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156 results for “underground”
Figure 5 in Going underground: postcranial morphology of the early Miocene marsupial mole Naraboryctes philcreaseri and the evolution of fossoriality in notoryctemorphians
Figure 5. Comparison of femora of Naraboryctes philcreaseri and Notoryctes typhlops: a, right femur of Naraboryctes philcreaseri (QM F57678) in cranial view; b, right femur of Notoryctes typhlops (SAM M637) in cranial view; c, QM F57678 in caudal view; d, SAM M637 in caudal view. Abbreviations: fh, femoral head; gtr, greater trochanter; icg, intercondylar groove; ltr, lesser trochanter; trf, trochanteric fossa; ttr, third trochanter.
Figure 8 in Going underground: postcranial morphology of the early Miocene marsupial mole Naraboryctes philcreaseri and the evolution of fossoriality in notoryctemorphians
Figure 8. Dated total evidence phylogeny based on 259 morphological characters and 9012 bp of sequence data from five nuclear genes (APOB, BRCA1, RBP3, RAG1, and VWF) analysed using MrBayes 3.2.2 assuming the Independent Gamma Rates (IGR) clock model, with topological and temporal constraints applied to selected internal nodes (see supplementary information). Notoryctes and Naraboryctes are indicated in bold. Blue bars represent 95% highest posterior density intervals (HPDs) on the divergence times. Nodes without Bayesian posterior probability (BPP) were constrained a priori.
Figure 1 in Going underground: postcranial morphology of the early Miocene marsupial mole Naraboryctes philcreaseri and the evolution of fossoriality in notoryctemorphians
Figure 1. Comparison of scapulae of Naraboryctes philcreaseri and Notoryctes typhlops in lateral view: a, left scapula of Naraboryctes philcreaseri (QM F57716); b, right scapula (reversed) of Notoryctes typhlops (SAM M637). Abbreviations: acr, acromion process; cau, "caudal" angle; cor, coracoid process; cra, "cranial" angle; inf, infraspinous fossa; psf, postscapular fossa; ssf, supraspinous fossa; ssp, scapular spine; sssp, secondary scapular spine.
Figure 1 in A new underground water explorer
Figure 1. – Location of the study site and topographic map of underground water area with location point of the observed European catfish. Picture of the European catfish taken at 25 m depth and 150 m from the cave entrance.
Video of Using LOD to crowdsource Dutch WW2 underground newspapers on Wikipedia, SWIB2016, Bonn, 29-11-2016
<p><strong> </strong> Video registration of presentation about <a title="File:Using LOD to crowdsource Dutch WW2 underground newspapers on Wikipedia, SWIB2016, 29-11-2016.pdf" href="https://commons.wikimedia.org/wiki/File:Using_LOD_to_crowdsource_Dutch_WW2_underground_newspapers_on_Wikipedia,_SWIB2016,_29-11-2016.pdf">Using Linked Open Data to crowdsource Dutch WW2 underground newspapers on Wikipedia</a>, <a href="https://swib.org/swib16/programme.html" rel="nofollow">SWIB2016</a>, 29-11-2016 in Bonn.</p> <p>The video describes a project <a title="nl:Wikipedia:Wikiproject/Verzetskranten" href="https://nl.wikipedia.org/wiki/Wikipedia:Wikiproject/Verzetskranten">to systematically describe and interlink 1,300 Dutch underground newspapers from World War 2</a> on Wikipedia using linked open data.</p> <p>The project extracts contextual information about the newspapers from a book, converts it to structured data, and generates Wikipedia stubs linked to metadata, full texts, and each other.</p> <p>Volunteers are expanding the stubs into full articles, improving access to information about this historical period.</p> <div> <h2>Abstract</h2> </div> <p>During the second World War some 1.300 illegal newspapers were issued by the Dutch resistance. Right after the war as many of these newspapers as possible were physically preserved by Dutch memory institutions. They were described in formal library catalogues that were digitized and brought online in the 1990s. In 2010 the national collection of underground newspapers - some 200.000 pages - was full-text digitized in Delpher, the national aggregator for historical full-texts. Having created online metadata and full-texts for these publications, the third pillar <em>context</em> was still missing, making it hard for people to understand the historic background of the newspapers. We are currently running a project to tackle this contextual problem. We started by extracting contextual entries from a hard-copy standard work on Dutch illegal press and combined these with data from the library catalogue and Delpher into a central LOD triple store. We then created links between historically related newspapers and used Named Entity Recognition to find persons, organisations and places related to the newspapers. We further semantically enriched the data using DBPedia. Next, using an article template to ensure uniformity and consistency, we generated 1.300 Wikipedia article stubs from the database. Finally, we sought collaboration with the Dutch Wikipedia volunteer community to extend these stubs into full encyclopedic articles. In this way we can give every newspaper its own Wikipedia article, making these WW2 materials much more visible to the Dutch public, over 80% of whom uses Wikipedia. At the same time the triple store can serve as a source for alternative applications, like data visualizations. This will enable us to visualize connections and networks between underground newspapers, as they developed over time between 1940 and 1945.</p> <div> <h2>Presentation slides</h2> </div> <ul> <li><a title="File:Using LOD to crowdsource Dutch WW2 underground newspapers on Wikipedia, SWIB2016, 29-11-2016.pdf" href="https://commons.wikimedia.org/wiki/File:Using_LOD_to_crowdsource_Dutch_WW2_underground_newspapers_on_Wikipedia,_SWIB2016,_29-11-2016.pdf">Using LOD to crowdsource Dutch WW2 underground newspapers on Wikipedia</a></li> <li><a href="https://swib.org/swib16/slides/janssen_using_lod.pdf" rel="nofollow">https://swib.org/swib16/slides/janssen_using_lod.pdf</a></li> <li><a href="https://doi.org/10.5281/zenodo.13132987" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13132987</a></li> </ul>
Evaluation of Envision Rating System for Underground Transportation Infrastructure
<p>Credit-by-credit evaluation of the Envision rating system for representative underground transportation projects. Related paper presented at the 2019 International Conference for Sustainable Infrastructure.</p>
London Underground Performance Data
<p>Raw data on train journey durations for London Underground. Data given by Mango Solutions, London https://www.mango-solutions.com/</p> <p>Shiny app at https://william-gilks.shinyapps.io/LondonUnderground/ requires upload of data from here.</p>
Figure 4 in Review of unique odd chromosome-numbered underground rodent species of the Palearctic region: Ellobius lutescens Thomas 1897 (Rodentia: Cricetidae)
Figure 4. Dorsal (a), ventral (b), and lateral (c) views of cranium and lateral view (d) of mandible of an adult male Ellobius lutescens (Dicle University, Faculty of Science, Department of Biology, Zoology Lab. Mammal collection number 741, from 20 km south of Iğdır, Turkey).
Figure 2 in Review of unique odd chromosome-numbered underground rodent species of the Palearctic region: Ellobius lutescens Thomas 1897 (Rodentia: Cricetidae)
Figure 2. Photograph of an adult Ellobius lutescens from Iğdır, Turkey. Photograph by Y Coşkun, collected on 24 April 2013.
Figure 11 in First researches of the underwater ecosystem communities of an underground channel built in 1950s (Balaklava bay, Sevastopol)
Figure 11. Variabilities of high meiobenthic taxa by their abundance (103 ind./m2) (colored bars) and by their total number (dotted line): A – in the bottom sediments, B – in the biofouling of the channel walls at the depth of 1.5 meters.
Figure 8 in First researches of the underwater ecosystem communities of an underground channel built in 1950s (Balaklava bay, Sevastopol)
Figure 8. Quantitative representation of macrozoobenthos in the channel wall biofouling at a depth of 1.5 m: A – by abundance (103 ind./m2), B – by biomass (g/m2).
Figure 6 in First researches of the underwater ecosystem communities of an underground channel built in 1950s (Balaklava bay, Sevastopol)
Figure 6. Channel wall biofoulings at the point 4: A, B – underwater edge on the northeast wall; C – at the depth of 1.5 m on the northeast wall; D, E – underwater edge on the southeast wall; F – at the depth of 1.5 m on the southeast wall.
Figure 5. A in First researches of the underwater ecosystem communities of an underground channel built in 1950s (Balaklava bay, Sevastopol)
Figure 5. A – General view of the biofouling at the point 3, B – Porifera colonies, C – Mussels druze on the halyard.
Figure 2 in First researches of the underwater ecosystem communities of an underground channel built in 1950s (Balaklava bay, Sevastopol)
Figure 2. Topography of anthropogenic objects in the underground channel on the base of submarines in Balaklava and benthic sampling stations. Topography of anthropogenic objects in the underground channel at the submarine base in Balaklava and the benthos sampling station.
Figure 1 in First researches of the underwater ecosystem communities of an underground channel built in 1950s (Balaklava bay, Sevastopol)
Figure 1. Museum of Military History of Fortifications "Balaklava" in Mount Tavros: A – entrance to the Museum from the side of the Balaklava Bay embankment (photo by A. Zatsepin); B – northern entrance part of the channel from the side of the embankment of the bay; C – southern entrance part of the channel; D, E – internal view of the channel with artificial illumination in its different parts.
Figure 9 in First researches of the underwater ecosystem communities of an underground channel built in 1950s (Balaklava bay, Sevastopol)
Figure 9. The species number ratio of higher taxa in the channel walls macrobiofouling, in comparison with similar data on rocks other areas of the Crimean coast.
Fig. 3 in Use of baits for the evaluation of underground termites (Blattodea: Rhinotermitidae) in different habitats of the southern Amazon region
Fig. 3. Queen of Heterotermes tenuis (center circle) in early stage of egg production (circle on lef), and colony formation inside the cardboard bait in southern Amazonia.
Fig. 2 in Use of baits for the evaluation of underground termites (Blattodea: Rhinotermitidae) in different habitats of the southern Amazon region
Fig. 2. Predominant termites in this study in southern Amazonia: (A) Nasutitermes sp. soldier; (B) Heterotermes tenuis soldier.
Fig. 1 in Use of baits for the evaluation of underground termites (Blattodea: Rhinotermitidae) in different habitats of the southern Amazon region
Fig. 1. Spatial arrangement of Termitrap® baits within plots for subterranean termite survey in different environments in southern Amazonia.
Piezomagnetic Fields Generated by the Gas Injection Process in Hutubi Ultralarge Underground Gas Storage System (China)
<p>All data used in paper "Piezomagnetic Fields Generated by the Gas Injection Process in Hutubi Ultralarge Underground Gas Storage System (China)".</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.