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1,320 results for “navigation”
Extended Kalman filters for close-range navigation to noncooperative targets
<p>The data sets provided here are associated to the paper “Extended Kalman filters for close-range navigation to noncooperative targets” available at this <a name="_Hlk36545040"></a><a href="https://doi.org/10.1016/j.asr.2023.10.038"><span>link</span></a>. These allow recreating the simulations discussed in Sections 5.2 – for the results plotted in Figure 7 – 5.3 (Figures 9-10), and 5.4 (Figures 11-12).</p> <p>That paper presents a set of dynamic filters for estimating the relative roto-translational state and the main parameters of a noncooperative target from an observing chaser satellite during close proximity operations. The proposed different options address a wide range of design possibilities for the architecture of the relative navigation system. All filters are derived from a common, general, core shaped as a dynamic multiplicative extended Kalman filter using dual quaternions. This allows exploiting the advantages of handling the pose (i.e., attitude and position) in a multiplicative fashion, while improving the accuracy in the estimation of the angular and linear relative velocities, as well as enabling the estimation of some meaningful parameters of the target spacecraft (e.g., the ratios of the moments of inertia, position and orientation of the principal axes frame). Moreover, by adopting relative kinematics and dynamics equations in dual quaternions, the inherent coupling of the six degrees-of-freedom motion is addressed with no approximations.</p> <p>All filters take as observations only the noisy pose measurements from an electro-optical device. For each proposed formulation, numerical simulations are carried out to show the behaviour of the filter within a scenario representative of close-range target inspection at conclusion of the mid-range rendezvous.</p>
Navigation and meteorological data collected during the Tara Pacific Expedition 2016-2019
<p>The Tara Pacific expedition (2016-2018) sampled coral ecosystems around 32 islands in the Pacific Ocean, and sampled the surface of oceanic waters at 249 locations, resulting in the collection of nearly 58,000 samples. The expedition was designed to systematically study corals, fish, plankton, and seawater, and included the collection of samples for advanced biogeochemical, molecular, and imaging analysis. Here we provide the continuous dataset originating from navigation and meteorological instruments acquiring continuously during the full course of the campaign.</p> <p> </p> <p>Variables/ descriptions and units:</p> <table> <tbody> <tr> <td>variable</td> <td>description</td> <td>units</td> </tr> <tr> <td>'dt'</td> <td>date-time stamp</td> <td>iso UTC</td> </tr> <tr> <td>'lat'</td> <td>latitude</td> <td>decimal degree</td> </tr> <tr> <td>'lon'</td> <td>longitude</td> <td>decimal degree</td> </tr> <tr> <td>'flag_origin_latlon'</td> <td>origin of the latitude and longitude</td> </tr> <tr> <td>'cog'</td> <td>course over ground</td> <td>degree</td> </tr> <tr> <td>'sog'</td> <td>speed over ground</td> <td>knots</td> </tr> <tr> <td>'sst_batos'</td> <td>Sea surface temperature measured by the navigation station</td> <td>°C</td> </tr> <tr> <td>'temperature_atm'</td> <td>Atmospheric temperature</td> <td>°C</td> </tr> <tr> <td>'pressure_sealevel'</td> <td>Atmospheric presure</td> <td>hp</td> </tr> <tr> <td>'relative_humidity'</td> <td>relative humidity </td> <td>%</td> </tr> <tr> <td>'apparent_windspeed_bow'</td> <td>apparent wind speed</td> <td>knots</td> </tr> <tr> <td>'apparent_winddir_bow'</td> <td>wind direction from the bow</td> <td>degree</td> </tr> <tr> <td>'apparent_wind_trueN'</td> <td>wind direction from north</td> <td>degree</td> </tr> <tr> <td>'true_wind_speed'</td> <td>knots</td> </tr> <tr> <td>'true_wind_dir'</td> <td>wind direction from north</td> <td>degree</td> </tr> <tr> <td>'sunzenith'</td> <td>sun position relative to zenith</td> <td>radian</td> </tr> <tr> <td>'sunazimuth'</td> <td>sun position relative to north</td> <td>radian</td> </tr> </tbody> </table>
Navigating protected areas networks for improving diffusion of conservation practices
<p>The Natura 2000 protected area network is the cornerstone of European Union's biodiversity conservation strategy. These protected areas range across multiple biogeographic regions, and they include a diversity of species assemblages along with a diversity of managing organizations, altogether making difficult to pool relevant sites to facilitate the flow of knowledge significant to their management. Here we introduce an approach to navigating protected area networks that has the potential to foster systematic identification of key sites for facilitating the exchange of knowledge and diffusion of information within the network. To demonstrate our approach, we abstractly represented Romanian Natura 2000 network as a co-occurrence network, with individual sites as nodes and shared species as edges, further combining into our analysis network topology, community detection, and network reduction methods. We identified most representative Natura 2000 sites that may increase the transfer of information within the national network of protected areas, detected clusters of sites and key sites for maintaining network cohesiveness, and highlighted the subsample of sites that retain the characteristics of the entire network. Our analysis provides implications for protected area prioritization by proposing a network perspective approach to collaboration rooted in ecological principles.</p>
Uncorrected inertial navigation dataset collected on board the R/V Akademik Tryoshnikov during the austral summer of 2016/2017 as part of the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>A HYDRINS Inertial Navigation System (INS) was installed on the R/V Akademik Tryoshnikov during the austral summer of 2016/2017 as it circumnavigated Antarctica as part of the Antarctic Circumnavigation Expedition (ACE).</p> <p>This dataset is uncorrected and as-was recorded and covers the period from November 2016 – April 2017. Data files are provided in text format which can be used in many software packages. Heading, attitude, position and speed data are provided.</p> <p>This dataset can be used alongside, for example, mulitbeam survey data and to give high positional accuracy.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ACE_place-X_Y.txt, data file, comma-separated values format</li> <li>data_file_header.txt, metadata, text format</li> <li>README.txt, metadata, text format</li> </ul> <p><strong>Dataset license</strong></p> <p>This inertial navigation dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Navigating deep learning strategies for large-area land cover mapping using very-high-resolution imagery in Senegal: Validation Data
<p><span><span>R</span><span>apid</span><span> advances in deep learning</span><span> for</span> <span>land cover </span><span>classification of </span><span>trees, shrubs and </span><span>very small</span> <span>agricultur</span><span>al</span> <span>fields</span> <span>using</span> <span>very high</span><span>-</span><span>resolution satellite </span><span>data </span><span>(< 2 m</span><span>)</span><span>,</span><span> has tremendous potential</span> <span>for resolving </span><span>current</span><span> challenges </span><span>in </span><span>quantifying</span> <span>land cover </span><span>change </span><span>in</span> <span>sub-</span><span>Saharan</span> <span>African (SSA</span><span>)</span><span>,</span> <span>due to</span> <span>growing </span><span>demand for food resources</span><span>.</span> <span>We</span> <span>conducted experiments </span><span>with</span><span> different training strategies for scaling up </span><span>UNet</span> <span>convolutional neural network </span><span>models for regional land cover mapping with multispectral </span><span>WorldView</span><span> (WV</span><span>)</span><span>-2 and –3,</span><span> imagery</span><span> in</span><span> three distinct regions of Senegal </span><span>which</span> <span>has</span><span> complex </span><span>seasonal wet/dry conditions and </span><span>cropland-savanna mosaics. </span></span></p> <p>The validation exercise of this research consisted in validating more than 70,000 km<sup>2</sup> across Senegal. The infrastructure was setup in the NASA SMCE system with a total of twelve George Mason University (GMU) students participating as operators. These operators validated more than 59 WV-2 and -3 images, each consisting of 200 stratified points in 5,000 x 5,000-pixel images. This effort resulted in a total of ~35,000 aggregated observations that are available through the eo-validation API for public consumption. Each validation point from this dataset has three individual observations.</p>
Two specific populations of GABAergic neurons originating from the medial and the caudal ganglionic eminences aid in proper navigation of callosal axons.
<p>Reduced motility of CC GABAergic guidepost neurons after E16.5.<em>In vitro</em> time-lapse sequences over a period of around three hours (sequential pictures taken at regular intervals) of GAD67-GFP<sup>+</sup> neuron dynamics in coronal CC slices of E14.5 (mov 1) and E16.5 (mov 2) GAD67-GFP<sup>+</sup>transgenic mice. Open arrowheads indicate the progression of neurons between sequential pictures while arrowheads highlight immobilized neurons. (A1–A6) At E14.5, the majority of the GAD67-GFP<sup>+</sup> neurons exhibit rapid movements within the white matter of the CC (open arrowheads). (B1–B6) By contrast, at E16.5, nearly all the GAD67-GFP<sup>+</sup> neurons exhibit a reduced motility within the white matter of the CC (arrowheads). </p> <p>Branching and outgrowth defects in the callosal axons of Nkx2.1<sup>−/−</sup>:GAD67-GFP mice brains. (mov 3 and mov 4) A pCAG-Ires-Tomato plasmid was injected into the lateral ventricle and electroporated into the dorsal pallium, to label the callosal projecting neurons, of E14.5 GAD67-GFP<sup>+</sup> living embryos that were allowed to develop until E16.5. 6. High power views of <em>in vitro</em> time-lapse sequences over a period of 120 min (at 20 min intervals) of Tomato-labeled callosal axons and GAD67-GFP<sup>+</sup> neurons on coronal CC slices of E16.5 Nkx2.1<sup>+/+</sup>:GAD67-GFP<sup>+</sup> (mov 3) and Nkx2.1<sup>−/−</sup>:GAD67-GFP<sup>+</sup> (mov4) embryos. In the Nkx2.1<sup>−/−</sup> brains, though the callosal axons progressed along normal path, they displayed disoriented branch extensions. </p>
Embodied Spatial Navigation Training in Mild Cognitive Impairment: A Proof-of-Concept Trial
<p>Raw data of included cognitive test and VR data Starting Grant Ricerca Finalizzata, code: SG-2018-12368175</p>
Navigating News Narratives: A Media Bias Analysis Dataset
<p>The prevalence of bias in the news media has become a critical issue, affecting public perception on a range of important topics such as political views, health, insurance, resource distributions, religion, race, age, gender, occupation, and climate change. The media has a moral responsibility to ensure accurate information dissemination and to increase awareness about important issues and the potential risks associated with them. This highlights the need for a solution that can help mitigate against the spread of false or misleading information and restore public trust in the media.</p><p><strong>Data description: </strong>This is a dataset for news media bias covering different dimensions of the biases: political, hate speech, political, toxicity, sexism, ageism, gender identity, gender discrimination, race/ethnicity, climate change, occupation, spirituality, which makes it a unique contribution. The dataset used for this project does not contain any personally identifiable information (PII).</p><p><strong>Data Format: </strong>The format of data is:</p><ul><li>ID: Numeric unique identifier.</li><li>Text: Main content.</li><li>Dimension: Categorical descriptor of the text.</li><li>Biased_Words: List of words considered biased.</li><li>Aspect: Specific topic within the text.</li><li>Label: Neutral, Slightly Biased , Highly Biased</li></ul><p><br><strong>Annotation Scheme: </strong>The annotation scheme is based on Active learning, which is Manual Labeling --> Semi-Supervised Learning --> Human Verifications (iterative process)</p><ul><li>Bias Label: Indicate the presence/absence of bias (e.g., no bias, mild, strong).</li><li>Words/Phrases Level Biases: Identify specific biased words/phrases.</li><li>Subjective Bias (Aspect): Capture biases related to content aspects.</li></ul><p><br><strong>List of datasets used : </strong>We curated different news categories like Climate crisis news summaries , occupational, spiritual/faith/ general using RSS to capture different dimensions of the news media biases. The annotation is performed using active learning to label the sentence (either neural/ slightly biased/ highly biased) and to pick biased words from the news.</p><p>We also utilize publicly available data from the following links. Our Attribution to others.</p><p> <strong>MBIC (media bias): </strong>Spinde, Timo, Lada Rudnitckaia, Kanishka Sinha, Felix Hamborg, Bela Gipp, and Karsten Donnay. "MBIC--A Media Bias Annotation Dataset Including Annotator Characteristics." arXiv preprint arXiv:2105.11910 (2021). <a href="https://zenodo.org/records/4474336">https://zenodo.org/records/4474336</a> </p><p><strong>Hyperpartisan news: </strong>Kiesel, Johannes, Maria Mestre, Rishabh Shukla, Emmanuel Vincent, Payam Adineh, David Corney, Benno Stein, and Martin Potthast. "Semeval-2019 task 4: Hyperpartisan news detection." In Proceedings of the 13th International Workshop on Semantic Evaluation, pp. 829-839. 2019. <a href="https://huggingface.co/datasets/hyperpartisan_news_detection">https://huggingface.co/datasets/hyperpartisan_news_detection</a> </p><p><strong>Toxic comment classification: </strong>Adams, C.J., Jeffrey Sorensen, Julia Elliott, Lucas Dixon, Mark McDonald, Nithum, and Will Cukierski. 2017. "Toxic Comment Classification Challenge." Kaggle. <a href="https://kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge">https://kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge</a>.</p><p><strong>Jigsaw Unintended Bias: </strong>Adams, C.J., Daniel Borkan, Inversion, Jeffrey Sorensen, Lucas Dixon, Lucy Vasserman, and Nithum. 2019. "Jigsaw Unintended Bias in Toxicity Classification." Kaggle. <a href="https://kaggle.com/competitions/jigsaw-unintended-bias-in-toxicity-classification">https://kaggle.com/competitions/jigsaw-unintended-bias-in-toxicity-classification</a>.</p><p><strong>Age Bias : </strong>Díaz, Mark, Isaac Johnson, Amanda Lazar, Anne Marie Piper, and Darren Gergle. "Addressing age-related bias in sentiment analysis." In Proceedings of the 2018 chi conference on human factors in computing systems, pp. 1-14. 2018. <a href="https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/F6EMTS">Age Bias Training and Testing Data - Age Bias and Sentiment Analysis Dataverse (harvard.edu)</a></p><p><strong>Multi-dimensional news Ukraine: </strong>Färber, Michael, Victoria Burkard, Adam Jatowt, and Sora Lim. "A multidimensional dataset based on crowdsourcing for analyzing and detecting news bias." In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 3007-3014. 2020. <a href="https://zenodo.org/records/3885351#.ZF0KoxHMLtV">https://zenodo.org/records/3885351#.ZF0KoxHMLtV</a> </p><p><strong>Social biases: </strong>Sap, Maarten, Saadia Gabriel, Lianhui Qin, Dan Jurafsky, Noah A. Smith, and Yejin Choi. "Social bias frames: Reasoning about social and power implications of language." arXiv preprint arXiv:1911.03891 (2019). <a href="https://maartensap.com/social-bias-frames/">https://maartensap.com/social-bias-frames/</a> </p><p> </p><p><strong>Goal of this dataset :</strong>We want to offer open and free access to dataset, ensuring a wide reach to researchers and AI practitioners across the world. The dataset should be user-friendly to use and uploading and accessing data should be straightforward, to facilitate usage.</p><p><strong>If you use this dataset, please cite us.</strong></p><p>Navigating News Narratives: A Media Bias Analysis Dataset © 2023 by <a href="https://www.linkedin.com/in/shainaraza/">Shaina Raza, Vector Institute </a>is licensed under <a href="http://creativecommons.org/licenses/by-nc/4.0/?ref=chooser-v1">CC BY-NC 4.0 </a></p><p> </p>
IODP Expedition 383 Navigation
Operational navigation data were measured using Trimble GPS systems and saved as navigational data files including configuration, data, logs, pictures, vehicle, and waypoints. Site Fix summary data and plots are presented in Microsoft Excel. Data are presented by expedition.
Transforming towards what? A review of futures thinking applied in the quest for navigating sustainability transformations
<p>This is the dataset used for the review article "Transforming towards what? A review of futures thinking applied in the quest for navigating sustainability transformations". The spreadsheet contains the bibliographic records and the data used and organized for its analysis. </p>
Sidewalk Environment for Visual Navigation
<p>This dataset contains low and high resolution panoramic images, coordinates, labels and a connectivity graph. In order to run this simulated environment, you will need at least one copy of the panoramic images, which are available in low (84x224 pixels) or high resolution (1280x3840 pixels). For more information, visit https://mweiss17.github.io/SEVN/.</p>
Evaluating Open Science Practices in Indoor Positioning and Indoor Navigation Research (Supplementary Material: Full Paper Listing and Analysis)
<p>Supplementary material of the paper:</p> <p>Title: "Evaluating Open Science Practices in Indoor Positioning and Indoor Navigation Research"<br>Subtitle: "A Survey of the IPIN's Reference Papers of 2022 and 2023 Editions"</p> <p>The paper is accepted to the "14th International Conference on Indoor Positioning and Indoor Navigation, IPIN 2024, Hong Kong, October 14-17, 2024, IEEE, 2024.</p> <p>An Author's accepted version of the manuscript is available here: <a href="../records/13684170" target="_blank" rel="noopener">https://zenodo.org/records/13684170</a> </p> <p>If you want to refer to this work, please cite this Zenodo entry as well as the published conference version.</p> <p> </p> <p>---------------------------------------</p> <p>This entry contains two files:</p> <ul> <li>"Paper Characterization Spreadsheet.xlsx": <strong>The spreadsheet of the full analysis of this work</strong>, as described in the paper. It characterizes various features of the analyzed papers and forms the raw data on which the analyses of our work were based.</li> <li>"Main features of the manuscripts analysed in Zenodo Record #12088175.pdf": A document summarizing the main features of the IPIN's Reference Papers of the 2022 and 2023 Editions, that contain some form of open resources (Open Data, Code, or Material).</li> </ul> <p> </p> <p> </p> <p> </p>
Navigating the complex policy landscape for carbon farming in The Netherlands and the EU -- Open Research Europe Extended Data-- Tables 1-6, Figures 1-2
<p>This is extended data for the article entitle 'Navigating the complex policy landscape for carbon farming in The Netherlands and the EU' submitted to Open Research Europe by Eise Spijker. </p>
prioritization data and result for the navigable danube
<p>Shape file containing data and results of the prioritization approach for the navigable Danube river for conservation and restortion planning.</p>
Dispersal of alien species in relation to the historic development of hydropower generation and navigation
<p>Dataset on dispersal of alien species in relation to the historic development of hydropower generation and Navigation along the River Danube.</p>
Suplementary material: Towards uncoding hepatotoxicity of approved drugs through navigation of multiverse and consensus chemical spaces
<p>Supplementary material: "Towards uncoding hepatotoxicity of approved drugs through navigation of multiverse and consensus chemical spaces"</p>
Navigating within the Safe Operating Space with Carbon Capture On-Board
<p>This file contains the data used to plot Figure 3 and Figure 4 in the manuscript Navigating within the Safe Operating Space with Carbon Capture On-Board published in ACS Sustainable Chemistry and Engineering.</p>
A multi-scale labeled dataset for boulder segmentation and navigation on small bodies
<p>The capability to detect boulders on the surface of small bodies is beneficial for vision-based applications such as hazard detection during critical operations, safety quantification, autonomous planning of scientific operations, and autonomous navigation. This task, however, is challenging due to the wide assortment of irregular shapes, the characteristics of the boulders population, and the rapid variability in the illumination conditions. Moreover, the lack of publicly available labeled datasets damps the research about data-driven algorithms. The following dataset has been designed and made publicly available to tackle these challenges. Its purpose is twofold. First, from the lessons learned from previous datasets, to develop a multi-purpose, high-fidelity dataset with boulders scattered across the surface of a small body. Second, to exploit domain randomization, artificial noise addition, scaling, and post-processing, enabling the design of data-driven pipelines. </p> <p>The methodology used to generate the dataset is illustrated in the work "A multi-scale labeled dataset for boulder segmentation and navigation on small bodies" by Mattia Pugliatti and Michele Maestrini, presented at the 74th IAC (International Astronautical Congress), 2024, Baku, Azerbaijan.</p> <p>The dataset contains the image-label pairs of 47502 samples, organized with the following structure: </p> <p>Dataset_PugliattiMaestrini_2023IAC<br> --img<br> --labels<br> --masks</p> <p>The dataset is comprised of 47502 samples. The "img" folder contains the input, 512x 512 grayscale images. The "labels" folder includes the .txt segmentation labels of the 15 most prominent boulders for each image detected with the methodology illustrated in the IAC paper. The "masks" dataset contains the segmentation masks for all image layers, with the values being encoded between 0 and 17 as uint8. The samples are named as XXXXXX_YYY. XXXXXX stands for the image's original ID during rendering. YYY corresponds to the sub-splits of the original image obtained at rendering: </p> <p> 001 - Top-Left crop<br> 002 - Top-Right crop<br> 003 - Bottom-Left crop<br> 004 - Bottom-right crop<br> 005 - Whole, resized</p> <p>The file "10000_ub_2023-01-18 00.09.43.txt" contains all the values of the rendering inputs detailed in the IAC paper.</p>
IODP Expedition 361 Navigation
<p>Operational navigation data were measured using Trimble GPS systems and saved as navigational data files including configuration, data, logs, pictures, vehicle, and waypoints. Site Fix summary data and plots are presented in Microsoft Excel. Data are presented by expedition.</p>
IODP Expedition 362 Navigation
<p>Operational navigation data were measured using Trimble GPS systems and saved as navigational data files including configuration, data, logs, pictures, vehicle, and waypoints. Site Fix summary data and plots are presented in Microsoft Excel. Data are presented by expedition.</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)
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.