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720 results for “Identification data”
Data from: StomataCounter: a neural network for automatic stomata identification and counting
Stomata regulate important physiological processes in plants and are often phenotyped by researchers in diverse fields of plant biology. Currently, there are no user friendly, fully-automated methods to perform the task of identifying and counting stomata, and stomata density is generally estimated by manually counting stomata. We introduce StomataCounter, an automated stomata counting system using a deep convolutional neural network to identify stomata in a variety of different microscopic images. We use a human-in-the-loop approach to train and refine a neural network on a taxonomically diverse collection of microscopic images. Our network achieves 98.1% identification accuracy on Ginkgo SEM micrographs, and 94.2% transfer accuracy when tested on untrained species. To facilitate adoption of the method, we provide the method in a publicly available website at http://www.stomata.science/.
Data from: Automated identification of insect vectors of Chagas disease in Brazil and Mexico: the Virtual Vector Lab
Identification of arthropods important in disease transmission is a crucial, yet difficult, task that can demand considerable training and experience. An important case in point is that of the 150+ species of Triatominae, vectors of Trypanosoma cruzi, causative agent of Chagas disease across the Americas. We present a fully automated system that is able to identify triatomine bugs from Mexico and Brazil with an accuracy consistently above 80%, and with considerable potential for further improvement. The system processes digital photographs from a photo apparatus into landmarks, and uses ratios of measurements among those landmarks, as well as (in a preliminary exploration) two measurements that approximate aspects of coloration, as the basis for classification. This project has thus produced a working prototype that achieves reasonably robust correct identification rates, although many more developments can and will be added, and—more broadly—the project illustrates the value of multidisciplinary collaborations in resolving difficult and complex challenges.
Image-based automated species identification: Can virtual data augmentation overcome problems of insufficient sampling?
<p></p><p>Automated species identification and delimitation is challenging, particularly in rare and thus often scarcely sampled species, which do not allow sufficient discrimination of infraspecific versus interspecific variation. Typical problems arising from either low or exaggerated interspecific morphological differentiation are best met by automated methods of machine learning that learn efficient and effective species identification from training samples. However, limited infraspecific sampling remains a key challenge also in machine learning.</p> <p>In this study, we assessed whether a data augmentation approach may help to overcome the problem of scarce training data in automated visual species identification. The stepwise augmentation of data comprised image rotation as well as visual and virtual augmentation. The visual data augmentation applies classic approaches of data augmentation and generation of artificial images using a Generative Adversarial Networks (GAN) approach. Descriptive feature vectors are derived from bottleneck features of a VGG-16 convolutional neural network (CNN) that are then stepwise reduced in dimensionality using Global Average Pooling and PCA to prevent overfitting. Finally, data augmentation employs synthetic additional sampling in feature space by an oversampling algorithm in vector space (SMOTE). Applied on four different image datasets, which include scarab beetle genitalia (Pleophylla, Schizonycha) as well as wing patterns of bees (Osmia) and cattleheart butterflies (Parides), our augmentation approach outperformed a deep learning baseline approach by means of resulting identification accuracy with non-augmented data as well as a traditional 2D morphometric approach (Procrustes analysis of scarab beetle genitalia).</p><p></p>
The Q-TOF proteomics data for the identification of mammalian 4-oxo-L-proline reductase (EC 1.1.1.104)
<p>The enclosed Zip file contains data files (RAW format) from MS^E experiment. The experiment was performed with the use of Acquity nanoUPLC coupled with a Synapt G2 HDMS Q-TOF mass spectrometer (Waters) fitted with a nanospray source. It aimed at the identification of proteins present in Fraction 28 from Superdex 200 purification step and the gel bands A, B, and C, coming from the SDS-PAGE analysis of this fraction.</p>
Data from: Identification of candidate loci for adaptive phenotypic plasticity in natural populations of spadefoot toads
<p>Phenotypic plasticity allows organisms to alter their phenotype in direct response to changes in the environment. Despite growing recognition of plasticity's role in ecology and evolution, few studies have probed plasticity's molecular bases—especially using natural populations. We investigated the genetic basis of phenotypic plasticity in natural populations of spadefoot toads (<i>Spea multiplicata</i>). <i>Spea</i> tadpoles normally develop into an 'omnivore' morph that is favored in long-lasting, low-density ponds. However, if tadpoles consume freshwater shrimp or other tadpoles, they can alternatively develop (via plasticity) into a 'carnivore' morph that is favored in shallow, high-density ponds. By combining natural variation in pond ecology and morph production with population genetic approaches, we identified candidate loci associated with morph (carnivores versus omnivores) and loci associated with adaptive phenotypic plasticity (adaptive versus maladaptive morph choice). Our candidate morph loci mapped to two genes, whereas our candidate plasticity loci mapped to 14 genes. In both cases, the identified genes tended to have functions related to their putative role in spadefoot tadpole biology. Our results thereby form the basis for future studies into the molecular mechanisms that mediate plasticity in spadefoots. More generally, these results illustrate how diverse loci might be deployed to mediate adaptive plasticity.Phenotypic plasticity allows organisms to alter their phenotype in direct response to changes in the environment. Despite growing recognition of plasticity's role in ecology and evolution, few studies have probed plasticity's molecular bases—especially using natural populations. We investigated the genetic basis of phenotypic plasticity in natural populations of spadefoot toads (<i>Spea multiplicata</i>). <i>Spea</i> tadpoles normally develop into an 'omnivore' morph that is favored in long-lasting, low-density ponds. However, if tadpoles consume freshwater shrimp or other tadpoles, they can alternatively develop (via plasticity) into a 'carnivore' morph that is favored in shallow, high-density ponds. By combining natural variation in pond ecology and morph production with population genetic approaches, we identified candidate loci associated with morph (carnivores versus omnivores) and loci associated with adaptive phenotypic plasticity (adaptive versus maladaptive morph choice). Our candidate morph loci mapped to two genes, whereas our candidate plasticity loci mapped to 14 genes. In both cases, the identified genes tended to have functions related to their putative role in spadefoot tadpole biology. Our results thereby form the basis for future studies into the molecular mechanisms that mediate plasticity in spadefoots. More generally, these results illustrate how diverse loci might be deployed to mediate adaptive plasticity.</p>
Data for "Machine-learning-aided atomic structure identification of interfacial ionic hydrates from AFM images"
<p>Dataset for Neural Network training and testing of paper entitled "Machine-learning-aided atomic structure identification of interfacial ionic hydrates from AFM images" (<a href="https://doi.org/10.1093/nsr/nwac282">https://doi.org/10.1093/nsr/nwac282</a>).</p> <p>Each file contains named-dependent simulated AFM Images at different tip height and corresponding atomic structure file in POSCAR format. (See detailed description in the manuscript <a href="https://doi.org/10.1093/nsr/nwac282">https://doi.org/10.1093/nsr/nwac282</a>)</p> <p> </p>
Identification of disinfection by‑product precursors by natural organic matter fractionation: a review - Full Data
<p>Data gathered for the meta-analysis performed in the review paper.</p> <p>Full references to the used data is found in the review paper itself.</p>
Data explanation for Manuscript "'An optimized hydroponic pipeline for large-scale identification of wheat genotypes with resilient biological nitrification inhibition (BNI) activity'"
<p>Data explanation for Manuscript "'An optimized hydroponic pipeline for large-scale identification of wheat genotypes with resilient biological nitrification inhibition (BNI) activity'"</p>
Resulting Pseudonymized Classification Data for "Automatic Core-Developer Identification on GitHub: A Validation Study"
<p>Resulting pseudonymized classification data of the study "Automatic Core-Developer Identification on GitHub: A Validation Study". The corresponding input data, from which the output data have been derived, can be found here: https://zenodo.org/record/7775078</p>
Pseudonymized Raw Data for "Automatic Core-Developer Identification on GitHub: A Validation Study"
<p>Pseudonymized raw data (i.e., commit data and issue data for 25 GitHub projects) that has been used as input for the study published as "Automatic Core-Developer Identification on GitHub: A Validation Study".</p> <p>The pseudonymized raw data has been extracted via the tools <a href="https://github.com/se-sic/codeface/">Codeface</a>, <a href="https://github.com/se-sic/GitHubWrapper/">GitHubWrapper</a>, <a href="https://github.com/mehdigolzadeh/BoDeGHa">BoDeGHa</a>, and <a href="https://github.com/se-sic/codeface-extraction/">codeface-extraction</a> (and additional manual corrections after sanity checks).</p>
Data for Objective identification of pressure wave events from networks of 1-Hz, high-precision sensors
<p>Data from pressure sensors assembled by Matthew Miller which consist of either a Bosch BMP388 or Bosch BME280 Adafruit breakout board connected to a Raspberry Pi Zero W single-board computer used to log the data. Data are recorded at 1-second intervals. The data are stored in .csv files: one for each day for each sensor. Sensors were placed in networks in the Toronto, ON, Canada, New York, NY, USA, and Raleigh, NC, USA metro areas. Code for processing these data can be found at https://doi.org/10.5281/zenodo.8087843.</p>
Benchmark data for "Model-X knockoffs reveal data-dependent limits on regulatory network identification"
<p>This collection of data was used in our manuscript tentatively entitled "<strong>Model-X knockoffs reveal data-dependent limits on regulatory network identification</strong>". It is entirely from public sources, but to enable easy repetition of our analyses, we collect it all here in the exact format we used. Links to related papers and code can be found at the <a href="https://github.com/ekernf01/knockoffs_paper">knockoffs paper</a> homepage.</p>
A Development of Inflammatory Bowel Disease Pattern Identification Algorithm Using Case Series Data
ClinicalTrials.gov study NCT04296500. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Data from: Identification of biomarkers for Barcelona Clinic Liver Cancer staging and overall survival of patients with hepatocellular carcinoma
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Identification of a Resting Bold Connectome Associated with Cognitive Reserve - Data and code archive
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Data from: Particle backtracking improves breeding subpopulation discrimination and natal-source identification in mixed populations
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Data from: Friends and Family: a software program for identification of unrelated individuals from molecular marker data. And from: Genetic diversity, relatedness and inbreeding of ranched and fragmented Cape buffalo populations in southern Africa
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Data from: Next-generation sequencing for rodent barcoding: species identification from fresh, degraded and environmental samples
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Data from: Effects of sample fixation on specimen identification in biodiversity assemblies based on proteomic data (MALDI-TOF)
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Data from: Molecular prey identification in Central European piscivores
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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.