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LEN-DB - Local earthquakes detection: a benchmark dataset of 3-component seismograms built on a global scale
<p>In this study ( <a href="http://www.sciencedirect.com/science/article/pii/S2666544120300010">The paper</a> ) we present a large dataset of 1,249,411 3-component seismograms, recorded along the vertical, north, and east components of 1487 broad-band or very broad-band receivers distributed worldwide, including 631,105 3-component seismograms generated by 304,878 local earthquakes and labeled as earthquakes (EQ), and 618,306 ones labeled as noise (AN). The choice of collecting only local earthquake-data is motivated by the fact that small-magnitude events, which generate relatively small amplitudes and are easily attenuated, are often problematic to detect but provide valuable information about earthquake processes. The labeled data are split into HDF5-Groups: <em>EQ</em> and <em>AN</em>. Each of these groups contains as many HDF5-Datasets as the number of 3-component seismograms; these are labeled in accordance to the format <em>net_sta_starttime</em>, where <em>net</em>, <em>sta</em>, and <em>starttime</em> represent the seismic network, station, and start time of the seismograms. Each HDF5-Dataset (i.e. each triplet of seismograms) has an attribute, which allows accessing the respective metadata. In addition, the HDF5-Group <em>Stations</em> allows accessing stations’ metadata through as many HDF5-Datasets (which are labeled in accordance to the format <em>net_sta)</em> as the number of receivers employed for collecting the waveforms.</p> <p>This global dataset is intended to be used for carrying out a multitude of seismological and signal processing tasks on single-station recordings, and its size particularly suits machine learning (ML) applications.. Application of ML to this dataset shows that a simple Convolutional Neural Network of 67,939 parameters allows discriminating between earthquakes and noise single-station recordings with high accuracy (93.2%), even if applied in regions not investigated by the training set. We make the dataset publicly available as a unique file in HDF5 data format, intending to provide the seismological and broader scientific community with a benchmark for time-series to be used as a testing ground in seismology and signal processing.</p>
AndroZooOpen: Collecting Large-scale Open Source Android Apps for the Research Community
<p>The raw collected data for the paper</p>
Fig. 1 in Large-scale migration of a school shark, Galeorhinus galeus, in the Southwestern Atlantic
Fig. 1. Schematic representation of the depth-averaged ocean circulation in the Southwestern Atlantic Ocean (adapted from Matano et al., 2010) showing the geographical locations of tagging of Galeorhinus galeus [Triangle, Nuevo Gulf (NG), 42°30'S, 65°55'W, depth 100 m], newest recapture in Uruguayan shelf waters (Star, 35°30'S, 51°48'W – depth 290 m) and the two old recapture sites (Circle, Mar del Plata, 38°24'S, 57°18'W, depth 40 m and Canal Culebra, 40°24'S, 62°06'W - depth 15 m, Irigoyen et al., 2015). Additional geographical locations are indicated with acronyms: SMG = San Matías Gulf, VP = Valdés Peninsula, AB = Anegada Bay, PM = Punta Médanos, STSF = Subtropical Shelf Front. The thick curved lines represent the path of the major ocean currents. The black line is the 200m isobath. Colours indicate bottom temperature (a, c) and bottom salinity (b, d) and black arrows indicate the bottom shelf circulation extracted from a numerical model (Palma et al., 2008). The yellow and gray lines in the left panels indicate the 11 and 17 °C isotherms respectively. The red and cyan lines in the right panels indicate the 33.93 and 33.5 isohalines.
Fig. 2. A in Large-scale migration of a school shark, Galeorhinus galeus, in the Southwestern Atlantic
Fig. 2. A) Releasing of a tagged Galeorhinus galeus, B) the recaptured tag on August 17, 2015, and C) length size distribution of school sharks captured and tagged in Nuevo Gulf (~ 42°30'S, Argentina) during February 15, 2015.
Supplementary Information Table for J. Geophys. Res. article: A Survey of Small-Scale Waves and Wave-Like Phenomena in Jupiter's Atmosphere Detected by JunoCam
<p>Text version of the Table in the Supplementary Information file associated with the J. Geophys. Res. article by Orton et al. (2020) A Survey of Small-Scale Waves and Wave-Like Phenomena in Jupiter’s Atmosphere Detected by JunoCam. The columns provide values for various sizes and orientations associated with waves detected by the Juno spacecraft's JunoCam visible imaging instrument. The values include the perijove, PJ (orbit close approach), image number, number of wave crests detected, the mean longitude and latitude, the length and width of the wave packet, the wavelength (mean distance between wave crests), and the tilt (angle of the wave crest with respect to the direction of the wave).</p>
Fig. 5 Lateral habitus a in Rapid, large-scale species discovery in hyperdiverse taxa using 1D MinION sequencing
Fig. 5 Lateral habitus a and diagnostic features of Megaselia
Figure 7 from: Korshunova T, Malmberg K, Prkić J, Petani A, Fletcher K, Lundin K, Martynov A (2020) Fine-scale species delimitation: speciation in process and periodic patterns in nudibranch diversity. ZooKeys 917: 15-50. https://doi.org/10.3897/zookeys.917.47444
Figure 7 Reproductive systems, schemes. AAmphorina farraniBAmphorina viriola sp. nov. CAmphorina andra sp. nov. DAmphorina linensisEAmphorina pallida. Abbreviations: am–ampulla, fgm–female gland mass, pg–supplementary ("penial") gland, pr–prostate, psh–penial sheath, rs–receptaculum seminis.
Figure 6 from: Korshunova T, Malmberg K, Prkić J, Petani A, Fletcher K, Lundin K, Martynov A (2020) Fine-scale species delimitation: speciation in process and periodic patterns in nudibranch diversity. ZooKeys 917: 15-50. https://doi.org/10.3897/zookeys.917.47444
Figure 6 Amphorina andra sp. nov. aA. andra sp. nov., paratype ZMMU Op-703, Croatia, a1, head; a2, cerata; a3, tail; a4, posterior part of radula (20 μm); a5, anterior part of radula (20 μm); a6, posterior central tooth (10 μm); a7, jaw (200 μm); a8, jaw details (50 μm); a9, stylet details (30 μm); a10, stylet (30 μm) bA. andra sp. nov., paratype GNM9720, Sweden, b1, head; b2, tail; b3, cerata; b4, posterior part of radula (20 μm); b5, anterior part of radula (20 μm); b6, jaw (light microscopy); b7, jaw (200 μm); b8, jaw details (20 μm) cA. andra sp. nov., paratype GNM9272, UK, с1, head; с2, tail; с3, cerata; с4, posterior part of radula (20 μm); с5, posterior part of radula (10 μm); с6, anterior part of radula (20 μm); с7, anterior part of radula (20 μm); с8, jaw (light microscopy); с9, jaw (SEM, 100 μm); с10, jaw details (20 μm).
Figure 5 from: Korshunova T, Malmberg K, Prkić J, Petani A, Fletcher K, Lundin K, Martynov A (2020) Fine-scale species delimitation: speciation in process and periodic patterns in nudibranch diversity. ZooKeys 917: 15-50. https://doi.org/10.3897/zookeys.917.47444
Figure 5 Amphorina viriola sp. nov., Sweden. aA. viriola sp. nov., holotype GNM9393, a1, head; a2, cerata; a3, tail; a4, posterior part of radula (30 μm); a5, anterior part of radula (30 μm); a6, jaw (light microscopy); a7, jaw (SEM, 100 μm); a8, jaw details (30 μm) bA. viriola sp. nov., paratype GNM9360, b1, head; b2, cerata; b3, posterior part of radula (10 μm); b4, posterior part of radula (20 μm); b5, anterior part of radula (50 μm); b6, jaw (light microscopy); b7, jaw (200 μm); b8, jaw details (20 μm); b9, stylet details (10 μm); b10, penis with stylet (100 μm) cA. viriola sp. nov., paratype GNM9263, с1, head; с2, cerata; с3, apical part of lateral teeth with possible denticles (1 μm); с4, posterior part of radula (20 μm); с5, anterior part of radula (20 μm); с6, jaw (light microscopy); с7, jaw (SEM, 200 μm); с8, jaw details (20 μm) dA. viriola sp. nov., paratype GNM9260, d1, head; d2, tail; d3, cerata; d4, posterior part of radula (30 μm); d5, anterior part of radula (30 μm); d6, jaw (light microscopy); d7, jaw (300 μm); d8, jaw details (30 μm).
Supplementary material 1 from: Korshunova T, Malmberg K, Prkić J, Petani A, Fletcher K, Lundin K, Martynov A (2020) Fine-scale species delimitation: speciation in process and periodic patterns in nudibranch diversity. ZooKeys 917: 15-50. https://doi.org/10.3897/zookeys.917.47444
: Data type: species data
Figure 4 from: Korshunova T, Malmberg K, Prkić J, Petani A, Fletcher K, Lundin K, Martynov A (2020) Fine-scale species delimitation: speciation in process and periodic patterns in nudibranch diversity. ZooKeys 917: 15-50. https://doi.org/10.3897/zookeys.917.47444
Figure 4 Amphorina farrani (Alder & Hancock, 1844) (a–c), A. linensis (Garcia-Gomez, Cervera & Garcia, 1990) (d, e) and A. pallida (Alder & Hancock, 1842) (f, g). aA. farrani, neotype GNM9268, UK, a1, head; a2, tail; a3, cerata; a4, posterior part of radula (SEM, scale bar 20 μm); a5, posterior part of radula (10 μm); a6, anterior part of radula (10 μm); a7, jaw (light microscopy); a8, jaw (SEM, 100 μm); a9, jaw details (20 μm); a10, details of stylet (3 μm); a11, penis with stylet (30 μm) bA. farrani, image from description of Eolis farrani in Alder and Hancock 1845 (not in copyright), b1, head; b2, tail with orange-yellow colouration; b3, cerata cA. farrani, France, Mediterranean, (external data – ZMMU Op-702), с1, head; с2, tail; с3, cerata; (internal data – GNM9278), с4, posterior part of radula (50 μm); с5, anterior part of radula (20 μm); с6, anterior part of radula (20 μm); с7, jaw (100 μm) dA. linensis GNM9392, Sweden, d1, head; d2, tail and cerata eA. linensis ZMMU Op-707, Mediterranean, Croatia, e1, head; e2, tail and cerata; e3, posterior part of radula (20 μm); e4, anterior part of radula (20 μm); e5, anterior part of radula (20 μm); e6, jaw (light microscopy); e7, jaw (SEM, 200 μm); e8, jaw details (50 μm); e9, stylet details (10 μm); e10, penis with stylet (100 μm) fA. pallida ZMMU Op-710, Norway, f1, head; f2, tail and cerata; f3, radula (100 μm); f4, penis with stylet (100 μm) gA. pallida ZMMU Op-712, Norway, g1, head; g2, tail and cerata; g3, posterior part of radula (100 μm); g4, anterior part of radula (30 μm); g5, jaw (light microscopy); g6, jaw (SEM, 100 μm); g7, jaw details (20 μm).
Figure 2 from: Korshunova T, Malmberg K, Prkić J, Petani A, Fletcher K, Lundin K, Martynov A (2020) Fine-scale species delimitation: speciation in process and periodic patterns in nudibranch diversity. ZooKeys 917: 15-50. https://doi.org/10.3897/zookeys.917.47444
Figure 2 The haplotype network based on cytochrome c oxidase subunit I (COI) molecular data showing genetic mutations occurring within species of the genus Amphorina (A). Statistical test of the reliability of the bathymetric distribution patterns (and correlated with depths of brackish and marine environments) of A. viriola sp. nov. (red bar) and A. andra sp. nov. (blue bar) in Swedish waters (B). All specimens of A. viriola sp. nov. occur strictly in a very shallow brackish water layer above the halocline (salinity usually ca. 24–25‰), whereas in the same geographic region A. andra sp. nov. occur only below the halocline (at ca. 15 m depth) in waters with more stable oceanic salinity at 34–35‰.
Figure 1 from: Korshunova T, Malmberg K, Prkić J, Petani A, Fletcher K, Lundin K, Martynov A (2020) Fine-scale species delimitation: speciation in process and periodic patterns in nudibranch diversity. ZooKeys 917: 15-50. https://doi.org/10.3897/zookeys.917.47444
Figure 1 Phylogenetic relationships of Amphorina nudibranchs based on the COI+16S+H3 concatenated dataset inferred by Bayesian Inference (BI). The posterior probabilities from BI/ bootstrap values for Maximum Likelihood (ML) are shown.
Figure 3 from: Korshunova T, Malmberg K, Prkić J, Petani A, Fletcher K, Lundin K, Martynov A (2020) Fine-scale species delimitation: speciation in process and periodic patterns in nudibranch diversity. ZooKeys 917: 15-50. https://doi.org/10.3897/zookeys.917.47444
Figure 3 Periodic-like presentation of colour variation patterns among all species of the genus Amphorina, represented as vertical rows. Three main periods (horizontal rows), each with several subperiods are presented with spotless body/colourless forms at the bottom to forms with a maximal number of spots/coloured body at the top. Note that different species fundamentally display similar colouration patterns, but not all species display all colourations, so some morphs in particular species (e.g., forms with extensive surface pigmentation and dark body in A. farrani, A. linensis, and A. pallida) can either be eventually discovered or do not exist, by some further constraints of the developmental system. Non-observed forms for each particular species are indicated as "unkn" = "unknown"). * = Image from Alder and Hancock 1845.
A chromosome-scale genome assembly resource for Myriosclerotinia sulcatula infecting sedge grass (Carex sp.)
<p>The fungus <em>Myriosclerotinia sulcatula</em> is a close relative of the notorious polyphagous plant pathogens <em>Botrytis cinerea</em> and <em>Sclerotinia sclerotiorum</em> but exhibits a host range restricted to plants from the <em>Carex</em>genus (<em>Cyperaceae</em> family). To date, there are no genomic resources available for fungi in the <em>Myriosclerotinia</em>genus. Here, we present a chromosome-scale reference genome assembly for <em>M. sulcatula</em>. The assembly contains 24 contigs with a total length of 43.53 Mbp, with scaffold N<sub>50</sub> of 2,649.7 kbp and N<sub>90</sub> of 1,133.1 kbp. BRAKER-predicted gene models were manually curated using WebApollo, resulting in 11,275 protein-coding genes that we functionally annotated. We provide a high-quality reference genome assembly and annotation for <em>M. sulcatula</em> as a resource for studying evolution and pathogenicity in fungi from the <em>Sclerotiniaceae</em> family. </p>
Supplementary material 1 from: Schulz AN, Mech AM, Allen CR, Ayres MP, Gandhi KJK, Gurevitch J, Havill NP, Herms DA, Hufbauer RA, Liebhold AM, Raffa KF, Raupp MJ, Thomas KA, Tobin PC, Marsico TD (2020) The impact is in the details: evaluating a standardized protocol and scale for determining non-native insect impact. NeoBiota 55: 61-83. https://doi.org/10.3897/neobiota.55.38981
Supplementary tables and figures
Data for A budget-based turbulence length scale diagnostic
<p>Data accompanying the paper titled: A budget-based turbulence length scale diagnostic.</p> <p>This repository contains configuration files, standard output statistics, transversely averaged perturbations of wind components, sub-samble statistics, post-processing script, figures and the fitting data file for four idealized simulations run with LES model MicroHH (van Heerwaarden et al., 2017): ARM, BOMEX, DYCOMS and GABLS1.</p> <p>ARM, BOMEX, and GABLS1 cases were run with version 1.0 of the MicroHH model: https://github.com/microhh/microhh .</p> <p>DYCOMS case was run version 2 of the MicroHH model: https://github.com/microhh/microhh2 .</p> <p>The new length scale diagnostic was tested on four idealized cases, all developed by the Global Energy and Water Cycle Experiment (GEWEX): the drizzling stratocumulus case based on the first research flight (RF01) of the second Dynamics and Chemistry of Marine Stratocumulus (DYCOMS-II) field study (Stevens et al., 2005), the continental cumulus case based on measurements over the Atmospheric Radiation Measurement (ARM) program Cloud and Radiation Testbed (CART) site in Oklahoma (Brown et al. 2002; Lenderink et al., 2004), the trade wind cumulus case based on observations from the Barbados Oceanographic and Meteorological Experiment (BOMEX) (Siebesma et al. , 2003), and the fourth test case is based on the GEWEX Atmospheric Boundary Layer Study (GABLS1) project (Holtslag, 2006; Beare et al., 2006; Cuxart et al., 2006).</p> <p>The files are organized in the following way: </p> <p>Each case has its own folder (ARM, BOMEX, DYCOMS and GABLS1). </p> <p>Each such folder contains a 'setup' sub-folder with the configuration files for the microHH simulation: an '.ini' file.</p> <p>The standard output statistic ('*default*.nc' file) and the transversely averaged perturbations of wind components ('*_UV.nc' file) are located in the 'statistics_output' sub-folder.</p> <p>The output of the sub-sampling statistic ('.nc' file) is located in 'statistics_subsample' sub-folder.</p> <p>Python script 'post-processing.py' processes data from all cases. The final figures (pLL.pdf, pTTT.pdf, pFL.pdf, Fsubs.pdf) and the fitting data file (df.csv) for publication are located in the 'post-processing' folder.</p>
FIGURE1. Lateral view of Siphamia guttulata (CSIRO H 6930-02, 25 mm SL). Scale bar = 5 mm. in Redescription and distributional range extension of the Speckled Siphonfish, Siphamia guttulata (Pisces: Apogonidae)
FIGURE1. Lateral view of Siphamia guttulata (CSIRO H 6930-02, 25 mm SL). Scale bar = 5 mm.
Figure 9. Paralepidonotus ampulliferus. a anterior end, dorsal view b 1 in Intertidal scale worms (Polychaeta, Polynoidae and Sigalionidae) from the northern coasts of the Persian Gulf and Gulf of Oman
Figure 9. Paralepidonotus ampulliferus. a anterior end, dorsal view b 1st right elytron c ampullae on elytra d fringing papillae e microtubercles f papillae on surface g ventral lamellae h 21st left parapodium, anterior view i notochaeta j upper neurochaeta k middle neurochaetae l lower neurochaeta.
Figure 7. Thormora jukesii. a 9 in Intertidal scale worms (Polychaeta, Polynoidae and Sigalionidae) from the northern coasts of the Persian Gulf and Gulf of Oman
Figure 7. Thormora jukesii. a 9th left parapodium, anterior view b neurochaeta c lower notochaeta d upper neurochaeta.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.