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3,684 results for “extract”
Automated diagnosis of prostate cancer using artificial intelligence. A systematic literature review. Extraction and Summary Forms.
<p>Extraction and summary forms of a systematic literature review protocol detailed by Barbara Kitchenham for automated diagnosis of prostate cancer using artificial intelligence articles. </p>
Data extraction format
<p>data extraction format used to determine the survival status of patients admitted to the intensive care unit.</p>
Extracting the Architecture of Microservices: An Approach for Explainability and Traceability
<p>Dataset and replication package</p>
Memory kernel extraction and mean first-passage time for fast-folding proteins (Q - trajectories)
<p>Fraction of native contacts reaction coordinate (Q) trajectories for the 8 proteins that appear in the Dalton et. al. PNAS 2023. For the original all-atom data from which the Q(t) were calculated, contact the group of David E. Shaw at Shaw Research (see the paper - K. Lindorff-Larsen, S. Piana, R. O. Dror, D. E. Shaw, How fast-folding proteins fold. Science 334, 517–520 (2011))</p> <p>Data contains:<br> - Q(t) trajectories for 8 proteins <br> - Corresponding free energy profiles<br> <br> Example analysis codes (written in C++) are included for:<br> - Free energy calculation<br> - Mean first-passage times<br> - Velocity-velocity correlation function and position-force correlation function calculations, needed for memory kernel extraction<br> - Memory kernel extraction (see Ayaz et. al. PNAS 2021)</p>
Preferential information extraction from space-based passive microwave measurements enables accurate characterization of snow depth variability at continental scales
<p>This is a repository contains </p> <p>1)training data (x_data, y_data, snow_max) </p> <p>2) Developed Deep Learning model (snow_model.py)</p> <p>3) Training weights (*.hdf files)</p> <p>for publication " Preferential information extraction from space-based passive microwave measurements enables accurate characterization of snow depth variability at continental scales"</p> <p> </p>
Data from: Drop it all: Extraction-free detection of non-indigenous marine species through optimized direct-droplet digital PCR
<p>Molecular biosecurity surveillance programs increasingly use environmental DNA (eDNA) for detecting marine non-indigenous species (NIS). However, the current molecular detection workflow is cumbersome, prone to errors and delays, and is limited in providing knowledge about eDNA beyond the spatial and temporal extent of the sampling. These limitations can hinder management efforts and restrict the "opportunity window" for a rapid response to new marine NIS incursions. Emerging innovative field-deployable digital droplet PCR (ddPCR) systems offer improved workflow efficiency by autonomously analyzing targeted free-floating extra-cellular eDNA (free-eDNA) signals. Despite their potential, these systems have not been tested in marine environments. Thus, an aquarium study was conducted with three distinct marine NIS: <span>the Mediterranean fanworm <em>Sabella spallanzanii</em>, the ascidian clubbed tunicate <em>Styela clava</em>, and the brown bryozoan <em>Bugula neritina</em></span> to evaluate the detectability of free-eDNA in seawater. The detectability of targeted free-eDNA was assessed by directly analyzing aquarium water samples using an optimized species-specific ddPCR assay, without filtration or DNA extraction, so-called, "direct-ddPCR". The results demonstrated the consistent detection of <em>Sabella spallanzanii</em> and <em>Bugula neritina</em> free-eDNA when these organisms were present in high abundance. Once organisms were removed, the free-eDNA signal exponentially declined, noting that free-eDNA persisted between 24-72 hours. Results indicate that organism biomass, specimen characteristics (e.g., stress and viability), and species-specific biological differences may influence free-eDNA detectability. These results are critical for implementing <em>in-situ</em> nucleic acid automated continuous sensing systems for marine biosurveillance, enabling point-of-need detection and <span>rapid management response to biosecurity threats. </span></p>
Sample extraction and SNP sequencing data for: Identification of sex-linked SNP markers in wild populations of monomorphic birds
<p><span>Single-nucleotide polymorphism (SNP) analyses are a powerful tool for population genetics, pedigree reconstruction and phenotypic trait mapping. However, the untapped potential of SNP markers to discriminate the sex of individuals in species with reduced sexual dimorphism or of individuals during immature stages remains a largely unexplored avenue. Here, we develop a novel protocol for molecular sexing of birds based on the detection of unique Z- and W-linked SNP markers. Our method is based on the identification of two unique loci, one in each sexual chromosome. Individuals are considered males when they show no calls for the W-linked SNP and are heterozygotic or homozygotic for the Z-linked SNP, while females show both Z- and W-linked SNP calls. We validated the method in the Jackdaw (<em>Corvus</em> <em>monedula</em>). The reduced sexual dimorphism in this species makes it difficult to sex individuals in the wild. We assessed the reliability of the method using 36 individuals of known sex and found that their sex was correctly assigned in 100% of cases. The sex-linked markers also proved to be widely applicable to discriminate males and females from a sample of 927 genotyped individuals of different maturity stages with an accuracy of 99.5%. Given that SNP markers are increasingly used in quantitative genetic analyses of wild populations, the approach we propose has a great potential to be integrated into broader genetic research programmes without the need for additional sexing techniques.</span></p>
MedProcNER Corpus: Gold Standard annotations for Clinical Procedures Information Extraction
<p><strong>MedProcNER</strong> stands for MEDical PROCedure Named Entity Recognition. It is a shared task and set of resources focused on the <strong>detection, normalization and indexing of clinical procedures in medical documents</strong> in <strong>Spanish</strong>. MedProcNER is complementary to the DisTEMIST corpus (https://temu.bsc.es/distemist) as they both use the same document collection.</p><p><strong>Please cite if you use this dataset:</strong></p><p>Lima-López S, Farré-Maduell E, Gascó L, Nentidis A, Krithara A, Katsimpras G, Paliouras G, Krallinger M. Overview of MedProcNER task on medical procedure detection and entity linking at BioASQ 2023. Working Notes of CLEF. 2023.</p><p>@article{lima2023overview, title={Overview of MedProcNER task on medical procedure detection and entity linking at BioASQ 2023}, author={Lima-L{\'o}pez, Salvador and Farr{\'e}-Maduell, Eul{\`a}lia and Gasc{\'o}, Luis and Nentidis, Anastasios and Krithara, Anastasia and Katsimpras, Georgios and Paliouras, Georgios and Krallinger, Martin}, journal={Working Notes of CLEF}, year={2023} }</p><p>This repository includes the Train Set of the task, which includes a total of 750 documents, plus the annotated Test Set's 250 documents. A gazetteer of possible <strong>SNOMED CT codes</strong> for the normalization and indexing tasks is also part of the bundle as a lexical resource. </p><p>In addition, a cross-mapping file of all SNOMED CT codes to <strong>MeSH</strong> is also included.</p><p>Finally, we release an experimental <strong>multilingual Silver Standard</strong> version derived from the Spanish Gold Standard in <strong>9 languages</strong>: <i><strong>English</strong></i>, <i><strong>Catalan</strong></i>, <i><strong>Italian</strong></i>, <i><strong>French</strong></i>, <i><strong>Portuguese</strong></i>, <i><strong>Romanian</strong></i>, <i><strong>Czech</strong></i>, <i><strong>Dutch</strong></i> and <i><strong>Swedish</strong></i>. </p><p>These documents have been generated using an automatic annotation transfer process that works as follows:</p><ol><li>The text files were translated with a neural machine translation system.</li><li>The annotations were translated with the same neural machine translation system.</li><li>The translated annotations were transferred to the translated text files using a lexical approach and custom dictionaries.</li></ol><p>MedProcNER was developed by the Barcelona Supercomputing Center's NLP for Biomedical Information Analysis and used as part of BioASQ @ CLEF 2023. For more information on the corpus, annotation scheme and task in general, please visit: <a href="https://temu.bsc.es/medprocner">https://temu.bsc.es/medprocner</a>.</p><p> </p><p><strong>Resources:</strong></p><ul><li><a href="https://temu.bsc.es/medprocner"><strong>Web</strong></a></li><li><a href="https://ceur-ws.org/Vol-3497/paper-002.pdf"><strong>Citation</strong></a><strong>: </strong>Lima-López S, Farré-Maduell E, Gascó L, Nentidis A, Krithara A, Katsimpras G, Paliouras G, Krallinger M. Overview of MedProcNER task on medical procedure detection and entity linking at BioASQ 2023. Working Notes of CLEF. 2023.</li><li><a href="https://doi.org/10.5281/zenodo.7817666"><strong>Annotation guidelines</strong></a></li><li><a href="https://ceur-ws.org/Vol-3497/"><strong>Proceedings and participant papers</strong></a></li><li><a href="https://ceur-ws.org/Vol-3497/paper-002.pdf"><strong>Overview paper</strong></a></li><li><a href="https://www.slideshare.net/MartinKrallinger/medprocnerproctemist-shared-task-on-clinical-procedure-detection-and-normalization-task-overview-presentation-at-bioasq-workshop-of-clef-2023-conference"><strong>Overview talk slides at BioASQ/CLEF</strong></a></li></ul><p> </p><p><strong>Additional resources and corpora</strong></p><p>If you are interested in MedProcNER, you might want to check out these corpora and resources:</p><ul><li><a href="https://zenodo.org/records/7614764">DisTEMIST</a> (Corpus of disease mentions and normalization to SNOMED CT, same document collection)</li><li><a href="https://zenodo.org/records/8413866">SympTEMIST </a>(Corpus of symptoms, signs and findings mentions and normalization to SNOMED CT, same document collection)</li><li><a href="https://zenodo.org/records/4270158">PharmaCoNER</a> (Corpus of medications, drugs, chemical substances, genes, proteins and vaccine mentions and normalization, same document collection)</li><li><a href="https://zenodo.org/records/7116201">MEDDOPROF</a> (Corpus of mentions of professions, occupations and working status and normalization, different document collection with some overlapping documents)</li><li><a href="https://zenodo.org/records/8403498">MEDDOPLACE</a> (Corpus of mentions of place-related entity mentions, including departments, nationalities or patient movements etc.. and normalization, different document collection with some overlapping documents)</li><li><a href="https://zenodo.org/records/4279323">MEDDOCAN</a> (Corpus of mentions of place-related entity mentions, including departments, nationalities or patient movements etc.. and normalization, modified synthetic verions of the document collection)</li><li><a href="https://zenodo.org/records/3978041">CANTEMIST</a> (Corpus of cancer tumor morphology mentions and normalization, different document collection)</li><li><a href="https://zenodo.org/records/3837305">CodiESp</a> (Corpus of clinical case reportes with assigned clinical codes from ICD10, Spanish version, same document collection)</li><li><a href="https://zenodo.org/records/7684093">LivingNER</a> (Corpus of mentions of species, including human/family members, pathogens, food, etc.. and normalization to NCBI Taxonomy, different document collection with some overlapping documents)</li><li><a href="https://zenodo.org/records/2560344">SPACCC-POS</a> (Corpus of clinical case reports in Spanish annotated with POS-tags, same document collection)</li><li><a href="https://zenodo.org/records/2560338">SPACCC-TOKEN</a> (Corpus of clinical case reports in Spanish annotated with token-tags (word mention boundaries), same document collection)</li><li><a href="https://zenodo.org/records/2560338">SPACCC-SPLIT</a> (Corpus of clinical case reports in Spanish annotated with sentence boundary-tags, same document collection)</li><li><a href="https://zenodo.org/records/5602914">MESINESP-2</a> (Corpus of manually indexed records with DeCS /MeSH terms comprising scientific literature abstracts, clinical trials, and patent abstracts, different document collection)</li></ul><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>
Green Synthesis of Zinc Oxide (ZnO) Nanoparticles Using Aqueous Extract of Shilajit: Their Characterization and Anticancer Activity
<p>UV analysis of aqueous extract of shilajit and zinc oxide nanoparticles (ZnO NPs) of shilajit extract showed the absorbance peaks at 422.40 nm and 357.90 nm. The abosrbance peaks confirms the presence of ZnO NPs in the reaction mixture.</p> <p>The FTIR spectrum of shilajit extract and ZnO NPs of shilajith extract showed the strong broad absorbance at 3428 cm−1 are due to the stretching vibration of hydroxyl (OH) functional groups. The absorption band at 1633 cm-1 corresponded the stretching vibration of N-H groups. The peak at 1414 cm<sup>-1</sup> observed in the spectrum indicate the presence of mono-substituted alkynes. The peak at 1163 cm<sup>-1</sup> showed the presence of C-O bonds.</p> <p>The XRD pattern of ZnO NPs of shilajith extract, characterized by their high intensity, were observed at specific 2θ peak values, namely 31.69°, 34.33°, 36.17°, 47.45°, 56.52°, 62.77°, 67.87° and 68.99°, and can be attributed to the lattice planes (100), (002), (101), (102), (110), (103), (112), and (201), respectively.</p> <p>The particle size of ZnO NPs of shilajit extract was measured to be 348 nm and the polydispersity index was found to be 0.322.</p>
SCI-3000: A Novel Dataset for the Task of Figure, Table and Caption Extraction from Scientific PDFs
<p>This dataset contains bounding boxes of figures, tables, captions in 34,791 pages extracted from 3000 open-access scientific publications from the fields of medicine, chemistry, physics, computer science, and technology. The underlying publications are also included in PDF form.</p> <p>For more details, refer to the README file.</p>
Increased impact sensitivity in ageing high explosives: Analysis of Amatol extracted from explosive remnants of war
<p>Millions of tonnes of explosive remnants of war remain in nature, and the volume is continuously growing. The explosive legacy of wars represents an increasing threat to the environment and to societal safety and security. As munitions continue to deteriorate, harmful constituents will eventually leak into the environment, poisoning ecological receptors and contaminating the surrounding soil and groundwater. Deteriorating munitions may also become increasingly sensitive to external stimuli and may be susceptible to accidental detonation. To thoroughly assess how to address these ageing munitions, we must first establish certain threshold values for the safe and secure handling and final disposal of the explosive ordnance. One key factor is to establish how the impact sensitivity of the explosives evolves over time. In the present work, we investigated the high explosive substance Amatol extracted from ageing explosive remnants of war. The results obtained in the analysis indicate that the high explosives in the examined specimens generally were much more sensitive to impact than previously assumed. Furthermore, the analysis revealed that the standardised methodology of impact sensitivity testing was insufficient for estimating the sensitivities in question, and a more careful statistical analysis was required.</p>
Results of integrating asynchronous data provenance methods, metadata extraction, and similarity detection into a standards-based RDMS
<p>Related to the thesis: "Asynchronous Tracking and Description of Research Data Changes in Distributed Systems with Interoperable Metadata"</p>
"CausalXtract: a flexible pipeline to extract causal effects from live-cell time-lapse imaging data" datasets
<p>Datasets from the article:</p> <p><strong>CausalXtract: a flexible pipeline to extract causal effects from live-cell time-lapse imaging data </strong></p> <p>by Franck Simon, Maria Colomba Comes, Tiziana Tocci, Louise Dupuis, Vincent Cabeli, Nikita Lagrange, Arianna Mencattini, Maria Carla Parrini, Eugenio Martinelli, Hervé Isambert.</p> <p> </p> <p>The <strong>original videos</strong> are uploaded as: "20161230.zip", "20170105.rar", "Video_2017_0517.zip".</p> <p><strong>Details </strong>for each <strong>experiment </strong>can be found in: "Experiments' details.zip".</p> <p>The <strong>ROIs </strong>(ROI: Region of Interest) are uploaded as .tif files in: "EXTRACTED ROIs.zip".</p> <p>The <strong>MATLAB data</strong> is uploaded in "MATLAB_DATA.rar" and includes: the cancer cells' trajectories (subfolder: "TUMOR TRAJECTORIES"), the immune cells' trajectories (subfolder: "IMMUNE TRAJECTORIES"), the ROIs videos as .mat files for the detection of cells (subfolder: "ROI MAT"), the ROIs further cropped for the extraction of shape descriptors (folder: "ROI_TU MAT"). The ROIs videos .mat files included in the last two subfolders are stopped after their apoptosis has been detected.</p>
extracted_features
<p>Extracted features from OSTrain, OSValidate, OSTest to be used in SUMo's pipeline.</p>
Advancing Mid-Rectal Cancer Surgery: Unveiling the Potential of Natural Orifice Specimen Extraction Surgery (NOSES) in Comparison to Conventional Laparoscopic-Assisted Resection (CLAR)
<p><strong>Supplementary Material_1</strong><strong>:</strong>These supplementary videos offer a comprehensive visual guide to the NOSES surgical procedure, highlighting each significant step with precision and clarity.</p> <p><strong><em>Supplementary Video 1: Mobilization of the Descending and Sigmoid Colon.</em></strong></p> <p>This video illustrates the crucial first step in the NOSES surgical procedure, demonstrating the meticulous mobilization of the descending and sigmoid colon.</p> <p><strong><em>Supplementary Video 2: Clipping of the Inferior Mesenteric Artery using Hem-o-Lock Clips.</em></strong></p> <p>In this video, the surgical team showcases the precise technique of clipping the inferior mesenteric artery with Hem-o-Lock clips, a key maneuver in the NOSES approach.</p> <p><strong><em>Supplementary Video 3: Dissection of the Mesorectum following Total Mesorectal Excision (TME).</em></strong></p> <p>This video provides an in-depth view of the mesorectal dissection, a critical component of the NOSES procedure, following the principles of Total Mesorectal Excision (TME).</p> <p><strong><em>Supplementary Video 4: Delivery of the Anvil in the Sigmoid and Resection of the Rectum using the Linear Stapler Echelon 60.</em></strong></p> <p>Watch as the surgical team delivers the anvil into the sigmoid and performs rectal resection using the advanced Linear Stapler Echelon 60.</p> <p><strong><em>Supplementary Video 5: Removal of the Anvil Connection Rod at the Distal End of the Sigmoid Colon.</em></strong></p> <p>This video showcases the precise removal of the anvil connection rod at the distal end of the sigmoid colon, ensuring a seamless transition in the surgical process.</p> <p><strong><em>Supplementary Video 6: Transanal Extraction of the Opened Distal Rectal Stump for Smooth Specimen Extraction.</em></strong></p> <p>Observe the meticulous transanal extraction of the opened distal rectal stump in this video, a step that facilitates smooth specimen extraction.</p> <p><strong><em>Supplementary Video 7: Extracorporeal Resection of the Rectal Specimen and Inspection for Adequate Distal Margin.</em></strong></p> <p>This video captures the extracorporeal resection of the rectal specimen, followed by a thorough inspection to ensure an adequate distal margin.</p> <p><strong><em>Supplementary Video 8: Passage of the Head of the Circular Stapler through the Anal Orifice and End-to-End Double-Stapled Anastomosis.</em></strong></p> <p>Witness the precise passage of the circular stapler through the anal orifice, culminating in an end-to-end double-stapled anastomosis.</p> <p><strong><em>Supplementary Video 9: Positioning of the Post-operative Peritoneal Drainage Tube near the Anastomosis Site</em></strong><em>.</em></p> <p>In this video, the surgical team demonstrates the placement of the post-operative peritoneal drainage tube near the site of the anastomosis, a crucial step in the NOSES procedure.</p>
Extracted Information for the Systematic Review of Survey Scales for Measuring Information Privacy Concerns on Social Network Sites
<p>The data set is part of a systematic literature review of survey scales for measuring information privacy concerns (IPCs) used in research on social network sites (SNSs).</p> <p>The results of this systematic literature review are available in Bartol, J., Vehovar, V., & Petrovčič, A. (2023). Systematic review of survey scales measuring information privacy concerns on social network sites. <em>Telematics and Informatics</em>, 102063. https://doi.org/10.1016/j.tele.2023.102063</p> <p>The article also includes a detailed description of the methods used in generating this data set.</p>
Materials Datasets with 273 compositional and structural features extracted from Matminer
<p>Materials Datasets with 273 compositional and structural features extracted from <a href="https://github.com/hackingmaterials/matminer">Matminer</a>. Materials datasets are retrieved using the python package <a href="https://github.com/usnistgov/jarvis">jarvis-tools</a>.</p>
Synthetic datasets for end-to-end Relation Extraction of relationships between Organisms and Natural-Products
<p>Synthetic datasets (training/validation) for end-to-end Relation Extraction of relationships between Organisms and Natural-Products. The datasets are provided for reproducibility purposes, but, can also be used to train new models.</p><p>As in the corresponding article, 3 subtypes of synthetic datasets are provided:</p><ul><li><i>Diversity-synt</i>:<strong> </strong>The seed literature references used in the generation process correspond to the top-500 extracted items per biological kingdoms using the <a href="https://github.com/idiap/gme-sampler">GME-sampler</a>.</li><li><i>Random-synt</i>:<strong> </strong>5 datasets of equivalent sizes as <i>Diversity-synt</i>, but using randomly sampled seed literature references.</li><li><i>Extended-synt</i>: A merge of <i>Diversity-synt and the 5 Random-synt datasets.</i><br> </li></ul><p><strong>All </strong>datasets were produced with <a href="https://huggingface.co/lmsys/vicuna-13b-v1.3">Vicuna-13b-v1.3</a>. Like the model, the produced synthetic data are also submitted to the License of the model used for generation, see the original <a href="https://github.com/facebookresearch/llama/blob/llama_v1/MODEL_CARD.md">LLaMA model card</a>.</p><p>LLaMA is licensed under the <a href="https://github.com/facebookresearch/llama/blob/llama_v1/MODEL_CARD.md">LLaMA License</a>, Copyright (c) Meta Platforms, Inc. All Rights Reserved. </p>
Extracting Educational Code Scenarios from Python Textbooks
<p><strong>Dataset Overview:</strong> This dataset complements the research project titled "Extracting Learning Scenarios from Python Textbooks." It consists of 1,017 chapter titles collected from 76 Python textbooks.</p><p><strong>Research Findings:</strong> Our analysis revealed that learning scenarios (referred to as "Scenarios") are a prevalent theme, constituting approximately 39.5% of the total chapter titles in comparison to other content categories. We further categorized these scenarios into four types, including Application Programming Interfaces, Data and Processing, Graphical User Interfaces, and other scenarios. Additionally, we identified a list of 19 Python modules commonly used within these scenarios.</p><p><strong>Purpose:</strong> We envision that this work and its insights can serve as stepping stones and lay the groundwork for further extraction and the effective application of how Python can be utilized for its diverse audience.</p>
An Assessment of Bone Augmentation in Post-Extraction Sockets
ClinicalTrials.gov study NCT00641316. IPD Sharing: Not stated. Countries: 1. Publications: 11.
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.