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548 results for “data classification”
Fig. 7 Erythraeus phalangoides, larva. a Leg I. b Leg II. c Leg III. d Tarsus I. e Tarsus II. f in Towards resolving the double classification in Erythraeus (Actinotrichida: Erythraeidae): matching larvae with adults using 28S sequence data and experimental rearing
Fig. 7 Erythraeus phalangoides, larva. a Leg I. b Leg II. c Leg III. d Tarsus I. e Tarsus II. f Tarsus III (d–f, only specialized setae shown)
Fig. 5 Erythraeus phalangoides, larva. a in Towards resolving the double classification in Erythraeus (Actinotrichida: Erythraeidae): matching larvae with adults using 28S sequence data and experimental rearing
Fig. 5 Erythraeus phalangoides, larva. a Gnathosoma (and scutum), dorsal view. b Odontus. c Gnathosoma, ventral view. d Palp tarsus
Fig. 3 Trees obtained under the implied weighting using three concavity values k in First phylogenetic analysis of the tribe Oligaphorurini (Collembola: Onychiuridae) inferred from morphological data, with implications for generic classification
Fig. 3 Trees obtained under the implied weighting using three concavity values k = 6 (a), 9 (b), and 12 (c)
Fig. 4 Abdominal sternite IV in First phylogenetic analysis of the tribe Oligaphorurini (Collembola: Onychiuridae) inferred from morphological data, with implications for generic classification
Fig. 4 Abdominal sternite IV showing organization of furcal remnant. a, b Oligaphorura ursi Fjellberg, 1984; c Micraphorura gamae Buşmachiu and Weiner, 2013; d Oligaphorura groenlandica (Tullberg, 1876); e Dimorphaphorura inya Weiner and Kaprus, 2014; f Protaphorura eichhorni (Gisin, 1954)
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 8. Performance in 1st Approach for three data set
<p>This work also deals with classification of multi class images under different constraints of<br> data set. The first experiment is carried out on images without noise, second with Gaussian noise<br> and filtered data set in third experiment. Performance of the classifier using statistical texture<br> features for two approaches are presented in the table 3. It is observed that performance n the first<br> experiment is best in the first data set i.e. data set without noise in both the approach, while the<br> performance is decreased if the same images are affected by Gaussian noise.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 9. Performance in 2nd Approach for three data set
<p>This work also deals with classification of multi class images under different constraints of<br> data set. The first experiment is carried out on images without noise, second with Gaussian noise<br> and filtered data set in third experiment. Performance of the classifier using statistical texture<br> features for two approaches are presented in the table 3. It is observed that performance n the first<br> experiment is best in the first data set i.e. data set without noise in both the approach, while the<br> performance is decreased if the same images are affected by Gaussian noise. This is because the<br> texture feature of the original images consists Gaussian pattern also. Filtering of the noise from the<br> second data set improves the result. The table also shows that feature extraction using blocking of<br> the image enhance the average classification rate in all the case.</p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 5. Flowchart of data classification
<p>The Random Forest is a powerful classification method because of the following. First, errors are minimized as a result of a random forest, synthesizing through training (learner). The second, random choice at every stage in the Random Forest will reduce the correlation between the learners in the synthesis of the results. In addition, we also found that the total error of layered forest trees depends on their individual errors in forest trees, as well as the correlation between the trees. The article uses the wrapper model (Christopher Tong, 2000) with the objective function for the evaluation, Random Forest algorithm is shown in figure 5. </p>
BRAIN Journal-Prediction of Thyroid Disease Using Data Mining Techniques-Figure 2. Attributes of the classification models used in the experiments
<p>The authors used for their experiments a data set (UCI, 2016) containing 756 records about persons with thyroid dysfunctions. The classification model has 22 attributes; the class attribute is the target and it has three possible values: hypothyroidism, hyperthyroidism and normal. The current data set was extracted and preprocessed from the original file. A description of the attributes used in the experiments is given in Figure 2 (an extract from thyroid.arff test file). </p>
Data supporting the conclusions of Atmospheric boundary layer classification with Doppler lidar
<p>This is data set includes Doppler wind lidar quantities which were calculated from Halo Photonics Streamline measurements between 2 September 2015 and 16 November 2016 at Hyytiälä, Finland and between 1 January 2015 and 31 December 2016 at Jũlich, Germany. The data set also includes the respective boundary layer classification results generated from the calculated lidar quantities from both of the sites.</p>
Leaf and wood classification framework for terrestrial LiDAR point clouds: Simulated data validation dataset
<p>Set of 200 3D point clouds used in the validation of "Leaf and wood classification framework for terrestrial LiDAR point clouds". This dataset is a collection of point clouds simulated by a Monte-Carlo ray tracing (librat) using four 3D tree models from the fourth phase RAMI exercise (Widlowski et al, 2015).</p>
Leaf and wood classification framework for terrestrial LiDAR point clouds: Field data validation dataset
<p>Set of 10 3D point clouds used in the validation of "Leaf and wood classification framework for terrestrial LiDAR point clouds". This dataset is a collection of single trees scanned around the globe, from different biomes (both forest and urban areas), using the Riegl VZ-400 terrestrial laser scanner.</p>
Dataset from "Combined Landsat and L-Band SAR Data Improves Land Cover Classification and Change Detection in Dynamic Tropical Landscapes"
<p>These are the output land cover and land cover change raster maps from the paper, "<a href="https://doi.org/10.3390/rs10020306">Combined Landsat and L-Band SAR Data Improves Land Cover Classification and Change Detection in Dynamic Tropical Landscapes</a>," published in Remote Sensing journal.</p> <p>ABSTRACT. Robust quantitative estimates of land use and land cover change are necessary to develop policy solutions and interventions aimed towards sustainable land management. Here, we evaluated the combination of Landsat and L-band Synthetic Aperture Radar (SAR) data to estimate land use/cover change in the dynamic tropical landscape of Tanintharyi, southern Myanmar. We classified Landsat and L-band SAR data, specifically Japan Earth Resources Satellite (JERS-1) and Advanced Land Observing Satellite-2 Phased Array L-band Synthetic Aperture Radar-2 (ALOS-2/PALSAR-2), using Random Forests classifier to map and quantify land use/cover change transitions between 1995 and 2015 in the Tanintharyi Region. We compared the classification accuracies of single versus combined sensor data, and assessed contributions of optical and radar layers to classification accuracy. Combined Landsat and L-band SAR data produced the best overall classification accuracies (92.96% to 93.83%), outperforming individual sensor data (91.20% to 91.93% for Landsat-only; 56.01% to 71.43% for SAR-only). Radar layers, particularly SAR-derived textures, were influential predictors for land cover classification, together with optical layers. Landscape change was extensive (16,490 km<sup>2</sup>; 39% of total area), as well as total forest conversion into agricultural plantations (3,214 km<sup>2</sup>). Gross forest loss (5,133 km<sup>2</sup>) in 1995 was largely from conversion to shrubs/orchards and tree (oil palm, rubber) plantations, and gross gains in oil palm (5,471 km<sup>2</sup>) and rubber (4,025 km<sup>2</sup>) plantations by 2015 were mainly from conversion of shrubs/orchards and forests. Analysis of combined Landsat and L-band SAR data provides an improved understanding of the associated drivers of agricultural plantation expansion and the dynamics of land use/cover change in tropical forest landscapes.</p>
Data for "On the impact of Citizen Science-derived data quality on deep learning based classification in marine images"
<p>This dataset contains all the annotations done by either citizen scientists or experts of the publication: "On the impact of Citizen Science-derived data quality on deep learning based classification in marine images"</p> <p><strong>CSP.csv</strong> -> CS annotations of the Citizen Science Primer-experiment</p> <p><strong>CSPExpert.csv</strong> -> Expert annotations of the Citizen Science Primer-experiment</p> <p><strong>CSS.csv</strong> -> CS annotations of the Citizen Science Study</p> <p><strong>CSSExpert.csv</strong> -> Expert annotations of the Citizen Science Study</p> <p>Visual exploration of the image data is possible in the BIIGLE 2.0 image annotation system at <a href="https://biigle.de/projects/139">https://biigle.de/projects/159</a> using the login <em><a href="mailto:cs@example.com">cs@example.com</a></em> and the password <em>plosonecs</em>.</p>
Sample data for "Classification Modeling for Hazardous Rip Current Prediction" Notebook
<p>This sample dataset is used in the notebook "Classification Modeling for Hazardous Rip Current Prediction" to demonstrate the application of using machine learning to identify hazardous rip current. The notebook is available in the NOAA Center for Artificial Intelligence GitHub Learning Journey repository (https://github.com/noaa-ncai/learning-journey). The full dataset is available via NOAA.</p>
Appendix Figures A.1 - A.15 of the paper "Advanced classification of hot subdwarf binaries using artificial intelligence techniques and Gaia DR3 data". This work has been accepted for publication in the journal Astronomy & Astrphysics (A&A) on September 24, 2024.
<p><strong>Figure captions:</strong></p> <p> </p> <p><strong>Fig. A.1.</strong> Heatmap with the number of common stars (true positives) labeled as binary for the five methods used.</p> <p> </p> <p><strong>Fig. A.2.</strong> Heatmap with the number of common stars (true negatives) labeled as single for the five methods used.</p> <p> </p> <p><strong>Fig. A.3.</strong> Color-magnitude diagrams, showing the 2815 stars of our sample from Sect. 3. Colors indicate the label predictions by SOM (left panel) and CNN (right panel).</p> <p> </p> <p><strong>Fig. A.4.</strong> K-S test comparing radial SOM (black) and CNN (blue).</p> <p> </p> <p><strong>Fig. A.5.</strong> Spectra of the star "LAMOSTJ112914.11+471501.7" (blue color) and in the background (gray color) the 35 stars classified as binary by Solano et al. (2022) with VOSA tools.</p> <p> </p> <p><strong>Fig. A.6.</strong> Spectra of the star "HD14829" (red color) and in the background (gray color) the 53 stars classified as single by Drilling et al. (2013).</p> <p> </p> <p><strong>Fig. A.7.</strong> Spectra of the star "Feige98" (red color) and in the background (gray color) the 53 stars classified as single by Drilling et al. (2013).</p> <p> </p> <p><strong>Fig. A.8.</strong> Spectra of the star "PG0304+184" (red color) and in the background (gray color) the 53 stars classified as single by Drilling et al. (2013).</p> <p> </p> <p><strong>Fig. A.9.</strong> Spectra of the star "PG1510+635" (red color) and in the background (gray color) the 53 stars classified as single by Drilling et al. (2013).</p> <p> </p> <p><strong>Fig. A.10.</strong> Cluster 0 of spectra (blue color) with the other spectra in the background (gray color).</p> <p> </p> <p><strong>Fig. A.11.</strong> Cluster 4 of spectra (brown color) with the other spectra in the background (gray color).</p> <p> </p> <p><strong>Fig. A.12.</strong> Cluster 1 of spectra (yellow color) with the other spectra in the background (gray color).</p> <p> </p> <p><strong>Fig. A.13.</strong> Cluster 3 of spectra (red color) with the other spectra in the background (gray color).</p> <p> </p> <p><strong>Fig. A.14.</strong> Cluster -1 of spectra (pink color) with the other spectra in the background (gray color).</p> <p> </p> <p><strong>Fig. A.15.</strong> Cluster 2 of spectra (green color) with the other spectra in the background (gray color).</p>
Classification of Text Data on Rare Diseases
<div>A dataset with text and labels for 3 categories: </div> <div> <div> </div> <div>- Rare Diseases</div> <div>- Non-Rare Diseases</div> <div>- Other</div> </div> <div> </div> <div>It is a subset of abstracts obtained from PubMed and sorted into the 3 classes on the basis of their MeSH terms.</div> <div> </div> <div>The dataset is provided for demonstration and methodology validation purposes. The original PubMed data was randomly under-sampled. </div> <p>The dataset consists of 3 files in the Tab-Separated Values (TSV) format, corresponding to the 3 splits used in the article:</p> <p>Rei L, Pita Costa J, Zdolšek Draksler T. Automatic Classification and Visualization of Text Data on Rare Diseases. _Journal of Personalized Medicine_. 2024; 14(5):545. https://doi.org/10.3390/jpm14050545</p>
Companion data artifacts: Technical framework demonstration for deep learning-based wood species classification with advanced sub-μ-CT imaging
<p>This is the companion data artifact collection for the IWAWA paper manuscript by Jannik Stebani, Tim Lewandrowski, Kilian Dremel, Simon Zabler and Volker Haag.It is generally to be used with the visualization and prediction showcases implemented in the Binder notebooks launched from this <a href="https://github.com/stebix/woodnet-showcase" target="_blank" rel="noopener">woodnet-showcase</a> GitHub repository.</p> <p>The artifacts amount to the following:</p> <ol> <li><code>acer-artifact.hdf5</code> : Exemplary <code>(256, 256, 256)</code> subvolume from a <em>Acer pseudoplatanus</em> sub-μ-CT scan</li> <li><code>pinus-artifact.hdf5</code> : Exemplary <code>(256, 256, 256)</code> subvolume from a <em>Pinus sylvestris </em>sub-μ-CT scan</li> <li><code>weights-artifact.pth</code> : Exemplary PyTorch trained weights for a woodnet/deep neural network to demonstrate classification of the above samples</li> </ol>
Data for paper "OC_Finder: A deep learning-based software for osteoclast segmentation, classification, and counting"
<pre>This folder contains the source data for the images we used to train the neural network. Tiff files are microscopic images, and each tiff file has two xls or csv files to indicate the location of osteoclasts or non-osteoclasts in each Tiff file. We performed data collection 3 times, and the data from each data collection is stored separately in 3 folders, "Dataset_1", "Dataset_2", "Dataset_3". The "Dataset_1" folder contains a total of 170 tiff images. They are from the cell culture of wild-type cells stimulated with 50 ng/ml RANKL with/without 100 ng/ml TNF-alpha or 10 ng/ml IL-1beta. The location of cells is recorded in xls files. "IMAGE_NAME osteoclasts.xls" is for the coordination of osteoclasts, and "IMAGE_NAME non-osteoclasts.xls" is for the coordination of non-osteoclasts. The x-coordinate is recorded in the "x" column, and the y-coordinate is in the "y" column, with x=0 and y=0 being the coordinates of the upper left corner of the image. Please ignore the columns other than x and y. The "Dataset_2" folder contains a total of 288 images. They are from the cell culture of wild-type cells or cells with gain-of-function mutation of SH3BP2 (KI) stimulated with 25 or 50 ng/ml of RANKL. The image names are composed of the information about the culture conditions, such as gender of cell sources (female and male), the genotype of the cell source (wt: wild-type, KI: Knock-In mutation in Sh3bp2 resulting in the increased osteoclastogenesis), the concentration of RANKL in the culture media (R25: 25 ng/ml of RANKL, R50: 50 ng/ml of RANKL), and the culture period with RANKL stimulation (day 3: cultured with RANKL for 3 days.), excepting the images with the name starting "Image_". The images with the name Image_NUMBER.tif are from the following culture condition; female KI R25. The location of cells is recorded in xls files. "IMAGE_NAME posi.xls" is for the coordination of osteoclasts, and "IMAGE_NAME nega.xls" is for the coordination of non-osteoclasts. The x-coordinate is recorded in the "x" column, and the y-coordinate is in the "y" column, with x=0 and y=0 being the coordinates of the upper left corner of the image. Please ignore the columns other than x and y. The "additional data" folder contains a total of 288 images, which are identical to the images in the "second batch" folder. Using the same images, we collected additional coordination to increase samples. The location of cells is recorded in csv files. "IMAGE_NAME new posi.xls" is for the coordination of osteoclasts, and "IMAGE_NAME new nega.xls" is for the coordination of non-osteoclasts. The x-coordinate is recorded in the "x" column, and the y-coordinate is in the "y" column, with x=0 and y=0 being the coordinates of the upper left corner of the image. Please ignore the columns other than x and y. Please contact Mizuho Kittaka <mkittaka@iu.edu> for the inquery about the dataset.</pre>
Figure 2. A in A classification of Danaus butterflies (Lepidoptera: Nymphalidae) based upon data from morphology and DNA
Figure 2. A, single most parsimonious tree based on amalgamated mtDNA 12S rRNA (344 bp†) and COI (676 bp†) genes (Appendix 2) for the genus Danaus, rooted by outgroups Tirumala septentrionis and T. limniace (†values before alignment). Tree statistics: characters in matrix 1025, variable informative characters 54, parsimony informative characters 139, heuristic search length 304, consistency index (CI) = 0.80, homoplasy index (HI) = 0.20, retention index (RI) = 0.81, rescaled consistency index (RCI) = 0.65. Bootstrap replicates/Bremer Support values are shown for each node where significant. Branch lengths are drawn proportional to nucleotide changes and indicated in parentheses. The MP topology depicted here is congruent with the MP strict consensus, NJ and ML algorithms not shown. ML branch significance is P <0.01 unless marked *. See Table 1 for provenances and sample sizes. B, single most parsimonious tree based on morphological, biochemical and colour gene characters (Appendix 1A, B) for the genus Danaus, rooted by outgroups T. septentrionis and T. limniace (characters from T. petiverana, T. hamata and T. ishmoides were used to fill gaps); †character alignment was fixed to character). Tree statistics: characters in matrix 67†, variable uninformative characters 8, parsimony informative characters 59, heuristic search length 95, CI = 0.84, HI = 0.16, RI = 0.90, RCI = 0.75. Other notes as in (A). C, one of 11 most parsimonious trees based on amalgamated nDNA 18S rRNA (525 bp†) and EF1-a (400 bp†) genes for the genus Danaus, rooted by outgroups Amauris niavius and A. ellioti (†values before alignment). Tree statistics: characters in matrix 926, variable uninformative characters 19, parsimony informative characters 24, heuristic search length 52, CI = 0.83, HI = 0.17, RI = 0.72, RCI = 0.60. Other notes as in (A).
Binary Classification as a Phase Separation Process (data repository)
<p><strong>For version 0.0.2 (from 2021) see below:</strong></p> <p>This is a data repository for the paper "Binary classification as a phase separation process", by Rafael Monteiro.</p> <ul> <li>Website with description of this project: <a href="https://rafael-a-monteiro-math.github.io/Binary_classification_phase_separation/index.html">https://rafael-a-monteiro-math.github.io/Binary_classification_phase_separation/index.html</a></li> <li>Github: <a href="https://github.com/rafael-a-monteiro-math/Binary_classification_phase_separation">https://github.com/rafael-a-monteiro-math/Binary_classification_phase_separation</a></li> </ul> <p>This is a second version, which I wrote using tensorflow. It is much smaller (5 Gb when decompressed), a remarkable improvement when compared to the more than 100 Gb of the previous version).</p> <p>The new files are </p> <ul> <li>PSBC_BCs.tar.gz</li> <li>PSBC_classifier_PCA.tar.gz</li> <li>PSBC_dataset.tar.gz</li> <li>PSBC_libs_grids_statistics.tar.gz</li> <li> PSBC_notebooks.tar.gz</li> </ul> <p>Their content is explained in the file README_v2.pdf</p> <p><strong>UPDATE: <a href="https://drive.google.com/drive/folders/18l_92HuHDWJDkZnvXRuyGedcyC_3YZ2M?usp=sharing">a Google Colab folder is also available</a>. You can also find all the data and libraries there, unpacked.</strong></p> <p>For usage, see the Git-hub. </p> <blockquote> <p><strong>NOTE)</strong> I will keep the content for the previous version available in my Github as well. It is still a "nice exercise" to do all that is done in this new version in numpy, as done there. <strong><em>(Or, I should say, they should be studied as a cautionary tale of what to avoid.)</em></strong></p> </blockquote> <p> </p> <p><strong>For version 0.0.1 (from 2020) see below:</strong></p> <p>This is a data repository for the paper "Binary classification as a phase separation process", by Rafael Monteiro.</p> <ul> <li>Website with description of this project: <a href="https://rafael-a-monteiro-math.github.io/Binary_classification_phase_separation/index.html">https://rafael-a-monteiro-math.github.io/Binary_classification_phase_separation/index.html</a></li> <li>Github: <a href="https://github.com/rafael-a-monteiro-math/Binary_classification_phase_separation">https://github.com/rafael-a-monteiro-math/Binary_classification_phase_separation</a></li> </ul> <p>Therein you will find</p> <ul> <li>Examples</li> <li>1D toy model examples</li> <li>Computational statistics</li> <li>Several trained PSBC on MNIST dataset, with different parameter configurations</li> <li>Extra simulations, investigating normalization properties, low dimensional models that fail due to "too much" model compression, and comparison among ANNs, KNNs, and the PSBC in 1D</li> </ul> <p>If you want to know</p> <ol> <li>how to read the data</li> <li>how to access computational statistics, raw data, and examples</li> <li>how to use the data stored in this data repository</li> </ol> <p>see the guide README.pdf on GitHub page at <a href="https://github.com/rafael-a-monteiro-math/Binary_Classification_Phase_Separation">Binary_Classification_Phase_Separation</a>, where a script that downloads (and organizes) all this data is also available ("download_PSBC.sh).</p> <p>I did not include a copy of the train-test set (0-1dubset of the MNIST database) in every folder with simulations. But you can find a copy of the normalized dataset in the tar ball "PSBC_Examples.tar.gz" as</p> <p>data_test_normalized_MNIST.csv and data_train_normalized_MNIST.csv.</p> <p> </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.