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1,188 results for “Deltas”
Cell-density resolved comparative analysis of D. shibae wild-type vs. delta-luxI1 overexpressing luxI1 in trans
GEO Series GSE42012. Dinoroseobacter shibae DFL 12 = DSM 16493. 12 samples. Type: Expression profiling by array.
Halobacterium delta-ura3 growth with added uracil: time course
GEO Series GSE14836. Halobacterium salinarum NRC-1. 23 samples. Type: Expression profiling by array.
CK1 Delta Is an mRNA Cap-Associated Protein That Drives Translation Initiation and Tumor Growth
GEO Series GSE146851. Homo sapiens. 15 samples. Type: Expression profiling by high throughput sequencing.
WALOWA (WAVE LOADS ON WALLS) - LARGE SCALE EXPERIMENTS IN THE DELTA FLUME
<p>WaLoWa stands for Wave Loads on Walls and is a Hydralab+ project funded by the European Union. Ghent University (Belgium), TU Delft (The Netherlands), RWTH Aachen (Germany), Politechnico Bari and University of Florence (Italy) and Flanders Hydraulics Research (Belgium) are jointly working on the WaLoWa project. The user team leader is Ghent University. The WaLoWa project is hosted by Deltares and the Delta Flume facility.</p> <p>When storm walls and buildings are located on top of a dike or promenade, overtopping waves can induce large forces on these structures as has e.g. been observed at the Belgian coast which has a specifically shallow foreshore. Especially during storm season and in times of sea level rise these loads can be highly destructive. It is therefore the key objective of WaLoWa to study overtopped wave loads on structures situated on top of a dike and in shallow foreshore conditions.</p>
Flight data for a conventional fixed wing and delta -wing drone applied to model-based navigation
<p>The following two bags permit the testing of the vehicle dynamic model-based navigation real-time software VDMc available <a href="https://gitlab.epfl.ch/laupre/vdm_c">here</a>. </p> <p><strong>TOPOPlane2_20221027_STIM14.bag</strong></p> <p>Data are saved in a <em>rosbag </em>while flying with the TOPOPlane2 drone on October 2nd, 2022. The <em>_tagged </em>suffix name means that the data come from the autopilot and the time is GNSS time-tagged with an internal routine. The bag contains the following topics:</p> <ul> <li>/GIINAV_POSE - solution of the INS/GNSS software (ros msg type: <a href="https://docs.ros.org/en/noetic/api/nav_msgs/html/msg/Odometry.html">nav_msgs/Odometry</a>)</li> <li>/GPS0 - binary output of the GNSS receiver. These data need to be parsed with the function <em>GNSSReceiver::gpsCallBack</em> in <a href="https://gitlab.epfl.ch/laupre/vdm_c/-/blob/Topoplane2/src/TP2/gnssreceiver.cpp?ref_type=heads">gnssreceiver.cpp</a> (ros msg type: <a href="https://docs.ros.org/en/melodic/api/std_msgs/html/msg/UInt8MultiArray.html">std_msgs/Uint8MultiArray</a>)</li> <li>/IMU0 - binary output of the IMU sensor. These data need to be parsed with the function <em>IMUReceiver::imuCallback</em> in <a href="https://gitlab.epfl.ch/laupre/vdm_c/-/blob/Topoplane2/src/TP2/imureceiver.cpp?ref_type=heads">imureceiver.cpp </a>(ros msg type: <a href="https://docs.ros.org/en/melodic/api/std_msgs/html/msg/UInt8MultiArray.html">std_msgs/Uint8MultiArray</a>)</li> <li>/airData - data from the surrey PitotTube and processed (custom ros msg type: surrey_sensor/AirData, available in <a href="https://gitlab.epfl.ch/laupre/vdm_c/-/blob/Topoplane2/include/AirData.h?ref_type=heads">AirData.h</a>) <ul> <li>GNSS time [s]</li> <li>airSpeed [m/s]</li> <li>baroAltitude [m]</li> <li>density [hPa]</li> </ul> </li> <li>/airpressure_tagged - dynamic air pressure of the PitotTube (Pa) (ros msg type: <a href="https://docs.ros.org/en/melodic/api/sensor_msgs/html/msg/FluidPressure.html">sensor_msgs/FluidPressure</a> ) </li> <li>/airspeed_tagged - transformed airspeed [m/s] and groundspeed [m/s] from Pitotube (ros msg type: <a href="https://docs.ros.org/en/noetic/api/mavros_msgs/html/msg/VFR_HUD.html">mavros_msgs/VFR_HUD</a>)</li> <li>/cc_tagged - control commands tagged with GNSS time (ros msg type: <a href="https://docs.ros.org/en/noetic/api/mavros_msgs/html/msg/RCOut.html">mavros_msgs/RCout</a>) <ul> <li>header, channels (autopilot value from 1000 to 2000). The conversion is done in the function CCReceiver::ccCallback in <a href="https://gitlab.epfl.ch/laupre/vdm_c/-/blob/Topoplane2/src/TP2/ccreceiver.cpp?ref_type=heads">ccreceiver.cpp</a> [aileron elevator rpm rudder]</li> </ul> </li> <li>/mavros/debug_value/debug_vector - This custom vector is used to exchange data from/to the Ground Control Station (GCS). It contains the solution of the INS/GNSS (Giinav) and VDMNav (ros msg type: <a href="https://docs.ros.org/en/noetic/api/mavros_msgs/html/msg/DebugValue.html">mavros_msgs/DebugValue</a>)</li> <li>/mavros/debug_value/named_value_float - Topic used to trigger the real-time simulated GNSS outage from the GCS (ros msg type: <a href="https://docs.ros.org/en/noetic/api/mavros_msgs/html/msg/DebugValue.html">mavros_msgs/DebugValue</a>) </li> <li>/mavros/debug_value/send - not used</li> <li>/mavros/imu/diff_pressure (ros msg type: <a href="https://docs.ros.org/en/melodic/api/sensor_msgs/html/msg/FluidPressure.html">sensor_msgs/FluidPressure</a>)</li> <li>/mavros/imu/static_pressure (ros msg type: <a href="https://docs.ros.org/en/melodic/api/sensor_msgs/html/msg/FluidPressure.html">sensor_msgs/FluidPressure</a>)</li> <li>/mavros/rc/out - control commands tagged with autopilot (system) time (ros msg type: <a href="https://docs.ros.org/en/noetic/api/mavros_msgs/html/msg/RCOut.html">mavros_msgs/RCout</a>)</li> <li>/mavros/wind_estimation - autopilot estimation of the wind (ros msg type: <a href="https://docs.ros.org/en/melodic/api/geometry_msgs/html/msg/TwistWithCovarianceStamped.html">geometry_msgs/TwistWithCovarianceStamped</a>)</li> <li>/surrey - raw data from the surrey PitotTube (custom ros msg type: surrey_sensor/Surrey, available in <a href="https://gitlab.epfl.ch/laupre/vdm_c/-/blob/Topoplane2/include/Surrey.h?ref_type=heads">Surrey.h</a>)</li> <li>/timeSync - mapping from autopilot time and GNSS time to tag the data autopilot data (*<em>_tagged</em>) with GNSS time (ros msg type: <a href="https://docs.ros.org/en/melodic/api/std_msgs/html/msg/Float32MultiArray.html">std_msgs/Float32MultiArray</a>)</li> </ul> <p><strong>concordeS_20230601.bag</strong></p> <p>These data are generated in a <em>formatted </em>version using the recorded flight with the ConcordeS1 drone on June 1st, 2023. The bag contains the following topics:</p> <ul> <li>/airData - data from the PitotTube (custom ros msg types: surrey_sensor/AirData, available from <a href="https://gitlab.epfl.ch/laupre/vdm_c/-/blob/Topoplane2/include/AirData.h?ref_type=heads">AirData.h</a>) <ul> <li>GNSS time [s]</li> <li>airSpeed [m/s]</li> <li>baroAltitude [m]</li> <li>density [hPa]</li> </ul> </li> <li>/cc_formatted - control commands (ros msg type: <a href="https://docs.ros.org/en/melodic/api/std_msgs/html/msg/Float64MultiArray.html">std_msgs/Float64MultiArray</a>) <ul> <li>data :[GNSS time [s], rpm [normalized value from autopilot channel 0-1000], LeftDeflection [degree], RightDeflection [degree]]</li> </ul> </li> <li>/giinav_formatted - INS/GNSS solution, used to initialize the in-flight VDM-based navigation filter (ros msg type: <a href="https://docs.ros.org/en/noetic/api/nav_msgs/html/msg/Odometry.html">nav_msgs/Odometry</a>) <ul> <li>pose: <ul> <li>position (WGS84) [x(lat) [rad], y(long) [rad], z(alt) [m]]</li> <li>orientation (NED) [x y z w] in quaternion</li> </ul> </li> <li>twist: <ul> <li>linear (NED) [x [m/s], y [m/s], z [m/s]]</li> <li>angular (body) [x [degree/s], y [degree/s], z [degree/s]]</li> </ul> </li> </ul> </li> <li>/gnss_formatted - GNSS position and velocity (ros msg type: <a href="https://docs.ros.org/en/noetic/api/nav_msgs/html/msg/Odometry.html">nav_msgs/Odometry</a>) <ul> <li>pose: <ul> <li>position (WGS84) [x(lat) [rad], y(long) [rad], z(alt) [m]]</li> <li>orientation - null</li> </ul> </li> <li>twist: <ul> <li>linear (NED) [x [m/s], y [m/s], z [m/s]]</li> <li>angular - null</li> </ul> </li> </ul> </li> <li>/imu_formatted - a triplet of accelerometer and gyroscope (ros msg type: <a href="https://docs.ros.org/en/noetic/api/sensor_msgs/html/msg/Imu.html">sensors_msgs/IMU</a>) <ul> <li>header</li> <li>orientation (NED) (quaternion) [x y z w]</li> <li>orientation_covariance</li> <li>angular_velocity (body) [degree/s] [x y z]</li> <li>angular_velocity_covariance</li> <li>linear_acceleration (body) [m/s2] [x y z]</li> <li>linear_<em>acceleration</em>_covariance</li> </ul> </li> <li>/toggleOnOffGnss - Simulate the activation of the GNSS outage for testing purposes (ros msg type: <a href="https://docs.ros.org/en/noetic/api/std_msgs/html/msg/Bool.html">std_msgs/Bool</a>)</li> </ul> <p>In addition, there is a complete example of VDMc with TOPOPLANE2 in the <strong>TOPOPlane2_20220826_STIM13.zip</strong> file. It contains</p> <ul> <li>The VDMc code in the vdm_c folder that you can run</li> <li>The input data: TP2_20220825_outage382606_120sbaro.bag, which contains the rosbag from the STIM13 flight, performed on August 25th, 2022</li> <li>The compilation and execution of the code is explained at : https://gitlab.epfl.ch/laupre/vdm_c/-/wikis/Installation</li> </ul>
The dataset of Synoptic Features of the Regional Daily Extreme Precipitation over the Yangtze River Delta during the Summer Seasons of 1979-2021
<p>The dataset contains the SOM results, the start and end dates of the Meiyu season, and the condition of extreme precipitation stations per day during the summer seasons of 1979-2021.</p>
DatasetOpen The dataset of Synoptic Features of the Regional Daily Extreme Precipitation over the Yangtze River Delta during the Summer Seasons of 1979-2021
<p>The dataset contains the SOM results, the start and end dates of the Meiyu season, and the condition of extreme precipitation stations per day during the summer seasons of 1979-2021.</p>
The dataset of Synoptic Features of the Regional Daily Extreme Precipitation over the Yangtze River Delta during the Summer Seasons of 1979-2021
<p>The dataset contains the SOM results, the start and end dates of the Meiyu season, and the condition of extreme precipitation stations per day during the summer seasons of 1979-2021.</p>
Dataset for "Vaccination Effects on SARS-CoV-2 Intra-Host Evolution During São Paulo's Delta and Omicron Waves"
<p><span>The COVID-19 pandemic, driven by SARS-CoV-2, has led to intensive vaccination campaigns worldwide. Despite widespread vaccination, the potential for vaccine escape variants necessitates ongoing research into the virus’s intra-host evolutionary dynamics. In this study we investigated the effects of vaccination on SARS-CoV-2 intra-host evolution during the Delta and Omicron waves in São Paulo, Brazil, analyzing 700 SARS-CoV-2 positive samples collected through the LabMovel initiative, from August 2021 to March 2022. Samples were categorized based on vaccination status (Unvaccinated, Spike protein-based vaccines, and Whole inactivated virus vaccine), enabling comparison of intra-host viral diversity across vaccinated and unvaccinated individuals for both Delta and Omicron VOCs. We evaluated intra-host genetic diversity by measuring intra-host single nucleotide variants (iSNVs), Faith's Phylogenetic Diversity (PD), and haplotype diversity using Normalized Shannon Entropy. For Delta, vaccinated groups exhibited higher haplotype diversity, yet no statistically significant difference was observed in the total number of iSNVs between vaccinated and unvaccinated individuals. Selective pressures in the Delta VOC showed neutral selection in vaccinated individuals, contrasting with purifying selection in unvaccinated individuals, though effect sizes were minimal. For Omicron, a bimodal distribution in Faith's PD across all groups suggests genetic drift events, aligning with Omicron’s rapid spread and high transmissibility. Observed intra-host diversity patterns were variant-specific, with Spike-based vaccines associated with a lower number of haplotypes in Omicron cases. These findings suggest that vaccination modulates SARS-CoV-2’s intra-host evolution, likely contributing to its mutational landscape in a variant-dependent manner. The results highlight variant-specific responses to vaccination, emphasizing the complex role of selective pressures in SARS-CoV-2 intra-host evolution. Our findings support ongoing genomic surveillance to understand vaccination's evolutionary impact on intra-host viral dynamics, particularly as new variants emerge.</span></p>
Fig. 1 in Springtails (Hexapoda: Collembola) of some plant communities of the Pechora delta
Fig. 1. Localities within the region under consideration. Рис. 1. ИЗученные локалитеты региона.
water level and discharge data in the Mekong Delta
<p>The dataset contains water level and discharge data at multiple station within the Mekong Delta. The timezone is local Vietnamese time.</p>
Mapping Soil Organic Carbon in the World's Largest Arid Mangrove Forest (Indus Delta, Pakistan): A Multi-Sensor Remote Sensing and Machine Learning Approach
<p><span>Mangrove forests play a crucial role in carbon sequestration, especially in arid regions where their ability to store carbon in soil is vital for mitigating climate change. The Indus Delta in Pakistan, the world’s largest arid mangrove forest system, lacks spatially explicit data on Soil Organic Carbon (SOC) despite its importance for conservation and carbon budgeting. This study aims to establish a baseline SOC map 2020 at 10 m spatial resolution using Sentinel-1 (Synthetic Aperture Radar) and Sentinel-2 (MultiSpectral Instrument) satellite imagery, integrated with in-situ soil sampling. SOC predictions were made using a Classification and Regression Tree (CART) machine learning model within the Google Earth Engine platform, leveraging 40 predictor variables, including spectral bands and derived indices. A total of 53 topsoil (0-10 cm) samples were collected in February 2020 across the Indus Delta, and SOC was analyzed using the Walkley-Black method. The results showed an average SOC value of 65.88 Mg C ha</span><span>⁻</span><span>¹ with substantial spatial variability, ranging from 15.06 Mg C ha</span><span>⁻</span><span>¹ to 138.03 Mg C ha</span><span>⁻</span><span>¹ with a total of 0.91 Pg C. The CART model demonstrated high accuracy, with an R² of 0.95 and an RMSE of 9.18 Mg C ha</span><span>⁻</span><span>¹. However, the region faces challenges such as seawater intrusion and salinity, which threaten its ability to sequester carbon. With the first high-resolution SOC map for the Indus Delta, this study provides valuable insights for ecosystem management, conservation planning, and carbon budgeting. These findings of this study have the potential to significantly influence initiatives like REDD+ and Blue Carbon projects, which aim to enhance carbon sequestration while addressing the ecological challenges facing Pakistan’s mangroves</span></p>
FABDEM V1-0 adjusted for the Ayeyarwady Delta in Myanmar by local spot height data from topographic maps
<p><strong>Title:</strong></p> <p>FABDEM V1-0 adjusted for the Ayeyarwady Delta in Myanmar by local spot height data from topographic maps</p> <p><strong>Citation:</strong></p> <p>Seeger, K., Minderhoud, P. S. J., Peffeköver, A., Vogel, A., Brückner, H., Kraas, F., Nay Win Oo, Brill, D. (2023): FABDEM V1-0 adjusted for the Ayeyarwady Delta in Myanmar by local spot height data from topographic maps. Zenodo, <a href="https://doi.org/10.5281/zenodo.7875856">https://doi.org/10.5281/zenodo.7875856</a>.</p> <p><strong>Supplement to:</strong></p> <p>Seeger, K., Minderhoud, P. S. J., Peffeköver, A., Vogel, A., Brückner, H., Kraas, F., Nay Win Oo, and Brill, D. (2023): Assessing land elevation in the Ayeyarwady Delta (Myanmar) and its relevance for studying sea level rise and delta flooding. EGUsphere [preprint], <a href="https://doi.org/10.5194/egusphere-2022-1425">https://doi.org/10.5194/egusphere-2022-1425</a>.</p> <p><strong>Abstract:</strong></p> <p>This digital elevation model is a version of the FABDEM V1-0 of Hawker et al. (2022; <a href="https://doi.org/10.1088/1748-9326/ac4d4f">https://doi.org/10.1088/1748-9326/ac4d4f</a>) that was adjusted for the Ayeyarwady Delta in Myanmar by local spot height data from topographic maps (scale 1:50,000) published in 2014 while source data was compiled between 2000 and 2004. The FABDEM V1-0 (Laurence Hawker, Jeffrey Neal (2021): FABDEM V1-0. <a href="https://doi.org/10.5523/bris.25wfy0f9ukoge2gs7a5mqpq2j7">https://doi.org/10.5523/bris.25wfy0f9ukoge2gs7a5mqpq2j7</a>; CC BY-NC-SA 4.0) was projected to the Myanmar 2000 datum and clipped to the Ayeyarwady Delta region of interest. The vertical reference of the FABDEM V1-0 was transformed to EGM96 before applying a conversion to continuous mean sea level based on mean dynamic topography data (CNES-CLS18 dataset of Mulet et al. (2021; <a href="https://doi.org/10.5194/os-17-789-2021">https://doi.org/10.5194/os-17-789-2021</a>) that we transposed to EGM96). Subsequently, inland water bodies were masked using the water body mask of the Copernicus DEM (Airbus Defence and Space, 2020: Copernicus Digital Elevation Model Product Handbook Version 3.0, Airbus, 38 pp.) and cell values with an elevation of more than 7 m below mean sea level were removed.</p> <p>From the topographic maps, the local spot heights outside of areas masked in the AD-DEM (Seeger et al. (2023): Local digital elevation model for the Ayeyarwady Delta in Myanmar (AD-DEM) derived from digitised spot and contour heights of topographic maps. Doi; CC-BY 4.0) were subtracted from elevation values of the FABDEM V1-0 at the same locations (~3630 spot heights). Empirical Bayesian Kriging with empirical data transformation and exponential modelling was applied to interpolate the height residuals and export the raster data at ~30 m grid cell resolution. The mask layer of the AD-DEM was applied to the height residual raster in order to correct for interpolations in areas of data paucity. Subsequently, the interpolated height residuals were subtracted from the pre-processed FABDEM. In delta areas outside the masked regions of the height residual raster, the elevation of the pre-processed FABDEM was maintained (see the figure in the uploaded dataset).</p> <p>For further information on processing of local and global elevation data for the Ayeyarwady Delta in Myanmar, including DEM interpolation, determination of local mean sea level and vertical datum conversions, as well as DEM performance, see the corresponding paper and supplementary material.</p> <p>File name: FABDEM_EGM96_MDT_AD_MMR2000_maskedCop_min7_adjusted_AD.tif</p> <p>File format: GEOTIFF file</p> <p>Spatial reference: MMR2000_46N</p> <p>Vertical reference: local continuous mean sea level, i.e., mean dynamic topography (CNES-CLS18 dataset of Mulet et al. (2021; <a href="https://doi.org/10.5194/os-17-789-2021">https://doi.org/10.5194/os-17-789-2021</a>) transposed to EGM96</p> <p>Cell size: 30 × 30 m</p>
Comparative analysis of D. shibae wild-type vs. delta-luxI1 supplemented with different acetylated homoserine-lactones
GEO Series GSE42010. Dinoroseobacter shibae DFL 12 = DSM 16493. 6 samples. Type: Expression profiling by array.
Identification of Ligands for the human gamma-delta TCR
GEO Series GSE39709. Homo sapiens. 12 samples. Type: Expression profiling by array.
Hyperactive PI3K-delta in Th1 and Th2 differentiation [RNA-seq]
GEO Series GSE277881. Mus musculus. 42 samples. Type: Expression profiling by high throughput sequencing.
Transcriptional program for induced delta cell on day10
GEO Series GSE52522. Mus musculus. 3 samples. Type: Expression profiling by array.
Transmission of survival signals through Delta-like 1 on activated CD4+ T cells
GEO Series GSE76693. Mus musculus. 3 samples. Type: Expression profiling by array.
Expression analysis of Shigella sonnei MS390 delta-rodZ mutant
GEO Series GSE21051. Shigella sonnei; Shigella sonnei Ss046. 4 samples. Type: Expression profiling by array.
Recombinant erythroid Kruppel-like factor fused to GATA1 up-regulates delta- and gamma-globin expression in erythroid cells primary adult human erythroid progenitor cells
GEO Series GSE189272. Homo sapiens. 12 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
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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.