This dataset contains images of 3d printed parts recorded while printing.

The dataset contains 4 classes and 34 shapes:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5666725%2F92b8fca57767fa55ae4e42d3972b2522%2F1.PNG?generation=1708440162571728&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5666725%2Fc36caa40d8d565bafa02d9f97112a777%2F2.PNG?generation=1708440216287321&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5666725%2F3ddeb2380e1106e9d482f3e6940235d3%2F3.PNG?generation=1708440227278455&alt=media" alt="">

Labels and methadata:

Recording Process

The dataset was recorded in the context of this work: https://github.com/NilsHagenBeyer/FDM_error_detection

The Images were recorded with ELP-USB13MAFKV76 digital autofocus camera with the Sony IMX214 sensor chip, which has a resolution of 3264x2448, which were later downscaled to 256x256px. All Prints were carried out on a customized Creality Ender-3 Pro 3D.

The Images were mainly recorded with a black printbed from camera position 1. For testing purposes the dataset contains also few images from camera postition 2 (oblique camera) with a black printbed (significant motion blurr) and camera postition 1 with a silver printbed. The positions can be seen in the image below.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5666725%2F253a5f4c3d83233ddbc943fc1f8273e0%2Fexp_setup.png?generation=1721130817484111&alt=media" alt="">

Folder Structure

├── general data

 └── all_images_no_filter.csv      # Full Dataset, unfiltered

 └── all_images.csv         # Full Dataset, no spaghetti error

 └── black_bed_all.csv       # Full Dataset, no silver bed

├── images

 └── all_images
 |   └── ...         # All Images: Full Dataset + Silver Bed + Oblique Camera
 |
 └── test_images_silver265
 |   └── ...         # Silver bed test images
 |
 └── test_images_oblique256
    └── ...         # Oblique camera test images

Renewable energy resources, including solar and wind energy, play a significant role in sustainable energy systems. However, the inherent uncertainty and intermittency of renewable generation pose challenges to the safe and efficient operation of power systems. Recognizing the importance of short-term (hours ahead) renewable generation forecasting in power systems operation, it becomes crucial to address the potential inaccuracies in these forecasts. To systematically evaluate the performance of controllers in the presence of imperfect forecasts, we generate synthetic forecasts using actual renewable generation profiles (one from solar and one from wind). These synthetic forecasts incorporate different levels of statistical error, allowing us to control and manipulate the accuracy of the predictions. The primary objective is to employ synthetic forecasts with controlled yet realistic error levels to systematically investigate how controllers adapt to variations in forecast accuracy, providing valuable insights into their robustness and effectiveness under real-world conditions.

The experiment here was to demonstrate that we can reliably measure the Reference Waveforms designed in the IEEE P1765 proposed standard and calculate EVM along with the associated uncertainties. The measurements were performed using NIST's calibrated sampling oscilloscope and were traceable to the primary standards. We have uploaded the following two datasets. (1) Table 3 contains the EVM values (in %) for the Reference Waveforms 1--7 after performing the uncertainty analyses. The Monte Carlo means are also compared with the ideal values from the calculations in the IEEE P1765 standard. (2) Figure 3 shows the complete EVM distribution upon performing uncertainty analysis for Reference Waveform 3 as an example. Each of the entries in Table 3 is associated with an EVM distribution similar to that shown in Fig. 3.

Error Tracking System is a database used to store & track error notifications sent by users of EPA's web site. ETS is managed by OIC/OEI. OECA's ECHO & OEI Envirofacts use it. Error notifications from EPA's home Page under "Contact Us" also uses it.

This dataset is inspired by a real story of something funny that happened to a data science team in a company. They had built a classifier that was suspiciously good, even when analyzing its performance on unseen validation data. It was too good to be true.

Your job is to find the error.

The following notebook explains the task:

https://www.kaggle.com/computingschool/shady-dataset-find-the-error

Abstract: Supplement of the article Grothe O, Kaplan A, Kouz K, Saugel B. "Computer program for error grid analysis in arterial blood pressure method comparison studies" to provide the error grid analysis suggested in Saugel B, Grothe O, Nicklas JY. "Error Grid Analysis for Arterial Pressure Method Comparison Studies. Anesthesia and analgesia 2018;126:1177-85. TechnicalRemarks: Detailed information for usage is provided in the article.

Datasets to evaluate the performance of point-based, rigid-body registration may be downloaded from this site. Registration is the process of transforming one coordinate frame to another coordinate frame. The datasets contain 3D position measurements obtained from three instruments: a laser tracker (LT), a motion capture system (System A), and a large-scale metrology system (System B). The positions are for points that are in a semi-regular, 5 x 5 x 5 grid. The grid covers a volume that is approximately (3 x 3 x 1.8) m [L x W x H]. The measurement uncertainties are ± 25e-03 mm for the laser tracker, ± 250e-03 mm for System B, while the accuracy of System A is only specified as sub-millimeter. The datasets for each instrument were collected in the instrument's local coordinate frame. The datasets contain measurements of 125 fiducials (points used for registration) and 16 test points. Test points are points that are not used for registration but to which a transformation is applied; these points are used to evaluate the performance of the registration.

Means and standard error for all data points shown in graphs.

The correction in Digital Elevation Models (DEMs) has always been a crucial aspect of remote sensing geoscience research. The burgeoning development of new machine learning methods in recent years has provided novel solutions for the correction of DEM elevation errors. Given the reliance of machine learning and other artificial intelligence methods on extensive training data, and considering the current lack of publicly available, unified, large-scale, and standardized multisource DEM elevation error prediction datasets for large areas, the multi-source DEM Elevation Error Prediction Dataset (DEEP-Dataset) is introduced in this paper. This dataset comprises four sub-datasets, based on the TerraSAR-X add-on for Digital Elevation Measurements (TanDEM-X) DEM and Advanced land observing satellite World 3D-30 m (AW3D30) DEM in the Guangdong Province study area of China, and the Shuttle Radar Topography Mission (SRTM) DEM and Advanced Spaceborne Thermal Emission and reflection Radiometer (ASTER) DEM in the Northern Territory study area of Australia. The Guangdong Province sample comprises approximately 40 000 instances, while the Northern Territory sample includes about 1 600 000 instances. Each sample in the dataset consists of ten features, encompassing geographic spatial information, land cover types, and topographic attributes. The effectiveness of the DEEP-Dataset in actual model training and DEM correction has been validated through a series of comparative experiments, including machine learning model testing, DEM correction, and feature importance assessment. These experiments demonstrate the dataset’s rationality, effectiveness, and comprehensiveness.Citation:YU Cuilin, WANG Qingsong, ZHONG Zixuan, ZHANG Junhao, LAI Tao, HUANG Haifeng. Elevation Error Prediction Dataset Using Global Open-source Digital Elevation Model[J]. Journal of Electronics & Information Technology, 2024, 46(9): 3445-3455. doi: 10.11999/JEIT240062原文：https://jeit.ac.cn/cn/article/doi/10.11999/JEIT240062

The penalized likelihood approach of Fan and Li (2001, 2002) differs from the traditional variable selection procedures in that it deletes the non-significant variables by estimating their coefficients as zero. Nevertheless, the desirable performance of this shrinkage methodology relies heavily on an appropriate selection of the tuning parameter which is involved in the penalty functions. In this work, new estimates of the norm of the error are firstly proposed through the use of Kantorovich inequalities and, subsequently, applied to the frailty models framework. These estimates are used in order to derive a tuning parameter selection procedure for penalized frailty models and clustered data. In contrast with the standard methods, the proposed approach does not depend on resampling and therefore results in a considerable gain in computational time. Moreover, it produces improved results. Simulation studies are presented to support theoretical findings and two real medical data sets are analyzed.

Table S3. Performance statistics on output rate. a. Output rate (%) performance statistics on PacBio data of ten methods using five SR coverages. b. Output rate (%) performance statistics on ONT data of ten methods using five SR coverages. (XLSX 19 kb)

The data contains a list of articles given low score by the RCT Tagger and an error analysis of them, which were used in a project associated with the manuscript "Evaluation of publication type tagging as a strategy to screen randomized controlled trial articles in preparing systematic reviews". Change made in this V3 is that the data is divided into two parts: - Error Analysis of 44 Low Scoring Articles with MEDLINE RCT Publication Type. - Error Analysis of 244 Low Scoring Articles without MEDLINE RCT Publication Type.

This webpage provides reports for SNAP activity, error rates and quality control.

This replication package reproduces the results for the paper entitled "Adding measurement error to location data to protect subject confidentiality while allowing for consistent estimation of exposure effects," which is published in The Journal of the Royal Statistical Society: Series C (Applied Statistics), DOI: https://doi.org/10.1111/rssc.12439. This package contains 2 Stata Do-Files (.do) that produce the simulated dataset and run one replication of the simulation (the main paper runs 1,000 replications), and 2 Stata Data Files (.dta) that are used for the simulation in the main paper. (2020-02-29).

This is an anonymous data that was primarily collected to identify therapeutic dietary errors in a tertiary care hospital in Pakistan.

This report compares the estimated (or realized) precisions of a key set of estimates with the targets for the 2012 National Survey on Drug Use and Health (NSDUH). The report provides an overall of the 2012 sample design, describes relative standard errors and design effects, and the use of domain specific design effects for approximating standard error.

satellite altimetry missions now provide more than 25 years of accurate, continuous and quasi-global measurements of sea level along the reference ground track of topex-poseidon. these measurements are used by different groups to build the global mean sea level (gmsl) record, an essential climate change indicator. estimating a realistic uncertainty of the gmsl record is of crucial importance for climate studies such as estimating precisely the current rate and acceleration of sea level, analyzing the closure of the sea level budget, understanding the causes for sea level rise, detecting and attributing the response of sea level to anthropogenic activity, or estimating the earth energy imbalance. ablain et al. (2015) estimated the uncertainty of the gmsl trend over the period 1993-2014 by thoroughly analyzing the error budget of the satellite altimeters and showed that it amounts to 0.5 mm.yr-1 (90% confidence level). here, we extend ablain et al. (2015) analysis by providing a comprehensive description of the uncertainties in the satellite gmsl record. we analyse 25 years of satellite altimetry data and estimate for the first time the error variance-covariance matrix for the gmsl record with a time resolution of 10 days. three types of errors that can affect satellite altimetry measurements are modelled (drifts, biases, noise) and combined together to derive a realistic estimate of the gmsl error variance-covariance matrix. from the error variance-covariance matrix, the uncertainty on any metrics related to gmsl can be derived including the 90% confidence envelop of the gmsl record on a 10-day basis, the gmsl trend and acceleration uncertainties over any time periods of 2 years and longer in between october 1992 and december 2017. over 1993-2017 we find a gmsl trend of 3.35+-0.4 mm.yr-1 (90% cl) and a gmsl acceleration of 0.12 +-0.07 mm.yr-2 (90% cl) in agreement (within error bars) with previous studies. the full gmsl error variance-covariance matrix is freely available here.

We propose the indirect inference estimator as a consistent method to estimate the parameters of a structural model when the observed series are contaminated by measurement error by considering the noise as a structural feature. We show that the indirect inference estimates are asymptotically biased if the error is neglected. When the condition for identification is satisfied, the structural and measurement error parameters can be consistently estimated. The issues of identification and misspecification of measurement error are discussed in detail. We illustrate the reliability of this procedure in the estimation of stochastic volatility models based on realized volatility measures contaminated by microstructure noise.

The risk indicators and corresponding qualitative terms.