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7,195 results for “SET”
Data Set of Extracted Summary Statistics from Equipment Sensor Data
<p>This data set was generated in accordance with the semiconductor industry and contains values of summary statistics from sensor recordings of the high-precision and high-tech production equipment. Basically, the semiconductor production consists of hundreds of process steps performing physical and chemical operations on so-called wafers, i.e. slices based on semiconductor material. In the production chain, each process equipment is equipped with several sensors recording physical parameters like gas flow, temperature, voltage, etc., resulting in so-called sensor data. Out of the sensor data, values of summary statistics are extracted. These are values like mean, standard deviation and gradients. To keep the entire production as stable as possible, these values are used to monitor the whole production in order to intervene in case of deviations.</p> <p>After the production, each device on the wafer is tested in the most careful way resulting in so-called wafer test data. In some cases, suspicious patterns occur in the wafer test data potentially leading to failure. In this case the root cause must be found in the production chain. For this purpose, the given data is provided. The aim is to find correlations between the wafer test data and the values of summary statistics in order to identify the root cause.</p> <p>The given data is divided into four data sets: "XTrain.csv", "YTrain.csv", "XTest.csv" and "YTest.csv". "XTrain.csv" and "XTest.csv" represent the values of summary statistics originating in the production chain separated for the purpose of training and validating a statistical model. Included are 114 observations of 77 parameters (values of summary statistics). The "YTrain.csv" and "YTest.csv" contain the corresponding wafer test data (144 observations of one parameter).</p>
US_Horror_Trailers_Data_Set
<p>This data set contains audio, colour, editing, and motion data for trailers for the fifty highest grossing horror films at the US box office from 2011 to 2015.</p> <p>All trailers were pre-processed to trim MPAA tag screens and YouTube channel promotional materials, and cropped to remove letterbox blanking.</p> <p>The sample is described in the file US_Horror_Trailers_Sample_Summary.csv, which contains the title of the film promoted, the URL for the trailer on YouTube (all URLs were correct as of 27 January 2021), the dimensions of the video file after pre-processing, the number of frames after pre-processing, the native frame rate, and the running time of a trailer after pre-processing in seconds.</p> <p>For each trailer the following information is available:</p> <ul> <li>Audio: the time contour of the normalized aggregated power envelope. One csv file per trailer with the naming convention: *_audio.csv</li> <li>Colour: three unnamed columns containing the average colour of each frame as an RGB triplet. One csv file per trailer with the naming convention: *_rgb.csv</li> <li>Motion: total magnitude and horizontal and vertical pixel displacement between consecutive frames. One csv file per trailer with the naming convention: *_motion.csv</li> <li>Editing: Shot length data in seconds. Shot length data is contained in the csv file US_Horror_Trailers_SL_Data.csv, with one column per data.</li> </ul> <p>All variables are time ordered from frame/shot/window 1 to <em>n</em> even when no explicit time variable is provided.</p> <p>A guide to visualising the data in this data set is available online here: <a href="https://rpubs.com/nr62_rp33/visualizing_trailers">https://rpubs.com/nr62_rp33/visualizing_trailers</a>.</p>
Data Set: fMRI Meta-Analyses
<p>This data set is the result of a systematic search in <strong>PubMed </strong>and <strong>APA PsycINFO</strong> for <strong>fMRI meta-analyses.</strong></p> <p>These records would be suitable for:</p> <ul> <li>meta-meta analysis on fMRI meta-analyses</li> <li>research questions regarding neuroimaging meta-analysis methodology (especially for those interested in coordination-based meta-analyses (CBMA): activation likelihood estimation (ALE) using GingerALE software, multi-level kernel density analysis (MKDA), seed-based d mapping (SDM) or image-based meta-analyses.</li> </ul> <p><strong>NOTE: These data are the raw search results from PubMed and PsycINFO and have been deduplicated but have NOT been screened for any inclusion criteria. This means you may find records in these results that are not, in fact, meta-analyses but still have the search terms (below) present in the title or abstract of the paper. </strong></p> <p>The data set is available in three formats: .csv, .ris, and a <a href="https://www.zotero.org/groups/4150721/fmri_meta-analyses">Zotero shared library</a></p> <p><strong>Search documentation</strong>: </p> <p>Search Date: May 21, 2021</p> <p>Conducted by: Meghan Testerman, Behavioral Sciences Librarian, Princeton University, mtesterman@princeton.edu</p> <p> </p> <p>PubMed: 433 records identified</p> <p>PubMed Search query (exact): (meta-analysis[Title]) AND (fMRI[Title/Abstract])</p> <p> </p> <p>PsycINFO: 289 records identified</p> <p>PsycINFO Search Query (exact): (TI meta-analysis) AND (TI fMRI OR AB fMRI)</p> <p><br> <strong>Total Results</strong></p> <p>Pubmed (433) + PsycINFO (289) = 722</p> <p>Deduplicates removed: 234</p> <p>Unique records: 488</p>
Dipole localisation predictions data set
<p>This data set contains prediction of ten dipole localisation algorithms computed using a simulated artificial lateral line (potential flow).</p>
Inferring the mammal tree: Species-level sets of phylogenies for questions in ecology, evolution, and conservation
<p>Big, time-scaled phylogenies are fundamental to connecting evolutionary processes to modern biodiversity patterns. Yet inferring reliable phylogenetic trees for thousands of species involves numerous trade-offs that have limited their utility to comparative biologists. To establish a robust evolutionary timescale for all ~6000 living species of mammals, we developed credible sets of trees that capture root-to-tip uncertainty in topology and divergence times. Our 'backbone-and-patch' approach to tree-building applies a newly assembled 31-gene supermatrix to two levels of Bayesian inference: (i) backbone relationships and ages among major lineages, using fossil node- or tip-dating; and (ii) species-level 'patch' phylogenies with non-overlapping in-groups that each correspond to one representative lineage in the backbone. Species unsampled for DNA are either excluded ('DNA-only' trees) or imputed within taxonomic constraints using branch lengths drawn from local birth-death models ('completed' trees). Joining time-scaled patches to backbones results in species-level trees of extant Mammalia with all branches estimated under the same modeling framework, thereby facilitating rate comparisons among lineages as disparate as marsupials and placentals. We compare our phylogenetic trees to previous estimates of mammal-wide phylogeny and divergence times, finding that (i) node ages are broadly concordant among studies, and (ii) recent (tip-level) rates of speciation are estimated more accurately in our study than in previous 'supertree' approaches where unresolved nodes led to branch length artifacts. Credible sets of mammalian phylogenetic history are now available for download at <a href="http://vertlife.org/phylosubsets">http://vertlife.org/phylosubsets</a>, enabling investigations of long-standing questions in comparative biology.</p>
VocalSketch Data Set v1.0.4
<p>This data set contains thousands of vocal imitations of a large set of diverse sounds. These imitations were collected from hundreds of contributors via Amazon's Mechanical Turk website. The data set also contains data on hundreds of people's ability to correctly label these vocal imitations, also collected via Amazon's Mechanical Turk. This data set will help the research community understand which audio concepts can be effectively communicated with this approach. We have released this data so the community can study the related issues and build systems that leverage vocal imitation as an interaction modality, such as search engines that can be queried by vocally imitating the desired sound.</p> <p> </p> <p><strong>This data set is a supplement to a paper. Please cite the following paper to reference this data set in a publication:</strong></p> <p>Cartwright, M., Pardo, B. VocalSketch: Vocally Imitating Audio Concepts. In <em>Proceedings of ACM Conference on Human Factors in Computing Systems</em> (2015). http://dx.doi.org/10.1145/2702123.2702387</p> <p> </p> <p>See https://github.com/interactiveaudiolab/VocalSketchDataSet for the latest updates to this data set.</p> <p> </p> <p>Interactive Audio Lab: http://music.eecs.northwestern.edu</p>
Data set from Pouzat and Chaffiol (2009) Journal of Neuroscience Methods 181:119.
<p><span>1</span></p> <p>1</p> <p>1This is the data set of Cockroach first olfactory relay recordings used in Pouzat and Chaffiol (2009) Automatic Spike Train Analysis and Report Generation. An Implementation with R, R2HTML and STAR <em>Journal of Neuroscience Methods</em> <strong>181</strong>: 119-1443. These data are also included in the R package STAR. The data are in HDF5 format.</p>
URLs from tweets for a 2014 sample of Twitter users and for a set of computer scientists
<p>The files in this dataset are used to analyse the tweeting behaviour of computer scientists on Twitter. They comprise</p> <ul> <li>a set of 989,529 tweet-URL pairs (<em>tweets_2014_researcher.tsv.bz2</em>) from 2014 from 6,271 users of the computer scientists sample in https://zenodo.org/record/12942 specified by time, tweet id, user id, and URL,</li> <li>a set of 300,053,850 tweet ids (<em>tweets_2014_sample.tsv.bz2</em>) from the 1% Twitter stream sample from 2014,</li> <li>a set of 671,304 tweet-URL pairs (<em>tweets_2014_sample_6271_users.tsv.bz2</em>) from the 1% Twitter stream sample from 2014 for 6,271 users specified by time, tweet id, user id, and URL,</li> <li>a set of the top 10,000 host names (<em>MAG_hosts_10000.tsv</em>) from the Microsoft Academic Graph data (http://blogs.msdn.com/b/msr_er/archive/2015/06/26/announcing-the-microsoft-academic-graph-let-the-research-begin.aspx), specified by rank, URL count, and host name, and</li> <li>a set of 340 host names of URL shortening services (<em>url_shortening_services.tsv</em>).</li> </ul>
Data set to article "Rapid serial processing of natural scenes: Color modulates detection but neither recognition nor the attentional blink"
<p>The file Data_Marxetal2014_AB.csv contains the data to the paper<br> Marx, S., Hansen-Goos, O., Thrun, M., & Einhäuser, W. (2014). Rapid serial processing of natural scenes: Color modulates detection but neither recognition nor the attentional blink. Journal of Vision, 14(14):4, 1-18, http://www.journalofvision.org/content/14/14/4, doi:10.1167/14.14.4.<br> as comma-separated value (csv) file</p> <p>Each row contains the data of one trial, represented by the following columns</p> <p>1 - number of the line<br> 2 - subject ID<br> 3 - experiment number<br> 4 - color condition (1: gray inverted, 2: gray original, 3: color inverted, 4: color original)<br> 5 - number of targets<br> 6 - SOA in ms<br> 7 - serial position of first target (0 if absent)<br> 8 - serial position of second target (0 if absent)<br> 9 - category of first target (1: feline, 2: avian, 3: ungulate, 4: canine)<br> 10 - category of second target (1: feline, 2: avian, 3: ungulate, 4: canine)<br> 11 - response to "How many animals?" (detection)<br> 12 - response to first category (recognition, 1: feline, 2: avian, 3: ungulate, 4: canine)<br> 13 - response to second category (recognition, 1: feline, 2: avian, 3: ungulate, 4: canine)</p>
Data set for article Veto, P., Einhäuser, W., & Troje, N.F. (2017). Biological motion distorts size perception. Scientific Reports, 7, 42576.
<p>In this data set you find 3 files containing data from Experiments 1, 2 & 3 of Veto P, Einhauser W & Troje NF (2017) Biological motion distorts size perception. Scientific Reports, 7, 42576; doi: 10.1038/srep42576</p> <p><br> The data are freely available for academic use only. If you use these data for a publication, please cite the aforementioned article.<br> If you have any questions regarding the data, please do not hesitate to contact Peter Veto at vettop@gmail.com</p> <p>Each row of the files contain data from one trial.<br> Columns:</p> <p>Experiment 1<br> 1 - Participant number<br> 2 - Block number<br> 3 - Trial number<br> 4 - Target orientation (1: Upright; -1: Inverted)<br> 5 - Stimulus width<br> 6 - Stimulus height<br> 7 - Response width<br> 8 - Response height</p> <p>Experiment 2<br> 1-8 Same as Experiment 1<br> 9 - Condition: dynamic (1) or static (2) target</p> <p>Experiment 3<br> 1 - Participant number<br> 2 - Block number<br> 3 - Trial number<br> 4 - Walker orientation<br> (1: upper walker upright, lower walker inverted;<br> 2: upper walker inverted, lower walker upright)<br> 5 - Condition<br> (1: upper target larger (21%) than lower target;<br> 2: upper target larger (10.5%) than lower target;<br> 3: target sizes are identical;<br> 4: lower target larger (10.5%) than upper target;<br> 5: lower target larger (21%) than upper target)<br> 6 - Inter stimulus interval (from end of walker presentation to onset of target circles)<br> (1: 17ms; 2: 100ms)<br> 7 - Response<br> (1: upper target was larger;<br> 2: lower target was larger)</p>
Data set for the physical, chemical and biochemical modelling of the primary sedimentation tanks at the WWTP of Eindhoven
<p>These files contain data about measurement campaigns on the primary sedimentation tanks of the WWTP of Eindhoven (The Netherlands) in 2013 and 2014 and the routinely collected data for 2011 till 2013.</p> <p>The data was processed in the PhD of Youri Amerlinck, entitled "Model refinements in view of wastewater treatment plant optimization: improving the balance in sub-model detail."</p> <p>http://www.biomath.ugent.be/biomath/publications/download/amerlinckyouri_phd.pdf</p> <p><br> WWTP of Eindhoven PST Routine Measurements 2011_2013.csv<br> January 5, 2011 - June 14, 2013: <br> Routine analysis for BOD5, COD, TKN, TP, PO4, TSS</p> <p>WWTP of Eindhoven PST Reduced Capacity 2013.csv<br> June 24, 2013 - July 23, 2013 - September 9, 2013<br> Evaluation of reducing the capacity of the PST (including dosing of chemicals) for CODT, CODS, TP, PO4 ,TSS </p> <p>WWTP of Eindhoven PST measurement campaign full ASM 20140506.csv<br> May 6, 2014: <br> Full ASM fractionation BOD5, CODT, CODS, TSS, VSS TP, PO4 ,TN, NH4, NO3, pH</p> <p>WWTP of Eindhoven PST measurement campaign full ASM and Cations 20140902.csv<br> September 2, 2014:<br> Full ASM fractionation (repetition) and cation analysis (BOD10, CODT, CODS, TSS, VSS, TP, PO4 ,TN, NH4, NO3, pH - Ca, Mg, Na, K, Fe)</p>
Data set for "Optogenetic stimulation of cortex to map evoked whisker movements in awake head-restrained mice"
<p>Data set for: Auffret M, Ravano VL, Rossi GMC, Hankov N, Petersen MFA, Petersen CCH (2017) Optogenetic stimulation of cortex to map evoked whisker movements in awake head-restrained mice. Neuroscience, http://dx.doi.org/10.1016/j.neuroscience.2017.04.004</p> <p>There are 9 files in this data upload:</p> <ol> <li>'2017_Auffret_Neuroscience.pdf' - this is a pdf version of the online publication.</li> <li>'Auffret_data.mat' - this is a Matlab data structure, which contains all the data for the publication.</li> <li>'Auffret_data.npy' - this is a Python data structure, which contains all the data for the publication. The Python data was generated from 'Auffret_data.mat' by 'DataViewer.py'.</li> <li>'Auffret_data.xlsx' - this is an Excel file, which contains all the data for the publication. This Excel file was generated from 'Auffret_data.mat'.</li> <li>'DataViewer.fig' - this is a Matlab Figure file, which is the GUI layout for 'DataViewer.m'.</li> <li>'DataViewer.m' - this is a Matlab Code, which displays the data contained in 'Auffret_data.mat'.</li> <li>'DataViewer.py' - this is a Python Code, which generates 'Auffret_data.npy' from 'Auffret_data.mat', and displays an example trial.</li> <li>'FigureViewer.fig' - this is a Matlab Figure file, which is the GUI layout for 'FigureViewer.m'.</li> <li>'FigureViewer.m' - this is a Matlab Code, which analyses the data in 'Auffret_data.mat', and displays the results in the same way as the published figures (Auffret et al., 2017).</li> </ol>
Datasets to article "Selection history alters attentional filter settings persistently and beyond top-down control"
<p>Single-Subject Behavioral and ERP mean amplitude data for Experiments 1 to 3.</p>
URLs from tweets for a 2014 sample of Twitter users and for a set of computer scientists
<p>The files in this dataset are used to analyse the tweeting behaviour of computer scientists on Twitter. They comprise</p> <ul> <li>a set of 989,529 tweet-URL pairs (<em>tweets_2014_researcher.tsv.bz2</em>) from 2014 from 6,271 users of the computer scientists sample in https://zenodo.org/record/12942 specified by time, tweet id, user id, and URL,</li> <li>a set of 300,053,850 tweet ids (<em>tweets_2014_sample.tsv.bz2</em>) from the 1% Twitter stream sample from 2014,</li> <li>a set of 605,080 tweet-URL pairs (<em>tweets_2014_sample_6694_users.tsv.bz2</em>) from the 1% Twitter stream sample from 2014 for 6,694 users specified by time, tweet id, user id, and URL,</li> <li>a set of the top 10,000 host names (<em>MAG_hosts_10000.tsv</em>) from the Microsoft Academic Graph data (http://blogs.msdn.com/b/msr_er/archive/2015/06/26/announcing-the-microsoft-academic-graph-let-the-research-begin.aspx), specified by rank, URL count, and host name, and</li> <li>a set of 340 host names of URL shortening services (<em>url_shortening_services.tsv</em>).</li> </ul> <p>In addition, the following rankings (based on the odds ratio) of domains, hosts, and URLs that appear in both the researcher dataset and the sample are included:</p> <ul> <li><em>domains_by_odds_ratio.tsv.bz2</em> - a ranking of 61,860 domains,</li> <li><em>hosts_by_odds_ratio.tsv.bz2</em> - a ranking of 80,384 hosts,</li> <li><em>publisher_domains_by_odds_ratio.tsv.bz2</em> - a ranking of 924 publisher domains,</li> <li><em>publisher_urls_by_odds_ratio.tsv.bz2</em> - a ranking of 4,227 publisher URLs.</li> </ul>
Nitric oxide (NO) data set (60--160 km) from SCIAMACHY mesosphere--lower thermosphere limb scans
<p><strong>Overview</strong><br> Contains the nitric oxide (NO) number densities (in cm<sup>-3</sup>) from 60 km to 160 km retrieved from SCIAMACHY mesosphere--lower thermosphere (MLT, 50--150 km) limb scans.</p> <p>SCIAMACHY is a UV-visible-near-infrared spectrometer which flies on ESA's Envisat and was operational from 08/2002 to 04/2012 (see Burrows et al., 1995 and Bovensmann et al., 1999 and references therein). The Mesosphere--Lower Thermosphere (MLT) measurement mode was carried out from 07/2008 until the end of the mission for one day every 15 days. This data set comprises 84 days of SCIAMACHY MLT NO measurements, each<br> containing about 15 orbits.</p> <p>The NO retrieval was carried out at the Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany, and is described in Bender et al., 2013. We used the SCIAMACHY geo-located atmospheric spectra (SCI_NL__1P) version 8.02 provided by ESA via their data browser at<br> https://earth.esa.int/web/guest/data-access/browse-data-products.<br> The spectra were calibrated with ESA's `SciaL1C` command line tool available for download at<br> https://earth.esa.int/web/guest/software-tools/content/-/article/scial1c-command-line-tool-4073.</p> <p>The SCIAMACHY NO data were compared to the results from ACE-FTS, MIPAS, and SMR in Bender et al., 2015, showing that all agree within the respective measurement uncertainties.</p> <p><strong>Acknowledgements</strong><br> The development of the retrieval was funded by the Helmholtz-society under the grant number VH-NG-624. The SCIAMACHY project, which was initiated by Professor Burrows in 1984, was funded by the German Aerospace Agency (DLR), the Netherlands Space Office NSO, formerly NIVR, and the Belgium ministry responsible for space. ESA funded the Envisat project. Professor Burrows of University of Bremen is the Principal Investigator. He and his research team comprising his colleagues in Bremen and international scientific collaborators led the scientific support and development of SCIAMACHY and the scientific exploitation of its data products.</p> <p>The SCIAMACHY instrument is developed by an industrial team headed by companies now known as Airbus SD on the German side and by Dutch Space on the Dutch side and included Belgium companies. The instrument and algorithm development is supported by the activities of the SCIAMACHY Science Advisory Group (SSAG), a team of scientists from various international institutions: University of Bremen (D), SRON (NL), SAO (USA), IASB (B), MPI Chemistry Mainz (D), KNMI (NL), University of Heidelberg (D), IMGA (I), CNRS-LPMA (F). Operational data processing is being performed by ESA and DLR-DFD within the ENVISAT ground segment. Support with respect to mission planning and operations is given by the SCIAMACHY Operations Support Team (SOST). The relevant work at the University of Bremen is funded by the University and State of Bremen.</p>
LAMASUS Land Use Management Data Set
<p>The land use management classes developed as part of the LAMASUS project have been aggregated to a 1 km grid (INSPIRE) provided as three geopackages and NUTS2 regions, provided as shares in each grid cell and number of pixels in each region, respectively. The NUTS2 data can be joined to the NUTS2 shapefile (contained in NUTS0_2016_01M_3035_corrected.zip, including all NUTS levels), which is provided here as some corrections to the NUTS layer have been made. A list of the land use management classes is provided in the excel file. Note that these aggregated files are consistent with official statistics from the Forest Resources Assessment of the Food and Agriculture Organization (FRA-FAO) for forest areas and Eurostat for cropland and grassland areas. </p> <div> <p>This dataset has been created as part of LAMASUS Project under the scope of Deliverable 2.1 titled "The LUM Geodatabase and Area Estimates of Land Use Change to 2018 ". The data is directly linked to the work described on pages 12-34, belonging to section 3. The Land Use Management (LUM) Geodatabase. The full text of the deliverable can be accessed via: <a href="https://www.lamasus.eu/wp-content/uploads/LAMASUS_D2.1_LUMGeodatabase.pdf">https://www.lamasus.eu/wp-content/uploads/LAMASUS_D2.1_LUMGeodatabase.pdf.</a></p> </div>
Fully differentiable, fully distributed River Discharge Prediction: data sets
<p>This repository contains the data sets used in: Scholz et al. (2025). Fully differentiable, fully distributed River Discharge Prediction.</p> <ul> <li><code>dem_1000.h5</code> based on EU-DEM v1.1, reprojected to RADOLAN grid: <a href="https://sdi.eea.europa.eu/catalogue/srv/api/records/3473589f-0854-4601-919e-2e7dd172ff50">https://sdi.eea.europa.eu/catalogue/srv/api/records/3473589f-0854-4601-919e-2e7dd172ff50</a></li> <li><code>efas.h5</code> based on EFAS historical: <a href="https://ewds.climate.copernicus.eu/datasets/efas-historical?tab=overview">https://ewds.climate.copernicus.eu/datasets/efas-historical?tab=overview</a></li> <li><code>era5_ssrd_neckar*.nc</code> based on ERA5 provided by ECMWF, reprojected to RADOLAN grid: <a href="https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5">https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5</a></li> <li><code>radolan_neckar_*.h5</code> based on RADOLAN rw product provided by the Deutsche Wetterdienst: <a href="https://opendata.dwd.de/climate_environment/CDC/grids_germany/hourly/radolan/">https://opendata.dwd.de/climate_environment/CDC/grids_germany/hourly/radolan/</a></li> </ul> <p>Due to copyright, the discharge data has to be downloaded manually from the Global Runoff Data Centre (<a href="https://grdc.bafg.de/">https://grdc.bafg.de/</a>), and then preprocessed with the provided <code>bafg_parser.py</code> python script. We use the following stations in our work:</p> <ul> <li>6335290: STEIN</li> <li>6335291: GAILDORF</li> <li>6335565: BAD IMNAU</li> <li>6335600: ROCKENAU SKA</li> <li>6335601: LAUFFEN</li> <li>6335602: PLOCHINGEN</li> <li>6335603: ROTTWEIL</li> <li>6335604: KIRCHENTELLINSFURT</li> <li>6335620: MOSBACH</li> <li>6335660: PFORZHEIM</li> <li>6335665: DENKENDORF</li> <li>6335671: ALTENSTEIG</li> <li>6335675: MURR</li> <li>6335676: OPPENWEILER</li> <li>6335680: SCHWABSBERG</li> <li>6335681: UNTERGRIESHEIM</li> <li>6335690: NEUSTADT</li> </ul> <p>To preprocess the discharge data, additionally the river network data "Fließgewässer (AWGN)" provided by the Landesanstalt für Umwelt Baden-Württemberg (LUBW) is required: <a href="https://rips-metadaten.lubw.de/trefferanzeige?docuuid=7251515f-6aed-4555-8319-ab6314155ab1">https://rips-metadaten.lubw.de/trefferanzeige?docuuid=7251515f-6aed-4555-8319-ab6314155ab1</a></p> <p> </p>
TCOM-HCl : Daily global gap-free stratospheric hydrogen chloride profile data set based on TOMCAT CTM and Occultation Measurements
<p>Methodology: </p> <p><span>The </span><strong><span>TOMCAT simulation</span></strong><span> was conducted at a T64L32 resolution, consistent with previous work by Dhomse et al. (2021, 2022), covering the period from 2000 to 2024. These simulations utilized </span><strong><span>ERA-5 reanalysis data</span></strong><span>.</span></p> <h3><span>HCl Profile Processing and Bias Correction</span></h3> <p><strong><span>Collocated HCl profiles</span></strong><span> are organized into five distinct latitude bins:</span></p> <ul> <li> <p><strong><span>NH polar</span></strong><span>: </span><span><span><span><span><span>9</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>N - </span><span><span><span><span><span>5</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>N</span></p> </li> <li> <p><strong><span>NH mid-lat</span></strong><span>: </span><span><span><span><span><span>2</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>N - </span><span><span><span><span><span>7</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>N</span></p> </li> <li> <p><strong><span>Tropics</span></strong><span>: </span><span><span><span><span><span>4</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>S - </span><span><span><span><span><span>4</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>N</span></p> </li> <li> <p><strong><span>SH mid-lat</span></strong><span>: </span><span><span><span><span><span>7</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>S - </span><span><span><span><span><span>2</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>S</span></p> </li> <li> <p><strong><span>SH polar</span></strong><span>: </span><span><span><span><span><span>9</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>S - </span><span><span><span><span><span>5</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>S</span></p> </li> </ul> <p><span>Initially, </span><strong><span>differences between TOMCAT and satellite measurements</span></strong><span> (primarily ACE-FTS data) are calculated for each zonal bin across 51 height levels (ranging from </span><span><span><span><span><span>10</span><span>,</span><span><span>km</span></span></span></span></span></span><span> to </span><span><span><span><span><span>60</span><span>,</span><span><span>km</span></span></span></span></span></span><span>).</span></p> <p><strong><span>Separate XGBoost regression models</span></strong><span> are then trained for these HCl differences at each height level within a given latitude bin. These trained models are subsequently used to estimate </span><strong><span>HCl bias corrections</span></strong><span> for all daytime TOMCAT grids (9132 days), specifically sampled at 1:30 PM local time at the equator. This yields grid-specific bias corrections that are applied to the original TOMCAT profiles.</span></p> <p><strong><span>Height-resolved HCl profile data</span></strong><span> are then interpolated onto 28 standard pressure levels (from </span><span><span><span><span><span>300</span><span>,</span><span><span>hPa</span></span></span></span></span></span><span> to </span><span><span><span><span><span>0.1</span><span>,</span><span><span>hPa</span></span></span></span></span></span><span>), using pressure levels directly from the TOMCAT grids. For overlapping latitude bins, values are averaged to ensure smoother fields near boundary regions.</span></p> <h3><span>Data Files</span></h3> <p><span>The dataset includes two files containing daily mean zonal mean HCl profiles:</span></p> <ul> <li> <p><code><span>zmhcl_TCOM_hlev_T2Dz_2000-2024_V1.1.nc</span></code><span>: Contains </span><strong><span>height level data</span></strong><span> (</span><span><span><span><span><span>10</span><span>,</span><span><span>km</span></span></span></span></span></span><span> to </span><span><span><span><span><span>60</span><span>,</span><span><span>km</span></span></span></span></span></span><span>).</span></p> </li> <li> <p><code><span>zmhcl_TCOM_plev_T2Dz_2000-2024_V1.1.nc</span></code><span>: Contains </span><strong><span>pressure level data</span></strong><span> (</span><span><span><span><span><span>300</span><span>,</span><span><span>hPa</span></span></span></span></span></span><span> to </span><span><span><span><span><span>0.1</span><span>,</span><span><span>hPa</span></span></span></span></span></span><span>).</span></p> </li> </ul> <h3><span>Reference Publication</span></h3> <p><span>This methodology, incorporating only ACE-FTS data and various minor algorithmic developments, is based on the following publication:</span></p> <p><span>Dhomse, S. S. and Chipperfield, M. P.: Using machine learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets, Earth Syst. Sci. Data, 15, 5105–5120, </span><a title="null" href="https://doi.org/10.5194/essd-15-5105-2023"><span>https://doi.org/10.5194/essd-15-5105-2023</span></a><span>, 2023</span></p>
TOMCAT CTM and Occultation measurement-based Stratospheric CFC12 (TCOM-CFC12) profile data set
<p>TOMCAT CTM and Occultation measurement-based Stratospheric CFC12 (TCOM-CFC12) profile data set </p> <p>Sandip S. Dhomse </p> <p>School of Earth and Enviro, University of Leeds, Leeds, UK</p> <p>National Centre for Earth Observations, University of Leeds, Leeds, UK</p> <p> email: s.s.dhomse@leeds.ac.uk</p> <p> Methodology: TOMCAT simulation is performed at T64L32 resolution for the 2000-2024 time period. Collocated CFC12 (CF2Cl2) profiles are divided in five latitude bins: SH polar (90S-50S), SH mid-lat (70S-20S), tropics (40S-40N), NH mid-lat (20N-70N) and NH polar (50N-90N). Initially, model-measurement differences are calculated for each zonal bins (51 height levels, 10km to 60km). Separate XGBoost regression models are trained for the differences between TOMCAT and measurements at each level for a given latitude bin. XGBoost model is then used to estimate error corrections for all the TOMCAT grids. Estimated corrections for a given model grid that are added to the original TOMCAT simulated daily (at 1.30 local time) CFC-12 profiles. Height resolved data are then interpolated on 28-pressure levels (300 - 0.1hPa). For overlapping latitude bins, we use averages and then calculate daily zonal mean values. For more details see attached presentation.</p> <p>Dataset also includes two files containing daily mean zonal mean CFC-12 profiles on height (10-60 km) and pressure (300-0.1 hPa) levels (9132 days/64 latitudes):</p> <p>zmcfc12_TCOM_hlev_T2Dz_2000-2024_V1.1.nc – height level data (10 to 60 km)</p> <p>zmcfc12_TCOM_plev_T2Dz_2000-2024_V1.1.nc – pressure level data (300 to 0.1 hPa)</p> <p>Daily 3D profiles on height and pressure levels would be made available on request. Xarrays “resample” can be used to get monthly means.</p>
Data set for paper "Ramparts around lakes on Titan impact winds and methane evaporation"
<p>Data and post-processing code used for the paper "Ramparts around lakes on Titan impact winds and methane evaporation", submitted to PSJ in 2024.</p> <p>Are made available:<br> - a list of the simulations (list_simulations_ramparts2D.pdf)<br> - the simulations' netCDF outputs (run-t##.nc.gz)<br> - the input files used to run the simulations (in input_files/)<br> - the post-processing python codes used to plot the figures (in post_processing_codes/)<br> - tables of latent heat flux and horizontal wind values (Tables_LH_and_Uwind.pdf)<br> - a gif of the horizontal wind in the reference run (u_wind_run-t04_speed.gif)<br> - a gif of the vertical wind in the reference run (w_wind_run-t04_speed.gif)</p>
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