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6,157 results for “knowledge”
Figure 7 in Improving knowledge of the cyclorrhaphan larva (Diptera)
Figure 7. Coelopa frigida (Coleophidae), third stage preserved larva, lateral view, head to the right, from the 2nd abdominal segment to the pseudocephalon, showing distribution of black spicules, length 5 mm.
Figure 5 in Improving knowledge of the cyclorrhaphan larva (Diptera)
Figure 5. Cibarial ridges and head skeleton pump in the basal sclerites of cyclorrhaphan larvae, mandibles to the left. a, head skeleton in situ, third stage larva of Alipumilio femoratus Shannon (Diptera, Syrphidae), ventro-lateral view, 1 = mandibles, 2 = cibarial ridges on the floor of the basal sclerite; b, Eumerus sp. Meigen (Syrphidae), lateral view, 3 = pharynx running along the floor of the basal sclerite, 4 = valve at the rear of the upturned ventral cornu, 5 = bands of muscle originating on the dorsal cornu and inserting on the roof of the pharynx and the ventral cornu that operate the pump, specimen prepared and donated by C.J. Hartley to the National Museums, Scotland.
Figure 4 in Improving knowledge of the cyclorrhaphan larva (Diptera)
Figure 4. Head skeletons of exemplar saprophages, lateral view, mandible towards centre; figures in parentheses are lengths of the basal sclerites in mm. a, Neophyllomyza acyglossa (Milichiidae) (0.2); b, Leucophora personata (Collin) (Anthomyiidae) (0.4); c, Chyromya femorellum (Fallén) (Chyromyidae) (0.4); d, Strongylophthalmyia ustulata (Zetterstedt) (Strongylophthalmyiidae) (0.4); e, Lonchaea nitens (Lonchaeidae) (0.3); f, Meoneura lamellata Collin (Carnidae) (0.2); g, Stegana coleoptrata (Drosophilidae) (0.4); h, Camilla fuscipes Collin (Camillidae) (0.3); i, Suillia variegata (Loew) (Heleomyzidae) (0.5); j, Lonchaea sylvatica (Lonchaeidae) (0.3).
Figs 22-23 in Contribution to the knowledge of the genus Quedius S , 1829 of Siberia and Russian Far East (Coleoptera: Staphylinidae: Staphylinini: Quediina)
Figs 22-23: (22) Type locality of Quedius conviva nov.sp.; (23) Type locality of Quedius amurensis nov.sp.
CoDEx: A Comprehensive Knowledge Graph Completion Benchmark
<p>This repository hosts the <strong>relational-only part</strong> of the CoDEx benchmark, which was presented at the EMNLP 2020 conference. You can access the paper <a href="https://www.aclweb.org/anthology/2020.emnlp-main.669.pdf">here</a> and the full dataset, including text and pretrained models, <a href="https://bit.ly/2EPbrJs">on GitHub</a>.</p> <p>Abstract:</p> <p><em>We present CoDEx, a set of knowledge graph completion datasets extracted from Wikidata and Wikipedia that improve upon existing knowledge graph completion benchmarks in scope and level of difficulty. In terms of scope, CoDEx comprises three knowledge graphs varying in size and structure, multilingual descriptions of entities and relations, and tens of thousands of hard negative triples that are plausible but verified to be false. To characterize CoDEx, we contribute thorough empirical analyses and benchmarking experiments. First, we analyze each CoDEx dataset in terms of logical relation patterns. Next, we report baseline link prediction and triple classification results on CoDEx for five extensively tuned embedding models. Finally, we differentiate CoDEx from the popular FB15K-237 knowledge graph completion dataset by showing that CoDEx covers more diverse and interpretable content, and is a more difficult link prediction benchmark. Data, code, and pretrained models are available <a href="https://bit.ly/2EPbrJs">here</a>.</em></p>
Dataset: Cybersecurity Knowledge and Skills Taught in Capture the Flag Challenges
<p>This repository contains supplementary materials for the following journal paper:</p> <p>Valdemar Švábenský, Pavel Čeleda, Jan Vykopal, Silvia Brišáková.<br> <em>Cybersecurity Knowledge and Skills Taught in Capture the Flag Challenges.</em><br> In Elsevier Computers & Security. 2020.<br> <a href="https://doi.org/10.1016/j.cose.2020.102154">https://doi.org/10.1016/j.cose.2020.102154</a></p> <p>Preprint available at: <a href="https://arxiv.org/abs/2101.01421">https://arxiv.org/abs/2101.01421</a></p> <p><strong>How to cite</strong></p> <p>If you use or build upon the materials, please use the BibTeX entry below to cite the original paper (not only this web link).</p> <pre><code>@article{Svabensky2020cybersecurity, author = {\v{S}v\'{a}bensk\'{y}, Valdemar and \v{C}eleda, Pavel and Vykopal, Jan and Bri\v{s}\'{a}kov\'{a}, Silvia}, title = {{Cybersecurity Knowledge and Skills Taught in Capture the Flag Challenges}}, journal = {Computers \& Security}, publisher = {Elsevier}, volume = {102}, year = {2020}, issn = {0167-4048}, url = {https://www.sciencedirect.com/science/article/pii/S0167404820304272}, doi = {10.1016/j.cose.2020.102154}, }</code></pre> <p><strong>Attached content</strong></p> <p>See the README.md file inside the attached ZIP file for more details.</p>
The Choice is Yours? How Algorithm Bias Impacts Fairness and Accessibility of Knowledge
<p><strong>Episode Summary</strong></p> <p>In this episode we talked about 'almighty' algorithms with Carlos Castillo, Lorenzo Porcaro, Marzieh Karimihaghighi, David Solans, and Francesco Fabbri from the Web Science & Social Computing Research Group, and the department of Engineering in Information & Communication Technologies, in Universitat Pompeu Fabra in Barcelona. We discussed how bias can enter into algorithm systems, how bias is measured, and what systems are impacted by it. </p> <p><strong>Episode Links</strong></p> <p><a href="https://www.upf.edu/web/wssc/">Web Science and Social Computing Research Group</a></p> <ul> <li><a href="https://www.upf.edu/web/etic/entry/-/-/24095/adscripcion/carlos-alberto-alejandro-castillo">Carlos Castillo</a></li> <li><a href="https://www.linkedin.com/in/marzieh-karimihaghighi-706b5554/?originalSubdomain=ir">Marzieh Karimihaghighi</a></li> <li><a href="https://www.linkedin.com/in/david-solans-noguero-48269b85/?originalSubdomain=es">David Solans</a></li> <li><a href="https://www.linkedin.com/in/francesco-fabbri/?originalSubdomain=it">Francesco Fabbri</a></li> <li><a href="https://www.linkedin.com/in/lorenzo-porcaro-7a8792b1/?originalSubdomain=es">Lorenzo Porcaro</a></li> </ul>
Data Set Knowledge Graph (DSKG)
<p>We present the <strong>Data Set Knowledge Graph (<a href="http://dskg.org">DSKG.org</a>)</strong>, an <strong>RDF</strong> <strong>dataset about datasets </strong>that are <strong>linked to publications</strong> (modeled in the Microsoft Academic Knowledge Graph, MAKG) that mention the datasets. The metadata of the datasets is based on datasets that are registered in <strong>OpenAIRE</strong> and <strong>Wikidata</strong>.</p> <p><strong>What exactly do we provide?</strong></p> <ol> <li>Periodically updated <strong><a href="http://dskg.org">RDF dump files</a></strong> of the Data Set Knowledge Graph.</li> <li><strong><a href="http://dskg.org">URI resolution</a></strong> of the Data Set Knowledge Graph within the Linked Open Data.</li> <li>A publicly accessible <strong><a href="http://dskg.org">SPARQL endpoint</a></strong> containing the latest Dataset Knowledge Graph data.</li> </ol> <p><strong>How big is the Dataset Knowledge Graph?</strong></p> <p>The <a href="http://dskg.org">Dataset Knowledge Graph</a> models, among others,</p> <ul> <li>2,208 datasets from all scientific disciplines</li> <li>813,551 links to 634,803 unique papers</li> <li>1,169 authors of datasets</li> <li>208 ORCID IDs.</li> </ul> <p><strong>Potential use cases:</strong></p> <ul> <li>Use the DSKG for the development of semantic search engines (e.g. use the metadata of the linked publications of the datasets for advanced search capabilities)</li> <li>Easier data integration by using the RDF standard vocabulary DCAT and by linking resources to other data sources (e.g., combining the DSKG with other dataset collections in RDF).</li> <li>Data analysis to measure and award the provisioning of datasets (e.g., determine the scientific influence of datasets and authors).</li> </ul>
Figure 5 Austrocarabodes parapustulatus Mahunka, 2009 in Contribution to the knowledge of oribatid mites of the genus Austrocarabodes (Acari, Oribatida, Carabodidae) of Madagascar
Figure 5 Austrocarabodes parapustulatus Mahunka, 2009, adult: a – dorsal view; b – ventral view (legs omitted); c – lateral view (legs omitted). Scale bar 100 μm.
Figure 4 in Contribution to the knowledge of oribatid mites of the genus Austrocarabodes (Acari, Oribatida, Carabodidae) of Madagascar
Figure 4 Austrocarabodes madagascarensisn. sp., adult, SEM micrographs: a – anterior view; b – posterior view; c – rostral seta; d – bothridial seta, humeral process, some notogastral setae and part of sejugal region, dorsal view; e – part of anoadanal region; f – gnathosoma, ventral view; g – gnathosoma and anterolateral part of prodorsum, lateral view; h – bothridial seta, bothridium, humeral process, some notogastral setae and sejugal region, lateral view. Scale bar 100 μm (a), scale bar 50 μm (b), scale bar 20 μm (c–h).
Figure 1 Austrocarabodes madagascarensis n in Contribution to the knowledge of oribatid mites of the genus Austrocarabodes (Acari, Oribatida, Carabodidae) of Madagascar
Figure 1 Austrocarabodes madagascarensis n. sp., adult: a – dorsal view; b – ventral view (legs omitted); c – lateral view (gnathosoma and legs omitted). Scale bar 100 μm.
Figure 8 in Contribution to the knowledge of oribatid mites of the genus Austrocarabodes (Acari, Oribatida, Carabodidae) of Madagascar
Figure 8 Austrocarabodes planisetus Mahunka and Mahunka-Papp, 2011, adult: a – dorsal view; b – ventral view (legs omitted); c – lateral view (legs omitted). Scale bar 100 μm.
Figure 3 Austrocarabodes madagascarensis n in Contribution to the knowledge of oribatid mites of the genus Austrocarabodes (Acari, Oribatida, Carabodidae) of Madagascar
Figure 3 Austrocarabodes madagascarensis n. sp., adult, SEM micrographs: a – dorsal view; b – ventral view; c – lateral view; d – rostral and lamellar setae and distal part of lamella, dorsoanterior view. Scale bar 100 μm (a–c), scale bar 20 μm (d).
Figure 6 Austrocarabodes parapustulatus Mahunka, 2009 in Contribution to the knowledge of oribatid mites of the genus Austrocarabodes (Acari, Oribatida, Carabodidae) of Madagascar
Figure 6 Austrocarabodes parapustulatus Mahunka, 2009, adult, SEM micrographs: a – dorsal view; b – ventral view; c – lateral view; d – rostral and lamellar setae and distal part of lamella, dorsoanterior view. Scale bar 100 μm (a–c), scale bar 20 μm (d).
Figure 7 Austrocarabodes parapustulatus Mahunka, 2009 in Contribution to the knowledge of oribatid mites of the genus Austrocarabodes (Acari, Oribatida, Carabodidae) of Madagascar
Figure 7 Austrocarabodes parapustulatus Mahunka, 2009, adult, SEM micrographs: a – dorsoanterior view; b – dorsoposterior view; c – bothridial seta and humeral process, dorsal view; d – some notogastral setae, dorsal view; e – bothridial seta, bothridium, humeral process, some notogastral setae and sejugal region, lateral view. Scale bar 100 μm (a, b), scale bar 20 μm (c–e).
Figure 2 Austrocarabodes madagascarensis n in Contribution to the knowledge of oribatid mites of the genus Austrocarabodes (Acari, Oribatida, Carabodidae) of Madagascar
Figure 2 Austrocarabodes madagascarensis n. sp., adult: a – leg I, left, paraxial view; b – femur, genu and tibia of leg II, right, antiaxial view; c – leg III, without tarsus, left, antiaxial view; d – leg IV, left, antiaxial view; e – subcapitulum, ventral view; f – palp, right, antiaxial view; g – chelicera, right, antiaxial view. Scale bar 20 μm (a–e; g), scale bar 10 μm (f).
Figure 9 in Contribution to the knowledge of oribatid mites of the genus Austrocarabodes (Acari, Oribatida, Carabodidae) of Madagascar
Figure 9 Austrocarabodes planisetus Mahunka and Mahunka-Papp, 2011, adult, SEM micrographs: a – dorsal view; b – ventral view; c – lateral view; d – rostral, lamellar and interlamellar setae, dorsal view. Scale bar 100 μm (a–c), scale bar 50 μm (d).
Figure 10 in Contribution to the knowledge of oribatid mites of the genus Austrocarabodes (Acari, Oribatida, Carabodidae) of Madagascar
Figure 10 Austrocarabodes planisetus Mahunka and Mahunka-Papp, 2011, adult, SEM micrographs: a – dorsoanterior view; b – interlamellar and bothridial and some notogastral setae, dorsoanterior view; c – bothridial seta and notogastral seta, dorsal view; d – anterior part of body, ventral view; e – gnathosoma and medioanterior part of leg tarsus I, lateral view; f – part of anoadanal region. Scale bar 100 μm (a), scale bar 50 μm (d), scale bar 20 μm (b, c, e, f).
Figure 13 in Additional contributions to the knowledge of Philippine predatory mites mainly of the subfamilies Cunaxinae and Cunaxoidinae (Acari: Prostigmata: Cunaxidae)
Figure 13 Scutopalus clavatus (Shiba), female: a – leg I; b – leg II; c – leg III (trochanter to genu);
Figure 11 in Additional contributions to the knowledge of Philippine predatory mites mainly of the subfamilies Cunaxinae and Cunaxoidinae (Acari: Prostigmata: Cunaxidae)
Figure 11 Scutopalus clavatus (Shiba), female: a – palp; b – subcapitulum; c – chelicera. Scale bar 50 µm.
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.