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Simulation performance of different planetary boundary layer schemes in WRF V4.3.1 on wind field over Sichuan Basin within "Gray zone" resolution
<p>Weather Research and Forecasting (WRF) model was <span>used</span> to <span>investigate the performance of different planetary </span>boundary layer <span>(PBL) </span>parameteri<span>z</span>ation schemes <span>on </span>simulat<span>ing</span> surface wind <span>fields over Sichuan Basin</span> at a spatial resolution of <span>0.33</span>km<span>.</span> <span>T</span>he <span>experiment</span> is based on <span>multi-</span>case stud<span>ies, so</span> 2<span>8 near-surface </span>wind events from 2021 to 2022 <span>were selected, and</span> <span>a</span> total of 112 <span>sensitivity </span>simulations were <span>carried out by</span> employing four common<span>ly used</span> <span>PBL </span>schemes: YSU, MYJ, MYNN2, and QNSE<span>, and</span> compared to observations.</p> <p>The mean 10 minutes observations of wind speed and direction during the study period from the Guanghan Airport are stored in one seperate txt file named OBS-28cases-every10min-10m-wind-Guanghan-Airoprt.txt.</p> <p>Wind field (U10, V10) at the central point from the WRF simulations of inner domain from all the PBL simulations are investigated, which are stored in txt format (.txt), and all the results are available from the first author(yuet@mail.iap.ac.cn).</p>
FireSR: A Dataset for Super-Resolution and Segmentation of Burned Areas
<p><br># FireSR Dataset</p> <p>## Overview</p> <p>**FireSR** is a dataset designed for the super-resolution and segmentation of wildfire-burned areas. It includes data for all wildfire events in Canada from 2017 to 2023 that exceed 2000 hectares in size, as reported by the National Burned Area Composite (NBAC). The dataset aims to support high-resolution daily monitoring and improve wildfire management using machine learning techniques.</p> <p>## Dataset Structure</p> <p>The dataset is organized into several directories, each containing data relevant to different aspects of wildfire monitoring:</p> <p>- **S2**: Contains Sentinel-2 images.<br> - **pre**: Pre-fire Sentinel-2 images (high resolution).<br> - **post**: Post-fire Sentinel-2 images (high resolution).</p> <p>- **mask**: Contains NBAC polygons, which serve as ground truth masks for the burned areas.<br> - **pre**: Burned area labels from the year before the fire, using the same spatial bounds as the fire events of the current year.<br> - **post**: Burned area labels corresponding to post-fire conditions.</p> <p>- **MODIS**: Contains post-fire MODIS images (lower resolution).</p> <p>- **LULC**: Contains land use/land cover data from ESRI Sentinel-2 10-Meter Land Use/Land Cover (2017-2023).</p> <p>- **Daymet**: Contains weather data from Daymet V4: Daily Surface Weather and Climatological Summaries.</p> <p>### File Naming Convention</p> <p>Each GeoTIFF (.tif) file is named according to the format: `CA_<year>_<province>_<id>.tif`, where:<br>- `CA` stands for Canada.<br>- `<year>` is the year of the wildfire event.<br>- `<province>` is the province code (e.g., AB for Alberta, BC for British Columbia).<br>- `<id>` is a unique identifier for the wildfire event.</p> <p>### Directory Structure</p> <p>The dataset is organized as follows:</p> <p>```<br>FireSR/<br>│<br>├── dataset/<br>│ ├── S2/<br>│ │ ├── post/<br>│ │ │ ├── CA_2017_AB_204.tif<br>│ │ │ ├── CA_2017_AB_2418.tif<br>│ │ │ └── ...<br>│ │ ├── pre/<br>│ │ │ ├── CA_2017_AB_204.tif<br>│ │ │ ├── CA_2017_AB_2418.tif<br>│ │ │ └── ...<br>│ ├── mask/<br>│ │ ├── post/<br>│ │ │ ├── CA_2017_AB_204.tif<br>│ │ │ ├── CA_2017_AB_2418.tif<br>│ │ │ └── ...<br>│ │ ├── pre/<br>│ │ │ ├── CA_2017_AB_204.tif<br>│ │ │ ├── CA_2017_AB_2418.tif<br>│ │ │ └── ...<br>│ ├── MODIS/<br>│ │ ├── CA_2017_AB_204.tif<br>│ │ ├── CA_2017_AB_2418.tif<br>│ │ └── ...<br>│ ├── LULC/<br>│ │ ├── CA_2017_AB_204.tif<br>│ │ ├── CA_2017_AB_2418.tif<br>│ │ └── ...<br>│ ├── Daymet/<br>│ │ ├── CA_2017_AB_204.tif<br>│ │ ├── CA_2017_AB_2418.tif<br>│ │ └── ...<br>```</p> <p>### Spatial Resolution and Channels</p> <p>- **Sentinel-2 (S2) Images**: 20 meters (Bands: B12, B8, B4)<br>- **MODIS Images**: 250 meters (Bands: B7, B2, B1)<br>- **NBAC Burned Area Labels**: 20 meters (1 channel, binary classification: burned/unburned)<br>- **Daymet Weather Data**: 1000 meters (7 channels: dayl, prcp, srad, swe, tmax, tmin, vp)<br>- **ESRI Land Use/Land Cover Data**: 10 meters (1 channel with 9 classes: water, trees, flooded vegetation, crops, built area, bare ground, snow/ice, clouds, rangeland)</p> <p>**Daymet Weather Data**: The Daymet dataset includes seven channels that provide various weather-related parameters, which are crucial for understanding and modeling wildfire conditions:</p> <p>| Name | Units | Min | Max | Description |</p> <p>|------|-------|-----|-----|-------------|</p> <p>| dayl | seconds | 0 | 86400 | Duration of the daylight period, based on the period of the day during which the sun is above a hypothetical flat horizon. |</p> <p>| prcp | mm | 0 | 544 | Daily total precipitation, sum of all forms converted to water-equivalent. |</p> <p>| srad | W/m^2 | 0 | 1051 | Incident shortwave radiation flux density, averaged over the daylight period of the day. |</p> <p>| swe | kg/m^2 | 0 | 13931 | Snow water equivalent, representing the amount of water contained within the snowpack. |</p> <p>| tmax | °C | -60 | 60 | Daily maximum 2-meter air temperature. |</p> <p>| tmin | °C | -60 | 42 | Daily minimum 2-meter air temperature. |</p> <p>| vp | Pa | 0 | 8230 | Daily average partial pressure of water vapor. |</p> <p>**ESRI Land Use/Land Cover Data**: The ESRI 10m Annual Land Cover dataset provides a time series of global maps of land use and land cover (LULC) from 2017 to 2023 at a 10-meter resolution. These maps are derived from ESA Sentinel-2 imagery and are generated by Impact Observatory using a deep learning model trained on billions of human-labeled pixels. Each map is a composite of LULC predictions for 9 classes throughout the year, offering a representative snapshot of each year.</p> <p>| Class Value | Land Cover Class |</p> <p>|-------------|------------------|</p> <p>| 1 | Water |</p> <p>| 2 | Trees |</p> <p>| 4 | Flooded Vegetation |</p> <p>| 5 | Crops |</p> <p>| 7 | Built Area |</p> <p>| 8 | Bare Ground |</p> <p>| 9 | Snow/Ice |</p> <p>| 10 | Clouds |</p> <p>| 11 | Rangeland |</p> <p><br>## Usage Tutorial</p> <p>To help users get started with FireSR, we provide a comprehensive tutorial with scripts for data extraction and processing. Below is an example workflow:</p> <p>### Step 1: Extract FireSR.tar.gz</p> <p>```bash<br>tar -xvf FireSR.tar.gz<br>```</p> <p>### Step 2: Tiling the GeoTIFF Files</p> <p>The dataset contains high-resolution GeoTIFF files. For machine learning models, it may be useful to tile these images into smaller patches. Here's a Python script to tile the images:</p> <p>```python<br>import rasterio<br>from rasterio.windows import Window<br>import os</p> <p>def tile_image(image_path, output_dir, tile_size=128):<br> with rasterio.open(image_path) as src:<br> for i in range(0, src.height, tile_size):<br> for j in range(0, src.width, tile_size):<br> window = Window(j, i, tile_size, tile_size)<br> transform = src.window_transform(window)<br> outpath = os.path.join(output_dir, f"{os.path.basename(image_path).split('.')[0]}_{i}_{j}.tif")<br> with rasterio.open(outpath, 'w', driver='GTiff', height=tile_size, width=tile_size, count=src.count, dtype=src.dtypes[0], crs=src.crs, transform=transform) as dst:<br> dst.write(src.read(window=window))</p> <p># Example usage<br>tile_image('FireSR/dataset/S2/post/CA_2017_AB_204.tif', 'tiled_images/')<br>```</p> <p>### Step 3: Loading Data into a Machine Learning Model</p> <p>After tiling, the images can be loaded into a machine learning model using libraries like PyTorch or TensorFlow. Here's an example using PyTorch:</p> <p>```python<br>import torch<br>from torch.utils.data import Dataset<br>from torchvision import transforms<br>import rasterio</p> <p>class FireSRDataset(Dataset):<br> def __init__(self, image_dir, transform=None):<br> self.image_dir = image_dir<br> self.transform = transform<br> self.image_paths = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith('.tif')]</p> <p> def __len__(self):<br> return len(self.image_paths)</p> <p> def __getitem__(self, idx):<br> image_path = self.image_paths[idx]<br> with rasterio.open(image_path) as src:<br> image = src.read()<br> if self.transform:<br> image = self.transform(image)<br> return image</p> <p># Example usage<br>dataset = FireSRDataset('tiled_images/', transform=transforms.ToTensor())<br>dataloader = torch.utils.data.DataLoader(dataset, batch_size=16, shuffle=True)<br>```</p> <p>## License</p> <p>This dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). You are free to share and adapt the material as long as appropriate credit is given.</p> <p>## Contact</p> <p>For any questions or further information, please contact:<br>- Name: Eric Brune<br>- Email: ebrune@kth.se</p>
High-spatial-resolution monthly precipitation dataset over China during 1901–2017
<p>The dataset with 0.5 arcminute (~1 km) was spatially downscaled from CRU TS v4.02 based on Delta downscaling method, including monthly precipitation from 1901.1 to 2017.12. The dataset covers the main land area of China. The dataset was evaluated by 496 national weather stations across China, and the evaluation indicated that the downscaled dataset is reliable for the investigations related to climate change across China.</p> <p>Another data download site is Loess plateau Scientific Data Center (http://loess.geodata.cn/). This is a Chinese website. This website publishes the updated histrorical dataset and future downscaled monthly precipitation under multiple SSP Scenarios and GCMs, with 1 km spatial resolution.</p> <p>/*************/ The dataset is updated yearly. Now, the period of the dataset is from 1901.1 to 2020.12.</p> <p>/*************/ The future 1km dataset from 2021-2100 is published.</p> <p><br> The data provider recommended the below publication as the reference.<br> Peng Shouzhang, Ding Yongxia, Liu Wenzhao, Li Zhi. 1 km monthly temperature and precipitation dataset for China from 1901 to 2017. Earth System Science Data, 2019, 11, 1931–1946, https://doi.org/10.5194/essd-11-1931-2019.</p>
RESOLUTE Japan - The Clinical Evaluation of the MDT-4107 Drug-Eluting Coronary Stent
ClinicalTrials.gov study NCT00927940. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Ultra-high-resolution CT vs. Conventional Angiography for Detecting Coronary Heart Disease
ClinicalTrials.gov study NCT04272060. IPD Sharing: NO. Countries: 1. Publications: 7.
The Medtronic RESOLUTE US Clinical Trial
ClinicalTrials.gov study NCT00726453. IPD Sharing: Not stated. Countries: 1. Publications: 5.
18F-AV-1451 High Resolution Autopsy Study
ClinicalTrials.gov study NCT02350634. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The CHARM Trial: Chemotherapy for Ablation and Resolution of Mucinous Pancreatic Cysts
ClinicalTrials.gov study NCT01475331. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Single Arm Trial With Resolute Onyx in ONE-Month DAPT for High-Bleeding Risk Patients Who Are Considered One-Month Clear (Onyx ONE Clear)
ClinicalTrials.gov study NCT03647475. IPD Sharing: NO. Countries: 1. Publications: 3.
Advanced Medical Therapy Versus Advanced Medical Therapy Plus Bariatric Surgery for the Resolution of Type 2 Diabetes
ClinicalTrials.gov study NCT00432809. IPD Sharing: Not stated. Countries: 1. Publications: 12.
Continued Anticonvulsants After Resolution of Neonatal Seizures: a Patient-centered Comparative Effectiveness Study
ClinicalTrials.gov study NCT02789176. IPD Sharing: YES. Countries: 1. Publications: 1.
RESOLUTE Japan SVS: The Clinical Evaluation of the MDT-4107 DES in the Treatment of De Novo Lesions in Small Diameter Native Coronary Arteries
ClinicalTrials.gov study NCT01150500. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Complex Sleep Apnea Syndrome (CompSAS) Resolution Study
ClinicalTrials.gov study NCT00915499. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.
High Resolution, 18F-PSMA PET-MRI Before Prostate Cancer HIFU or Radical Prostatectomy
ClinicalTrials.gov study NCT04461509. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Medtronic Resolute Onyx 2.0 mm Clinical Study
ClinicalTrials.gov study NCT02412501. IPD Sharing: Not stated. Countries: 1. Publications: 1.
High-resolution, Relational, Resonance-based, Electroencephalic Mirroring (HIRREM) to Relieve Insomnia
ClinicalTrials.gov study NCT01971567. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Clot Lysis: Evaluating Accelerated Resolution of Intraventricular Hemorrhage Phase III
ClinicalTrials.gov study NCT00784134. IPD Sharing: NO. Countries: 9. Publications: 7.
Effect of Use of DryNites Absorbent Pants on the Rate of Spontaneous Resolution of Paediatric Nocturnal Enuresis (NE)
ClinicalTrials.gov study NCT04620356. IPD Sharing: NO. Countries: 1. Publications: 1.
Investigation of Hemodynamics and Radiomics Based on High Resolution Magnetic Resonance Imaging for Predicting the Outcomes of Intracranial Dissecting Aneurysm
ClinicalTrials.gov study NCT07335029. IPD Sharing: NO. Countries: 1. Publications: 2.
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.