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3,684 results for “Extraction”
Topography extraction and topographic change measurements using PlanetScope data: Shisper Glacier (Pakistan)
<p>The study area is located in the north flank of Hunza Valley in the Central Karakoram. Shisper glacier covers ~53.7 km2 at an elevation range of 2567-611 m a.s.l. It is a surge-type glacier, which has recently gained the attention of the scientific community and disaster response agencies during its surge. In 2018, the glacier surged beyond the confluence with the outlet stream of Mochwar glacier. The blockage resulted in the creation of a lake, which then drained causing a GLOF (Glacial Lake Outbreak Flood) and has recently begun reforming. The melt water from the two glaciers feeds hydropower plants in the Hunza valley and is a major source of fresh water for agriculture. Glacier-related hazards threaten both the town of Hassanabad and the Karakoram Highway, the only paved road through the mountain range. Here we show the potential of CubSat data to monitor such glaciers, which are not easily accessible to field observation and their potential impact on power generation, water resources and infrastructure.</p> <p>We use multi-date L1B DOVE-C PlanetScope data to extract two DEMs in 2017 and 2019 over the study area in order to compute the elevation difference caused by the glacier surge.</p> <p>Supplementary material for our paper: Optimization of optical image geometric modeling, application to topography extraction and topographic change measurements using PlanetScope and SkySat imagery.</p>
L4E - Maximum tree height extracted from LiDAR transects over the Brazilian Amazon
<p>Between 2016 and 2018, the EBA airborne missions (conducted by the Brazilian National Institute for Space Research (INPE) and funded by Amazon Fund) collected airborne lidar transects of 375 ha (12.5 x 0.3 km) each. A majority of the transects were flown over randomly selected locations of old growth and second growth as forests defined by the PRODES and TerraClass databases (PRODES, INPE, 2016; TerraClass, INPE, 2014). PRODES separates forests from non-forest while TerraClass identifies second growth forest and other land covers.</p>
Kidney glomeruli-ROIs extracted from histological slides stained with HE or PAS.
<p>Kidney glomeruli-ROIs extracted from histological slides stained with HE or PAS. The multicenter test set of 78 regions of interest together with annotations. ROIs were extracted from 20 WSIs representing various human kidney pathologies. WSI's came from four sources: three independent medical centers and TCGA. Slides from three sources were stained with HE and slides from one center were stained with PAS. Slides were digitalized on Pannoramic 250 Flash II (3DHISTECH, Budapest, Hungary), Hamamatsu NanoZoomer S60 Digital slide scanner, or using Aperio AT Turbo (Leica Biosystems, Vista, CA). ROIs were extracted for x10, that corresponding to the pixel size ~10um.</p>
Supplementary Dataset for "Extracting near-field seismograms from ocean-bottom pressure gauge inside the focal area: application to the 2011 Mw 9.1 Tohoku-Oki earthquake"
<p>Datasets S1 contains the results obtained by the analysis in this study, such as the spatial and temporal configuration of the basis functions. Dataset S2 contains the ocean-bottom pressure gauge data used in this study.</p> <p>The manuscript is available at: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL091664</p> <p> </p> <p> </p> <div> </div>
Data Set of Extracted Summary Statistics from Equipment Sensor Data
<p>This data set was generated in accordance with the semiconductor industry and contains values of summary statistics from sensor recordings of the high-precision and high-tech production equipment. Basically, the semiconductor production consists of hundreds of process steps performing physical and chemical operations on so-called wafers, i.e. slices based on semiconductor material. In the production chain, each process equipment is equipped with several sensors recording physical parameters like gas flow, temperature, voltage, etc., resulting in so-called sensor data. Out of the sensor data, values of summary statistics are extracted. These are values like mean, standard deviation and gradients. To keep the entire production as stable as possible, these values are used to monitor the whole production in order to intervene in case of deviations.</p> <p>After the production, each device on the wafer is tested in the most careful way resulting in so-called wafer test data. In some cases, suspicious patterns occur in the wafer test data potentially leading to failure. In this case the root cause must be found in the production chain. For this purpose, the given data is provided. The aim is to find correlations between the wafer test data and the values of summary statistics in order to identify the root cause.</p> <p>The given data is divided into four data sets: "XTrain.csv", "YTrain.csv", "XTest.csv" and "YTest.csv". "XTrain.csv" and "XTest.csv" represent the values of summary statistics originating in the production chain separated for the purpose of training and validating a statistical model. Included are 114 observations of 77 parameters (values of summary statistics). The "YTrain.csv" and "YTest.csv" contain the corresponding wafer test data (144 observations of one parameter).</p>
Negative Sampling Improves Hypernymy Extraction Based on Projection Learning
<p>We present a new approach to extraction of hypernyms based on projection learning and word embeddings. In contrast to classification-based approaches, projection-based methods require no candidate hyponym-hypernym pairs. While it is natural to use both positive and negative training examples in supervised relation extraction, the impact of negative examples on hypernym prediction was not studied so far. In this paper, we show that explicit negative examples used for regularization of the model significantly improve performance compared to the state-of-the-art approach on three datasets from different languages.</p> <p>The <strong>russian</strong> model.</p> <p>$ python -V; pip show tensorflow numpy scipy scikit-learn gensim | egrep -i '(name|version)'<br> Python 3.5.2 :: Continuum Analytics, Inc.<br> Name: tensorflow<br> Version: 0.12.1<br> Name: numpy<br> Version: 1.12.0<br> Name: scipy<br> Version: 0.18.1<br> Name: scikit-learn<br> Version: 0.18.1<br> Name: gensim<br> Version: 0.13.4.1</p> <p>The <strong>english</strong><strong>-combined</strong> model has been trained using the well-known word embeddings dataset based on Google News: GoogleNews-vectors-negative300.bin on EVALution, BLESS, K&H+N, ROOT09 combined. The <strong>english</strong><strong>-</strong><strong>evalution</strong> model is traned on EVALution only.</p> <p>$ python -V; pip show tensorflow numpy scipy scikit-learn gensim | egrep -i '(name|version)'<br> Python 3.5.2 :: Anaconda custom (64-bit)<br> Name: tensorflow<br> Version: 0.12.1<br> Name: numpy<br> Version: 1.11.3<br> Name: scipy<br> Version: 0.18.1<br> Name: scikit-learn<br> Version: 0.18.1<br> Name: gensim<br> Version: 0.13.4.1</p>
Systemic Treatment with Cigarette Smoke Extract Affects Zebrafish Visual Behaviour, Intraocular Vasculature Morphology and Outer Segment Phagocytosis
<p>Underlying dataset and analysis tests of the results described in the article "Systemic Treatment with Cigarette Smoke Extract Affects Zebrafish Visual Behaviour, Intraocular Vasculature Morphology and Outer Segment Phagocytosis".</p>
Deep learning to extract the meteorological by-catch of wildlife cameras: Supporting data, models and code
<p>This repository contains the data, models and code to train and deploy deep learning models related to the paper "Deep learning to extract the meteorological by-catch of wildlife cameras" published in the journal Global Change Biology (<a href="https://doi.org/10.1111/gcb.17078"><strong>https://doi.org/10.1111/gcb.17078</strong></a>).</p>
Extracted Source Properties Catalog for "Monitoring the X-ray Variability of Bright X-ray Sources in M33"
<p>Supplemental data to the article "Monitoring the X-ray Variability of Bright X-ray Sources in M33" accepted for publication in ApJ. Contains all extracted source properties for the 56-source final catalog, including single-ObsID extractions and merged values. See ReadMe for column descriptions and additional comments.</p>
Extracted raw data from: Global dominance of lianas over trees is driven by forest disturbance, climate, and topography
<p>In a meta-analysis, we use an unprecedented dataset, representing 556 unique locations worldwide, distributed across 44 countries and six continents to show for the first time that lianas (woody vines) thrive relatively better than trees when forests are disturbed, temperature increase, precipitation decrease, and particularly in tropical lowlands. We demonstrate that liana dominance can persist for decades post-disturbance and hinder the recovery of disturbed forests, especially when climate favours lianas. With implications for the global carbon sink, our findings suggest that degraded tropical forests with environmental conditions favouring lianas should be the highest priority to consider for restoration management.</p>
Definitions of terms extracted from data-related European Union laws, version 3
<p>Collection of definitions of terms in English, French, German, Italian and Spanish extracted from the following data-related European laws:</p> <ol> <li> <p><a href="http://data.europa.eu/eli/dir/2007/2/oj?locale=en">Directive 2007/2/EC of the European Parliament and of the Council of 14 March 2007 establishing an Infrastructure for Spatial Information in the European Community (<strong>INSPIRE</strong>)</a></p> </li> <li> <p><a href="http://data.europa.eu/eli/reg/2016/679/2016-05-04?locale=en">Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (<strong>General Data Protection Regulation</strong>) (Text with EEA relevance)</a></p> </li> <li> <p><a href="https://eur-lex.europa.eu/eli/reco/2018/790/oj?locale=en">Commission Recommendation (EU) 2018/790 of 25 April 2018 on <strong>access to and preservation of scientific information</strong></a></p> </li> <li> <p><a href="https://eur-lex.europa.eu/eli/reg/2018/1807/oj?locale=en">Regulation (EU) 2018/1807 of the European Parliament and of the Council of 14 November 2018 on a framework for the <strong>free flow of non-personal data</strong> in the European Union (Text with EEA relevance)</a></p> </li> <li> <p><a href="https://data.europa.eu/eli/dir/2019/790/oj?locale=en">Directive (EU) 2019/790 of the European Parliament and of the Council of 17 April 2019 on <strong>copyright and related rights in the Digital Single Market</strong> and amending Directives 96/9/EC and 2001/29/EC (Text with EEA relevance)</a></p> </li> <li> <p><a href="https://eur-lex.europa.eu/eli/dir/2019/1024/oj?locale=en">Directive (EU) 2019/1024 of the European Parliament and of the Council of 20 June 2019 on open data and the re-use of public sector information (recast) (<strong>Open Data Directive</strong>)</a></p> </li> <li><a href="https://eur-lex.europa.eu/eli/reg/2021/695/oj?locale=en">Regulation (EU) 2021/695 of the European Parliament and of the Council of 28 April 2021 establishing <strong>Horizon Europe</strong> – the Framework Programme for Research and Innovation, laying down its rules for participation and dissemination, and repealing Regulations (EU) No 1290/2013 and (EU) No 1291/2013 (Text with EEA relevance)</a></li> <li> <p><a href="https://eur-lex.europa.eu/eli/reg/2022/868/oj?locale=en">Regulation (EU) 2022/868 of the European Parliament and of the Council of 30 May 2022 on European data governance and amending Regulation (EU) 2018/1724 (<strong>Data Governance Act</strong>) (Text with EEA relevance)</a></p> </li> <li> <p><a href="https://eur-lex.europa.eu/eli/reg/2022/1925/oj?locale=en">Regulation (EU) 2022/1925 of the European Parliament and of the Council of 14 September 2022 on contestable and fair markets in the digital sector and amending Directives (EU) 2019/1937 and (EU) 2020/1828 (<strong>Digital Markets Act</strong>) (Text with EEA relevance)</a></p> </li> <li> <p><a href="https://eur-lex.europa.eu/eli/reg/2022/2065/oj?locale=en">Regulation (EU) 2022/2065 of the European Parliament and of the Council of 19 October 2022 on a Single Market For Digital Services and amending Directive 2000/31/EC (<strong>Digital Services Act</strong>) (Text with EEA relevance)</a></p> </li> <li> <p><a href="https://eur-lex.europa.eu/eli/reg_impl/2023/138/oj?locale=en">Commission Implementing Regulation (EU) 2023/138 of 21 December 2022 laying down a list of specific <strong>high-value datasets</strong> and the arrangements for their publication and re-use (Text with EEA relevance)</a></p> </li> <li> <p><a href="https://eur-lex.europa.eu/eli/reg/2023/2854/oj?locale=en">Regulation (EU) 2023/2854 of the European Parliament and of the Council of 13 December 2023 on harmonised rules on fair access to and use of data and amending Regulation (EU) 2017/2394 and Directive (EU) 2020/1828 (<strong>Data Act</strong>)</a></p> </li> <li> <p><a href="https://eur-lex.europa.eu/eli/reg/2024/903/oj?locale=en">Regulation (EU) 2024/903 of the European Parliament and of the Council of 13 March 2024 laying down measures for a high level of public sector interoperability across the Union (<strong>Interoperable Europe Act</strong>)</a></p> </li> <li> <p><a href="https://eur-lex.europa.eu/eli/reg/2024/1689/oj?locale=en">Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (<strong>Artificial Intelligence Act</strong>) Text with EEA relevance.</a></p> </li> <li><a href="https://eur-lex.europa.eu/eli/reg/2024/2847/oj?locale=en">Regulation (EU) 2024/2847 of the European Parliament and of the Council of 23 October 2024 on horizontal cybersecurity requirements for products with digital elements and amending Regulations (EU) No 168/2013 and (EU) 2019/1020 and Directive (EU) 2020/1828 (<strong>Cyber Resilience Act</strong>) (Text with EEA relevance)</a></li> </ol>
S68 | HSDBTPS | Transformation Products Extracted from HSDB Content in PubChem
<p>This is the collection associated with list S68 HSDBTPS Transformation Products Extracted from HSDB Content in PubChem on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>HSDBTPS is a list of metabolites / transformation products extracted from the "Metabolites/Metabolism" section from HSDB (Hazardous Substance Data Bank) in PubChem (<a href="https://pubchem.ncbi.nlm.nih.gov/source/11933">https://pubchem.ncbi.nlm.nih.gov/source/11933</a>). Dataset DOI: <a href="https://doi.org/10.5281/zenodo.3827487">10.5281/zenodo.3827487</a>.</p> <p>Entries automatically extracted from HSDB are manually validated to remove mismatching CIDs, and add additional CIDs not captured using the descriptions provided. Files are created with a default to not import any data until this has been checked. Please report any mismatches, despite best efforts it is possible that errors are present, all files are under version control so that entries can be corrected/updated/enhanced over time and recommitted.</p> <p>Recent uploads (2022 and later) are handled with ShinyTPs (<a href="https://gitlab.lcsb.uni.lu/eci/shinytps">code</a> + article from Palm et al 2023 DOI:<a href="https://doi.org/10.1021/acs.estlett.3c00537">10.1021/acs.estlett.3c00537</a>). The original code associated with this deposit is located <a href="https://gitlab.lcsb.uni.lu/eci/pubchem/-/tree/master/annotations/tps/">here</a>. </p> <p>Updates:</p> <p>16 May 2020: Added the source file (extract of all HSDB Metabolites/Metabolism entries as is from JSON file), plus an updated S68_HSDBTPS_StructInfoOnly.csv with one new structure and renamed to include S68, plus the first draft of the Transformations table. 28 May 2020: added InChIKey and DIXSID files. 11 June 2020: added updated Transformations and Structure files to contain new CIDs and resulting reactions from new PubChem registrations. Dec 23, 2022: first dataset from Emma Palm added, using her TP curation app. DTXSID file dropped. 23 Mar 2023: added Biosystem and Enzyme columns, filling in biosystem where appropriate. 24 Mar 2023: new azo dye reactions from Emma Palm added. 1 Apr 2023: more azo dye reactions from Emma Palm. 4 Apr 2023: added new CIDs. 27 June 2023: added many new substances, including new CIDs. 28 June 2023 added transformations, 30 June 2023 added new CIDs. 11 July added missing CIDs to transformations table. 18 Nov 2023: added new reactions from Jolly Komolo (no new CIDs). 1 Dec 2023: new reactions from Marie, incl. two new CIDs. 19 Dec 2023: new reactions from Marie, incl. 1 new CID, updated CIDs from Dec 1. 29 April 2024: new reactions from Marie, CID updated. 16 July 2024: added reactions from Olga. 26 July 2024: added BPA=>MBP. 6 Aug 2024: adjusted many triazine names, added one new reaction. 27 Nov 2024: added Griseofulvin reactions. 8 Feb 2025: added Sertraline TP. 14 Mar 2025: added missing CID for O-Demethyl phosphamidon.</p>
WikiMed and PubMedDS: Two large-scale datasets for medical concept extraction and normalization research
<p>Two large-scale, automatically-created datasets of medical concept mentions, linked to the <a href="https://uts.nlm.nih.gov/uts/umls/home">Unified Medical Language System (UMLS)</a>.</p> <p><strong>WikiMed</strong></p> <p>Derived from Wikipedia data. Mappings of Wikipedia page identifiers to UMLS Concept Unique Identifiers (CUIs) was extracted by crosswalking Wikipedia, Wikidata, Freebase, and the NCBI Taxonomy to reach existing mappings to UMLS CUIs. This created a 1:1 mapping of approximately 60,500 Wikipedia pages to UMLS CUIs. Links to these pages were then extracted as mentions of the corresponding UMLS CUIs.</p> <p>WikiMed contains:</p> <ul> <li>393,618 Wikipedia page texts</li> <li>1,067,083 mentions of medical concepts</li> <li>57,739 unique UMLS CUIs</li> </ul> <p>Manual evaluation of 100 random samples of WikiMed found 91% accuracy in the automatic annotations at the level of UMLS CUIs, and 95% accuracy in terms of semantic type.</p> <p><strong>PubMedDS</strong></p> <p>Derived from biomedical literature abstracts from <a href="https://pubmed.ncbi.nlm.nih.gov/">PubMed</a>. Mentions were automatically identified using distant supervision based on Medical Subject Heading (MeSH) headers assigned to the papers in PubMed, and recognition of medical concept mentions using the high-performance <a href="https://allenai.github.io/scispacy/">scispaCy</a> model. MeSH header codes are included as well as their mappings to UMLS CUIs.</p> <p>PubMedDS contains:</p> <ul> <li>13,197,430 abstract texts</li> <li>57,943,354 medical concept mentions</li> <li>44,881 unique UMLS CUIs</li> </ul> <p>Comparison with existing manually-annotated datasets (NCBI Disease Corpus, BioCDR, and MedMentions) found 75-90% precision in automatic annotations. Please note this dataset is <em>not </em>a comprehensive annotation of medical concept mentions in these abstracts (only mentions located through distant supervision from MeSH headers were included), but is intended as data for <em>concept n</em><em>ormalization</em> research.</p> <p>Due to its size, PubMedDS is distributed as 30 individual files of approximately 1.5 million mentions each.</p> <p><strong>Data format</strong></p> <p>Both datasets use JSON format with one document per line. Each document has the following structure:</p> <pre><code class="language-json">{ "_id": "A unique identifier of each document", "text": "Contains text over which mentions are ", "title": "Title of Wikipedia/PubMed Article", "split": "[Not in PubMedDS] Dataset split: <train/test/valid>", "mentions": [ { "mention": "Surface form of the mention", "start_offset": "Character offset indicating start of the mention", "end_offset": "Character offset indicating end of the mention", "link_id": "UMLS CUI. In case of multiple CUIs, they are concatenated using '|', i.e., CUI1|CUI2|..." }, {} ] }</code></pre> <p><strong>Version history</strong></p> <table align="left"> <thead> <tr> <th scope="col">Version</th> <th scope="col">Notes</th> </tr> </thead> <tbody> <tr> <td>1.0.0</td> <td>Initial release</td> </tr> <tr> <td>1.0.1</td> <td>Corrected duplication error in WikiMed.zip file</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p>
Front polylines extracted from DWD Maps
<p>Weather fronts extracted from surface analysis maps of the German Weather Service (Deutscher Wetterdienst, DWD) for the years 2015 to 2020</p> <p>Fronts are described by an identifier (warm, cold, occ) followed by several [lat, lon] coordinate pairs in degree [North, East]</p> <p>Stationary-Fronts are extracted as either cold or warm fronts</p> <p>e.g.</p> <p>warm [75.69211706913667, 29.558180185836815] [75.12348547753966, 26.89164117647018] ...</p> <p>No guarantee is given regarding correctness or completeness of extracted fronts</p>
Model America - Chicago Archetype extract from ORNL's AutoBEM
<p>Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (<a href="https://bit.ly/AutoBEM">bit.ly/AutoBEM</a>).</p> <p><strong>Critical note: Building multipliers and models will be updated soon.</strong></p> <p>Archetype metadata, models, and multipliers are provided for 93 building <strong>archetypes</strong> located within the city of Chicago (United States):</p> <p> </p> <ol> <li><strong>Data (12KB *.csv) - minimalist list of each building (rows) for the following fields (columns)</strong> <ol> <li>ID - unique building ID</li> <li>Area - estimate of total conditioned floor area (ft<sup>2</sup>)</li> <li>CZ - ASHRAE Climate Zone designation</li> <li>Height - building height (ft)</li> <li>NumFloors - number of floors (above-grade) (IECC = Residential)</li> <li>BuildingType - DOE prototype building designation (IECC=residential) as implemented by OpenStudio-standards</li> <li>Standard - building vintage</li> <li>WWR_surfaces - percent of each facade (pair of points from Footprint2D) covered by fenestration/windows (average 14.5% for residential, 40% for commercial buildings)</li> <li>Area2D - footprint area (ft<sup>2</sup>)</li> <li>Num_build_per_zone - Number of this building type/vintage in WRF zone</li> <li>Total_zone_area - Total area of this building type/vintage in WRF zone (ft<sup>2</sup>)</li> <li>Area_multiplier - Scaling factor for building type/vintage for building in WRF zone</li> </ol> </li> <li><strong>Models (7.69MB *.zip) - EnergyPlus building energy models named according to ID</strong> <ul> <li>Each model has approximately 3,000 building input descriptors that can be extracted. Please see the EnergyPlus (v9.4) 2,784-page <a href="https://energyplus.net/sites/all/modules/custom/nrel_custom/pdfs/pdfs_v9.4.0/InputOutputReference.pdf">Input/Output Reference Guide </a>for everything that can be retrieved or simulated from these models.</li> </ul> </li> </ol>
Scholarly articles written by women extracted from Indexes of Archaeological Papers (1891-1907) and Gomme's Index of Archaeological Papers 1665-1890
<p>The dataset `List-of-Women-in-Archaeological-Indexes_cleaned.tsv` contains scholarly articles written by women extracted from annual <em>Indexes of Archaeological Papers</em> published between 1891 and 1907 inclusive and George Laurence Gomme's <a href="https://archive.org/details/indexofarchaeolo00gommrich/page/n5/mode/2up"><em>Index of Archaeological Papers 1665-1890</em></a>, referred to hereafter as the source datasets. These Indexes were published in London, initially by the Congress of Archaeological Societies directly, and from 1898 by Archibald Constable & Co. The Indexes were sent to Societies subscribing to the Congress, but could also be acquired separately. A list of indexes consulted in available on our <a href="https://www.zotero.org/groups/4509023/beyond_notability/collections/FDKSUL8J">Zotero library</a>.</p> <p>The dataset is published in .tsv and .xslx formats.</p> <p>This is v2 of the dataset, including some cleaned publication titles and the addition of the socities that published each journal (v2.1 fixes a faulty dataset export in v2).</p>
Data for: Prior exposure of a fungal parasite to cyanobacterial extracts does not impair infection of its Daphnia host
<p>This dataset supports the findings of the study 'Prior exposure of a fungal parasite to cyanobacterial extracts does not impair infection of its <em>Daphnia</em> host', published in Hydrobiologia (https://doi.org/10.1007/s10750-022-04889-7)</p>
DrCyZ: Techniques for analyzing and extracting useful information from CyZ.
<p>DrCyZ: Techniques for analyzing and extracting useful information from CyZ.</p> <p>Samples from NASA Perseverance and set of GAN generated synthetic images from Neural Mars.</p> <p>Repository: <a href="https://github.com/decurtoidiaz/drcyz">https://github.com/decurtoidiaz/drcyz</a></p> <p><br> Subset of samples from (includes tools to visualize and analyse the dataset):</p> <p>CyZ: MARS Space Exploration Dataset. [<a href="https://doi.org/10.5281/zenodo.5655473">https://doi.org/10.5281/zenodo.5655473</a>]</p> <p>Images from NASA missions of the celestial body.</p> <p>Repository: <a href="https://github.com/decurtoidiaz/cyz">https://github.com/decurtoidiaz/cyz</a></p> <p>Authors:</p> <p>J. de Curtò c@decurto.be</p> <p>I. de Zarzà z@dezarza.be</p> <p>------------------------------------------<br> File Information from DrCyZ-1.1<br> ------------------------------------------</p> <p> • Subset of samples from Perseverance (drcyz/c).<br> ∙ png (drcyz/c/png).<br> PNG files (5025) selected from NASA Perseverance (CyZ-1.1) after t-SNE and K-means Clustering. <br> ∙ csv (drcyz/c/csv).<br> CSV file.</p> <p> • Resized samples from Perseverance (drcyz/c+).<br> ∙ png 64x64; 128x128; 256x256; 512x512; 1024x1024 (drcyz/c+/drcyz_64-1024).<br> PNG files resized at the corresponding size. <br> ∙ TFRecords 64x64; 128x128; 256x256; 512x512; 1024x1024 (drcyz/c+/tfr_drcyz_64-1024).<br> TFRecord resized at the corresponding size to import on Tensorflow.</p> <p> • Synthetic images from Neural Mars generated using Stylegan2-ada (drcyz/drcyz+).<br> ∙ png 100; 1000; 10000 (drcyz/drcyz+/drcyz_256_100-10000)<br> PNG files subset of 100, 1000 and 10000 at size 256x256.</p> <p> • Network Checkpoint from Stylegan2-ada trained at size 256x256 (drcyz/model_drcyz).<br> ∙ network-snapshot-000798-drcyz.pkl</p> <p> • Notebooks in python to analyse the original dataset and reproduce the experiments; K-means Clustering, t-SNE, PCA, synthetic generation using Stylegan2-ada and instance segmentation using Deeplab (<a href="https://github.com/decurtoidiaz/drcyz/tree/main/dr_cyz+">https://github.com/decurtoidiaz/drcyz/tree/main/dr_cyz+</a>).<br> ∙ clustering_curiosity_de_curto_and_de_zarza.ipynb<br> K-means Clustering and PCA(2) with images from Curiosity.<br> ∙ clustering_perseverance_de_curto_and_de_zarza.ipynb<br> K-means Clustering and PCA(2) with images from Perseverance.<br> ∙ tsne_curiosity_de_curto_and_de_zarza.ipynb<br> t-SNE and PCA (components selected to explain 99% of variance) with images from Curiosity.<br> ∙ tsne_perseverance_de_curto_and_de_zarza.ipynb<br> t-SNE and PCA (components selected to explain 99% of variance) with images from Perseverance.<br> ∙ Stylegan2-ada_de_curto_and_de_zarza.ipynb<br> Stylegan2-ada trained on a subset of images from NASA Perseverance (DrCyZ).<br> ∙ statistics_perseverance_de_curto_and_de_zarza.ipynb<br> Compute statistics from synthetic samples generated by Stylegan2-ada (DrCyZ) and images from NASA Perseverance (CyZ).<br> ∙ DeepLab_TFLite_ADE20k_de_curto_and_de_zarza.ipynb<br> Example of instance segmentation using Deeplab with a sample from NASA Perseverance (DrCyZ).</p>
Relative abundance of the CHC extracts of each population replicate's of I. uriae ticks from Iceland after log centered ratio transformation
<p>Relative abundance of the CHC extracts of each population replicate’s of I. uriae ticks from Iceland after log centered ratio transformation.</p>
TBGA: A Large-Scale Gene-Disease Association Dataset for Biomedical Relation Extraction
<p>This repository contains the TBGA dataset. TBGA is a large-scale, semi-automatically annotated dataset for Gene-Disease Association (GDA) extraction. The dataset consists of three text files, corresponding to train, validation, and test sets, plus an additional JSON file containing the mapping between relation names and IDs. Each record in train, validation, or test files corresponds to a single GDA extracted from a sentence. Records are represented as JSON objects with the following structure:</p> <ul> <li><strong>text:</strong> sentence from which the GDA was extracted.</li> <li><strong>relation:</strong> relation name associated with the given GDA.</li> <li><strong>h: </strong>JSON object representing the gene entity, composed of: <ul> <li><strong>id: </strong>NCBI Entrez ID associated with the gene entity.</li> <li><strong>name:</strong> NCBI official gene symbol associated with the gene entity.</li> <li><strong>pos: </strong>list consisting of starting position and length of the gene mention within text.</li> </ul> </li> <li><strong>t:</strong> JSON object representing the disease entity, composed of: <ul> <li><strong>id: </strong>UMLS CUI associated with the disease entity.</li> <li><strong>name:</strong> UMLS preferred term associated with the disease entity.</li> <li><strong>pos:</strong> list consisting of starting position and length of the disease mention within text.</li> </ul> </li> </ul> <p>TBGA contains over 200,000 instances and 100,000 bags.<br> The zip file consists of one folder, named TBGA, containing the files corresponding to the dataset.</p> <p>If you use or extend our work, please cite the following: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-022-04646-6#citeas<br> TBGA paper can be found at: <a href="https://rdcu.be/cKkY2">https://rdcu.be/cKkY2</a><br> TBGA code is available at: https://github.com/GDAMining/gda-extraction</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.