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12,753 results for “Corrections”
Soil carbon maintained by perennial grasslands but lost in field crop systems over 30 years in a temperate Mollisol according to longitudinal, compaction-corrected, full-soil profile analysis
<p>To mitigate climate change, some seek to store carbon from the atmosphere in agricultural soils. However, our understanding of how agriculture affects soil organic carbon (SOC) is muddied by studies 1) lacking longitudinal data, 2) ignoring bulk density changes, or 3) sampling only surface soils. To better understand SOC trends, here we measured changes over 30 years in density-corrected, full-soil-depth (90 cm) SOC stocks under 6 cropping systems and a restored prairie in a Mollisol of southern Wisconsin, USA. Cash-grain systems and alfalfa-based systems lost SOC. Prairie and rotationally-grazed pasture maintained SOC. Average SOC losses for cash-grain and alfalfa-based systems were -0.82 (±0.12) and -0.64 (±0.17) Mg C ha<sup>-1</sup> yr<sup>-1</sup>, respectively. Sensitivity analysis showed that incomplete methodologies overestimated SOC improvements. Our findings using more comprehensive methods demonstrate the inadequacy of row-crop systems and the need for well-managed grasslands to protect SOC in productive agricultural soils of the Upper Midwest USA.</p>
Fig. 1 in A List of Pselaphine Species (Insecta, Coleoptera, Staphylinidae) Collected from Mt. Phia Oac, North Vietnam in 2017 with a Corrected Checklist of the Pselaphine Species Known from Vietnam
Fig. 1. The location of Mt. Phia Oac on the map of N Vietnam.
Figure 1 in New Data On Distribution Of Two Palaearctic Leptaleus Laferté-Sénectère, 1849 (Coleoptera: Anthicidae) Species With New Records And A Correction
Figure 1. Leptaleus chaudoiri (Kolenati, 1846), specimen from Islamabad, Pakistan.
Fig. 3 in Additions and corrections to the list of Psocoptera of Unguja Island (Zanzibar, Tanzania)
Fig. 3. The previously misidentified specimen of Stenocaecilius (from Georgiev 2021): S. casarum.
Fig. 1 in Additions and corrections to the list of Psocoptera of Unguja Island (Zanzibar, Tanzania)
Fig. 1. Light microscope photograph of a female L. puber from Unguja Island (10x).
Multi-sample batch correction with Harmony and SnapATAC2
<p>Input datasets for the Galaxy Tutorial "Multi-sample batch correction with Harmony and SnapATAC2.</p>
Chromophore-driven optical path reduction as an indirect inner filter effect correction strategy in fluorescence microplate assays - experimental data
<p>Experimental data for the paper entitled <em>Chromophore-driven optical path reduction as an indirect inner filter effect mitigation strategy in fluorescence microplate assays</em> (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.saa.2025.127049" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.saa.2025.127049</span></span></a>). </p> <p>An Excel file with multiple worksheets is provided for the following:</p> <p>1. Fluorescence data:</p> <p>The worksheets contain fluorescence values measured in tetraplicates using two microplate readers (Tecan Spark M10, Tecan, Austria and SpectraMax iD3, Molecular Devices, USA), with baseline values recorded in octuplicates. The results were averaged and baseline-corrected. Detailed notes on the titration experiments are included in the manuscript. Data include both IFE-uncorrected fluorescence and fluorescence corrected (fully or partially) by the addition of an absorbing chromophore.</p> <p>2. Absorbance data:</p> <p>All measured absorbance values obtained using the Tecan Spark M10 microplate reader with UV-Vis transparent microplates are included. The absorbance values at specific wavelengths suitable for the Lakowicz IFE correction method or for determining the concentration of the compound are given separately.</p> <p>3. Fluorescein spectra:</p> <p>UV-Vis and fluorescence excitation/emission spectra for highly dilute fluorescein concentrations where the inner filter effect (IFE) is negligible are reported. These measurements were performed to obtain “true” excitation and emission spectra without the influence of IFE quenching effects.</p> <p>4. ZINFE/NINFE IFE corrections:</p> <p>The dataset contains the results of ZINFE/NINFE IFE corrections (<a href="https://doi.org/10.1021/acs.analchem.2c01031">https://doi.org/10.1021/acs.analchem.2c01031</a>) applied to the fluorescence data and processed using the web service available at <a href="https://ninfe.science">https://ninfe.science</a>.</p> <p>The notation of the fluorophores and microplates is the same as in the manuscript. For reasons of visual clarity, the numerical titration indices are not in subscript.</p> <p> </p>
Fig. 2 in Correction of the holotype citations of three vascular plants at the herbarium of the National Institute of Biological Resources, Korea
Fig. 2. Holotype of Isoetes coreana Y.H. Chung & H.K. Choi.
Fig. 3 in Correction of the holotype citations of three vascular plants at the herbarium of the National Institute of Biological Resources, Korea
Fig. 3. Holotype of Huperzia jejuensis B.Y. Sun & J. Lim.
Dataset Prenatal correction of XLHED
<p>This dataset is related to "Prenatal Correction of X-Linked Hypohidrotic Ectodermal Dysplasia" (Holm Schneider, Florian Faschingbauer, Sonia Schuepbach-Mallepell, Iris Körber, Sigrun Wohlfart, Angela Dick, Mandy Wahlbuhl, Christine Kowalczyk-Quintas, Michele Vigolo, Neil Kirby, Corinna Tannert, Oliver Rompel, Wolfgang Rascher, Matthias W. Beckmann, Pascal Schneider)</p>
A method for correcting staggered pulse repetition time (PRT) and dual Pulse Repetition Frequency (PRF) processor errors
<p>A method for correcting dual-PRF and staggered-PRT induced radial velocity errors. This method is being developed by the authors at the University of Oklahoma and is considered to be a work in progress. v2 of the algorithm will be made available in the coming months (late 2019 or early 2020).</p>
correction_sol_rad_2018_v.1.0
<p>Addtition Data for Correction of Solar Radiation on Patscherkofel.</p> <p>A Project by students of ACINN, University of Innsbruck, 2018.</p> <p>For more Information see: https://transparency-lecture.github.io/docs/home/</p>
R code for Rezek et al 2018 "Two million years of flaking stone ..." CORRECTION
<p>The code and the dataset for Rezek et al 2018 Two million years of flaking stone and the evolutionary efficiency of stone tool technology, Nature Ecology and Evolution. The dataset was also published with the paper doi:10.1038/s41559-018-0488-4</p>
Corrective saccades influence velocity judgments and interception
<p>Here you can find the data for the paper "Corrective saccades influence velocity judgments and interception" by</p> <p>Alexander Goettker, Eli Brenner, Karl Gegenfurtner & Cristina de la Malla.</p>
359,569 commits with source code density; 1149 commits of which have software maintenance activity labels (adaptive, corrective, perfective)
<p>This dataset comes as SQL-importable file and is compatible with the widely available MariaDB- and MySQL-databases.</p> <p>It is based on (and incorporates/extends) the dataset "<em>1151 commits with software maintenance activity labels (corrective,perfective,adaptive)</em>" by Levin and Yehudai (<a href="https://doi.org/10.5281/zenodo.835534">https://doi.org/10.5281/zenodo.835534</a>).</p> <p>The extensions to this dataset were obtained using <em>Git-Tools</em>, a tool that is included in the <strong>Git-Density</strong> (<a href="https://doi.org/10.5281/zenodo.2565238">https://doi.org/10.5281/zenodo.2565238</a>) suite. For each of the projects in the original dataset, Git-Tools was run in <em>extended</em> mode.</p> <p>The dataset contains these tables:</p> <ul> <li><strong>x1151</strong>: The original dataset from Levin and Yehudai. <ul> <li>despite its name, this dataset has only 1,149 commits, as two commits were duplicates in the original dataset.</li> <li>This dataset spanned 11 projects, each of which had between 99 and 114 commits</li> <li>This dataset has <strong>71</strong> features and spans the projects <em>RxJava, hbase, elasticsearch, intellij-community, hadoop, drools, Kotlin, restlet-framework-java, orientdb, camel</em> and <em>spring-framework</em>.</li> </ul> </li> <li><strong>gtools_ex</strong> (short for <em>Git-Tools, extended</em>) <ul> <li>Contains <strong>359,569</strong> commits, analyzed using Git-Tools in extended mode</li> <li>It spans all commits and projects from the x1151 dataset as well.</li> <li>All 11 projects were analyzed, from the initial commit until the end of January 2019. For the projects <em>Intellij</em> and <em>Kotlin</em>, the first 35,000 resp. 30,000 commits were analyzed.</li> <li>This dataset introduces <strong>35 new</strong> features (see list below), 22 of which are <em><strong>size</strong></em>- or <em><strong>density</strong></em>-related.</li> </ul> </li> </ul> <p>The dataset contains these views:</p> <ul> <li><strong>geX_L</strong> (short for Git-<em>tools, extended, with labels</em>) <ul> <li>Joins the commits' labels from <em>x1151</em> with the extended attributes from <em>gtools_ex</em>, using the commits' hashes.</li> </ul> </li> <li><strong>jeX_L</strong> (short for <em>joined, extended, with labels</em>) <ul> <li>Joins the datasets <em>x1151</em> and <em>gtools_ex</em> entirely, based on the commits' hashes.</li> </ul> </li> </ul> <p> </p> <p>Features of the <strong>gtools_ex</strong> dataset:</p> <ul> <li><strong>SHA1</strong></li> <li><strong>RepoPathOrUrl</strong></li> <li><strong>AuthorName</strong></li> <li><strong>CommitterName</strong></li> <li><strong>AuthorTime </strong>(UTC)</li> <li><strong>CommitterTime </strong>(UTC)</li> <li><strong>MinutesSincePreviousCommit</strong>: Double, describing the amount of minutes that passed since the previous commit. Previous refers to the <strong>parent</strong> commit, not the previous in time.</li> <li><strong>Message</strong>: The commit's message/comment</li> <li><strong>AuthorEmail</strong></li> <li><strong>CommitterEmail</strong></li> <li><strong>AuthorNominalLabel</strong>: All authors of a repository are analyzed and merged by Git-Density using some heuristic, even if they do not always use the same email address or name. This label is a unique string that helps identifying the same author across commits, even if the author did not always use the exact same identity.</li> <li><strong>CommitterNominalLabel</strong>: The same as <em>AuthorNominalLabel</em>, but for the committer this time.</li> <li><strong>IsInitialCommit</strong>: A boolean indicating, whether a commit is preceded by a parent or not.</li> <li><strong>IsMergeCommit</strong>: A boolean indicating whether a commit has more than one parent.</li> <li><strong>NumberOfParentCommits</strong></li> <li><strong>ParentCommitSHA1s</strong>: A comma-concatenated string of the parents' SHA1 IDs</li> <li><strong>NumberOfFilesAdded</strong></li> <li><strong>NumberOfFilesAddedNet</strong>: Like the previous property, but if the net-size of all changes of an added file is zero (i.e. when adding a file that is empty/whitespace or does not contain code), then this property does not count the file.</li> <li><strong>NumberOfLinesAddedByAddedFiles</strong></li> <li><strong>NumberOfLinesAddedByAddedFilesNet</strong>: Like the previous property, but counts the net-lines</li> <li><strong>NumberOfFilesDeleted</strong></li> <li><strong>NumberOfFilesDeletedNet</strong>: Like the previous property, but considers only files that had net-changes</li> <li><strong>NumberOfLinesDeletedByDeletedFiles</strong></li> <li><strong>NumberOfLinesDeletedByDeletedFilesNet</strong>: Like the previous property, but counts the net-lines</li> <li><strong>NumberOfFilesModified</strong></li> <li><strong>NumberOfFilesModifiedNet</strong>: Like the previous property, but considers only files that had net-changes</li> <li><strong>NumberOfFilesRenamed</strong></li> <li><strong>NumberOfFilesRenamedNet</strong>: Like the previous property, but considers only files that had net-changes</li> <li><strong>NumberOfLinesAddedByModifiedFiles</strong></li> <li><strong>NumberOfLinesAddedByModifiedFilesNet</strong>: Like the previous property, but counts the net-lines</li> <li><strong>NumberOfLinesDeletedByModifiedFiles</strong></li> <li><strong>NumberOfLinesDeletedByModifiedFilesNet</strong>: Like the previous property, but counts the net-lines</li> <li><strong>NumberOfLinesAddedByRenamedFiles</strong></li> <li><strong>NumberOfLinesAddedByRenamedFilesNet</strong>: Like the previous property, but counts the net-lines</li> <li><strong>NumberOfLinesDeletedByRenamedFiles</strong></li> <li><strong>NumberOfLinesDeletedByRenamedFilesNet</strong>: Like the previous property, but counts the net-lines</li> <li><strong>Density</strong>: The ratio between the two sums of all lines added+deleted+modified+renamed and their resp. gross-version. A density of zero means that the sum of net-lines is zero (i.e. all lines changes were just whitespace, comments etc.). A density of of 1 means that all changed net-lines contribute to the gross-size of the commit (i.e. no useless lines with e.g. only comments or whitespace).</li> <li><strong>AffectedFilesRatioNet</strong>: The ratio between the sums of <em>NumberOfFilesXXX</em> and <em>NumberOfFilesXXXNet</em></li> </ul> <p> </p> <p>This dataset is supporting the paper <strong>"<em>Importance and Aptitude of Source code Density for Commit Classification into Maintenance Activities</em></strong><strong>"</strong>, as submitted to the <em>QRS2019</em> conference (The 19th IEEE International Conference on Software Quality, Reliability, and Security). Citation: Hönel, S., Ericsson, M., Löwe, W. and Wingkvist, A., 2019. Importance and Aptitude of Source code Density for Commit Classification into Maintenance Activities. In <em>The 19th IEEE International Conference on Software Quality, Reliability, and Security</em>.</p>
Validating additive correction schemes against gradient-based extrapolations
<p>Supporting information for the paper.<br> <br> Python files are used to generate figures, which will be placed in the "figures" folder. The table include files in "si" are also automatically generated.</p> <p>Each of the other folders includes all data (inputs, shell scripts and outputs) of the Psi4 calculations for a given species, with the exception of the NCDT16 folder, which is a benchmark code developed previously.</p>
Test Instrument Example for ASE'18 paper 'Assessing the Type Annotation Burden" : Corrected Question Numbers
<p>Artifact for ASE'18 paper "Assessing the Type Annotation Burden". This PDF shows an example test instrument (a Qualtrics survey) with all 20 questions (20 code artifacts).</p> <p> </p> <p>A picture of the options in the drop-down box is shown on the last page (note: the order of elements in the drop-down was randomized for each test.</p>
Tomographic data for demonstrating distortion correction methods
<p>Datasets were used to demonstrate the improvement of a tomographic data after distortion correction was applied.</p> <p>Details in the conference paper: </p> <p>Nghia T. Vo, Robert C. Atwood, and Michael Drakopoulos "Preprocessing techniques for removing artifacts in synchrotron-based tomographic images", Proc. SPIE 11113, Developments in X-Ray Tomography XII, 111131I (10 September 2019); https://doi.org/10.1117/12.2530324</p> <p>Distortion coefficients were calculated using vounwarp package: </p> <p><a href="https://github.com/DiamondLightSource/vounwarp/blob/master/vounwarp/examples/example_01.py">https://github.com/DiamondLightSource/vounwarp/blob/master/vounwarp/examples/example_01.py</a></p> <p>Slices can be reconstructed using the <a href="https://github.com/DiamondLightSource/Savu">Savu</a> pipeline: <a href="https://github.com/DiamondLightSource/Savu/blob/master/savu/plugins/corrections/dark_flat_field_correction.py">flat-field correction</a>, <a href="https://github.com/DiamondLightSource/Savu/blob/master/savu/plugins/corrections/distortion_correction_dev.py">distortion correction</a>, <a href="https://github.com/DiamondLightSource/Savu/blob/master/savu/plugins/centering/vo_centering.py">center of rotation calculation</a>, <a href="https://github.com/DiamondLightSource/Savu/blob/master/savu/plugins/ring_removal/remove_all_rings.py">ring artifact removal</a>, and<a href="https://github.com/DiamondLightSource/Savu/blob/master/savu/plugins/reconstructions/astra_recons/astra_recon_gpu.py"> FBP reconstruction</a>.</p>
Fig. 6 in Correction to: Alcyonidium kuklinskii sp. nov., a new species of Antarctic ctenostome bryozoan with a key to all Antarctic species of the genus
Fig. 6 Map showing the type localities of all Antarctic species of Alcyonidium
Figures 11–12 in On the correct identification of Miltochrista jeremyhollowayi (Bucsek, 2014) and Miltochrista malayproducta (Bucsek, 2012) (Lepidoptera: Erebidae: Arctiinae: Lithosiini)
Figures 11–12. Miltochrista spp.: female genitalia. The specimens are deposited in MWM/ZSM.
ScienceDex guides
Understand access before you commit
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.