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NOBEL-BOX: A Ship-Based Low-Cost Instrument for Real-Time Ocean Monitoring and Analysis
<p>This data is the measurement result obtained from the NOBEL-BOX instrument. The principle of NOBEL-BOX is to attach sensors in a container connected to a microcontroller and then measure directly. This data results from measurements using fresh water and sea water mixed to see the response from NOBEL BOX. Furthermore, data was also obtained from sea measurements in Pangandaran, West Java, Indonesia. These measurements include pH, water and water temperature, dissolved oxygen, TDS, and salinity. The use of this parameter is to see the condition of the sea so that it becomes a reference in mitigating and managing the ocean.</p>
Data for "ChickenSense: A low-cost deep learning-based solution for real-time poultry feed intake monitoring using sound technology"
<p>The dataset includes two types of data: scale and audio. The scale data is recorded using a hanging scale and measures the feeders' weight with a 5 Hz frequency. The audio data contains 1824 hours of recorded audio using a piezo-electric sensor attached to a bucket feeder to capture vibrations as audio signals with a sampling rate of 48 kHz. Each .wav file contains 15 minutes of audio recording.<br> Along with the raw data, a part of the dataset containing 36 30-second audio recordings was sampled and labeled in four classes, including pecking, singing, anomaly, and silence, resulting in 3319 labeled events. </p>
Data from: Automatic patient-level recognition of four Plasmodium species on thin blood smear by a Real Time Detector Transformer (RT-DETR) object detection algorithm: a proof-of-concept and evaluation
<p>Automatic patient-level recognition of four <em>Plasmodium</em> species on thin blood smear by a Real Time Dectector Transformer (RT-DETR) object detection algorithm: a proof-of-concept and evaluation</p> <p>Emilie Guemas, Baptiste Routier, Théo Ghelfenstein-Ferreira, Camille Cordier, Sophie Hartuis, Bénédicte Marion, Sébastien Bertout, Emmanuelle Varlet-Marie, Damien Costa, Grégoire Pasquier</p> <p><strong>Abstract:</strong></p> <p>Malaria remains a global health problem with 247 million cases and 619,000 deaths in 2021. Diagnostic of <em>Plasmodium</em> species is important for administering the appropriate treatment. The gold-standard diagnosis from accurate species identification remains the thin blood smear. Nevertheless, this method is time-consuming and requires highly skilled and trained microscopists. To overcome these issues, new diagnostic tools based on deep learning are emerging. This study aimed to evaluate the performances of a RT-DETR (Real-Time Detection Transformer)object detection algorithm to discriminate <em>Plasmodium</em> species on thin blood smears images. The algorithm was trained and validated on a dataset consisting in 24,720 images from 475 thin blood smears corresponding to 2,002,597 labels. Performances were calculated with a test dataset of 4,508 images from 170 smears corresponding to 358,825labels coming from six French university hospital. At the patient level, the RT-DETR algorithm exhibited an overall accuracy of 79.4% (135/170) with a recall of 74% (40/54) and 81.9% (95/116) for negative and positive smears, respectively. Among <em>Plasmodium </em>positive smears, the global sensitivity was 82.7% (91/110) with a sensitivity of 90% (38/42), 81.8% (18/22) and 76.1% (35/46) for <em>P. falciparum</em>, <em>P. malariae </em>and <em>P. ovale/vivax,</em> respectively. The YOLOv5 model achieved a World Health Organization (WHO) competence level 2 for species identification. Besides, the RT-DETR algorithm may be run in real-time on low-cost devices such as a smartphone and could be suitable for deployment in low-resource setting areas where microscopy experts are lacking.</p> <p><strong>Data collection:</strong></p> <p>The training and validation dataset included 24,720 pictures taken from 475 manually May Grunwald-Giemsa (MGG)-stained thin blood smears from the Montpellier University Hospital collection and for a smaller part from the Toulouse University Hospital collection. In Montpellier, the pictures were taken with a Flexcam C1 microscope camera (Leica) attached to a Leica DM 2000 microscope and Leica DF450C microscope camera adapted with a Leica DM2500 microscope at X1000 magnification. Labelling of pictures was performed manually, and then automatically with manual correction with a Computer Visual Annotation Tools (CVAT) free software. Nine categories of labels were used: white blood cells (n=3,338), red blood cells (n=1,887,781), platelets (n=48,520), <em>Trypanosoma brucei </em>(n=2,773), and red blood cells infected by <em>P. falciparum </em>(n=43,545), <em>P. ovale </em>(n=4,651), <em>P. vivax </em>(n=4,115), <em>P. malariae </em>(n=2,849) and <em>Babesia divergens</em> (n=5,142).</p> <p>The test dataset included 4,508 pictures taken from 170 thin blood smears from the same number of patients from the Parasitology laboratories of University Hospitals of Montpellier, Toulouse, Rouen, Lille, Nantes and Saint-Louis in Paris (Table 1). Among these 170 patients, 54 were not infected, including two patients with Howell-Jolly bodies, and 116 were infected with hematozoa. For each patient, between 20 and 30 photos were taken from one thin blood smear with at least one hematozoan parasite per picture for infected patients.</p> <p>Accurate species diagnostic was made by a senior parasitologist, and for recent smears, it was confirmed by specific PCR, either performed locally (Toulouse) or at the Malaria French National Reference Center (Montpellier, Saint Louis, Rouen, Lille, Nantes).</p>
Interpretation results used in the paper "Real-Time Dual-Para FWI-Inv of GPR Data Based on Robust Deep Learning"
<p>We provide the updated synthetic data and inversion results used for all interpretations presented in the paper "Real-Time Dual-Parameter FWI Inversion of GPR Data Based on Robust Deep Learning."</p>
Now or Never? Global Review of Reactive and Near Real-Time Conservation Actions For Cetaceans
<p><span>Table S1: Information on resulting publications (<em>n</em> = 64) reviewed in this study.</span></p>
Mobile Technology and Data Analytics to Identify Real-time Predictors of Caregiver Well-Being
ClinicalTrials.gov study NCT04556591. IPD Sharing: YES. Countries: 1. Publications: 4.
Log2Lose: Incenting Weight Loss and Dietary Self-monitoring in Real-time to Improve Weight Management Among Adults With Obesity
ClinicalTrials.gov study NCT04770909. IPD Sharing: YES. Countries: 1. Publications: 2.
Validation of an indoor real-time location system for tracking sheep
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Inducing representational change in the hippocampus through real-time neurofeedback
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Using a real-time location system to detect behavioral changes in ewes with subclinical mastitis and their lambs
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Monitoring the compaction of single DNA molecules in Xenopus egg extract in real time
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CovidCounties is an interactive real time tracker of the COVID19 pandemic at the level of US counties
Open the record for dataset details and reuse information.
Rapid and real-time identification of fungi up to the species level with long amplicon Nanopore sequencing from clinical samples
<p>Samples collected from fungal cultures, skin of dogs and ZymoBIOMICS<sup>TM </sup>mock community (which includes <em>Saccharomyces cerevisiae</em> and <em>Cryptococcus neoformans</em>). The amplicons length of the fungal cultures and ZymoBIOMICS<sup>TM </sup>mock community is 3,5 Kb and 6 Kb, while the <em>Malassezia spp</em> samples used as control is 3,5 Kb. The amplicons length of the four samples from the skin is 3,5 Kb.</p>
Research data for dissertation 'Real-time Tomographic Reconstruction'
<p><br> This repository contains research data and software used for the research presented in the dissertation "Real-time tomographic reconstruction" by Jan-Willem Buurlage. Made permanently available as required by the research data policy of Leiden University.</p> <p><br> *<strong>Chapter 2</strong>*: A modern interface for BSP programs</p> <p>This chapter outlines the design and use of a new interface for bulk-synchronous parallel programs called Bulk. The 'chapter2/' directory programs contain source code for the three numerical experiments used to verify Bulk: a benchmarking program 'benchmark.cpp' that measures the BSP parameters, source code for the fast-fourier transform 'fft.cpp' and a comparison to a BSPlib implementation 'fft_bspedupack.cpp'. The Bulk library itself is also included.</p> <p><br> *<strong>Chapter 3</strong>*: Geometric partitioning for tomography</p> <p>This chapter describes a partitioning method that is based on the acquisition geometry underlying the tomographic system matrix, rather than the nonzero pattern. The main results are presented in four large tables, which can be generated using the programs implemented in 'chapter3/src/tableX.cpp'. The main dependency is the "TPT" which was developed alongside this research, and is also included in the directory. The tasks have high computational cost. The runtime measurements were performed on the Lisa supercomputer of SURFsara.</p> <p>The acquisition geometries used are represented in TOML files in 'data/geometries'.</p> <p><br> *<strong>Chapter 4</strong>*: A projection-based partitioning method</p> <p>This chapter describes a refinement of the previous method, that is based on a continuous model of the projections that make up the acquisition geometry. The programs used to generate the numerical results as well as most of the illustrations are contained in the directory 'chapter4/'. In particular (files relative to this directory):</p> <p>- Figure 4.3 and 4.4: generated by 'src/overlap.cpp'<br> - Figure 4.5: generaed by 'python/plot_partitioning_blender.py'<br> - Table 4.1 and 4.2: generated by 'src/grcb.cpp' and 'TPT/tools/generate_comvol_table.cpp'<br> - Figure 4.6: Generated by running 'Pleiades/src/reconstruct.cpp' on a GPU<br> cluster of 8 nodes with a 40 Gbit Mellanox Infiniband connection. Each node<br> has four NVIDIA GeForce GTX TITAN X GPUs, two Intel Xeon E5-2630 v3 CPUs<br> running at 2.40GHz, and 128GB RAM</p> <p>The acquisition geometries used are represented in TOML files in 'data/geometries', and were used as input to the programs above.</p> <p><br> *<strong>Chapter 5</strong>*: Real-time quasi-3D tomographic reconstruction</p> <p>This chapter describes a new reconstruction framework and software package based around the idea of reconstructing arbitrarily oriented slices that can be adjusted on the fly.</p> <p>- All results in the paper were generated directly using the RECAST3D software, and the code snippets are directly given as examples in the corresponding software folder.<br> - The benchmark results in Table 5.1 were produced on a node with two Intel Xeon E5-2623v3 processors, 128 GB RAM, and two dual-GPU NVIDIA GTX TITAN Z cards for a total of 4 GPUs with 6GB RAM each. To obtain these numbers, 'RECAST3D/slicerecon/src/slicerecon_server.cpp' should be run with a '--bench' flag.<br> - The data used for the experimental verification, presented in Figure 5.8, can be found at <https://doi.org/10.5281/zenodo.1154166>.</p> <p><br> *<strong>Chapter 6</strong>*: Application of quasi-3D reconstruction to synchrotron tomography</p> <p>This chapter describes an application of the RECAST3D software to synchrotron tomography. The results were generated using the RECAST3D software, as explained above. Adapters to make it work together with the GigaFRoST camera were written in Python, and a slightly modified version of RECAST3D was used for generating some of the results presented in the paper. Later, these changes were merged back into RECAST3D. The original code, which was used to generated some of the results shown in the chapter, is given in the Zipfile 'chapter6/tomcat-live-master.zip'.<br> </p>
Real-time benchmark dynamics of the Ohmic Spin-Boson Model computed with Time-Dependent Variational Matrix Product States. (TDVMPS) coupling strength and temperature parameter space
<p>Data describing the complete propagators (maps) for the evolution of the Ohmic Spin-Boson Model are made available, here. Using a time-dependent variotnal matrix product states (TDVMPS) respresentation of the complete spin-environment wave function, non -perturbative results are presented over a wide range of coupling strengths, temperatures and initial conditions. The results in this repository are associated with the article: </p> <p>https://www.preprints.org/manuscript/202012.0016/v1 </p> <p>A mathematica notebook that allows the data to be visualised and manipulated is also provided. </p>
Digital Twin of 3D Printer in Real Time Mode
<p>Digital Twin operating in real-time mode. (Please note that the window on the top right of the video is from Repetier (3D Printing Application) and it was added as a visual guide on what the 3D printer was printing. It was not part of the study.</p>
Deep Reinforcement Learning for END-To-END Local Motion Planning of Autonomous Aerial Robots in Unknown Outdoor Environments: Real-Time Flight Experiments
<p> </p> <p>Videos for the real flight tests and the simulation experiments </p>
Dataset for Anomaly Detection Using Inter-Arrival Curves for Real-time Systems
<p>The dataset shows the input files and detailed results for the experiments discussed in the paper. A README file provide more details on the data.</p>
Real time, in-situ deuteriding of uranium encapsulated in grout; effects of temperature on the uranium-deuterium reaction
<p>To accurately predict the initiation and evolution of uranium hydride potentially present in nuclear waste containers, studies of simulated conditions are required. Here, for the first time, the uranium-deuterium reaction was examined in-situ, in real time, whilst within grouted media. A deuterium gas control rig and stainless steel-quartz glass reaction cell were configured on a synchrotron beam line to collect X-ray diffraction and X-ray tomography data. It was found that deuteride formation, and thus hydride formation, was limited by the uranium and grout thermal conductivities and deuteride initiation only commenced above a threshold temperature. Strong adherence between uranium oxide and grout was also observed.</p>
For the people by the people: citizen science web interface for real-time monitoring of tick risk areas in Finland
<p>Ticks and tick-borne diseases (TBDs) form a significant and growing threat to human health and well-being in Europe, with increasing numbers of tick-borne encephalitis (TBE) and Lyme borreliosis cases being reported during the past few decades. Increasing knowledge of tick risk areas and seasonal activity remains the primary method for preventing TBDs. Crowdsourcing provides the best alternative for rapidly obtaining data on tick occurrence on a national level.</p> <p>In order to produce and share up-to-date data about tick risk areas in Finland, an online platform, Punkkilive (www.punkkilive.fi/en), was launched in April 2021. On the website, users can submit and browse tick observations, report tick numbers and hosts, and upload pictures of ticks.</p> <p>Here, we looked at trends in the crowdsourced data from 2021, assessed the effect of local tick species on seasonality of observations, and examined sampling bias in the data.</p> <p>The high number of tick observations (n=78 837) highlights that there was demand for such a service. Approximately 97% of 5573 uploaded pictures represented ticks. Seasonal patterns of tick observations varied across Finland, highlighting variability in the risk associated with the two human-biting tick species <em>Ixodes ricinus</em> and <em>I. persulcatus</em>, the latter having a shorter, unimodal activity peak in late spring–early summer. Tick numbers were low and the proportion of new sightings was high in northern Finland, as may be expected near the latitudinal distribution limits of both species. While the number of inhabitants generally explained the number of tick observations well, geographically weighted regression models also identified areas that deviated from this general pattern.</p> <p>This study offers a prime example of how crowdsourcing can be applied to track vectors of zoonotic diseases, to the benefit of both researchers and the public. Areas with more or fewer observations than predicted based on number of inhabitants were revealed, wherein more specific analyses may reveal factors contributing to lower or higher risk levels that may be used in increasing awareness. We hope that the success of Punkkilive serves to highlight the usefulness of citizen science in the prevention of vector-borne diseases.</p>
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.