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FIG. 1 in Description of a New Blind and Rare Species of Xyliphius (Siluriformes: Aspredinidae) from the Amazon Basin Using High-Resolution Computed Tomography
FIG. 1. Holotype of Xyliphius sofiae, ANSP 182322, 44.1 mm SL, Río Amazonas in vicinity of Iquitos, Loreto, Peru. (A–C) Alcohol preserved (scale bar ¼ 5 mm). (D) Live. Photos by M. Sabaj.
FIG. 9 in Description of a New Blind and Rare Species of Xyliphius (Siluriformes: Aspredinidae) from the Amazon Basin Using High-Resolution Computed Tomography
FIG. 9. Ventral view of Weberian complex in select species of Xyliphius (anterior is top). (A) X. lepturus, FMNH 99488, 72.1 mm SL. (B) X. melanopterus, FMNH 99493, 81.9 mm SL. (C) Xyliphius sofiae, ANSP 182322, 44.1 mm SL. cv: complex vertebra; gbc: gas bladder chamber (line points to portion encapsulated by bone in B); hc: hemal canal; in: intercalarium; lal: lateral line tubules; pcv: parapophysis complex vertebra; pv5: parapophysis vertebra five; r6: rib 6; sc: scaphium; tr: tripus; v6: vertebra six. Scale bar ¼ 2 mm.
Computational Metabolomics - raw data files for a tutorial
<p>20 data files for a tutorial (computational metabolomics). Sample metadata file and the known target list is also available. </p>
Systematic benchmarking of computational methods to identify spatially variable genes: Part 1
<div> <div> <div> <p>Spatially resolved transcriptomics offers unprecedented insight by enabling the profiling of gene expression within the intact spatial context of cells, effectively adding a new and essential dimension to data interpretation. To efficiently detect spatial structure of interest, an essential step in analyzing such data involves identifying spatially variable genes. Despite researchers having developed several computational methods to accomplish this task, the lack of a comprehensive benchmark evaluating their performance remains a considerable gap in the field. Here, we present a systematic evaluation of 14 methods using 60 simulated datasets generated by four different simulation strategies, 12 real-world transcriptomics, and three spatial ATAC-seq datasets. We find that spatialDE2 consistently outperforms the other benchmarked methods, and Moran’s I achieves competitive performance in different experimental settings. Moreover, our results reveal that more specialized algorithms are needed to identify spatially variable peaks. </p> <p> </p> </div> </div> </div>
Linear cross-entropy certification of quantum computational advantage in Gaussian Boson Sampling
<p>This repository contains the data used to obtain the numerical results of the paper "Linear cross-entropy certification of quantum computational advantage in Gaussian Boson Sampling" (https://arxiv.org/abs/2403.15339).</p>
FIGURE 5. Micro-computed tomography 3D images. A–D in A new species of sponge crab of the genus Epigodromia McLay 1993 (Crustacea: Brachyura: Dromiidae) from the southeastern Arabian Sea, with notes on the Zoogeography
FIGURE 5. Micro-computed tomography 3D images. A–D, dorsal view; B–C, frontal view with chelipeds outer view. A, B, Epigodromia mclayi sp. nov., holotype, male (cw 11.43 mm, cl 10.33 mm), (IO/SS/BRC00370), southeastern Arabian Sea, Tamil Nadu, India; C–D, Epigodromia gilesii Alcock, 1900, male (cw 5.8 mm, cl 6.05 mm), (IO/SS/BRC00372), Malabar coast, southeastern Arabian Sea, India.
STEM CO-EDUCATIONAL ENVIRONMENTS COMBINING EDUCATIONAL ROBOTICS AND COMPUTATIONAL THINKING
<h1><span>AMBIENTES COEDUCATIVOS STEM QUE COMBINAN ROBÓTICA EDUCATIVA Y PENSAMIENTO COMPUTACIONAL </span></h1> <p><span> </span></p> <p><strong><span>STEM CO-EDUCATIONAL ENVIRONMENTS COMBINING EDUCATIONAL ROBOTICS AND COMPUTATIONAL THINKING</span></strong></p> <p> </p> <p> </p> <p> </p> <p><span>Resumen en español </span></p> <p><span>El presente artículo de investigación se enfoca en abordar las desigualdades de género en el ámbito educativo, especialmente en las disciplinas STEM, mediante el uso de la robótica educativa y el pensamiento computacional. Para ello se realiza una revisión sistemática de literatura, con el objetivo de analizar tendencias, desafíos y oportunidades en la implementación de estas estrategias en Colombia y su impacto en el cierre de brechas de género. Aunque no se encontró una revisión que sintetice la evidencia de estos temas de manera conjunta, el estudio actual busca llenar ese vacío y contribuir a intervenciones educativas futuras más equitativas.</span></p> <p><span>Palabras clave (en español): </span><span>Educación STEM, Robótica educativa, Pensamiento computacional, Brechas de género, Vocaciones científicas</span><span>.</span><span> </span></p> <p>Resumen en inglés</p> <p><span>This research article focuses on addressing gender inequalities in education, especially in STEM disciplines, through the use of educational robotics and computational thinking. To this end, a systematic literature review is conducted, with the aim of analyzing trends, challenges and opportunities in the implementation of these strategies in Colombia and their impact on closing gender gaps. Although no review was found that synthesizes the evidence on these topics together, the current study seeks to fill this gap and contribute to more equitable future educational interventions.</span></p> <p><span>Palabras clave (en inglés): STEM education, educational robotics, Computational thinking, Gender gaps, Scientific vocations. </span></p>
Violation of no-signaling on a public quantum computer
<p>Dataset for Violation of no-signaling on a public quantum computer</p>
Figure 15 in Cranial anatomy of the Triassic rhynchosaur Mesosuchus browni based on computed tomography, with a discussion of the vomeronasal system and its deep history in Reptilia
Figure 15. Computed tomography slices of the maxillary and dentary tooth rows of ºAM-PK-6536, Mesosuchus browni, showing intermedate condition of tooth implantation and arrangement. A, sagiưal section of less maxilla; B, transverse section of right maxilla; C, transverse section of right dentary. Dashed yellow line indicates region of seperation between tooth base and alveolar bone of the maxilla.
Figure 14 in Cranial anatomy of the Triassic rhynchosaur Mesosuchus browni based on computed tomography, with a discussion of the vomeronasal system and its deep history in Reptilia
Figure 14. Strict consensus tree calculated from our re-analysis of the character matrix constructed by Ezcurra et al. (2016), with adjusted scorings for Mesosuchus browni. Mesosuchus browni is presented in red text, and the clades Rhynchosauria and Rhynchosauridae are labelled.
Figure 13 in Cranial anatomy of the Triassic rhynchosaur Mesosuchus browni based on computed tomography, with a discussion of the vomeronasal system and its deep history in Reptilia
Figure 13. Strict consensus tree calculated from our re-analysis of the character matrix constructed by Scheyer et al. (2020) with adjusted scorings for Mesosuchus browni. Mesosuchus browni is presented in red text, and the clades Rhynchosauria and Rhynchosauridae are labelled.
Figure 10 in Cranial anatomy of the Triassic rhynchosaur Mesosuchus browni based on computed tomography, with a discussion of the vomeronasal system and its deep history in Reptilia
Figure 10. Digitally isolated left mandible of SAM-PK-6536, Mesosuchus browni, in: A, lateral; B, medial; C, ventral; D, anterior; and E, posterior views.
Figure 12 in Cranial anatomy of the Triassic rhynchosaur Mesosuchus browni based on computed tomography, with a discussion of the vomeronasal system and its deep history in Reptilia
Figure 12. Internal anatomy of left dentary and splenial of SAM-PK-6536, Mesosuchus browni: A, left dentary in lateral view; B, left dentary in dorsal view; C, left dentary in anterior view; D, left splenial in anterior view; E, left splenial in posterolateral view; F, left splenial in medial view.
Figure 11 in Cranial anatomy of the Triassic rhynchosaur Mesosuchus browni based on computed tomography, with a discussion of the vomeronasal system and its deep history in Reptilia
Figure 11. Internal anatomy of the left maxilla and right jugal of SAM-PK-6536, Mesosuchus browni: A, left maxilla in lateral view; B, left maxilla in anterior view; C, left maxilla in medial view; D, left maxilla in posterior view; E, right jugal in dorsal view; F, right jugal in lateral view; and G, right jugal in anterior view.
Figure 2 in Cranial anatomy of the Triassic rhynchosaur Mesosuchus browni based on computed tomography, with a discussion of the vomeronasal system and its deep history in Reptilia
Figure 2. Schematic tree displaying the generally accepted phylogenetic relationships of the taxa we use for general comparisons in our description of Mesosuchus browni.
Figure 9 in Cranial anatomy of the Triassic rhynchosaur Mesosuchus browni based on computed tomography, with a discussion of the vomeronasal system and its deep history in Reptilia
Figure 9. Digitally isolated septomaxilla of SAM-PK-6536, Mesosuchus browni, in coronal section: A, through centre of Jacobson's chamber in posterolateral view; and B, through vomer in posterior view.
Figure 7 in Cranial anatomy of the Triassic rhynchosaur Mesosuchus browni based on computed tomography, with a discussion of the vomeronasal system and its deep history in Reptilia
Figure 7. Digitally isolated palatal region of SAM-PK-6536, Mesosuchus browni, in: A, dorsal; B, posterior; and C, medial views.
Figure 8 in Cranial anatomy of the Triassic rhynchosaur Mesosuchus browni based on computed tomography, with a discussion of the vomeronasal system and its deep history in Reptilia
Figure 8. Digitally isolated palatal complex of SAM-PK-6536, Mesosuchus browni, in: A, dorsal; B, right lateral; and C, anterior views.
Figure 3 in Cranial anatomy of the Triassic rhynchosaur Mesosuchus browni based on computed tomography, with a discussion of the vomeronasal system and its deep history in Reptilia
Figure 3. Digitally segmented skull of SAM-PK-6536, Mesosuchus browni, in: A, right lateral; B, left lateral; C, anterior; and D, posterior views.
Figure 1 in Cranial anatomy of the Triassic rhynchosaur Mesosuchus browni based on computed tomography, with a discussion of the vomeronasal system and its deep history in Reptilia
Figure 1. Volume renderings of the skull of SAM-PK-6536, Mesosuchus browni, in: A, left lateral; B, right lateral; C, ventral; D, dorsal; E, posterior; and F, anterior views.
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.