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1,321 results for “Navigation”
Linked collectors and determiners for: Navigator, a new endemic genus of Cetoniinae (Coleoptera: Scarabaeidae) from Australia, with descriptions of two new species and behavioural studies.
Natural history specimen data linked to collectors and determiners held within, "Navigator, a new endemic genus of Cetoniinae (Coleoptera: Scarabaeidae) from Australia, with descriptions of two new species and behavioural studies". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/f9f36ad7-2af7-4b6f-bc33-37418b11470b">https://bionomia.net/dataset/f9f36ad7-2af7-4b6f-bc33-37418b11470b</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/f9f36ad7-2af7-4b6f-bc33-37418b11470b">https://gbif.org/dataset/f9f36ad7-2af7-4b6f-bc33-37418b11470b</a>. Formatted as a Frictionless Data package.
NAVIGATING BUSINESS INTELLIGENCE TOOLS: STRATEGIES TO DRIVE BUSINESS GROWTH
<p><span>This study explores the evolution and current state of business intelligence (BI) tools and their strategic role in driving business growth. The research utilizes a combination of market analysis, industry case studies, and theoretical frameworks, including the DIKW hierarchy and Resource-Based View, to examine BI adoption trends. The results highlight the importance of data quality, cross-functional collaboration, and user adoption in maximizing BI effectiveness. Key findings indicate that cloud-based and self-service BI tools significantly improve data-driven decision-making, while challenges remain in data governance and integration. To address these challenges, organizations must implement robust data policies and empower users through training and self-service capabilities. The study concludes that integrating BI tools into digital transformation initiatives provides a competitive edge, enabling strategic planning, operational efficiency, and innovation. This research offers new insights into how organizations can leverage BI tools for sustained growth and enhanced decision-makingl.</span></p>
Multimodal Trajectory Prediction via Topological Invariance for Navigation at Uncontrolled Intersections
<p>A pre-trained model of the paper "Multimodal Trajectory Prediction via Topological Invariance for Navigation at Uncontrolled Intersections," CoRL 2020.</p>
Interindividual differences influence multisensory processing during spatial navigation
<p>The dataset contains the raw data from two experiments. We investigate the ability of using auditory, visual or audiovisual landmarks for spatial navigation through the environment.</p>
Dataset of Public Objects in Uncontrolled Environment for Navigation Aiding
<p>Computer vision is a new approach to navigation aiding that assist visually impaired people to travel independently. A deep learning-based solution implemented on a portable device that uses a monocular camera to capture public objects could be a low-cost and handy navigation aid. By recognizing public objects in the street and estimating the distance between the users, visually impaired people are able to avoid obstacles in the outdoor environment and walk safely. This dataset is created for navigation aiding and contains 3 classes of objects which commonly exist on the pavement in the city. It is verified that the dataset is of high quality for object detection and distance estimation, and is ultimately utilized for navigation aid solution.</p>
Improving Robustness of Deep Neural Networks for Aerial Navigation by Incorporating Input Uncertainty
<p>CEA covered the scenario of UAV navigation through a set of gates with unknown locations using a DNN-based navigation model. The implemented navigation model uses two DL components (perception and control), and uses (Bayesian) uncertainty estimation methods to capture the uncertainty (confidence) associated with the predictions of each component. The safety requirements in the UAV mission are related to the confidence (uncertainty) associated with the predictions from these components. CEA observed and analysed the uncertainty from each DNN under specific situations that can pose a risk to the UAV mission. Then, the observations were used to define STL rules to track the confidence of the DNN-based navigation system. Finally, mitigation behaviours (e.g., hover, land, DNN-based autonomous flight) are triggered depending on the satisfaction (or violation) of the STL rules. Moreover, the proposed ROS2-based architecture for safe navigation contributed to the definition and improvement of the COMP4DRONES reference architecture, showing in practice how the proposed safety monitoring architecture relates and integrates with the components from other system functions.</p>
Datasets used to train the models in "Deep learning for denoising High-Rate Global Navigation Satellite System data."
<p>Datasets used to train the models in "Deep learning for denoising High-Rate Global Navigation Satellite System data." Additional information can be found at https://github.com/amtseismo/hrgnss_denoising.</p>
Navigating the Complex Solid Form Landscape of the Quercetin Flavonoid Molecule
<p>Data related to the paper published in Crystal Growth & Design, with doi:https://doi.org/10.1021/acs.cgd.3c00584.</p> <p>Quercetin, a naturally occurring bioflavonoid substance widely used in the nutraceutical and food industries, exists in various solid forms that can have different physicochemical properties, thus impacting this compound’s performance in various applications. In this work, we will clarify the complex solid-form landscape of this molecule. Two elusive isostructural solvates of quercetin were obtained from ethanol and methanol. The obtained crystals were characterized experimentally, but the crystallographic structure could not be solved due to their high instability. Nevertheless, the desolvated structure resulting from a high-temperature treatment (or prolonged storage at ambient conditions) of both these two labile crystals was characterized and solved via powder X-ray diffraction and solid-state nuclear magnetic resonance (SSNMR). This anhydrous crystal structure was compared with another anhydrous quercetin form obtained in our previous work, indicating that, at least, two different anhydrous polymorphs of quercetin exist. Navigating the solid-form landscape of quercetin is essential to ensure accurate control of the functional properties of food, nutraceutical, or pharmaceutical products containing crystal forms of this substance.</p>
IODP Expedition 382 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>
Optical Navigation Dataset for Solar System Small Bodies
<p>This dataset has been curated for the purpose of training and evaluating a variety of local feature extractors intended for optical navigation in the proximity of Solar System small bodies (SSSBs). It aims to serve as a resource for researchers in the field and it is referenced in the related article titled "CNN-based local features for navigation near an asteroid" [1]. Additionally, the associated Python code for this dataset can be found in [2].</p> <p>The dataset is a compilation of images obtained from four distinct space missions focused on SSSBs, specifically NEAR Shoemaker (Eros) [3], Hayabusa (Itokawa) [4], Rosetta (67P/Churyumov-Gerasimenko) [5, 6], and OSIRIS-REx (Bennu) [7]. It also incorporates synthetic data generated through the utilization of a Bennu shape model [8] and OpenGL-based rendering software [9, 10]. Access to mission-specific images is available through the NASA Planetary Data System (PDS), and for the Rosetta mission, via the ESA Planetary Science Archive [11].</p> <p>The prefix <code>rot-</code> has been applied to subsets in which images have been pre-rotated to orient the SSSB's rotation axis upwards within the image frame. These subsets are primarily intended for training purposes and encompass image pairs with pixel correspondences that can be found in the <code>aflow</code> directory. Pixel correspondences are stored as 16-bit PNG images, where the G- and B-channels respectively represent the x and y image coordinates. To facilitate data compression and storage, a fixed scaling coefficient of 8 has been employed to convert the pixel correspondence float array into a 16-bit integer array to be used by the PNG compression. These pixel correspondence files can be loaded using the <code>navex.datasets.tools.load_aflow</code> function from [2].</p> <p>On the other hand, subsets designated with a <code>-d</code> postfix include depth information (<code>*.d</code> files) and are exclusively employed during the evaluation of the proposed feature extractors. The depth data is stored as scaled grayscale 16-bit integer arrays using PNG compression. A custom additional header accompanies these images, providing two 32-bit float values, namely the subtracted offset <em>v<sub>0</sub> </em>and the scale multiplier <em>s</em> utilized in the calculation of image pixel values as <em>v</em>' = (<em>v</em> - <em>v<sub>0</sub></em>)·<em>s</em>. To access the depth data as a 32-bit float array, researchers can utilize the <code>navex.datasets.tools.load_mono</code> function from [2].</p> <p>Please note that the file paths in e.g. <code>rot-cg67p-osinac.tar</code> and <code>cg67p-osinac-d.tar</code> archives are the same, so you need to either rename the extracted folder, extract them to different folders, or only extract the archive that you need.</p> <p>For clarity, it should be noted that subsets lacking the aforementioned pre- or postfixes do not contain paired images and consequently lack pixel correspondences. These subsets were exclusively used for feature extractor training in [1].</p> <p>The dataset also includes <code>*.ckpt</code> files, which are the trained feature extractor models referred to in [1]. More details about how to use them can be found in [2].</p>
Landmark-Aware Visual Navigation (LAVN) Dataset
<p>We present a \textbf{L}andmark-\textbf{A}ware \textbf{V}isual \textbf{N}avigation (LAVN) dataset to allow for supervised learning of human-centric exploration policies. We collect RGB observation and human point-click pairs as a human annotator explores virtual and real world environments with the goal of full coverage exploration of the space. These human point-clicks serve as direct supervision for waypoint prediction when learning to explore in environments. The human annotators also provide distinct landmark examples along each trajectory, which we intuit will simplify the task of map or graph building and localization. </p>
Developing a Health Insurance Navigation Program for Survivors of Childhood Cancer
ClinicalTrials.gov study NCT04520061. IPD Sharing: NO. Countries: 1. Publications: 10.
CBCT-Guided Navigational Bronchoscopy For Lung Nodules
ClinicalTrials.gov study NCT04758403. IPD Sharing: YES. Countries: 1. Publications: 24.
Telemedicine Control Tower for the Operating Room: Navigating Information, Care and Safety
ClinicalTrials.gov study NCT03923699. IPD Sharing: NO. Countries: 1. Publications: 4.
Pilot Study on Training Emerging Adults Skills in Navigating College
ClinicalTrials.gov study NCT05560425. IPD Sharing: YES. Countries: 1. Publications: 13.
Data from: behaviorMate: An Intranet of Things approach for adaptable control of behavioral and navigation-based experiments
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Data from: Distinct catecholaminergic pathways projecting to hippocampal CA1 transmit contrasting signals during navigation in familiar and novel environments
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Collective navigation as a solution to noisy navigation and its vulnerability to population loss
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Carbon dioxide and blood-feeding shift visual cue tracking during navigation in Aedes aegypti mosquitoes
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GENT900000065568 - Recueil de plusieurs placcarts fort utiles au pays de Haynnau, dont les chartes dudit pays renvoient à quantité d'iceux. Avec le decret de l'an 1601, l'edit perpetuel, le reglement de la navigation, mesures des heritages
<p>Titel: <em>Recueil de plusieurs placcarts fort utiles au pays de Haynnau, dont les chartes dudit pays renvoient à quantité d'iceux. Avec le decret de l'an 1601, l'edit perpetuel, le reglement de la navigation, mesures des heritages.</em></p> <p>Publisher: Simeon de la Roche</p> <p>Place: Mons.</p> <p>Year:1664</p> <p>Used version: The copy we used for the transcriptions is held at Ghent University.</p> <p>Link digitised version of the book: <a href="https://books.google.nl/books?id=kfE9AAAAcAAJ&dq=Recueil+de+plusieurs+placcarts+fort+utiles+au+pays+de+Haynnau,+dont+les+chartes+dudit+pays+renvoient&hl=nl&source=gbs_navlinks_s">https://books.google.nl/books?id=kfE9AAAAcAAJ&dq=Recueil+de+plusieurs+placcarts+fort+utiles+au+pays+de+Haynnau,+dont+les+chartes+dudit+pays+renvoient&hl=nl&source=gbs_navlinks_s</a></p> <p>(Main) Language:French</p> <p>Province: Hainaut.</p> <p>Font:Roman.</p> <p>Model used: French_18thC_Print (public model in Transkribus)</p> <p>Version of Transkribus used: v.1.9.1.</p> <p>Other info: Abbyy FineReader v.11 has been used.</p> <p>Link model: For more information on the HTR-model used, please visit: <a href="https://lab.kb.nl/dataset/entangled-histories-ordinances-low-countries">https://lab.kb.nl/dataset/entangled-histories-ordinances-low-countries</a>.</p> <p>Transcription conventions:</p> <ul> <li> <p>The abbreviations have been written out into full words.</p> </li> <li> <p>The hyphens at the end of a line have been kept (when there).</p> </li> </ul> <p>If you are in need of the original scans of the documents, please contact <a href="mailto:xxxxxx@kb.nl">dataservices@kb.nl</a>.</p> <p>This transcription is part of the dataset created with the ‘Entangled Histories’-project.</p> <p>PI: dr. C.A. Romein;<br> Scientific Programmer: S.F. Veldhoen, MSc;<br> Project Manager: drs. M. de Gruijter.</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.