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Figure 3 in Trigonotarbus johnsoni Pocock, 1911, revealed by X-ray computed tomography, with a cladistic analysis of the extinct trigonotarbid arachnids
Figure 3. Tomographic reconstruction of Trigonotarbus johnsoni based on specimen NHMUK I. 15860. A, dorsal view. B, fourth right leg, podomeres labelled. C, ventral view. D, ventrolateral view of the coxo-sternal region. Abbreviations: 1–12, segment number; CE1−2, coxal endites 1–2; CH, chelicerae; CL, clypeus; FE, femur; L1−L4, legs 1–4; ME, median eye tubercle; MT, metatarsus; PA, patella; PP, pedipalp; PR, projection; TA, tarsus; TI, tibia; TR, trochanter. Scale bars: A, C = 2 mm, B = 1 mm, D = 0.5 mm.
Figure 1 in Trigonotarbus johnsoni Pocock, 1911, revealed by X-ray computed tomography, with a cladistic analysis of the extinct trigonotarbid arachnids
Figure 1. Reconstructions of representatives of nine trigonotarbid families, shown to scale. A, Palaeocharinus rhyniensis (Palaeocharinidae). B, Archaeomartus levis (Archaeomartidae). C, Anthracomartus hindi (Anthracomartidae). D, Anthracosiro woodwardi (Anthracosironidae). E, Trigonotarbus johnsoni (Trigonotarbidae). F, Lissomartus schucherti (Lissomartidae). G, Aphantomartus pustulatus (Aphantomartidae). H, Eophrynus prestvicii (Eophrynidae). I, Kreischeria wiedei (Kreischeriidae). Scale bar = 10 mm.
Figure 5 in Trigonotarbus johnsoni Pocock, 1911, revealed by X-ray computed tomography, with a cladistic analysis of the extinct trigonotarbid arachnids
Figure 5. The results of the cladistic analysis. The four trees show, as labelled, the agreement subtree and strict consensus of both the equally weighted analysis and implied weights analyses with concavity constants (k) of 0.25, 1, 3, and 10.
Figure 4 in Trigonotarbus johnsoni Pocock, 1911, revealed by X-ray computed tomography, with a cladistic analysis of the extinct trigonotarbid arachnids
Figure 4. An idealized reconstruction of Trigonotarbus johnsoni based on the computed tomography scan of NHMUK I. 15860 and additional hand specimens. Scale bar = 2 mm.
SeBS: A Serverless Benchmark Suite for Function-as-a-Service Computing
<p>This upload contains the software prototype, data, analysis scripts, and replication scripts for the paper "SeBS: A Serverless Benchmark Suite for Function-as-a-Service Computing" (ACM/IFIP Middleware 2021).</p> <p>With our artifact we provide the following components:</p> <ul> <li> <p><code>serverless-benchmarks</code> - source code of the benchmark suite</p> </li> <li> <p><code>data</code> - benchmarking results obtained for the paper</p> </li> <li> <p><code>analysis</code> - Python plotting and analysis scripts used for data analysis</p> </li> <li> <p><code>experiments</code> - scripts helping to reproduce the experiments</p> </li> <li> <p><code>docker</code> - compressed Docker images that were used for our experiments.</p> </li> </ul> <p>Our data has been obtained in January 2020, July and August 2020, and November 2020.</p>
FIGURE 1 in First fossil representative of Cerylonidae (Coleoptera: Coccinelloidea) described using X-ray micro-computed tomography, from Eocene Baltic amber
FIGURE 1. Protostomopsis pandema gen. et sp. nov., holotype, No MP/4233/col. AG/no. 9441 [ISEA]: A, B – dorsal habitus photomicrograph, and corresponding X-ray μCT rendering; C, D – ventral habitus photomicrograph, and corresponding X-ray μCT rendering. Scale bars represent 0.25 mm.
FIGURE 7 in First fossil representative of Cerylonidae (Coleoptera: Coccinelloidea) described using X-ray micro-computed tomography, from Eocene Baltic amber
FIGURE 7. Distribution of Ostomopsinae: extant species of Ostomopsis (green), and Eocene record of fossil Protostomopsis pandema gen. et sp. nov. (red).
FIGURE 6 in First fossil representative of Cerylonidae (Coleoptera: Coccinelloidea) described using X-ray micro-computed tomography, from Eocene Baltic amber
FIGURE 6. Protostomopsis pandema gen. et sp. nov., MP/4233/col. AG/no.9441 [ISEA], X-ray μCT rendering of aedeagus: A – dorsal view; B – ventral view; C – lateral view. Abbreviations: ap—apex; ba—base. Scale bar represents 0.1 mm.
FIGURE 3 in First fossil representative of Cerylonidae (Coleoptera: Coccinelloidea) described using X-ray micro-computed tomography, from Eocene Baltic amber
FIGURE 3. Protostomopsis pandema gen. et sp. nov., paratype, No P3300.138 [RSKM], habitus: A – dorsal view; B – ventral view; C – right lateral view. Scale bar represents 0.5 mm.
FIGURE 2 in First fossil representative of Cerylonidae (Coleoptera: Coccinelloidea) described using X-ray micro-computed tomography, from Eocene Baltic amber
FIGURE 2. Protostomopsis pandema gen. et sp. nov., holotype, No MP/4233/col. AG/no. 9441 [ISEA] surrounded by fungal hyphae: A, B – right lateral habitus photomicrograph, and corresponding X-ray μCT rendering; C, D – left lateral habitus phot- omicrograph, and corresponding X-ray μCT rendering. Scale bars represent 0.25 mm.
FIGURE 4 in First fossil representative of Cerylonidae (Coleoptera: Coccinelloidea) described using X-ray micro-computed tomography, from Eocene Baltic amber
FIGURE 4. Protostomopsis pandema gen. et sp. nov., paratype, No P3300.138 [RSKM]: A – habitus, ventrolateral view; B – details of abdomen showing apical margin of ventrite 5. Scale bars represent 0.5 mm for Fig. A, 0.1 mm for Fig. B.
Analyzed Benchmarks on Experiments for a Complications for Computational Experiments from Modern Processors
<p>For details see: </p> <p>Johannes K. Fichte, Markus Hecher, Ciaran McCreesh, Anas Shahab: Complications for Computational Experiments from Modern Processors, Proceedings of the 27th International Conference on Principles and Practice of Constraint Programming (CP'2021).</p> <p>For the benchmark set, we refer to https://www.cs.uni-potsdam.de/wv/projects/sets/set-industrial-09-12.tar.xz or https://www.cs.uni-potsdam.de/wv/projects/sets. The instances are also available on Zenodo at: https://doi.org/10.5281/zenodo.3989071</p> <p>The tested solver is available at: https://github.com/arminbiere/cadical </p>
High-resolution X-ray computed tomography images of Bentheim sandstone under elevated stress
<p>A dry sample of Bentheim (or Bentheimer) sandstone was characterized using 3D X-Ray microscopy (Versa XRM-500, XRadia-Zeiss) at three different confining pressures of 1 MPa, 20 MPa, and 30 MPa and two voxel sizes of (1.5854 µm)<sup>3</sup> and (3.3452 µm)<sup>3</sup>. The 5-mm-diameter, 20-mm-long dry sample was placed inside a custom-made pressure sell (Lebedev et al, 2017). The sample was subjected to confining pressure of 20 MPa and 3200 radiographs were acquired, then confining pressure was reduced to 1MPa and the sample was imaged again, finally, the sample was pressurized up to 30MPa and the final image set was taken. Image reconstruction was done using internal software (XRadia-Zeiss).</p>
T-REX: computational model data
<p>Datasets generated by the computational neural mass network model of the brain during the study of " A multiscale brain network model links Alzheimer's disease-mediated neuronal hyperactivity to large-scale oscillatory slowing".</p> <p>Data includes raw files of simulated neurophysiology timeseries, generated by iterating the Brainnet coupled neural mass model in Brainwave, available from <a href="http://home.kpn.nl/stam7883/brainwave.html">http://home.kpn.nl/stam7883/brainwave.html</a>. </p> <p>Each file contains data of a single scenario and can be viewed using Brainwave.</p>
Memory-Efficient Fixpoint Computation
<p>These are the raw datasets used in <a href="https://link.springer.com/chapter/10.1007/978-3-030-65474-0_3">Memory-Efficient Fixpoint Computation (SAS2020)</a></p>
Open citations involving Computer Science publications listed in DBLP
<p>Data used in the presentation "Open citations in Informatics" held during ECSS 2021. It includes six different files obtained using the software available at <a href="https://github.com/essepuntato/ecss-2021">https://github.com/essepuntato/ecss-2021</a>.</p>
Dataset for CIIE 2018 folio number 2018062802133: A Computational Literature Review of Educational Innovation
<p>Dataset for CIIE 2018 folio number 2018062802133: A Computational Literature Review of Educational Innovation</p>
Data for "Computational Desire Line Analysis of Cyclists on the Dybbølsbro Intersection in Copenhagen"
<p>Data for the paper "Computational Desire Line Analysis of Cyclists on the Dybbølsbro Intersection in Copenhagen"</p> <p>**Paper (preprint)**: [https://arxiv.org/abs/2211.01301](https://arxiv.org/abs/2211.01301) </p>
PROGRESS IN COUPLING COMPUTATIONAL THERMODYNAMICS AND COMPUTATIONAL FLUID DYNAMICS TO SUPPORT MOLTEN SALT REACTOR APPLICATIONS (Revised)
<p>This research was undertaken, in part, thanks to funding from the Canada Research Chairs program and the Discovery Grant Program of the Natural Sciences and Engineering Research Council of Canada. This study is in direct support of the SAMOSAFER project.</p> <p> </p>
UTS Digital Forensic, Computer-Assisted
<p>data uts digital forensic computer-assisted</p>
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.