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105 results for “AIS data”
Environmental and AIS data collected during the EUMarineRobots Trans-National Access activities experiments using the NATO STO-CMRE Littoral Ocean Observatory Network testbed (Release 2)
<p>Environmental and AIS data collected during the second phase of EUMR TNA experiments using the CMRE LOON testbed. Environmental data consists of temperature measured across the water column; sound velocity measured close to the surface and close to the sea bottom; meteorological data at the surface (i.e., pressure, temperature, wind speed and direction, humidity and rain). The environmental dataset is complemented with Automatic Identification System (AIS) data for the ships transiting close to the LOON area (Gulf of La Spezia, Italy)</p> <p>Temperature measured across the water column in the LOON area (Gulf of La Spezia, Italy). The dataset includes measurements for:<br> i) June 9-11, 17-18, 25-26 - 2021<br> ii) July 5-7, 21-27, 30-31 - 2021<br> iii) August 3-5, 10-14, 19-21, 23-24, 28-30 - 2021</p> <p><br> Meteorological data at the surface (i.e., pressure, temperature, wind speed and direction, humidity and rain) in the LOON area (Gulf of La Spezia, Italy). The dataset includes measurements for:<br> i) June 9-11, 17-18, 25-26 - 2021<br> ii) July 5-7, 21-27, 30-31 - 2021<br> iii) August 3-5, 10-14, 19-21, 23-24, 28-30 - 2021</p> <p><br> Sound velocity measured close to the surface (SVP1) and close to the sea bottom (SVP2) in the LOON area (Gulf of La Spezia, Italy). The dataset includes measurements for:<br> i) June 9-11, 17-18, 25-26 - 2021<br> ii) July 5-7, 21-27, 30-31 - 2021<br> iii) August 3-5, 10-14, 19-21, 23-24, 28-30 - 2021</p> <p>SVP1 data missing for June 17-18 (2021) and July 5-7 (2021).</p> <p><br> Automatic Identification System (AIS) data for the ships transiting close to the LOON area (Gulf of La Spezia, Italy). The dataset includes AIS data for:<br> i) June 9-11 - 2021</p> <p>AIS recorded data not available after June 11, 2021</p> <p>For reference, see: "Environmental data collected on the CMRE LOON tested during the EUMR project: dataset description", Petroccia, Roberto; Zappa, Giovanni; Cimino, Giampaolo; Grati, Alberto; Alves, João. CMRE-DA-2021-001. July 2021, available at https://www.cmre.nato.int/research/publications/latest-techreports/1638-cmre-da-2021-001</p>
Experimental Data for the paper Training AI to Recognize Realizable Gauss diagrams: the Same Instances Confound AI and Human Mathematicians
<p>This upload contains supplementary materials for the paper <br> Training AI to Recognize Realizable Gauss diagrams: the Same Instances Confound AI and Human Mathematicians, by Abdullah Khan, Alexei Lisitsa and Alexei Vernitski, to appear in Proceedings of ICAART 2022 (14th International Conference on Agents and Artificial Intelligence, 3-5 February, 2022), SCITEPRESS. </p>
Generative AI in University Communication, 2nd Wave - Survey Data (May 2024)
<p>Der Datensatz mit dem Titel "Generative KI in der Hochschulkommunikation, 2. Welle - Umfragedaten (Mai 2024)" erfasst Informationen zur Einführung und Nutzung von generativer künstlicher Intelligenz (KI) im Kontext der Hochschulkommunikation. Die Umfrage, die im Mai 2024 unter 318 deutschen Hochschulen durchgeführt wurde, von denen 82 geantwortet haben, untersucht verschiedene Aspekte, darunter Bekanntheit und Wissen über verschiedene KI-Tools (z.B. ChatGPT), Diskussionen in Gremien, das Vorhandensein von Richtlinien für die Nutzung, das Vorhandensein von Arbeitsgruppen für generative KI, strategische Ziele und Initiativen, Schulungsangebote für generative KI-Tools und die wahrgenommene Bedeutung von generativen KI-Tools in der Hochschulkommunikation. Ziel des Datensatzes ist es, Einblicke in die aktuelle Landschaft und Praxis der Integration generativer KI im universitären Umfeld zu geben. Die Daten der ersten Erhebung sind unter https://doi.org/10.5281/zenodo.10254904 zu finden.</p> <p>The dataset, titled "Generative AI in University Communication - Survey Data (May 2024)," captures information related to the adoption and utilization of generative artificial intelligence (AI) in the context of university communication. This survey, conducted in June 2024 among 318 German universities of which 82 responded, explores various aspects, including awarenes and knowledge of various AI tools (e.g. ChatGPT), discussions in committees, the existence of guidelines for usage, the presence of working groups for generative AI, strategic goals and initiatives, training offerings for generative AI tools, and the perceived importance of generative AI tools in university communication. The dataset aims to provide insights into the current landscape and practices regarding the integration of generative AI within university settings. Data of the first wave can be found here: https://doi.org/10.5281/zenodo.10254904<br><br>More information here: <a href="https://www.hof.uni-halle.de/projekte/hochki/">https://www.hof.uni-halle.de/projekte/hochki/</a></p>
Supporting data for Emerging AI-based weather prediction models as downscaling tools
<p>Supporting data for "Emerging AI-based weather prediction models as downscaling tools" by Nikolay Koldunov, T. Rackow, Christian Lessig, S. Danilov, S. Cheedela, D. Sidorenko, Irina Sandu, Thomas Jung</p> <p><a href="https://t.co/PSUCUvh9lf" target="_blank" rel="noopener noreferrer nofollow"><span>https://</span>doi.org/10.48550/arXiv<span>.2406.17977</span></a></p>
Link Discovery on AIS and Contextual data sets
<p>This data set contains the links discovered between AIS synopses provided in https://zenodo.org/record/2576152 and contextual data sets available at https://zenodo.org/record/2576584 . Specifically, the detected relations and the contextual data sets used are:</p> <p>a) C1 ports of Brittany, World Port Index and SeaDataNet fishing ports for proximity relation "nearto", stored in AIS_nearto_ports.ttl.7z</p> <p>b) C4 fishing areas (European Commission) for "within" relation stored in AIS_within_fishingAreas.ttl.7z</p> <p>c) C5 fishing interdiction for "within" relation stored in AIS_within_FishingConstraints.ttl.7z</p> <p>d) C5 Natura2000 for "within" relation stored in AIS_within_natura2000.ttl.7z</p> <p>Please notice that the attached data sets contain only the detected links. Description of the resources is provided in the corresponding TTL files.</p>
Data for publication 'Recreational vessels without Automatic Identification System (AIS) dominate anthropogenic noise contributions to a shallow water soundscape' (Scientific Reports 2019)
<p>Data on vessel tracks and underwater noise levels presented in the publication Hermannsen, L., Mikkelsen, L., Tougaard, J., Beedholm, K., Johnson, M. and P. T. Madsen, "Recreational vessels without Automatic Identification System (AIS) dominate anthropogenic noise contributions to a shallow water soundscape", Scientific Reports 9:15477 (<a href="https://doi.org/10.1038/s41598-019-51222-9">https://doi.org/10.1038/s41598-019-51222-9</a>).</p>
Data Science Tasks used in "AI Support for Data Scientists: An Empirical Study on Workflow and Alternative Code Recommendation"
<p>This entry contains the supplementary files for a scientific article. </p> <p>The dataset contains the necessary files for the two data science tasks used in the experiment study from scientific article "AI Support for Data Scientists: An Empirical Study on Workflow and Alternative Code Recommendation"</p> <p> </p>
Generative AI for designing and validating easily synthesizable and structurally novel antibiotics: Data and Models
<p>This repository contains data and models used in the following paper.</p> <p>Swanson, K., Liu, G., Catacutan, D., Zou, J. & Stokes, J. <a href="https://www.nature.com/articles/s42256-024-00809-7">Generative AI for designing and validating easily synthesizable and structurally novel antibiotics</a>. <em>Nature Machine Intelligence, </em>2024.</p> <p>The data and models are meant to be used with the <a href="https://github.com/swansonk14/SyntheMol">SyntheMol</a> code. More details about how to use the data and models with the code are available <a href="https://github.com/swansonk14/SyntheMol/tree/main/docs">here</a>.</p> <p>The Data.zip file has the following structure. Note that the numbers for the Data subdirectories correspond to the supplementary data numbers in the paper (e.g., 1_training_data corresponds to Supplementary Data 1).</p> <p>Data</p> <p> 1_training_data: The <em>Acinetobacter baumannii</em> inhibition data used to train antibiotic property prediction models.</p> <p> 2_chembl: Known antibiotic and antibacterial molecules from <a href="https://www.ebi.ac.uk/chembl/">ChEMBL</a>, which are used to compute the novelty of generated antibiotic candidates.</p> <p> 4_real_space: Data files and statistics for the <a href="https://enamine.net/compound-collections/real-compounds/real-space-navigator">Enamine REAL Space</a>. The molecular building blocks file is version 2021 q3-4 while all other REAL Space details are computed from the full enumerated REAL space version 2022 q1-2 (downloaded on August 30, 2022).</p> <p> 5_generations_clogp: Compounds generated by SyntheMol using Chemprop models trained to predict cLogP.</p> <p> 6_generations_chemprop: Compounds generated by SyntheMol using Chemprop models trained to predict <em>A. baumannii</em> inhibition.</p> <p> 7_generations_chemprop_rdkit: Compounds generated by SyntheMol using Chemprop-RDKit models trained to predict <em>A. baumannii</em> inhibition.</p> <p> 8_generations_random_forest: Compounds generated by SyntheMol using random forest models trained to predict <em>A. baumannii</em> inhibition.</p> <p> 9_synthesized: Information on the 58 SyntheMol-generated compounds that were successfully synthesized by Enamine.</p> <p>The Models.zip file contains one folder for each model used in the paper. Note that each model is technically an ensemble of ten individual models, so each directory contains ten model files.</p>
Data from: Two complementary AI approaches for predicting UMLS semantic group assignment: heuristic reasoning and deep learning
<p><strong>Objective</strong>: Use heuristic, deep learning (DL), and hybrid AI methods to predict semantic group (SG) assignments for new UMLS Metathesaurus atoms, with target accuracy ≥ 95%.</p> <p><strong>Materials and Methods</strong>: We used train-test datasets from successive 2020AA-2022AB UMLS Metathesaurus releases. Our heuristic "waterfall" approach employed a sequence of seven different SG prediction methods. Atoms not qualifying for a method were passed on to the next method. The DL approach generated BioWordVec and SapBERT embeddings for atom names, BioWordVec embeddings for source vocabulary names, and BioWordVec embeddings for atom names of the second-to-top nodes of an atom's source hierarchy. We fed a concatenation of the four embeddings into a fully connected multi-layer neural network with an output layer of 15 nodes (one for each SG). Both methods were capable of estimating the probability that their predicted SG for an atom would be correct. We developed two hybrid SG prediction methods combining the strengths of heuristic and DL methods.</p> <p><strong>Results</strong>: The heuristic waterfall approach accurately predicted 94.3% of SGs for 1,563,692 new unseen atoms. The DL accuracy on the same dataset was also 94.3%. The hybrid approaches achieved an average accuracy of 96.5%.</p> <p><strong>Conclusion</strong>: Our study demonstrated that AI methods can predict SG assignments for new UMLS atoms with sufficient accuracy to be potentially useful as an intermediate step in the time-consuming task of assigning new atoms to UMLS concepts (CUIs). We showed that for SG prediction, combining heuristic methods and DL methods can produce better results than either alone.</p>
Satellite images and road-reference data for AI-based road mapping in Equatorial Asia
<p><span>1. </span><span>INTRODUCTION</span></p> <p><span>For the purposes of training AI-based models to identify (map) road features in rural/remote tropical regions on the basis of true-colour satellite imagery, and subsequently testing the accuracy of these AI-derived road maps, we produced a dataset of 8904 satellite image 'tiles' and their corresponding known road features across Equatorial Asia (Indonesia, Malaysia, Papua New Guinea).</span><span> </span></p> <p><span>2. </span><span>FURTHER INFORMATION</span></p> <p><span>The following is a summary of our data. Fuller details on these data and their underlying methodology are given in the corresponding article, under consideration by the journal Remote Sensing as of September 2023: </span></p> <p><span>Sloan, S., Talkhani, R.R., Huang, T., Engert, J., Laurance, W.F. (2023) Mapping remote roads using artificial intelligence and satellite imagery. Under consideration by Remote Sensing.</span></p> <p><span>Correspondence regarding these data can be directed to:</span></p> <p><span>Sean Sloan</span></p> <p>Department of Geography, Vancouver Island University, Nanaimo, B.C, Canada</p> <p><span><a href="mailto:sean.sloan@viu.ca"><span>sean.sloan@viu.ca</span></a></span>; </p> <p><span>Tao (Kevin) Huang</span></p> <p>College of Science and Engineering, James Cook University, Cairns, Queensland 4878, Australia</p> <p><a href="mailto:tao.huang1@jcu.edu.au">tao.huang1@jcu.edu.au</a><span> </span></p>
Assessing the Performance of Artificial Intelligence (AI)-Augmented Electronic Health Record (EHR) Data Abstraction for Clinical Trial Patient Screening
ClinicalTrials.gov study NCT06561217. IPD Sharing: NO. Countries: 1. Publications: 1.
Satellite images and road-reference data for AI-based road mapping in Equatorial Asia
Open the record for dataset details and reuse information.
Data from: Two complementary AI approaches for predicting UMLS semantic group assignment: heuristic reasoning and deep learning
Open the record for dataset details and reuse information.
AI-Derived West Nile Virus Determinants Maps - 2018 Europe - Data
<p>Data for AI-Derived West Nile Virus Determinants Maps</p> <p>2018 Europe</p> <ol> <li>Original (nominal value) data</li> <li>SHAP-derived effect data </li> </ol>
Data from: Using satellite AIS to improve our understanding of shipping and fill gaps in ocean observation data to support marine spatial planning
1. A key stage underpinning marine spatial planning (MSP) involves mapping the spatial distribution of ecological processes and biological features, as well the social and economic interests of different user groups. One sector, merchant shipping (vessels that transport cargo or passengers), however, is often poorly represented in MSP due to a perceived lack of fine-scale spatially explicit data to support decision making processes. 2. Here, using the Republic of Congo as an example, we show how publicly accessible satellite derived Automatic Identification System (S-AIS) data can address gaps in ocean observation data for shipping at a national scale. We also demonstrate how fine-scale (0.05 km2 resolution) spatial data layers derived from S-AIS (intensity, occupancy) can be used to generate maps of vessel pressure to provide an indication of patterns of impact on the marine environment and potential for conflict with other ocean-user groups. 3. We reveal that passenger vessels, offshore service vessels, bulk carrier and cargo vessels and tankers account for 93.7% of all vessels and vessel traffic annually, and that these sectors operate in a combined area equivalent to 92% of Congo's exclusive economic zone(EEZ) – far exceeding the areas allocated for other user-groups (conservation, fisheries and petrochemicals). We also show that the shallow coastal waters and habitats of the continental shelf are subject to more persistent pressure associated with shipping; and that the potential for conflict among user groups is likely to be greater with fisheries, whose zones are subject to the highest vessel pressure scores than with conservation or petrochemical sectors. 4. Synthesis and applications. Shipping dominates ocean use, and so excluding this sector from decision making could lead to increased conflict among user groups, poor compliance and negative environmental impacts. This study demonstrates how Satellite derived Automatic Identification System data can provide a comprehensive mechanism to fill gaps in ocean observation data and visualise patterns of vessel behaviour and potential threats to better support marine spatial planning at national scales.13-Feb-2018
The Piraeus AIS data set expressed under the vesselAI ontology
<p>See also <a href="https://github.com/gsantipantakis/dataExtraction/releases/">https://github.com/gsantipantakis/dataExtraction/releases/</a> for data extraction to tab separated values (TSV) files.</p>
A sample of the training data used in the paper "A Hybrid Physics-AI (HyPhAI) approach for probability fields advection: Application to cloud cover nowcasting"
<p>Copyright (2024) EUMETSAT</p>
Experts fail to reliably detect AI-generated histological data
<p>This repository contains material related to the paper "<em>Experts fail to reliably detect AI-generated histological data</em>":</p> <ul> <li>Dreambooth parameters (<em>dreambooth_parameters.zip</em>)</li> <li>Images displayed during the study <em>(images.zip)</em></li> <li>Data collected during the survey (<em>results-survey.xlsx</em>)</li> <li>R Code to reproduce results and figures (<em>analysis_code.zip</em>)</li> </ul> <p>Please find our associated work here:</p> <p>Hartung, J., Reuter, S., Kulow, V.A., Fähling, M., Spreckelsen, C., and Mrowka, R. (2024). Experts fail to reliably detect AI-generated histological data. Sci Rep <em>14</em>, 28677. https://doi.org/10.1038/s41598-024-73913-8.</p>
AI tutorial data
<p>Preprocessing demo</p>
Protein structure data for "AI-predicted protein deformation encodes energy landscape perturbation"
<p>AF2-predicted protein structures of WT and mutant proteins that have corresponding ddG measurements in the ThermoMutDB database of protein mutant stability measurements. PDB structures are compressed using <a href="https://github.com/steineggerlab/foldcomp/">FoldComp</a>, and saved in "structures.zip".</p> <p>Summary of the final dataset and results can be found in "results_summary.pkl".</p> <p>Code used to plot figures can be found in "code4figs.zip".</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.