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193 results for “labeled data”
Comparative analysis of statistical methods used for detecting differential expression in label-free mass spectrometry proteomics - Data Supplement
<p>This the is Data Supplement for the article "Comparative analysis of statistical methods used for detecting differential expression in label-free mass spectrometry proteomics" submitted to the Journal of Proteomics 2015.</p>
Raw Data for 'A Systematic Classification and Labelling Approach to Support a Circular Economy Ecosystem for NdFeB-Type Magnet'
<p>Raw Data for the paper "A Systematic Classification and Labelling Approach to Support a Circular Economy Ecosystem for NdFeB-Type Magnet" </p>
Data from "Single-molecule fluorescence multiplexing by multi-parameter spectroscopic detection of nanostructured FRET labels"
<p>Source data for all figures; full table of all nucleic acid sequences used; Supplementary Video 1 showing rotating view of Fig. 4e.</p>
ATELIER. Self-Labelling dynamic Loss function training data
<p>This dataset contains all the statistics collected during the training of multiple Neural Networks, which uses the following architecture technologies:</p> <p>Siamese Neural Networks, Dynamic loss function, modified during the training through Reinforcement learning and a 'Curriculum-Learning' cycle in between training cycles. The goal of the Models is to correctly identify anomalies in the QoE of real-time videos through the network KPIs.</p> <p>The dataset contains 3 type of experiment with different levels of model complexity.</p> <p>Please refer to the associated github repository and the published paper for a detailed description of both the dataset and the infrastructure that generated the dataset.<br>Journal paper title: "ATELIER: Service Tailored and Limited-Trust Network Analytics Using Cooperative Learning"</p>
Pacific black ducks tri-axial accelerometer data with behaviour labels
<p>The tri-axial accelerometer datasets from Pacific black ducks (<em>Anas superciliosa</em>) was measured at 25 Hz. Fifty tri-axial measurements, totalling 2 seconds, were used to form a behaviour segment. Each dataset includes 9343 segments each forming a row in the dataset. Each row contains 151 columns. The first 150 columns are ACC measurements from three orthogonal axes, arranged as x, y, z, x, y, z, ...,x, y, z. The final column is of type character containing the corresponding behaviour. The two datasets contains 16 and 8 behaviour type labels, respectively. </p>
FIGURE 8. Type specimens and label data. A in Korean species of the Atheta Thomson subgenus Dimetrota Mulsant & Rey (Coleoptera: Staphylinidae: Aleocharinae) with a description of new species
FIGURE 8. Type specimens and label data. A: lectotype of Atheta (Dimetrota) altaica; B: syntype of A. (D.) sublaevana [= A. (D.) atramentaria]; C: holotype of A. (D.) sulputrida [= A. (D.) atramentaria]; D: lectotype of A. (D.) furtiva; E: holotype of A. (D.) machonryongica; F: holotype of A. (D.) photaechonica; G: lectotype of A. (D.) subsericans; H: syntype of A. (Dimetrota) weisei; I: holotype of A. (D.) chagangensis = A. (Badura) tokiokai.
Kashtanka.pet labeled data
<p>Data partition from kashtanka.pet</p>
Graph-based Leaf–Wood Separation Method for Individual Trees Using Terrestrial Lidar Point Clouds: Labeled validation data
<p>Set of 10 manually labeled point clouds used in the performance assessment of "Graph-based Leaf–Wood Separation Method for Individual Trees Using Terrestrial Lidar Point Clouds". This dataset is a collection of single trees scanned from different regions covering tropical, temperate, and boreal species—with heights ranging from 5.4 to 43.7 m, using the Riegl VZ-400 and Leica ScanStation C10 terrestrial laser scanner. These data have been preprocessed. For the original data, see https://doi.org/10.5061/dryad.10hq7;<br> https://doi.org/10.5061/dryad.np5hqbzp6;<br> https://doi.org/10.1594/PANGAEA.942856.</p>
Mapping forest in the Swiss Alps treeline ecotone with explainable deep learning: label data
<p>Label data used in the paper "Nguyen T.-A., Kellenberger B., Tuia D. (2022), <em>Mapping forest in the Swiss Alps treeline ecotone with explainable deep learning</em>" (<a href="https://doi.org/10.1016/j.rse.2022.113217">https://doi.org/10.1016/j.rse.2022.113217</a>).</p> <p>Code available at <a href="https://github.com/thienanhng/ExplainableForestMapping">github.com/thienanhng/ExplainableForestMapping</a>.</p>
Disconnection Labelled Reaction Data
<p>Dataset containing reaction centers used to train the disconnection aware model</p>
FLIM Data and analysis of the hands-on sessions "CF44-1 Label-free metabolic FLIM with 2 photon excitation"
<p>This FLIM dataset on HeLa cells and human astrocytes was acquired during the hands-on sessions <em>CF44-1 Label-free metabolic FLIM with 2 photon excitation</em> during the <a href="https://www.bioimaging.bmc.med.uni-muenchen.de/gerbiflim2024/flimprogram/index.html">German BioImaging </a><a href="https://www.bioimaging.bmc.med.uni-muenchen.de/gerbiflim2024/flimprogram/index.html">workshop on FLIM in Munich</a>.</p> <p>The FLIM data are analyzed with the open source software FLUTE available on GitHub: <a href="https://github.com/LaboratoryOpticsBiosciences/FLUTE"><strong><em>https://github.com/LaboratoryOpticsBiosciences/FLUTE</em></strong></a></p> <p>and published on Biological imaging Journal: <a href="https://www.cambridge.org/core/journals/biological-imaging/article/flute-a-python-gui-for-interactive-phasor-analysis-of-flim-data/862F290EC14187741BDA6B58E9868FA2"><strong><em>Gottlieb, D., Asadipour, B., Kostina, P., Ung, T., & Stringari, C. (2023). FLUTE: A Python GUI for interactive phasor analysis of FLIM data. Biological Imaging, 1-22. doi:10.1017/S2633903X23000211</em></strong></a></p>
Examined specimen data from collection labels in Megachile (Austrochile) taxonomic revision
<p>The species in the subgenus <em>Austrochile </em>Michener, 1965<em> </em>(genus <em>Megachile</em>) construct brood cells from resin that are stuck individually to twigs or in clusters on the bark of trees. We therefore propose 'resin pot bees' as common name for the bees in this subgenus. The species are revised and redescribed, and 78 additional species are described. The following names were synonymized: <em>Chalicodoma</em> (<em>Austrochile</em>) <em>subferox</em> Meade-Waldo, 1915 syn. nov. = <em>Megachile</em> (<em>Austrochile</em>) <em>kirbyana</em> Cockerell, 1906 comb. nov.; <em>Chalicodoma</em> (<em>Austrochile</em>) <em>recisa Cockerell, </em>1913 syn. nov., <em>Chalicodoma</em> (<em>Austrochile</em>) <em>simpliciformis</em> Cockerell, 1918 syn. nov., <em>Chalicodoma</em> (<em>Austrochile</em>) <em>kirbiella</em> Rayment, 1953 syn. nov. = <em>Megachile</em> (<em>Austrochile</em>) <em>modesta</em> Smith, 1892 comb. nov.; <em>Chalicodoma</em> (<em>Austrochile</em>) <em>subremotula</em> Rayment, 1934 syn. nov., <em>Chalicodoma</em> (<em>Austrochile</em>) <em>portlandiana</em> Rayment, 1953 syn. nov. = <em>Megachile</em> (<em>Austrochile</em>) <em>rottnestensis</em> Rayment, 1934 comb. nov.; <em>Chalicodoma</em> (<em>Austrochile</em>) <em>sexmaculata</em> Smith, 1868 syn. nov. = <em>Megachile</em> (<em>Rozenapis</em>) <em>ignita</em> Smith, 1853.</p> <p>All descriptions are accompanied by high resolution diagnostic images and distribution maps. Data on flower visitation and phenology are given. Dichotomous keys to both sexes of the species are provided and interactive keys to the species will be available on the LUCID website.</p>
Event Abstraction on Partially Ordered Event Data using Label Propagation
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FIGURES 6 A–Y. Evacanthini types and label data. A–D in Revision of the evacanthine leafhoppers (Hemiptera: Cicadellidae: Evacanthinae) of the Indian subcontinent
FIGURES 6 A–Y. Evacanthini types and label data. A–D. Apphia burmanica Distant; E–H. Apphia assamensis (Ramakrishnan); I–L. Apphia himalayana (Distant); M–P. Bundera venata Distant; Q–T. Evacanthus bellus (Distant); U–Y. E. extremus (Walker).
Data from "Label-free chemical imaging flow cytometry by high-speed multicolor stimulated Raman scattering"
<p>Data from "Label-free chemical imaging flow cytometry by high-speed multicolor stimulated Raman scattering" published in PNAS.</p>
Fig. 3. Type specimens and label data. A in Korean Species of Bellatheta Roubal (Coleoptera: Staphylinidae: Aleocharinae), with Two New Combinations and a New Synonym
Fig. 3. Type specimens and label data. A) Holotype of Atheta (Dimetrota) myohyangsani, ISEA, B) Holotype of A. (D.) namphoensis, ISEA.
Label-Free Quantification (LFQ) LC-MS Data for the Profiling of Domain-specific VCP/p97 Interactions in Living Cells
<p>This deposit includes LFQ LC-MS data for tandem-IP samples from VCP-L278AbK and VCP-D592AbK crosslinking experiments, their VCP-L278BocK and VCP-D592BocK controls, and SpectroMine output files.</p>
Sampled data for varifying the correctness of CHEN-AND (Labeled Dataset for Chinese and English Joint Author Name Disambiguation)
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Data and code for "A Linear Time Solution to the Labeled Robinson-Foulds Distance Problem"
<p><strong>Data and code for "A Linear Time Solution to the Labeled Robinson-Foulds Distance Problem"</strong></p> <p>Samuel Briand, Christophe Dessimoz, Nadia El-Mabrouk, Yannis Nevers</p> <p> </p> <p><strong>Experimental data</strong></p> <p>The __ALF\_Output__ directory contains the results obtained from ALF with parameters specified in the paper, as well as additional files generated in the downstream analysis (see below)</p> <p>The __Partitions__ directory contains one directory by partitioning of the 100 species from ALF in nested sets. Each contains three folder and a file.</p> <p> The summary.txt directory report which family are part of the nested set.</p> <p> The Allfamily directory contains the FASTA file of the 100 gene families generated with ALF, with only the species selected in the partition.</p> <p> The Aln directory contains the MSA for each gene family as generated with MAFFT with the selected species set</p> <p> The FTree directory contains the gene tree for each family as generated with FastTree with the selected species set</p> <p>The __Script__ directory containst the files used to generated the data from the ALF directory, as well as downstream analysis</p> <p> </p> <p> </p> <p>To reproduce the results start by runing __rewriteSeq.py__ , which is used for generating the Partitions. It takes as parameter the ALF directory, the directory in which you wish to generate the partitions, and the path to ALF's genomes FASTA files. If the partition file already exist, you can use the -r option to redo the random selection, otherwise it will generate file for the previous random selection. </p> <p>Example command :</p> <p>python rewriteSeq.py -i ../ALF\_output -o ../Partitions -g ../ALF\_output/DB</p> <p> </p> <p>Then, by runing __rewriteTree.py__ you will generated the reference trees used for the RF comparisons, as well as species tree used for each partitions. It takes as parameters the ALF directory , the Partitions directory and the species file of the partitions used to create the reference tree (smallest of all partitions)</p> <p>Example command :</p> <p>python rewriteTree.py -i ../ALF\_output/ -p ../Partitions/ -s ../Partitions/Part10/summary.txt</p> <p> </p> <p>Then, the script __launchFastTree.sh__ will, by partitions, generate a MSA using MAFFT and a phylogenetic tree using FastTree. It takes as parameter the Partitions directory and the number of the identifier of the partition for which you wish tu run it. Notes that the afforementionned software need to be installed before hand.</p> <p>Example command:</p> <p>bash launchFastTree.sh ../Partitions 10</p> <p> </p> <p>Finally, the __LRFAnalysis.ipynb__ file is a Jupyer Notebook used to run downstream analysis of RF and LRF on the different Partitions, including figure generation. Path to the data directory can be set in the 4th block of the Notebook.</p> <p> </p> <p><strong>Comparison of RF, LRF, and ELRF</strong></p> <p> </p> <p>The code to compare is provided as a Jupyter notebook in the directory "Comparison with RF and ELRF". The input NOX4 family from Ensembl version 99 is provided. The output figures are provided as PDF but they can be regenerated by running the notebook.</p> <p> </p>
Labelled data for P/S separation with CNN
<p>This is the part of training labelled Dataset-B for the publication "P/S separation of multi-component seismic data at land surface based on deep learning".</p> <p>There are 500-shot data labels:</p> <p>Train/, Val/ & Test/ are the separated file for Training, Validation & Testing</p> <p>The labelled data size are nx*nz=1001*3001 using a IEEE float format</p> <p>One can use the Seisic Unix command to plot for QC: ximage < shotx_mod_1.dat n1=3001 perc=99 & </p> <p> </p> <p>In each directory, the files are named as follows: </p> <p>The horizontal component:</p> <p> shotx_mod_*.dat </p> <p>The vertical component:</p> <p> shotz_mod_*.dat </p> <p>The P-wave label :</p> <p> shotp_*.dat </p> <p>The S-wave label :</p> <p> shots_*.dat </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.