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2,516 results for “Seri”
FIGURE 8 in Rediscovery of the type series of the Sacred Shrew, Sorex religiosus I. Geoffroy Saint-Hilaire, 1826, with additional notes on mummified shrews of ancient Egypt (Mammalia: Soricidae)
FIGURE 8. Taphonomic features of the Passalacqua shrew mummies. A, distorted thoracic vertebrae of ÄM 690; B, distorted thoracic vertebrae of ÄM 7050; C, broken braincase of ÄM 7055; D, broken braincase of ÄM 7051. X-rays (A-C) and Micro-CTscan (D).
FIGURE 7 in Rediscovery of the type series of the Sacred Shrew, Sorex religiosus I. Geoffroy Saint-Hilaire, 1826, with additional notes on mummified shrews of ancient Egypt (Mammalia: Soricidae)
FIGURE 7. Micro-CT-scans of a cross section through the head and the lateral view of the skull of an embalmed shrew (ÄM 690) from the Passalacqua Collection. The skull is surrounded by dry tissue and mineral dust. Width of bony braincase is 6.8 mm. Please see interactive 3D scan in Fig. S2.
FIGURE 6 in Rediscovery of the type series of the Sacred Shrew, Sorex religiosus I. Geoffroy Saint-Hilaire, 1826, with additional notes on mummified shrews of ancient Egypt (Mammalia: Soricidae)
FIGURE 6. Crocidura religiosa, as seen by the Victorian artist P.J. Smit. Detail from plate 23 in Anderson & de Winton (1902).
FIGURE 4 in Rediscovery of the type series of the Sacred Shrew, Sorex religiosus I. Geoffroy Saint-Hilaire, 1826, with additional notes on mummified shrews of ancient Egypt (Mammalia: Soricidae)
FIGURE 4. Lectotype ÄM 690 of Sorex religiosus I. Geoffroy Saint-Hilaire, 1826. A, External view of mummy; B, X-ray of entire specimen. Scale in mm. Photograph courtesy of S. Steiss, Berlin; X-ray image courtesy of C. Schmidt, Berlin.
FIGURE 3 in Rediscovery of the type series of the Sacred Shrew, Sorex religiosus I. Geoffroy Saint-Hilaire, 1826, with additional notes on mummified shrews of ancient Egypt (Mammalia: Soricidae)
FIGURE 3. Dorsal views of the skulls of mummified (micro-CT-scans) and extant (camera lucida drawings) specimens of shrews. C. religiosa: A, Passalacqua specimen ÄM 690 (GLS 15.3 mm), C, Giza, Egypt, BMNH 10.6.18.5 (GLS 15.6 mm). C. pasha: B, Passalacqua specimen ÄM 697 (GLS 14.1 mm), D, Khartoum, Sudan, BMNH 9.9.2.31 (GLS 14.1 mm).
Monthly MODIS LST data related to the article: A new fully gap-free time series of land surface temperature from MODIS LST data
<p>Temperature time series with high spatial and temporal resolutions are important for several applications. The new MODIS Land Surface Temperature (LST) collection 6 provides numerous improvements compared to collection 5. However, being remotely sensed data in the thermal range, LST shows gaps in cloud-covered areas. With a novel method [1] we fully reconstructed the daily global MODIS LST products MOD11C1 and MYD11C1 (spatial resolution: 3 arc-min, i.e. approximately 5.6 km at the equator). For this, we combined temporal and spatial interpolation, using emissivity and elevation as covariates for the spatial interpolation. Here we provide a time series of these reconstructed LST data aggregated as monthly average, minimum and maximum LST maps.</p> <p>[1] Metz M., Andreo V., Neteler M. (2017): A new fully gap-free time series of Land Surface Temperature from MODIS LST data. Remote Sensing, 9(12):1333. DOI: http://dx.doi.org/10.3390/rs9121333</p> <p>LICENSE: Open Data Commons Open Database License (ODbL) http://opendatacommons.org/licenses/odbl/</p> <p>Acknowledgments: We are grateful to the NASA Land Processes Distributed Active Archive Center (LP DAAC) for making the MODIS LST data available. The dataset is based on MODIS Collection V006.</p> <p><strong>The data available here for download are the reconstructed global MODIS LST products MOD11C1/MYD11C1 at a spatial resolution of 3 arc-min</strong> (approximately 5.6 km at the equator; see https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table), <strong>aggregated to monthly data</strong>. The data are provided in GeoTIFF format. The Coordinate Reference System (CRS) is identical to the MOD11C1/MYD11C1 product as provided by NASA. In WKT as reported by GDAL:<br> <br> GEOGCS["Unknown datum based upon the Clarke 1866 ellipsoid",<br> DATUM["Not specified (based on Clarke 1866 spheroid)",<br> SPHEROID["Clarke 1866",6378206.4,294.9786982138982,<br> AUTHORITY["EPSG","7008"]]],<br> PRIMEM["Greenwich",0],<br> UNIT["degree",0.0174532925199433]]<br> </p> <p><strong>File name</strong> abbreviations:</p> <ul> <li>avg = average of daily averages</li> <li>min = minimum of daily minima</li> <li>max = maximum of daily maxima</li> </ul> <p>Meaning of <strong>pixel values</strong>:</p> <ul> <li>The <strong>pixel values</strong> are coded in <strong>degree Celsius * 100</strong> (hence, to obtain °C divide the pixel values by 100.0).</li> </ul> <p>Version <strong>changelog</strong>:</p> <ul> <li>V1.1.0: GeoTIFF metadata updated.</li> <li>V1.0.0: original upload</li> </ul>
Time series used in the manuscript "Causal dependences between the coupled ocean-atmosphere dynamics over the Tropical Pacific, the North Pacific and the North Atlantic"
<p>These 6 files contain time series built using reanalyses datasets of the ECMWF as discussed in the manuscript "Causal dependences between the coupled ocean-atmosphere dynamics over the Tropical Pacific, the North Pacific and the North Atlantic" submitted for discussion in the journal "Earth System Dynamics".</p>
Google Trends time series for the term "topic modeling"
<p>Dataset received from Google Trends for the phrase "topic modeling'' on 31 January 2024 using the URL <a href="https://trends.google.de/trends/explore?date=all&q=topic\%20modeling&hl=de">https://trends.google.de/trends/explore?date=all&q=topic\%20modeling&hl=de</a></p> <p><em>Data obtained from Google LLC, which is the ultimate owner of these data. Published for academic and non-commercial replication purposes only.</em></p>
Tooth enamel LC-MS dataset for: "Enamel histomorphometry, growth patterns and developmental trajectories of the first decidous molar in an Italian early medieval skeletal series"
<p>(n)LC-MS .raw data of tooth enamel samples from archaeological human remains. </p> <p>The dataset is related to the publication of Magri et al. </p>
Fig. 2 in A phylogenetic investigation of the taxonomically problematic Eucalyptus odorata complex (E. section Adnataria series Subbuxeales): evidence for extensive interspecific gene flow and reticulate evolution
Fig. 2. Neighbour-net networks of E. odorata complex taxa and co-occurring members of E. section Adnataria created in Splitstree V.4 using uncorrelated-P distances of SNPs generated by A. ddRADseq and B. DArTseq. Tips are coloured by species, with shapes used to distinguish different major groups: E. series Hetereophloiae (inverse triangles), E. series Melliodorae (hexagons), E. series Buxeales (triangles), the grey-box taxa (diamonds), mallee members of E. series Subbuxeales not in the E. odorata complex (squares), and the E. odorata complex (circles) coloured consistent with Fig. 1.
Data from: Simultaneous determination of crystallization age and "uncommon" initial lead composition in a series of Phanerozoic magmatic allanites
<h2><span>Submitted Article</span></h2> <h2><span>Simultaneous determination of crystallization age </span><span><em>and</em></span><span> “uncommon” initial lead composition in a series of Phanerozoic magmatic allanites</span></h2> <p><span><span>Etienne SKRZYPEK</span></span><span><sup><span>a*</span></sup></span><span><span>, Daniela GALLHOFER</span></span><span><sup><span>a</span></sup></span><span><span>, Christoph HAUZENBERGER</span></span><span><sup><span>a</span></sup></span><span><span>, Isabella HAAS</span></span><span><sup><span>a</span></sup></span><span><span>, Anh Thi Quynh NONG</span></span><span><sup><span>a</span></sup></span><span><span>, Harald FRITZ</span></span><span><sup><span>a</span></sup></span><span><span>, Milan KOHÚT</span></span><span><sup><span>b</span></sup></span><span><span>, Viljem PODGORŠEK</span></span><span><sup><span>c</span></sup></span><span><span>, Zmago ŽORŽ</span></span><span><sup><span>d</span></sup></span></p>
Synthetic dataset and prediction files for the paper "Denoising of Geodetic Time Series Using Spatiotemporal Graph Neural Networks: Application to Slow Slip Event Extraction", by Costantino et al. (2024)
<p>Synthetic database used for training and evaluation of SSEdenoiser</p>
Animated Series Production Schedule "Jaci na Selva de Pedra" (1st Season)
<p>Brazilian Animated Series "Jaci na Selva de Pedra" Production Schedule based on Belisa Proença's production model.</p>
SHIFT Project Series "7 Myths About Beyond-5G Debunked with Evidence"
<p>SHIFT's conrotium has addressed and debunked some of the most common misconceptions surrounding Beyond-5G technology. To wrap up this enlightening series, a video was released that summarizes all the myths and the research-based facts.<br>Watch this and get a comprehensive overview of how Beyond-5G is set to transform our world positively and sustainably.</p>
ERA5-Land monthly: Total precipitation, monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
<p>ERA5-Land total precipitation monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)</p> <p>Source data:<br>ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.</p> <p>Total precipitation:<br>Accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of precipitation are depth in metres. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step.</p> <p>Processing steps:<br>The original hourly ERA5-Land data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (<a href="https://chelsa-climate.org/">https://chelsa-climate.org/</a>). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: <br>1. spatially aggregate CHELSA to the resolution of ERA5-Land <br>2. calculate proportion of ERA5-Land / aggregated CHELSA <br>3. interpolate proportion with a Gaussian filter to 30 arc seconds <br>4. multiply the interpolated proportions with CHELSA <br>Using proportions ensures that areas without precipitation remain areas without precipitation. Only if there was actual precipitation in a given area, precipitation was redistributed according to the spatial detail of CHELSA.</p> <p>The spatially enhanced daily ERA5-Land data has been aggregated to monthly resolution, by calculating the sum of the precipitation per pixel over each month.</p> <p>File naming:<br><code>ERA5_land_monthly_prectot_sum_30sec_YYYY_MM_01T00_00_00_int.tif</code> <br>e.g.:<code>ERA5_land_monthly_prectot_sum_30sec_2023_12_01T00_00_00_int.tif</code></p> <p>The date within the filename is year and month of aggregated timestamp.</p> <p>Pixel values:<br>mm * 10<br>Scaled to Integer, example: value 218 = 21.8 mm</p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br>north: 28:18N<br>south: 14:42N<br>west: 17:05W<br>east: 4:49W</p> <p>Temporal extent:<br>January 2019 - December 2023</p> <p>Spatial resolution:<br>30 arc seconds (approx. 1000 m)</p> <p>Temporal resolution:<br>monthly</p> <p>Lineage:<br>Dataset has been processed from original Copernicus Climate Data Store (ERA5-Land) data sources. As auxiliary data CHELSA climate data has been used.</p> <p>Software used:<br>GRASS GIS 8.3.2</p> <p>Format: GeoTIFF</p> <p>Original ERA5-Land dataset license:<br><a href="https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf</a></p> <p>CHELSA climatologies (V1.2): Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a><br>Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a></p> <p>Representation type: Grid</p> <p>Processed by:<br>mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Contact: <br>mundialis GmbH & Co. KG, info@mundialis.de</p> <p> </p>
Sequential LIBS Spectral Analysis of Artemisia absinthium: Dataset Series from Eight Repeated Measurements
<p>Sequential LIBS Spectral Analysis of Artemisia absinthium: Dataset Series from Eight Repeated Measurements, Raw and Normalized Data</p>
Monthly precipitation series for Mediterranean countries
<p>Raw monthly precipitation series for different countries of the Mediterranean region.</p>
Monthly time series of spatially enhanced relative humidity for Europe at 1000 m resolution (2000 - 2023) derived from ERA5-Land data
<p>Overview:<br>ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.</p> <p>Processing steps:<br>The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (<a href="https://chelsa-climate.org/">https://chelsa-climate.org/</a>). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: <br>1. spatially aggregate CHELSA to the resolution of ERA5-Land <br>2. calculate difference of ERA5-Land - aggregated CHELSA <br>3. interpolate differences with a Gaussian filter to 30 arc seconds <br>4. add the interpolated differences to CHELSA</p> <p>Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 12/2023.</p> <p>Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997):</p> <p><code>maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))</code></p> <p><code>actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))</code></p> <p><code>relative humidity = actual water pressure / maximum water pressure</code></p> <p>The resulting relative humidity has been aggregated to monthly averages.</p> <p>Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000].</p> <p>The data have been reprojected to EU LAEA.</p> <p>File naming scheme (YYYY = year; MM = month):<br><code>ERA5_land_rh2m_avg_monthly_YYYY_MM.tif</code></p> <p>Projection + EPSG code:<br>EU LAEA (EPSG: 3035)</p> <p>Spatial extent:<br>north: 6874000<br>south: -485000<br>west: 869000<br>east: 8712000</p> <p>Spatial resolution:<br>1000 m</p> <p>Temporal resolution:<br>Monthly</p> <p>Pixel values:<br>Percent * 10 (scaled to Integer; example: value 738 = 73.8 %)</p> <p>Software used:<br>GDAL 3.2.2 and GRASS GIS 8.0.0/8.3.2</p> <p>Original ERA5-Land dataset license:<br><a href="https://apps.ecmwf.int/datasets/licences/copernicus/">https://apps.ecmwf.int/datasets/licences/copernicus/</a></p> <p>CHELSA climatologies (V1.2):<br>Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a><br>Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a></p> <p>Processed by:<br>mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC</p> <p>Data is also available in Latitude-Longitude/WGS84 (EPSG: 4326) projection: <a href="https://doi.org/10.5281/zenodo.6146383">https://doi.org/10.5281/zenodo.6146383</a></p>
The GPS time series of 2005 Kashmir earthquake
<p>These data includes the near-field GPS data provided by Jouanne et al. (2011), InSAR data provided by Wang & Fialko (2014), and the far-field GPS data processed in this work. Additionally, we have uploaded the coseismic rupture models from Avouac et al. (2006) and Yan et al. (2013). The far-field GPS data includes the original time series, the fitting interseismic velocities, and the fitting time series of interseismic and postseismic. These datasets are organized into corresponding folders, with filenames that clearly describe the contents of each file.</p>
FIGURE 22 in Trilobites and agnostids from the Goyder Formation (Cambrian Series 3, Guzhangian; Mindyallan), Amadeus Basin, central Australia
FIGURE 22. Liostracina sp. Specimen comes from spot locality AS178. A, B, C, D, CPC42356, partial cranidium; A, dorsal view; B, oblique anterolateral view; C, lateral view; D, anterior view. All scale bars are 1 mm.
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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.
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.