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Concatenated Data from the Chang'E-1 and -2 Microwave Radiometers
<p>This dataset includes a binary table collecting all data released by NAOC from the Chang'E 1 and 2 Microwave Radiometers, as well as gridded, map-projected versions of those data. Please see readme.md for a detailed description of contents.</p>
ERA5 based training, validation and evaluation data for retrievals combining 22-58 GHz with 175-340 GHz microwave radiometer measurements during MOSAiC
<p>This data set is used for the training, validation and evaluation of retrievals of temperature and specific humidity profiles, as well as integrated water vapour from simlulated or measured microwave brightness temperatures (TBs), which are described in <strong>[1]</strong>.</p> <p>The data set consists of yearly files (2001-2018, 6-hourly resolution) that include data from the European Centre for Medium-Range Weather Forecasts's ERA5 reanalysis <strong>[2]</strong> and simulated TBs in the microwave spectrum. TB simulations were performed with PAMTRA <strong>[3,4]</strong> on the native ERA5 model level resolution at frequencies of a low frequency Humidity and Temperature Profiler (HATPRO, 22-58 GHz) and of a Low Humidity Profiler (LHUMPRO-243-340, aka MiRAC-P, 175-340 GHz). Afterwards, the ERA5 model level data has been interpolated to a new height grid (dimension 'z'), of which the lowest 43 indices equal the height grid of the retrieval that is developed with this data set. The upper 11 indices are included for additional TB simulations needed for the information content estimation performed and are not used for the retrievals to avoid the tropopause.</p> <p>The trained retrieval is applied to observations from the HATPRO and MiRAC-P that were installed onboard the research vessel Polarstern during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition.</p> <p><strong>[1]:</strong> Walbröl, A., Griesche, H. J., Mech, M., Crewell, S., and Ebell, K.: Combining low- and high-frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products, Atmospheric Measurement Techniques, 17, 6223-6245, https://doi.org/10.5194/amt-17-6223-2024, 2024.</p> <p><strong>[2]:</strong> Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global reanalysis, Quarterly Journal of the Royal Meteorological Society, 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.</p> <p><strong>[3]:</strong> Mech, M., Maahn, M., Kneifel, S., Ori, D., Orlandi, E., Kollias, P., Schemann, V., and Crewell, S.: PAMTRA 1.0: the Passive and Active Microwave radiative TRAnsfer tool for simulating radiometer and radar measurements of the cloudy atmosphere, Geoscientific Model Development, 13, 4229–4251, https://doi.org/10.5194/gmd-13-4229-2020, 2020.</p> <p><strong>[4]:</strong> Mech, M., Maahn, M., Ori, D., Kneifel, S., and Orlandi, E.: PAMTRA Package – Passive and Active Microwave TRANsfer, available at: https://github.com/igmk/pamtra (last access: 6 September 2020), 2019c.</p>
Surface brightness temperatures measured by the HATPRO microwave radiometer onboard the RV Polarstern during the ATWAICE expedition PS144 to the Arctic in summer 2022
<p>The data set contains daily files of raw microwave radiation measurements by the HATPRO microwave radiometer (see Rose et al., 2015) onboard about 22 m height at the top deck (starboard) of RV Polarstern during cruise PS131 (ATWAICE expedition, see Kanzow, 2023). Via a mirror construction the radiometers were observing the surface at a viewing angle of about 53° off-nadir for 15 min each hour. The actual viewing angle could vary by a few degree because of ship motion. The data covers the range July 11, 2022 to August 11, 2022. The radiation measurements are given as brightness temperatures in seven K band channels (22.24 - 31.4 GHz), vertical polarization, and seven V band (51.26 - 58 GHz) channels, horizontal polarization. </p> <p>Version 2 of the uploaded data is quality-controlled (see the flag variable).</p>
Surface brightness temperatures measured by the MiRAC-P microwave radiometer onboard the RV Polarstern during the ATWAICE expedition PS144 to the Arctic in summer 2022
<p>The data set contains daily files of raw microwave radiation measurements by the MiRAC-P (or LHUMPRO-243-340) microwave radiometer (see Mech et al., 2019) onboard about 22 m height at the top deck (starboard) of RV Polarstern during cruise PS131 (ATWAICE expedition). Via a mirror construction the radiometers were observing the surface at a viewing angle of about 53° off-nadir for 15 min each hour. The actual viewing angle could vary by a few degree because of ship motion. The data covers the range July 11, 2022 to August 11, 2022. The radiation measurements are given as brightness temperatures in six double side band averaged G band (183.31 +/- 0.6 to 183.31 +/- 7.5 GHz), vertical polarization, and one higher frequency (243 GHz) channel, horizontal polarization. The 340 GHz channel was malfunctioning. </p> <p>Version 2 of the uploaded data is quality-controlled (see the flag variable).</p>
Wind fields from aggregated retrievals from the WIRA-C Doppler wind radiometer in tropical and arctic lattitudes
<p>These data sets contain the retrieved wind fields from aggregated retrievals from the WIRA-C Doppler wind radiometer from two campaigns.</p> <p>The first campaign took place in the southern hemisphere at the Maïdo observatory on La Réunion Island (France), located in the Indian ocean at 21°S, 55°E. Data from April, May and June 2017 are included.</p> <p>For the second (and still ongoing) campaign, WIRA-C is located at the ALOMAR observatory on Andøya (Norway) at 69°N, 16°E. Data from September, October and November are included.</p>
OU/NSSL CLAMPS Microwave Radiometer and Surface Meteorological Data from LAPSE-RATE
<p>The microwave radiometer measures downwelling microwave radiance in 14 channels from 22.2 to 60.0 GHz at 1 s temporal resolution. It is one of the core instruments in the Collaborative Lower Atmospheric Mobile Profiling System (CLAMPS-1) facility.</p> <p>A simple statistical retrieval is performed to retrieve precipitable water vapor (PWV) and liquid water path (LWP) from the observations at 23.8 and 31.4 GHz (Turner et al. 2007).</p> <p>This microwave radiometer has a Vaisala all-weather met station included. This station is positioned about two feet above the back of the CLAMPS trailer. It is possible that the trailer is affecting these met station observations in some conditions. Note that the wind data in these files are likely not representative of true environmental wind. Due to power requirements, CLAMPS was positioned close to a building, which likely influenced the wind speed and direction. Any wind data should be used with caution.</p> <p> </p> <p>References:</p> <p>Turner, D. D., S. A. Clough, J. C. Liljegren, E. E. Clothiaux, K. E. Cady-Pereira, and K. L. Gaustad, 2007: Retrieving Liquid Wat0er Path and Precipitable Water Vapor From the Atmospheric Radiation Measurement (ARM) Microwave Radiometers. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, <strong>45</strong>, 3680–3690, <a href="https://doi.org/10.1109/TGRS.2007.903703">https://doi.org/10.1109/TGRS.2007.903703</a>.</p>
On the use of a consumer-grade 360-degree camera as a radiometer for scientific applications: calibration dataset
<p>Studying the geometric distribution of the light field in terms of absolute radiometry required expensive and complex instruments. New types of compact 360-degree cameras have recently appeared on the consumer technology market. Some of these allow users to access raw imagery, offering sensor-level data that can be directly exploited for absolute light quantification. This paves the way for easy-to-use, inexpensive and accessible radiance cameras that can be operated in a wide range of natural environments. </p> <p>This dataset presents raw format images captured with the camera Insta360 ONE for its calibration and characterization. These experiments include geometric calibration, relative illumination evaluation, spectral response determination, absolute spectral radiance calibration, as well as linearity and dark frame analysis. In addition, we are providing data from a calibration validation experiment based on co-located measurements of the sky's downward radiance using a 360-degree calibrated camera and a scientific radiometer: the Compact Optical Profiling System (C-OPS, Biospherical Instruments Inc.).</p> <p>This repository contains raw files as taken by the camera's imaging sensor. In most cases, no data processing has been carried out. The entire data set is contained in a .zip file which includes the following sub-folders (in alphabetical order):</p> <ul> <li><strong>absolute-radiance</strong>: data (.dng, .tsv , .hdf5) acquired for <em>absolute spectral radiance</em> calibration</li> <li><strong>darkframe: </strong>DNG raw images taken for <em>dark frame</em> analysis</li> <li><strong>geometric: </strong>DNG images used for the <em>geometric calibration</em></li> <li><strong>immersion-factor: </strong>DNG images for the calculation of the immersion factor</li> <li><strong>linearity:</strong> DNG images for <em>linearity</em> assessment (gain and exposure time)</li> <li><strong>relative-illumination: </strong>DNG images for <em>roll-off (relative illumination) </em>characterization</li> <li><strong>relative-spectral-response: </strong>data (.asc, .dng) taken for relative spectral response characterization</li> <li><strong>verification&validation: </strong>time series data (.tsv, .dng) of the calibration validation experiment </li> </ul> <p>Each folder contains a <strong>README </strong>file explaining the additional subfolders and their files. As you may notice, some of the subfolders are named "lensclose" or "lensfar". They refers to the data acquired with the fish-eye optic assembly that is closer or farther from the top of the 360-degree camera respectively. Some calibrations were performed both in water and in air (geometric calibration, relative-illumination). In that regard, the images were placed in folders refering to "air" and "water". The routines (coded in python) for the data processing can be found in the following <a href="https://github.com/RaphaelLarouche/radiance_camera_insta360/tree/master_v01">Github repository</a> (master_v01) or the <a href="../records/4660994">Zenodo stored version</a>. The useful scripts are located in the <em>calibration</em> directory, and the<strong> README</strong> for each folder points to the revelant code for analysis of the files they contain. For additional information, all the methodologies are described in the <a href="https://arxiv.org/abs/2305.07103">arXiv preprint</a>. </p>
Neural Network predictions and ERA5 reference of integrated water vapour, and temperature and specific humidity profiles based on simulated microwave radiometer observations
<p>This data set contains predictions of the Neural Network retrievals described in <strong>[1]</strong>, where simulated microwave radiometer observations (brightness temperatures, TBs) from the evaluation data subset of <strong>[2]</strong> (years 2001, 2006, 2011, 2015) were used as input to the Neural Network. As described in Section 3.2 of <strong>[1]</strong>, we trained an ensemble of 20 Neural Networks for each retrieved atmospheric quantity and applied them to the ERA5 evaluation data set to estimate the robustness of the retrievals with respect to random perturbations. The following atmospheric quantities were retrieved: </p> <ul> <li>temperature profile (variable name 'temp_p', filename suffix 'temp_test_417'),</li> <li>boundary layer temperature profile (variable name 'temp_p', filename suffix 'temp_test_424'),</li> <li>specific humidity profile (variable name 'q_p', filename suffix 'q_test_472'),</li> <li>integrated water vapour (variable name 'iwv_p', filename suffix 'iwv_test_126')</li> </ul> <p>The cryptic 3-digit filename suffixes represent different settings of the Neural Network retrieval. More information can be found in <strong>[3]</strong>. Variables that do not have the "_p" suffix are ERA5 data and used as reference to estimate errors of the retrievals by comparing them with the predictions. The dimension 'n_s' represents the ERA5 data sample number while the dimension 'n_rand' designates the ensemble of Neural Networks.</p> <p>These files can be created when running run_NN_retrieval (contained in NN_retrieval.py, see <strong>[3]</strong>) with exec_type='20_runs' and eval_mode=True and test_id either "126", "417", "424" or "472". However, as this might take some hours, we provide them here.</p> <p> </p> <p><strong>[1]:</strong> Walbröl, A., Griesche, H. J., Mech, M., Crewell, S., and Ebell, K.: Combining low- and high-frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products, Atmospheric Measurement Techniques, 17, 6223-6245, https://doi.org/10.5194/amt-17-6223-2024, 2024.</p> <p><strong>[2]:</strong> Walbröl, A., and Mech, M.: ERA5 based training, validation and evaluation data for retrievals combining 22-58 GHz with 175-340 GHz microwave radiometer measurements during MOSAiC (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.10997365, 2024.</p> <p><strong>[3]: </strong>Walbröl, A.: Codes for: Combining low and high frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products (1.0.1). Zenodo. <a href="https://doi.org/10.5281/zenodo.11123136" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11123136</a>, 2024.</p>
Brightness temperature data and weather station data from general scans of the microwave radiometer HATPRO
<p>This set contains brightness temperature observations and associated weather station data from microwave radiometer HATPRO for three events on Jul 27 and Aug 1, 2023.</p>
Datasets used in a Transformer network for image inversion of multi-dimensional nonuniform aperture synthesis radiometers
<p><span>该数据集于 2023 年 11 月在中南大学生成,并通过 matlab 仿真软件进行仿真和收集。主要用于图像重建网络的训练和测试。</span>该数据集由原始场景亮度数据、能见度数据和一维、二维和三维非均匀天线阵列图像重建的能见度函数对应的频域采样点位置数据,以及使用其他一些常规方法进行图像重建获得的亮度数据组成。此外,为了验证所提方法的有效性,在工作频率为 33.5 Ghz 的原型 8 元一维非均匀天线阵列上进行了室内实验,并生成了测量数据集。</p> <p>具体来说,名为 Tb_in、V2_noise、T3_AAF 和 T2_idft 的四个仿真数据集存储在名为 1d 的 zip 包中。</p> <p>Tb_IN_1d存储了一维非均匀天线对应的原始场景亮温数据,该数据选自西北工业大学制作的遥感影像场景分类公共数据集。</p> <p>V2_noise存储了包含各种误差的能见度函数值,主要是通过将原始场景亮温图像输入到运行在接收频率为 33.5 GHz 的非均匀积分孔径辐射计模拟程序中得到的。</p> <p>T2_idft 和 T3_AAF 分别是使用逆离散傅里叶变换和阵列因子形成方法进行图像重建获得的明亮温度数据。这两组数据都可用于后续的比较实验。</p> <p>名为 Tb_IN_2d、Tb_out_2d、VS_2d 和 VS_P_2d 的四个数据集存储在名为 2d 的 zip 包中。</p> <p>Tb_IN_2d内部存储的是二维非均匀天线对应的原始场景亮温数据,该数据选自西北工业大学制作的遥感影像场景分类公共数据集。</p> <p>VS_2d为二维非均匀天线阵列对应的能见度函数值,主要是将原始场景亮温图像输入到接收频率为 33.5 GHz 的非均匀集成孔径辐射计模拟程序中得到的。</p> <p>VS_P_2d存储了二维非均匀天线阵列的能见度函数对应的频域采样点位置,该值主要通过计算能见度函数值得到。</p> <p>Tb_out_2d文件存储了使用传统方法进行图像重建得到的亮温值,该数据也用于后续与所提方法获得的数据的比较实验。</p> <p>名为 Tb_IN_3d、Tb_out_3d、VS_3d 和 VS_P_3d 的四个数据集存储在名为 3d 的 zip 包中。</p> <p>Tb_IN_3d内部存储的是 3D 非均匀天线对应的原始场景亮温数据,该数据选自西北工业大学制作的遥感图像场景分类公共数据集。</p> <p>VS_3d是三维非均匀天线阵列对应的能见度函数值,是将原始场景亮温图像输入到接收频率为 33.5 GHz 的非均匀积分孔径辐射计仿真程序中得到的。</p> <p>VS_P_3d存储了三维非均匀天线阵列的能见度函数对应的频域采样点位置,该位置是通过计算能见度函数值得到的。</p> <p>Tb_out_3d文件存储了使用常规方法进行图像重建得到的亮温值,该数据也用于后续与所提方法获得的数据的比较实验。</p> <p>名为 Array2_R、Array3_R、V2_noise 和 Tb_out 的四个数据集存储在名为 8mm8 channel 的 zip 包中。</p> <p>存储在 Array2_R 和 Array3_R 中的是系统在不同位置测量的目标点源的相关矩阵,矩阵中元素的值反映了测试点对目标点源的检测能力。根据此相关矩阵,可以计算可见性值。</p> <p>存储在 V2_noise 内部的是包含各种误差的测量可见性函数的样本。对这些数据进行实验主要是为了验证所提方法的有效性。</p> <p>存储在 Tb_out 中的是使用测试数据集获得的亮温结果数据,用于测试训练的网络。</p>
Dataset for "LOROS: Laboratory Simulations of the Optical RadiOmeter composed of CHromatic Imagers (OROCHI) Experiment of the Martian Moons eXploration (MMX) Mission"
<p>This dataset hosts the image and numerical data analysed and derived in the accompanying Stabbins & Kameda article for the special issue of Progress in Earth and Planetary Science on instrumentation and preparations for the JAXA Martian Moons eXploration (MMX) mission. The paper describes and validates the performance of the Laboratory OROCHI Simulator (LOROS).</p> <p>OROCHI (Optical RadiOmeter composed of CHromatic Imagers) is a multispectral multi-view imaging system for the JAXA MMX spacecraft, that will image Phobos and Deimos across 8 visible and near-infrared spectral channels with unprecedented spatial resolution, recording data that in synergy with the other instruments of the MMX spacecraft and rover will constrain hypotheses on the origin of the Martian moons.</p> <p>LOROS is a laboratory simulator of OROCHI, constructed from commercial off-the-shelf parts.</p> <p>The dataset for the characterisation and validation of LOROS is composed of the following sub-sets:</p> <p>A. Modulation Transfer Function<br>B. Expected Reflectance of Carbonaceous Chondrite & Dark Spectralon<br>C. Radiometric Calibration<br>D. Dark Spectralon Validation</p> <div> <h2>Dataset A: Modulation Transfer Function</h2> This dataset includes the table of results of MTF measurements of the slant-edge target at 5 different random orientations in the range of ~7--10°: <div>- <code>mtf_results_07122023.csv</code></div> <br> <div>and the region-of-interest images, for each orientation and each LOROS channel, used to perform the analysis via the <a href="https://sourceforge.net/p/mtfmapper/home/Home/" target="_blank" rel="noopener">MTF Mapper software</a>:</div> <div>- <code>mtf_measurements_07122023</code></div> <br> <div>The directory tree of measurements, for the <em>n</em>th orientation, is illustrated below. Region-of-interest images are stored under <code>img</code>, and are averaged over 25 repeat images to minimise random noise, have had dark frames subtracted, and have been converted from 12-bit to 8-bit grayscale images for compatibility with the MTF Mapper software. Modulation Transfer Function (MTF) and Spatial Frequency Response (SFR) diagnostics generated by MTF Mapper are stored in the <code>results</code> directory.</div> <div> </div> <div><code>mtf_measurements_07122023</code></div> <div><code>├── mtf_knifeedge_low_07122023_*n*</code></div> <div><code>│ ├── img</code></div> <div><code>│ │ ├── 0_850_img_ave.tif</code></div> <div><code>│ │ ├── 1_475_img_ave.tif</code></div> <div><code>│ │ ├── ...</code></div> <div><code>│ ├── results</code></div> <div><code>│ │ ├── 0_850_img_ave_annotated.jpg</code></div> <div><code>│ │ ├── 0_850_img_ave_edge_mtf_values.txt</code></div> <div><code>│ │ ├── 0_850_img_ave_edge_sfr_values.txt</code></div> <div><code>│ │ ├── 1_475_img_ave_annotated.jpg</code></div> <div><code>│ │ ├── ...</code></div> <div><code>├── mtf_knifeedge_low_07122023_*n+1*</code></div> <div><code>│ ├── img</code></div> <div><code>│ │ ├── ...</code></div> <div> </div> <div>This data constitutes part of <strong>Table 1</strong> and <strong>Figure 2</strong> of the manuscript.</div> <div> <h2>Dataset B: Expected Reflectance of Carbonaceous Chondrite & Dark Spectralon</h2> This dataset includes the high-resolution ($\delta\lambda$=1 nm) reference reflectance spectra of the representative Carbonaceous Chondrite meteorite (<a href="https://westernreflectancelab.com/visor/graph/?results-selection=16136&results-item=16136&results-item=15972&results-item=231&results-item=230&graph=&form-TOTAL_FORMS=1&form-INITIAL_FORMS=0&form-MIN_NUM_FORMS=0&form-MAX_NUM_FORMS=1000&form-0-sample_name=nogoya&form-0-any_field=meteorite&form-0-id=&sort_params=-sample_name&page_selected=1&jump-to-page=" target="_blank" rel="noopener">Nogoya)</a> and the 5% reflectance Spectralon calibration target (<a href="https://www.labsphere.com/wp-content/uploads/2021/09/SpectralonStandards.pdf" target="_blank" rel="noopener">SCT5</a>):<br> <div>- <code>highres_input.csv</code></div> <br> <div>and the resampled spectra of these materials expected for OROCHI and LOROS filter wavelengths:</div> <br> <div>- <code>loros_observation.csv</code></div> <div>- <code>orochi_observation.csv</code></div> <br> <div><code>B_expected_reflectance</code></div> <div><code>├── README.md</code></div> <div><code>├── highres_input.csv</code></div> <div><code>├── loros_observation.csv</code></div> <div><code>└── orochi_observation.csv</code></div> <br> <div>This data constitutes <strong>Table 1</strong> and <strong>Figure 10</strong> of the manuscript.</div> <div> </div> <div> <div> <h2>Dataset C: Radiometric Calibration</h2> This dataset contains the image and derived data for 4 experiments with different illumination conditions for characterising the radiometric response of each of the 8 channels of LOROS.</div> <div><br> <div>This dataset contributes to <strong>Tables 2 - 4</strong> and <strong>Figures 3 - 9</strong> of the manuscript.</div> <br> <div>The final derived metrics are hosted in the spreadsheet:</div> <br> <div>- <code>measured_sensor_properties.csv</code></div> <br> <div>and image data and intermediary derived properties for each experiment are stored in the</div> <br> <div>- <code>experiments</code></div> <br> <div>directory.</div> <br> <div><code>C_radiometric_calibration</code></div> <div><code>├── README.md</code></div> <div><code>├── experiments</code></div> <div><code>│ ├── F*S5L10</code></div> <div><code>│ ├── F*S99L10</code></div> <div><code>│ ├── FGS99L2</code></div> <div><code>│ └── FGS99L10</code></div> <div><code>└── measured_sensor_properties.csv</code></div> <br> <h3><code>experiments</code> Directories</h3> In the directory of each experiment are sub-directories hosting Photon Transfer and Dark Transfer datasets, and a spreadsheet of derived metrics of these.<br> <div> </div> <div><code>C_radiometric_calibration</code></div> <div><code>├── README.md</code></div> <div><code>├── experiments</code></div> <div><code>│ ├── F*S5L10</code></div> <div><code>│ │ ├── dark_transfer_curve</code></div> <div><code>│ │ ├── photo_transfer_curve</code></div> <div><code>│ │ └── F*S5L10_derived_properties.csv</code></div> <div><code>│ └── ...</code></div> <div><code>└── measured_sensor_properties.csv</code></div> <div> </div> </div> <div> </div> <div><strong>Derived Properties</strong><br> <div> </div> <div>The spreadsheet (<code>[experiment]_derived_properties.csv</code>) collecting the properties derived from each experiment holds the following information, that has been extracted from the Photon Transfer and Dark Transfer curves as described in §4.2 of the manuscript:</div> <br> <div><code>camera # The camera number and wavelength</code></div> <div><code>k_adc # Sensitivity (e-/DN)</code></div> <div><code>full_well_e # Saturation Capacity (electrons)</code></div> <div><code>full_well_dn # Saturation Capacity (Digital Numbers)</code></div> <div><code>read_noise_e # Read Noise (electrons)</code></div> <div><code>read_noise_dn # Read Noise (Digital Numbers)</code></div> <div><code>bias_e # Offset (electrons)</code></div> <div><code>bias_dn # Offset (Digital Numbers)</code></div> <div><code>dark_current_e # Dark Current (electrons/second)</code></div> <div><code>dark_current_dn # Dark Current (Digital Numbers/second)</code></div> <div><code>DR # Dynamic Range</code></div> <div><code>lin_min # Minimum Linearity Error</code></div> <div><code>lin_max # Maximum Linearity Error</code></div> <div><code>linearity # Average Linearity Error</code></div> <div><code>snr_max # Maximum Signal-to-Noise Ratio</code></div> <div><code>t_exp_min # Minimum Exposure used in experiment (seconds)</code></div> <div><code>t_exp_max # Maximum Exposure used in experiment (seconds)</code></div> <div><code>expected_response # Expected Response (or 'Digital Flux') for OROCHI^12 at Phobos (Digital Numbers/second)</code></div> <div><code>response # Fitted Response (or 'Digital Flux') (Digital Numbers/second)</code></div> <br> <div>These values are given for each channel of LOROS, as well as the expected values for LOROS in off-the-shelf configuration (with no gain adjustment), LOROS with the gain adjustment, and OROCHI if downsampled to 12-bit resolution digital numbers.</div> <br> <div>This data constitutes <strong>Table 2</strong> of the manuscript.</div> <br> <div><strong>Dark Transfer Curve</strong></div> <br> <div>The <code>dark_transfer_curve</code> directory hosts the derived Dark Transfer Curve data (<code>derived_data</code>) and the source region-of-interest dark image pair data (<code>raw_data</code>) for each LOROS channel.</div> <br> <div><code>dark_transfer_curve</code></div> <div><code>├── derived_data</code></div> <div><code>│ ├── F*S5L10_0_850_dtc.csv</code></div> <div><code>│ ├── F*S5L10_1_475_dtc.csv</code></div> <div><code>│ ├── F*S5L10_2_400_dtc.csv</code></div> <div><code>│ ├── F*S5L10_3_550_dtc.csv</code></div> <div><code>│ ├── F*S5L10_4_725_dtc.csv</code></div> <div><code>│ ├── F*S5L10_5_950_dtc.csv</code></div> <div><code>│ ├── F*S5L10_6_650_dtc.csv</code></div> <div><code>│ └── F*S5L10_7_550_dtc.csv</code></div> <div><code>└── raw_data</code></div> <div><code>├── 0_850</code></div> <div><code>│ ├── 850_10095570us_1_calibration.tif</code></div> <div><code>│ ├── 850_10095570us_2_calibration.tif</code></div> <div><code>│ ├── 850_104us_1_calibration.tif</code></div> <div><code>│ ├── 850_104us_2_calibration.tif</code></div> <div><code>│ ├── ...</code></div> <div><code>├── 1_475</code></div> <div><code>├── 2_400</code></div> <div><code>├── 3_550</code></div> <div><code>├── 4_725</code></div> <div><code>├── 5_950</code></div> <div><code>├── 6_650</code></div> <div><code>├── 7_550</code></div> <div><code>└── camera_config.csv</code></div> <br> <div>The <code>raw_data</code> directory hosts a dark image pair for each exposure time used, and the <code>camera_config.csv</code> spreadsheet gives metadata for the system configuration, including the coordinates and dimensions of the region-of-interest for each channel.</div> <br> <div>The dark transfer curve for each experiment and each channel (<code>[experiment]_[channel]_[wavelength]_dtc</code>) gives the data derived from each raw image data, with the following values:</div> <br> <div><code>exposure # exposure duration (seconds)</code></div> <div><code>n_pix # number of pixels in the region of interest</code></div> <div><code>mean # average value of the region of interest</code></div> <div><code>std_t # total standard deviation of the region of interest</code></div> <div><code>std_rs # read+shot-noise standard deviation, copmuted from the difference of the image pair</code></div> <br> <div>This data constitutes <strong>Figures 5 and 8</strong> of the manuscript.</div> <br> <div><strong>Photon Transfer</strong></div> <br> <div>The <code>photon_transfer_curve</code> directory hosts the derived Photon Transfer Curve data (<code>derived_data</code>) and the source region-of-interest illuminated image pairs and associated dark frame image data (<code>raw_data</code>) for each LOROS channel.</div> <br> <div><code>photo_transfer_curve</code></div> <div><code>├── derived_data</code></div> <div><code>│ ├── F*S5L10_0_850_ptc.csv</code></div> <div><code>│ ├── F*S5L10_1_475_ptc.csv</code></div> <div><code>│ ├── F*S5L10_2_400_ptc.csv</code></div> <div><code>│ ├── F*S5L10_3_550_ptc.csv</code></div> <div><code>│ ├── F*S5L10_4_725_ptc.csv</code></div> <div><code>│ ├── F*S5L10_5_950_ptc.csv</code></div> <div><code>│ ├── F*S5L10_6_650_ptc.csv</code></div> <div><code>│ └── F*S5L10_7_550_ptc.csv</code></div> <div><code>└── raw_data</code></div> <div><code>├── 0_850</code></div> <div><code>│ ├── 850_104us_1_calibration.tif</code></div> <div><code>│ ├── 850_104us_2_calibration.tif</code></div> <div><code>│ ├── 850_104us_d_drk.tif</code></div> <div><code>│ ├── 850_105828us_1_calibration.tif</code></div> <div><code>│ ├── ...</code></div> <div><code>├── 1_475</code></div> <div><code>├── 2_400</code></div> <div><code>├── 3_550</code></div> <div><code>├── 4_725</code></div> <div><code>├── 5_950</code></div> <div><code>├── 6_650</code></div> <div><code>├── 7_550</code></div> <div><code>└── camera_config.csv</code></div> <br> <div>The <code>raw_data</code> directory hosts an image pair and dark frame for each exposure time used, and the <code>camera_config.csv</code> spreadsheet gives metadata for the system configuration, including the coordinates and dimensions of the region-of-interest for each channel.</div> <br> <div>The photon transfer curve for each experiment and each channel (<code>[experiment]_[channel]_[wavelength]_ptc</code>) gives the data derived from each raw image data, with the following values across the region-of-interest:</div> <br> <div><code>exposure # exposure duration (seconds)</code></div> <div><code>n_pix # number of pixels in the region of interest</code></div> <div><code>mean # average value (Digital Numbers)</code></div> <div><code>std_t # total standard deviation (Digital Numbers)</code></div> <div><code>std_rs # read+shot-noise standard deviation (Digital Numbers), computed from the difference of the image pair</code></div> <div><code>d_mean # average value of the dark (Digital Numbers)</code></div> <div><code>d_dsnu # Dark Signal Nonuniformity (Digital Numbers)</code></div> <div><code>std_s # Shot Noise (read noise removed) (Digital Numbers)</code></div> <div><code>k_adc # Sensitivity (note this the point-wise sensitivity, rather than fitted) (electrons/Digital Number)</code></div> <div><code>linearity # Linearity Error (point-wise distance to least-squares linear fit) (%)</code></div> <div><code>snr # Signal-to-Noise Ratio, derived from shot-noise (point-wise)</code></div> <div><code>snr_t # Signal-to-Noise Ratio, derived from total noise (point-wise)</code></div> <div><code>e- # Electron count, derived from sensitivity</code></div> <div><code>e-_noise # Electron shot-noise, derived from sensitivity</code></div> <br> <div>This data constitutes <strong>Figures 3, 4, 6, 7 & 9</strong> of the manuscript.</div> <br> <div><strong>Measured Sensor Properties</strong></div> <br> <div>The <code>measured_sensor_properties.csv</code> spreadsheet collects and averages the following metrics over the 4 experiments performed, to give the values for each channel, along with the expected values for LOROS in off-the-shelf configuration, gain-adjusted LOROS, and OROCHI downsampled to 12-bit resolution.</div> <br> <div><code>SNR Max</code></div> <div><code>Dynamic Range (dB)</code></div> <div><code>Dynamic Range (bits)</code></div> <div><code>Sensitivity (e-/DN)</code></div> <div><code>Saturation Capacity (e-)</code></div> <div><code>Saturation Capacity (DN)</code></div> <div><code>Read Noise (e-)</code></div> <div><code>Read Noise (DN)</code></div> <div><code>Nonlinearity (%)</code></div> <div><code>Dark Signal@30°C (e-/s)</code></div> <div><code>Dark Signal@30°C (DN/s)</code></div> <div><code>Bias (e-)</code></div> <div><code>Bias (DN)</code></div> <div><code>DSNU1288 (DN)</code></div> <div><code>DSNU1288 (e-)</code></div> <div><code>PRNU1288 (%)</code></div> <br> <div>This data constitutes <strong>Table 3</strong> of the manuscript.</div> <div> </div> <div> <h2>Dataset D: Dark Spectralon Validation</h2> This dataset contains the raw image and derived data used to demonstrate the ability of LOROS to measure the spectral reflectance of the 5% reflectance Spectralon calibration target (<a href="https://www.labsphere.com/wp-content/uploads/2021/09/SpectralonStandards.pdf" target="_blank" rel="noopener">SCT5</a>).<br> <div>The image data is hosted in the directory:</div> <br> <div>- <code>raw_data</code></div> <br> <div>and the processed data (e.g. reflectance products) are hosted in the directory:</div> <br> <div>- <code>processed_data</code></div> <br> <div><code>D_dark_spectralon_validation</code></div> <div><code>├── processed_data</code></div> <div><code>│ ├── SCT5</code></div> <div><code>│ └── SCT99</code></div> <div><code>├── raw_data</code></div> <div><code>│ ├── SCT5</code></div> <div><code>│ ├── SCT5_dark</code></div> <div><code>│ ├── SCT99</code></div> <div><code>│ └── SCT99_dark</code></div> <div><code>└── README.md</code></div> <br> <div><strong>Raw Data</strong></div> <br> <div>The raw data directory contains images captured of <code>SCT5</code> and <code>SCT99</code> (99% reflectance white Spectralon), and accompanying dark frames, hosted in the <code>SCT5_dark</code> and <code>SCT99_dark</code> frames respectively.</div> <br> <div>For each channel, 25 repeat images have been captured for the illuminated and dark frames.</div> <br> <div><strong>Processed Data</strong></div> <br> <div>The processed SCT99 and SCT5 datasets differ slightly. Both include:</div> <br> <div><code>├── img</code></div> <div><code>├── rfl</code></div> <div><code>└── rois</code></div> <br> <div>directories, with the SCT99 scene also including a <code>cal</code> directory.</div> <br> <div><code>img</code> hosts a set of <code>context</code> figures, showing the regions of interest selected, <code>fits</code> hosts the floating point mean (<code>ave</code>), standard error (<code>err</code>), standard deviation (<code>std</code>) and single-frame (<code>one</code>), all in units of Digital Number, after dark frame subtraction, flat-fielding and linearity correction. <code>uint8</code> hosts the same data rescaled to 8-bit resolution, for quick-view.</div> <br> <div><code>rfl</code> hosts the same set as <code>img</code>, after conversion to units of reflectance against the results of the SCT99 calibration (see §3.5 of the manuscript).</div> <br> <div><code>rois</code> gives plots of the mean and error of the reflectance spectrum of the region of interest, as well as the Signal-to-Noise Ratio, as well as the data for each region-of-interest (<code>roi_data</code>).</div> <br> <div><code>cal</code> also gives context figures for each channel region-of-interest, as converted to units of reflectance coefficients (1/DN/s).</div> </div> </div> </div> </div> </div>
Astroclimate measurements on several points over Eastern hemisphere in 2-mm and 3-millimeter atmospheric transparency windows using tipping radiometer
<p>We are presenting results of the atmospheric propagation observations which our research group has been conducting since 2012. Over this time, we have gathered statistical data of atmospheric propagation over a number of sites which possibly can be used for radio astronomical observations in the millimetre and sub-millimetre bandwidths. The Zenith opacity was studied by the atmospheric dip method in the 3 mm and 2 mm atmospheric windows. The dataset is recommended to use for estimation of atmospheric propagation for the purpose of radio astronomy and telecommunications.</p> <p>The “tau-meter” (named MIAP-2) allows us to estimate an integral absorption by using the atmospheric dip method. The hardware includes a radiometric system comprising two self-contained radiometers operating in two different bands of 84-99 GHz (λ ~ 3 mm) and 132-148 GHz (λ ~ 2 mm), a rotary support, a control, and a firmware system. The radiometers work in modulation mode within 36 Hz modulation frequency. Floating mirror allows radiometer to scan the sky in the interval of 0 – 88.5 elevation angles (6 total). The output record contains the voltages of synchronous detector for each angle in 2 wavebands. The voltage is proportional to brightness temperature of the sky in corresponding waveband. (It has negative value for technical reasons.)</p> <p>The dataset contains raw data observed by radiometer: Local date and time, a several detector voltages on different elevation angles for 2 wavebands, as well as service information header. The first 6 columns contain optical depth calculated by old method and it’s no more used in data processing since the new algorithm has been invented in 2018.</p>
Dataset - Dual-mode room temperature self-calibrating photodiodes approaching cryogenic radiometer uncertainty
<p>This page contains selected data from the publication "Dual-mode room temperature self-calibrating photodiodes approaching cryogenic radiometer uncertainty", Marit S Ulset <em>et al</em> 2022 <em>Metrologia</em> <strong>59</strong> 035008, <strong>DOI</strong> 10.1088/1681-7575/ac6a94.</p> <p> </p> <p>Description of files:</p> <p>Fig5.txt: Data for Fig 5a. Plotted values of non-equivalence as a function of beam position on the photodiode.</p> <p>Fig6.txt: Non-equivalence (gamma) in parts per million (ppm) and responsivity in mK/mW as a function of power level P in mW. </p> <p>Fig7.txt: Spectral directional emissivity determined under 10° with respect to the sample surface normal for Wafer P7 at 20°C. Uncertainty is given as standard uncertainty (k=1).</p> <p>Fig14.txt: Calculated time constants for the four different steps in a thermal heating cycle (electrical low, optical, electrical high, optical). The average value in the published paper contains an error, as one dataset was used twice. The file also shows correct the average value, when all datasets are used only once. A corrigendum was submitted to Metrologia, but it was considered not necessary, and hence not published. </p> <p>Fig15.txt: Plotted values for apparent IQD in parts per million (ppm) for three different calculation algorithms. Uncertainty is given as propagated type A standard uncertainty.</p> <p>Fig16.txt: Plotted values for measured IQD in parts per million (ppm) as a function of absorbed optical power in µW, for two different measurement methods - OC and FB method.</p>
Radiometer network dataset of 10 Hz spectral irradiance and derived variables (LIAISE campaign)
<p><strong>Description</strong></p> <p>Measurements from the spatial network of 15 radiometers deployed at the LIAISE field campaign in 2021.</p> <p>14 July 2021 until 28 July 2021.</p> <p><strong>Dataset Contents</strong></p> <ul> <li><em>rsds: </em>Total shortwave downwelling solar irradiance @ 10 Hz, 1 sec, and 1 minute resolution (Level 2, derived dataset)</li> <li><em>prw: </em>Total column integrated water vapour @ 1 sec resolution (Level 2, derived dataset)</li> <li><em>spectrum: </em>Pre-calibrated solar spectral irradiance measurements @ 10 Hz resolution (Level 1, source dataset)</li> <li><em>raw data:</em> straight from the sensors (Level 0)</li> </ul> <p><strong>Dataset Quality</strong></p> <p>All data is quality controlled and completed with metadata and quality flags. Level 2 data is calibrated against high quality references, and derived from Level 1 data.</p> <p>Methodology, performance, and usage all described in detail in an upcoming pre-print.</p> <p><strong>References and more info</strong></p> <ul> <li><a href="https://egusphere.copernicus.org/preprints/2022/egusphere-2022-726/">Radiometer reference paper (FROST)</a></li> <li>LIAISE <a href="https://liaise.aeris-data.fr/">campaign website</a>, <a href="https://liaise.aeris-data.fr/page-catalogue/?uuid=594dbc5d-986e-4679-b0ec-7d1e29b5cab9">LIAISE database</a></li> <li>Dataset description paper: <a href="https://arxiv.org/abs/2307.06980">pre-print on Arxiv</a></li> <li>Code to produce these data <a href="https://doi.org/10.5281/zenodo.10159129">on Zenodo</a></li> </ul> <p><strong>Version History</strong></p> <p>v1.2: fixed bug of incorrect wavelength labeling, all bands but 900 and 940 nm were affected. This only concerns the 10 Hz spectrum data, rest unchanged.</p> <p>v1.1: added raw (level 0) data, fixed typo in file name for the 10 Hz spectrum zip (L2 -> L1).</p>
Corresponding Dataset of Advanced Water Vapor Radiometer Data for Juno Gravity Science
<p><br> Corresponding Dataset of Advanced Water Vapor<br> Radiometer Data for Juno Gravity Science<br> Troposphere Calibrations<br> README FILE<br> Dustin Buccino<br> August 24, 2021<br> Jet Propulsion Laboratory<br> California Institute of Technology</p> <p>=============================================================================<br> INTRODUCTION<br> =============================================================================</p> <p> This dataset contains high rate data collected by the Advanced Water<br> Vapor Radiometer (AWVR) at the Deep Space Network's Goldstone Complex in <br> California. This dataset is provided in order to supplement the submitted<br> article to the "Radio Science" journal</p> <p> Buccino, D.R., et al (2021), Performance of Earth Troposphere <br> Calibration Measurements with the Advanced Water Vapor Radiometer <br> for the Juno Gravity Science Investigation, Radio Science, submitted<br> October 2021.</p> <p> ******************************************************************<br> * ANY USERS OF JUNO GRAVITY SCIENCE DATA ARE HIGHLY ENCOURAGED *<br> * TO INSTEAD REFER TO THE OFFICIAL ARCHIVE ON THE NASA PLANETARY *<br> * DATA SYSTEM. THIS SUPPLEMENTAL DATA SET DOES NOT CONTAIN ANY *<br> * GRAVITY SCIENCE DATA; IT ONLY CONTAINS HIGHER RATE AWVR DATA *<br> ******************************************************************</p> <p> Additional Juno Gravity Science Data may be found at the Planetary Data<br> System:</p> <p> Buccino, D. R. (2016). Juno jupiter gravity science raw data set <br> V1.0, JUNO-J-RSS-1 JUGR-V1.0, NASA planetary data system (PDS). <br> Retrieved from https://atmos.nmsu.edu/PDS/data/jnogrv_1001/<br> </p> <p>=============================================================================<br> ARCHIVE INFORMATION<br> =============================================================================</p> <p> This archive contains several data types, located within subdirectories.<br> <br> ROOT<br> `- PJ03/<br> <br> This directory contains all PJ-03 related data, including<br> path delay, path delay rate, calibration values, and frequency<br> residuals.<br> <br> `- PJ06/<br> <br> This directory contains all PJ-06 related data, including<br> path delay, path delay rate, and calibration values.<br> <br> `- PJ08/<br> <br> This directory contains all PJ-08 related data, including<br> path delay, path delay rate, and calibration values.<br> <br> `- ADEV/<br> <br> This directory contains troposphere scintillation Allan deviations<br> from each perijove. Files are named using the start time of the file,<br> in YYYYMMDDHHMM format, where YYYY is the year, MM is the month,<br> DD is the day of month, HH is the hour, and MM is the minute.<br> <br> `- STATS/<br> <br> This directory contains the Juno perijove frequency residual <br> statistics. Only one file is present in this directory.</p> <p>=============================================================================<br> FILE FORMAT<br> =============================================================================</p> <p> This dataset contains two separate file formats as described below.<br> ASCII plain-text files are given with the "*.txt" extension and the<br> comma-separated text files are given with the "*.csv" extension.</p> <p><br> TXT FILES<br> -------------------------------------------------------------------------</p> <p> The ASCII plain-text files are human-readable, space-delimited<br> text files. Each column is defined by a header row which provides<br> a description of each column. Additional comments may be optionally<br> specified by starting a row with the character "#".</p> <p> CSV FILES<br> -------------------------------------------------------------------------</p> <p> The Comma-Separated Value (CSV) files are plain-text files. Values in<br> each data file are separated using a comma ",". Each column is defined <br> by a header row which provides a description of each column.</p> <p><br> =============================================================================<br> FIGURE REPRODUCTION<br> =============================================================================</p> <p> This section will describe the data that are used to produce the figures<br> in the article that describes this dataset.</p> <p> FIGURE 1<br> -------------------------------------------------------------------------</p> <p> Figure 1 is a photograph and is not included in this dataset.</p> <p> FIGURE 2<br> -------------------------------------------------------------------------</p> <p> Figure 2 is produced using files within the "PJ03", "PJ06", and "PJ08"<br> directories.<br> <br> The first row of subfigures are produced by plotting the final three <br> columns of "pjXX_bt_zenith.txt" as a function of time.<br> <br> The second row of subfigures are produced by plotting the path delay<br> componets as a function of time from the "pjXX_pd_awvr.txt" and <br> "pjXX_pd_tsac.txt" data files.</p> <p><br> FIGURE 3<br> -------------------------------------------------------------------------</p> <p> Figure 3 is produced using files within the "PJ03", "PJ06", and "PJ08"<br> directories.<br> <br> The first row of subfigures are produced by plotting the last column<br> of "pjXX_freq_awvr.txt" and "pjXX_freq_tsac.txt".<br> <br> The second row of subfigures are produced by differencing the values.<br> <br> FIGURE 4<br> -------------------------------------------------------------------------</p> <p> Figure 4 is produced using files within the "ADEV" directory.<br> <br> Each individual file within the "ADEV" directory contains the Allan <br> deviation. Each Allan deviation is plotted on a log-log scale and is<br> color-mapped to the calendar date. The file naming convention gives<br> the calendar date of data collection, with the filenames starting with<br> YYYYMMDD, where YYYY is 4-digit year, MM is 2-digit month, and DD is<br> 2-digit day of month in UTC time.</p> <p> FIGURE 5<br> -------------------------------------------------------------------------</p> <p> Figure 5 is produced using files within the "PJ03" directory. The<br> frequency residual from the "pj03_resid_awvr.csv" and<br> "pj03_resid_tsac.csv" is simply plotted as a function of time.<br> <br> FIGURE 6<br> -------------------------------------------------------------------------</p> <p> Figure 6 is produced using files within the "STATS" directory. This <br> directory contains a single file, "AWVR_stats_jul2021_v3.csv" and<br> contains the root-mean-square of the frequency residuals from Juno <br> perijove passes. The root-mean-square of the frequency residuals<br> are plotted using a bar plot and the percent improvement is plotted <br> with a scatterplot.<br> </p> <p>=============================================================================<br> ACKNOWLEDGMENTS<br> =============================================================================</p> <p>This work was carried out at the Jet Propulsion Laboratory, <br> California Institute of Technology, under contract with the National <br> Aeronautics and Space Administration. Government sponsorship acknowledged.</p> <p>=============================================================================<br> PRIMARY POINT OF CONTACT<br> =============================================================================</p> <p>Dustin Buccino<br> Jet Propulsion Laboratory<br> Planetary Radar and Radio Sciences<br> (818) 393 - 1072<br> Dustin.R.Buccino@jpl.nasa.gov</p> <p>=============================================================================<br> ACRONYMS AND ABBREVIATIONS<br> =============================================================================</p> <p> ASCII American Standard Code for Information Interchange<br> DOY Day of year<br> DSN Deep Space Network<br> JPL Jet Propulsion Laboratory<br> NAIF Navigation Ancillary Information Facility<br> NASA National Aeronautics and Space Administration<br> PDS Planetary Data System<br> RS Radio Science<br> RSS Radio Science Subsystem<br> SIS Software Interface Specification<br> TXT Text file<br> UTC Universal Time, Coordinated<br> </p>
Data support to Characteristics of persistent heavy fog events retrieved by microwave radiometer profiler during 2010–2013 in northern China
<p>The dataset include the observation data from microwave radiometer, tethered balloon and the surface.</p>
Data production of ''Lunar Titanium and Frequency‐Dependent Microwave Loss Tangent ... LRO Diviner Lunar Radiometers"
<p>These are the image data of "Lunar Titanium and Frequency‐Dependent Microwave Loss Tangent as Constrained by the Chang'E‐2 MRM and LRO Diviner Lunar Radiometers".</p>
Dataset for Angular Dependence and Spatial Distribution of Jupiter's Centimeter-Wave Thermal Emission from Juno's Microwave Radiometer
<p>This dataset comprises all processed data (in HDF5 format) used in figures and discussion in the paper "Angular Dependence and Spatial Distribution of Jupiter's Centimeter-Wave Thermal Emission from Juno's Microwave Radiometer".</p>
Dataset for the paper "Ensemble optimization retrieval algorithm of hydrometeor profiles for the Ice Cloud Imager submillimeter-wave radiometer'
<p>1. "Retrieval_Database" file contains the pre-calculated retrieval database.</p> <p>2. "Algorithm_Input" file contains the input of the ensemble optimization retrieval algorithm.</p> <p>3. "TrueProfiles" file contains the true profiles corresponding to the input brightness temperatures. </p> <p>4. "Algorithm_Output" file contains the output of the ensemble optimization retrieval algorithm.</p>
Lunar heat flow constrained from Chang'E-2 Microwave Radiometer and Diviner observations at the Moon's south pole
<p>This data repository includes the Chang'E-2 microwave radiometer data which is provided by the Ground Research and Application System of Chinese Lunar Exploration Program.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.