Find research datasets worth reusing

opencc-by-4.0Jan 2024View details →

Mapping the global distribution of C4 vegetation using observations and optimality theory

This dataset includes annual C4 vegetation distribution and its uncertainty from 2001 to 2019. We also provide the distribution of C4 natural grasses and C4 crops during the same period, as well as the code and interim dataset to generate the main figures. Please refer to manuscript for more details: Luo, X., Zhou, H., Satriawan, T.W., Tian, J., Zhao, R., Keenan, T.F., Griffith, D. M., Sitch, S. Smith, N.G. & Still, C.J. (2024). Mapping the global distribution of C4 vegetation using observations and optimality theory. Nature Communications. https://doi.org/10.1038/s41467-024-45606-3. Update (Nov 2023): we have updated the observational constraint from a linear model to a non-linear model - logistic curve, to better depict how C4 photosynthetic advantage translates into C4 grass coverage changes (C4_distribution_NUS_v2.2.nc). Update (August 2023): we corrected the issue caused by a bias in the remote sensing grassland base map, and released the version 2 of the C4 vmap (C4_distribution_NUS_v2.nc). Update (June 2023): we noticed there is a critical issue in the version 1 of our C4 map, due to the quality of remote sensing grassland base map used. We are now working on providing a new version (V2) in the next few months (Jun 2023).

opencc-by-4.0Dec 2015View details →

L4A - Biomass map of the Brazilian Amazon and uncertainty

The AGB final map (further referred as EBA - Estimativa de Biomassa para a Amazônia - map) presented a maximum AGB value of 518 Mg ha-1, a mean AGB of 174 Mg ha-1, and a standard deviation of 102 Mg ha-1. The map is provided in TIF format, projected using EPSG 4236. The uncertainty map is provided in TIF format, projected using EPSG 4236. The information is offered in Mg ha-1.

opencc-by-4.0Feb 2024View details →

Mapping of STEAM practices - anonymised dataset

This dataset is the result of the initial dissemination of a survey submitted to STEAM practitioners, aiming at gaining insight on their on-going STEAM practices, which have informed the project's mapping of STEAM practices (Deliverable 4.2).

opencc-by-4.0Dec 2022View details →

Precision viticulture dataset for detailed vineyard mapping composed of geotagged smartphone ground images, phytosanitary status, UAV orthomosaics, 3D point clouds, and RTK GNSS data - Northern Spain, July 2022

This dataset offers a rich multimodal collection of data from vineyards, designed to enhance agricultural research with a focus on vineyard management and disease monitoring. It includes geotagged smartphone ground images in ".7z" format for detailed plant-level analysis, a ".csv" file detailing plants' phytosanitary status for health assessment, UAV-derived 3D Point Clouds and orthomosaics in ".las" and ".tiff" formats for aerial landscape views, and RTK GNSS data in ".shp" format for precise plant geolocations. This dataset can be combined with other datasets to enable a comprehensive view of the vineyards and improve its value: <div> <ul> <li>Ariza-Sentís, Mar, Sergio Vélez, and João Valente. ‘Dataset on UAV RGB Videos Acquired over a Vineyard Including Bunch Labels for Object Detection and Tracking’. Data in Brief 46 (February 2023): 108848. <a href="https://doi.org/10.1016/j.dib.2022.108848">https://doi.org/10.1016/j.dib.2022.108848</a>.</li> <li>Vélez, Sergio, Mar Ariza-Sentís, and João Valente. ‘VineLiDAR: High-Resolution UAV-LiDAR Vineyard Dataset Acquired over Two Years in Northern Spain.’ Data in Brief, October 2023, 109686. <a href="https://doi.org/10.1016/j.dib.2023.109686">https://doi.org/10.1016/j.dib.2023.109686</a>.</li> <li> <div> <div>Vélez, Sergio, Mar Ariza-Sentís, and João Valente. ‘Dataset on Unmanned Aerial Vehicle Multispectral Images Acquired over a Vineyard Affected by Botrytis Cinerea in Northern Spain’. Data in Brief 46 (February 2023): 108876. <a href="https://doi.org/10.1016/j.dib.2022.108876">https://doi.org/10.1016/j.dib.2022.108876</a>.</div> <div> </div> </div> </li> </ul> </div>

opencc-by-4.0Feb 2024View details →

FAIR Evaluations of University and College Repositories at DataCite Using MetaDIG Mappings for Four Use Cases.

This spreadsheet has the results of an evaluation of FAIRness of 387 University and College DataCite repositories using techniques developed in the MetaDIG project. It is possible to compare scores from different repositories and to create rose diagrams showing the results for any of the repositories.

opencc-by-4.0Feb 2024View details →

Data for Paper "Scalable Semantic 3D Mapping of Coral Reefs with Deep Learning"

Example Data for DeepReefMap This dataset contains input videos in MP4 format taken with GoPro Hero 10 Cameras in Reefs in the Red Sea to demonstrate the DeepReefMap tool, which is described in the paper "Scalable Semantic 3D Mapping of Coral Reefs with Deep Learning" by Sauder et al. It contains a directory for model checkpoints for semantic segmentation, and for the 3D SLAM component: ``` checkpoints/         segmentation_net.pth         sfm_net.pth ``` It also contains videos to run the reconstruction with. See the detailed instructions for running reconstructions in https://github.com/josauder/mee-deepreefmap ``` input_videos/         GX_SINGLE_VIDEO.MP4         GX_VIDEO_1_OF_2.MP4         GX_VIDEO_2_OF_2.MP4 ```

A taxonomy to map evidence on the co-benefits, challenges, and limits of carbon dioxide removal

This repository is linked to the following article: <ul> <li>Prütz, R., Fuss, S., Lück, S. Stephan, L. & Rogelj, J., A taxonomy to map evidence on the co-benefits, challenges, and limits of carbon dioxide removal. Commun Earth Environ 5, 197 (2024). <a href="https://doi.org/10.1038/s43247-024-01365-z">https://doi.org/10.1038/s43247-024-01365-z</a></li> </ul> This repository includes:  <ul> <li>The literature-based data set that was compiled and used to develop the taxonomy of carbon dioxide removal side effects</li> <li>Code to run the machine learning classifier and to process and visualise the data</li> <li>Training data and an abbreviation list which is required to run the provided code</li> </ul>

University of New Hampshire Pressure Mapped Munition (PMM) Experiments 2019 - 2021

This project contains the data collected by the University of New Hampshire (UNH) Coastal Processes Lab for three field experiments focusing on investigating munition mobility in nearshore/surfzone regions. The first experiment took place on May 17 2019, the second experiment took place on February 1-2 2021, and the last experiment to place on October 26-27 2021. The data was collected at Wallis Sands Beach in Rye, New Hampshire.  The main instrument utilized was the Pressure Mapped Munition (PMM), which is a fully autonomous cylindrical surrogate munition that can resolve the pressure field on its surface. The PMM is constructed from a 229 mm long section of 304 stainless steel pipe with a 140 mm outside diameter and 12.7 mm wall thickness. To make autonomous measurements of surface pressure and relative position, the PMM houses 16 high resolution TE Connectivity MS5837-02BA absolute pressure sensors and a Lord MicroStrain 3DM®-GX5-25 inertial measurement unit (IMU). There are two rings of pressure sensors around the cylinder. Pressure sensors 1-8 are in ring 1 and pressure sensors 9-16 are in ring 2. Each ring is 6.35 cm from the ends of the cylinder and the rings are 15.24 cm apart. The radial spacing between sensors around each ring is 45°, which minimizes the arc length and height difference between sensors while maintaining axial pairs of sensors. This orientation allows for sensor redundancy in the case of sensor failure and allows for error correction if faulty values from one of the sensors is suspected. When the PMM is lying flat the vertical displacement between the topmost and bottommost sensor is 129 - 140 mm depending on the orientation of the instrument.  For the May experiment, a Nortek Vector was deployed along with the PMM. The Nortek Vector is an Acoustic Doppler Velocimeter (ADV) which collects high-resolution, single-point, 3-dimensional velocity data (Nortek, 2022b). For this experiment the ADV transducer was about 30 cm above the sediment bed while the ADV pressure sensor was about 50 cm above the sediment bed. The Nortek Vector was not deployed for the Feburary and October experiments. For the February and October experiments, another instrument, the Pressure Stick (PS), was deployed with the PMM. The Pressure Stick is a fully autonomous pressure-profiling instrument. The PS contains eight micro-controlled, time-synced TE Connectivity MS5837-02BA absolute pressure and temperature sensors distributed along a 70 cm distance to measure pressure throughout the water column and into the sediment bed. The Pressure Stick allows for a quantification of intermittent bed instability (momentary liquefaction) and its potential role in munition mobility. More information about the PS can be found in Marry & Foster (2024). Global Positioning System (GPS) surveys of the beach profile were also completed on February 2, 2021 (at the end of the experiment) and October 26, 2021 (at the beginning of the experiment), and the survey data is included in this project. Data of the offshore wave conditions as measured by the Jeffrey's Ledge waverider buoy (station 44098) from the National Data Buoy Center (NDBC) (https://www.ndbc.noaa.gov/station_page.php?station=44098) for all three experiments are given here as well. This effort was supported by Strategic Environmental Research and Development Program (SERDP, Project Number 17 MR-2731). These datasets are presented in the final report for Project Number 17 MR-2731. <h2>Dataset files</h2> A description of each data file is given below: <h3>Pressure Mapped Munition (PMM) Data</h3> 1. PMM_20190517.txt      This file contains the raw data from the PMM for the experiment on May 17, 2019      - Date = date of sample      - Time = time of sample      - P1 = pressure from sensor 1 in mbar      - P2 = pressure from sensor 2 in mbar      - P3 = pressure from sensor 3 in mbar      - P4 = pressure from sensor 4 in mbar      - P5 = pressure from sensor 5 in mbar      - P6 = pressure from sensor 6 in mbar      - P7 = pressure from sensor 7 in mbar      - P8 = pressure from sensor 8 in mbar      - P9 = pressure from sensor 9 in mbar      - P10 = pressure from sensor 10 in mbar      - P11 = pressure from sensor 11 in mbar      - P12 = pressure from sensor 12 in mbar      - P13 = pressure from sensor 13 in mbar      - P14 = pressure from sensor 14 in mbar      - P15 = pressure from sensor 15 in mbar      - P16 = pressure from sensor 16 in mbar      - T = temperature in degrees Celsius 2. IMU_20190517.txt      This file contains the raw IMU data from the PMM for the experiment on May 17, 2019      - Date = date of sample      - Time = time of sample      - yaw = orientation of the IMU in the yaw direction, measured in degrees      - pitch = orientation of the IMU in the pitch direction, measured in degrees. Due to the orientation of the IMU in the PMM, the pitch time series refers to when there are changes in rotation along the long axis of the PMM (i.e. when it rolls).       - roll = orientation of the IMU in the roll direction, measured in degrees. Due to the orientation of the IMU in the PMM, the roll time series refers to when there are changes in rotation along the short axis of the PMM (i.e. when it tilts and one end of the cylinder is higher than the other end).  3. PMM_20210202.txt      This file contains the raw data from the PMM for the experiment on February 1-2, 2021      - Date = date of sample      - Time = time of sample      - P1 = pressure from sensor 1 in mbar      - P2 = pressure from sensor 2 in mbar      - P3 = pressure from sensor 3 in mbar      - P4 = pressure from sensor 4 in mbar      - P5 = pressure from sensor 5 in mbar      - P6 = pressure from sensor 6 in mbar      - P7 = pressure from sensor 7 in mbar      - P8 = pressure from sensor 8 in mbar      - P9 = pressure from sensor 9 in mbar      - P10 = pressure from sensor 10 in mbar      - P11 = pressure from sensor 11 in mbar      - P12 = pressure from sensor 12 in mbar      - P13 = pressure from sensor 13 in mbar      - P14 = pressure from sensor 14 in mbar      - P15 = pressure from sensor 15 in mbar      - P16 = pressure from sensor 16 in mbar      - T = temperature in degrees Celsius 4. IMU_20210202.txt      This file contains the raw IMU data from the PMM for the experiment on February 1-2, 2021      - Date = date of sample      - Time = time of sample      - yaw = orientation of the IMU in the yaw direction, measured in degrees      - pitch = orientation of the IMU in the pitch direction, measured in degrees. Due to the orientation of the IMU in the PMM, the pitch time series refers to when there are changes in rotation along the long axis of the PMM (i.e. when it rolls).       - roll = orientation of the IMU in the roll direction, measured in degrees. Due to the orientation of the IMU in the PMM, the roll time series refers to when there are changes in rotation along the short axis of the PMM (i.e. when it tilts and one end of the cylinder is higher than the other end).  5. PMM_20211026.txt      This file contains the raw data from the PMM for the experiment on October 26-27, 2021      - Date = date of sample      - Time = time of sample      - P1 = pressure from sensor 1 in mbar      - P2 = pressure from sensor 2 in mbar      - P3 = pressure from sensor 3 in mbar      - P4 = pressure from sensor 4 in mbar      - P5 = pressure from sensor 5 in mbar      - P6 = pressure from sensor 6 in mbar      - P7 = pressure from sensor 7 in mbar      - P8 = pressure from sensor 8 in mbar      - P9 = pressure from sensor 9 in mbar      - P10 = pressure from sensor 10 in mbar      - P11 = pressure from sensor 11 in mbar      - P12 = pressure from sensor 12 in mbar      - P13 = pressure from sensor 13 in mbar      - P14 = pressure from sensor 14 in mbar      - P15 = pressure from sensor 15 in mbar      - P16 = pressure from sensor 16 in mbar      - T = temperature in degrees Celsius 6. IMU_20211026.txt      This file contains the raw IMU data from the PMM for the experiment on October 26-27, 2021      - Date = date of sample      - Time = time of sample      - yaw = orientation of the IMU in the yaw direction, measured in degrees      - pitch = orientation of the IMU in the pitch direction, measured in degrees. Due to the orientation of the IMU in the PMM, the pitch time series refers to when there are changes in rotation along the long axis of the PMM (i.e. when it rolls).       - roll = orientation of the IMU in the roll direction, measured in degrees. Due to the orientation of the IMU in the PMM, the roll time series refers to when there are changes in rotation along the short axis of the PMM (i.e. when it tilts and one end of the cylinder is higher than the other end).  <h3>Nortek Vector Data</h3> Vector_20190517.txt      This file contains the Nortek Vector data from the experiment on May 17 2019.      - Vec_time = time of Vector samples      - Vpress = pressure in mbar      - Vpress_smooth = smoothed pressure in mbar      - Vu = cross-shore velocity in m/s      - Vv = along-shore velocity in m/s      - Vw = vertical velocity in m/s <h3>Pressure Stick (PS) Data</h3> 1. PS_20210202.txt     This file contains the raw data from the Pressure Stick for the experiment on February 1-2, 2021     - Date = date of sample     - Time = time of sample     - P1 = pressure from sensor 1 (topmost sensor) in mbar, sensor 1 is about 25 cm above the sediment bed     - P2 = pressure from sensor 2 in mbar, sensor 2 is about 15 cm above the sediment bed     - P3 = pressure from sensor 3 in mbar, sensor 3 is about 9 cm above the sediment bed     - P4 = pressure from sensor 4 in mbar, sensor 4 is about 3 cm above the sediment bed     - P5 = pressure from sensor 5 in mbar, sensor 5 is about 3 cm in the sediment bed     - P6 = pressure from sensor 6 in mbar, sensor 6 is about 13 cm in the sediment bed     - P7 = pressure from sensor 7 in mbar, sensor 7 is about 28 cm in the sediment bed     - P8 = pressure from sensor 8 (bottom-most sensor) in mbar, sensor 8 is about 43 cm in the sediment bed     - T1 = temperature from sensor 1 (topmost sensor) in degrees Celsius, sensor 1 is about 25 cm above the sediment bed     - T2 = temperature from sensor 2 in degrees Celsius, sensor 2 is about 15 cm above the sediment bed     - T3 = temperature from sensor 3 in degrees Celsius, sensor 3 is about 9 cm above the sediment bed     - T4 = temperature from sensor 4 in degrees Celsius, sensor 4 is about 3 cm above the sediment bed     - T5 = temperature from sensor 5 in degrees Celsius, sensor 5 is about 3 cm in the sediment bed     - T6 = temperature from sensor 6 in degrees Celsius, sensor 6 is about 13 cm in the sediment bed     - T7 = temperature from sensor 7 in degrees Celsius, sensor 7 is about 28 cm in the sediment bed     - T8 = temperature from sensor 8 (bottom-most sensor) in degrees Celsius, sensor 8 is about 43 cm in the sediment bed  2. PS_20211026.txt     This file contains the raw data from the Pressure Stick for the experiment on October 26-27, 2021     - Date = date of sample     - Time = time of sample     - P1 = pressure from sensor 1 (topmost sensor) in mbar, sensor 1 is about 19 cm above the sediment bed     - P2 = pressure from sensor 2 in mbar, sensor 2 is about 9 cm above the sediment bed     - P3 = pressure from sensor 3 in mbar, sensor 3 is about 3 cm above the sediment bed     - P4 = pressure from sensor 4 in mbar, sensor 4 is about 3 cm in the sediment bed     - P5 = pressure from sensor 5 in mbar, sensor 5 is about 9 cm in the sediment bed     - P6 = pressure from sensor 6 in mbar, sensor 6 is about 19 cm in the sediment bed     - P7 = pressure from sensor 7 in mbar, sensor 7 is about 34 cm in the sediment bed     - P8 = pressure from sensor 8 (bottom-most sensor) in mbar, sensor 8 is about 49 cm in the sediment bed     - T1 = temperature from sensor 1 (topmost sensor) in degrees Celsius, sensor 1 is about 19 cm above the sediment bed     - T2 = temperature from sensor 2 in degrees Celsius, sensor 2 is about 9 cm above the sediment bed     - T3 = temperature from sensor 3 in degrees Celsius, sensor 3 is about 3 cm above the sediment bed     - T4 = temperature from sensor 4 in degrees Celsius, sensor 4 is about 3 cm in the sediment bed     - T5 = temperature from sensor 5 in degrees Celsius, sensor 5 is about 9 cm in the sediment bed     - T6 = temperature from sensor 6 in degrees Celsius, sensor 6 is about 19 cm in the sediment bed     - T7 = temperature from sensor 7 in degrees Celsius, sensor 7 is about 34 cm in the sediment bed     - T8 = temperature from sensor 8 (bottom-most sensor) in degrees Celsius, sensor 8 is about 49 cm in the sediment bed  <h3>GPS Data</h3> 1. UNH_GPS_WS_20210202.txt (.pos)     This file contains data from a GPS survey taken on February 2nd 2021 as the post-deployment survey for the February experiment.      - GPST = time stamp of the survey sample      - latitude = latitude in degrees     - longitude = longitude in degrees     - height = elevation height, relative to WGS84/ellipsoidal in meters     - Q = quality of the data point. Q = 1 is a 'good' data point, Q = 2 is an 'okay' data point, and Q > 2 are 'bad' data points.      - ns = Number of satellites      2. UNH_GPS_WS_20211026.txt (.pos)     This file contains data from a GPS survey taken on October 26th 2021 as the pre-deployment survey for the October experiment.      - GPST = time stamp of the survey sample      - latitude = latitude in degrees     - longitude = longitude in degrees     - height = elevation height, relative to WGS84/ellipsoidal in meters     - Q = quality of the data point. Q = 1 is a 'good' data point, Q = 2 is an 'okay' data point, and Q > 2 are 'bad' data points.      - ns = Number of satellites <h3>Waverider Buoy (NDBC station 44098) Data</h3> 1. NDBC_44098_JeffreysLedgeBuoy_May2019.txt     This file contains offshore wave data from Jeffrey's Ledge waverider buoy (station 44098) from the National Data Buoy Center (NDBC)(https://www.ndbc.noaa.gov/station_page.php?station=44098) throughout the May 2019 experiment (May 17th 2019 00:08:00 - May 18th 2019 12:38:00). Please see "Description of Measurements" at https://www.ndbc.noaa.gov/measdes.shtml for a discussion of each of the variables in this file. 2. NDBC_44098_JeffreysLedgeBuoy_February2021.txt     This file contains offshore wave data from Jeffrey's Ledge waverider buoy (station 44098) from the National Data Buoy Center (NDBC)(https://www.ndbc.noaa.gov/station_page.php?station=44098) throughout the February 2021 experiment (February 1st 2021 01:26:00 - Feburary 2nd 2021 09:56:00). Please see "Description of Measurements" at https://www.ndbc.noaa.gov/measdes.shtml for a discussion of each of the variables in this file. 3. NDBC_44098_JeffreysLedgeBuoy_October2021.txt     This file contains offshore wave data from [Jeffrey's Ledge waverider buoy (station 44098) from the National Data Buoy Center (NDBC)](https://www.ndbc.noaa.gov/station_page.php?station=44098) throughout the October 2021 experiment (October 26th 2021 00:26:00 - October 27th 2021 23:56:00). Please see "Description of Measurements" at https://www.ndbc.noaa.gov/measdes.shtml for a discussion of each of the variables in this file.

opencc-by-4.0Jan 2024View details →

Adult baobab trees's distribution map across Sahel

The baobab tree (Adansonia digitata L.) is an integral part of rural livelihoods throughout the African continent. However, the combined effects of climate change and increasing global demand for baobab products are currently exerting pressure on the sustainable utilization of these resources. Here we employ sub-meter resolution satellite imagery to identify nearly 3 million baobab trees in the Sahel, a dryland region of 1.5 million km2. This achievement is considered an essential step towards improving valuable woody species' management and monitoring system. To prevent mismanagement of this specific tree species, we aggregated every single adult baobab tree map to 5 × 5 km grids. We also classified the baobab trees using the tree crown diameters( small: 3-9m; medium 9m-13m; large: >13m).  The baobab tree count map is also available for this three different size classes.

Change detection technique comparison in long-term wetland monitoring: datasets and maps of the Poitevin Marsh (France)

<h3>For a full description of the methodology and results, please see the following article:</h3> <div> <div>Demarquet, Q., Rapinel, S., Gore, O., Dufour, S., Hubert-Moy, L., 2024. Continuous change detection outperforms traditional post-classification change detection for long term monitoring of wetlands. International Journal of Applied Earth Observation and Geoinformation 133, 104142. <a href="https://doi.org/10.1016/j.jag.2024.104142">https://doi.org/10.1016/j.jag.2024.104142</a></div> <div> </div> <div>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> </div> <h3># Datasets</h3> Points datasets are projected in WGS84 (EPSG:4326), and are provided in the open source GeoPackage format. The first dataset (Dataset_1.gpkg) contains training and validation points for random forest classification of EUNIS habitats in the Poitevin Marsh. This dataset consists of 3360 training and 840 validation points (total:  4200). Fields description: <ul> <li>"ID": unique identifier</li> <li>"CLASS": EUNIS first level habitat type, classified as following: <ul> <li>1: EUNIS habitat A</li> <li>2: EUNIS habitat B</li> <li>3: EUNIS habitat C1J5</li> <li>4: EUNIS habitat C3</li> <li>5: EUNIS habitat E</li> <li>6: EUNIS habitat G</li> <li>7: EUNIS habitat I</li> <li>8: EUNIS habitat J</li> </ul> </li> <li>"DATE": Date associated with EUNIS habitat sample</li> <li>"LON": Point longitude in decimal degrees</li> <li>"LAT": Point latitude in decimal degrees</li> <li>"TYPE": Either training ("train") or validation ("test") sample</li> </ul> The second dataset (Dataset_2.gpkg) contains points for the Olofsson correction method. This dataset consists of 326 points where the change classes are classified as following: -10 (wetland loss), 10 (wetland gain), 100 (stable existing wetland), and 200 (stable damaged wetland). Fields description: <ul> <li>"ID": unique identifier</li> <li>"LON": Point longitude in decimal degrees</li> <li>"LAT": Point latitude in decimal degrees</li> <li>"REFERENCE": Change class reference</li> <li>"CCDC": Change class obtained from the Continuous Change Detection and Classification approach</li> <li>"PCCD": Change class obtained from the Post-Classification Change Detection approach</li> </ul> Supplementary layout files (Dataset_1.qml and Dataset_2.qml) support formatting of the points in QGIS software. -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- <h3># EUNIS habitat</h3> Maps are projected in WGS84 (EPSG:4326), and are provided in the GeoTiff format at 30m of spatial resolution.  Habitat maps are given for the two approaches in years 1984 and 2022: <ul> <li>CCDC: Continuous Change Detection and Classification (CCDC_HABITAT_1984.tif and CCDC_HABITAT_2022.tif)</li> <li>PCCD: Traditional post-classification approach (PCCD_HABITAT_1984.tif and PCCD_HABITAT_2022.tif)</li> </ul> Supplementary layout files (CCDC_HABITAT_1984.qml, CCDC_HABITAT_2022.qml, PCCD_HABITAT_1984.qml, PCCD_HABITAT_2022.qml) support formatting of raster layers in QGIS software. -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- <h3># Change detection during the 1984-2022 period</h3> Maps are projected in WGS84 (EPSG:4326), and are provided in the GeoTiff format at 30m of spatial resolution. Raster values follow the classification scheme used in Dataset_2. Change detection maps are given for the two approaches: <ul> <li>CCDC: Continuous Change Detection and Classification (CCDC_CHANGE_1984_2022.tif)</li> <li>PCCD: Traditional post-classification approach (PCCD_CHANGE_1984_2022.tif)</li> </ul> Supplementary layer files (CCDC_CHANGE_1984_2022.qml and PCCD_CHANGE_1984_2022.qml) support formatting of the raster layers in QGIS software. -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- <h3># GEE repository</h3> To get direct access to GEE scripts and assets, please follow those two links: https://code.earthengine.google.com/?accept_repo=users/demarquetquentin/CCDC_Poitevin https://code.earthengine.google.com/?asset=projects/ee-quen-dem/assets/CCDC_Poitevin

Data and supplementary material used for Soundscapes to Landscapes soundscape mapping

This repository contains supporting data products to enable the soundscape mapping outlined in the associated publication (DOI forthcoming). Data were used to extract acoustic recording location environmental data for training random forest models to spatially predict 2021 ecoacoustic metrics. The accompanying code will be linked to the GitHub repository. Files include: Data: <ul> <li>clustered_fold_k10.rsd: indices of the model data used if geoCV approach</li> <li>extracted_predictors_vif3.csv: site-specific predictor values extracted from predictors_annual_20230223.tif</li> <li>final_predictors_vif3.csv: a two column table summarizing the VIF selected predictors</li> <li>final_sites_2017-2021.csv: the list of 1,195 potential sites</li> <li>predictor_sprmn_corr.csv: correlation matrix for predictors in model data</li> <li>predictors_annual_20230223.tif: all predictors </li> <li>response_df_200623.csv: site level ecoacoustic metrics</li> </ul> Results: <ul> <li>map_correlations.tar: pairwise response map correlations</li> <li>pdps.tar: partial dependence plot data</li> <li>performance.tar: model performance summaries</li> <li>predictions_maps.tar: final median and IQR model prediction surfaces</li> <li>variable_importance.tar: summaries for variable importance analyses</li> </ul> Contact Colin Quinn at cq73@nau.edu for questions related to this repository or the underlying work. Original wav recordings are expected to be made publicly available on the NASA DAACs in the near future.

Maps of the simulations and observations (TURBAN-D17)

<h3>Basic information</h3> This dataset contains two folders; in output-measurements are daily averaged TURDATA (see <a href="../records/10655033">https://zenodo.org/records/10655033</a>) results for air quality measurements. In folder output-simulations contains simulation results for the so-called Legerova domain, an area in the city of Prague, Czech Republic, centered around the traffic-heavy streets Legerova and Sokolská. All times are in UTC (local time in winter, CET, is UTC +01:00, summer time, CEST, is UTC +02:00). In total 6 episodes in 2022 and 2023 was selected: <ol> <li>s1 2022-07-17 00:00:00 - 2022-07-20 00:00:00</li> <li>s2: 2022-08-02 00:00:00 - 2022-08-05 00:00:00</li> <li>s3: 2022-09-22 00:00:00 - 2022-09-25 00:00:00</li> <li>s4: 2022-12-08 00:00:00 - 2022-12-11 00:00:00</li> <li>s5: 2023-01-27 00:00:00 - 2023-01-30 00:00:00</li> <li>s6: 2023-02-13 00:00:00 - 2023-02-16 00:00:00</li> </ol> For all episode’s boundary conditions from regional model ICON (see icon in folder nomenclature) were used. However, for a very stable condition during s6 two additional regional models were used (WRF and ALADIN; see wrf or alad) that provided boundary conditions closer to the observed meteorology. For more detailed description of the experiments see the TURBAN project website at <a href="https://www.project-turban.eu/">https://www.project-turban.eu/</a>. <h3>General organisation</h3> Each selected epizode (s1-s6) has three folders; post-processed results from the PALM model as averaged ASCII (output-ascii) or GeoTiff (output-gis) files that can be viewed in many GIS applications and maps in the PNG format (output-png). Each variable was averaged from original 10min values to 1 and 24-hour averages. <h3>Modelled variables</h3> Each subfolder includes 3 subfolders with variables. Variable bio_pet is the Physiological Equivalent Temperature (PET), bio_utci is the Universal Thermal Climate Index (UTCI) and kc_PM10 is the concentration of PM10 at 3m above ground. Episodes s4 and s6 includes 3 more subfolders: kc_NO is the concentration of NO, kc_NO2 is the concentration of NO2 and kc_PM25 is the concentration of PM2.5 at 3m above ground. <h3>File nomenclature</h3> Each file (PRJ, TIF, ASC or PNG) has the same nomenclature. An example (bio_utci_abs-01h_20190724_1200-1300.png) could be parsed as: variable name (bio_utci), processed output (abs-01h), date (20190724) and averaged period (1200-1300). So, the result is a map with hourly averaged UTCI for 24 Jul 2019 between 12:00 and 13:00 UTC. <h3>Important note</h3> During the processing phase a few potentially important problems were identified and need to be analysed in detail. One of them are extremely overestimated concentrations due to stable conditions from boundary condition inputs. In certain situations, it can happen that the overall best regional meteorological model can provide inappropriate input conditions for some episode. This needs to be checked in detail before any following interpretation. The issue will be addressed in an upcoming manuscript that will be available through the project website. <h3>Acknowledgements</h3> The PALM simulations, and pre- and postprocessing were performed partially on the HPC infrastructure of the Institute of Computer Science of the Czech Academy of Sciences (ICS), supported by the long-term strategic development financing of the ICS (RVO:67985807) and partially on the IT4I HPC infrastructure supported by the Ministry of Education, Youth and Sports of the Czech Republic through the e-INFRA CZ (ID:90254). The work was performed within the project TURBAN (TO01000219; TURBAN – Turbulent-resolving urban modelling of air quality and thermal comfort) supported by Norway Grants and Technology Agency of the Czech Republic.

Using the traditional microscope for mineral grain orientation determination: A prototype image analysis pipeline for optic-axis mapping (POAM). Original dataset.

The data repository contains data obtained with the microscope Nikon Eclipse LV100ND that was stitched with <a href="https://imagej.net/plugins/trakem2/">TrakEM2 software</a>. The files allow reproducing the results obtained and plot in <a href="https://doi.org/10.1111/jmi.13284">Acevedo et al. (2024)</a> "Using the traditional microscope for mineral grain orientation determination: A prototype image analysis pipeline for optic-axis mapping (POAM)." by Acevedo Zamora, M. A., Schrank, C. E., & Kamber, B. S. The prototype uses MatLab scripts (<a href="https://github.com/marcoaaz/AcevedoEtAl._2024a_POAM">AcevedoEtAl._2024a_POAM</a>) that were documented in the paper Supplementary Material 1. The metadata can be found in Supplementary Material 3 and follows the structure of this data repository. The user needs downloading and changing the paths to run the same scripts and reproduce the results. Note: After download, unzip and merge (copy-paste) the folders (parts 1, 2 and 3). Before merging, the containing folder should be re-named to 'paper 2_datasets' to match exactly the MatLab scripts and reproduce our work. The remaining questions should be addressed to Marco Acevedo (maaz.geologia@gmail.com ; marco.acevedozamora@qut.edu.au) Thanks.

Maps of the detailed spatially and temporally attributed emission for area of Legerova and Sokolska (TURBAN-D18)

<h3>Basic information</h3> This dataset contains six folders with maps of input data for simulations published in project TURBAN as result D17 (see <a href="../records/10982836">https://zenodo.org/records/10982836</a>). Each folder contains air quality inputs for the so-called Legerova domain, an area in the city of Prague, Czech Republic, centred around the traffic-heavy streets Legerova and Sokolská. All times are in UTC (local time in winter, CET, is UTC +01:00, summer time, CEST, is UTC +02:00). In total 6 episodes in 2022 and 2023 were selected: <ol> <li>s1 2022-07-17 00:00:00 - 2022-07-20 00:00:00</li> <li>s2: 2022-08-02 00:00:00 - 2022-08-05 00:00:00</li> <li>s3: 2022-09-22 00:00:00 - 2022-09-25 00:00:00</li> <li>s4: 2022-12-08 00:00:00 - 2022-12-11 00:00:00</li> <li>s5: 2023-01-27 00:00:00 - 2023-01-30 00:00:00</li> <li>s6: 2023-02-13 00:00:00 - 2023-02-16 00:00:00</li> </ol> For more detailed description of the experiments see the TURBAN project website at <a href="https://www.project-turban.eu/">https://www.project-turban.eu/</a>. <h3>General organisation, variables and file nomenclature</h3> Each selected epizode (s1-s6) has three subfolders; input files in ASCII (output-ascii) or GeoTiff (output-gis) formats that can be viewed in many GIS applications. In the third subfolder are maps in the PNG format (output-png). Each subfolder includes 4 subfolders with emissions summarized in all layers above ground. Variable vsrc_PM10 is the concentration of volume source emissions (VSRC) of the PM10, vsrc_PM25 is the concentration of PM2.5, vsrc_NO is the concentration of NO and vsrc_NO2 is the concentration of NO2. Each file (PRJ, TIF, ASC or PNG) has the same nomenclature. An example (vsrc_NO_abs-01h_20220717_1200-1300.png) could be parsed as: variable name (vsrc_NO), processed input (abs-01h), date (20220717) and period (1200-1300). So, the result is a map with emission fluxes of NO between 12:00 and 13:00 UTC 24 Jul 2019. <h3>Emissions (see section 2.4.3 in Resler et al., 2024)</h3> The data were processed from datasets published by CHMI, data collected by the Municipality of Prague and its organizations, data obtained by the researcher (ATEM) while providing expert studies in the past, and results of previous research projects. The input data of the used emission sources can be divided into two basic groups: emission from local heating and transport sources. Emissions for local heating were determined by calculations based on data from CHMI and the Czech Statistical Office (CZSO). Emissions from the transport sources were modeled using the MEFA transportation emission model which is recommended for the use in the Czech Republic by the Ministry of Environment of the Czech Republic. The model takes into account factors such as road gradient, the number of vehicles on the road, the flow of traffic, the composition of car types, and the emission characteristics of the individual car types. The emission calculation is based on data from the traffic census provided by the Prague Technical Administration of Roads (TSK Praha) and on data from the census of the composition of the transportation fleet in Prague built in the MEFA emission model. The data are based on regular surveys of the fleet composition carried out in Prague (Karel et al., 2021). The dust resuspension was computed according to the methodology published by the Ministry of Environment (Karel et. al., 2015). This methodology is based on US EPA methodology AP-42 (EPA, 2011) and was adjusted for the conditions of the Czech Republic. For the garages and parking lots, the results of the project TH03030496 (Karel et al., 2020) were used and for the bus stations, publicly available data about transportation were gathered from the Prague Public Transit Company (DPP). The disaggregation of the annual emissions into hourly intervals was then performed according to the type of source. For combustion sources distribution of emissions to days was done according to natural gas supply profiles for category DOM4 were used (OTE, 2024) and complemented by daily profiles for SNAP 2 (van der Gon, 2011). For transport sources, the census data from TSK Praha was utilized for all streets where it was available. For Legerova and Sokolská streets, hourly traffic intensity data were obtained and used directly for the selected episodes. For streets that were not covered by regular traffic surveys, the spatial and temporal distribution of the traffic intensities were based on analysis and evaluation of the relevant studies for the particular area (e.g. urban planning studies, Environmental Impact Assessment (EIA), etc.) and combined with information like street type, location, traffic regime, and pavement type. This approach allowed us to specify the distribution of the transportation intensities on smaller streets. For the detailed modeling of emissions from rail transport (diesel locomotives), the data of train rides were obtained from the Railway Administration (SŽ) and emission factors from the EMEP/EEA Air Pollutant Emission Inventory Guidebook 2019 (EEA, 2019) were used. Emissions from river ships were obtained from the CHMI national database and spatially distributed to the area of the river. Spatial transformation of the line and point emission into the corresponding areas was done with the utilization of the surrogates representing corresponding areas (e.g. areas of the street traffic lines and parking places for traffic emission and areas of the building roofs for local heating sources). This not only ensured the reasonable spatial distribution of the emission in the street canyon but also decreased the gradients of the emission field and with this proneness of the model to numerical inaccuracy of the micro-scale model. The processing of the emission sources into hourly emission flows was done in the emission model FUME recently extended for processing of the PALM emission (Belda et al., 2024). <h3>Acknowledgements</h3> The PALM simulations, and pre- and postprocessing were performed partially on the HPC infrastructure of the Institute of Computer Science of the Czech Academy of Sciences (ICS), supported by the long-term strategic development financing of the ICS (RVO:67985807) and partially on the IT4I HPC infrastructure supported by the Ministry of Education, Youth and Sports of the Czech Republic through the e-INFRA CZ (ID:90254). The work was performed within the project TURBAN (TO01000219; TURBAN – Turbulent-resolving urban modelling of air quality and thermal comfort) supported by Norway Grants and Technology Agency of the Czech Republic. <h3>Literature</h3> Note that some sources are available only in Czech language. Belda, M., et al. (2024) FUME 2.0 – Flexible Universal processor for Modeling Emissions, EGUsphere [preprint]. <a href="https://doi.org/10.5194/egusphere-2023-2740">https://doi.org/10.5194/egusphere-2023-2740</a> Karel, J., et al. (2020) Projekt TH03030496 - Zmapování a emisní bilance neevidovaných zdrojů emisí zneči&scaron;ťujících látek na území městských aglomerací. Mapa neevidovaných zdrojů emisí zneči&scaron;ťujících látek na území aglomerace CZ01 Praha. Partially available at: <a href="https://www.atem.cz/neevidovane_zdroje.php">https://www.atem.cz/neevidovane_zdroje.php</a> Karel, J., et al. (2015) Metodika pro výpočet emisí částic pocházejících z resuspenze ze silniční dopravy, CENEST, s. r. o., Prague. Available at: <a href="https://www.mzp.cz/C1257458002F0DC7/cz/doprava/$FILE/OOO-resuspenze_metodika-20190708.pdf">https://www.mzp.cz/C1257458002F0DC7/cz/doprava/$FILE/OOO-resuspenze_metodika-20190708.pdf</a> Karel J., et. al. (2021) Zpráva o dynamické skladbě vozového parku na území hlavního města Prahy v roce 2020, Prague 2021. Available upon request from the Environmental Protection Division of the Prague Municipality. EPA (2011) Compilation of Air Pollutant Emission Factors, Volume I, AP-42. Section 13.2.1. Paved roads. EPA Research Triangle Park, US, 2003, updated 2011. Available at: <a href="https://www.epa.gov/air-emissions-factors-and-quantification/ap-42-compilation-air-emissions-factors-stationary-sources">https://www.epa.gov/air-emissions-factors-and-quantification/ap-42-compilation-air-emissions-factors-stationary-sources</a> van der Gon, H.D., et al. (2011) Description of Current Temporal Emission Patterns and Sensitivity of Predicted AQ for Temporal Emission Patterns. EU FP7 MACC Deliverable Report D_D-EMIS_1.3. Available at: <a href="https://atmosphere.copernicus.eu/sites/default/files/2019-07/MACC_TNO_del_1_3_v2.pdf">https://atmosphere.copernicus.eu/sites/default/files/2019-07/MACC_TNO_del_1_3_v2.pdf</a> EEA (2019) European Environment Agency, EMEP/EEA air pollutant emission inventory guidebook 2019 – Technical guidance to prepare national emission inventories, Publications Office. Available at: <a href="https://data.europa.eu/doi/10.2800/293657">https://data.europa.eu/doi/10.2800/293657</a> OTE (2024) Gas Load Profiles - temperature and recalculated TDD. Available at: <a href="https://www.ote-cr.cz/en/statistics/gas-load-profiles/normalized-lp?set_language=en">https://www.ote-cr.cz/en/statistics/gas-load-profiles/normalized-lp?set_language=en</a>

Quality controlled observations of hourly incoming shortwave radiation data at the surface for solar resource mapping in Norway (2016-2020).

Observed hourly incoming shortwave radiation data at the surface of Norway for the years 2016-2020 along with quality control flags, visualization plots and a descriptive report. The data has been collected, visually inspected and quality controlled within the SunPoint project (SUn in Norway - POtential and INTegration of the solar energy resource, Norwegian Research Council project 320750). The main data source is frost.met.no but some gaps were filled with data directly obtained by the station holders. There are three NetDCF files for 47 stations selected after quality control: <ul> <li>rsds_1hr_selection_v5_2016-2020.nc: Raw data</li> <li>rsds_flagged_1hr_selection_v5_2016-2020.nc: Raw data with flags</li> <li>rsds_cleaned_1hr_selection_v5_2016-2020.nc: Filtered data (i.e. all flagged data has been removed)</li> </ul> and one NetCDF file for all available stations (106 stations) <ul> <li>rsds_1hr_frost_and_more_2016-2020.nc</li> </ul> Version 3.5 of the McClear clear-sky model is used for flagging which reduces the bias to ground measurements compared to earlier versions.  The visualization and automated quality control routines are available in the Scripts.zip file (python).

opencc-by-4.0Nov 2024View details →

Proportions of Phytoplankton Functional Groups (PFT) retrieved using a Self-Organizing Map in the North Atlantic

Concentrations of diagnostic pigments are retrieved from satellite data (Chl-a + Rrs at 4 wavelengths + SST) using a SOM trained on a global in-situ HPLC dataset (see El Hourany et al. 2019). Pigments are converted to PFTs using empirical coefficients. Three sets of coefficients are used and the results are averaged (see El Hourany et al. 2024). The PFT data is expressed as the proportion of each group in the total community abundance: <code>Proportion_i = (alpha_i * Pig_i) / sum_j(alpha_j * Pig_j)</code> There are seven PFT groups (diatoms, dinoflagellates, haptophytes, green algae, cryptophytes, pelagophytes, prokaryotes). The satellite input Chl-a data is included as well. It was retrieved from the Globcolour portal (<a href="https://hermes.acri.fr/">https://hermes.acri.fr/</a>) in 2022. The CHL-1 product for case 1 waters is used. It uses the AVW merging method: single-sensor level-2 Chl-a data is merged from multiple sensors (SeaWiFS, MERIS, MODIS-Aqua, VIIRS-NPP/JPSS1, OLCI-A/B). The data spans from 2002 to 2020, at a daily resolution. It is available on a regular latitude/longitude grid, at a resolution of 1/24° (approximately 4 km) in a window of bounds 15°N−55°N ; 82°W−40°W. <h2>Storage</h2> All variables are stored in the same daily file: <code>PFT_SOM-DAP_GLOB_4km_daily/[year]/[month]/PFT_[year][month][day]_GLOB_4.nc</code>. The PFT proportions are stored as percentages (ranging between 0 and 100). Files are NetCDF4, and the metadata follow CF conventions. The PFT variables are stored using linear packing (see <a href="https://nco.sourceforge.net/nco.html#Linear-Packing">https://nco.sourceforge.net/nco.html#Linear-Packing</a>) as 16-bits unsigned integers (<code>NC_USHORT</code>), with a scale factor of 1.54e-3. This means values are discretized between 0 and ~100.92 (<code>=(2**16 - 2) * 1.54e-3</code>), with a discretization step of 1.54e-3. As for the PFT variables, these values are in percent points. <h2>References</h2> <ul> <li>El Hourany, R., Abboud-Abi Saab, M., Faour, G., Aumont, O., Crépon, M., Thiria, S. “Estimation of secondary phytoplankton pigments from satellite observations using Self-Organizing Maps (SOMs)”, J. Geophys. Res. Oceans 124, 1357–1378, <a href="https://doi.org/10.1029/2018jc014450">https://doi.org/10.1029/2018jc014450</a>, 2019</li> <li> <div>El Hourany, R, Pierella Karlusich J., Zinger L., Loisel H., Levy M., and Bowler C. “Linking Satellites to Genes with Machine Learning to Estimate Phytoplankton Community Structure from Space”, Ocean Science 20 no. 1, 217−39. <a href="https://doi.org/10.5194/os-20-217-2024">https://doi.org/10.5194/os-20-217-2024</a>, 2024</div> </li> </ul> <h2>Changelog</h2> <h3>v1.2</h3> <ul> <li>[2024-01-24] Finished re-arranging data and checked validity. 553 missing days / source files (about 6% of total data).</li> <li>[2023-12-01] Fix SST projection at PFT generation. Data fully re-generated.</li> </ul> <h3>v1.1</h3> <ul> <li>[2023-08-30] Store PFT data using linear packing. </li> </ul> <h3>v1.0</h3> <ul> <li>[2023-08-03] those data are reorganised, and metadata is added, using the script <a href="https://gitlab.in2p3.fr/clementhaeck/submeso-color/-/blob/develop/Compute/arrange_pft_data.py?ref_type=heads">https://gitlab.in2p3.fr/clementhaeck/submeso-color/-/blob/develop/Compute/arrange_pft_data.py?ref_type=heads</a></li> <li>[2023-07] data generated with the SOM were stored on `spirit:/data/lollier/PFT`</li> </ul>

opencc-by-4.0Jun 2024View details →

Redistribution of the map of the water surfaces of the Flemish Region (status 2024)

This is a redistribution of the dataset '<a href="https://www.vlaanderen.be/datavindplaats/catalogus/watervlakken-versie-2024" target="_blank" rel="noopener">Watervlakken - versie 2024</a>’ (Water surfaces - edition 2024), originally published by the Research Institute for Nature and Forest (INBO) and distributed by 'Informatie Vlaanderen' under a CC-BY compatible license. More specifically, this Zenodo record redistributes the GeoPackage file from the original data source, in order to support reproducible, analytical workflows on Flemish Natura 2000 habitats and regionally important biotopes. The digital map of standing water surfaces (edition 2024) is a georeferenced digital file of standing surface waters in Flanders (northern Belgium). The file contains 93 201 polygons with an area between 1.45 m² and 2.47 km² and can be considered as the most complete and accurate representation of lentic water bodies presently available for the Flemish territory. The map is based on topographic map layers, orthophoto images, the Digital Terrain Model of Flanders version II, results of a water prediction model and, to a lesser extent, field observations. It can be used for a wide range of applications in research, policy preparation and policy implementation, management planning and evaluation that consider the distribution and characteristics of stagnant water bodies. The map is also relevant internationally, including updates for the National Wetland Inventories (Ramsar). Furthermore, its unique reference to each object will considerably facilitate related data management. For this new edition of Watervlakken (2024), the orthophoto images of 2021, 2022 and 2023 and the digital terrain model of Flanders have been used. This edition also uses the results of an AI prediction model for water developed by VITO. Data from various Regional Landscapes, ad hoc user reports and field observations have been used to digitise additional polygons, make shape corrections or remove filled ponds from the map layer. For a number of water surfaces, new data on the Flemish type according to the European Water Framework Directive (WFD type), water depth and connectivity have been added to the attribute table. The data source is produced, owned and administered by the Research Institute for Nature and Forest (INBO, Department of Environment of the Flemish government).

opencc-by-4.0Jul 2024View details →

Three Dimensional Multiscalar Neurovascular Nephron Connectivity Map of the Human Kidney Across the Lifespan - Supporting Movie Files

This is a collection of movies related to the manuscript "Three Dimensional Multiscalar Neurovascular Nephron Connectivity Map of the Human Kidney Across the Lifespan" by McLaughlin et al to describe kidney organization using 3D light sheet fluorescence microscopy.  The preprint manuscript associated with these movies is  Three Dimensional Multiscalar Neurovascular Nephron Connectivity Map of the Human Kidney Across the Lifespan Liam McLaughlin, Bo Zhang, Siddharth Sharma, Amanda L. Knoten, Madhurima Kaushal, Jeffrey M. Purkerson, Heidy Huyck, Gloria S. Pryhuber, Joseph P. Gaut, Sanjay Jain bioRxiv 2024.07.29.605633; doi: <a href="https://doi.org/10.1101/2024.07.29.605633">https://doi.org/10.1101/2024.07.29.605633</a> Movie Legends Movie 1: 3D view of the entire slice showing key structures. 3D light sheet fluorescence microscopy 5x movie of reference adult sample SK3, demonstrating glomeruli, collecting ducts, nerves, and blood vessels. 0:00s — Raw signal. 0:10s — Segmentations. Annotations are in the movie. Movie 2: Relationship of nerves with glomeruli and juxtaglomerular apparatus. The movie depicts innervation of glomeruli in 2D optical sections, containing glomeruli, Tuj1(labels TUBB3)-stained nerves, and CGRP-stained sensory nerves. 0:22s — Innervation of the JGA. 0:33 s— Innervation of the Macula Densa. 0:47s — Innervation of the outer boundary of the Bowman’s Capsules. Movie 3: Neuro-nephron connectivity. The movie explores innervation between different structures of the same nephron, and between nephrons in both 3D and 2D optical sections, containing glomeruli, Tuj1 (TUBB3)-stained nerves, CGRP-stained sensory nerves, proximal (convoluted) tubule, thick ascending limb, distal convoluted tubule, and collecting duct. 0:00-1:53min — 3D relationships. 0:38s — Innervation of glomerulus JGA. 1:02min — Post-JGA innervation of medullary ray structures. 1:54min-end — 2D relationships. 2:38min — Interglomerular/internephron innervation. Movie 4: Neurovascular – nephron patterns in the medulla. The movie shows innervation pattern within the medulla. 0:00s—5x adult medullary innervation pattern in 3D; 0:34s—in 2D also showing Vasa Recta and Collecting Duct; 0:48s—in 3D at 20x resolution. 1:04min—20x adult medullary innervation of proximal tubule, thick ascending limb, and Vasa Recta in 3D; 1:31min—view if the previous in 2D. 2:11min—5x adult medullary innervation pattern in 3D; 2:40min—in 2D also showing Vasa Recta and Collecting Duct; 2:54min—in 3D at 20x resolution; 3:15—in 2D at 20x resolution. Movie 5: Network motifs. Exploring 3D neuroglomerular networks at 5x and 20x resolution. 0:00s — Raw 5x signal from young adult sample SK2. 0:16s— Segmented 5x SK2 with 20x coregistrations. 0:26 — Exploring SK2 20x FOV. 0:38 — 20x network featuring hourglass motif. 1:19 5x “Type I” network in SK2. 1:41 — Segmented 5x adult SK3 sample featuring a “Type 2” network containing a lattice motif. 2:23 — Exploring SK3 20x FOV, featuring a network with a lattice motif. Movie 6: LSFM movie of pediatric kidney 3D lightsheet 5x image of neonatal sample SK414, containing glomeruli, collecting ducts, nerves, and blood vessels. 0:00s — Raw signal. 0:22s — Segmentations. 1:34min — Overlayed segmentations. Movie 7: Neuronephron connectivity time course Exploring neuronephro-networks across a life time course in 1mm3 20x images. 0:00 — Raw neonatal. 0:17sec — Segmented neonatal. 0:47 sec— Raw infant. 0:54 — Segmented infant with network. 1:13min — Raw young adult. 1:23min — Segmented young adult with network. 1:33min — Raw adult. 1:43min — Segmented adult featuring network with keychain motif. 1:49min — Raw aged. 1:59min — Segmented aged with network featuring pyramid motif.   Movie 8: Mother Glomeruli Evaluating distributions and innervation of mother glomeruli in neuroglomerular networks. 0:00 - Large 20x 3D Network sample SK1 FOV8. 0:13 - Sample SK1 5x Network in 2D. 0:36 - Mother glomerulus neural quantifications SK1. 0:41 Large 20x 3D Network sample SK3 FOV12. 0:56. Large 20x 2D Network sample SK3 FOV12. 1:21 - Mother glomerulus neural quantifications SK3.   Movie 9: Segmentations Demonstrating accuracy of segmentations that combine supervised ML with manual validation in Sample SK2 FOV5. 0:00 - AQP2 labelled Collecting Duct and NPHS1 labelled Glomerulus. 0:26 - Tuj1 labelled nerve.   metadata_analyzed_images: Metadata for images that were analyzed. Includes metadata .txt files for all stitched, downsampled samples, as well as .csv metadata for certain raw .czi files (pre-processing).

opencc-by-4.0Dec 2024View details →

Flood Hazard Maps and Associated Data for Case Study: Funding rules that promote equity in climate adaptation outcomes

Inundation grids for multiple return periods and multiple scenarios. Please see the underlying study for more details about the methods. The data here can be reproduced following the code and instructions at this repository: https://github.com/CoRE-Lab-UCF/Pollack_et_al_2024/tree/main. Also available here: https://doi.org/10.5281/zenodo.14515896.