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Figures 1–7. Psyrassa spp. 1–3 in The multiform genus Psyrassa Pascoe (Coleoptera: Cerambycidae: Elaphidiini): new species, new records, synonyms and transfers
Figures 1–7. Psyrassa spp. 1–3) Psyrassa xestioides (Bates, 1872), female. 1) Specimen 1, dorsal habitus. 2) Specimen 1, metanepisternum. 3) Specimen 2, metanepisternum. 4–7) Metanepisternum. 4) Psyrassa basicornis Pascoe, 1866, male from Nicaragua (Granada). 5) P. testacea Linsley, 1935, male from Costa Rica (Limón). 6) P. ludmilakozlovae Santos-Silva et al., 2017, holotype female from Colombia (Boyacá). 7) P. subpicea (White, 1853), male from Honduras (Olancho).
Figures 30–40. Psyrassa cylindricollis Linsley, 1935 in The multiform genus Psyrassa Pascoe (Coleoptera: Cerambycidae: Elaphidiini): new species, new records, synonyms and transfers
Figures 30–40. Psyrassa cylindricollis Linsley, 1935, specimens from Mexico (Jalisco). 30–33) Female (1): 30) Dorsal habitus. 31) Ventral habitus. 32) Prothorax, lateral view. 33) Antennomeres III–V. 34) Female (2), dorsal habitus. 35) Female (3), dorsal habitus. 36–40) Male: 36) Lateral habitus. 37) Dorsal habitus. 38) Ventral habitus. 39) Head, frontal view. 40) Apical spine of the antennomeres III–V.
Data from: RockNet: Rockfall and earthquake detection and association via multitask learning and transfer learning
<p>Seismological data can provide timely information for slope failure hazard assessments, among which rockfall waveform identification is challenging for its high waveform variations across different events and stations. A rockfall waveform does not have typical body waves as earthquakes do, so researchers have made enormous efforts to explore characteristic function parameters for automatic rockfall waveform detection. With recent advances in deep learning, algorithms can learn to automatically map the input data to target functions. We develop RockNet via multitask and transfer learning; the network consists of a single-station detection model and an association model. The former discriminates rockfall and earthquake waveforms. The latter determines the local occurrences of rockfall and earthquake events by assembling the single-station detection model representations with multiple station recordings. RockNet achieves macro F1 scores of 0.990 and 0.981 in terms of discriminating earthquakes and rockfalls from other events with the single-station detection and association models, respectively.</p>
Part 1: Dataset and script for a manuscript entitled 'Host-specific subtelomere: structural variation and horizontal transfer in asexual filamentous fungal pathogens'
<p>Datasets, scripts and instructions for reproducing some of the results in the manuscript. The file subtelomere.tar needs to be unpacked on a Linux system. After unpacking it, go to the directory subtelomere, which contains a number of subdirectories. One subdirectory is named data, which contains genome assemblies and is used to hold datasets of short reads; the datasets of short reads in the files Data.One.Focb.tar, Data.One.Focb-2.tar and reads.tar on the four-part depository need to be placed in the subdirectory subtelomere/data/reads/. The other subdirectories under the directory subtelomere contain instructions and scripts for reproducing some of the results in the manuscript. Please see the README and z.cmd files in each subdirectory.</p> <p>The file Data.One.Focb-2.tar contains 22 files of paired-end reads from F. oxysporum f.sp. cubense tropical race 1 isolate N2 (SRA accession: SRR550150, SRR550151), and F. oxysporum f.sp. cubense TR4 isolates Hainan.B2 (SRR550152), My-1 (SRR7226877), La-2 (SRR7226878), Vn-2 (SRR7226879), Leb1.2C (SRR7226880), JV11 (SRR7226881), Phi2.6C (SRR7226882), Pak1.1A (SRR7226883), UK0001 (SRR9733598).</p>
Part 2: Dataset and script for a manuscript entitled 'Host-specific subtelomere: structural variation and horizontal transfer in asexual filamentous fungal pathogens'
<p>Datasets, scripts and instructions for reproducing some of the results in the manuscript. The file subtelomere.tar needs to be unpacked on a Linux system. After unpacking it, go to the directory subtelomere, which contains a number of subdirectories. One subdirectory is named data, which contains genome assemblies and is used to hold datasets of short reads; the datasets of short reads in the files Data.One.Focb.tar, Data.One.Focb-2.tar and reads.tar on the four-part depository need to be placed in the subdirectory subtelomere/data/reads/. The other subdirectories under the directory subtelomere contain instructions and scripts for reproducing some of the results in the manuscript. Please see the README and z.cmd files in each subdirectory.</p> <p>The file Data.One.Focb.tar contains 16 files of paired-end reads from F. oxysporum f.sp. cubense TR4 isolates II-5 (SRA accession: SRR10054446), S1B8 (SRR10054447), JV14 (SRR10054448), FOC.TR4-5 (SRR10054449), FOC.TR4-1 (SRR10054450), Col2 (SRR10103605), Col4 (SRR10125423), Col17 (SRR10747097).</p> <p> </p>
Part 4: Dataset and script for a manuscript entitled 'Host-specific subtelomere: structural variation and horizontal transfer in asexual filamentous fungal pathogen
<p>Datasets, scripts and instructions for reproducing some of the results in the manuscript. The file subtelomere.tar needs to be unpacked on a Linux system. After unpacking it, go to the directory subtelomere, which contains a number of subdirectories. One subdirectory is named data, which contains genome assemblies and is used to hold datasets of short reads; the datasets of short reads in the files Data.One.Focb.tar, Data.One.Focb-2.tar and reads.tar on the four-part depository need to be placed in the subdirectory subtelomere/data/reads/. The other subdirectories under the directory subtelomere contain instructions and scripts for reproducing some of the results in the manuscript. Please see the README and z.cmd files in each subdirectory.</p> <p>The file reads.tar contains 44 files of paired-end reads from <em>F. oxysporum </em>f.sp. <em>lycopersici</em> isolate Fol069 (SRA accession: SRR307106, SRR307107, SRR307113, SRR307115, SRR307123, SRR307257, SRR307266), isolate Fol072 (SRR307122, SRR307092, SRR307091, SRR307090, SRR307086, SRR307281, SRR307250), isolate Fol4287 (SRR7690004, SRR3139043), and F. oxysporum f.sp. radicis-cucumerinum isolate Forc016 (SRR3139027, SRR3139028), isolate Forc024 (SRR3139029, SRR3139030), isolate Forc031 (SRR3139031, SRR3139032).</p>
Fig. 3 in Transfer of the endemic Malagasy species of Kosteletzkya to Hibiscus and Perrierophytum (Malvaceae)
Fig. 3. – Known distribution of the Hibiscus L. and five Perrierophytum Hochr. species treated in this paper.
Fig. 2 in Transfer of the endemic Malagasy species of Kosteletzkya to Hibiscus and Perrierophytum (Malvaceae)
Fig. 2. – Hibiscus retrobracteatus (Hochr.) M.M. Hanes & Callm.: A. Flower; B. Fruits. [A: Bardot-Vaucoulon 1555; B: Ranirison 663] [Photos: A: M. Bardot-Vaucoulon; B: P. Ranirison]
Fig. 1 in Transfer of the endemic Malagasy species of Kosteletzkya to Hibiscus and Perrierophytum (Malvaceae)
Fig. 1. – Label of the isotype of Kosteletzkya macrantha var.herbacea Hochr. at P with Perrier de la Bâthie's handwriting. [Perrier de la Bâthie 5465, P] [P00037088; © Muséum national d'Histoire naturelle, Paris]
A near-field Head-Related Transfer Function (HRTF) data set of KEMAR with high distance resolution
<p>A near-field Head-Related Transfer Function (HRTF) data set measured on a KEMAR head and torso simulator with high distance resolution and multiple elevations is presented ('KEMAR_NFHRIRmea_1cm.sofa'). HRTFs are measured at 83448 spatial points at distances ranging from 20 to 110 cm, elevations from -25° to 35°, and azimuths from 0° to 355°. The distance resolution of the HRTF data is 1 cm, higher than that of any existing public near-field HRTF databases. Therefore, the dataset enables further exploration of the distance dependence of near-field HRTFs, and is beneficial for applications of realistic and dynamic binaural rendering of nearby sound sources. An additional data set of simulated HRTFs with 1.5 cm distance resolution is also provided ('KEMAR_NFHRIRsim_1.5cm.sofa') for a direct comparison with the measured HRTFs or other purposes.</p>
Database of the artistic transfer in the building factory in the ancient kingdom of Seville in the transition to the Modern Age [Dataset]
<p>Los datos recopilados se almacenan en una base de datos de Microsoft Access® que se ha diseñado para integrarse físicamente en un sistema SIG y a herramientas para modelos orientado a Grafo, como Gephi®. La estructura de esta base de datos está compuesta por entidades y relaciones que son representadas por tablas de entidades principales (E), tablas de clasificación/valores - entidades secundarias (Ee) y relaciones (RS). Cada una de las tablas está formada por una serie de registros (cada registro corresponde a una entidad o elemento) y por columnas o campos. Existen 5 tablas de entidades principales: “E1 profesionales”, “E2 mecenas”, “E3 edificios”, “E4 parte_edificio” (objetos asociados a arquitectura y estructura); 4 tablas de entidades secundarias: “Ee1 categoria agente”, “Ee2 tipo-clasificacion”, “Ee3 autor-dato” y “Ee4 ref-docu”; y 2 tablas de Relaciones: “RS1 Evento” y “RS2 tiene pariente”. El objetivo es identificar los eventos relacionados con la actividad constructiva durante el fenómeno del Gótico Tardio, mediados del Siglo XV y XVI de los profesionales que han trabajado en el antiguo Reino de Sevilla con el fin de analizar los movimientos, actividades y red de relaciones de los profesionales en el tiempo y espacio.</p>
A qualitative assessment on the acceptability of providing cash transfers and social health insurance for tuberculosis-affected families in Ho Chi Minh City, Vietnam
<p>Seven transcripts from qualitative interviews about the acceptability of cash transfers and social health insurance in Vietnam. </p>
A multi-station volcano-tectonic earthquakes monitoring based on Transfer Learning techniques.
<p>A multi-station volcano-tectonic earthquakes monitoring based on Transfer Learning techniques.</p> <p>Manuel Titos (1), Ligdamis Gutiérrez (2,3), Carmen Benítez (1), Pablo Rey Devesa (2,3), Ivan Koulakov (4) and Jesús. M. Ibáñez (2,3)</p> <p><br> <strong>Institutions associated:</strong></p> <p>(1) CITIC, Department of Signal Processing, Telematic and Communications, University of Granada, 18071. Granada. Spain.<br> (2) Department of Theoretical Physics and Cosmos. Science Faculty. Avd. Fuentenueva s/n. University of Granada. 18071. Granada. Spain.<br> (3) Andalusian Institute of Geophysiscs. Campus de Cartuja. University of Granada. C/Profesor Clavera 12. 18071. Granada. Spain.<br> (4) Laboratory for Seismic Forward and Inverse Problems, Institute of Petroleum Geology and Geophysics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia</p> <p><br> <strong>Acknowledgment:</strong></p> <p>This is a short text to acknowledge the contributions of specific colleagues, institutions, or agencies that aided the efforts of the authors.</p> <p>a) This work is part of the research by the Spanish FEMALE project (PID2019-106260GB-I00). <strong>FEMALE </strong>(<em>Forecasting Volcanic Eruptions Using Signal Processing and Machine Learning Techniques on Seismic Signals</em>) https://femalevolcanoes.es/</p> <p><br> b) JMI and LG were partially funded by the Spanish project PROOF-FOREVER (EUR2022.134044).</p> <p><br> <strong>Keywords:</strong></p> <p>Automatic volcanic monitoring, real-time monitoring, Artificial Intelligence, Transfer Learning, Recurrent Neural Networks, Temporal Convolutional Networks.</p> <p> </p> <p><strong>Data availability statement:</strong></p> <p>Seismic data from Bezymianny volcano (2017), Kamchatka, Russia.</p> <p> </p> <p><strong>Contents:</strong></p> <p>Seismic Data from Bezymianny volcano recorded at stations BZ01, BZ02, BZ06 and BZ10.<br> The data represent the vertical component of the seismic signal, associated to the period analyzed in the study:</p>
Multi-fidelity Gaussian Process Emulation for Atmospheric Radiative Transfer Models
<p>This repository contains several datasets of spectral atmospheric transfer functions (i.e. path radiance, transmittances, spherical albedo) simulated with MODTRAN6 atmospheric radiative transfer model. The simulations are stored in hdf5 files using the Atmospheric Look-up table Generator (ALG) toolbox (<a href="https://doi.org/10.5194/gmd-13-1945-2020">https://doi.org/10.5194/gmd-13-1945-2020</a>). Each dataset has an associated .xml file that includes the configuration of ALG/MODTRAN6 executions. All datasets include the input atmospheric/geometric variables that are summarized in the following table. Each dataset file has a random distribution (based on latin hypercube sampling) these input variables with varying number of points (e.g. train500.h5 contains 500 samples). The <em>reference </em>dataset contains 10000 samples and was used as reference for evaluating Gaussian Processes emulators.</p> <table> <tbody><tr> <th>Input Variables</th> <th>Units</th> <th>Min</th> <th>Max</th> </tr> </tbody><tbody> <tr> <td>O3 column concentration</td> <td>atm-cm</td> <td>0.25</td> <td>0.45</td> </tr> <tr> <td>Columnar Water Vapor</td> <td>g/cm2</td> <td>0.2</td> <td>4</td> </tr> <tr> <td>Aerosol Optical Thickness</td> <td>-</td> <td>0.04</td> <td>0.6</td> </tr> <tr> <td>Asymmetry parameter</td> <td>-</td> <td>0.5</td> <td>0.85</td> </tr> <tr> <td>Angstrom exponent</td> <td>-</td> <td>0.1</td> <td>2</td> </tr> <tr> <td>Single Scattering Albedo</td> <td>-</td> <td>0.8</td> <td>1</td> </tr> <tr> <td>Surface elevation</td> <td>km</td> <td>0</td> <td>2.5</td> </tr> <tr> <td>Solar Zenith Angle</td> <td>deg</td> <td>0</td> <td>70</td> </tr> <tr> <td>Relative Zenith Angle</td> <td>deg</td> <td>0</td> <td>180</td> </tr> </tbody> </table> <p> </p>
Dataset associated with publication "Direct observation of coherence transfer and rotational-to-vibrational energy exchange in optically centrifuged CO2 super-rotors" to be published in Nature Communications
<p>This dataset contains all data to compose figures in the associated manuscript. Some of the images are presented in MatLab .mat files. If a different format is needed, please contact the corresponding author. </p>
Input and Output simulation data of the THOR GCM for the paper Dynamical and radiative effects resulting from the deep non-hydrostatic vs deep quasi-hydrostatic equations in the global circulation model THOR with an added non-grey radiative transfer scheme
<p>The input and ouput simulation data of the THOR GCM for Dynamical and radiative effects resulting from the deep non-hydrostatic vs deep quasi-hydrostatic equations in the global circulation model THOR with an added non-grey radiative transfer scheme</p> <p>Global circulation models (GCMs) play an important role in contemporary investigations of exoplanet atmospheres. Different GCMs evolve various sets of dynamical equations which can result in obtaining different atmospheric properties between models. In this study, we investigate the effect of different dynamical equation sets on the atmospheres of hot Jupiter exoplanets. We compare GCM simulations using the quasi-primitive dynamical equations (QHD) and the deep Navier-Stokes equations (NHD) in the GCM THOR. We utilise a two-stream non-grey "picket-fence" scheme to increase the realism of the radiative transfer scheme. We perform GCM simulations covering a wide parameter range grid of system parameters in the population of exoplanets. Our results show significant differences between simulations with the NHD and QHD equation sets at lower gravity, higher rotation rates or at higher irradiation temperatures. The parameter exploration shows the relevance of choosing dynamical equation sets dependent on system and planetary properties.Climate states of hot Jupiters seemed to be more diverse than previously thought. There are exceptions to prograde superrotation. Overall, our study shows the evolution of different climate states which arise just due to different selection of Navier-Stokes equations and approximations. We show the shortcomings of approximations in GCMs made for Earth, but used for non Earth-like planets.</p>
Spatially resolved mock observations of stellar kinematics: full radiative transfer treatment of the simulated galaxies of the Auriga project
<p>We present realistic mock spectroscopic observations for state-of-the-art hydrodynamical simulations, using high spectral resolution stellar population models (FSPS) and full radiative transfer treatment with SKIRT. The dataset contains IFS-like spectroscopy of the stellar continuum for 10 (of the 30) galaxies in the Auriga Project at z=0. One galaxy at z=0.8 (Au 6) and another at z=3 (Au 29).</p>
Figure 2 in A redescription of Eulimnadia rivolensis (Brady, 1886) (Branchiopoda: Spinicaudata: Limnadiiidae), and its transfer to Paralimnadia
Figure 2. Digital images of male P. rivolensis from NMV J55640. A, male carapace; B, male head, C, male telson; D, male claspers, E, female head.
Figure 4 in A redescription of Eulimnadia rivolensis (Brady, 1886) (Branchiopoda: Spinicaudata: Limnadiiidae), and its transfer to Paralimnadia
Figure 4. SEM images of eggs of P. rivolensis. A, from type locality Rivoli Bay, SA, freshwater swamps, NMV J14426; B, from Campbelltown Tas, AM P55640; C, from Bruny Island, Tas, NMV J46600; D, from Central Australia, NMV J54016; E, from Flinders Island, Tas, AM P97387; F, swamps from near Lake Muir, WA, WAM C57251
Figure 1 in A redescription of Eulimnadia rivolensis (Brady, 1886) (Branchiopoda: Spinicaudata: Limnadiiidae), and its transfer to Paralimnadia
Figure 1. Drawings of types of P. rivolensis, male lectotype BMNH 1890.2.1.9. A, carapace; B head, C, telson and cercopod; D, an antennal flagellum; E trunk segments XIV to XVVII dorsa; F, clasper I with insert of long palp of clasper II; female from paralectotype NMV J14426; G, head; H, telson and cercopod. Scale bars 1 mm.
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.