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10,801 results for “data correction”
MultiScent-20 Data - Binary responses (correct vs. incorrect answers) data from full sample, for each odour
<p>Data used for IRT analysis in the article</p> <p>"The Multiscent-20: A Digital Odour Identification Test Developed with Item<br> Response Theory", with the following authors:</p> <p>Marcio Nakanishi 1, *, Pedro Renato de Paula Brandão 2, *, Gustavo Subtil Magalhães<br> Freire 1 , Luis Gustavo do Amaral Vinha 3 , Marco Aurélio Fornazieri 4 , Wilma Terezinha<br> Anselmo Lima 5 , Claudia Galvão 6 , Thomas Hummel 7</p> <p> </p> <p>1 Department of Otorhinolaryngology, University Hospital of Brasília, University of<br> Brasília, Brasília, DF, Brazil<br> 2 Neurology Department, Hospital Sírio-Libanês, Brasília, DF, Brazil<br> 3 Statistics Department, University of Brasília, DF, Brazil<br> 4 State University of Londrina, Pontifical Catholic University of Paraná<br> 5 Department of Ophthalmology and Otorhinolaryngology, Ribeirão Preto Medical<br> School-University of São Paulo, Brazil<br> 6 NOAR Brasil Ltda, São Paulo-SP, Brazil<br> 7 Smell and Taste Clinic, Department of Otorhinolaryngology, Technical University of<br> Dresden, Dresden, Germany</p> <p> </p> <p> </p> <p>MultiScent-20 descrition:</p> <p>The Multiscent-20 is a portable tablet computer with an integrated hardware<br> system consisting of a central processing unit, touchscreen, Wi-Fi antenna, USB dock,<br> power supply, and rechargeable battery. Additionally, it contains an odour system<br> composed of 20 microcartridges, an air filter, a system that generates a dry air stream,<br> and an odour-dispensing opening. The device is capable of presenting 20 different<br> odours from individual odour capsules, which store the olfactory stimuli by incorporating<br> the odours into an oil-resistant polymer. The capsules are loaded through an insertion<br> port on the back of the device.<br> A software-controlled processing unit governs the flow of dry air, generating a<br> constant air stream that passes through the capsule, releasing individual odours<br> through a small opening at the upper front of the device. In this study, each scent was<br> presented for 5 seconds, with an interval of at least 6 seconds between odour<br> presentations. Each capsule contained 35 µL of oil-based odour solution, allowing the<br> device to maintain consistent odour intensity and identifiability for up to 100 activations,<br> as per the manufacturer's instructions. The Givaudan Flavours and Fragrance<br> Corporation (São Paulo, Brazil) prepared the odours used in the capsules.<br> A digital application (software) was developed to present odours and record<br> olfactory function test results. The device delivers odours through a dry air system,<br> leaving no residue in the environment or on users. Users can access the test using an<br> application on the device (screen version) or mobile phone. The results are accessible<br> through the integrated software, allowing for storage and analysis of the data.</p>
Data from: Spectroscopic approach to correction and visualisation of bright-field light transmission microscopy biological data
Open the record for dataset details and reuse information.
Five-minute average horizontal wind velocity data combined from both sensors (which has been corrected for air-flow distortion) from the Antarctic Circumnavigation Expedition (ACE) 2016/2017 legs 0 to 4.
<p><strong>Dataset abstract</strong></p> <p>The horizontal wind velocity data from the Antarctic Circumnavigation Expedition (ACE) 2016/2017 legs 0 to 4 has been corrected for air-flow distortion. The measurements from both the port and starboad side anemometer were averaged to five-minute resolution and have been combined via vector averaging of the data. The ten meter neutral wind speed (U10N) has been estimated using ERA-5 surface heat fluxes, which were interpolated onto the ship's track, and the COARE 3.5 drag coefficient. This data set provides a continous and high-resolution record of the wind speed and direction near to the ship's location.</p> <p><strong>Dataset contents</strong></p> <ul> <li>wind-observations-port-stbd-corrected-combined-5min-legs0-4.csv, data file, comma-separated values</li> <li>data_file_header, metadata, text format</li> <li>README.txt, metadata, text format</li> </ul> <p><strong>Dataset license</strong></p> <p>This five-minute averaged wind velocity dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
One-minute average horizontal wind velocity data (which has been corrected for air-flow distortion) from the Antarctic Circumnavigation Expedition (ACE) 2016/2017 legs 0 to 4.
<p><strong>Dataset abstract</strong></p> <p>One-minute average horizontal wind velocity data from the Antarctic Circumnavigation Expedition (ACE) 2016/2017 legs 0 to 4. The data has been filtered for spurious observations and the true wind correction has been redone using the quality checked one-minute ship track velocity data. The data has been corrected for air-flow distortion, which was caused by the ship's super structure.</p> <p><strong>Dataset contents</strong></p> <ul> <li>wind-observations-stbd-corrected-1min-legs0-4.csv, data file, comma-separated values</li> <li>wind-observations-port-corrected-1min-legs0-4.csv, data file, comma-separated values</li> <li>data_file_header, metadata, text format</li> <li>README.txt, metadata, text format</li> </ul> <p><strong>Dataset license</strong></p> <p>This one-minute averaged wind velocity dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
All ChemCam data and corrected Li data
<p>Supplementary data for JGR manuscript "Boron and Lithium in Calcium Sulfate Veins: Tracking Precipitation of Diagenetic Materials in Vera Rubin Ridge, Gale Crater". Excel files contain all LIBS data collected by the Curiosity rover between sol 1-2311 and corrected Li data for vein targets.</p>
A sample image data for ImageJ Bleach Correction Plugin
<p>This sample image data is for testing the ImageJ Bleach Correction plugin. Data was acquired by Boryana Petrova in the Christian H. Haering Lab (EMBL) in 2011 and provided as a sample for development to Kota Miura</p> <p><a href="https://imagej.net/Bleach_Correction">https://imagej.net/Bleach_Correction</a></p> <p>For more details, see "README.md".</p>
Accounting for imperfect detection in data from museums and herbaria when modeling species distributions: Combining and contrasting data-level versus model-level bias correction
The digitization of museum collections as well as an explosion in citizen science initiatives has resulted in a wealth of data that can be useful for understanding the global distribution of biodiversity, provided that the well-documented biases inherent in unstructured opportunistic data are accounted for. While traditionally used to model imperfect detection using structured data from systematic surveys of wildlife, occupancy models provide a framework for modelling the imperfect collection process that results in digital specimen data. In this study, we explore methods for adapting occupancy models for use with biased opportunistic occurrence data from museum specimens and citizen science platforms using 7 species of Anacardiaceae in Florida as a case study. We explored two methods of incorporating information about collection effort to inform our uncertainty around species presence: (1) filtering the data to exclude collectors unlikely to collect the focal species and (2) incorporating collection covariates (collection type, time of collection, and history of previous detections) into a model of collection probability. We found that the best models incorporated both the background data filtration step as well as collector covariates. Month, method of collection and whether a collector had previously collected the focal species were important predictors of collection probability. Efforts to standardize meta-data associated with data collection will improve efforts for modeling the spatial distribution of a variety of species.
TVD differencing on three-dimensional unstructured meshes with monotonicity-preserving correction of mesh skewness (Accompanying data)
<p>This data set contains the data accompanying the article F. Denner and B. van Wachem, TVD differencing on three-dimensional unstructured meshes with monotonicity-preserving correction of mesh skewness, Journal of Computational Physics (2015), http://dx.doi.org/10.1016/j.jcp.2015.06.008.</p>
WDS-RDA-F11 Publishing Data Workflows WG Synthesis FINAL CORRECTED
<p>Final, revised version of http://dx.doi.org/10.5281/zenodo.33413 (published 6 November 2015)</p>
ParaMask, a new method to identify multicopy genomic regions, corrects major biases in whole-genome sequencing data. Additional Datasets.
<p>Data supporting the main figures of the "ParaMask, a new method to identify multicopy genomic regions, corrects major biases in whole-genome sequencing data" manuscript and a copy of the ParaMask software and scripts for analysis, and SV calls from longreads. README files are included.</p>
Data, code and supplementary plots for "A hierarchical spline model for correcting and hindcasting temperature data"
<p>Data, code and supplementary plots for the paper "A hierarchical spline model for correcting and hindcasting temperature data". Please see README.txt for detailed description of the files and relevant instructions.</p>
Data from: Evaluation of different bias correction methods for dynamical downscaled future projections of the California Current Upwelling System
<p class="Abstract">Biases in global Earth System Models (ESMs) are an important source of errors when used to obtain boundary conditions for regional models. Here we examine historical and future conditions in the California Current System (CCS) using three different methods to force the regional model: (1) interpolation of ESM output to the regional grid with no bias correction; (2) a "seasonally-varying" delta method that obtains a season-dependent mean climate change signal from the ESM for a 30-year future period; and (3) a "time-varying" delta method that includes the interannual variability of the ESM over the 1980–2100 period. To compare these methods, we use a high-resolution (0.1˚) physical-biogeochemical regional model to dynamically downscale an ESM projection under the RCP8.5 emission scenario. Using different downscaling methods, the sign of future changes agrees for most of the physical and ecosystem variables, but the spatial patterns and magnitudes of these changes differ, with the seasonal- and time-varying delta simulations showing more similar changes. Not correcting the ESM forcing leads to amplification of biases in some ecosystem variables as well as misrepresentation of the California Undercurrent and CCS source waters. In the non-bias corrected and time-varying delta simulations, most of the ecosystem variables inherit trends and decadal variability from the ESM, while in the seasonally-varying delta simulation, the future variability reflects the observed historical variability (1980–2010). Our results demonstrate that bias correcting the forcing prior to downscaling improves historical simulations and that the bias correction method may impact the spatial and temporal variability of future projections. </p>
Data for: The meta-analysis of the effects of spatial sampling bias correction on presence only species distribution models
<p>This dataset contains information extracted from 70 studies identified through a systematic review of the peer-reviewed literature (Web of Science and SCOPUS databases both searched on the 13/02/2023) to evaluate the effect of spatial sampling bias correction methods in presence-only species distribution models.</p>
Detrended and corrected GNSS data from Lake Taupō, New Zealand, 2014–2021
<p>If using this data please cite the following publication:</p> <p>Schuler, J., Hreinsdóttir, S., Illsley-Kemp, F., Holden, C., Townend, J., Villamor, P. The response of Taupō Volcano to the M7.8 Kaikōura Earthquake. <em>Journal of Geophysical Research - Solid</em> <em>Earth. </em>2024.</p> <p>Also refer to this publication for full details on the methods used to derive the dataset. </p>
Replication Data for the Paper "Is there a secular decline in disruptive patents? Correcting for measurement bias"
<p><strong>Working Paper Title: The Illusive Slump of Disruptive Patents</strong></p> <p>The repository contains replication data and scripts for the paper: </p> <p>Jeffrey T. Macher, Christian Rutzer, Rolf Weder,<br>Is there a secular decline in disruptive patents? Correcting for measurement bias,<br>Research Policy,<br>Volume 53, Issue 5,<br>2024,<br>104992,<br>ISSN 0048-7333,<br>https://doi.org/10.1016/j.respol.2024.104992</p> <p>The core component of the repository is the file `replication_file.R` which is an R script to replicate all figures and tables of the paper.</p> <p>The script relies on multiple data files. The datasets contain CD-values for granted USPTO utility patents for the years 1976-2016. All data is available in two formats, `.fst' (from the R package fst) and `.csv'.</p> <p> </p> <p><strong>Dataset Descriptions</strong></p> <p><strong>Main Datasets:</strong></p> <p>1. `dat_cd5_no_trunc`: This dataset contains the CD5 index of patents based on a method without truncation. </p> <p>2. `dat_cd5_trunc_1975`: This dataset contains the CD5 index of patents based on a truncation method as in Park et al. (Nature, 2023).</p> <p>3. `dat_cd5_no_trunc_app_adj`: This dataset contains the CD5 index of patents based on a method without truncation and including citations to patent applications granted by 2021. </p> <p> </p> <p><strong>Additional Datasets:</strong></p> <p>4. `dat_cd5_trunc_1985`: This dataset contains the CD5 index of patents based on a truncation of all backward citations to patents published before 1985.</p> <p>5. `dat_cd5_trunc_1995`: This dataset contains the CD5 index of patents based on a truncation of all backward citations to patents published before 1995.</p> <p>6. `dat_cd5_no_trunc_app`: This dataset contains the CD5 index of patents based on a method without truncation and including citations to patent applications granted by 2021, as well as those not yet granted. </p> <p>7. `age_bwc_untrunc`: This dataset contains the age of backward citations using untruncated data.</p> <p>8. age_bwc_trunc: This dataset contains the age of backward citations using truncated data as in Park et al. (Nature, 2023).</p> <p>9. `age_bwc_untrunc_app_adj`: This dataset contains the age of backward citations using untruncated data and considering citations of patent applications granted until 2021.</p> <p>10. `dat_cd10_trunc_1975`: This dataset contains the CD10 index of patents based on a truncation method as in Park et al. (Nature, 2023). </p> <p>11. `dat_cd10_no_trunc_app_adj`: This dataset contains the CD10 index of patents based on a method without truncation and including citations to patent applications granted by 2021. </p> <p>12. `dat_cd2021_trunc_1975`: This dataset contains the CD index as of 2021 of patents based on a truncation method as in Park et al. (Nature, 2023). </p> <p>13. `dat_cd2021_no_trunc_app_adj`: This dataset contains the CD index as of 2021 of patents based on a method without truncation and including citations to patent applications granted by 2021. </p> <p> </p> <p>To successfully run the `replication_file.R' script, make sure all data files are in the directory and the `mainDir1' variable at the beginning of the script is set to the correct path to where the data is stored. In addition, set the `mainDir2' variable to the folder where you want to store the figures created by the script. </p> <p>If you have any questions, please contact christian.rutzer@unibas.ch</p>
Data folder for the Batch Effect correction part of OBRWR
<p>Data folder for the batch effect correction of phosphoproteomics assay conducted in Dr. Destaing's lab.</p> <p>Batch effect assessment using PCA.</p> <p>Proposing two correction methods : one using PCA, the second (and the one used) using MLM.</p>
Data from "Hardware-efficient quantum error correction using concatenated bosonic qubits"
<p>Includes data for characterizing the bit-flip and logical phase-flip rates of the logical memory. See the README for more details.</p>
Data from: Demographic correction – a tool for inference from individuals to populations
<p>Estimation of responses of organisms to their environment using experimental manipulations, and comparison of such responses across sets of species, is one of the primary tools in ecology research. The most common approach is to compare response of a single life stage of species to an environmental factor and use this information to draw conclusions about population dynamics of these species. Such approach ignores the fact that interspecific fitness differences measured at a single life stage are not directly comparable and cannot be extrapolated to lifetime fitness of individuals and thus species' population dynamics. Comparison of one life stage only while omitting demographic information can strongly bias conclusions, both in experimental studies with a few species, and in large comparative studies.</p> <p>We illustrate the effect of this omission using both an exaggerated fictitious example, and biological data on congeneric species differing in their demography. We are showing, taking simple assumptions, that different demography can completely revert conclusions reached by a comparison based on an experiment focusing on a single life stage.</p> <p>We show that a "demographic correction", namely translating observed effects into differences in outcomes of demographic models, is a solution to this problem. It requires turning the detected effects from the experiment into changes of transition probabilities of projection matrix models. Although such solution is limited by the low number of species with demographic data available, we believe that existing data (and data likely to be collected in the near future) permit at least approximate handling of this problem.</p>
Data from: Correcting a bias in the computation of behavioral time budgets that are based on supervised learning
<p>Supervised learning of behavioral modes from body-acceleration data has become a widely used research tool in Behavioral Ecology over the past decade. One of the primary usages of this tool is to estimate behavioral time budgets from the distribution of behaviors as predicted by the model. These serve as the key parameters to test predictions about the variation in animal behavior. In this paper we show that the widespread computation of behavioral time budgets is biased, due to ignoring the classification model confusion probabilities. Next, we introduce <em>the confusion matrix correction for time budgets</em> -- a simple correction method for adjusting the computed time budgets based on the model's confusion matrix. Finally, we show that the proposed correction is able to eliminate the bias, both theoretically and empirically in a series of data simulations on body acceleration data of a fossorial rodent species (Damaraland mole-rat, <em>Fukomys damarensis</em>). Our paper provides a simple implementation of <em>the confusion matrix correction for time budgets</em>, and we encourage researchers to use it to improve accuracy of behavioral time budget calculations.</p>
Replication Data for: Exact two-component Hamiltonians for relativistic quantum chemistry: Two-electron picture-change corrections made simple
<p>Based on self-consistent field (SCF) atomic mean-field (amf) quantities, we present two simple, yet computationally efficient as well as numerically accurate algebraic approaches to remedy both scalar-relativistic <em>and</em> spin-orbit two-electron picture-change effects (PCE) arising within an exact two-component (X2C) Hamiltonian framework. Both approaches, dubbed as amfX2C and e(xtended)amfX2C, allow us to uniquely tailor PCE corrections to either mean-field models, <em>viz</em>. Hartree–Fock or Kohn–Sham DFT, in the latter case also avoiding the need of a point-wise calculation of exchange–correlation PCE corrections. We assess the numerical performance of these PCE correction models on spinor energies of group-18 (closed-shell) and group-16 (open-shell) diatomic molecules, achieving a consistent ≈ 10−5-Hartree accuracy with regard to reference four-component data. Additional tests include SCF calculations of molecular properties such as absolute contact density and contact density shifts in copernicium fluoride compounds (CnFn, n=2,4,6), as well as equation-of-motion coupled cluster calculations of X-ray core ionization energies of 5<em>d</em> and 6<em>d</em>-containing molecules where we observe an excellent agreement with reference data. To conclude, we are confident that our (e)amfX2C PCE correction models constitute a fundamental milestone towards a universal and reliable relativistic two-component quantum chemical approach, maintaining the accuracy of the parent four-component one at a fraction of its computational cost.</p>
ScienceDex guides
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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.