Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
6,639
datasets available to search
ShareScore release 0.9.0
Dataset results
6,639 results for “Failure”
RIBuild: Analysis of models for failure
<p>The dataset consists of data used for analysing a number of models, each related to a specific failure mode or failure mechanism that affects the material properties of building materials. Three RIBuild partners (KUL, UNIVPM, RISE) were responsible of performing laboratory tests to evaluate the models chosen to characterize a specific failure mode (frost, algae, mould). One RIBuild partner (DTU/AAU) used measurement data from a WP3 test setup to validate simulations of wood rot in wooden beam ends.</p> <p>Further details to be found in RIBuild deliverable D2.2.</p> <p>Overview of data files to be found in 'RIBuild data WP2 Model analysis' as part of this dataset.</p>
Public Available Data Set of Process Flows from Internal Physical Inspections in the Failure Analysis Laboratory
<p>This data set was generated in accordance with the semiconductor industry and contains data of certain process flows in Failure Analysis (FA) laboratories focusing on the identification and analysis of anomalies or malfunctions in semiconductor devices. It comprises logistic data about the processing steps for the so-called Internal Physical Inspection (IPI).</p><p>A so-called IPI job is given as a sequence of tasks that must be performed to complete the job they belong to. It has an assigned unique ID and timestamps indicating the submission, the end, and the deadline to be met. A job also has an IPI classification assigned to it, providing general guidelines on the operations to be performed.</p><p>Every task within a job has its own type and working time, as well as the assigned resources. There are two main resources involved:</p><p> - the equipment; the machine used to perform the task,</p><p> - the operator; the person who performed the task.</p><p>In addition, general information about the type of the device to be analyzed is also available, such as the given (anonymized) package and basictype. Data also include the number of stressed samples within a device and the samples a task is performed on.</p><p>The dataset includes data from 4 years, specifically from January 2020 to December 2022.</p><p>Finally, the exact column structure is given as follows (python 3.9.5 datatype):</p><ul><li>JOB_ID [int64]: the unique ID of the job</li><li>JOB_SUBMISSION_DATE [object]: the date of the job submission</li><li>JOB_REQ_END_DATE [object]: the required end date (deadline)</li><li>JOB_FINISH_DATE [object]: the actual end date</li><li>JOB_BASICTYPE_H [object]: the given basictype denotation</li><li>JOB_PACKAGE_H [object]: the package denotation of the device</li><li>JSH_QTY_STRESSED [float64]: number of stressed samples</li><li>TASK_SUBMISSION_DATE [object]: the date of the task submission</li><li>TASK_WORKING_TIME [float64]: the amount of time (hours) the task needs to be completed</li><li>TASK_SAMPLE_NO [object]: the samples the task was performed on </li><li>TASK_CEQ_ID [float64]: the ID of the machine used to perform the task</li><li>TASK_CTKS_ID [int64]: the ID representing the task type</li><li>TASK_USR_ID [int64]: the ID of the operator performing the task</li><li>CIPI_LEVEL_0 [object]: a series of IPI classifications, indicating what is required to execute for a specific job</li></ul>
Run-to-failure data set of ball bearings subjected to time-varying load and speed conditions
<p>This data set consist of experimental data collected during 17 run-to-failure experiments on ball bearings subjected to time-varying load and speed conditions. No defect was initiated in the bearings before the experiments. A detailed description file is enclosed.</p> <p>Version 2024-04-02: All experiments B01 through B17 are uploaded. Furthermore, Figure 1 of the description file has been updated.</p> <p>Please also cite our paper, when using this data set: Javanmardi, A., Aimiyekagbon, O. K., Bender, A. ., Kimotho, J. K., Sextro, W., & Hüllermeier, E. (2024). Remaining Useful Lifetime Estimation of Bearings Operating under Time-Varying Conditions. <em>PHM Society European Conference</em>, <em>8</em>(1), 9. https://doi.org/10.36001/phme.2024.v8i1.4101</p>
Soybean data for paper: "Increase of simultaneous soybean failures due to climate change"
<p>Input and Output data used in the paper: ""Increase of simultaneous soybean failures due to climate change""</p> <p>Upon use of part of this dataset, please cite authors and paper related.</p> <p>Input:</p> <p>Observed soybean data obtained from official authorities pre-processed and regularised at 0.5 x 0.5 spatial resolution:</p> soy_yield_1975_2016_05x05_1prc.nc 43.6 MB soy_yield_arg_1974_2019_05x05.nc 44.6 MB soy_yields_US_all_1975_2020_05x05.nc 47.7 MB soybean_harvest_area_calculated_americas_hg.nc soybean_yields_america_detrended_1978_2016.nc 78.8 MB <p> </p> <p>Outputs:</p> <p>Hybrid model outputs for soybean yield from 2015-2100l with trends at 0.5 x 0.5 spatial resolution:</p> hybrid_trend_gfdl-esm4_ssp126_default_yield_soybea ... 14.6 MB hybrid_trend_gfdl-esm4_ssp585_default_yield_soybea ... 14.6 MB hybrid_trend_ipsl-cm6a-lr_ssp126_default_yield_soy ... 14.6 MB hybrid_trend_ipsl-cm6a-lr_ssp585_default_yield_soy ... 14.6 MB hybrid_trend_ukesm1-0-ll_ssp126_default_yield_soyb ... 14.6 MB hybrid_trend_ukesm1-0-ll_ssp585_default_yield_soyb ... 14.6 MB <p>Hybrid model outputs for soybean yield from 2015-2100 without trends at 0.5 x 0.5 spatial resolution:</p> hybrid_gfdl-esm4_ssp126_default_yield_soybean_2015 ... 7.3 MB hybrid_gfdl-esm4_ssp585_default_yield_soybean_2015 ... 7.3 MB hybrid_ipsl-cm6a-lr_ssp126_default_yield_soybean_2 ... 7.3 MB hybrid_ipsl-cm6a-lr_ssp585_default_yield_soybean_2 ... 7.3 MB hybrid_ukesm1-0-ll_ssp126_default_yield_soybean_20 ... 7.3 MB hybrid_ukesm1-0-ll_ssp585_default_yield_soybean_20 ... 7.3 MB
Landslides from Space - Bento Rodrigues Dam Failure, Brazil (5th November 2015)
<p>On 5th November 2015, an iron ore tailings dam in Bento Rodrigues suffered a failure. About 60 million cubic meter of iron waste flowed down the valley. Two villages were partly destroyed and the drinking water supply of a few hundred thousand people were effected. The river Doce will be affected by this disaster for many decades.</p> <p>The pre-event acquisition is from 5th November 2015 (Landsat-8) and the post-event acquisition is from 26th December 2015 (Sentinel-2).<br> <br> <em>Contains modified Copernicus Sentinel and Landsat data (2015) </em></p>
Data inputs and results from AI-supported title and abstract screening "Lack of evidence regarding markers identifying acute heart failure in patients with COPD: an AI-supported systematic review"
<p>These comma-separated data files were used to conduct the AI supported screening of [Lack of Evidence Regarding Markers Identifying Acute Heart Failure in Patients with COPD: An AI-supported Systematic Review (working title)], following the methodology described in the publication (URL/doi to be uploaded).</p> <p>These files provide insight into the AI-supported screening process and the choices made by the human reviewer.</p>
Dataset of the cut-slope failure in the A-7 highway (S Spain)
<div> <p>The dataset contains various datasets and media related to the landslide analysis on the <strong>failure occurred in 11 March 2021 in the Km 354.3 of the A-7 Highway (S Spain)</strong>. Each folder contains specific types of data collected and processed during different stages of the cut-slope assessment, including raw data, processed materials, and media documentation.</p> <h3>Folder Structure</h3> <p><strong>DEMs:</strong> Digital Elevation Models (DEMs) representing different stages of the cut-slope. These DEMs serve as raw data for calculating volumes and understanding changes in the slope’s morphology over time.</p> <p><strong>FailureVolumes:</strong> Processed datasets used for detailed volume calculations of the landslide. These datasets represent the surfaces used for calculating the volume of the material displaced by the landslide.</p> <p><strong>Orthoimages: </strong>Orthoimages of the cut-slope at various stages. These images were acquired by drone, providing high-resolution, georeferenced views that facilitate visual analysis and comparison across different points in time.</p> <p><strong>PointClouds: </strong>Point cloud data in LAZ file format, representing different stages of the cut-slope. These point clouds offer a detailed spatial representation of the slope, valuable for further processing and 3D modeling.</p> <strong>Videos: </strong>Videos captured by drones, documenting the cut-slope’s condition on each data acquisition day during the emergency response and recovery phases. These videos provide a visual context for understanding the progression of the landslide and recovery efforts. <p>________________</p> <p>Details on the dataset production and its analysis are provided in the following paper:</p> <p>Galve, J.P., Pérez-García, J.L., Ruano, P., Gómez-López, J.M., Reyes-Carmona, C., Moreno-Sánchez, M., Jerez-Longres, P.S., Ghadimi, M., Barra, A., Mateos, R.M., Monserrat, O., Azañón, J.M. (2025) Applications of UAV Digital Photogrammetry in landslide emergency response and recovery activities: the case study of a slope failure in the A-7 highway (S Spain). Landslides. <a href="https://link.springer.com/article/10.1007/s10346-024-02449-9">https://doi.org/10.1007/s10346-024-02449-9</a></p> <p>The dataset includes materials gathered, acquired and processed during the investigation’s emergency and recovery phases. However, <strong>work at the site is ongoing</strong>. <strong>Please contact us (<a href="mailto:jpgalve@ugr.es">jpgalve@ugr.es</a>) if you require additional information</strong>, as new data may have been generated since this dataset was published.</p> <div> </div> </div> <p> </p>
Age-related proteostatic imbalance exacerbates heart failure with preserved ejection fraction pathogenesis in old mice
<p>Heart failure with preserved ejection fraction (HFpEF) is a leading cause of hospitalization and death in the elderly. While aging strongly increases the incidence of HFpEF, the specific influences of aging on HFpEF at molecular and pathophysiological levels remain unclear. Here, we show that aged mice, when subjected to chronic metabolic and hypertensive stress (2-hit stress), develop an aggravated cardiometabolic HFpEF phenotype compared to younger counterparts. Aged HFpEF mice also display unique pathological characteristics reminiscent of those found in HFpEF patients. We demonstrate that age-related dysfunction in protein quality control (PQC) exacerbates proteostatic stress in HFpEF. Specifically, we demonstrate that increased protein synthesis induced by 2-hit stress combines with age-related impairment in protein degradation in aged HFpEF hearts, culminating in the accumulation of protein aggregates. These findings underscore the importance of incorporating aging into preclinical HFpEF models and support the therapeutic potentials of targeting PQC mechanisms to ameliorate disease outcomes.</p> <p>The deposited data are lc-ms data acquired on the Thermo QEx-Plus system. For any questions, please contact mike kinter mike-kinter at omrf.org</p> <p>This upload contains the bulk of the LC-MS data. But, due to file sizes, and addition group of files can be found at doi 10.5281/zenodo.11094720</p>
Myocardial ultrastructure of human heart failure with preserved ejection fraction
<p>These transmission electron micrographs were obtained from endocardial biopsies of patients with heart failure and preserved ejection fraction, or from non-failling control myocardium. The myocardium is from the right side of the ventricular septum. Images are shown at various magnification levels indicated in the title of the image. Images with titles: HH_DM+ or HH_DM-; Mixed_DM+ or Mixed_DM-; OB_DM+ or OB_DM-; or NF_DM+ or NF_DM- show examples from the primary groups, HH represents HFpEF patients with primarily hypertensive hypertrophic heart disease and the least obesity; OB represents HFpEF patients with primarily severe obesity and the least hypertensive hypertrophic disease; Mixed matches obesity and hypertensive hypertrophic heart disease in HFpEF patients to levels obsserved in the HH and OB groups, and NF is non-failing controls. </p> <p>Additional images are shown for NF, HH, OB, and Mixed from the remaining patients in this study are provided at two magnification levels. These are provided as individual pictures as well. </p> <p> </p>
Heart Failure eQTLs companion to "Pathologic gene network rewiring implicates PPP1R3A as a central cardioprotective factor in pressure overload heart failure"
<p>These are the results of a QTL analysis companion to "Pathologic gene network rewiring implicates PPP1R3A as a central cardioprotective factor in pressure overload heart failure". We performed RNA expression measurements and obtained genotype information in genome-wide markers for 313 patients (177 failing hearts , 136 donor, non-failing [control] hearts) using Affymetrix expression and Affymetrix Human 6.0 respectively.<strong> </strong>Prior to eQTL discovery, we used PEER to find hidden covariates that could confound signals in our data as well as filtering any genotypes with major allele frequencies less than 5%. To test associations between gene expression in each cohort separately, we used QTLTools with an additive model accounting for gender, age, sample site, and the PEER factors as covariates. We corrected for eQTL multiple association testing using a 10000 permutations per locus in a 2 megabase window and a false discovery rate cutoff of 5%. To select the number of PEER factors, we performed the full analysis multiple times from 1 to 15 PEER factors and observed a saturation of new QTLs being discovered when using 10 factors.</p> <p>Four files are provided, two for each cohort (cases and controls):</p> <p>- peer_[cases|controls]_nominal.txt: Nominal associations with a p-value threshold of 0.001</p> <p>- peer_[cases|controls]_permutations_all.significant.txt: All significant associations detected after the QTLtools permutation test.</p> <p>The column names are those from QTLtools, in order:</p> <p><br> 1. The phenotype ID<br> 2. The chromosome ID of the phenotype<br> 3. The start position of the phenotype<br> 4. The end position of the phenotype<br> 5. The strand orientation of the phenotype<br> 6. The total number of variants tested in cis<br> 7. The distance between the phenotype and the tested variant (accounting for strand orientation)<br> 8. The ID of the tested variant ( in Affy 6.0 SNP ids)<br> 9. The chromosome ID of the variant<br> 10. The start position of the variant<br> 11. The end position of the variant<br> 12. The nominal P-value of association between the variant and the phenotype<br> 13. The corresponding regression slope<br> 14. A binary flag equal to 1 is the variant is the top variant in cis</p>
Defective HNF4alpha-dependent gene expression as a driver of hepatocellular failure in alcoholic hepatitis [Suppl Data]
<p>Alcoholic hepatitis (AH) is a life-threatening condition characterized by profound hepatocellular dysfunction for which targeted treatments are urgently needed. Identification of molecular drivers is hampered by the lack of suitable animal models. By performing RNA sequencing in livers from patients with different phenotypes of alcohol-related liver disease (ALD), we show that the development of AH is characterized by the defective activity of liver-enriched transcription factors (LETFs). TGFb1is a key upstream transcriptome regulator in AH and induces the use of HNF4aP2 promoter in hepatocytes, which results in defective metabolic and synthetic functions. Gene polymorphisms in LETFs including HNF4aare not associated with the development of AH. In contrast, epigenetic studies show that AH livers have profound changes in DNA methylation state and chromatin remodeling, affecting HNF4a-dependent gene expression. We conclude that targeting TGFb1and epigenetic drivers that modulate HNF4a-dependent gene expression could be beneficial to improve hepatocellular function in patients with AH.</p>
Structured Power Grid Simulation Dataset for Machine Learning: Failure and Survival Events in Grid2Op's L2RPN WCCI 2022 Environment
<p>This dataset was developed for and used in the paper titled <em>"Fault Detection for Agents in Power Grid Topology Optimization: A Comprehensive Analysis"</em> by Malte Lehna, Mohamed Hassouna, Dmitry Degtyar, Sven Tomforde, and Christoph Scholz, presented at the <em>Workshop on Machine Learning for Sustainable Power Systems (ML4SPS)</em>, part of <em>ECML PKDD 2024</em>. While the paper is pending formal publication, a preprint version is available on arXiv.</p> <p>The dataset contains structured training, validation, and test data comprising failure and survival events observed in transmission power grid simulations. These were generated using Grid2Op with the WCCI 2022 L2RPN environment. Each data instance is labeled with one of four classes, representing survival or impending failure in 1, 3, and 5 timesteps. This dataset was used to train, validate and test machine learning models that predict grid agent failures in topology optimization tasks. </p>
Automatically Reproducing Timing-Dependent Flaky-Test Failures
<p>This artifact contains the source code for FlakeRake, a tool for automatically reproducing timing-dependent flaky-test failures. It also includes raw and processed results produced in the evaluation of FlakeRake</p> <p> </p> <p>Contents:</p> <p> </p> <p>Timing-related APIs that FlakeRake considers adding sleeps at: timing-related-apis</p> <p>Anonymized code for FlakeRake (not runnable in its anonymized state, but included for reference; we will publicly release the non-anonymized code under an open source license pending double-blind review): flakerake.tgz</p> <p>Failure messages extracted from the FlakeFlagger dataset: 10k_reruns_failures_by_test.csv.gz </p> <p>Output from running isolated reruns on each flaky test in the FlakeFlager dataset: 10k_isolated_reruns_all_results.csv.gz (all test results summarized into a CSV), 10k_isolated_reruns_failures_by_test.csv.gz (CSV including just test failures, including failure messages), 10k_isolated_reruns_raw_results.tgz (includes all raw results from reruns, including the XML files output by maven)</p> <p>Output from running the FlakeFlagger replication study (non-isolated 10k reruns):flakeFlaggerReplResults.csv.gz (all test results summarized into a CSV), 10k_reruns_failures_by_test.csv.gz (CSV including just failures, including failure messages), flakeFlaggerRepl_raw_results.tgz (includes all raw results from reruns, including the XML files output by maven - this file is markedly larger than the 10k isolated reruns results because we ran *all* tests in this experiment, whereas the 10k isolated rerun experiment only re-ran the tests that were known to be flaky from the FlakeFlagger dataset).</p> <p>Output from running FlakeRake on each flaky test in the FlakeFlagger dataset:</p> <p>For bisection mode: results-bis.tgz</p> <p>For one-by-one mode: results-obo.tgz</p> <p>Scripts used to execute FlakeRake using an HPC cluster: execution-scripts.tgz<br> Scripts used to execute rerun experiments using an HPC cluster: flakeFlaggerReplScripts.tgz<br> Scripts used to parse the "raw" maven test result XML files in this artifact into the CSV files contained in this artifact: parseSurefireXMLs.tgz </p> <p>Output from running FlakeRake in “reproduction” mode, attempting to reproduce each of the failures that matched the FlakeFlagger dataset (collected for bisection mode only): results-repro-bis.tgz</p> <p>Analysis of timing-dependent API calls in the failure inducing configurations that matched FlakeFlagger failures: bis-sleepyline.cause-to-matched-fail-configs-found.csv</p>
A Systematic Literature Review of Machine Learning for Uncovering Software Faults and Failures
<p>This data set contains the results of an extensive, systematic literature review on the use of machine learning (ML) for uncovering software faults and failures. Covering the period of 2019 to 2022, this literature review identifies 874 relevant publications, classified into six distinct quality assurance tasks. Results show a compound annual growth rate (CAGR) of relevant publications of 38% over the last five years.</p> <p>This literature review particularly analyzed in how far these relevant papers leverage synergies between different quality assurance tasks. Results show that only 3% of all relevant papers leverage such synergies, indicating ample opportunities for future research. For example, a single type of quality assurance activity may not suffice to deliver the expected software quality. Ideally, one would use a suitable combination of different types of activities – such as combining dynamic testing with static code analysis. Also, leveraging the synergies between different quality assurance activities can increase the effectiveness of the individual activities. For example, having a good estimate of the fault density of a software component (e.g., using deep learning-driven fault prediction techniques) could help optimize and prioritize testing effort and budget.</p>
KG for heart failure gene expression data
<p>Pre processed gene expression data for different heart failure. Includes count table, gene patiens metadata, gene lenght</p>
Failure without tears: Two-step attachment in a climbing cactus
<p>Climbing plants can be extremely adaptable to diverse habitats and capable of colonizing perturbed, unstructured and even moving environments. The timing of the attachment process, whether instantaneous (e.g., a pre-formed hook) or slow (growth process) crucially depends on the environmental context and the evolutionary history of the group concerned. We observed how spines and adhesive roots develop and tested their mechanical strength in the climbing cactus <em>Selenicereus setaceus</em> (Cactaceae) in its natural habitat. Spines are formed on the edges of the triangular cross section of the climbing stem and originate in soft axillary buds (areoles). Roots are formed in the inner hard core of the stem (wood cylinder) and grow via tunnelling through soft tissue, emerging from the outer skin. We measured maximal spine strength and root strength via simple tensile tests using a field measuring Instron device. Spine and root strengths differ, and this has a biological significance for the support of the stem. Our measurements indicate that the measured mean strength of a single spine could theoretically support an average force of 2.8 N. This corresponds to an equivalent stem length of 2.62 m (mass of 285 g). The measured mean strength of root could theoretically support an average of 13.71 N. This corresponds to a stem length of 12.91 m (mass of 1398 g). We introduce the notion of two-step attachment in climbing plants. In this cactus, the first step deploys hooks to attach to a substrate; this process is instantaneous and is highly adapted for moving environments. The second step involves root growth for more solid attachment to the substrate; this process involves “slow” root growth and adhesion. The advantage of the two-step strategy is that fast hook attachment can steady the plant for the slower root attachment. This strategy is effective in highly variable and heterogeneous environments when climbing plants are faced with constant moving and perturbed environmental conditions in many ecosystems. We discuss how two-step mechanisms are of interest for technical anchoring applications particularly for “soft-bodied” artefacts, which have to deploy hard and stiff materials originating from a soft compliant body and where tasks involving attachment take place in highly unstable and unpredictable environments.</p>
Time-of-failure prediction of the Achoma landslide, Peru, from high frequency Planetscope satellites
<p><strong>Introduction</strong></p> <p>This repository contains the data used for the study of the slope instability of Achoma, Peru, described in Lacroix et al. (submitted). Specifically, the repository contains a time series of horizontal ground displacements, obtained from high frequency PlanetScope satellite between 2017 and 2020. It also contains two Digital Elevation Models, one from before the Achoma failure obtained with Pléaides stero images, and the other from just after the Achoma failure obtained with drone imagery.</p> <p>The data and methods used for the elaboration of this data repository are described in detail in Lacroix et al. (submitted). In this repository we also provide a short summary and overview of the data and methods used.</p> <p><strong>Data</strong></p> <p>A total of 79 PlanetScope scenes were used to produce the time series of horizontal horizontal ground displacements maps. Table 1 provides an overview of these data.</p> <p>Table1: Data used for the creation of this repository</p> <table> <tbody> <tr> <td> <p>Application</p> </td> <td> <p>Platforms</p> </td> <td> <p>Acquisition dates</p> </td> </tr> <tr> <td> <p>Pre-failure DEM</p> </td> <td> <p>Pléiades</p> <p> </p> </td> <td> <p>2017/05/13</p> </td> </tr> <tr> <td> <p>Post-failure DEM</p> </td> <td> <p>Drone</p> </td> <td> <p>2020/06/19</p> </td> </tr> <tr> <td> <p>Horizontal ground displacement</p> </td> <td> <p>PlanetScope</p> </td> <td> <p>79 scenes from 2017/11/27 to 2020/06/17</p> </td> </tr> </tbody> </table> <p><br> </p> <p><strong>Methods</strong></p> <p>The horizontal ground displacement maps, both along the NS and the EW directions (file names NSxxxxxxxx.tif and Ewxxxxxxxx.tif, where xxxxxxxx is the date in the format yyyymmdd) were created using the offset tracking methodology described in Bontemps et al. (2018), consisting of: (1) correlation of all the pairs of images using Mic-Mac (Rupnik et al., 2017), (2) masking the low correlation coefficient values (CC<0.7), (3) mosaicking correction, similar to stripe corrections (Bontemps et al., 2018), that we obtained by subtracting the median value of the stacked profile in the along-stripe direction, taking into account only stable areas, (4) least square inversion of the redundant system per pixel, weighted by the time separation between pairs (Bontemps et al., 2018), (5) correction of illumination effects (Lacroix et al., 2019), based on the 2 years of data between November 2017 and December 2019.</p> <p>The pre-failure DEM was computed from Ames Stereo Pipeline (Shean et al. 2016) and the methodology developed in (Lacroix, 2016) applied to the Pléiades stereo images (file name DEM_20170513_shifted_vertical2.tif ).</p> <p>The post-failure DEM was processed using the Structure from Motion-Multi View Stereo (SfM-MVS) methodology with the Agisoft Metashape Professional 1.5.5 software applied on 1824 pictures taken from the drone (file name Achoma_DEM_2020.06.20_UTM19S_50cm.tif ).<br> </p> <p><strong>Acknowledgements</strong></p> <p>P.L. acknowledge the support from the French Space Agency (CNES) through the TOSCA, PNTS, and ISIS programs.</p> <p><strong>Dataset attribution</strong></p> <p>This dataset is licensed under a Creative Commons CC BY 4.0 International License.</p> <p><strong>Dataset Citation</strong></p> <p>Lacroix, P., Huanca, J., Angel, L., Taipe, E.: Data Repository: Time-of-failure prediction of the Achoma landslide, Peru, from high frequency Planetscope satellites. Dataset distributed on Zenodo: 10.5281/zenodo.7866962</p>
A Novel Approach to Heart Failure Prediction and Classification through Advanced Deep Learning Model
<p>A Novel Approach to Heart Failure Prediction and Classification through Advanced Deep Learning Model</p>
Experiments for "Automata Theoretic Approach to Verification of MPLS Networks under Link Failures"
<p><strong>Prerequisites:</strong></p> <ol> <li>A 2020 linux distribution on an x86_64 platform</li> <li>python3 installation with os, sys, json and statistics packages installed for computation of timing statistics</li> <li>pdflatex for visualization of reachability matrices</li> </ol> <p><strong>Contents:</strong></p> <ul> <li>The <em>bin</em> folder contains the pre-compiled binaries for our tool and the backend verifier for pushdown-systems, <em>moped.</em></li> <li>The <em>sources</em> folder contains a snapshot of source-code used for producing the tool binary, namely <ul> <li>AalWiNes, also found at https://github.com/DEIS-Tools/AalWiNes in release v0.92.J (337f1e4c44f8bce2060897b88e706f7ceca38319)</li> <li>PDAAAL, used for pushdown-system manipulation, also found at https://github.com/DEIS-Tools/PDAAAL in release v0.2.2.J (ed1e7fbb4cacd6ffb240a514cfe8472f83d91f60)</li> </ul> </li> <li>The <em>nested</em> folder contains scripts and models for reproducing the results of Table IV</li> <li>The <em>nn-net</em> folder contains scripts and models for constructing reachability-matrices (Table V to Table XII) and operator specific queries</li> <li>Within the <em>nn-net</em> folder, you also find a mapping from alphabetical naming of NORDUnet routers in the paper to numericals <em>(nn-net/index-alpha-map.txt)</em></li> </ul> <p><strong>P-Rex vs HSA (Table IV)</strong></p> <p>We here re-use the HSA-timings computed in <a href="https://doi.org/10.1145/3281411.3281432">https://doi.org/10.1145/3281411.3281432</a></p> <p>The timings for our tool can be obtained by the following commands</p> <pre><code class="language-bash">cd nested ./compute_nested.sh | grep "#"</code></pre> <p>which should compute a sequence of lines in the terminal equal to</p> <pre><code class="language-bash">### Running N0 with aalwines ### Memory: 27064 Kb Time 0.02 seconds ### Running N1 with aalwines ### Memory: 37404 Kb Time 0.03 seconds ### Running N2 with aalwines ### Memory: 47704 Kb Time 0.03 seconds ### Running N3 with aalwines ### Memory: 57784 Kb Time 0.04 seconds ### Running N4 with aalwines ### Memory: 68368 Kb Time 0.04 seconds ### Running N5 with aalwines ### Memory: 78468 Kb Time 0.05 seconds ### Running N6 with aalwines ### Memory: 88980 Kb Time 0.06 seconds </code></pre> <p><strong>Operator Queries</strong></p> <p>The experiments on the queries of the operators can be computed via the commands</p> <pre><code class="language-bash">cd nn-net ./compute_operator.sh </code></pre> <p>which should complete less than 10 minutes.</p> <p>The traces and timings are outputted directly to the terminal.</p> <p><strong>Reachability Matrices (Table V to Table XII)</strong></p> <p>This package folder comes pre-populated with the raw computation results. Notice that the <em>compute_grid.sh</em> command will invalidate these results.</p> <p>To compute the full set of reachability tables, you can use the following commands</p> <pre><code class="language-bash">cd nn-net ./make_queries.sh ./compute_grid.sh</code></pre> <p>Notice, however, that this computation in total takes more than two days to complete on a single core.</p> <p>To reduce the size of the experiment, clear the contents of the <em>query</em> sub-folder an modify the <em>make_queries.sh</em> file by commenting out unwanted queries and parameter-combinations.</p> <p>To obtain a matrix, the command</p> <pre><code class="language-bash">./make_grid.sh aalwines IP IP 0 > ip_ip.tex pdflatex ip_ip.tex</code></pre> <p>which generated the IP IP matrix with 0 failures.<br> For the second argument, the following values are supported <em>IP, MPLS</em> while the third argument can also attain the value of <em>ANY</em>. The fourth argument gives the number of link failures.</p> <p>Timing information for the reachability-matrices can be obtained by</p> <pre><code class="language-bash">python3 ./timing_stats.py aalwines 3 IP_IP_UNDER_0</code></pre> <p>where the third argument determines the reduction-type used, and the fourth argument gives the specific query and engine mode used. In the given example we attain the statistics for reduction-mode 3 for the IP-IP matrix using under-approximation on zero link-failures.</p> <p>The <em>pre-reduction</em> and <em>post-reduction</em> elements of the output give the size of the constructed pushdown system before (and after) reduction respectively.</p>
A cross-study transcriptional patient map of heart failure defines conserved multicellular coordination in cardiac remodeling
<p>Collection of auxiliary data to reproduce the results from "<strong>A cross-study transcriptional patient map of heart failure defines conserved multicellular coordination in cardiac remodeling</strong>". Source code is available at: https://github.com/saezlab/reheat2_pub<br><br>We provide processed data to facilitate access to the results, for the original count data, please see the associated manuscript for references to the original datasets. </p> <p> </p> <p> </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.