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281 results for “Senegal”

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zenodo48/100

Navigating deep learning strategies for large-area land cover mapping using very-high-resolution imagery in Senegal: Validation Data

<p><span><span>R</span><span>apid</span><span> advances in deep learning</span><span> for</span> <span>land cover </span><span>classification of </span><span>trees, shrubs and </span><span>very small</span> <span>agricultur</span><span>al</span> <span>fields</span> <span>using</span> <span>very high</span><span>-</span><span>resolution satellite </span><span>data </span><span>(&lt; 2 m</span><span>)</span><span>,</span><span> has tremendous potential</span> <span>for resolving </span><span>current</span><span> challenges </span><span>in </span><span>quantifying</span> <span>land cover </span><span>change </span><span>in</span> <span>sub-</span><span>Saharan</span> <span>African (SSA</span><span>)</span><span>,</span> <span>due to</span> <span>growing </span><span>demand for food resources</span><span>.</span> <span>We</span> <span>conducted experiments </span><span>with</span><span> different training strategies for scaling up </span><span>UNet</span> <span>convolutional neural network </span><span>models for regional land cover mapping with multispectral </span><span>WorldView</span><span> (WV</span><span>)</span><span>-2 and &ndash;3,</span><span> imagery</span><span> in</span><span> three distinct regions of Senegal </span><span>which</span> <span>has</span><span> complex </span><span>seasonal wet/dry conditions and </span><span>cropland-savanna mosaics.&nbsp;</span></span></p> <p>The validation exercise of this research consisted in validating more than 70,000 km<sup>2</sup> across Senegal. The infrastructure was setup in the NASA SMCE system with a total of twelve George Mason University (GMU) students participating as operators. These operators validated more than 59 WV-2 and -3 images, each consisting of 200 stratified points in 5,000 x 5,000-pixel images. This effort resulted in a total of ~35,000 aggregated observations that are available through the eo-validation API for public consumption. Each validation point from this dataset has three individual observations.</p>

opencc-by-4.0Oct 2024View details →
zenodo44/100

Kallaama: A Transcribed Speech Dataset about Agriculture in the Three Most Widely Spoken Languages in Senegal

<p>This data is transcribed speech data, in Wolof, Pulaar and Sereer.</p> <p>The recordings are about agriculture. The recorded consist of farmers, agricultural advisers, and agri-food business managers.&nbsp;Type of recordings comprise interactive radio programmes, focus groups, voice messages, push messages and interviews. Therefore, spontaneous speech is prevailing. Quality of audio may vary depending on the type of programme.</p> <p>Content description :</p> <ul> <li><strong>speech_dataset_wol.tar.gz:</strong> Wolof (ISO Code 639-2: wol) speech dataset contains 55 hours of transcribed speech, including almost 13 hours of validated content check by an expert. It also contains a XSAMPA lexicon (49,132 phonetised entries) and a text corpus (1,140,508 words).</li> <li><strong>speech_dataset_fuc.tar.gz:</strong> Pulaar (ISO Code 639-2: fuc) speech dataset contains nearly 32 hours of transcribed speech, including around 11 hours of validated content check by an expert. It also contains a text corpus (742,024 words).</li> <li><strong>speech_dataset_srr.tar.gz:</strong> Sereer (ISO Code 639-2: srr) speech dataset contains 38 hours of transcribed speech, including nearly 11 hours of validated content check by an expert.<br>In total, these resources provide 125 hours of transcribed speech in the 3 most widely spoken languages in Senegal, including 35 hours of checked transcriptions.</li> </ul> <p>This work is a result of the Kallaama project, funded by Lacuna Fund for 1 year, in 2023.&nbsp;</p> <p>See the <a title="Kallaama speech dataset" href="https://github.com/gauthelo/kallaama-speech-dataset" target="_blank" rel="noopener">GitHub repository</a> for more details about the dataset.</p>

opencc-by-4.0Mar 2024View details →
zenodo44/100

Inventory data of woody plants surveyed and measured in North Senegal (Ferlo) in 2015-2017

<p>This dataset gathers measurements from field inventory on woody vegetation carried in the sylvo-pastoral zone of Ferlo (Senegalese Sahel) in 2015-2016-2017. The data consist of dendrometric measurements, location and species for 3215 woody individuals (trees, bushes, and shrubs) belonging to 25 species and 11 families.</p> <p><strong>Sites </strong></p> <p>The study sites are located in the Northern Sandy Pastoral Region of Senegal around the deep wells of Widou Thiengoly (15.99&deg;&nbsp;N, 15.32&deg;&nbsp;W) and Tess&eacute;k&eacute;r&eacute; (15.85&deg; N, 15.06&deg;&nbsp;W). The vegetation formation is an open savanna, with a relatively low woody cover.</p> <p>For the <strong>field work of 2015</strong>, we applied a stratified sampling according to the topography and the distance to the studied deep wells. We inventoried 139 plots of 0.25 ha each. The center of each plot was marked by a gps point. The plots were located at increasing distances from the boreholes: 2, 3.5, 5, 7.5, 10, 12.5 and 15&nbsp;km (20 plots per distance). For each distance, we randomly selected at least six plots in depressions (43 plots in total), the other plots being located on slopes or on hilltops, with a total vertical drop of several meters (96 plots in total). Whenever possible, the plots in each category of topography were distributed between the two soil types. In total, 86 plots were allocated to the ferruginous soils and 53 plots to the sub-arid brown red soils.&nbsp;</p> <p>In<strong> 2016, </strong>additional woody plants were surveyed within 10 circular plots with a variable radius between 27 m and 52 m, so that at least 10 individuals were counted for each plot. <strong>In 2017, </strong>woody plants were surveyed within 30 square plots of 0.25 ha each. Woody individuals were all geotagged in 2016 and 2017. &nbsp;</p> <p><strong>Field measurements</strong></p> <p>Adults woody plants (with a circumference superior to 10 cm at ground level) were inventoried within square plots (2015, 2017) or circular (2016). Species name was identified for all individuals and recorded following the taxonomic referential of the African Plant Database (version 3.4.0). Three types of dendrometric measurements were performed on woody plants: (i) circumference, measured at 30 cm from ground level, except for shrubs for which circumference was measured at ground level; (ii) tree height, measured by an ultrasonic hypsometer Vertex IV (Haglof Inc.) (iii) two perpendicular crown diameters. GPS points were taken (GPSMAP 62, Garmin Inc.) at the center of each plot (for 2015) and, in some cases for each individual (2016-2017).</p> <p><strong>Data structure and metadata</strong></p> <p>Data are encoded in a single file, using comma-delimited format and UTF-8 encoding. Each row describes one individual woody plant with its corresponding measurements. The following table presents the variables (columns) contained in the dataset.</p> <p>Shapefile format is also available (same data as the .csv).</p> <table> <tbody> <tr> <td> <p><strong>Variable name</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> <td> <p><strong>Definition</strong></p> </td> </tr> <tr> <td> <p>Tree_id</p> </td> <td> <p>-</p> </td> <td> <p>Unique identifier of the individual, with a 13 characters length. The 4 characters following the &ldquo;y&rdquo; indicate the year of the inventory. For instance, &ldquo;tr.y2015.0034&rdquo; is referring to the woody plant number 34 inventoried in 2015.</p> </td> </tr> <tr> <td> <p>Plot_id</p> </td> <td> <p>-</p> </td> <td> <p>Plot identifier</p> </td> </tr> <tr> <td> <p>Plot_area_ha</p> </td> <td> <p>ha</p> </td> <td> <p>Area of the inventoried plot</p> </td> </tr> <tr> <td> <p>Species</p> </td> <td> <p>-</p> </td> <td> <p>Genus, species, subspecies names and botanical authors</p> </td> </tr> <tr> <td> <p>Family</p> </td> <td> <p>-</p> </td> <td> <p>Family name</p> </td> </tr> <tr> <td> <p>Growth_form</p> </td> <td> <p>-</p> </td> <td> <p>Shrub, bush or tree</p> <p>Growth form expresses the extent of growth and the potential branching of the main-shoot axis. In this work, we refer to three types of growth form: shrub, bush and tree. A shrub refers to a small woody plant with a height below 2 meters and multi-stemmed. A tree designates a woody plant taller than 5 to 6 meters, generally presenting a single trunk. A bush, or a dwarf tree as in P&eacute;rez-Harguindeguy et al. (2013), is the intermediary between a shrub and a tree. Its height is usually between 2 to 6 meters and it is often multi-stemmed. Because of intra-specific traits variation, the mentioned growth form is valid for our study area.</p> </td> </tr> <tr> <td> <p>Circ30_m</p> </td> <td> <p>m</p> </td> <td> <p>Circumference measured at 30 cm from ground level. &ldquo;NA&rdquo; indicates missing data (for the individuals measured in 2017).</p> </td> </tr> <tr> <td> <p>Height_m</p> </td> <td> <p>m</p> </td> <td> <p>Tree height. &ldquo;NA&rdquo; indicates missing data (for the individuals measured in 2017).</p> </td> </tr> <tr> <td> <p>Dcrown1_m</p> </td> <td> <p>m</p> </td> <td> <p>First diameter of the crown</p> </td> </tr> <tr> <td> <p>Dcrown2_m</p> </td> <td> <p>m</p> </td> <td> <p>Second diameter of the crown (perpendicular to the first diameter)</p> </td> </tr> <tr> <td> <p>Geoloc_method</p> </td> <td> <p>-</p> </td> <td> <p>Geolocation method; indicates if it is the center of the plot which was geolocated (&ldquo;geoloc.plot&rdquo;, for individuals in 2015) or the woody plant (&ldquo;geoloc.tree&rdquo;, for 2016-2017).</p> </td> </tr> <tr> <td> <p>Lat_dd</p> </td> <td> <p>Decimal degrees</p> </td> <td> <p>North latitude of the plot if the geoloc_method == &ldquo;geoloc.plot&rdquo; and of the woody plant if the geoloc_method == &ldquo;geoloc.tree&rdquo;.</p> </td> </tr> <tr> <td> <p>Long_dd</p> </td> <td> <p>Decimal degrees</p> </td> <td> <p>West longitude of the plot if the geoloc_method == &ldquo;geoloc.plot&rdquo; and of the woody plant if the geoloc_method == &ldquo;geloc.tree&rdquo;.</p> </td> </tr> <tr> <td> <p>Topography</p> </td> <td> <p>-</p> </td> <td> <p>Local topography of the plot. Indicates if the plot is located within a depression (lowland) or on a hilltop.</p> </td> </tr> <tr> <td> <p>Date</p> </td> <td> <p>-</p> </td> <td> <p>Date of the survey: dd-mm-yy</p> </td> </tr> </tbody> </table> <p>&nbsp;</p>

opencc-by-4.0Aug 2022View details →
zenodo44/100

National Checklists 2017: Senegal 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 Senegal collected using effechecka and geonames polygons

opencc-zeroAug 2024View details →
zenodo44/100

National Checklists 2019: Senegal 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 Senegal collected using effechecka and geonames polygons

opencc-zeroAug 2024View details →
zenodo44/100

UAV outputs and associated field measurement of the herbaceous and tree of the Senegalese savanna across Senegal

<p>This dataset contains UAV outputs (mosaic, surface and terrain model) and field measurement of vegetation that were made in northern and Eastern Senegal.</p> <p>Sites</p> <p>National gradient measurements</p> <p>For the national gradients, the measurements were made on 45 different plots in two different field campaign. One in the Northern part at the end of September 2020 and the other in South eastern part of Senegal in middle of October. The selection of the site was a combination of accessibility (not far from the road) and diversity of vegetation. The average rainfall for the period 1981-2018 was ranging from 221 mm.y-1 to 468 mm. y-1 for the Northern Part and ranging 759 mm.y-1 to 1246 mm y-1 for the south eastern part.</p> <p>UAV flight plan</p> <p>We used a low-cost UAV with an RGB (Red Green Blue) captor integrated in the UAV.&nbsp; The UAV was an Anafi of Parrot with PIX4D capture application using the double gird flight plan in a square generally of 100m*100m; The height of the flight was 80m with an overlap of 80% at low speed with 80&deg; angle &deg;. &nbsp;The flights were made at any time during the day.</p> <p>Field measurement.</p> <p>Herbaceous Biomass.</p> <p>3 squares of 1 m&sup2; were sampled. All the aboveground biomass was cut and weighted in fresh. A composite sample was made for each site and weighted dry to evaluated the dry matter content and so the dry matter of each sample.</p> <p>The height of 5 herbaceous individuals selected randomly were measured. We recorded the species composition with percentage of cover of each species. We collected an herbarium sample each time we had a new species. The sample were used to identified the species by the IFAN herbarium team. The positions of the squared was mark with a wood triangle painted on the ground.</p> <p>Tree measurement.</p> <p>Four trees were measured on the field. It was the four woody individuals the closest to the first square of herbaceous measurements were made in each direction (Northwest, North east, South West, South East).</p> <p>The distance to the first square of each tree were measured using a telemeter. The height was also measured with a laser telemeter. The circumference at 0.30cm and 1.3 cm were measured. The diameter of the tree crown in the north-south direction and in the west-east direction were measured to the crow area calculated assuming that the crown was a circle.</p> <p>The species were recorded. We collected an herbarium sample each time we had a new species. The sample were used to identified the species by the IFAN herbarium team.</p> <p>Image analysis.</p> <p>The images taken during each flight were processed using a PiX4D mapper (Pix4D SA, Lausanne, Switzerland). 3D mapping is the basic parameter proposed in the software. For each plot, an orthophotograph, a digital surface model, and a digital elevation model were computed and exported in GeoTIFF format.</p> <p>Data organization</p> <p>The data are organized in two separated folders for each dataset.</p> <p>Each dataset folders contains four folders:</p> <ul> <li>DSM that contains the surface model in tiff</li> <li>DTM that contains the terrain 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> <p>The shapefile&rdquo; national-shape.shp&quot; contains the positions of both tree and herbaceous samples. In some case it was hard to position the squared or the tree. The position and the shape of the object are not well defined.</p> <p>The file &ldquo;tree-national.xlsx&rdquo; contains the information on the tree measurement. The ID that contains the site and the positions of the trees, the distance from the squared in m that indicate the distance of the tree to the biomass square. The height H (in m), the trunk circumference at 1.30m (TC1.3) and at 0.3m(TC0.3) in cmand the area of crown (Area). The species is also described.</p> <p>The file &ldquo; herbacous_national.xlsx&rdquo; contains the information on the herbaceous layer.</p> <p>For each square, the height of the herbaceous layer (H), Fresh mass (FM), Dry matter content (DMC) and dry Mass (DM) are presented; The last columns of the file are the different species with the percentage of cover in each case.</p> <p>&nbsp;</p>

opencc-by-4.0Jul 2021View details →
zenodo44/100

Waves Hindcast on the Senegalese Coast over the Last Four Decades (from 1980 to 2021). [A Dataset use in : SAMOU, M.S.; BERTIN, X.; SAKHO, I.; LAZAR, A.; SADIO, M.; DIOUF, M.B. Wave Climate Variability along the Coastlines of Senegal over the Last Four Decades. Atmosphere 2023]

<p>Computed from the WW3 Model, the last Four Decades Wave Hindcast is available on the Senegalese Coast through this present Dataset. Covering the period 1980 to 2021, this high resolution hindcast, both spatial (0.05x0.05) and temporal (1 h) provided all the wave parameters such as: the significant wave heights, the mean wave periods, the wave directions and the peak wave periods (to compute from wave frequencies) with an hourly interval.</p> <p>More details on this data (e.g., model implementation and validation) can be obtained in: SAMOU, M.S.;&nbsp; BERTIN, X.; SAKHO, I.; LAZAR, A.; SADIO, M.; DIOUF, M.B. Wave&nbsp; Climate Variability along the&nbsp; Coastlines of Senegal over the Last Four Decades. <em>Journal Atmosphere 2023</em>].</p>

opencc-by-4.0Jul 2023View details →
zenodo40/100

Fig. 4. A–D in Descriptions of species of Stegelleta Thorne, 1938 (Nematoda, Rhabditida, Cephalobidae) from California, New Zealand and Senegal, and a revision of the genus

Fig. 4. A–D. Stegelleta ophioglossa Andrássy, 1967. A. Pharyngeal region. B. Anterior end, surface view. C. Female gonad. D. Female tail. E–G. Stegelleta tuarua Yeates, 1967. E. Pharyngeal region. F. Anterior end, surface view. G. Male tail. Scale bar = 20 µm.

opencc-by-3.0Jun 2014View details →
zenodo40/100

Fig. 2 in Descriptions of species of Stegelleta Thorne, 1938 (Nematoda, Rhabditida, Cephalobidae) from California, New Zealand and Senegal, and a revision of the genus

Fig. 2. Stegelleta incisa (Thorne, 1937), SEM micrographs. A–B. Anterior end, left lateral view. C. Anterior part of lateral field. D. Deirid (arrow). E. Vulval region. F. Female tail, subventral view. G. Female tail, left sublateral view (arrow points at phasmid). H. Female tail, lateral view. Scale bars = 5 µm.

opencc-by-3.0Jun 2014View details →
zenodo40/100

Fig. 3 in Descriptions of species of Stegelleta Thorne, 1938 (Nematoda, Rhabditida, Cephalobidae) from California, New Zealand and Senegal, and a revision of the genus

Fig. 3. Stegelleta laterocornuta sp. nov., SEM micrographs. A. Vulval opening. B. Anal opening. C–D. Anterior end, left subventral view. E. Anterior end, left lateral view (arrows in C–E point at the long acute tine extending along the primary axil on the lateral lips). Scale bars = 2 µm.

opencc-by-3.0Jun 2014View details →
zenodo40/100

Fig. 1. A–E in Descriptions of species of Stegelleta Thorne, 1938 (Nematoda, Rhabditida, Cephalobidae) from California, New Zealand and Senegal, and a revision of the genus

Fig. 1. A–E. Stegelleta incisa (Thorne, 1937). A. Pharyngeal region. B. Female gonad. C. Anterior end, surface view. D. Female tail. E. Male tail. F–J. Stegelleta laterocornuta sp. nov. F. Pharyngeal region. G. Female gonad. H. Anterior end, surface view. I. Female tail. J. Male tail. Scale bar = 20 µm.

opencc-by-3.0Jun 2014View details →
zenodo40/100

Figs 24–32. 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 24–32. Mastogloia belaensis M.Voigt. Light micrographs (LM) of valves from the Lac de Guiers population (Van de Vijver sample SEN-42). 24–28. LM views of several smaller valves showing variation in valve size and shape. 29–30. LM views of the partectal ring with the partecta. 31. LM view of an entire valve with removed partectal ring showing the pseudosepta (arrows). 32. Entire frustule in girdle view. Scale bar: 10 μm.

opencc-by-3.0Dec 2017View details →
zenodo40/100

Fig. 69 in Morphology of two Mastogloia species (Bacillariophyta) from Lac de Guiers (Senegal) and comparison with the type material of M. braunii

Fig. 69. World distribution of Mastogloia braunii s. lat. according to the literature. Circles: recent records. Squares: fossil records. Filled symbols indicate confirmed (illustrated) records. 331 locations were found based on 271 references.

opencc-by-3.0Dec 2017View details →
zenodo40/100

Figs 1–5 in Morphology of two Mastogloia species (Bacillariophyta) from Lac de Guiers (Senegal) and comparison with the type material of M. braunii

Figs 1–5. Mastogloia braunii Grunow. Light micrographs (LM) of valves from the type population (Grunow 23583 – capsule 0645, Vienna, Austria). 1–3. LM views of 3 valves showing variation in valve size and shape. The arrows in Fig. 2 indicate shortened striae near the central area. 3–4. Same valve taken at different foci. 4–5. LM views of the partectal ring with the partecta. Scale bar: 10 μm.

opencc-by-3.0Dec 2017View details →
zenodo40/100

Figs 46–55 in Morphology of two Mastogloia species (Bacillariophyta) from Lac de Guiers (Senegal) and comparison with the type material of M. braunii

Figs 46–55. Mastogloia senegalensis Van de Vijver, Fofana, Sow &amp; Ector sp. nov. Light micrographs of valves from the Lac de Guiers type population (Van de Vijver sample SEN-42). 46–51. LM views of several specimens showing variation in valve size and shape (the arrows in Fig. 46 show typical bifurcating striae near the central area). 52–53. LM views of the partectal ring with the partecta. 54. LM view of an entire valve with removed partectal ring showing the pseudosepta. 55. LM view of an entire valve with removed partectal ring showing the valve interior. Scale bar: 10 μm.

opencc-by-3.0Dec 2017View details →
zenodo40/100

Figs 56–59 in Morphology of two Mastogloia species (Bacillariophyta) from Lac de Guiers (Senegal) and comparison with the type material of M. braunii

Figs 56–59. Mastogloia senegalensis Van de Vijver, Fofana, Sow &amp; Ector sp. nov. Scanning electron micrographs (SEM) of valves from the Lac de Guiers type population (Van de Vijver sample SEN-42). 56. SEM girdle view of an entire frustule showing the partectal pores and the mantle areolae. 57. SEM external view of an entire valve with typical undulating raphe branches. 58. SEM external detail of the apex and the axial area with the depressed grooved on both sides of the raphe. 59. SEM external detail of the valve mantle. Scale bars: 10 µm.

opencc-by-3.0Dec 2017View details →
zenodo40/100

Figs 39–45. 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 39–45. Mastogloia belaensis M.Voigt. Scanning electron micrographs (SEM) of valves from the Lac de Guiers population (Van de Vijver sample SEN-42). 39. SEM internal view of an entire valve with the typical partectal ring. 40–41. SEM internal details of the partectal ring near the valve apices showing the cleft with the lacunae. 42. SEM internal detail of the central area. 43. SEM internal detail of the valve apex with the pseudoseptum. 44. SEM internal detail of the partecta showing the partectal walls with 2–4 series of small, rounded pores. 45. SEM internal view of the inner areolae arranged in groups of 4–8 per pseudoloculus. Scale bars: 39–43 = 10 µm; 44 = 5 µm; 45 = 1 µm.

opencc-by-3.0Dec 2017View details →
zenodo40/100

Figs 66–68 in Morphology of two Mastogloia species (Bacillariophyta) from Lac de Guiers (Senegal) and comparison with the type material of M. braunii

Figs 66–68. Mastogloia baldjikiana Grunow. Light micrographs (LM) of valves from slide 545 (Baldjick, Types du Synopsis des diatomées de Belgique, Van Heurck collection, BR). 66–67. Same valve taken at different foci. 66, 68. LM views of 2 valves showing variation in valve size and shape. 67. LM view of the partectal ring with the partecta. Scale bar: 10 μm.

opencc-by-3.0Dec 2017View details →
zenodo40/100

Figs 17–23. 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 17–23. Mastogloia belaensis M.Voigt. Light micrographs (LM) of valves from the Lac de Guiers population (Van de Vijver sample SEN-42). LM views of several specimens showing variation in valve size and shape. Scale bar: 10 μm.

opencc-by-3.0Dec 2017View details →
zenodo40/100

Figs 12–16 in Morphology of two Mastogloia species (Bacillariophyta) from Lac de Guiers (Senegal) and comparison with the type material of M. braunii

Figs 12–16. Mastogloia braunii Grunow. Scanning electron micrographs (SEM) of valves from the type population (Grunow 23583 – capsule 0645, Vienna, Austria). 12. SEM internal view of an entire valve with the partectal ring and series of partectal pores. 13. SEM internal detail of the partecta with the flange connecting the partecta with the valve margins. 14. SEM internal detail of the partecta showing the partectal walls with 2–4 series of small, rounded pores. 15–16. SEM internal details of the partectal ring near the valve apices showing the cleft with the lacunae. Scale bars: 12 = 1 µm; 13–16 = 10 µm.

opencc-by-3.0Dec 2017View details →

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record