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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.
Fig. 1 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. 1. Part of the Bayesian phylogenetic tree of Buccinoidea Rafinesque, 1815 obtained with the cox- 1, 16S and 28S concatenated dataset representing family Buccinidae Rafinesque, 1815. The full tree is available as supplementary material (Supplementary file 1). Subfamily Siphonaliinae Finlay, 1928 is collapsed. Vertical numbered lines on the right mark the subfamilies of Buccinidae: 1 = Beringiinae Golikov & Starobogatov, 1975; 2 = Neptuneinae Stimpson, 1865; 3 = Volutopsinae Habe & Sato, 1973; 4 = Parancistrolepidinae Habe, 1972. Posterior probabilities and bootstrap values are shown for each medium supported node. Highly supported nodes are marked with black dots. The colors of the text refer to valid genus (orange = Volutharpa P. Fischer, 1856) and species referred to or morphologically attributable to formerly recognized (sub)genera of Buccininae: red = Thysanobuccinum Golikov & Gulbin in Golikov, 1980; blue = Ovulatibuccinum Golikov & Sirenko, 1988; green = Bathybuccinum Golikov & Sirenko, 1988.
Fig. 4 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. 4. Buccinum hasegawai sp. nov. A–B. Holotype (ZIN 62775 (Buc274)), SL 18.5 mm. C. Paratype 1 (ZIN 62902 (Buc276)) from the type locality, SL 14.8 mm. D. Paratype 2 (MIMB 42300 (Buc273)), Urup I., 450–460 m, SL 14.7 mm. E. Paratype (ZIN 26228), Iturup I., 414 m, SL 16.1 mm. F. Paratype (ZIN 26228), Iturup I., 414 m, SL 16.2 mm, lateral view to show the penis. G. Buccinum bombycinum Dall, 1907 (ZIN 48100) Iturup I., 910–920 m, SL 15.3 mm. H–I. Syntype (USNM 1105311), Japan, Honshu I., Suruga Bay, 536 m, SL 23.9 mm. I = enlarged upper part of the shell to show the sculpture. Scale bars: A–H = same scale; I = 5 mm. Photos H–I: courtesy of USNM.
Fig. 2 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. 2. Sequenced species of Buccinum Linnaeus, 1758 previously attributed to Thysanobuccinum Golikov & Gulbin in Golikov, 1980, Bathybuccinum Golikov & Sirenko, 1988 and Ovulatibuccinum Golikov & Sirenko, 1988. A–B. Buccinum tunicatum Golikov & Gulbin, 1977 (Buc265), Okhotsk Sea, off Urup I., depth 142 m, R/V Akademik Oparin, 56 cr., sta 7, SL 20.4 mm. C. Buccinum sp. 2 (Buc175), off Onagawa, Miyagi, Honshu I., Japan, depth 3302–3311 m, SL 20.3 mm. D–D'. Buccinum cf. tunicatum (Buc269), Okhotsk Sea coast of Urup I., 2 m, R/V Akademik Oparin, 56 cr., sta 26, SL 8.7 mm. D = at the same scale as others; D' = enlarged. E–E'. Buccinum bicordatum (Golikov & Sirenko, 1988) (Buc188), off Onagawa, Miyagi, Honshu I., Japan, depth 342–343 m, SL 8.4 mm. E = at the same scale as others; E' = enlarged. F. Buccinum unicordatum (Golikov & Sirenko, 1988) (Buc266), Kurile Is, Iturup I., Prostor Bay, depth 264–270 m, SL 15.9 mm. G. Buccinum chinoi nom. nov. (Buc190), off Onagawa, Miyagi, Honshu I., Japan, depth 342–343 m, SL 9.0 mm. H. Buccinum fimbriatum (Golikov & Sirenko, 1988) (Buc275), Kurile Is, Simushir I., depth 436 m, R/V Akademik Oparin, 56 cr., stn 19, SL 16.6 mm. I. Buccinum sp. 1 (Buc189), off Onagawa, Miyagi, Honshu I., Japan, depth 342–343 m, SL 22.4 mm. All shells (except D', E') at the same scale.
Fig. 3 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. 3. Sequenced species of Buccinum Linnaeus, 1758, Volutharpa P. Fischer, 1856, and Plicibuccinum Golikov & Gulbin, 1977. A. Buccinum tenuisulcatum Golikov & Gulbin, 1977 (Buc264), Okhotsk Sea, Kurile Is, Onekotan I., depth 571–580 m, R/V Akademik Oparin, 56 cr., stn 68, SL 29.4 mm. B. Buccinum cyaneum Bruguière, 1789 (Buc281), Barents Sea, Teriberka Inlet, depth 10–17 m, SL 18.1 mm. C–D. Volutharpa ampullacea (Middendorff, 1848) (Buc277), Kurile Is, off Chirpoi I., depth 148–147 m, SL 14.1 mm. E. Buccinum nipponense Dall, 1907 (Buc187), off Otsuchi, Iwate, Honshu I., Japan, depth 479–484 m, SL 40.8 mm. F. Buccinum percrassum Dall, 1883 (Buc283), Kurile Is, Simushir I., 1–6 m, SL 33.5 mm. G–H. Buccinum cf. kobjakovae Golikov & Gulbin, 1977 (Buc278), Kurile Is, Simushir I., R/V Akademik Oparin, 56 cr., stn 19, 46°40.6′ N, 151°58.4′ E, depth 436 m, SL 12.1 mm. I. Plicibuccinum declivis (Habe & Ito, 1976) (Buc289), Japan Sea, Primorje, R/V Akademik Oparin, 64 cr., stn 73, 43°43.5′ N, 135°22.9′ E, depth 45–49 m, SL 36.2 mm. Shells not to scale.
Data (corrected spectra per pixels) from "Artificial reefs efficiency changes among types as revealed by underwater hyperspectral imagery"
<p>open access data related to the paper "<strong>Artificial reefs efficiency changes among types as revealed by underwater hyperspectral imagery</strong>" Riera E., Ungerman M., Pey A., Rigot G., Hubas C., Rossi F. (under revision to Restoration Ecology)</p> <ul> <li>pixels_corrected_spectra.npy: data matrix of the corrected spectra for each pixels</li> <li>wavelenghts.npy: the vector of the wavelengths </li> <li>pixels_metada.csv: metadata related the corrected spectra for each pixels</li> </ul> <p>To be processed on python for further statistical analyses. script available on github: <a href="https://github.com/ELI-RIERA/HYPER3D">https://github.com/ELI-RIERA/HYPER3D</a></p>
Data for Stochastically accelerated perturbative triples correction in coupled cluster calculations
<p>This files contains all the data used to perform the plots in the "Stochastically accelerated perturbative triples correction in coupled cluster calculations" article.</p>
FedscGen: privacy-aware federated batch effect correction of single-cell RNA sequencing data -- Preprocessed datasets
<div> <div> <div> <div> <p>This dataset accompanies the publication "FedscGen: Privacy-Aware Federated Batch Effect Correction of Single-Cell RNA Sequencing Data" and includes eight single-cell RNA sequencing (scRNA-seq) datasets used to benchmark the FedscGen and scGen methods. The datasets are provided in <code>.h5ad</code> format and include comprehensive metadata necessary for replication and further analysis.</p> <h3>Datasets</h3> <p>We analyze various datasets to compare FedscGen against scGen (centralized) in terms of batch correction. For simplicity, we refer to the dataset by abbreviations:</p> <ol> <li> <p><strong>Cell Line (CL)</strong>:</p> <ul> <li>Derived from the 293t_jurkat experiment with three batches: Zheng et al., 2017.</li> </ul> </li> <li> <p><strong>Human Dendritic Cells (HDC)</strong>:</p> <ul> <li>scRNA-seq data of human dendritic cells across two batches: Villani et al., 2017.</li> </ul> </li> <li> <p><strong>Human Pancreas (HP)</strong>:</p> <ul> <li>Consolidated data from five sources with 14,767 cells each: Baron et al., 2016; Muraro et al., 2016; Segerstolpe et al., 2016; Wang et al., 2016; Xin et al., 2016.</li> </ul> </li> <li> <p><strong>Mouse Brain (MB)</strong>:</p> <ul> <li>Merged datasets with 691,600 and 141,606 cells: Saunders et al., 2018; Rosenberg et al., 2018.</li> </ul> </li> <li> <p><strong>Mouse Cell Atlas (MCA)</strong>:</p> <ul> <li>Data focusing on 11 cell types from various organs: Han et al., 2018; The Tabula Muris Consortium, 2018.</li> </ul> </li> <li> <p><strong>Mouse Hematopoietic Stem and Progenitor Cells (MHSPC)</strong>:</p> <ul> <li>Data from SMART-seq2 and MARS-seq protocols: Nestorowa et al., 2016; Paul et al., 2015.</li> </ul> </li> <li> <p><strong>Mouse Retina (MR)</strong>:</p> <ul> <li>Data from two unassociated laboratories with 26,830 and 44,808 cells: Macosko et al., 2015; Shekhar et al., 2016.</li> </ul> </li> <li> <p><strong>PBMC (human Peripheral Blood Mononuclear Cell)</strong>:</p> <ul> <li>scRNA-seq data with two batches: Zheng et al., 2017.</li> </ul> </li> </ol> <p><strong>Usage Notes</strong>: Each dataset is provided in <code>.h5ad</code> format, compatible with common single-cell analysis tools such as Scanpy. Detailed metadata is included within each file.</p> <p><strong>Keywords</strong>: Single-cell RNA sequencing, scRNA-seq, Batch effect correction, Privacy-aware, Federated learning, scGen, FedscGen, Clinical multi-center studies, Genomics, Bioinformatics</p> <p><strong>Contact</strong>: For questions or further information, please contact Mohammad Bakhtiari at <a href="mailto:mohammad.bakhtiari@uni-hamburg.de.">mohammad.bakhtiari@uni-hamburg.de.</a></p> <p><strong>License</strong>: Creative Commons Attribution 4.0 International (CC BY 4.0)</p> </div> </div> </div> </div> <div> <div> <div> </div> </div> </div>
Figure 2 in New Data On Distribution Of Two Palaearctic Leptaleus Laferté-Sénectère, 1849 (Coleoptera: Anthicidae) Species With New Records And A Correction
Figure 2. Leptaleus chaudoiri (Kolenati, 1846), male aedeagus. A – specimen from Cyprus, 'CYP RUS TROODOS Mts. Dhiarizos Riv., on light eastAgios Georgios leg. PREISS', 18 VII 99; B – specimen from 'Greece, Rodos, Koljmbia, 18– 25.06.2001 Leg. L. Fancello'; C – specimen from Iran, 'IRAN, Gilan Province S Astara, 5 km W Lavandvil: Koteh Komeh, 180 m 38°18'26" N, 048°47'14" E 10.10.2011, leg. Frisch'; D – specimen from Nepal, 'NEPAL, Prov. Bheri 28°38'23" N, 81°17'14" E Chisapani, KarnaliUfer 17.VI.2005; 180 m leg.: J. Weipert'; E – specimen from Pakistan, Islamabad sect. 7 (see studied material).
Figure 2. Correct Classified Instances for different data sets-Intelligent System for Diagnosis of a Three-Phase Separator
<p>The data mining models may be considered a superior</p> <p>technique that may be successful</p> <p>applied in diagnosis and may be develop in the futu</p> <p>re on the base of more training data to increase</p> <p>the accuracy of results.</p> <p>Industrial processes are dynamic processes with ran</p> <p>dom behavior and whose evolution over</p> <p>time cannot be predicted unless it is well known th</p> <p>e process model and use advanced predictive</p> <p>techniques. Consequently, design and implement an a</p> <p>utomated online monitoring and diagnosis</p> <p>three-phase separator remains a future direction of</p> <p>research conducted so far.</p> <p>Conceptually, this system should have permanent acc</p> <p>ess to data collected from field</p> <p>transducers, to be able to identify the type of fau</p> <p>lt occurred, to locate the fault and provide</p> <p>recommendations to remedy abnormal operating condit</p> <p>ion. Also, updating the database defects with</p> <p>new types of defects occurred and the adequate solu</p> <p>tions adopted for eliminating errors in the</p> <p>operating mode is an important feature to be consid</p> <p>ered during the design of the online diagnosis</p> <p>system. This is possible if the system would have s</p> <p>elf-learning capabilities. To acquire this "skill",</p> <p>the automatic online diagnosis system may contain a</p> <p>diagnosis module based on artificial neural</p> <p>networks.</p>
Bias correction of simulated Brazilian wind power generation based on reanalysis data
<p>Available data:</p> <p>- Brazilian wind power generation time series derived from MERRA-2 reanalysis data with wind speed and wind power bias correction.</p> <p>- Wind speed correction factors derived from INMET wind speeds (http://www.inmet.gov.br/portal/) as well as wind power correction factors dervied from ONS wind power generation time series are also provided.</p> <p>- Simulation of about 38 years of wind power generation with fixed capacity.</p> <p>Data used for validation:</p> <p>- Historical wind power generation data, which were used for validation of simulated time series, can be found at the ONS homepage (http://ons.org.br/Paginas/resultados-da-operacao/historico-da-operacao/geracao_energia.aspx).</p> <p> </p> <p>Other Links:</p> <p>- Information on this will soon be found here: https://refuel.world/</p> <p>- Code for generating time series, validation and analysis: https://github.com/KatharinaGruber/BrazilWind</p> <p>- Master thesis belonging to data: https://doi.org/10.5281/zenodo.1471221</p>
Experimental sloshing pressure data from "Improving stability of moving particle semi-implicit method by source terms based on time-scale correction of particle-level impulses"
<p>3D sloshing in a prismatic tank under translational coupled surge-sway (X and Y axis) motions. The main dimensions are height H<sub>T</sub> = 0.54m, width W<sub>T</sub> = 0.84m and length L<sub>T</sub> = 0.72m. The filling ratio of 50% (H<sub>F</sub> = 0.27m). The periods of surge and sway excitations are T<sub>s</sub> = 1.25s with amplitude motions of A<sub>x</sub> = 0.0144m (0.02 x length) and A<sub>y</sub> = 0.0168m (0.02 x width).</p> <p>Files:</p> <p><strong>experimental_slosh_h50_20cycles_p1_dt0p000100.txt</strong>: Experimental pressure data at sensor P1</p> <p><strong>slosh_3d_exp_timer</strong>: Experimental movie</p> <p><strong>sloshing_tank_dimensions.pdf</strong>: Tank main dimensions</p> <p> </p> <p>The experimental data was used in:</p> <p>Cheng, L.Y., Amaro Junior, R.A., Favero, E.H. (2021). Improving stability of moving particle semi-implicit method by source terms based on time-scale correction of particle-level impulses. Engineering Analysis with Boundary Elements, 131, 118-145. Available at. doi: <a href="https://doi.org/10.1016/j.enganabound.2021.06.018">https://doi.org/10.1016/j.enganabound.2021.06.018</a></p>
Tracking Data I/II of the publication "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction"
<p>DATA belonging to the paper<br> "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction"<br> Katharina Löffler, Tim Scherr, Ralf Mikut<br> doi: https://doi.org/10.1101/2021.03.16.435631</p> <p>-----------------------------</p> <p>To investigate the influence of different segmentation errors on the tracking performance we simulate errorneous segmentation data:<br> - under-segmentation (referred to as "merge" in the folders), over-segmentation("split"), False Negatives ("remove"), combination of the aforementioned errors ("mixed")<br> - percentages: 1,2,5,10,20 of errorneous masks per dataset<br> - runs: 5 randomly initialized runs per combination<br> - datasets: Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ each with two image sequences<br> ---> in total 4 (error types) * 5 (percentage) * 5 (runs) * 2 (data sets) * 2 (image sequences) = 400 datasets</p> <p>The datasets can be recreated by running our code https://git.scc.kit.edu/KIT-Sch-GE/2021-cell-tracking<br> ----------------------------</p> <p>RESULTS<br> We evuated the four tracking algorithms KIT-Sch-GE(1), KTH-SE, MU-Lux-CZ and our proposed algorithm on the aforementioned datasets and compare their performance using the CTC metrics DET, SEG and TRA.<br> This repository contains all metrics as xls files and all tracking results as image sequences.</p> <p><br> <strong>xls files</strong><br> -----------<br> compare_all_trackers_on_synt_bm.csv<br> Comparing the tracking algorithms MU-Lux-CZ, KTH-SE, KIT-Sch-GE(1) and the proposed tracking algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets).<br> Reported scores are DET, SEG and TRA from the Cell Tracking Challenge<br> (Fig8 and Fig9 and Supplementary Figures 3 and 4 are created from this data)</p> <p><br> compare_postprocessing_on_synth_bm.csv<br> Comparing the different post-processing strategies of the proposed tracking algorithm algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets).<br> Reported scores are DET, SEG and TRA from the Cell Tracking Challenge<br> (Fig7 and Fig8 and Supplementary Figures 1 and 2 are created from this data)</p> <p><strong>PLEASE NOTE: the folder compare_postprocessing_synth_bm is provided in the repository 10.5281/zenodo.5227610 due to size restrictions.</strong></p> <p><strong>folders </strong>(decompressed approximately 90GB of data!)<br> -----------<br> tracking_data<br> compare_all_synth_bm<br> Contains all tracking results for each tracking algorithm on the synthetically degraded datasets ()</p> <p> compare_all_synth_bm_no_error<br> Contains the tracking results for each tracking algorithm provided with the perfect ground truth segmentation data</p> <p> compare_postprocessing_synth_bm [<strong>will be stored in 10.5281/zenodo.5227610 due to size restrictions</strong>]<br> Contains all tracking resuls for each postprocessing configuration of the proposed cell tracking algorithm<br> the leaf folders are names run_xPOSTPROCESSING where x is the run number and POSTPROCESSING the postprocessing key<br> Postprocessing keys: ("no untangle" or "no masks" is indicated by an overline in the paper)<br> all ("untangle + masks" in the paper)<br> nd ("no untangle + masks")<br> nd_ns-l ("no untangle + no masks")<br> ns-l ("untangle + no masks")</p>
Corrected data for GSM4949911
<p>Corrected scATAC-seq data for GEO Accession GSM4949911. The original version had an error that caused fragments.tsv.gz to be missing a unique barcode for each cell. This is corrected in this version. Because the UUID-based barcode generation was re-run, the corresponding metadata and matrix files were also updated.</p> <p>We are working with GEO to get these updated files in place on GEO as the official version of record.</p>
Bias-corrected monthly air temperature data over South Siberia for 1979-2020 (CTSS 1.0)
<p>Bias-<strong>C</strong>orrected Air <strong>T</strong>emperature data over <strong>S</strong>outh <strong>S</strong>iberia (<strong>CTSS 1.0</strong>) contains monthly air temperature at 2m for the area within the coordinates 50–65 N, 60–120 E for the period from January 1979 to December 2020. CTSS data were combined from monthly total air temperture data from ERA5 reanalysis European Centre for Medium-Range Weather Forecasts (Copernicus Climate Change…, 2017) and temperature data records from ground weather stations (Bulygina et al., 2014). The ERA5 data were scaled according to the derived correction coefficient. The additive coefficient for each month and weather station were calculated and extrapolated to the study area using the ordinary kriging method. Data spatial resolution is 0.25° in the latitude and 0.25° in the longitude. CTSS reproduces the spatial variability of temperature more precisely than can be done from the weather station observation network. Data provided in NetCDF (Network Common Data Form) format.</p> <p>Copernicus Climate Change Service (C3S), 2017. <em>ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate.</em> Copernicus Climate Change Service Climate Data Store (CDS), Available at: <a href="https://cds.climate.copernicus.eu/cdsapp#!/home"><em>https://cds.climate.copernicus.eu/cdsapp#!/home</em></a></p> <p><em>Bulygina O.N., Razuvaev V.N., Trofimenko L.T., Shvets N.V., 2014. Description of the monthly air temperature data at weather stations of Russia.</em> Certificate of state registration of the database No. 2014621485 Available at: <a href="http://meteo.ru/data/156-temperature"><em>http://meteo.ru/data/156-temperature</em></a></p>
Data from: Spectroscopic approach to correction and visualisation of bright-field light transmission microscopy biological data
<p>The most realistic information about the transparent sample such as a live cell can be obtained only using bright-field light microscopy. At high-intensity pulsing LED illumination, we captured a primary 12-bit-per-channel (bpc) response from an observed sample using a bright-field wide-field microscope equipped with a high-resolution (4872x3248) image sensor. In order to suppress data distortions originating from the light interactions with undesirable elements in the optical path, poor sensor reproduction (geometrical defects of the camera sensor and some peculiarities of sensor sensitivity), this uncompressed 12-bpc data underwent a kind of correction after simultaneous calibration of all the parts of the experimental arrangement. Moreover, the final intensities of the corrected images are proportional to the photon fluxes detected by a camera sensor. It can be visualized in 8-bpc intensity depth after the Least Information Loss compression [Lect. Notes Bioinform. 9656, 527 (2016)].</p>
Bias-corrected data from the preoperational MiKlip system for decadal climate predictions used in the PNRA-IPSODES project
<p>This dataset contains a selection of bias-corrected data from the preoperational MiKlip system for decadal climate predictions (Mueller et al., 2018) used within the Italian research project PNRA18_00199-IPSODES. The adopted method for bias correction is described in the file bias_correction.pdf. Also data from the assimilation run are provided. Nomenclature of variables follows that of the original MiKlip output.</p> <p>Mueller, W., et al. A Higher‐resolution Version of the Max Planck Institute Earth System Model (MPI‐ESM1.2‐HR). J. Adv. Model. Earth Syst. 10, 1383-1413 (2018)</p>
Supporting Data for MODIS Aqua L2 SST Correction Project
<p>This dataset consists of two parts:</p> <p>A) Three data objects needed to ‘fix’ several issues associated with the L2 sea surface temperature (SST) dataset obtained from the MODIS Aqua instrument by the Ocean Biology Processing Group (OBPG) at NASA’s Goddard Space Flight Center. These files are required by the Matlab script, build_and_fix_orbits.m, in the GitHub repo git@github.com:pcornillon/MODIS_L2.git. This script ‘unmasks’ pixels improperly flagged as bad in the vicinity of large SST gradients and those flagged as bad because the difference between the retrieved SST and a reference field exceeds a given threshold. The script also regrids the L2 fields to compensate for the bow-tie effect, which tends to jumble the pixel locations in the along-track direction. The files are:</p> <p>1) weights_and_locations_from_31191.mat - used to correct for the bow-tie effect.</p> <p>2) SST_Range_for_Declouding.mat - are monthly climatologies of the minimum and maximum temperatures in 5x5 degree cells determined from the MODIS Aqua L2 SST dataset for 2002-2019. These fields are used to unmask pixels flagged as cloudy based on the reference SST test. The original test used was for a fixed SST range independent of time-of-year and location.</p> <p>3) Separation_and_Angle_Arrays.n4 - a file containing several fields, one for the along-track separation of pixels at each pixel location, a second for the along-scan separation and a third for the angle the perpendicular to the scan line makes counterclockwise of east. These data are used to determine the eastward and northward components of the SST gradient determined from the 'fixed' SST fields.</p> <p>B) A set of files used to test build_and_fix_orbits.m. These files are contained in four folders zipped into one file, MODIS_R2019.zip, plus the output file, AQUA_MODIS.20100619T052031.L2.SST.nc4, written by build_and_fix_orbits.m. The .zip file contains the data for one complete MODIS Aqua orbit beginning at 05h20 GMT on 19 June 2010. The folders in this file are: </p> <p>1) Data_from_OBPG_for_PO-DAAC - metadata files copied from a portion of the OBPG SST granules.</p> <p>2) Day - daytime granules obtained from the OBPG’s web site.</p> <p>3) Night - nighttime granules obtained from the OBPG’s web site.</p> <p>4) Orbits - a files indicating which OBPG granules make up individual orbits for June 2010</p> <p> </p>
Inner filter effect correction for fluorescence measurements in microplates (AddAbs) - experimental data
<p>Experimental data for the paper entitled <em>Reducing the Inner Filter Effect in Microplates by Increasing Absorbance? Linear Fluorescence in Highly Concentrated Fluorophore Solutions in the Presence of an Added Absorber</em> (<a href="https://doi.org/10.1021/acs.analchem.3c01295">https://doi.org/10.1021/acs.analchem.3c01295</a>).</p> <p>Separate worksheets are provided for the following:</p> <p>1. Fluorescence measurements - raw values in triplicate; 2 worksheets for transparent (T) and nontransparent (NT) microplates, each with 15 titrations: L<sub>1</sub>-L<sub>12</sub> and H<sub>1</sub>-H<sub>3</sub>,</p> <p>2. Absorbance measurements - raw values in triplicate (<em>λ</em><sub>ex</sub> = 345 nm, <em>λ</em><sub>em</sub> = 390 nm); 1 worksheet for T microplates only, with 4 titrations: L<sub>1</sub>-L<sub>4</sub>,</p> <p>3. Fluorescence measurements - averaged, baseline-corrected averaged and normalized baseline-corrected averaged triplicates; 2 worksheets for T and NT microplates, each with 15 titrations: L<sub>1</sub>-L<sub>12</sub> and H<sub>1</sub>-H<sub>3</sub>,</p> <p>4. ZINFE/NINFE-corrected fluorescence in T microplates; 12 worksheets for 12 titrations: L<sub>1</sub>-L<sub>9</sub> and H<sub>1</sub>-H<sub>3</sub>,</p> <p>5. ZINFE/NINFE-corrected fluorescence in NT microplates; 12 worksheets for 12 titrations: L<sub>1</sub>-L<sub>9</sub> and H<sub>1</sub>-H<sub>3</sub>.</p> <p>The results of the ZINFE and NINFE correction methods obtained using the online calculator service written in Javascript: https://ninfe.science (version 09.5.2022.)</p> <p>For additional details please visit: https://glymech.pharma.hr//GlyMech.html.</p>
Data for "Global Precipitation Correction Across a Range of Climates Using CycleGAN"
<p># Data for "Global Precipitation Correction Across a Range of Climates Using CycleGAN"</p> <p>This repository contains the data used in the paper "Global Precipitation Correction Across a Range of Climates Using CycleGAN" by J. McGibbon et al. (2023, *in review*).</p> <p>`train_val` contains the model outputs for the training and validation sets used in the paper. Model spinup data is not included. The C384 runs are stored as a single series along a concatenated time axes. All C384 data has been coarsened to C384 resolution.</p> <p>`ramping_data` contains the model outputs from the 4-year, 3-month ramping simulation, including spinup data.</p> <p>`predicted` is included for convenience, and contains the model outputs from the "best" model, as was used to create figures shown in the paper. This model output was created from the CycleGAN using the validation dataset (stored in `train_val`) as input, and years 2 and 3 of the ramping simulation in `ramping_data` (following the 3-month spinup period). Ramping predictions are stored as separate datasets for the C48 (backwards) prediction and the C384 prediction. Validation data is stored alongside its respective target data in a single netCDF file, labelled "processed-agg". These filenames are intentionally left unchanged so that they correspond with the names of files used in the `process_combined_aggregate.py` script in `projects/cyclegan` of the code DOI for this paper, which was used to create its figures.</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.