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9,302 results for “DETECT”
Dataset: Survey on the actual situation of antibiotic resistant bacteria detection by nucleic acid amplification test in clinical microbiology laboratories at hospitals in Japan: Online survey of participants in workshops organized by the Nara Association of Medical Technologists
<p>The coronavirus disease 2019 pandemic has led to the widespread use of the nucleic acid amplification test (NAAT), along with an increase in demand for SARS-CoV-2 tests. NAAT has been used to detect antimicrobial resistance (AMR) genes since before the pandemic, but the test has been performed in a limited number of facilities. We investigated the current status and background of Japanese clinical laboratories by surveying the implementation of genotypic AST in NAAT, which has become widespread owing to the pandemic. This means that 59% of the respondents possessed NAAT and were using it for genotypic AST. GeneXpert and FilmArray were introduced in the majority of cases (62.5% and 82.6%, respectively), with the pandemic as the trigger. More than half of the respondents cited “rapid detection” (56.0%) and “ICT requests” (52.4%) as the reasons for introducing the system. Regarding usefulness, “contribution to infectious disease treatment” (74.1%) showed the highest percentage. Among the respondents who cited “not implemented”, the most frequent responses were “I have no plans, but I want to do it.” (38.1%) and “would do so if requested by a physician” (33.3%). The most common reason for not implementing the system was concern about increased workload (52.9%). We believe that this is due to changes in the working environment caused by the pandemic and the characteristics of Japanese society. In the future, to promote the adoption of genotypic AST, it will be necessary to approach it through reports on its usefulness from domestic facilities, and simultaneously, improving and enhancing efficiency in work processes will also be essential.</p>
Dataset: Highly sensitive single molecule detection of macromolecule ion beams
<p>Figure 6 & Figure 7.zip: Figure 6 Superconducting nanowire detector (SSPD) beam profile of a mass-selected Insulin (Ins5+) ion beam at 1000 eV of impact energy.; Figure 7 Area-normalized molecule detection quantum yield. - raw data and Origin 2022 files</p><p>Voltage Shifts.zip: Calibration of deflection electrodes with linear stage - raw data and Origin 2022 files</p><p>ESI source stability.opju: 1 Hour measurement of the source stability with a TOF-MS - Origin 2022 file</p><p>Figure 2.opju: Impact energy in ESI-QMS-SSPD vs. ESI-QMS-Phosphorscreen mass spectra. - Origin 2022 file</p><p>Figure 3.opju: Signal background and complexity in ESI-QMS-SSPD vs. ESI-TOF-MCP spectra. - Origin 2022 file</p><p>Figure 4.opju: Influence of Energy/Mass/Momentum/structure on the detection mechanism. - Origin 2022 file</p><p>Figure 5.opju: Confirmation of the hot-spot model for SSPD detector D2 - Origin 2022 file</p><p>Figure S2.opju: Identification of protein mass spectra. - Origin 2022 file</p><p>Figure S3.opju: ESI-QMS-SSPD spectrum measured with SSPD. - Origin 2022 file</p><p>Figure S5.opju: Beam profile at different deflection voltages. - Origin 2022 file</p><p>Figure S9.opju: Photo-activated ESI-QMS-SSPD spectrum of Ru-complex modified Insulin. - Origin 2022 file</p><p>Linear stage position readback.txt: Position readback when lifting the Linear Stage recorded with a laser triangulometer. - raw data</p>
Data from: Using controlled subsurface releases to investigate the effect of leak variation on above-ground natural gas detection
<p class="MsoNormal"><span>Pipelines transport natural gas (NG) in all stages between production and the end user. The NG composition, pipeline depth, and pressure vary significantly between extraction and consumption. As methane (CH<sub>4</sub>), the primary component of NG is both explosive and a potent greenhouse gas, NG leaks from underground pipelines pose both a safety and environmental threat. Leaks are typically found when an observer detects a CH<sub>4</sub> enhancement as they pass through the downwind above-ground NG plume. The likelihood of detecting a plume depends, in part, on the size of the plume, which is contingent on both environmental conditions and intrinsic characteristics of the leak. To investigate the effects of leak characteristics, this study uses controlled NG release experiments to observe how the above-ground plume width changes with changes in the gas composition of the NG, leak rate, and depth of the subsurface emission. Results show that plume width generally decreases when heavier hydrocarbons are present, the leak rate is reduced, and as leak depth decreases from 0.9 to 0.6 m. The above surface CH<sub>4</sub> plume is undetectable when leaks are 1.8 m deep. As most survey methods typically prioritize leaks based on the leak size, this study shows that the effect of NG density on above-ground plume width is only 4%, equivalent to the effect of leak rate. This suggests that reported leaks in areas with heavier hydrocarbons could currently be missed or underestimated. Furthermore, this study shows that leaks from pipelines laid in covers meeting minimum depth requirements of 0.9 m could be easier to detect compared to those buried shallower. Overall, this study illustrates that leak survey protocols for flowlines and gathering lines should be different from distribution pipelines and tailored to the compositions of the transported NG to report emissions accurately.</span></p>
Reproduction package for the paper: "Detection of ultra-fast radio bursts from FRB 20121102A"
<p><br># Reproduction package for the paper "Detection of ultra-fast radio bursts from FRB 20121102A"<br>Authors: Mark P. Snelders, K. Nimmo, J.W.T. Hessels, Z. Bensellam, L.P. Zwaan, P. Chawla, O.S. Ould-Boukattine, F. Kirsten, J.T. Faber and V. Gajjar.<br>arXiv link: https://arxiv.org/abs/2307.02303<br>DOI published article: Nature Astronomy, 19 October 2023, https://doi.org/10.1038/s41550-023-02101-x<br><br>This work has been made possible by an NWO Vici grant (Principal investigator, J.W.T.H.). <br><br>## Raw Data<br><br><strong>- The data are 100% publicly available and are explained in great detail in the following post: http://seti.berkeley.edu:8000/frb-data/</strong><br><strong>- The data are available from the Breakthrough Initiatives Open Data Portal with target name FRB121102: https://breakthroughinitiatives.org/opendatasearch</strong><br><br>In this paper we have re-processed and re-analysed data from the Green Bank Telescope that made use of the Breakthrough Listen digital backend. I will call this the GBT BL data. Below you can find links to multiple papers, GitHub repositories and blogposts that explain the GBT BL data.</p><p>- My paper describing the search and analysis of the ultra-fast radio bursts:<br> * https://ui.adsabs.harvard.edu/abs/2023arXiv230702303S/abstract<br> * https://www.nature.com/articles/s41550-023-02101-x<br>- First detection of the bursts at 8 GHz: https://ui.adsabs.harvard.edu/abs/2018ApJ...863....2G/abstract<br>- More bursts from the same dataset with machine learning detections: https://ui.adsabs.harvard.edu/abs/2018ApJ...866..149Z/abstract<br>- Explaining the Breakthrough Listen project: https://ui.adsabs.harvard.edu/abs/2017AcAau.139...98W/abstract<br>- Explaining the GBT breakthrough listen recorder: https://ui.adsabs.harvard.edu/abs/2018PASP..130d4502M/abstract<br>- Explaining the data formats: https://ui.adsabs.harvard.edu/abs/2019PASP..131l4505L/abstract<br>- Python 2 code to work with the baseband data: https://github.com/greghell/extractor (NOTE THAT IT IS PYTHON 2!!) (I recommend using Python 2.7 if you make use of that repo)<br>- Structure of the baseband data: https://github.com/UCBerkeleySETI/breakthrough/blob/master/doc/RAW-File-Format.md<br>- More information: https://github.com/UCBerkeleySETI/breakthrough/blob/master/GBT/waterfall.md<br>- A version of dspsr, called bl-dspsr, that can work with the GBT BL baseband data: https://github.com/UCBerkeleySETI/bl-dspsr<br><br>## Software<br><br>- The data was processed on multiple machines with various operating systems, which include, but are not limited to, macOS, Ubuntu and centOS.<br>- All the used software is open source, see the section above for more information, and also see the 'software' section in the paper.<br><br>## Figures and Tables</p><p>The files in this Zenodo package should be self-explanatory. E.g. `table_1.tar` contains all the scripts/notebooks/files needed to make table_1, and also contains table 1 itself. <br>The file: 'general_info.tar' is basically a txt file with the same info as provided here and it contains an offline version of the Breakthrough Listen blogpost that that explains the raw data. <br>The file: `helper_functions.tar` is a tarball that contains a Python file with a collection of helper functions that are used in the Jupyter notebooks (and the figures are made in the Jupyter notebooks). It also contains some files that are needed to e.g. remove the instrumental delay from the data.<br> <br>## End-to-End analysis scripts<br>The Python code/Jupyter notebooks in the tarfiles are end-to-end. </p><p>## Intermediate data products </p><p>The file `data_and_data_info.tar` contains two intermediate data products (both several gigabytes in size) and a txt file explaining the files and how they were made. Due to the Zenodo file size limitations I cannot upload everything. Please contact me at snelders@astron.nl or m.p.snelders@uva.nl or via ORCID to request any other files. <br><br><br><br> </p>
Host biomarkers and combinatorial scores for the detection of serious and invasive bacterial infection in pediatric patients with fever without source
<h3><span>Background </span></h3> <p><span>Improved tools are required to detect bacterial infection in children with fever without source (FWS), especially when younger than 3 years old. The aim of the present study was to investigate the diagnostic accuracy of a host signature combining for the first time two viral-induced biomarkers, tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) and interferon </span><span>γ</span><span>-induced protein-10 (IP-10), with a bacterial-induced one, C-reactive protein (CRP), to reliably predict bacterial infection in children with fever without source (FWS) and to compare its performance to routine individual biomarkers (CRP, procalcitonin (PCT), white blood cell and absolute neutrophil counts, TRAIL, and IP-10) and to the Labscore.</span></p> <h3><span>Methods</span></h3> <p><span>This was a prospective diagnostic accuracy study conducted in a single tertiary center in children aged less than 3 years old presenting with FWS. Reference standard etiology (bacterial or viral) was assigned by a panel of three independent experts. Diagnostic accuracy (AUC, sensitivity, specificity) of host individual biomarkers and combinatorial scores was evaluated in comparison to reference standard outcomes (expert panel adjudication and microbiological diagnosis). </span></p> <h3><span>Results </span></h3> <p><span>241 patients were included. 68 of them (28%) were diagnosed with a bacterial infection and 5 (2%) with invasive bacterial infection (IBI). Labscore, ImmunoXpert, and CRP attained the highest AUC values for the detection of bacterial infection, respectively 0.854 (0.804–0.905), 0.827 (0.764–0.890), and 0.807 (0.744–0.869). Labscore and ImmunoXpert outperformed the other single biomarkers with higher sensitivity and/or specificity and showed comparable performance to one another although slightly reduced sensitivity in children < 90 days of age.</span></p> <h3><span>Conclusion </span></h3> <p><span>Labscore and ImmunoXpert demonstrate high diagnostic accuracy for safely discriminating bacterial infection in children with FWS aged under and over 90 days, </span><span>supporting their adoption in the assessment of febrile patients. </span></p>
Data from: Interactive bioacoustic playback as a tool for detecting and exploring nonhuman intelligence: "Conversing" with an Alaskan humpback whale
<p>Here we report on a rare and opportunistic acoustic turn-taking with an adult female humpback whale, known as Twain, in Southeast Alaska. Post hoc acoustic and statistical analyses of a 20-minute acoustic exchange between the broadcast of a recorded contact call, known as a 'whup/throp', with call responses by Twain revealed an intentional human-whale acoustic (and behavioral) interaction. Our results show that Twain participated both physically and acoustically in three phases of interaction (Phase 1: Engagement, Phase 2: Agitation, Phase 3: Disengagement), independently determined by blind observers reporting on surface behavior and respiratory activity of the interacting whale. A close examination of both changes to the latency between Twain's calls and the temporal matching to the latency of the exemplar across phases indicated that Twain was actively engaged in the exchange during Phase 1 (Engagement), less so during Phase 2 (Agitation), and disengaged during Phase 3 (Disengagement). These results, while preliminary, point to several for effective playback design, namely the importance of salient, dynamic and adaptive playbacks, that should be utilized in experimentation with whales and other interactive nonhuman species.</p>
Multiple sequence alignments: Detection and isolation of a new member of Burkholderiaceae‑related endofungal bacteria from Saksenaea boninensis sp. nov., a new thermotolerant fungus in Mucorales
<p><strong>Methods:</strong></p><p>Nucleotide sequences were aligned independently for each region using MAFFT v7.212 (Katoh and Standley, 2013). The obtained alignment blocks were subject to Gblocks 0.91b (Castresana, 2000) to remove poorly aligned positions with the relaxed selection setting described in Talavera & Castresana (2007) using the following parameters (-t = d -b2 = 9 -b3 = 10 -b4 = 5 -b5 = h). After automatically removing gaps, the alignment blocks were viewed using MEGA 6.06 software (Tamura et al., 2013) and poorly aligned positions at either end of the alignments were removed manually. Pairwise distances of the nucleotide sequences (ITS2, ITS1-5.8S-ITS2, LSU, and tef1) of the ex-type strains of seven <i>Saksenaea</i> spp. and the representative isolate <i>S. boninensis</i> Sak4 were calculated by MEGA 6.06 software (Tamura et al. 2013). Multiple sequence alignment of 16S rRNA gene of the family <i>Burkholderiaceae</i> was prepared for the phylogeny of a bacterial endosymbiont. Multiple sequence alignments of ITS, LSU, and tef1 genes of <i>Saksenaea</i> spp. (Mucorales) were separately prepared for the phylogeny of a fungal host. Concatenated dataset of these genes were also prepared. All nucleotide sequences were retrieved from GenBank (See "Sequence_ID.csv" and taxon names of each alignment). </p><p> </p><p><strong>Description of files:</strong></p><p><strong>A. Phylogeny of the family </strong><i><strong>Burkholderiaceae</strong></i><strong> (Bacterial endosymbiont):</strong></p><p>1. Burkholderiaceae_16S_RAW.fasta</p><p>Non-aligned dataset of 16S rRNA gene of the family <i>Burkholderiaceae</i>.</p><p> </p><p>2. Burkholderiaceae_16S_aligned.fasta</p><p>Aligned dataset of 16S rRNA gene of the family <i>Burkholderiaceae</i>.</p><p> </p><p><strong>B. Phylogenies of </strong><i><strong>Saksenaea</strong></i><strong> spp. (Fungal host):</strong></p><p>1. Sequence_ID_v2.csv</p><p>Taxon names, accession numbers, and sequence ID for the concatenated multiple sequence alignment are listed.</p><p> </p><p>2. Saksenaea_ITS_RAW_v2.fasta</p><p>Non-aligned dataset of ITS1-5.8S-ITS2 region of <i>Saksenaea</i> spp. </p><p> </p><p>3. Saksenaea_ITS_aligned_v2.fasta</p><p>Aligned dataset of ITS1-5.8S-ITS2 region of <i>Saksenaea</i> spp. Only used for ITS1-5.8S-ITS2 phylogeny.</p><p> </p><p>4. Saksenaea_LSU_RAW_v2.fasta</p><p>Non-aligned dataset of LSU gene region of <i>Saksenaea</i> spp. </p><p> </p><p>5. Saksenaea_LSU_aligned_v2.fasta</p><p>Aligned dataset of LSU gene region of <i>Saksenaea</i> spp. Only used for LSU phylogeny.</p><p> </p><p>6. Saksenaea_tef1_RAW_v2.fasta</p><p>Non-aligned dataset of tef1 gene region of <i>Saksenaea</i> spp. </p><p> </p><p>7. Saksenaea_tef1_aligned_v2.fasta</p><p>Aligned dataset of tef1 gene region of <i>Saksenaea</i> spp. Only used for tef1 phylogeny.</p><p> </p><p><strong><Concatenated dataset 1 (ITS2, LSU, tef1)></strong></p><p>8. Saksenaea_ITS2_for_concatenated_RAW_v2.fasta</p><p>Non-aligned dataset of ITS2 region of <i>Saksenaea</i> spp. used for preparation of a concatenated dataset 1.</p><p> </p><p>9. Saksenaea_ITS2_for_concatenated_aligned_v2.fasta</p><p>Aligned dataset of ITS2 region of <i>Saksenaea</i> spp. used for preparation of a concatenated dataset 1.</p><p> </p><p>10. Saksenaea_LSU_for_concatenated_RAW_v2.fasta</p><p>Non-aligned dataset of LSU gene region of <i>Saksenaea</i> spp. used for preparation of concatenated datasets 1 and 2.</p><p> </p><p>11. Saksenaea_LSU_for_concatenated_aligned_v2.fasta</p><p>Aligned dataset of LSU gene region of <i>Saksenaea</i> spp. used for preparation of concatenated datasets 1 and 2.</p><p> </p><p>12. Saksenaea_tef1_for_concatenated_RAW_v2.fasta</p><p>Non-aligned dataset of tef1 gene region of <i>Saksenaea</i> spp. used for preparation of concatenated datasets 1 and 2.</p><p> </p><p>13. Saksenaea_tef1_for_concatenated_aligned_v2.fasta</p><p>Aligned dataset of tef1 gene region of <i>Saksenaea</i> spp. used for preparation of concatenated datasets 1 and 2.</p><p> </p><p>14. Saksenaea_ITS2_LSU_tef1_concatenated_dataset1.fasta</p><p>Concatenated dataset of three multiple sequence alignments (9, 11, and 13). This concatenated dataset was used for the main phylogeny of <i>Saksenaea</i> spp.</p><p> </p><p><strong><Concatenated dataset 2 (ITS1-5.8S-ITS2, LSU, tef1)></strong></p><p>15. Saksenaea_ITS_for_concatenated_RAW_v2.fasta</p><p>Non-aligned dataset of ITS1-5.8S-ITS2 region of <i>Saksenaea</i> spp. used for preparation of a concatenated dataset 2.</p><p> </p><p>16. Saksenaea_ITS_for_concatenated_aligned_v2.fasta</p><p>Aligned dataset of ITS1-5.8S-ITS2 region of <i>Saksenaea</i> spp. used for preparation of a concatenated dataset 2.</p><p>Blank sequences were inserted for five isolates of <i>Saksenaea longicolla</i> after the alignment.</p><p> </p><p>17.Saksenaea_ITS_LSU_tef1_concatenated_dataset2.fasta</p><p>Concatenated dataset of three multiple sequence alignments (15, 11, and 13). This concatenated dataset was used for the main phylogeny of <i>Saksenaea</i> spp.</p><p> </p><p><strong>C. Pairwise distances of the ex-type strains of </strong><i><strong>Saksenaea</strong></i><strong> spp.</strong></p><p>1. Saksenaea_ITS_type_RAW.fasta</p><p>Non-aligned dataset of ITS1-5.8S-ITS2 region of the ex-type strains of <i>Saksenaea</i> spp.</p><p> </p><p>2. Saksenaea_ITS2_type_aligned.fasta</p><p>Aligned dataset of ITS2 region of the ex-type strains of <i>Saksenaea</i> spp.</p><p> </p><p>3.Saksenaea_ITS_type_aligned.fasta</p><p>Aligned dataset of ITS1-5.8S-ITS2 region of the ex-type strains of <i>Saksenaea</i> spp. without <i>Saksenaea longicolla</i>.</p><p> </p><p>4. Saksenaea_LSU_type_RAW.fasta</p><p>Non-aligned dataset of LSU gene region of the ex-type strains of <i>Saksenaea </i>spp.</p><p> </p><p>5. Saksenaea_LSU_type_aligned.fasta</p><p>Aligned dataset of LSU gene region of the ex-type strains of <i>Saksenaea</i> spp.</p><p> </p><p>6. Saksenaea_tef1_type_RAW.fasta</p><p>Non-aligned dataset of tef1 gene region of the ex-type strains of <i>Saksenaea</i> spp.</p><p> </p><p>7. Saksenaea_tef1_type_aligned.fasta</p><p>Aligned dataset of tef1 gene region of the ex-type strains of <i>Saksenaea</i> spp.</p>
EnderScope: A low-cost 3D printer based scanning microscope for microplastic detection
<p>The EnderScope is a novel, low-cost microscope for automated scanning and detection of microplastics in filtered seawater samples. This microscope is based on the mechanics of a low-cost 3D printer (Creality Ender 3). The hotend of the printer is replaced with an optics module, allowing for the reliable and calibrated motion system of the 3D printer to be used for automated scanning over a large area (>20x20 cm). </p> <p>Here we present the optical and mechanical validtion of the EnderScope. Along with data from some proof of concept experiments that show the EnderScope is capable of detecting plastics in environmental seawater samples.</p>
Dataset for varroa mite detection on sticky boards
<p>This Zenodo entry contains several supplementary files to the paper <i>"Analysis of Varroa mite colony infestation level using new open software based on deep learning techniques"</i> by Jose Divasón, Ana Romero, Francisco Javier Martínez de Pisón, Miguel A. Silvestre, Pilar Santolaria and Jesús L. Yániz.</p><p>The code is also publicly available in Github: <a href="https://github.com/jodivaso/varroa_detector">https://github.com/jodivaso/varroa_detector</a></p><p>The following files are included in this entry:</p><ul><li><strong>dataset.zip: </strong>this file contains the dataset that consists of 64 images (dimensions 8064 x 6048) of sticky boards with varroa mites. The compressed file also includes the labels (the annotations).</li><li><strong>images_deblurGAN.zip: </strong>this file contains the same 64 images, but after applying deblurGAN techniques.</li><li><strong>df_dataset.csv: </strong>CSV containing the information which images belong to the training set and which belong to the validation set.</li><li><strong>models.zip:</strong> it contains the different deep learning models that have been developed.</li><li><strong>example.zip: </strong>compressed file that includes a model (based on Faster R-CNN with resnet18 as backbone and fpn) and one image.</li></ul>
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.
Rulers2023: An Annotated Dataset of Synthetic and Real Images for Ruler Detection Using Deep Learning
<p>Annotated datasets of synthetic and real ruler images:<br>1. Synthetic-train<br>2. Real-train<br>3. Real-test</p><p><strong>Citation:</strong> Matuzevičius D. Rulers2023: An Annotated Dataset of Synthetic and Real Images for Ruler Detection Using Deep Learning. <i>Electronics</i>. 2023; 12(24):4924. https://doi.org/10.3390/electronics12244924</p><p> </p>
CAS Landslide Dataset: A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide Detection
<p>In this work, we present the CAS Landslide Dataset, a large-scale and multisensor dataset for deep learning-based landslide detection, developed by the Artificial Intelligence Group at the Institute of Mountain Hazards and Environment, Chinese Academy of Sciences (CAS). The dataset aims to address the challenges encountered in landslide recognition. With the increase in landslide occurrences due to climate change and earthquakes, there is a growing need for a precise and comprehensive dataset to support fast and efficient landslide recognition. In contrast to existing datasets with dataset size, coverage, sensor type and resolution limitations, the CAS Landslide Dataset comprises 20,865 images, integrating satellite and unmanned aerial vehicle data from nine regions. To ensure reliability and applicability, we establish a robust methodology to evaluate the dataset quality. We propose the use of the Landslide Dataset as a benchmark for the construction of landslide identification models and to facilitate the development of deep learning techniques. Researchers can leverage this dataset to obtain enhanced prediction, monitoring, and analysis capabilities, thereby advancing automated landslide detection.</p> <p>If you use our data, please cite our work published in Scientific Data.</p> <p>Xu, Y., Ouyang, C., Xu, Q. <em>et al.</em> CAS Landslide Dataset: A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide Detection. <em>Sci Data</em> <strong>11</strong>, 12 (2024). https://doi.org/10.1038/s41597-023-02847-z</p>
Dataset for honey bee audio detection
<p>The database contains recordings of bees and drones in wav format, without compression. The sampling frequency is 44.1kHz.</p><p>The database contains 10000 one-second recordings of bees and 1700 one-second recordings of drones.</p>
SPACEtomo training dataset for lamella detection using YOLOv8
<p><strong>Training data used to train a YOLOv8 model for cryoFIB-milled lamella detection on whole grid TEM montages.</strong></p> <p>This dataset contains 614 LM map pieces of varying pixel sizes (rescaled to 400 nm/pixel) containing 1076 examples of lamellae. Lamellae were classified into "good", "contaminated", "thick" and "broken" lamellae. An additional 534 LM map pieces not containing any lamellae were added to the training dataset. To enhance rotational invariance, the 614 LM map pieces were added to the training dataset again with flipped axes.</p> <p>Thanks to <span>Matthias </span><span>P</span><span>ö<span>ge</span></span><span>, Gregor Weiss, Sven Klumpe, <span>Anna Bieber</span><span> and</span><span> Cristina Capitanio for providing whole grid TEM maps.</span></span></p>
Data for White-tailed Deer Detection Intensity Increases When Coyotes Are Present
<p>Data and code used for fitting two-species occupancy model with continuous time detection process.</p>
Fault Detection and Inventory Management in Manufacturing with Plastic Bricks: A Dataset
<p><strong>Dataset for Smart Manufacturing</strong></p> <p>The dataset contains images of plastic bricks showcasing various colors, shapes, and minor surface damages, designed to represent the use-cases of quality classification and inventory management in manufacturing. The use cases are separated and include 3 categories for quality classification and 24 categories for inventory classification. The data is analyzed in the publication titled "Demonstrating Computer Vision to Small- and Medium-sized Enterprises in Manufacturing: Towards Overcoming Costs and Implementation Challenges". The associated research explores the development of a simple computer vision demonstrator and its demonstration to small- and medium-sized enterprises. </p> <p><strong>Structure of Files</strong></p> <blockquote> <p>quality_classification (224 images)</p> <p> defect</p> <p> defect_free</p> <p> empty</p> <p>inventory_classification (2732 images)</p> <p> beige_large</p> <p> beige_small</p> <p> blue_bright_large</p> <p> 21 further categories (colour_shape)</p> </blockquote>
Multimodal Epigenetic Sequencing Analysis (MESA) of Cell-free DNA for Non-invasive Colorectal Cancer Detection
<p>Processed data (feature-by-sample matrices) of non-disruptive bisulfite-free methylation sequencing for cfDNA samples from 4 clinical cohorts (Cohort 1, Cohort 2, Cohort 3, and cfTAPS dataset). Codes used to repeat the results in our paper can be found https://rpubs.com/LiYumei/926228 and https://github.com/ChaorongC/MESA. </p>
HUSM and GMicro: PPG Peak detection
<p>Photoplethysmography (PPG) is a non-invasive technique that measures changes in blood flow volume through optical means. Accurate detection of peaks in the PPG waveform allows the extraction of the heart rate variability (HRV), a recognized indicator for the health of the cardiac and autonomic nervous systems. We make available a novel dataset comprising 66 minutes of PPG recordings from healthy individuals and patients with cardiopulmonary diseases, in three distinct phases of an acquisition protocol, with annotations for every systolic peak present.</p> <p>The dataset composition and organization are detailed in the paper that acompanies the dataset.</p>
Articulatory features of plosive consonants for early detection of Parkinson's disease
<p>These datasets contain temporal and spectral features extracted from the Voice Onset Time (VOT) segments of /p/, /t/ and /k/ syllables for a audio database composed of 27 individuals diagnosed with PD and 27 healthy controls. Data are presented both in CSV and Excel formats.</p> <p>These datasets have been created and used in a full computer-aided diagnosis system. All information about the feature extraction process is presented in:</p> <p>David Montaña, Yolanda Campos-Roca, Carlos J. Pérez, A Diadochokinesis-based expert system considering articulatory features of plosive consonants for early detection of Parkinson’s disease, Computer Methods and Programs in Biomedicine, Volume 154, 2018, 89-97, ISSN: 0169-2607, DOI: 10.1016/j.cmpb.2017.11.010.</p> <p> </p> <p> </p>
Supplementary data for: simultaneous species detection and discovery with environmental DNA metabarcoding: a freshwater mollusk case study
<p>Environmental DNA (eDNA) sampling is a powerful tool for rapidly characterizing biodiversity patterns for specious, cryptic taxa with incomplete taxonomies. One such group that are also of high conservation concern are North American freshwater gastropods. In particular, springsnails of the genus <em>Pyrgulopsis</em> (Family: Hydrobiidae) are prevalent throughout the western United States where >140 species have been described. Many of the described species are narrow endemics known from a single spring or locality and it is believed that there are likely many additional species which have yet to be described. The distribution of these species across the landscape is of interest because habitat loss and degradation, climate change, groundwater mining, and pollution have resulted in springsnail imperilment rates as high as 92%. Determining distributions with conventional sampling methods is limited by the fact that these snails are often <5 mm in length with few distinguishing morphological characters, making them both difficult to detect and to identify. In order to facilitate detection of <em>Pyrgulopsis</em> we developed an eDNA metabarcoding protocol that is both inexpensive and capable of rapid, accurate detection of all known <em>Pyrgulopsis</em> species. When compared with conventional collection techniques, our pipeline consistently resulted in detection at sites previously known to contain <em>Pyrgulopsis </em>springsnails and at a cost per site that is likely to be substantially less than the conventional sampling and individual barcoding that has been done historically. Additionally, because our method uses eDNA extracted from filtered water it is non-destructive and suitable for the detection of endangered species where "no take" restrictions may be in effect. This effort represents both a tool which is immediately applicable to a group of high conservation concern across western North America and a case study in the broader application of eDNA sampling for landscape assessments of cryptic taxa of conservation concern.</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.