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9,302 results for “DETECT”
Figure S1: Tissue detection in StrataQuest software via creation of a digital mask; Figure S2: StrataQuest workflow for detection of SOX2+ nuclei and thresholding.
<p><strong>Figure S1: Tissue detection in StrataQuest software via creation of a digital mask</strong><strong>.</strong> A digital ‘mask’ is created to measure the area of tissue for quantification of cell densities. The tissue mask is automatically generated by StrataQuest software via conversion of the scanned image from RGB to grayscale and then application of an intensity threshold. Manual adjustments are made to the tissue mask to remove necrotic areas and/or staining artefacts. Only nuclei present within the tissue mask are quantified. Representative tumour cores with low (A) and high (B) SOX2 densities, respectively, with corresponding overlaid tissue masks are shown (C-D; purple colour). Tissue mask generation is based on haematoxylin staining so is not affected by the level of DAB staining. Scale bars 200µm.</p> <p><strong>Figure S2: StrataQuest workflow for detection of SOX2+ nuclei and thresholding. </strong>Inbuilt colour deconvolution algorithms within StrataQuest software separate SOX2 DAB (brown) staining from haematoxylin (blue) staining to produce grayscale images for each channel. Nuclear segmentation was then performed on the resulting grayscale DAB image to detect brown-stained nuclei. This is possible as SOX2 expression is localised to the nucleus. Segmented nuclear masks overlaid onto the DAB (brown) channel (A) and the original colour image (B) allow visualisation of the segmentation algorithm. The software then calculates parameters such as nuclear size, haematoxylin intensity and DAB intensity for each nuclear mask, which are reported on a scattergram, with each dot representing a single nuclear mask. A scattergram displaying DAB intensity vs haematoxylin intensity is used to threshold and accurately detect SOX2+ stained nuclei (C). Gated nuclei from the scattergram (C) can be visualised on an image of segmented nuclear masks overlaid onto the RGB image (D). Red nuclei represent SOX2+ cells (red gate on C) and green nuclei are SOX2- cells (green gate on C). Scale bars 50μm.</p>
German Traffic Sign Detection Benchmark - 3 different types of backdoor patterns added
<p>Datasets are split in Training and Test datasets</p> <p>GTSRB_backdoor_green_1.zip - a green squared pattern sized 1% of the images area is added randomly around the center in an interval of +/-20% of time images width and height</p> <p>GTSRB_backdoor_green_0_5.zip - a green squared pattern sized 0.5% of the images area is added randomly around the center in an interval of +/-20% of time images width and height</p> <p>GTSRB_backdoor_black_1.zip - a black squared pattern sized 1% of the images area is added randomly around the center in an interval of +/-20% of time images width and height</p>
Targeted indication of imaging for detection of vesicoureteric reflux after pediatric febrile urinary tract infections based on a multiparametric computational tool
<p>This is the original data used for our study on differentiated indication of a voiding cystourethrography after pediatric febrile urinary tract infections. </p>
Mars novelty detection Mastcam labeled dataset
<p>These datasets were used for experiments in the paper:<br> Kerner, H. R., Wagstaff, K. L., Bue, B. D., Wellington, Jacob, S., D. F., Bell III, J. F., Kwan, C., Ben Amor, H. Comparison of novelty detection methods for multispectral images in rover-based planetary exploration missions. Under review, 2020.</p> <p>All source images are publicly released Experiment Data Records (EDRs) archived by the Planetary Data System (PDS).</p>
MCMC data for A semi-supervised Bayesian approach for simultaneous protein sub-cellular localisation assignment and novelty detection
<p>These are unprocessed Markov-chain Monte-Carlo datasets accompanying the manuscript "A semi-supervised Bayesian approach for simultaneous protein sub-cellular localisation assignment and novelty detection"</p>
Weakly Supervised Airplane Detection Dataset: WSADD
<p>The images in this dataset includes airport and nearby areas of different countries (mainly from China, the United States, the United Kingdom, France, Japan, and Singapore) taken from the Google Earth satellite. The dataset comprises 700 remote sensing images in total, of which 400 images contain an aircraft target (the ‘’positive sample set’’) and the other 300 do not (the ‘’negative sample set’’). In the process of dataset construction, the spatial resolution of the image was controlled between 0.3 m and 2 m, and the size was fixed to 768×768. We sought to collect images from different sensors during different daytimes, different seasons, and different light intensities to ensure that the dataset has a high diversity.</p>
Could dementia be detected from UK primary care patients' records by simple automated methods earlier than by the treating physician? A retrospective case-control study - Extended Data
<p>Extended data for Article published in Wellcome Open Research (Appendices 1,2 & 3). </p> <p>Abstract of Article: </p> <p><strong>Background:</strong> Timely diagnosis of dementia is a policy priority in the United Kingdom (UK). Primary care physicians receive incentives to diagnose dementia; however, 33% of patients are still not receiving a diagnosis. We explored automating early detection of dementia using data from patients’ electronic health records (EHRs). We investigated: a) how early a machine-learning model could accurately identify dementia before the physician; b) if models could be tuned for dementia subtype; and c) what the best clinical features were for achieving detection.</p> <p><strong>Methods:</strong> Using EHRs from Clinical Practice Research Datalink in a case-control design, we selected patients aged >65y with a diagnosis of dementia recorded 2000-2012 (cases) and matched them 1:1 to controls; we also identified subsets of Alzheimer’s and vascular dementia patients. Using 77 coded concepts recorded in the 5 years before diagnosis, we trained random forest classifiers, and evaluated models using Area Under the Receiver Operating Characteristic Curve (AUC). We examined models by year prior to diagnosis, subtype, and the most important features contributing to classification.</p> <p><strong>Results:</strong> 95,202 patients (median age 83y; 64.8% female) were included (50% dementia cases). Classification of dementia cases and controls was poor 2-5 years prior to physician-recorded diagnosis (AUC range 0.55-0.65) but good in the year before (AUC: 0.84). Features indicating increasing cognitive and physical frailty dominated models 2-5 years before diagnosis; in the final year, initiation of the dementia diagnostic pathway (symptoms, screening and referral) explained the sudden increase in accuracy. No substantial differences were seen between all-cause dementia and subtypes.</p> <p><strong>Conclusions:</strong> Automated detection of dementia earlier than the treating physician may be problematic, if using only primary care data. Future work should investigate more complex modelling, benefits of linking multiple sources of healthcare data and monitoring devices, or contextualising the algorithm to those cases that the GP would need to investigate.</p>
ShimFall&ADL: Triaxial accelerometer fall and activities of daily living detection dataset
<p> </p> <p><strong>ShimFall&ADL dataset</strong></p> <p> </p> <p><strong>Version </strong>1.0 (2020-06-19)</p> <p><strong>Please cite as:</strong> "T. Althobaiti, S. Katsigiannis, N. Ramzan, Triaxial accelerometer-based Fall and Activities of Daily Life detection using machine learning, Sensors, 20(13), 3777, 2020. doi: 10.3390/s20133777"</p> <p> </p> <p><strong>Disclaimer</strong><br> While every care has been taken to ensure the accuracy of the data included in the ShimFall&ADL dataset, the authors and the University of the West of Scotland do not provide any guaranties and disclaim all responsibility and all liability (including without limitation, liability in negligence) for all expenses, losses, damages (including indirect or consequential damage) and costs which you might incur as a result of the provided data being inaccurate or incomplete in any way and for any reason. 2020, University of the West of Scotland, Scotland, United Kingdom.</p> <p><br> <strong>Contact</strong><br> For inquiries regarding the ShimFall&ADL dataset, please contact:<br> Dr Stamos Katsigiannis, Stamos.Katsigiannis@uws.ac.uk, University of the West of Scotland<br> Prof. Naeem Ramzan, Naeem.Ramzan@uws.ac.uk, University of the West of Scotland</p> <p> </p> <p><strong>Acknowledgment</strong></p> <p>The authors would like to thank Md. Hasan Shahriar for the data collection under his MSc project.</p> <p> </p> <p><strong>Dataset summary</strong><br> The ShimFall&ADL dataset contains recordings from 35 individuals, acquired using a chest-strapped Shimmer v2 tri-axial accelerometer, recording at a 50Hz sampling rate. Experiments were conducted in a controlled environment at a research lab in the University of the West of Scotland. Thirty five (35) healthy individuals were recruited among young or mid-aged volunteers, aged between 19 and 34 years old, having a body weight between 52 and 113 kg, and a body height between 1.45 and 1.82 m.</p> <p>Participants performed the following activities of daily living (ADL):<br> Jumping<br> Lying down<br> Bending/picking up<br> Sitting to a chair<br> Standing up from a chair<br> Walking</p> <p>Participants performed the following falls:<br> Steep (hard)<br> Front (soft)<br> Front (hard)<br> Left (soft)<br> Left (hard)<br> Right (soft)<br> Right (hard)<br> Back (soft)<br> Back (hard)</p> <p><br> <strong>Data</strong><br> Each ".dat" file in the dataset corresponds to one event for one individual and contains 101 accelerometer samples corresponding to the event. Each row of the file corresponds to one 3-channel sample, dividing the x, y, z axes values using the "\t" character, as follows:<br> Row 1: x1\ty1\tz1<br> Row 2: x2\ty2\tz2<br> ...<br> Row N: xN\tyN\tzN</p> <p>The files within the dataset are named as follows:<br> adl_<ADL activity>_<Participant ID>.dat<br> <Fall Type>fall_<soft,hard>_<Participant ID>.dat</p> <p>For example, the file "adl_standingfromchair_18.dat" corresponds to the accelerometer recording of the 18th participant, performing the "standing up from chair" ADL. The file, "leftfall_soft_11.dat" corresponds to the accelerometer recording of the 11th participant, performing a soft left fall.</p> <p><br> <strong>Additional information</strong><br> For additional information regarding the creation of the ShimFall&ADL dataset, please refer to the associated publication: "T. Althobaiti, S. Katsigiannis, N. Ramzan, Triaxial accelerometer-based Fall and Activities of Daily Life detection using machine learning, Sensors, 20(13), 3777, 2020. doi: 10.3390/s20133777"</p>
Dataset used for detecting DNS over HTTPS by Machine Learning.
<p><strong> </strong>The dataset consists of three different data sources:</p> <ol> <li> DoH enabled Firefox</li> <li>DoH enabled Google Chrome</li> <li>Cloudflared DoH proxy</li> </ol> <p>The capture of web browser data was made using the Selenium framework, which simulated classical user browsing. The browsers received command for visiting domains taken from Alexa's top 10K most visited websites. The capturing was performed on the host by listening to the network interface of the virtual machine. Overall the dataset contains almost 5,000 web-page visits by Mozilla and 1,000 pages visited by Chrome.</p> <p>The Cloudflared DoH proxy was installed in Raspberry PI, and the IP address of the Raspberry was set as the default DNS resolver in two separate offices in our university. It was continuously capturing the DNS/DoH traffic created up to 20 devices for around three months.</p> <p>The dataset contains 1,128,904 flows from which is around 33,000 labeled as DoH. We provide raw pcap data, CSV with flow data, and CSV file with extracted features.</p> <p>The CSV with extracted features has the following data fields:</p> <p>- Label (1 - Doh, 0 - regular HTTPS)<br> - Data source<br> - Duration<br> - Minimal Inter-Packet Delay<br> - Maximal Inter-Packet Delay<br> - Average Inter-Packet Delay<br> - A variance of Incoming Packet Sizes<br> - A variance of Outgoing Packet Sizes<br> - A ratio of the number of Incoming and outgoing bytes<br> - A ration of the number of Incoming and outgoing packets<br> - Average of Incoming Packet sizes<br> - Average of Outgoing Packet sizes<br> - The median value of Incoming Packet sizes<br> - The median value of outgoing Packet sizes<br> - The ratio of bursts and pauses<br> - Number of bursts<br> - Number of pauses<br> - Autocorrelation<br> - Transmission symmetry in the 1st third of connection<br> - Transmission symmetry in the 2nd third of connection<br> - Transmission symmetry in the last third of connection</p> <p>The observed network traffic does not contain privacy-sensitive information. </p> <p>The zip file structure is:</p> <pre><code>|-- data | |-- extracted-features...extracted features used in ML for DoH recognition | | |-- chrome | | |-- cloudflared | | `-- firefox | |-- flows...............................................exported flow data | | |-- chrome | | |-- cloudflared | | `-- firefox | `-- pcaps....................................................raw PCAP data | |-- chrome | |-- cloudflared | `-- firefox |-- LICENSE `-- README.md</code></pre> <p><br> When using this dataset, please cite the original work as follows:</p> <pre><code>@inproceedings{vekshin2020, author = {Vekshin, Dmitrii and Hynek, Karel and Cejka, Tomas}, title = {DoH Insight: Detecting DNS over HTTPS by Machine Learning}, year = {2020}, isbn = {9781450388337}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3407023.3409192}, doi = {10.1145/3407023.3409192}, booktitle = {Proceedings of the 15th International Conference on Availability, Reliability and Security}, articleno = {87}, numpages = {8}, keywords = {classification, DoH, DNS over HTTPS, machine learning, detection, datasets}, location = {Virtual Event, Ireland}, series = {ARES '20} } </code></pre> <p> </p>
Towards automated early detection of risks for a CO2 plume containment from permanent seismic monitoring data
<p>This storage contains the training data for neural networks proposed in a manuscript 'Towards automated early detection of risks for a CO2 plume containment from permanent seismic monitoring data'. The data consists of output from reservoir simulations of a small-scale CO2 injection at CO2CRC Otway Project Stage 2C (Victoria, Australia). The output is presented as a set of images, where each pixel in a portable network graphics is a plume thickness for a particular injection scenario at a particular day after the injection has commenced. The format is unsigned integer 16-bit. The data set contains images of two major types:</p> <p>1. REALISTIC: plumes are obtained from reservoir simulations in a complex geological model that was calibrated on an extensive set of geophysical surveys. File naming follows this convention 'plume_thick_real_scenario_%S_day_%N.png', where %S represents a string that encodes the injection scenario name and %N denotes day number after the injection started.</p> <p>2. VANILLA: plumes are obtained from reservoir simulations in a simple model of a reservoir that reflects only few typical features of the Otway injection interval. 'plume_thick_vanilla_scenario_%S_day_%N.png', where %S represents a string that encodes the injection scenario name and %N denotes day number after the injection started.</p>
Data from: Early detection of chronic hepatitis B and risk factor assessment in Turkish migrants, Middle Limburg, Belgium.
Background Turkey is an intermediate hepatitis B virus (HBV) endemic country. However, prevalence among Turkish migrants in Belgium is unknown, especially in those born in Belgium with a foreign-born parent, i.e. second-generation migrants (SGM). Aims To evaluate the prevalence of HBV infection and associated risk factors in Turkish first-generation migrants (FGM), i.e. foreign-born, and SGM. Methods Between September 2017 and May 2019, free outreach testing for hepatitis B surface antigen (HBsAg), hepatitis B core antibodies (anti-HBc), and antibodies against HBsAg was offered to Turkish migrants in Middle-Limburg, Belgium. Face-to-face questionnaire assessed HBV risk factors. HBsAg positive patients were referred and followed up. Turkish SGM were stratified into birth cohort born before and after 1987, since those born after 1987 should be covered by the universal infant vaccination program. Results A total of 1,081/1,113 (97.1%) Turkish did go for HBV testing. Twenty-six (2.4%) were HBsAg positive; 11/26 were unaware of their status and 10/11 were successfully referred. HBsAg prevalence was 3.0% in FGM and 1.5% in SGM, p = .070. Only one out of seven HBsAg positive SGM was born after 1987. In the multiple generalized estimating equations model, the most important risk factors for anti-HBc positivity were male gender ( p = .021), older age ( p < .001), FGM ( p < .001), low educational level of the mother ( p = .003), HBV infected mother ( p = .008), HBV infected siblings ( p = .002), HBV infected other family member ( p = .004), gynaecological examination in Turkey or unsafe male circumcision ( p = .032) and dental treatment in Turkey ( p = .049). Conclusion Outreach testing was well-accepted and referral to specialist care was generally successful. National HBV screening should be implemented in the Turkish FGM population and might be considered in SGM not covered by primary prevention strategies.
Data from: High detectability with low impact: Optimising large PIT tracking systems for cave-dwelling bats
Passive integrated transponder (PIT) tag technology permits the 'resighting' of animals tagged for ecological research without the need for physical re-trapping. While this is effective if animals pass within centimetres of tag readers, short-distance detection capabilities have prevented the use of this technology with many species. To address this problem, we optimised a large (15 m-long) flexible antenna system to provide a c. 8 m2 vertical detection plane for detecting animals in flight. We installed antennas at two roosting caves, including the primary maternity cave, of the critically endangered southern bent-winged bat (Miniopterus orianae bassanii) in south-eastern Australia. Testing of these systems indicated PIT-tags could be detected up to 105 cm either side of the antenna plane. Over the course of a three-year study, we subcutaneously PIT-tagged 2966 bats and logged over 1.4 million unique detections, with 97% of tagged bats detected at least once. The probability of encountering a tagged bat decreased with increasing environmental 'noise' (unwanted signal) perceived by the system. During the study we mitigated initial high noise levels by earthing both systems, which contributed to an increase in daily detection probability (based on the proportion of individuals known to be alive that were detected each day) from <0.2 (noise level ≥30%) to 0.7-0.8 (noise level 5-15%). Conditional on a low (5%) noise level, model-based estimates of daily encounter probability were highest (>0.8) during peak breeding season when both female and male southern bent-winged bats congregate at the maternity cave. In this paper we detail the methods employed and make methodological recommendations for future wildlife research using large antennas, including earthing systems as standard protocol and quantifying noise metrics as a covariate influencing the probability of detection in subsequent analyses. Our results demonstrate that large PIT antennas can be used successfully to detect small volant species, extending the scope of PIT technology and enabling a much broader range of wildlife species to be studied using this approach.
A metacommunity approach for detecting species influenced by mass effect
<p>1. Mass effect, allowing species to persist in unfavourable habitats, and dispersal limitation, preventing species from reaching favourable habitats, are the two major dispersal processes. While dispersal limitation can be detected by experimental or modeling techniques, mass effect is more challenging to evaluate, which hampers our ability to disentangle the influence of the environment vs. dispersal on species distribution. This is undesirable for biomonitoring programs built on known species-environment relationships.</p> <p>2. We developed an approach for detection of species influenced by mass effect. We tested it on stream diatoms, a widely used taxonomic group for stream biomonitoring, from four French watersheds. This approach combined (1) an appropriate spatial framework, the Asymmetric Eigenvector Map (AEM), used in species distribution modeling to measure the relative influence of dispersal vs. niche processes, (2) an analysis of negative co-occurrence patterns to separate mass effect from dispersal limitation, and (3) a measurement of niche breadths to distinguish between non-spatially structured generalists and species influenced by mass effect.</p> <p>3. We propose that species characterized by low negative co-occurrence values, a high correlation to spatial factors and average to low niche breadths are sensitive to mass effect.</p> <p>4. Synthesis and applications: We suggest that the sensitivity of species towards mass effect should represent a new ecological trait to be considered for fundamental and applied issues concerning ecology and water quality assessment. Almost all of the species identified here as influenced by mass effect are contributing to the calculation of different diatom-based indices (e.g., Biological Diatom Index or Specific Pollution-sensitivity Index) and should be treated with caution when assigning ecological status classes to water bodies.</p>
Far-red pentamethine cyanine dyes as fluorescent probes for detection of serum albumins
<p><span>Benzothiazole based cyanine dyes with bridged groups in pentamethine chain were studied as potential far-red fluorescent probes for protein detection. Spectral-luminescent properties were characterized for unbound dyes and in the presence of serum albumins (bovine (BSA), human (HSA), equine (ESA)), and globular proteins (β-lactoglobulin, ovalbumin). We have observed that the addition of albumins leads to a significant increase in dyes fluorescence intensity. However, the fluorescent response of dyes in the presence of other globular proteins was noticeably lower. The value of fluorescence quantum yield for dye bearing sulfonate group complexed with HSA amounted to 42% compared with 0.2 % for the free dye. The detection limit of HSA by this dye was about > 0.003 mg/ml that indicates the high sensitivity of dye to low HSA concentrations. Modeling of structure of the dyes complexes with albumin molecules was performed by molecular docking. According to these data, dyes could bind to up to five sites on the HSA molecule; the most preferable are the hemin-binding site in subdomain IB and <span>the dye-binding site in the pocket between subdomains IA, IIA, and IIIA.</span> This work confirms that pentamethine cyanine dyes could be proposed as powerful far-red fluorescent probes applicable for highly sensitive detection of albumins.</span></p>
Data from: Metabarcoding on planktonic larval stages: an efficient approach for detecting and investigating life cycle dynamics of benthic aliens
High-throughput sequencing (HTS) technologies offer new promise to support surveillance programs targeting marine non-indigenous species (NIS). Metabarcoding might surpass traditional monitoring methods, for example through its ability to detect rare species, a key feature in early detection of NIS. Another interest of this approach is the identification of organisms difficult to identify based on morphology only (e.g., early developmental stages), making it relevant in the context of management programs. Because many marine benthic NIS have a bi-phasic bentho-pelagic life cycle, targeting their pelagic larval stages in zooplankton may allow early detection and assessment of their establishment and potential spread. We illustrate this approach with an analysis of bulk-DNA retrieved from a time-series of zooplankton samples collected over 22 months in one bay in Brittany (France). Using HTS of amplicons obtained with two markers (COI and 18S) and a metabarcoding approach, 12 NIS were identified and their temporal larval dynamics were monitored. Importantly, we chose to focus on a closed list of species, from four metazoan classes encompassing 52 NIS reported within the study area or nearby seas, with molecular references available or obtained locally for 42 of them. The use of a custom-designed database allowed the detection of three NIS otherwise not detected when using public databases. Interestingly, NIS known to have a short-lived larval stage were detected (e.g., the bryozoan Bugula neritina or the tunicate Corella eumyota). For two molluscs Ruditapes philippinarum and Crepidula fornicata, metabarcoding results were compared to those obtained using traditional methods (i.e., barcoding of individual larvae and morphology, respectively) to show the reliability of the approach in detecting and assessing the extent of their reproductive periods. Our results also revealed that the Pacific oyster Crassostrea gigas, a notorious invasive species, failed to reproduce in the study bay, showing that metabarcoding on larval stages also provides information regarding the establishment success (or failure) of NIS. While metabarcoding has its limitations and biases, this study demonstrates its effectiveness for surveillance of targeted NIS, notably to support management strategies like the European Marine Strategy Framework Directive (MSFD).
Data from: Extending the climatological concept of 'Detection and Attribution' to global change ecology in the Anthropocene
<ol> <li>Research into global change ecology is motivated by the need to understand the role of humans in changing biotic systems. Mechanistic understanding of ecological responses requires the separation of different climatic parameters and processes that often operate on diverse spatiotemporal scales. Yet most environmental studies do not distinguish the effects of internal climate variability from those caused by external, natural (e.g. volcanic, solar, orbital) or anthropogenic (e.g. greenhouse gases, ozone, aerosols, land-use) forcing factors.</li> <li>We suggest extending the climatological concept of 'Detection and Attribution' (DA) to unravel abiotic drivers of ecological dynamics in the Anthropocene. We therefore apply DA to quantify the relative roles of natural versus industrial temperature change on elevational shifts in the outbreak epicentres of the larch budmoth (LBM; <i>Zeiraphera diniana</i> or <i>griseana</i> Gn.); the classic example of a cyclic forest defoliating insect.</li> <li>Our case study shows that anthropogenic warming shifts the epicentre of travelling LBM waves upward, which disrupts the intensity of population outbreaks that occurred regularly over the past millennium in the European Alps. Our findings demonstrate the ability of DA to detect ecological responses beyond internal system variability, to attribute them to specific external climate forcing factors, and to identify climate-induced ecological tipping points.</li> <li>In order to implement the climatological concept of 'Detection and Attribution' successfully into modern global change ecology, future studies should combine high-resolution paleoenvironmental reconstructions and state-of-the-art climate model simulations to inform inference-based ecosystem models.</li> </ol>
Recombination detection program (RDP4) results for ACMV and ACMBFV DNA-B
<p>A DNA-B multiple alignment (Zenodo record 3964979) was scanned with RDP4.100 (D. Martin et al. 2015). No events had statistical support from more than 4 of the 7 methods used to scan for signals of recombination.</p> <p>These results are described in a paper by Crespo-Bellido et al. (2021) https://doi.org/10.1128/JVI.00541-21</p>
Recombination detection program (RDP4) results for EACMV-like DNA-B sequences (7 "species")
<p>A DNA-B multiple alignment (Zenodo record 3965023) was scanned with RDP4.97 (D. Martin et al. 2015). The input sequences belong to 7 “species”: CMMGV, EACMCV, EACMV, EACMKV, EACMMV, EACMZV, SACMV. Recombination breakpoints for events with statistical support from at least 5 of 7 methods were adjusted per the manual. These events were explicitly accepted, with the exception of one event which lost significance after adjustment of breakpoints. A CSV file with the results was exported and then saved a second time with Excel to normalize format (a comma in between every column).</p> <p>These results are described in a paper by Crespo-Bellido et al. (2021) https://doi.org/10.1128/JVI.00541-21</p>
Under the karst: detecting hidden subterranean assemblages using eDNA metabarcoding in the caves of Christmas Island, Australia
<p>Subterranean ecosystems are understudied and challenging to conventionally survey given the inaccessibility of underground voids and networks. In this study, we conducted a eukaryotic environmental (eDNA) metabarcoding survey across the karst landscape of Christmas Island, (Indian Ocean, Australia) to evaluate the utility of this non-invasive technique to detect subterranean aquatic 'stygofauna' assemblages. Three metabarcoding assays targeting the mitochondrial 16S rRNA and nuclear 18S genes were applied to 159 water and sediment samples collected from 23 caves and springs across the island. Taken together, our assays detected a wide diversity of chordates, cnidarians, porifera, arthropods, molluscs, annelids and bryozoans from 71 families across 60 orders. We report a high level of variation between cave and spring subterranean community compositions which are significantly influenced by varying levels of salinity. Additionally, we show that dissolved oxygen and longitudinal gradients significantly affect biotic assemblages within cave communities. Lastly, we combined eDNA-derived community composition and environmental (water quality) data to predict potential underground interconnectivity across Christmas Island. We identified three cave and spring groups that showed a high degree of biotic and abiotic similarity indicating likely local connectivity. This study demonstrates the applicability of eDNA metabarcoding to detect subterranean eukaryotic communities and explore underground interconnectivity.</p>
Data and processing for "Current detection using a Josephson parametric upconverter"
<p>This directory contains all the code and data necessary to produce the figures of the paper titled "Current detection using a Josephson parametric upconverter" written by Felix E. Schmidt, Daniel Bothner, Ines C. Rodrigues, Mario F. Gely, Mark D. Jenkins and Gary A. Steele.</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.