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138 results for “advanced analysis”
Datasets for paper 'Cabello, V., Renner, A., Giampietro, M. 2019. Relational analysis of the resource nexus in arid land crop production. Advances in Water Resources 130:258-629'
<p>Datasets produced for the paper Cabello, V., Renner, A., Giampietro, M. 2019.<em> </em>Relational analysis of the resource nexus in arid land crop production. <em>Advances in Water Resources </em>130:258-269</p>
GLARE: Guided LexRank for Advanced Retrieval in Legal Analysis
<p>This page includes legal datasets used in the paper GLARE: Guided LexRank for Advanced Retrieval in Legal Analysis.</p> <p>The Guided Lexrank algorithm is applied to dataset <a href="../api/records/13696090/draft/files/special_appeal.csv/content" target="_blank" rel="noopener noreferrer">special_appeal.csv</a> to summarize the texts of legal documents. The obtained summary and the texts of the topics contained in dataset <a href="../api/records/13696090/draft/files/themes.csv/content" target="_blank" rel="noopener noreferrer">themes.csv</a> are submitted to the BM25 algorithm for similarity assessment. From a list of topics, the GLARE method produces a ranking with suggested topics for a given document.</p>
ACR PET phantom raw data and templates for advanced analysis
<p>A zipped folder containing raw PET data of the ACR phantom, which was acquired first for 30 minutes without any activity outside the axial field of view (FOV), followed by another 30 minutes of acquisition with activity outside the FOV.</p> <p>Each acquisition comes with the UTE mu-map in DICOM format, included in both raw data folders, <raw> and <raw_ofov>.</p> <p>Since the MR-based mu-maps are not of sufficient accuracy, the synthetic mu-map has been included (and also the generated hardware mu-map).</p> <p>The design for the templates for generating the synthetic mu-map, NAC PET image, and sampling VOIs are included in folder <design>.</p> <p> </p>
Surrogate Machine Learning for Parmec Advanced Gas-cooled Reactor (AGR) Analysis
<p>Data associated with research towards a surrogate machine learning model for the Advanced Gas-cooled Reactor (AGR). This data was generated using the Parmec software package [<a href="https://parmes.org/parmec/index.html">1</a>] and can be used to train machine learning models using the Surrogate Machine Optimisation and Learning (SMOL) framework [<a href="https://gitlab.cs.man.ac.uk/q59494hj/parmec_agr_ml_surrogate">2</a>]. Visit the aforementioned repository, clone the code, then download the files into repository folder.</p> <p>If you are not planning on working with data augmentation, exclude the files with flip and rotate in the title, e.g. dataset__flip_13_rotate_123_cases.pkl.<br> <br> 1. Koziara, T., 2019. Parmec documentation. URL: <a href="https://parmes.org/parmec/index.html">https://parmes.org/parmec/index.html</a> [Online; accessed 05-August-2022].</p> <p>2. Github Repository. URL: <a href="https://gitlab.cs.man.ac.uk/q59494hj/parmec_agr_ml_surrogate">https://gitlab.cs.man.ac.uk/q59494hj/parmec_agr_ml_surrogate</a> [Online; accessed 05-August-2022].</p>
Dataset and Source Code for the Paper: A Framework for Developing Strategic Cyber Threat Intelligence from Advanced Persistent Threat Analysis Reports Using Graph-Based Algorithms
<p>Here are the data set and source code related to the paper: "A Framework for Developing Strategic Cyber Threat Intelligence from Advanced Persistent Threat Analysis Reports Using Graph-Based Algorithms"</p> <p>1- aptnotes-downloader.zip : contains source code that downloads all APT reports listed in https://github.com/aptnotes/data and https://github.com/CyberMonitor/APT_CyberCriminal_Campagin_Collections</p> <p>2- apt-groups.zip : contains all APT group names gathered from https://docs.google.com/spreadsheets/d/1H9_xaxQHpWaa4O_Son4Gx0YOIzlcBWMsdvePFX68EKU/edit?gid=1864660085#gid=1864660085 and https://malpedia.caad.fkie.fraunhofer.de/actors and https://malpedia.caad.fkie.fraunhofer.de/actors</p> <p>3- apt-reports.zip : contains all deduplicated APT reports gathered from https://github.com/aptnotes/data and https://github.com/CyberMonitor/APT_CyberCriminal_Campagin_Collections</p> <p>4- countries.zip : contains country name list.</p> <p>5- ttps.zip : contains all MITRE techniques gathered from https://attack.mitre.org/resources/attack-data-and-tools/</p> <p>6- malware-families.zip : contains all malware family names gathered from https://malpedia.caad.fkie.fraunhofer.de/families</p> <p>7- ioc-searcher-app.zip : contains source code that extracts IoCs from APT reports. Extracted IoC files are provided in report-analyser.zip. Original code repo can be found at https://github.com/malicialab/iocsearcher</p> <p>8- extracted-iocs.zip : contains extracted IoCs by ioc-searcher-app.zip</p> <p>9- report-analyser.zip : contains source code that searchs APT reports, malware families, countries and TTPs. I case of a match, it updates files in extracted-iocs.zip.</p> <p>10- cti-transformation-app.zip : contains source code that transforms files in extracted-iocs.zip to CTI triples and saves into Neo4j graph database.</p> <p>11- graph-db-backup.zip : contains volume folder of Neo4j Docker container. When it is mounted to a Docker container, all CTI database becomes reachable from Neo4j web interface. Here is how to run a Neo4j Docker container that mounts folder in the zip:</p> <p>docker run -d --publish=7474:7474 --publish=7687:7687 --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/data:/data --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/plugins:/plugins --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/logs:/logs --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/conf:/conf --env 'NEO4J_PLUGINS=["apoc","graph-data-science"]' --env NEO4J_apoc_export_file_enabled=true --env NEO4J_apoc_import_file_enabled=true --env NEO4J_apoc_import_file_use__neo4j__config=true --env=NEO4J_AUTH=none neo4j:5.13.0</p> <h4><strong>web interface: http://localhost:7474</strong></h4> <h4><strong>username: neo4j</strong></h4> <h4><strong>password: neo4j</strong></h4> <p> </p>
Advancing Vanadium Redox Flow Battery Analysis: A Deep Learning Framework for High-Throughput 3D Visualization and Bubble Quantification via Synchrotron X-ray Tomography
<p>Dataset and model of UTILE-Redox - Deep Learning based Tool for Autonomous 3D Bubble Analysis of Vanadium Flow Batteries from Synchrotron X-ray Imaging. This project focuses on the deep learning-based automatic analysis of Vanadium Redox Flow Batteries (VRFB) Synchrotron X-ray tomographies. This repository contains the Python implementation of the UTILE-Redox software for automatic volume analysis, feature extraction, and visualization of the results.</p>
Constraints on the emplacement of Martian nakhlite igneous rocks and their source volcano from advanced micro-petrofabric analysis
<p>Martian nakhlite meteorite electron backscatter data and magma body unit thickness calculation code.</p>
spectrapepper: A Python toolbox for advanced analysis of spectroscopic data for materials and devices.
<p>spectrapepper is a Python package that makes advanced analysis of spectroscopic data easy and accessible through straightforward, simple, and intuitive code. This library contains functions for every stage of spectroscopic methodologies, including data acquisition, pre-processing, processing, and analysis. In particular, advanced and high statistic methods are intended to facilitate, namely combinatorial analysis and machine learning, allowing also fast and automated traditional methods. The following is a short list of some main procedures that spectrapepper package enables: i) Baseline removal functions, ii) Normalization methods, iii) Noise filters, trimming tools, and despiking methods, iv) Chemometric algorithms to find peaks, fit curves, and deconvolution of spectra, v) Combinatorial analysis tools, such as Spearman, Pearson, and n-dimensional correlation coefficients, vi) Tools for Machine Learning applications, such as data merging, randomization, and decision boundaries, and vii) Sample data and examples</p>
Performance of Advanced Ambu Bag System among Adult Patients with Mechanical Ventilation: A Mixed-Effects Analysis
<p>We conducted the study at the Department of Stroke Care of the Can Tho Central General Hospital, Vietnam. There are eight intensive care beds for critical illness. The study was performed according to the Helsinki Declaration and approved by the Can Tho Central General Hospital ethics committee. All patients gave written informed consent by a legal surrogate. We enrolled patients with mechanical ventilation between November 2022 and September 2023. The inclusion criteria were: (1) patients aged 16 years and older, (2) pulse rate less than 120 times per minute, (3) systolic blood pressure from 110 to 160 mmHg, (4) peripheral oxygen saturation (SpO<sub>2</sub>) greater than 90%, (5) spontaneous breathing rate less than 28 times per minute, (6) end-tidal carbon dioxide (EtCO<sub>2</sub>) from 20 to 45 mmHg, (7) secretion required suction less than one time per hour, (8) positive end-expiratory pressure less than or equal to 5 cmH<sub>2</sub>O, (9) fraction of inspired oxygen less than or equal to 60%, (10) minute ventilation less than 15 liters per minute, (11) diameter of a tracheal or tracheostomy tube greater than or equal to 7.0 mm, (12) no usage of sedation, (13) normal ST wave in the electrocardiogram. Patients were excluded from the trial if they had one of the following conditions: (1) acute myocardial infarction, (2) acute pulmonary embolism, or (3) new dangerous arrhythmias appeared in this episode (multiform ventricular ectopy, bigeminy or trigeminy ventricular ectopy, coupled ventricular ectopy, R-on-T ventricular ectopy, high-grade atrioventricular heart block, supraventricular tachycardia, atrial fibrillation, atrial flutter, ventricular tachycardia, ventricular fibrillation), (4) using vasopressors or inotropic agents. Patients could withdraw from the study at any time without giving any reason. Besides, the patient stopped the trial of the advanced Ambu bag system immediately when one of the signs appeared, such as (1) the peripheral oxygen saturation lower than 90% prolonging more than 1 minute, (2) the end-tidal carbon dioxide greater than 45 mmHg or less than 15 mmHg prolonging more than 10 minutes, (3) pulse rate greater than 120 times per minute or less than 60 times per minute prolonging more than 10 minutes, (4) systolic blood pressure greater than 170 mmHg prolonging more than 10 minutes, (5) appearing dangerous arrhythmias, (6) progressive cognitive impairment (based on Grady coma scale), or (7) any abnormal sign that the physician evaluated the patient required respiratory support immediately with conventional mechanical ventilation.</p> <p> The following is the meaning of the variables in the study:</p> <p>age: Age of study participants.</p> <p>gender: Gender of study participants (0: Woman, 1: Man).</p> <p>day1: Day of admission to the hospital</p> <p>day2: Intervention day.</p> <p>dia1: Major disease.</p> <p>dia2: Cause of respiratory failure.</p> <p>nihss1: National Institute of Health Stroke Scale on admission</p> <p>hsg: Severity of cerebral hemorrhage (0: No hemorrhagic stroke, hi1: Scattered small petechiae, no mass effect, hi2: Confluent petechiae, no mass effect, ph1: Hematoma within infarcted tissue, occupying <30%, no substantive mass effect, ph2: Hematoma occupying 30% or more of the infarcted tissue, with obvious mass effect, 3a: Parenchymal hematoma remote from infarcted brain tissue, 3b: Intraventricular hemorrhage, 3c: Subarachnoid hemorrhage, 3d: Subdural hemorrhage)</p> <p>aspects1: Alberta stroke program early CT score of anterior circulation on CTscan</p> <p>aspect2: Alberta stroke program early CT score of anterior circulation on DWI- Diffusion-weighted Imaging.</p> <p>aspects3: Alberta stroke program early CT score of posterior circulation on CTscan</p> <p>aspects4: Alberta stroke program early CT score of posterior circulation on DWI- Diffusion-weighted Imaging.</p> <p> </p> <p>nihss2: National Institute of Health Stroke Scale before intervention</p> <p>grady: Grady coma scale before intervention</p> <p> </p> <p>rtpa: Use alteplase (0: No, 1: Yes)</p> <p>thromb: Thrombectomy (0: No, 1: Yes)</p> <p>crani: Craniectomy (0: No, 1: Yes).</p> <p>coil: Endovascular coiling (0: No, 1: Yes)</p> <p> </p> <p>mode: Ventilation mode</p> <p>mv: Mechanical ventilation (L/min)</p> <p>fio2: Fraction of inspired oxygen (%)</p> <p>peep: Positive end-expiratory pressure (cmH<sub>2</sub>O)</p> <p>sc: Static compliance (mL/cmH<sub>2</sub>O)</p> <p>alv: Pulmonary consolidation (0: No, 1: ¼ lung, 2: ½ lung, 3: ¾ lung, 4: Complete lung)</p> <p>sf: spo2/fio2 ratio.</p> <p> </p> <p>p13a: Number of pulse beats at time -13 (conventional mechanical ventilation stage)</p> <p>s13a: Systolic blood pressure at time -13 (conventional mechanical ventilation stage)</p> <p>d13a: Diastolic blood pressure at time -13 (conventional mechanical ventilation stage)</p> <p>sp13a: SpO<sub>2</sub> at time -13 (conventional mechanical ventilation stage)</p> <p>e13a: EtCO<sub>2</sub> at time -13 (conventional mechanical ventilation stage)</p> <p> </p> <p>p12a: Number of pulse beats at time -12 (conventional mechanical ventilation stage)</p> <p>s12a: Systolic blood pressure at time -12 (conventional mechanical ventilation stage)</p> <p>d12a: Diastolic blood pressure at time -12 (conventional mechanical ventilation stage)</p> <p>sp12a: SpO<sub>2</sub> at time -12 (conventional mechanical ventilation stage)</p> <p>e12a: EtCO<sub>2</sub> at time -12 (conventional mechanical ventilation stage)</p> <p> </p> <p>p11a: Number of pulse beats at time -11 (conventional mechanical ventilation stage)</p> <p>s11a: Systolic blood pressure at time -11 (conventional mechanical ventilation stage)</p> <p>d11a: Diastolic blood pressure at time -11 (conventional mechanical ventilation stage)</p> <p>sp11a: SpO<sub>2</sub> at time -11 (conventional mechanical ventilation stage)</p> <p>e11a: EtCO<sub>2</sub> at time -11 (conventional mechanical ventilation stage)</p> <p> </p> <p>p10a: Number of pulse beats at time -10 (conventional mechanical ventilation stage)</p> <p>s10a: Systolic blood pressure at time -10 (conventional mechanical ventilation stage)</p> <p>d10a: Diastolic blood pressure at time -10 (conventional mechanical ventilation stage)</p> <p>sp10a: SpO<sub>2</sub> at time -10 (conventional mechanical ventilation stage)</p> <p>e10a: EtCO<sub>2</sub> at time -10 (conventional mechanical ventilation stage)</p> <p> </p> <p>p9a: Number of pulse beats at time -9 (conventional mechanical ventilation stage)</p> <p>s9a: Systolic blood pressure at time -9 (conventional mechanical ventilation stage)</p> <p>d9a: Diastolic blood pressure at time -9 (conventional mechanical ventilation stage)</p> <p>sp9a: SpO<sub>2</sub> at time -9 (conventional mechanical ventilation stage)</p> <p>e9a: EtCO<sub>2</sub> at time -9 (conventional mechanical ventilation stage)</p> <p> </p> <p>p8a: Number of pulse beats at time -8 (conventional mechanical ventilation stage)</p> <p>s8a: Systolic blood pressure at time -8 (conventional mechanical ventilation stage)</p> <p>d8a: Diastolic blood pressure at time -8 (conventional mechanical ventilation stage)</p> <p>sp8a: SpO<sub>2</sub> at time -8 (conventional mechanical ventilation stage)</p> <p>e8a: EtCO<sub>2</sub> at time -8 (conventional mechanical ventilation stage)</p> <p> </p> <p>p7a: Number of pulse beats at time -7 (conventional mechanical ventilation stage)</p> <p>s7a: Systolic blood pressure at time -7 (conventional mechanical ventilation stage)</p> <p>d7a: Diastolic blood pressure at time -7 (conventional mechanical ventilation stage)</p> <p>sp7a: SpO<sub>2</sub> at time -7 (conventional mechanical ventilation stage)</p> <p>e7a: EtCO<sub>2</sub> at time -7 (conventional mechanical ventilation stage)</p> <p> </p> <p>p6a: Number of pulse beats at time -6 (conventional mechanical ventilation stage)</p> <p>s6a: Systolic blood pressure at time -6 (conventional mechanical ventilation stage)</p> <p>d6a: Diastolic blood pressure at time -6 (conventional mechanical ventilation stage)</p> <p>sp6a: SpO<sub>2</sub> at time -6 (conventional mechanical ventilation stage)</p> <p>e6a: EtCO<sub>2</sub> at time -6 (conventional mechanical ventilation stage)</p> <p> </p> <p>p5a: Number of pulse beats at time -5 (conventional mechanical ventilation stage)</p> <p>s5a: Systolic blood pressure at time -5 (conventional mechanical ventilation stage)</p> <p>d5a: Diastolic blood pressure at time -5 (conventional mechanical ventilation stage)</p> <p>sp5a: SpO<sub>2</sub> at time -5 (conventional mechanical ventilation stage)</p> <p>e5a: EtCO<sub>2</sub> at time -5 (conventional mechanical ventilation stage)</p> <p> </p> <p>p4a: Number of pulse beats at time -4 (conventional mechanical ventilation stage)</p> <p>s4a: Systolic blood pressure at time -4 (conventional mechanical ventilation stage)</p> <p>d4a: Diastolic blood pressure at time -4 (conventional mechanical ventilation stage)</p> <p>sp4a: SpO<sub>2</sub> at time -4 (conventional mechanical ventilation stage)</p> <p>e4a: EtCO<sub>2</sub> at time -4 (conventional mechanical ventilation stage)</p> <p> </p> <p>p3a: Number of pulse beats at time -3 (conventional mechanical ventilation stage)</p> <p>s3a: Systolic blood pressure at time -3 (conventional mechanical ventilation stage)</p> <p>d3a: Diastolic blood pressure at time -3 (conventional mechanical ventilation stage)</p> <p>sp3a: SpO<sub>2</sub> at time -3 (conventional mechanical ventilation stage)</p> <p>e3a: EtCO<sub>2</sub> at time -3 (conventional mechanical ventilation stage)</p> <p> </p> <p>p2a: Number of pulse beats at time -2 (conventional mechanical ventilation stage)</p> <p>s2a: Systolic blood pressure at time -2 (conventional mechanical ventilation stage)</p> <p>d2a: Diastolic blood pressure at time -2 (conventional mechanical ventilation stage)</p> <p>sp2a: SpO<sub>2</sub> at time -2 (conventional mechanical ventilation stage)</p> <p>e2a: EtCO<sub>2</sub> at time -2 (conventional mechanical ventilation stage)</p> <p> </p> <p>p1a: Number of pulse beats at time -1 (conventional mechanical ventilation stage)</p> <p>s1a: Systolic blood pressure at time -1 (conventional mechanical ventilation stage)</p> <p>d1a: Diastolic blood pressure at time -1 (conventional mechanical ventilation stage)</p> <p>sp1a: SpO<sub>2</sub> at time -1 (conventional mechanical ventilation stage)</p> <p>e1a: EtCO<sub>2</sub> at time -1 (conventional mechanical ventilation stage)</p> <p> </p> <p>p0a: Number of pulse beats at time 0 (conventional mechanical ventilation stage)</p> <p>s0a: Systolic blood pressure at time 0 (conventional mechanical ventilation stage)</p> <p>d0a: Diastolic blood pressure at time 0 (conventional mechanical ventilation stage)</p> <p>sp0a: SpO<sub>2</sub> at time 0 (conventional mechanical ventilation stage)</p> <p>e0a: EtCO<sub>2</sub> at time 0 (conventional mechanical ventilation stage)</p> <p> </p> <p>p1b: Number of pulse beats at time +1 (advanced Ambu bag system stage)</p> <p>s1b: Systolic blood pressure at time +1 (advanced Ambu bag system stage)</p> <p>d1b: Diastolic blood pressure at time +1 (advanced Ambu bag system stage)</p> <p>sp1b: SpO<sub>2</sub> at time +1 (advanced Ambu bag system stage)</p> <p>e1b: EtCO<sub>2</sub> at time +1 (advanced Ambu bag system stage)</p> <p> </p> <p>p2b: Number of pulse beats at time +2 (advanced Ambu bag system stage)</p> <p>s2b: Systolic blood pressure at time +2 (advanced Ambu bag system stage)</p> <p>d2b: Diastolic blood pressure at time +2 (advanced Ambu bag system stage)</p> <p>sp2b: SpO<sub>2</sub> at time +2 (advanced Ambu bag system stage)</p> <p>e2b: EtCO<sub>2</sub> at time +2 (advanced Ambu bag system stage)</p> <p> </p> <p>p3b: Number of pulse beats at time +3 (advanced Ambu bag system stage)</p> <p>s3b: Systolic blood pressure at time +3 (advanced Ambu bag system stage)</p> <p>d3b: Diastolic blood pressure at time +3 (advanced Ambu bag system stage)</p> <p>sp3b: SpO<sub>2</sub> at time +3 (advanced Ambu bag system stage)</p> <p>e3b: EtCO<sub>2</sub> at time +3 (advanced Ambu bag system stage)</p> <p> </p> <p>p4b: Number of pulse beats at time +4 (advanced Ambu bag system stage)</p> <p>s4b: Systolic blood pressure at time +4 (advanced Ambu bag system stage)</p> <p>d4b: Diastolic blood pressure at time +4 (advanced Ambu bag system stage)</p> <p>sp4b: SpO<sub>2</sub> at time +4 (advanced Ambu bag system stage)</p> <p>e4b: EtCO<sub>2</sub> at time +4 (advanced Ambu bag system stage)</p> <p> </p> <p>p5b: Number of pulse beats at time +5 (advanced Ambu bag system stage)</p> <p>s5b: Systolic blood pressure at time +5 (advanced Ambu bag system stage)</p> <p>d5b: Diastolic blood pressure at time +5 (advanced Ambu bag system stage)</p> <p>sp5b: SpO<sub>2</sub> at time +5 (advanced Ambu bag system stage)</p> <p>e5b: EtCO<sub>2</sub> at time +5 (advanced Ambu bag system stage)</p> <p> </p> <p>p6b: Number of pulse beats at time +6 (advanced Ambu bag system stage)</p> <p>s6b: Systolic blood pressure at time +6 (advanced Ambu bag system stage)</p> <p>d6b: Diastolic blood pressure at time +6 (advanced Ambu bag system stage)</p> <p>sp6b: SpO<sub>2</sub> at time +6 (advanced Ambu bag system stage)</p> <p>e6b: EtCO<sub>2</sub> at time +6 (advanced Ambu bag system stage)</p> <p> </p> <p>p7b: Number of pulse beats at time +7 (advanced Ambu bag system stage)</p> <p>s7b: Systolic blood pressure at time +7 (advanced Ambu bag system stage)</p> <p>d7b: Diastolic blood pressure at time +7 (advanced Ambu bag system stage)</p> <p>sp7b: SpO<sub>2</sub> at time +7 (advanced Ambu bag system stage)</p> <p>e7b: EtCO<sub>2</sub> at time +7 (advanced Ambu bag system stage)</p> <p> </p> <p>p8b: Number of pulse beats at time +8 (advanced Ambu bag system stage)</p> <p>s8b: Systolic blood pressure at time +8 (advanced Ambu bag system stage)</p> <p>d8b: Diastolic blood pressure at time +8 (advanced Ambu bag system stage)</p> <p>sp8b: SpO<sub>2</sub> at time +8 (advanced Ambu bag system stage)</p> <p>e8b: EtCO<sub>2</sub> at time +8 (advanced Ambu bag system stage)</p> <p> </p> <p>p9b: Number of pulse beats at time +9 (advanced Ambu bag system stage)</p> <p>s9b: Systolic blood pressure at time +9 (advanced Ambu bag system stage)</p> <p>d9b: Diastolic blood pressure at time +9 (advanced Ambu bag system stage)</p> <p>sp9b: SpO<sub>2</sub> at time +9 (advanced Ambu bag system stage)</p> <p>e9b: EtCO<sub>2</sub> at time +9 (advanced Ambu bag system stage)</p> <p> </p> <p>p10b: Number of pulse beats at time +10 (advanced Ambu bag system stage)</p> <p>s10b: Systolic blood pressure at time +10 (advanced Ambu bag system stage)</p> <p>d10b: Diastolic blood pressure at time +10 (advanced Ambu bag system stage)</p> <p>sp10b: SpO<sub>2</sub> at time +10 (advanced Ambu bag system stage)</p> <p>e10b: EtCO<sub>2</sub> at time +10 (advanced Ambu bag system stage)</p> <p> </p> <p>p11b: Number of pulse beats at time +11 (advanced Ambu bag system stage)</p> <p>s11b: Systolic blood pressure at time +11 (advanced Ambu bag system stage)</p> <p>d11b: Diastolic blood pressure at time +11 (advanced Ambu bag system stage)</p> <p>sp11b: SpO<sub>2</sub> at time +11 (advanced Ambu bag system stage)</p> <p>e11b: EtCO<sub>2</sub> at time +11 (advanced Ambu bag system stage)</p> <p> </p> <p>p12b: Number of pulse beats at time +12 (advanced Ambu bag system stage)</p> <p>s12b: Systolic blood pressure at time +12 (advanced Ambu bag system stage)</p> <p>d12b: Diastolic blood pressure at time +12 (advanced Ambu bag system stage)</p> <p>sp12b: SpO<sub>2</sub> at time +12 (advanced Ambu bag system stage)</p> <p>e12b: EtCO<sub>2</sub> at time +12 (advanced Ambu bag system stage)</p> <p> </p> <p>p13b: Number of pulse beats at time +13 (advanced Ambu bag system stage)</p> <p>s13b: Systolic blood pressure at time +13 (advanced Ambu bag system stage)</p> <p>d13b: Diastolic blood pressure at time +13 (advanced Ambu bag system stage)</p> <p>sp13b: SpO<sub>2</sub> at time +13 (advanced Ambu bag system stage)</p> <p>e13b: EtCO<sub>2</sub> at time +13 (advanced Ambu bag system stage)</p> <p> </p> <p>p14b: Number of pulse beats at time +14 (advanced Ambu bag system stage)</p> <p>s14b: Systolic blood pressure at time +14 (advanced Ambu bag system stage)</p> <p>d14b: Diastolic blood pressure at time +14 (advanced Ambu bag system stage)</p> <p>sp14b: SpO<sub>2</sub> at time +14 (advanced Ambu bag system stage)</p> <p>e14b: EtCO<sub>2</sub> at time +14 (advanced Ambu bag system stage)</p>
Shaping History: Advanced Machine Learning Techniques for the Analysis and Dating of Cuneiform Tablets over Three Millennia - Datasets
<p>Our research leverages advanced deep learning methods to classify cuneiform tablets by their historical periods, focusing on shape analysis rather than textual content. Utilizing a dataset of over 94,000 images from the Cuneiform Digital Library Initiative, we introduce a novel toolset powered by Variational Auto-Encoders (VAEs) to enhance model interpretability. By highlighting the predictive power of tablet silhouettes for historical period classification with a ResNet model reaching 61\% macro-F1 score,, our approach allows researchers to explore changes in tablet shapes across different eras. This methodology not only complements traditional archaeological methods but also enriches the field of document analysis and diplomatics, offering valuable tools for historians and epigraphists to understand ancient Mesopotamian cultures.</p> <p>The Datasets attached include:</p> <ul> <li>All CDLI IDs used and their URLs on CDLI</li> <li>All tablet silhouettes - Black and white representations</li> <li>A height-to-width ratio table, of the largest component extracted from the silhouettes.</li> <li>VAE encodings per tablet, extracted from the VAE model</li> </ul>
Matrix Framework for Analysis: Building a Matrix from Advanced Projects
<p>This raw dataset is part of a Framework Global - Matrix that enables a comprehensive and holistic assessment, simplifying comparisons and guiding decision-makers in selecting the alternatives that offer the greatest value in terms of financial return, social and environmental impact, and regulatory compliance. To streamline the analysis and comparison of indicators, the author utilized categories linked to Sustainable Competitiveness and the World Business Council for Sustainable Development (WBCSD). As a result, the indicators contribute to the functionality of the matrix, facilitating the identification of key projects related to the desired area and enabling future application of the data in sustainability-focused initiatives.</p>
FIGURE 1 in The Integrated Plant Record (IPR) analysis: Methodological advances and new insights into the evolution of European Palaeogene/Neogene vegetation
FIGURE 1. Zelkova zelkovifolia is a representative of the broad-leaved deciduous component.
Data for "Advanced Structural Health Monitoring Method by Integrated Isogeometric Analysis and Distributed Fiber Optic Sensing"
<p>This dataset includes the experiment and simulation data of a new structural health monitoring system using distributed fiber optic sensing (DFOS) and Isogeometric Analysis (IGA).</p> <p>The experiment setup was a 5mm thick PVC pipe with a fiber optic cable wrapped around the outer surface of the pipe. The PVC pipe was subjected to an applied deformation and the distributed strains along the optical fiber was measured with a Neubrescope (NBX7031) instrument using Rayleigh backscattering technology.</p> <p>The simulation was performed using the in-house code JWRIAN-IGA developed in Joining and Welding Research Institute, Osaka University. The simulated data includes deformation, stress and strain distributions of the pipe, and projected one-dimensional fiber strains. The visualization files are post-processed with ParaView software.</p>
Data from: Advancing Pyrus phylogeny: Deep genome skimming-based inference coupled with paralogy analysis yields a robust phylogenetic backbone and an updated infrageneric classification of the pear genus (Maleae, Rosaceae)
Open the record for dataset details and reuse information.
Supplementary material 3 from: Vasiliţa C, Popovici OA, Talamas E, Johnson N, Masner L, Tortorici F, Fusu L (2021) Molecular analysis reveals Latonius planus Kononova to be a derived species of Trissolcus Ashmead. In: Lahey Z, Talamas E (Eds) Advances in the Systematics of Platygastroidea III. Journal of Hymenoptera Research 87: 267-289. https://doi.org/10.3897/jhr.87.63533
Phylogenetic reconstruction on the partitioned data set of Taekul et al. (2014), Bayesian analysis. Posterior probability indicated at nodes
Supplementary material 1 from: Vasiliţa C, Popovici OA, Talamas E, Johnson N, Masner L, Tortorici F, Fusu L (2021) Molecular analysis reveals Latonius planus Kononova to be a derived species of Trissolcus Ashmead. In: Lahey Z, Talamas E (Eds) Advances in the Systematics of Platygastroidea III. Journal of Hymenoptera Research 87: 267-289. https://doi.org/10.3897/jhr.87.63533
Phylogenetic reconstruction on the non-partitioned data set of Taekul et al. (2014) with a degenerated COI alignment, ML analysis
FoodEx2vec: Poincaré Embeddings of a FoodEx2 Hierarchy for Advanced Food Data Analysis
<p>A dataset consisting of Poincaré embeddings of the FoodEx2 hierarchy, as well as the clustering results obtained with the Poincaré embeddings.</p> <p>It consists of two folders:</p> <ol> <li>foodex2vec_clusters - where the clustering using the Poincaré embeddings can be seen;</li> <li>foodex2vec_poincare_embeddings - the actual Poincaré embeddings with different dimensions. </li> </ol>
Data and analysis for the paper Qubits made by advanced semiconductor manufacturing
<p>Data and analysis scripts for the paper:</p> <p>Qubits made by advanced semiconductor manufacturing</p> <p>by A.M.J. Zwerver,<em> et al.</em></p>
Advancing crop disease early warning in South Asia by complementing expert surveys with internet media scraping - supporting data and analysis
<p>Contains data from experiments and analysis that support the associated paper currently under review.</p> <p>Also contains scripts to:</p> <p>- Get the latest scraped media reports from the online dashboard (see doi: 10.5281/zenodo.8024493), and generate proxy surveys from them;</p> <p>- Simulate wheat rust spore production based on available survey datasets.</p> <p>- Analyse results of surveys, and models of spore production and spore dispersal, and reproduce the figures contained in the associated paper and its supplement.</p> <p>Requirements: Docker installed and 6GB of available storage space.</p> <p>Steps to use:</p> <p>1) Download and unzip the contained file.</p> <p>2) (Linux) Run setup.sh.</p> <p> (Windows/OSX/other) Look in setup.sh for instructions on where to unzip the files contained in supporting_data/ and build the Docker image and container.</p> <p>3) Start and enter the Docker container, then run /home/code/sources-media-reports/run_all.sh.</p>
Wavefront Analysis and Contrast Sensitivity After Cataract Surgery With Akreos Advanced Optics Intraocular Lens Implantation
ClinicalTrials.gov study NCT00863759. IPD Sharing: Not stated. Countries: 1. Publications: 4.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.