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3,421 results for “Amazon”
Fig. 7 in Xiphocentronidae (Trichoptera: Psychomyioidea) from the Andean foothills: first species of Machairocentron and Xiphocentron described in the Peruvian Amazon
Fig. 7. Xiphocentron matsigenka sp. nov., holotype, male genitalia (MUSM-ENT-0320566). A. Lateral view, with detail of paraproct. B. Dorsal view. C. Ventral view. D. Phallus, dorsal and lateral views, respectively. E. Phallus in full length, dorsal view.
Fig. 6 in Xiphocentronidae (Trichoptera: Psychomyioidea) from the Andean foothills: first species of Machairocentron and Xiphocentron described in the Peruvian Amazon
Fig. 6. Xiphocentron harakbut sp. nov.,holotype, male genitalia (MUSM-ENT-0320564). A. Lateral view, with detail of paraproct. B. Dorsal view. C. Ventral view. D. Phallus, dorsal and lateral views, respectively. E. Left inferior appendage, photograph, lateroventral view.
Tweets acerca dos imaginários algorítmicos na venda de livros pela Amazon
<p>Esta planilha compõe a metodologia da pesquisa de mestrado intitulada: Imaginários Algorítmicos e do ciclo de sedução da Amazon: um estudo de caso sobre a mediação algorítmica e a venda de livros na plataforma. Com base em sua elaboração, analisamos 1.065 tweets coletados, durante um período de três meses (04/02/2022 a 04/05/2022), com o objetivo de mapear os sentidos produzidos pelos usuários, no contexto de interação com a plataforma, e os imaginários que decorrem deles.</p> <p>Para tanto, elaboramos quatro categorias de análise, seguindo o proposto pela Análise de Conteúdo de Bardin (2016). São elas: 1) mobilização do imaginário por meio dos sentidos; 2) mobilização do imaginário durante a relação de consumo com a <em>Amazon</em>; 3) reconhecimento pelo usuário das ações algorítmicas; 4) referente a lógica de atuação da <em>Amazon</em> na venda de livros.<br> <br> A tabela é composta por 14 colunas, separadas por letras do alfabeto. A) refere-se ao tweet ID; B) a identificação da conta do usuário do Twitter; C) o texto elaborado pelo usuário; D) Categoria 01; E) Categoria 02; F) Categoria 03; G) Categoria 04; H) Tipo de tweet (compõe os imaginários, perfis de vendas ou outros assuntos que divergem do foco da pesquisa); I) Tweets repetidos que também compõem os perfis de vendas e afiliados da Amazon; J) Retweets; K) replys (R = respostas e M= menções; L) data e hora dos posts; M) idioma; N) número de seguidores dos usuários. </p>
L1A - Discrete airborne LiDAR transects collected by EBA in the Brazilian Amazon (Mato Grosso, Amazonas e Pará)
<p>In two campaigns (2016/2017 and 2017/2018), we collected LiDAR transects across the Brazilian Amazon. Some transects were randomly distributed over the forest and secondary forest, some were randomly distributed over the deforestation arch, and others overlapped field plots to allow for model calibration. Each transect covered a minimum of 375 hectares (12.5 km x 300 m) and was surveyed by emitting full-waveform laser pulses from a Trimble Harrier 68i airborne sensor (Trimble; Sunnyvale, CA) aboard a Cessna aircraft (model 206). The average point density was set at four returns per m², the field of view was 30°, the flying altitude was 600 m, and the transect width on the ground was approximately 494 m. Global Navigation Satellite System (GNSS) data were collected on a dual-frequency receiver (L1/L2). The pulse footprint was below 30 cm, based on a divergence angle between 0.1 and 0.3 milliradians. Horizontal and vertical accuracy were controlled to be under 1 m and 0.5 m, respectively.</p> <p>We used the PRODES forest mask (2015) and secondary vegetation (forest regrown after complete forest clearing) from TerraClass (2014) to distribute the transects. To calibrate and validate the airborne LiDAR predictions of biomass, we intentionally overlapped some transects with field plots from 15 research partners. In 2017/2018, we complemented the expanded the transects survey improving the representation of secondary forest based on TerraClass (INPE, 2014). To calibrate and validate the airborne LiDAR predictions of biomass. The metadata about each transect is included in the shapefile hosted at Zenodo repository (<a href="https://doi.org/10.5281/zenodo.4968706">https://doi.org/10.5281/zenodo.4968706</a>).</p> <p>To position the transects, we randomly generated center points with X, Y coordinates and assigned a random alpha slope angle to each point. We visually inspected the start points to ensure they were within the forest or secondary vegetation mask. If the start point was not entirely within a forest, as seen by satellite image, we discarded the seed point and selected another one. For each point, we created a shapefile with a 12.5 km x 300 m polygon. For both campaigns, if there were any conflicts with the flight plan (e.g., proximity to an airport or military restrictions), the company making the flights requested repositioning it to the closest allowed area.</p> <p>This deposit delivers data from Mato Grosso (1 zip file), Amazonas (2 zip files) and Pará (8 zip files).</p>
L1A - Discrete airborne LiDAR transects collected by EBA in the Brazilian Amazon (Roraima e Amapá)
<p>In two campaigns (2016/2017 and 2017/2018), we collected LiDAR transects across the Brazilian Amazon. Some transects were randomly distributed over the forest and secondary forest, some were randomly distributed over the deforestation arch, and others overlapped field plots to allow for model calibration. Each transect covered a minimum of 375 hectares (12.5 km x 300 m) and was surveyed by emitting full-waveform laser pulses from a Trimble Harrier 68i airborne sensor (Trimble; Sunnyvale, CA) aboard a Cessna aircraft (model 206). The average point density was set at four returns per m², the field of view was 30°, the flying altitude was 600 m, and the transect width on the ground was approximately 494 m. Global Navigation Satellite System (GNSS) data were collected on a dual-frequency receiver (L1/L2). The pulse footprint was below 30 cm, based on a divergence angle between 0.1 and 0.3 milliradians. Horizontal and vertical accuracy were controlled to be under 1 m and 0.5 m, respectively.</p> <p>We used the PRODES forest mask (2015) and secondary vegetation (forest regrown after complete forest clearing) from TerraClass (2014) to distribute the transects. To calibrate and validate the airborne LiDAR predictions of biomass, we intentionally overlapped some transects with field plots from 15 research partners. In 2017/2018, we complemented the expanded the transects survey improving the representation of secondary forest based on TerraClass (INPE, 2014). To calibrate and validate the airborne LiDAR predictions of biomass. The metadata about each transect is included in the shapefile hosted at Zenodo repository (<a href="https://doi.org/10.5281/zenodo.4968706">https://doi.org/10.5281/zenodo.4968706</a>).</p> <p>To position the transects, we randomly generated center points with X, Y coordinates and assigned a random alpha slope angle to each point. We visually inspected the start points to ensure they were within the forest or secondary vegetation mask. If the start point was not entirely within a forest, as seen by satellite image, we discarded the seed point and selected another one. For each point, we created a shapefile with a 12.5 km x 300 m polygon. For both campaigns, if there were any conflicts with the flight plan (e.g., proximity to an airport or military restrictions), the company making the flights requested repositioning it to the closest allowed area.</p> <p>This deposit delivers data from Amapá (1 zip file) and Roraima (1 zip file).</p>
L1A - Discrete airborne LiDAR transects collected by EBA in the Brazilian Amazon (Acre e Rondônia)
<p>In two campaigns (2016/2017 and 2017/2018), we collected LiDAR transects across the Brazilian Amazon. Some transects were randomly distributed over the forest and secondary forest, some were randomly distributed over the deforestation arch, and others overlapped field plots to allow for model calibration. Each transect covered a minimum of 375 hectares (12.5 km x 300 m) and was surveyed by emitting full-waveform laser pulses from a Trimble Harrier 68i airborne sensor (Trimble; Sunnyvale, CA) aboard a Cessna aircraft (model 206). The average point density was set at four returns per m², the field of view was 30°, the flying altitude was 600 m, and the transect width on the ground was approximately 494 m. Global Navigation Satellite System (GNSS) data were collected on a dual-frequency receiver (L1/L2). The pulse footprint was below 30 cm, based on a divergence angle between 0.1 and 0.3 milliradians. Horizontal and vertical accuracy were controlled to be under 1 m and 0.5 m, respectively.</p> <p>We used the PRODES forest mask (2015) and secondary vegetation (forest regrown after complete forest clearing) from TerraClass (2014) to distribute the transects. To calibrate and validate the airborne LiDAR predictions of biomass, we intentionally overlapped some transects with field plots from 15 research partners. In 2017/2018, we complemented the expanded the transects survey improving the representation of secondary forest based on TerraClass (INPE, 2014). To calibrate and validate the airborne LiDAR predictions of biomass. The metadata about each transect is included in the shapefile hosted at Zenodo repository (<a href="https://doi.org/10.5281/zenodo.4968706">https://doi.org/10.5281/zenodo.4968706</a>).</p> <p>To position the transects, we randomly generated center points with X, Y coordinates and assigned a random alpha slope angle to each point. We visually inspected the start points to ensure they were within the forest or secondary vegetation mask. If the start point was not entirely within a forest, as seen by satellite image, we discarded the seed point and selected another one. For each point, we created a shapefile with a 12.5 km x 300 m polygon. For both campaigns, if there were any conflicts with the flight plan (e.g., proximity to an airport or military restrictions), the company making the flights requested repositioning it to the closest allowed area.</p> <p>This deposit delivers data from Acre (1 zip file) and Rondônia (1 zip file).</p>
FIG. 19 in Two new genera of Acanthopidae (Mantodea) from the Amazon region, with description of a new species
FIG. 19. — Humid forest of low altitude at the edge of the Kaw track, type locality of Royacanthops soukana (Roy, 2002) n. comb. Photo: Jérémie Lapèze.
FIG. 18 in Two new genera of Acanthopidae (Mantodea) from the Amazon region, with description of a new species
FIG. 18. — Parvacanthops parva (Beier, 1942) n. comb., male abdomen apex: A, holotype terminalia; B, holotype genitalia, ventral view. Scale bar: 1 mm.
FIG. 7 in Two new genera of Acanthopidae (Mantodea) from the Amazon region, with description of a new species
FIG. 7. — Royacanthops confusa Schwarz & Moulin, n. gen., n. sp., male paratype SMNK-Mant 00003: A, dorsal view; B, ventral view. Scale bars: 10 mm.
FIG. 16 in Two new genera of Acanthopidae (Mantodea) from the Amazon region, with description of a new species
FIG. 16. — Parvacanthops parva (Beier, 1942) n. comb., left foreleg, posterior view. Scale bar: 1 mm.
FIG. 20 in Two new genera of Acanthopidae (Mantodea) from the Amazon region, with description of a new species
FIG. 20. — Humid forest of low altitude at Montagne des Chevaux, locality of the male of Royacanthops soukana (Roy, 2002) n. comb. Photo: Stéphane Brûlé.
FIG. 14 in Two new genera of Acanthopidae (Mantodea) from the Amazon region, with description of a new species
FIG. 14. — Parvacanthops parva (Beier, 1942) n. comb., male holotype MIZ 125056: A, dorsal view; B, labels. Scale bars: 10 mm.
FIG. 13 in Two new genera of Acanthopidae (Mantodea) from the Amazon region, with description of a new species
FIG. 13. — Royacanthops confusa Schwarz & Moulin, n. gen., n. sp., male abdomen apices: A, holotype terminalia; B, paratype terminalia; C, holotype genitalia, ventral view; D, paratype genitalia, ventral view. Scale bars: 1 mm.
FIG. 12 in Two new genera of Acanthopidae (Mantodea) from the Amazon region, with description of a new species
FIG. 12. — Royacanthops confusa Schwarz & Moulin, n. gen., n. sp., female forewing, reconstructed and mirrored. Scale bar: 5 mm.
FIG. 11 in Two new genera of Acanthopidae (Mantodea) from the Amazon region, with description of a new species
FIG. 11. — Royacanthops confusa Schwarz & Moulin, n. gen., n. sp., left forelegs: A, male holotype, posterior view; B, male holotype, anterior view; C, male paratype, posterior view; arrows point to the aberrant posteroventral spine configuration referred to in the text; D, male paratype, anterior view; E, female allotype, posterior view; arrow points to the aberrant posteroventral spine referred to in the text; F, female allotype, anterior view. Scale bar: 5 mm.
FIG. 9 in Two new genera of Acanthopidae (Mantodea) from the Amazon region, with description of a new species
FIG. 9. — Royacanthops confusa Schwarz & Moulin, n. gen.,n. sp., heads,anterior view: A, male holotype; B, male paratype; C, female allotype. Scale bars: 1 mm.
FIG. 10 in Two new genera of Acanthopidae (Mantodea) from the Amazon region, with description of a new species
FIG. 10. — Royacanthops confusa Schwarz & Moulin, n. gen., n. sp., anterior part of pronotum, dorsolateral view: A, male holotype; B, male paratype; C, female allotype. Scale bar: 1 mm.
FIG. 6 in Two new genera of Acanthopidae (Mantodea) from the Amazon region, with description of a new species
FIG. 6. — Royacanthops confusa Schwarz & Moulin, n. gen., n. sp., male holotype ex. SMNK-Mant 00005: A, dorsal view; B, ventral view. Scale bars: 10 mm.
FIG. 8 in Two new genera of Acanthopidae (Mantodea) from the Amazon region, with description of a new species
FIG. 8. — Royacanthops confusa Schwarz & Moulin, n. gen. n. sp., female allotype SMNK-Mant 00006: A, dorsal view; B, ventral view. Scale bars: 10 mm.
FIG. 4 in Two new genera of Acanthopidae (Mantodea) from the Amazon region, with description of a new species
FIG. 4. — Royacanthops soukana (Roy, 2002) n. comb., male MNHN-EP-EP7504: A, genitalia, ventral view; B, genitalia, dorsal view. Abbreviations: see Material and methods. Scale bar: 1 mm.
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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.