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281 results for “Senegal”
Figs 60–65 in Morphology of two Mastogloia species (Bacillariophyta) from Lac de Guiers (Senegal) and comparison with the type material of M. braunii
Figs 60–65. Mastogloia senegalensis Van de Vijver, Fofana, Sow & Ector sp. nov. Scanning electron micrographs (SEM) of valves from the Lac de Guiers type population (Van de Vijver sample SEN-42). 60. SEM internal view of an entire valve with the partectal ring. 61–62. SEM internal details of the partectal ring near the valve apices showing the cleft on each apex. 63. SEM internal detail of the central area and part of the partectal ring. 64. SEM internal detail of the partecta showing the partectal walls with 2–4 series of small, rounded pores loosely aggregated in distinct plaques. 65. SEM internal view of a valve apex without the partectal ring (note the small pseudoseptum). Scale bars: 60–63, 65 = 10 µm; 64 = 1 µm.
Figs 33–38. Mastogloia belaensis M in Morphology of two Mastogloia species (Bacillariophyta) from Lac de Guiers (Senegal) and comparison with the type material of M. braunii
Figs 33–38. Mastogloia belaensis M.Voigt. Scanning electron micrographs (SEM) of valves from the Lac de Guiers population (Van de Vijver sample SEN-42). 33. SEM girdle view of an entire frustule showing the partectal pores and the mantle areolae. 34. SEM external view of an entire valve with typical undulating raphe branches. 35. SEM external detail of the central area. 36. SEM external detail of the valve apex. 37. SEM external detail of the apices and girdle bands of an entire frustule. 38. SEM external detail of the valve mantle with the transapically elongated mantle areolae and the row of rounded pseudoloculi on the valve face/mantle junction. Scale bars: 10 µm.
Figure 3. Diplecogaster tonstricula n in Diplecogaster tonstricula, a new species of cleaning clingfish (Teleostei: Gobiesocidae) from the Canary Islands and Senegal, eastern Atlantic Ocean, with a review of the Diplecogaster-ctenocrypta species-group
Figure 3. Diplecogaster tonstricula n. sp., CCML uncat., paratype, specimen 1, 22.9 mm SL. Head lateral line system. (A) Dorsal view of head; (B) ventral view of head. Bar 1 mm.
qdgc Senegal
<p>QDGC tables delivered in geopackage file<br> - - - - - - - - - - - - - - - - - - - - - -<br> QDGC represents a way of making (almost) equal area squares covering a specific area to represent specific qualities of the area covered. The squares themselves are based on the degree squares covering earth. Around the equator we have 360 longitudinal lines , and from the north to the south pole we have 180 latitudinal lines. Together this gives us 64800 segments or tiles covering earth.<br> <br> <br> <br> <br> Within each geopackage file you will find a number of tables with these names:<br> <br> <br> <br> <br> -tbl_qdgc_01<br> -tbl_qdgc_02<br> -tbl_qdgc_03<br> -tbl_qdgc_04<br> -tbl_qdgc_05<br> -etc<br> <br> <br> <br> <br> The attributes for each table are:<br> <br> <br> <br> <br> qdgc Unique Quarter Degree Grid Cell reference string<br> area_reference Country<br> level_qdgc QDGC level<br> cellsize degrees decimal degree for the longitudal and latitudal length of the cell<br> lon_center Longitude center of the cell<br> lat_center Latitudal center of the cell<br> area_km2 Calculated area for the cell<br> geom Geometry<br> <br> <br> <br> <br> Metadata<br> --------<br> Geodata GCS_WGS_1984<br> Datum: D_WGS_1984<br> Prime Meridian: 0<br> <br> <br> <br> <br> Areas are calculated with different versions of Albers Equal Area Conic using the PostGIS function st_area. For the African continent I have used Africa Albers Equal Area Conic which will look like this:<br> - st_area(st_transform(geom, 102022))/1000000)<br> <br> <br> <br> <br> Licensing<br> ---------<br> Creative Commons Attribution 4.0 International<br> <br> <br> <br> <br> Conditions<br> ----------<br> Delivered to the user as-is. No guarantees. If you find errors, please tell me and I will try to fix it. Suggestions for improvements can be addressed to the github repository: https://github.com/ragnvald/qdgc<br> <br> <br> <br> <br> Thankyou<br> --------<br> The work has over the years been supported and received advice and moral support from many organisations and stakeholders. Here are some of them:<br> - Tanzania Wildlife Research Institute<br> - Dept of Biology, NTNU, Norway<br> - Norwegian Environment Agency<br> - Eivin Røskaft, Steven Prager, Howard Frederick, Julian Blanc, Honori Maliti, Paul Ramsey<br> <br> <br> <br> <br> References<br> ----------<br> * http://en.wikipedia.org/wiki/QDGC<br> * http://www.mindland.com/wp/projects/quarter-degree-grid-cells/about-qdgc/<br> * http://en.wikipedia.org/wiki/Lambert_azimuthal_equal-area_projection<br> * http://www.safe.com<br> <br> <br> <br> <br> <br> <br> <br> <br> Ragnvald Larsen<br> Trondheim 21th of January, 2021<br> <br> <br> <br> <br> ragnvald@mindland.com<br> www.mindland.com</p>
Transport Starter Data Kit: Historical socio-transport data for Senegal
<p>This Transport Starter Data Kit contains historical annual data (1990–2021) on passenger and freight activity, segregated by mode and fuel. Additionally, historical data on energy intensities, load factors, vehicle stock, population (total, urban, rural, growth), and GDP (total, agriculture, construction, mining, manufacturing, service, energy, growth) are included in the kit, within the 'Data' tab. The historical data can be used as a foundation for transport-energy modelling and/or to identify areas of improvement. This data was verified through consultation with relevant stakeholders before publishing. The definition used for each vehicle mode is found in the 'Definitions' tab, and the description of each data observation status is found in the 'Notes' tab. All data sources are linked where possible.</p>
Infrastructure Climate Resilience Assessment Data Starter Kit for Senegal
<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, 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>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 (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> 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>
National Checklists: Senegal 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>
UAV outputs and associated field measurement of the herbaceous of a Sahelian Rangeland during the wet season in Northern Senegal
<p>This dataset contains UAV outputs (mosaic and digital surface model) and field measurement of vegetation (shapefile) that were made in northern Senegal.</p> <p><strong>Site gradient measurement</strong></p> <p>The data was collected on a plot of the Centre of Zootechnical Researches of Dahra / ISRA during 2020 rainy season (from July 19, 2020, to September 17, 2020). The average rainfall for the period 1981-2018 was ranging from 221 mm.y-1 to 468 mm. y-1. The vegetation in the field is a herbaceous savannah where <em>Vachellia tortilis</em> and <em>Balanites aegyptiaca</em> are the dominant trees.</p> <p><strong>Field measurement.</strong></p> <p><strong>UAV flight plan</strong></p> <p>We used two different drones : Bluegrass and Anafi of Parrot. The Bluegrass of Parrot was used from 19/07/2020 to 04/08/2020. The Bluegrass flights were done at 60 meters of altitude, with a speed of 2 m s<sup>-1</sup>, and 90% of overlap rate between images, on a double grid of 100 m x 100 m. Anafi of Parrot was used for the rest of the season. The Anafi flights were done at 60 meters of altitude, with a speed of 2 m s<sup>-1</sup>, and 90% of overlap rate between images, on a double grid of 100 m x 100 m and the angle of inclination of the camera fixed at 80°. The flights have been done with PIX4D capture application at earlier in the day every two days. A total of 61 drone flights were conducted over the rainy season.</p> <p><strong>Herbaceous Biomass</strong></p> <p>Every two days , after drone flight, herbaceous measurements were carried out, in three plots of 1 m² distributed respectively under the crown of a tree, at the edge of the crown, and at a distance from the edge of the crown equal to the height of the tree. These plots were rotated among the trees in the field until all four azimuths of trees were covered.We collected Fresh mass and dry mass.</p> <p><strong>Image analysis.</strong></p> <p>The drone images taken for each day of collect, were analyzed in the software PIX4DMapper (Pix4D SA, Lausanne, Switzerland) by the Structure from Motion method. We used precisely the 3D mapping option of the software. Then for each flight we computed and exported an orthophotograph and a digital surface model.</p> <p><strong>Data organization</strong></p> <p>The data contains :</p> <ul> <li>DSM that contains the surface model in tiff</li> <li>Mosaic that the orthomosaic in tiff.</li> <li>Data that contains the shapefile with the position and table with the field measurements</li> </ul>
FIGURE 12 in A Composite Section of Fossiliferous Late Cretaceous- Early Paleogene Localities in Senegal and Preliminary Description of a New Late Maastrichtian Vertebrate Fossil Assemblage
FIGURE 12. Pycnodus jonesae (SEN 056) from the Late Maastrichtian Cap de Naze Formation discovered at the North Quarry of Poponguine. Left prearticular in occlusal view. Scale = 1 cm.
FIGURE 11 in A Composite Section of Fossiliferous Late Cretaceous- Early Paleogene Localities in Senegal and Preliminary Description of a New Late Maastrichtian Vertebrate Fossil Assemblage
FIGURE 11. Maastrichtian invertebrates (internal molds formed in situ) of the Cap de Naze Formation discovered at the North Quarry of Poponguine. A, Turritellidae cf. Mesalia (SEN 060) natural cast in?abapertural view; and B, Naticidae indet. (SEN 071), natural cast in apical view. Scale = 1 cm.
FIGURE 8 in A Composite Section of Fossiliferous Late Cretaceous- Early Paleogene Localities in Senegal and Preliminary Description of a New Late Maastrichtian Vertebrate Fossil Assemblage
FIGURE 8. The Islet of Poponguine locality where the Thanetian beds of the Poponguine Formation crop out. A, Overview of locality showing units 2–5 (see text for lithology); B, detail of fine coquina limestone (unit 2) with small cross stratification; C, detail of thick coquina limestone (unit 4) with oblique stratifications dominated by lamellibranches; D, detail of thick coquina limestone (unit 5) with turritellids; and E, detail of limestone breccia (unit 6).
FIGURE 7 in A Composite Section of Fossiliferous Late Cretaceous- Early Paleogene Localities in Senegal and Preliminary Description of a New Late Maastrichtian Vertebrate Fossil Assemblage
FIGURE 7. Ndayane Cliff locality at Poponguine with the middle-late Danian Ndayane Formation cropping out. Buildings now cover the brachyanticline (upper right). The Maastricthian Cap de Naze Formation consisting of calcareous sandstone with hard ground underlies the Ndayane Formation unconformably. As seen in the center of the photograph, the beach of Poponguine obscures part of unit 1 of the Ndayane Formation. Unit 1 consists of calcareous sandstone with beds of marls and unit 2 of marls with calcite rosettes.
FIGURE 6 in A Composite Section of Fossiliferous Late Cretaceous- Early Paleogene Localities in Senegal and Preliminary Description of a New Late Maastrichtian Vertebrate Fossil Assemblage
FIGURE 6. Contact between the Danian Ndayane Formation and the overlying Thanetian Poponguine Formation in the North Quarry of Poponguine locality.
FIGURE 14 in A Composite Section of Fossiliferous Late Cretaceous- Early Paleogene Localities in Senegal and Preliminary Description of a New Late Maastrichtian Vertebrate Fossil Assemblage
FIGURE 14. Late Maastrichtian Testudines from the Upper Cap de Naze Formation of the North Quarry of Poponguine. A, Partial plastron in ventral view (SEN 054). Dotted lines mirrored to show extent of preservation. B, Peripheral carapace fragment in dorsal view (SEN 054). C, Partial neural bone in dorsal view (SEN 080). Abbreviations: ent, entoplastron; epi, epiplastron; hyo, hyoplastron. Scale = 2 cm.
FIGURE 3 in A Composite Section of Fossiliferous Late Cretaceous- Early Paleogene Localities in Senegal and Preliminary Description of a New Late Maastrichtian Vertebrate Fossil Assemblage
FIGURE 3. Composite stratigraphic section correlating four fossiliferous Late Cretaceous–Early Paleogene localities of Western Senegal. Units are unique to each locality (i.e., unit 2 of the Ndayane Formation at the North Quarry locality is not necessarily the same as unit 2 of the Ndayane Formation at the Ndayane Cliff at Poponguine locality). Abbreviations: Camp, Campanian; CdN., Cap de Naze; Dan, Danian; Fm, formation; fs, fine sand; ms, medium sand; L, late; M/L, Middle to Late; Maas, Masstrichtian; Mid, middle; mst, mudstone; Nda., Ndayane Formation; Popo., Poponguine; pst, packstone; u, unit; wst, wackestone. Gray caps on three of the localities are Plio-Pleistocene ferruginous rocks.
FIGURE 13 in A Composite Section of Fossiliferous Late Cretaceous- Early Paleogene Localities in Senegal and Preliminary Description of a New Late Maastrichtian Vertebrate Fossil Assemblage
FIGURE 13. Late Maastrichtian dyrosaurid crocodyliforms of the Cap de Naze Formation discovered at the North Quarry of Poponguine. A, B, Caudal vertebrae (SEN 062 and 064) from proximal region of tail in right lateral view; C, caudal vertebra (SEN 065) from middle of tail in right lateral view; D, distal caudal vertebra (SEN 073) in right lateral view; E, proximal right metatarsal II (SEN 069) in dorsal view; F, proximal right metatarsal II (SEN 059) from a larger individual; and G, isolated tooth crown (SEN 067) in labial view. Scalebars: A–F = 2 cm; G =1 cm.
FIGURE 1. A in A Composite Section of Fossiliferous Late Cretaceous- Early Paleogene Localities in Senegal and Preliminary Description of a New Late Maastrichtian Vertebrate Fossil Assemblage
FIGURE 1. A, Regional geologic map of western Senegal indicating names and ages of geologic formations exposed (after Roger et al., 2009), with black box indicating area under study (expanded in B). B, Positions of the four localities of the Ndayane/Poponguine area described in this paper: 1, Cap de Naze; 2, North Quarry of Poponguine; 3, Ndayane Cliff at Poponguine; and 4, Islet of Poponguine. All are in close proximity despite the variation in lithology among them. C, Position of field area in the Senegalese–Mauritanian Basin in western Senegal, West Africa.
FIGURE 2 in A Composite Section of Fossiliferous Late Cretaceous- Early Paleogene Localities in Senegal and Preliminary Description of a New Late Maastrichtian Vertebrate Fossil Assemblage
FIGURE 2. Composite of formation names for Late Cretaceous–Early Paleogene rocks of western Senegal.
FIGURE 4 in A Composite Section of Fossiliferous Late Cretaceous- Early Paleogene Localities in Senegal and Preliminary Description of a New Late Maastrichtian Vertebrate Fossil Assemblage
FIGURE 4. The four units visible at the Cap de Naze Cliff locality with the end Campanian Paki Formation and the Late Maastrichtian Cap de Naze Formation cropping out under a Pliocene capping. A, units 1–4 (figure modified from Cuny et al., 2012: fig. 2) and B, units 1–3.
Fig. 3 in Plagiorchis sp. in small mammals of Senegal and the potential emergence of a zoonotic trematodiasis
Fig. 3. Phylogenetic relationships among Plagiorchis spp. inferred by Maximum Likelihood (A) and Bayesian Inference (B) analyses of the cytochrome c oxidase subunit 1 gene data. The taxon Fasciola hepatica (GenBank™ AP017707) was used as outgroup. Nodal support ≥ 80% from likelihood bootstrap replicates and Bayesian posterior probabilities is indicated with an asterisk.
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.