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12,753 results for “Corrections”
Drinov Orthography for Post-OCR Correction dataset
<p>The Drinov Orthography for Post-OCR Correction (DOPOC) dataset was created by annotating a historical newspaper collection provided by the <a href="https://digital.libplovdiv.com/en">National Library "Ivan Vazov"</a> (NLIV) in Plovdiv, Bulgaria. We consider printed versions of these documents, which we manually annotate and align at the character level in the same format as the one from the ICDAR 2019 post-OCR correction competition.</p>
Output of global termite CH4 emission estimation (unit corrected: g CH4/m2/yr)
<p>NetCDF file: grid map of annual emissions from 1901 to 2021</p>
NO2 Corrected Timeseries of AOD at 440,400 nm, Angstrom Exponent for Two Sites in Rome
<p>Dataset Created in the framework of QA4EO wp2360. Timeseries of AOD 440nm for AERONET stations SAP and ISAC in Rome, Italy, AOD 400nm for Skynet station in SAP and corresponding ånström exponents, corrected for Total NO2 effect, using PNG data. Time period 2017-2022</p>
Precipitation oxygen isoscape for mainland China from 1870 to 2017 generated based on data fusion and bias correction of iGCMs simulations
<p>The dataset includes the stable oxygen isotope of precipitation for the mainland of China over the 1870-2017 period, at a spatial resolution of 50-60 km and a monthly temporal resolution. In order to make full use of observations to integrate the advantages of various iGCMs, the combination of data fusion and bias correction methods are used. Some physical-based ancillary data are introduced in the fusion methods, including elevation and meteorological data, to enrich the climate and terrain information in the process of data fusion. Specifically,</p><p>(1) for the 1979-2001 period, nine simulations from six iGCMs (CAM2, GISS E, HadAM3, IsoGSM2, LMDZ4, and MIROC32) and ancillary data are fused with observations by using the CNN fusion method;</p><p>(2) for the 2002-2007 period, seven simulations from four iGCMs (GISS E, IsoGSM2, LMDZ4, and MIROC32) and ancillary data are fused by using the CNN fusion method;</p><p>(3) for the 1969-1978 period, four simulations from three iGCMs (CAM2, GISS E, and HadAM3) and ancillary data are fused by using the CNN fusion method;</p><p>(4) for the 1958-1968 and 2008-2017 periods, two iGCM simulations (CAM2 and HadAM3 for 1958-1968 and IsoGSM2 and LMDZ4 zoomed for 2008-2017) are corrected by using two BCMs, and ensemble mean (mean of four simulations) is then calculated;</p><p>(5) for the 1870-1957 period, one iGCM simulation (HadAM3) is corrected by using two BCMs, and the ensemble mean (mean of two simulations) is then calculated.</p><p>Compared with the existing iGCMs, the isoscape has high quality and stability for a large region in China at the monthly scale. However, it should be noted that the isoscape may be more reliable for the common periods of most iGCMs (1969-2007), but mediocre for other periods. </p>
Data supporting Tutorial: EPI Distortion Correction
<p>This data support Tutorial: EPI Distortion Correction by Barbara Dymerska for the Weekend Course during the ISMRM 2023 session: Artifacts & Correction Strategies on Sunday, 04 June 2023:</p> <p>https://github.com/fil-physics/ISMRM2023_educational</p> <p> </p>
Corrections for Geostationary Cloud Liquid Water Path Using Microwave Imagery
<p>Netcdf files containing a set of correctional factors for GOES-16 and GOES-17 cloud liquid-water path (LWP). The correctional factors for both satellites are fractional corrections of microwave imager LWP divided by GOES-16/17 LWP at a given solar zenith, GOES sensor zenith, relative azimuth (solar - sensor zenith), and low-cloud fraction.</p> <p>Uncorrected GOES-16/17 LWP is derived from GOES retrieved cloud-optical thickness and cloud-top effective radius, and it is multiplied by the corresponding correctional factor (i.e. the bins which the uncorrected values are in).</p>
Simulation and data analysis for "Center-of-Mass Corrections in Associated Particle Imaging"
<p>IPython notebook used to run simulations and generate all plots the paper "Center-of-Mass Corrections in Associated Particle Imaging". The notebook also includes some more analysis and plots not shown in the paper.</p>
High Frequency Meteorological, Drift-Corrected Dissolved Oxygen, and Thermistor Temperature Data - Lake Sunapee Buoy, NH, USA, 2007 – 2013
The Lake Sunapee Protective Association (Sunapee, New Hampshire, USA) has been operating an instrumented buoy on Lake Sunapee (maximum depth 33.7 meters) beginning on 27 August 2007. The environmental sensors on the buoy from 2007 - 2013 provided information on weather conditions, lake thermal structure, and oxygen dynamics, and their data can be used to calculate physical and biological variables such as buoyancy frequency, thermocline depth, thermal stability, and lake metabolism. The sensors were programmed to collect environmental data every 10 minutes. The buoy collected meteorological data 1.7 meters above the lake surface, including wind speed and direction (Vaisala WXT52 anemometer), air temperature and humidity (Vaisala HMP50), and photosynthetically active radiation (PAR Li-Cor). The water temperature sensors (TempLine thermistors from Apprise Technology in 2007-2010; NexSens T-node sensors 2010-2013) were situated at 0.5-2 meter intervals from 0-14 meters deep with the bottom sensor approximately 1 meter from the sediments. The dissolved oxygen (Zebra-Tech d-opto) sensor was deployed at approximately 1 meter below the surface and recorded oxygen concentration (mg/L), oxygen saturation (%), and temperature at the sensor (oxygen saturation is not included in this dataset). The buoy was anchored at approximately 15 meter deep water near the Loon Island lighthouse in the northern half of the lake, near the deepest part of the lake (43.390 N, -72.057 W). During the winter of 2007 - 2008, the buoy froze into the ice and continually recorded data with the uppermost thermistor below the bottom of the ice. In winter of 2008 - 2009, the buoy was damaged by ice and data were not collected until re-deployment on 29 July 2009. In subsequent years, the buoy was deployed from April or May to October or November at the Loon Island location and limited data were obtained during the winter months at the Sunapee Harbor (43.386 N, -72.081 W). In 2013, the buoy was taken offl
Sensor data associated with Lucius et al. 2020 – Using machine learning to correct for nonphotochemical quenching in high-frequency in vivo fluorometer data.
This document describes a dataset used to produce Using machine learning to correct for nonphotochemical quenching in high-frequency, in vivo fluorometer data, as reported in: Lucius, M.A., Johnston, K.E., Eichler, L.W., Farrell, J.L., Moriarty, V.W. and Relyea, R.A. (2020), Using machine learning to correct for nonphotochemical quenching in high‐frequency, in vivo fluorometer data. Limnol Oceanogr Methods, 18: 477-494. https://doi.org/10.1002/lom3.10378 The dataset consists of high-frequency water quality and meterological sensor data collected from two autonomous vertical profiling platforms deployed on Lake George, NY during the ice-free months of 2017-2019. Water quality data include depth-referenced measurements of chlorophyll fluorescence, water temperature and dissolved oxygen. Meteorological data include surface-incident total radiation as well as two derived values: solar azimuth and 1-hr rolling average of total radiation. Finally, using interpolated data from regularly collected subsurface profiles of photosynthetically active radiation, estimates of subsurface total radiation were estimated and included in this dataset. This dataset does not include raw data. The data used were subjected to quality control procedures of the Jefferson Project, as well as additional outlier removal measures and the creation of derived data (as previously described and described in detail in Lucius et al. 2020).
Correcting False Memories: The Effect of Mnemonic Generalization on Original Memory Traces
Open the record for dataset details and reuse information.
Brainport, Urban driving, request route, VRU detection, route correction
<p><strong>Scenario description</strong>:</p> <p>Complete use case: Request vehicle, choose route on CEMA and GeoFenching VRU detection with 4 smartphone detections<br> Test detection of multiple VRUs close to each other and compare with camera detections.<br> Test GeoFence area of detection with different pedestrian walking paths (for pedestrian prediction)<br> Make comparison with GeoFenching enabled (runs 1-12) and with GeoFencing disabled (runs 13-25)</p> <p><strong>Session description</strong>:</p> <p>Smartphone 3120 sends request, route 2 is being blocked by crowd (depending on run this changes), vehicle receives correct route and drives from west to east. Underway 2 groups of each 2 VRU crosses, or walk alongside the road (see Test Plan Table 3 for exact walking patterns).</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle</p> <p>Dataset Description This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_EAI2Mobile</strong>: Data from the service to the mobile</p> <p>Dataset Description This dataset contains information sent to the mobile about the Estimated Arrival time and position</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with absolute coordinates</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with relative coordinates</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_IOT_CEMA_Message</strong>: Data from the service to the vehicle</p> <p>Dataset Description This dataset contains information from the Crowd Estimation and Mobility Analytics service</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_IOT_FlowRadar_Message</strong>: Data from the vehicle to the service</p> <p>Dataset Description This dataset contains the GPS informaton (speed,position,heading) from the vehicle</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services</p> <p>Dataset Description Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle.</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_IOT_VehicleStatus</strong>: Data sent from the vehicle to the service</p> <p>Dataset Description This dataset contains the current status of the vehicle</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_PositioningSystem</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed,longitude,latitude,heading from the GPS</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_SmartphoneGPS</strong>: Data sent by the mobile to the service</p> <p>Dataset Description This dataset contains the GPS informaton (speed,position,heading) from the mobile</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_SmartphoneStatus</strong>: Data sent from the mobile to the service</p> <p>Dataset Description This dataset contains the current status of the mobile</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_TaxiRequest</strong>: Data sent from the mobile to the service</p> <p>Dataset Description This dataset contains the requests for a taxi from the mobile phones</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o temperature and battery state of the vehicles</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p>
Microsecond ALEX FRET analysis notebook using FRETbursts - corrections, FRET burst analysis of recurring molecules, FCS, 2CDE & BVA
<p>The herein Python notebook uses FRETbursts (download from here: https://github.com/tritemio/FRETBursts) to show how to analyze microsecond alternating laser excitation (usALEX) confocal-based FRET measurements of freely diffusing single molecules. It includes a step-by-step calculation and implementation of correction factors, donor fluorescence leakage to the acceptor detection channel (Lk), acceptor fluorescence caused by acceptor excitation by the laser intended for donor excitation (Dir), the imbalance in acceptor/donor fluorecence quantum yields and detection efficiencies (Gamma) and the imbalance in donor/acceptor excitation yields (Beta). The notebook implments a global Gamma correction, assuming the Gamma correction factor is constant for all FRET populations, based on the procedure from Lee et al. 2005. Burst search for showing the FRET population is a dual-channel burst search. After correction, the corrected FRET histogram is presented (after burst selection takes into account Beta & Gamma corrected burst sizes). We also present analysis of bursts from recurring molecules, as well as the FCS (a bit irrelevant here, due to the lasr alternation in microeconds), 2CDE & BVA plots, helping in identifying whether a FRET population is a time average of FRET states, occurring faster then molecular diffusion time, or wheather the FRET population is static and represents a single conformational state. The sample data is a result of measurement of 50 pM of hairpin 3 presented in Tsukanov et al. 2013, labeled with ATTO dyes (ATTO 550 & ATTO 647N as donor and acceptor dyes, respectively) - 532 & 640 nm cw excitation, with an alternation period of 50 microseconds. </p>
Is science self-correcting?
<p><strong>Episode Summary:</strong></p> <p>In this episode we are discussing whether science is broken or whether it is self-correcting, and whether the Open Science movement is evidence that science does in fact self-correct. Our interview guest will be Daniel Engber who is a science journalist from Slate magazine. We will cover problems and challenges in the practice of science, what can be done to improve the situation, and how Open Science principles are helping to change things.</p> <p><strong>Resources and Links:</strong></p> <ul> <li>Engber Articles <ul> <li><a href="https://slate.com/technology/2017/05/science-is-broken-how-much-should-we-fix-it.html">Science Is Broken. How Much Should We Fix It?</a></li> <li><a href="https://slate.com/technology/2018/01/in-defense-of-the-replication-movement.html">For Better Science, Bring on the Revolutionaries</a></li> <li><a href="https://slate.com/health-and-science/2017/06/daryl-bem-proved-esp-is-real-showed-science-is-broken.html">Daryl Bem Proved ESP Is Real Which means science is broken</a></li> <li><a href="http://www.slate.com/articles/health_and_science/future_tense/2016/04/biomedicine_facing_a_worse_replication_crisis_than_the_one_plaguing_psychology.html">Cancer Research Is Broken</a></li> </ul> </li> <li>Discover Magazine article: <a href="http://blogs.discovermagazine.com/neuroskeptic/2017/06/19/science-broken-self-correcting/#.W_5us2hKhPY">Is Science Broken, Or Is It Self-Correcting?</a></li> <li><a href="https://doi.org/10.7554/eLife.28699.001">Pressure for Novelty</a> article</li> </ul> <p><strong>Episode Quotes:</strong></p> <p>“Science is only self-correcting if people take the trouble to correct it”</p>
Spatially corrected dataset for natural variation across Arabidopsis diversity panel in early responses to salt stress
<p>The spatially corrected data of Arabidopsis thaliana accessions, which were grown according to the established protocol for studying salt stress in soil experiment (described in detail here dx.doi.org/10.17504/protocols.io.4xzgxp6), in the PSI facility, Czech Republic.</p> <p>The spatial correction was done using the asreml package. The data was subsequently used for the Genome-Wide Association Study. </p>
Counted Biopores dataset used in 'RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation'
<p>Counted biopores dataset used in the article: 'RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation'</p> <p>Originally collected as part of a field trial at the University of Bonn in 2012, described in the following paper:</p> <p>Eusun Han, Timo Kautz, Ute Perkons, Marcel Lüsebrink, Ralf Pude, and Ulrich Köpke.Quantification of soil biopore density after perennial fodder cropping.Plant and Soil, 394(1-2):73–85, sep 2015. ISSN 15735036. doi:10.1007/s11104- 015- 2488- 3</p>
Counted Nodules dataset used in 'RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation'
<p>Counted Nodules dataset used in the article: 'RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation'</p>
Datasets used in: "Correcting for sparsity and interdependence in glycomics by accounting for glycan biosynthesis"
<p>Datasets included in: Bokan Bao+, Benjamin P. Kellman+, Austin W. T. Chiang, Austin K. York, Mahmoud A. Mohammad, Morey W. Haymond, Lars Bode, and Nathan E. Lewis. 2019. “<strong>Correcting for Sparsity and Non-Independence in Glycomic Data through a System Biology Framework.</strong>” bioRxiv. <a href="https://doi.org/10.1101/693507">https://doi.org/10.1101/693507</a></p> <p><strong>Central Datasets</strong><br> - Github_Yang2019_EPO<br> - paper_hmo<br> These are the HMO and EPO datasets used throughout the majority of the manuscript. They are formatted consistent with the github code repository: https://github.com/LewisLabUCSD/GlyCompare</p> <p><strong>Additional Datasets</strong><br> - Webapp_Jin2017_Mucin<br> - Webapp_Riley2019_SiteSpecN<br> - Webapp_Sibile2016_Glycolipid<br> These are additional datasets explored in the final figure and supplement of the manuscript formatted for the webapp: https://glycompare.herokuapp.com/</p> <p>All datasets but Riley2019 have structural data, Riley2019 only contains compositional data</p> <p><strong>Detailed Descriptions</strong></p> <ul> <li>Github_Yang2019_EPO <ul> <li>Sixteen MALDI-TOF glycoprofiles of EPO, where each EPO glycoprofile was produced in a different glycoengineered</li> </ul> </li> <li>CHO cell line- paper_hmo <ul> <li>Forty-eight HPLC glycoprofiles of HMO from six mothers22.</li> </ul> </li> <li>Webapp_Jin2017_Mucin <ul> <li>Mucin-type O-glycans from tumor and normal samples from gastrointestinal cancers</li> </ul> </li> <li>Webapp_Riley2019_SiteSpecN <ul> <li>Site-specific N-glycosylation in mouse brain</li> </ul> </li> <li>Webapp_Sibile2016_Glycolipid <ul> <li>Glycolipid abundance in Rat eye, brain and blood<br> </li> </ul> </li> </ul>
Fig. 1 in World catalogue of the Hydrophiloidea (Coleoptera): additions and corrections II (2006-2010)
Fig. 1. Distribution of newly described species since HANSEN (1999) by faunal region and catalogue update (SHORT & HEBAUER 2006, this paper). Boundaries used to define regions are identical to those used by HANSEN (1999), except we have combined the Australian and 'Pacific' Regions into one.
Data from Study: Respiratory Motion Correction of PET using MR-Constrained PET-PET Registration
<p>This dataset contains the data used to arrive at the conclusions in the research article <em>Respiratory Motion Correction of PET using MR-Constrained PET-PET Registration</em>, by Balfour et al [<em>BioMedical Engineering OnLine</em> 2015, <strong>14</strong>:85].</p> <p>This study was based upon motion-affected PET images simulated from real dynamic MR image volumes, simulated and reconstructed using the Software for Tomographic Image Reconstruction ("STIR", see http://stir.sourceforge.net/). This dataset includes data from MR scans of 4 healthy volunteers (male, aged 22-33).</p> <p>Three types of data are provided, which should be sufficient for repeating the findings of the study:</p> <ul> <li>Reconstructed PET image volumes, split into 6 respiratory bins ("gates") for each simulation</li> <li>The dynamic 3D MR volumes used to derive the respiratory motion of each volunteer</li> <li>Text files outline which dynamics have NOT been used for PET simulation - these are the ones used to make the motion model in the study</li> </ul> <p>These MR volumes were registered and combined with the head-foot position of the right hemidiaphragm to form a respiratory motion model, which was subsequently used to constrain PET to PET image registration, attempting to correct for the motion in the PET images.</p> <p>For more detailed information regarding the method, please refer to the article.</p> <p>The PET data is split into several sub-categories:</p> <ul> <li>Volunteer ID (4 possibilities, anonymised)</li> <li>Lesion position (9 possibilities - see article for locations)</li> <li>Lesion diameter, in millimetres (10 or 14 mm)</li> <li>Respiratory gate number, ranging from 1 (most inhaled) to 6 (most exhaled)</li> </ul> <p>Note that there are two types of each simulation: with motion, and without motion. These are included in the respective zip files for each volunteer ID.</p> <p> </p>
1151 commits with software maintenance activity labels (corrective,perfective,adaptive)
<p>Data format: CSV</p> <p>Separator character: '#'</p> <p><strong>This dataset contains 1151 commits manually labeled with maintenance activities ("c" for corrective, "p" for perfective, "a" for adaptive)</strong> according to the definition by Mockus et al. in <em>"Mockus, A. and Votta, L.G., 2000, October. Identifying Reasons for Software Changes using Historic Databases. In icsm (pp. 120-130)"</em>.</p> <p>In addition, this dataset also contains <strong>further information (features) extracted from the commits</strong>:</p> <ol> <li>The <strong>source code changes</strong> performed by the commit author as part of a given commit (statement added, statement removed, etc.) <ul> <li>The source code change taxonomy is detailed in <em>"Fluri, B. and Gall, H.C., 2006, June. Classifying change types for qualifying change couplings. In Program Comprehension, 2006. ICPC 2006. 14th IEEE International Conference on (pp. 35-45). IEEE."</em></li> </ul> </li> <li>A binary indication (1/0) whether a given commit contains any of the <strong>keywords from a pre-computed </strong>(according to a word frequency analysis)<strong> set of keywords</strong> <strong>indicative of each maintenance activity</strong>.</li> </ol> <p>The dataset consists of commits sampled from the following open source projects:</p> <ol> <li>RxJava</li> <li>hbase</li> <li>elasticsearch</li> <li>intellij-community</li> <li>hadoop</li> <li>drools</li> <li>kotlin</li> <li>restlet-framework-java</li> <li>orientdb</li> <li>camel</li> <li>spring-framework </li> </ol> <p>This dataset is a supporting material for the paper <strong>"Boosting Automatic Commit Classification Into Maintenance Activities By Utilizing Source Code Changes", to appear in PROMISE 2017.</strong></p>
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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.