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3,684 results for “extract”
Teaser Local Climate Zone maps extracted from the global map of Local Climate Zones.
<p>Local Climate Zone teaser maps for the 15 largest functional urban areas stratified by urban ecoregion. The data is extracted from the global 100 m spatial resolution LCZ map, and is used to plot Figures 5, 6 and 7 of Demuzere et al. (2022a). The full global LCZ map is available via Demuzere et al. (2022b).<br> <br> See readme.txt for more details.</p> <p><em>Demuzere, M., Kittner, J., Martilli, A., Mills, G., Moede, C., Stewart, I. D., Vliet, J. van, & Bechtel, B. (2022a). A global map of Local Climate Zones to support earth system modelling and urban scale environmental science. Earth Syst. Sci. Data Discuss.<br> Demuzere M, Kittner J, Martilli A, et al. Global map of Local Climate Zones. Zenodo. 2022b. doi:10.5281/zenodo.6364594</em></p>
Event Registry dataset with multiple extracted features (both sparse and dense) and degraded by OCR
<p>This is the same dataset as:</p> <p>Guillaume Bernard. (2022). Event Registry dataset with multiple extracted features (both sparse and dense) (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6630367</p> <p>But with texts degraded by OCR as described in:</p> <p>Guillaume Bernard. (2022). Event Registry dataset texts with OCR degradations and synthesised segmentation (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6631305</p>
Event Registry titles dataset with multiple extracted features (both sparse and dense) and degraded by OCR
<p>This is the same dataset as:</p> <p>Guillaume Bernard. (2022). Event Registry titles only dataset with multiple extracted features (both sparse and dense) (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6630447</p> <p>But with texts degraded by OCR as described in:</p> <p>Guillaume Bernard. (2022). Event Registry titles dataset texts with OCR degradations (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6630828</p>
FibVid dataset with multiple extracted features (both sparse and dense) and degraded by OCR
<p>This is the same dataset as:</p> <p>Guillaume Bernard. (2022). Fibvid dataset with multiple extracted features (both sparse and dense) (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6630409</p> <p>But with texts degraded by OCR as described in:</p> <p>Guillaume Bernard. (2022). FibVid dataset texts with OCR degradations (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6630758</p>
DISTANT-CTO: A Zero Cost, Distantly Supervised Approach to Improve Low-Resource Entity Extraction Using Clinical Trials Literature
<p><strong>Datasets</strong></p> <ol> <li>DISTANT-CTO is a weakly-labelled dataset of 'Intervention' and 'Comparator' entity annotated sentences. The dataset was obtained using candidate generation the approach described in "DISTANT-CTO: A Zero Cost, Distantly Supervised Approach to Improve Low Resource Entity Extraction Using Clinical Trials Literature". <ol> <li>distantcto_high_conf.txt - ds conf 1.0 (full dataset)</li> <li>extraction1_pos_posnegtrail_conf09.txt - ds conf 0.9 (partial dataset)</li> </ol> </li> <li>The physio test set is a dataset comprising 153 PICO annotated randomized controlled trial abstracts from Physiotherapy and Rehabilitation. This dataset was used as an additional benchmark to evaluate the generalization power of the weakly annotated dataset and NER model for this sub-domain.</li> </ol> <p> </p> <p><strong>Utility</strong></p> <p>The dataset could be used as an input for training 'Intervention' named-entity recognition (NER) models.</p> <p> </p> <p><strong>Availability</strong></p> <p>This directory includes extraction1_pos_posnegtrail_conf09.txt - This text data file contains all the weak annotations (source intervention terms mapped onto target sentences) from clinicaltrials.org (CTO) with a confidence score of 0.9 and above.</p> <p>The directory also includes ‘physio_sent_annot2POS_posnegtrail.txt’ – This data file contains manually annotated (Intervention entity) data from the physiotherapy and rehabilitation domain. It follows a roughly similar structure as described in the ‘Description for long targets’ section. (‘Participant’ and ‘Outcome’ annotations are removed from this file)</p>
Extracted MSR GitHub Repository URLs
<p>This dataset contains text files of <a href="https://github.com">GitHub</a> URLs pointing to hosted git repositories.</p> <p>These URLs come from mining software repository (MSR) datasets. URLs are built by taking the repository owner's name (OWNER) and it's name (REPO) and appending them to https://github.com/. There is one URL per line. <em>URLs have not been tested for their current availibility</em>. An example URL format is provided below:</p> <pre><code>https://github.com/OWNER/REPO</code></pre> <p> Current URLs are from the following datasets:</p> <ul> <li>libraies.io January 12th, 2020 dataset <ul> <li>Jeremy Katz, "Libraries.io Open Source Repository and Dependency Metadata". Zenodo, Jan. 12, 2020. doi: 10.5281/zenodo.3626071.</li> </ul> </li> <li>RepoReapers/reaper dataset <ul> <li>Munaiah, N., Kroh, S., Cabrey, C. et al. Curating GitHub for engineered software projects. Empir Software Eng 22, 3219–3253 (2017). https://doi.org/10.1007/s10664-017-9512-6</li> </ul> </li> <li>GH Torrent dataset <ul> <li>G. Gousios, “The GHTorent dataset and tool suite,” in <em>Proceedings of the 10th Working Conference on Mining Software Repositories</em>, San Francisco, CA, USA, May 2013, pp. 233–236.</li> </ul> </li> </ul>
EconBiz Images for Text Extraction from Scholarly Figures
<p>Scholarly figures are data visualizations like bar charts, pie charts, line graphs, maps, scatter plots or similar figures. Text extraction from scholarly figures is useful in many application scenarios, since text in scholarly figures often contains information that is not present in the surrounding text. This dataset is a corpus of 121 scholarly figures from the economics domain evaluating text extraction tools. We randomly extracted these figures from a corpus of 288,000 open access publications from <a href="https://www.econbiz.de/">EconBiz</a>. The dataset resembles a wide variety of scholarly figures from bar charts to maps. We manually labeled the figures to create the gold standard.</p> <p>We adjusted the provided gold standard to have a uniform format for all datasets. Each figure is accompanied by a TSV file (tab-separated values) where each entry corresponds to a text line which has the following structure:</p> <ul> <li>X-coordinate of the center of the bounding box in pixel</li> <li>Y-coordinate of the center of the bounding box in pixel</li> <li>Width of the bounding box in pixel</li> <li>Height of the bounding box in pixel</li> <li>Rotation angle around its center in degree</li> <li>Text inside the bounding box</li> </ul> <p>In addition we provide the ground truth in JSON format. A schema file is included in each dataset as well. The dataset is accompanied with a ReadMe file with further information about the figures and their origin.</p> <p>If you use this dataset in your own work, please cite one of the papers in the references.</p>
S118 | PFASFCCMIGEX | 68 PFAS in Migrating & Extractable Food Contact Chemicals (FCCmigex)
<p>This is the collection associated with list S118 PFASFCCMIGEX 68 PFAS in Migrating & Extractable Food Contact Chemicals (FCCmigex) 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>List of the 68 PFASs identified in migrating and extractable food contact chemicals (FCCmigex- see <a href="../records/10551195">S112</a>) by the Food Packaging Forum based on 47 studies as published in Phelps et al (2024) DOI:<a href="https://pubs.acs.org/doi/10.1021/acs.est.3c03702">10.1021/acs.est.3c03702</a>.</p>
Models and Datasets for "Extracting Paleoweather from Paleoclimate: A Deep Learning Reconstruction of Northern Hemisphere Summertime Atmospheric Blocking over the Last Millennium"
<p><strong>Associated publication:</strong> <em>Karamperidou, C., Extracting Paleoweather from Paleoclimate: A Deep Learning Reconstruction of Northern Hemisphere Summertime Atmospheric Blocking over the Last Millennium, Nature Communications Earth & Environment, (2024)</em></p> <p> </p> <p><strong>This repository contains:</strong></p> <ul> <li>the architecture and weights of PaleoBlockNet v1.0</li> <li>the following ensemble DL reconstructions of JJA frequency of blocked days inferred by PaleoBlockNet: <ol> <li>the 10-member NTREND-based DL reconstruction; uses as input the NTREND DA N.Hemisphere MJJA surface temperature anomaly by King et al. (2021)</li> <li>the 100-member PHYDA-based DL reconstruction; uses as input the PHYDA JJA surface temperature anomaly by Steiger et al. (2018)</li> <li>the 12-member LME-based DL reconstruction; uses as input the CESM-LME surface temperature anomaly; this is a sensitivity experiment (see publication for details).</li> </ol> </li> <li>Integrated Gradients that assign importance to the input features for PaleoblockNet's blocking inferences </li> <li>train-validate-test samples to use with sample scripts from the Gituhub repo github/ckaramp-research/paleoblocknet</li> </ul> <p> </p> <p><strong>If you use this dataset, please cite the associated publication and the present repository.</strong></p> <p>To <strong>interactively explore</strong> the datasets, a web interface has been developed and can be accessed at <a href="https://www2.hawaii.edu/~ckaramp/paleoblocknet">https://www2.hawaii.edu/~ckaramp/paleoblocknet</a></p> <p>Contact the author Christina Karamperidou (<a title="Karamperidou Research Group" href="https://www2.hawaii.edu/~ckaramp" target="_blank" rel="noopener">https://www2.hawaii.edu/~ckaramp</a>) for more information about the details of these datasets.</p>
Improving Hypernymy Extraction with Distributional Semantic Classes
<p>In this paper, we show for the first time how distributionally-induced semantic classes can be helpful for extraction of hypernyms. We present a method for (1) inducing sense-aware semantic classes using distributional semantics and (2) using these induced semantic classes for filtering noisy hypernymy relations. Denoising of hypernyms is performed by labeling each semantic class with its hypernyms. On one hand, this allows us to filter out wrong extractions using the global structure of the distributionally similar senses. On the other hand, we infer missing hypernyms via label propagation to cluster terms. We conduct a large-scale crowdsourcing study showing that processing of automatically extracted hypernyms using our approach improves the quality of the hypernymy extraction both in terms of precision and recall. Furthermore, we show the utility of our method in the domain taxonomy induction task, achieving the state-of-the-art results on a benchmarking dataset.</p> <p>This particular page contains datasets related to the paper. Namely the input induced word senses, a database of hypernyms, and the output clusters of senses labeled with hypernyms -- the distributional semantic classes. The semantic classes are of two granularities, as described in the paper (coarse and fine grained). </p>
MMoveT15: A Twitter Dataset for Extracting and Analysing Migration-Movement Data of the European Migration Crisis 2015
<p>In the 2015 migration crisis thousands of refugees and migrants crossed the border to Hungary, Austria and Germany. The movements of these people are reflected in social media, especially on Twitter. We present a dataset of 3275 Tweets form the months September and October 2015. These Tweets are annotated regarding their relevance to the quantitative movement of refugees/migrants into Hungary, Austria and Germany. We present this dataset for a posterior analysis of the 2015 migration crisis or as a basis for an early warning or forecasting system</p>
A Semi-Automatic Classification Approach for River Shape Extraction from Sentinel-2 Imagery
<p>To extract the river from satellite imagery, at first we have to classify the waterbody from satellite imagery. Then we will differentiate the river from waterbody. We have used three different techniques to classify the waterbody from satellite imagery. At first, pixel based iso-cluster unsupervised classification was used to classify the waterbody in Sentinel 2 imagery. We excluded the supervised classification in decided methodology as we are interested about automatic process of river extraction. Then we have used Segment mean shift classification tool as image segmentation techniques. Indices based NDWI (Normalize difference water indices) classification was also used in this research.</p>
Extract from the Berlin State Library's Main Catalog
<p>The data set is based on the main catalog of the library. Currently, the following fields are extracted:</p> <ul> <li>title</li> <li>author (+ optional GND ID)</li> <li>publisher</li> <li>place of publication</li> <li>country of publication</li> <li>year of publication</li> </ul> <p>The extract has been created by the processPicaPlus script available <a href="https://github.com/elektrobohemian/StabiHacks">here</a>. Attention, some special characters might not have been extracted correctly in versions <1.0.0.</p> <p><em>Change Log:</em></p> <p>0.2.0 </p> <p>fixes various encoding issues for non-ASCII characters</p> <p>0.3.0 </p> <p>added year of publication; added separate files for languages: rus, pol, rum, cze, slo, gre with minor encoding issues</p> <p> </p> <p><em>Dataset Characteristics</em></p> <p>The following languages are available in separate data files:</p> <ul> <li>eng</li> <li>ger</li> <li>lat</li> <li>fre</li> <li>ita</li> <li>spa</li> <li>por</li> <li>dut</li> <li>swe</li> <li>dan</li> <li>nor</li> <li>ice</li> <li>fry</li> <li>rus*</li> <li>pol*</li> <li>rum*</li> <li>cze*</li> <li>slo*</li> <li>gre*</li> </ul> <p>*: Language file might be subject to character encoding issues.</p> <p>The other languages are present in the data set but have not been separated, i.e., they are combined in one data file:</p> <p>'fre', 'rus', 'pol', 'ger', 'eng', 'lit', 'dan', 'dut', 'spa', 'swe', 'ita', 'lat', 'nor', 'ind', 'bul', 'grc', 'fry', 'rum', 'cze', 'slo', 'bel', 'ice', 'fin', 'gre', 'hun', 'tur', 'enm', 'hrv', 'est', 'srp', 'roh', 'syr', 'wen', 'mal', 'afr', 'slv', 'mac', 'smi', 'nds', 'qmw', 'pra', 'oci', 'bre', 'san', 'alb', 'baq', 'non', 'ara', 'chm', 'per', 'cat', 'gmh', 'sla', 'arm', 'ukr', 'por', 'chu', 'heb', 'arc', 'gle', 'tib', 'lav', 'geo', 'crp', 'hin', 'mul', 'chi', 'epo', 'kor', 'kan', 'vot', 'csb', 'glg', 'kaz', 'frm', 'jpn', 'bur', 'srd', 'sal', 'ira', 'bos', 'mol', 'rom', 'tat', 'aze', 'yid', 'mar', 'mak', 'pli', 'rys', 'tgk', 'map', 'vie', 'tuk', 'oss', 'ota', 'tut', 'ben', 'sun', 'tir', 'bak', 'chv', 'ber', 'khm', 'may', 'pan', 'uzb', 'swa', 'kir', 'egy', 'dum', 'nep', 'cop', 'mon', 'tam', 'urd', 'zxx', 'wel', 'mis', 'ng', 'goh', 'dt', 'en', 'fao', 'fro', 'pus', 'kur', 'cus', 'hau', 'uig', 'sit', 'dt.', 'cpf', 'tgl', 'qoj', 'tag', 'raj', 'fiu', 'xal', 'kbd', 'udm', 'scr', 'gag', 'kas', 'scc', 'pro', 'tha', 'dar', 'dr', 'sna', 'ewe', 'de', 'dra', 'ang', 'ine', 'zza', 'und', 'ave', 'amh', 'crh', 'jav', 'cpe', 'akk', 'dsb', 'qce', 'guj', 'ltz', 'got', 'bua', 'peo', 'mdr', 'nob', 'ava', 'che', 'sux', 'kok', 'zap', 'nl', 'inc', 'sah', 'gem', 'law', 'bem', 'sin', 'qdo', 'hsb', 'som', 'lao', 'kam', 'kom', 'abk', 'roa', 'cau', 'ady', 'bat', 'mlt', 'sai', 'xho', 'paa', 'sot', 'bnt', 'lug', 'myn', 'kar', 'qhe', 'kin', 'zul', 'tsn', 'apa', 'nso', 'yao', 'yor', 'bih', 'nog', 'nap', 'loz', 'nbl', 'kon', 'nya', 'snh', 'chn', 'run', 'suk', 'fur', 'osa', 'bra', 'den', 'kpe', 'kal', 'tig', 'wol', 'gla', 'lad', 'mos', 'cre', 'krc', 'ge', 'fr', 'dak', 'fij', 'mad', 'srr', 'kum', 'her', 'nai', 'cel', 'inh', 'kro', 'hit', 'pal', 'tmh', 'tsw', 'bam', 'kab', 'kik', 'kua', 'lub', 'luo', 'nub', 'tem', 'znd', 'mai', 'tai', 'qkr', 'ful', 'man', 'lol', 'sag', 'tog', 'hai', 'arg', 'fat', 'nav', 'niu', 'ibo', 'ido', 'men', 'qju', 'gaa', 'vol', 'nah', 'mlg', 'nic', 'ijo', 'sus', 'orm', 'smo', 'mag', 'tyv', 'mnc', 'cos', 'mdf', 'kaa', 'dua', 'gez', 'ton', 'ven', 'snd', 'syc', 'nym', 'nia', 'sem', 'chg', 'fan', 'twi', 'mas', 'ina', 'ile', 'art', 'ori', 'qai', 'arw', 'mao', 'bas', 'kmb', 'tiv', 'bal', 'tar', 'tpi', 'abs', 'asm', 'qqa', 'iku', 'min', 'rup', 'tel', 'or', 'tah', 'aka', 'day', 'qqg', 'lah', 'lus', 'sio', 'oto', 'alg', 'shn', 'ndo', 'haw', 'tso', 'mus', 'cai', 'qev', 'new', 'zha', 'grn', 'khi', 'ssw', 'nde', 'bla', 'grb', 'mun', 'din', 'sam', 'mwr', 'cor', 'sat', 'cho', 'ger,', 'que', 'btk', 'glv', 'rar', 'jk', 'nno', 'cmc', 'mga', 'jw', 'iro', 'sog', 'hat', 'dzo', 'mkh', 'bik', 'ban', 'ilo', 'pam', 'ts', 'sme', 'myv', 'qnn', 'jpr', 'qte', 'yap', 'bis', 'sga', 'qkj', 'pap', 'ath', 'ipk', 'phi', 'sco', 'del', 'moh', 'iri', 'gae', 'ryl', 'our', 't--', 'grk', 'ssa', 'awa', 'efi', 'jrb', 'enk', 'kru', 'oji', 'arn', 'car', 'gsw', 'lez', 'war', 'ace', 'qrn', 'wln', 'ceb', 'aar', 'bug', 'kaw', 'chr', 'cpp', 'tet', 'aym', 'ces', 'hmo'</p>
Extracted Illustrations of the Berlin State Library's Digitized Collections (part 1 of 4)
<p>The dataset consists of various illustrations extracted from 26,233 historical books and other media offered in the Berlin State Library's Digitized Collections. The media objects are older than 1920.</p> <p>Version 1.0 contains of 594,890 extracted illustrations in total.</p> <p>The extraction of illustrations is driven by the coordinates given by the ABBYY FineReader OCR engine (in ALTO XML) . The extracted illustrations have not been resized but compressed and saved in JPEG format.</p> <p>Pre-trained models in order to separate color scales, hand-written signatures, library stamps or the like from interesting content are available under: <a href="https://github.com/elektrobohemian/imi-unicorns">https://github.com/elektrobohemian/imi-unicorns</a>.</p> <p>The extracts for each media object are stored in separated sub-folders and tar files named after the PPN (a unique ID used in the library) to facilitate further processing. Additional metadata can be obtained with help of the PPN as described here: <a href="https://github.com/elektrobohemian/StabiHacks/blob/master/ppn-howto.md">https://github.com/elektrobohemian/StabiHacks/blob/master/ppn-howto.md</a> .</p> <p>The dataset is published as a set of ZIP files, each fitting on a Blu Ray disc. <strong>After decompression, the contents will consume ca. 166 GB.</strong></p> <p><em>Change Log for Version</em></p> <ol> <li>original dataset</li> <li>added color histograms (RGB, separated by channel) in JSON and Python pickle format as extracted by the <a href="https://pillow.readthedocs.io/en/stable/">Pillow</a> package (see https://github.com/elektrobohemian/StabiHacks/tree/master/image-tools)</li> </ol> <p><strong>This is part 1 of 4. The following datasets contain the other ZIP files (8 files in total):</strong></p> <ul> <li><a href="https://doi.org/10.5281/zenodo.2598145">https://doi.org/10.5281/zenodo.2598145</a></li> <li><a href="https://doi.org/10.5281/zenodo.2598261">https://doi.org/10.5281/zenodo.2598261</a></li> <li><a href="https://doi.org/10.5281/zenodo.2598270">https://doi.org/10.5281/zenodo.2598270</a></li> </ul> <p> </p>
MiRoR11 - P2 - Annotated corpus for primary and reported outcomes extraction
<p>Annotated corpus for outcome extraction</p> <p>This folder contains 2 subfolders:<br> 1. Primary_outcomes - a corpus annotated for primary outcomes<br> The folder contains the following files:<br> po_sent_marked_p1_1000.txt - sentences 1 - 1000 of the annotated corpus, ConstruKT format; coordinated outcomes annotated as single entity<br> po_sent_marked_p2_1000.txt - sentences 1001 - 2000 of the annotated corpus, ConstruKT format; coordinated outcomes annotated as single entity</p> <p>po_sent_marked_col_p1.txt - sentences 1 - 1000 of the annotated corpus, tabulated format; coordinated outcomes annotated as single entity<br> po_sent_marked_col_p2.txt - sentences 1001 - 2000 of the annotated corpus, tabulated format; coordinated outcomes annotated as single entity</p> <p>po_sent_marked_col_p1_coord.txt - sentences 1 - 1000 of the annotated corpus, tabulated format; coordinated outcomes annotated as separate entities<br> po_sent_marked_col_p2_coord.txt - sentences 1001 - 2000 of the annotated corpus, tabulated format; coordinated outcomes annotated as separate entities</p> <p>Subfolders:<br> po - the corpus for 10-fold cross-validation (10 subfolders with train/dev/test sets); coordinated outcomes annotated as separate entities<br> po_coord - the corpus for 10-fold cross-validation (10 subfolders with train/dev/test sets); coordinated outcomes annotated as separate entities</p> <p>2. Reported_outcomes - a corpus annotated for reported outcomes<br> The corpus contains sentences from Results and Conclusions sections of the articles for which primary outcomes were annotated. The first part of reported outcomes corpus contains sentences from articles for the first half of the primary outcomes corpus (sentences 1 - 1000). The second part of reported outcomes corpus contains sentences from articles for the second half of the primary outcomes corpus (sentences 1001 - 2000).</p> <p>The folder contains the following files:<br> res_sent_marked_p1.txt - first part of the annotated corpus, ConstruKT format<br> res_sent_marked_p2.txt - second part of the annotated corpus, ConstruKT format</p> <p>res_sent_marked_p1_col.txt - first part of the annotated corpus, tabulated format<br> res_sent_marked_p2_col.txt - first part of the annotated corpus, tabulated format</p> <p>Subfolders:<br> rep - the corpus for 10-fold cross-validation (10 subfolders with train/dev/test sets)</p>
Radiocarbon Palaeolithic Europe Database v25 June 2019 extract
<p>The database, version 25 (first version was available in2002), contains now <strong>13093</strong> site forms, (most of them with their geographical coordinates), comprising <strong>15500 </strong>radiometric data: Conv. <sup>14</sup>C and AMS <sup>14</sup>C (<strong>12500 </strong>items), TL (<strong>1023</strong> items), OSL (<strong>627</strong> items), ESR, Th/U and AAR (<strong>2015</strong> items) from the European (Russian Siberia included) Lower, Middle and Upper Palaeolithic. All <sup>14</sup>C dates are conventional dates BP. 263 new sites are incorporated and 447 sites have a corrected or an updated content. The latest version is available at https://ees.kuleuven.be/en/geography/projects/14c-palaeolithic/.</p>
Graph topological features extracted from expression profiles of neuroblastoma patients
<p><strong>Introduction</strong></p> <p>This dataset contains the data described in the paper titled "A deep neural network approach to predicting clinical outcomes of neuroblastoma patients." by Tranchevent, Azuaje and Rajapakse. More precisely, this dataset contains the topological features extracted from graphs built from publicly available expression data (see details below). This dataset does not contain the original expression data, which are available elsewhere. We thank the scientists who did generate and share these data (please see below the relevant links and publications).</p> <p> </p> <p><strong>Content</strong></p> <p>File names start with the name of the publicly available dataset they are built on (among "Fischer", "Maris" and "Versteeg"). This name is followed by a tag representing whether they contain raw data ("raw", which means, in this case, the raw topological features) or TF formatted data ("TF", which stands for TensorFlow). This tag is then followed by a unique identifier representing a unique configuration. The configuration file "Global_configuration.tsv" contains details about these configurations such as which topological features are present and which clinical outcome is considered.</p> <p>The code associated to the same manuscript that uses these data is at <a href="https://gitlab.com/biomodlih/SingalunDeep">https://gitlab.com/biomodlih/SingalunDeep</a>. The procedure by which the raw data are transformed into the TensorFlow ready data is described in the paper.</p> <p> </p> <p><strong>File format</strong></p> <p>All files are TSV files that correspond to matrices with samples as rows and features as columns (or clinical data as columns for clinical data files). The data files contain various sets of topological features that were extracted from the sample graphs (or Patient Similarity Networks - PSN). The clinical files contain relevant clinical outcomes.</p> <p>The raw data files only contain the topological data. For instance, the file "Fischer_raw_2d0000_data_tsv" contains 24 values for each sample corresponding to the 12 centralities computed for both the microarray (<em>Fischer-M</em>) and RNA-seq (<em>Fischer-R</em>) datasets. The TensorFlow ready files do not contain the sample identifiers in the first column. However, they contain two extra columns at the end. The first extra column is the sample weights (for the classifiers and because we very often have a dominant class). The second extra column is the class labels (binary), based on the clinical outcome of interest.</p> <p> </p> <p><strong>Dataset details</strong></p> <p>The <em>Fischer</em> dataset is used to train, evaluate and validate the models, so the dataset is split into train / eval / valid files, which contains respectively 249, 125 and 124 rows (samples) of the original 498 samples. In contrast, the other two datasets (<em>Maris</em> and <em>Versteeg</em>) are smaller and are only used for validation (and therefore have no training or evaluation file).</p> <p>The <em>Fischer</em> dataset also has more data files because various configurations were tested (see manuscript). In contrast, the validation, using the <em>Maris</em> and <em>Versteeg</em> datasets is only done for a single configuration and there are therefore less files.</p> <p>For <em>Fischer</em>, a few configurations are listed in the global configuration file but there is no corresponding raw data. This is because these items are derived from concatenations of the original raw data (see global configuration file and manuscript for details).</p> <p> </p> <p><strong>References</strong></p> <p>This dataset is associated with Tranchevent L., Azuaje F.. Rajapakse J.C., A deep neural network approach to predicting clinical outcomes of neuroblastoma patients.</p> <p>If you use these data in your research, please do not forget to also cite the researchers who have generated the original expression datasets.</p> <p><em>Fischer</em> dataset:</p> <ul> <li>Zhang W. et al., Comparison of RNA-seq and microarray-based models for clinical endpoint prediction. Genome Biology 16(1) (2015). doi:10.1186/s13059-015-0694-1</li> <li>Wang C. et al., The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance. Nat. Biotechnol. 32(9), 926–932. doi:10.1038/nbt.3001</li> </ul> <p><em>Versteeg</em> dataset:</p> <ul> <li>Molenaar J.J. et al., Sequencing of neuroblastoma identifies chromothripsis and defects in neuritogenesis genes. Nature 483(7391), 589–593. doi:10.1038/nature10910</li> </ul> <p><em>Maris</em> dataset:</p> <ul> <li>Wang Q. et al., Integrative genomics identifies distinct molecular classes of neuroblastoma and shows that multiple genes are targeted by regional alterations in DNA copy number. Cancer Res. 66(12), 6050–6062. doi:10.1158/0008-5472.CAN-05-4618</li> </ul>
Additional data for publication: Simple protocol for combined extraction of exocrine secretions and RNA in small arthropods.
<p>Additional data and results are given in this repository. It contains the trimmed reads (fastp; raw reads also on SRA accession numbers SRR29851544-SRR29851549, Bioproject PRJNA1136254), full busco reports for individual transcriptomes, assembly of all six RNAseqs together (transcriptome as base for differential expression analysis) and results of salmon.</p>
A proteome-wide quantitative platform for nanoscale spatially resolved extraction of membrane proteins into native nanodiscs
<p><strong>EM Quantitation:</strong></p> <p>Raw data gathered from EM images taken to determine nanodisc population size distribution.</p> <p> </p> <p><strong>NNB TGN46 analysis:</strong></p> <p>Data analysis of the Native Nanobleach experiments of TGN46 in native nanodiscs to determine population distribution of oligomeric organizations.</p> <p> </p> <p><strong>Polymer conditions:</strong></p> <p>Physiochemical characteristic and extraction conditions for all polymers in the library both commercially available and in-house.</p> <p> </p> <p><strong>Protein groups polymer screen original file:</strong></p> <p>Original output of MaxQuant data processing of polymer screen data.</p> <p> </p> <p><strong>Organelle matching:</strong></p> <p>Code used for mathcing proteins identified in the proteomics output to organelle or residence for all organellar annotations.</p> <p> </p> <p><strong>Polymer code:</strong></p> <p>Code used to process and normalize the MaxQuant output and calulate extraction efficiency across all detected proteins.</p> <p> </p> <p><strong>MAP Library Details:</strong></p> <p>Graphic and table explaining chemical details of all polymer used in the screen, both commerically available and in-house synthesized.</p> <p> </p> <p><strong>NNB TGN46:</strong></p> <p>Raw scope files for the TIRF microscopy single molecule step photobleaching experiment with TGN46.</p> <p> </p> <p><strong>Organellar Breakdown Database:</strong></p> <p>Proteins detected in the polymer screen through proteomics experiments stratified into organelle of residence.</p> <p> </p> <p><strong>Human Proteome FASTA:</strong></p> <p>The FASTA file used for proteome searching in processing the proteomics data to build the screening database.</p> <p> </p> <p><strong>Hand Curated Organellar Proteomes:</strong></p> <p>Organellar proteomes used for organellar sorting and identification of proteins detected in the screen.</p> <p> </p> <p><strong>Polymer SEC Superdex75:</strong></p> <p>Size exculsion chromatography traces for chloroSMA series of polymers. Was used to characterize length and population polydispersity.</p> <p> </p> <p><strong>Negative Stain Raw:</strong></p> <p>RAW TEM scope images of purified synaptophysin-vamp2 containing nanodiscs. Populatoin size distribution was determined.</p> <p> </p> <p><strong>FSEC Polymer CS80:</strong></p> <p>Fluoresence size exclusion chromatogram for purified synaptophysin-vamp2 containing nanodiscs to ensure population homogeneity and purity.</p> <p><strong>NMR Raw data:</strong></p> <p>NMR raw files for characterizing the in-house synthesized Chloro-SMA series and AASTY series.</p> <p> </p>
Left Atrium 3D Models Extracted from Static CT Images (AF and SR Models)
<p>A cohort of 45 x 2 patient-specific 3D-models from static computed tomography (CT) images provided by the University Medical Center Hamburg-Eppendorf in Germany. The study complies with EU Regulation 2016/679 and has recieved ethical approval from the regional committee. </p> <p>The cohort has been used in two research papers:</p> <ol> <li>Kjeldsberg et al. (2024), Estimation of inlet flow rate in simulations of left atrial flows: A proposed optimized and reference-based algorithm with application to sinus rhythm and atrial fibrillation, <em>J Biomech</em> [Accepted]</li> <li>Kjeldsberg et al. (2024), Beyond CHA2DS2–VASc: hemodynamic and morphologic discriminants for thrombus formation and stroke in atrial fibrillation patients,<em> Ann. Biomed. Eng</em>. [Submitted]</li> </ol> <p><strong>models_af.zip </strong>contains 45 3D surface models (.vtp) extracted during onset of atrial diastole where <strong>A</strong>trial <strong>F</strong>ibrillation movement was applied using a motion algorithm [*]</p> <p><strong>models_sr.zip </strong>contains 45 3D surface models (.vtp) extracted during onset of atrial diastole where <strong>S</strong>inus <strong>R</strong>hytmn movement was applied using a motion algorithm [*]</p> <p> </p> <p>[*] For details on the motion algorithm, see <a href="Harrison, J., 2024. Medical Imaging, Shapes and Statistics for Stroke Prediction in Atrial Fibrillation (Doctoral dissertation, Inria & Université Cote d'Azur, CNRS, I3S, Sophia Antipolis, France).">Harrison – Medical Imaging, Shapes and Statistics for Stroke Prediction in Atrial Fibrillation </a></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.