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12,753 results for “Corrections”
TCSIF: A temporally consistent global GOME-2A SIF dataset with correction of sensor degradation
<p><strong>TCSIF: A temporally consistent global GOME-2A SIF dataset with correction of sensor degradation</strong></p> <p> </p> <p><strong>Description:</strong></p> <p>The global monthly GOME-2A SIF dataset (2007–2021) with correction of temporal degradation. The corrected global GOME-2 SIF dataset can be obtained in two types. The daily level2 dataset is provided in hdf5 format(compressed in the zip files named "{Year}{Quater}.zip"). The name of the hdf5 files was SIF_daily_YYYYMMDD.h5, YYYY, MM, and DD represent the year, month, and date, respectively. The level3 datasets which were aggregated monthly from the level2 dataset, have a spatial resolution of 0.5°and were saved in TIFF format in chronological order from 2007 to 2021 (compressed in the file "Level3.zip"). The name of the files was SIFpar_evi_monthly _YYYYMM.tif, where SIF was product type, par, and evi represented upscaled parameters, monthly represented temporal scale, YYYY and MM was the year and month, respectively. The SIF output was stored in the hdf5 files along with other variables of interest for further processing and visualization. See the appendix for the structure of the hdf5 file.</p> <p> </p> <p><strong>cloud_fraction</strong><strong>[float]</strong>:</p> <p>Description: Effective cloud fraction derived from GOME-2 Level1B product.</p> <p>Units: none</p> <p><strong>latitude</strong><strong>[float]</strong>:</p> <p>Description: Pixel center latitude.</p> <p>Units: degrees N</p> <p><strong>longitude</strong><strong>[float]</strong>:</p> <p>Description: Pixel center longitude.</p> <p>Units: degrees E</p> <p><strong>latitude_bounds</strong><strong>[float]</strong>:</p> <p>Description: Latitude of the boundary corners for each pixel.</p> <p>Units: degrees N</p> <p><strong>longitude _bounds</strong><strong>[float]</strong>:</p> <p>Description: Longitude of the boundary corners.</p> <p>Units: degrees E</p> <p><strong>SIF_740</strong><strong>[float]</strong>:</p> <p>Description: SIF signal at 740nm retrieved using the 735–758 nm fitting window.</p> <p>Units: mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup></p> <p><strong>SIF_daily</strong><strong> [float]</strong>:</p> <p>Description: SIF signal at 740nm with correction of day-length.</p> <p>Units: mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup></p> <p><strong>Sigma_i</strong><strong>[float]</strong>:</p> <p>Description: The squre of single retrieval error of SIF_740.</p> <p>Units: (mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup>)<sup>2</sup></p> <p><strong>Solar_zenith_angle</strong><strong> [float]</strong>:</p> <p>Description: Solar zenith angle.</p> <p>Units: degrees</p> <p><strong>Solar_azimuth_angle</strong><strong> [float]</strong>:</p> <p>Description: Solar azimuth angle.</p> <p>Units: degrees</p> <p><strong>Viewing _zenith_angle</strong><strong> [float]</strong>:</p> <p>Description: Viewing zenith angle.</p> <p>Units: degrees</p> <p><strong>Viewing_azimuth_angle</strong><strong>[float]</strong>:</p> <p>Description: Viewing azimuth angle.</p> <p>Units: degrees</p> <p><strong>chi2</strong><strong>[float]</strong>:</p> <p>Description: The reduced chi-square value calculated based on the the fitting residuals.</p> <p>Units: None</p> <p><strong>Rad_NIR</strong><strong>[float]</strong>:</p> <p>Description: The average radiance within the 735~758 nm window</p> <p>Units: mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup></p> <p><strong>ps_NIR</strong><strong>[float]</strong>:</p> <p>Description: The average reflectance within at around 780 nm.</p> <p>Units: None</p> <p><strong>ps_red</strong><strong>[float]</strong>:</p> <p>Description: The average reflectance within the 665~680 nm window</p> <p>Units: None</p> <p><strong>NDVI</strong><strong>[float]</strong>:</p> <p>Description: Calculated by the TOA reflectance at red band (around 680 nm) and near-infrared band (around 780nm).</p> <p>Units: None</p>
Bias corrected era5 skin temperature over the Arctic sea ice – 1981 to 2018 monthly means and climatology
<p>This dataset is generated in the context of the peer-reviewed study of Zampieri et al., 2023. The users can find a detailed description of the bias correction strategy and information on the scientific value of the dataset in the paper. Please, do not hesitate to contact me to obtain further information and suggestions on how to employ this dataset for your specific purpose. </p> <p><strong>References:</strong></p> <p>Zampieri, L.,<strong> </strong>Arduini, G., Holland, M., Keeley, S., Mogensen, K., Shupe, M., Tietsche, S. (2023) A machine learning correction model of the winter clear-sky temperature bias over the Arctic sea ice in atmospheric reanalyses. <em>Monthly Weather Review</em>. DOI:<a href="https://doi-org.cuucar.idm.oclc.org/10.1175/MWR-D-22-0130.1">10.1175/MWR-D-22-0130.1</a></p> <p><strong>Acknowledgments:</strong></p> <p>As part of the Virtual Earth System Research Institute (VESRI), funding for the Multiscale Machine Learning In coupled Earth System Modeling (M2LInES) project was provided to Lorenzo Zampieri by the generosity of Eric and Wendy Schmidt by recommendation of the Schmidt Futures program. </p>
A Study to Evaluate the Effects of Pevonedistat on the Corrected QT (QTc) Interval in Participants With Advanced Solid Tumors
ClinicalTrials.gov study NCT03330106. IPD Sharing: YES. Countries: 1. Publications: 1.
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.
A dynamic in vitro model of Down Syndrome neurogenesis with Trisomy 21 gene dosage correction
Open the record for dataset details and reuse information.
Dataset of trend-preserving bias-corrected daily temperature, precipitation and wind from NEX-GDDP and CMIP5 in the Qinghai-Tibet Plateau——Part Ⅱ
<p>A bias-corrected dataset containing daily meteorological data of the Qinghai-Tibet Plateau has been generated, by using a trend-preserving bias-correction, the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) approach together with a high-quality gridded meteorological dataset based on ground observation (CN05.1). The data set contains daily bias-corrected values of maximum/minimum near-surface air temperature, precipitation and mean near-surface wind speed from 15 models from the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5) and their downscaled high-resolution dataset (NEX-GDDP) in the Qinghai-Tibet Plateau (QTP) during 1986-2095. This dataset can provide important reference for the study on future climate change and its impacts in the Qinghai-Tibet Plateau region.</p> <p><strong>Note: For Tmin in historical periods, the values larger than 2606 refer to no data. Set them to NaN before using, for example (Matlab): Tmin(Tmin>2606)=nan;</strong></p> <p>More details about this dataset can be found in the article: S. Chen, T. Ye, W. Liu, A. Wang and P. Shi. Evaluation and bias correction of the historical and future near-surface climate forcing in NEX-GDDP and CMIP5 over the Qinghai-Tibet plateau[J], Plateau Meteorology (in Chinese), 2020, DOI: 10.7522/j.issn.1000-0534. 2020. 00019.</p>
Dataset of trend-preserving bias-corrected daily temperature, precipitation and wind from NEX-GDDP and CMIP5 in the Qinghai-Tibet Plateau——Part Ⅰ
<p>A bias-corrected dataset containing daily meteorological data of the Qinghai-Tibet Plateau has been generated, by using a trend-preserving bias-correction, the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) approach together with a high-quality gridded meteorological dataset based on ground observation (CN05.1). The data set contains daily bias-corrected values of maximum/minimum near-surface air temperature, precipitation and mean near-surface wind speed from 15 models from the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5) and their downscaled high-resolution dataset (NEX-GDDP) in the Qinghai-Tibet Plateau (QTP) during 1986-2095. This dataset can provide important reference for the study on future climate change and its impacts in the Qinghai-Tibet Plateau region.</p> <p><strong>Note: For Tmax in historical periods, the values larger than 2606 refer to no data. Set them to NaN before using, for example (Matlab): Tmax(Tmax>2606)=nan;</strong></p> <p>More details about this dataset can be found in the article: S. Chen, T. Ye, W. Liu, A. Wang and P. Shi. Evaluation and bias correction of the historical and future near-surface climate forcing in NEX-GDDP and CMIP5 over the Qinghai-Tibet plateau[J], Plateau Meteorology (in Chinese), 2020, DOI: 10.7522/j.issn.1000-0534. 2020. 00019.</p>
The dataset of the ASE'20 paper titled "Automated Patch Correctness Assessment: How Far are We?"
<p>This is the experiment result of the ASE'20 paper titled "<strong>Automated Patch Correctness Assessment: How Far are We?</strong>".</p> <p>If you use our data for academic research, please cite our paper as:</p> <pre><code class="language-html">@inproceedings{wang2020automated, title={Automated Patch Correctness Assessment: How Far are We?}, author={Wang, Shangwen and Wen, Ming and Lin, Bo and Wu, Hongjun and Qin, Yihao and Zou, Deqing and Mao, Xiaoguang and Jin, Hai}, booktitle={Proceedings of the 35th International Conference on Automated Software Engineering (ASE)}, year={2020}, organization={ACM} }</code></pre> <p>The file <strong><em>Patches.zip</em></strong> includes all the patches we take into consideration in this study. Note that 269 patches come from "<a href="http://arxiv.org/pdf/1909.13694">Automated Patch Assessment for Program Repair at Scale</a> (Ye et al.), Technical report 1909.13694, arXiv, 2019".</p> <p>The file <em><strong>Patches_for_Static</strong></em> include all the class files we used for static method.</p> <p>The file <em><strong>Tests-oracle </strong></em>includes all the test cases generated by <strong>Evosuite</strong> and <strong>Randoop</strong> on the fixed version programs.</p> <p>The file <em><strong>Tests-buggy</strong></em> includes all the test cases generated by <strong>Evosuite</strong> and <strong>Randoop</strong> on the buggy version programs.</p> <p>The file <em><strong>DiffTGen-result</strong></em> includes ingredients and output information of <strong>DiffTGen</strong>.</p> <p>The file <em><strong>Daikon-output</strong></em> includes inferred invariants of each patch and its corresponding ground-truth.</p> <p>The file <em><strong>PATCH-SIM_result</strong></em> includes the output vector files from <strong>PATCH-SIM</strong> and<strong> E-PATCH-SIM</strong>.</p> <p>The file <em><strong>Training_result</strong></em> includes the output of six ML algorithms with or without oracle.</p> <pre><code class="language-xml">Chart: 1-26; Closure: 14, 18, 31, 33, 38, 40, 57, 62, 63, 70, 73, 86, 92, 93, 115, 123, 126; Lang: 6, 7, 10, 16, 20, 21, 22, 24, 26, 27, 33, 35, 38, 39, 41, 43, 44, 45, 50, 51, 55, 57, 58, 59, 60, 61, 63; Math: 2, 3, 4, 5, 6, 8, 20, 22, 25, 28, 30, 31, 32, 33, 34, 35, 39, 41, 49, 50, 53, 56, 57, 58, 59, 60, 61, 63, 65, 68, 70, 71, 73, 74, 75, 79, 80, 81, 82, 85, 86, 88, 89, 90, 93, 97, 98, 99, 104; Time: 4, 7, 11, 14, 15, 19. </code></pre> <p>Please note that for bugs in the above table, the Evosuite tests on the fixed version programs are reused from <a href="https://arxiv.org/abs/1909.13694">a previous study</a>. We thank <strong>He Ye</strong>, <strong>Matias Martinez</strong>, and <strong>Martin Monperrus</strong> so much for sharing their data.</p> <p> </p> <p><strong>Notice!</strong> For patches under the folder <em>Patches_ICSE</em>, those under <em>Ddifferent</em> and <em>Dsame</em> folders are all correct patches. <em>Different</em> and <em>Same</em> only indicate whether the patch is syntactically identical to the ground truth patch.</p> <p>Patches generated for Mockito project (2 in total): Kali-A-Mockito-10; Arja-Mockito-10</p> <p>Patches do not pass plausibility check (6 in total): Kali-Closure-133; kPAR-Chart-12; FixMiner-Chart-12; patch1-Lang-6-SketchFix-plausible; patch2-Lang-6-SketchFix-plausible; patch1-Math-2-SOFix</p> <p>Patches that are mistakenly labeled (12 in total): patch2-Lang-51-Jaid; patch1-Lang-43-CapGen; patch2-Lang-43-CapGen; patch2-Math-53-CapGen; patch2-Math-53-Jaid; jKali-Lang-7; ACS-Lang-35; Arja-Math-35; SimFix-Math-72; SimFix-Closure-19; Arja-Math-50; SimFix-Lang-60</p> <p>Detailed reasons for the mislabeled patches: 1. the ground-truth patch modifies multiple locations while the generated patch only modifies one of them (2/12, SimFix-Math-72, SimFix-Lang-60); 2. the edit points in the generated patch are different from those in ground-truth patch (8/12, patch2-Lang-51-Jaid, patch2-Math-53-Jaid, patch1-Lang-43-CapGen, patch2-Lang-43-CapGen, patch2-Math-53-CapGen, ACS-Lang-35, SimFix-Closure-19, Arja-Math-50); 3. the generated patch doesnot fulfill the intended function in ground-truth (2/12, jKali-Lang-7, Arja-Math-35).</p> <p>Take <em>Arja-Math-50</em> as an example, this patch deletes a conditional statement which deals with an unexpected input (<em>null</em>) in the method <em><strong>verifyBracketing</strong></em>.<em><strong> </strong></em>However, in the oracle program, this conditional statement still exists. Then, <strong>Randoop</strong> generated a test case by calling <strong><em>verifyBracketing</em></strong> with a <em>null</em> argument. This test passed on the ground-truthpatch but failed on the patch generated by Arja due to the removeof the exception handling statements. As a result, this patch is actually overfitting but mistakenly labeled as correct. We have confirmed this case with Kui Liu, the first author of the recent ICSE'20 paper (Title: <em>On the Efficiency of Test Suite based Program Repair</em>) which makes up our patch benchmark.</p> <p> </p> <p>Border line Patches (3 in total): ACS-Lang-7; kPAR-Lang-7; TBar-Lang-7. Reasons for overfitting: Evosuite generates some tests that fail on those patches, e.g., test049 in Seed 1; the Java documentation above the function states that it needs to deal with the situation where the input cannot be converted. Reasons for correct: it synthesizes the correct modification; currently, in the program, <em>createBigDecimal()</em> is not called directly in other part of the production code except <em>createNumber()</em> and the test code. In our paper, we consider these three patches as correct and that's why Evosuite has 3 false positives.</p>
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>
Degradation corrected 0.05 degree GOME-2 SIF datasets in Amazon area
<p>An 8-day instrument degradation corrected 0.05 degree GOME-2 SIF dataset in Amazon area from 2010 to 2018. PK dataset from the spatially downscaled sun-induced fluorescence global product proposed by Gregory Duveiller in 2020 is corrected based on a pseudo-invariant method and then masked. Mean value composite method is used to produce monthly data. Files are organized in TIF format.</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>
Corrected super-resolution microscopy enables nanoscale imaging of auto-fluorescent lung macrophages
<p>Observing the cell surface and underlying cytoskeleton at nanoscale resolution using super-resolution microscopy has enabled many insights into cell signalling and function. However, the nanoscale dynamics of tissue-specific immune cells have been relatively little studied. Tissue macrophages, for example, are highly auto-fluorescent, severely limiting the utility of light microscopy. Here, we report a correction technique to remove auto-fluorescent noise from Stochastic Optical Reconstruction Microscopy (STORM) datasets. Simulations identified a moving median filter as an accurate and robust correction technique. Using this, we were able to visualise lung macrophages activated through Fc receptors by antibody-coated glass slides. Accurate, nanoscale quantification of macrophage morphology revealed that activation induced the formation of cellular protrusions tipped with MHC class I protein. These data are consistent with a role for lung macrophage protrusions in antigen presentation. We further show that the tetraspanin and extracellular vesicle (EV) marker CD81 appears in ring-shaped structures (mean diameter 93 ± 50 nm) at the surface of activated lung macrophages, likely marking the secretion of extracellular vesicles. Moreover, this correction method for super-resolution microscopy is widely applicable to other challenging biological samples.</p>
corrected_setiferous_punctures_in_3rd_elytral_interval
<p>corrected filename for uploads: Habitus of an Agonum specimen, setiferous punctures in 3rd elytral interval marked</p>
SDOML Dataset Not Corrected for Degradation Over Time
<p>This dataset is a version of the Galvez et al. 2019 (A Machine-learning Data Set Prepared from the NASA Solar Dynamics Observatory Mission) dataset not corrected for degradation over time.</p> <p>This dataset is required to reproduce Fig.4 of Dos Santos et al. 2021 (arXiv: 2012.14023)</p> <p><br> This dataset contains extreme ultraviolet (EUV) images from the Atmospheric Image Assembly at the Solar Dynamics Observatory. The specs of the data are:</p> <ul> <li>Data from 2010 to 2020. </li> <li>Temporal resolution is 1 day.</li> <li>Spatial resolution is ~4.8 arcsec (512 x 512 image)</li> <li>Images not corrected for instrumental degradation over time,</li> <li>Images corrected for exposure time.</li> <li>7 EUV channels.</li> <li>The total size of the data is ~22.1 TB compressed.</li> </ul>
A global GOME-2 monthly SIF dataset (2007-2018) with correction of temporal degradation
<p>Monthly instrument degradation corrected 0.5 degree GOME-2 SIF datasets on a global scale from 2007 to 2018. The sun-induced fluorescence global product Joiner et al. (2017) is firstly uscaled to a monthly resolution using a APAR based algorithm by Hu et al. (2018). Then, the mothly GOME-2 SIF was corrected based on a pseudo-invariant method to eliminate the temporal degradation of GOME-2 satellite sensor. Files are organized in TIF format.</p>
Dataset for submission of "Correction of river bathymetry parameters using the stage–discharge rating curve"
<p>Dataset for submission of "Correction of river bathymetry parameters using the stage–discharge rating curve". </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>
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.