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74 results for “error correction”

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zenodo48/100

Data supporting 'Empirical correction of systematic orthorectification error in Sentinel-2 velocity fields for Greenlandic outlet glaciers'

Note: An updated dataset covering the majority of Greenland's marine-terminating glaciers is available as part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) project through the National Snow and Ice Data Center (NSIDC) at <a href="https://doi.org/10.5067/B28FM2QVVYWY">https://doi.org/10.5067/B28FM2QVVYWY</a>.  Data supporting the paper: <blockquote> Chudley, T. R., Howat, I. M., Yadav, B. N., & Noh, M. J. (2022). Empirical correction of systematic orthorectification error in Sentinel-2 velocity fields for Greenlandic outlet glaciers. The Cryosphere. 16, 2629–2642, https://doi.org/10.5194/tc-16-2629-2022 </blockquote> Dataset consists of four netCDF files containing stacked Sentinel-2 velocity data of four Greenlandic outlet glaciers (Helheim Glacier, Jakobshavn Isbræ, Store Glacier, and Kangerlussuaq) between 2017 and 2021. Velocity data are derived and corrected following the methods outlined in Chudley et al. (2022).   NetCDF files are created by, and tested to be readable by, Python's xarray package. The dimensions of the netCDF file are as follows: <ul> <li>X - x coordinates in NSDIC Sea Ice Polar Stereographic North (EPSG:3413).</li> <li>Y - y coordinates in NSDIC Sea Ice Polar Stereographic North (EPSG:3413).</li> <li>time - temporal midpoint of velocity field.</li> </ul> The variables of the netCDF file are as follows: <ul> <li>dmag - the absolute magnitude of the velocity, in metres per day.</li> <li>dx - the velocity in the x direction, in metres per day.</li> <li>dy - the velocity in the y direction, in metres per day.</li> <li>date1 - the date and time of the first scene acquisition.</li> <li>date2 - the date and time of the second scene acquisition.</li> <li>baseline - the temporal baseline, in days, between scene acquisitions.</li> <li>orbit_pair - the combination of orbital pathways in the string format 'RXXX_RYYY', where XXX is relative orbit number of the first scene and YYY the relative orbit number of the second scene.</li> <li>mag_rmse - the root mean square error of the absolute velocity of the off-ice area. </li> <li>dx_mean - the mean velocity of the off-ice area in the x direction.</li> <li>dx_sd - the standard deviation of the velocity of the off-ice area in the x direction.</li> <li>dy_mean - the mean velocity of the off-ice area in the x direction.</li> <li>dy_sd - the standard deviation of the velocity of the off-ice area in the y direction.</li> </ul>

opencc-by-4.0Jun 2022View details →

zenodo48/100

Kinetochore life histories reveal an Aurora B dependent error correction mechanism in anaphase

Dataset of kinetochore tracks in human RPE1 cells showing chromosome dynamics and segregation from prometaphase through to anaphase as described in detail in Sen, Harrison, Burroughs and McAinsh, 2021, https://doi.org/10.1101/2021.03.30.436326 Tracks correspond to 3D time-lapse movies of Ndc80-eGFP and were acquired in the 488nm channel using 1\% laser power, 50 ms exposure time/z-plane, 93 z-planes, 307 nm z-step, which results in 4.7 s/z-stack time frame. Cells are subject to nocodazole arrest-and-release or equivalent treatment with DMSO as indicated in the folder names, and some cells are subject to additional treatment with ZM to inhibit Aurora B (also indicated in folder names). Tracks were produced using kinetochore tracking software, KiT v2.3 (see Armond et al., 2016, Bioinformatics), available from https://github.com/cmcb-warwick/KiT/

opencc-by-4.0Oct 2021View details →

zenodo44/100

Demonstrating real-time and low-latency quantum error correction with superconducting qubits

Data associated with results presented in "Demonstrating real-time and low-latency quantum error correction with superconducting qubits". HDF5 files include raw data collected during experiments. Datasets for experiments performed with different number of measurement rounds are saved in separate groups. The group attributes contain information including the total number of measurement rounds. Groups also contain the stim circuits associated with each experiment, which are used for software decoding, and qubit_mappings, which maps each stim coordinate to the corresponding qubit ID on the Ankaa-2 device. Each group has a hard_measurements and soft_measurements group containing the hard and soft measurement results. Measurement results are grouped in datasets per qubit, storing results in the order of measurement execution during the experiment, and with each row representing a separate repetition of the experiment. When decoding with the FPGA decoder we also store the decoder register outcomes in decoder_shot_results. In particular, the first column indicates the logical correction computed by the FPGA decoder – values 0 and 2 correspond to no logical error detected and 1 corresponds to logical error being detected by the decoder. The HDF5 file with data for the fast-feedback experiment ("fast_feedback_raw_data.h5") includes the reference_data group storing reference data. It contains the "delays" group (used to measure T1 in FigS4(d)), "measurement_fidelity" group (used to calculate measurement confusion matrix in Fig S4e, and "double_measurement" group (used to compute post-measurement state distribution in Fig S4f). Also included are files containing the logical error probabilities (LEPs), and CSV files containing timings, both containing data used to plot figures.

opencc-by-4.0Oct 2024View details →

zenodo44/100

A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction - Syndromes Dataset

Simulated sydromes measurement of quantum surface code error correction. Used for the paper: "<a href="https://doi.org/10.48550/arXiv.2307.09463">A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction</a>". File names: <code>d-<surface_code_distance>_pfr-<physical_fault_rate>_nb-<number_of_samples></code> Each file is formatted as csv with the following columns: <ul> <li>label: binary label (0: no error, 1: error)</li> <li>syndromes: syndrome measurement sequence (tuples of the form (round, syndromes))</li> <li>quantity: number of samples for this label + syndrome sequence</li> </ul> Only distance 3 is currently available with 10M samples for each physical fault rate. The data generation relies on <a href="https://github.com/quantumlib/Stim" target="_blank" rel="noopener">Stim</a>.

opencc-by-4.0May 2024View details →

zenodo44/100

A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction - Simulation Data

Simulation output data used to generate figures of the paper: "<a href="https://doi.org/10.48550/arXiv.2307.09463">A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction</a>"

opencc-by-4.0May 2024View details →

zenodo44/100

Data for "Optimization of decoder priors for accurate quantum error correction"

Datasets of surface code and repetition code memory experiments executed on Google's Sycamore quantum processor. See README at the root of each zip archive for detailed description of each dataset.

opencc-by-4.0May 2024View details →

zenodo44/100

Data for "Quantum error correction below the surface code threshold"

Datasets of the surface code and repetition code memory experiments executed on Google's quantum processor. See README at the root of each zip archive for detailed description of each dataset.

opencc-by-4.0Aug 2024View details →

zenodo40/100

Determining non-significant bits on a C++ implementation of the LeNet-5 convolutional neural network to be used for storing error correcting codes to protect weights and biases. Robustness assessment of the network after integrating the proposed codes.

The architecture of the LeNet-5 convolutional neural network (CNN) was defined by LeCun in its paper "Gradient-based learning applied to document recognition" (<a href="https://ieeexplore.ieee.org/document/726791">https://ieeexplore.ieee.org/document/726791</a>) to classify images of hand written digits (MNIST dataset).This architecture has been customized to use Rectified Linear Unit (ReLU) as activation functions instead of Sigmoid.It consists of the following layers:<ul><li>conv1: Convolution 2D, 1 input channel (28x28), 3 output channels (28x28), kernel size 5, stride 1, padding 2.</li><li>relu1: Rectified Linear Unit (3@28x28).</li><li>max1: Subsampling buy max pooling (3@14x14).</li><li>conv2: Convolution 2D, 3 input channels (14x14), 6 output channels (14x14), kernel size 5, stride 1, padding 2.</li><li>relu2: Rectified Linear Unit (6@14x14).</li><li>max2: Subsampling buy max pooling (6@7x7).</li><li>fc1: Fully connected (294, 147)</li><li>fc2: Fully connected (147, 10)</li></ul>The fault hypotheses for this work include the occurrence of:<ul><li>S0/S1: multiple adjacent stuck-at-0 and stuck-at-1 faults to determine the least significant bits of weights and biases that could be used to store the proposed error correcting codes.</li><li>BF: single, double, and triple bit-flip faults to assess the robustness of the considered CNN</li></ul>In the memory cells containing all the parameters of the CNN:  <ul><li>w: weights (float32)</li><li>b: biases (float32)</li></ul>All the images (10000) from the MNIST dataset have been used as workload.The weights and biases of the LeNet-5 architecture have been protected using six different error correcting codes that have been deployed in the least significant bits of these elements.The parity check matrices (H = P I) that define these ECCs are:<ul><li>SEC(32, 26) (Hamming) under a classic policy (see methodology below):</li></ul>        11010010001000011101101000 100000        10101001000100011011010100 010000        01100100100010010110110010 001000        00011100010001001110001101 000100        00000011110000100001111011 000010        00000000001111100000000111 000001<ul><li>SEC(23, 18) (Hamming) under a conservative policy (see methodology below):</li></ul>        111100001111000000 10000        110011101000111000 01000        101011010100100110 00100        010110110010010101 00010        001101110001001011 00001<ul><li>SEC(13, 9) (Hamming) under an aggressive policy (see methodology below):</li></ul>        110111000 1000        101100110 0100        011010101 0010        111001011 0001<ul><li>DEC(32, 21) (low redundancy and reduced overhead DEC) under a classic policy (see methodology below):</li></ul>        111000011001010010000 10000000000        110110000011101000000 01000000000        101011000110000010001 00100000000        100101101000110001000 00010000000        011010101100100000100 00001000000        010101010100001001010 00000100000        001100110010010100100 00000010000        000011110001000110010 00000001000        000000001111001101001 00000000100        000000000000111100111 00000000010        000000000000000011111 00000000001<ul><li>DEC(28, 18) (low redundancy and reduced overhead DEC) under a conservative policy (see methodology below):</li></ul>        111111000000000000 1000000000        110100111100000000 0100000000        110000100011110000 0010000000        001110010011001100 0001000000        101100001010101010 0000100000        010001001101010110 0000010000        001011000101101001 0000001000        101000011000110101 0000000100        010001110000011011 0000000010        000010100110000111 0000000001<ul><li>DEC(17, 9) (low redundancy and reduced overhead DEC) under an aggressive policy (see methodology below):</li></ul>        111110000 10000000        111001100 01000000        110101010 00100000        101010110 00010000        101101001 00001000        100110101 00000100        100011011 00000010        110000111 00000001This dataset contains the raw data obtained from:<ul><li>running exhaustive fault injection campaigns for increasingly multiple stuck-at faults in the least significant bits of all weights and biases (simultaneously) and for all the images in the workload.</li><li>running statistical fault injection campaigns for single, double, and triple bit-flip faults, randomly targeting the considered locations and images in the workload.</li></ul><h3>Files information</h3><ul><li>no_ecc folder: Results obtained for the original (not protected) version of the CNN.<ul><li>golden_run.csv: Prediction obtained for all the images considered in the workload in the absence of faults (Golden Run). This is intended to act as oracle to determine the impact of injected faults.</li><li>sampling_SBF_10000.csv: Prediction obtained for running 10000 statistical fault injection experiments for single bit-flip faults.</li><li>sampling_DBF_10000.csv: Prediction obtained for running 10000 statistical fault injection experiments for double bit-flip faults.</li><li>sampling_TBF_10000.csv: Prediction obtained for running 10000 statistical fault injection experiments for triple bit-flip faults.</li><li>locating_sensitive_bits folder: Prediction obtained for all the images considered in the workload in presence of stuck-at-0/stuck-at-1 faults that simultaneously target the N least significant bits of all weights and biases. There is one file for each parameter of type of fault and range of targeted bits. Files for bits in the range [11, 0] are not included as they obtain eactly the same results as the Golden Run (faults do not alter the behaviour of the network).</li></ul></li><li>sec/classic, sec/conservative, and sec/aggressive folders: They contain the results obtained for the CNN protected by SEC(32, 26), SEC(23, 18), and SEC(13, 9), respectively.<ul><li>golden_run.csv: Prediction obtained for all the images considered in the workload in the absence of faults (Golden Run). This is intended to act as oracle to determine the impact of injected faults. It must be noted that this file could be different that the golden_run.csv file for the original version of the CNN, as deploying the ECC in the weights and biases may have affected the behaviour of the network.</li><li>sampling_SBF_10000.csv: Prediction obtained for running 10000 statistical fault injection experiments for single bit-flip faults. They should all be tolerated by the definition of the ECC.</li><li>sampling_DBF_10000.csv: Prediction obtained for running 10000 statistical fault injection experiments for double bit-flip faults. They could be more harmful than for the unprotected version of the CNN, as the ECC may erroneously flip correct bits.</li></ul></li><li>dec/classic, dec/conservative, and dec/aggressive folders: They contain the results obtained for the CNN protected by DEC(32, 21), DEC(28, 18), and DEC(17, 9), respectively.<ul><li>golden_run.csv: Prediction obtained for all the images considered in the workload in the absence of faults (Golden Run). This is intended to act as oracle to determine the impact of injected faults. It must be noted that this file could be different that the golden_run.csv file for the original version of the CNN, as deploying the ECC in the weights and biases may have affected the behaviour of the network.</li><li>sampling_DBF_10000.csv: Prediction obtained for running 10000 statistical fault injection experiments for double bit-flip faults. They should all be tolerated by the definition of the ECC.</li><li>sampling_TBF_10000.csv: Prediction obtained for running 10000 statistical fault injection experiments for triple bit-flip faults. They could be more harmful than for the unprotected version of the CNN, as the ECC may erroneously flip correct bits.</li></ul></li></ul><h3>Methodology information</h3>First, the CNN was used to classify all the images of the workload in the absence of faults to get a reference to determine the impact of faults. This is golden_run.csv file.To locate non-significant bits in weights and biases, fault injection experiments were executed targeting all elements of all parameters of the CNN using the following procedure:<ul><li>The initial mask targeted only the least significant bit</li><li>Until the mask targets all bits of the elements (32 bits as they are single-precision floating point values):<ul><li>Affect the bits (setting them to 0 or 1 in case of stuck-at-0 or stuck-at-1 faults) identified by the mask for all elements of all parameters.</li><li>Classify all the images of the workload in the presence of this fault. The obtained output was stored in a given .csv file.</li><li>Remove the fault from the CNN by restoring the affected bits to its previous value.</li><li>Add the next adjacent bit to the mask, so it targets an additional least significant bit.</li></ul></li></ul>The analysis of the obtained results may help in determining which bits can be used to store an ECC:<ul><li>which bits never affect the behaviour of the CNN, as the predicted classification is exactly the same than in the absence of faults.</li><li>which bits midly affect the behaviour of the CNN, as although the predicted classifications differ from those in the absence of faults, the accuracy of the network is barely affected.</li><li>which bits greatly affect the behaviour of the CNN, as the accuracy of the network is significantly affected.</li></ul>Accordingly, three different policies have been identified for deploying an ECC using these bits:<ul><li>Classic policy: The ECC protects as much bits as possible.</li><li>Conservative policy: The ECC protects all those bits that may affect the prediction of the network.</li><li>Aggressive policy: The ECC protects only those bits that significantly affect the accuracy of the network.</li></ul>After designing and deploying a single ECC and a double ECC for each of the identified policies, fault injection experiments were executed to verify their behaviour in the presence of faults.Single and double ECCs were tested against single and double bit-flip, respectively (all faults should be tolerated,) and double and triple bit-flips, respectively (a correct bit could be erroneously flipped.)Due to the heavy computational load of the decoders, statistical injection was used to run the required fault injection campaigns with a sample size (number of experiments) of 10000.Each experiment consisted in:<ul><li>Randomly selecting the image to process, and the parameter, element, and bits (mask) to be targeted by the fault.</li><li>Affecting the bits (inverting them) identified by the mask.</li><li>Classifying the selected image of the workload in the presence of this fault. The obtained output was stored in a given .csv file.</li><li>Removing the fault from the CNN by restoring the affected bits to its previous value.</li></ul><h3>List of variables (Name : Description (Possible values))</h3><ul><li>IMGID: Integer number identifying the considered image (1-9999).</li><li>TENSORID: Integer number identiying the parameter affected by the fault (0 - No fault, 1 - conv1.w, 2 - conv1.b, 3 - conv2.w, 4 - conv2.b, 5 - fc1.w, 6 - fc1.b, 7 - fc2.w, 8 - fc2.b).</li><li>ELEMID: Integer number identiying the element of the parameter affected by the fault (-1 - No fault, [0-2] - conv1.b, [0-74] - conv1.w, [0-5] - conv2.b, [0-149] - conv2.w, [0-146] - fc1.b, [0-43217] - fc1.w, [0-9] - fc2.b, [0-1469] - fc2.w).</li><li>MASK: 8-digit hexadecimal number identifying those bits affected by the fault ([00000000 - No fault, FFFFFFFF - all 32 bits faulty]).</li><li>FAULT: String identiying the type of fault (NF - No fault, BF - bit-flip, S0 - Stuck-at-0, S1 - Stuck-at-1).</li><li>SOFTMAX: 10 decimal numbers obtained after applying the softmax function to the provided output. They represent the probability of the image of belonging to the corresponding category for classification.</li><li>PRED: Integer number representing the category predicted for the processed image.</li><li>LABEL: integer number representing the actual category for the processed image.</li></ul>

opencc-by-4.0Nov 2023View details →

zenodo40/100

Linked collectors and determiners for: Correction of collecting number errors in the protologues of sixty-four taxon names from China.

Natural history specimen data linked to collectors and determiners held within, "Correction of collecting number errors in the protologues of sixty-four taxon names from China". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/057ddfc8-53dc-4dc3-89ce-058fcc84ff71">https://bionomia.net/dataset/057ddfc8-53dc-4dc3-89ce-058fcc84ff71</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/057ddfc8-53dc-4dc3-89ce-058fcc84ff71">https://gbif.org/dataset/057ddfc8-53dc-4dc3-89ce-058fcc84ff71</a>. Formatted as a Frictionless Data package.

opencc-zeroJul 2024View details →

zenodo40/100

Tracking Data I/II of the publication "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction"

DATA belonging to the paper "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction" Katharina Löffler, Tim Scherr, Ralf Mikut doi: https://doi.org/10.1101/2021.03.16.435631 ----------------------------- To investigate the influence of different segmentation errors on the tracking performance we simulate errorneous segmentation data: - under-segmentation (referred to as "merge" in the folders), over-segmentation("split"), False Negatives ("remove"), combination of the aforementioned errors ("mixed") - percentages: 1,2,5,10,20 of errorneous masks per dataset - runs: 5 randomly initialized runs per combination - datasets: Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ each with two image sequences ---> in total 4 (error types) * 5 (percentage) * 5 (runs) * 2 (data sets) * 2 (image sequences) = 400 datasets The datasets can be recreated by running our code https://git.scc.kit.edu/KIT-Sch-GE/2021-cell-tracking ---------------------------- RESULTS We evuated the four tracking algorithms KIT-Sch-GE(1), KTH-SE, MU-Lux-CZ and our proposed algorithm on the aforementioned datasets and compare their performance using the CTC metrics DET, SEG and TRA. This repository contains all metrics as xls files and all tracking results as image sequences. xls files ----------- compare_all_trackers_on_synt_bm.csv Comparing the tracking algorithms MU-Lux-CZ, KTH-SE, KIT-Sch-GE(1) and the proposed tracking algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets). Reported scores are DET, SEG and TRA from the Cell Tracking Challenge (Fig8 and Fig9 and Supplementary Figures 3 and 4 are created from this data) compare_postprocessing_on_synth_bm.csv Comparing the different post-processing strategies of the proposed tracking algorithm algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets). Reported scores are DET, SEG and TRA from the Cell Tracking Challenge (Fig7 and Fig8 and Supplementary Figures 1 and 2 are created from this data) PLEASE NOTE: the folder compare_postprocessing_synth_bm  is provided in the repository 10.5281/zenodo.5227610 due to size restrictions. folders (decompressed approximately 90GB of data!) ----------- tracking_data     compare_all_synth_bm     Contains all tracking results for each tracking algorithm on the synthetically degraded datasets ()     compare_all_synth_bm_no_error     Contains the tracking results for each tracking algorithm provided with the perfect ground truth segmentation data     compare_postprocessing_synth_bm [will be stored in 10.5281/zenodo.5227610 due to size restrictions]     Contains all tracking resuls for each postprocessing configuration of the proposed cell tracking algorithm     the leaf folders are names run_xPOSTPROCESSING where x is the run number and POSTPROCESSING the postprocessing key     Postprocessing keys: ("no untangle" or "no masks" is indicated by an overline in the paper)     all ("untangle + masks" in the paper)     nd ("no untangle + masks")     nd_ns-l ("no untangle + no masks")     ns-l ("untangle + no masks")

opencc-by-4.0Aug 2021View details →

zenodo40/100

Reactive Correction of Object Placement Errors for Robotic Arrangement Tasks (Video)

Supplementary video for the paper "Reactive Correction of Object Placement Errors for Robotic Arrangement Tasks".

opencc-by-4.0Feb 2023View details →

zenodo36/100

Data from "Hardware-efficient quantum error correction using concatenated bosonic qubits"

Includes data for characterizing the bit-flip and logical phase-flip rates of the logical memory.  See the README for more details.

opencc-by-4.0Dec 2024View details →

zenodo36/100

A method for correcting staggered pulse repetition time (PRT) and dual Pulse Repetition Frequency (PRF) processor errors

A method for correcting dual-PRF and staggered-PRT induced radial velocity errors. This method is being developed by the authors at the University of Oklahoma and is considered to be a work in progress. v2 of the algorithm will be made available in the coming months (late 2019 or early 2020).

opencc-by-4.0Jul 2018View details →

zenodo36/100

Tracking Data II/II of the publication "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction"

DATA belonging to the paper "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction" Katharina Löffler, Tim Scherr, Ralf Mikut doi: https://doi.org/10.1101/2021.03.16.435631 ----------------------------- To investigate the influence of different segmentation errors on the tracking performance we simulate errorneous segmentation data: - under-segmentation (referred to as "merge" in the folders), over-segmentation("split"), False Negatives ("remove"), combination of the aforementioned errors ("mixed") - percentages: 1,2,5,10,20 of errorneous masks per dataset - runs: 5 randomly initialized runs per combination - datasets: Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ each with two image sequences ---> in total 4 (error types) * 5 (percentage) * 5 (runs) * 2 (data sets) * 2 (image sequences) = 400 datasets The datasets can be recreated by running our code https://git.scc.kit.edu/KIT-Sch-GE/2021-cell-tracking ---------------------------- RESULTS We evuated the four tracking algorithms KIT-Sch-GE(1), KTH-SE, MU-Lux-CZ and our proposed algorithm on the aforementioned datasets and compare their performance using the CTC metrics DET, SEG and TRA. This repository contains all metrics as xls files and all tracking results as image sequences. PLEASE NOTE: this repository contains only the folder compare_postprocessing_synth_bm All other datasets and files are provided in 10.5281/zenodo.5227595 due to size restrictions. xls files ----------- compare_all_trackers_on_synt_bm.csv Comparing the tracking algorithms MU-Lux-CZ, KTH-SE, KIT-Sch-GE(1) and the proposed tracking algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets). Reported scores are DET, SEG and TRA from the Cell Tracking Challenge (Fig8 and Fig9 and Supplementary Figures 3 and 4 are created from this data) compare_postprocessing_on_synth_bm.csv Comparing the different post-processing strategies of the proposed tracking algorithm algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets). Reported scores are DET, SEG and TRA from the Cell Tracking Challenge (Fig7 and Fig8 and Supplementary Figures 1 and 2 are created from this data)   folders (decompressed approximately 90GB of data!) ----------- tracking_data     compare_all_synth_bm     Contains all tracking results for each tracking algorithm on the synthetically degraded datasets ()     compare_all_synth_bm_no_error     Contains the tracking results for each tracking algorithm provided with the perfect ground truth segmentation data     compare_postprocessing_synth_bm [will be stored in 10.5281/zenodo.5227610 due to size restrictions]     Contains all tracking resuls for each postprocessing configuration of the proposed cell tracking algorithm     the leaf folders are names run_xPOSTPROCESSING where x is the run number and POSTPROCESSING the postprocessing key     Postprocessing keys: ("no untangle" or "no masks" is indicated by an overline in the paper)     all ("untangle + masks" in the paper)     nd ("no untangle + masks")     nd_ns-l ("no untangle + no masks")     ns-l ("untangle + no masks")

opencc-by-4.0Aug 2021View details →

ClinicalTrials.gov36/100

Correcting Residual Errors With Spectral, Ultrasound, Traditional Speech Therapy

ClinicalTrials.gov study NCT03737318. IPD Sharing: NO. Countries: 1. Publications: 19.

closedIPD-NOFeb 2026View details →

ClinicalTrials.gov36/100

Safety and Suitability of ICL for Correction of Refractive Errors Without the Use of Dispersive OVDs

ClinicalTrials.gov study NCT06371079. IPD Sharing: Not stated. Countries: 1. Publications: 3.

restrictedIPD-UNDECIDEDFeb 2026View details →

dryad36/100

Topographic data to support the analysis of error and uncertainty that degrade topographic corrections of remotely sensed data

Open the record for dataset details and reuse information.

publicNov 2022View details →

dryad36/100

Predictive coding and internal error correction in speech production

Open the record for dataset details and reuse information.

publicOct 2022View details →

zenodo32/100

A heterometallic [LnLn'Ln] lanthanide complex as a qubit with embedded quantum error correction. Open data set

Includes data relevant for publication with DOI <a href="https://doi.org/10.1021/acsnano.0c03167">10.1039/d0sc03107k</a> plus a table with information on how the data were obtained and processed.

opencc-by-3.0Nov 2020View details →

dryad32/100

Data from: Correction of location errors for presence-only species distribution models

1. Species distribution models (SDMs) for presence-only data depend on accurate and precise measurements of geographic and environmental covariates that influence presence and abundance of the species. Some data sets, however, may contain both systematic and random errors in the recorded location of the species. Environmental covariates at the recorded location may differ from those at the true location and result in biased parameter estimates and predictions from SDMs. 2. Regression calibration is a well-developed statistical method that can be used to correct the bias in estimated coefficients and predictions from SDMs when the recorded geographic location differ from the true location for some, but not all locations. We expand the application of regression calibration methods to SDMs and provide illustrative examples using simulated data and opportunistic records of whooping cranes (Grus americana). 3. We found we were able to successfully correct the bias in our SDM parameters estimated from simulated data and opportunistic records of whooping cranes using regression calibration. 4. When modeling species distributions with data that have geographic location errors, we recommend researchers consider the effect of location errors. Correcting for location errors requires that at least a portion of the data have locations recorded without error. Bias correction can result in an increase in variance; this increase in variance should be considered when evaluating the utility of bias correction.

opencc-zeroDec 2012View details →

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