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FIGURE 1a–d in Catalog of the Ulidiidae (Diptera: Tephritoidea) of Chile
FIGURE 1a–d. Pterotaenia fasciata (Wiedemann, 1830), lectotype (NHMW) sex unrecognized: a. habitus, dorsal view; b. habitus, lateral view; c. labels; d. head, dorso-frontal view. Scales: 1 mm.
FIGURE 17 in Catalog of the Ulidiidae (Diptera: Tephritoidea) of Chile
FIGURE 17. Chilean distribution of Lipsana insulaepaschalis Enderlein, 1938, Notogramma azapae Steyskal, 1991, Physiphora alceae (Preyssler, 1791), Seioptera importans Hennig, 1941.
FIGURE 5a–b. a. Ceroxys friasi Steyskal, 1991 in Catalog of the Ulidiidae (Diptera: Tephritoidea) of Chile
FIGURE 5a–b. a. Ceroxys friasi Steyskal, 1991, paratype female (USNM): habitus, lateral view and labels; b. Ceroxys pallidus Steyskal, 1991 paratype female (USNM): habitus, lateral view and labels. Scales: 1.0 mm. Although the labels indicate that the specimens from both figures (a and b) are paratypes, their data labels do not correspond to the information on the paratypes from the original description (Steyskal, 1991:27); therefore, these are not part of the type series of each species.
FIGURE 16 in Catalog of the Ulidiidae (Diptera: Tephritoidea) of Chile
FIGURE 16. Chilean distribution of Euxesta eluta Loew, 1868, E. calligyna (Bigot, 1857), E. obliquestriata Hendel, 1909, and E. penacamposi Steyskal, 1973.
FIGURE 8a–e. Euxesta mazorca Steyskal, 1974 in Catalog of the Ulidiidae (Diptera: Tephritoidea) of Chile
FIGURE 8a–e. Euxesta mazorca Steyskal, 1974, holotype female (USNM): a. habitus, dorsal view; b. habitus, lateral view; c. wing; d. labels; e. head, dorso-frontal view. Scales: 1 mm.
FIGURE 9a–c. Euxesta penacamposi Steyskal, 1973 in Catalog of the Ulidiidae (Diptera: Tephritoidea) of Chile
FIGURE 9a–c. Euxesta penacamposi Steyskal, 1973, paratype male (MEUC): a. habitus, dorsal view; b. habitus, lateral view; c. labels. Scales: 1 mm.
FIGURE 11a–e. Notogramma azapae Steyskal, 1991 in Catalog of the Ulidiidae (Diptera: Tephritoidea) of Chile
FIGURE 11a–e. Notogramma azapae Steyskal, 1991, holotype male (USNM): a. habitus, dorsal view; b. habitus, lateral view and pupa; c. head, dorso-frontal view; d. labels; e. wing. Scales: 1 mm.
FIGURE 15 in Catalog of the Ulidiidae (Diptera: Tephritoidea) of Chile
FIGURE 15. Chilean distribution of Ceroxys friasi Steyskal, 1991, C. pallidus Steyskal, 1991, Chaetopsis sp., and Stictoedopa ruizi Brèthes, 1926.
The earthquake catalog in the Changning shale gas field based on dense array
Open the record for dataset details and reuse information.
FIGURES 35–38. Austroleptis fulviceps Malloch, 1932 in An illustrated catalog of the South American Austroleptidae (Diptera: Tabanomorpha), with a compilation of all known records and new records from Chile and Argentina
FIGURES 35–38. Austroleptis fulviceps Malloch, 1932, female, holotype [USNM] © National Museum of Natural History, Washington, D.C. (USA).
FIGURES 39–42. Austroleptis fulviceps Malloch, 1932 in An illustrated catalog of the South American Austroleptidae (Diptera: Tabanomorpha), with a compilation of all known records and new records from Chile and Argentina
FIGURES 39–42. Austroleptis fulviceps Malloch, 1932, female, paratype [NMHUK] © The Natural History Museum, London (United Kingdom).
FIGURES 8–18 in An illustrated catalog of the South American Austroleptidae (Diptera: Tabanomorpha), with a compilation of all known records and new records from Chile and Argentina
FIGURES 8–18. Additional specimens of Austroleptis atriceps Malloch, 1932. 8–11. Female [CNC]. 12–14. Female [NMSA]. 15–18. Male [CNC].
FIGURE 73–79 in An illustrated catalog of the South American Austroleptidae (Diptera: Tabanomorpha), with a compilation of all known records and new records from Chile and Argentina
FIGURE 73–79. Geographic distribution of Austroleptis in South America. 73. Map with all the records of Austroleptidae (data compiled in Table 1). 74. Map of Austroleptis atrata. 75. Map of A. atriceps. 76. Map of A. breviflagella. 77. Map of A. fulviceps. 78. Map of A. penai. 79. Map with records of Austroleptis sp. 1, Austroleptis sp. 2, and additional records of undetermined specimens of the genus. Square indicates the holotype of each species.
FIGURES 61–72 in An illustrated catalog of the South American Austroleptidae (Diptera: Tabanomorpha), with a compilation of all known records and new records from Chile and Argentina
FIGURES 61–72. Specimens of undescribed species of Austroleptis. 61–64. Austroleptis sp. 1, male [CNC]. 65–68. Austroleptis sp. 1, female [CNC]. 69–72. Austroleptis sp. 2, female [RBINS].
FIGURES 54–60 in An illustrated catalog of the South American Austroleptidae (Diptera: Tabanomorpha), with a compilation of all known records and new records from Chile and Argentina
FIGURES 54–60. Austroleptis penai Nagatomi & Nagatomi, 1987. 54–57. Female, holotype [CNC] © Canadian National Collection, Ottawa (Canada). 58–60. Female [NMSA].
FIGURES 23–34 in An illustrated catalog of the South American Austroleptidae (Diptera: Tabanomorpha), with a compilation of all known records and new records from Chile and Argentina
FIGURES 23–34. Additional specimens of Austroleptis breviflagella Nagatomi & Nagatomi, 1987. 23–25. Male [NMSA]. 26–29. Female [RBINS]. 30–34. Females [CNC].
FIGURES 1–7. Austroleptis atriceps Malloch, 1932 in An illustrated catalog of the South American Austroleptidae (Diptera: Tabanomorpha), with a compilation of all known records and new records from Chile and Argentina
FIGURES 1–7. Austroleptis atriceps Malloch, 1932 [NHMUK] (1–4. Holotype, female; 5–7. Paratype, female) © The Natural History Museum, London (United Kingdom).
FIGURES 19–22 in An illustrated catalog of the South American Austroleptidae (Diptera: Tabanomorpha), with a compilation of all known records and new records from Chile and Argentina
FIGURES 19–22. Austroleptis breviflagella Nagatomi & Nagatomi, 1987, female, holotype [CNC] © Canadian National Collection, Ottawa (Canada).
FIGURES 43–53 in An illustrated catalog of the South American Austroleptidae (Diptera: Tabanomorpha), with a compilation of all known records and new records from Chile and Argentina
FIGURES 43–53. Additional specimens of Austroleptis fulviceps Malloch, 1932. 43–49. Female [CNC]. 50–53. Male [CNC].
Parameter estimation catalogs for binary neutron star mergers detected with next-generation gravitational wave detectors
<div> <p>Next-generation gravitational wave (GW) observatories, such as the Einstein Telescope (ET) and the Cosmic Explorer, will provide access to the population of binary neutron star (BNS) mergers throughout cosmic history and yield precise parameter estimates. Here, we publish the results of a comprehensive study evaluating BNS merger detection prospects using the ET alone or in a network of current or next-generation detectors up to redshift equal to 1. We publicly release all the parameter estimation for 10 years of observations of BNSs in the form of catalogs. These catalogs are made available to the community for multi-messenger studies, multi-probe cosmology, and nuclear study to constrain the neutron star (NS) equation of state (EOS). They can be used to focus on specific events (for example golden events with high signal-to-noise ratio) or for statistical studies on the BNS populations. </p> <p>Our simulations assessed the perspectives for detecting the optical emission of BNS mergers in the era of next-generation detectors, considering how uncertainties in BNS population properties, NS mass distribution, and the EOS might affect the detection rate and parameter estimation. The study is published in <a href="https://arxiv.org/abs/2411.02342" target="_blank" rel="noopener">Loffredo, Hazra, Dupletsa, Branchesi et al. 2024</a> arXiv:2411.02342 (submitted to A&A).</p> </div> <h3>BNS merger rate</h3> <p>As shown in <a href="https://ui.adsabs.harvard.edu/abs/2021MNRAS.502.4877S/abstract" target="_blank" rel="noopener">Santoliquido et al. (2021)</a>, the common envelope ejection efficiency parameter, α, determines one of the main sources of uncertainty for the number of BNS mergers per year. In order to evaluate the impact of the uncertainties of the BNS merger rate normalization on our results, we generate two catalogues of BNS mergers assuming α to be either <strong>0.5</strong> or <strong>1.0</strong>. </p> <h3>NS mass distribution</h3> <p>We draw the component masses of the NS binaries, M_1 and M_2, from two different mass distributions: <strong>Gaussian</strong> and<br><strong>uniform</strong> mass distributions. The Gaussian distribution is centred at 1.33 M⊙ with a standard deviation of 0.09 M⊙. The uniform mass distribution ranges in [1.1 M⊙, M_max], where M_max depends on the selected EOS.</p> <h3>Equation of state (EOS)</h3> <p>Since the NS EOS affects both the GW and EM signals expected from BNS mergers, we consider<br>two different EOSs, namely the <strong>APR4</strong> and <strong>BLh</strong> microscopic EOSs.</p> <h3>Detector configuration</h3> <p>Given the two values of α (0.5 and 1.0), the two mass distributions (uniform and Gaussian), and the two EOSs (BLh and APR4), we have a total of 8 different population sets, which constitute our injections for the gravitational signal analysis. For each of these datasets, we consider the following GW detector configurations:</p> <ul> <li>ET in its triangular design of 10 km arms, located in Sardinia, alone and operating together with (<strong>ET_delta_10_cryo</strong>): <ul> <li>the current ground-based network LIGO-Hanford, LIGO-Livingston, Virgo, KAGRA, LIGO-India (<strong>LVKI</strong>) </li> <li>one L-shaped CE with 40 km arms, located in the USA (<strong>1CE</strong>)</li> <li>2 CEs, both with 40 km arms, one in the USA and one in Australia (<strong>2CE</strong>)</li> </ul> </li> <li>ET in its 2L-shaped interferometer configuration of 15 km arms misaligned at 45 deg (one located in Sardinia and the other in the Netherlands); we consider the same networks as above, using the 2L-configuration instead of the triangular one (<strong>ET_2L_15_cryo_45deg</strong>). </li> </ul> <p>We thus have eight different detector networks giving a total of 64 simulations available in this repository. </p> <h3>Catalog description</h3> <p>The parameter estimation of the injected GW signals by the various detector networks is obtained through the Fisher matrix software <strong>GWFish</strong> (<a href="https://ui.adsabs.harvard.edu/abs/2023A%26C....4200671D/abstract" target="_blank" rel="noopener">Dupletsa et al. 2023</a>). The Fisher analysis method approximates the likelihood with a multivariate Gaussian distribution. All the parameters [M_1, M_2, dL, ι, RA, DEC, Ψ, phase, tc, Λ_1, Λ_2] are considered for the Fisher matrix derivation. The uncertainties on parameters coming from the covariance matrix (the inverse of the Fisher matrix) are given at 1σ. We implement a duty cycle of 85% for each of the L-shaped detectors, and for each of the three nested detectors composing the triangle. </p> <ul> <li><strong>Signals_<em>{BNS_merger_rate}</em>_<em>{EOS}</em>_<em>{NS_mass_distribution}</em>_<em>{Detector_configuration}</em>.txt </strong>contains the parameters describing a GW event and the corresponding network signal-to-noise ratio (SNR) <ul> <li><strong>mass_1: </strong>primary mass of the binary in [Msol] (in detector frame) (M_1)</li> <li><strong>mass_2:</strong> secondary mass of the binary in [Msol] (in detector frame) (M_2)</li> <li><strong>luminosity_distance:</strong> the luminosity distance of the merger in [Mpc]</li> <li><strong>dec:</strong> declination angle in [rad]. It varies in [−𝜋/2,+𝜋/2]</li> <li><strong>ra:</strong> right ascension in [rad]. It varies in [0,2/𝑝𝑖]</li> <li><strong>theta_jn:</strong> the angle between the line of observation and the total angular momentum (orbital, spin and GR corrections) of the binary [rad] (it reduces to the so-called inclination angle or <strong>iota</strong> if the spin component is absent); it ranges in [0,𝜋]</li> <li><strong>psi:</strong> the polarization angle in [rad]; it ranges in [0,𝜋]</li> <li><strong>geocent_time:</strong> merger time as GPS time in [s]</li> <li><strong>phase:</strong> the initial phase of the merger in [rad]; it ranges in [0,2𝜋]</li> <li><strong>redshift: </strong>the redshift of the merger</li> <li><strong>lambda_1: </strong>dimensionless tidal polarizabilty of primary component</li> <li><strong>lambda_2:</strong> dimensionless tidal polarizabilty of secondary component</li> <li><strong>network_SNR:</strong> network SNR for a the given event</li> </ul> </li> <li><strong>Errors_<em>{BNS_merger_rate}</em>_<em>{EOS}</em>_<em>{NS_mass_distribution}</em>_<em>{Detector_configuration}</em>.txt </strong>contains the <div> <div>parameter errors for each event. The first column is <strong>network_SNR</strong>, the following columns repeat the injected parameters as above and the relative errors <strong>err_<em>{parameter}</em></strong><em>. </em>The last column is the error on sky localisation (<strong>err_sky_location</strong>) at 90% credible interval. </div> </div> </li> </ul> <h3>Further details </h3> <p>Further details on the assumptions we made to produce these catalogs can be found in <a href="https://arxiv.org/abs/2411.02342" target="_blank" rel="noopener">Loffredo et al. 2024</a>, while further details on GWFish can be found on <a href="https://colab.research.google.com/github/janosch314/GWFish/blob/main/gwfish_tutorial.ipynb" target="_blank" rel="noopener">this tutorial</a>. We also provide the jupyter notebook <strong>paper_plots.ipynb</strong>, to reproduce Figs. 10, 11, 13, D.1, D.5, D.6. </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.