Multi-view Traffic Intersection Dataset (MTID)

MTID er et datasæt med trafikovervågning. Det indeholder optagelser af ét kryds, der er optaget fra to forskellige perspektiver i samme tidsrum. Trafikanterne er blevet grundigt annoteret til pixel-præcision.The Multi-view Traffic Intersection Dataset (MTID) is a traffic surveillance dataset containing footage of the same intersection from multiple points of view. Traffic in all views has been carefully annotated to pixel-level accuracy

Spatially Variant Super-Resolution (SVSR) benchmarking dataset

The Spatially Variant Super-Resolution (SVSR) benchmarking dataset contains 1119 low-resolution images that are degraded by complex noise of varying intensity and type and their corresponding noise free X2 and X4 high-resolution counterparts, for evaluation of the robustness of real-world super-resolution methods. Additionally, the dataset is also suitable for evaluation of denoisers

39 - Expert level evaluations for explainable AI(XAI) methods in the medical domain

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LISA Traffic Sign Dataset

Verdens største datasæt med billeder af amerikanske trafikskilte.The World's largest dataset containing pictures of US traffic signs

BrackishMOT

This is the first underwater MOT dataset captured in turbid waters. It contains 98 sequences of fish, crabs, jellyfish, and more

AutoFish

The AutoFish dataset comprises 1500 high-quality images of fish on a conveyor belt. It features 454 unique fish with class labels, IDs, manual length measurements, and a total of 18,160 instance segmentation masks

Machine learning in general practice: scoping review of administrative task support and automation

Abstract Background Artificial intelligence (AI) is increasingly used to support general practice in the early detection of disease and treatment recommendations. However, AI systems aimed at alleviating time-consuming administrative tasks currently appear limited. This scoping review thus aims to summarize the research that has been carried out in methods of machine learning applied to the support and automation of administrative tasks in general practice. Methods Databases covering the fields of health care and engineering sciences (PubMed, Embase, CINAHL with full text, Cochrane Library, Scopus, and IEEE Xplore) were searched. Screening for eligible studies was completed using Covidence, and data was extracted along nine research-based attributes concerning general practice, administrative tasks, and machine learning. The search and screening processes were completed during the period of April to June 2022. Results 1439 records were identified and 1158 were screened for eligibility criteria. A total of 12 studies were included. The extracted attributes indicate that most studies concern various scheduling tasks using supervised machine learning methods with relatively low general practitioner (GP) involvement. Importantly, four studies employed the latest available machine learning methods and the data used frequently varied in terms of setting, type, and availability. Conclusion The limited field of research developing in the application of machine learning to administrative tasks in general practice indicates that there is a great need and high potential for such methods. However, there is currently a lack of research likely due to the unavailability of open-source data and a prioritization of diagnostic-based tasks. Future research would benefit from open-source data, cutting-edge methods of machine learning, and clearly stated GP involvement, so that improved and replicable scientific research can be done

Machine learning in general practice: scoping review of administrative task support and automation

Abstract Background Artificial intelligence (AI) is increasingly used to support general practice in the early detection of disease and treatment recommendations. However, AI systems aimed at alleviating time-consuming administrative tasks currently appear limited. This scoping review thus aims to summarize the research that has been carried out in methods of machine learning applied to the support and automation of administrative tasks in general practice. Methods Databases covering the fields of health care and engineering sciences (PubMed, Embase, CINAHL with full text, Cochrane Library, Scopus, and IEEE Xplore) were searched. Screening for eligible studies was completed using Covidence, and data was extracted along nine research-based attributes concerning general practice, administrative tasks, and machine learning. The search and screening processes were completed during the period of April to June 2022. Results 1439 records were identified and 1158 were screened for eligibility criteria. A total of 12 studies were included. The extracted attributes indicate that most studies concern various scheduling tasks using supervised machine learning methods with relatively low general practitioner (GP) involvement. Importantly, four studies employed the latest available machine learning methods and the data used frequently varied in terms of setting, type, and availability. Conclusion The limited field of research developing in the application of machine learning to administrative tasks in general practice indicates that there is a great need and high potential for such methods. However, there is currently a lack of research likely due to the unavailability of open-source data and a prioritization of diagnostic-based tasks. Future research would benefit from open-source data, cutting-edge methods of machine learning, and clearly stated GP involvement, so that improved and replicable scientific research can be done

JAMBO

The JAMBO dataset contains 3290 underwater images of the seabed captured by an ROV in temperate waters in the Jammer Bay area off the North West coast of Jutland, Denmark. All the images have been annotated by six annotators to contain one of three classes: sand, stone, or bad. The dataset was presented and used in the ECCV 2024 Computer Vision for Ecology (CV4E) Workshop paper "Underwater Uncertainty: A Multi-Annotator Image Dataset for Benthic Habitat Classification"

1QGJ : ARABIDOPSIS THALIANA PEROXIDASE N

Experimental Technique/Method:X-RAY DIFFRACTION Resolution:1.9 Classification:OXIDOREDUCTASE Release Date:2000-03-08 Deposition Date:1999-04-29 Revision Date:2008-04-26#2011-07-13 Molecular Weight:66012.63 Macromolecule Type:Protein Residue Count:600 Atom Site Count:4885 DOI:10.2210/pdb1qgj/pdb Abstract: The structure of the neutral peroxidase from Arabidopsis thaliana (ATP N) has been determined to a resolution of 1.9 A and a free R value of 20.5%. ATP N has the expected characteristic fold of the class III peroxidases, with a C(alpha) r.m.s.d. of 0.82 A when compared with horseradish peroxidase C (HRP C). HRP C is 54% identical to ATP N in sequence. When the structures of four class III plant peroxidases are superimposed, the regions with structural differences are non-randomly distributed; all are located in one half of the molecule. The architecture of the haem pocket of ATP N is very similar to that of HRP C, in agreement with the low small-molecule substrate specificity of all class III peroxidases. The structure of ATP N suggests that the pH dependence of the substrate turnover will differ from that of HRP C owing to differences in polarity of the residues in the substrate-access channel. Since there are fewer hydrogen bonds to haem C17 propionate O atoms in ATP N than in HRP C, it is suggested that ATP N will lose haem more easily than HRP C. Unlike almost all other class III plant peroxidases, ATP N has a free cysteine residue at a similar position to the suggested secondary substrate-binding site in lignin peroxidase