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113 results for “Temporal resolution”

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edi64/100

Microbial Observatory at North Temperate Lakes LTER High-resolution temporal and spatial dynamics of microbial community structure in freshwater bog lakes 2005 - 2009 original format (Reformatted to the ecocomDP Design Pattern)

This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/349/4. The abstract below was extracted from the Level 0 data package and is included for context: The North Temperate Lakes - Microbial Observatory seeks to study freshwater microbes over long time scales (10+ years). Observing microbial communities over multiple years using DNA sequencing allows in-depth assessment of diversity, variability, gene content, and seasonal/annual drivers of community composition. Combining information obtained from DNA sequencing with additional experiments, such as investigating the biochemical properties of specific compounds, gene expression, or nutrient concentrations, provides insight into the functions of microbial taxa. Our 16S rRNA gene amplicon datasets were collected from bog lakes in Vilas County, WI, and from Lake Mendota in Madison, WI. Ribosomal RNA gene amplicon sequencing of freshwater environmental DNA was performed on samples from Crystal Bog, North Sparkling Bog, West Sparkling Bog, Trout Bog, South Sparkling Bog, Hell’s Kitchen, and Mary Lake. These microbial time series are valuable both for microbial ecologists seeking to understand the properties of microbial communities and for ecologists seeking to better understand how microbes contribute to ecosystem functioning in freshwater.

openCC (other)Dec 2022View details →

edi56/100

Microbial Observatory at North Temperate Lakes LTER High-resolution temporal and spatial dynamics of microbial community structure in freshwater bog lakes 2005 - 2009 original format

The North Temperate Lakes - Microbial Observatory seeks to study freshwater microbes over long time scales (10+ years). Observing microbial communities over multiple years using DNA sequencing allows in-depth assessment of diversity, variability, gene content, and seasonal/annual drivers of community composition. Combining information obtained from DNA sequencing with additional experiments, such as investigating the biochemical properties of specific compounds, gene expression, or nutrient concentrations, provides insight into the functions of microbial taxa. Our 16S rRNA gene amplicon datasets were collected from bog lakes in Vilas County, WI, and from Lake Mendota in Madison, WI. Ribosomal RNA gene amplicon sequencing of freshwater environmental DNA was performed on samples from Crystal Bog, North Sparkling Bog, West Sparkling Bog, Trout Bog, South Sparkling Bog, Hell’s Kitchen, and Mary Lake. These microbial time series are valuable both for microbial ecologists seeking to understand the properties of microbial communities and for ecologists seeking to better understand how microbes contribute to ecosystem functioning in freshwater.

openCC (other)Dec 2022View details →

edi48/100

Microbial Observatory at North Temperate Lakes LTER High-resolution temporal and spatial dynamics of microbial community structure in freshwater bog lakes 2005 - 2009 original format (Reformatted to a Darwin Core Archive)

This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/344/6, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/349/4. The abstract below was extracted from the Level 0 data package and is included for context: The North Temperate Lakes - Microbial Observatory seeks to study freshwater microbes over long time scales (10+ years). Observing microbial communities over multiple years using DNA sequencing allows in-depth assessment of diversity, variability, gene content, and seasonal/annual drivers of community composition. Combining information obtained from DNA sequencing with additional experiments, such as investigating the biochemical properties of specific compounds, gene expression, or nutrient concentrations, provides insight into the functions of microbial taxa. Our 16S rRNA gene amplicon datasets were collected from bog lakes in Vilas County, WI, and from Lake Mendota in Madison, WI. Ribosomal RNA gene amplicon sequencing of freshwater environmental DNA was performed on samples from Crystal Bog, North Sparkling Bog, West Sparkling Bog, Trout Bog, South Sparkling Bog, Hell’s Kitchen, and Mary Lake. These microbial time series are valuable both for microbial ecologists seeking to understand the properties of microbial communities and for ecologists seeking to better understand how microbes contribute to ecosystem functioning in freshwater.

openCC0Aug 2021View details →

zenodo44/100

Temporal super-resolution microscopy using a hue-encoded shutter

This dataset contains the data to reproduce the figures in our paper called "Temporal super-resolution microscopy using a hue-encoded shutter", Biomedical Optics Express, 2019. Together with the data, the code is available on <a href="https://github.com/idiap/hesm_distrib">Idiap's GitHub page</a>.

opencc-by-4.0Aug 2019View details →

zenodo44/100

The S&M-HSTPM2d5 dataset: High Spatial-Temporal Resolution PM 2.5 Measures in Multiple Cities Sensed by Static & Mobile Devices

This S&M-HSTPM2d5 dataset contains the high spatial and temporal resolution of the particulates (PM2.5) measures with the corresponding timestamp and GPS location of mobile and static devices in the three Chinese cities: Foshan, Cangzhou, and Tianjin. Different numbers of static and mobile devices were set up in each city. The sampling rate was set up as one minute in Cangzhou, and three seconds in Foshan and Tianjin. For the specific detail of the setup, please refer to the Device_Setup_Description.txt file in this repository and the data descriptor paper. After the data collection process, the data cleaning process was performed to remove and adjust the abnormal and drifting data. The script of the data cleaning algorithm is provided in this repository. The data cleaning algorithm only adjusts or removes individual data points. The removal of the entire device's data was done after the data cleaning algorithm with empirical judgment and graphic visualization. For specific detail of the data cleaning process, please refer to the script (Data_cleaning_algorithm.ipynb) in this repository and the data descriptor paper. The dataset in this repository is the processed version. The raw dataset and removed devices are not included in this repository. The data is stored as a CSV file. Each CSV file which is named by the device ID represents the data that was collected by the corresponding device. Each CSV file has three types of data: timestamp as the China Standard Time (GMT+8), geographic location as latitude and longitude, and PM2.5 concentration with the unit of microgram per cubic meter. The CSV files are stored in either Static or Mobile folder which represents the devices' type. The Static and Mobile folder are stored in the corresponding city's folder. To access the dataset, any programming language that can access CSV files is appropriate. Users can also open the CSV file directly. The get_dataset.ipynb file in this repository also provides an option of accessing the dataset. To successfully execute ipynb file, Jupyter Notebook with Python 3.0 is required. The following python library is also required: get_dataset.ipynb:     1. os library     2. pandas library Data_cleaning_algorithm.ipynb:     1. os library     2. pandas library     3. datetime library     4. math library The instruction of installing the libraries above can be found online. After installing the Jupyter Notebook with Python 3.0 and the required libraries, users can try to open the ipynb file with Jupyter Notebook and follow the instruction inside the file.  For questions or suggestions please e-mail Xinlei Chen <xinlei.chen@sv.cmu.edu>

opencc-by-4.0Sep 2020View details →

zenodo44/100

Data repository of multi-temporal high-resolution data products of ecosystem structure derived from country-wide airborne laser scanning surveys of the Netherlands

This data repository contains a set of multi-temporal data products of ecosystem structure derived from four national ALS surveys of the Netherlands (AHN1–AHN4) (folders: 1_AHN1, 2_AHN2, 3_AHN3, and 4_AHN4). Four sets of 25 LiDAR-derived vegetation metrics representing ecosystem height, cover, and structural variability are provided at 10 m spatial resolution, providing valuable data sources for a wide range of ecological research and field beyond. A preview of all generated LiDAR metrics are also provided (folder: 5_Maps). All 25 LiDAR metrics were calculated using Laserfarm workflow  (<a href="https://laserfarm.readthedocs.io/en/latest/">https://laserfarm.readthedocs.io/en/latest/</a>) (building on the user-extendable features from the “Laserchicken” software: <a href="https://laserchicken.readthedocs.io/en/latest/#features">https://laserchicken.readthedocs.io/en/latest/#features</a>). All metrics are calculated with the normalized point cloud. More details on metric calculation are provided on GitHub (Laserchicken: <a href="https://github.com/eEcoLiDAR/laserchicken">https://github.com/eEcoLiDAR/laserchicken</a> and Laserfarm: <a href="https://github.com/eEcoLiDAR/Laserfarm">https://github.com/eEcoLiDAR/Laserfarm</a>), as well as on the “Laserchicken” documentation page (<a href="https://laserchicken.readthedocs.io/en/latest/">https://laserchicken.readthedocs.io/en/latest/</a>). We also provided masks to minimize the influence of water surfaces, buildings and roads, powerlines and NA values in the data products (folder: 6_Masks).  To supplement the generated data products, we also provided a set of raster layers that contains point/pulse density of each AHN survey and the DTM and DSM raster layers for each AHN dataset (folder: 7_Auxiliary_data). To test the robustness of the LiDAR metrics, we also compared the metrics generated from different pulse densities across different habitat types (folder: 8_Sensitivity_analysis). Two use cases demonstrated the utility of the presented data products: (use case 1) monitoring forest structural change across time using multi-temporal ALS data and (use case 2) comparison of vegetation structural difference within Natura 2000 sites. The used data are also provided (folder: 9_Use_case). Note that all the raster layers are provided at 10 m resolution under the local Dutch coordinate system “RD_new” (EPSG: 28992, NAP:5709). To gain more insights of the pre-classification accuracy of the AHN datasets, we also conducted a preliminary assessment of the effect of terrain filtering on vegetation change detection across AHN datasets (i.e. AHN2–AHN4). The data used in this analysis are made available (folder: 10_Ground_classification). An overview of all the folders in the repository: 1.     AHN1 2.     AHN2 3.     AHN3 4.     AHN4 5.     Maps Those folders contain four sets of 25 LiDAR metrics at 10 m resolution generated from each AHN dataset. The file names and their corresponding LiDAR metrics can be found in Table 1. An additional folder (5_Maps) contains the maps (.pdf format) of all 25 metrics for each AHN dataset. 6. Masks <ul> <li>ahn3_10m_mask_building_road_water.tif</li> <li>ahn4_10m_mask_building_road_water.tif</li> <li>ahn4_10m_mask_powerline.tif</li> <li>ahn1_10m_NA_mask.tif</li> <li>ahn2_10m_NA_mask.tif</li> <li>ahn3_10m_NA_mask.tif</li> <li>ahn4_10m_NA_mask.tif</li> </ul>   It contains two mask layers of water surfaces, buildings and roads for both AHN3 and AHN4 data products based on the Dutch cadaster data (TOP10NL) from 2018 (corresponding to AHN3) and 2021 (corresponding to AHN4) (<a href="https://www.kadaster.nl/zakelijk/producten/geo-informatie/topnl">https://www.kadaster.nl/zakelijk/producten/geo-informatie/topnl</a>). In the masks, water surfaces, buildings and roads were merged into one class with pixel value assigned to 1 and the rest has the pixel value of 0. There is also a powerline mask generated from the AHN4 dataset at 10 m resolution, where pixels containing powerlines were assigned a value of 1 and the rest as NoData. We provide those masks to minimize the inaccuracies of the data products caused by human infrastructures and water surfaces. We also provided a mask for each AHN dataset where NA value occurs — areas with no vegetation points (“unclassified” class in the AHN datasets). Pixels with NA value were assigned with a value of 1 and the rest as 0. 7. Auxiliary data (1) Point_density <ul> <li>ahn1_10m_point_density.tif</li> <li>ahn2_10m_point_density.tif</li> <li>ahn3_10m_point_density.tif</li> <li>ahn4_10m_point_density.tif</li> </ul> (2) Pulse_density <ul> <li>ahn3_10m_pulse_density.tif</li> <li>ahn4_10m_pulse_density.tif</li> </ul> (3) Flighttime <ul> <li>ahn3_10m_flighttime.tif</li> <li>ahn4_10m_flighttime.tif</li> </ul> (4) DTM_DSM <ul> <li>ahn2_10m_dtm.tif</li> <li>ahn2_10m_dsm.tif</li> <li>ahn3_10m_dtm.tif</li> <li>ahn3_10m_dsm.tif</li> <li>ahn4_10m_dtm.tif</li> <li>ahn4_10m_dsm.tif</li> </ul> It contains four raster layers representing the point density of each AHN dataset, two raster layers for pulse density of the AHN3 and AHN4, two raster layers for flight timestamp of the AHN3 and AHN4, and six DTM and DSM layers for AHN2–AHN4. All raster layers are provide at 10 m resolution. 8. Sensitivity analysis <ul> <li>Dunes</li> <li>Marsh</li> <li>Grassland</li> <li>Shrubland</li> <li>Woodland</li> <li>Code</li> <li>Figure</li> </ul> It contains the 25 metrics generated from point clouds with the original and down-sampled pulse densities (original pulse density of the AHN4, pulse density of the AHN3, ½ of the pulse density of the AHN3, and ¼ of the pulse density of AHN3) for each habitat type (i.e. dunes, marsh, grassland, shrubland, and woodland). We also provided the code and the figures generated from this analysis. 9. Use_case (1) Multi-temporal_AHN <ul> <li>Data</li> <li>Usecase_multi-temporal_AHN.R</li> </ul> It contains the input data for the use case data processing (i.e. Data folder), including the shapefile of the area (i.e. shp folder), and extracted pixel value from six selected LiDAR metrics from AHN1–AHN5 (i.e. Metrics folder), and the selected LiDAR metrics of the area (e.g. Hp95 folder), and the R code for data processing (i.e. Usecase_multi-temporal_AHN.R). (2) Natura2000 <ul> <li>Data</li> <li>Natura2000_end2021_HABITATCLASS.csv</li> <li>Natura2000_NL_habitat_grouped.csv</li> <li>Usecase_Natura2000.R</li> </ul> It contains a folder of the input data used for the use case (i.e. Data folder), including the shapefile (i.e. shp folder) of the Natura 2000 sites in the Netherlands (i.e. Nature2000_NL_RDnew.shp) and the 100 random sample plots from each habitat type (e.g. woodland_points.shp), and the LiDAR metrics from AHN4 used for demonstrating the vegetation  structure within each habitat type (i.e. AHN4_metrics folder). The table “Natura2000_end2021_HABITATCLASS.csv” is the original attribute table of Natura 2000 sites, including information related to the description of habitat classes (column “DESCRIPTION”), the code corresponding to the habitat class (column “HABITATCODE”), the code for the specific site (column “SITECODE”), and the percentage of the cover of a specific habitat class in one site (column “PERCENTAGECOVER”). The table “Natura2000_NL_habitat_grouped.csv” contains two subtabs, one (i.e. “Habitatclass”) is the copy of the original attribute table of Natura 2000 sites in the Netherlands, and the other one (i.e. “Habitat_class_summary”) is the grouped habitat type based on the dominant habitat class (i.e. class with the highest percentage cover) in each site. Different colors indicate different habitat types, corresponding to the colors in the first tab (“Habitatclass”) where the dominant habitat class was highlighted for each site. 10. Ground classification <ul> <li>Raw_point_cloud</li> <li>Computed_metrics </li> <li>Plottings_and_code</li> <li>ArcGIS_project</li> </ul> It contains four subfolders: (1) The original point cloud for each sample area (AHN2–AHN4) (subfolder: Raw_point_cloud); (2) The 25 LiDAR metrics computed from the original point clouds with pre-classification of AHN and from the new terrain filtering method across AHN2–AHN4 (subfolder: Computed_metrics); (3) Generated violin plots for the comparison of vegetation change detection and the python code employed (subfolder: Plottings_and_code); (4) an ArcGIS project which the shapefiles of the study area and sample plots are provided (subfolder: ArcGIS_project). Code availability Jupyter Notebooks for processing AHN datasets: <a href="https://github.com/ShiYifang/AHN">https://github.com/ShiYifang/AHN</a> Laserfarm workflow repository: <a href="https://github.com/eEcoLiDAR/Laserfarm">https://github.com/eEcoLiDAR/Laserfarm</a> Laserchicken software repository: <a href="https://github.com/eEcoLiDAR/laserchicken">https://github.com/eEcoLiDAR/laserchicken</a> Code for downloading AHN dataset: <a href="https://github.com/ShiYifang/AHN/tree/main/AHN_downloading">https://github.com/ShiYifang/AHN/tree/main/AHN_downloading</a> Code for generating masks for AHN datasets: <a href="https://github.com/ShiYifang/AHN/tree/main/AHN_masks">https://github.com/ShiYifang/AHN/tree/main/AHN_masks</a> Code for demonstration of ecological use cases: <a href="https://github.com/ShiYifang/AHN/tree/main/Use_case">https://github.com/ShiYifang/AHN/tree/main/Use_case</a>

opencc-by-4.0Oct 2024View details →

zenodo44/100

High-resolution, Decadal to Weekly Geomorphic Change Analysis of the Elbow River in Calgary, using Multi-temporal Lidar and Repeat Terrestrial Laser Scanning

This directory contains files related to the scientific research project of Luc van Dijk at the Department of Earth, Energy, and Environment, University of Calgary. The project title is "High-resolution, Decadal to Weekly Geomorphic Change Analysis of the Elbow River in Calgary, using Multi-temporal Lidar and Repeat Terrestrial Laser Scanning". This project in the field of geomorphology was a collaboration between the University of Calgary and Utrecht University in the Netherlands. The project was completed on October 27, 2023. Below is a description of the files in this directory. DisplacementVolumeDistributions_TLS.xlsxExcel file containing tabular data of the normalized sediment displacement volumes that were obtained using TLS. Each tab in the Excel file represents a period of interest in 2023. The data in this file were used to generate the 'histogram-like' figures in the report. DoD_rasters.zipFolder containing the aerial lidar DEMs of Difference (DoDs) for each period of interest. The DoDs are 'waterless', i.e. the water surface is masked. The suffix of the file name before the file extension (e.g., ..._10cm.tif) indicates the maximum REM value that was used for the automated masking of the water surface extent (see report section 3.1.2). If the file name contains "large", it refers to the upstream greater area (see report section 3.1.3).Within this folder is another folder called 'Clipped2AOIs'. This folder contains the same DoDs, but covering only the extents of the sites of interest ('AOIs' = Areas Of Interest). FilteredPointClouds_TLS.zipFolder containing the processed and filtered point clouds that were acquired throughout the summer of 2023 using TLS. These point clouds have been pre-processed and filtered to remove vegetation (see report section 3.2). They are grouped in sub-folders per acquisition date. The filenames are numbered to location, i.e. 'elbow1', 'elbow2', 'elbow3' and 'elbow4'. These correspond to the sites of interest: Glenmore Dam, golf club, Sandy Beach and Riverdale, respectively. PythonScripts_Discharge_Rainfall.zipFolder containing the Python scripts that were made to process the discharge and rainfall data that were sourced from Environment Canada and The City of Calgary (see report section 3.3). The scripts themselves contain descriptions of their purpose. PythonScripts_DisplacementVolumeAnalysis.zipFolder containing the Python scripts that were made to process and analyze the aerial lidar DoDs and the TLS rasterized difference point clouds (M3C2 output). The 'convert2pickle' scripts converted the sizable rasters to smaller pickle files, which were easier and faster to work with. The 'chart' scripts load the data from the pickle files, analyze them and produce the 'histogram-like' figures in the report. The scripts themselves contain descriptions of their purpose. RainfallDischargeData.xlsxExcel file containing the discharge and rainfall data from Environment Canada and The City of Calgary. The data came from different sources in different formats and were combined into this single table. RasterizedDifferencedPointClouds_M3C2.zipFolder containing the rasterized results of the differenced TLS point clouds (M3C2 output) (see report section 3.2.4). The filenames are numbered to location, i.e. 'Elbow1', 'Elbow2', 'Elbow3' and 'Elbow4'. These correspond to the sites of interest: Glenmore Dam, golf club, Sandy Beach and Riverdale, respectively. The numeric sequence in the file name indicates the start and end date of the change analysis in a 'mm-dd' format. The suffixes '_dist', '_unc' and '_sig' refer to the three output layers of the M3C2 algorithm: distance, uncertainty and significance of change. The main files of interest are the '.tif' files. Files sharing the same name, but with different extensions (.tfw, .tif.aux.xml, .tif.xml) are supplementary/auxiliary files for the '.tif' file, generated by ArcGIS Pro. ScarpsOfInterest_shapefile.zipFolder containing a polygon shapefile describing the extents and locations of the sites of interest. The main file of interest is the '.shp' file. The other files with the same name, but different extensions (.cpg, .dbf, .prj, .sbn, .sbx, .shp.xml, .shx) are supplementary/auxiliary files for the '.shp' file, generated by ArcGIS Pro.

opencc-by-4.0Oct 2023View details →

zenodo44/100

High temporal and spatial resolution emission inventory for maritime shipping emissions on the North Sea and Baltic Sea (2015)

A temporally and spatially highly resolved emission inventory for the North Sea and Baltic Sea for the year 2015, created with current emission factors and ship activity data. The emissions inventory is available as 396 csv files, one for each day in 2015 and December 2014, grouped as monthly archives.  Note that due to the underlying ship activity data and the geographic boundaries, the time index in the Datetime column in the ship_emissions_YYYYMMDD.csv files is not equidistant. For example, since vessels leave the geographic area and reenter later, no data is available for the time the vessel is not within the area. The underlying model source code is available on Github, with a release of the associated version on Zenodo: [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.6951672.svg)](https://doi.org/10.5281/zenodo.6951672)

opencc-by-4.0Jul 2022View details →

zenodo44/100

Characterisation and calibration of low-cost PM sensors at high temporal resolution to reference grade performances - dataset

This repository contains the data used for the analysis of the paper "Characterisation and calibration of PM sensors at high temporal resolution to reference grade performances" submitted to Heliyon and available as a pre-print: Bulot, Florentin M. J. and Ossont, Steven J. and Morris, Andrew and Basford, Philip J. and Easton, Natasha H. C. and Mitchell, Hazel L. and Foster, Gavin L. and Cox, Simon J. and Loxham, Matthew, Characterisation and Calibration of Low-Cost Pm Sensors at High Temporal Resolution to Reference-Grade Performance. Available at SSRN: <a href="https://ssrn.com/abstract=4360707">https://ssrn.com/abstract=4360707</a> or <a href="http://dx.doi.org/10.2139/ssrn.4360707">http://dx.doi.org/10.2139/ssrn.4360707</a>   The code used to conduct the data analysis is available at <a href="https://doi.org/10.5281/zenodo.7261417">https://doi.org/10.5281/zenodo.7261417</a>   .   The files are available in .csv and in .rds (for R) formats. For details about the measurement equipment used during this study, please refer to the methods section of the paper. Description of the files. 202007_to_202107_nocs - contains the data from the low-cost sensors It contains the following headers: - "sensor" - sensor id - "site" - name of the air quality monitor hosting the sensor - "median_PM1" - PM1 mass concentration (ug/m3) - "median_PM10" - PM10 mass concentration (ug/m3) - "median_PM25" - PM25 mass concentration (ug/m3) - "median_PM4" - PM4 mass concentration (ug/m3) (only available for SPS30) - "median_n05" - particle number concentration (SPS30) of particles between 0.3um and 0.5um - "median_n1" - particle number concentration (SPS30) of particles between 0.3um and 1um - "median_n10" - particle number concentration (SPS30) of particles between 0.3um and 10um - "median_n25" - particle number concentration (SPS30) of particles between 0.3um and 2.5um - "median_n4" - particle number concentration (SPS30) of particles between 0.3um and 4um - "median_gr03um" - particle number concentration (PMS5003) of particles >0.3um - "median_gr05um" - particle number concentration (PMS5003) of particles >0.5um - "median_gr100um" - particle number concentration (PMS5003) of particles >10um - "median_gr10um" - particle number concentration (PMS5003) of particles >1um - "median_gr25um" - particle number concentration (PMS5003) of particles >2.5um - "median_gr50um" - particle number concentration (PMS5003) of particles >5um - "median_pm100_cf1" - PM10 mass concentration with cf1 calibration for PMS5003 - "median_pm10_cf1" - PM1 mass concentration with cf1 calibration for PMS5003 - "median_pm25_cf1" - PM25 mass concentration with cf1 calibration for PMS5003 - "date" - date, format "yyyy-mm-dd HH:MM:SS GMT"      df_pm_2min - contains the PM mass concentration data from the Fidas 200S. It contains the following headers: - "PM2.5" - PM2.5 mass concentration (ug/m3) Fidas 200S - "PM10" - PM10 mass concentration (ug/m3) Fidas 200S - "PMtot" - PM total mass concentration (ug/m3) Fidas 200S - "PM1" - PM1 mass concentration (ug/m3) Fidas 200S - "date" - date, format "yyyy-mm-dd HH:MM:SS GMT"     df_weather_2min - contains the weather data from the Fidas 200S It contains the following headers: - "rh" - relative humidity (%) - "dew_point_temperature" -  dew point temperature (Celsius) - "air_pressure" - Air pressure (hPa) - "temperature" - temperature (Celsius) - "date" - date, format "yyyy-mm-dd HH:MM:SS GMT"

opencc-by-4.0Oct 2022View details →

zenodo44/100

Data supporting 3D Super-resolution Optical Fluctuation Imaging with Temporal Focusing with two-photon excitation

Data to support the publication combining temporal focusing two photon excitation with super-resolution optical fluctuation imaging. <div>This research was funded by National Centre of Science, grant number: 2022/47/B/ST7/03465. For the purpose of Open Access, the author has applied a</div> <div>CC-BY public copyright licence to any author Accepted Manuscript (AAM) version arising from this submission</div>

opencc-by-4.0Apr 2024View details →

zenodo44/100

Resampled FY4A GIIRS radiances from targeted observations for Typhoon Maria(2018) with high temporal resolution of 15 minutes

GIIRS_SSEC_Maria.tar.gz is the FY-4A GIIRS targeted observations for Typhoon Maria(2018) with 15 minutes temporal resolution, 00z – 23z 10 July, 2018. The radiances are re-sampled data generated at Space Science and Engineering Center of the University of Wisconsin-Madison. These are the data used in the study of Ma et al.(2021). The HDF files are the original GIIRS targeted observations for Typhoon Maria (2018) with 15 minutes temporal resolution which are used in the study of Yin et al.(2021). These radiances have not been re-sampled and for details please refer to Version 1.0 of this dataset: http://doi.org/10.5281/zenodo.4656877 (Han & Yin, 2021).

opencc-by-4.0Jul 2021View details →

zenodo44/100

Receptor exocytosis imaged with high temporal resolution for diverse receptor cargos

Cells perceive and interact with their environment in part through the expression, activation, and regulation of receptors on their plasma membrane. These receptors are dynamically trafficked from the plasma membrane in a process called endocytosis and delivered to the plasma membrane via exocytosis. Different receptors take diverse routes through the cell before being delivered via exocytosis. The data in this project focuses on 3 prototypical plasma membrane receptors - the B2 adrenergic receptor, the µ opioid receptor, and the transferrin receptor. Using a pH-sensitive green fluorescent protein variant, we visualized these receptors in cells as they recycled to the plasma membrane. We subsequently hand-labeled a subset of the data in order to build an automated image analysis method that could be used to detect receptor exocytosis across diverse imaging conditions. This repository contains our primary microscopy data from these studies as well as the labeling for use in supervised machine learning. These data support <a href="http://arxiv.org/abs/2106.07623">Evans et al 2021</a> and subsequent publications. Data Collection TIFF image stacks were collected using a Nikon Eclipse TiE Inverted Microscope using TIRF illumination with a solid state 488nm laser through a Nikon 60x/1.49NA TIRF objective and captured using an Andor iXon 897+ EMCCD camera. The camera was windowed to a 300x300 pixel view and images were collected with a 18.5ms exposures (~54Hz). Images were collected across two days, with two coverslips of each condition collected on day 1, and one coverslip collected on day 2. DNA Constructs The 3 cargos imaged in these data are the transferrin receptor (TfR), the B2-adrenergic receptor (B2AR, B2), and the µ opioid receptor (MOR). Constructs encoding these receptors, tagged extracellularly with the ph-sensitive GFP variant Superecliptic pHluorin (SpH, <a href="https://www.cell.com/biophysj/fulltext/S0006-3495(00)76468-X">Sankaranarayanan et al. 2000</a>, have been previously described in <a href="http://www.nature.com/articles/nn1679">Yudowski et al. 2006</a> for B2AR, <a href="https://www.jneurosci.org/content/30/35/11703">Yu et al. 2010</a> for MOR, and <a href="https://www.molbiolcell.org/doi/10.1091/mbc.e08-08-0892">Yudowski et al. 2009</a> for TfR. Cell Culture HEK293 cells were cultured in DMEM High Glucose (Hyclone) supplemented with 10% Heat Inactivated FBS (Gibco). Cells expressing B2 and MOR were stably selected from transient transfection using G418. Cells expressing TfR were transfected 3 days before the experiments presented here using Effectene following manufacturers' instructions. Before imaging, cells were transferred to 25mm diameter #1.5 glass coverslips (Electron Microscopy Sciences). Two days after plating, experiments began. Imaging conditions Cells were imaged in L-15 minimal media supplemented with 1% FBS. For MOR and B2, cells were imaged for 1 minute at ~0.16Hz without perturbation. Then agonist was added (10µM DAMGO for MOR, 10µM isoproterenol for B2) to the media and cells were imaged for 5 minutes to ensure that receptors clustered and internalized. After internalization, cells were bleached with 100% laser power for 1 minute and then imaged at 54Hz to visualize exocytic events. exocytosis was captured for up to 20 minutes after initial treatment, one cell at a time. For TfR, a single frame was taken before bleaching to show receptor expression levels and then cells were bleached and imaged as described above. Data blinding After collection, files were renamed as described in map.md. All metadata files and internalization imaging were separated into the 2 "extras" folders. The exocytosis movies were 'scrambled' to hide cargo identity using the included scrambler.py file. OPP_scramble.log described the mapping of scrambled filenames to the original imaging. Human labeling A subset of the images (22, with roughly equal representation across cargos) were hand labeled for exocytic events. Images were viewed in FIJI <a href="https://www.nature.com/articles/nmeth.2019">Schindelin et al. 2012</a> nad played back at 0.5x. When exocytic events were identified by eye, the playback was paused and the appearance of an event was found through manual advancing of the frames of the movie. The event was labeled using the Cell Counter plugin. Each movie was watched twice to identify as many events as possible. Labeled events are saved a <movie-name>-ZYW-1.xml in this dataset. Data organization All exocytic event movies and any matching human labeling are included in this base directory. All internalization movies and all metadata for all movies are included in the Extras folder for the day that movie was recorded. Coverslip and cargo identity are listed in map.md and the ground truth for cargo identity is in OPP_scramble.log

opencc-by-4.0Dec 2022View details →

zenodo44/100

High Temporal Resolution Records of Hansbreen Ice Flow Velocity for Years 2006-2019

This repository contains the datasets of the positions of 16 mass balance stakes, horizontal velocity (m/yr) and accuracy of velocity (m/yr) for Hansbreen, a tidewater glacier in southern Svalbard. Data were derived from GNSS measurements conducted in the period 2006-2019. Stake positions are given in UTM zone 33X, and elevation in geoidal height (EGM96). Additionally, we provide files with annual, summer and winter velocities (m/yr) with a standard deviation of velocity, estimated for the hydrological year. The file &bdquo;Hansbreen_preprocessing_code_stakes.zip” contains the code used for the velocity estimation.

opencc-by-4.0Jul 2023View details →

zenodo40/100

Configuration files for model stations presented in the manuscript "Sensitivity of shelf sea marine ecosystems to temporal resolution meteorological forcing"

This repository contains configuration files for running GOTM-FABM-ERSEM at stations L4 and CCS to produce results presented in the manuscript "Sensitivity of shelf sea marine ecosystems to meteorological forcing" in addition to meteorology files for running the sensitivity analysis presented in the manuscript. Ncfiles containing model results for all scenarios presented in the manuscript are also included within the zip files for both stations GOTM code is freely available from:  https://github.com/gotm-model/code FABM code is freely available from: https://github.com/fabm-model/fabm.git ERSEM code is freely available from: <a href="https://www.pml.ac.uk/Modelling_at_PML/Access_Code">https://www.pml.ac.uk/Modelling_at_PML/Access_Code</a>   Instructions for compiling GOTM-FABM-ERSEM can be found in the ERSEM git repository after registering for the code using the link above.  Versions/commits for the model code used to create results presented in this manuscript are: GOTM: commit 38e5d5b77adc7b3b5364aed7d7e4921b04b1781f  FABM: commit 69da88c87ec59a51d1e2143c1f76111526ed6498  ERSEM: Version 19.04

opencc-by-4.0Dec 2019View details →

zenodo40/100

Close range hyperspectral camera dataset with high temporal resolution of strawberry with eco-physiological data of one leaf

This high temporal resolution dataset of a strawberry plant was captured in two experiments, each lasting 100h. On leaf was inserted into a leaf chamber of the LI-6400XT gas exchange system, capturing information on transpiration, photosynthesis and stomatal conductance. Environmental characteristics are also captured at canopy height. These experiments were conducted in a growth chamber at ILVO (Melle, Belgium) and only covered conditions that did not result in stress in the plant. As such, this dataset attempts to capture subtle dynamic variation in the plant.

opencc-by-4.0Jun 2020View details →

zenodo40/100

1-km high resolution model outputs using the WRF and WRF-Hydro model Raw data from the manuscipt "Process-based Atmosphere-Hydrology-Malaria Modeling: Performance for Spatio-temporal Malaria Transmission Dynamics in Sub-Saharan Africa "

Here we provide the model outputs from the numerical climate model WRF (Weather Research and Forecasting) and its hydrological coupled model WRF-Hydro for the Health and Demographic Surveillance Systems (HDSS) site regions of Nouna in Burkina Faso. Model results are used for investigating the influence of surface hydrology representation, environmental and climate-sensitive driver factors on malaria incidence. The experiments use the following model configuration: 1km horizontal resolution with 200*200 grid points, WSM6 microphysics, ACM2 PBL, and RRTM & Dudhia radiation scheme. WRF uses the Noah LSM, and WRF-Hydro uses the Noah LSM with enhanced lateral hydrological description (https://ral.ucar.edu/projects/wrf_hydro/overview). These simulations were conducted in the Karlsruhe Steinbuch Centre for Computing (SCC) Horeka. Model outputs are provided in daily step (originally derived from the hourly output). Filename with "wrf-hydro_pr_2000-2020_d02-1km.nc" provides Precipitation, n mm/day"wrf-hydro_tas_2000-2020_d02-1km.nc" provides mean temperature in Celsius, "wrf-hydro_tasmax_2000-2020_d02-1km.nc" provides maximum temperature in Celsius, "wrf-hydro_tasmin_2000-2020_d02-1km.nc" provides minmum temperature in Celsius, "wrf-hydro_dtr_2000-2020_d02-1km.nc" provides diurnal temperature ranges in Celius, "wrf-hydro_rh_2000-2020_d02-1km.nc" provides relative humudity in % and "wrf-hydro_sw_2000-2020_d02-1km.nc" provides the surface hydrology.

opencc-by-4.0Mar 2024View details →

zenodo40/100

HTW Berlin weather data with a temporal resolution of 1 Hz and 1/60Hz (2017-2021)

These data sets from 2017-2021 contain the weather data measured on the roof at the University of Applied Sciences for Engineering and Economics in Berlin (HTW Berlin).  The data sets have a resolution of 1 Hz and 1/60 Hz and are stored in a Matlab .mat and .csv files. The data sets contain the solar irradiance, wind speed (x,y,z), air pressure, air temperature and humidity. The documentation has more detailed information about the equipment used and the data collected. The homepage of our research group: https://solar.htw-berlin.de/

opencc-by-sa-4.0Nov 2021View details →

zenodo40/100

Gastruloids as in vitro models of embryonic blood development with spatial and temporal resolution

Rawdata (images and flow cytometry) for the manuscript "Gastruloids as in vitro models of embryonic blood development with spatial and temporal resolution".

opencc-by-4.0Jun 2022View details →

zenodo40/100

Four-dimensional wind fields retrieved from GIIRS hyperspectral measurements with 15-minute temporal resolution during Typhoon Maria (2018)

These data were four-dimensional wind fields retrieved from GIIRS hyperspectral measurements with 15-minute temporal resolution during Typhoon Maria (2018). They were also the output results of the findings of Ma et al. (2021).

opencc-by-4.0Jul 2021View details →

dryad40/100

Data for: Tracking the temporal dynamics of insect defoliation by high-resolution radar satellite data

1. Quantifying tree defoliation by insects over large areas is a major challenge in forest management, but it is essential in ecosystem assessments of disturbance and resistance against herbivory. However, the trajectory from leaf-flush to insect defoliation to refoliation in broadleaf trees is highly variable. Its tracking requires high temporal- and spatial-resolution data, particularly in fragmented forests. 2. In a unique replicated field experiment manipulating gypsy moth Lymantria dispar densities in mixed-oak forests, we examined the utility of publicly accessible satellite-borne radar (Sentinel-1) to track the fine-scale temporal trajectory of defoliation. The ratio of backscatter intensity between two polarizations from radar data of the growing season constituted a canopy development index (CDI) and a normalized CDI (NCDI), which were validated by optical (Sentinel-2) and terrestrial laser scanning (TLS) data as well by intensive caterpillar sampling from canopy fogging. 3. The CDI and NCDI strongly correlated with optical and TLS data (Spearman's ρ=0.79 and 0.84, respectively). The ∆NCDIDefoliation (A-C) significantly explained caterpillar abundance (R2=0.52). The NCDI at critical time-steps and ΔNCDI related to defoliation and refoliation well discriminated between heavily and lightly defoliated forests. 4. We demonstrate that the high spatial and temporal resolution and the cloud independence of Sentinel-1 radar potentially enable spatially unrestricted measurements of the highly dynamic canopy herbivory. This can help monitor insect pests, improve the prediction of outbreaks, and facilitate the monitoring of forest disturbance, one of the high priority Essential Biodiversity Variables, in the near future.

opencc-zeroSep 2021View details →

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Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record