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Figures 2–4. Parochodaeus perdidus, n in Correction of the misidentifications and confusion surrounding Ochodaeus grandiceps Fairmaire, 1897 (Coleoptera: Ochodaeidae), and the description of a new species of ochodaeid from Cuba
Figures 2–4. Parochodaeus perdidus, n.sp. 2) Dorsal habitus of holotype. 3) Holotype labels. 4) Oblique lateral view of holotype, showing the ventrally projected pollex of the right protibia in lateral view (arrow), and bituberculate clypeus.
FIG. 3 in On the distinction of Atractocarpus heterophyllus (Montrouz.) Guillaumin & Beauvis. and A. bracteatus Schltr. & K.Krause (Rubiaceae): in search of the correct name for the type species of Atractocarpus Schltr. & K.Krause
FIG. 3. — Atractocarpus heterophyllus (Montrouz.) Guillaumin & Beauvis.: A, view of the apex of a female individual in fruit; B, detail of stipules at branch apex; C, transversal section of a fruit; D, longitudinal section of a fruit; E, developed placenta at fruit stage, in dorsal and ventral views; F, longitudinal section of a seed; G, embryo. Scale bars: A-E, 3 cm; F, G, 2 mm. Based on MacKee 26464 (P) and unpublished N. Hallé's drawings of New Caledonian Rubiaceae in sched (P).
FIG. 2 in On the distinction of Atractocarpus heterophyllus (Montrouz.) Guillaumin & Beauvis. and A. bracteatus Schltr. & K.Krause (Rubiaceae): in search of the correct name for the type species of Atractocarpus Schltr. & K.Krause
FIG. 2. — Atractocarpus bracteatus Schltr. & K.Krause: A, female flower; B, longitudinal section of a female flower (corolla and stamens detached from hypanthium); C, detail of stipules at branch apex; D, entire fruit and longitudinal section of a fruit; E, transversal section of a fruit; F, entire seed and its longitudinal section; G, embryo. Scale bars: A-C, 1.5 cm; D, 6 cm; E, 1.5 cm; F, 5 mm; G, 2 mm. Based on Mouly 149 (P).
FIG. 1 in On the distinction of Atractocarpus heterophyllus (Montrouz.) Guillaumin & Beauvis. and A. bracteatus Schltr. & K.Krause (Rubiaceae): in search of the correct name for the type species of Atractocarpus Schltr. & K.Krause
FIG. 1. — Atractocarpus bracteatus Schltr. & K.Krause: A, view of a female individual in fruit; B, male inflorescence, and detail of a flower with ant; C, mature fruit. Scale bars: A, 10 cm; B, C, 1 cm. Based on pictures by Daniel & Irène Létocart (www.endemia.nc).
FIG. 4 in Révision du groupe de l'Aloe divaricata Berger; correction d'une synonymie, combinaisons nouvelles et description d'un nouvel hybride
FIG. 4. — Aloe divaricata Berger subsp. tulearensis (T.A.McCoy & Lavranos) J.-P. Castillon, comb. nov., stat. nov. près de Saint-Augustin.
FIG. 3 in Révision du groupe de l'Aloe divaricata Berger; correction d'une synonymie, combinaisons nouvelles et description d'un nouvel hybride
FIG. 3. — Carte de distribution des différentes sous-espèces d'Aloe divaricata Berger et de l'espèce hybride x Aloe anosyana J.-P. Castillon, hybr. nov.
FIG. 2 in Révision du groupe de l'Aloe divaricata Berger; correction d'une synonymie, combinaisons nouvelles et description d'un nouvel hybride
FIG. 2. — Aloe divaricata Berger subsp. divaricata: A, une plante en provenance de Port-Bergé en culture à Tuléar; B, dans les forêts côtières, 20 km au nord de Majunga.
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>
Figs. 2–5 in Antilochus (Neaeretus) pterobrachys sp. nov. and the correct name of the subgenus Afroantilochus (Hemiptera: Heteroptera: Pyrrhocoridae)
Figs. 2–5. Antilochus (Neaeretus) pterobrachys sp. nov., J holotype, male genitalia. 2 – pygophore, dorsal view; 3 – pygophore, posterior view; 4 – pygophore, lateral view; 5 – paramere, most exposed view (a – tip of paramere, b – small spine opposite the tip). (Photo: P. Kment).
Fig. 1 in Length correction for early-juvenile Brazilian herring Sardinella janeiro (Eigenmann, 1894) after preservation in formalin, ethanol and freezing
Fig. 1. Mean percentage shrinkage of total length for earlyjuvenile of Sardinella janeiro during storage in freezing (open triangle), 2.5% formalin (full circle), 5% formalin (open circle), 70% ethanol (full square) and 95% ethanol (open square).
Fig. 3 in Length correction for early-juvenile Brazilian herring Sardinella janeiro (Eigenmann, 1894) after preservation in formalin, ethanol and freezing
Fig. 3. The relationship between fresh total length and relative (%) shrinkage in total length after 60 days of preservation for early-juvenile of Sardinella janeiro. (a) Freezing (open triangle), (b) 2.5% formalin (full circle), 5% formalin (open circle), (c) 70% ethanol (full square) and 95% ethanol (open square).
Fig. 2 in Length correction for early-juvenile Brazilian herring Sardinella janeiro (Eigenmann, 1894) after preservation in formalin, ethanol and freezing
Fig. 2. Mean percentage shrinkage of body mass for earlyjuvenile of Sardinella janeiro during storage in freezing (open triangle), 2.5% formalin (full circle), 5% formalin (open circle), 70% ethanol (full square) and 95% ethanol (open square).
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>
The coevolution of effort and replication, recreated, replicated and corrected
<p>We replicated “The natural selection of bad science” by Paul Smaldino and Richard McElreath (2016). The replication was successful with one exception. We find that selection acting on scientist’s propensity for replication frequency caused a brief period of exuberant replication not observed in the original paper due to a coding error. This difference does not, however, change the authors’ original conclusions.</p> <p>The three panels displayed here concern Figure 5 – titled “The coevolution of effort and replication” – of the original study. Panel (a) is based on the original data and is a recreation of the original figure. Panel (b) is the result of a replication based on the same coding error and panel (c) displays the corrected result.</p> <p>While effort, false positive rate (α), and false-discovery rate converge to the originally reported values when the simulated steps are extended beyond the original 1e6 time steps, the replication rate’s progress and convergence are different from the original.</p> <p>The results of this corrected model show the following pattern: Starting with a high effort, low effort replications are more attractive than conducting novel research (that is, employing this strategy received higher payoffs), which results in the replication rate reaching nearly 100% after ~730,000 steps. At this point the decline of effort has made low-effort novel research more attractive than low-effort replications (because publishing a novel positive result is associated with a higher payof than publishing a replication) and consequently the replication rate decreases again. With the decline of effort, alpha rises up to 0.67, comparable with the value reported in the study by Smaldino and McElreath (2016).</p> <p>Smaldino, P. E., & McElreath, R. (2016). The natural selection of bad science. <em>Royal Society Open Science</em>, <em>3</em>(9), 160384. <a href="https://doi.org/10.1098/rsos.160384">https://doi.org/10.1098/rsos.160384</a></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>
Stability of the next-to-tribimaximal mixings under radiative corrections with the variation of SUSY breaking scale
<pre>We analyse the stability of the next-to-tribimaximal mixings ($NTBM$) under radiative corrections with the variation of SUSY breaking scale ($m_s$) for both normal and inverted hierarchical cases at the fixed value of seesaw scale $M_R = 10^{15}$ GeV and $\tan \beta = 30$. All the neutrino oscillation parameters receive varying radiative corrections irrespective of the $m_s$ values at the electroweak scale, which are all within $3\sigma$ range of the latest global fit data. NH case is found to be more stable than IH case for all four cases.</pre>
Correcting for probe wandering by precession path segmentation
<p>Scanning precession electron diffraction datasets and processing scripts used in the journal publication "<strong>Correcting for probe wandering by precession path segmentation</strong>".</p> <p>DOI link to publication: <a href="http://doi.org/10.1016/j.ultramic.2023.113715">https://doi.org/10.1016/j.ultramic.2023.113715</a></p> <p> </p> <p><strong>Prerequisites</strong></p> <p>To run the scripts necessary to process the data, the open source packages JupyterLab and HyperSpy need to be installed. These notebooks were created with these package versions:</p> <ul> <li>hyperspy 1.6.4</li> <li>jupyterlab 3.2.0</li> </ul> <p> </p> <p><strong>Data files</strong></p> <p>There are three data types:</p> <ul> <li>Scanning precession electron diffraction (SPED) datasets contain the .hspy extension. There are two SPED datasets acquired by precession path segmentation, and one regular dataset: <ul> <li><em>SPED_256x256_22x22_10186nm_NBD_a5_spot1nm_CL20cm_125msExp_3000msFB_subframing_x8_pivotoff_01.hspy</em> is a precession path segmentation dataset presented in figure 2 in the article.</li> <li><em>SPED_zoom1_256x256_12x12_5556nm_NBD_a5_spot1nm_CL20cm_10msExp_3000msFB_subframing_x8.hspy</em> is a precession path segmentation dataset presented in figures 1 and 3 in the article.</li> <li><em>SPED_zoom1_256x256_12x12_5556nm_NBD_a5_spot1nm_CL20cm_10msExp_300msFB.hspy</em> is a regular SPED dataset of the same region as the dataset just above (also containing "zoom1" in its name).</li> </ul> </li> <li>A series of diffraction patterns with de-rocking switched off can be found in the <em>eight_segments.npy</em> file. This dataset was recorded by Dr. Tina Bergh at Department of Chemical Engineering, Norwegian University of Science and Technology, and is meant to be used as an illustration of the precession segments used in figure 1 in the article.</li> <li>Virtual bright field images of the precession path segmented scans, before and after rigid correction in SmartAlign, can be found with the .png extension. The files starting with "pivot01..." are images from the precession path segmentation scan with an intentional pivot point misalignment, while the files starting with "small..." are from the well aligned scan. The files ending with "_reg" are regular, i.e., non-corrected compound VBF images, while the files ending with "_cor" are SmartAlign rigidly corrected VBF images.</li> </ul> <p> </p> <p><strong>Processing scripts</strong></p> <p>There are two processing scripts:</p> <ul> <li><em>p01_slicing_segments.ipynb</em> is used to process the precession path segmentation datasets, from slicing of the raw data to constructing virtual bright field images.</li> <li><em>p02_image_processing.ipynb</em> is used to create the images found in the article and for image analysis. The latter includes blur quantification and measuring edge sharpness.</li> </ul> <p> </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
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.