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721 results for “Namibia”
Micrometeorological data from Etosha Heights Conservation Centre, Namibia, 2023-ongoing
This data set contains half-hourly micrometeorological data from six weather stations distributed across Etosha Heights Private Reserve in northern Namibia. Data collection began at the end of May 2023, and is ongoing. The data are part of a project funded by Colgate University's Picker Interdisciplinary Science Institute, in collaboration with Giraffe Conservation Foundation and the Namibia University of Science and Technology, aimed at better understanding animal movement. That data are being coupled with gps data from a variety of animals within the reserve and adjacent Etosha National Park.
Sample of HYPERNETS Hyperspectral Surface Reflectance Measurements for Satellite Validation from the Gobabeb Site in Namibia
<p>The HYPERNETS project (www.hypernets.eu; Ruddick et al. 2024) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical satellite products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu; Kuusk et al. 2024) dedicated to land and water surface reflectance validation with instrument pointing capabilities. This instrument has been deployed over various sites covering a range of water and land types and a range of climatic and logistic conditions. Here, we provide the first fully quality-checked data for the Gobabeb HYPERNETS site in Namibia (GHNA). The HYPERNETS data products were processed using the HYPERNETS_processor (De Vis et al. 2024b).</p> <p>The provided NetCDF files are the L2B hypernets products with surface reflectances, their associated uncertainties and error-correlation information. The reflectance in these products is the Hemispherical-conical Reflectance Factor (HCRF) defined as: HCRF = π L / E where L is the conical upwelling radiance (with field of view of 5 degrees) and E is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance). These reflectances have dimensions of wavelength and series, where each series is a set of measurements for a given geometry (combination of viewing zenith and azimuth angle). In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, number of valid scans used, and quality flags (typically no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info.</p> <p>The GHNA site has minimal daily variation in surface cover and weather conditions and is an ideal location for sustained, homogeneous measurements. The site is well characterised as it is very close to an instrument already recognised as a radiometric calibration site (GONA) as part of the RadCalNet network (Bialek et al. 2016). The HYPERNETS site itself (23.60153 degrees S, 15.12589 degrees E) is 650 m from the RadCalNet site, and is located on a gravel plain near a dry riverbed which separates it from the neighbouring dune sea. The HYPSTAR®-XR sensor was installed May 2022 at the top of a 9m mast on an extended 1 m horizontal boom to minimise interruption of the field of view. Data are collected every 30 minutes between 9am and 6pm local time (UTC+02) between viewing zenith angles of 0 and 60 degrees. No measurements are taken at 2pm and 2:30pm local time to avoid the hottest part of the day.</p> <p>The HYPSTAR®-XR (eXtended Range) instruments deployed at each land HYPERNETS site consist of a VNIR and a SWIR sensor and autonomously collect data between 380-1700 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm and the SWIR sensor has 220 channels between 1000 and 1700 nm with a FWHM of 10 nm. The hypernets_processor (De Vis et al. 2024b) automatically processes all this data into various products, including the L2A surface reflectance product provided here. All of the products have associated uncertainties (divided into random and systematic uncertainties, including error-correlation information) which were propagated using the CoMet toolkit (www.comet-toolkit.org). For an example using these data for satellite vicarious calibration, see De Vis et al. 2024a).</p> <p>To obtain this dataset, we start from the full GHNA data record and omit data that does not pass the relevant quality checks (QC). Some QC are performed during the near-real time processing done by the hypernets_processor (see https://hypernets-processor.readthedocs.io/en/latest/content/atbd/processing/quality_checks.html) to produce the L2A files. Then, a number of site-specific QC are performed as post-processing to produce the L2B files. These site-specific QC cover things such as removing flags in the L2A data, avoiding periods with bad deployment conditions, removing unsuitable viewing and solar angles, as well as poorly performing wavelength ranges and individual sequences. Any potential misalignment of the sensor is also corrected, affecting L1D irradiances, and L2B reflectances. These corrected data are then used in a more stringent clear sky check, and in a check that verifies the reflectances are within realistic ranges for a given angle and time of year for the given site. </p> <p>There was a rain event in Gobabeb in March 2025, resulting in the growth of grass at the site. We expect the site will be back to its normal surface cover in the near future. Since the rain event, less data passed the site-specific QC. A dedicated QC will be developed for this period, as the data with grass surface cover will still be useful for satellite validation. These updated data will be made available in the future. </p>
National Checklists 2017: Namibia 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 Namibia collected using effechecka and geonames polygons
National Checklists 2019: Namibia 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 Namibia collected using effechecka and geonames polygons
Climate change perception interview questions for Kunene, Namibia
<p>The files contain interview question used to collect data on climate change perception in Kunene Region Namibia, from a pastoralist Himba tribe.</p>
FIGURE 5 in From the frying pan: an unusual dwarf shrub from Namibia turns out to be a new brassicalean family
FIGURE 5. Landsat image on which is shown the village of Tses in Namibia and the three seasonal pans (coloured green) from where Tiganophyton karasense is known at present: A = Groot Pan; B = Kleinvaalgras Pan; C = Pan at Middelplaas. These pans receive their drainage from the calcrete-covered Weissrand Plateau, the extensive palely coloured area dotted with darkly coloured depressions (dayas). Linear aeolian dunes of the Kalahari (KD) are visible in the upper right-hand corner of the image and believed to have covered the now exposed Weissrand Plateau in the distant past. Image: NASA, based on a tri-decadal global Landsat 7 orthorectified ETM+ Pan-sharpened image.
FIGURE 3. Tiganophyton karasense. A in From the frying pan: an unusual dwarf shrub from Namibia turns out to be a new brassicalean family
FIGURE 3. Tiganophyton karasense. A. Branchlet with short and long shoots; parts of oldest stem with scars left by withered short shoots. B. Short shoot with three flowers. C. Long shoot developing from apex of a short shoot. D. Long shoot leaves; dorsal view left, ventral view right. E. Short shoot leaves; dorsal view left, ventral view right. F. Semi-stylized depiction of a flower (in reality floral parts closely packed with gynophore tightly appressed to ovary), with calyx, two front stamens and a front petal removed. Note gynophore bent in near S-shape, horizontal orientated bilobed ovary, and gynobasic style (portion of style between ovary lobes indicated with stippling; ovary lobes opaque). G. Flower (side view). H. Calyx viewed from outside. I. Calyx opened out and viewed from inside. J. Nutlet. K. Flattened remains of flower with nutlet enclosed in the persistent calyx. Scale bar = 10 mm (A), or 1 mm (B–K). A, C, J & K from Swanepoel 364 and B & C–I from Swanepoel 365. Artist: Daleen Roodt.
FIGURE 1 in From the frying pan: an unusual dwarf shrub from Namibia turns out to be a new brassicalean family
FIGURE 1. Portion of the Brassicales phylogenetic tree based on three plastid (ndhF, matK and rbcL) and one mitochondrial (matR) markers. The core Brassicales clades comprise samples from Brassicaceae, Capparaceae, Cleomaceae, Resedaceae, Gyrostemonaceae, Pentadiplandraceae, Tovariaceae, and Emblingiaceae. Numbers on nodes are bootstrap percentages from the RAxML analysis.
FIGURE 4 in From the frying pan: an unusual dwarf shrub from Namibia turns out to be a new brassicalean family
FIGURE 4. Topographical map showing the known distribution (black dots) of Tiganophyton karasense in southeastern Namibia. The insert shows a map of southern Africa with names of countries; the grey rectangle indicates the area depicted by the topographical map.
FIGURE 2. Tiganophyton karasense. A. Plant habit and habitat. B in From the frying pan: an unusual dwarf shrub from Namibia turns out to be a new brassicalean family
FIGURE 2. Tiganophyton karasense. A. Plant habit and habitat. B. Part of an old long shoot showing short shoots with their rosettes of foliage leaves (mainly) and bracts. C. Young, actively elongating long shoots with short shoots not yet fully developed in leaf axils; arrows indicate where a long shoot emerges from the apex of a short shoot. D. Long shoot densely covered with short shoots, the latter bearing flowers. Photographs: W. Swanepoel.
qdgc Namibia
<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>
Infrastructure Climate Resilience Assessment Data Starter Kit for Namibia
<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>
Transport Starter Data Kit: Historical socio-transport data for Namibia
<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>
Figs. 1-6. 1-2 in Two new species of genus Singilis Rambur, 1837 (Coleoptera: Carabidae: Lebiini) from Namibia
Figs. 1-6. 1-2 – Habitus of Singilis: 1– S. (s.str.) puchneri sp.n., Holotype; 2 – S. (Protosingilis) bulirschi sp.n., Holotype; 3-4 – Aedeagus: 3 – S. (s.str.) puchneri sp.n., 4 – S. (Protosingilis) bulirschi sp.n.; 5-6 – Apical labial palpomere: 5 – S. (s.str.) puchneri sp.n., 6 – S. (Protosingilis) bulirschi sp.n.
Figure 9 in Description of a new endemic genus of the Namib Desert and adjacent biomes in Namibia (Tineoidea: Tineidae: Hapsiferinae)
Figure 9 – Rooiklipia spec., female genitalia, A: R. mirabib, ventral; B–C: R. michaelmeyi spec. nov., B: ventral, C: lateral.
Figure 11 in Description of a new endemic genus of the Namib Desert and adjacent biomes in Namibia (Tineoidea: Tineidae: Hapsiferinae)
Figure 11 – Landscape at Rooiklip guest farm, dry season 2019, with Gamsberg (2349 m) in the background.
Figure 13 in Description of a new endemic genus of the Namib Desert and adjacent biomes in Namibia (Tineoidea: Tineidae: Hapsiferinae)
Figure 13 – Best RAxML tree topology of the COI-5P fragment, with bootstrap values displayed above nodes.
When female leaders believe that men make better leaders: Empowerment in community-based water management in rural Namibia
<p>The code in this repository can be used to reproduce the figures and regression analysis in Stata (some packages need to be installed via SSC) but the data file can of course be used with other programs like R or SPSS.</p> <ul> <li>"female_leaders.do" includes the Stata code</li> <li>"female_leaders.dta" includes the data from the behavioral task and surveys data in Stata format (.dta) & "female_leaders.xlsx" includes these data in Excel format (.xlsx)</li> </ul>
FIG. 27 in Skeleton of Early Miocene Bathyergoides neotertiarius Stromer, 1923 (Rodentia, Mammalia) from Namibia: behavioural implication
FIG. 27. — Femora of extant fossorial rodents and GT 50'06: Cryptomys hottentotus (Lesson, 1826) AZ 834, right femur; A, anterior view; F, posterior view, K, medial view; Bathyergus janetta Thomas & Schwann, 1904 TM 39332, left femur; B, anterior view; G, posterior view; L, medial view; Heliophobius argentocinereus Peters, 1846 TM 45931, left femur; C, anterior view; H, posterior view; M, medial view; Tachyoryctes splendens (Rüppell, 1835) 820 38 M 1, right femur; D, anterior view; I, posterior view; N, medial view; Bathyergoides neotertiarius Stromer, 1923 GT 50'06, left femur E, anterior view; J, posterior view; O, medial view. Scale bars: 1 cm.
FIG. 18 in Skeleton of Early Miocene Bathyergoides neotertiarius Stromer, 1923 (Rodentia, Mammalia) from Namibia: behavioural implication
FIG. 18. — Left calcaneum of GT 50'06: A, dorsal view; B, lateral view; C, plantar view; D, medial view; E, distal view; F, Proximal view. Scale bar: 1 cm.
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.