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Figure 2 in Bat fly (Diptera: Streblidae) and common vampire bat (Chiroptera: Phyllostomidae) association in Honduras: prevalence, mean intensity, infracommunities and influence of the biological characteristics of the host
Figure 2. Infestation intensity of Strebla wiedemanni, Trichobius joblingi, T. parasiticus and T. caecus, collected on Desmodus rotundus between May 2018 to November 2019 in Honduras.
Figure 1 in Bat fly (Diptera: Streblidae) and common vampire bat (Chiroptera: Phyllostomidae) association in Honduras: prevalence, mean intensity, infracommunities and influence of the biological characteristics of the host
Figure 1. Geographic location of the study sites in Honduras: 1) Joya Grande, 2) Río Arcagual, 3) Los Ochales, 4) Lainez, 5) El Hizopo, 6) Cuevas de Talgua, 7) UNAG, 8 Kurpha. See Table 1 for details.
Figures 2–5 in One size doesn′t fit all: Singularities in bat species richness and activity patterns in wind-energy complexes in Brazil and implications for environmental assessment
Figures 2–5. Bat activity based on the pooled number of echolocation pulses per hour in four wind-energy complexes in northeastern Brazil, from September 2015 to January 2017: (2) Curva dos Ventos, municipality of Caetité, state of Bahia; (3) Cristal, municipality of Morro do Chapéu, state of Bahia; (4) Modelo, municipality of João Câmara, state of Rio Grande do Norte; (5) Fonte dos Ventos, municipality of Tacaratu, state of Pernambuco.
Figure 1 in One size doesn′t fit all: Singularities in bat species richness and activity patterns in wind-energy complexes in Brazil and implications for environmental assessment
Figure 1. Wind-energy complexes in northeastern Brazil studied for the presence and activity of insectivorous bats from September 2015 to January 2017.
Fig. 1 in Records of European free-tailed bat Tadarida teniotis (Rafinesque, 1814) (Mammalia: Chiroptera) in Bulgaria
Fig. 1. Sonogram of echolocation calls of a group of emerging from the daily roost European free-tailed bats (Tadarida teniotis) near Madzharovo town.
Fermi-GBM and Swift-BAT Data Release Related to Analysis of Gravitational-Wave Candidates from the Third Gravitational-wave Observing Run
<p>This material contains the data products associated with A Joint Fermi-GBM and Swift-BAT Analysis of Gravitational-Wave Candidates from the Third Gravitational-wave Observing Run [1]. It is based, in part, on data products associated with GWTC-2.1 [2][3] and GWTC-3 [4][5] from the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration, the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration, and the <a href="https://gwcenter.icrr.u-tokyo.ac.jp/en/">KAGRA</a> Collaboration provided under a Creative Commons Attributional 4.0 International license. For more information, see the paper (<a href="https://arxiv.org/abs/2308.13666">https://arxiv.org/abs/2308.13666</a>) and the included README.txt.</p> <p>The data release includes the following directories:</p> <ul> <li><strong>3sigma_upper_limit_maps:</strong> 3 sigma flux upper limits reported by GBM as a function of sky position over a 10-1000 keV energy range.</li> <li><strong>5sigma_upper_limit_maps: </strong>5 sigma flux upper limits reported by GBM and BAT as a function of sky position over a 15-350 keV energy range.</li> <li><strong>gbm_targeted_results: </strong>GRB candidates from the GBM Targeted Search.</li> <li><strong>gbm_temporal_offset_analysis: </strong>input files to the time offset analysis applied to GBM on-board triggers and candidates from the GBM Untargeted Search.</li> <li><strong>bbh_model_fluxes: </strong>predicted gamma-ray fluxes over the 10-1000 keV energy range for likely BBH mergers.</li> <li><strong>gw_localizations:</strong> GW localization files used to overlay the 90% credible area onto GBM upper limits as a function of sky position.</li> <li><strong>m1_m2_contours: </strong>90% credible region paths for the GW component masses m1, m2.</li> </ul> <p>The data release also includes a set of example scripts to show how the contents of the data files are used. Refer to the included README.txt for more details.</p> <p><strong>References:</strong></p> <p><a href="https://arxiv.org/abs/2308.13666">[1] Fletcher, C. et al 2023, arXiv, 2308.13666</a><br> <a href="https://doi.org/10.5281/zenodo.6513631">[2] LIGO Scientific Collaboration and Virgo Collaboration. 2022, Zenodo, 6513631</a><br> <a href="https://doi.org/10.5281/zenodo.5759108">[3] LIGO Scientific Collaboration and Virgo Collaboration. 2021, Zenodo, 5759108</a><br> <a href="https://doi.org/10.5281/zenodo.5546663">[4] LIGO Scientific Collaboration and Virgo Collaboration and KAGRA Collaboration. 2021, Zenodo, 5546663</a><br> <a href="https://doi.org/10.5281/zenodo.5546665">[5] LIGO Scientific Collaboration and Virgo Collaboration and KAGRA Collaboration 2021, Zenodo, 5546665</a></p> <p><br> </p>
Fig. 6. Habenaria proiteana J.A.N. Bat., A.A in Checklist and molecular phylogenetics reveal three taxonomic novelties in Habenaria (Orchidaceae, Orchidoideae) from Chapada dos Veadeiros, Goiás, Brazil
Fig. 6. Habenaria proiteana J.A.N. Bat., A.A.Vale & Bianch. sp. nov. A–B. Habit. C–E. Leaves. F. Flowers. G–I. Dissected perianth. J–L. Bract, pedicellate ovary, gynostemium and spur, lateral view. M. Gynostemium, side view. N. Gynostemium, front view. O. Gynostemium, back view. P. flattened connective, anther and lateral appendages (auricles). Q. Rostellum, upper view. R. Rostellum, lateral view. A–H, J–K and M–R from Batista et al. 2376 (BHCB); I and L from Hatschbach et al. 58383 (MBM). Abbreviations: ac = anther canal; an = anther; au = auricles; ca = caudicle; co = connective; rs = rostellum side-lobes; st = stigmatophores; vi = viscidium.
Fig. 5. Habenaria proiteana J.A.N. Bat., A.A in Checklist and molecular phylogenetics reveal three taxonomic novelties in Habenaria (Orchidaceae, Orchidoideae) from Chapada dos Veadeiros, Goiás, Brazil
Fig. 5. Habenaria proiteana J.A.N. Bat., A.A.Vale & Bianch. sp. nov. A. Inflorescence. B. Flower, front view. C. Flower, side view. D–E. Flower, ¾ lateral view. F. Gynostemium, front view. Habenaria cultellifolia. G. Habitat (Chapadas dos Veadeiros, Goiás). H. Vegetative part. I. Flower, front view. A–D, F from Batista et al. 2376 (BHCB); E from Batista & Bianchetii 3101 (BHCB); G–I from Batista et al. 2959 (BHCB). Abbreviations: ac = anther canals; an = anther; au = auricles; co = connective; rm = rostellum mid lobe; rs = rostellum side-lobes; st = stigmatophores; vi = viscidium. All photographs by J.A.N. Batista.
Fig. 1 A in DarkCideS 1.0, a global database for bats in karsts and caves
Fig. 1 A schematic diagram showing the features, contents, and potential applications of the DarkCideS 1.0 database. The database is a centralised, collaborative, and open-access platform that contains information on cave-dwelling bat species and their distribution.
Fig. 2 in DarkCideS 1.0, a global database for bats in karsts and caves
Fig. 2 Percentage of species data completeness according to biogeographical realm (a) and family-level (b) between IUCN estimates (red bars) and sampled caves from DarkCideS 1.0 (black bars) species richness, the proportion of endemism, and proportion of threatened species worldwide.
Fig. 5 in DarkCideS 1.0, a global database for bats in karsts and caves
Fig. 5 Schematic diagram showing the updating workflow of the database from new data entry. The DarkCideS database aims to be a long-term biodiversity data exchange platform by including new data from fieldwork and assessments. Authors can upload their dataset containing species records, geographical information, and landscape threats on the web page. The corresponding authors will receive new data entries for validation before being merged into the database.
Fig. 3 in DarkCideS 1.0, a global database for bats in karsts and caves
Fig. 3 The geographical data turnover of the current database version: (a) geographical locations of all bat caves included in the database, (b) percent distribution of species occurrence in terms of the biogeographical realm and terrestrial biome, (c) country-level turnover.
Data from: Strategic predatory pursuit of the stealthy, highly maneuverable, slow flying bat Corynorhinus townsendii
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Edge effects and vertical stratification of aerial insectivorous bats across the interface of primary-secondary Amazonian rainforest
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Data from: Diversification rates have no effect on the convergent evolution of foraging strategies in the most speciose genus of bats, Myotis
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Data for Diniz et al. (2022) Changing the main course: strong bat visitation to the ornithophilous mistletoe Psittacanthus robustus (Loranthaceae) in a Neotropical savanna (Biotropica)
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Data for: Drivers affecting habitat use in Afrotropical hipposiderid and pteropodid bats
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Data for: Predicting habitat suitability for Townsend’s big-eared bats across California in relation to climate change
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Data from: Consistent long-distance foraging flights across years and seasons at colony level in a Neotropical bat
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Fluorescent biomarkers demonstrate prospects for spreadable vaccines to control disease transmission in wild bats
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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.