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.

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.

CVS_ERRORHISTORY_TBL:

This table definition is identical similar to the DataErrors table. The difference is that the error history table contains a record of every known error found in the CVS data (errors in terms of deviations from the established rules of the data collection procedures or project-specific data limitations) for which measurements did not exist as required. The errors identified here no longer exist in the database tables, however. The missing or erroneous data have been corrected or replaced through a local imputation process.

Some records over time have been moved to other tables not described in this document. These tables have the same fields as the ErrorHistory table.

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.

In “Gamma Discounting” Martin L. Weitzman concludes that certainty equivalent discount rates should decline significantly over time. He draws this conclusion from fitting a Gamma distribution to the responses of 2,160 economists asked to give a discount rate estimate and calculating effective discount rates from it. The paper to which this dataset pertains replicates Weitzman's calculations on the basis of the data of the above mentoned survey, and repeats the calcualtions with more appropiate methods.

Intermediate annotations from the FEVER dataset that describe original facts extracted from Wikipedia and the mutations that were applied, yielding the claims in FEVER.

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.

Government Effectiveness: Standard Error in Nigeria was reported at 0.21315 in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Nigeria - Government Effectiveness: Standard Error - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

In this paper we derive both primal and dual-cost systems in which the stochastic specifications arise from the model (random environment or measurement errors and optimization errors)?not tacked on at the end after the deterministic system is worked out. Derivation of the error structures is based on cost-minimizing behavior on the firms. The primal systems constitute the production function and the first-order conditions of cost minimization. We consider two dual-cost systems. The first dual system is based on the cost function and cost share equations. The second dual system is based on a multiplicative general error production model that is an alternative to McElroy's additive general error production model. Our multiplicative general error model gives a clear and intuitive economic meaning to the error components. The resulting cost system is easy to estimate compared to the alternative cost systems. The error components in the multiplicative general error model can capture heterogeneity in the technology parameters even in a cross-sectional model. Panel data are not necessary to estimate either the primal or dual systems. The models are estimated using data on 72 fossil fuel-fired steam electric power generation plants (observed for the period 1986-1999) in the USA.

A person has nonseparable preferences when her preference on an issue depends on the outcome of other issues. A model of survey responses in which preferences are measured with error implies that responses will change depending on the order of questions and vary over time when respondents have nonseparable preferences. Results from two survey experiments confirmthat changes in survey responses due to question order are explained by nonseparable preferences but not by the respondent’s level of political information, partisanship, or ideology.

1. Location error occurs when the true location is different than the reported location. Because habitat characteristics at the true location may be different than those at the reported location, ignoring location error may lead to unreliable inference concerning species-habitat relationships.
2. We explain how a transformation known in the spatial statistics literature as a change of support (COS) can be used to correct for location errors when the true locations are points with unknown coordinates contained within arbitrary shaped polygons.
3. We illustrate the flexibility of the COS by modeling the resource selection of Whooping Cranes (Grus americana) using citizen contributed records with locations that were reported with error. We also illustrate the COS with a simulation experiment.
4. In our analysis of Whooping Crane resource selection, we found that location error can result in up to a five-fold change in coefficient estimates. Our simulation study shows that location error can result in coefficient estimates that have the wrong sign, but a COS can efficiently correct for the bias.

This data repository contains two folders: * 01 Equal Traffic Intensities – Raw data for the analysis of decomposition error in tandem queues with equal traffic intensities, * 02 Bottleneck Analyses – Raw data for the analysis of decomposition error in tandem queues with bottlenecks. The first folder contains a training data and a test data file. The second folder contains three files: * Data set with downstream bottleneck queues, * Data set with upstream bottleneck queues, * Data set with similar traffic intensities.

The USDA Forest Service (USFS) builds two versions of percent tree canopy cover (TCC) data to serve needs of multiple user communities. These datasets encompass the conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include: - The raw model outputs referred to as the annual Science data; and - A modified version built for the National Land Cover Database referred to as NLCD data. They are available at the following locations: https://data.fs.usda.gov/geodata/rastergateway/treecanopycover https://apps.fs.usda.gov/fsgisx01/rest/services/RDW_LandscapeAndWildlife NLCD: https://www.mrlc.gov/datahttps://apps.fs.usda.gov/fsgisx01/rest/services/RDW_LandscapeAndWildlife. The Science data are the initial annual model outputs that consist of two images: percent tree canopy cover (TCC) and standard error. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset, and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2008 through 2021 are available. The Science data were produced using a random forests regression algorithm. For standard error data, the initial standard error estimates that ranged from 0 to approximately 45 were multiplied by 100 to maintain data precision in unsigned 16 bit space (e.g., 45 = 4500). Therefore, standard error estimates pixel values range from 0 to approximately 4500. The value 65534 represents the non-processing area mask where no cloud or cloud shadow-free data are available to produce an output, and 65535 represents the background value. The Science data are accessible for multiple user communities, through multiple channels and platforms. For information on the NLCD TCC data and processing steps see the NLCD metadata. Information on the Science data and processing steps are included here.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

Abstract: This code was used for the numerical experiments in the preprint (CRC Preprint 2021/26; URL: https://www.waves.kit.edu/downloads/CRC1173_Preprint_2021-26.pdf) and in the paper "Error analysis of multirate leapfrog-type methods for second-order semilinear odes" by C. Carle and M. Hochbruck. TechnicalRemarks: The scripts inside the subfolders are intended to reproduce the figures from the preprint Error analysis of multirate leapfrog-type methods for second-order semilinear ODEs by Constantin carle and Marlis Hochbruck Requirements The codes are tested with Ubuntu 20.04.2 LTS and Python 3.8.5 and the following version of its modules: numpy - 1.17.4

This surname list is a part of the dataset that I used in my master’s thesis “Error analysis of surname rendering in Finnish-to-English machine translation”. My master thesis is deposited at the Helsinki University Library.

The surname list was extracted from the The Finnish News Agency Archive 2019–2021 (stt-fi-2019-2021-src). Permission to access the corpus can be obtained through the Language Bank of Finland. Corpus cannot be shared with third parties even if permission is granted.

I cannot share the full dataset, as it contains sentences from the corpus.

I am sharing only the list of surnames that I analyse in my master's thesis. The list contains 4,000 surnames extracted from the corpus. The list also contains the identification numbers of news articles from which the surnames were extracted and other metadata. Anyone with the permission to use the The Finnish News Agency Archive 2019–2021 corpus can use the id number to identify the news articles and recreate the dataset.

Reference:

STT. (2022). Finnish News Agency Archive 2019-2021, source [text corpus]. Kielipankki. Retrieved March 10, 2023, from http://urn.fi/urn:nbn:fi:lb-2022030202

Control of Corruption: Standard Error in Luxembourg was reported at 0.16688 in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Luxembourg - Control of Corruption: Standard Error - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

Table S2. Performance statistics on accuracy. a. Accuracy performance statistics on PacBio data of ten methods using five SR coverages. b. Accuracy performance statistics on ONT data of ten methods using five SR coverages. (XLSX 22 kb)

Prediction Error EEG & Motion Study

This is the dataset of the study "prediction error". In short, 19 participants were tested in a virtual reality (VR) reach-to-object task. In the task participants experienced visual, visual with vibrotactile or visual with vibrotactile (all subjects) and electrical muscle stimulation (EMS) feedback (10 subjects) . In 25% of trials the feedback, 'button selection', was provided prematurely, resulting in prediction error / mismatch ERPs (oddball style paradigm). Participants rated their interactive experience on the Immersion and Presence Questionnaire (IPQ) and their workload on the NASA-TLX.

Details about the study can be found in the following publication(s): - Detecting Visuo-Haptic Mismatches in Virtual Reality using the Prediction Error Negativity of Event-Related Brain Potentials. Lukas Gehrke, Sezen Akman, Pedro Lopes, Albert Chen, Avinash Kumar Singh, Hsiang-Ting Chen, Chin-Teng Lin and Klaus Gramann | In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). ACM, New York, NY, USA, Paper 427, 11 pages. DOI: https://doi.org/10.1145/3290605.3300657

A full repository including data, experimental VR protocol (Unity), and publication resources can be found at OSF, doi: 10.17605/OSF.IO/X7HNM

Available Data

Data include 64 channel EEG + 1 reference and 12 channel Motion (6DOF right hand, 6DOF head). Motion metadata is formatted to current preliminary BIDS Motion, see how to get involved.

Data Format and Derivation

Original data were recorded in .xdf format using labstreaminglayer. A \sourcedata directory is currently missing since our .xdf files did not comply with GDPR.

The PE²rr corpus contains source language texts from different domains along with their automatically generated translations into several morphologically rich languages, their post-edited versions, and error annotations of the performed post-edit operations. The main advantage of the corpus is the fusion of post-editing and error classification tasks, which have usually been seen as two independent tasks, although naturally they are not.