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6,452 results for “applicative”
Data for: Application Performance Monitoring: Trade-Off between Overhead Reduction and Maintainability
<p>Monitoring of a software system provides insights into its runtime behavior, improving system analysis and comprehension. System-level monitoring approaches focus, e.g., on network monitoring, providing information on externally visible system behavior. Application-level performance monitoring frameworks, such as Kieker or Dapper, allow to observe the internal application behavior, but introduce runtime overhead depending on the number of instrumentation probes.<br /> We report on how we were able to significantly reduce the runtime overhead of the Kieker monitoring framework. For achieving this optimization, we employed micro-benchmarks with a structured performance engineering approach. During optimization, we kept track of the impact on maintainability of the framework. In this paper, we discuss the emerged trade-off between performance and maintainability in this context.<br /> To the best of our knowledge, publications on monitoring frameworks provide none or only weak performance evaluations, making comparisons cumbersome. However, our micro-benchmark, presented in this paper, provides a basis for such comparisons. Our experiment code and data are available as open source software such that interested researchers may repeat or extend our experiments for comparison on other hardware platforms or with other monitoring frameworks.</p> <p>This dataset supplements the paper and contains the raw experimental data as well as several generated diagrams for each experiment.</p>
OptiSpot: Minimizing Application Deployment Cost using Spot Cloud Resources
<p>1. Attached files: </p> <p>This archive contains 1800 MATLAB files, each one containing the results of a single experiment.<br /> The name of each file follows the following format:</p> <p> A_B_C_D_E_F_G.mat</p> <p>Where the fields A, B, C, D, E, F, and G are described as follows.</p> <p>A: number of users.<br /> Considered values are: 1000, 2000, 5000, 10000.</p> <p>B: maximum response time in milliseconds.<br /> Considered values are: 60, 80, 100, 200.</p> <p>C: overbid time cap in hours.<br /> Considered values are: 5, 20, 80, 0 (note: 0 is a code used to express infinite hours).</p> <p>D: Amazon region.<br /> Considered values are: us-east, eu-west.</p> <p>E: Operating system.<br /> Considerede values are: Windows, Linux.</p> <p>F: Optimization algorithm.<br /> Considered values are: heuristic (which is OptiSpot), fmincon.</p> <p>G: Experiment seed.<br /> Considered values are from 1 to 30</p> <p>2. Data format:</p> <p>MATLAB data format, can be loaded from MATLAB using the following command:</p> <p>results = load(filename);</p> <p>results is defined as a structure with the following fields:</p> <p>results.cost<br /> Type: scalar, positive real number.<br /> Desc: hourly cost in US dollars.</p> <p> <br /> results.time<br /> Type: scalar, positive real number.<br /> Desc: total time (in seconds) needed by the algorithm to compute the solution.</p> <p>results.evaluations<br /> Type: scalar, positive integer number.<br /> Desc: number of constraints evaluations needed by the algorithm to compute the <br /> solution.</p> <p><br /> results.d<br /> Type: matrix, non negative positive real number. <br /> Desc: association matrix between rented resources (columns) and application <br /> components (rows). The sum of all the elements of this matrix is equal to<br /> the ECUs used by the application.</p>
Developing Digital Image Processing methods to quantify internal and interfacial convection in the Hele-Shaw cell, with applications to the laboratory ice-ocean boundary layer
<p>This dataset provides the video and image files obtained from Schlieren optical experiment 3 performed in the <span>Laboratoire de Glaciologie (GLACIOL)</span> at the Universite de libre Bruxelles. A document detailing the visual data and supporting figures is presented (DataOverview.pdf). </p>
Short-Term Synchronous and Asynchronous Ambient Noise Tomography in Urban Areas: Application to Karst Investigation
<p>We used DSurfTomo (<a href="https://github.com/HongjianFang/DSurfTomo">HongjianFang/DSurfTomo: Direct inversion of surface dispersion data based on ray tracing (github.com)</a>) for the tomography.</p> <p>ABC2_2023.dat is the travel time of C1 and C2 cross-correlation functions, used in our tomography.</p> <p>ManualDSurfTomoV1.3.pdf is the manual of DSurfTomo, including the data format description for ABC2_2023.dat.</p> <p>yunqiVs3D.txt is the interpolated 3D Vs model, including longitude, latitude, depth (meter), Vs (m/s).</p> <p>Previous version error: I forgot to write the Vs value.</p>
MultiCaRe: An open-source clinical case dataset for medical image classification and multimodal AI applications
<p>The dataset contains multi-modal data from over 70,000 open access and de-identified case reports, including metadata, clinical cases, image captions and more than 130,000 images. Images and clinical cases belong to different medical specialties, such as oncology, cardiology, surgery and pathology. The structure of the dataset allows to easily map images with their corresponding article metadata, clinical case, captions and image labels. Details of the data structure can be found in the file data_dictionary.csv.</p> <p>More than 90,000 patients and 280,000 medical doctors and researchers were involved in the creation of the articles included in this dataset. The citation data of each article can be found in the metadata.parquet file.</p> <p>Refer to the examples showcased in this <a href="https://github.com/mauro-nievoff/MultiCaRe_Dataset">GitHub repository</a> to understand how to optimize the use of this dataset.<br><br>The license of the dataset as a whole is CC BY-NC-SA. However, its individual contents may have less restrictive license types (CC BY, CC BY-NC, CC0). For instance, regarding image filess, 66K of them are CC BY, 32K are CC BY-NC-SA, 32K are CC BY-NC, and 20 of them are CC0.</p>
Metadata for the CSTE application
<p>The reproducible R codes for CSTE, an R Shiny application for the covariate-specific treatment effect curve</p>
ForensicVM Windows 11 Virtualization Dataset: Cloud and Local Application Data Retrieval with Virtualization and Password Bypass Challenge
<p>A Windows 11 Pro dataset designed in VirtualBox, complete with local and cloud apps, is to be virtually analyzed for crucial evidence. Bypassing the straightforward 'Bart' password is essential for access, yet original passwords should remain unchanged for others to attempt the same challenge. The task involves determining the password's nature, Bart's motives, identifying involved cloud applications, and extracting data both offline and online. The viability of dead box forensics for complete data retrieval is questioned, alongside what additional information network access could unveil. The challenge includes identifying two financial applications, extracting their data, and gathering cryptocurrency-related information, presented as an engaging forensicVM showcase by Nuno Mourinho, Mario Candeias, and Rogerio Bravo (Escola Superior de Tecnologia e Gestão de Beja, Instituto Politécnico de Beja).</p>
A high-throughput 3D X-ray histology facility for biomedical research and preclinical applications - Supplementary Data
<p><strong>Videos</strong></p><ul><li><strong>Video 1</strong> A video going through the Z stack in single slices. This is a cross- sectional view of the XRH image stack along the XY plane. XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.</li><li><strong>Video 2 </strong>A video going through the Y stack in single slices. This is a cross- sectional view of the XRH image stack along the XZ plane. XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.</li><li><strong>Video 3 </strong>A video going through the X stack in single slices. This is a cross- sectional view of the XRH image stack along the YZ plane. XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.</li><li><strong>Video 4 </strong>3D X-ray histology (XRH) is a µCT -based workflow tailored to fit seamlessly into current histology workflows in biomedical and pre-clinical research, as well as clinical histopathology. Microanatomical detail can be captured from standard (non-stained) formalin-fixed and paraffin-embedded (FFPE) tissue blocks.</li><li><strong>Video 5</strong> Average Intensity Projection (AIP) of the sample through the Histologically relevant plane. This is a 2D visualisation rendering the Average Intensity of 20x single XY slices along the z-axis of the stack. XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.</li><li><strong>Video 6 </strong>Maximum Intensity Projection (MIP) of the sample through the Histologically relevant plane. This is a 2D visualisation rendering the Maximum Intensity of 20x single XY slices along the z-axis of the stack. XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.</li><li><strong>Video 7 </strong>Standard deviation projection of the sample going through the histologically relevant plane. This is a 2D visualisation rendering the Standard Deviation of 20x single XY slices along the z- axis of the stack. XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.</li></ul><p><i>* <strong>Videos 5 -7</strong> are also referred to as "thick-slice rolls" </i>- <i>Thick-slice rolling is a 2D thick-slice viewing that allows rolling of a pre-selected number of slices (n) along the z-axis of the 3D data. A single thick-slice roll forwards is accomplished by translating the thick-slice by one single slice forwards; that is moving forward by one (+1) slice from the first and nth element and reapplying the criteria or operations to the new slice sub-stack.</i><br> </p><p><strong>The questionnaire used to collect feedback about the needs of the XRH community.</strong></p><ul><li>Survey.docx</li><li>Survey.pdf</li></ul><p><br><strong>Exemplar report of a semi-automatically generated augmented PDF file</strong> that contain sample information, imaging settings, still images with descriptive figure legends, and links to corresponding online videos</p><ul><li>DEMO02019-FFPE_report_99EbPXG.pdf</li></ul><p> </p><p>= = = = = = = = = = = = = = = = <br><strong>System performance data ZIP</strong><br>= = = = = = = = = = = = = = = = </p><p>This ZIP file contains imaging data collected through different systems and setups at the XRH facility at the μ-VIS X-ray Imaging Centre at the University of Southampton for the purpose of acceptance and/or system performance characterisation. Below is an overview of the folder structure and its contents</p><p>The following files are X-ray imaging data collected on September 28, 2017, using the Med-X system and a Jima phantom at 55 kV peak and 7 Watts. </p><ul><li>20170928_MEDX_1642_JIMA_55kVp7W-2.tif</li><li>20170928_MEDX_1642_JIMA_55kVp7W.tif</li><li>20170928_MEDX_1642_JIMA_55kVp7W.tif.profile.xml</li></ul><p>This PDF document is related to a QRM MicroCT bar pattern phantom, and its specifications</p><ul><li>QRM-MicroCT-Barpattern-Phantom.pdf</li></ul><p>Graphs showing the calculated focal-spot size as a function of the X-ray power (W) for the Molybdenum rotating target calculated using Edge Modulation function testing. The performance is then compared with the performance of the Reflection target across the same range of powers. Raw data can be found in XRH_QRM_Refl-vs-Rot-TargetComparison_SingleReconSlices_5umPixelSize folder. Test performed in July 2021. </p><ul><li>XRH_202107_MoRot-testing_EdgeModFunction-QRMrecons+RotReflCompar.png</li></ul><p> </p><p><i><strong>/ XRH-XT-H-225-ST_FocalSpots</strong></i><br>This directory contains radiographic data collected using the XRH system with a JIMA phantom and MoRt (Molybdenum rotating), TT (Transmission), and Reflection targets.</p><ul><li>20200113_XRH_Jima test MoRT 55kV 15W.tif, 20200113_XRH_Jima test MoRT 55kV 30W.tif, etc.: <br>These files represent radiographs taken on January 13, 2020, using the XRH system, Jima phantom, MoRT target at 55 kVp and varying wattages.</li><li>20200207_XRH_JIMA 80kV TT1a.tif, 20200207_XRH_JIMA 80kV TT1b.tif, etc.<br>Similar to the above, these files are from February 7, 2020, and use 80 kVp with a TT target.</li><li>20231115_XRH_reflW_80kVp6W.tif, 20231115_XRH_reflW_80kVp6W_02.tif, etc.<br>These files are from November 15, 2023, and collected using the XRH system with a Reflection target at 80 kVp and 6 Watts.</li></ul><p><i><strong>/ XRH_QRM_Refl-vs-Rot-TargetComparison_SingleRadioFromCTs_5umPixelSize</strong></i><br>This directory contains single radiographs taken with a pixel size of 5 micrometers using the Molybdenum rotating (MoRt), and the Reflection target using tungsten (W) and Molybdenum (Mo) metals.</p><p><i><strong>/ XRH_QRM_Refl-vs-Rot-TargetComparison_SingleReconSlices_5umPixelSize</strong></i><br>This directory contains sinlge reconstruction slices of the setups mentioned above. Slices are exported from CT volumes and were used for the Edge Modulation function study. </p><p>For interpretation of the filenames in the folders listed above please see below and refer to specific files and folders for detailed information and results related to each imaging session:</p><ul><li><i><xx>kVp or <xx>kV </i>:Imaging at a peak voltage of <xx> kVp.</li><li><i><y>W</i> :Imaging at <y> Watts;<i> </i>"." is represented with "-"; i.e. 20210705_XRH_2766_PJB_TEST03552-EQPMT_W_6-9W is acquired using a power of 6.9 W</li><li><i>MoRt, TT, Refl </i> :Molybdenum, Transmission, and Reflection targets, respectively.</li><li><i>_W_ and _Mo_ </i> :Tungsten and Molybdenum target materials.</li><li><i>_horiz</i> :Reconstruction slices in line with the X-ray beam's propagation direction.</li><li><i>_vert</i> :Reconstruction slices normal to the X-ray beam's propagation direction and parallel to the detector plane.</li></ul>
User Feedback Dataset from the Top 15 Downloaded Mobile Applications
<p>This dataset comprises user feedback data collected from 15 globally acclaimed mobile applications, spanning diverse categories. The included applications are among the most downloaded worldwide, providing a rich and varied source for analysis. <i><strong>The dataset is particularly suitable for Natural Language Processing (NLP) applications</strong></i>, such as text classification and topic modeling.</p><p><strong>List of Included Applications:</strong></p><ul><li>TikTok</li><li>Instagram</li><li>Facebook</li><li>WhatsApp</li><li>Telegram</li><li>Zoom</li><li>Snapchat</li><li>Facebook Messenger</li><li>Capcut</li><li>Spotify</li><li>YouTube</li><li>HBO Max</li><li>Cash App</li><li>Subway Surfers</li><li>Roblox</li><li>Data Columns and Descriptions:</li></ul><p><strong>Data Columns and Descriptions:</strong></p><ul><li>review_id: Unique identifiers for each user feedback/application review.</li><li>content: User-generated feedback/review in text format.</li><li>score: Rating or star given by the user.</li><li>TU_count: Number of likes/thumbs up (TU) received for the review.</li><li>app_id: Unique identifier for each application.</li><li>app_name: Name of the application.</li><li>RC_ver: Version of the app when the review was created (RC).</li></ul><p><strong>Terms of Use:</strong></p><p>This dataset is open access for scientific research and non-commercial purposes. Users are required to acknowledge the authors' work and, in the case of scientific publication, cite the most appropriate reference:</p><p>M. H. Asnawi, A. A. Pravitasari, T. Herawan, and T. Hendrawati, "The Combination of Contextualized Topic Model and MPNet for User Feedback Topic Modeling," in IEEE Access, vol. 11, pp. 130272-130286, 2023, doi: <a href="https://doi.org/10.1109/ACCESS.2023.3332644">10.1109/ACCESS.2023.3332644</a>.</p><blockquote><p>Researchers and analysts are encouraged to explore this dataset for insights into user sentiments, preferences, and trends across these top mobile applications. If you have any questions or need further information, feel free to contact the dataset authors.</p></blockquote>
Data for the publication "Sodium Triflate Water-in-Salt Electrolyte in Advanced Battery Applications: A First-principles Based Molecular Dynamics Study"
<p>The datasets 'CONTCAR_aiMLMD' and 'CONTCAR_AIMD' represent the final structures obtained from the aiMLMD and AIMD simulations, respectively. These simulations were conducted using VASP at T=333K and c=9.25 m.</p> <p>The datasets 'NP.rdf' and 'MSD_NP.xlsx' represent the radial pair distribution functions at different time steps and the time-dependent variations of mean squared displacement for sodium in 10 segments of the classical MD trajectory. The associated MD simulation was performed using a nonpolarizable force field in the LAMMPS package at T=333K and c=9.25 m. The file 'dataNP.lmp' includes the initial configuration for this simulation. The GROMOS parameters were employed for LJ interactions of sodium and all other force field parameters were set according to Table 1 in the manuscript.</p> <p>The datasets 'P.rdf' and 'MSD_P.xlsx,' respectively, represent the radial pair distribution functions at different time steps and the time-dependent variations of mean squared displacement for sodium in 10 segments of the classical MD trajectory. These data were obtained employing the Drude oscillator model in the LAMMPS package at T=333K and c=10 m. The file 'dataP.lmp' includes the initial configuration for this simulation. The simulation was conducted using the optimal force field parameters 'Sys. 1,' as described in table 3 of the manuscript.</p> <p>The second column in the files 'NP.rdf' and 'NP.rdf' represents the distance from sodium. The subsequent odd columns display the radial distribution functions for the Na-C, Na-F, Na-S, Na-O, Na-Na, Na-Hw, and Na-Ow pairs, while the even columns present the coordination numbers for the same atom pairs.</p>
Data and Code for Publication "Estimating inter-individual Mahalanobis distances from mixed incomplete high-dimensional data: Application to human skeletal remains from 3rd to 1st millennia BC Southwest Germany"
<p>Data and code for publication: H. Rathmann, S. Lismann, M. Francken, A. Spatzier, Estimating inter-individual Mahalanobis distances from mixed incomplete high-dimensional data: Application to human skeletal remains from 3<sup>rd</sup> to 1<sup>st</sup> millennia BC Southwest Germany. <em>Journal of Archaeological Science</em> 156: 105802. <a href="https://doi.org/10.1016/j.jas.2023.105802">https://doi.org/10.1016/j.jas.2023.105802</a></p> <p>The repository contains:</p> <ul> <li>“R code for FLEXDIST.txt”: R code for executing FLEXDIST, a tool to estimate inter-individual Mahalanobis-type distances, taking correlations among variables into account, applicable to multiple variable scales (nominal, ordinal, continuous, or any mixture thereof), accommodating missing values, and handling high-dimensional data. <strong>Please refer to the latest version of this repository for the most up-to-date R code</strong>.</li> <li>“data.csv”: Pre-processed dataset comprising 85 dental morphological features collected from 64 archaeological human remains from Final Neolithic to Early Iron Age Southwest Germany used for analysis.</li> <li>“complete dataset.xlsx”: Complete dataset comprising 199 dental morphological features collected from 144 archaeological human remains from Final Neolithic to Early Iron Age Southwest Germany.</li> </ul>
Stability Increase of Phenolic Acid Decarboxylase by a Combination of Protein and Solvent Engineering Unlocks Applications at Elevated Temperatures
<p>Enzymatic decarboxylation of biobased hydroxycinnamic acids gives access to phenolic styrenes for adhesive production. Phenolic acid decarboxylases are proficient enzymes that have been applied in aqueous systems, organic solvents, biphasic systems, and deep eutectic solvents, which makes stability a key feature. Stabilization of the enzyme would increase the total turnover number and thus reduce the energy consumption and waste accumulation associated with biocatalyst production. In this study, we used ancestral sequence reconstruction to generate thermostable decarboxylases. Investigation of a set of 16 ancestors resulted in the identification of a variant with an unfolding temperature of 78.1 °C and a half-life time of 45 h at 60 °C. Crystal structures were determined for three selected ancestors. Structural attributes were calculated to fit different regression models for predicting the thermal stability of variants that have not yet been experimentally explored. The models rely on hydrophobic clusters, salt bridges, hydrogen bonds, and surface properties and can identify more stable proteins out of a pool of candidates. Further stabilization was achieved by the application of mixtures of natural deep eutectic solvents and buffers. Our approach is a straightforward option for enhancing the industrial application of the decarboxylation process.</p>
Data to Support Predictive Models for Detrital Titanite Provenance with application to the Nanga Parbat syntaxial massif, western Himalaya."
<p>The files published here are metadata that are being used to support a manuscript currently (Mar, 2024) undergoing final reviews in Journal of Geophysical Research: Earth Surface.</p> <p>The intention of these data and code is to support a publication that is about generating a predictive categorisation scheme for the mineral titanite.</p> <p>The code to generate the titanite classification schemes was created in Python3, using Jupyter Notebook. The files also provide more motivation for why a predictive categorisation scheme for the mineral titanite is desirable, and other similar context. Chiefly, the dataset and random forest models published here will allow us to trace titanite in detritus.</p> <p>For info on running Jupyter Notebook, please visit (<a href="https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/execute.html">https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/execute.html</a>) to seek instructions. We also provide a readme file with some instructions. If you get really stuck, just email the authors.</p> <p>Our Model can be compared to similar previously published works (e.g. <a href="https://doi.org/10.1111/ter.12574">https://doi.org/10.1111/ter.12574</a>). Model was trained using skikit-learn v1.41.</p> <p>The supplementary file "Table_S4_Merged.csv" was used to train and generate the model.</p> <p>Your unknowns must contain the correct elements and labelling for the code to successfully run, these details are provided in the code (Titanite_Random_Forest_Model1_Mar24.ipynb). A template is also provided for you to paste your unknown data into (titanite_data_template.csv)</p> <p>Any new published data are titanite compositional or isotopic data collected by LA-ICP-MS. Description of how those data were collected is given in "OSullivan_et_al_Supp..." file.</p> <p>Some of the data, information and code in this submission has been subject to change after journal review, this is a second version of this content.</p> <p>References for the dataset compilation are provided in File S3.</p> <p>If you have any queries contact:<br>Gary O'Sullivan, Trinity College Dublin</p>
Data for :Nitrogen availability in digestates from full-scale biogas plants following soil application as affected by operation parameters and input feedstocks
<p>This archive contains data for the paper "Nitrogen availability in digestates from full-scale biogas plants following soil application as affected by operation parameters and input feedstocks". Obtained from a soil incubation experiment for 80 days.</p><p> </p>
Datasets of synthetic workflows for evaluating a multi-objective and multi-constrained scheduling approach for cyber-physical applications
<p>These datasets of synthetic workflows (task graphs) were generated to evaluate the performance and scalability of a multi-objective and multi-constrained scheduling approach for workflow applications of various structures, sizes, and sensing/actuating requirements in a cyber-physical system (CPS) based on the edge-hub-cloud paradigm. The examined CPS comprised four edge devices (i.e., single-board computers, each attached to an unmanned aerial vehicle (UAV) equipped with sensors/actuators) interacting with a hub device (e.g., a laptop), which in turn communicated with a more computationally capable cloud server. All system devices featured heterogeneous multicore processors with different processing core failure rates and varied sensing/actuating or other specialized capabilities. Our objectives were the minimization of the overall latency, the minimization of the overall energy consumption, and the maximization of the overall reliability of the workflow application in the specific CPS, under deadline, reliability, memory, storage, energy, capability, and task precedence constraints.</p> <p>We generated 25 random task graphs with 10, 20, 30, 40, and 50 nodes (5 task graphs for each size), utilizing the Task Graphs For Free (TGFF) random task graph generator [1],[2]. Additional task parameters (e.g., execution time, power consumption, memory, storage, output data size, capability, reliability threshold) were included post-generation, using appropriate values. More details are provided in README.txt.<br><br>References:<br>[1] R. P. Dick, D. L. Rhodes, and W. Wolf, "TGFF: Task graphs for free," Proceedings of the Sixth International Workshop on Hardware/Software Codesign (CODES/CASHE), 1998, pp. 97-101, doi: 10.1109/HSC.1998.666245.<br>[2] R. P. Dick, D. L. Rhodes, and K. Vallerio, "TGFF," https://robertdick.org/projects/tgff/.</p>
EAT example applications
<p>These are example applications for the <a href="../doi/10.5281/zenodo.10306435">Ensemble and Assimilation Tool (EAT).</a></p> <p>Instructions for running these examples can be found in the contained <code>README.md</code>.</p> <p>This updated version uses Jupyter notebooks for all three applications. It requires EAT version 1.1 or higher.</p>
Roughness and Energy Losses Induced by Mussel Growth on the Walls of Hydraulic Structures and Application to a Water Transfer Project
<p>This file contains the ADV data of <em>Roughness and Energy Losses Induced by Mussel Growth on the Walls of Hydraulic Structures and Application to a Water Transfer Project</em>.</p>
Calibration of non-local damage models from full-field measurements: application to discrete element fields.
<p>The codes, datasets, and results from the manuscript 'Calibration of non-local damage models from full-field measurements: application to discrete element fields' are available here.</p> <p> </p> <p>This repository is organized into four folders:</p> <ol> <li><strong>'Ideal Case' folder</strong>: This corresponds to Section ‘3. Application of the calibration method on a 1D ideal case' of the manuscript. In this folder, you will find the <a href="https://freefem.org/" target="_blank" rel="noopener">FreeFEM+</a> and <a href="https://www.python.org/" target="_blank" rel="noopener">Python</a> scripts and a makefile to run them. These codes reproduce all the figures, as well as the complete dataset associated with Figures 1-7.</li> <li><strong>'Virtual Testing Machine' folder</strong>: This corresponds to Section 4, 'Introduction of a Virtual Testing Machine' of the manuscript. Here, you will find: <ul> <li>The data for Figure 9, contains the force vs. CMOD response for all sizes and geometries.</li> <li>The data for Figure 10, contains the scores associated with this parametric identification.</li> </ul> </li> <li><strong>'Green Functions' folder</strong>: This includes the <a href="https://freefem.org/" target="_blank" rel="noopener">FreeFEM++</a> code to solve the boundary value problem for obtaining Green's function of the Eikonal equation. The code is demonstrated with both a non-damaged case and a polynomial damage case. In the first case, Green’s function corresponds to the weighting functions of the Implicit Gradient method (<a href="https://doi.org/10.1002/(SICI)1097-0207(19961015)39:19<3391::AID-NME7>3.0.CO;2-D" target="_blank" rel="noopener">Peerlings et al., 1996</a>).</li> <li><strong>'Real Case' folder</strong>: This corresponds to Section 5, 'Application of the Proposed Calibration Procedure to Virtual Tests.' Here, you will find: <ul> <li>The complete 'Damage vs. Strain' Dataset was generated with the Virtual Testing Machine. Figures 12-15 correspond to this dataset, processed as explained in the manuscript.</li> <li>The 'damage vs. damage driving variable' dataset for each characteristic length. Figure 16 in the manuscript corresponds to this dataset.</li> <li>The data for Figures 17 and 18, contain the evolution of the error with the characteristic length for different evolution laws.</li> </ul> </li> </ol>
Data for: COVID-19 patents/patent applications (Jan. 2020 – Oct. 2021)
<p>This dataset contains information regarding both applications and granted patents on COVID-19 disease</p> <p>Derwent Innovation database was used for data mining (accessed on Nov. 20, 2021).</p> <p>The patent search was carried out on by means of a precise set of keywords and performed in the title/abstract/claims search field.</p> <p>6,148 Inpadoc patent families were retrieved. </p> <p>The <em>XLS file</em> contains information related to Title, Abstract - DWPI , First Claim, Priority Number, Priority Date, Application Number, Application Date, Publication Number, Publication Date, IPC – Current, CPC – Current, Assignee/Applicant, Optimized Assignee, INPADOC Family Members. </p> <p>The top countries/regions are China (3,271), WO (1,057), India (487), United States (455).</p> <p>The top IPC codes are listed in the following table:</p> <p> </p> <table align="center"> <tbody> <tr> <td> <p><strong>IPC</strong></p> </td> <td> <p><strong>Definition</strong></p> </td> <td> <p><strong>No. of patents/applications</strong></p> </td> </tr> <tr> <td> <p>A61P 31/14</p> </td> <td> <p><em>Antivirals for RNA viruses</em></p> </td> <td> <p>1565</p> </td> </tr> <tr> <td> <p>G01N 33/569</p> </td> <td> <p><em>Biological material •• Chemical analysis of biological material ••• Immunoassay; Biospecific binding assay; Materials therefor •••• for microorganisms</em></p> </td> <td> <p>783</p> </td> </tr> <tr> <td> <p>C12Q 1/70</p> </td> <td> <p><em>Measuring or testing processes • involving virus or bacteriophage</em></p> </td> <td> <p>642</p> </td> </tr> <tr> <td> <p>A61P 11/00</p> </td> <td> <p><em>Drugs for disorders of the respiratory system</em></p> </td> <td> <p>582</p> </td> </tr> <tr> <td> <p>A61K 39/215</p> </td> <td> <p><em>Medicinal preparations containing antigens or antibodies • Viral antigens •• Coronaviridae, e.g., avian infectious bronchitis virus</em></p> </td> <td> <p>377</p> </td> </tr> </tbody> </table> <p><strong>Value of the dataset</strong>: prior art searches; patent landscape analysis </p> <p><strong>Steps to reproduce data</strong>: </p> <p>CTB=("covid-19" OR "covid 19" OR "covid19" ADJ "SARS-CoV-2" OR "SARS-CoV2" OR "sarscov2" ADJ "2019 ncov" OR "2019-nCoV" OR "2019nCoV" ADJ "covid-2019" OR "covid 2019" OR "COVID2019" OR "severe acute respiratory syndrome coronavirus 2" OR "2019 novel coronavirus" OR "coronavirus disease 2019" OR "novel corona virus" OR "novel coronavirus" OR "new corona virus" OR "new coronavirus" OR "Wuhan coronavirus")</p> <p>CTB=title/abstract/claims</p>
Dataset for "Implementation of disequilibrium chemistry to spectral retrieval code ARCiS and application to 16 exoplanet transmission spectra. Indication of disequilibrium chemistry for HD 209458b and WASP-39b"
<p>This is the supplemental materials for the Astronomy & Astrophysics publication "Implementation of disequilibrium chemistry to spectral retrieval code ARCiS and application to 16 exoplanet transmission spectra. Indication of disequilibrium chemistry for HD 209458b and WASP-39b". Please refer to "README.md" for details.</p>
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.