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16 results for “Engineering Sciences”
Data of the article Analysis of the self-archiving policies of journals in the highest rank category of the Finnish journal classification system within computer science, physics and electronic engineering
<p>The publication forum level three journals representing the three fields of science of computer science, computer science and electrical engineering were identified by utilizing the MinEdu field search filter while searching for the top-ranked journals from the publication channel search (https://www.tsv.fi/julkaisufoorumi/haku.php?lang=en), which is based on Field of Science, Statistics Finland classification (https://www.stat.fi/meta/luokitukset/tieteenala/001-2010/index_en.html). The data were extracted during august 2017 consists of total of 127 individual journals. It is worth noting that circa 30 journals were classified into more than one fields of sciences under scrutiny. First, the journals were divided into representing gold and hybrid model journals. Second, green open access policies of the identified hybrid journals were analyzed using Laakso’s (2014) publisher policy coding framework. Also publishers of the individual journals were identified and subsequently added to the data.</p> <p>NOTE! The data includes the shortest embargo to either institutional or subject repositories. For example, Elsevier had no embargo to opening accepted manuscripts from arXiv subject repository and thus no embargoes to Elsevier's journals are included within this datasheet.</p> <p>Data is in CSV. format</p> <p> </p> <p> </p>
Research Software Engineers Supporting Science: Survey Responses
<p>Raw survey data for "Not everyone can use git: Research Software Engineers’ recommendations for scientist-centred software support (and what researchers think of them)", a talk given by Caroline Jay at RSE16, Manchester, UK.</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 5. Differences in Validation Criteria between Classical Engineering Sciences and the Field of Brain- Like Artificial Intelligence for Automation
<p>A usual validation procedure in classical fields of engineering and computer sciences as well as in Applied AI, which is currently the dominant AI research domain, is to analyze and implement different potential methods to solve a given problem and to then compare their performance. What is thus usually desired are comparable, quantifiable results. In comparison, the starting situation is<br> different in the field of Brain-Like AI (see Figure 5).</p>
Social Science Theories in Software Engineering Research - Replication Package
<p>Replication package for the article "Social Science Theories in Software Engineering Research".</p> <p>See README file for additional details.</p>
Dataset: Problem-centred interviews results for Matching Data Life Cycle and Research Processes in Engineering Sciences
<p>The authors would like to thank the Federal Government and the Heads of Government of the Länder, as well as the Joint Science Conference (GWK), for their funding and support within the framework of the NFDI4Ing consortium. Funded by the German Research Foundation (DFG) - project number 442146713.</p>
Supplementary Material - Women's Journey in STEM Education in Brazil: A Rapid Review on Engineering and Computer Science
<p>Supplementary Material for the Rapid Review - Women's Journey in STEM Education in Brazil in Engineering and Computer Science courses</p>
Survey on how do Brazilian Software Engineering Researchers Perceive and Practice Open Science
<p>This document provides our survey questionnaire and the responses of 31 researchers.</p>
Supplementary information to "What does ChatGPT know about natural science and engineering?"
<p>This Excel workbook contains the survey data and data analysis from the manuscript "What does ChatGPT know about natural science and engineering?" by Schulze Balhorn et al.</p>
MSL Curiosity Rover Images with Science and Engineering Classes
<p> </p> <p><strong>Please note that the file msl-labeled-data-set-v2.1.zip</strong><strong> below contains the latest images and labels associated with this data set. </strong></p> <p> </p> <p><strong>Data Set Description</strong></p> <p>The data set consists of 6,820 images that were collected by the Mars Science Laboratory (MSL) Curiosity Rover by three instruments: (1) the Mast Camera (Mastcam) Left Eye; (2) the Mast Camera Right Eye; (3) the Mars Hand Lens Imager (MAHLI). With the help from Dr. Raymond Francis, a member of the MSL operations team, we identified 19 classes with science and engineering interests (see the "Classes" section for more information), and each image is assigned with 1 class label. We split the data set into training, validation, and test sets in order to train and evaluate machine learning algorithms. The training set contains 5,920 images (including augmented images; see the "Image Augmentation" section for more information); the validation set contains 300 images; the test set contains 600 images. The training set images were randomly sampled from sol (Martian day) range 1 - 948; validation set images were randomly sampled from sol range 949 - 1920; test set images were randomly sampled from sol range 1921 - 2224. All images are resized to 227 x 227 pixels without preserving the original height/width aspect ratio.</p> <p><strong>Directory Contents</strong></p> <ul> <li>images - contains all 6,820 images</li> <li>class_map.csv - string-integer class mappings</li> <li>train-set-v2.1.txt - label file for the training set</li> <li>val-set-v2.1.txt - label file for the validation set</li> <li>test-set-v2.1.txt - label file for the test set</li> </ul> <p>The label files are formatted as below:</p> <p>"Image-file-name class_in_integer_representation"</p> <p><strong>Labeling Process</strong></p> <p>Each image was labeled with help from three different volunteers (see Contributor list). The final labels are determined using the following processes:</p> <ul> <li>If all three labels agree with each other, then use the label as the final label.</li> <li>If the three labels do not agree with each other, then we manually review the labels and decide the final label.</li> <li>We also performed error analysis to correct labels as a post-processing step in order to remove noisy/incorrect labels in the data set. </li> </ul> <p><strong>Classes</strong></p> <p>There are 19 classes identified in this data set. In order to simplify our training and evaluation algorithms, we mapped the class names from string to integer representations. The names of classes, string-integer mappings, distributions are shown below:</p> <p>Class name, counts (training set), counts (validation set), counts (test set), integer representation</p> <p>Arm cover, 10, 1, 4, 0</p> <p>Other rover part, 190, 11, 10, 1</p> <p>Artifact, 680, 62, 132, 2</p> <p>Nearby surface, 1554, 74, 187, 3</p> <p>Close-up rock, 1422, 50, 84, 4</p> <p>DRT, 8, 4, 6, 5</p> <p>DRT spot, 214, 1, 7, 6</p> <p>Distant landscape, 342, 14, 34, 7</p> <p>Drill hole, 252, 5, 12, 8</p> <p>Night sky, 40, 3, 4, 9</p> <p>Float, 190, 5, 1, 10</p> <p>Layers, 182, 21, 17, 11</p> <p>Light-toned veins, 42, 4, 27, 12</p> <p>Mastcam cal target, 122, 12, 29, 13</p> <p>Sand, 228, 19, 16, 14</p> <p>Sun, 182, 5, 19, 15</p> <p>Wheel, 212, 5, 5, 16</p> <p>Wheel joint, 62, 1, 5, 17</p> <p>Wheel tracks, 26, 3, 1, 18</p> <p> </p> <p><strong>Image Augmentation</strong></p> <p>Only the training set contains augmented images. 3,920 of the 5,920 images in the training set are augmented versions of the remaining 2000 original training images. Images taken by different instruments were augmented differently. As shown below, we employed 5 different methods to augment images. Images taken by the Mastcam left and right eye cameras were augmented using a horizontal flipping method, and images taken by the MAHLI camera were augmented using all 5 methods. Note that one can filter based on the file names listed in the train-set.txt file to obtain a set of non-augmented images.</p> <ul> <li>90 degrees clockwise rotation (file name ends with -r90.jpg)</li> <li>180 degrees clockwise rotation (file name ends with -r180.jpg)</li> <li>270 degrees clockwise rotation (file name ends with -r270.jpg)</li> <li>Horizontal flip (file name ends with -fh.jpg)</li> <li>Vertical flip (file name ends with -fv.jpg)</li> </ul> <p><strong>Acknowledgment</strong></p> <p>The authors would like to thank the volunteers (as in the Contributor list) who provided annotations for this data set. We would also like to thank the PDS Imaging Note for the continuous support of this work.</p>
Open Data Package: Lessons Learned from Developing a Sustainability Awareness Framework for Software Engineering Using Design Science.
<p>Open Data Package for the paper: Stefanie Betz, Birgit Penzenstadler, Leticia Duboc, Ruzanna Chitchyan, Sedef Akinli Kocak, Ian Brooks, Shola Oyedeji, Jari Porras, Norbert Seyff, and Colin C. Venters. 2024. Lessons Learned from Developing a Sustainability Awareness Framework for Software Engineering Using Design Science. ACM Trans. Softw. Eng. Methodol. 24 00, JA, Article 00 (March 2024), 39 pages. https://doi.org/10.1145/3649597 25</p>
The influence coefficients used in Wind Energy Science paper "A computationally efficient engineering aerodynamic model for swept wind turbine blades"
<p>The influence coefficients for the convective correction with full double-precision floating-point accuracy. This is the supplement for the research article: "A computationally efficient engineering aerodynamic model for swept wind turbine blades", submitted to Wind Energy Science journal.</p> <p>Code language: Fortran</p>
Studies Rejected and Selected on the Systematic Mapping of Open Science Practices in Software Engineering
<p>Files in .xlsx format, describing the rejected and selected studies, recovered in a Systematic Mapping about Open Science Practices in Software Engineering.</p>
Supplementary Materials of the Tutorial: "Promotion of Open Science in Requirements Engineering: Leveraging the ORKG and ORKG Ask for FAIR Scientific Information"
<h1>Summary</h1> <p>This collection contains all the supplementary materials of the second tutorial titled "<a href="https://conf.researchr.org/details/RE-2025/RE-2025-tutorials/1/Promotion-of-Open-Science-in-Requirements-Engineering-Leveraging-the-ORKG-and-ORKG-A" target="_blank" rel="noopener">Promotion of Open Science in Requirements Engineering: Leveraging the ORKG and ORKG Ask for FAIR Scientific Information</a>", accepted at the <a href="https://conf.researchr.org/home/RE-2025" target="_blank" rel="noopener">33rd IEEE International Requirements Engineering Conference 2025</a>.</p> <p>The materials complement the tutorial sessions and provide participants with resources to enhance their understanding and application of open science principles in the field of Requirements Engineering (RE) by leveraging the <a href="https://orkg.org/" target="_blank" rel="noopener">Open Research Knowledge Graph (ORKG)</a> and <a href="https://ask.orkg.org/" target="_blank" rel="noopener">ORKG Ask</a> for FAIR scientific information. These materials contain all the presentation slides and exercise materials so that everyone can repeat the theoretical presentations independently and carry out the practical exercises themselves at any time.</p> <h1>Contents</h1> <h2>1. Slides - All slides used in the tutorial.</h2> <table> <tbody> <tr> <td><strong>Files</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>0. RE25 Tutorial - All Sessions.pdf</td> <td>The complete set of all slides used in the tutorial, which are also provided individually for each session of the tutorial.</td> </tr> <tr> <td>1. RE25 Tutorial - Welcome.pdf</td> <td>The welcome with an overview of the content of the tutorial.</td> </tr> <tr> <td>2. RE 25 Tutorial - Introduction to Open Science in RE.pdf</td> <td>The theoretical introduction to open science regarding its importance, benefits, and incentives for researchers themselves and the wider RE community.</td> </tr> <tr> <td>3. RE25 Tutorial - Introduction to ORKG and ORKG Ask.pdf</td> <td>The theoretical introduction to the Open Research Knowledge Graph (ORKG) and ORKG Ask.</td> </tr> <tr> <td>4. RE25 Tutorial - Using SciKGTeX.pdf</td> <td>The practical exercise, with detailed step-by-step instructions on how to use the LaTeX package <a href="https://github.com/Christof93/SciKGTeX" target="_blank" rel="noopener">SciKGTeX</a> to create a FAIR-annotated publication and import it into the ORKG.</td> </tr> <tr> <td>5. RE25 Tutorial - Using the ORKG.pdf</td> <td>The practical exercise, with detailed step-by-step instructions on how to use the <a href="https://orkg.org/" target="_blank" rel="noopener">ORKG </a>to describe publications regarding their scientific information and use these descriptions to create and publish an ORKG comparison.</td> </tr> <tr> <td>6. RE25 Tutorial - Using the ORKG Ask and ORKG CSV Import.pdf</td> <td>The practical exercise, with detailed step-by-step instructions on how to use <a href="https://ask.orkg.org/" target="_blank" rel="noopener">ORKG Ask</a> and the <a href="https://orkg.org/" target="_blank" rel="noopener">ORKG</a> CSV Import to describe publications regarding their scientific information and use these descriptions to create and publish an ORKG comparison.</td> </tr> <tr> <td>7. RE25 Tutorial - Reflection and Closing.pdf</td> <td>The summary, reflection, and closing of the tutorial with an outlook to the future of <a href="https://gitlab.com/TIBHannover/orkg/ExtracTable" target="_blank" rel="noopener">ExtracTable</a>.</td> </tr> </tbody> </table> <h2>2. Exercise Materials - All exercise materials used in the tutorial.</h2> <h3>2.1 SciKGTeX Materials</h3> <table> <tbody> <tr> <td><strong>Folder</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>SciKGTeX_Example</td> <td> <p>The folder contains an example publication and all required SciKGTeX files for annotating the scientific information.</p> <p>Files:</p> <ol> <li>example.tex : LaTeX file of the example publication.</li> <li>scikgtex.lua : Required LaTeX package file for using SciKGTeX.</li> <li>scikgtex.sty : Required LaTeX package file for using SciKGTeX.</li> <li>project.zip : Zip file containing all above files for uploading as a project in Overleaf.</li> </ol> <p><em>Remark:</em> SciKGTeX is constantly being further developed. For the latest version of the required files, please refer to the corresponding <a href="https://github.com/Christof93/SciKGTeX" target="_blank" rel="noopener">GitHub project</a>.</p> </td> </tr> <tr> <td>SciKGTeX_Solution</td> <td> <p>The folder contains an Overleaf project with the solution for a possible annotation of the example publication provided.</p> <p>Files:</p> <ol> <li>example.pdf : PDF with annotations embedded into the PDF's XMP metadata.</li> <li>example.tex : LaTeX file of the example publication with annotations.</li> <li>output.xmp_metadata.xml : XMP file generated by SciKGTeX to check the annotations created.</li> <li>scikgtex.lua : Required LaTeX package file for using SciKGTeX.</li> <li>scikgtex.sty : Required LaTeX package file for using SciKGTeX.</li> <li>project.zip : Zip file containing all above files for uploading as a project in Overleaf.</li> </ol> <p><em>Remark:</em> SciKGTeX is constantly being further developed. For the latest version of the required files, please refer to the corresponding <a href="https://github.com/Christof93/SciKGTeX" target="_blank" rel="noopener">GitHub project</a>.</p> </td> </tr> </tbody> </table> <h3>2.2 ORKG Materials</h3> <table> <tbody> <tr> <td><strong>Folder</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>ORKG_Exemplary_Comparison</td> <td> <p>The folder contains a created ORKG comparison as a PDF and PNG file, consisting of 4 exemplary publications that were described with the ORKG template provided for the tutorial.</p> </td> </tr> <tr> <td>ORKG_Exemplary_Publications</td> <td> <p>The folder contains 20 PDF files with short summaries of scientific findings on empirical research practices from 20 different publications of the IEEE International Requirements Engineering Conference. The participants have received these PDFs to enter them in the ORKG and then create an ORKG Comparison.</p> <p><em>Remark:</em> We have provided the short summaries instead of the full publications to simplify the extraction process due to time constraints.</p> </td> </tr> <tr> <td>ORKG_Template</td> <td> <p>The folder contains an overview of the ORKG template used in the tutorial as a PNG file and an N3 file of its RDF structure.</p> </td> </tr> </tbody> </table> <h3>2.3 ORKG Ask & ORKG CSV Import Materials</h3> <table> <tbody> <tr> <td><strong>File</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>empty_orkg_csv_file_for_orkg_csv_import.csv</td> <td> <p>The file contains an empty template for creating an ORKG CSV file for the ORKG CSV Import with own content.</p> </td> </tr> <tr> <td>empty_orkg_csv_file_for_orkg_csv_import.xlsx</td> <td> <p> </p> The file contains an empty template for creating an ORKG CSV file for the ORKG CSV Import with own content. <p> </p> </td> </tr> <tr> <td>orkg_ask_synthesized_answer_and_link_to search.txt</td> <td>The file contains the synthesized answer with references from ORKG Ask for the question "What is the state of the art in empirical research applied in requirements engineering?" with a link to the associated saved search.</td> </tr> <tr> <td>original_orkg_ask_export_for_orkg_csv_import.csv</td> <td> <p>The file contains the original content of an exported ORKG Ask result table that is revised in the tutorial to create an ORKG Comparison using the ORKG CSV Import.</p> </td> </tr> <tr> <td>original_orkg_ask_export_for_orkg_csv_import.xlsx</td> <td> <p>The file contains the original content of an exported ORKG Ask result table that is revised in the tutorial to create an ORKG Comparison using the ORKG CSV Import.</p> </td> </tr> <tr> <td>revised_orkg_ask_export_for_orkg_csv_import.csv</td> <td> <p>The file contains the revised content of an exported ORKG Ask result table that is used in the tutorial to create an ORKG Comparison using the ORKG CSV Import.</p> </td> </tr> <tr> <td>revised_orkg_ask_export_for_orkg_csv_import.xlsx</td> <td> <p>The file contains the revised content of an exported ORKG Ask result table that is used in the tutorial to create an ORKG Comparison using the ORKG CSV Import.</p> </td> </tr> </tbody> </table> <h1>Usage Notes</h1> <p>These materials are intended for use by the participants of the tutorial, the broader RE community, and everyone interested in open science. They are provided to support the long-term transition towards FAIR scientific information and to empower researchers to integrate open science infrastructures into their work.</p> <h1>License</h1> <p>The materials are released under <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" rel="noopener">Creative Commons Attribution 4.0 International (CC BY 4.0) license</a>, allowing for reuse and distribution in accordance with open science practices.</p>
STEM (Science, Technology, Engineering & Math) Familia Talk
ClinicalTrials.gov study NCT03766906. IPD Sharing: NO. Countries: 1. Publications: 0.
HUYGENS ACP CALIBRATED ENGINEERING & SCIENCE DATA
This data set is composed of data produced during the descent to Titan on January 14 2005 from the ACP instrument (Engineering data) and also from the GCMS instrument (Science data) which analyses the gaseous products from ACP.
HUYGENS ACP CALIBRATED ENGINEERING & SCIENCE DATA
This data set is composed of data produced during the descent to Titan on January 14 2005 from the ACP instrument (Engineering data) and also from the GCMS instrument (Science data) which analyses the gaseous products from ACP.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.