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720 results for “Identification data”
YOGData: Labelled data (YOLO and Mask R-CNN) for yogurt cup identification within production lines
<p><strong>D</strong><strong>ata abstract:</strong><br> The YogDATA dataset contains images from an industrial laboratory production line when it is functioned to quality yogurts. The case-study for the recognition of yogurt cups requires training of Mask R-CNN and YOLO v5.0 models with a set of corresponding images. Thus, it is important to collect the corresponding images to train and evaluate the class. Specifically, the YogDATA dataset includes the same labeled data for Mask R-CNN (coco format) and YOLO models. For the YOLO architecture, training and validation datsets include sets of images in jpg format and their annotations in txt file format. For the Mask R-CNN architecture, the annotation of the same sets of images are included in json file format (80% of images and annotations of each subset are in training set and 20% of images of each subset are in test set.) <br> </p> <p><strong>Paper abstract:</strong><br> The explosion of the digitisation of the traditional industrial processes and procedures is consolidating a positive impact on modern society by offering a critical contribution to its economic development. In particular, the dairy sector consists of various processes, which are very demanding and thorough. It is crucial to leverage modern automation tools and through-engineering solutions to increase their efficiency and continuously meet challenging standards. Towards this end, in this work, an intelligent algorithm based on machine vision and artificial intelligence, which identifies dairy products within production lines, is presented. Furthermore, in order to train and validate the model, the YogDATA dataset was created that includes yogurt cups within a production line. Specifically, we evaluate two deep learning models (Mask R-CNN and YOLO v5.0) to recognise and detect each yogurt cup in a production line, in order to automate the packaging processes of the products. According to our results, the performance precision of the two models is similar, estimating its at 99\%. </p> <p> </p>
Data from: Identification of a minority population of LMO2+ breast cancer cells that integrate into the vasculature and initiate metastasis.
<p>Metastasis is responsible for the majority of breast cancer-related deaths, however, identifying the cellular determinants of metastasis has remained challenging. Here, we identified a minority population of immature THY1+/VEGFA+ tumor epithelial cells in human breast tumor biopsies that display angiogenic features and are marked by the expression of the oncogene, LMO2. Higher abundance of LMO2+ basal cells correlated with tumor endothelial content and predicted poor distant recurrence-free survival in patients. Using MMTV-PyMT/Lmo2CreERT2 mice, we demonstrated that Lmo2 lineage-traced cells integrate into the vasculature and have a higher propensity to metastasize. LMO2 knockdown in human breast tumors reduced lung metastasis by impairing intravasation, leading to a reduced frequency of circulating tumor cells. Mechanistically, we find that LMO2 binds to STAT3 and is required for STAT3 activation by TNFα and IL6. Collectively, our study identifies a population of metastasis-initiating cells with angiogenic features and establishes the LMO2-STAT3 signaling axis as a therapeutic target in breast cancer metastasis.</p>
Characterization of data sources for emerging risks identification - Data collection for the identification of emerging risks related to food and feed
<p>The data set presents the results of the quality assessment of data sources by the working group on data collection for the identification of emerging risks related to food and feed. For this assessment, the WG defined text descriptors and quality parameters (i.e. link with indicators, data type, geographic and period coverage, language, edition, timeliness, accessibility, clarity and comparability). These data sources were linked to eleven priority indicators (i.e. the ESCO indicators) and qualitatively assessed and profiled.</p>
Training data for ChIP-seq data analysis (Galaxy Training Material): Identification of the binding sites of the Estrogen receptor
<p>The data provided here are part of a Galaxy Training Network tutorial that analyzes ChIP-seq data from a study published by Ross-Inness et al., 2012 (DOI:10.1038/nature10730) to identify the binding sites of the Estrogen receptor, a transcription factor known to be associated with different types of breast cancer.</p>
FIGURE 8 in An analysis of fossil identification guides to improve data reporting in citizen science programs
FIGURE 8. Cluster analyses of all subjects as individuals. Subject labels denote which field guide the subject tested, a red 'C' for color photos, a black 'G' for grayscale photos, and a blue 'I' for illustrations. Author names are abbreviated "DaBu" for Dava Butler, "DoEs" for Donald Esker and "KrJu" for Kristopher Juntunen.
FIGURE 1 in An analysis of fossil identification guides to improve data reporting in citizen science programs
FIGURE 1. Map of the United States and surrounding regions, showing the location of the state of Florida and FMMS (Google 2017). 1B: Geologic map of the State of Florida, showing the geographic distribution of rocks and the location of the FMMS (Google 2017; Scott et al. 2001). 1C: Geologic map of the region around the FMMS, showing the distribution of rocks and location of the site (Google 2017; Scott et al. 2001). 1D: Photograph of FMMS, taken by Fred Mazza.
Figure 2. Results for firm name "Váhostav" are in table. Each row defines a firm with its name, identification number and address. Then a connection is specified (whether it be a person or another firm).-Browsing Semantic Data in Slovakia
<p>We have searched for firm “Váhostav”, which is a rather big firm in Slovakia, with many press articles published about31. On Figure 2 there is a browsing window, for SBR data results, displaying tabular structure, which was refined from SBR dataset by continuous querying.</p>
A comparison of automatic cell identification methods for single-cell RNA-sequencing data
<p>Benchmark datasets used to evaluate the performance of 22 classifiers for cell type classification for scRNA-seq data</p>
Fig. 1 in Molecular identification and epidemiological data of Anisakis spp. (Nematoda: Anisakidae) larvae from Southeastern Pacific Ocean off Peru
Fig. 1. Scanning electron micrographs of Anisakis type I and II.1a and 2a. Cephalic end. Detail of the structures: oral cavity (oc), tooth (t), excretory pore (ep), subventral lip bulge (s). 1b. caudal end of Anisakis pegreffii. 2b. caudal end of Anisakis physeteris. Detail of the structures: anal pore (ap), mucron (m).
Fig. 2 in Molecular identification and epidemiological data of Anisakis spp. (Nematoda: Anisakidae) larvae from Southeastern Pacific Ocean off Peru
Fig. 2. Phylogenetic tree based on mtDNA cox2 gene sequences exploring the relationships among Anisakis species. The relationship was drawn using Bayesian inference (BI) and maximum likelihood (ML) methods. Posterior probability value (first) and nodal support is shown as bootstrap value (second) on the basis of 10 million generations for BI and 1000 replicates (only bootstrap values greater than 80% are shown) for ML, respectively. Scale bar indicate nucleotide substitutions per site. GenBank accession numbers are shown in parentheses. Hysterothylacium deardorffoverstreetorum was used as an outgroup.
Fig. 1 Retrieved data distribution. A in Same information, new applications: revisiting primers for the avian COI gene and improving DNA barcoding identification
Fig. 1 Retrieved data distribution. A Distribution of published primers for the barcode region of the avian COI gene throughout the years. B Number of complete COI sequences available for each bird order
Source code and experimental data of human brain tissue (visual cortex, corona radiata) for poro-viscoelastic parameter identification
<p>Computer code and experimental data that we used for our inverse parameter identification of poro-viscoelastic material parameters for two different brain regions: visual cortex (gray matter) and corona radiata (white matter). The experimental data comprises large-strain cyclic loading and compression/tension relaxation. For details see the corresponding publication: "Model-driven exploration of poro-viscoelasticity in human brain tissue: Be careful with the parameters!".</p> <p>Further explanation regarding the specimen preparation, experimental setup, as well as the assignment of regions and governing regions can be found in Hinrichsen, J., Reiter, N., Bräuer, L. et al. Inverse identification of region-specific hyperelastic material parameters for human brain tissue. Biomech Model Mechanobiol (2023). <a href="https://doi.org/10.1007/s10237-023-01739-w" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s10237-023-01739-w</a>.</p> <p>The file "<span>nonlinear-poro-viscoelasticity.cc</span>" contains our C++ Finite Element code based on the open source library deal.II. It is accompanied by an exemplary parameter file.</p> <p><strong>Funding:</strong> The support from the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) through the grants BU 3728/1-1, BU 3728/3-1, STE 544/70-1 as well as through project number 460333672 CRC1540 Exploring Brain Mechanics is gratefully acknowledged.</p>
A Submesoscale Eddy Identification Dataset Derived from GOCI I Chlorophyll–a Data based on Deep Learning
<p>This is an observational dataset on submesoscale eddies, which obtains from high–resolution chlorophyll–a distribution images from GOCI I. We employed a combination of digital image processing, filtering, YOLOv7–X, and small object detection techniques, along with specific chlorophyll image enhancement processing, to extract information on submesoscale eddies, including their time, polarity, geographical coordinates of the eddy center, eddy radius, coordinates of the upper left and lower right corners of the prediction box, area of the eddy's inner ellipse, and confidence score, which covers eight daily periods between 00:00 and 08:00 (UTC) from April 1, 2011, to March 31, 2021. We identified a total of 19,136 anticyclonic eddies and 93,897 cyclonic eddies at a confidence threshold of 0.2. The mean radius of anticyclonic eddies is 24.44 km (range 2.5 km to 44.25 km), while that of cyclonic eddies is 12.34 km (range 1.75 km to 44 km). The unprecedented hourly resolution dataset on submesoscale eddies provides information on their distribution, morphology, and energy dissipation, making it a significant contribution to understanding marine environments and ecosystems, as well as improving climate model predictions. The article doi associated with this dataset is https://doi.org/10.5194/essd-2024-188.</p>
EEG/LFP/EMG data from freely-behaving rats across the sleep/wake cycle (includes manual identification of behavioral state in 4s epochs)
<p>Open-access data set that contains raw EEG/LFP/EMG data recorded from freely-behaving rats. 40 complete 24-hr recordings across 10 male, Sprague Dawley rats are included. Additionally, manual behavioral state classification is provided for all recordings in 4s epochs. These data form the testing and training set for a recently accepted article (Ellen J.G. and Dash, M.B. <em>An automated neural network for automated behavioral state classification rats</em>, peerJ (<em>in press)</em>) which details an open-source artificial neural network for automated classification of behavioral state. R-code for the automated classifier and detailed instructions for its implementation are freely available at: <a href="https://github.com/jellen44/AutomaticSleepScoringTool"><em> https://github.com/jellen44/AutomaticSleepScoringTool</em></a></p> <p>For additional details about the data set, please see the uploaded readme file.</p> <p> </p>
Figure 2 in The maximum likelihood identification method applied to insect morphometric data
Figure 2. Classification based on landmarks (and semilandmarks). Values in percent are the proportions of correct assignments averaged over the complete set of data, mixing the various species, but restricted to the landmark-based method (including here the combination of landmarks and semilandmarks). Each value is shown with an error bar which is its standard deviation. The left part shows the results from the maximum likelihood method applied to the centroid size and to the shape variables. The right part shows the average scores obtained with the Mahalanobis classification. Abbreviation: first PC—the shape variables used were the few first PC of shape variables that the MLi classification selected as the most discriminant set of PC; all PC—the maximum number of PC that could be used by the Mahalanobis classification.
Figure 4 in The maximum likelihood identification method applied to insect morphometric data
Figure 4. Principal component analysis of sandfly data. The factor map of the two first principal components (PC) describing the morphospace of female sandfly data: S. bailyi, S. barraudi and P. stantoni. Unidentified males show a convex hull covering both female S. bailyi and S. barraudi, leaving two specimens outside the female hulls.
Figure 1 in The maximum likelihood identification method applied to insect morphometric data
Figure 1. Measurements on Triatominae. A. Digitization of the contour of the eggs of Triatomini and Rhodniini. B. Landmarks as digitized on the wing of Panstrongylus chinai. C. Traditional measurement of eggs of the tribe Triatomini (Panstrongylus sp. and Triatoma sp., left) and the tribe Rhodniini (Rhodnius sp., right). Abbreviation: op.dim—dimensions of the operculum; max.leng— maximum length; max.dim—maximum diameter.
Figure 3 in The maximum likelihood identification method applied to insect morphometric data
Figure 3. Classification based on harmonics. Values in percent are the proportions of correct assignments averaged over the complete set of data, mixing the various species, but restricted to the outline-based method. Each value is shown with an error bar which is its standard deviation. The left part shows the results from the maximum likelihood method applied to the perimeter of the contour and to the shape variables. The right part shows the average scores obtained with the Mahalanobis classification. Abbreviation: first PC— the shape variables used were the few first PC of shape variables that the MLi classification selected as the most discriminant set of PC; all PC—the maximum number of PC that could be used by the Mahalanobis classification.
Identification of Research Data and Software (re)Use Indications in High Energy Physics related Scholarly Works (1951 - 2018)
<p>This dataset contains research data and software (re)use indications in High Energy Physics related scholarly works. A minimal random sample of scholarly works that contain highly processed research data was taken from HEPData and manually read for research data and software (re)use indications. The sample contains 368 works, which were published between 1951 and 2018.</p> <p>The data was collected in 2019 for a PhD thesis on research data and software (re)use indications in scholarly works.</p>
PERCEIVE: WP1: Framework for comparative analysis of the perception of Cohesion Policy and identification with the European Union at citizen level in different European countries: Survey at citizen level and data relative to regional performance of the Cohesion Policy and institutional quality
<p>1. Orignal PERCEIVE survey data (STATA file)</p> <p>2. description of survey questions, descriptive results (word file)</p> <p>3. EU Deliverable document with descriptive analysis of survey questions</p> <p> </p> <p>***please cite the following when using the microdata:</p> <p>Bauhr, M., & Charron, N. (2020). The EU as a savior and a saint? Corruption and public support for redistribution. <em>Journal of European Public Policy</em>, <em>27</em>(4), 509-527.</p> <p>https://www.tandfonline.com/doi/full/10.1080/13501763.2019.1578816</p>
ScienceDex guides
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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.