Dataset

This dataset was created by Daniel M.

Released under CC0: Public Domain

Contents

This repository contains electron spectrum datasets supporting the publication Active energy compression of a laser-plasma electron beam. Two HDF5 files are provided:

- energy_compressor_data.h5: Contains electron spectra measured in two configurations:
- Unstabilized: Measurements without energy stabilization.
- Stabilized: Measurements after energy stabilization was applied.

- phase_scan_data.h5: Contains electron spectra and beam charge data acquired during a 360° RF phase scan.

This dataset repository contains sample codes and data used for the 2D results in the publication. The dataset (data2D_gzipped_famfiles_%d.h5) is a family of files under the HDF5 format that has been compressed under GZIP (compression factor = 8). All files must be within the same directory for readability. The compression for these files is transparent (no need to decompress, just straight reading). TF/Keras-based Python code is also attached under an open-source license. Further comments for the sample code are inside the files.

This file comprises a hdf5-compressed table intended for use with the Python package Pandas. Its columns describe 42 metrics, or computational details on those metrics; its rows are scenes, indexed by a string according the format "yyyy-mm-dd-s-n", where:- y: year- m: month- d: day- s: satellite (a - Aqua, t - Terra)- n: scene number on the dateThe file's metadata contains a dictionary that converts column headers into more legible descriptions. See e.g. https://stackoverflow.com/a/29130146 for instructions to load this data. Use keyword 'mydata' to access the data and metadata in the file.

This deposition contains the results from a simulation of reconstructions of undersampled atomic force microscopy (AFM) images. The reconstructions were obtained using weighted iterative thresholding compressed sensing algorithms.

The deposition consists of:

An HDF5 database containing the results from simulations of reconstructions of undersampled atomic force microscopy images (weighted_it_reconstructions.hdf5).

The Python script which was used to create the database (weighted_it_reconstructions.py).

MD5 and SHA256 checksums of the database and Python script files (weighted_it_reconstructions.MD5SUMS / weighted_it_reconstructions.SHA256SUMS).

The HDF5 database is licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/) . Since the CC BY 4.0 license is not well suited for source code, the Python script is licensed under the BSD 2-Clause license (http://opensource.org/licenses/BSD-2-Clause) .

The files are provided as-is with no warranty as detailed in the above mentioned licenses.

The database is split into four parts:

weighted_it_reconstructions.hdf5.tar.xz.part-00

weighted_it_reconstructions.hdf5.tar.xz.part-01

weighted_it_reconstructions.hdf5.tar.xz.part-02

weighted_it_reconstructions.hdf5.tar.xz.part-03

These four parts must be concatenated before the database can be extracted from the tar.xz archive. On Unix-like systems this may be done using:

cat weighted_it_reconstructions.hdf5.tar.xz.part-* > weighted_it_reconstructions.hdf5.tar.xz

after which the archive may be extracted, e.g., using:

tar xfJ weighted_it_reconstructions.hdf5.tar.xz

WARNING: The extracted HDF5 database has a size of 70 GiB.

The simulation results in the database are based on "Atomic Force Microscopy Images of Cell Specimens" by Christian Rankl licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). The original images are available at http://dx.doi.org/10.5281/zenodo.17573. The original images are provided as-is without warranty of any kind. Both the original images as well as adapted images are part of the dataset.

This is a 4 image lysozyme datasets derived from https://doi.org/10.5281/zenodo.3352357. The specific 4 images are able to be processed by the software package DIALS using commands in the linked dataset above. The images were rounded to integer and compressed to save file space using this script:

import shutil, h5py
import numpy as np

shutil.copyfile('../lyso009a_0087.JF07T32V01_master.h5', 'lyso009a_0087.JF07T32V01_master_4img.h5')
h5 = h5py.File('lyso009a_0087.JF07T32V01_master_4img.h5', 'r+')

data = h5["entry/data/data"][()]
del h5["entry/data/data"]
h = h5["entry/data"]
subset = data[5:9].astype(np.int32)
h.create_dataset("data", subset.shape, subset.dtype, subset, compression="gzip", compression_opts=9)

h5.close()

This depository contains raw data from 14 experiments performed on adult locusts. The data are contained in HDF5 files (http://www.hdfgroup.org/HDF5/). They are stored as compressed integers coded on 16 bits as they came out of the A/D card. Recording details can be found in Pouzat, Mazor and Laurent (2002) Using noise signature to optimize spike-sorting and to assess neuronal classification quality. Journal of Neuroscience Methods 122: 43-57 (a pre-print version is available: http://xtof.perso.math.cnrs.fr/pdf/Pouzat+:2002.pdf). Each data file is subdivided in Groups corresponding the type of acquisition performed: one or several epochs of spontaneous activity recording; repetitive stimulation with a given odor. Each group is made of one (if say a single epoch of 60 seconds of spontaneous recording was made) or several (if say 100 stimulation with Citral were made) (sub-)groups containing the data of all the channels that were recorded during that epoch. Each of these sub-groups is made of 4 to 16 data sets: 1 dimensional arrays containing the raw data recorded from one of the 16 channels of our probe (made of 4 tetrodes) during a single acquisition epoch. All channels were sampled at 15 kHz. Each data file has attributes (metadata) README and LabBook. The first, README contains a shortened version of the present text; the second, LabBook contains a transcript of the lab book corresponding to the experiment. Most groups have a log_file_content attribute. This attribute contains a copy a text file that was automatically generated during data acquisition. Some recording details can be found there as well as the precise time and data of each recorded epoch. The data were kept for 14 years on CDs and about a third of the recordings got lost because of CD corruption! What's left still make 15 GBytes of data after compression: a substantial amount. This CD corruption explains why some groups don't have a log_file_content attribute: it was on a corrupted CD.

Of the 14 experiments, 12 contain antennal lobe (the first olfactory relay of insects) recordings, 1 contains antennal lobe and alpha lobe recordings and 1 contains only alpha lobe recordings. Here the alpha lobe location should be taken with a little bit of caution since the latter is not as easy to locate as the antennal lobe in the locust. All data files start with the locust prefix, followed by the experiment year, month and date, like locust20000214.hdf5 an experiment performed on February 14 2000. Due to file size restriction on Zenodo, two experiments are split into two data files: locust20010124b_part1.hdf5 and locust20010124b_part2.hdf5 as well as locust20010214_part1.hdf5 and locust20010214_part2.hdf5. On two dates, two different experiments were performed: locust20010124a.hdf5 and locust20010124b_part1.hdf5 / locust20010124b_part2.hdf5 as well as locust20010208a.hdf5 and locust20010208b.hdf5. When a stimulation was applied, the following code is used: Odor name / Number of stimulation / Inter-stimulation interval / duration before the odor pulse / odor pulse duration / post pulse duration [odor dilution when several dilutions were used]. All times are in seconds.

A very brief description at the group level of the files content follows (see the LabBook attribute of each individual file for details):

locust20000214.hdf5:

Citral / 70 / 30 / 3 / 0.5 / 6.5

Cherry / 120 / 30 / 3 / 0.5 / 6.5

Octaldehyde / 60 / 30 / 3 / 0.5 / 6.5

locust20000421.hdf5:

Spontaneous: 60 seconds of spontaneous activity

1-Hexanol / 30 / 10 / 3 / 1 / 5.5

Hexanal / 25 / 10 / 3 / 1 / 5.5

Cis-3-hexen-1-ol / 25 / 10 / 3 / 1 / 5.5

Trans-2-hexen-1-ol / 25 / 10 / 3 / 1 / 5.5

1-Hexen-3-ol / 25 / 10 / 3 / 1 / 5.5

3-Pentanone / 25 / 10 / 3 / 1 / 5.5

1-Heptanol / 25 / 10 / 3 / 1 / 5.5

1-Octanol / 25 / 10 / 3 / 1 / 5.5

2-Heptanone / 25 / 10 / 3 / 1 / 5.5

3-Heptanone / 25 / 10 / 3 / 1 / 5.5

Citral / 25 / 10 / 3 / 1 / 5.5

Apple / 25 / 10 / 3 / 1 / 5.5

Mint / 25 / 10 / 3 / 1 / 5.5

Strawberry / 25 / 10 / 3 / 1 / 5.5

Octaldehyde / 25 / 10 / 3 / 1 / 5.5

1-Octanol / 25 / 10 / 3 / 1 / 5.5 [10^-5]

1-Octanol / 25 / 10 / 3 / 1 / 5.5 [10^-4]

1-Octanol / 25 / 10 / 3 / 1 / 5.5 [10^-3]

1-Octanol / 25 / 10 / 3 / 1 / 5.5 [10^-2]

1-Octanol / 25 / 10 / 3 / 1 / 5.5 [10^-1]

1-Octanol / 25 / 10 / 3 / 1 / 5.5 [1]

locust20000423.hdf5:

Spontaneous first: 60 seconds of spontaneous activity

1-Hexanol / 25 / 10 / 3 / 1 / 5.5

Hexanal / 25 / 10 / 3 / 1 / 5.5

Cis-3-hexen-1-ol / 25 / 10 / 3 / 1 / 5.5

1-Hexen-3-ol / 25 / 10 / 3 / 1 / 5.5

1-Heptanol / 25 / 10 / 3 / 1 / 5.5

2-Heptanone / 25 / 10 / 3 / 1 / 5.5

3-Heptanone / 25 / 10 / 3 / 1 / 5.5

Citral / 25 / 10 / 3 / 1 / 5.5

Apple / 25 / 10 / 3 / 1 / 5.5

Amyl Acetate / 25 / 10 / 3 / 1 / 5.5

1-Hexanol / 25 / 10 / 3 / 1 / 5.5

Spontaneous second: 60 seconds of spontaneous activity

locust20000613.hdf5:

Cis-3-hexen-1-ol / 50 / 30 / 3 / 1 / 16 [1]

Cis-3-hexen-1-ol / 10 / 30 / 3 / 1 / 16 [1/100]

Cis-3-hexen-1-ol / 50 / 30 / 3 / 1 / 16 [1/10]

Cis-3-hexen-1-ol / 50 / 30 / 3 / 1 / 16 [1]

Cherry / 21 / 30 / 3 / 1 / 16

locust20000616.hdf5:

Spontaneous first: 60 seconds of spontaneous activity

Cis-3-hexen-1-ol / 50 / 30 / 3 / 1 / 16 [1]

Spontaneous second: 60 seconds of spontaneous activity

Spontaneous third: 60 seconds of spontaneous activity

Cis-3-hexen-1-ol / 50 / 30 / 3 / 1 / 16 [1/100]

Cis-3-hexen-1-ol / 50 / 30 / 3 / 1 / 16 [1/10]

locust20000901.hdf5:

Vanilla / 5 / 30 / 3 / 1 / 16

Spontaneous: 60 seconds of spontaneous activity

Cherry / 30 / 30 / 3 / 1 / 16

Spontaneous: 60 seconds of spontaneous activity

Benzaldehyde / 30 / 30 / 3 / 1 / 16

Spontaneous: 60 seconds of spontaneous activity

Mint / 20 / 30 / 3 / 1 / 16

Hexanal / 15 / 30 / 3 / 1 / 16

Spontaneous: 60 seconds of spontaneous activity

Cis-3-hexen-1-ol / 30 / 30 / 3 / 1 / 16

Spontaneous: 60 seconds of spontaneous activity

Trans-2-hexen-1-ol / 30 / 30 / 3 / 1 / 16

locust20010124a.hdf5:

Spontaneous: 2x29 seconds of spontaneous activity

Spontaneous: 60x29 seconds of spontaneous activity

Spontaneous: 80x29 seconds of spontaneous activity

locust20010124b_part1.hdf5 and locust20010124b_part2.hdf5:

Spontaneous: 60x29 seconds of spontaneous activity

Spontaneous: 191x29 seconds of spontaneous activity

Spontaneous: 59x29 seconds of spontaneous activity

locust20010131.hdf5:

Spontaneous: 90x29 seconds of spontaneous activity

Spontaneous: 70x29 seconds of spontaneous activity

Spontaneous: 5x59 seconds of spontaneous activity

Spontaneous: 3x59 seconds of spontaneous activity

Spontaneous: 3x59 seconds of spontaneous activity

Spontaneous: 3x59 seconds of spontaneous activity

Spontaneous: 3x59 seconds of spontaneous activity

Spontaneous: 3x59 seconds of spontaneous activity

Spontaneous: 3x59 seconds of spontaneous activity

WithoutAntenna: 3x59 seconds of spontaneous activity (after antennal nerve cut)

WithoutAntenna: 3x59 seconds of spontaneous activity (after antennal nerve cut)

WithoutAntenna: 3x59 seconds of spontaneous activity (after antennal nerve cut)

locust20010201:

Continuous: 90x29 seconds of spontaneous activity

Continuous: 20x29 seconds of spontaneous activity

Citral / 50 / 30 / 3 / 1 / 25

Citral / 50 / 30 / 10 / 1 / 18

Citral / 50 / 30 / 10 / 1 / 18

Continuous: 50x29 seconds of spontaneous activity

Continuous: 45x29 seconds of spontaneous activity

locust20010208a.hdf5:

Spontaneous: 50x29 seconds of spontaneous activity

Spontaneous: 80x29 seconds of spontaneous activity

locust20010208b.hdf5:

Spontaneous: 50x29 seconds of spontaneous activity

Spontaneous: 50x29 seconds of spontaneous activity

Citral / 50 / 30 / 10 / 1 / 18

Citral / 120 / 30 / 10 / 1 / 18

Citral / 50 / 30 / 10 / 1 / 18

Citral / 25 / 30 / 10 / 1 / 18

locust20010214_part1.hdf5 and locust20010214_part2.hdf5:

Spontaneous: 30x29 seconds of spontaneous activity

Spontaneous: 30x29 seconds of spontaneous activity

Cis-3-hexen-1-ol / 25 / 30 / 10 / 1 / 18

Citral / 25 / 30 / 10 / 1 / 18

Vanilla / 25 / 30 / 10 / 1 / 18

Octanol / 25 / 30 / 10 / 1 / 18

Mint / 25 / 30 / 10 / 1 / 18

Cis-3-hexen-1-ol / 25 / 30 / 10 / 1 / 18

Spontaneous: 30x29 seconds of spontaneous activity

Spontaneous: 30x29 seconds of spontaneous activity

Cis-3-hexen-1-ol / 30 / 30 / 10 / 1 / 18

Cis-3-hexen-1-ol / 11 / 30 / 10 / 1 / 18

Cis-3-hexen-1-ol / 30 / 30 / 10 / 1 / 18

Cis-3-hexen-1-ol / 30 / 30 / 10 / 1 / 18

locust20010217.hdf5:

Spontaneous: 10x29 seconds of spontaneous activity

Spontaneous: 2x29 seconds of spontaneous activity

Spontaneous: 30x29 seconds of spontaneous activity

Spontaneous: 10x29 seconds of spontaneous activity

Spontaneous: 10x29 seconds of spontaneous activity

Spontaneous: 10x29 seconds of spontaneous activity

Spontaneous: 10x29 seconds of spontaneous activity

Spontaneous: 10x29 seconds of spontaneous activity

Spontaneous: 10x29 seconds of spontaneous activity

Lesional and non-lesional patches derived from 148 FCD patients is available as a HDF5 dataset (v1.0.0; doi: 10.5061/dryad.h70rxwdgm or 10.5281/zenodo.3239446). To create this dataset, for each of the 148 FCD patients, we sampled at most 1,000 (_N1000.h5) or 1,500 (_N1500.h5) cortical patches (or # voxels in the lesion, whichever is lower) of size 16×16×16 within the lesion.The same number of cortical patches were sampled randomly outside the lesion. The resulting lesional and non-lesional patches were concatenated, shuffled (to add another layer of randomization), and saved along with their binary labels (compressed HDF5 dataset).

For axis=1, index 0 is T1 and 1 is FLAIR.

TCSIF: A temporally consistent global GOME-2A SIF dataset with correction of sensor degradation

Description:

The global monthly GOME-2A SIF dataset (2007–2021) with correction of temporal degradation. The corrected global GOME-2 SIF dataset can be obtained in two types. The daily level2 dataset is provided in hdf5 format(compressed in the zip files named "{Year}{Quater}.zip"). The name of the hdf5 files was SIF_daily_YYYYMMDD.h5, YYYY, MM, and DD represent the year, month, and date, respectively. The level3 datasets which were aggregated monthly from the level2 dataset, have a spatial resolution of 0.5°and were saved in TIFF format in chronological order from 2007 to 2021 (compressed in the file "Level3.zip"). The name of the files was SIFpar_evi_monthly _YYYYMM.tif, where SIF was product type, par, and evi represented upscaled parameters, monthly represented temporal scale, YYYY and MM was the year and month, respectively. The SIF output was stored in the hdf5 files along with other variables of interest for further processing and visualization. See the appendix for the structure of the hdf5 file.

cloud_fraction[float]:

Description: Effective cloud fraction derived from GOME-2 Level1B product.

Units: none

latitude[float]:

Description: Pixel center latitude.

Units: degrees N

longitude[float]:

Description: Pixel center longitude.

Units: degrees E

latitude_bounds[float]:

Description: Latitude of the boundary corners for each pixel.

Units: degrees N

longitude _bounds[float]:

Description: Longitude of the boundary corners.

Units: degrees E

SIF_740[float]:

Description: SIF signal at 740nm retrieved using the 735–758 nm fitting window.

Units: mW m-2 nm-1 sr-1

SIF_daily [float]:

Description: SIF signal at 740nm with correction of day-length.

Units: mW m-2 nm-1 sr-1

Sigma_i[float]:

Description: The squre of single retrieval error of SIF_740.

Units: (mW m-2 nm-1 sr-1)2

Solar_zenith_angle [float]:

Description: Solar zenith angle.

Units: degrees

Solar_azimuth_angle [float]:

Description: Solar azimuth angle.

Units: degrees

Viewing _zenith_angle [float]:

Description: Viewing zenith angle.

Units: degrees

Viewing_azimuth_angle[float]:

Description: Viewing azimuth angle.

Units: degrees

chi2[float]:

Description: The reduced chi-square value calculated based on the the fitting residuals.

Units: None

Rad_NIR[float]:

Description: The average radiance within the 735~758 nm window

Units: mW m-2 nm-1 sr-1

ps_NIR[float]:

Description: The average reflectance within at around 780 nm.

Units: None

ps_red[float]:

Description: The average reflectance within the 665~680 nm window

Units: None

NDVI[float]:

Description: Calculated by the TOA reflectance at red band (around 680 nm) and near-infrared band (around 780nm).

Units: None

This dataset contains data used in the publication entitled "Nano-scale characterisation of sheared β'' precipitates in a deformed Al-Mg-Si alloy". This publication concerns how β'' precipitates are sheared by dislocations during deformation. The data contained in this repository are data acquired on various transmission electron microscopes of specimens of the aluminium alloy AA6060 in peak aged condition after uniaxial compression to 5%, 10%, and 20%, in addition to the undeformed reference alloy.

There are five main types of data:

Transmission electron microscopy (TEM) images

High-resolution TEM images

High angle annular dark field (HAADF) scanning TEM (STEM) images

Scanning precession electron diffraction (SPED) data.

Cross-sectional data of precipitates in undeformed and 20% compressed conditions.

Data for the TEM, HRTEM, and STEM images are kept in zipped folders due to the large number of images (several hundreds for each compression condition). Folders are named following the format of "_", where technique refers to TEM, HRTEM, or STEM. Images are provided in both .hdf format and .jpg format (to aid in navigating the data). Please see HDF Group for more information regarding the HDF file format, and HDF View for softaware to read and show HDF data. The Python package HyperSpy, is also useful for loading the HDF data for inspection, analysis, and presentation.

For some STEM images, a stack of short-exposure STEM images acquired and analysed using the SmartAlign plugin to Gatan Digital Micrograph is available. SmartAlign offers the possibility of rigidly and non-rigidly aligning the STEM images in the stack in order to reduce effect of specimen drift and scan noise during acquisition. The conventional STEM images are found in the zip archive labelled "STEM". When the filenames of the STEM images include "SAstack" and/or "SAimage", a STEM SmartAlign stack or the average through a non-rigidly aligned stack is available of the same field of view. In such cases, both the SmartAlign stack and the through-stack image is provided in the metadata in the .hdf file (note that not all stacks have been aligned, and in such cases no through-stack image is available). In addition, the SmartAlign stacks themselves are available in the subfolder "STEM\SmartAlign" within each STEM folder. The through-stack images of the smart align stacks are also provided separately in the subfolder "STEM\SmartAlign\Aligned". For the 20% compressed case, a lowloss electron energy loss spectroscopy (EELS) spectrum and thickness maps of the imaged areas are also provided, in the subfolder "STEM\EELS".

The SPED data, acquired using the ASTAR system of NanoMegas, is provided as .hdf5 files in the root directory of the repository. They should be read using and pyXem. The attached Jupyter Notebook "SPED_data_inspection.ipynb" can be used to access the SPED datasets. These datasets are 4D datasets, with two spatial and two reciprocal dimensions. They have been decomposed using the non-negative matrix factorization algorithm (NMF) used in HyperSpy. These decomposition results are included in the .hdf5 files. In addition, parameters used in the preprocessing of the datasets are attached in the metadata in these files. The metadata of these files are also provided separately as .txt files.

Finally, measurements of the precipitate cross-sectional area and circularity is available as .csv files with the first column being the row index, the second the cross-sectional areas of precipitates measured in nanometers squared, the third column is the perimeters of the precipitates measured in nanometers, and column four is the circularity of the precipitates.

OFFL/L3_CO Bu veri kümesi, CO konsantrasyonlarının çevrimdışı yüksek çözünürlüklü görüntülerini sağlar. Karbonmonoksit (CO), troposfer kimyasını anlamak için önemli bir atmosferik iz gazdır. Bazı kentsel alanlarda önemli bir atmosfer kirleticidir. CO'nun başlıca kaynakları fosil yakıtların yanması, biyokütle yakma ve metanın atmosferik oksidasyonudur.

These data sets contain ionospheric scintillation (GPS L1) observations (S4 and (\mathbf{\sigma_\phi}) indexes) from 5 different GNSS receivers.

The data are from two nights, 20/21 February and 27/28 November of 2013, and stored in different files. The files are in the HDF5 format using zlib compression and were generated by a Python script using the Pandas library.

Each file contain the following folders:

/csm_maker_calc

/csm_own_calc

/csm_raw

/gpstation6_maker_calc

/gpstation6_own_calc

/gpstation6_raw

/gsv_4004b_lisn_calc

/gsv_4004b_maker_calc

/septentrio_maker_calc

/septentrio_own_calc

/septentrio_raw

/su_iono_sdr_own_calc

/su_iono_sdr_raw

The suffix maker_calc means that the indices were calculated in real time or post-processed using receiver manufacturer's tools. The suffix own_calc means that the indices were calculated using our own methodology. The suffix raw corresponds to the raw data (power and phase cycles). And finally the suffix lisn_calc corresponds to the S4 index obtained from the LISN network website.

This data set contains numerical simulation results from experiments for the paper "Reconstruction Algorithms in Undersampled AFM
Imaging", published in IEEE Journal of Selected Topics in Signal Processing.

The data set consists of a set of HDF5 files containing the simulation results as well as MD5 and SHA checksums of the HDF5 databases for validating the integrity of the data after download.

The data set is licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/).

Python scripts used for producing these results as well as Python scripts for extracting images and data used in the accompanying paper from the database can be found in the accompanying deposition http://dx.doi.org/10.5281/zenodo.32959.

The data set contains images, and reconstructed versions of these, originally published in the data set available at http://dx.doi.org/10.5281/zenodo.17573.

OFFL/L3_HCHO このデータセットは、大気中のホルムアルデヒド（HCHO）濃度のオフライン高解像度画像を提供します。 ホルムアルデヒドは、非メタン揮発性有機化合物（NMVOC）のほぼすべての酸化連鎖の中間ガスであり、最終的には CO2 になります。非メタン揮発性有機化合物（NMVOC）は、NOx、CO、CH4 とともに、対流圏の O3 の最も重要な前駆体の一つです。遠隔大気中の主な HCHO 源は CH4 の酸化です。大陸では、植生、火災、交通、工業施設から排出される高濃度の NMVOC の酸化により、HCHO レベルが局所的に大幅に上昇します。ホルムアルデヒド分布の季節変動と年次変動は、主に気温の変化と火災イベントに関連していますが、人為的な活動の変化にも関連しています。境界層内の HCHO 濃度は、短寿命炭化水素の放出に直接関連しており、そのほとんどは宇宙から直接観測できません。詳細 OFFL L3 プロダクト OFFL L3 プロダクトを作成するには、次のようなコマンドを使用して、プロダクトのバウンディング ボックス内のデータがある領域を見つけます。 harpconvert --format hdf5 --hdf5-compression 9 -a 'tropospheric_HCHO_column_number_density_validity>50;derive(datetime_stop {time})' S5P_OFFL_L2_HCHO_20190116T171037_20190116T185207_06531_01_010105_20190123T104749.nc grid_info.h5 次に、すべてのデータを 1 つの大きなモザイクに統合します（異なる時間で異なる値を持つ可能性のあるピクセルの値の領域平均）。モザイクから、オルソ補正されたラスターデータを含む一連のタイルを作成します。 1 つのタイルに対する harpconvert 呼び出しの例: harpconvert --format hdf5 --hdf5-compression 9 -a 'tropospheric_HCHO_column_number_density_validity>50;derive(datetime_stop {time}); bin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01); keep(tropospheric_HCHO_column_number_density, tropospheric_HCHO_column_number_density_amf, HCHO_slant_column_number_density,cloud_fraction,sensor_altitude, sensor_azimuth_angle, sensor_zenith_angle,solar_azimuth_angle, solar_zenith_angle)' S5P_OFFL_L2_HCHO_20190116T171037_20190116T185207_06531_01_010105_20190123T104749.nc output.h5 Sentinel-5 Precursor Sentinel-5 Precursor は、大気汚染をモニタリングするために欧州宇宙機関が 2017 年 10 月 13 日に打ち上げた衛星です。搭載センサーは、Tropomi（TROPOspheric Monitoring Instrument）と呼ばれることがよくあります。 CH4 を除くすべての S5P データセットには、Near Real-Time（NRTI）と Offline（OFFL）の 2 つのバージョンがあります。CH4 は OFFL のみで利用できます。NRTI アセットは OFFL アセットよりもカバーする範囲は狭いですが、取得後すぐに表示されます。OFFL アセットには、単一の軌道からのデータが含まれています（地球の半分が暗いため、単一の半球のデータのみが含まれています）。 データ内のノイズのため、特にクリーンな地域や SO2 排出量が少ない地域では、負の垂直列の値が観測されることがよくあります。外れ値（-0.001 mol/m^2 より小さい垂直列）を除き、これらの値をフィルタしないことをおすすめします。 元の Sentinel 5P レベル 2（L2）データは、緯度/経度ではなく時間でビン分割されます。Earth Engine にデータを取り込めるように、各 Sentinel 5P L2 プロダクトは L3 に変換され、軌道ごとに 1 つのグリッドが保持されます（つまり、プロダクト間の集計は行われません）。 子午線をまたぐソースプロダクトは、_1 と _2 の接尾辞が付いた 2 つの Earth Engine アセットとして取り込まれます。 L3 への変換は、bin_spatial オペレーションを使用して harpconvert ツールによって行われます。ソースデータは、QA 値が次の値より小さいピクセルを削除するようにフィルタされます。 AER_AI の場合は 80% NO2 の tropospheric_NO2_column_number_density バンドで 75% O3 と SO2 以外のすべてのデータセットで 50% O3_TCL プロダクトは（harpconvert を実行せずに）直接取り込まれます。

OFFL/L3_AER_AI このデータセットは、吸収性エアロゾル指数（AAI）とも呼ばれる UV エアロゾル指数（UVAI）の高解像度オフライン画像を提供します。 AAI は、2 つの波長における UV スペクトル範囲のレイリー散乱の波長依存性の変化に基づいています。観測された反射率とモデル化された反射率の差が AAI になります。AAI が正の値の場合、塵や煙などの紫外線を吸収するエアロゾルが存在することを示します。これは、砂塵の発生、火山灰、バイオマス燃焼によるエピソード的なエアロゾル プルームの進化を追跡するのに役立ちます。 使用される波長はオゾン吸収が非常に少ないため、エアロゾル光学的厚さの測定とは異なり、雲が存在する場合でも AAI を計算できます。そのため、毎日グローバルなカバレッジを確保できます。 この L3 AER_AI プロダクトでは、354 nm と 388 nm の波長での測定値のペアを使用して、吸収性エアロゾル指数が計算されます。COPERNICUS/S5P/OFFL/L3_SO2 プロダクトには、340 nm と 380 nm の波長を使用して計算された absorbing_aerosol_index が含まれています。 OFFL L3 プロダクト OFFL L3 プロダクトを作成するには、次のようなコマンドを使用して、プロダクトのバウンディング ボックス内のデータがある領域を見つけます。 harpconvert --format hdf5 --hdf5-compression 9 -a 'absorbing_aerosol_index_validity>50;derive(datetime_stop {time})' S5P_OFFL_L2_AER_AI_20181030T213916_20181030T232046_05427_01_010200_20181105T210529.nc grid_info.h5 次に、すべてのデータを 1 つの大きなモザイクに統合します（異なる時間で異なる値を持つ可能性のあるピクセルの値の領域平均）。モザイクから、オルソ補正されたラスターデータを含む一連のタイルを作成します。 1 つのタイルに対する harpconvert 呼び出しの例: harpconvert --format hdf5 --hdf5-compression 9 -a 'absorbing_aerosol_index_validity>50;derive(datetime_stop {time}); bin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01); keep(absorbing_aerosol_index,sensor_altitude,sensor_azimuth_angle, sensor_zenith_angle,solar_azimuth_angle,solar_zenith_angle)' S5P_OFFL_L2_AER_AI_20181030T213916_20181030T232046_05427_01_010200_20181105T210529.nc output.h5 Sentinel-5 Precursor Sentinel-5 Precursor は、大気汚染をモニタリングするために欧州宇宙機関が 2017 年 10 月 13 日に打ち上げた衛星です。搭載センサーは、Tropomi（TROPOspheric Monitoring Instrument）と呼ばれることがよくあります。 CH4 を除くすべての S5P データセットには、Near Real-Time（NRTI）と Offline（OFFL）の 2 つのバージョンがあります。CH4 は OFFL のみで利用できます。NRTI アセットは OFFL アセットよりもカバーする範囲は狭いですが、取得後すぐに表示されます。OFFL アセットには、単一の軌道からのデータが含まれています（地球の半分が暗いため、単一の半球のデータのみが含まれています）。 データ内のノイズのため、特にクリーンな地域や SO2 排出量が少ない地域では、負の垂直列の値が観測されることがよくあります。外れ値（-0.001 mol/m^2 より小さい垂直列）を除き、これらの値をフィルタしないことをおすすめします。 元の Sentinel 5P レベル 2（L2）データは、緯度/経度ではなく時間でビン分割されます。Earth Engine にデータを取り込めるように、各 Sentinel 5P L2 プロダクトは L3 に変換され、軌道ごとに 1 つのグリッドが保持されます（つまり、プロダクト間の集計は行われません）。 子午線をまたぐソースプロダクトは、_1 と _2 の接尾辞が付いた 2 つの Earth Engine アセットとして取り込まれます。 L3 への変換は、bin_spatial オペレーションを使用して harpconvert ツールによって行われます。ソースデータは、QA 値が次の値より小さいピクセルを削除するようにフィルタされます。 AER_AI の場合は 80% NO2 の tropospheric_NO2_column_number_density バンドで 75% O3 と SO2 以外のすべてのデータセットで 50% O3_TCL プロダクトは（harpconvert を実行せずに）直接取り込まれます。

OFFL/L3_AER_LH 此数据集提供紫外线气溶胶指数(UVAI)（也称为吸收层高度[ALH]）的离线高分辨率图像。ALH 对云污染非常敏感。不过，气溶胶和云很难区分，因此对于所有小于0.05 的 FRESCO 有效云分数，系统都会计算ALH。…

OFFL/L3_AER_AI Tập dữ liệu này cung cấp hình ảnh có độ phân giải cao ngoại tuyến về Chỉ số khí dung UV (UVAI), còn được gọi là Chỉ số khí dung hấp thụ (AAI). AAI được tính dựa trên những thay đổi phụ thuộc vào bước sóng trong hiện tượng tán xạ Rayleigh trong dải quang phổ tia cực tím cho một cặp bước sóng. Sự khác biệt giữa kết quả phản xạ quan sát được và kết quả phản xạ được mô hình hoá dẫn đến …

OFFL/L3_O3 Cet ensemble de données fournit des images hors connexion haute résolution des concentrations d'ozone en colonne totale. Consultez également COPERNICUS/S5P/OFFL/L3_O3_TCL pour obtenir les données des colonnes troposphériques. Dans la stratosphère, la couche d'ozone protège la biosphère des dangereux rayonnements ultraviolets solaires. Dans la troposphère, il agit comme un agent nettoyant efficace, mais à forte concentration, il devient également nocif pour la santé des humains, des animaux et de la végétation. L'ozone est également un important gaz à effet de serre qui contribue au changement climatique en cours. Depuis la découverte du trou d'ozone antarctique dans les années 1980 et le protocole de Montréal qui a suivi, réglementant la production de substances appauvrissant la couche d'ozone contenant du chlore, l'ozone est surveillé de manière systématique depuis le sol et l'espace. Pour ce produit, deux algorithmes fournissent l'ozone total : GDP pour le quasi-temps réel et GODFIT pour les produits hors connexion. GDP est actuellement utilisé pour générer les produits opérationnels d'ozone total à partir de GOME, SCIAMACHY et GOME-2, tandis que GODFIT est utilisé dans les projets ESA CCI et Copernicus C3S. En savoir plus Manuel utilisateur du produit OFFL L3 Product Pour créer nos produits OFFL L3, nous recherchons des zones dans le cadre de sélection du produit contenant des données à l'aide d'une commande comme celle-ci : harpconvert --format hdf5 --hdf5-compression 9 -a 'O3_column_number_density_validity>50;derive(datetime_stop {time})' S5P_OFFL_L2_O3_20181005T225147_20181006T003317_05073_01_010102_20181012T001415.nc grid_info.h5 Nous fusionnons ensuite toutes les données en une grande mosaïque (en calculant la moyenne des valeurs des pixels qui peuvent avoir des valeurs différentes à différents moments). À partir de la mosaïque, nous créons un ensemble de tuiles contenant des données raster orthorectifiées. La valeur qa est ajustée avant l'exécution de harpconvert pour répondre à tous les critères suivants : ozone_total_vertical_column dans [0, 0.45] ozone_effective_temperature in [180, 260] ring_scale_factor dans [0, 0.15] effective_albedo dans [-0.5, 1.5] Exemple d'appel harpconvert pour une vignette : harpconvert --format hdf5 --hdf5-compression 9 -a 'O3_column_number_density_validity>50;derive(datetime_stop {time}); bin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01); keep(O3_column_number_density,O3_effective_temperature,cloud_fraction, sensor_altitude,sensor_azimuth_angle, sensor_zenith_angle, solar_azimuth_angle,solar_zenith_angle)' S5P_OFFL_L2_O3_20181005T225147_20181006T003317_05073_01_010102_20181012T001415.nc output.h5 Sentinel-5 Precursor Sentinel-5 Precursor est un satellite lancé le 13 octobre 2017 par l'Agence spatiale européenne pour surveiller la pollution atmosphérique. Le capteur embarqué est souvent appelé Tropomi (TROPOspheric Monitoring Instrument). Tous les ensembles de données S5P, à l'exception de CH4, comportent deux versions : Near Real-Time (NRTI) et Offline (OFFL). CH4 n'est disponible qu'en mode OFFL. Les assets NRTI couvrent une zone plus petite que les assets OFFL, mais apparaissent plus rapidement après l'acquisition. Les éléments OFFL contiennent des données provenant d'une seule orbite (qui, en raison de la moitié de la Terre étant dans l'obscurité, ne contient des données que pour un seul hémisphère). En raison du bruit dans les données, des valeurs de colonne verticale négatives sont souvent observées, en particulier au-dessus des régions propres ou pour les faibles émissions de SO2. Il est recommandé de ne pas filtrer ces valeurs, sauf pour les valeurs aberrantes, c'est-à-dire pour les colonnes verticales inférieures à -0,001 mol/m^2. Les données Sentinel-5P de niveau 2 (L2) d'origine sont regroupées par période, et non par latitude/longitude. Pour pouvoir ingérer les données dans Earth Engine, chaque produit Sentinel-5P de niveau 2 est converti au niveau 3, en conservant une seule grille par orbite (c'est-à-dire qu'aucune agrégation n'est effectuée sur les produits). Les produits sources couvrant l'antiméridien sont ingérés en tant que deux assets Earth Engine, avec les suffixes _1 et _2. La conversion au niveau 3 est effectuée par l'outil harpconvert à l'aide de l'opération bin_spatial. Les données sources sont filtrées pour supprimer les pixels dont les valeurs de contrôle qualité sont inférieures à : 80 % pour AER_AI 75 % pour la bande tropospheric_NO2_column_number_density de NO2 50 % pour tous les autres ensembles de données, à l'exception de l'O3 et du SO2 Le produit O3_TCL est ingéré directement (sans exécuter harpconvert).

OFFL/L3_HCHO Cet ensemble de données fournit des images hors connexion haute résolution des concentrations de formaldéhyde (HCHO) dans l'atmosphère. Le formaldéhyde est un gaz intermédiaire dans presque toutes les chaînes d'oxydation des composés organiques volatils non méthaniques (COVNM), qui aboutissent finalement à du CO2. Les composés organiques volatils non méthaniques (COVNM) sont, avec les NOx, le CO et le CH4, parmi les précurseurs les plus importants de l'O3 troposphérique. La principale source de HCHO dans l'atmosphère lointaine est l'oxydation du CH4. Sur les continents, l'oxydation des NMVOC élevés émis par la végétation, les incendies, le trafic et les sources industrielles entraîne des augmentations importantes et localisées des niveaux de HCHO. Les variations saisonnières et interannuelles de la distribution de formaldéhyde sont principalement liées aux changements de température et aux incendies, mais aussi aux changements dans les activités anthropiques. Les concentrations de HCHO dans la couche limite peuvent être directement liées à l'émission d'hydrocarbures à courte durée de vie, qui ne peuvent généralement pas être observés directement depuis l'espace. En savoir plus OFFL L3 Product Pour créer nos produits OFFL L3, nous recherchons des zones dans le cadre de sélection du produit contenant des données à l'aide d'une commande comme celle-ci : harpconvert --format hdf5 --hdf5-compression 9 -a 'tropospheric_HCHO_column_number_density_validity>50;derive(datetime_stop {time})' S5P_OFFL_L2_HCHO_20190116T171037_20190116T185207_06531_01_010105_20190123T104749.nc grid_info.h5 Nous fusionnons ensuite toutes les données en une grande mosaïque (en calculant la moyenne des valeurs des pixels qui peuvent avoir des valeurs différentes à différents moments). À partir de la mosaïque, nous créons un ensemble de tuiles contenant des données raster orthorectifiées. Exemple d'appel harpconvert pour une vignette : harpconvert --format hdf5 --hdf5-compression 9 -a 'tropospheric_HCHO_column_number_density_validity>50;derive(datetime_stop {time}); bin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01); keep(tropospheric_HCHO_column_number_density, tropospheric_HCHO_column_number_density_amf, HCHO_slant_column_number_density,cloud_fraction,sensor_altitude, sensor_azimuth_angle, sensor_zenith_angle,solar_azimuth_angle, solar_zenith_angle)' S5P_OFFL_L2_HCHO_20190116T171037_20190116T185207_06531_01_010105_20190123T104749.nc output.h5 Sentinel-5 Precursor Sentinel-5 Precursor est un satellite lancé le 13 octobre 2017 par l'Agence spatiale européenne pour surveiller la pollution atmosphérique. Le capteur embarqué est souvent appelé Tropomi (TROPOspheric Monitoring Instrument). Tous les ensembles de données S5P, à l'exception de CH4, comportent deux versions : Near Real-Time (NRTI) et Offline (OFFL). CH4 n'est disponible qu'en mode OFFL. Les assets NRTI couvrent une zone plus petite que les assets OFFL, mais apparaissent plus rapidement après l'acquisition. Les éléments OFFL contiennent des données provenant d'une seule orbite (qui, en raison de la moitié de la Terre étant dans l'obscurité, ne contient des données que pour un seul hémisphère). En raison du bruit dans les données, des valeurs de colonne verticale négatives sont souvent observées, en particulier au-dessus des régions propres ou pour les faibles émissions de SO2. Il est recommandé de ne pas filtrer ces valeurs, sauf pour les valeurs aberrantes, c'est-à-dire pour les colonnes verticales inférieures à -0,001 mol/m^2. Les données Sentinel-5P de niveau 2 (L2) d'origine sont regroupées par période, et non par latitude/longitude. Pour pouvoir ingérer les données dans Earth Engine, chaque produit Sentinel-5P de niveau 2 est converti au niveau 3, en conservant une seule grille par orbite (c'est-à-dire qu'aucune agrégation n'est effectuée sur les produits). Les produits sources couvrant l'antiméridien sont ingérés en tant que deux assets Earth Engine, avec les suffixes _1 et _2. La conversion au niveau 3 est effectuée par l'outil harpconvert à l'aide de l'opération bin_spatial. Les données sources sont filtrées pour supprimer les pixels dont les valeurs de contrôle qualité sont inférieures à : 80 % pour AER_AI 75 % pour la bande tropospheric_NO2_column_number_density de NO2 50 % pour tous les autres ensembles de données, à l'exception de l'O3 et du SO2 Le produit O3_TCL est ingéré directement (sans exécuter harpconvert).

OFFL/L3_SO2：這個資料集提供大氣中二氧化硫(SO2) 濃度的離線高解析度圖像。二氧化硫 (SO2) 會透過自然和人為過程進入地球大氣層。它在區域和全球化學反應中扮演重要角色，影響範圍從短期汙染到氣候效應都有。僅限…