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3,684 results for “extract”
Multiscale Spatial Patterns in Giant Dike Swarms Identified through Objective Feature Extraction Datasets
<p>S1 - Linked dike clusters for the Columbia River Flood Basalt group including the four identified subswarms: Chief Joseph, Monument, Ice Harbor, and Steens as compiled in Morriss et al., 2020. This dataset uses the a UTM Zone 11N projection (EPSG:26911).</p> <p>S2 - Linked dike clusters for the Deccan Traps including the four identified subswarms: Saurashtra, Narmada-Tapi, Central and Coastal. Due to their overlap Central and Coastal Swarms have been combined in this dataset into the Central Swarm. This dataset uses the a WGS 84 projection (EPSG:3857). </p> <p>S3 - Dike segment data for Spanish Peaks and Dike Mountain located in the Rio Grande Rift of Colorado. This dataset was digitized using QGIS based on the map by Johnson (1961). This dataset uses the a UTM Zone13N projection (EPSG:32613). The file includes the start, end points, and midpoints of the dikes; segment length; calculated $\rho$ and $\theta$ for the Hough Transform; the origin used for the Hough Transform which is different for each subswarm (xc,yc); dike rock type if known; and a unique identification calculated based on the start and endpoints. This dataset has been preprocessed to remove curving dikes and is the data set used to produce later products (Data set S4). </p> <p>S4 - Linked dike clusters for the Spanish Peaks and Dike Mountain. This dataset was produced using the Agglomerative Clustering algorithms using the parameters set in Table 1. This dataset uses the a UTM Zone 13N projection (EPSG:32613). </p> <p> </p> <p> </p> <p>These datasets were produced using the Agglomerative Clustering algorithms using the parameters set in Table 1. The datasets are in the format of a CSV file but can be read into GIS programs using Well Known Text (WKT) linestring. TThe file includes the start and end points of the average line in the cluster and it's mid points, cluster length and width (Xstart, Xend, Xmid, Ymid, in meters and UTM coordinates, Dike Cluster Width or R\_Width, Dike Cluster Length or R\_Length all in meters); calculated average $\rho$ and $\theta$ for the Hough Transform $\rho$ units measured in meters, $\theta$ units measured in degrees, unless otherwise stated); the origin used for the Hough Transform which is different for each subswarm ($xc$,$yc$, meters in UTM coordinates); average slope and intercept (AvgSlope, AvgIntercept meters); range and standard deviation for $\rho$ and $\theta$ for all objects in the cluster ($\rho$ units measured in meters, $\theta$ units measured in degrees); cluster size (Size); sum of segment lengths in a cluster (SegmentLSum, meters); whether the cluster crosses between negative and positive values (ClusterCrossesZero, boolean); overlap as calculated in the main text where the length of overlap is normalized by the sum of segment lengths in a cluster; maximum number of overlapping segments (nOverlapingSegments); twist angle which is the difference in angle betweeen the average cluster line and the average line formed by cluster midpoints (EnEchelonAngleDiff, degrees); the p-value for the midpoint line fit of the segments where $p<0.05$ is considered to be a significant fit (EEPValue); the maximum, median, and minimum segment nearest neighbors distances in the cluster which is calculated using the cartesian midpoints of each segment and normalized by the Cluster Length (MaxSegNNDist, MedianSegNNDist, MinSegNNDist); characterization of each cluster as filtered or not, filtered clusters are of size greater than $3$ and have a MaxSegNNDist of less than $0.5$ (TrustFilter, boolean); the date edited (Date\_Changed), and the clustering parameters used for each cluster (Rho\_Threshold in meters, Theta\_Threshold in degrees) and a unique identification calculated based on the start and endpoints (ClusterHash). </p>
Number of publications in French for the winners of the Nobel Prize for Literature (extraction of the BnF catalog)
<p>Python script and dataset.</p> <p>The dataset contains the number of publications in French one can find in the catalog of the Bibliothèque nationale de France (BnF) for every winner of the Nobel Prize of Literature.</p> <p>Idea from LIBER : <a href="https://twitter.com/kiru/status/1677413039132352512">https://twitter.com/kiru/status/1677413039132352512</a></p>
Рис. 1–5. Новые виΔы Λистоверток, самцы, общий виΔ и генитаΛии. 1, 3–5 – Phtheochroa tabasarana sp. n.; 2 – Celypha laminaria sp. n. 1–2 – гоΛотип, габитус; 3–5 – генитаΛии: 3 – эΔеагус, 4 – генитаΛии, эΔеагус извΛечен, 5 – генитаΛии, ΛатераΛьно. Масштабная Λинейка ΔΛя рисунков 3–5 – 1 мм. Figs 1–5. New species of leaf-rollers, males, general view and genitalia. 1, 3–5 – Phtheochroa tabasarana sp. n.; 2 – Celypha laminaria sp. n. 1–2 – holotype, habitus; 3–5 – genitalia: 3 – aedeagus, 4 – genitalia, aedeagus extracted, 5 – genitalia laterally. Scale bar for Figs 3–5 – 1 mm. in Two new species of leaf-rollers (Lepidoptera: Tortricidae) from the East Caucasus
Рис. 1–5. Новые виΔы Λистоверток, самцы, общий виΔ и генитаΛии. 1, 3–5 – Phtheochroa tabasarana sp. n.; 2 – Celypha laminaria sp. n. 1–2 – гоΛотип, габитус; 3–5 – генитаΛии: 3 – эΔеагус, 4 – генитаΛии, эΔеагус извΛечен, 5 – генитаΛии, ΛатераΛьно. Масштабная Λинейка ΔΛя рисунков 3–5 – 1 мм. Figs 1–5. New species of leaf-rollers, males, general view and genitalia. 1, 3–5 – Phtheochroa tabasarana sp. n.; 2 – Celypha laminaria sp. n. 1–2 – holotype, habitus; 3–5 – genitalia: 3 – aedeagus, 4 – genitalia, aedeagus extracted, 5 – genitalia laterally. Scale bar for Figs 3–5 – 1 mm.
OIE4PA: Open Information Extraction for the Public Administration
<p>Tenders are powerful means of investment of public funds and represent a strategic development resource.<br> Despite the efforts made so far by governments at national and international levels to digitalise documents related to the Public Administration sector, most of the information is still available in an unstructured format only. <br> With the aim of bridging this gap, we present OIE4PA, our latest study on extracting and classifying relations from tenders of the Public Administration.<br> Our work focuses on the Italian language, where the availability of linguistic resources to perform Natural Language Processing tasks is considerably limited. <br> For evaluation purposes, we built a dataset composed of 2,000 triples extracted from Italian tenders, which have been manually annotated by two human experts.</p> <p>The dataset, compressed in a single zip file, is composed of:</p> <ul> <li>The corpus of 6,262 texts extracted from Italian public tenders (corpus_tenders)</li> <li>The training set of 1,600 annotated triples (training_set)</li> <li>The test set of 400 annotated triples (test_set)</li> <li>The set U of 14,096 triples used for the self-training (u_triples_dd)</li> <li>a compressed archive that contains both the extracted triples and the index for each supervised approach (extraction) <br> </li> </ul>
Image for the dataset in "Extraction of stratigraphic exposures on visible images using a supervised machine learning technique"
<p>This is the original and hand-masked image for the dataset used in a research paper "Extraction of stratigraphic exposures on visible images using a supervised machine learning technique".</p> <p>The content is</p> <ul> <li>Original images with hand-masked images (original_images_NOGUCHIandShoji.zip) <ul> <li>training/* : original images used for the training dataset generation (60 files)</li> <li>training_masks/* : hand-masked images for the training dataset generation (60 files)</li> <li>validation/* : original images used for the training dataset generation (10 files)</li> <li>validation_masks/* : hand-masked images for the validation dataset generation (10 files)</li> <li>test/* : original images used as the test data (5 files)</li> <li>test_masks/* : hand-masked images used as the test data (5 files).</li> </ul> </li> </ul> <p>Note that original images include images obtained using <em>google-image-download</em>, a Python script published on GitHub (<a href="https://github.com/Joeclinton1/google-images-download/tree/patch-1">https://github.com/Joeclinton1/google-images-download/tree/patch-1</a>, Copyright © 2015-2019 Hardik Vasa). The whole images we obtained by <em>google-image-download</em> were labeled as noncommercial reuse with modification.</p> <p>For more details, please refer to a research paper "Extraction of stratigraphic exposures on visible images using a supervised machine learning technique".</p> <p>Correspondence: Rina Noguchi (r-noguchi@env.sc.niigata-u.ac.jp)</p>
Data for cyanobacteria, nutrients and color from 588 lakes in Finland Norway Sweden and UK extracted from the WISER database
<p>Data on phosphorus, color, cyanobacteria biovolume, total phytoplankton biovolume and cyanobacteria proportion of the total phytoplankton biovolume used for GAMM</p>
MEDDOPLACE Corpus: Gold Standard annotations for Medical Documents Place-related Content Extraction
<p><strong>MEDDOPLACE</strong> stands for MEDical DOcument PLAce-related Content Extraction. It is a shared task and set of resources focused on the detection, normalization (entity linking/toponym resolution) and classification of different kinds of places, as well as related types of information such as clinical departments, nationalities or patient movements, in medical documents in Spanish.</p> <p>This repository includes the corpus' <strong>train and test sets</strong> in multiple formats, as well as the <strong>SNOMED gazetteer</strong>, <strong>cross-mapping</strong> between SNOMED and MeSH and the <strong>multilingual silver standard in 8 languages </strong>(Catalan, English, French, Italian, Dutch, Portuguese, Romanian and Swedish). For more information, please check the attached README file.</p> <p>MEDDOPLACE was developed by the Barcelona Supercomputing Center's NLP for Biomedical Information Analysis and used as part of IberLEF 2023. For more information on the corpus, annotation scheme and task in general, please visit: <a href="https://temu.bsc.es/meddoplace">https://temu.bsc.es/meddoplace</a>.</p> <p> </p> <p><strong>Please cite if you use this resource:</strong></p> <p>Salvador Lima-López, Eulàlia Farré-Maduell, Antonio Miranda-Escalada, Vicent Brivá-Iglesias and Martin Krallinger. NLP applied to occupational health: MEDDOPROF shared task at IberLEF 2021 on automatic recognition, classification and normalization of professions and occupations from medical texts. In Procesamiento del Lenguaje Natural, 67. 2021.</p> <pre><code>@article{meddoplace, title={MEDDOPLACE Shared Task overview: recognition, normalization and classification of locations and patient movement in clinical texts}, author={Lima-López, Salvador and Farré-Maduell, Eulàlia and Brivá-Iglesias, Vicent and Gasco-Sanchez, Luis and Krallinger, Martin}, journal = {Procesamiento del Lenguaje Natural}, volume = {71}, year={2023}, issn = {1135-5948},<br>DOI = {10.26342/2023-71-23}, url = {http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6561/3961}, pages = {301--311} }</code></pre> <p><strong>Related Links:</strong></p> <p>- MEDDOPLACE website: <a href="https://temu.bsc.es/meddoplace">https://temu.bsc.es/meddoplace</a></p> <p>- MEDDOPLACE overview paper: <a href="http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6561">http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6561</a></p> <p>- Annotation Guidelines (Spanish): <a href="https://doi.org/10.5281/zenodo.7775234">https://doi.org/10.5281/zenodo.7775234</a></p> <p>- Annotation Guidelines (English): <a href="https://doi.org/10.5281/zenodo.7928145">https://doi.org/10.5281/zenodo.7928145</a></p> <p><strong>License</strong></p> <p>This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p> <p><strong>Contact</strong></p> <p>If you have any questions or suggestions, please contact us at:</p> <p>- Salvador Lima-López (<salvador [dot] limalopez [at] gmail [dot] com>)<br>- Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)</p>
Dataset for the article "Beyond PLFA: Concurrent extraction of neutral and glycolipid fatty acids provides new insights into soil microbial communities"
<p>The following are data and code used for statistical analysis and figure plotting in the manuscript</p> <p>Gorka et al. (2023) "Beyond PLFA: Concurrent extraction of neutral and glycolipid fatty acids provides new insights into soil microbial communities", Soil Biology and Biochemistry</p> <p>It contains the following files:</p> <p>1. Pure lipid standard data</p> <ul> <li>Total ion chromatogram (TIC) area data (<strong>area.csv</strong>)</li> <li>Assignment of lipids that the measured fatty acids originate from (<strong>LipidClass.csv</strong>)</li> <li>An R script reproducing the calculations and plotting for Fig. 2 and Fig. S1 (<strong>pure_lipids.R</strong>)</li> </ul> <p>2. Microbial pure culture fatty acid data data</p> <ul> <li>TIC area data of the PLFA, NLFA, and GLFA data from pure culture extracts (<strong>area.csv</strong>)</li> <li>Files needed for calculating the data and assigning taxonomic groups in the R code (<strong>weights.csv</strong>, <strong>C_atoms.csv</strong>, <strong>species_list.csv</strong>)</li> <li>An R script reproducing the calculations and plotting for Fig. 3, Fig. 4, Fig. S2, and Fig. S3 (<strong>pure_cultures.R</strong>)</li> </ul> <p>3. Soil fatty acid data</p> <ul> <li>Absolute abundance data in nmol C g<sup>-1</sup> dry weight of the PLFA, NLFA, and GLFA data from soil extracts (<strong>nmolC.csv</strong>)</li> <li>Taxonomic group assignments of fatty acids needed to run the R code (<strong>phylum.csv</strong>)</li> <li>An R script reproducing the calculations and plotting for Fig. 5, and Fig. S4 (<strong>soil.R</strong>)</li> </ul>
Figure 3. After extracting a in Observations Of Novel Feeding Tactics In Guira Cuckoo Guira Guira (Aves: Cuculidae)
Figure 3. After extracting a larva the cuckoos descended to the ground to soften it up before consumption.
Dataset from : "Automatic extraction of former WWI battlefields from ancient maps"
<p>The folder contains 3 shapefiles usable in GIS (geographic information system). These data result from the processing of the french map of devastated regions ("carte des régions dévastées"). The map was edited in 1920 by the geographic service of French army. The objective was to classify lands depending on the intensity of destruction, and to locate areas where substantial restoration work was necessary. The 47 map sheets of the collection at scale 1:50,000 have been scanned and can be obtained from the National Geographic Institute (IGN) in .jpg format. The map shows large red-colored zones representing heavily damaged front-line area by trenches and bombing according to the map legend. There are also red-hatched features locating destroyed cities, roads and destroyed or cut forests. The blue-colored symbols show new constructions, such as memorials and cemeteries. For the methodology of georeferencing, classification and vectorization, see Nelly Paradelle, Marianne Laslier, Guillaume DeCocq, "Automatic extraction of former WWI battlefields from ancient maps," Proc. SPIE 12727, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV, 127270H (17 October 2023);<a href="https://doi.org/10.1117/12.2684009"> https://doi.org/10.1117/12.2684009</a></p><p>-MANUAL_ENVELOPE.shp : This dataset contains the envelope bordering the local destructions from the dataset "RED POLYGONS", and drawn manually within QGIS.</p><p>-RED_POLYGONS.shp : This dataset contains only polygons of local destructions (cities, roads, buildings, destroyed or cut forests etc.) extracted from the map of devastated regions</p><p>-RED_ZONE.shp : This dataset contains only polygons of the large red-colored areas representing heavily damaged front-line area by trenches and bombing extracted from the map of devastated regions</p><p>Files with extension .qmd provide metadata.</p>
Systematic Literature Review - Selected Articles - Data Extraction
<p>- CSV with the raw data extracted from abstract + full-text review from the selected articles.</p> <p>- CSV with normalized data.</p> <p>- CSV with the normalized data after being a feature engineering step in which some columns have been deleted.</p> <p>- CSV with formal context to extract the concepts lattice.</p>
VAMDC extraction with identifier = 7525b68d-0ed7-464d-bbe9-c0e70adc2003
<div>This is a dataset extracted from https://cdms.astro.uni-koeln.de/cdms/tap/ VAMDC node.</div><div>Query originating this dataset: query=SELECT * WHERE MoleculeStoichiometricFormula = 'CF+' </div><div> Data source version: 2019-11-27</div><div>Data format: XSAMS 12.07</div><div>Query uuid in VAMDC query store: 7525b68d-0ed7-464d-bbe9-c0e70adc2003</div>
Assessing a New Jaw Support Device During Third Molar Extractions
ClinicalTrials.gov study NCT03975920. IPD Sharing: YES. Countries: 1. Publications: 19.
Evaluating The Safety And Clinical Efficacy Of Elderberry Extract In Patients With Influenza
ClinicalTrials.gov study NCT03410862. IPD Sharing: NO. Countries: 1. Publications: 1.
Broccoli Sprout Extracts Trial to See if NRF2 is Enhanced by Sulforaphane Treatment in Patients With COPD
ClinicalTrials.gov study NCT01335971. IPD Sharing: YES. Countries: 1. Publications: 2.
Extraction of clinical phenotypes for Alzheimer disease dementia from clinical notes using natural language processing
Open the record for dataset details and reuse information.
Data from: How many specimens make a sufficient training set for automated three dimensional feature extraction?
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Extracting abundance information from DNA-based data
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Hot carrier extraction from 2D semiconductor photoelectrodes
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Data from: A comprehensive suite for extracting neuron signals across multiple sessions in one-photon calcium imaging
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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.