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193 results for “labeled data”
FIGURE 12. Platynaspis wittmeri (Bielawski): a, b. adult, dorsal view (BMNH); c–e. holotype: c. holotype, dorsal view; d. label data; e. abdomen and genitalia (Image credit for c–e: Matthias Borer, NHMB). in A review of Platynaspini (Coleoptera: Coccinellidae) of the Indian subcontinent, including description of a new genus from north-eastern India and Bangladesh
FIGURE 12. Platynaspis wittmeri (Bielawski): a, b. adult, dorsal view (BMNH); c–e. holotype: c. holotype, dorsal view; d. label data; e. abdomen and genitalia (Image credit for c–e: Matthias Borer, NHMB).
Label-free data mining of scientific literature by unsupervised syntactic distance analysis
<p>1. OER.zip: literature resources, mined data and manual annotation of OER</p> <p>2. OLED.zip: literature resources and mined data of OLED</p> <p>3. PVK_ligand.zip: literature resources, mined data and manual annotation of perovskite ligand</p> <p>4. PVK_solvent.zip: literature resources, mined data and manual annotation of perovskite solvent</p> <p>5. syn_vec.part01.rar - syn_vec.part01.rar: word vector model trained by synthesis paragraphs in USPTO</p> <p>6. crossref_vec.part01.rar - crossref_vec.part10.rar: word vector trained by abstracts in Crossref</p>
Datasets for a data-centric image classification benchmark for noisy and ambiguous label estimation
<p>This is the official data repository of the Data-Centric Image Classification (DCIC) Benchmark. The goal of this benchmark is to measure the impact of tuning the dataset instead of the model for a variety of image classification datasets. Full details about the collection process, the structure and automatic download at</p> <p>Paper: https://arxiv.org/abs/2207.06214</p> <p>Source Code: https://github.com/Emprime/dcic</p> <p>The license information is given below as download.</p> <p><strong>Citation</strong></p> <p>Please cite as</p> <pre><code>@article{schmarje2022benchmark, author = {Schmarje, Lars and Grossmann, Vasco and Zelenka, Claudius and Dippel, Sabine and Kiko, Rainer and Oszust, Mariusz and Pastell, Matti and Stracke, Jenny and Valros, Anna and Volkmann, Nina and Koch, Reinahrd}, journal = {36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks}, title = {{Is one annotation enough? A data-centric image classification benchmark for noisy and ambiguous label estimation}}, year = {2022} }</code></pre> <p>Please see the full details about the used datasets below, which should also be cited as part of the license.</p> <pre><code>@article{schoening2020Megafauna, author = {Schoening, T and Purser, A and Langenk{\"{a}}mper, D and Suck, I and Taylor, J and Cuvelier, D and Lins, L and Simon-Lled{\'{o}}, E and Marcon, Y and Jones, D O B and Nattkemper, T and K{\"{o}}ser, K and Zurowietz, M and Greinert, J and Gomes-Pereira, J}, doi = {10.5194/bg-17-3115-2020}, journal = {Biogeosciences}, number = {12}, pages = {3115--3133}, title = {{Megafauna community assessment of polymetallic-nodule fields with cameras: platform and methodology comparison}}, volume = {17}, year = {2020} } @article{Langenkamper2020GearStudy, author = {Langenk{\"{a}}mper, Daniel and van Kevelaer, Robin and Purser, Autun and Nattkemper, Tim W}, doi = {10.3389/fmars.2020.00506}, issn = {2296-7745}, journal = {Frontiers in Marine Science}, title = {{Gear-Induced Concept Drift in Marine Images and Its Effect on Deep Learning Classification}}, volume = {7}, year = {2020} } @article{peterson2019cifar10h, author = {Peterson, Joshua and Battleday, Ruairidh and Griffiths, Thomas and Russakovsky, Olga}, doi = {10.1109/ICCV.2019.00971}, issn = {15505499}, journal = {Proceedings of the IEEE International Conference on Computer Vision}, pages = {9616--9625}, title = {{Human uncertainty makes classification more robust}}, volume = {2019-Octob}, year = {2019} } @article{schmarje2019, author = {Schmarje, Lars and Zelenka, Claudius and Geisen, Ulf and Gl{\"{u}}er, Claus-C. and Koch, Reinhard}, doi = {10.1007/978-3-030-33676-9_26}, issn = {23318422}, journal = {DAGM German Conference of Pattern Regocnition}, number = {November}, pages = {374--386}, publisher = {Springer}, title = {{2D and 3D Segmentation of uncertain local collagen fiber orientations in SHG microscopy}}, volume = {11824 LNCS}, year = {2019} } @article{schmarje2021foc, author = {Schmarje, Lars and Br{\"{u}}nger, Johannes and Santarossa, Monty and Schr{\"{o}}der, Simon-Martin and Kiko, Rainer and Koch, Reinhard}, doi = {10.3390/s21196661}, issn = {1424-8220}, journal = {Sensors}, number = {19}, pages = {6661}, title = {{Fuzzy Overclustering: Semi-Supervised Classification of Fuzzy Labels with Overclustering and Inverse Cross-Entropy}}, volume = {21}, year = {2021} } @article{schmarje2022dc3, author = {Schmarje, Lars and Santarossa, Monty and Schr{\"{o}}der, Simon-Martin and Zelenka, Claudius and Kiko, Rainer and Stracke, Jenny and Volkmann, Nina and Koch, Reinhard}, journal = {Proceedings of the European Conference on Computer Vision (ECCV)}, title = {{A data-centric approach for improving ambiguous labels with combined semi-supervised classification and clustering}}, year = {2022} } @article{obuchowicz2020qualityMRI, author = {Obuchowicz, Rafal and Oszust, Mariusz and Piorkowski, Adam}, doi = {10.1186/s12880-020-00505-z}, issn = {1471-2342}, journal = {BMC Medical Imaging}, number = {1}, pages = {109}, title = {{Interobserver variability in quality assessment of magnetic resonance images}}, volume = {20}, year = {2020} } @article{stepien2021cnnQuality, author = {St{\c{e}}pie{\'{n}}, Igor and Obuchowicz, Rafa{\l} and Pi{\'{o}}rkowski, Adam and Oszust, Mariusz}, doi = {10.3390/s21041043}, issn = {1424-8220}, journal = {Sensors}, number = {4}, title = {{Fusion of Deep Convolutional Neural Networks for No-Reference Magnetic Resonance Image Quality Assessment}}, volume = {21}, year = {2021} } @article{volkmann2021turkeys, author = {Volkmann, Nina and Br{\"{u}}nger, Johannes and Stracke, Jenny and Zelenka, Claudius and Koch, Reinhard and Kemper, Nicole and Spindler, Birgit}, doi = {10.3390/ani11092655}, journal = {Animals 2021}, pages = {1--13}, title = {{Learn to train: Improving training data for a neural network to detect pecking injuries in turkeys}}, volume = {11}, year = {2021} } @article{volkmann2022keypoint, author = {Volkmann, Nina and Zelenka, Claudius and Devaraju, Archana Malavalli and Br{\"{u}}nger, Johannes and Stracke, Jenny and Spindler, Birgit and Kemper, Nicole and Koch, Reinhard}, doi = {10.3390/s22145188}, issn = {1424-8220}, journal = {Sensors}, number = {14}, pages = {5188}, title = {{Keypoint Detection for Injury Identification during Turkey Husbandry Using Neural Networks}}, volume = {22}, year = {2022} }</code></pre> <p>Addition: This repository also contains the original data from the paper "Annotating Ambiguous Images" (https://arxiv.org/abs/2306.12189). The data is created based on the original datasets and license from https://osf.io/t98fz/ and https://osf.io/nqjyw/</p>
Deep and fast label-free Dynamic Organellar Mapping - Imaging data
<p>Imaging data from the article "Deep and fast label-free Dynamic Organellar Mapping", published in Nature Communications by Schessner et al., from Fig. 6, 7 and Supp. Fig. 6c.</p> <p>Widefield images were captured on a Leica DMi8 inverted microscope equipped with an iTK LMT200 motorised stage, a 63x/1.47 oil objective (HC PL APO 63x/1.47 OIL) and a Leica DFC9000 GTC Camera, and controlled with LAS X (Leica Application Software X).</p> <p>Fig6_GLG1_TGOLN2_GALNT2_SuppFig6c_LC3B: Widefield imaging of wild-type HeLa cells cultured for 1h in either: 1) full growth medium (Control); 2) EBSS to starve the cells (Starve); 3) full growth medium plus 100 nM BafA (Control + BafA); or 4) EBSS plus 100 nM BafA (Starve + BafA). Cells were labelled with anti-GALNT2 (Alexa Fluor 488), in combination with either anti-GLG1 (Alexa Fluor 568) or anti-TGOLN2 (Alexa Fluor 680), as shown in Fig. 6, or were labelled with anti-LC3B (Alexa Fluor 488), as shown in Supp. Fig. 6c. In all images, cells were stained with DAPI to label nuclei.</p> <p>Fig7_GALNT2_GLG1_TM9SF2_TGOLN2_GOLIM4_SDF4: Widefield imaging of HeLa cells left untreated in full growth medium (0h) or cultured in the presence of 100 nM BafA for 0.5, 1, 2, 4, 6 or 8 hours, before fixation. Cells were labelled with anti-GALNT2 (Alexa Fluor 647) in combination with either anti-GLG1, anti-TM9SF2, anti-TGOLN2, anti-GOLIM4 or anti-SDF4 (Alexa Fluor 555). In all images, cells were stained with DAPI and phalloidin-488 to label nuclei and cytoplasm, respectively.</p>
Data from: Ophthalmologic evaluation of severely obese patients undergoing bariatric surgery: a pilot, monocentric, prospective, open-label study
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Data from: Single-dose oral ciprofloxacin prophylaxis as a response to a meningococcal meningitis epidemic in the African meningitis belt: a three-arm, open-label, cluster-randomized trial
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Data from: Novel reverse radioisotope labelling experiment reveals carbon assimilation of marine calcifiers under ocean acidification conditions
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Data from: Non-invasive measurement of metabolic rates in wild, free-living birds using doubly labelled water
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Data from: Disentangling effects of air and soil temperature on C allocation in cold environments: a 14C pulse labelling study with two plant species
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Pacific black ducks tri-axial accelerometer data with behaviour labels
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Examined specimen data from collection labels in Megachile (Austrochile) taxonomic revision
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Learning protein fitness models from evolutionary and assay-labeled data
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Figure 3 from: Dupont S, Humphries J, Butcher AJ, Baker E, Balcells L, Price BW (2020) Ahead of the curve: three approaches to mass digitisation of vials with a focus on label data capture. Research Ideas and Outcomes 6: e53606. https://doi.org/10.3897/rio.6.e53606
Figure 3 A lateral image of ReVILE with the side panel removed to show the camera (a), turntable (b), stepper motor (c), Arduino Uno and motor driver controllers (d), front light panels (e), back light panel (f), light shield (g) and position of vial (arrow).
Figure 1 from: Dupont S, Humphries J, Butcher AJ, Baker E, Balcells L, Price BW (2020) Ahead of the curve: three approaches to mass digitisation of vials with a focus on label data capture. Research Ideas and Outcomes 6: e53606. https://doi.org/10.3897/rio.6.e53606
Figure 1 MALICE vial setup showing the acrylic mirrors (a) LEGO mirror tilt arms (b), formex base (c) and LEGO friction joint (d)
Figure 5 from: Dupont S, Humphries J, Butcher AJ, Baker E, Balcells L, Price BW (2020) Ahead of the curve: three approaches to mass digitisation of vials with a focus on label data capture. Research Ideas and Outcomes 6: e53606. https://doi.org/10.3897/rio.6.e53606
Figure 5 MALICE: Image output of MALICE including original output image (a) and the final processed image (b).
Figure 7 from: Dupont S, Humphries J, Butcher AJ, Baker E, Balcells L, Price BW (2020) Ahead of the curve: three approaches to mass digitisation of vials with a focus on label data capture. Research Ideas and Outcomes 6: e53606. https://doi.org/10.3897/rio.6.e53606
Figure 7 ReVILE: Two Rollout photography outputs of ReVILE. Left to right: a frame from the video (rotated 90° clockwise) showing the vial itself; the uncropped rollout image, covering more than one full rotation; the cropped rollout image, showing only one 360° rotation; the cropped rollout image, "shifted" across (by transferring a manually-defined block of pixel columns from the left side of the image to the right) to show the complete label
Data from: Proximity labeling reveals novel interactomes in live Drosophila tissue
Gametogenesis is dependent on intercellular communication facilitated by stable intercellular bridges connecting developing germ cells. During Drosophila oogenesis, intercellular bridges (referred to as ring canals) have a dynamic actin cytoskeleton that drives their expansion to a diameter of 10μm. While multiple proteins have been identified as components of ring canals (RCs), we lack a basic understanding of how RC proteins interact together to form and regulate the RC cytoskeleton. We optimized a procedure for proximity-dependent biotinylation in live tissue using the APEX enzyme to interrogate the RC interactome. APEX was fused to four different RC components (RC-APEX baits) and 55 unique high-confidence preys were identified. The RC-APEX baits produced almost entirely distinct interactomes that included both known RC proteins as well as uncharacterized proteins. The proximity ligation assay was used to validate close-proximity interactions between the RC-APEX baits and their respective preys. Further, an RNAi screen revealed functional roles for several high-confidence prey genes in RC biology. These findings highlight the utility of enzyme-catalyzed proximity labeling for protein interactome analysis in live tissue and expand our understanding of RC biology.
Data from: Using quantum dots as pollen labels to track the fates of individual pollen grains
1. Despite a long history of significant advances in understanding natural selection and evolution, the field of plant reproductive biology has largely studied plant mating without directly tracking pollen movement due to a lack of suitable pollen-tracking methods. 2. Here, we develop and test a novel pollen-tracking technique using quantum dots as pollen-grain labels. Quantum dots are semiconductor nanocrystals that are so small, they behave like atoms. When exposed to UV light, they emit extremely bright light in both visible and infrared wavelengths We tested the suitability of non-toxic CuInSexS2-x/ZnS (core/shell) quantum dots with oleic acid ligands as pollen-grain labels.Using a micropipette, we dispensed quantum dots dissolved in hexane in minute volumes (0.15–0.5 µl) directly onto dehisced anthers of four different plant species from four different families [Wachendorfia paniculata (Haemodoraceae), Sparaxis villosa (Iridaceae), Arctotheca calendula (Asteraceae), Oxalis purpurea (Oxalidaceae)]. 3. After application, the hexane solvent evaporated immediately, leaving behind quantum dots that remained attached to pollen grains of the four different plant species even after agitation in a polar solvent. This suggests a lipophilic interaction between oleic-acid ligands on quantum dots, and pollenkitt surrounding pollen grains. We also showed that most pollen grains within anthers of the same four plant species were labelled with quantum dots after applying a volume of quantum-dot solution sufficient to cover an individual anther. To test whether quantum-dot pollen-labels influenced pollen transport, we conducted pollen transfer trials (one donor, ten sequential recipients) on S. villosa using captively-reared honey bees to ensure bees were free of external pollen prior to experiments. We found no difference in pollen transport to recipients from donor flowers with labelled or unlabelled pollen grains. 4. We demonstrate that quantum dots can be used as pollen labels allowing subsequent tracking of pollen fates. This method is relatively inexpensive (<$500 for equipment and ca. $0.02 per labelled anther thereafter) and can be simply and directly applied to anthers of most flowers in the lab and field. The ability to track pollen grain movement in situ, may help to address an historically neglected aspect of plant reproductive ecology and evolution.
Data from: Label-free sensitive detection of influenza virus using PZT discs with a synthetic sialylglycopolymer receptor layer
We describe rapid, label-free detection of Influenza A viruses using the first radial mode of oscillations of lead zirconate titanate (PZT) piezoelectric disks with a 2-mm radius and 100-µm thickness fabricated from a piezoelectric membrane. The disks are modified with a synthetic sialylglycopolymer receptor layer,and the coated disks are inserted in a flowing virus suspension. Label-free detection of the virus is achieved by monitoring the disk radial mode resonance frequency shift. Piezo transducers with sialylglycopolymer sensor layers exhibited a long lifetime, a high sensitivity, and the possibility of regeneration. We demonstrate positive, label-free detection of Influenza A viruses at concentrations below 10^5 virus particles per millilitre. We show that label-free, selective, sensitive detection of Influenza viruses by home appliances is possible in principle.
2003_2020 auroral data and predicted labels
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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.