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175 results for “built”
Figs 1–8 in A new Callosides species from Ecuador with uniquely built membranous mandibles (Coleoptera: Hybosoridae: Anaidinae)
Figs 1–8. Callosides mafik sp. nov. 1 – habitus of holotype in dorsal view; 2 – habitus of holotype in lateral view; 3 – left mandible; 4 – detail of pronotal sculpture; 5 – detail of elytral sculpture; 6 – metathoracic wing; 7 – apical part of aedeagus in lateral view; 8 – apical part of aedeagus in dorsal view. Scale bar (Figs 3, 6–8) = 0.5 mm.
Synset-based dataset built by using semantic-based feature reduction techniques.
<p>Dataset was built by applying an enhanced semantic-based feature selection method over to publicly available datasets: SpamAssassin (SA.zip) and Youtube Comments (YT.zip).</p>
Physical state of water controls friction of gabbro-built faults
<p>Experimental data</p> <p>six columns in the CSV. file, from left to right, are: time (s), displacement (mm), friction coefficient, axial displacement (mm), pore pressure (MPa) and temperature (℃). </p>
Fig. 8 in Characteristics of thermal processes in ant nests built under stones (Hymenoptera: Formicidae)
Fig. 8 – Daily temperature changes at characteristic points of the system for thick stone (A, open space; B, penumbra; C, shadow); Same, for thin stone: D, open space; E, penumbra; F, shadow
Fig. 9 in Characteristics of thermal processes in ant nests built under stones (Hymenoptera: Formicidae)
Fig. 9 – Temperature distribution along the height of a thick stone and in the soil under the stone (A, open space; B, penumbra; C, shadow) and for a thin stone (D, open space; E, penumbra; F, shadow).
Fig. 6 in Characteristics of thermal processes in ant nests built under stones (Hymenoptera: Formicidae)
Fig. 6 – Number of workers, larvae and pupae found under stones (A-C, cold; D-F, warm; G-I, hot) during April – August 2021. A, D, G: workers; B, E, H: larvae; C, F, I: pupae.
Fig. 5 in Characteristics of thermal processes in ant nests built under stones (Hymenoptera: Formicidae)
Fig. 5 – Number of workers, larvae and pupae found under stones in the nests of three ant species (A-C, Myrmica rubra; D-F, Lasius niger; G-I, Formica cinerea) during April - August 2021. A, D, G: workers; B, E, H: larvae; C, F, I: pupae. 1-4 indicates the number of week in each month.
Fig. 4 in Characteristics of thermal processes in ant nests built under stones (Hymenoptera: Formicidae)
Fig. 4 – Linear dimensions of stones with Myrmica rubra (M_r), Lasius niger (L_n), Formica cinerea (F_c) nests. D1 and D2 are diameter 1 and 2; H is height. All measures are in cm.
Fig. 1 in Characteristics of thermal processes in ant nests built under stones (Hymenoptera: Formicidae)
Fig. 1 – Place of research at the confluence of the rivers Desna and Lyubich (Litky village, Kyiv region, Ukraine); 1, location of stone 2; 2, location of stones 1, 3-12, 19-20; 3, location of stones 13-18.
Fig. 10 in Characteristics of thermal processes in ant nests built under stones (Hymenoptera: Formicidae)
Fig. 10 – Two-dimensional temperature field of the "thick stone-soil" system for open space: A, time 10-00; B, time 14-00: Temperature scale in °C. The heights of rectangles are: the height above the sole (for the part above the dashed line) and the depth below the soil (for the part below the dashed line).
Fig. 7 in Characteristics of thermal processes in ant nests built under stones (Hymenoptera: Formicidae)
Fig. 7 – Relationship between the number of ants and their brood and the linear dimensions of the stone (average diameter, cm). A, workers in mid- June (R2 = 0.46788, p = 0.00088136); B, workers at the end of July (R2 = 0.27579, p = 0.017422); C, larvae at the end of May (R2 = 0.25457, p = 0.023286); D, pupae in mid-June (R2 = 0.16688, p = 0.073735).
Experimental Database of deterministic wave prediction built from synchronous measurements from an X-band pulse radar and met-ocean sensors deployed on the Floatgen floating wind turbine and its vicinity on SEM-REV test site.
<p>This dataset is a deliverable of the FLOATECH project, funded under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101007142.<br> The aim of this dataset is a result of the field experiments carried out at the Floatgen FOWT located at the SEM-REV test site.</p>
Sandvík 2020, stone built hearth
A stone built corner hearth/oven from a Viking period coastal building in Sandvík, Strandir, northwestern Iceland. See here for more details of the project - https://www.facebook.com/FornarStrandir Source: Objaverse 1.0 / Sketchfab
Implemented Algorithm built and loaded succ. run 1.
Open the record for dataset details and reuse information.
Data repository for " Built-in Bernal gap in large-angle-twisted monolayer-bilayer graphene"
<p>This is the data presented in the manuscript " Built-in Bernal gap in large-angle-twisted monolayer-bilayer graphene", <em>Commun Phys</em> <strong>7</strong>, 391 (2024). https://doi.org/10.1038/s42005-024-01887-0</p>
Paper dataset of "ARAMIS: a Martian radiative environment model built from GEANT4 simulations"
<p>Supplementary materila dataset concerning the article : <a href="https://doi.org/10.1051/swsc/2024032">https://doi.org/10.1051/swsc/2024032 .</a></p> <p>In the version v1, the proton flux where not giving for both cone and total view which is corrected here. Beside there was a wrong upload file for proton_aramis_2015_2016.dat, which has been corrected as well.</p>
Euro-Calliope: Pre-built models
<p>Ready to use models of the European electricity system built for use in <em>Calliope</em>. Models are available on three different spatial resolutions: continental, national, and regional.</p> <p>In addition, Euro-Calliope models can be built manually which adds more configuration options. To learn how to build Euro-Calliope manually, head over to <a href="https://euro-calliope.readthedocs.io">Euro-Calliope’s documentation</a>.</p> <p><strong>At a glance</strong></p> <p>Euro-Calliope models the European electricity system with each location representing an administrative unit. It is built on three spatial resolutions: on the continental level as a single location, on the national level with 34 locations, and on the regional level with 497 locations. At each location, renewable generation capacities (wind, solar, bioenergy) and balancing capacities (battery, hydrogen) can be built. In addition, hydro electricity and pumped hydro storage capacities can be built up to the extent to which they exist today. All capacities are used to satisfy electricity demand on all locations where demand is based on historic data. Locations are connected through transmission lines of either unrestricted capacity or projections. Using <a href="https://www.callio.pe">Calliope</a>, the model is formulated as a linear optimisation problem with total monetary cost of all capacities as the minimisation objective. Due to the flexibility of Calliope and the availability of the routines building the model all components can be adapted to the modeller’s needs.</p> <p><strong>Prepare</strong></p> <ol> <li> <p>Install a Gurobi license on your computer (<a href="https://www.gurobi.com/downloads/end-user-license-agreement-academic/">academic license</a> comes at no cost), or <a href="https://euro-calliope.readthedocs.io/en/latest/model/customisation/#manual-changes">choose a different solver</a>.</p> </li> <li> <p>Install Calliope and all required dependencies. The easiest way to do so is using <a href="https://conda.io/">conda</a> or <a href="https://mamba.readthedocs.io/">mamba</a>. Using conda, you can install Calliope:</p> </li> </ol> <pre><code>cd pre-built-euro-calliope-v1.1.0 conda env create -f environment.yaml conda activate euro-calliope</code></pre> <p><strong>Run</strong></p> <p>There are three models in the directory of the pre-builts – one for each of the three spatial resolutions continental, national, and regional. You can run all three models out-of-the-box, but you may want to modify the model. By default, the model runs for the first day of January only. To run the example model on the continental resolution type:</p> <pre><code>calliope run ./continental/example-model.yaml</code></pre> <p><strong>Customise</strong></p> <p>The pre-built models are examples and very likely require customisation to fit your purpose. Once you’ve managed to run them, it’s a good point in time to learn about <a href="https://euro-calliope.readthedocs.io/en/latest/model/customisation/">model customisation options in Euro-Calliope</a>.</p> <p><strong>More information</strong></p> <p>For more information on Euro-Calliope and how to use and modify the models, see <a href="https://euro-calliope.readthedocs.io">Euro-Calliope’s documentation</a>.</p> <p><strong>License and attribution</strong></p> <p>Euro-Calliope is developed and maintained within the <a href="https://www.callio.pe">Calliope project</a>.<br> <br> This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p> <p>Contains modified Copernicus Atmosphere Monitoring Service information 2020. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.</p> <p>Contains modified data from <a href="https://www.renewables.ninja/">Renewables.ninja</a>.</p> <p>Contains modified data from <a href="https://open-power-system-data.org">Open Power System Data</a>.</p>
Figure 25. Simple apparatus built with a in Neurobiology and vision of jumping spiders (Araneae: Salticidae)
Figure 25. Simple apparatus built with a balloon to represent the eye tube of the right AME, a roll of paper to secure the anterior end of the tube, and attached rubber bands used to model the action of each of the four (1-4) extraocular muscles, as well as the two circular ocular muscles (5, 6). Muscle numbers correspond to those assigned by Land (1969b). By pulling on one or more rubber bands, the likely action of the respective muscles could be observed directly. The action of the circular muscles was more challenging to model, but I was able to simulate their contraction by pulling the ends against a loop in the middle of each band.
National Data Files for Pre-built Sector-coupled Euro-Calliope Model
<p>National time series data derived from the <a href="https://zenodo.org/record/5774988#.YtUQ9-zP3Ph">Sector-coupled Euro-Calliope Pre-built Model</a></p>
AgsSAT Multiannual (2017-2021) Sentinel-2 Built-Up Indices Composites
<p>AgsSAT Multiannual (2017-2021) Sentinel-2 Built-Up Indices Composites</p> <p>The 2,815 images available for the state of Aguascalientes, Mexico for the years 2017 to 2021 were processed using the Open Data Cube (ODC) platform [Lewis et al. (2017), Gavin et al. (2018), <a href="https://www.opendatacube.org/">https://www.opendatacube.org/</a>]. These images correspond to multiple coverages of the region of interest. The images were then used to generate cloud-free annual composites by applying geometric median (geomedian) algorithm, as defined in [Roberts et al. (2017)]. </p> <p>Geomedian algorithm produces a pixel-level summary for every pixel, in this case this means that each summary corresponds to a 10m x 10m region in the territory and its observations throughout a calendar year. </p> <p>All these summary pixels form a 12-band (coastal aerosol, blue, green, red, vegetation red edge 5, vegetation red edge 6, vegetation red edge 7, near-infrared, narrow nir, water vapor, swir1 and swir2) composite of the state of Aguascalientes. </p> <p>Another product called GeoMad was generated, which calculates the robust dispersion statistic called MAD, as defined in [Roberts, D., Dunn, B., & Mueller, N. (2018)]. In the resulting image composite, each of the three-pixel bands represents the variation over three distances: Spectral Distance (smad), Euclidean Distance (emad) and the Bray-Curtis Distance (bcmad). </p> <p>More bands were generated to represent different environmental conditions during the study years (2017-2021), these conditions can be captured by analyzing various combinations of bands, these combinations are also called spectral indices, which allow detecting vegetation, presence of water, urbanization, etc., Finally, 28 indices divided into 4 categories were calculated: </p> <p>Vegetation Indices </p> <p>(Atmospherically Resistant Vegetation Index, Kaufman 1972) </p> <p>(Enhanced Vegetation Index, Huete 2002): </p> <p>(Modified Soil Adjusted Vegetation Index, Qi Et Al. 1994) </p> <p>(Normalized Difference Chlorophyll Index, Mishra & Mishra, 2012) </p> <p>(Normalised Difference Moisture Index, Gao 1996) </p> <p>(Normalized Difference Vegetation Index, Rouse 1973) </p> <p>(Optimized Soil Adjusted Vegetation Index, Rondeaux. 1996) </p> <p>(Simple Ratio Vegetation Index Jordan, C.F.1 969) </p> <p>(Soil Adjusted Vegetation Index, Huete 1988) </p> <p>(Visible Atmospherically Resistant Index, Gittleson 2002) </p> <p> </p> <p>Built-up Indexes </p> <p>(Band Ration For Built-up Area, Waqar 2012) </p> <p>(Built-up Area Extraction Index, Bouzekri 2015) </p> <p>(Built-up Index, He Et Al. 2010) </p> <p>(Index-based Built-up Index, Xu 2008) </p> <p>(New Built-up Index, Jieli Et Al. 2010) </p> <p>(Normalized Difference Built-up Index, Zha 2003) </p> <p>(Normalized Built-up Area Index, Waqar 2012) </p> <p>(Urban Index, Kawamura 1996) </p> <p> </p> <p>Water Indices </p> <p>(Modified Normalized Difference Water Index, Xu 1996) </p> <p>(Normalized Difference Water Index, Mcfeeters 1996) </p> <p>(Water Index, Fisher 2016) </p> <p> </p> <p>Other Indices </p> <p>(Bare Soil Index, Rikimaru Et Al. 2002) </p> <p>(Bare Soil Index, Wanhui 2004) </p> <p>(Burn Area Index, Martin 1998) </p> <p>(Clay Minerals Ratio, Drury 1987) </p> <p>(Ferrous Minerals Ratio, Segal 1982) </p> <p>(Iron Oxide Ratio, Segal 1982) </p> <p>(Normalized Burn Ratio, Lopez Garcia 1991) </p> <p>(Normalised Difference Snow Index, Hall 1995). </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.