Find research datasets worth reusing

Echolocation clicks and anthropogenic detections with neural network labels in Hawaiian Island HARP data from Kona, Kaua`i, and Pearl and Hermes Reef

This dataset consists of echolocation clicks and detections of anthropogenic signals at three sites in the Hawaiian Islands Archipelago. These sites are Hawaii/Hawaii_K, Kauai/KA, and Pearl and Hermes Reef/PHR. Echolocation clicks were grouped into 5 minute bins, for which summary data is provided. Files are in .mat format that can be read using any desired coding language using a netcdf reading script. Files are separated by site, deployment, and neural network class (i.e. sitedeployment_cbins_class or site_deployment_cbins_class). Manual labels are provided.

opencc-by-4.0Jul 2021View details →

An automated system for inspecting rock faces and detecting potential rock falls using machine learning

Rockfall is a hazard in mountainous areas threatening infrastructure and human lives. Rockfall hazards are often mitigated by manual inspections using pry bars. The inspector must access the rock face, hit the rock surface, detect, and remove the loose rocks. This method is very labor demanding, unsafe, and challenging. This research presents a method that automatize the inspection of rock blocks that are prone to rockfall events. A robot is developed to replace the manual hammer tap process and collect the sound data remotely; subsequently, the sound signal is used to identify different types of the discontinuity in rocks in controlled laboratory environment. Machine learning is used to train the method to discriminate between intact rock and rock that may be prone to fall. This methodology was successfully applied to laboratory tests on rock. Finally, the research involves the implementation of this system in field to understand the potential and limitations of the proposing system in automatizing the rock inspections. This research enables the inspectors to collect data remotely, detect loose rocks, and save data for future references.

opencc-by-4.0Jul 2021View details →

Bridge Cracks Monitoring: Detection, Measurement, and Comparison using Augmented Reality

Crack occurrence and propagation are among critical factors that affect the performance and lifespan of civil infrastructures such as bridges. Consequently, numerous crack detection and measurement methods have been proposed and developed in the recent decades in the areas of Structural Health Monitoring and non-destructive testing. Many novel technologies have emerged with the potential to overcome the limitations of the presented techniques of crack detection and characterization. Crack detection and characterization method used in this research lies in supplementing human visual inspection capabilities in a systematic manner through an appropriate level of automation. The Augmented Reality (AR) tool developed in this project allows a user to perform tasks in a real-world environment while visually receiving supplementary 3D computer-generated information to support the tasks. More specifically, we developed a crack detection/characterization tool in this research and deployed it in Microsoft HoloLens smart glasses. This AR tool provides the user with automatic data collection capability through AR headset camera and is a means of hands-free data sharing for inspectors while conducting their normal inspection. We conducted several laboratory and field experiments by which we evaluated the effectiveness of the developed crack detection and measurement system. The result confirm that the AR tool devised in this project has the potential to help the inspection process in terms of time, comfort and accuracy.

opencc-by-4.0Nov 2021View details →

Smart Battery Management System for Electric Vehicles: Selflearning Algorithms for Simultaneous State and Parameter Estimation, and Stress Detection

The project proposes to develop parameter-varying SOH-coupled models for lithium-ion battery and self-learning algorithms to learn the model for simultaneous state and parameter estimation and fault detection. The traditional battery models use constant parameters, limiting their accuracy for predicting the state of the charge and health over the complete life-cycle. In practice, the battery parameters vary with the change in the state of charge and state of health. SOH-coupled models can be used to estimate the state of charge and health accurately. Further, obtaining the model parameters is also a challenging task for designing filters or observers for state estimation. A self-learning algorithm can eliminate the requirement of the model parameters. In this project, three SOH-coupled models are proposed and validated experimentally. The models are also used to design extended Kalman filters (EKF) for the state of charge, state of health, core and surface temperature, and internal resistance estimation. The results showed that the SOHcoupled models are more effective when compared to the uncoupled models in the literature. Further, it was found that EKFs based state estimation errors were within 1%. The self-learning algorithm using a two-layer neural network showed the ability to learn the models in real-time. However, the state estimation errors are higher for the self-learning scheme compared to the EKF based approaches. This is due to the limited measurement and online training schemes utilized to train neural networks. This requires further investigation in hyper-parameter tuning for implementation. Finally, a model-based fault detection scheme was proposed to detect internal thermal fault at its onset. The SOHcoupled model is reformulated to incorporate the internal resistance as a state. The EKF is used as a fault detection observer. The proposed fault detection scheme is validated using numerical simulation. It was observed that the fault detection scheme with SOH coupled electro-thermal-aging model could effectively detect a thermal fault at its incipient state.

Age-specific differences in Asian Elephant defecation, dung decay, detection and their implication for dung count

In vertebrate population estimation, converting faecal density into animal density requires information on faecal production rate, decay rate, and faecal density. Differences in the above factors for long-lived species across age classes were not evaluated. We have evaluated these factors associated with the dung count of Asian elephants (Elephas maximus) in the tropical forest of Southern India. The defecation rate of elephants was determined in semi-wild elephants at the Mudumalai elephant camp. The relationship between dung bolus diameter and age was determined to estimate the age of the elephant. Total and age-specific elephant density based on dung bolus diameter were estimated. A total of 24 transect lines of 2-4 km (125 km) were sampled in the study area. An experiment was conducted to assess detection probability across the age classes of dung piles. The dung decay rates across age classes and seasons were determined by marking fresh dung piles (n=1551). The dung-based age structure assessment and its limitations were evaluated. The mean defecation rate was 13.51±0.51 per day. The defecation rate was significantly lower for the younger age class and increased with the age of elephants. Defecation rates were significantly lower in the wet season than in the dry. The dung-boli diameter positively increased with the age of elephants, and the growth curve can be used to predict the age and age structure of the elephant population. The disparity in the dung production rate results in the lower availability of younger age class (Juvenile and calf) dung in the transect for counting, that results in lower dung abundance. The detection probability of dung piles of younger age classes was low (0.58). The survival rates of dung piles of younger age classes were lower and increased with the age of elephants in the wet season. Hence, demography assessment of the population based on dung needs to consider age-specific differences in dung production, decay, and detection probability. Through demography assessment using dung provides insight into population age structure, it has limitations in predicting age structure for young elephants.

Data from: Detection of silent SARS-CoV-2 infection (S1 File)

Background: To control COVID-19 pandemic is of critical importance to the global public health. To capture the prevalence in an accurate and timely manner and to understand the mode of nosocomial infection are essential for its preventive measure. Methods: We recruited 685 healthcare workers (HCW's) at Tokyo Shinagawa Hospital prior to the vaccination with COVID-19 vaccine. Sera of the subjects were tested by assays for the titer of IgG against S protein's receptor binding domain (IgG (RBD)) or IgG against nucleocapsid protein (IgG (N)) of SARS-CoV-2. Together with PCR data, the positive rates by these methods were evaluated. Results: Overall positive rates among HCW's by PCR, IgG (RBD), IgG (N) with a cut-off of 1.4 S/C (IgG (N)1.4), and IgG (N) with a cut-off of 0.2 S/C (IgG (N)0.2) were 3.5%, 9.5%, 6.1%, and 27.7%, respectively. Positive rates of HCW's working in COVID-19 ward were significantly higher than those of HCW's working in non-COVID-19 ward by all the four methods. Concordances of IgG (RBD), IgG (N)1.4, and IgG (N)0.2 against PCR were 97.1%, 71.4%, and 88.6%, respectively. By subtracting the positive rates of PCR from that of IgG (RBD), the rate of overall silent infection and that of HCW's in COVID-19 ward were estimated to be 6.0% and 21.1%, respectively. Conclusions: For the prevention of nosocomial infection of SARS-CoV-2, identification of silent infection is essential. For the detection of ongoing infection, periodical screening with IgG (RBD) in addition to PCR would be an effective measure. For the surveillance of morbidity in the population, on the other hand, IgG (N)0.2 could be the most reliable indicator among the three serological tests.

Sacramento River green sturgeon acoustic telemetry detections

Abstract for publication titled: Intraspecific variation in migration timing of green sturgeon in the Sacramento River system Understanding movement patterns of anadromous fishes is critical to conservation management of declining wild populations and preservation of habitats. Yet, the duration of observations for individual animals can constrain accurate descriptions of movements. In this study, we synthesized over a decade (2006-2018) of acoustic telemetry tracking observations of green sturgeon (Acipenser medirostris) in the Sacramento River system to describe major anadromous movement patterns. We observed that green sturgeon exhibited a unimodal in-migration during the spring months but had a bimodal distribution of out-migration timing, split between an 'early' out-migration (32%) group during May - June, or alternatively, holding in the river until a 'late' out-migration (68%), November - January. Focusing on these out-migration groups, we found that river discharge, but not water temperature, may cue the timing of migration, and that fish showed a tendency to maintain out-migration timing between subsequent spawning migration events. We recommend that life history descriptions of green sturgeon in this region reflect the distinct out-migration periods described here. Furthermore, we encourage the continued use of biotelemetry to describe migration timing and life history variation, not only this population but other green sturgeon populations and other species.

opencc-zeroNov 2022View details →

Data from: Sharing detection heterogeneity information among species in community models of occupancy and abundance can strengthen inference

1. The estimation of abundance and distribution and factors governing patterns in these parameters is central to the field of ecology. The continued development of hierarchical models that best utilize available information to inform these processes is a key goal of quantitative ecologists. However, much remains to be learned about simultaneously modeling true abundance, presence, and trajectories of ecological communities. 2. Simultaneous modeling of the population dynamics of multiple species provides an interesting mechanism to examine patterns in community processes and, as we emphasize herein, to improve species-specific estimates by leveraging detection information among species. Here we demonstrate a simple but effective approach to share information about observation parameters among species in hierarchical community abundance and occupancy models, where we use shared random effects among species to account for spatiotemporal heterogeneity in detection probability. 3. We demonstrate the efficacy of our modeling approach using simulated abundance data, where we recover well our simulated parameters using N-mixture models. Our approach substantially increases precision in estimates of abundance compared to models that do not share detection information among species. We then expand this model, and apply it to repeated detection/non-detection data collected on six species of tits (Paridae) breeding at 119 1 km2 sampling sites across a P. montanus hybrid zone in northern Switzerland (2004-2020). We find strong impacts of forest cover and elevation on population persistence and colonisation in all species. We also demonstrate evidence for interspecific competition on population persistence and colonization probabilities, where the presence of marsh tits reduces population persistence and colonisation probability of sympatric willow tits, potentially decreasing gene flow among willow tit subspecies. 4. While conceptually simple, our results have important implications for the future modeling of population abundance, colonization, persistence, and trajectories in community frameworks. We suggest potential extensions of our modeling in this paper, and discuss how leveraging data from multiple species can improve model performance and sharpen ecological inference.

opencc-by-4.0Sep 2021View details →

Genome graphs detect human polymorphisms in active epigenomic states during influenza infection: code and processed data

Manuscript, figure, and analysis code and processed data for the Groza et al (2022) preprint.

opencc-zeroMay 2022View details →

Data from: Detecting sub-micron space weathering effects in lunar grains with synchrotron infrared nanospectroscopy

Space weathering processes induce changes to the physical, chemical, and optical properties of space-exposed soil grains. For the Moon, space weathering causes reddening, darkening, and diminished contrast in reflectance spectra over visible and near-infrared wavelengths. The physical and chemical changes responsible for these optical effects occur on scales below the diffraction limit of traditional far-field spectroscopic techniques. Recently developed super-resolution spectroscopic techniques provide an opportunity to understand better the optical effects of space weathering on the sub-micrometer length scale. This paper uses synchrotron infrared nanospectroscopy to examine depth-profile samples from two mature lunar soils in the mid-infrared, 1500–700 cm-1 (6.7–14.3 µm). Our findings are broadly consistent with prior bulk observations and theoretical models of space weathered spectra of lunar materials. These results provide a direct spatial link between the physical/chemical changes in space-exposed grain surfaces and spectral changes of space-weathered bodies.

opencc-zeroMay 2022View details →

Data from: The importance of population heterogeneities in detecting social learning as the foundation of animal cultural transmission

High levels of within-population behavioural variation can have drastic demographic consequences, thus changing the evolutionary fate of populations. A major source of within-population heterogeneity is personality. Nonetheless, it is still relatively rarely accounted for in social learning studies that constitute the most basic process of cultural transmission. Here, we perform in female mosquitofish (Gambusia holbrooki) a social learning experiment in the context of mate choice, a situation called mate copying, and for which there is strong evidence that it can lead to the emergence of persistent traditions of preferring a given male phenotype. When accounting for the global tendency of females to prefer lager males but ignoring differences in personality we detected no evidence for mate copying. However, when accounting for the bold-shy dichotomy, we found that bold females did not show any evidence for mate copying, while shy females showed significant amounts of mate copying. This illustrates how the presence of variation in personality can hamper our capacity to detect mate copying. We conclude that mate copying may be more widespread than we thought because many studies ignored the presence of within-population heterogeneities.

opencc-by-4.0May 2022View details →

Datasets and Code for "Hypothesis Tests with Functional Data for Surface Quality Change Detection in Surface Finishing Processes"

This is the set of data and computer code used for reproducing the results in Jin, Tuo, Tiwari, Bukkapatnam, Aracne-Ruddle, Lighty, Hamza, and Ding, 2022, “Hypothesis tests with functional data for surface quality change detection in surface finishing processes,” IISE Transactions, in press.

openMay 2022View details →

CT-FAN: A Multilingual dataset for Fake News Detection

By downloading the data, you agree with the terms & conditions mentioned below: Data Access: The data in the research collection may only be used for research purposes. Portions of the data are copyrighted and have commercial value as data, so you must be careful to use them only for research purposes.  Summaries, analyses and interpretations of the linguistic properties of the information may be derived and published, provided it is impossible to reconstruct the information from these summaries. You may not try identifying the individuals whose texts are included in this dataset. You may not try to identify the original entry on the fact-checking site. You are not permitted to publish any portion of the dataset besides summary statistics or share it with anyone else. We grant you the right to access the collection's content as described in this agreement. You may not otherwise make unauthorised commercial use of, reproduce, prepare derivative works, distribute copies, perform, or publicly display the collection or parts of it. You are responsible for keeping and storing the data in a way that others cannot access. The data is provided free of charge. Citation Please cite our work as <pre>@InProceedings{clef-checkthat:2022:task3, author = {K{\"o}hler, Juliane and Shahi, Gautam Kishore and Stru{\ss}, Julia Maria and Wiegand, Michael and Siegel, Melanie and Mandl, Thomas}, title = "Overview of the {CLEF}-2022 {CheckThat}! Lab Task 3 on Fake News Detection", year = {2022}, booktitle = "Working Notes of CLEF 2022---Conference and Labs of the Evaluation Forum", series = {CLEF~'2022}, address = {Bologna, Italy},} </pre> <pre>@article{shahi2021overview, title={Overview of the CLEF-2021 CheckThat! lab task 3 on fake news detection}, author={Shahi, Gautam Kishore and Stru{\ss}, Julia Maria and Mandl, Thomas}, journal={Working Notes of CLEF}, year={2021} }</pre> Problem Definition: Given the text of a news article, determine whether the main claim made in the article is true, partially true, false, or other (e.g., claims in dispute) and detect the topical domain of the article. This task will run in English and German. Task 3: Multi-class fake news detection of news articles (English) Sub-task A would detect fake news designed as a four-class classification problem. Given the text of a news article, determine whether the main claim made in the article is true, partially true, false, or other. The training data will be released in batches and roughly about 1264 articles with the respective label in English language. Our definitions for the categories are as follows: <ul> <li> False - The main claim made in an article is untrue. </li> <li> Partially False - The main claim of an article is a mixture of true and false information. The article contains partially true and partially false information but cannot be considered 100% true. It includes all articles in categories like partially false, partially true, mostly true, miscaptioned, misleading etc., as defined by different fact-checking services. </li> <li> True - This rating indicates that the primary elements of the main claim are demonstrably true. </li> <li> Other- An article that cannot be categorised as true, false, or partially false due to a lack of evidence about its claims. This category includes articles in dispute and unproven articles. </li> </ul> Cross-Lingual Task (German) Along with the multi-class task for the English language, we have introduced a task for low-resourced language. We will provide the data for the test in the German language. The idea of the task is to use the English data and the concept of transfer to build a classification model for the German language. Input Data The data will be provided in the format of Id, title, text, rating, the domain; the description of the columns is as follows: <ul> <li>ID- Unique identifier of the news article</li> <li>Title- Title of the news article</li> <li>text- Text mentioned inside the news article</li> <li>our rating - class of the news article as false, partially false, true, other</li> </ul> Output data format <ul> <li>public_id- Unique identifier of the news article</li> <li>predicted_rating- predicted class</li> </ul> Sample File <pre><code>public_id, predicted_rating 1, false 2, true</code></pre> IMPORTANT! <ol> <li>We have used the data from 2010 to 2022, and the content of fake news is mixed up with several topics like elections, COVID-19 etc.</li> </ol> Baseline: For this task, we have created a baseline system. The baseline system can be found at <a href="https://zenodo.org/record/6362498">https://zenodo.org/record/6362498</a> Related Work <ul> <li>Shahi GK. AMUSED: An Annotation Framework of Multi-modal Social Media Data. arXiv preprint arXiv:2010.00502. 2020 Oct 1.<a href="https://arxiv.org/pdf/2010.00502.pdf">https://arxiv.org/pdf/2010.00502.pdf</a></li> <li>G. K. Shahi and D. Nandini, “FakeCovid – a multilingual cross-domain fact check news dataset for covid-19,” in workshop Proceedings of the 14th International AAAI Conference on Web and Social Media, 2020. <a href="http://workshop-proceedings.icwsm.org/abstract?id=2020_14">http://workshop-proceedings.icwsm.org/abstract?id=2020_14</a></li> <li>Shahi, G. K., Dirkson, A., & Majchrzak, T. A. (2021). An exploratory study of covid-19 misinformation on twitter. Online Social Networks and Media, 22, 100104. doi: <a href="https://dx.doi.org/10.1016%2Fj.osnem.2020.100104">10.1016/j.osnem.2020.100104</a></li> <li>Shahi, G. K., Struß, J. M., & Mandl, T. (2021). Overview of the CLEF-2021 CheckThat! lab task 3 on fake news detection. Working Notes of CLEF.</li> <li>Nakov, P., Da San Martino, G., Elsayed, T., Barrón-Cedeno, A., Míguez, R., Shaar, S., ... & Mandl, T. (2021, March). The CLEF-2021 CheckThat! lab on detecting check-worthy claims, previously fact-checked claims, and fake news. In European Conference on Information Retrieval (pp. 639-649). Springer, Cham.</li> <li>Nakov, P., Da San Martino, G., Elsayed, T., Barrón-Cedeño, A., Míguez, R., Shaar, S., ... & Kartal, Y. S. (2021, September). Overview of the CLEF–2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake News. In International Conference of the Cross-Language Evaluation Forum for European Languages (pp. 264-291). Springer, Cham.</li> </ul>

opencc-by-4.0May 2022View details →

GNSS and levelling data to detect ground deformation along the Upper Adriatic Sea coastal area (Italy)

This geodetic dataset includes both Global Navigation Satellite System (GNSS) and levelling data. GNSS measurements were recorded by continuous stations managed by public institutions and private companies, while levelling measurements were obtained by the use of benchmarks managed by ENI S.p.A.   This dataset is used in the manuscript entitled "Multi-technique geodetic detection of onshore and offshore subsidence along the Upper Adriatic Sea coasts" to estimate deformation around the littoral area of Ravenna (Italy) (Polcari et al., 2022). The GNSS data, from permanent stations RAVE, PCTA, FIUN and ANGA covers the period from around 1998 to 2018. The files in .csv format contain displacement time series with respect to the Adria-fixed reference frame and for PCTA, FIUN and ANGA also with respect to RAVE GNSS station. The levelling data refer to campaigns that took place in 2002, 2003, 2004, 2005, 2007, 2009, 2011, 2014, and 2017.  The file named Original.csv contains the original height measurements for each benchmark, while the file named Ref.RAVE.csv contains the mean velocity and the displacement calculated for all 147 benchmarks. In this last file the data were scaled with respect to the mean velocity of the benchmark located near the RAVE station.

openodc-byDec 2021View details →

Night and Day Instance Segmented Park (NDISPark) Dataset: a Collection of Images taken by Day and by Night for Vehicle Detection, Segmentation and Counting in Parking Areas

The Dataset A collection of images of parking lots for vehicle detection, segmentation, and counting. Each image is manually labeled with pixel-wise masks and bounding boxes localizing vehicle instances. The dataset includes about 250 images depicting several parking areas describing most of the problematic situations that we can find in a real scenario: seven different cameras capture the images under various weather conditions and viewing angles. Another challenging aspect is the presence of partial occlusion patterns in many scenes such as obstacles (trees, lampposts, other cars) and shadowed cars. The main peculiarity is that images are taken during the day and the night, showing utterly different lighting conditions. We suggest a three-way split (train-validation-test). The train split contains images taken during the daytime while validation and test splits include images gathered at night. In line with these splits we provide some annotation files: <ul> <li> train_coco_annotations.json and val_coco_annotations.json --> JSON files that follow the golden standard MS COCO data format (for more info see <a href="https://cocodataset.org/#format-data">https://cocodataset.org/#format-data</a>) for the training and the validation splits, respectively. All the vehicles are labeled with the COCO category 'car'. They are suitable for vehicle detection and instance segmentation. </li> <li> train_dot_annotations.csv and val_dot_annotations.csv --> CSV files that contain xy coordinates of the centroids of the vehicles for the training and the validation splits, respectively. Dot annotation is commonly used for the visual counting task. </li> <li> ground_truth_test_counting.csv --> CSV file that contains the number of vehicles present in each image. It is only suitable for testing vehicle counting solutions. </li> </ul>   Citing our work If you found this dataset useful, please cite the following paper <blockquote> <pre>@inproceedings{Ciampi_visapp_2021,   doi = {10.5220/0010303401850195},   url = {https://doi.org/10.5220%2F0010303401850195},   year = 2021,   publisher = {{SCITEPRESS} - Science and Technology Publications},   author = {Luca Ciampi and Carlos Santiago and Joao Costeira and Claudio Gennaro and Giuseppe Amato},   title = {Domain Adaptation for Traffic Density Estimation},   booktitle = {Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications} } </pre> </blockquote> and this Zenodo Dataset <blockquote> <pre>@dataset{ciampi_ndispark_6560823,   author = {Luca Ciampi and Carlos Santiago and Joao Costeira and Claudio Gennaro and Giuseppe Amato},   title = {{Night and Day Instance Segmented Park (NDISPark) Dataset: a Collection of Images taken by Day and by Night for Vehicle Detection, Segmentation and Counting in Parking Areas}},   month = may,   year = 2022,   publisher = {Zenodo},   version = {1.0.0},   doi = {10.5281/zenodo.6560823},   url = {https://doi.org/10.5281/zenodo.6560823} } </pre> </blockquote>   Contact Information If you would like further information about the dataset or if you experience any issues downloading files, please contact us at <a href="mailto:mobdrone@isti.cnr.it">luca.ciampi@isti.cnr.it</a>

opencc-by-4.0Jun 2022View details →

RNA modification detection using direct RNA sequencing and nanoDoc2

The core of nanoDoc2 includes a machine-learning algorithm in which a 6-mer segmented raw current signal is compared by Deep-One-Class classification using a Wavenet-based neural network. As an output, an RNA modification is detected by a statistical score in each candidate position. Herein, we describe the detailed instructions on how to use nanoDoc2 for signal segmentation, train/test the neural network and finally predict RNA modifications present in nanopore direct RNA sequence data.

opencc-by-4.0May 2022View details →

DCASE 2022 Task 5: Few-shot Bioacoustic Event Detection Evaluation Set

General Description The evaluation set for task 5 of DCASE 2022 "Few-shot Bioacoustic Event Detection" consists of 46 audio files acquired from different bioacoustic sources.  The first 5 annotations are provided for each file, with events marked as positive (POS) for the class of interest.  This dataset is to be used for evaluation purposes during the task Folder Structure Evaluation_Set.zip     |___DC/         |____*.wav         |____*.csv     |___CT/         |____*.wav         |____*.csv     |___CHE/         |____*.wav         |____*.csv     |___MGE/         |____*.wav         |____*.csv     |___MS/         |____*.wav         |____*.csv     |___QU/         |____*.wav         |____*.csv   Evaluation_Set_5shots.zip has the same structure but contains only the *.wav files. Evaluation_Set_5shots_annotations_only.zip has the same structure but contains only the *.csv files The subfolders denote different recording sources and there may or may not be overlap between classes of interest from different wav files. Annotation structure Each line of the annotation csv represents an event in the audio file. The column descriptions are as follows: [ Audiofilename, Starttime, Endtime, Q ] Development Set The development set for the same task can be found at: <a href="http://doi.org/10.5281/zenodo.4543504">https://doi.org/10.5281/zenodo.6012309</a> Open Access This dataset is available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.   Contact info Please send any feedback or questions to: Ines Nolasco: i.dealmeidanolasco@qmul.ac.uk

opencc-zeroJun 2022View details →

Improved biodiversity detection using a large-volume environmental DNA sampler with in situ filtration and implications for marine eDNA sampling strategies

Metabarcoding analysis of environmental DNA samples is a promising new tool for marine biodiversity and conservation. Typically, seawater samples are obtained using Niskin bottles and filtered to collect eDNA. However, standard sample volumes are small relative to the scale of the environment, conventional collection strategies are limited, and the filtration process is time consuming. To overcome these limitations, we developed a new large – volume eDNA sampler with in situ filtration, capable of taking up to 12 samples per deployment. We conducted three deployments of our sampler on the robotic vehicle Mesobot in the Flower Garden Banks National Marine Sanctuary in the northwestern Gulf of Mexico and collected samples from 20 to 400 m depth. We compared the large volume (~40 – 60 liters) samples collected by Mesobot with small volume (~2 liters) samples collected using the conventional CTD rosette – mounted Niskin bottle approach. We sequenced the V9 region of 18S rRNA, which detects a broad range of invertebrate taxa, and found that while both methods detected biodiversity changes associated with depth, our large volume samples detected approximately 66% more taxa than the CTD small volume samples. We found that the fraction of the eDNA signal originating from metazoans relative to the total eDNA signal decreased with sampling depth, indicating that larger volume samples may be especially important for detecting metazoans in mesopelagic and deep ocean environments. We also noted substantial variability in biological replicates from both the large volume Mesobot and small volume CTD sample sets. Both of the sample sets also identified taxa that the other did not – although the number of unique taxa associated with the Mesobot samples was almost four times larger than those from the CTD samples. Large volume eDNA sampling with in situ filtration, particularly when coupled with robotic platforms, has great potential for marine biodiversity surveys, and we discuss practical methodological and sampling considerations for future applications.

opencc-zeroJun 2022View details →

Underlying data for 'Rapid molecular assays for the detection of the four dengue viruses in infected mosquitoes'

The pantropic emergence of severe dengue disease can partly be attributed to the co-circulation of different dengue viruses (DENVs) in the same geographical location. Effective monitoring for circulation of each of the four DENVs is critical to inform disease mitigation strategies. In low resource settings, this can be effectively achieved by utilizing inexpensive, rapid, sensitive and specific assays to detect viruses in mosquito populations. In this study, we developed four rapid DENV tests with direct applicability for low-resource virus surveillance in mosquitoes. The test protocols utilize a novel sample preparation step, a single-temperature isothermal amplification, and a simple lateral flow detection. Analytical sensitivity testing demonstrated tests could detect down to 1,000 copies/µL of virus-specific DENV RNA, and analytical specificity testing indicated tests were highly specific for their respective virus, and did not detect closely related flaviviruses. All four DENV tests showed excellent diagnostic specificity and sensitivity when used for detection of both individually infected mosquitoes and infected mosquitoes in pools of uninfected mosquitoes. With individually infected mosquitoes, the rapid DENV-1, -2 and -3 tests showed 100% diagnostic sensitivity (95% CI = 69% to 100%, n=8 for DENV-1; n=10 for DENV 2,3) and the DENV-4 test showed 92% diagnostic sensitivity (CI: 62% to 100%, n=12) along with 100% diagnostic specificity (CI: 48–100%) for all four tests. Testing infected mosquito pools, the rapid DENV-2, -3 and -4 tests showed 100% diagnostic sensitivity (95% CI = 69% to 100%, n=10) and the DENV-1 test showed 90% diagnostic sensitivity (55.50% to 99.75%, n=10) together with 100% diagnostic specificity (CI: 48–100%). Our tests reduce the operational time required to perform mosquito infection status surveillance testing from > two hours to only 35 minutes, and have potential to improve accessibility of mosquito screening, improving monitoring and control strategies in low-income countries most affected by dengue outbreaks.