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242 results for “Suriname”
National Checklists 2017: Suriname Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>A list of species from Suriname collected using effechecka and geonames polygons
National Checklists 2019: Suriname Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>A list of species from Suriname collected using effechecka and geonames polygons
Figure 2 in The rare rhinoceros beetle, Ceratophileurus lemoulti Ohaus, 1911, in French Guiana and Suriname (Coleoptera, Scarabaeidae, Dynastinae, Phileurini)
Figure 2. Male specimen of Ceratophileurus lemoulti Ohaus collected at Raleighvallen, Suriname. Photograph by Alain and Marcel Galant.
Figure 1 in The rare rhinoceros beetle, Ceratophileurus lemoulti Ohaus, 1911, in French Guiana and Suriname (Coleoptera, Scarabaeidae, Dynastinae, Phileurini)
Figure 1. Male specimen of Ceratophileurus lemoulti Ohaus collected in French Guiana (YPC). Photograph by Yannig Ponchel. Aedeagus (aed) attached to card mount underneath specimen, in oblique view.
Figure 3 in The rare rhinoceros beetle, Ceratophileurus lemoulti Ohaus, 1911, in French Guiana and Suriname (Coleoptera, Scarabaeidae, Dynastinae, Phileurini)
Figure 3. Distribution map for Ceratophileurus lemoulti Ohaus. Collection localities in black circles.
Infrastructure Climate Resilience Assessment Data Starter Kit for Suriname
<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; Baugh et al, 2024)</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, 2025)</li> <li>railways (OpenStreetMap, 2025)</li> <li>power plants (Global Energy Observatory et al, 2018)</li> <li>power transmission lines (Arderne et al, 2020)</li> </ul> <p>Contextual information:</p> <ul> <li>elevation (European Union and ESA, 2021)</li> <li>land-use and land cover (Copernicus Climate Change Service and Climate Data Store, 2019)</li> <li>administrative boundaries from geoBoundaries (Runfola 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=12278">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> Baugh, Calum; Colonese, Juan; D'Angelo, Claudia; Dottori, Francesco; Neal, Jeffrey; Prudhomme, Christel; Salamon, Peter (2024): Global river flood hazard maps. European Commission, Joint Research Centre (JRC) [Dataset] PID: <a href="http://data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif">data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif</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> Copernicus Climate Change Service, Climate Data Store, (2019): Land cover classification gridded maps from 1992 to present derived from satellite observation. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: <a href="https://doi.org/10.24381/cds.006f2c9a">10.24381/cds.006f2c9a</a> (Accessed on 09-AUG-2024) </li> <li> Copernicus DEM - Global Digital Elevation Model (2021) DOI: <a href="https://doi.org/10.5270/ESA-c5d3d65">10.5270/ESA-c5d3d65</a> (produced using Copernicus WorldDEM™-90 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved) </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 (2025) 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> Runfola D, Anderson A, Baier H, Crittenden M, Dowker E, Fuhrig S, et al. (2020) geoBoundaries: A global database of political administrative boundaries. PLoS ONE 15(4): e0231866. DOI: <a href="https://doi.org/10.1371/journal.pone.0231866">10.1371/journal.pone.0231866</a>. </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>
Figure 2 in Cyclocephala kuijteni (Scarabaeidae: Dynastinae: Cyclocephalini) a new species from Suriname
Figure 2. Cyclocephala spp., lateral views of the aedeagi. a) C. kuijteni n. sp., holotype. b) C. hardyi Endrödi, paratype. c) C. castanea (Olivier). d) C. pygidialis Joly, from Joly (2000).
Figure 1. Cyclocephala spp., dorsal habitus. a in Cyclocephala kuijteni (Scarabaeidae: Dynastinae: Cyclocephalini) a new species from Suriname
Figure 1. Cyclocephala spp., dorsal habitus. a) C. kuijteni n. sp., holotype. b) C. hardyi Endrödi, paratype. c) C. castanea (Olivier). d) C. pygidialis Joly, paratype.
National Checklists: Suriname Species List
Data from: GBIF.org (23 January 2025) GBIF Occurrence Download <a href="https://doi.org/10.15468/dl.vd2ajk" target="_blank" rel="noopener">https://doi.org/10.15468/dl.vd2ajk</a>
Fig. 22 in The genus Lestes (Odonata: Lestidae) Leach, 1815, in Surinam
Fig. 22, Lestes tenuatus Rambur; 23, Lestes mediorufus Calvert; 24, Lestes edentatus spec, nov.; 25, Lestes forficula Rambur; 26, Lestes curvatus spec, nov.; 27. Lestes trichonus spec. nov. 22-27, terminal segments of female abdomen, left lateral aspect [6.3 x 2.5].
Figs 10-11 in The genus Lestes (Odonata: Lestidae) Leach, 1815, in Surinam
Figs 10-11, Lestes basidens spec, nov.; 12-13, Lestes edentatus spec. nov. 10,12, tenth abdominal segment and caudal appendages of male, dorsal aspect [12.5 x 2.5]; 11,13, the same, left lateral aspect [12.5 X 2.5].
Fig. 1 in The genus Lestes (Odonata: Lestidae) Leach, 1815, in Surinam
Fig. 1, Lestes basidens spec, nov.; 2, Lestes mediorufus Calvert; 3, Lestes forficula Rambur; 4, Lestes curvatus spec, nov.; 5, Lestes tenuatus Rambur; 6, Lestes tenuatus Rambur, lectotype; 7, Lestes edentatus spec, nov.; 8, Lestes trichonus spec, nov.; 9, Lestes sublatus Hagen in Selys. 1-9, pectoral colour pattern [12.5 X 0.63],
Figs 18-19 in The genus Lestes (Odonata: Lestidae) Leach, 1815, in Surinam
Figs 18-19, Lestes tenuatus Rambur; 20-21, Lestes forficula Rambur. 18,20, tenth abdominal segment and caudal appendages of male, dorsal aspect [12.5 X 2.5]; 19,21, the same, left lateral aspect [12.5 X 2.5].
Figs 14-15 in The genus Lestes (Odonata: Lestidae) Leach, 1815, in Surinam
Figs 14-15, Lestes curvatus spec, nov.; 16-17, Lestes mediorufus Calvert. 14,16, tenth abdominal segment and caudal appendages of male, dorsal aspect [12.5 X 2.5]; 15,17, the same, left lateral aspect [12.5 X 2.5].
Fig. 28-30 in The genus Lestes (Odonata: Lestidae) Leach, 1815, in Surinam
Fig. 28-30, Lestes sublatus Hagen in Selys; 31-32, Lestes forficula Rambur; 33, Lestes curvatus spec. nov. 28, terminal segments of female abdomen, left lateral aspect [6.3 X 2.5]; 29, 31, stigma of right fore wing [6.3 X 2.5]; 30, 32, stigma of right hind wing [6.3 X 2.5]. 33, caudal appendages of female, dorsal aspect [6.3 X 2.5].
Fig. 1 in Short Communication Range extension and some morphological characteristics of Ptychoglossus brevifrontalis, Boulenger, 1912 (Squamata: Alopoglossidae) in Suriname
Fig. 1. Map showing the occurrence of P. brevifrontalis in Suriname. The blue dot represents the first recorded specimen from Suriname while the red triangle depicts the specimen collected at the BNP. Fig. 2. Lateral view of head of P. brevifrontalis showing the labials. Fig. 3. Anal scales of P. brevifrontalis.
Fig. 4 in Short Communication Range extension and some morphological characteristics of Ptychoglossus brevifrontalis, Boulenger, 1912 (Squamata: Alopoglossidae) in Suriname
Fig. 4. Habitat where Ptychoglossus brevifrontalis was collected at the BNP. Picture by A. Gangadin.
Figures 11-15. Rhynostelis multiplicata, female specimen from Suriname. 11 in Revision of the rare anthidiine bee genus Rhynostelis Moure & Urban (Hymenoptera, Apidae)
Figures 11-15. Rhynostelis multiplicata, female specimen from Suriname. 11, head, anteroventral view; 12, head, posterodorsal view; 13, habitus, dorsal view; 14, metasoma, dorsal view; 15, habitus, lateral view. Scale line = 2.0 mm (Figs. 11, 12 and 14). Scale line = 5.0 mm (Figs. 13 and 15).
Figures 1-4 in New records of species of Nitornus Stål, 1859 (Hemiptera: Reduviidae: Stenopodainae) from French Guiana and Surinam
Figures 1-4. Nitornus spp., male specimens. Scale bars: 5.0 mm. 1-2. Specimens from French Guiana. 3-4. Specimens from Surinam. 1. Nitornus lobulatus Stål, 1859. 2-4. Nitornus parkoi (Costa Lima & Campos Seabra, 1945). 1-3. Dorsal view. 4. ventral view. / Nitornus spp., especÍmenes machos. Barras de escala: 5,0 mm. 1-2. EspecÍmenes de la Guayana Francesa. 3-4. EspecÍmenes de Suriname. 1. Nitornus lobulatus Stål, 1859. 2-4. Nitornus parkoi (Costa Lima y Campos Seabra, 1945). 1-3. Vista dorsal. 4. Vista ventral.
Fig. 9 in Checklist of the amphibians and reptiles of the Lely Mountains, eastern Suriname
Fig. 9. Overview of the airstrip in the Lely Mountains with parts of the forest visible where the June 2016 surveys were conducted. Photo by T. Gazoni.
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.