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679 results for “retrieval”
PM2.5 4 days forecast from December, 22 2020 retrieved from Copernicus Monitoring Service
<p>Dataset used in the Galaxy Pangeo tutorials on Xarray.</p> <p>Data is in netCDF format and is from <a href="https://ads.atmosphere.copernicus.eu/">Copernicus Air Monitoring Service</a> and more precisely PM2.5 (<a href="https://en.wikipedia.org/wiki/Particulates#Size,_shape_and_solubility_matter">Particle Matter < 2.5 μm</a>) 4 days forecast from December, 22 2021. This dataset is very small and there is no need to parallelize our data analysis. Parallel data analysis with Pangeo is not covered in this tutorial and will make use of another dataset.</p> <p> </p> <p><strong>This dataset is not meant to be useful for scientific studies.</strong></p>
Training and test data for retrievals based on MiRAC-P observations during MOSAiC
<p>The dataset consists of one netCDF file that contains the entire training and test data for the retrieval of integrated water vapour (prw) from brightness temperatures (tb) measured by the MiRAC-P (microwave radiometer for Arctic clouds, aka. LHUMPRO-243-340). A neural network retrieval has been developed to derive the prw. The trained retrieval is applied on the MiRAC-P observations gathered onboard the research vessel Polarstern during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. For the data to be specialized on Arctic conditions they are based on ERA-Interim reanalysis. An IDL-based radiative transfer model has been used to simulate brightness temperatures. The elevation angle (ele) is always 90° because the MiRAC-P performed zenith scans only throughout the MOSAiC campaign.</p>
FORUM end-to-end simulator emissivity retrievals
<p>The dataset contains a set of netCDF files for the cases treated in the paper "Emissivity Retrievals with FORUM's End-to-end Simulator: Challenges and Recommendations". </p> <p>The OUTPUTS/ folder has three sub-folders:</p> <ul> <li>Final/ has all the full FEES retrieval run outputs used for the analysis</li> <li>SGM/ has the synthetic scene outputs shown in Figure 2</li> <li>transmittance_results/ has the transmittance for scene 67N 18E (not a default output of the FEES) shown in Figure 3</li> </ul> <p>Each run folder in Final/ contains a set of FEES output files, out of which the following two are most important for the analysis:</p> <ul> <li>fe2es_l2m_out_d0001.nc - the output of the L2M retrieval module</li> <li>fe2es_sgm_ref_d0001_acq3.nc - the reference atmosphere (synthetic scene) for that case</li> </ul> <p>In addition the final emissivity jacobian d_emi_num_full.dat and the atmosphere steps in the iterative retrieval process 00x_L2M_OUT.nc are also used in the analysis of the paper and are included in the folders together with additional files that are relevant for the retrieval settings.</p> <p>The code and additional data for the analysis in the paper can be found here: https://github.com/mayaby/FORUM_emissivity </p>
Data: Spatio-temporal prediction of soil moisture using soil maps, topographic indices and SMAP retrievals
<p><strong>Data used in:</strong></p> <p>Schönauer, M., Prinz, R., Väätäinen, K., Astrup, R., Pszenny, D., Lindeman, H., et al. (2022). Spatiotemporal prediction of soil moisture using soil maps, topographic indices and SMAP retrievals. <em>International Journal of Applied Earth Observation and Geoinformation</em>, 102730. doi: 10.1016/j.jag.2022.102730</p>
Supplementary Information: Exoplanet atmosphere retrievals in 3D using phase curve data with ARCiS: application to WASP-43b. Chubb and Min, A&A (2022).
<p>Supplementary information containing additional figures of the journal article 'Exoplanet atmosphere retrievals in 3D using phase curve data with ARCiS: application to WASP-43b' by K. L. Chubb and M. Min, published in Astronomy & Astrophysics (2022).</p>
Commodity Dataset | Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform
<p>(Commodity data in raster format) Supplementary materials for “Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform” that had been published on Land MDPI (2020). doi:<a href="https://doi.org/10.3390/land9100377">10.3390/land9100377</a> </p> <p>The data included:</p> <p>1) Raster data of commodity maps (TIFF Compressed in ZIP)</p> <p>2) READ ME for the dataset (DOCX)</p> <p>3) Legend for raster data in ArcGIS Format (LYR)</p> <p> </p>
Data Supplement for ApJ Paper: Stumbling over planetary building blocks: AU Microscopii as an exampleof the challenge of retrieving debris-disk dust properties
<p>Computational data set underlying a manuscript analyzing visible to near infrared wavelength Hubble-STIS spectrographic coronagraph observations of the AU Microscopii debis disk. To interpret this multi-wavelength data set, we calculated light scattering properties of three different debris disk dust grain shape models: spheres, porous spheres, and more realistic irregular agglomerated debris particles. Calculations of scattering efficiency and phase function were done over a range of dust grain sizes and material refractive indices.</p> <p>This bundle contains three .hdf table files. Each table file contains a size parameter array (1D), complex refractive index array (1D), scattering efficiency array (1D), and scattering phase function array (2D). The 1st dimension of each array is the same size. The 2nd dimension of the scattering phase function is 60, the number of scattering angles calculated at an angular resolution of 3 degrees.</p>
Fig. 3 in The Relationship Between Fish Length And Otolith Size And Weight Of The Australian Anchovy, Engraulis Australis (Clupeiformes, Engraulidae), Retrieved From The Food Of The Australasian Gannet, Morus Serrator (Suliformes, Sulidae), Hauraki Gulf, New Zealand
Fig. 3. Fish total length relationship with: A — otolith length; B — otolith width; C — otolith weight.
Fig. 2. A in The Relationship Between Fish Length And Otolith Size And Weight Of The Australian Anchovy, Engraulis Australis (Clupeiformes, Engraulidae), Retrieved From The Food Of The Australasian Gannet, Morus Serrator (Suliformes, Sulidae), Hauraki Gulf, New Zealand
Fig. 2. A, Engraulis australis, 138 mm TL; B, Otolith of Engraulis australis, 135 mm TL showing otolith sizes, length (OL) and width (OW).
Bangla Information Retrieval Test Collection
<p>There are several IR test collections available in English (e.g. http://ir.dcs.gla.ac.uk/resources/test_collections/). Unfortunately, there is no Gold standard dataset available to test the effectiveness of Bangla IR. So, we have created a document collection containing 182 short stories, novels, and essays written by Rabindranath Tagore11 and 1000 newspaper articles published in 2013 crawled from the Bangla newspaper Prothom Alo12. The collection contains 100 newspaper articles each from one of the ten categories: বাংলাদেশ/ Bānlādēśa(EN: `Bangladesh'), খেলা/ khēlā(EN: `sports'), বিজ্ঞান ও প্রযুক্তি/ bijñāna ō prayukti(EN: `technology'), বিনোদন/ binōdana(EN: `entertainment'), আন্তর্জাতিক/ āntarjātika(EN: `international'), অর্থনীতি/ arthanīti(EN: `economy'), জীবনযাপন/ jībanayāpana(EN: `life-style'), মতামত/ matāmata(EN: `opinion'), শিক্ষা/ śikṣā(EN: `education') and আমরা/ āmarā(EN:`we-are'). There are 94 queries in the dataset, 26 queries belonging to complexity levels 1 and 2, 19 queries in complexity level 3 and 23 queries in complexity level 4. The definition of the complexity level of a query is described below:</p> <p>Complexity Level 1: The query contains exact words, phrases or sentence from the document.</p> <p>Complexity Level 2: The query is not present as it is in the document. There is a slight deviation.</p> <p>Complexity Level 3: The query is a generalised phrase capturing the overall story or the document’s theme.</p> <p>Complexity Level 4: It is a general query not related to any specific document.</p>
DATASET - Improving Remote Sensing of Extreme Events with Machine Learning: Application to IASI LST Retrievals
<p>Data for experiments presented in the paper "Improving Remote Sensing of Extreme Events with Machine Learning: Application to IASI LST Retrievals" </p>
A simple model for daily basin-wide thermodynamic sea ice thickness growth retrieval: Data
<p>Data for:</p> <p>Anheuser, J., Liu, Y., and Key, J.: A daily basin-wide sea ice thickness retrieval methodology: Stefan's Law Integrated Conducted Energy (SLICE), The Cryosphere Discuss. [preprint], <a href="https://doi.org/10.5194/tc-2021-333">https://doi.org/10.5194/tc-2021-333</a>, in review, 2021.</p> <p> </p> <p>Scripts for producing data and figures can be found at:</p> <p>https://doi.org/10.5281/zenodo.6561431</p> <p> </p> <p> </p>
Data archive for the peer-reviewed journal article "Information content and aerosol property retrieval potential for different types of in situ polar nephelometer data"
<p>Data archive accompanying the peer-reviewed journal article "Information content and aerosol property retrieval potential for different types of in situ polar nephelometer data". This article was accepted for publication in the journal <em>Atmospheric Measurement Techniques</em> in 2022. The original contributions presented in the study are included in the article and its supplementary information. The GRASP-OPEN model was used to perform forward calculations: this model is publicly available on the official GRASP website (https://www.grasp-open.com/; last access: 14 September, 2022). The specific GRASP-OPEN model outputs that were used for the study are contained in this data archive. </p>
RAL IMS retrieval of SO2 and sulphates (January to April 2022)
<p><strong>RAL IMS retrieval of SO2 and sulphates</strong></p> <p><strong>Description of the product</strong></p> <p>The RAL (Rutherford Appleton Laboratory) Infrared/Microwave Sounder (IMS) retrieval core scheme (Siddans, 2019) uses an optimal estimation spectral fitting procedure to retrieve atmospheric and surface parameters jointly from co-located measurements by IASI (Infrared Atmospheric Sounding Interferometer), AMSU (Advanced Microwave Sounding Unit) and MHS(Microwave Humidity Sounder) on MetOp-B spacecraft, using RTTOV 12 (Radiative Transfer for TOVS)(Saunders et al., 2017) as the forward radiative transfer model. The use of RTTOV 12 enables the quantitative retrieval of volcanic-specific aerosols (sulphate aerosol) and trace gases (SO2). The present dataset includes IMS SO2 and sulphate aerosols retrievals from its near-real time implementation. The IMS scheme retrieves the SO2 in the sensitive region around 1100-1200 cm<sup>−1</sup>, in ppbv assuming a uniform vertical mixing ratio. It retrieves sulphate-specific AOD (Aerosol Optical Depth) at 1170 cm<sup>−1</sup> (i.e. the peak of the mid-infrared extinction cross section (Sellitto and Legras, 2016)), assuming a Gaussian extinction coefficient profile shape peaking at 20 km altitude, with 2 km full-width half-maximum. The bulk of the spectroscopic information on SO2 and sulphate aerosols, in the IMS scheme, thus comes from the IASI Fourier transform spectrometer (Clerbaux et al., 2009).</p> <p>We refer to the two retrieved products as IMS SO2 and IMS SA OD.</p> <p><strong>References</strong></p> <p>Clerbaux, C., Boynard, A., Clarisse, L., George, M., Hadji-Lazaro, J., Herbin, H., Hurtmans, D., Pommier, M., Razavi, A., Turquety, S., Wespes, C., and Coheur, P.-F.: Monitoring of atmospheric composition using the thermal infrared IASI/MetOp sounder, Atmospheric Chemistry and Physics, 9, 6041–6054, https://doi.org/10.5194/acp-9-6041-2009, 2009.</p> <p>Saunders, R., Hocking, J., Rundle, D., Rayer, P., Hayemann, S., Matricardi, A., Lupu, C., Brunel, P., and Vidot, J.: RTTOV-12 SCIENCE AND VALIDATION REPORT; Version : 1.0, Doc ID : NWPSAF-MO-TV-41, https://nwp-saf.eumetsat.int/site/download/documentation/rtm/docs_rttov12/rttov12_svr.pdf, 2017.</p> <p>Sellitto, P. and Legras, B.: Sensitivity of thermal infrared nadir instruments to the chemical and microphysical properties of UTLS secondary sulfate aerosols, Atmospheric Measurement Techniques, 9, 115–132, https://doi.org/10.5194/amt-9-115-2016, 2016.</p> <p>Siddans, R.: Water Vapour Climate Change Initiative (WV-CCI) - Phase One, Deliverable 2.2; Version 1.0, https://climate.esa.int/documents/1337/Water_Vapour_CCI_D2.2_ATBD_Part2-IMS_L2_product_v1.0.pdf, 2019.</p> <p><strong>Description of the data</strong></p> <p>The archive IMS-2022.tgz contains level 3 daily gridded files for the two retrieved products IMS SO2 and IMS SA OD in the period 13 January to 30 April 2022. A few days are missing between 9 March and 13 March. The first 8 letters of the name of each file contain the date. There are 4 files per day as the two products are in separate files and there is a file collecting day-time orbits and another one for night-time orbits every day.</p> <p>For the 28 April 2022, the four files are</p> <p>20220428_ims_metopb_tir_qnrt_aot0_day_global_g0.5_qc0.nc SA OD day-time orbits</p> <p>20220428_ims_metopb_tir_qnrt_aot0_night_global_g0.5_qc0.nc SA OD night-time orbits</p> <p>20220428_ims_metopb_tir_qnrt_so2_day_global_g0.5_qc0.nc SO2 day-time orbits</p> <p>20220428_ims_metopb_tir_qnrt_so2_night_global_g0.5_qc0.nc SO2 night-time orbits</p> <p>The names for other dates can be derived by changing the first 8 letters.</p> <p>The format is netdcf4 that is readable with many programming languages and graphics packages.</p> <p>The data are on a [-90,90] x [-180,180] lat x lon grid with resolution 0.25°, that is a 720 x 1440 array of centered values.</p> <p>For both SO2 and SA OD, the values are in the ‘data’ variable. The variable ‘qa_value’ is a quality control value used to screen values for plotting; 0 means do not plot; -1 means mask is not defined so the mask is not used (data will be plotted).</p> <p>SO2 units are ppbv (assuming a uniform mixing ratio vertical profile ). SA OD is an optical depth with no unit.</p> <p><strong>Reading software</strong></p> <p>A python package to read and process the data is available at https://github.com/bernard-legras/ASTuS/tree/master/IMS and in the IMS-reader.tgz archive</p>
OMPS-LP ozone profiles retrieved at the University of Bremen - IUP
<p>This data set includes ozone profiles retrieved at the University of Bremen (IUP) from OMPS-LP observations. The retrieval range is 8.5 to 60.5 km. The format is harmonized according to ESA CCI guidelines. For detailed information about the retrieval and the data set, see "Retrieval of ozone profiles from OMPS limb scattering observations", Arosio, C. et al. (2018).</p>
TEAMx-PC22 (TEAMx pre-campaign 2022) - Radial velocity and coplanar-retrieved horizontal wind fields from KITcube Leosphere/Vaisala Windcube WLS200s-124 and WLS200s-159
<p><strong>Abstract</strong></p> <p>This data set was collected during the TEAMx pre-campaign in summer 2022 (TEAMx-PC22) in the Inn Valley Target Area, Austria.</p> <p><strong>Data description</strong></p> <p>This data set is comprised of a single TAR file containing 1536 hourly NetCDF files. Within these, radial velocities from KITcube Leosphere/Vaisala Windcube WLS200s-124 and WLS200s-159 Doppler wind lidars, as well as coplanar-retrieved horizontal wind speed components in their common scanning plane are stored. </p> <p>The time period is 29 June 2022, 00:00 UTC - 31 August 2022, 23:58 UTC.</p> <p>More details about the variables, lidar locations, scan details, as well as post-processing can be found in the NetCDF metadata. The wind fields stored in the NetCDF files are also available in daily animation form under an accompanying Zenodo Video/Audio data set (DOI: 10.5281/zenodo.7212837).</p>
Supplementary data for: Comparison of optical flow derivation techniques for retrieving tropospheric winds from satellite image sequences
<p>This study introduces a validation technique for quantitative comparison of algorithms which retrieve winds from passive detection of cloud- and water vapor-drift motions, also known as Atmospheric Motion Vectors (AMVs). The technique leverages airborne wind-profiling lidar data collected in tandem with 1-min refresh rate geostationary satellite imagery. AMVs derived with different approaches are used with accompanying numerical weather prediction model data to estimate the full profiles of lidar-sampled winds which enables ranking of feature tracking, quality control, and height-assignment accuracy and encourages meso-scale, multi-layer, multi-band wind retrieval solutions. The technique is used to compare the performance of two brightness motion, or "optical flow," retrieval algorithms used within AMVs, 1) Patch Matching (PM; used within operational AMVs) and 2) an advanced Variational Optical Flow (VOF) method enabled for most atmospheric motions by new-generation imagers. The VOF AMVs produce more accurate wind retrievals than the PM method within the benchmark in all imager bands explored. It is further shown that image regions with low texture and multi-layer-cloud scenes in visible and infrared bands are tracked significantly better with the VOF approach, implying VOF produces representative AMVs where PM typically breaks down. It is also demonstrated that VOF AMVs have reduced accuracy where the brightness texture does not advect with the mean wind (e.g. gravity waves), where the image temporal noise exceeds the natural variability, and when the height-assignment is poor. Finally, it is found that VOF AMVs have improved performance when using fine-temporal refresh rate imagery, such as 1-min versus 10-min data.</p>
Touché24-Image-Retrieval-and-Generation-for-Arguments
<div> <p>Data for the <a href="https://touche.webis.de/clef24/touche24-web/image-retrieval-for-arguments.html">Image Retrieval/Generation for Arguments</a> task at Touché 2024.</p> <p>Only the main.zip and nodes.zip are uploaded here due to space restrictions. Find the web page screenshots and web archives here: <a href="https://files.webis.de/corpora/corpora-webis/corpus-touche-image-retrieval-and-generation-24/">https://files.webis.de/corpora/corpora-webis/corpus-touche-image-retrieval-and-generation-24/</a></p> </div>
Dataset and Replication Package for the View-Based Retriever Approach To Reverse Engineering Software Architecture Models
<div> <div><span>Dataset and replication package for the view-based Retriever approach to reverse engineering software architecture models. Each Dataset project is structured as follows:</span></div> <ul> <li><span>The .ruleengine.yml file contains the configuration for running the Retriever approach.</span> <ul> <li><span>The repository value is the ID of a GitHub repository.</span></li> <li><span>The current_version value is the latest version of the retriever approach used to build the architectural models.</span></li> <li><span>The rules values are the rules used to build the architectural models.</span></li> </ul> </li> <li><span>The model_re folder contains the architectural model of the system automatically generated by the Retriever approach.</span> <ul> <li><span>The pcm folder contains the Palladio Component Model (PCM) of the system.</span></li> <li><span>The uml folder contains the PlantUML model.</span></li> </ul> </li> <li><span>The model_gs folder contains our manual gold standards for the system.</span></li> </ul> <div><span>The easiest way to use our approach is to use the CLI application with the given parameters: ./eclipse -i /path/to/input/directory -o /path/to/output/directory -r supported_rules</span></div> </div>
Look-up table data used in the GPM Spectral Latent Heating retrieval for the midlatitudes
<h3>Overview</h3> <p>This dataset contains the look-up table (LUT) data used for the midlatitude retrieval of the Global Precipitation Measurement (GPM) Spectral Latent Heating (SLH) V07 product. The LUTs that tie heating profiles to precipitation characteristics are constructed using Local Forecast Model simulations of eight extratropical cyclones around Japan. The LUTs for the following six categories: convective, shallow stratiform, downward increasing (DI) deep stratiform, downward decreasing (DD) deep stratiform, SUB0 (where the 0°C level is near the surface) deep stratiform, and OTHER. The precipitation top height (PTH) serves as an index for LUTs for convective and shallow stratiform, while the maximum precipitation rate (PMAX) is used as an index for LUTs for three deep stratiform and OTHER types. Note that LUTs for DD, DI, and SUB0 are subdivided based on whether the PMAX appears above or at the surface.</p> <p> </p> <h3>Contents</h3> <p>There are two directories named "data" and "fortran90".</p> <p>In the directory named "data", there are the following nine files for each-type LUT:</p> <p> 1. Convective LUT: lut_convective _midlatSLH.grd</p> <p> 2. Shallow stratiform LUT: lut_shallow-stratiform _midlatSLH.grd</p> <p> 3. DD deep strtiform LUT with PMAX above the surface with: lut_DD-stratiform_pmaxABOVEsfc_midlatSLH.grd</p> <p> 4. DD deep strtiform LUT with PMAX at the surface with: lut_DD-stratiform_pmaxATsfc_midlatSLH.grd</p> <p> 5. DI deep strtiform LUT with PMAX above the surface with: lut_DI-stratiform_pmaxABOVEsfc_midlatSLH.grd</p> <p> 6. DI deep strtiform LUT with PMAX at the surface with: lut_DI-stratiform_pmaxATsfc_midlatSLH.grd</p> <p> 7. SUB0 deep strtiform LUT with PMAX above the surface with: lut_SUB0-stratiform_pmaxABOVEsfc_midlatSLH.grd</p> <p> 8. SUB0 deep strtiform LUT with PMAX at the surface with: lut_SUB0-stratiform_pmaxATsfc_midlatSLH.grd</p> <p> 9. OTHER LUT: lut_OTHER_midlatSLH.grd</p> <p>The LUT data are in the plain binary format (single-precision real number, little endian).</p> <p>The LUT values of latent heating, apparent heat source minus radiative heating, and apparent moisture sink are included in each file. Unit is K/h. </p> <p>The convective and shallow stratiform LUTs have 176 PTH bins and 176 vertical level bins, from the surface to 21.875 km at each 125 m.</p> <p>On the other hand, the deep stratiform and OTHER LUTs have 35 PMAX bins defined as follows: 0.2–0.3, 0.3–0.7, 0.7–1, 1–1.5, 1.5–2, 2–2.5, 2.5–3, 3–4, 4–5, 5–6, 6–7, 7–8, 8–9, 9–10, 10–11, 11–12, 12–13, 13–14, 14–15, 15–16, 16–17, 17–18,18–19, 19–20, 20–21, 21–22, 22–23, 23–24, 24–25, 25–26, 26–27, 27–28, 28–29, 29–30, ≥30 mm/h. There are 353 vertical level bins for altitudes standardized by the level of PMAX, PTH, and precipitation bottom height, from -8.8 to 8.8 with an interval of 0.05.</p> <p>Please see Yokoyama et al. (2024, J. Appl. Meteor. Climatol., under review) for details.</p> <p> </p> <p>In the directory named "fortran90", there are two fortran90 programs to read the LUT data. The program named “read_lut_type-pth.f90” is for the convective and shallow stratiform LUTs, while the program named “read_lut_type-pmax.f90” is for the deep stratiform and OTHER LUTs.</p> <p> </p> <p>The data would be updated. </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.