Find research datasets worth reusing

opencc-by-4.0Apr 2021View details →

Figure 1 in Determination of an efficient and reliable method for PCR detection of borrelial DNA from engorged ticks

Figure 1. Different amount of starting material – A. The whole individual of partially engorged tick; B. Anterior half of fully-engorged tick (above red line); C. Paired DNA extraction – mouthparts (1) and a part of scutum (2).

opencc-by-4.0Apr 2021View details →

Figure 2 in Determination of an efficient and reliable method for PCR detection of borrelial DNA from engorged ticks

Figure 2. Results of PCR amplification – A1. Efficient PCR with clear band of 250bp presented as successful amplification, determined by using a positive control; All amplified bands with sizes differing from the positive control were defined as non-specific alleles; Four types of different PCR results: A2. Non-specific alleles; A3. Poor amplification; A4. A combination of poor amplification and non-specific alleles; B. Unsuccessful amplification detected in paired DNA extraction, after using DNA obtained from mouthparts (B1), whereas target region was amplified for the same sample but using DNA obtained from a part of scutum (B2); a 50 bp DNA ladder (BioLabs, New England) (A, B).

openmit-licenseJun 2023View details →

Benchmark for energy efficient obstacle detection on head mounted wearable for the vision impaired

Here we present a novel benchmark dataset with the associated challenge, that is to detect obstacles based on head-mounted sensors and lightweight wearable devices to assist Blind and Visually Impaired individuals (BVIs) navigate in indoor environments.  The challenge encompasses three objectives: (1) as accurately as possible to detect the obstacles on the pathway that likely lead to a collision; (2) as durably as possible on a given amount of battery power for the detection algorithm or model to run; (3) as reliably as possible to compensate natural head turns so nearby objects would not trigger false alarms.  The data provided in the benchmark are collected from the following head mounted sensors: (i) nine low-cost ultrasonic sensors; (ii) one high-end ultrasonic sensor with a larger detection range but higher power consumption; (iii) a 9-Degrees of Freedom (DOF) Inertial Measurement Unit (IMU).  The resulting dataset consists of more than 188,000 unique sequences obtained from multiple subjects walking in three different indoor scenarios.  This benchmark is to facilitate and encourage accurate yet fast obstacle detection solutions that can really benefit BVIs.

Fig. 9 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Fig. 9 Overall framework of proposed automated malaria diagnosis and species identification. CNN, Convolutional neural network; RBC, red blood cell; YOLO, You Only Look Once (model)

Fig. 8 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Fig. 8 Examples of false positive predictions by the YOLOv4-RC3_4 model. YOLO, You Only Look Once (model)

Fig. 6 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Fig. 6 Comparison of detection performance by the original YOLOv4 model and the YOLOv4-RC3_4 model. Red arrows indicate cells not detected by the original YOLOv4 model, green arrows indicate the same cells detected by the YOLOv4-RC3_4 model. YOLO, You Only Look Once (model)

Fig. 3 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Fig. 3 Network structure of YOLOv4. CSP, cross-spatial connection; SPP, spatial pyramid pooling layer; PANet, Path Aggregation Network; CBM, Convolutional, Batch Normalisation, and Activation; CBL, Convolutional, Batch normalisation, and Leaky-ReLU; Conv, convolutional; Concat, concatenation

Fig. 4 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Fig. 4 Building blocks of the residual learning module. CBM, Convolutional, Batch normalisation and Mish (modules)

Fig. 5 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Fig. 5 Visual representation of the removal of residual blocks from C3 and C4 Res-block body. YOLO,You Only Look Once (model)

Fig. 1 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Fig. 1 Comparison of malaria diagnosis using deep learning CNN models and deep learning object detectors. CNN, Convolutional neural network

Fig. 2 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Fig. 2 Cropping of infected cells using the coordinates of predictions by the object detectors. RBC, Red blood cell; YOLO,You Only Look Once (model)

opencc-by-4.0Dec 2023View details →

Fig. 1 in Pre-treatment of canine plasma with heat, rather than acid, efficiently enhances Dirofilaria immitiS antigen detection

Fig. 1 Detection of Dirofilaria immitis antigen by ELISA for twofold-diluted plasma samples with and without treatment. A spectrophotometric OD reading at a wavelength of 415 nm was obtained for each sample using the microplate reader SpectraMax® i Series-Spectramax Id3. Filled black circle, untreated sera. Open blue circle, ICD-1 protocol. Filled blue circle, ICD-2 protocol. Open red circle, ICD-3 protocol. Filled red circle, ICD-4 protocol. The error was represented as mean ± 95% confidence interval

opencc-by-4.0Nov 2022View details →

Figure 3 in Are recaptures of banded birds efficient at detecting altitudinal migrations in the Atlantic Forest?

Figure 3. Schematic illustration of the altitudinal movements recorded on the slope of Núcleo Curucutu by the recapture of individually marked birds. White lines indicate the two lines of mist-nets at each locality. Source: Google Earth Pro (Image Landsat/Copernicus 2015).

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Listening and watching: do camera traps or acoustic sensors more efficiently detect wild chimpanzees in an open habitat?

1. With one million animal species at risk of extinction, there is an urgent need to regularly monitor threatened species. However, in practice this is challenging, especially with wide-ranging, elusive and cryptic species or those that occur at low density. 2. Here we compare two non-invasive methods, passive acoustic monitoring (n=12) and camera trapping (n=53), to detect chimpanzees (Pan troglodytes) in a savanna-woodland mosaic habitat at the Issa Valley, Tanzania. With occupancy modelling we evaluate the efficacy of each method, using the estimated number of sampling days needed to establish chimpanzee absence with 95% probability, as our measure of efficacy. 3. Passive acoustic monitoring was more efficient than camera trapping in detecting wild chimpanzees. Detectability varied over seasons, likely due to social and ecological factors that influence party size and vocalization rate. The acoustic method can infer chimpanzee absence with less than ten days of recordings in the field during the late dry season, the period of highest detectability, which was five times faster than the visual method. 4. Synthesis and applications: Despite some technical limitations, we demonstrate that passive acoustic monitoring is a powerful tool for species monitoring. Its applicability in evaluating presence/absence, especially but not exclusively for loud call species, such as cetaceans, elephants, gibbons or chimpanzees provides a more efficient way of monitoring populations and inform conservation plans to mediate species-loss.

opencc-zeroFeb 2020View details →

opencc-by-4.0Apr 2021View details →

Figure 3 in Determination of an efficient and reliable method for PCR detection of borrelial DNA from engorged ticks

Figure 3. Ratio of four different PCR types in presented ticks categories and isolation groups.

opencc-by-4.0Feb 2022View details →

Dataset and processing scripts for manuscript titled "Broadband polarization insensitivity and high detection efficiency in high-fill-factor superconducting microwire single-photon detectors"

The manuscript "Broadband polarization insensitivity and high detection efficiency in high-fill-factor superconducting microwire single-photon detectors" contains results for high-efficiency, low-polarization-sensitivity superconducting microwire single-photon detectors. All of the data gathered and the python scripts used for processing is being provided as a zip archive, along with a digital signature file. The dataset contains crucial procedures regarding the equipment calibrations used for accurate device efficiency measurements.

dryad36/100

Increasing efficiency and reducing bias in the detection of seed-dispersal interactions based on mist-netted birds

Efficient and unbiased sampling of ecological interactions is essential to our understanding of the functions they mediate. Seed dispersal by frugivorous birds is a key mutualism for plant regeneration and community dynamics. Mist-netting is one of the most widely used methods to sample avian seed dispersal through the identification of seeds in droppings of captured birds kept inside cloth bags. However, birds may drop seeds on the ground before being extracted from the net, leading to a fraction of missing information due to ineffective sampling. Worryingly, this fraction could be unevenly distributed across bird and plant species, leading to sampling biases. Here, we assess the effectiveness of using a 1- m wide mesh below mist nets to sample seeds dropped by entangled birds. We used data from birds mist-netted during one-year-round. We sampled nearly 50% of interaction events and 75% of dispersed seeds on the mesh band below the mist nets (i.e. lost information without this optimization). The proportion of seeds sampled on the mesh bands was not evenly distributed among bird species but strongly related to bird size, ranging from 57-63% in warblers to 84-94% in thrushes. Moreover, the proportion of seeds sampled on the mesh was negatively related to seed size, although this relationship was weaker. We also evaluated accumulation curves of species and pairwise interactions with increasing sampling effort, both with and without using the mesh bands. The number of seed species sampled increased by 21% when using the mesh bands and the number of pairwise interactions by 36%. Our findings provide strong evidence on how inefficient and biased traditional mist-netting can be for sampling community-wide seed-dispersal interactions. We thus urge the use of mesh bands in future studies to increase sampling effectiveness and avoid biases, which will ultimately improve our understanding of the seed dispersal function.

opencc-zeroAug 2022View details →

Fig. 7 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Fig. 7 Confusion matrix of predictions by YOLOv4 models. YOLO,You Only Look Once (model)

Dataset for the article "Efficient Computation of Magnetic Polarizability Tensor Spectral Signatures for Object Characterisation in Metal Detection"

Datasets to accompany the article "Efficient Computation of Magnetic Polarizability Tensor Spectral Signatures for Object Characterisation in Metal Detection". Written by J. Elgy and P. D. Ledger.  The datasets include data files, meshes, and code for recreating the results from the paper. This requires the open source MPT-Calculator software available at <a href="http://github.com/MPT-Calculator/MPT-Calculator">https://github.com/MPT-Calculator/MPT-Calculator</a> (v.1.5.0). J. Elgy and P. D. Ledger gratefully acknowledge the financial support received from EPSRC in the form of grant EP/V009028/1.