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679 results for “retrieval”
Sample Sentinel-1 SAR data for sea ice type retrieval
<p>Sample Sentinel-1 SAR data for sea ice type retrieval processed with thermal noise removal (<a href="https://ieeexplore.ieee.org/document/8126233">https://ieeexplore.ieee.org/document/8126233</a>).</p> <p>Original data is available at ESA Scientific Hub <a href="https://scihub.copernicus.eu/">https://scihub.copernicus.eu/</a></p> <p> </p>
Learning Unsupervised Knowledge-Enhanced Representations to Reduce the Semantic Gap in Information Retrieval (Evaluation datasets)
<p>This dataset contains all the runs, pools, plots and analyses to reproduce the results presented in the paper: "Learning Unsupervised Knowledge-Enhanced Representations to Reduce the Semantic Gap in Information Retrieval ", 2020.</p>
MVCNN++: CAD model shape classification and retrieval using multi-view convolutional neural networks
<p>Deep neural networks have shown promising success towards the classification and retrieval tasks for images and text data. While there have been several implementations of deep networks in the area of computer graphics, these algorithms do not translate easily across different datasets, especially for shapes used in product design and manufacturing domain. Unlike datasets used in the 3D shape classification and retrieval in the computer graphics domain, engineering level description of 3D models do not yield themselves to neat distinct classes. The current study looks at an improved form of the 3D shape deep learning algorithm for classification and retrieval through the use of techniques such as relaxed classification, use of prime angled camera angles for capturing feature detail and transfer learning for reducing the amount of data and processing time needed to train shape recognition algorithms. The proposed algorithm (MVCNN++) builds on top of multi-view convolutional neural network (MVCNN) algorithm, improving its efficacy for manufacturing part classification by enabling use of part metadata, yielding an improvement of almost 6% over the original version. With the explosive growth of 3D product models available in publicly available repositories, search and discovery of relevant models is critical to democratizing access to design models.</p>
Bilingual Dataset for Information Retrieval and Question Answering over the Spanish Workers Statute
<p>A bilingual dataset of questions and answers over a key document in Spanish labor law legislation is presented. The document contains 150 questions and their respective answers in the form of one part number from the 130 parts in which the Workers Statute is divided (articles and other provisions), and with the most relevant excerpt of information for the answer.</p>
Experimental Data for the Paper 'Rotation-Aware Representation Learning for Remote Sensing Image Retrieval'
<p><strong>Experimental Data for the Paper 'Rotation-Aware Representation Learning for Remote Sensing Image Retrieval'</strong></p> <p>In this repository, we provide the implementation of the algorithms developed in the paper 'Rotation-Aware Representation Learning for Remote Sensing Image Retrieval' along with the experimental results.<br> The goal is to provide the elements needed to validate and reproduce our research work as well as all the tools needed to reach the same conclusions as we did.<br> The licences valid for the elements of this repository are discussed under point "2. Licenses" below.</p> <p><em><strong>1. Structure</strong></em></p> <p>The repository contains the following items:</p> <ol> <li>"data" - the results from our experiments</li> <li>"lib" - some external functions used in the experiments</li> <li>"make_data" - the training and test data</li> <li>"fmt-vgg.py" - the FMT-RAN model</li> <li>"stn.py" - the STN module of ST-RAN</li> <li>"st_ran.py" - the ST-RAN model</li> <li>"README" - this text here.</li> <li>"LICENSE" - the <a href="https://mit-license.org/">MIT License</a></li> </ol> <p><strong><em>2. License</em></strong></p> <p>The following licenses apply for the files and folders:</p> <ul> <li>The files "stn.py" and "spatial_transformer_tutorial.py" in the folder "lib" are from the GitHub repository <a href="https://github.com/GHamrouni/stn-tuto">https://github.com/GHamrouni/stn-tuto</a> and therefore are under the copyright of its repository owner Ghassen Hamrouni.</li> <li>All other files are under the <a href="https://mit-license.org/">MIT License</a>.</li> </ul> <p>The <a href="https://mit-license.org/">MIT License</a> is included here as file "LICENSE".</p> <p><em><strong>3. Contact</strong></em></p> <p>1. Dr. <a href="http://iao.hfuu.edu.cn/146">Zhize WU</a>, <a href="mailto:wuzz@hfuu.edu.cn">wuzz@hfuu.edu.cn</a><br> 2. Dr. <a href="http://iao.hfuu.edu.cn/5">Thomas WEISE</a>, <a href="mailto:tweise@hfuu.edu.cn">tweise@hfuu.edu.cn</a>, <a href="http://mailto:tweise@ustc.edu.cn">tweise@ustc.edu.cn</a></p> <p><a href="http://iao.hfuu.edu.cn">Institute of Applied Optimization</a>, <br> School of Artificial Intelligence and Big Data, <br> Hefei University, South Campus 2, Jinxiu Dadao 99, <br> Hefei Economic and Technological Development Area, <br> Shushan District, Hefei 230601, Anhui, China</p>
Cuttlefish retrieve whether they smelt or saw a previously encountered item
<p><span>According to the Source Monitoring Framework, the origin of a memory is remembered through the retrieval of specific features (<i>e.g.</i> perceptive, sensitive, affective signals). In two source discrimination tasks, we studied the ability of cuttlefish to remember the modality in which an item had been presented several hours ago. In experiment 1, cuttlefish were able to retrieve the modality of presentation of a crab (visual vs olfactory) sensed before 1h and 3hrs delays. In experiment 2, cuttlefish were trained to retrieve the modality of presentation of fish, shrimp, and crabs. After training, cuttlefish performed the task with another item never encountered before (e.g. mussel). Cuttlefish successfully passed transfer tests without and with delay (3hrs). This study is the first to show the ability to discriminate between two sensory modalities (<i>i.e. </i>see vs smell) in an animal. Taken together, these results suggest that cuttlefish can retrieve perceptual features of a previous event, namely whether they had seen or smelled an item. </span></p>
ImageCLEF 2016 Bentham Handwritten Retrieval Dataset
<p>Dataset compiled for the ImageCLEF 2016 Handwritten Scanned Document Retrieval challenge. It is derived from a subset of pages from unpublished manuscripts written by the philosopher and reformer Jeremy Bentham, that have been digitised and transcribed under the Transcribe Bentham project [Causer 2012]. More details about the dataset and the challenge are found in the overview paper at http://ceur-ws.org/Vol-1609/16090233.pdf the slides of the overview presentation at http://imageclef.org/system/files/Villegas16_CLEF_Handwritten-Overview_presentation.pdf or the evaluation web page http://imageclef.org/2016/handwritten.</p> <p>[Causer 2012] T. Causer and V. Wallace, Building a Volunteer Community: Results and Findings from Transcribe Bentham, Digital Humanities Quarterly, Vol. 6 (2012), http://www.digitalhumanities.org/dhq/vol/6/2/000125/000125.html</p>
Question Oriented Software Text Retrieval
<p>Dataset-1: Question-answer pairs on “Lucene” collected from StackOverflow. As mentioned in paper [36], we first get 5,587 questions and 7,872 answers from the StackOverflow with tag “lucene”, where 1,826 questions with positive votes are kept and labeled. We use these question and their 2,460 answers for original classifier training and testing.</p> <p>Dataset-2: Question-answer pairs on “Java” collected from StackOverflow. We need more data to train the classifier models and evaluate our approach. Then we extend our data collection scope and randomly pick 50,000 questions with tag “Java” on StackOverflow. It may cost too much time if we judge the types of these question accurately and manually. We filter all the questions using regular expressions (e.g. the question includes phrases “how to” , “how can” or “what is the best way to”, etc., are labeled with “how to” tag). Finally, 11,003 questions and the corresponding 16,255 answers are selected. Table IV briefly describes these two datasets.</p> <p>Dataset-3: FAQs of seven well-known open source projects. In software development, FAQs are used by many projects as part of their documentation. Compared with the data from StackOverflow, the FAQs are more formal and accurate. We want to investigate whether our approach is valid in search- ing these questions’ answers and whether the classifiers are affected by our learning examples. Table V illustrates the 7 open source projects and the numbers of their FAQs. All of them are the top level projects (TLPs) in Apache.</p>
A dataset of atmospheric ozone above the Mexico City basin retrieved from FTIR remote sensing observations made at two different ground altitudes
<p>This dataset of atmospheric ozone (O<sub>3</sub>) has been generated from solar absorption spectra measured in central Mexico using ground-based Fourier-Transform Infrared (FTIR) spectrometers. The FTIR experiments have been operated by the “Spectroscopy and Remote Sensing” Research Group of the Centro de Ciencias de la Atmósfera of the Universidad Nacional Autónoma de México (http://www.atmosfera.unam.mx/espectroscopia/index.html).</p> <p>The dataset covers measurements made between November 2012 and February 2014 applying two different FTIR spectrometers. The first instrument offers very high resolution spectra and contributes to NDACC (Network for the Detection of Atmospheric Composition Change). It is located at the mountain observatory of Altzomoni (ALTZ) about 1700m above the Mexico City basin. The second instrument has a medium spectral resolution and is located inside of Mexico City at the Universidad Nacional Autónoma de México (UNAM) at a horizontal distance of about 60km to the mountain observatory.</p> <p>The here provided dataset consists of two NETCDF data-files for each station and a MATLAB script for reading the NETCDF files. The files “ALTZ_IFS125_O3.nc” and “UNAM_IFS125_O3.nc” contain the retrieved O<sub>3</sub> state vectors, the O<sub>3</sub> averaging kernels and the O<sub>3</sub> a priori profiles, together with auxiliary data: observation time, observation geometry, instrumental settings, atmospheric temperature and humidity profiles. The data as well as the method for combining the two different observations are presented in Plaza-Medina et al. (2017), which should be consulted for more details.</p> <p>The files “ALTZ_IFS125_O3_Jac+Gain.nc” and “UNAM_IFS125_O3_Jac+Gain.nc” contain the Jacobians (for O<sub>3</sub> as well as for error sources) and the Gain matrix, together with the auxiliary data. The MATLAB script “readNETCDF_and_combine2FTIR.m” reads the NETCDF files and performs the operations needed for the generation of a combined product, thereby exploiting the synergetic effects of two observations made in coincidence but at different ground altitudes.</p> <p>A related dataset with Altzomoni O<sub>3</sub> profiles obtained by applying slightly different retrieval settings is available at the NDACC database (ftp://ftp.cpc.ncep.noaa.gov/ndacc/station/altzomoni/hdf/ftir/). Further datasets of atmospheric parameters as measured by different techniques are available at the webpage of the Red Universitario de Observaciones Atmosfericas (www.ruoa.unam.mx).</p>
Identification of genetic variants associated with anterior cruciate ligament rupture and AKC standard coat color in the Labrador Retriever
<p>Canine anterior cruciate ligament (ACL) rupture is a common complex disease. Prevalence of ACL rupture is breed-dependent. In an epidemiological study, yellow coat color was associated with increased risk of ACL rupture in the Labrador Retriever. ACL rupture risk variants may be linked to coat color through genetic selection or through linkage with coat color genes. To investigate these associations, Labrador Retrievers were phenotyped as ACL rupture cases or controls and for coat color and were single nucleotide polymorphism (SNP) genotyped. After filtering, ~697K SNPs were analyzed using GEMMA and mvBIMBAM for multivariate association. Functional annotation clustering analysis with DAVID was performed on candidate genes. A large 8Mb region on chromosome 5 that included <em>ACSF3</em>, as well as 32 additional SNPs, met genome-wide significance at <em>P</em><6.07E-7 or Log<sub>10</sub>(BF) = 3.0 for GEMMA and mvBIMBAM, respectively. On chromosome 23, SNPs were located within or near <em>PCCB</em> and <em>MSL2</em>. On chromosome 30, a SNP was located within <em>IGDCC3</em>. SNPs associated with coat color were also located within <em>ADAM9</em>,<em> FAM109B</em>,<em> SULT1C4</em>,<em>RTDR1</em>,<em> BCR</em>, and <em>RGS7</em>. <em>DZIP1L</em> was associated with ACL rupture. Several significant SNPs on chromosomes 2, 3, 7, 24, and 26 were located within uncharacterized regions or long non-coding RNA sequences. This study validates associations with the previous ACL rupture candidate genes <em>ACSF3</em> and <em>DZIP1L</em> and identifies novel candidate genes. These variants could act as targets for treatment or as factors in disease prediction modeling. The study highlighted the importance of regulatory SNPs in the disease, as several significant SNPs were located within non-coding regions.</p>
English-language articles related to petrophysics retrieved from the SSCI sub-database of Web of Science core database (Time: 2000.01.01-2022.12.31)
<p>According to the research content of petrophysics, petrophysics can be divided into eight branches: rock electricity, rock acoustics, rock nuclear physics, rock mechanics, rock thermophysics, rock nuclear magnetic resonance spectroscopy(NMR), rock gravimetry (density), and rock magnetism. This dataset presents the scientific literature retrieved by selecting the "Science Citation Index Expanded(SCI-EXPANDED)-- 1982-present" sub-library from the core collection database of Web of Science. </p>
Absorbing Aerosol Optical Central Height (AOCH) retrieved from TROPOMI with UIowa's AOCH-O2AB algorithm
<p>Absorbing Aerosol Optical Centroid Height (AOCH) retrieved from TROPOMI with UIowa’s AOCH-O<sub>2</sub>AB algorithm. Dataset for analyzing dust and smoke cases over Asia during 2021-2023.</p> <p>More information about this dataset can be found in: </p> <p>Chen, X., Wang, J., Xu, X. G., Zhou, M., Zhang, H. X., Garcia, L. C., Colarco, P. R., Janz, S. J., Yorks, J., McGill, M., Reid, J. S., de Graaf, M., and Kondragunta, S.: First retrieval of absorbing aerosol height over dark target using TROPOMI oxygen B band: Algorithm development and application for surface particulate matter estimates, Remote Sensing of Environment, 265, 18, <a href="https://doi.org/10.1016/j.rse.2021.112674">https://doi.org/10.1016/j.rse.2021.112674</a>, 2021.</p>
Supplementary Material: The Importance of Optical Wavelength Data on Atmospheric Retrievals of Exoplanet Transmission Spectra
<p>Supplementary material for "The Importance of Optical Wavelength Data on Atmospheric Retrievals of Exoplanet Transmission Spectra" DOI: <a href="https://ui.adsabs.harvard.edu/link_gateway/2024arXiv240307801F/doi:10.48550/arXiv.2403.07801" target="_blank" rel="noreferrer noopener">10.48550/arXiv.2403.07801</a></p> <p>Contents of this record:</p> <ul> <li>The retrieved atmospheric parameters for the population (see supplementary_material.pdf).</li> <li>The retrieval statistics per planet for the wavelength ranges 0.3-4.5, 0.6-4.5, and 1.1-4.5 microns (see supplementary_material.pdf).</li> <li>Planet specific retrieved spectra for the wavelength ranges 0.3-4.5, 0.6-4.5, and 1.1-4.5 microns.</li> <li>Retrieved parameter cornerplots per planet for the wavelength ranges 0.3-4.5, 0.6-4.5, and 1.1-4.5 microns.</li> </ul> <p>(NOTE: the retrieval model and priors for the results displayed in this record are specified in tables 2 and 3 of the paper.)</p>
Turkmenistan methane point source detections and retrievals from Landsat 5 (1986-2011)
<p>Global atmospheric methane concentrations rose by 10-15 ppb/yr in the 1980s before abruptly slowing to 2-8 ppb/yr in the early 1990s. This period in the 1990s is known as the "methane slowdown" and has been attributed to the collapse of the former Soviet Union (USSR) in December 1991, which may have decreased the methane emissions from oil and gas operations. Here we develop a methane plume detection system based on probabilistic deep learning and human-labelled training data. We use this method to detect methane plumes from Landsat 5 satellite observations over Turkmenistan from 1986 to 2011. We focus on Turkmenistan because economic data suggest it could account for half of the decline in oil and gas emissions from the former USSR. We find an increase in both the frequency of methane plume detections and the magnitude of methane emissions following the collapse of the USSR. We estimate a national loss rate from oil and gas infrastructure in Turkmenistan of more than 10% at times, which suggests the socioeconomic turmoil led to a lack of oversight and widespread infrastructure failure in the oil and gas sector. Our finding of increased oil and gas methane emissions from Turkmenistan following the USSR's collapse casts doubt on the long-standing hypothesis regarding the methane slowdown, begging the question: "what drove the 1992 methane slowdown?"</p>
NOAA PSL thermodynamic profiles retrieved from a combination of active and passive remote sensors and numerical weather prediction models with the optimal estimation physical retrieval TROPoe at Platteville, CO, USA
<p>This dataset contains retrieved profiles of thermodynamic variables obtained using the Tropospheric Remotely Observed Profiling via Optimal Estimation (TROPoe) physical retrieval from various combinations of input data collected by passive and active remote sensing instruments, in-situ surface platforms, and numerical weather prediction models deployed at the Platteville, CO, USA, site in fall 20221-winter 2022. Among the employed instruments are Microwave Radiometers (MWRs), Infrared Spectrometers (IRS), Radio Acoustic Sounding Systems (RASS), ceilometers, surface sensors, and information from the operational Rapid Refresh numerical weather prediction model.</p> <p>The dataset also includes 15 radiosounding launched for assessing the retrievals.</p> <p>For further information, please see:</p> <p>Bianco, L., Adler, B., Bariteau, L., Djalalova, I. V., Myers, T., Pezoa, S., Turner, D. D., and Wilczak, J. M.: Sensitivity of thermodynamic profiles retrieved from ground-based microwave and infrared observations to additional input data from active remote sensing instruments and numerical weather prediction models, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2023-263, in review, 2024.</p>
Data For: A Framework for Automated Supraglacial Lake Detection and Depth Retrieval in ICESat-2 Photon Data Across the Greenland and Antarctic Ice Sheets
<p>HDF5 data files for 1249 supraglacial lakes detected in ICESat-2 ATL03 data over Central West Greenland (melt seasons 2019 and 2020) and the Amery Ice Shelf Catchment (melt seasons 2018-19 and 2020-21). Each HDF5 data file is associated with a .jpg "quicklook" file of the same name, showing ATL03 photon elevations with the estimated along-track fits to the lake surface and lakebed and the resulting maximum lake depth, along with the corresponding ICESat-2 ground track over cloud-free concurrent satellite imagery.</p> <p>The data files are structured as following: </p> <div> <div> <div> <div> <div> <pre>group: depth_data/ - dataset: bathymetry_confidence - dataset: lakebed_fit_elevation_meters - dataset: lat - dataset: lon - dataset: surface_fit_elevation_meters - dataset: water_depth_meters - dataset: x_along_track_meters group: fluid_bathymetry_peaks/ - dataset: elevation_meters - dataset: peak_prominence - dataset: x_along_track_meters group: mframe_data/ - dataset: delta_time - dataset: density_ratio_1 - dataset: density_ratio_2 - dataset: density_ratio_3 - dataset: density_ratio_4 - dataset: major_frame_id - dataset: passes_bathymetry_check - dataset: passes_flatness_check - dataset: photon_density_peak_elevation - dataset: q_1_number_peaks - dataset: q_2_prominece - dataset: q_3_elev_spread - dataset: q_4_alignment - dataset: q_s - dataset: x_along_track_meters_end - dataset: x_along_track_meters_start group: photon_data/ - dataset: afterpulse_probability - dataset: fluid_signal_confidence - dataset: geoid_elevation_meters - dataset: lat - dataset: lon - dataset: photon_elevation_above_geoid_meters - dataset: pulse_saturation_level - dataset: x_along_track_meters group: properties/ - dataset: beam_number - dataset: beam_strength - dataset: cycle_number - dataset: granule_id - dataset: gtx - dataset: ice_sheet - dataset: lake_quality - dataset: lat - dataset: lon - dataset: melt_season - dataset: rgt - dataset: sc_orient - dataset: surface_elevation - dataset: time_utc </pre> </div> </div> </div> </div> </div>
Dataset for PurpleAir OEM retrieval
<p>The EXCEL spreadsheet available here has the raw measurements used to retrieve hygroscopic growth factor from Purple Air sensor measurements. This work will be submitted for publication in November, 2024.</p>
Video Supplement for Himes et al. (2024): "Using neural networks for near-real-time aerosol retrievals from OMPS Limb Profiler measurements"
<p>This archive contains the video supplement for</p> <p>Using neural networks for near-real-time aerosol retrievals from OMPS Limb Profiler measurements</p> <p>by Himes et al. (2024), submitted to Atmospheric Measurement Techniques. The file contains an animation of the V2.1 and NRT average retrieved extinction coefficient between 19.5--21.5 km at 997 nm for the 2024 Ruang eruptions.</p>
The data to create figures in (Sulfur Dioxide Distribution at the Venusian Cloud-top Retrieved from Akatsuki UV Images).
<p>The data to create figures.</p>
Global Terrestrial Ecosystem Carbon Flux Inferred from TanSat XCO2 Retrievals
<p>TanSat is China’s first greenhouse gases observing satellite. In recent years, substantial progresses have been achieved on retrieving column-averaged CO<sub>2</sub> dry air mole fraction (XCO<sub>2</sub>). However, relatively few attempts have been made to estimate terrestrial net ecosystem exchange (NEE) using TanSat XCO<sub>2</sub> retrievals. In this study, based on the GEOS-Chem 4D-Var data assimilation system, we infer the global NEE from April 2017 to March 2018 using TanSat XCO<sub>2</sub>. Evaluations against independent CO<sub>2</sub> observations and comparison with previous estimates indicate that the inverted land sinks in the northern middle latitudes and southern temperate regions are improved to a certain extent, however, they are obviously overestimated in northern high latitudes and underestimated in tropical lands (mainly northern Africa), respectively. </p> <p>There are 4 monthly mean variables in this dataset, including prior NEE, posterior NEE, prior ocean flux, and posterior ocean flux, which are all in a spatial resolution of 5 deg by 4 deg. </p>
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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.