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137 results for “SLAM”
SLAMS photometric catalogue
<p>Photometric catalogues from the SLAMS survey, described in 'Discovery of a thin stellar stream in the SLAMS survey' (arxiv.org/abs/1711.09103). Columns and units:<br> 1) right ascension J2000 (degrees)<br> 2) declination J2000 (degrees)<br> 3) r-band magnitude (mag)<br> 4) r-band magnitude error (mag)<br> 5) r-band spread_model star/galaxy classifier<br> 6) r-band spread_model error<br> 7) g-band magnitude (mag)<br> 8) g-band magnitude error (mag)<br> 9) g-band spread_model star/galaxy classifier<br> 10) g-band spread_model error<br> 11) E(B-V) reddening (mag)</p>
Visual-inertial input datasets for SLAM applications containing extreme and human-like motion patterns
<p>Recorded datasets in compressed rosbag format, which contain visual and IMU sensor information that are bearing high resemblance to the movement of a human player with a handheld AR-capable device.</p> <p>For machine learning training and validation tasks, separate dataset are available containing motion patterns in a wide range from steady camera image to extremely challenging movements.</p>
UAV-based monocular SLAM video datasets in vineyards with RTK ground truth
<p>The dataset provides a UAV-based monocular visual SLAM data, designed to evaluate the potential of using monocular visual SLAM in vineyards. It includes videos in ".mp4" format collected by UAV, and "xlsx" tables which include latitude, longitude, height, speed in x, y and z, comjpass, pitch, roll. The ".xlsx" tables were measured by RTK and can be used as ground truth of UAV trajectory and pose.</p> <p>This dataset can be combined with other datasets to enable a comprehensive view of the vineyards:</p> <p>Vélez S, Ariza-Sentís M, Valente J. EscaYard: Precision viticulture multimodal dataset of vineyards affected by Esca disease consisting of geotagged smartphone images, phytosanitary status, UAV 3D point clouds and Orthomosaics. Data in Brief. 2024 Jun 1;54:110497. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.dib.2024.110497" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.dib.2024.110497</span></a></p> <p><span>Ariza-Sentís M, Wang K, Cao Z, Vélez S, Valente J. GrapeMOTS: UAV vineyard dataset with MOTS grape bunch annotations recorded from multiple perspectives for enhanced object detection and tracking. Data in Brief. 2024 Jun 1;54:110432. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.dib.2024.110432" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.dib.2024.110432</a></span></p> <p> </p> <p> </p>
Data for "Excitation of Low- and High-frequency Magnetosonic Whistler Waves Associated with SLAMS in the Terrestrial Foreshock" by Yao et al.
<p>The database includes the plasma data used in instability analyses and the theoretical analysis results based on the linear model.</p> <h3>Captions:</h3> <div><strong>Plasma_input.mat</strong> file is the plasma data used in the instability analyses, which is used to plot Figure 2a-2d.</div> <ul> <li><strong><em>B</em></strong>: magnetic field strength</li> <li><strong><em>n</em></strong>: plasma number density</li> <li><strong><em>Te_para</em></strong>: parallel electron temperature</li> <li><strong><em>Te_perp</em></strong>: perpendicular electron temperature</li> <li><strong><em>Tp</em></strong>: proton temperature</li> </ul> <div> </div> <div><strong>WWs_theo_predictions.mat</strong> file is the calculation result of linear growth rate and wave frequency. The results are used to plot Figure 2e and 2f.</div> <ul> <li><strong><em>f_theo</em></strong>: wave frequency in theoretical predictions</li> <li><strong><em>gamma_theo</em></strong>: growth rate in theoretical predictions</li> </ul>
Database of Short Large-Amplitude Magnetic Structures (SLAMS) detected by spacecraft 1 of the Cluster mission in the foreshock of Earth
<p>Database of Short Large-Amplitude Magnetic Structures (SLAMS) detected in the foreshock of Earth by spacecraft 1 of the Cluster mission between the years 2002-2012.</p> <p>An automated algorithm has been used for SLAMS identification followed by a manual verification process to remove bow shock oscillations and other false detections. SLAMS have been defined to have an amplitude of at least two times the background magnetic field. </p> <p>More details on the creation of the database are given in the following publication:</p> <p><span><span lang="EN-US">Bergman, S.</span></span><span lang="EN-US">, Karlsson, T., Wong Chan, T. K., & Trollvik, H. (2025). Statistical properties of Short Large</span><span lang="EN-US">‐</span><span lang="EN-US">Amplitude Magnetic Structures (SLAMS) in the foreshock of Earth from Cluster measurements. <em>Journal of Geophysical Research: Space Physics</em>, 130. </span><a href="https://doi.org/10.1029/2024JA033568"><span lang="EN-US">https://doi.org/10.1029/2024JA033568</span></a></p> <p>Contact: S. Bergman, sofiabergmanphd@gmail.com </p>
VC-SLAM Versatile Corpus for Semantic Labeling And Modeling
<p>Benchmark Corpus for semantic labeling and modeling.</p> <p>This corpus contains 101 data sets from different open data portals.<br> Each data set consists of the following data:</p> <ul> <li>Raw csv data [rawdata_csv]</li> <li>Large json data sample [json_sample_large]</li> <li>Small json data sample [json_sample_small]</li> <li>Raw data in csv format [rawdata_csv]</li> <li>Raw data samples in csv format [rawdata_csv_samples]</li> <li>Mappings to translate between csv and json files [csv_json_mappings]</li> <li>Textual description / Metadata [descriptions]</li> <li>Semantic model as rdf/ttl [semantic_models]</li> <li>Mappings describing mapping between raw data attributes and concepts from the ontology [mappings]</li> <li>List of attributes that have been ignored during modeling [ignored_attributes]</li> </ul> <p>Additionally the corpus contains a target ontology as rdf/ttl [ontology].</p> <p>The individual data sets are licensed by the licenses specified in the attached Excel sheet (DataSetOverview.xlsx)</p> <p> </p> <p>These data are provided "as is", without any warranties of any kind. The data are provided under the Creative Commons Attribution 4.0 International license.</p>
Data Results from Performance Modelling of SLAM methods
<p>Data from runs/bencmarks of SLAM methods GMapping, SLAM Toolbox and Hector SLAM.</p> <p>Results include data for multiple performance metrics, parameters of the robot sensors, and environment features. The data contains results from many runs executed with various combinations of parameters in order to create a statistical model of the SLAM performance in function of characteristics of the robot and environment.</p>
Developments in taxonomy could see safari hunters killing 25 types of antelope, instead of the previous 9, to achieve the 'spiral horned grand slam'. in Taxonomy anarchy hampers conservation
Developments in taxonomy could see safari hunters killing 25 types of antelope, instead of the previous 9, to achieve the 'spiral horned grand slam'.
Dataset: Slam Corp. (SLAM) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Slam Corp. (SLAMW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Slam Corp. (SLAMU) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Örebro University basement SLAM dataset - Radar, Velodyne
<p>Dataset recorded using the Taurob Tracker in Örebro University. The data includes a novel radar sensor and velodyne data.</p>
Dortmund SLAM dataset - Radar, Velodyne
<p>Dataset recorded using the Taurob Tracker in Dortmund in field tests with firemen. The data includes a novel radar sensor and velodyne data. There is also part with smoke blocking the measurements of the velodyne.</p>
Visual and visual-inertial SLAM: State-of-the-Art, Classification and Experimental Benchmarking
<p>IRSTV dataset used in the publication "Visual and visual-inertial SLAM: State-of-the-Art, Classification and Experimental Benchmarking", Myriam Servières, Valérie Renaudin, Alexis Dupuis<sup> </sup>and Nicolas Antigny<sup>,</sup> Hidawi Journal of Sensors, To be published</p>
Autonomous Mobile Robots: Past, Present and Future of SLAM 2013
<p>“Autonomous Mobile Robots: Past, Present and Future of SLAM” 2013. In:Workshop<br /> at the First RSI/ISM International Conference on Robotics and Mechatronics by<br /> Sharif University of Technology. Presenter: Prof. Hamid D. Taghirad, 2013.</p>
Data for SLAM Datasets
<div>This project contains a dataset generated with a Kobuki Turtlebot 2 and a RP LiDAR A2. The goal of the project is that the user can experiment the issues that LiDAR has detecting glass walls or mapping with excessive light. The topics available contain robot odometry, laser scans, poseupdates, commands given to the robot, its trajectory, clocks, and the needed transforms to perform a map. With the bags included in bag folder you can replicate the ros topics generated during an indoor walkthrough of UC3M corridors.</div> <div> <div>This information can also be found in the next link, please check it for updates: <a href="https://gitlab.netcom.it.uc3m.es/predict-6g/slam-datasets" target="_blank" rel="noopener">PREDICT 6G / SLAM Datasets · GitLab (uc3m.es)</a></div> <div> </div> <div> <p><em>This work has been partly funded by the European Commission Horizon Europe SNS JU <a href="https://predict-6g.eu/" target="_blank" rel="nofollow noreferrer noopener">PREDICT-6G</a> (GA 101095890) and <a href="https://hexa-x-ii.eu/" target="_blank" rel="nofollow noreferrer noopener">Hexa-X-II</a> (GA 101095759) projects, and the Spanish Ministry of Economic Affairs and Digital Transformation and the European Union-NextGeneration-EU through the UNICO 5G I+D <a href="https://unica6g.it.uc3m.es/6g-edgedt/" target="_blank" rel="nofollow noreferrer noopener">6G-EDGEDT</a> and <a href="https://unica6g.it.uc3m.es/6g-datadriven/" target="_blank" rel="nofollow noreferrer noopener">6G-DATADRIVEN</a>.</em></p> <p><em>This work has been funded by the Spanish Ministry of Economic Affairs and Digital Transformation under European Union NextGeneration-EU projects TSI-063000-2021-59 RISC6G and TSI-063000-2021-63 MAP-6G.</em></p> </div> </div>
Richardson Model R-5 Slam Fire Shotgun Low Poly
* 2048x2048 texture + normal map * low poly game ready model Richardson Model R-5 Single Shot Shotgun 12 ga. Barrel length 24" Excellent Bore , Marked clearly on top of the Receiver is "Richardson Industries, Inc. New Haven Conn.USA Model R-5 12ga. For those of you out their not familiar with a slamfire shot-gun, this is a factory gun made after WW2 by the Richardson Industries, a company owned by the famous Lt Iliff D. Richardson, during WW2 Lt Richardson fought the Japanese on the Philippines, it is their that he developed this unusual simple shotgun Source: Objaverse 1.0 / Sketchfab
CamVox: A Low-cost and Accurate Lidar-assisted Visual SLAM System
<p>Abstract— Combining lidar in camera-based simultaneous localization and mapping (SLAM) is an effective method in improving overall accuracy, especially at outdoor large scale scenes. Recent development of low-cost lidars (e.g. Livox lidar) enable us to explore such SLAM systems with lower budget and higher performance. In this paper we propose CamVox by adapting Livox lidars into visual SLAM (ORB-SLAM2) by exploring the lidars’ unique features. Based on the unique scan pattern of Livox lidars, we propose an automatic lidarcamera calibration method that will work in uncontrolled scenes. The long depth detection range also benefit a more accurate mapping. Comparison of CamVox with visual SLAM (VINS-mono) and lidar SLAM (LOAM) are evaluated on the same dataset to demonstrate the performance. We open sourced our hardware, code and dataset on GitHub. (https://github.com/ISEE-Technology/CamVox)</p> <p>This contains our dataset in SUSTech campus with loop closure (CamVox.bag) and the Lidar-camera Synchronization ARM(stm32) code (synchronization.zip ).</p>
GRAND-SLAM analysis of SARS-CoV-2 data from Finkel et al., Nature 2021 (https://www.nature.com/articles/s41586-021-03610-3)
<p>This is the processed SLAM-seq data from Finkel et al., Nature 2021 (https://www.nature.com/articles/s41586-021-03610-3). The zip file contains the full output from the processing pipeline (including the mapped reads, the scripts to run the pipeline and the output). The json file is required if you want to start from scratch. The file sars.tsv.gz is the GRAND-SLAM output table.</p> <p> </p> <p>To generate the GRAND-SLAM output yourself, first <a href="https://github.com/erhard-lab/gedi/wiki/Preparing-genomes">prepare</a> the human (ensembl v90) and the SARS-CoV-2 genome (NC_045512). Then run:</p> <pre><code class="language-bash">gedi -e Slam -trim5p 15 -reads sars.cit -genomic h.ens90 SARS-CoV2 -prefix grandslam_t15/sars -plot -progress </code></pre> <p>To generate the cit file you have to modify the first lines in start.bash to match the paths on your file system, and then run it.</p> <p>You can also start from scratch (i.e., the json file):</p> <ol> <li><a href="https://github.com/erhard-lab/gedi/wiki/Preparing-genomes">Prepare</a> the human genome (ensembl v90), the SARS-CoV-2 genome (NC_045512), the human rRNA sequence (U13369.1), and the Mycoplasma hominis sequence</li> <li>Prepare the joint STAR index for the human and virus genome by calling gedi -e GenomicUtils -p -m star -g h.ens90 SARS-CoV2</li> <li>Modify the starindex entry in the json file to match your file system</li> <li>Run: gedi -e Pipeline -r parallel -j sars.json rnaseq_mapping.sh report.sh grandslam.sh</li> </ol> <p>Software versions:</p> <ul> <li>gedi toolkit 1.0.4</li> <li>GRAND-SLAM 2.0.7</li> <li>cutadapt 3.4</li> <li>Bowtie 2 version 2.3.0</li> <li>STAR version 2.5.3a</li> </ul>
GRAND-SLAM analysis of HCMV infection of ZAP knockout cells from Gonzalez-Perez et al., mBio 2021 (https://doi.org/10.1128/mbio.02683-20)
<p>This is the processed SLAM-seq data from Gonzalez-Perez et al., mBio 2021 (https://doi.org/10.1128/mbio.02683-20). The zip file contains the full output from the processing pipeline (including the mapped reads, the scripts to run the pipeline and the output). The json file is required if you want to start from scratch. The file sars.tsv.gz is the GRAND-SLAM output table.</p> <p><br> To generate the GRAND-SLAM output yourself, first prepare the human (ensembl v90) and the HCMV genome (EF999921). Then run:</p> <p>gedi -e Slam -double -reads sars.cit -genomic h.ens90 HCMV_TB40_BAC4 -prefix grandslam_du/zap_bac4 -plot -progress</p> <p>[Zum Verschieben anwählen und ziehen]</p> <p>To generate the cit file you have to modify the first lines in start.bash to match the paths on your file system, and then run it.</p> <p>You can also start from scratch (i.e., the json file):</p> <p> Prepare the human genome (ensembl v90), the HCMV genome (EF999921), the human rRNA sequence (U13369.1), and the Mycoplasma hominis sequence<br> Prepare the joint STAR index for the human and virus genome by calling gedi -e GenomicUtils -p -m star -g h.ens90 HCMV_TB40_BAC4<br> Modify the starindex entry in the json file to match your file system<br> Run: gedi -e Pipeline -r parallel -j zap_bac4.json rnaseq_mapping.sh report.sh grandslam.sh</p> <p>Software versions:</p> <p> gedi toolkit 1.0.2<br> GRAND-SLAM 2.0.5a<br> Trimmomatic 0.39<br> STAR version 2.5.3a</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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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.