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105 results for “AIS data”
Figure 5 from: Ding H-L, Wang P, Qian Z-X, Lin J-H, Zhou Z-C, Hwang C-C, Ai H-M (2016) Revision of sinistral land snails of the genus Camaena (Stylommatophora, Camaenidae) from China based on morphological and molecular data, with description of a new species from Guangxi, China. ZooKeys 584: 25-48. https://doi.org/10.3897/zookeys.584.7173
Figure 5 - Shell of Camaena poyuensis sp. n. A Holotype, FJIQBC 18484, Poyue, Bama, Guangxi, China B Paratype, FJIQBC18489, from type locality C Life photograph of paratype, FJIQBC 18485, from type locality. Scale: 10 mm.
Figure 4 from: Ding H-L, Wang P, Qian Z-X, Lin J-H, Zhou Z-C, Hwang C-C, Ai H-M (2016) Revision of sinistral land snails of the genus Camaena (Stylommatophora, Camaenidae) from China based on morphological and molecular data, with description of a new species from Guangxi, China. ZooKeys 584: 25-48. https://doi.org/10.3897/zookeys.584.7173
Figure 4 - Reproductive system of sinistral Chinese sinistral species of Camaena. A Camaena cicatricosa (FJIQBC 18483, Guiping, Guangxi, China) B Camaena obtecta (FJIQBC 18743, Longbang, Jinxi, Guangxi, China) C Camaena inflata (FJIQBC 18797, Qianlin park, Guiyang, Guizhou, China) D Camaena connectens (FJIQBC 18832, Tianbao, Malipo, Yunnan, China) E Camaena poyuensis sp. n. (FJIQBC 18484, Poyue, Bama, Guangxi, China). Abbreviations: V, verge; AG, albumen gland; BC, bursa copulatrix; E, epiphallus; F, flagellum; HD, hermaphroditic duct; P, penis; PR, penis retractor muscle; PBC, pedunculus of bursa copulatrix; VD, vas deferens.
Figure 3 from: Ding H-L, Wang P, Qian Z-X, Lin J-H, Zhou Z-C, Hwang C-C, Ai H-M (2016) Revision of sinistral land snails of the genus Camaena (Stylommatophora, Camaenidae) from China based on morphological and molecular data, with description of a new species from Guangxi, China. ZooKeys 584: 25-48. https://doi.org/10.3897/zookeys.584.7173
Figure 3 - Shells of sinistral Chinese species of Camaena. A Camaena cicatricosa (FJIQBC 18483, Guiping, Guangxi, China) B Camaena obtecta (FJIQBC 18743, Longbang, Jingxi, Guangxi, China) C Camaena inflata (FJIQBC 18782, Qianlin park, Guiyang, Guizhou, China) D Camaena connectens (FJIQBC 18826, Tianbao, Malipo, Yunnan, China) E Camaena seraphinica (syntype, IZCAS HMT-0001a; Dingan, Tianlin, Guangxi, China). Red circle indicates a hump beside the umbilicus. Scale = 10 mm.
Figure 2 from: Ding H-L, Wang P, Qian Z-X, Lin J-H, Zhou Z-C, Hwang C-C, Ai H-M (2016) Revision of sinistral land snails of the genus Camaena (Stylommatophora, Camaenidae) from China based on morphological and molecular data, with description of a new species from Guangxi, China. ZooKeys 584: 25-48. https://doi.org/10.3897/zookeys.584.7173
Figure 2 - Maximum Likelihood tree based on analysis of the concatenated dataset of COI, 16S and ITS2 sequences. Numbers beside nodes indicate bootstrapping support (%) for main clades. See Table 2 for sampling locations.
Figure 1 from: Ding H-L, Wang P, Qian Z-X, Lin J-H, Zhou Z-C, Hwang C-C, Ai H-M (2016) Revision of sinistral land snails of the genus Camaena (Stylommatophora, Camaenidae) from China based on morphological and molecular data, with description of a new species from Guangxi, China. ZooKeys 584: 25-48. https://doi.org/10.3897/zookeys.584.7173
Figure 1 - Map of locations of Camaena species. Camaena cicatricosa: A Nanning, Guangxi, China B Guiping, Guangxi, China C Yangchun, Guangdong, China D Gaoming, Canton, Guangdong, China E Yingde, Guangdong, China F Shantou, Guangdong, China. Camaena obtecta: G Buhaitun, Jinxi, Guangxi, China H Longbang, Jingxi, Guangxi, China I Cao Bang, Vietnam (type locality). Camaena inflata: J Qianlin park, Guiyang, Guizhou, China K Ziyun, Guiyang, Guizhou, China. Camaena connectens: L Tianbao, Malipo, Yunnan, China M Ha Giang, Vietnam (type locality). Camaena poyuensis sp. n.: N Poyue, Bama, Hechi, Guangxi, China (type locality). Camaena seraphinica: O Dingan, Tianlin, Guangxi, China (type locality).
Evaluation of ECG Transmission and AI Models Using Apple Watch ECGs and Symptoms Data Collected Using a Mayo iPhone App
ClinicalTrials.gov study NCT05324566. IPD Sharing: NO. Countries: 1. Publications: 0.
[5G-IANA] UC1 - Data processed by the AI object detection algorithm
<p>Bounding boxes of objects detected by the artificial intelligence algorithm for each video frame received.</p>
Article: AI-MAPE Versus SA-SGM - Complete Data Bank
<p>Mechanistic models provide an in-depth understanding of important biophysical systems. In fields such as developmental biology, these models are inherently complex, as they require genuinely spatial-stochastic descriptions of the underlying systems. This complexity poses significant challenges for inferring model parameters. Recently, modern deep-learning techniques have been integrated with simulation-based inference, creating an exciting new approach for estimating parameters of such models. Their overall goal is to computationally replicate target empirical observations and to develop powerful prediction tools for uncovering hidden system dynamics. However, these modern approaches remain broadly general and are often difficult to implement for specific spatial-stochastic problems, particularly within developmental biology. This difficulty raises the question of how much more valuable these modern approaches are compared to classical techniques. In this study, we compare one modern approach, <em>AI-MAPE</em>, inspired by the sequential neural posterior estimation (SNPE) algorithm, against one classical approach, <em>SA-SGM</em>, inspired by the simulated annealing (SA) algorithm. Our findings show that, while the inferred parameter sets generally agree between the two approaches, the AI-powered method, at comparable computational effort, provides significantly richer and more regular inferred distributions. This results in more detailed information about parameter interactions and synergies than the SA-inspired method.</p>
Data augmentation with Generative AI for DoW attack detection in serverless architectures
<p>Serverless computing is one of the latest paradigms in cloud computing. It offers a framework for the development of event-driven applications whose functions are executed in a scalable environment provided by the corresponding cloud platform. In this way, resources are obtained on demand, paying only for the time the function is running. This new model has new vulnerabilities and, therefore, new types of cybersecurity attacks. However, there are still not enough transaction datasets for serverless systems with a sufficient amount of data to develop advanced detection methods for this type of threat. Therefore, we present this dataset that has been built with generative AI to advance the development of models that can effectively deal with these threats.</p>
MMETHANE: predicting host status from microbial composition and metabolomics data with interpretable AI
<p>Data to reproduce figures:</p> <ol> <li>"real_data.xlsx" contains data to reproduce Figure 2.</li> <li>"semisyn_data.xlsx" contains data to reproduce Figure 3.</li> <li>"fingerprint_data.xlsx" contains data to reproduce Supplementary Figure 1.</li> </ol>
Study to Collect Real-world Performance and Safety Data on Penumbra System® in Population With Acute Ischemic Stroke (AIS).
ClinicalTrials.gov study NCT07107022. IPD Sharing: NO. Countries: 2. Publications: 0.
An AI Platform Integrating Imaging Data and Models, Supporting Precision Care Through Prostate Cancer's Continuum
ClinicalTrials.gov study NCT05384002. IPD Sharing: YES. Countries: 1. Publications: 0.
Data Collection Protocol for the Development of CHLOE-OQ, an AI Model for Assessing Oocytes Quality.
ClinicalTrials.gov study NCT06928337. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Modelling and AI Using Sensor Data to Personalise REHABilitation Following Joint Replacement
ClinicalTrials.gov study NCT04289025. IPD Sharing: YES. Countries: 1. Publications: 0.
Data Collection and Professional Simulated Use Study to Develop an Embryo Quality Artificial Intelligence (AI) Model
ClinicalTrials.gov study NCT07017972. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
AI-Agent for Automated Diagnosis and Predicting Using EHR and Multimodal Data
ClinicalTrials.gov study NCT06791499. IPD Sharing: NO. Countries: 1. Publications: 0.
AI-Based Real-Time Detection of Surgical Smoke Using Endoscopic Data
ClinicalTrials.gov study NCT07397000. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data Collection for CV-3E AI Software Development
ClinicalTrials.gov study NCT06916845. IPD Sharing: NO. Countries: 1. Publications: 0.
Training Data Collection & AI Development
ClinicalTrials.gov study NCT05378854. IPD Sharing: YES. Countries: 1. Publications: 0.
AI-Driven Genotype Prediction Using EHR and Multimodal Data
ClinicalTrials.gov study NCT06791421. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
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.