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391 results for “Roughness”
Elevation, mesh elements, and strickler roughness coefficient for the Virginia Coastal Reserve
 This dataset contains a mesh and associated elements for the southern Delmarva Peninsula
Dataset for Lattice Boltzmann simulation of water flow through rough nanopores
<p>All the datasets used to produce the figures in our paper "<strong>Lattice Boltzmann simulation of water flow through rough nanopores</strong>".</p>
Experimental data for 'The transfer of forces through rough surface contact in concrete'
<p>The data contained in this archive was produced within the research project described in the following thesis:<br> <em>Tirassa M. (2020). The transfer of forces through rough surface contact in concrete. Ph.D. Thesis, EPFL, Lausanne, Switzerland.</em><br> The research project was funded by the Swiss National Science Foundation through research grant 200021_169649.<br> The data relates to the topic of force transfer across concrete cracks and interfaces (rebar-to-concrete) subjected to mixed mode kinematics with constant opening angle. It comprises the measured forces (normal and tangential) and displacements (crack opening and sliding). Moreover, some of the resulting surfaces (scanned using a digital microscope) are included.</p>
Dataset for paper "Flow Resistance for a Varying Density of Obstacles on Smooth and Rough Beds"
<p>This dataset contains the experimental data that supports the paper:</p> <p>Guillén-Ludeña, S., Lopez, D., Mignot, E., & Riviere, N. (2019). Flow Resistance for a Varying Density of Obstacles on Smooth and Rough Beds. <em>Journal of Hydraulic Engineering</em>, <em>146</em>(2), 04019059.</p> <p>The file Readme.txt contains al explanations regarding the data organization.</p>
Scott-E surface roughness and thermal model results
<p>Supporting Information for "Geomorphic evidence for the presence of ice deposits in lunar permanently shadowed regions" by Moon et al. </p> <p><strong>Dataset S1 – S4.</strong></p> <p>Dataset S1. ds01_ElevRough.asc is an ASCII raster file for elevation-derived roughness [unitless].<br> Dataset S2. ds02_BriRough.asc is an ASCII raster file for brightness-derived roughness [DN/m].<br> Dataset S3. ds03_Tmax.asc is an ASCII raster file for modeled annual maximum temperature [K].<br> Dataset S4. ds04_Dice.asc is an ASCII raster file for modeled depth to thermally stable water ice [m].</p> <p>All maps are in south polar stereographic projection.</p>
Non-linear variation in clinging performance with surface roughness in geckos
<p>Understanding the challenges faced by organisms moving within their environment is essential to comprehending the evolution of locomotor morphology and habitat use. Geckos have developed adhesive toe pads that enable exploitation of a wide range of microhabitats. These toe pads, and their adhesive mechanisms, have typically been studied using a range of artificial substrates, usually significantly smoother than those available in nature. Although these studies have been fundamental in understanding the mechanisms of attachment in geckos, it is unclear whether gecko attachment simply gradually declines with increased roughness as some researchers have suggested, or whether the interaction between the gekkotan adhesive system and surface roughness produces non-linear relationships. To understand ecological challenges faced in their natural habitats, it is essential to use test surfaces that are more like surfaces used by geckos in nature. We tested gecko shear force (i.e., frictional force) generation as a measure of clinging performance on three artificial substrates. We selected substrates that exhibit microtopographies with peak-to-valley heights similar to those of substrates used in nature, to investigate performance on a range of surfaces smooth (glass), and fine-grained (fine sandpaper) to rough (coarse sandpaper). We found that shear force did not decline monotonically with roughness, but varied non-linearly among substrates. Clinging performance was greater on glass and coarse sandpaper than on fine sandpaper, and clinging performance was not significantly different between glass and coarse sandpaper. Our results demonstrate that performance on different substrates varies, probably depending on the underlying mechanisms of the adhesive apparatus in geckos.</p>
Data from: Searching for diamonds in the apomictic rough: a case study involving Boechera lignifera (Brassicaceae)
The genus Boechera is one of the most difficult species complexes in North America, with about 70 sexual diploids and hundreds of apomictic taxa representing diverse combinations of nearly every known sexual genome. In this study, we set out to clarify the taxonomy of Boechera lignifera, which currently includes a small number of sexual diploid populations in addition to the widespread apomictic diploid upon which the name is based. Using data from cytological studies, microsatellite DNA analyses, geography, and morphology, we demonstrate that the apomictic populations are genetically quite divergent from the sexual diploids. We propose the name Boechera kelseyana to accommodate the sexual diploid taxon, which occurs entirely south of the geographic range of B. lignifera. Boechera kelseyana is consistently separable from B. lignifera based on pollen and seed morphology, the length and proximal orientation of fruiting pedicels, differences in the branching and orientation of trichomes on the lowers stems, and the number of flowers and cauline leaves on unbranched fertile stems.
Data from: Staying close to home? Genetic differentiation of rough-toothed dolphins near oceanic islands in the central Pacific Ocean
Rough-toothed dolphins have a worldwide tropical and subtropical distribution, yet little is known about the population structure and social organization of this typically open-ocean species. Although it has been assumed that pelagic dolphins range widely due to the lack of apparent barriers and unpredictable prey distribution, recent evidence suggests rough-toothed dolphins exhibit fidelity to some oceanic islands. Using the most comprehensively extensive dataset for this species to date, we assess the isolation and interchange of rough-toothed dolphins at the regional and oceanic scale within the central Pacific Ocean. Using mtDNA and microsatellite genotyping (nDNA), we analyzed samples of insular communities from the main Hawaiian (Kaua'i n = 93, O'ahu n = 9, Hawai'i n = 57), French Polynesian (n = 70) and Samoan (n = 16) archipelagos, and pelagic samples off the Northwestern Hawaiian Islands (n = 18). An overall AMOVA indicated strong genetic differentiation among islands (mtDNA FST = 0.265; p < 0.001; nDNA FST = 0.038; p < 0.001), as well as among archipelagos (mtDNA FST = 0.299; p < 0.001; nDNA FST = 0.055; p < 0.001). Shared haplotypes (n = 4) between the archipelagos may be a product of a relatively recent divergence and/or periodic exchange from poorly understood pelagic populations. Analyses using STRUCTURE and GENELAND identified four separate management units among archipelagos and within the Hawaiian Islands. These results confirm the presence of multiple insular populations within the Pacific and island-specific genetic isolation among populations attached to islands in each archipelago. Insular populations seem most prevalent where oceanographic conditions indicate high local productivity or a discontinuity with surrounding oligotrophic areas. Our findings have important implications for a little studied species that faces increasing anthropogenic threats around oceanic islands.
The supplementary materials for "Roughness prediction of end milling surface for behavior mapping of digital twined machine tools".
<p>This is the supplementary materials for a paper named "Roughness prediction of end milling surface for behavior mapping of digital twined machine tools" published on the Digital Twin journal.</p>
Pisgah Roughly Smoothed Bowl (2241p2172)
**Pisgah Roughly Smoothed bowl** Location: Warren Wilson site (31Bn29), Buncombe County, North Carolina. Period: Mississippian, Pisgah phase (AD 1000-1400). Material: ceramic. Dimensions: height, 6.2 cm; diameter, 13.8 cm; thickness, 5.4 mm. Notes: Catalog no. 2241p2086, p2172, p2176, North Carolina Archaeological Collection, Research Laboratories of Archaeology, University of North Carolina at Chapel Hill. Model by Abigail Gancz. Source: Objaverse 1.0 / Sketchfab
Experimental measurements of the effects of surface roughness on large-scale downburst-like impinging jets at the WindEEE Dome laboratory
<p>Thunderstorm downbursts originate as negatively buoyant currents of cold air descending from cumulonimbus clouds. Upon impacting the ground, a strong radial outflow develops with maximum wind velocities occurring at the near-ground level. These types of flows pose serious hazard to the natural and built environment. Their restricted time and spatial extent as well as their intermittent and non-Gaussian fluctuating nature make them extremely challenging to be recorded and analyzed through classic full-scale measurements in nature. Alongside synoptic-scale extra tropical cyclones, downbursts govern the wind climate at the mid-latitude areas around the globe. Recent trends in climate change studies suggest both an increased intensity as well as frequency of occurrence of these events. Therefore, their scientific comprehension urges serious consideration.</p> <p>In the context of the project THUNDERR “Detection, simulation, modelling and loading of thunderstorm outflows to design wind-safer and cost-efficient structures”, financed by the European Research Council (ERC) Advanced Grant 2016 (grant No. 741273, P.I. Prof. Giovanni Solari, University of Genoa), an extensive experimental campaign was recently conducted at the WindEEE Dome wind chamber. This campaign focused on measuring downburst-like flows (DLFs) generated by large-scale impinging jets. The dataset presented here encompasses a portion of the measurements collected during this comprehensive experimental initiative. Specifically, this series of tests aimed to unravel the role of surface roughness in potentially altering the configuration and dynamics of the overall radial outflow. The term "surface roughness" pertains to the ground patch under examination, encompassing both the natural features of the terrain and any obstacles, such as buildings, present on the ground. While surface roughness plays a decisive role in changing the shape and magnitudes of the wind speed vertical profiles for extra-tropical cyclones, the scientific literature has not yet thoroughly addressed its impact on downburst winds, which are different being dominated by intense vortex dynamics.</p> <p>Impinging jets, considered representative for the simulation of downburst like flow (DLF), are here simulated as transient phenomena through the opening and closing of the bell-mouth that connects the test chamber and the upper plenum of the dome, the latter being pressurized before releasing the jet. As a result, the velocity records exhibit a distinct pattern, featuring a sudden ramp-up of velocity, followed by a velocity peak, a statistically-stationary phase, and ultimately, a gradual velocity deceleration—mirroring the behavior observed in real-world scenarios.</p> <p>The database consists of six ASCII tab-delimited text files, denoted as ‘windspeedDB89z0eq007.txt’, ‘windspeedDB89z0eq020.txt’, ‘windspeedDB89z0eq320.txt’, ‘windspeedDB124z0eq007.txt’, ‘windspeedDB124z0eq020.txt’, and ‘windspeedDB124z0eq320.txt’, aligning with the two jet intensities and three rough surfaces employed in the experiments. These filenames correspond to: (i) centerline jet velocities at the nozzle outlet section, with values of <em>Wjet</em> = 8.9 and 12.4 m/s (indicated as “<em>Wjet</em>” in the database files); (ii) equivalent full-scale roughness lengths <em>z0eq</em> = 0.007, 0.020, 0.32 m (“<em>z0eq</em>” in the database files, see details below). Each file encompasses wind speed timeseries, detailed as follows:</p> <p>The three-component velocity measurements were recorded by means of 11 Cobra probes (sampling frequency 2,500 Hz) mounted on a stiff mast. The heights (<em>z</em>) of the probes were <em>z</em> = 0.040, 0.070, 0.100, 0.125, 0.150, 0.200, 0.300, 0.400, 0.500, 0.700, 1.000 m above the surface. Within the database files, the wind speed linked to various heights is labeled as “<em>v_zXXXXmm</em>”. In this notation, '<em>v</em>' designates the velocity component: longitudinal ‘<em>U</em>’ (along the horizontal axis of the probe), corresponding to the radial outflow of the downburst, with a positive value when the flow is directed toward the probe. Transversal, ‘<em>V</em>’, represents the velocity component transverse to the probe's centerline axis, having a positive value when the flow is directed right-to-left concerning an observer facing the probe's head. The vertical component is denoted as ‘<em>W</em>’ with a positive value indicating an upward direction. The term “XXXX” signifies the height of the probe, specified in millimeters (mm). The mast with the Cobra probes was subsequently positioned at ten radial <em>r</em> distances with respect to the jet impingement position in the range <em>r/D</em> (<em>D</em> = 3.2 m is the jet diameter) between 0.2–2.0 with an increment of 0.2. Note that the position <em>r/D</em> = 0.8 was adjusted to <em>r/D</em> = 0.75. This modification was necessary due to irregularities on the chamber floor at <em>r/D</em> = 0.8, which could have otherwise introduced bias into the measurements. The radial distance is identified with “<em>r/D_distance</em>” in the dataset files. The ceiling height of the testing chamber is <em>H</em> = 3.75 m, which leads to <em>H/D</em> > 1 allowing for a full vertical development of the downburst radial outflow. For every <em>r/D</em> position, each experiment with the same initial condition (i.e., <em>Wjet</em>) was repeated 10 times (“<em>repetition#</em>” in the database files) to inspect the repeatability of the tests and their variance. Each velocity record lasted 12 s (12 × 2,500 = 30,000 samples) and the duration of the downburst-like part of the record varied between 3–5 s. Overall, 6,600 total time series (2 <em>Wjet</em> × 3 rough surface × 10 repetitions × 10 <em>r/D</em> positions × 11 heights <em>z</em>) of downburst-like outflows were recorded during this set of experimental tests.</p> <p>The reported accuracy of Cobra probes from the manufacturer is +/- 0.5 m/s and +/- 1° for velocity and yaw/pitch angles respectively, up to approximately 30% of turbulence intensity. All velocity magnitudes below 1 m/s were removed and converted to NaN (Not a Number) in the database due to the poor accuracy of Cobra probes for velocities below this threshold. In addition, some velocity values were reported as null in the instrument readings due to the incoming flow being outside the probe's spatial cone of measurement (+/- 45° in respect to the probe horizontal axis). These values are flagged as NULL values in the database. This notation aligns with that utilized in a preceding database of measurements collected within the same experimental campaign at the WindEEE Dome (Canepa et al., 2021; <a href="https://doi.org/10.1594/PANGAEA.931205">https://doi.org/10.1594/PANGAEA.931205</a>).</p> <p>DLFs were tested on three different surfaces: (i) WindEEE Dome bare floor; (ii) Carpet; (iii) Artificial grass. A 1 m × 8 m rectangular section was selected from each of the three surfaces for testing purposes. Each surface was positioned with a 1 m offset relative to the geometric location of the jet impingement. It was identified by an equivalent full-scale roughness length, “<em>z0eq</em>”based on matching atmospheric boundary layer profiles measured in WindEEE in boundary layer mode with standard ESDU (Engineering Science Data Unit) profiles. A total of 15 different Atmospheric Boundary Layer (ABL)-like profiles were tested inside the chamber by varying the rotation-per-minute (rpm) of the fans across the 4 rows of the 60-fan wall—a peripheral wall of the hexagonal WindEEE Dome chamber comprising a matrix of 4 × 15 (rows × columns) fans that is used to produce ABL -like flows. A specific configuration of the 60-fan wall and a length scale of 1:200 were chosen based on correlation analysis between physically reproduced ABL profiles and curve fitting through Eq. A1.8 of the ESDU 82026. This scale is deemed suitable for both ABL and downburst winds produced at the laboratory. Through a linear fitting of the measured data on the <em>U – ln(z)</em> chart, employing the logarithmic law-of-the-wall (dependent on roughness length <em>z0</em> and friction velocity <em>u*</em>), the equivalent roughness lengths for the three surfaces were determined: <em>z0eq</em> = 0.007, 0.020, 0.320 m for the WindEEE Dome bare floor, carpet, and artificial grass, respectively.</p> <p>In summary, each experimental velocity time series consists of 30,000 rows, with 33 columns detailing the three velocity components (<em>U</em>, <em>V</em>, <em>W</em>) across the 11 Cobra probe heights. Columns 34 to 37 provide information on the repetition number, radial position of measurement, equivalent full-scale roughness length, and jet intensity. The subsequent timeseries within the dataset refer to the parameters in columns 34 to 37, each one spanning its entire range in the specified order.</p> <p>Researchers can leverage this database to validate and calibrate numerical and analytical models of thunderstorm winds, in addition to interpreting full-scale measurements of the phenomenon. It also serves as a valuable resource for the fluid dynamics community, particularly those interested in the physical comprehension of downscaled flows or the surface flow dynamics of large Reynolds number impinging jets.</p>
Seafloor roughness reduces melting of the East Antarctic ice sheets
<p>MITgcm model setup and<span> </span>MATLAB script and data that support figures for "<strong>Seafloor roughness reduces melting of East Antarctic ice shelves</strong>" by Y. Liu, M. Nikurashin, and B. Pena-Molino</p> <p><strong>MITgcm model setup: </strong></p> <p>We provide the two MITgcm model configurations of the Denman regional model, using BedMachine and SRTM15+ bathymetry datasets, described in the main text of the paper. Both simulations can be run from a pickup file corresponding to 5 years from the beginning of the simulation, when the model is well equilibrated. Complete model outputs used for the analysis in the paper can be produced by running the simulations for additional 5 years.</p> <p>(Contents)</p> <ul> <li><strong><em>denman_0025_RYF_SHI_tides_bedmachine.zip</em></strong> (code, parameter files, and initial and boundary conditions to run the simulation with BedMachine bathymetry)</li> <li><strong><em>denman_0025_RYF_SHI_tides_srtm15.zip</em></strong> (code, parameter files, and initial and boundary conditions to run the simulation with SRTM15+ bathymetry)</li> <li><strong><em>denman_external_forcing_files.zip</em></strong> (3-hourly atmospheric forcing files that are used for both simulations take nearly 100Gb of disk space. Due to Zenodo size limit of 50GB, we provide the original JRA-55 forcing files and a Matlab script that interpolates them onto the regional model grid.)</li> </ul> <p>(How to build and run)</p> <p>The reader is referred to MITgcm documentation for instructions on how to download, compile and run the model: https://mitgcm.readthedocs.io/en/latest/getting_started/getting_started.html</p> <p><strong>MATLAB script and data:</strong></p> <ul> <li><strong><em>data & script.zip</em></strong> includes the raw data saved in Matlab data format and the script to create the figures in the paper. Please download all files into a folder and run the script.m under MATLAB.</li> </ul>
Simulation Results in the Paper "Propagation of Slow Slip Events on Rough Faults: Clustering, Back Propagation, and Re-rupturing" [Dataset]
<p>Data file "simulations.mat" contains the 5 simulations of slow slip events on flat or rough faults. </p> <table> <tbody> <tr> <td>structure array</td> <td>description</td> <td>reference</td> </tr> <tr> <td>s0</td> <td> 2.5 km long flat fault</td> <td>Fig. 2b</td> </tr> <tr> <td>s1</td> <td>2.5 km long rough fault</td> <td>Fig. 2c</td> </tr> <tr> <td>s2</td> <td>10 km long rough fault</td> <td>Fig. 5</td> </tr> <tr> <td>s3</td> <td>10 km long fractal fault</td> <td>Fig. 7</td> </tr> </tbody> </table> <p>structure array consists of:</p> <p>t: time (s)</p> <p>x: distance (m)</p> <p>v: slip rate (m/s)</p> <p>slip: accumulated slip (m)</p> <p>tau: shear stress (Pa)</p> <p>sigma: normal stress (Pa)</p> <p>notes: description</p> <p> </p> <p>Data file "catalog.mat" contains 3 simulated slow slip events' catalogs on flat and rough faults, c0, c1, and c2, in Fig. 4a, 4b, and 4c, respectively.</p> <p>It consists of:</p> <p>l: rupture length (m)</p> <p>time: time (s)</p> <p>notes: description</p>
The metre-scale roughness of asteroid (101955) Bennu from the OSIRIS-REx Laser Altimeter: Processed OLA v21 pointclouds for RMS deviation (L = 0.2 m to L = 20.0 m) [Dataset]
<p>Pointclouds:</p> <ul> <li>Pointcloud (.pcd) files for OLA RMS deviation calculation with fields: <br> <ul> <li>'Location': node points (XYZ points on Bennu's surface)</li> <li>'Intensity': height delta between the node and a neighbouring point at distance L from the node. 'Height' is defined as height above an orthogonally regressed plane fit to points within 2L of the node. </li> </ul> </li> </ul> <p> Tables:</p> <ul> <li>2020_06_bt_final_ma.xlsx: <ul> <li>Crater catalog from Bierhaus et al (2022)</li> </ul> </li> <li>bierhaus2023_craters_gt5m_lt80m_olav21_roi_flags.xlsx: <ul> <li>Subset of catalog from Bierhaus et al. (2022) with craters between 5 m - 80 m diameter used for interior-exterior roughness ratio in Bierhaus et al. (2023) and Rossmann et al. (2024).</li> </ul> </li> </ul> <p> </p>
The effects of surface roughness on the spectral (300-1400 nm) bidirectional reflectance distribution function (BRDF) of sea ice
<p>Please cite the following publication when using the data:</p> <p><br> Lamare, M. L., Hedley, J. D., and King, M. D.: The effects of surface roughness on the calculated, spectral, conical–conical reflectance factor as an alternative to the bidirectional reflectance distribution function of bare sea ice, The Cryosphere, 17, 737–751, https://doi.org/10.5194/tc-17-737-2023, 2023.</p> <p>"BRF_results" contains BRDF output files from the radiative-transfer model PlanarRad.</p> <p>BDRF was computed for three different types of sea ice with varying roughness parameters and <br> thicknesses. </p> <p>The folder tree is constructed with the following structure:</p> <p>BRF_results<br> - Solar Zenith angles<br> - Roughness parameters<br> - Sea ice thicknesses<br> - Wavelengths<br> - Sea ice types</p>
Data Set: The influence of roughness on experimental fault mechanical behaviour and associated microseismicity
<p>This is the data set used to create the figures for the submitted manuscript, The influence of roughness on experimental fault mechanical behaviour and associated microseismicity. Submitted July 2022 to the Journal of Geophysical Research: Solid Earth. Article DOI: 10.1029/2022JB025113</p>
Preparatory Slip in Laboratory Faults: Effect of Roughness and Loading Rate
<p>Mechanical and acoustic emission data presented in the GRL manuscript: 'Preparatory Slip in Laboratory Faults: Effect of Roughness and Loading Rate', 2022</p>
On following pages: 19. Striped Dolphin (Stenella coeruleoalba); 20. Atlantic Spotted Dolphin (Stenella frontalis); 21. Spinner Dolphin (Stenella longirostris); 22. Rough-toothed Dolphin (Steno bredanensis). in Delphinidae
On following pages: 19. Striped Dolphin (Stenella coeruleoalba); 20. Atlantic Spotted Dolphin (Stenella frontalis); 21. Spinner Dolphin (Stenella longirostris); 22. Rough-toothed Dolphin (Steno bredanensis).
On following pages: 3. Highveld Golden Mole (Amblysomus septentrionalis); 4. Marley's Golden Mole (Amblysomus Congo Golden Mole (Huetia leucorhina); 8. Somali Golden Mole (Huetia tytonis); 9. Gunning''s Golden Mole (Neamblysomus arendsi); 12. Sclater''s Golden Mole (Chlorotalpa sclateri); 13. Duthie''s Golden Mole (Chlorotalpa duthieae); 14. Stuhlmann's Golden Mole (Chrysochloris visagiel); 17. Rough-haired Golden Mole (Chrysospalax villosa); 18. Giant Golden Mole (Cryptochloris zyl); 21. Grant's Golden Mole (Eremitalpa grant). marley); 5. Fynbos Golden Mole (Amblysomus corriae); 6. Yellow Golden Mole (Calcochloris obtusirostris); 7. gunning); 10. Juliana's Golden Mole (Neamblysomus julianae); 11. Arend''s Golden Mole (Carpitalpa Golden Mole (Chrysochloris stuhlmanni); 15. Cape Golden Mole (Chrysochloris asiatica); 16. Visagie's (Chrysospalax trevelyani); 19. De Winton's Golden Mole (Cryptochloris wintoni); 20. Van Zyl's Golden Mole in Chrysochloridae
On following pages: 3. Highveld Golden Mole (Amblysomus septentrionalis); 4. Marley's Golden Mole (Amblysomus Congo Golden Mole (Huetia leucorhina); 8. Somali Golden Mole (Huetia tytonis); 9. Gunning''s Golden Mole (Neamblysomus arendsi); 12. Sclater''s Golden Mole (Chlorotalpa sclateri); 13. Duthie''s Golden Mole (Chlorotalpa duthieae); 14. Stuhlmann's Golden Mole (Chrysochloris visagiel); 17. Rough-haired Golden Mole (Chrysospalax villosa); 18. Giant Golden Mole (Cryptochloris zyl); 21. Grant's Golden Mole (Eremitalpa grant). marley); 5. Fynbos Golden Mole (Amblysomus corriae); 6. Yellow Golden Mole (Calcochloris obtusirostris); 7. gunning); 10. Juliana's Golden Mole (Neamblysomus julianae); 11. Arend''s Golden Mole (Carpitalpa Golden Mole (Chrysochloris stuhlmanni); 15. Cape Golden Mole (Chrysochloris asiatica); 16. Visagie's (Chrysospalax trevelyani); 19. De Winton's Golden Mole (Cryptochloris wintoni); 20. Van Zyl's Golden Mole
Distribution. Most of Europe, from the British Is and NW France E to W Siberia as far E as Irtysh and Ob rivers, and from S Sweden, S Finland, and S Karelia (Russia) S to N Italy and N Balkans; marginally present also in NW Kazakhstan. In E Europe and in Asia the border roughly follows the extreme extension of the taiga in the N (northernmost record is from Pechora River close to 68°N) and the steppe-forest—steppe transition in the S. Present on some Is in the Baltic Sea and around Denmark (Oland, Funen, Zeeland, Bjgrng, Tasinge, Tung, Langeland, Riigen, Usedom, and Wollin), around Great Britain (Sky, Mull, Anglesey, Wight, and Jersey), offshore W coast of France (Ouessant and Ré), and on Cres (Croatia) as the only Mediterranean I. in Talpidae
Distribution. Most of Europe, from the British Is and NW France E to W Siberia as far E as Irtysh and Ob rivers, and from S Sweden, S Finland, and S Karelia (Russia) S to N Italy and N Balkans; marginally present also in NW Kazakhstan. In E Europe and in Asia the border roughly follows the extreme extension of the taiga in the N (northernmost record is from Pechora River close to 68°N) and the steppe-forest—steppe transition in the S. Present on some Is in the Baltic Sea and around Denmark (Oland, Funen, Zeeland, Bjgrng, Tasinge, Tung, Langeland, Riigen, Usedom, and Wollin), around Great Britain (Sky, Mull, Anglesey, Wight, and Jersey), offshore W coast of France (Ouessant and Ré), and on Cres (Croatia) as the only Mediterranean I.
ScienceDex guides
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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.