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19,293 results for “western”
Fig. 1 in Elaphoidella stygobiotica (Copepoda: Harpacticoida: Canthocamptidae), a new species of cave-dwelling copepod from western Thailand
Fig. 1. Map of the sampling site of Elaphoidella stygobiotica, new species: A, location of Kanchanaburi Province; B, location of the Lawa cave [square (■) = capital city, circle (●) = cave location]; C, location of sampling sites inside the cave [diamond (◊) = pools of stagnant water, star (★) = sampling site]; D, photo of the sampling site.
Fig. 4 in Elaphoidella stygobiotica (Copepoda: Harpacticoida: Canthocamptidae), a new species of cave-dwelling copepod from western Thailand
Fig. 4. Elaphoidella stygobiotica, new species, female (holotype): A, antennule; B, antenna; C, mandible; D, maxillule; E, maxilla; F, maxilliped.
Fig. 3 in Elaphoidella stygobiotica (Copepoda: Harpacticoida: Canthocamptidae), a new species of cave-dwelling copepod from western Thailand
Fig. 3. Elaphoidella stygobiotica, new species, SEM photo of female: A, habitus, dorsal view; B, prosome and urosomite 1, dorsal view; C, prosomite 2–4 and urosome, dorsal view; D, anal somite and caudal rami, ventral view; E, urosomite 1 with P5 and urosomite 2 with P6, ventral view.
Fig. 5 in Elaphoidella stygobiotica (Copepoda: Harpacticoida: Canthocamptidae), a new species of cave-dwelling copepod from western Thailand
Fig. 5. Elaphoidella stygobiotica, new species, female (holotype): A, P1; B, P2; C, P3 (black arrows indicate inner setae); D, P4; E, P5; F, P5 variation.
Fig. 5 in A new Pipistrelle bat from the oceanic Island of Príncipe (Western Central Africa)
Fig. 5.—(A) Dorsal; (B) lateral; (C) ventral; (D) apical (above), and basal (bellow) views of the baculum of the paratype Pseudoromicia principis sp. nov. (EBD 17358M). Photographs by Laura Torrent.
Fig. 6 in A new Pipistrelle bat from the oceanic Island of Príncipe (Western Central Africa)
Fig. 6.—(A) Detail of the upper incisors (top left) and drawings of the (a) lower incisors and (b) upper incisors (bottom) of the holotype of Pseudoromicia principis sp. nov. (EBD 17475M). Photograph by Joxerra Aihartza and drawings by Joaquín López-Rojas. (B) Details of the rhinarium of (c) the holotype of P. principis sp. nov. (EBD 17475M) and of (d) Nycticeinops happoldorum (ZFMK-MAM-2009.0029), from Hutterer et al. (2019). Photograph by Laura Torrent. (C) Drawing of the tragi of: (e) P. principis sp. nov. (EBD 17358M, paratype); (f) P. brunnea from Van Cakenberghe and Happold (2013) and (g) P. brunnea (DM13229) from Monadjem et al. (2013) and (D) dorsal and ventral views (from left to right) of bacula of: (h) P. principis sp. nov. (EBD 17358M, paratype); (i) P. rendalli from Hill and Harrison (1987); and (j) P. brunnea (DM13229) from Monadjem et al. (2013). Drawings of P. principis sp. nov. baculum by Joaquín López-Rojas.
Fig. 4 in A new Pipistrelle bat from the oceanic Island of Príncipe (Western Central Africa)
Fig. 4.—Skull and mandible of the holotype of Pseudoromicia principis sp. nov. (EBD 17475M) in dorsal, ventral, lateral, front and back views as well as mandible top view. Photographs by Joxerra Aihartza.
Fig. 2 in A new Pipistrelle bat from the oceanic Island of Príncipe (Western Central Africa)
Fig. 2.—Bayesian phylogenetic reconstruction based on the mitochondrial gene cytochrome b (Cytb). Values above branches represent Bayesian posterior probabilities and values below branches are bootstrap values of maximum likelihood (support values <50 are not shown). The right column presents sequences GenBank accession numbers. The sequence JQ956446 is listed in GenBank as Pseudoromicia tenuipinnis but it was probably misidentified. See Table 1 for a complete list of the specimens used and main text for details of the molecular analyses.
Figure 3 in Seasonal incidence of Raoiella indica Hirst (Acari: Tenuipalpidae) on different varieties of date palm in Kachchh region of Western India
Figure 3. Pattern of distribution of red palm mite, Raoiella indica in different directions on three different varieties (pooled).
Fig. 3 in Life cycle of Carabus granulatus Linnaeus 1758 (Coleoptera, Carabidae) in Western Transbaikalia
Fig. 3. Seasonal dynamics of Carabus granulatus activity on saline meadow in 2018. Abbreviations as in Fig. 1.
Fig. 2 in Life cycle of Carabus granulatus Linnaeus 1758 (Coleoptera, Carabidae) in Western Transbaikalia
Fig. 2. Seasonal dynamics of Carabus granulatus activity on steppe meadow in 2018. Abbreviations as in Fig. 1.
Fig. 1 in Life cycle of Carabus granulatus Linnaeus 1758 (Coleoptera, Carabidae) in Western Transbaikalia
Fig. 1. Seasonal dynamics of Carabus granulatus activity on floodplain meadow in 2018. Abbreviations: t – teneral imago, i – immature imago, m – mature imago, s – spent imago.
Figs 31–36 in First record of the genus Alainites Waltz & McCafferty, 1994 (Ephemeroptera, Baetidae) from India with the description of a new species from the North-western Himalayas
Figs 31–36. Alainites neeru sp. nov. 31–32. Paratype (AMC). 33–36. Holotype (AMC). 31. Tergalius I. 32. Tergalius IV. 33. Posterior margin of tergum IV denticulation. 34–35. Paraproct. 36. Closer view of paraproct.
Figs 27–30 in First record of the genus Alainites Waltz & McCafferty, 1994 (Ephemeroptera, Baetidae) from India with the description of a new species from the North-western Himalayas
Figs 27–30. Alainites neeru sp. nov., paratype (AMC), thorax and abdomen of larva. 27. Foreclaw. 28. Hindwing pad (pointed by arrow). 29. Tergal segments III–X. 30. Tergal segments VIII–X.
Figs 14–18 in First record of the genus Alainites Waltz & McCafferty, 1994 (Ephemeroptera, Baetidae) from India with the description of a new species from the North-western Himalayas
Figs 14–18. Alainites neeru sp. nov., paratype (AMC), mouthparts of larva. 14. Labium. 15. Glossae and paraglossae. 16. Closer view of labial palp segment III. 17. Maxilla. 18. Closer view of crown of maxilla.
Figs 19–21 in First record of the genus Alainites Waltz & McCafferty, 1994 (Ephemeroptera, Baetidae) from India with the description of a new species from the North-western Himalayas
Figs 19–21. Alainites neeru sp. nov., paratype (AMC), legs of larva. 19. Forefemur. 20. Middle femur. 21. Hind femur.
Figs 22–26 in First record of the genus Alainites Waltz & McCafferty, 1994 (Ephemeroptera, Baetidae) from India with the description of a new species from the North-western Himalayas
Figs 22–26. Alainites neeru sp. nov., paratype (AMC), legs of larva. 22. Foretibia and foretarsus. 23. Outer marginal setation of foretibia. 24. Inner marginal setation of foretibia. 25. Middle tibia and middle tarsus. 26. Hind tibia and hind tarsus.
Figs 7–13 in First record of the genus Alainites Waltz & McCafferty, 1994 (Ephemeroptera, Baetidae) from India with the description of a new species from the North-western Himalayas
Figs 7–13. Alainites neeru sp. nov. paratypes (AMC), mouthparts of larva. 7. Left mandible. 8. Right mandible. 9. Prostheca and incisor of left mandible. 10. Closer view of left mandible. 11–12. Prostheca and incisor of right mandible. 13. Prostheca of right mandible.
Figs 1–5 in First record of the genus Alainites Waltz & McCafferty, 1994 (Ephemeroptera, Baetidae) from India with the description of a new species from the North-western Himalayas
Figs 1–5. Alainites neeru sp. nov. 1. Holotype, ♀ (AMC), mature larva. 2–6. Paratype (AMC). 2. Immature larva. 3. Antenna. 4. Labrum. 5. Labrum, sub-marginal setae (arrows indicate 1+2 long, simple setae). 6. Setae of the ventral surface of the labrum.
Infrastructure Climate Resilience Assessment Data Starter Kit for Western Sahara
<p> This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems. </p> <p> These extracts are derived from global datasets which have been clipped to the national scale (or subnational, in cases where national boundaries have been split, generally to separate outlying islands or non-contiguous regions), using Natural Earth (2023) boundaries, and is not meant to express an opinion about borders, territory or sovereignty. </p> <p> Human-induced climate change is increasing the frequency and severity of climate and weather extremes. This is causing widespread, adverse impacts to societies, economies and infrastructures. Climate risk analysis is essential to inform policy decisions aimed at reducing risk. Yet, access to data is often a barrier, particularly in low and middle-income countries. Data are often scattered, hard to find, in formats that are difficult to use or requiring considerable technical expertise. Nevertheless, there are global, open datasets which provide some information about climate hazards, society, infrastructure and the economy. This "data starter kit" aims to kickstart the process and act as a starting point for further model development and scenario analysis. </p> <p>Hazards:</p> <ul> <li>coastal and river flooding (Ward et al, 2020)</li> <li>extreme heat and drought (Russell et al 2023, derived from Lange et al, 2020)</li> <li>tropical cyclone wind speeds (Russell 2022, derived from Bloemendaal et al 2020 and Bloemendaal et al 2022)</li> </ul> <p>Exposure:</p> <ul> <li>population (Schiavina et al, 2023)</li> <li>built-up area (Pesaresi et al, 2023)</li> <li>roads (OpenStreetMap, 2023)</li> <li>railways (OpenStreetMap, 2023)</li> <li>power plants (Global Energy Observatory et al, 2018)</li> <li>power transmission lines (Arderne et al, 2020)</li> </ul> <p> The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people. </p> <p> To learn more about related concepts, there is a free short course available through the Open University on <a href="https://www.open.edu/openlearncreate/course/view.php?id=11539">Infrastructure and Climate Resilience</a>. This <a href="https://opsis.eci.ox.ac.uk/courses/2-infra-for-resil/">overview of the course</a> has more details. </p> <p> These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow: </p> <ul> <li> <a href="https://github.com/tomalrussell/snkit"><code>snkit</code></a> helps clean network data </li> <li> <a href="https://github.com/nismod/snail"><code>nismod-snail</code></a> is designed to help implement infrastructure exposure, damage and risk calculations </li> </ul> <p> The <a href="https://github.com/nismod/open-gira"><code>open-gira</code></a> repository contains a larger workflow for global-scale open-data infrastructure risk and resilience analysis. </p> <p> For a more developed example, some of these datasets were key inputs to a regional climate risk assessment of current and future flooding risks to transport networks in East Africa, which has a related online visualisation tool at <a href="https://east-africa.infrastructureresilience.org/">https://east-africa.infrastructureresilience.org/</a> and is described in detail in Hickford et al (2023). </p> <p><strong>References</strong></p> <ul> <li> Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the global power system using open data [Dataset]. In Nature Scientific Data (1.1.1, Vol. 7, Number Article 19). Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.3628142">10.5281/zenodo.3628142</a> </li> <li> Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2020): STORM tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/12705164.v3">10.4121/12705164.v3</a> </li> <li> Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan); Martinez, Andrew B.; et al. (2022): STORM climate change tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/14510817.v3">10.4121/14510817.v3</a> </li> <li> Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. (2018) Global Power Plant Database. Published on Resource Watch and Google Earth Engine; <a href="http://resourcewatch.org/">resourcewatch.org/</a> </li> <li> Hickford et al (2023) Decision support systems for resilient strategic transport networks in low-income countries – Final Report. Available online: <a href="https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries">https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries</a> </li> <li> Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI: <a href="https://doi.org/10.1029/2020EF001616">10.1029/2020EF001616</a> </li> <li> Natural Earth (2023) Admin 0 Map Units, v5.1.1. [Dataset] Available online: <a href="https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details/">www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details</a> </li> <li> OpenStreetMap contributors, Russell T., Thomas F., nismod/datapkg contributors (2023) Road and Rail networks derived from OpenStreetMap. [Dataset] Available at <a href="https://global.infrastructureresilience.org">global.infrastructureresilience.org</a> </li> <li> Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030) European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea">data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea</a>, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA </li> <li> Russell, T., Nicholas, C., & Bernhofen, M. (2023). Annual probability of extreme heat and drought events, derived from Lange et al 2020 (Version 2) [Dataset]. Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.8147088">10.5281/zenodo.8147088</a> </li> <li> Schiavina M., Freire S., Carioli A., MacManus K. (2023): GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe">data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe</a>, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE </li> <li> Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, et al. (2020) Aqueduct Floods Methodology. Technical Note. Washington, D.C.: World Resources Institute. Available online at: <a href="https://www.wri.org/publication/aqueduct-floods-methodology">www.wri.org/publication/aqueduct-floods-methodology</a>. </li> </ul>
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.