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679 results for “retrieval”
Retrieval and Validation of Total Seasonal Liquid Water Amounts in the Percolation Zone of Greenland Ice Sheet Using L-band Radiometry
<p>This repository contains the dataset associated with the analyses presented in the following study:</p> <p>Hossan, A., Colliander, A., Vandecrux, B., Schlegel, N.-J., Harper, J., Marshall, S., and Miller, J. Z.: <em>Retrieval and validation of total seasonal liquid water amounts in the percolation zone of the Greenland Ice Sheet using L-band radiometry</em>, <strong>The Cryosphere</strong>, 19, 4237–4258, <a href="https://doi.org/10.5194/tc-19-4237-2025" target="_new">https://doi.org/10.5194/tc-19-4237-2025</a>, 2025.</p> <p>In this study, we demonstrated the capability of NASA's Soil Moisture Active Passive (SMAP) L-band radiometer to estimate surface and subsurface liquid water amounts (LWA) in the percolation zone of the Greenland Ice Sheet. The article presents our initial retrieval algorithm, validation results, and highlights the potential for developing a Greenland-wide LWA data product.</p> <p><strong>Contents of this Repository</strong></p> <p>This repository includes:</p> <ul> <li><strong>SMAP-retrieved daily, vertically integrated LWA gridded initial data products</strong> (2015–2023), derived from enhanced-resolution SMAP TB observations. These data include spatial coordinates, acquisition dates, and a melt flag indicator.</li> <ul> <li>SMAP_LWA_time_series_AWS contains daily time series at a AWS location (point observation)</li> <li>Samimi_EBM_LWA_time_series_AWS contains corresponding time series of LWA estimated by Samimi model forced by PROMICE AWS.</li> <li>GEMB_LWA_time_series_AWS contains corresponding time series of LWA estimated by GEMB model forced by PROMICE AWS</li> <li>The locations and name ID of the AWS are given in AWS.txt/xls file</li> <li>L_band_LWA_yyyy.nc files contain daily LWA and TB data over the entire percolation zone</li> </ul> <li><strong>Corresponding vertically polarized brightness temperature (TBV) data</strong>, including their winter mean and standard deviation.</li> <li><strong>Model-based LWA estimates used for validation</strong>, including outputs from:</li> <ul> <li>The locally calibrated <strong>Energy and Mass Balance (EMB)</strong> model.</li> <li>The <strong>Glacier Energy and Mass Balance (GEMB)</strong> model within NASA’s <strong>Ice-sheet and Sea-level System Model (ISSM)</strong>.</li> </ul> </ul> <p><strong>Retrieval and Validation Codebase</strong></p> <p>The MATLAB scripts and tools used for the microwave retrieval algorithm, radiative transfer modeling, inversion process, and comparative validation with in situ AWS-driven model outputs are available at the following GitHub repository:</p> <p>🔗 <a href="https://github.com/HossanAlamgir/SMAP_MWA_Retrieval_and_Validation_GrIS" target="_new">https://github.com/HossanAlamgir/SMAP_MWA_Retrieval_and_Validation_GrIS</a><br><em>(Last accessed: 17 September 2025)</em></p> <p>The codebase includes:</p> <ul> <li>Preprocessing routines for SMAP TB data.</li> <li>Implementation of the radiative transfer forward model.</li> <li>Inversion and threshold-based detection algorithms.</li> <li>Validation scripts for comparison against AWS-forced EMB and GEMB model outputs.</li> </ul> <p><strong>Relevance</strong></p> <p>These data and methods support ongoing efforts to improve surface mass balance (SMB) estimates and enhance projections of Greenland’s contribution to global sea level rise.</p> <p> </p>
Supplementary material for "Phase Retrieval from overexposed PSF" article
<p>Supplementary material for "Phase Retrieval from overexposed PSF" article, submitted to Optics Communications (http://dx.doi.org/10.2139/ssrn.4267855).</p> <p>The pdf files present the results of the phase retrieval task as described in Sections 5 and 6 of the article and provide an easy-to-navigate tool for browsing through the parameter space of the simulation and of the algorithm. Click on the parameter values in the right-hand side of the browser page to navigate to the corresponding result.</p>
AOD and FMF dataset retrieved from NNAero in eastern and northern China from 2010 to 2020
<p>In this work, the development of an artificial Neural Network for AEROsol retrieval (NNAero) is presented. NNAero uses data from the NASA MODerate resolution Imaging Spectroradiometer (MODIS) flying on the NASA Terra and Aqua satellites. The MODIS-derived spectral reflectances of solar radiation at the top of the atmosphere (TOA) and at the surface were used together with ground-based Aerosol Robotic Network (AERONET) measurements of Aerosol Optical Depth (AOD) and FMF to train a Convolutional Neural Network (CNN) for the joint retrieval of FMF and AOD. The NNAero results over northern and eastern China were validated against an independent reference AERONET dataset (i.e. not used in training the CNN). The results show that 68% of the NNAero AOD values are within the MODIS expected error (EE) envelope over land of ± (0.05 + 15%), which is similar to the results from the MODIS Deep Blue (DB) algorithm (63% within EE), and both are better than the Dark Target (DT) algorithm (31% within EE). The validation of the NNAero FMF vs AERONET data shows a significant improvement with respect to the DT FMF, with Root Mean Squared Prediction Errors (RMSE) of 0.1567 (NNAero) and 0.34 (DT). The NNAero method shows the potential of improved retrieval of the FMF.</p> <p>If you use this dataset for related scientific research,please cite the below-listed corredponding references first(Chen X et al. ,RSE, 2020 )</p> <p>Chen, X., de Leeuw, G., Arola, A., Liu, S., Liu, Y., Li, Z., &amp; Zhang, K. (2020). Joint retrieval of the aerosol fine mode fraction and optical depth using MODIS spectral reflectance over northern and eastern China: Artificial neural network method. Remote Sensing of Environment, 249, 112006.<a href="https://doi.org/10.1016/j.rse.2020.112006">https://doi.org/10.1016/j.rse.2020.112006</a></p>
Four-dimensional horizontal wind fields retrieved from FY4B GIIRS during typhoon Mulan (2022)
<p>This dataset is the four-dimensional (time, pressure, u, v) wind fields retrieved from FY4B GIIRS observations during typhoon Mulan (2022) with a 15-minute interval. </p>
Fig. 3 in A new technique and software to optimize compression and data retrieval in the Material Examined section of taxonomic publications
Fig. 3. Resulting order after multiple sorting according to the columns typeStatus, country, state, city and locality, in that order. This sequence of column names corresponds to the variable Sorting Order in the Gridit software.
Fig. 2 in A new technique and software to optimize compression and data retrieval in the Material Examined section of taxonomic publications
Fig. 2. Columns swapped according to the desired order for the information in the final text. The order of the column names (scientificName, typeStatus, sex, country, etc.) corresponds to the variable Display Order in the Gridit software.
Fig. 1 in A new technique and software to optimize compression and data retrieval in the Material Examined section of taxonomic publications
Fig. 1. Spreadsheet data from some of the specimens of Distictus tibialis (Brullé, 1846) cited in Supeleto et al. (2019). Mandatory columns and column names in the Gridit software marked in red.
Fig. 5 in A new technique and software to optimize compression and data retrieval in the Material Examined section of taxonomic publications
Fig. 5. Data from the spreadsheet in Fig. 4, excluding header and the column scientificName, copied and pasted into a text editor; resulting tabs (cells) replaced with comma. Sequences of "ib" and "?" in each row were grouped together with a preceding number (e.g., "11ib" in row 2) that indicates the total of subsequent repeats. Each row represents a unique collecting event. The final text generated from this file is shown in Table 2.
Fig. 4 in A new technique and software to optimize compression and data retrieval in the Material Examined section of taxonomic publications
Fig. 4. Repetitions in each column identified with the ib code for all columns (strict usage of the gridsetting technique).
Bibliographic data on datasets affiliated to Poznan University of Technology and indexed in Data Citation Index (retrieved by Web of Science service in January 2023))
<p>The file contains the number of datasets published by the researchers affiliated to Poznan University of Technology and indexed in Data Citation Index provided by Web of Science (database updated 10.01.2023). The Search was performed using the name of institution in the 'Affiliation' field. Dataset contains two files in two diffrent formats: plain text and xls.</p>
Hyperspectral Mixture Models in the CHIME Mission Implementation for Topsoil Texture Retrieval
<p>This dataset provides the steps of the image analysis techniques used to soil texture classes retrieval related to the paper 'Hyperspectral Mixture Models in the CHIME Mission Implementation for Topsoil Texture Retrieval' in wich the principles of the spectral mixture analyses are used.</p>
Probabilistic Estimates of Convective Instability in Individual MRO-MCS Temperature Retrievals
<p>Heavens, Nicholas G. (2023), “Probabilistic Estimates of Convective Instability in Individual MRO-MCS Temperature Retrievals”, Zenodo, V1, doi: 10.5281/zenodo.7828010.</p> <p> </p> <p><strong>Title:</strong> Probabilistic Estimates of Convective Instability in Individual MRO-MCS Temperature Retrievals</p> <p> </p> <p><strong>Author:</strong> Nicholas G. Heavens, Space Science Institute, Boulder, CO, USA and London, UK (nheavens@spacescience.org)</p> <p> </p> <p>Date: 15 April 2023</p> <p> </p> <p><strong>Overview</strong>: This dataset contains analyses of convective instabilities in individual MRO-MCS temperature profiles for a manuscript currently under review at the <em>Planetary Science Journal</em>.</p> <p> </p> <p>Averaged data products based on these analyses already have been archived as Heavens (2022):</p> <p> </p> <p>The data products are estimates of the probability of convective instability derived from diagnosis of convective instability in retrieved temperature profiles from Mars Climate Sounder on board Mars Reconnaissance Orbiter (MRO-MCS)</p> <p> </p> <p>If you are using this dataset and are feeling confused or wish there were some additional information from the article in this dataset, please contact me. A complete account of the contents and a restatement of this description is included as MCS_CONVINST_INDFILE_SURVEY_DESC.PDF</p> <p> </p> <p>Acknowledgments: The archiving of this dataset is supported by NASA’s Mars Data Analysis Program (80NSSC19K1215).</p> <p> </p> <p><strong>Contents:</strong></p> <p> </p> <ol> <li><em>MCS_CONVINST_INDFILE_SURVEY_DESC.pdf</em></li> </ol> <p>A copy of this description in PDF format.</p> <p> </p> <ol> <li><em>MCSconvinst_indivncfiles.tar.gz</em></li> </ol> <p>This compressed tar archive contains 63196 Network Common Data Format (netCDF) files (~ 20 GB uncompressed) named in the form:</p> <p> </p> <p>xxxxxx_convinst.nc, where xxxxxx is the re-centered orbit number of MRO defined by Heavens et al. (2018). The contents of this file type is:</p> <p> </p> <p>Variables:</p> <p> </p> <ol> <li>entrynum: Number to index retrievals in the file</li> <li>Pgrid: The pressure (Pa) level grid</li> <li>lapserateunstableprob_rets: The probability (%) of convective instability in an individual profile at a given pressure level</li> <li>altrets: The altitude grid (km) of each individual profile in relative to the Mars Orbiter Laser Altimeter (MOLA) areoid</li> <li>ampmrets: A flag to indicate whether MRO is in its ascending PM orbit (~ 15:00 LST at the Equator) or in its descending AM orbit (~ 3:00 at the Equator). Precise local time can be estimated by using the MRO-MCS dataset directly or using sclkrets.</li> <li>latrets: The latitude of the profile in degrees north.</li> <li>longrets: The longitude of the profile in degrees east</li> <li>loopflagrets: A flag to indicate the presence of nearby loops; flag = 1 indicates the presence of a nearby loop.</li> <li>lsrets: Areocentric longitude (degrees) of each individual profile</li> <li>myrets: Mars Year of each individual profile</li> <li>sclkrets: SCLK (seconds since 1 January 1980 00:00:00 UTC) for each individual profile</li> </ol> <p> </p> <p><strong>References</strong></p> <p> </p> <p>Heavens, N. (2022), Epitomic Data for a Multiannual Record of Convective Instability in Mars's Middle Atmosphere from the Mars Climate Sounder, Mendeley Data, V1, doi: 10.17632/fmgvbpwdk7.1</p> <p> </p> <p>Heavens, N.G., A. Kleinböhl, M.S. Chaffin, J.S. Halekas, D.M. Kass, P.O. Hayne, D.J. McCleese, S. Piqueux, J.H. Shirley, and J.T. Schofield, 2018, Hydrogen escape from Mars enhanced by deep convection in dust storms, <em>Nature Astron</em>., 2, 126–132, doi: 10.1038/s41550-017-0353-4.</p>
Digital Humanities Retrieval Evaluation Dataset for Art, Architecture and Life Sciences (DHREAAL)
<p>This dataset, titled "Digital Humanities Retrieval Evaluation Dataset for Art, Architecture, and Life Sciences," emphasizes semantic retrieval, including ground truth data for both content-based and instance-based image retrieval. Utilized to compare various promising approaches and refine the evaluation method, this dataset serves as a precursor to a more extensive dataset. Additional details can be found in the dataset's related publication.</p> <p>Currently, the dataset is still under development. In its next iteration, the dataset will include options for retrieving image syntax and temporal aspects, in addition to the semantics.</p>
MAGARA: A Multi-Angle Geostationary Aerosol Retrieval Algorithm
<p>MAGARA, NOAA bias-corrected, and AERONET datasets used for analysis of manuscipt titled "MAGARA: A Multi-Angle Geostationary Aerosol Retrieval Algorithm".</p>
MarrowDLD: a microfluidic method for label-free retrieval of fragile bone marrow cells
<p>We introduce here a label-free cytometry microsystem, MarrowDLD, based on deterministic lateral displacement. MarrowDLD enables the isolation of fragile cells based on intrinsic size properties while preserving their viability and functionality. Bone marrow adipocytes, obtained from mouse and human stromal line differentiation, as well as megakaryocytes, from primary human CD34+ hematopoietic stem and progenitor cells, were used for validation.</p>
Dataset for surface spectral emissivity retrieval
<p>We provide two databases, one for clear sky conditions and another for cloudy sky conditions, for both January and July 2021. Each database consists of input files for the CLAIM (CLouds and Atmospheric Inversion Module) code, as described in Sgheri et al. 2022, as well as output files. The input files contain data regarding the surface, atmospheric composition, and potential clouds. Additionally, the output files encompass computed variables essential for our analysis, including errors, precipitable water vapor, surface temperature, and thermal contrast.</p>
Supplementary data for: Comparison of optical flow derivation techniques for retrieving tropospheric winds from satellite image sequences
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Measuring semantic memory using associative and dissociative retrieval tasks
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NH and ME Landsat chlorophyll-a retrieval algorithms and in situ measurements 2000 (Landsat 7), 2013-2015 (Landsat 8)
Predicting algal blooms has become a priority for scientists, municipalities, businesses, and citizens. Remote sensing offers solutions to the spatial and temporal challenges facing existing lake research and monitoring programs that rely primarily on high-investment, in situ measurements. Techniques to remotely measure chlorophyll-a (chl-a) as a proxy for algal biomass have been limited to specific large water bodies in particular seasons and narrow chl-a ranges. Thus, a first step toward prediction of algal blooms is generating regionally robust algorithms using in situ and remote sensing data. This study explores the relationship between in-lake measured chl-a data from Maine and New Hampshire lakes and remotely-sensed chl-a retrieval algorithm outputs. Landsat 8 images were obtained and then processed after required atmospheric and radiometric corrections. Six previously developed algorithms were tested on a regional scale on eleven scenes from 2013-2015 covering 192 lakes. Additionally, data related to one Landsat 7 scene (2000) are included in this data set. Boucher, J, K.C. Weathers, H. Norouzi, B. Steele. In Press. Assessing the effectiveness of Landsat 8 chlorophyll-a retrieval algorithms for regional freshwater monitoring. Ecological Applications.
Near-global CFC-11 retrieved from Atmospheric Infrared Sounder (AIRS) from 2003 to 2018
<p>CFC-11 surface mole fractions were retrieved from monthly clear-sky nadir-view Atmospheric Infrared Sounder (AIRS) observations on 30-deg by 10-deg over 55S-55N. </p> <p>Dataset is available at https://github.com/Huang-Group-UMICH/CFC-11-retrievals-from-AIRS.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.