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2,235 results for “engineering”
Figure 5 in Les engins et techniques de pêche utilisés dans la baie de Loango, République du Congo, et leurs incidences sur les prises accessoires
Figure 5. - Courbe réponse du nombre de tortues capturées par événement de pêche en fonction de la taille des mailles (cm, maille étirée). L'intervalle de confiance à 95% apparaît en pointillés. +: valeurs observées. [Response curve of the number of sea turtles caught by fishing event according to mesh size (in cm mesh stretched). The 95% confidence interval appears in dotted lines. +: observed values.]
Figure 2 in Les engins et techniques de pêche utilisés dans la baie de Loango, République du Congo, et leurs incidences sur les prises accessoires
Figure 2. - Courbe réponse de la masse de poissons et crustacés (kg) en fonction de la taille des mailles (cm, maille étirée). L'intervalle de confiance à 95% apparaît en pointillés. +: valeurs observées. [Response curve of fish weight (kg) by fishing event according to mesh size (in cm mesh stretched). The 95% confidence interval appears in dotted lines. +: observed values.]
Figure 1 in Les engins et techniques de pêche utilisés dans la baie de Loango, République du Congo, et leurs incidences sur les prises accessoires
Figure 1. - Carte du littoral congolais (République du Congo) et de la zone d'étude: la baie de Loango. [Map of the Congo coastline (Republic of Congo) and of the study area: Loango Bay.]
Los Angeles, California, Earthquake Dataset with Feature-Engineered Variables
<p>This dataset includes detailed records of seismic events in Southern California, such as magnitudes, depths, and locations, filtered to focus on a 100 km radius around Los Angeles from January 1, 2012, to September 1, 2024. It also includes a target variable representing the maximum earthquake magnitude within 30 days of each event, along with additional engineered features for use in machine learning and neural network algorithms to improve earthquake forecasting.</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>
Enhancing Motivation in Software Engineering Education through Gamified Agile Project-based Learning
<p>Project-based learning (PBL), e.g., student software development projects, is an essential part of today's Software Engineering (SE) education. They allow students to work on real-world projects and gain practical experience as a team. However, several challenges arise in such projects, including learning new technologies and dealing with communication and coordination issues within the team. These factors can lead to a lack of motivation to contribute to the project and a decrease in productivity, potentially resulting in an insufficient project outcome. This paper aims to promote student motivation in PBL and increase team productivity by applying gamification. We conducted a user and requirements analysis to identify the needs of students and supervisors of such projects. Based on the insights, we designed and implemented DinoDev, a gamified project management tool that combines project management features with gamification elements. The DinoDev concept was evaluated in a student project, indicating increased motivation and team productivity. The findings are valuable for advancing research on using gamification in PBL and for lecturers to improve their students' motivation and team productivity in SE education.</p>
Protein engineering using variational free energy approximation
<p>Data generated by PREVENT model and used in manuscript "Protein engineering using variational free energy approximation". Contains raw input data, R scripts and Jupyter Notebooks to process data and output figures used in the main text and supplementary materials of the manuscripts.</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>
Dopant Engineering for Spiro-OMeTAD Hole-Transporting Materials towards Efficient Perovskite Solar Cells
<p>Optoelectronic, photovoltaic, and supplementary characterization data for “Dopant Engineering for Spiro-OMeTAD Hole-Transporting Materials towards Efficient Perovskite Solar Cells”, DOI:10.1002/adfm.202102124</p> <ul> <li>CV.zip: Data (cyclic voltammograms) described in Figures S3–S5 in Origin (*opj) file format.</li> <li>EPR.zip: Data (EPR spectra) described in Figure 5, Figure S2, and Table S2 in Origin (*opj) file format.</li> <li>Optical.zip: Data (UV-vis, PL, and TRPL spectra) described in Figure 4 and Table S1 in Origin (*opj) file format.</li> <li>PV.zip: Data (photovoltaic characteristics) described in the Figures 2–3, Table 1, Figure S1, and Figure S6 in Origin (*opj) file format.</li> </ul>
Biomarker Signatures of Quality for Engineering Nasal Chondrocyte-Derived Cartilage
<p>Data underlying the figures in the publication “Biomarker Signatures of Quality for Engineering Nasal Chondrocyte-Derived Cartilage”, published in <em>Front Bioeng Biotechnol., </em><strong>2020</strong>, 8: 283. (DOI: 10.3389/fbioe.2020.00283).</p> <p>Table of contents:</p> <p><strong>1. biochem_summary</strong>; Biochemistry data and MBS of pellets made from NC and PC mixtures in the titration experiment.</p> <p><strong>2. dirty-clean-biochem</strong>; Biochemistry data and MBS of pellets made from passage 2 cells from dirty/clean biopsies.</p> <p><strong>3. identity_all</strong>; All passage 1 and passage 2 cells of type pure NC and pure PC from the titration and dirty/clean datasets. All PCR data included.</p> <p><strong>4. identity_titration</strong>; Passage 1 and 2 and pellets made from pure NC and PC populations in the titration experiment. All PCR data included. Average (avg) refers to the average of the biologicsl replicates (which are already the average of the experimental replicates of Data 5).</p> <p><strong>5. identity_titration_avg</strong>; Passage 1 and 2 and pellets made from pure NC and PC populations in the titration experiment. All PCR data included. All experimental replicate information is included (which are the average of the technical replicates).</p> <p><strong>6. PCR_cells_summary</strong>; For the purity and potency assays, mixtures of increasing NC to PC cell ratios were created. The PCR data from those mixtures are here for passage 2 and for the pure populations as passage 1.</p> <p><strong>7. PCR_pellet_summary</strong>; For the purity and potency assays, mixtures of increasing NC to PC cell ratios were created. PCR data from engineered pellets are here included.</p> <p><strong>8. PCRdirty-clean</strong>; PCR data of cells obtained from dirty/clean biopsies at passage 0,1, and 2.</p> <p><strong>9. PCRsummary_all</strong>; Combined Data 5, 7, and 8.</p> <p><strong>10. Proliferation_TOT</strong>; Proliferation rates of cells from clean and dirty biopsies. </p>
Research on Cognition in Software Engineering
<p>This dataset includes the primary studies selected for literature review on cognition in software engineering. </p>
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>
Supporting Data for Human Factors in Developing Automated Vehicles:A Requirements Engineering Perspective
<p>This data set complements our manuscript in submission with the title:</p> <p>"Human Factors in Developing Automated Vehicles: A Requirements Engineering Perspective"</p> <p>We provide two files:</p> <p>a) the interview guide</p> <p>b) an overview that maps from themes to example quotes and codes derived from particular interview subjects</p>
Engineering the Radiative Dynamics of Thermalized Excitons with Metal Interfaces
<p>Here we analyze the emission properties of excitons in two metal structures: placed near a single metal (silver) interface and placed symmetrically at the center of a Fabry-Perot microcavity. The exciton is modeled as a point dipole and as an extended 2D dipole. With the momentum dependent emission rates for the extended exciton, we can also examine its temperature dependent behavior by applying a momentum distribution. We investigate the cases of a Maxwell-Boltzmann statistical distribution and a Bose-Einstein statistical distribution (since excitons form composite bosons). </p> <p>The dataset provided are the Mathematica codes used to generate the plots in our paper. The point dipole results are contained in the notebook files: Emission Rate 2, Multiple Interfaces, Multiple Interfaces_Par (1), and Multiple Interfaces_Perp. The extended exciton results are contained in the notebook files: Extended Exciton 2-3 and BE stats. </p>
Dataset with survey answers about enginering studies opinion in each kind of high school Spanish studies (Compulsory Secondary Education, Vocational Education and Upper Secondary Education)
<p>The first line includes each question and the rest of the tuples include one answer per each filled survey. Depending on the type of high school studies, you find one different survey, because the questions are adapted to each particular high school education.</p> <p> </p>
Towards a Data-Driven Requirements Engineering Approach: Automatic Analysis of User Reviews
<p>6000 French user reviews from three applications on Google Play (Garmin Connect, Huawei Health, Samsung Health) are labelled manually. We selected four labels: rating, bug report, feature request and user experience.</p> <ul> <li><strong>Ratings</strong> are simple text which express the overall evaluation to that app, including praise, criticism, or dissuasion.</li> <li><strong>Bug reports</strong> show the problems that users have met while using the app, like loss of data, crash of app, connection error, etc.</li> <li><strong>Feature requests</strong> reflect the demande of users on new function, new content, new interface, etc.</li> <li>In <strong>user experience</strong>, users describe their experience in relation to the functionality of the app, how does certain functions be helpful.</li> </ul> <p>As we can observe from the following table, that shows examples of labelled user reviews, each review belongs to one or more categories.</p> <table> <tbody> <tr> <th>App</th> <th>Total</th> <th>Rating</th> <th>Bug report</th> <th>Feature request</th> <th>User experience</th> </tr> </tbody> <tbody> <tr> <td>Garmin Connect</td> <td>2000</td> <td>1260</td> <td>757</td> <td>170</td> <td>493</td> </tr> <tr> <td>Huawei Health</td> <td>2000</td> <td>1068</td> <td>819</td> <td>384</td> <td>289</td> </tr> <tr> <td>Samsung Health</td> <td>2000</td> <td>1324</td> <td>491</td> <td>486</td> <td>349</td> </tr> </tbody> </table> <p> </p> <h2>New Dataset</h2> <p>Based on this dataset, we developed a labeled dataset containing 6,000 English and 6,000 French reviews for classification, as well as 1,200 bilingual reviews for clustering. The new dataset has been made publicly available on Zenodo at the following link: <a href="../records/11066414">https://zenodo.org/records/11066414</a></p>
Replication package for Workshop on Software Engineering 22' - What does the pytest plugins data say?
<p>This database stores the information used to run the experiment in the article: <strong>What does the pytest plugins data say?</strong></p>
Replication Package: Model-Driven Engineering for the Interoperability of Simulation Modeling Languages: a Case Study in the Space Industry
<p>Replication package "Architectural Support for Software Performance in Continuous Software Engineering: a Systematic Mapping Study".</p>
Supplementary materials of the active learning methodology for online engineering education
<p>These are the supplementary materials for the active learning methodology for engineering education integrating online and mobile learning. The methodology is focused on teaching electronics, physical computing, basic robotics, and programming. The purpose of the methodology was to provide active learning, experimentation, and reflection in online classes to the students increasing their motivation and self-efficacy.</p> <p>The file contains the following elements:</p> <ol> <li>Rubric employed to evaluate the student-created videos and blogs.</li> <li>The survey's questions of the different courses that employ online and mobile learning modalities.</li> <li>A set of URLs with examples of the blogs and videos constructed by the students.</li> </ol>
IDMT-ISA-Electric-Engine Dataset
<p>The IDMT-ISA-ELECTRIC-ENGINE dataset contains sound files of three similar units of an electrical engine (2ACT Motor Brushless DC 42BLF01, 4000 RPM, 24VDC), which simulate different acoustic conditions. The operational states “good”, “heavy load” and “broken” were provoked by a change of supply voltage and loading weight leading to a change of the operating sound. In March 2017 the IDMT_ISA_ELECTRIC_ENGINE dataset was recorded at Fraunhofer Institute for Digital Media Technology (IDMT). In each file, only one of the engines is active at the same time assuming an engine can only have one of the three operational states. The dataset consists of recordings of the electric engine plus the following background noise types:</p> <ul> <li>File duration: 42.32 minutes</li> <li># Operational State “good”: 774</li> <li># Operational State “broken”: 789</li> <li># Operational State “heavyload”: 815</li> <li># Total WAV Files: 2378</li> <li>Sampling rate: 44.1KHz</li> <li>Resolution: 32-bit</li> <li>Mono audio</li> </ul>
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.