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282 results for “data engineering”
Data for: A Benchmark Engineering Methodology to Measure the Overhead of Application-Level Monitoring
<p>Application-level monitoring frameworks, such as Kieker, provide insight into the inner workings and the dynamic behavior of software systems. However, depending on the number of monitoring probes used, these frameworks may introduce significant runtime overhead. Consequently, planning the instrumentation of continuously operating software systems requires detailed knowledge of the performance impact of each monitoring probe.</p> <p>In this paper, we present our benchmark engineering approach to quantify the monitoring overhead caused by each probe under controlled and repeatable conditions. Our developed MooBench benchmark provides a basis for performance evaluations and comparisons of application-level monitoring frameworks. To evaluate its capabilities, we employ our benchmark to conduct a performance comparison of all available Kieker releases from version 0.91 to the current release 1.8.</p> <p>This dataset supplements the paper and contains the raw experimental data as well as several generated diagrams for each experiment.</p>
Research data supporting "Pericyte seeded dual peptide scaffold with improved endothelialization for vascular graft tissue engineering"
<p>Raw research data supporting the paper:</p> <p>Campagnolo, P. <em>et al</em>., Pericyte seeded dual peptide scaffold with improved endothelialization for vascular graft tissue engineering, 2016, Advanced Healthcare Materials, 5(23), 3046-3055.</p> <p> </p>
Research data supporting "Online quantitative monitoring of live cell engineered cartilage growth using diffuse fiber-optic Raman spectroscopy"
<p>Research data supporting the publication:</p> <p>M. Bergholt, 2017, Online quantitative monitoring of live cell engineered cartilage growth using diffuse fiber-optic Raman spectroscopy, Biomaterials, Volume 140, September 2017, Pages 128–137, DOI: 10.1016/j.biomaterials.2017.06.015</p>
Research data supporting "Enzyme Prodrug Therapy Engineered into Electrospun Fibers with Embedded Liposomes for Controlled, Localized Synthesis of Therapeutics"
<p>Research data supporting the publication: Chandrawati R. et al., 2017, Enzyme Prodrug Therapy Engineered into Electrospun Fibers with Embedded Liposomes for Controlled, Localized Synthesis of Therapeutics, Advanced Healthcare Materials. DOI: 10.1002/adhm.201700385</p>
Fracture data from Monte Carlo simulation and practical engineering cases
<p>This data is mainly used for inferring each fracture size and spatial pattern from its trace exposed on the rock mass outcrop.</p>
Reference data and analysis software for "Four-color single-molecule imaging with engineered tags resolves the molecular architecture of signaling complexes in the plasma membrane"
<p>Reference data set for the single molecule co-tracking analysis presented in "Four-color single-molecule imaging with engineered tags resolves the molecular architecture of signaling complexes in the plasma membrane". Corresponding author for further inquiries:</p> <p>Prof. Dr. Jacob Piehler</p> <p>University of Osnabrück, Department of Biology/Chemistry, Division of Biophysics, Barbarastr. 11, 49076 Osnabrück, Germany</p> <p>https://www.biophysik.uni-osnabrueck.de/</p>
Data and R code from: Relics of beavers past: time and population density drive scale-dependent patterns of ecosystem engineering
<p><span>Like many ecological processes, natural disturbances exhibit scale-dependent dynamics that are largely a function of the magnitude, frequency, and scale at which they are assessed. Ecosystem engineers create patch-scale disturbances that affect ecological processes, yet we know little about how these effects scale across space or vary through time. Here, we investigate how patch disturbances by beavers (<i>Castor canadensis</i>), ecosystem engineers renowned for their pond-creation behavior, affect ecological processes across space and time. We evaluated how beaver population recovery influenced surface water dynamics in relation to population density over 70 years across multiple spatial scales (pond, watershed, and regional) in northern Minnesota. Surface water area was positively related to population density at the watershed scale; however, despite variation in beaver densities (and therefore surface water area) at the watershed scale, regional-scale surface water area was stable through time. This stability appears to have been driven by asynchronous beaver density fluctuations among watersheds, combined with the increasing importance of abandoned ponds. Beavers initially created and occupied larger ponds with greater surface water area, but through time shifted towards occupying smaller ponds. As ponds accumulated on the landscape proportionally more surface water was stored within abandoned ponds, which offset the smaller size of occupied ponds. Beaver engineering—driven by density-dependent mechanisms and the legacy effects from abandoned ponds—not only follows general patterns of patch disturbance dynamics by creating a spatial mosaic of patches, but the organism-created mosaic also appears to generate ecological stability at greater spatial scales. We suggest restoring beavers to landscapes is a viable method for increasing surface water storage and will ultimately help advance numerous conservation and rewilding objectives. Our study demonstrates that ecosystem engineering effects can be scale-dependent, indicating researchers should evaluate the ecological impact of engineers across diverse spatiotemporal scales to fully understand their functional roles in ecosystems.</span></p>
Open access data from the International Design Engineering Annual (IDEA) Challenge 2021
<p>Open access dataset from the IDEA challenge 2021. </p> <p>The generation of this dataset has been undertaken as part of the ProtoTwin project (Improving the product development process through integrated revision control and twinning of digital-physical models during prototyping). The work was conducted at the University of Bristol in the Design and Manufacturing Futures Lab (<a href="http://www.dmf-lab.co.uk/">http://www.dmf-lab.co.uk</a>) and is funded by the Engineering and Physical Sciences Research Council (EPSRC), Grant reference <a href="https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/R032696/1">EP/R032696/1</a>. The dataset was generated in collaboration with the Norwegian Technical University (NTNU), University of Zagreb and University of Twente.</p> <p>For more information please contact Mark (mark.goudswaard @ bristol.ac.uk) or James ( james.gopsill @ bristol.ac.uk)</p>
Data for "Engineering Higgs dynamics by spectral singularities"
<p>These files contain the data for the article "Engineering Higgs dynamics by spectral singularities" (<a href="https://doi.org/10.48550/arXiv.2205.06826">https://doi.org/10.48550/arXiv.2205.06826</a>).</p> <p>In each .zip file, it is possible to find not only the relevant data, but also a script (.sh file extension) to generate each one of the 5 figures shown in the paper and Supplemental Information Material. The file "Phasediagram.zip" contains the data corresponding to the dynamical phase diagrams (Fig. 1 of the manuscript) for the model of flat and graphene-like density of states (DOS).</p> <p>The data for the representative dynamical phases and Fourier Transforms for the constant DOS and graphene-like DOS model can be found in the files "Dynamics_flatDOS.zip" and "Dynamics_grapheneDOS.zip" respectively. </p> <p>The Fourier Transforms of the x-component of pseudospins texture are stored in the files "FFT_x-componentofpseudospintexture_flatDOS.zip" and "FFT_x-componentofpseudospintexture_grapheneDOS.zip" for the flat DOS and graphene DOS case respectively.</p> <p>For all the .txt files inside the .zip files, we have added at the top of each column a brief description of the data recorded below. Some notations have been used and read as follows: "Delta" indicates the superconducting order parameter, and "\xi_k" means the quasiparticle energy.</p>
Supplementary data for "ENGINEERED ADAPTATION MECHANISMS BETWEEN MARINE AND FRESHWATER ENVIRONMENTS IN FISH AFTER THE FLOOD" for the ICC 2023 in Cedarville, Ohio
<p>Supplementary data for "ENGINEERED ADAPTATION MECHANISMS BETWEEN MARINE AND FRESHWATER ENVIRONMENTS IN FISH AFTER THE FLOOD" for the ICC 2023 in Cedarville, Ohio.</p> <p>These include FishBase annotation, mtDNA sequence similarity matrixes, clustering, and statistics for nine fish orders:</p> <p>1. Acipenseriformes</p> <p>2. Angulliformes</p> <p>3. Beloniformes</p> <p>4. Characiformes</p> <p>5. Clupeiformes</p> <p>6. Cyprinodontiformes</p> <p>7. Elasmobranchii</p> <p>8. Pleuronectiformes</p> <p>9. Salmoniformes</p>
Data - Teaching and Learning of Introduction to Software Engineering Experimentation to Distance-Learning Students: a Quasi-Experiment
<p>Data from a quasi-experiment on "Teaching and Learning of Introduction to Software Engineering Experimentation to Distance-Learning Students: a Quasi-Experiment"</p> <p> </p> <p>Funded by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) 311503/2022-5</p>
3D Laser Scanning Data: Public Square in Murcia and Engineering Laboratory at the University of Alicante
<p>This dataset includes 3D terrestrial laser scans obtained using the Leica C10 ScanStation. The data covers two distinct scenarios:</p> <ol> <li> <p><strong>Public Square in Murcia Capital</strong>: This dataset includes two scan positions within a public square located in Murcia. Three HDTarget markers were placed, and their center or vertex coordinates are provided in the accompanying _vertices.txt file. The scans were conducted with the laser scanner leveled, but they are not registered.</p> </li> <li> <p><strong>Engineering Laboratory at the University of Alicante</strong>: This dataset consists of two scans of the Ground Engineering Laboratory at the University of Alicante. The scans were conducted with the same leveled laser scanner, and no targets were used. Between the two scans, some elements in the laboratory were slightly moved, which can be identified by comparing the point clouds.</p> </li> </ol>
Data for the paper: The Role of Glycerol in Manufacturing Freeze-Dried Chitosan and Cellulose Foams for Mechanically Stable Scaffolds in Skin Tissue Engineering
<p>The Dataset contains all the data, described in the article "<strong>The Role of Glycerol in Manufacturing Freeze-Dried Chitosan </strong><br><strong>and Cellulose Foams for Mechanically Stable Scaffolds in Skin Tissue Engineering</strong>."</p> <p><strong><em>Abstract</em></strong><br>Various strategies have extensively explored enhancing the physical and biological properties of chitosan and cellulose scaffolds for skin tissue engineering. This study presents a straightforward method involving the addition of glycerol into highly porous structures of two polysaccharide complexes: chitosan/carboxymethyl cellulose (Chit/CMC) and chitosan/oxidized cellulose (Chit/OC); during a one-step freeze-drying process. Adding glycerol, especially to Chit/CMC, significantly increased stability, prevented degradation, and improved mechanical strength by nearly 50%. Importantly, after 21 days of incubation in enzymatic medium Chit/CMC scaffold has almost completely decomposed, while foams reinforced with glycerol exhibited only 40% mass loss. It is possible due to differences in multivalent cations and polymer chain contraction, resulting in varied hydrogen bonding <br>and, consequently, distinct physicochemical outcomes. Additionally, the scaffolds with glycerol improved the cellular activities resulting in over 40% higher proliferation of fibroblast after 21 days of incubation. It was achieved by imparting water resistance to the highly absorbent material and aiding in achieving a balance between hydrophilic and hydrophobic properties. This study clearly indicates the possible elimination of additional crosslinkers and multiple fabrication steps that can reduce the cost of scaffold production for skin tissue engineering applications while tailoring mechanical strength and degradation.</p> <p><strong>Figure 2.</strong> Morphology. SEM micrographs of the internal structure of the freeze-dried scaffolds. Results of porosity analysis. The methodology and data are described in the README file in the folder.</p> <p><strong>Figure 3.</strong> Mechanical test results. Representative stress-strain curves from the tensile test of all freeze-dried scaffolds, where (A)<br>– measurement performed in dry conditions, (B) – measurement performed in wet conditions. All are described in the README file in the folder.</p> <p><strong>Figure 4. </strong>Swelling behavior of all scaffolds. B – Two representative vials with a visual demonstration of swelling, samples marked with circles: Chit/CMC sample submerged in the PBS (blue circle) and Chit/CMC/Glyc sample floating on the surface (green circle). The arrows lead to photos of scaffolds taken from vials directly after swelling. C – Gel fraction analysis in aqueous solution after 24 <br>6 h. D – Time after which the water droplet is absorbed into the scaffold. E – Photographs of water droplet shape changes on Chit/CMC and Chit/CMC/Glyc scaffolds over time. All are described in the README file in the folder.</p> <p><strong>Figure 5</strong>. Fourier Transform Infrared Spectroscopy (ATR-FTIR) analysis results. Details are in the README file in the folder.</p> <p><strong>Figure 6.</strong> The FTIR spectra of eluates from degraded scaffolds collected on a microscopic glass slide. Details are in the README file in the folder.</p> <p><strong>Figure 7.</strong> The degradation studies of all scaffolds over 21 days of experiments in A – enzymatic medium. B – cell culture medium. Details are in the README file in the folder.</p> <p><strong>Figure 9. </strong>Cell experiments and toxicity analysis. Cytotoxicity of eluates taken from degraded scaffolds. B – Direct fibroblast seeding on scaffolds during 14 days of culture period. C – Direct fibroblast seeding on scaffolds during 14 days of culture period without control to better see the effect of glycerol. Details are in the README file in the folder.</p>
Research data supporting "Raman spectroscopic imaging for quantification of depth-dependent and local heterogeneities in native and engineered cartilage"
<p>Research data supporting the publication: Albro M. et al., 2018, npj Regenerative Medicine, DOI: https://doi.org/10.1038/s41536-018-0042-7.</p>
Research data supporting "Engineering anisotropic muscle tissue using acoustic cell patterning"
<p>Raw research data supporting the publication:</p> <p>Armstron, JPK et al., "Engineering anisotropic muscle tissue using acoustic cell paterning", Advanced Materials, DOI: 10.1002/adma.201802649 (2018)</p>
Survey Data Set Part 1 - Attitudes Towards Videos as a Documentation Option for Communication in Requirements Engineering
<p>In 2017, we conducted an online survey to explore software professionals' attitudes towards videos as a documentation option for communication in requirements engineering. The survey covered the following topics:</p> <ul> <li>Demographics</li> <li>Attitude towards videos as a medium in RE including its strengths, weaknesses, opportunities, and threats</li> <li>Current production and use of videos in RE, respectively the obstacles that prevent the production and use of videos</li> </ul> <p>64 out of 106 software professionals from industry and academia completed the survey. The survey was implemented in LimeSurvey and distributed across several communication channels such as LinkedIn, ResearchGate, and a mailing list of a German RE professionals group.</p> <p>This dataset includes the following files:</p> <ul> <li>"Raw and analyzed data.xlsx" contains the raw and analyzed survey responses which are anonymized <ul> <li>This data includes <em>demographics </em>and <em>attitude</em>.</li> <li>The data on <em>video production and use</em> are included in: <a href="https://zenodo.org/record/4064741">Survey Data Set Part 2 - Attitudes Towards Videos as a Documentation Option for Communication in Requirements Engineering</a>.</li> </ul> </li> <li>"Survey - Offline version.docx" contains the questions and possible answers of the survey</li> <li>"Survey - Offline version.pdf" contains the questions and possible answers of the survey</li> </ul> <p>This survey was designed, conducted, and analyzed by Oliver Karras (<a href="https://twitter.com/KarrasOliver">@KarrasOliver</a>).</p>
Evaluation data used in "An innovative STEM outreach model (OH-Kids) to foster the next generation of geoscientists, engineers, and technologists"
<p>This repository contains all data of the evaluation questionnaire used to assess modifications in pupils’ perceptions of same water resources concepts and science and scientist resulting from the application of OH-Kids outreach model in six Mexican primary schools (n=344 pupils).</p>
Original data and code for "Wave-function engineering on superconducting substrates: Chiral Yu-Shiba-Rusinov molecules"
<p>We provide all experimental data and the code to simulate the tight-binding YSR patterns in the paper "Wave-function engineering on superconducting substrates: Chiral Yu-Shiba-Rusinov molecules"</p>
Data Echoes: Tracking Data Availability and Integrity in Software Engineering Research
<p><strong>This is the dataset of the report: Data Echoes: Tracking Data Availability and Integrity in Software Engineering Research</strong></p> <p>It contains the following information of all the papers from ASE, FSE, and ICSE in 2023:</p> <ul> <li>Paper title</li> <li>Keyword</li> <li>Is the source data available and accessible in the paper?</li> <li>If the source data is not available, do the authors explain why?</li> <li>Hosting platforms</li> <li>Access mode</li> <li>License</li> <li>Is their experiment data reused from previous work, or newly generated specifically for this study, or combination of both? </li> <li>Do the authors change/modify their experiment data before experiment?</li> <li>What modifications do they perform?</li> <li>Does the link provide detailed instructions about how to replicate their paper?</li> <li>Does the link contains their complete experiment data, their source code or other materials that are necessary to replicate their experiments?</li> <li>What's the data format inside the link?</li> <li>What's the content of the link?</li> </ul> <p> </p> <p>We collect the data in a rush.</p> <p>If you want to use this dataset and find any errors, please contact us ;-)</p> <p> </p> <p>Our emails:</p> <ul> <li>echo.xiangchen@gmail.com</li> <li>zhifengyao731@gmail.com</li> </ul>
Data set of paper Model-Driven System-Performance Engineering for Cyber-Physical Systems
<p>This data set contains the raw and processed data of the paper <em>Model-Driven System-Performance Engineering for Cyber-Physical Systems</em>, published in the proceedings of ESWEEK’21.</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.