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679 results for “retrieval”
Retrieval results for optically thin clouds in the trades
<p>ASTER satellite observations at 15 m pixel resolution are used to extract the signal of optically thin clouds during the EUREC4A field campaign (https://doi.org/10.5194/essd-2021-18). The signal of optically thin clouds is derived as a residual from the all-sky minus the simulated clear-sky (https://doi.org/10.5281/zenodo.4842675) and minus the known cloudy signal according to following a common cloud masking scheme. The paper describing the method, dataset, and results is intended for publication in the journal of Atmospheric Chemistry and Physics (ACP) under Mieslinger et al., 2021.</p> <p>The dataset includes basic information of the relevant input variables to clear-sky radiative transfer simulations as well as the resulting probability density function over reflectance values and for discrete flag values (clear-sky, optically thin clouds, clouds) given an ASTER observation. This data builds the basis for any derived quantities such as the area fraction or the expected reflectance corresponding to a certain flag value.</p>
Data for the publication "Retrieving ice-nucleating particle concentration and ice multiplication factors using active remote sensing validated by in situ observations"
<p>This repository contains the data for the paper:</p> <p>Wieder, J., Ihn, N., Mignani, C., Haarig, M., Bühl, J., Seifert, P., Engelmann, R., Ramelli, F., Kanji, Z. A., Lohmann, U., and Henneberger, J.: Retrieving ice nucleating particle concentration and ice multiplication factors using active remote sensing validated by in situ observations, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2022-67, in review, 2022.</p> <p>More information can be found in the README files.</p> <p>Note that the scripts to reproduce the figures of the publication are available on request.</p>
Retrieval coefficients for HATPRO observations during MOSAiC
<p>Retrieval files required to run the IDL program MWR_PRO <strong>[1]</strong> script 'pl_mk_pol.sh' to process raw HATPRO brightness temperatures and retrieve integrated water vapour (IWV, also called prw), liquid water path (LWP, also called clwvi), zenith humidity profiles (hze), zenith temperature profiles (tze) and boundary layer temperature profiles (tel).</p> <p> </p> <p>The retrieval files contain the coefficients acquired via regression with linear only (tel) or also quadratic terms (prw, clwvi, hze, tze) based on Ny-Alesund radiosonde measurements. The coefficients have been determined by Nomokonova et al. <strong>[2]</strong>.<br> <br> <strong>[1]</strong>: Walbröl, Andreas. (2022). Codes for: Atmospheric temperature, water vapour and liquid water path from two microwave radiometers during MOSAiC (v2.1.0). Zenodo. <a href="https://doi.org/10.5281/zenodo.6673957">https://doi.org/10.5281/zenodo.6673957</a></p> <p><strong>[2]</strong>: Nomokonova, T., Ebell, K., Löhnert, U., Maturilli, M., Ritter, C., and O'Connor, E.: Statistics on clouds and their relation to thermodynamic conditions at Ny-Ålesund using ground-based sensor synergy, Atmos. Chem. Phys., 19, 4105–4126, <a href="https://doi.org/10.5194/acp-19-4105-2019">https://doi.org/10.5194/acp-19-4105-2019</a>, 2019.</p>
Touché22-Image-Retrieval-for-Arguments
<p>Data for the <a href="https://touche.webis.de/clef22/touche22-web/image-retrieval-for-arguments.html">Image Retrieval for Arguments</a> task at Touché 2022.</p> <p>This version is lacking the touche22-image-search-archives.zip and touche22-image-search-screenshots.zip for space restrictions. Please get them from <a href="https://files.webis.de/corpora/corpora-webis/corpus-touche-image-search-22/">https://files.webis.de/corpora/corpora-webis/corpus-touche-image-search-22/</a></p>
SeasoNet: A Seasonal Scene Classification, Segmentation and Retrieval Dataset for Satellite Imagery over Germany
<p>This dataset consists of 1,759,830 multi-spectral image patches from the Sentinel-2 mission, annotated with image- and pixel-level land cover and land usage labels from the German land cover model LBM-DE2018 with land cover classes based on the CORINE Land Cover database (CLC) 2018. It includes pixel synchronous examples from each of the four seasons, plus an additional snowy set, spanning the time from April 2018 to February 2019. The patches were taken from 519,547 unique locations, covering the whole surface area of Germany, with each patch covering an area of 1.2km x 1.2km. The set is split into two overlapping grids, consisting of roughly 880,000 samples each, which are shifted by half the patch size in both dimensions. The images in each of the both grids themselves do not overlap.</p> <p><strong>Contents</strong></p> <p>Each sample includes:</p> <ul> <li>3 10m resolution bands (RGB), 120px x 120px</li> <li>1 10m resolution band (infrared), 120px x 120px</li> <li>6 20m resolution bands, 60px x 60px</li> <li>2 60m resolution bands, 20xp x 20px</li> <li>1 pixel-level label map</li> <li>2 binary masks for cloud and snow coverage</li> <li>2 binary masks for easy and medium segmentation difficulties, marks areas <300px and <100px respectively</li> <li>1 JSON-file containing additional meta-information</li> </ul> <p>The meta.csv contains the following information about each sample:</p> <ul> <li>Which season it belongs to</li> <li>Which of the two grids it belongs to</li> <li>Coordinates of the patch center</li> <li>Whether it was acquired from Sentinel-2 Satellite A or B</li> <li>Date and time of image acquisition</li> <li>Snow and cloud coverage percentages</li> <li>Image-level multi-class labels</li> <li>Three additional image-level urbanization labels, based on the center pixel (details below)</li> <li>The path to the sample</li> </ul> <p><strong>Classes</strong></p> <table> <thead> <tr> <th scope="col">ID</th> <th scope="col">Class</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>Continuous urban fabric</td> </tr> <tr> <td>2</td> <td>Discontinuous urban fabric</td> </tr> <tr> <td>3</td> <td>Industrial or commercial units</td> </tr> <tr> <td>4</td> <td>Road and rail networks and associated land</td> </tr> <tr> <td>5</td> <td>Port areas</td> </tr> <tr> <td>6</td> <td>Airports</td> </tr> <tr> <td>7</td> <td>Mineral extraction sites</td> </tr> <tr> <td>8</td> <td>Dump sites</td> </tr> <tr> <td>9</td> <td>Construction sites</td> </tr> <tr> <td>10</td> <td>Green urban areas</td> </tr> <tr> <td>11</td> <td>Sport and leisure facilities</td> </tr> <tr> <td>12</td> <td>Non-irrigated arable land</td> </tr> <tr> <td>13</td> <td>Vineyards</td> </tr> <tr> <td>14</td> <td>Fruit trees and berry plantations</td> </tr> <tr> <td>15</td> <td>Pastures</td> </tr> <tr> <td>16</td> <td>Broad-leaved forest</td> </tr> <tr> <td>17</td> <td>Coniferous forest</td> </tr> <tr> <td>18</td> <td>Mixed forest</td> </tr> <tr> <td>19</td> <td>Natural grasslands</td> </tr> <tr> <td>20</td> <td>Moors and heathland</td> </tr> <tr> <td>21</td> <td>Transitional woodland/shrub</td> </tr> <tr> <td>22</td> <td>Beaches, dunes, sands</td> </tr> <tr> <td>23</td> <td>Bare rock</td> </tr> <tr> <td>24</td> <td>Sparsely vegetated areas</td> </tr> <tr> <td>25</td> <td>Inland marshes</td> </tr> <tr> <td>26</td> <td>Peat bogs</td> </tr> <tr> <td>27</td> <td>Salt marshes</td> </tr> <tr> <td>28</td> <td>Intertidal flats</td> </tr> <tr> <td>29</td> <td>Water courses</td> </tr> <tr> <td>30</td> <td>Water bodies</td> </tr> <tr> <td>31</td> <td>Coastal lagoons</td> </tr> <tr> <td>32</td> <td>Estuaries</td> </tr> <tr> <td>33</td> <td>Sea and ocean</td> </tr> </tbody> </table> <p><strong>Urbanization classes</strong></p> <ul> <li><strong>SLRAUM</strong> <ul> <li>0: None</li> <li>1: Ländlicher Raum (~ rural area)</li> <li>2: Städtischer Raum (~ urban area)</li> </ul> </li> <li><strong>RTYP3</strong> <ul> <li>0: None</li> <li>1: Ländliche Regionen (~ rural areas)</li> <li>2: Regionen mit Verstädterungsansätzen (~ urbanizing areas)</li> <li>3: Städtische Regionen (~ urban areas)</li> </ul> </li> <li><strong>KTYP4</strong> <ul> <li>0: None</li> <li>1: Dünn besiedelte ländliche Kreise</li> <li>2: Kreisfreie Großstädte</li> <li>3: Ländliche Kreise mit Verdichtungsansätzen</li> <li>4: Städtische Kreise</li> </ul> </li> </ul> <p>Further information on the urbanization classes can be found here:</p> <p><strong>SLRAUM</strong></p> <p><a href="https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/kreise/staedtischer-laendlicher-raum/kreistypen.html">https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/kreise/staedtischer-laendlicher-raum/kreistypen.html</a></p> <p><strong>RTYP3</strong></p> <p><a href="https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/regionen/siedlungsstrukturelle-regionstypen/regionstypen.html">https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/regionen/siedlungsstrukturelle-regionstypen/regionstypen.html</a></p> <p><strong>KTYP4</strong></p> <p><a href="https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/kreise/siedlungsstrukturelle-kreistypen/kreistypen.html">https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/kreise/siedlungsstrukturelle-kreistypen/kreistypen.html</a></p> <p><strong>License of landcover model</strong></p> <p>Bundesamt für Kartographie und Geodäsie</p> <p>dl-de/by-2-0 from <a href="https://www.govdata.de/dl-de/by-2-0">https://www.govdata.de/dl-de/by-2-0</a></p> <p>© GeoBasis-DE / <strong>BKG</strong> 2022</p> <p><strong>Source of landcover model</strong></p> <p><a href="https://gdz.bkg.bund.de/index.php/default/catalog/product/view/id/1071/s/corine-land-cover-5-ha-stand-2018-clc5-2018/">https://gdz.bkg.bund.de/index.php/default/catalog/product/view/id/1071/s/corine-land-cover-5-ha-stand-2018-clc5-2018/</a></p>
FloX data for SIF retrieval testing
<p>Zip - Folders containing calibrated reflectance, reflected radiance and incoming radiance retrieved from measurements with FloX field spectrometers over Alfalfa and Forage (2018 in Grosseto, Italy), Cover crop (2020 in Julich, Germany) and Oak canopy (2020 in Observatoire de Haute-Provence (OHP), France) during clear sky conditions. The incoming radiance is measured with optics of hemispherical (~180°) field of view and the reflected radiance with conical (~25°) field of view. In addition, SCOPE simulated full spectral fluorescence and true reflectance are supplied.</p> <p>The data is organized in sub-folders inside the zip-repositories which are named according to the date of recording, containing CSV-files which hold the hyperspecteral information. One file is associated to the integrated full-range (FULL) spectrometer between 340nm and 1000nm, and the fluorescence-range (FLUO) spectrometer between 650nm and 810nm, containing incoming radiance, reflected radiance or reflectance, respectively. The first collumn contains the associated central wavelengths, the first row the time of recording, each subsequent field of the table contains the calibrated radiance (in W m<sup>-2</sup> sr<sup>-1</sup> nm<sup>-1</sup>) or reflectance (unitless) values.</p>
Related Works for the National Science Foundation of Sri Lanka and the National Sleep Foundation retrieved from the DataCite Commons.
<p>These data were retrieved in order to help understand the use of funder identifiers associated with common acronyms like NSF. They include the following fields: doi, registrationAgency, type, publisher, publicationYear, funderName, funderIdentifier, and awardNumber retrieved from DataCite Commons using the query</p> <div> <div>{organization(id: "' + ror + '") {id name works(first:2000) { totalCount pageInfo {endCursor hasNextPage} nodes {doi type registrationAgency {name} publisher {name} publicationYear fundingReferences {funderName funderIdentifier awardNumber}}}}}'</div> <div> </div> <div>The files are identifier with RORs:</div> <div><a href="../api/records/11116776/draft/files/00zc1hf95_relatedWorks_20240505_10.csv/content" target="_blank" rel="noopener noreferrer">00zc1hf95_relatedWorks_20240505_10.csv</a> are data for the National Sleep Foundation</div> <div><a href="../api/records/11116776/draft/files/010xaa060_relatedWorks_20240505_10.csv/content" target="_blank" rel="noopener noreferrer">010xaa060_relatedWorks_20240505_10.csv</a> are data for the National Science Foundation of Sri Lanka</div> <div> </div> <div>A blog post describing this work is at https://metadatagamechangers.com/blog/2024/4/12/funder-acronyms-are-still-not-enough</div> <div> </div> </div>
Supplementary run files for the paper "Learning Effective Representations for Retrieval using Self-Distillation with Adaptive Relevance Margins"
<p>TREC-Format run files of all trained models as supplementary material for the paper "Learning Effective Representations for Retrieval using Self-Distillation with Adaptive Relevance Margins".</p> <p>File naming follows the schema: <code>{model}-{loss variant}-{in-batch usage}-{dataset}.txt.gz</code></p>
Data set of pup retrieval test in control and V1b vasopressin receptor knockout mice
<div> <div> <div> <p>This repository contains the images and code used in the paper "Computer vision analysis of mother-infant interaction identified efficient pup retrieval in V1b receptor knockout mice".</p> <p>For effective nursing, close contact between lactating mothers and their infants is necessary. However, evaluation of maternal motivation to retrieve pups is challenging, because multiple infants were randomly accessed multiple times in changing background. We applied computer vision and deep learning analysis in this process to precisely calculate maternal behavior. Object detection in an open filed test identified less entry into the center area and less moving distance in virgin female mice lacking V1b vasopressin receptor (V1bKO) than wild-type (WT) mice. Although this character was replicated in a V1bKO mother, a pup retrieval test showed that total distances among a V1bKO mother and infants come closer in a shorter time than with a WT mother. In the medial preoptic area, V1b receptor message was partly detected in galanin- and c-fos-positive neurons after the mother was stimulated by infants. Our deep learning analysis effectively evaluated mother-infant relationship in V1bKO mice.</p> </div> </div> </div>
RemoTeC full-physics retrieval GOSAT/TANSO-FTS Level 2 bias-corrected XCO2 2009-2023, version 2.4.1 operated at Heidelberg University
<p>The data set contains bias-corrected column averaged dry air mole fractions (XCO2) retrieved with the RemoTeCv2.4.1 full-physics algorithm (Butz et al. 2011, Guerlet et al. 2013) applied on GOSAT TANSO-FTS Level 1B (L1B) data from 2009-04-18 to 2023-08-29. The GOSAT TANSO-FTS L1B data product is produced by JAXA/NOIES/MOE and provided by ESA. The XCO2 data together with related variables are aggregated as daily files, only good quality retrievals are included.</p> <p>If the data is used for publications, please contact andre.butz@uni-heidelberg.de to discuss potential co-authorship and technical details.</p> <p> </p> <p>Summary:</p> <p>Shortname: REMOTEC_L2_CO2_GOSAT</p> <p>Longname: RemoTeC full-physics retrieval GOSAT/TANSO-FTS Level 2 bias-corrected XCO2 version 2.4.1</p> <p>DOI: 10.5281/zenodo.12773070</p> <p>Version: 2.4.1</p> <p>Format: netCDF</p> <p>Spatial Coverage: -180.0,-90.0,180.0,90.0</p> <p>Temporal Coverage: 2009-04-18 to 2023-08-29</p>
Annotation of metagenome-assembled genomes retrieved from Amazon river basin metagenomes
<p> </p> <p><strong>Annotation of metagenome-assembled genomes retrieved from Amazon river basin metagenomes</strong></p> <p> </p> <p> RELEASE MAG-2018/01<br> --------------------------------------</p> <p> </p> <p>1. INTRODUCTION</p> <p>Here is deposited the genes and proteins annotation from metagenome-assembled genomes (MAGs) retrieved from Amazon river basin metaganomes (SRP044326, PRJEB25171 and SRP039390) were deposited under European Nucleotide Archive - ENA project PRJEB25176. Briefly, metagenomes were coassembled in groups by geographical location with Megahit v.1.0 and the contigs were used to a reads mapping and binning with BWA-MEM (version 0.7.12-r1039), SamTools (version 1.3.1) and Metabat (v2.12.1). MAGs with overall quality greater than 50, calculated with CheckM (version 1.0.11), were selected for refining precedures. Contigs outliers were eliminated by using RefineM (version 0.0.23). Finished MAGs were then annotated by Prokka (version 1.11) pipeline, and with the other most completes databases up to date (KEGG, UniProtKB, dbCAN, PFAM, eggNOG and COG).</p> <p> </p> <p>2. LOCATION</p> <p> </p> <p> MAGs sequences are available under ENA project PRJEB25176.</p> <p> </p> <p> ENA_accession Isolate<br> -------------------- --------------<br> ERZ494218 AM_0118<br> ERZ494219 AM_0219<br> ERZ494220 AM_0226<br> ERZ494221 AM_0228<br> ERZ494222 AM_0233<br> ERZ494223 AM_0240<br> ERZ494224 AM_0244<br> ERZ494225 AM_0256<br> ERZ494226 AM_0268<br> ERZ494227 AM_0275<br> ERZ494228 AM_0466<br> ERZ494229 AM_0507<br> ERZ494230 AM_0510<br> ERZ494231 AM_0519<br> ERZ494232 AM_0528<br> ERZ494233 AM_0546<br> ERZ494234 AM_0608<br> ERZ494235 AM_0615<br> ERZ494236 AM_0616<br> ERZ494237 AM_0619<br> ERZ494238 AM_0621<br> ERZ494239 AM_0630<br> ERZ494240 AM_0643<br> ERZ494241 AM_0729<br> ERZ494242 AM_0764<br> ERZ494243 AM_0832<br> ERZ494244 AM_0849<br> ERZ494245 AM_0854<br> ERZ494246 AM_0876<br> ERZ494247 AM_0902<br> ERZ494248 AM_0936<br> ERZ494249 AM_1003<br> ERZ494250 AM_1104<br> ERZ494251 AM_1111<br> ERZ494252 AM_1205<br> ERZ494253 AM_1312<br> ERZ494254 AM_1409<br> ERZ494255 AM_1503<br> ERZ494256 AM_1603<br> ERZ494257 AM_1606<br> ERZ494258 AM_1801<br> ERZ494259 AM_1811<br> ERZ494260 AM_2104<br> ERZ494261 AM_2116<br> ERZ494262 AM_2124<br> ERZ494263 AM_2202<br> ERZ494264 AM_2207<br> ERZ494265 AM_2208<br> ERZ494266 AM_2324<br> ERZ494267 AM_2502<br> ERZ494268 AM_2804<br> </p> <p>3. ACKNOWLEDGEMENTS<br> </p> <p>This work is a joint effort of Laboratory of molecular biology from Federal<br> University of São Carlos, São Paulo, Brazil (LBM/UFSCAR) and Protists group<br> of Institut del Ciencias del Mar, Barcelone, Spain (ICM). We are grateful to<br> Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), as well as, the spanish funding organ Consejo Superior de Investigaciones Científicas (CSIC).</p> <p>This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.</p> <p> </p> <p>4. CONTACT INFORMATION</p> <p> Current curators:</p> <p> - Célio Dias Santos Júnior (celio.diasjunior@gmail.com)<br> - Flavio Henrique-Silva (dfhs@ufscar.br)<br> - Ramiro R. Logares (ramiro.logares@icm.csic.es)<br> </p> <p>5. COPYRIGHT NOTICE</p> <p> Amazon River Basin Metagenome-Assembled Genomes Annotation - AM/MAGs<br> Copyright (C) 2018 The AMnrGC consortium.</p> <p> This database is provided “as is” and without any warranty of any kind,<br> of openly available. You can redistribute and/or modify it<br> as you wish, under the terms of Creative Commons CC BY 4.0:</p> <p> https://creativecommons.org/licenses/by/4.0/</p> <p>___________________<br> Barcelone, Feb/2018</p>
Dataset for "Method to retrieve cloud condensation nuclei number concentrations using lidar measurements"
<p>This repository contains the source data for the manuscript "<strong>Method to retrieve cloud condensation nuclei number concentrations using lidar measurements</strong>" published in <em>Atmospheric Measurement Techniques</em>. In situ measured data from five filed campaigns and corresponding theoretical simulated CCN number concentrations, lidar extinction and backscatter are included.</p>
Models and Data for Simple Applications of BERT for Ad Hoc Document Retrieval
<p>This submission includes all pretrained models, test data and prediction files for the arXiv paper "<a href="https://arxiv.org/abs/1903.10972">Simple Applications of BERT for Ad Hoc Document Retrieval</a>". Please follow the instructions at the <a href="https://github.com/castorini/birch">Birch repo</a> to reproduce the results.</p>
Benchmark protocol for exoplanet forward model and retrieval
<p>Benchmark protocol for giant exoplanet atmosphere tools, presented in Baudino et al. 2017 <a href="https://doi.org/10.3847/1538-4357/aa95be">https://doi.org/10.3847/1538-4357/aa95be</a></p> <p>The original data to reproduice the protocol are used in a jupyter notebook "Tutorial.ipynb" including all the plot routines to help to compare with you own models</p>
Cross-Domain Modeling of Sentence-Level Evidence for Document Retrieval
<p>This submission includes all pretrained models, test data and prediction files for the EMNLP 2019 paper "Cross-Domain Modeling of Sentence-Level Evidence for Document Retrieval". Please follow the instructions in the emnlp bran at the <a href="https://github.com/castorini/birch/tree/emnlp">Birch repo</a> to reproduce the results.</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>
Supplementary Material: Knobs and dials of retrieving JWST transmission spectra. I. The importance of p-T profile complexity
<p>This is supplementary material to <a title="Schleich et al. (2024)" href="https://www.aanda.org/articles/aa/abs/2024/10/aa51845-24/aa51845-24.html" target="_blank" rel="noopener">Schleich et al. (2024)</a>. The content of the provided data repository (also described in the file "content.txt") is as follows:</p> <p> </p> <h2>ADDITIONAL ANALYSIS</h2> <p>This folder contains a collection of ancillary data products for the evaluation of the retrievals performed in this work.</p> <ul> <li>'bayes-factor' contains the data tables for evaluating the Bayes' factor for each separate collection of models(*)</li> <li>'corner-plots' contains a collection of all corner plots associated with the individual input cases</li> <li>'fit-residuals' contains all fit residuals for the individual atmospheric retrievals performed in this work (used to make Fig. C.1)</li> <li>'resampled-pt-profiles' contains resampled p-T profiles to generate Figs. 6 and F.1</li> <li>'retrieval-accuracy' contains additional plots related to the accuracy of each retrieval (used to make Fig. 5, as well as Figs. E.1 - E.5)</li> </ul> <p><br>(*) SIDE NOTE:<br>Table headers in the "bayes-factor" data tables reference evidence reported from MultiNest (variable "Z"), and calculated Bayes factor (variable R). The case with log(R) = 0 is necessarily the reference case, and outliers are marked in a binary table with 1 (|log(R)| > 5) or 0 (|log(R)| < 5). In all cases, "log" refers to the natural logarithm.</p> <ul> <li>If someone actually reads this, I'm sorry. I also spent way too much time trying to track down if the values reported in MultiNest are natural or base-10 logarithm. I have now been convinced that it is worth it, always, to either specify "ln" for the base-e logarithm, or give the base of your logarithm if your write it down (i.e. log_10(X)) -Simon.</li> </ul> <h1> </h1> <h2>RETRIEVAL RESULTS</h2> <p>This folder contains the data products associated with the retrieval runs for each synthetic spectrum. The sub-directories are aranged by the following keys:</p> <ul> <li>'drs' and 'pandexo' refere to the two noise cases considered</li> <li>'inv-t' and 'norm-t' refere to the two underlying p-T profiles used to make the synthetic spectra</li> <li>'hpc', 'mpc', and 'lpc' refere two the three cloud-top pressure cases considerd</li> </ul> <p>Each individual folder contains (1) the TauREx parameter files for running retrievals using the selection of p-T profiles, (2) a folder called 'results', which containts the associated data products, and (3) a folder called 'chains', which stores the ancillary data products associated with the MultiNest sampling runs of each retrieval.</p> <p> We note that for the "drs_inv-t_mpc" case, the chains for the isothermal, 2-point, and 4-point runs have been lost</p> <p> </p> <h2>SYNTHETIC SPECTRA</h2> <p>This folder contains data products associated with the sample of synthetic transmission spectra.</p> <ul> <li>'pt-profile_*.csv' are csv-files containing the p-T points used to make Figure 1 , and to generate the synthetic transmissions spectra</li> <li>'forward-models' contains TauREx parameter files and forward models for the sample of synthetic transmission spectra. Each of the sub-directories also contains a faux-spectrum representing the wavelength-map of NIRSpec PRISM <ul> <li>'no-clouds' contains contains the above for generating Figure 3.</li> <li>'inv-t' contains forward models using the "inverse" p-T profile</li> <li>'norm-t' contains forwrad models using the "monotonic" p-T profile</li> </ul> </li> </ul>
A Synthetic Hyperspectral Dataset for Development and Validation of Phytoplankton Size Class Retrieval Models
<p><strong>A Synthetic Hyperspectral Dataset for Development and Validation of Phytoplankton Size Class Retrieval Models.</strong></p> <p>Please refer to the following scientific paper for a description of the dataset.</p> <blockquote> <p>Holtrop, T.; Van Der Woerd, H.J. (accepted) HYDROPT: An Open-Source Framework for Fast Inverse Modelling of Multi- and Hyperspectral Observations from Oceans, Coastal and Inland Waters. <em>Remote Sens. </em><strong>2021</strong>, 13, 0.</p> </blockquote>
Direct sun retrievals of nitrogen dioxide (NO2) total columns from Brewer #067, Rome, Italy (reprocessed with algorithm BNALG2)
<p>Cloud-screened and quality-filtered direct sun retrievals of nitrogen dioxide (NO2) vertical column densities (VCDs) derived from MkIV Brewer #067 measurements in Rome (wavelengths 425.02, 431.40, 437.35, 442.83, 448.08, and 453.20 nm) and processed using the Brewer Nitrogen Dioxide Algoritm BNALG2. Calibration is carried out with Bootstrap Estimation techniques. The values represent averages of 5 samples.</p> <p>In the latest version, days with obviously erroneous data (NO2 VCD > 99.9% percentile) have been removed.</p> <p>A detailed description of the method has been accepted as a research article by the ESSD journal (H. Diémoz et al., Advanced NO2 retrieval technique for the Brewer spectrophotometer applied to the 20-year record in Rome, Italy, Earth Syst. Sci. Data, 2021).</p>
Data used in the "H-FISTA: A hierarchical algorithm for phase retrieval with application to pulsar dynamic spectra" publication
<p>Dynamic spectra used in the "A new approach to phase retrieval for pulsar dynamic spectra" publication. These data can be used to reproduce the results in the paper</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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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.