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278 results for “Sahara”
Electron microscopy of particles collected by different techniques from field measurements in the Moroccan Sahara during FRAGMENT 2019
<p>An intensive field campaign between 4-30 September 2019 was conducted at a major source region on the edge of the Saharan desert in Morocco (29.83 °N 5.87 °W) in the context of the FRontiers in dust minerAloGical coMposition and its Effects upoN climaTe (FRAGMENT) project. Samples were collected with three different sampling techniques, namely: flat-plate sampler (FPS), free-wing impactor (FWI), and a micro-orifice uniform deposit impactor (MOUDI). Substrates in the MOUDI and FWI were collected two times a day with a typical sampling duration of a few minutes to avoid overloading the substrate for individual particle analysis. For the flat-plate sampler, the average exposure time was half a day. Here we present dataset of the elemental composition and morphology of more than 300,000 freshly emitted individual particles by performing offline analysis in the laboratory using Scanning Electron Microscopy (SEM) coupled with Energy-Dispersive X-ray Spectrometry (EDX).</p>
Data presented in González-Flórez et al. 2023 "Insights into the size-resolved dust emission from field measurements in the Moroccan Sahara", Atmos. Chem. Phys.
<p>Meteorological, dust and saltation data used in González-Flórez et al., 2023. Data are based on measurements taken during an intensive dust field campaign conducted in the context of the FRontiers in dust minerAloGical coMposition and its Effects upoN climaTe (FRAGMENT) project. The campaign took place in September 2019 in a small ephemeral lake, locally named "L'Bour", located in the Lower Drâa Valley in Morocco. The description of the data is provided below:</p> <p>- t.nc: time series of temperature measured with four aspirated shield temperature sensors (Campbell Scientific 43502 fan-aspirated shield with 43347 RTD Temperature probe) placed at heights of 1m, 2m, 4m and 8m.</p> <p>- t005.nc time series of temperature measured with a temperature and relative humidity probe (Campbell Scientific HC2A-S3) at 0.5m height.</p> <p>- rh005.nc: time series relative humidity measured with a temperature and relative humidity probe (Campbell Scientific HC2A-S3) at 0.5m height.</p> <p>- wspd.nc: time series of wind speed measured with five 2-D sonic anemometers (Campbell Scientific WINDSONIC4-L) placed at heights of 0.4m, 0.8m, 2m, 5m and 10m.</p> <p>- sdir.nc: time series of wind direction measured with five 2-D sonic anemometers (Campbell Scientific WINDSONIC4-L) placed at heights of 0.4m, 0.8m, 2m, 5m and 10m.</p> <p>- radout.nc: time series of outgoing long wave radiation measured with a four-component net radiometer (Campbell Scientific NR01-L radiometer) placed at 1.5m height.</p> <p>- p015.nc: times series barometric pressure measured with a barometer (Campbell Scientific CS106) at around 1.5m height.</p> <p>- u_star_law.nc: time series friction velocity calculated through the law of the wall method.</p> <p>- z0_law.nc: time series of roughness length calculated through the law of the wall method.</p> <p>- zeta_law.nc: time series of dimensionless height, zref/L, where zref is the reference height (zref=2m) and L is the Obukhov length calculated through the law of the wall method.</p> <p>- psd_lower_15avg_20190904_000000_integrated_bins.nc: time series of 15-min average number concentrations in integrated size bin resolution measured with an optical particle counter (Fidas 200S, Palas GmbH) at ~1.8m height.</p> <p>- psd_upper_15avg_20190904_000000_integrated_bins.nc: time series of 15-min average number concentrations in integrated size bin resolution measured with an optical particle counter (Fidas 200S, Palas GmbH) at ~3.5m height and corrected for systematic bias based on an intercomparison between the two Fidas at the end of the campaign.</p> <p>- diff_flux_nb_15avg_20190904_000000_integrated_bins.nc: time series of 15-min average number diffusive flux calculated using the flux-gradient method.</p> <p>- q_15avg.nc: time series of 15-min average saltation flux calculated based on measurements with optical gate devices at heights of 0.05m, 0.15m and 0.3m as part of the Standalone AeoliaN Transport Real-time Instrument (SANTRI, Desert Research Institute).</p> <p>- geometric_diameters_integrated_size_bins.csv: containing the minimum, maximum and mean logarithmic optical diameter of the integrated size bins.</p> <p>- optical_diameters_integrated_size_bins: containing the minimum, maximum and mean logarithmic geometric diameter of the integrated size bins.</p> <p>SANTRI data were processed by Martina Klose (<a href="mailto:martina.klose@kit.edu">martina.klose@kit.edu</a>) and the rest by Cristina González Flórez (<a href="mailto:cristina.gonzalez@bsc.es">cristina.gonzalez@bsc.es</a>). Please, cite González-Flórez et al. (2023, ACP) if you use these data. If the data become the key main component of a paper then co-authorship may be offered. Contact Carlos Pérez García-Pando (<a href="mailto:carlos.perez@bsc.es">carlos.perez@bsc.es</a>) if more details are needed.</p> <p>This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 773051, FRAGMENT).</p>
Figure 43 in Cretaceous Crocodyliforms from the Sahara
Figure 43. Phylogeny of stem crocodyliforms. Maximum parsimony and bootstrap analysis of representative stem crocodyliforms scored for 252 characters using the protosuchian Orthosuchus as an outgroup. Taxon names in red highlight those species described here. The character list, character-state matrix, and apomorphy list (for one of the minimum-length trees) are available in the Appendix. A Strict consensus tree based on 4 minimum-length trees (TL = 986, consistency index = 0.34; retention index = 0.64) from maximum-parsimony analysis using PAUP* (Swofford 1998) of all 42 ingroup crocodyliforms, which places Hsisosuchus and Thalattosuchia at the base of Crocodyliformes, recognizes a diverse Notosuchia including Anatosuchus and Araripesuchus, and positions several taxa including Kaprosuchus and Laganosuchus within Neosuchia. B 50%-majority-rule consensus tree based on 2000 bootstrap replicate parsimony analyses on 40 ingroup crocodyliforms (excluding for computational effi ciency the poorly known taxa Araripesuchus rattoides and Laganosuchus thaumastos). The bootstrap result recognizes less structure at the base of Metasuchia and within Notosuchia. Taxon names (circled numbers) are positioned on nodes and stems to reflect their node- and stem-based phylogenetic definitions, respectively (Sereno 2005; Larsson and Sues 2007). Abbreviations: 1, Mesoeucrocodylia; 2, Thalattosuchia; 3, Metasuchia; 4, Notosuchia; 5, Neosuchia; 6, Sebecia; 7, Mahajangasuchidae; 8, Pholidosauridae; 9, Crocodylia.
Figure 40 in Cretaceous Crocodyliforms from the Sahara
Figure 40. Lower jaws of the crocodyliform Laganosuchus thaumastos gen. n. sp. n. Cast (UCRC PVC9) of the posterior portion of the lower jaws (MNN IGU13). A Left side in dorsal view. B Right side in dorsal view. Dashed line indicates missing bone. Scale bar equals 5 cm. Abbreviations: a, angular; ar, articular; gl, glenoid; cp, coronoid process; pb, pathologic bone; rp, retroarticular process; ru, rugosities; sa, surangular.
Figure 35 in Cretaceous Crocodyliforms from the Sahara
Figure 35. Skull of the crocodyliform Kaprosuchus saharicus gen. n. sp. n. Detailed views of the external nares and orbital region (MNN IGU12). A Snout end in dorsal view. B Orbital, antorbital, and coronoid regions of the skull in right lateral view. Scale bars equal 5 cm. Abbreviations: a, angular; antfe, antorbital fenestra; antfo, antorbital fossa; apap, articular surface for the palpebral; asaf, anterior surangular foramen; d3, dentary tooth 3; en, external naris; j, jugal; l, lacrimal; m, maxilla; n, nasal; nf, narial fossa; pm, premaxilla; pmru, premaxillary rugosity; pob, postorbital bar (jugal portion); sa, surangular.
Figure 22 in Cretaceous Crocodyliforms from the Sahara
Figure 22. Endocast of the crocodyliform Araripesuchus wegeneri. Endocast (UCRC PVC5) prototyped from a computed-tomography scan of skull MNN GAD19. Th e endocast lacks a portion of the pituitary fossa and right and left labyrinths. A Lateral view. B Dorsal view. C Ventral view. Scale bar equals 1 cm. Abbreviations: cer, cerebrum; cnII, cranial nerve II (optic nerve); lsin, longitudinal sinus; opt, optic lobe; pit, pituitary fossa; vfo, ventral fossa.
Figure 26 in Cretaceous Crocodyliforms from the Sahara
Figure 26. Manus and pes of the crocodyliform Araripesuchus wegeneri. A Right manus in dorsal view (MNN GAD22). B Right pes in dorsal view (MNN GAD22). Scale bar equals 1 cm in A and 2 cm in B. Abbreviations: I-V, digits I-V; mc1–5, metacarpal 1–5; mt1–4, metatarsal 1–4; ph, phalanx; un, ungual.
Figure 34 in Cretaceous Crocodyliforms from the Sahara
Figure 34. Skull of the crocodyliform Kaprosuchus saharicus gen. n. sp. n. Drawings matching the cranium and lower jaws (MNN IGU12) in Fig. 33. A Cranium and lower jaws in left lateral view. B Cranium in dorsal view. C Cranium in ventral view. Dashed line indicates missing bone or tooth crown. Scale bar equals 20 cm. Abbreviations: a, angular; antfe, antorbital fenestra; antfo, antorbital fossa; apap, articular surface for palpebral; ar, articular; asaf, anterior surangular foramen; bo, basioccipital; bs, basisphenoid; ch, choana; d, dentary; d1–3, 8, 16, dentary tooth 1–3, 8, 16; dd3, 8, diastema for dentary tooth d3, d8; ec, ectopterygoid; Ef, Eustachian foramen; emf, external mandibular fenestra; en, external naris; f, frontal; fd1, 2, 5, fossa for dentary tooth 1, 2, 5; gef, groove for ear flap; j, jugal; jfo, jugal fossa; l, lacrimal; m, maxilla; m1, 3, 7, 10, maxillary tooth 1, 3, 7, 10; n, nasal; nfo, narial fossa; p, parietal; pf, prefrontal; pl, palatine; pm, premaxilla; pm1–3, premaxillary tooth 1–3; po, postorbital; pt, pterygoid; q, quadrate; qc, quadrate cotylus; qj, quadratojugal; rp, retroarticular process; sa, surangular; se, septum; sof, suborbital fenestra; sq, squamosal; sqh, squamosal horn; tm, tooth mark; vg, vascular groove.
Figure 24 in Cretaceous Crocodyliforms from the Sahara
Figure 24. Caudal skeleton of the crocodyliform Araripesuchus wegeneri. Flexed, articulated tail showing paired dorsal osteoderm rows in dorsal view, lateral osteoderm row in lateral view and ventral osteoderm rows in ventral view (MNN GAD20). Scale bar equals 5 cm. Abbreviations: k, keel; l do, left dorsal osteoderm; l vo, left ventral osteoderm; r do, right dorsal osteoderm; r lo, right lateral osteoderm; r vo, right ventral osteoderm.
Figure 33 in Cretaceous Crocodyliforms from the Sahara
Figure 33. Skull of the crocodyliform Kaprosuchus saharicus gen. n. sp. n. Cast (UCRC PVC8) of cranium and lower jaws (MNN IGU12), which were separated from a cast of the skull (which remains in one piece). Left maxillary teeth 1 and 8 were missing and are based on the corresponding right maxillary teeth. Dentary teeth 9–16 cannot be seen as a result of the adduction of the jaws but were visualized and then reconstructed on the basis of a computed-tomographic scan. A portion of the right side of the skull table is not preserved and is a reflection from the left side. Most of the occiput is not preserved and has been reconstructed. A Cranium and lower jaws in left lateral view. B Cranium in dorsal view. C Cranium in ventral view. Scale bar equals 20 cm.
Figure 9 in Cretaceous Crocodyliforms from the Sahara
Figure 9. Dentary of the crocodyliform Anatosuchus minor. Pencil drawing of mid-section of the left dentary including alveoli 7–14 (MNN GAD18). A Dorsal view. B Ventral view (reversed). Parallel lines indicate broken bone; double-dash pattern indicates matrix. Scale bar equals 1 cm. Abbreviations: ad7, 12, alveolus of dentary tooth 7, 12; asp, articular surface for splenial; d14, dentary tooth 14; fo, foramen; Mc, Meckel's canal; sh, shelf.
Figure 1 in Corrigenda: Sereno PC, Larsson HCE (2009) Cretaceous Crocodyliforms from the Sahara. ZooKeys 28: 1–143.
Figure 1. Lower jaw of Kaprosuchus saharicus. Drawing of the lateral view of the lower jaw (MNN IGU12). Scale bar equals 10 cm. Abbreviations: a, angular; ar, articular; asaf, anterior surangular foramen; d, dentary; d1-3, 8, 16, dentary tooth 1-3, 8, 16; emf, external mandibular fenestra; qc, quadrate cotylus; ri, ridge; rp, retroarticular process; sa, surangular.
qdgc Western Sahara
<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 23rd of January, 2021<br> <br> <br> <br> <br> ragnvald@mindland.com<br> www.mindland.com</p>
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>
Fig. 9 in Revision of deep-water species in Granulina (Gastropoda: Granulinidae) from Mauritania and Western Sahara
Fig. 9. Granulina sigridae sp. nov., Mauritania.A –I. Timiris coral mound chain, MSM16–3/GeoB14876. A –B. Paratype (SMF373040). A. Ventral view, height 2.9 mm, width 1.8 mm. B. Columellar folds. C–D. Holotype (SMF359034). C. Ventral view, height 3.1 mm, width 1.9 mm. D. Columellar folds. E– G. Paratype (SMF373040). E –F. Ventral and side views, height 3.2 mm, width 1.8 mm, tumidity 1.6 mm. G. Micro-sculpture above second columellar tooth, see arrow in E–F. H. Paratype (SMF373040), ventral view, height 3.0 mm, width 1.8 mm. I. Paratype (SMF373040), side view, height 3.1 mm, tumidity 1.6 mm. J–K. Tamxat Mounds, MSM16–3/GeoB14904, ventral view, height 2.8 mm, width 1.7 mm.
Fig. 7 in Revision of deep-water species in Granulina (Gastropoda: Granulinidae) from Mauritania and Western Sahara
Fig. 7. Granulina ronaldi sp. nov., Mauritania, off Banc d'Arguin, CANCAP/3.154. A–D. Holotype (SMF359026). A–B. Ventral and side views, height 2.9 mm, width 1.7 mm, tumidity 1.4 mm. C. Columellar folds. D. Micro-sculpture above second columellar fold, see arrow in C. E–F. Paratype (SMF359020), ventral and side views, height 2.5 mm, width 1.5 mm, tumidity 1.3 mm. G–H. Paratype (SMF359027), central and side views, height 2.5 mm, width 1.4 mm, tumidity 1.3 mm, callus line indicated by white dots. I–J. Paratype (SMF359029), ventral and side views, height 2.5 mm, width 1.5 mm, tumidity 1.2 mm.
Fig. 6 in Revision of deep-water species in Granulina (Gastropoda: Granulinidae) from Mauritania and Western Sahara
Fig. 6. Granulina reginae sp. nov., Mauritania. A –D. Timiris Mound Complex. A –C. Holotype, POS346/ GeoB11587 (SMF359019). A–B. Ventral view, height 2.2 mm, width 1.4 mm, C. Micro-sculpture above second fold, see arrow in B. D. Paratype, POS346/GeoB11588 (SMF359021), ventral view, height 2.0 mm, tumidity 1.2 mm. E–J. Paratypes (SMF359025), off Banc d'Arguin, MSM16–3/GeoB14799. E–F. Ventral and side view, height 2.1 mm, width 1.3 mm, tumidity 1.1 mm. G. Micro-sculpture above second fold, see arrows in E, H. H. Columellar folds. I–J. Ventral and side view, height 2.2 mm, width 1.4 mm, tumidity 1.1 mm.
Fig. 5 in Revision of deep-water species in Granulina (Gastropoda: Granulinidae) from Mauritania and Western Sahara
Fig. 5. Location map of the new species in Granulina Jousseaume, 1888 off Mauritania and Western Sahara. White circles show all investigated stations; colour symbols show locations of shells from new species. Granulina reginae sp. nov. is presented by yellow squares, G. sigridae sp. nov. as red diamonds, G. sandrae sp. nov. as blue triangles, and G. ronaldi sp. nov. as green circles. Bathymetric data from GEBCO; contours 500 m
Fig. 4 in Revision of deep-water species in Granulina (Gastropoda: Granulinidae) from Mauritania and Western Sahara
Fig. 4. Ganulina aff. crassa, off Nouakchott, Mauritania. A–D. M44/133–KG615. E–F. M60/77–KG960. A–B. Ventral and side view, height 1.8 mm, width 1.2 mm, tumidity 1.0 mm. C. Micro-sculpture above second columellar fold, see arrow in D. D. Columellar folds and labial denticles. E. Ventral view, height 2.0 mm, width 1.3 mm. F. Ventral view, height 2.0 mm, width 1.3 mm.
Fig. 3 in Revision of deep-water species in Granulina (Gastropoda: Granulinidae) from Mauritania and Western Sahara
Fig. 3. Granulina crassa Smriglio, Gubbioli & Mariottini, 2000, Western Sahara, off Cap Blanc, M44/235–KG649. A–D. Ventral and side views, height 2.1 mm, width 1.3 mm, tumidity 1.0 mm. C. Columellar folds. D. Micro–sculpture above second columellar fold, see arrow in C. E–F. Ventral and side views, height 2.1 mm, width 1.3 mm, tumidity 1.0 mm. G–H. Views and dimensions as E–F. I – J. Views and dimensions as E–F.
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.