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886 results for “Kazakhstan”
FIG. 9 in Horse sacrifice in a Pazyryk culture kurgan: the princely tomb of Berel'(Kazakhstan). Selection criteria and slaughter procedures
FIG. 9. — Representation of a mounted, horned horse, in front of which is a person applying the metallic part of an axe to the extremity of the animal's nose: Tamgaly III n°23 (Kazakhstan — photo H.-P. Francfort, with his kind permission).
FIG. 8 in Horse sacrifice in a Pazyryk culture kurgan: the princely tomb of Berel'(Kazakhstan). Selection criteria and slaughter procedures
FIG. 8. — Observation of traces of blows to the skulls on the Berel' horses; an example of a Kurgan skull n°18 (on the left); schematic reconstruction of the slaying of a harnessed horse decorated with fake ibex horns (in the middle); axe (upper photo, Mongolian Altai, photo from T. Turbat with his kind permission).
FIG. 3 in Horse sacrifice in a Pazyryk culture kurgan: the princely tomb of Berel'(Kazakhstan). Selection criteria and slaughter procedures
FIG. 3. — The funerary chamber and the monoxylous casket. In the foreground, under the plastic, is the zone reserved for the horses (photo S. Lepetz).
FIG. 4 in Horse sacrifice in a Pazyryk culture kurgan: the princely tomb of Berel'(Kazakhstan). Selection criteria and slaughter procedures
FIG. 4. — The heap of organic material (muscle, skin, fur); Heads of horses « A » (in front) and « C » (behind). Harnessing elements in gold-plated wood are visible (photo S. Lepetz).
FIG. 5 in Horse sacrifice in a Pazyryk culture kurgan: the princely tomb of Berel'(Kazakhstan). Selection criteria and slaughter procedures
FIG. 5. — Position of horses in upper and lower levels (drawing of funerary chamber: A. Cornet; drawing of horses: S. Lepetz; CAD: S. Lepetz).
Figs 7–11 in Contribution to the knowledge of Pselaphinae (Coleoptera: Staphylinidae) from Kazakhstan
Figs 7–11. Euplectus baizakovi sp. nov. 7 – mesoventrite and metaventrite, male (lmcf – lateral metacoxal fovea); 8 – abdomen dorsal, male (mbf – mediobasal foveae, lptf – lateral paratergal fovea); 9 – abdomen ventral, male (blf – basolateral fovea, md – median depression); 10 – abdomen ventral, female (blf – basolateral fovea); 11 – metatarsus, male.
Figs 1–6 in Contribution to the knowledge of Pselaphinae (Coleoptera: Staphylinidae) from Kazakhstan
Figs 1–6. Euplectus baizakovi sp. nov., male. 1 – head dorsal (ff – frontal fovea, oc – occipital carina, vf – vertexal fovea), 2 – head ventral (gf – gular foveae), 3 – left antenna, 4 – pronotum and base of elytra (bef – basal elytral fovea, lpf – lateral pronotal fovea), 5 – left elytron (bef – basal elytral fovea, ds – discal stria, shef – subhumeral elytral fovea, ss – sutural stria), 6 – prosternum and mesoventrite (apsf – anteroprosternal fovea, lmsf – lateral mesoventral fovea, lpcf – lateral procoxal fovea, mmsf – medial mesoventral fovea)
Figs 6-11 in On some Palaearctic Xantholinini, primarily from Kazakhstan (Coleoptera: Staphylinidae: Staphylininae)
Figs 6-11: Atopolinus subnigroaeneus (COIFFAIT) (6-8) and Medhiama schawalleri BORDONI (9- 11): (6, 9) habitus; (7, 10) forebody; (8, 11) aedeagus. Scale bars: 6-7, 9-10: 1.0 mm; 8, 11: 0.5 mm.
Figs 1-5 in On some Palaearctic Xantholinini, primarily from Kazakhstan (Coleoptera: Staphylinidae: Staphylininae)
Figs 1-5: Vulda afghanica (COIFFAIT): (1) habitus; (2) forebody; (3) head; (4) aedeagus; (5) distal portion of aedeagus. Scale bars: 1-2: 1.0 mm; 3: 0.5 mm; 4: 0.2 mm; 5: 0.1 mm.
FIG. 4 in Statistical comparisons of late Caradoc (Ordovician) brachiopod faunas around the Iapetus Ocean, and terranes located around Australia, Kazakhstan and China
FIG. 4. — Proposed palaeogeographic reconstruction; Kazakh terranes: 1, Altai-Sayan; 2, Chu-Ili; 3, Chingiz; limits of terranes unknown. Scale bar: 4000 km.
FIG. 3 in Statistical comparisons of late Caradoc (Ordovician) brachiopod faunas around the Iapetus Ocean, and terranes located around Australia, Kazakhstan and China
FIG. 3. — Assemblage scores on axes 1 and 2 of detrended correspondence analysis (DCA) based on presence/absence of genera. Key: same as Figure 2.
FIG. 2 in Statistical comparisons of late Caradoc (Ordovician) brachiopod faunas around the Iapetus Ocean, and terranes located around Australia, Kazakhstan and China
FIG. 2. — Cluster analysis on presence/absence data of 27 localities using 173 genera; UPGMA, Dice Index of similarity (values are displayed on the vertical axis). Data: see Appendix. Shaded areas represent the five clusters numbered 1 to 5. Key for assemblage: see Appendix.
Figs 1, 2 in Two new species of the genus Macrosiphoniella Del Guercio, 1911 (Hemiptera, Aphidomorpha: Aphididae) inhabits yarrow in Kazakhstan
Figs 1, 2. Macrosiphoniella (Macrosiphoniella) kazakhstanica Kadyrbekov, Tleppaeva et Kolov, sp. n. 1 – head with rostrum; 2 – abdomen with siphunculi and cauda.
Figs 3–6 in Two new species of the genus Macrosiphoniella Del Guercio, 1911 (Hemiptera, Aphidomorpha: Aphididae) inhabits yarrow in Kazakhstan
Figs 3–6. Macrosiphoniella (Macrosiphoniella) sarmatica Kadyrbekov, Tleppaeva et Kolov, sp. n. 1 – frons; 2 – third and forth antennal segments; 3 – ultimate rostral segment;
A multispectral UAV Imagery dataset of wheat, soybean, and barley crops in East Kazakhstan
<p>This study introduces a dataset of crop imagery captured during the 2022 growing season in the Eastern Kazakhstan region. The images were acquired using a multispectral camera mounted on an unmanned aerial vehicle (DJI Phantom 4). The agricultural land, encompassing 27 hectares and cultivated with wheat, barley, and soybean, was subjected to five aerial multispectral photography sessions throughout the growing season. This facilitated thorough monitoring of the most important phenological stages of crop development in the experimental design, which consisted of 27 plots, each covering one hectare. The collected imagery underwent enhancement and expansion, integrating a sixth band that embodies the normalized difference vegetation index (NDVI) values, in conjunction with the original five multispectral bands (Red, Green, Blue, Infrared, and Near Infrared). This amplification enables a more effective evaluation of vegetation health and growth, rendering the enriched dataset a valuable resource for the progression and validation of crop monitoring and yield prediction models, as well as for the exploration of precision agriculture methodologies.</p>
A multispectral UAV Imagery dataset of wheat, soybean, and barley crops in East Kazakhstan
<p>This study introduces a dataset of crop imagery captured during the 2022 growing season in the Eastern Kazakhstan region. The images were acquired using a multispectral camera mounted on an unmanned aerial vehicle (DJI Phantom 4). The agricultural land, encompassing 27 hectares and cultivated with wheat, barley, and soybean, was subjected to five aerial multispectral photography sessions throughout the growing season. This facilitated thorough monitoring of the most important phenological stages of crop development in the experimental design, which consisted of 27 plots, each covering one hectare. The collected imagery underwent enhancement and expansion, integrating a sixth band that embodies the normalized difference vegetation index (NDVI) values, in conjunction with the original five multispectral bands (Blue, Green, Red, Red Edge and Near Infrared Red). This amplification enables a more effective evaluation of vegetation health and growth, rendering the enriched dataset a valuable resource for the progression and validation of crop monitoring and yield prediction models, as well as for the exploration of precision agriculture methodologies.</p>
A multispectral UAV Imagery dataset of wheat, soybean, and barley crops in East Kazakhstan
<p>This study introduces a dataset of crop imagery captured during the 2022 growing season in the Eastern Kazakhstan region. The images were acquired using a multispectral camera mounted on an unmanned aerial vehicle (DJI Phantom 4). The agricultural land, encompassing 27 hectares and cultivated with wheat, barley, and soybean, was subjected to five aerial multispectral photography sessions throughout the growing season. This facilitated thorough monitoring of the most important phenological stages of crop development in the experimental design, which consisted of 27 plots, each covering one hectare. The collected imagery underwent enhancement and expansion, integrating a sixth band that embodies the normalized difference vegetation index (NDVI) values, in conjunction with the original five multispectral bands (Blue, Green, Red, Red Edge and Near Infrared Red). This amplification enables a more effective evaluation of vegetation health and growth, rendering the enriched dataset a valuable resource for the progression and validation of crop monitoring and yield prediction models, as well as for the exploration of precision agriculture methodologies.</p>
A multispectral UAV Imagery dataset of wheat, soybean, and barley crops in East Kazakhstan
<p>This study introduces a dataset of crop imagery captured during the 2022 growing season in the Eastern Kazakhstan region. The images were acquired using a multispectral camera mounted on an unmanned aerial vehicle (DJI Phantom 4). The agricultural land, encompassing 27 hectares and cultivated with wheat, barley, and soybean, was subjected to five aerial multispectral photography sessions throughout the growing season. This facilitated thorough monitoring of the most important phenological stages of crop development in the experimental design, which consisted of 27 plots, each covering one hectare. The collected imagery underwent enhancement and expansion, integrating a sixth band that embodies the normalized difference vegetation index (NDVI) values, in conjunction with the original five multispectral bands (Blue, Green, Red, Red Edge and Near Infrared Red). This amplification enables a more effective evaluation of vegetation health and growth, rendering the enriched dataset a valuable resource for the progression and validation of crop monitoring and yield prediction models, as well as for the exploration of precision agriculture methodologies.</p>
Рис. 2–6. ÀетаΛи строения пауков семейства Gnaphosidae. 2 – Gnaphosa cumensis; 3 – G. cf.cumensis; 4 – Haplodrassus rugosus; 5–6 – Zelotes fuscus. 2–4 – паΛьпа самца, вентраΛьно; 5 – эпигина, вентраΛьно; 6 – эпигина, ΑорсаΛьно. Масштабные Λинейки 0.2 мм. Figs 2–6. Family Gnaphosidae, details of structure. 2 – Gnaphosa cumensis; 3 – G. cf. cumensis; 4 – Haplodrassus rugosus; 5–6 – Zelotes fuscus. 2–4 – male palp, ventral view; 5 – epigyne, ventral view; 6 – epigyne, dorsal view. Scale bars 0.2 mm. in New data on spiders (Aranei) of the Naurzum State Natural Reserve (Kostanay Region, Kazakhstan)
Рис. 2–6. ÀетаΛи строения пауков семейства Gnaphosidae. 2 – Gnaphosa cumensis; 3 – G. cf.cumensis; 4 – Haplodrassus rugosus; 5–6 – Zelotes fuscus. 2–4 – паΛьпа самца, вентраΛьно; 5 – эпигина, вентраΛьно; 6 – эпигина, ΑорсаΛьно. Масштабные Λинейки 0.2 мм. Figs 2–6. Family Gnaphosidae, details of structure. 2 – Gnaphosa cumensis; 3 – G. cf. cumensis; 4 – Haplodrassus rugosus; 5–6 – Zelotes fuscus. 2–4 – male palp, ventral view; 5 – epigyne, ventral view; 6 – epigyne, dorsal view. Scale bars 0.2 mm.
Рис. 1. Наурзумский заповеΑник и его распоΛожение в Костанайской обΛасти (Казахстан). Fig. 1. Naurzum Reserve and its location in the Kostanay Region (Kazakhstan). in New data on spiders (Aranei) of the Naurzum State Natural Reserve (Kostanay Region, Kazakhstan)
Рис. 1. Наурзумский заповеΑник и его распоΛожение в Костанайской обΛасти (Казахстан). Fig. 1. Naurzum Reserve and its location in the Kostanay Region (Kazakhstan).
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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)
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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.