CVS_DATAERRORS_TBL:

The data errors table was where any validation errors or height recalculation utility errors were sent. (Errors in terms of deviations from the established rules of the data collection procedures or project-specific data limitations). Error records corrected were moved to the ErrorHistory table and any remaining records were removed, including warnings, leaving this table blank.

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

A corpus of machine-annotated incident reports of medication errors

Our dataset contains 478,175 named entities related to medication errors and also differentiates between incident types by recognising discrepancies between what was intended and what actually occurred.

When using this dataset, one should also cite the following original data source: Medical Adverse Event Information Collection Project [Iryō jiko jōhō shūshū-tō jigyō]　 Japan Council for Quality Health Care; 2022 [Available from: https://www.med-safe.jp/index.html.]

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 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.

DASSLE 1.0 (Dataset of Authentic and Synthetic Slovene Language Errors) comprises 7,385 manually prepared entries, each consisting of a Slovene sentence containing a single, specific language problem, its corrected version, and metadata including both coarse- and fine-grained correction classifications, as well as the source of the example.

Language problems are divided into five top-level categories: spelling, orthography, morphology, vocabulary, and syntax. These are further specified using 128 fine-grained error types, aligned with the typology developed for the Šolar 3.0 corpus. The typology is explained at https://wiki.cjvt.si/books/11-developmental-corpus-solar/page/introduction-to-tags and in more detail in the annotation guidelines at https://wiki.cjvt.si/books/11-developmental-corpus-solar/page/annotation-guidelines.

The examples in DASSLE 1.0 were sourced from four distinct origins, combining both authentic and synthetic data creation. From Šolar 3.0, the corpus of student writing with teacher-provided corrections, sentences were manually reviewed and edited to contain only one clearly defined language problem. In Gigafida 2.0, the reference corpus of standard written Slovene, examples were either manually corrected or deliberately corrupted to introduce typical deviations from the current norm. Synthetic examples were generated using GPT-4o, which was prompted with authentic sentence pairs; outputs were manually reviewed to select only those most closely resembling natural language use. A small number of examples were collected from Jezikovna svetovalnica, based on real language queries submitted by speakers.

The dataset is primarily intended for the development and evaluation of natural language processing tools for automatic error detection and correction for Slovene. It is available in TSV format, accompanied by a README document that describes its contents in more detail.

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.

Abstract (en): Using recent results in the measurement error literature, we show that the official US unemployment rate substantially underestimates the true level of unemployment, due to misclassification errors in the labor force status in the Current Population Survey. During the period from January 1996 to August 2011, the corrected monthly unemployment rates are between 1 and 4.4 percentage points (2.1 percentage points on average) higher than the official rates, and are more sensitive to changes in business cycles. The labor force participation rates, however, are not affected by this correction.

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.

RMSPE: root mean square prediction error; ASE: average standard error; SMPE: standardized mean prediction error; SRMS: standardized root mean square; Δ: thermal shock.

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

Net errors and omissions (BoP, current US$) in Japan was reported at --22365514161 USD in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Japan - Net errors and omissions, adjusted (BoP, current US$) - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

ABSTRACT: Purpose: to characterize the types of errors committed in Staggered Spondaic Words testing by patients undergoing auditory processing evaluation, and correlate these findings with age, gender, educational level and auditory processing disorder (APD) sub-profile. Methods: the Staggered Spondaic Words test results were obtained from a private database, which evaluated patients aging from 7 to 19 years, between June 2011 and September 2013. Results: the most frequent types of errors detected were: word omission (76.66%), word substitution (45%) and replacement by an adjacent word (20%). The APD sub-profiles observed were auditory decoding deficit coupled with integration deficit (38.33%), auditory decoding deficit (23.33%), normal result (20%), and others (18,34%). When the conditions were compared, we observed a greater number of errors in competing conditions. In relation to age and educational level, the errors occurred in greater number among younger patients with lower levels of educational. The correlation between the total number of errors and gender was not statistically significant. Conclusion: the types of errors made in the Staggered Spondaic Words test were characterized and correlated with the proposed variables (gender, age, educational level and APD sub-profile), emphasizing the importance of the test, which is frequently used in auditory processing evaluations for the diagnosis of human communication disorder, and in the identification of children at risk for learning disorders.

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.

Japan BoP: Net Errors and Omissions data was reported at -1,711.221 JPY bn in Feb 2025. This records a decrease from the previous number of 382.396 JPY bn for Jan 2025. Japan BoP: Net Errors and Omissions data is updated monthly, averaging 22.644 JPY bn from Jan 1996 (Median) to Feb 2025, with 350 observations. The data reached an all-time high of 4,064.192 JPY bn in Jan 2023 and a record low of -2,795.222 JPY bn in Jul 2018. Japan BoP: Net Errors and Omissions data remains active status in CEIC and is reported by Bank of Japan. The data is categorized under Global Database’s Japan – Table JP.JB001: Balance of Payment: BPM6: Summary.

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

All data (including input and output files) for computations performed in the production of this paper.

These detailed tables present the standard errors for the totals and prevalence estimates of the number of days and types of substance use, poly-drug use, nicotine dependence, substance dependence by age of first use, source of substances, social context of substance use, and drunk/drugged driving from the 2010 National Survey on Drug Use and Health (NSDUH). Substances examined include illicit drugs, marijuana, cocaine, heroin, hallucinogens, inhalants, and the nonmedical use of prescription-type pain relievers, tranquilizers, stimulants, and sedatives, and alcohol. Standard errors are provided for totals and prevalence estimates of lifetime, past year, and past month use by age group, gender, race/ethnicity, education level, employment status, geographic area, pregnancy status, college enrollment status, and probation/parole status. Comparisons are made between 2010 and 2009.

Turkey: Balance of payments, net errors and omissions: The latest value from 2023 is -14062 million USD, a decline from 23737 million USD in 2022. In comparison, the world average is -638.70 million USD, based on data from 148 countries. Historically, the average for Turkey from 1974 to 2023 is -46.12 million USD. The minimum value, -14537 million USD, was reached in 2017 while the maximum of 23737 million USD was recorded in 2022.