duration X ms each. Those Matlab data files are meant to be analysed with the Analysis_repeated_erasure code. Among the usefull variables contained in those files we identifiy :</li> </ul> <ol> <li>V: applied protocol, position of center of the wells in nm</li> <li>x: position signal in nm</li> <li>sigma_m: calibration of the standard deviation used by the feedback</li> <li>x0: threshold of the virtual potential</li> <li>tau1: duration of step1</li> <li>tau2: duration of step 2</li> <li>tau4: half resting time between step1 and step2</li> <li>X1: initial half distance between the wells</li> <li>dt: acquisition sampling time</li> </ol> <p><br> </p> <p> </p>
Data from: Erasure-cooling, control, and hyper-entanglement of motion in optical tweezers
Open the record for dataset details and reuse information.
Erasure Dataset
<p>This spreadsheet contains +400 creative works that use erasure techniques, methods, or processes in their composition and display. This dataset has been compiled by Álvaro Seiça between 2018-20.</p> <p>This project received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 793147, ARTDEL.</p> <p>More info at https://artdel.net</p>
Erasure typology
<p>This table illustrates a typology of erasure (updated Oct. 25, 2021). The fields and taxonomy have been refined along with the documentation of creative works that employ erasure techniques—dataset of +400 works available in this repository.</p> <p>This project received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 793147, ARTDEL.</p> <p>More info at https://artdel.net</p>
Data for "Fault-tolerant quantum architectures based on erasure qubits"
<p>Example Stim circuits used to simulate Floquet codes implemented with erasure qubits. Each circuit is the converted stabilizer circuit for a given pattern of erasure check detection events. To view the circuit diagram, please download the file and change the extension to html.</p>
Dataset: Learning efficient erasure protocols for an underdamped memory
<p>Data set for the article "Learning efficient erasure protocols for an underdamped memory", by Nicolas Barros, Stephen Whitelam, Sergio Ciliberto, and Ludovic Bellon. Available on <a href="https://arxiv.org/abs/2409.15050" target="_blank" rel="noopener">arXiv</a>.</p> <p>Inside the compressed file, you will find several folders.</p> <p>It contains:</p> <p>The pdf file of the article, as well as the movies provided in the supplementary material.</p> <p>The folder "Single Erasure" contains the data used for all the protocols attempted in a single iteration. One can find the raw data of the time traces (position z, threshold z0, and center of the well z1) inside the folders "ErasureTo0" and "Erasureto1", depending on the direction of the erasure. Snapshots of the velocities and position pdf are inside "xPDF" and "vPDF", as well as the resulting work distribution "WorkPDF..." and the final success rates "Success...." (a 0 denotes a failure of the erasure protocol, 1 a success).</p> <p>The folder "Chained Erasure" contains the results concerning the repetition of the erasure protocols, repeated 100 times with random targets. For each protocol, we provide the full time traces (z, z0, z1), as well as the resulting work distribution "W...", the mean temperature after each erasure "T...", and the survival rate "R...".</p> <p>Files starting with z, z1, z0 correspond to the raw data of the experimental trajectories and protocols. Each line represents an independent realization of an experiment, with a sampling frequency of 2 MHz.</p> <p>Please contact the authors for any additional data or questions about this dataset.</p>
Testing the Addition of Total Ablative Therapy to Usual Systemic Therapy Treatment for Limited Metastatic Colorectal Cancer, The ERASur Study
ClinicalTrials.gov study NCT05673148. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Role of Tet1 in genomic imprinting erasure [RNA-seq]
GEO Series GSE49761. Mus musculus. 13 samples. Type: Expression profiling by high throughput sequencing.
Erasure of Tet-oxidized 5-methylcytosine by a SRAP nuclease
GEO Series GSE81222. Mus musculus. 31 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Methylation profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
An ExtendedCulture System that Supports Human Primordial Germ Cell-Like Cells Survival and Initiation of DNA Methylation Erasure
GEO Series GSE139115. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing; Methylation profiling by high throughput sequencing.
Genome-wide erasure of DNA methylation in mouse primordial germ cells is affected by Aid deficiency
GEO Series GSE19960. Mus musculus. 12 samples. Type: Methylation profiling by high throughput sequencing.
Transgenerational Sperm DMRs and Low CpG Density Sites Escape Methylation Erasure in Embryonic Epigenetic Inheritance
GEO Series GSE211135. Rattus norvegicus. 13 samples. Type: Methylation profiling by high throughput sequencing.
Fate Erasure Logic of Gene Networks Underlying Direct Neuronal Conversion of Somatic Cells by MicroRNAs III
GEO Series GSE283008. Homo sapiens. 16 samples. Type: Expression profiling by high throughput sequencing.
PGC reversion to pluripotency involves erasure of DNA methylation from imprinting control centers followed by locus-specific re-methylation
GEO Series GSE69354. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
Fate Erasure Logic of Gene Networks Underlying Direct Neuronal Conversion of Somatic Cells by MicroRNAs II
GEO Series GSE283006. Homo sapiens. 24 samples. Type: Expression profiling by high throughput sequencing.
Tet-mediated imprinting erasure following reprogramming of spermatogonial stem cells to induced pluripotent stem cells
GEO Series GSE64856. Mus musculus. 9 samples. Type: Expression profiling by high throughput sequencing.
Fate Erasure Logic of Gene Networks Underlying Direct Neuronal Conversion of Somatic Cells by MicroRNAs I
GEO Series GSE283004. Homo sapiens. 20 samples. Type: Expression profiling by high throughput sequencing.
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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)
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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.