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12,753 results for “Corrections”
Bias Correction of CRCM5-LE for Hydrological Bavaria
<p>The frequency and intensity of extreme hydrometeorological events are anticipated to rise as a result of climate change. For precise analysis, especially in low-flow assessments, it is crucial to have data on precipitation and temperature with high spatial and sub-daily resolution. However, such data is often lacking in both density and duration. The <a href="https://www.climex-project.org/">ClimEx-II </a>project (Climate Change and Hydrological Extreme Events 2nd Phase) is dedicated to enhancing our understanding of these shifts in hydrological extremes.</p> <p>The Canadian Regional Climate Model version 5 Large Ensemble (CRCM5-LE; Leduc et al. (2019)) under RCP 8.5 builds the climatic boundary conditions for the hydrological modelling. The ensemble covers a European and a North American domain, each comprising 50 members from 1951 to 2100. The SDCLIREF v2 (Lehr- und Forschungseinheit für physische Geographie und komplexe Umweltsysteme 2024), a sub-daily (3h), high-resolution (500m) data set for the domain of Bavaria and hydrologically important neighbouring catchments marks the reference data set. </p> <p>In ClimEx-II, bias correction is a crucial step before downscaling (regional) climate model simulations to higher resolutions as it adjusts local inconsistencies in the climate model. The corrected and downscaled meteorological inputs can be used to drive a hydrological model (Emami and Koch 2018, Fang et al. 2015). The quality of the corrected data depends on the method used. Therefore, this data set comprises a comparison of the input and output data from three different bias correction methods, UBC (Cannon et al., 2015), MBCn (Cannon 2018) and VBC (Funk et al., 2024) for three diverse climate regions in Bavaria:</p> <ul> <li>Fränkische Saale Salz is a franconian catchment</li> <li>Iller Kempten is a pre-alpine catchment</li> <li>Hart an der Ziller is an alpine catchment</li> </ul> <p>Each catchment comprises six to seven grid cells of a 12 km resolution. Five climate variables of hydrological importance are corrected in a 3-hourly temporal resolution per grid cell:</p> <ul> <li>Near-Surface Dewpoint Temperature in °C (<em>dew</em>)</li> <li>Precipitation in kg/m2 (<em>pr</em>)</li> <li>Surface Downwelling Shortwave Radiation in W/m2 (<em>rsds</em>)</li> <li>Near-Surface Wind Speed in m/s (<em>sfcWind</em>)</li> <li>Near-Surface Air Temperature in °C (<em>tas</em>)</li> </ul> <p>The environment in each file comprises the inputs to the bias correction</p> <ul> <li><strong>mp_dts</strong>: CRCM5-LE model data during the projection period (2011-2030) before correction</li> <li><strong>mc_dts</strong>: CRCM5-LE model data during calibration period (1991-2010)</li> <li><strong>oc_dts</strong>: SDCLIREF v2 reference data during calibration period (1991-2010)</li> </ul> <p>,the outputs from the bias correction comparison during the projection period</p> <ul> <li><strong>vbc</strong>: CRCM5-LE model data during the projection period (2011-2030) after correction by VBC</li> <li><strong>mbcn</strong>: CRCM5-LE model data during the projection period (2011-2030) after correction by MBCn</li> <li><strong>ubc</strong>: CRCM5-LE model data during the projection period (2011-2030) after correction by UBC</li> </ul> <p>and the held-out reference data for validation</p> <ul> <li><strong>op_dts</strong>: SDCLIREF v2 reference data during the projection period (2011-2030).</li> </ul> <p>Each of the above-presented data sets consists of six columns. The five climate variables are indexed by their abbreviations. The sixth column <em>time</em> contains a string marking the respective timestamp. All CRCM5-LE model data contain a seventh column indicating the respective ensemble member. The evaluation results by Wasserstein Distance and Model Correction Inconsistency from Funk et al. (2024) are captured in the three additional files starting with <em>06_*</em>.</p>
A Pixel-scale Corrected Nighttime Light Dataset (PCNL, 1992-2024) Combining DMSP-OLS and NPP-VIIRS
<h1><strong>Updated to 2024! Welcome to download!</strong></h1> <p>We proposed a set of DMSP-OLS and NPP-VIIRS inter-correction methods and produced a pixel-scale corrected nighttime light dataset (PCNL).</p> <p>Two sets of reliable global nighttime light datasets were chosen as the basis. CCNL-DMSP (1992-2013) is a consistent and corrected nighttime light dataset produced from DMSP-OLS. CCNL-DMSP mainly solved three problems of DMSP-OLS, namely, interannual inconsistency, saturation and blooming. The annual VNL-VIIRS dataset available on the Earth Observation Group website was also used, and the monthly median masked data of V21/V22 was selected. Using filtering and employing outlier removal, VNL-VIIRS has removed sunlit, moonlit and cloudy pixels, and has discarded biomass burning pixels.</p> <p>PCNL shows a great temporal and spatial consistency at both the pixel scale and the regional scale.</p> <p> </p> <p><strong>Please refer to the paper for detailed information.</strong></p> <div> <div>Li, S., Cao, X*., Zhao, C., Jie, N., Liu, L., Chen, X., Cui, X., (2023). Developing a Pixel-Scale Corrected Nighttime Light Dataset (PCNL, 1992–2021) Combining DMSP-OLS and NPP-VIIRS. <em>Remote Sensing,</em> 15, 3925. <a href="https://doi.org/10.3390/rs15163925" target="_blank" rel="noopener">https://doi.org/10.3390/rs15163925</a></div> </div> <p> </p> <p><strong>Other recent publications using PCNL:</strong></p> <p>Li, S., Cao, X.*, (2024). Monitoring the modes and phases of global human activity development over 30 years: Evidence from county-level nighttime light. <em>International Journal of Applied Earth Observation and Geoinformation</em>, 126, 103627. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jag.2023.103627" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.jag.2023.103627</a></p> <div> <div> <div>Li, S., Cao, X.*, Liu, L., Li, A., (2025). Inequality of divided and shared socio-economic resources in 15-minute cities of China. <em>Geography and Sustainability, </em>100337. <a href="https://doi.org/10.1016/j.geosus.2025.100337" target="_blank" rel="noopener">https://doi.org/10.1016/j.geosus.2025.100337</a></div> </div> </div>
Cryo-4D-STEM datasets on cells and cellular organelles for demonstrating a dose-Efficient cryo-EM technique: tilt-Corrected Scanning Transmission Electron Microscopy
<p>This upload contains three 4D-STEM datasets in .raw format for demonstrating a dose-efficient cryo-EM technique for thick samples: tilt-corrected Scanning Transmission Electron Microscopy (tcBF-STEM). The dataset dimension is 128130256*256. Data were acquired on vitrified intact E.coli cells and isolated human cell organelles. This upload also contains the EFTEM images in .mrc acqired in the same ROI as the 4D-STEM dataset. </p> <p>It also contains analysis of the manuscript's Fig 3 and Ext. data fig 8. </p>
Determining non-significant bits on a C++ implementation of the LeNet-5 convolutional neural network to be used for storing error correcting codes to protect weights and biases. Robustness assessment of the network after integrating the proposed codes.
<p>The architecture of the LeNet-5 convolutional neural network (CNN) was defined by LeCun in its paper "Gradient-based learning applied to document recognition" (<a href="https://ieeexplore.ieee.org/document/726791">https://ieeexplore.ieee.org/document/726791</a>) to classify images of hand written digits (MNIST dataset).</p><p>This architecture has been customized to use Rectified Linear Unit (ReLU) as activation functions instead of Sigmoid.</p><p>It consists of the following layers:</p><ul><li><strong>conv1</strong>: Convolution 2D, 1 input channel (28x28), 3 output channels (28x28), kernel size 5, stride 1, padding 2.</li><li><strong>relu1</strong>: Rectified Linear Unit (3@28x28).</li><li><strong>max1</strong>: Subsampling buy max pooling (3@14x14).</li><li><strong>conv2</strong>: Convolution 2D, 3 input channels (14x14), 6 output channels (14x14), kernel size 5, stride 1, padding 2.</li><li><strong>relu2</strong>: Rectified Linear Unit (6@14x14).</li><li><strong>max2</strong>: Subsampling buy max pooling (6@7x7).</li><li><strong>fc1</strong>: Fully connected (294, 147)</li><li><strong>fc2</strong>: Fully connected (147, 10)</li></ul><p>The fault hypotheses for this work include the occurrence of:</p><ul><li><strong>S0</strong>/<strong>S1</strong>: multiple adjacent stuck-at-0 and stuck-at-1 faults to determine the least significant bits of weights and biases that could be used to store the proposed error correcting codes.</li><li><strong>BF</strong>: single, double, and triple bit-flip faults to assess the robustness of the considered CNN</li></ul><p>In the memory cells containing all the parameters of the CNN: </p><ul><li><strong>w</strong>: weights (float32)</li><li><strong>b</strong>: biases (float32)</li></ul><p>All the images (10000) from the MNIST dataset have been used as workload.</p><p>The weights and biases of the LeNet-5 architecture have been protected using six different error correcting codes that have been deployed in the least significant bits of these elements.</p><p>The parity check matrices (H = P I) that define these ECCs are:</p><ul><li><strong>SEC(32, 26)</strong> (Hamming) under a <i>classic policy </i>(see methodology below):</li></ul><p><i> 11010010001000011101101000 100000</i></p><p><i> 10101001000100011011010100 010000</i></p><p><i> 01100100100010010110110010 001000</i></p><p><i> 00011100010001001110001101 000100</i></p><p><i> 00000011110000100001111011 000010</i></p><p><i> 00000000001111100000000111 000001</i></p><ul><li><strong>SEC(23, 18)</strong> (Hamming) under a <i>conservative policy</i> (see methodology below):</li></ul><p><i> 111100001111000000 10000</i></p><p><i> 110011101000111000 01000</i></p><p><i> 101011010100100110 00100</i></p><p><i> 010110110010010101 00010</i></p><p><i> 001101110001001011 00001</i></p><ul><li><strong>SEC(13, 9)</strong> (Hamming) under an <i>aggressive policy </i>(see methodology below):</li></ul><p><i> 110111000 1000</i></p><p><i> 101100110 0100</i></p><p><i> 011010101 0010</i></p><p><i> 111001011 0001</i></p><ul><li><strong>DEC(32, 21)</strong> (low redundancy and reduced overhead DEC) under a <i>classic policy </i>(see methodology below):</li></ul><p><i> 111000011001010010000 10000000000</i></p><p><i> 110110000011101000000 01000000000</i></p><p><i> 101011000110000010001 00100000000</i></p><p><i> 100101101000110001000 00010000000</i></p><p><i> 011010101100100000100 00001000000</i></p><p><i> 010101010100001001010 00000100000</i></p><p><i> 001100110010010100100 00000010000</i></p><p><i> 000011110001000110010 00000001000</i></p><p><i> 000000001111001101001 00000000100</i></p><p><i> 000000000000111100111 00000000010</i></p><p><i> 000000000000000011111 00000000001</i></p><ul><li><strong>DEC(28, 18)</strong> (low redundancy and reduced overhead DEC) under a <i>conservative policy </i>(see methodology below):</li></ul><p><i> 111111000000000000 1000000000</i></p><p><i> 110100111100000000 0100000000</i></p><p><i> 110000100011110000 0010000000</i></p><p><i> 001110010011001100 0001000000</i></p><p><i> 101100001010101010 0000100000</i></p><p><i> 010001001101010110 0000010000</i></p><p><i> 001011000101101001 0000001000</i></p><p><i> 101000011000110101 0000000100</i></p><p><i> 010001110000011011 0000000010</i></p><p><i> 000010100110000111 0000000001</i></p><ul><li><strong>DEC(17, 9)</strong> (low redundancy and reduced overhead DEC) under an <i>aggressive policy </i>(see methodology below):</li></ul><p><i> 111110000 10000000</i></p><p><i> 111001100 01000000</i></p><p><i> 110101010 00100000</i></p><p><i> 101010110 00010000</i></p><p><i> 101101001 00001000</i></p><p><i> 100110101 00000100</i></p><p><i> 100011011 00000010</i></p><p><i> 110000111 00000001</i></p><p>This dataset contains the raw data obtained from:</p><ul><li>running exhaustive fault injection campaigns for increasingly multiple stuck-at faults in the least significant bits of all weights and biases (simultaneously) and for all the images in the workload.</li><li>running statistical fault injection campaigns for single, double, and triple bit-flip faults, randomly targeting the considered locations and images in the workload.</li></ul><h3>Files information</h3><ul><li><i>no_ecc </i>folder: Results obtained for the original (not protected) version of the CNN.<ul><li><i>golden_run.csv</i>: Prediction obtained for all the images considered in the workload in the absence of faults (Golden Run). This is intended to act as oracle to determine the impact of injected faults.</li><li><i>sampling_SBF_10000.csv</i>: Prediction obtained for running 10000 statistical fault injection experiments for single bit-flip faults.</li><li><i>sampling_DBF_10000.csv</i>: Prediction obtained for running 10000 statistical fault injection experiments for double bit-flip faults.</li><li><i>sampling_TBF_10000.csv</i>: Prediction obtained for running 10000 statistical fault injection experiments for triple bit-flip faults.</li><li><i>locating_sensitive_bits </i>folder: Prediction obtained for all the images considered in the workload in presence of stuck-at-0/stuck-at-1 faults that simultaneously target the N least significant bits of all weights and biases. There is one file for each parameter of type of fault and range of targeted bits. Files for bits in the range [11, 0] are not included as they obtain eactly the same results as the Golden Run (faults do not alter the behaviour of the network).</li></ul></li><li><i>sec/classic</i>, <i>sec/conservative</i>, and <i>sec/aggressive</i> folders: They contain the results obtained for the CNN protected by SEC(32, 26), SEC(23, 18), and SEC(13, 9), respectively.<ul><li><i>golden_run.csv</i>: Prediction obtained for all the images considered in the workload in the absence of faults (Golden Run). This is intended to act as oracle to determine the impact of injected faults. It must be noted that this file could be different that the golden_run.csv file for the original version of the CNN, as deploying the ECC in the weights and biases may have affected the behaviour of the network.</li><li><i>sampling_SBF_10000.csv</i>: Prediction obtained for running 10000 statistical fault injection experiments for single bit-flip faults. They should all be tolerated by the definition of the ECC.</li><li><i>sampling_DBF_10000.csv</i>: Prediction obtained for running 10000 statistical fault injection experiments for double bit-flip faults. They could be more harmful than for the unprotected version of the CNN, as the ECC may erroneously flip correct bits.</li></ul></li><li><i>dec/classic</i>, <i>dec/conservative</i>, and <i>dec/aggressive </i>folders: They contain the results obtained for the CNN protected by DEC(32, 21), DEC(28, 18), and DEC(17, 9), respectively.<ul><li><i>golden_run.csv</i>: Prediction obtained for all the images considered in the workload in the absence of faults (Golden Run). This is intended to act as oracle to determine the impact of injected faults. It must be noted that this file could be different that the golden_run.csv file for the original version of the CNN, as deploying the ECC in the weights and biases may have affected the behaviour of the network.</li><li><i>sampling_DBF_10000.csv</i>: Prediction obtained for running 10000 statistical fault injection experiments for double bit-flip faults. They should all be tolerated by the definition of the ECC.</li><li><i>sampling_TBF_10000.csv</i>: Prediction obtained for running 10000 statistical fault injection experiments for triple bit-flip faults. They could be more harmful than for the unprotected version of the CNN, as the ECC may erroneously flip correct bits.</li></ul></li></ul><h3>Methodology information</h3><p>First, the CNN was used to classify all the images of the workload in the absence of faults to get a reference to determine the impact of faults. This is <i>golden_run.csv</i> file.</p><p>To locate non-significant bits in weights and biases, fault injection experiments were executed targeting all elements of all parameters of the CNN using the following procedure:</p><ul><li>The initial mask targeted only the least significant bit</li><li>Until the mask targets all bits of the elements (32 bits as they are single-precision floating point values):<ul><li>Affect the bits (setting them to 0 or 1 in case of stuck-at-0 or stuck-at-1 faults) identified by the mask for all elements of all parameters.</li><li>Classify all the images of the workload in the presence of this fault. The obtained output was stored in a given .csv file.</li><li>Remove the fault from the CNN by restoring the affected bits to its previous value.</li><li>Add the next adjacent bit to the mask, so it targets an additional least significant bit.</li></ul></li></ul><p>The analysis of the obtained results may help in determining which bits can be used to store an ECC:</p><ul><li>which bits never affect the behaviour of the CNN, as the predicted classification is exactly the same than in the absence of faults.</li><li>which bits midly affect the behaviour of the CNN, as although the predicted classifications differ from those in the absence of faults, the accuracy of the network is barely affected.</li><li>which bits greatly affect the behaviour of the CNN, as the accuracy of the network is significantly affected.</li></ul><p>Accordingly, three different policies have been identified for deploying an ECC using these bits:</p><ul><li><strong>Classic policy</strong>: The ECC protects as much bits as possible.</li><li><strong>Conservative policy</strong>: The ECC protects all those bits that may affect the prediction of the network.</li><li><strong>Aggressive policy</strong>: The ECC protects only those bits that significantly affect the accuracy of the network.</li></ul><p>After designing and deploying a single ECC and a double ECC for each of the identified policies, fault injection experiments were executed to verify their behaviour in the presence of faults.</p><p>Single and double ECCs were tested against single and double bit-flip, respectively (all faults should be tolerated,) and double and triple bit-flips, respectively (a correct bit could be erroneously flipped.)</p><p>Due to the heavy computational load of the decoders, statistical injection was used to run the required fault injection campaigns with a sample size (number of experiments) of 10000.</p><p>Each experiment consisted in:</p><ul><li>Randomly selecting the image to process, and the parameter, element, and bits (mask) to be targeted by the fault.</li><li>Affecting the bits (inverting them) identified by the mask.</li><li>Classifying the selected image of the workload in the presence of this fault. The obtained output was stored in a given .csv file.</li><li>Removing the fault from the CNN by restoring the affected bits to its previous value.</li></ul><h3>List of variables (Name : Description (Possible values))</h3><ul><li><strong>IMGID</strong>: Integer number identifying the considered image (1-9999).</li><li><strong>TENSORID</strong>: Integer number identiying the parameter affected by the fault (0 - No fault, 1 - conv1.w, 2 - conv1.b, 3 - conv2.w, 4 - conv2.b, 5 - fc1.w, 6 - fc1.b, 7 - fc2.w, 8 - fc2.b).</li><li><strong>ELEMID</strong>: Integer number identiying the element of the parameter affected by the fault (-1 - No fault, [0-2] - conv1.b, [0-74] - conv1.w, [0-5] - conv2.b, [0-149] - conv2.w, [0-146] - fc1.b, [0-43217] - fc1.w, [0-9] - fc2.b, [0-1469] - fc2.w).</li><li><strong>MASK</strong>: 8-digit hexadecimal number identifying those bits affected by the fault ([00000000 - No fault, FFFFFFFF - all 32 bits faulty]).</li><li><strong>FAULT</strong>: String identiying the type of fault (NF - No fault, BF - bit-flip, S0 - Stuck-at-0, S1 - Stuck-at-1).</li><li><strong>SOFTMAX</strong>: 10 decimal numbers obtained after applying the softmax function to the provided output. They represent the probability of the image of belonging to the corresponding category for classification.</li><li><strong>PRED</strong>: Integer number representing the category predicted for the processed image.</li><li><strong>LABEL</strong>: integer number representing the actual category for the processed image.</li></ul>
ATMS Limb Correction Coefficients for NOAA-20 and SNPP platforms
Limb correction coefficients used to create limb adjusted brightness temperature. Four separate files provide separate land and sea correction coefficients for two different ATMS sensors, one onboard on the NOAA-20 platform and the other onboard on the SNPP platform. Reference publication (older coefficients used in paper): Zhang K; Zhou L; Goldberg M; Liu X; Wolf W; Tan C, et al. A Methodology to Adjust ATMS Observations for Limb Effect and Its Applications. Journal of Geophysical Research: Atmospheres. 2017;122(21):11,347-11,56. https://doi.org/10.1002/2017JD026820
Characterization of a loss-of-function NAPB mutation in monozygotic triplets affected with epilepsy and autism using cortical neurons from proband-derived and CRISPR-corrected iPSC lines. Author names and affiliations
<p>RNA-seq data of matured cortical neurons (8-weeks old) derived from induced pluripoent stem cells (iPSC). There are three replicates (Rep1, Rep2, Rep3) for each sample with Forwad read (R1_001.fastq.gz) and reverse read (R2_001.fastq.gz).</p> <p>CtrlF: Control Father sample</p> <p>CtrlM: Control mother sample</p> <p>NDD_01: Proband sample</p> <p>NDD_04: Proband sample</p> <p>NDD_05: Proband sample</p>
Experimental absorption spectra used in "Spectral profile of ro-vibrational transitions of HCl broadened by He, Ar and SF6: testing the β-correction to the Hartmann-Tran profile and the speed dependent (complex) hard collision model"
<p>Experimental absorption spectra used in the article entitled <strong>"Spectral profile of ro-vibrational transitions of HCl broadened by He, Ar and SF<sub>6</sub>: testing the β-</strong><strong>correction to the Hartmann-Tran profile and the speed dependent (complex) hard collision model", </strong>to be published in the Journal of Quantitative Spectroscopy and Radiative Transfer. Accepted 19 March 2024.</p> <div> <div> <div> <div> <h3><a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jqsrt.2024.108977" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.jqsrt.2024.108977</a></h3> </div> </div> </div> </div> <p> </p> <p>Comma separated ASCII files with 1 header line.</p> <p>First column is wavenumber in cm-1</p> <p>The rest of the columns contain the napierian absorbance at the total pressure given in the header. </p> <p>Mind the units!: pressure of Ar-mixtures is expressed in Torr, pressure of He- and SF6-mixtures is expressed in mbar</p> <p> </p>
Corrected Diophantine Rational Sextuples List
<p>Phillip E. Gibbs published a list of 644 Diophantine Sextuples in <a href="https://www.researchgate.net/profile/Philip-Gibbs-2/publication/309429323_A_Survey_of_Rational_Diophantine_Sextuples_of_Low_Height/links/581099ae08aef2ef97b28ed8/A-Survey-of-Rational-Diophantine-Sextuples-of-Low-Height.pdf">https://www.researchgate.net/profile/Philip-GiA Survey of Rational Diophantine Sextuples of Low Height</a> in Annex A.</p> <p>Unfortunately the indexing was incorrect. The downloaded file has made corrections to his original list, and carefully followed the index scheme which he used as much as possible.</p>
Fig 10 in Correction: Integrated Taxonomy Reveals Hidden Diversity in Northern Australian Fishes: A New Species of Seamoth (Genus Pegasus)
Fig 10. Molecular species identification of Pegasus species using Genetic treeML trees. (A) sequences from the 16S gene; (B) sequences from the COI gene. Trees are based on the K2 evolutionary distance model and are shown here with mined Pegasus and Eurypegasus sequences from GenBank. The trees are shown here with an E. draconis outgroup. Bootstrap support values (following 1000 replicates) are shown above the nodes. https://doi.org/10.1371/journal.pone.0251680.g001
Fig. 1 in Arundo micrantha Lam. (Poaceae), the correct name for Arundo mauritanica Desf. and Arundo mediterranea Danin
Fig. 1. – Holotypus of Arundo micrantha Lam. [DesFontaines s.n., P-LA] [© MNHN CRHST-CNRS. Reproduced with permission]
Fortnight Climatology Average of Corrected Chlorophyll-a Concentration from MODIS Level-2 Data in the Algerian Basin (2003-2018)
<p>We deposited the corrected MODIS Level-2 Chlorophyll data after a specific processing steps applied for the first time to the standard cloud-corrected Level-2 Chlorophyll fields to detect and remove spurious local patterns that affect data quality, even in time series averages.</p> <p>From a purely spatial point of view, the quality of the Chlorophyll field is improved more significantly by the outlier removal than the previous synthetic representation suggests.</p>
Figure 2 in The correct identity of Centruroides hoffmanni Armas, 1996 (Scorpiones: Buthidae), with the description of a new species from Chiapas, Mexico
Figure 2. Geographical distribution of the authentic Centruroides hoffmanni (circles) and C. concordia sp. n. (triangles), with the tYpe localities indicated in red. Image frame represents 3,800 x 1,550 km (a) and 600 x 245 km (b).
Figure 1 in The correct identity of Centruroides hoffmanni Armas, 1996 (Scorpiones: Buthidae), with the description of a new species from Chiapas, Mexico
Figure 1. Adults of the authentic Centruroides hoffmanni: a–b) preserved male (RTO) from Salina Cruz, Oaxaca, dorsal and ventral views; c–d) preserved female topotYpe (RTO) from La Gloria Farm, Chiapas, dorsal and ventral views; e) live female from Salina Cruz, Oaxaca, photographed in its natural habitat under the loose bark of a fence post (photo courtesY David Hoferek). Scale bar in mm (a–d).
Inner filter effect correction for fluorescence measurements in microplates (ZINFE and NINFE) - experimental data
<p>Experimental data for the paper entitled <em>Inner Filter Effect Correction for Fluorescence Measurements in Microplates Using Variable Vertical Axis Focus</em> (https://doi.org/10.1021/acs.analchem.2c01031).</p> <p>Separate datasets are provided for data with background correction, without background correction, and absorbance-corrected data.</p> <p>All results were obtained using the online calculator service written in Javascript: https://ninfe.science (version 15.9.2021.).</p> <p>For additional details please visit: https://glymech.pharma.hr//GlyMech.html.</p> <p> </p>
Datasets for Background and Shading Correction of Optical Microscopy Images by BaSiC
<p>This repository holds all the example data for publication: "<strong>A BaSiC tool for background and shading correction of optical microscopy images, Nature Communications (2017)</strong>" DOI: <a href="https://doi.org/10.1038/ncomms14836">https://doi.org/10.1038/ncomms14836</a>. A downsampled version is available at Zenodo repository with DOI: <a href="https://zenodo.org/record/6974039#.YvD8G3ZBxD8">10.5281/zenodo.6974039</a>.</p>
Dublin's Corrected Mean Sea Level (1938-2016)
<p>These datasets have been created and published to accompany the paper "A newly reconciled data set for identifying sea level rise and variability in Dublin Bay" by Shoari Nejad et al 2021.</p> <p> Filename format: <dataset_name>_dublin_<variable_names>_<startyear>_to_<endyear>.csv.</p> <p>Time and date variables are described using standard names: Year, Month, Day, Hour, Minute, Date (dd/mm/yyyy). All times are UTC. Sea level variables are described in the format <variable_name>_<unit>_<datum>. Ordnance Datum Malin (ODM) is used with geoid model OSGM15. LAT is 2.599 m below ODM for Dublin Port. None of the datasets is adjusted for atmospheric effects. </p>
Paulinella micropore KR01 manually corrected gene models from selected KEGG pathways
<p>Major results files produced from the analysis of the nucleotide biosynthesis, DNA replication, and histidine metabolism pathways in <em>Paulinella micropore</em> KR01.</p> <p> </p> <p><code>manually_corrected_genes.genome.gff3.gz</code></p> <p>Gene models (in GFF3 format) of manually corrected <em>P. micropora</em> KR01 genes.</p> <p> </p> <p><code>manually_corrected_genes.cds.fna.gz</code></p> <p>CDS of manually corrected <em>P. micropora</em> KR01 genes.</p> <p> </p> <p><code>manually_corrected_genes.pep.faa.gz</code></p> <p>Protein sequences of manually corrected <em>P. micropora</em> KR01 genes.</p> <p> </p> <p><code>fasta.tar.gz</code></p> <p>Sequences used for phylogenetic analysis of major KEGG Orthologs from the nucleotide biosynthesis, DNA replication, and histidine metabolism pathways.</p> <p> </p> <p><code>aln.tar.gz</code></p> <p>Alignments produced by <code>mafft</code> v7.453 (‘--localpair --maxiterate 1000’) that were used for phylogenetic analysis of the major KEGG Orthologs.</p> <p> </p> <p><code>tree.tar.gz</code></p> <p>Consensus trees produced by <code>iqtree</code> v1.6.12 (‘-m LG+R7 -bb 2000 -quiet’) that were used for phylogenetic analysis of the major KEGG Orthologs.</p>
Bias-corrected CORDEX daily precipitation dataset for the Carpathian Region
<p>This dataset contains bias-corrected regional climate model (RCM) daily outputs for daily precipitation under the RCP8.5 scenario.</p> <p><br> The reference dataset is CARPATCLIM (Szalai et al., 2013) which covers the the Carpathian Region for the period 1961-2010.</p> <p>The dataset contains bias corrected daily outputs of the following high-resolution (0.11o) RCMs from the framework of EURO-CORDEX (Jacob et al., 2014) and Med-CORDEX (Ruti et al., 2016):<br> - ALADIN<br> - CCLM<br> - HIRHAM<br> - RACMO<br> - RCA4<br> - RegCM<br> - REMO<br> - WRF</p> <p> </p> <p>The dataset covers the following periods with grid spacing of 0.11o on a regular lon/lat grid (between latitudes 44°N and 50°N, and longitudes 17°E and 27°E):</p> <p>- 1976-2005</p> <p>- 2021-2050</p> <p>- 2070-2099</p> <p> </p> <p>File format: NetCDF</p> <p>All data have been created following the work of Mezghani et al. (2017).</p> <p>Using the dataset please cite the following reference paper (also further details are given there): <a href="https://doi.org/10.28974/idojaras.2020.1.2">https://doi.org/10.28974/idojaras.2020.1.2</a>.</p> <p> </p> <p>References:<br> Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O.B., Bouwer, L.M., Braun, A., Colette, A., Déqué, M., Georgievski, G., Georgopoulou, E., Gobiet, A., Menut, L., Nikulin, G., Haensler, A., Hempelmann, N., Jones, C., Keuler, K., Kovats, S., Kröner, N., Kotlarski, S., Kriegsmann, A., Martin, E., van Meijgaard, E., Moseley, C., Pfeifer, S., Preuschmann, S., Radermacher, C., Radtke, K., Rechid, D., Rounsevel, M., Samuelsson, P., Somot, S., Soussana, J.-F., Teichmann, C., Valentini, R., Vautard, R., Weber, B. and Yiou, P. (2014) EURO-CORDEX New high resolution climate change projections for European impact research. Reg. Environ. Change, 14, 563–578. https://doi.org/10.1007/s10113-013-0499-2</p> <p><br> Mezghani, A., Dobler, A., Haugen, J.E., Benestad, R.E., Parding, K.M., Piniewski, M., Kardel, I. and Kundzewicz, Z.W. (2017) CHASE-PL Climate Projection dataset over Poland – bias adjustment of EURO-CORDEX simulations. Earth Syst. Sci. Data, 9, 905–925. https://doi.org/10.5194/essd-9-905-2017</p> <p><br> Ruti, P.M., Somot, S., Giorgi, F., Dubois, C., Flaounas, E., Obermann, A., Dell'Aquila, A., Pisacane, G., Harzallah, A., Lombardi, E., Ahrens, B., Akhtar, N., Alias, A., Arsouze, T., Aznar, R., Bastin, S., Bartholy, J., Béranger, K., Beuvier, J., Bouffies-Cloché, S., Brauch, J., Cabos, W., Calmanti, S., Calvet, J.-C., Carillo, A., Conte, D., Coppola, E., Djurdjevic, V., Drobinski, P., Elizalde-Arellano, A., Gaertner, M., Galán, P., Gallardo, C., Gualdi, S., Goncalves, M., Jorba, O., Jordi, G., L'Heveder, B., Lebeaupin-Brossier, C., Li, L., Liguori, G., Lionello, P., Maciás, D., Nabat, P., Onol, B., Raikovic, B., Ramage, K., Sevault, F., Sannino, G., Struglia, M.V., Sanna, A., Torma, C. and Vervatis, V. (2016) MED-CORDEX initiative for Mediterranean climate studies. Bulletin of the American Meteorological Society, 97, 1187–1208. https://doi.org/10.1175/BAMS-D-14-00176.1</p> <p><br> Szalai, S., Auer, I., Hiebl, J., Milkovich, J., Radim, T., Stepanek, P., Zahradnicek, P., Bihari, Z., Lakatos, M., Szentimrey, T., Limanowka, D., Kilar, P., Cheval, S., Deak, Gy., Mihic, D., Antolovic, I., Mihajlovic, V., Nejedlik, P., Stastny, P., Mikulova, K., Nabyvanets, I., Skyryk, O., Krakovskaya, S.,Vogt, J., Antofie, T. and Spinoni, J. (2013) Climate of the Greater Carpathian Region. Final Technical Report. http://www.carpatclim-eu.org<br> </p>
Fig. 5 in Taxonomic status of genera of Buccininae (Neogastropoda, Buccinidae) updated based on molecular data with description of new species and corrections of nomenclature of Buccinum
Fig. 5. Buccinum bizikovi sp. nov. A–B. Holotype (ZIN 62781), SL 20.0 mm. C. Paratype (ZIN 62782), SL 20.0 mm, lateral view to show the penis. D. South-eastern Sakhalin, depth 78 m, SL 21.5 mm, ZIN 62783. E–F. Buccinum ovulum Dall, 1895 (ZIN 48103), Kurile Is, Iturup I., depth 605–620 m, SL 16.9 mm. G. Holotype (USNM 106997), Aleutian Is, Andreanof Is, Amukta Pass, depth 454 m, SL 25.6 mm. Photo G: courtesy of USNM.
Fig. 6 in Taxonomic status of genera of Buccininae (Neogastropoda, Buccinidae) updated based on molecular data with description of new species and corrections of nomenclature of Buccinum
Fig. 6. Buccinum hasegawai sp. nov. (A–C) and Buccinum bizikovi sp. nov. (D–F). A–B. Paratype (ZIN 62777), SL 16.5 mm. A. Radula. B. Operculum. C. Paratype (ZIN 62782), penis. D. Paratype (ZIN 62782), SL 16.5 mm, radula. E. Paratype (ZIN 62782), SL 19.1 mm, operculum. F. Paratype (ZIN 62782), SL 16.5 mm, penis. Scale bars: C, F = 1 mm.
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.