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1,255 results for “High-resolution”

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dryad36/100

Tracing pathways from high-resolution tractography, transcription, and temporal dimensions

The neural circuits supporting human cognition are topics of enduring interest. The lack of tools available to map circuits has precluded our ability to trace the evolution of the human connectome. We harnessed high-resolution connectomic, anatomic, and transcriptomic data to develop enhanced tools to test for modifications in developmental programs across species. We found corresponding ages across species and transcriptionally define neurons with stereotypical projections in humans and macaques. We used these data to test for modifications in frontal cortex circuit. Frontal cortex circuitry development is extended in primates, which is concomitant with an expansion in cortico-cortical pathways compared with mice in adulthood. These parameters varied little across humans and macaques. We identify a collection of conserved features in frontal cortex circuits in studied primates. We demonstrate that the integration of transcriptional and connectomic data across temporal dimensions is a robust approach to trace the evolution of connections in primates. This dataset contains scripts as well as diffusion MR scans of mouse brains.

opencc-zeroDec 2022View details →

zenodo36/100

High-resolution remotely sensed datasets for saltwater intrusion across the Delmarva Peninsula

Abstract: Saltwater intrusion (SWI) on coastal farmlands can change the soil properties (physical and chemical), rendering it unusable for agricultural purposes. Globally, over a quarter of arable land is negatively impacted by soil salinization, including more than 50% of irrigated land. These salt-impacted lands account for more than 30% of food production worldwide. However, the visible impacts of SWI on coastal ecosystems are challenging to map due to the fine spatial resolution of the salt patches. Here we provide the first mapping of the early visual evidences of SWI impacts on the Delmarva (Delaware, Maryland, Virginia) Peninsula region's farmlands by quantifying and mapping the proportions of the farmlands where the spectral signature of a white salt patch was detected. We focus our effort on fourteen counties on the Delmarva Peninsula. We utilized very high-resolution (1-m) aerial imagery from the National Agriculture Imagery Program (NAIP) and seasonal information derived from the moderate resolution (30-m) Landsat satellite imagery collection. Using a Random Forest algorithm with 100 trees and over 94,240 reference points for training and testing, we developed high-resolution geospatial datasets for the study area for two time-steps: 2011-2013 and 2016-2017. The nine coastal Maryland counties witnessed an average of 79% increase in the salt patches on farmlands. The average increase across the state of Delaware is 81%. Virginia experienced an average of 243% increase in these salt patches. While the expansion rate is alarming, the absolute area with these salt deposits remained rather small even in 2017: about 122 ha in Virginia; 339 ha in Delaware; and 445 ha in Maryland. Visible white salt patches remained a small fraction of total farmlands in each of these counties, ranging between 0.01% and 0.18% in 2011-2013, and between 0.01% and 0.39% in 2016-2017. -  - - - - - - - - - - - - - - -  - - - - - - - - - - - - - - This collection of gridded data layers provides the spatial distribution of salt patches along with seven other land cover classes for 14 counties in the Delmarva (Delaware, Maryland and Virginia) Peninsula in the United States of America (USA). We developed high-resolution datasets for the study area for two time-steps: 2011-2013 and 2016-2017. The geospatial datasets are classified images for each time-step and have eight land cover categories as shown below: Raster value             Land cover/use category 1                                  Forest 2                                  Marsh 3                                  Salt patch 4                                  Built 5                                  Open water 6                                  Farmland 7                                  Bare soil 8                                  Other vegetation   Input Data: These geospatial data layers are derived using aerial data from the National Agriculture Imagery Program (NAIP) and satellite data from Landsat 5, 7, and 8. We accessed ortho-rectified NAIP images from June-July 2011 (Maryland), May 2012 (Virginia), September 2013 (Delaware), June 2016 (Virginia), June 2017 (Maryland), and July-August 2017 (Delaware) on the Google Earth Engine (GEE) platform. Cloud-masked top-of-atmosphere (TOA) reflectance images from Landsat 5 (2011, 2012), Landsat 7 (2013), and Landsat 8 (2016, 2017) were obtained using GEE. We derived several spectral indices from the original NAIP and Landsat bands and then used those as inputs into a Random Forest (RF) classifier on GEE. Methods: NAIP data contains 4 spectral bands (red, blue, green, and near-infrared) and have a 1 m spatial resolution. Several spectral indices were calculated from the NAIP imagery and used as input into the RF classifier, such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and a Shadow Index (SI). A Principal Component Analysis (PCA) was used to generate four additional bands. In addition, four smoothed NAIP bands were generated using a 3x3 boxcar kernel. NDVI = (Near-infrared – Red) / (Near-infrared + Red) NDWI = (Green – Near-infrared) / (Green + Near-infrared) SI = (256 – Blue) * (256 + Blue) In order to address limited spectral resolution of the NAIP data and lack of year-long coverage, we incorporated seasonal information from Landsat data. Landsat is a series of satellites launched by the National Aeronautics and Space Administration (NASA) with satellite images distributed through the United States Geological Survey (USGS). Landsat data includes red, green, blue, near-infrared, shortwave infrared, aerosol, cirrus, panchromatic, and thermal bands. All bands are collected at a 30 m resolution except the panchromatic band, which is collected at a 15 m resolution and the thermal bands which are collected at a 100 m resolution. In this work, Landsat 5 was used for 2011 and 2012, Landsat 7 was used for 2013, and Landsat 8 was used for 2016 and 2017. Landsat data (spatial resolution: 30 m) were fused with NAIP data to a resolution of 1 m. An Enhanced Vegetation Index (EVI) was calculated from Landsat bands for each of the four seasons (June-August, September-November, December-February, and March-May) and was then used as input into the RF classifier. Seasonal data was reduced using a median reducer. Landsat thermal bands for each season were also used in the classification. Again, bands were smoothed using a 3x3 boxcar kernel. EVI = (NIR-Red) / (NIR+6*Red-7.5*Blue+1) A Random Forest (RF) classifier was used with input data comprised of the four NAIP bands, four PCA bands from NAIP, three indices from NAIP, four smoothed NAIP bands, four smoothed seasonal EVI bands from Landsat, and four smoothed seasonal thermal bands (from Landsat 5) or eight when (for Landsat 7 or 8) – all sampled to a 1 m resolution to match the NAIP input bands. Due to the high resolution of the input data, there is a considerable 'salt-and-pepper' effects or speckle effects on the classified image, especially for the salt deposit class and its surroundings. As a post-processing step to reduce such speckle effects, we applied a majority filter to the classified image using eight pixel neighbors. For example, any solitary salt patch pixel was reclassified as the majority land cover within the immediate neighborhood. Furthermore, we considered only patches of 10 or more connected 'salt patch' pixels as a valid salt signature. We also used a road mask to minimize the confusion between impervious streets and salt deposits. Accuracy assessment: A total of 94,240 reference points were collected from ground surveys and visual interpretation of NAIP imagery from both time periods. 70% of these points were used to train the RF classifier and 30% were used to test accuracy. We calculated user’s accuracy, producer’s accuracy, overall accuracy, kappa statistic, and the F-Score as shown below. <table> <tbody> <tr> <td> Delaware 2013 </td> </tr> <tr> <td> Categories </td> <td> User’s accuracy </td> <td> Producer’s accuracy </td> <td> F-score </td> <td> Overall </td> <td> Kappa </td> </tr> <tr> <td> Forest </td> <td> 88.46% </td> <td> 90.89% </td> <td> 0.90 </td> <td> 86.37% </td> <td> 0.83 </td> </tr> <tr> <td> Marsh </td> <td> 84.03% </td> <td> 80.13% </td> <td> 0.82 </td> </tr> <tr> <td> Salt patch </td> <td> 97.02% </td> <td> 71.18% </td> <td> 0.82 </td> </tr> <tr> <td> Built </td> <td> 94.56% </td> <td> 95.06% </td> <td> 0.95 </td> </tr> <tr> <td> Water </td> <td> 91.01% </td> <td> 96.63% </td> <td> 0.94 </td> </tr> <tr> <td> Farmland </td> <td> 83.16% </td> <td> 86.19% </td> <td> 0.85 </td> </tr> <tr> <td> Bare Soil </td> <td> 87.70% </td> <td> 87.30% </td> <td> 0.88 </td> </tr> <tr> <td> Other Vegetation </td> <td> 82.46% </td> <td> 84.20% </td> <td> 0.83 </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> Delaware 2017 </td> </tr> <tr> <td> Categories </td> <td> User’s accuracy </td> <td> Producer’s accuracy </td> <td> F-score </td> <td> Overall </td> <td> Kappa </td> </tr> <tr> <td> Forest </td> <td> 95.07% </td> <td> 90.30% </td> <td> 0.93 </td> <td> 91.37% </td> <td> 0.90 </td> </tr> <tr> <td> Marsh </td> <td> 88.86% </td> <td> 92.44% </td> <td> 0.91 </td> </tr> <tr> <td> Salt patch </td> <td> 91.82% </td> <td> 85.59% </td> <td> 0.89 </td> </tr> <tr> <td> Built </td> <td> 87.58% </td> <td> 93.54% </td> <td> 0.90 </td> </tr> <tr> <td> Water </td> <td> 92.68% </td> <td> 90.48% </td> <td> 0.92 </td> </tr> <tr> <td> Farmland </td> <td> 91.61% </td> <td> 93.61% </td> <td> 0.93 </td> </tr> <tr> <td> Bare Soil </td> <td> 95.67% </td> <td> 87.67% </td> <td> 0.91 </td> </tr> <tr> <td> Other Vegetation </td> <td> 91.16% </td> <td> 90.24% </td> <td> 0.91 </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> Maryland 2011 </td> </tr> <tr> <td> Categories </td> <td> User’s accuracy </td> <td> Producer’s accuracy </td> <td> F-score </td> <td> Overall </td> <td> Kappa </td> </tr> <tr> <td> Forest </td> <td> 88.32% </td> <td> 90.97% </td> <td> 0.90 </td> <td> 87.20% </td> <td> 0.85 </td> </tr> <tr> <td> Marsh </td> <td> 87.14% </td> <td> 82.08% </td> <td> 0.85 </td> </tr> <tr> <td> Salt patch </td> <td> 96.74% </td> <td> 78.76% </td> <td> 0.87 </td> </tr> <tr> <td> Built </td> <td> 89.02% </td> <td> 88.50% </td> <td> 0.89 </td> </tr> <tr> <td> Water </td> <td> 92.74% </td> <td> 96.10% </td> <td> 0.94 </td> </tr> <tr> <td> Farmland </td> <td> 84.31% </td> <td> 89.83% </td> <td> 0.87 </td> </tr> <tr> <td> Bare Soil </td> <td> 87.53% </td> <td> 90.05% </td> <td> 0.89 </td> </tr> <tr> <td> Other Vegetation </td> <td> 86.11% </td> <td> 80.57% </td> <td> 0.83 </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> Maryland 2017 </td> </tr> <tr> <td> Categories </td> <td> User’s accuracy </td> <td> Producer’s accuracy </td> <td> F-score </td> <td> Overall </td> <td> Kappa </td> </tr> <tr> <td> Forest </td> <td> 88.66% </td> <td> 88.66% </td> <td> 0.89 </td> <td> 87.34% </td> <td> 0.84 </td> </tr> <tr> <td> Marsh </td> <td> 87.65% </td> <td> 88.35% </td> <td> 0.88 </td> </tr> <tr> <td> Salt patch </td> <td> 93.29% </td> <td> 68.30% </td> <td> 0.79 </td> </tr> <tr> <td> Built </td> <td> 93.44% </td> <td> 86.92% </td> <td> 0.90 </td> </tr> <tr> <td> Water </td> <td> 92.36% </td> <td> 92.36% </td> <td> 0.92 </td> </tr> <tr> <td> Farmland </td> <td> 83.84% </td> <td> 94.55% </td> <td> 0.89 </td> </tr> <tr> <td> Bare Soil </td> <td> 92.86% </td> <td> 82.61% </td> <td> 0.87 </td> </tr> <tr> <td> Other Vegetation </td> <td> 86.56% </td> <td> 76.00% </td> <td> 0.81 </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> Virginia 2012 </td> </tr> <tr> <td> Categories </td> <td> User’s accuracy </td> <td> Producer’s accuracy </td> <td> F-score </td> <td> Overall </td> <td> Kappa </td> </tr> <tr> <td> Forest </td> <td> 84.65% </td> <td> 91.18% </td> <td> 0.88 </td> <td> 86.88% </td> <td> 0.84 </td> </tr> <tr> <td> Marsh </td> <td> 87.03% </td> <td> 84.39% </td> <td> 0.86 </td> </tr> <tr> <td> Salt patch </td> <td> 97.67% </td> <td> 72.41% </td> <td> 0.83 </td> </tr> <tr> <td> Built </td> <td> 90.97% </td> <td> 86.24% </td> <td> 0.89 </td> </tr> <tr> <td> Water </td> <td> 94.17% </td> <td> 87.39% </td> <td> 0.91 </td> </tr> <tr> <td> Farmland </td> <td> 86.87% </td> <td> 91.81% </td> <td> 0.89 </td> </tr> <tr> <td> Bare Soil </td> <td> 85.82% </td> <td> 83.04% </td> <td> 0.84 </td> </tr> <tr> <td> Other Vegetation </td> <td> 83.60% </td> <td> 80.59% </td> <td> 0.82 </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> Virginia 2016 </td> </tr> <tr> <td> Categories </td> <td> User’s accuracy </td> <td> Producer’s accuracy </td> <td> F-score </td> <td> Overall </td> <td> Kappa </td> </tr> <tr> <td> Forest </td> <td> 84.65% </td> <td> 86.00% </td> <td> 0.85 </td> <td> 85.83% </td> <td> 0.83 </td> </tr> <tr> <td> Marsh </td> <td> 86.25% </td> <td> 90.72% </td> <td> 0.88 </td> </tr> <tr> <td> Salt patch </td> <td> 90.61% </td> <td> 62.12% </td> <td> 0.74 </td> </tr> <tr> <td> Built </td> <td> 88.70% </td> <td> 77.72% </td> <td> 0.83 </td> </tr> <tr> <td> Water </td> <td> 92.12% </td> <td> 84.41% </td> <td> 0.88 </td> </tr> <tr> <td> Farmland </td> <td> 85.17% </td> <td> 90.36% </td> <td> 0.88 </td> </tr> <tr> <td> Bare Soil </td> <td> 83.30% </td> <td> 88.54% </td> <td> 0.86 </td> </tr> <tr> <td> Other Vegetation </td> <td> 84.49% </td> <td> 84.17% </td> <td> 0.84 </td> </tr> </tbody> </table> While our datasets have an overall high accuracy, a few caveats should be considered when utilizing the data for other applications. Misclassifications of salt patches might arise from a flooding event immediately prior to the image acquisition or spectral similarity with marsh. Misclassifications might also arise from spectral similarities between crop fields and other vegetation, which typically encompasses open fields and lawns. Shadows are sometimes misclassified as water, or built. The algorithm used in this work often under-predicted salt patches, because the typical bright white signature of these patches can be altered when those areas become wet, leading these areas to be classified as crop fields. Some of the areas classified as salt patches might be bleached siliceous minerals visible on the soil surface.   Data format: The spatial resolution of all the derived datasets is 1 m. These georeferenced datasets are distributed in GEOTIFF format, and are compatible with GIS and/or image processing software, such as R and ArcGIS. The GIS-ready raster files can be used directly in mapping and geospatial analysis. Code: Sample code is available at <a href="https://code.earthengine.google.com/a3c66ac5f06a796fc221a5c902486806">https://code.earthengine.google.com/a3c66ac5f06a796fc221a5c902486806</a>. The user would need to upload study area boundaries and reference points in order to successfully run these codes. Datasets for download: <ul> <li>Two zipped data layers for Delaware:</li> </ul> <ol> <li>DE_3counties_2013</li> <li>DE_3counties_2017</li> </ol> These data layers cover 3 counties: Kent, New Castle, Sussex. <ul> <li>Two zipped data layers for Maryland:</li> </ul> <ol> <li>MD_9counties_2011</li> <li>MD_9counties_2017</li> </ol> These data layers cover 9 counties: Caroline, Cecil, Dorchester, Kent, Queen Anne's, Somerset, Talbot, Wicomico, Worcester. <ul> <li>Two zipped data layers for Virginia:</li> </ul> <ol> <li>VA_2counties_2012</li> <li>VA_2counties_2016</li> </ol> These data layers cover 2 counties: Accomack, Northampton. We also provided a color map (DELMARVA_ColorMap.clr) that can be used with these data files.  Data citation: Mondal, P., Walter, M., Miller, J., Epanchin-Niell, R., Yawatkar, V., Nguyen, E., Gedan, K. and Tully, K. 2022. High-resolution remotely sensed datasets for saltwater intrusion across the Delmarva Peninsula. Available at: 10.5281/zenodo.6685695. Accessed DAY MONTH YEAR.

opencc-by-4.0Dec 2022View details →

zenodo36/100

National High-Resolution Cropland Classification of Japan with Agricultural Census Information and Multi-temporal Multi-modality datasets

Multi-modality datasets offer advantages for processing frameworks with complementary information, particularly for large-scale cropland mapping. Extensive training datasets are required to train machine learning algorithms, which can be challenging to obtain. To alleviate the limitations, we extract the training samples from the agricultural census information. We focus on Japan and demonstrate how agricultural census data in 2015 can map different crop types for the entire country. Due to the lack of Sentinel-2 datasets in 2015, this study utilized Sentinel-1 and Landsat-8 collected across Japan and combined observations into composites for different prefecture periods (monthly, bimonthly, seasonal). Recent deep learning techniques have been investigated the performance of the samples from agricultural census information. Finally, we obtain nine crop types on a countrywide scale (around 31 million parcels) and compare our results to those obtained from agricultural census testing samples as well as those obtained from recent land cover products in Japan. The generated map accurately represents the distribution of crop types across Japan and achieves an overall accuracy of 87% for nine classes in 47 prefectures.

opencc-by-4.0Jan 2023View details →

zenodo36/100

A Daily High-Resolution (1 km) Human Thermal Index Collection over the North China Plain from 2003 to 2020

The daily High spatial resolution human Thermal Index Collection over the North China Plain (HiTIC-NCP) includes near-surface air temperature (SAT) and 11 commonly used human-perceived temperature indices: indoor Apparent Temperature (ATin), outdoor shaded Apparent Temperature (ATout), Discomfort Index (DI), Effective Temperature (ET), Heat Index (HI), Humidex (HMI), Modified Discomfort Index (MDI), Net Effective Temperature (NET), Wet-Bulb Temperature (WBT), simplified Wet-Bulb Globe Temperature (sWBGT), and Wind Chill Temperature (WCT). This daily dataset has a high spatial resolution of 1 km × 1 km and covers the North China Plain from January 2003 to December 2020. It has high accuracy with averaged determination coefficient, mean absolute error, and root mean squared error of 0.987, 0.970 °C, and 1.292 °C, respectively. The dataset is stacked by year and each stack consists of 365 daily images in NetCDF format by day of the year. The unit of the dataset is 0.01 degree Celsius (°C), and the values are stored in an integer type (Int16) to save storage space, and thus need to be divided by 100 to get the values in degree Celcius when in use. The geographic coordinate system of the dataset is World Geodetic System (WGS) 1984 Coordinate System. Naming rules and other details can be found in "README.pdf". If you have any questions when using the HiTIC-NCP dataset, please feel free to contact Mr. Xiang Li via <a href="mailto:lixiang97@mail2.sysu.edu.cn">lixiang97@mail2.sysu.edu.cn</a>, Dr. Ming Luo via <a href="mailto:luom38@mail.sysu.edu.cn">luom38@mail.sysu.edu.cn</a>, or Dr. Yongquan Zhao via <a href="mailto:zhaoyq66@mail.sysu.edu.cn">yqzhao@link.cuhk.edu.hk</a>. More details on the procedure of producing the HiTIC-NCP dataset and its accuracy assessment can be found in: Li, X., Luo, M*., Zhao, Y*., Zhang, H., Ge, E., Huang, Z., Wu, S., Wang, P., Wang X., Tang Y. (2023). A daily high-resolution (1 km) human thermal index collection over the North China Plain from 2003 to 2020. Scientific Data, 10, 634. https://doi.org/10.1038/s41597-023-02535-y

opencc-by-4.0Jan 2023View details →

zenodo36/100

High-resolution digital elevation models and orthomosaics generated from historical aerial photographs (since the 1960s) of the Bale Mountains in Ethiopia

This dataset contains the results of photogrammetric processing (Digital Elevation Models, Orthomosaics and  subset data used for volumetric calculation and visualization) named: “DEM_1967.7z”: inside the zipped folder “1967_DEM.tif” (digital elevation model produced for the year 1967), “DEM_1984.7z”: inside the zipped folder “1984_DEM.tif” exist (digital elevation model produced for the year 1984). In addition, under “1967_Orthomosaic.7z" and "1984_Orthomosaic.7z” zipped folders, there are orthomosaic files produced namely, “1967_orthomosaic.tif” and "1984_orthomosaic.tif” for the year 1967 and 1984, respectively. The DEMs and Orthomosaics  subset from the results for sites (data example 1 and data example 2) reside under "Data_Examples.zip". Accuracy of the resulted data were assessed and the extracted elevation values are under "Accuracy_assessment.zip".  All DEMs and Orthomosaics are in GeoTIFF format in the Adindan UTM Zone 37 N (EPSG: 20137) projected coordinate system.     Potential application of the presented dataset include: 1. watershed management 2. analyses of historical landscape change 3. detailed mapping and analyses of geological and archaeological features, as well as natural resources 4. analyses of geomorphological processes 5. socioecological patterns and dynamics 6. modelling and planning for telecommunications  7. biodiversity research.  The inputs for the above resulted DEMs and Orthomosaics are found under Zenodo repository "10.5281/zenodo.7271617".

opencc-by-4.0Oct 2022View details →

zenodo36/100

High-resolution digital elevation models and orthomosaics generated from historical aerial photographs (since the 1960s) of the Bale Mountains in Ethiopia

This dataset contains the inputs used for Structure from Motion Multiview Stereo photogrammetry processing for the year 1967 and 1984 i.e Unprocessed scanned historical aerial Photographs, camera position coordinates, flight index and Ground Control Points. All the scanned historical aerial photographs data are in TIFF format except four photographs in JPEG format under a zipped folder ("1967_Scanned_HAPs_Part1.7z and 1967_Scanned_HAPs_Part2.7z" for the 1967 Historical Aerial Photographs and "1984_Scanned_HAPs_Part1.7z and 1984_Scanned_HAPs_Part2.7z" for the 1984  Historical Aerial Photographs). The "Flight_Index.Zip" contains shapefiles of the camera position and polygon of consecutive aerial photograph index; "GCP.Zip" contains text file of the GCPs used for the 1967 and 1984; and "Camera_Position.Zip" contains the file of the camera position (Label, Easting, Northing and Altitude) of each historical aerial photographs.  The results of the above dataset could be accessible on Zenodo repository "10.5281/zenodo.7269999". Anyone can reuse the presented dataset to produce DEMs and Orthomosaics; and use for the following application  areas: 1. watershed management 2. analyses of historical landscape change 3. detailed mapping and analyses of geological and archaeological features, as well as natural resources 4. analyses of geomorphological processes 5. socioecological patterns and dynamics 6. modelling and planning for telecommunications  7. biodiversity research.

opencc-by-4.0Oct 2022View details →

dryad36/100

Data from: An expanded Smithian-Spathian (Early Triassic) boundary from a reefal build-up record in Oman: Implications for conodont taxonomy, high-resolution biochronology and the carbon isotope record

Some 2.7 Ma after the Permian-Triassic boundary mass extinction (PTME), a stepwise extinction of the nekton (ammonoids and conodonts) ended at the Smithian-Spathian boundary (SSB) during an episode of climate cooling. SSB records from continental shelves are usually affected by an unconformity, suggesting a forced regression of glacio-eustatic origin. Here, we document a new 30 m-thick SSB section from Jebel Aweri (Batain Plain, Oman) that provides an exceptionally complete and expanded record preserved in an exotic block. Most of this SSB section consists of metazoan reefal build-ups that formed in shallow water on an offshore sea mount. In Wadi Musjah (Hawasina nappes, Oman), another exotic block records the SSB in a deeper water setting represented by Hallstatt-type facies. These two sections provide a unique perspective on the early Spathian rapid re-diversification of conodonts. They led to a thorough revision of conodont taxonomy around the SSB and to the construction of the highest resolution biochronological scheme for this time interval in the Tethys. A total of five SSB sections from Oman representing both offshore sea mounts and lower slope deposits were included in a high-resolution, quantitative Unitary Associations analysis. The resulting 8 conodont biozones are intercalibrated with ammonoid zones and with the carbonate carbon isotope record ultimately placing the SSB in the interval of separation between UAZ3 and UAZ4. Only the association of Nv. pingdingshanensis with Ic. crassatus can be used to unambiguously characterize the base of the Spathian.

opencc-zeroFeb 2023View details →

zenodo36/100

Raw Data for the article: High-Resolution Secretome Analysis of Chemical Hypoxia Treated Cells Identifies Putative Biomarkers of Chondrosarcoma

Chondrosarcoma is the second most common bone tumor, accounting for 20% of all cases. Little is known about the pathology and molecular mechanisms involved in the development and in the metastatic process of chondrosarcoma. As a consequence, there are no approved therapies for this tumor and surgical resection is the only treatment currently available. Moreover, there are no available biomarkers for this type of tumor, and chondrosarcoma classification relies on operator-dependent histopathological assessment. Reliable biomarkers of chondrosarcoma are urgently needed, as well as greater understanding of the molecular mechanisms of its development for translational purposes. Hypoxia is a central feature of chondrosarcoma progression. The hypoxic tumor microenvironment of chondrosarcoma triggers a number of cellular events, culminating in increased invasiveness and migratory capability. Herein, we analyzed the effects of chemically-induced hypoxia on the secretome of SW 1353, a human chondrosarcoma cell line, using high-resolution quantitative proteomics. We found that hypoxia induced unconventional protein secretion and the release of proteins associated to exosomes. Among these proteins, which may be used to monitor chondrosarcoma development, we validated the increased secretion in response to hypoxia of glyceraldehyde 3-phosphate dehydrogenase (GAPDH), a glycolytic enzyme well-known for its different functional roles in a wide range of tumors. In conclusion, by analyzing the changes induced by hypoxia in the secretome of chondrosarcoma cells, we identified molecular mechanisms that can play a role in chondrosarcoma progression and pinpointed proteins, including GAPDH, that may be developed as potential biomarkers for the diagnosis and therapeutic management of chondrosarcoma.

opencc-by-4.0Feb 2023View details →

zenodo36/100

High-resolution spatial multi-omics datasets

Supplementary raw data. The raw microscopy data are not uploaded owing to their large size (6.8 Tb), but are available upon reasonable request (Long Cai: lcai@caltech.edu, Yodai Takei: ytakei@caltech.edu). These supplementary data contain additional files for RNA seqFISH+, DNA seqFISH+, and sequential immunofluorescence from cell culture and adult mouse cerebellum experiments. DNA seqFISH+ datasets (provided as a tar.gz folder for each replicate): Super-resolved DNA spot locations by DNA seqFISH+ along with sequential immunofluorescence intensity at the rounded voxel location. RNA seqFISH datasets (provided as a zip folder): Super-resolved mRNA or intron spot locations. Sequential immunofluorescence table (provided as a csv file): Mean voxel intensity of each immunofluorescence marker per nucleus for the adult mouse cerebellum datasets. Note that voxel sizes are 103 nm for x and y, and 250 nm for z in our experimental setting. Please find the uploaded readme.txt file for more details.

opencc-by-4.0Mar 2023View details →

zenodo36/100

supplementary data for Bemelmans et al., 2023, High-resolution InSAR reveals localised pre-eruptive deformation inside the crater of Agung volcano, Indonesia.

This repository contains the supplementary materials for the paper "High-resolution InSAR reveals localised pre-eruptive deformation inside the crater of Agung volcano, Indonesia." to be published in JGR: Solid Earth. The dataset contains files assiciated with the StaMPS time series processing. Each dataset has its own folder containing: <ol> <li>*_data.csv : data file containing latitude, longitude, incidence angle, heading and LOS displacement for each acquistion (date is listed in the column name in the format yyyymmdd).</li> <li>parms.mat : parameter file used for StaMPS processing of that dataset</li> </ol> The dataset also contains input and results from the GBIS modelling (/GBIS_results/). the *.inp files are the input files for each model inversion where the letter ('M','T','P','Y', or 'D') refer to the Mogi (point), McTigue (sphere), penny-shaped crack, Yang (ellipsoid), and dyke (also sill) model used for that run. folders with the same name as the *.inp file contain the inversion results (invert_*.mat), a summary table (summary_*.txt) and several figures showing the distribution and convergence of the model inversion. The input for the GBIS inversions is stored in /GBIS_results/INSAR_input/ the file <a href="https://zenodo.org/api/files/f30be116-1e2f-4d52-8fcf-a54e11ab691f/matlab_functions.zip?versionId=490de786-f245-45b5-92bb-d2ae0d50be56">matlab_functions.zip </a>contains matlab functions used for data processing, visualisation, storage and conversion. the file <a href="https://zenodo.org/api/files/f30be116-1e2f-4d52-8fcf-a54e11ab691f/GBISv1_1_MJWB.zip?versionId=cec83c81-a0b7-4e4a-8aa8-ac81e32b8772">GBISv1_1_MJWB.zip </a>contains GBIS code adapted by the author to perform statistical analysis of the model inversion, perform region-of-interest based subsampling and store modeled results as shapefiles for further processing.

opencc-by-4.0Mar 2023View details →

zenodo36/100

High-resolution figures and shape analyses files of Mengel et al.

High-resolution figures and shape analyses files of Mengel et al. 2023 "The morphological diversity of dragon lacewing larvae changed more over geological time scales than anticipated" in Insects (MDPI)

opencc-by-4.0May 2023View details →

zenodo36/100

Model output for "A high-resolution physical-biogeochemical model for marine resource applications in the Northwest Atlantic (MOM6-COBALT-NWA12)"

This dataset contains the numerical model output files that were used in the analysis presented in "A high-resolution physical-biogeochemical model for marine resource applications in the Northwest Atlantic (MOM6-COBALT-NWA12)", submitted to Geoscientific Model Development.

opencc-by-4.0May 2023View details →

zenodo36/100

High-resolution version for Fig. 1 in Rev. Sci. Instrum 94, 053706 (2023)

Here is the same figure as Fig. 1 in Rev. Sci. Instrum 94, 053706 (2023), but with much higher resolution.

opencc-by-4.0May 2023View details →

zenodo36/100

Candidate High-Resolution Mass Spectrometry-Based Reference Method for the Quantification of Procalcitonin in Human Serum Using a Characterized Recombinant Protein as a Primary Calibrator

Dataset related to Huu-Hien Huynh, Vincent Delatour, Maxence Derbez-Morin, Qinde Liu, Amandine Boeuf, and Joëlle Vinh, (2022) Candidate High-Resolution Mass Spectrometry-Based Reference Method for the Quantification of Procalcitonin in Human Serum Using a Characterized Recombinant Protein as a Primary Calibrator. Anal. Chem. 2022, 94, 10, 4146–4154.

opencc-by-4.0Jul 2023View details →

dryad36/100

Dataset for: Utilizing high-resolution genetic markers to track population-level exposure of migratory birds to renewable energy development

With new motivation to increase the proportion of energy demands met by zero-carbon sources, there is a greater focus on efforts to assess and mitigate the impacts of renewable energy development on sensitive ecosystems and wildlife, of which birds are of particular interest. One challenge for researchers, due in part to a lack of appropriate tools, has been estimating the effects from such development on individual breeding populations of migratory birds. To help address this, we utilize a newly developed, high-resolution genetic tagging method to rapidly identify the breeding population of origin of carcasses recovered from renewable energy facilities and combine them with maps of genetic variation across geographic space (called 'genoscapes') for five species of migratory birds known to be exposed to energy development, to assess the extent of population-level effects on migratory birds. We demonstrate that most avian remains collected were from the largest populations of a given species. In contrast, those remains from smaller, declining populations made up a smaller percentage of the total number of birds assayed. Results suggest that application of this genetic tagging method can successfully define population-level exposure to renewable energy development and may be a powerful tool to inform future siting and mitigation activities associated with renewable energy programs.

opencc-zeroAug 2023View details →

zenodo36/100

High-Resolution Vegetation Height Maps for Switzerland in 2017-2020

This dataset comprises 10m-resolution vegetation height maps for Switzerland spanning from 2017 to 2020. It encompasses both mean and maximum vegetation height data, generated through the integration of Sentinel-2 and airborne laser scanning information.

opencc-by-4.0Aug 2023View details →

dryad36/100

Data from: High-resolution crossover maps for each bivalent of Zea mays using recombination nodules

Recombination nodules (RNs) mark sites of crossing over along pachytene synaptonemal complexes (SCs). Thus, RNs provide the highest resolution cytological marker currently available for defining the frequency and distribution of crossovers along the length of chromosomes because they are observed by electron microscopy. Using the maize inbred line KYS, we have prepared an SC karyotype in which each SC was identified by relative length and arm ratio and related to the proper linkage group using inversion heterozygotes. We mapped 4272 RNs on 2080 identified SCs to produce high-resolution maps of RN frequency and distribution on each bivalent. Average RN frequency per bivalent is closely correlated with SC length. The total length of the RN map is about two-fold shorter than most linkage maps, but there is good correspondence between the relative lengths of the different maps when individual bivalents are considered. Each bivalent has a unique distribution of crossing over, but all bivalents share a high frequency of distal RNs and a severe reduction of RNs at and near kinetochores. The frequency of RNs at knobs is either similar to or higher than the average frequency of RNs along the SCs. These RN maps represent an independent measure of crossing over along maize bivalents.

opencc-zeroSep 2023View details →

zenodo36/100

GRACE High-Resolution Trend Mascons - Greenland Ice Sheet (2007-2015)

High-resolution mascon trend solution, computed for the Greenland Ice Sheet over the time period from January 2007 and January 2015, where each mascon regression model (including the trend) has been directly estimated from the Gravity Recovery and Climate Experiment (GRACE) Level 1B data. The GAD product has not been restored, meaning the ocean mascons are consistent with the Level 2 GSM product information.Description of columns in the dataset:<ol><li>Latitude center (deg)</li><li>Longitude center (deg)</li><li>Mass change trend (cm w.e. / yr)</li><li>Mass change trend uncertainty (cm w.e. / yr)</li><li>Latitude minimum (deg)</li><li>Latitude maximum (deg)</li><li>Longitude minimum (deg)</li><li>Longitude maximum (deg)</li><li>Area of mascon (sq. km)</li><li>Label of mascon</li></ol>When citing this dataset, please also include this citation:Loomis, B. D., D. Felikson, T. J. Sabaka, and B. Medley (2021). High‐spatial‐resolution mass rates from GRACE and GRACE‐FO: Global and ice sheet analyses.   Journal of Geophysical Research: Solid Earth, <a href="https://doi.org/10.1029/2021JB023024">https://doi.org/10.1029/2021JB023024</a>

opencc-by-4.0Oct 2023View details →

ClinicalTrials.gov36/100

Multi-Center Trial of High-resolution Transrectal Ultrasound Versus Standard Low-resolution Transrectal Ultrasound for the Identification of Clinically Significant Prostate Cancer

ClinicalTrials.gov study NCT02079025. IPD Sharing: Not stated. Countries: 2. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov36/100

High-resolution, Relational, Resonance-based, Electroencephalic Mirroring (HIRREM) to Relieve Insomnia

ClinicalTrials.gov study NCT01971567. IPD Sharing: Not stated. Countries: 1. Publications: 3.

restrictedIPD-UNDECIDEDFeb 2026View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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