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46 results for “computer science”
Computer code accompanying Schraivogel, D. et al. "High-speed fluorescence image-enabled cell sorting" Science, 2022. doi: 10.1126/science.abj3013
<p>Computer code accompanying Schraivogel et al. "High-speed fluorescence image-enabled cell sorting". Details are provided in the manuscript's data and materials availability section and table 3.</p> <p> </p> <p>We provide three directories:</p> <p>(1) R code to reproduce figures (ICS2021_0.1.0.tar.gz)</p> <p>(2) Python code to reproduce figures (ICS_Fiji_Plugin.zip)</p> <p>(3) Code for ICS/CellView Fiji plugins (ICSPython.zip)</p> <p> </p> <p>Code for (1) and (3) has also been shared via Github:</p> <p>https://github.com/benediktrauscher/ICS</p> <p>https://github.com/embl-cba/ICS</p> <p> </p> <p>We recommend downloading the ICS Fiji plugins via Github or to install them using the Fiji update site to ensure you're using the most recent version.</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>
A Systematic Mapping of the Classification of Open Educational Resources for Computer Science Education in Digital Sources (Data)
<p>Data from a Systematic Mapping of the classification of Open Educational Resources for Computer Science Education.</p> <p>Content:</p> <ul> <li>Studies selected</li> <li>Digital sources used to classify Open Educational Resources for Computer Science Education</li> <li>Computer Science domains explored by Open Educational Resources</li> <li>Approaches for the classification of Open Educational Resources for Computer Science Education</li> </ul>
International E- Conference on "Recent Trends in Chemical Science, Physical Science, Life Science and Computer Technology (ICRTCPLCT–2022)
<p><strong>International E- Conference</strong> on “Recent Trends in Chemical Science, Physical Science, Life Science and Computer Technology (ICRTCPLCT–2022)” By Anjuman Islam Janjira Degree College of Science, Murud on <strong>29<sup>th</sup> March 2022.</strong></p>
Supplemental Material: Computational Experiments in Computer Science Research: A literature survey
<p>This laboratory package contains supplemental material from the study: "Supplemental Material: Computational Experiments in Computer Science Research: A literature survey". The supplementary material contains:<br> 1. The list of the 26 primary studies.<br> 2. The .xlsx file of the dataset used to analyze the RQ.<br> 3. The list of figures published in the scientific article.</p>
Grades of Computer Science Students in a Nigerian University
<p><strong>Brief Description of Dataset</strong></p> <p>The dataset contains information about students in a 5-year Bachelor of Technology Degree in Computer Science from a North Eastern Nigerian University of Technology. The year of enrolment of the students ranges from 2005 to 2015. In the dataset, “NA” means that the student did not attempt the course.</p> <p><strong>Data Cleaning</strong></p> <p>First steps: the student marks that are less than 40 are excluded, as the course has to be retaken to be passed with a minimum of 50 marks. In addition, courses that are taken outside of graduation audit by students are eliminated. </p> <p>There were 430 students screened for enrolment in the study with 95 being excluded because they did not take the course within the period of degree program for their early exemption. The exact ages of the participants are unknown other than all students enrolled were aged above 18 years of age.</p> <p> </p> <p> </p>
Co-authoring graphs of research teams in a laboratory in computer science
<p>Our aim is to study inter-organisational collaborations initiated by researchers in their research activity. We considered the co-authoring graph involving at least researchers from LORIA (<a href="https://www.loria.fr/fr/">https://www.loria.fr/fr/</a>), a French laboratory in computer science.</p> <p>The dataset is collected from the open French archive HAL (<a href="https://data.archives-ouvertes.fr/">https://data.archives-ouvertes.fr/</a>).</p> <p>Each file encodes (in <a href="http://www.graphviz.org/about/">DOT</a>) the co-authoring graph of a team of LORIA. A node represents a researcher, two nodes are linked only if the corresponding researchers published together over the three considered years 2017, 2018 and 2019. An affiliation attribute is attached to each considered node.</p> <p>The name of researchers and the teams as well as the HAL:id are anonymised. Only affiliations remain the same.</p>
Impostor Syndrome in Final Year Computer Science Students: An Eye Tracking and Biometrics Study
<p>These are the artifacts that are associated with the paper: <strong>Impostor Syndrome in Final Year Computer Science Students: An Eye Tracking and Biometrics Study</strong></p> <p>This paper was accepted for publication at the 18th International Conference on Augmented Cognition</p> <p>The preprint of the paper is available at: https://arxiv.org/abs/2404.10194</p>
SelfCode 2.0: Annotated Corpus of Student Self-Explanations to Introductory JAVA Programs in Computer Science
<p><strong>Dataset Description:</strong> This dataset was collected during a lab study conducted in Spring 2022 for introductory JAVA programming. Students had to provide line by explanations to four JAVA programs in the experimental condition of the study. The JAVA Programs were selected from the examples made available in the <a title="PCEX" href="https://dl.acm.org/doi/abs/10.1145/3279720.3279726" target="_blank" rel="noopener">PCEX Worked Examples interface</a>. The explanations collected were then split by the number of attempts. Students could attempt twice based on the feedback provided using the the PCEX interface and in their third attempt they filled in the blanks to complete an explanation to the particular line of code. In this dataset, we only have the annotated examples of explanations provided by students. The explanations were annotated on their correctness (binary rating 0 or 1), completeness (binary rating 0 or 1) and similarity (rating scale 1 to 5).</p> <p><strong>Correctness:</strong> Given the line of code and context of the line in the program, if the student explanation covers **only** the topics relevant to the line of code</p> <p><strong>Completeness:</strong> Given the line of code and context of the line in the program, if the student explanation covers **all** the topics relevant to the line of code</p> <p><strong>Similarity:</strong> Given the line of code, the context of the line in the program and an expert explanation to the line of code, the metric compares the similarity on a rating scale from 1 to 5, defined in the following manner:</p> <p>1 - expert and student explanations are very different,</p> <p>2 -- expert and student explanations are somewhat alike, but there are major differences in the concepts / topics explained</p> <p>3 -- expert and student explanations are similar but there are differences in the concepts / topics explained</p> <p>4 -- expert and student explanations are similar and have few differences in the concepts / topics explained</p> <p>5 -- expert and student explanations are very similar.</p> <p> </p> <p>Overall 3000 single attempts (corresponding to 40 student explanation submission) were annotated against different various expert explanation pairs.</p> <p> </p> <p><strong>Dataset Summary:</strong></p> <p><strong>Explanation Type N Definition<br></strong>Experts 2 Source Code Line-by-Line Explanations by Experts<strong><br></strong>Students 60 (annotated 40) Source Code Line-by-Line Explanations by Students</p> <table> <tbody> <tr> <td>COUNT of std_sent_count</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>std_sent_count</td> <td>1</td> <td>2</td> <td>3</td> <td>4</td> <td>5</td> <td>6</td> <td>Grand Total</td> </tr> <tr> <td>1</td> <td>1854</td> <td>367</td> <td>245</td> <td>107</td> <td>34</td> <td>33</td> <td>2640</td> </tr> <tr> <td>2</td> <td>222</td> <td>46</td> <td>40</td> <td>12</td> <td>6</td> <td>6</td> <td>332</td> </tr> <tr> <td>3</td> <td>21</td> <td>5</td> <td>5</td> <td>5</td> <td>1</td> <td>2</td> <td>39</td> </tr> <tr> <td>4</td> <td>2</td> <td>1</td> <td>2</td> <td>3</td> <td> </td> <td> </td> <td>8</td> </tr> <tr> <td>Grand Total</td> <td>2099</td> <td>419</td> <td>292</td> <td>127</td> <td>41</td> <td>41</td> <td>3019</td> </tr> </tbody> </table> <p> </p> <p><strong>Sample Data:</strong></p> <p><strong>Program:</strong> PointTester; Line number: 12; Line code: private int y;<br><strong>Expert1: </strong>Every object of the Point class will have its own y-coordinate. Therefore, we<br>need to declare an instance variable for the class to store the y-coordinate of the point.<br>We declare it as int because we want to have integer coordinates for the point. Note<br>that an instance variable is a variable defined in a class, for which each instantiated<br>object of the class has a separate copy, or instance.<br><strong>Expert2:</strong> The instance variables are declared as private to prevent direct access to<br>them from outside the class. In this way, no unexpected modifications to a Point<br>object’s data are possible.<br><strong>Student1: </strong>initialize a private value inside the point class with no value yet<br><strong>Student2:</strong> Declares the private int variable y.<br><strong>Student3: </strong>Creates a private int that can only be accessed by class Point called int y<br>...<br><strong>Student59:</strong> private variable used to store the value entered into the value of the y<br>coordinate</p> <p> </p> <p><strong>Kappa Scores:</strong></p> <div> <table> <tbody> <tr> <td> <p>Round</p> </td> <td> <p>Row Numbers</p> </td> <td> <p>Correctness Rating Agreement %age</p> </td> <td> <p>Correctness Rating Kappa</p> </td> <td> <p>Sufficiency Rating Agreement %age</p> </td> <td> <p>Sufficiency Rating Kappa</p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>1000 - 1432 </p> </td> <td> <p>92.9</p> </td> <td> <p>0.365</p> </td> <td> <p>0.708</p> </td> <td> <p>-0.0123</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>1432 - 1864</p> </td> <td> <p>94.2</p> </td> <td> <p>0.263</p> </td> <td> <p>77.6</p> </td> <td> <p>0.329</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>1864 – 1964 </p> </td> <td> <p>75.3</p> </td> <td> <p>0</p> </td> <td> <p>70.3</p> </td> <td> <p>0.299</p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>1964 -- 2064 </p> </td> <td> <p>86</p> </td> <td> <p>0.108</p> </td> <td> <p>74.7</p> </td> <td> <p>0.275</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>2064 – 2264</p> </td> <td> <p>95.5</p> </td> <td> <p>-0.0158</p> </td> <td> <p>81.5</p> </td> <td> <p>0.312</p> </td> </tr> <tr> <td> <p>6</p> </td> <td> <p>2264 – 2464</p> </td> <td> <p>83.5</p> </td> <td> <p>0.039</p> </td> <td> <p>86.5</p> </td> <td> <p>0.648</p> </td> </tr> <tr> <td> <p>7</p> </td> <td> <p>2464 – 2864</p> </td> <td> <p>92</p> </td> <td> <p>0.103</p> </td> <td> <p>74.5</p> </td> <td> <p>0.188</p> </td> </tr> <tr> <td> <p>8</p> </td> <td> <p>2864 -- 3005</p> </td> <td> <p>86.5</p> </td> <td> <p>-0.026</p> </td> <td> <p>72.3</p> </td> <td> <p>0.117</p> </td> </tr> </tbody> </table> </div> <p> </p> <p> </p> <p><strong>Citation Format:</strong><br>If using this dataset in your project please cite:</p> <p>Lekshmi-Narayanan, A.-B., Chapagain, J., Brusilovsky, P., & Rus, V. (2023). SelfCode 2.0: Annotated Corpus of Student Self-Explanations to Introductory JAVA Programs in Computer Science [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10912669</p> <p><strong>Acknowledgements:</strong><br>This project was funded as a part of the NSF AWARD # 1822752</p> <p> </p> <p> </p>
In 2017, Plantix, a free smartphone app that helps identify plant damage, was introduced to the Indian state of Andhra Pradesh, with an extension partner. Plantix was created by Progressive Environmental and Agricultural Technologies (PEAT), a German startup. Two PEAT cofounders, Charlotte Schuman (second from the right) and Alex Kennepohl (center, with eyeglasses), confer about the smartphone app with students from Angrau University. Farmers and gardeners can transmit their plant images to Plantix, which uses deep learning and computer vision to help identify diseases and pests. The smartphone app offers symptom descriptions, treatment recommendations, and potential preventive actions. Photographs: Courtesy of PEAT GmbH. in Deep learning brings speed, accuracy to the life sciences.
In 2017, Plantix, a free smartphone app that helps identify plant damage, was introduced to the Indian state of Andhra Pradesh, with an extension partner. Plantix was created by Progressive Environmental and Agricultural Technologies (PEAT), a German startup. Two PEAT cofounders, Charlotte Schuman (second from the right) and Alex Kennepohl (center, with eyeglasses), confer about the smartphone app with students from Angrau University. Farmers and gardeners can transmit their plant images to Plantix, which uses deep learning and computer vision to help identify diseases and pests. The smartphone app offers symptom descriptions, treatment recommendations, and potential preventive actions. Photographs: Courtesy of PEAT GmbH.
Kaisa Helminen is CEO of Fimmic Oy, a Finnish company that created the first commercial tool integrating deep learning and computer vision for pathology research. Photograph: Sebastian Mardones / Health Capital Helsinki. in Deep learning brings speed, accuracy to the life sciences.
Kaisa Helminen is CEO of Fimmic Oy, a Finnish company that created the first commercial tool integrating deep learning and computer vision for pathology research. Photograph: Sebastian Mardones / Health Capital Helsinki.
Backgroun in Computational Tools (Data Science Summer School Goettingen)
<p>Here is a dataset to understand the computational tools background of participants</p>
Three principles of data science: predictability, computability, and stability (PCS)
<p>This repository contains the data, codes, and PCS documentation described in: <em>Three principles of data science: predictability, computability, and stability (PCS)</em>.</p>
Computational History of Philosophy of Science (Comp HOPOS) Dataset
<p>The Computational History of Philosophy of Science (Comp HOPOS) aims to be a comprehensive set of article and (when available) book chapter metadata for philosophy of science. The dataset covers the full run of over 40 journals and 3 major book series in the field. An automated author disambiguation script is used to construct canonical names for each author, and a combination of gender attribution methods is used to attribute the gender of each author. The full code used to generate the dataset is available at <a href="https://github.com/dhicks/comp-HOPOS">https://github.com/dhicks/comp-HOPOS</a>. See the file data_dictionary.txt for data dictionary and additional information.</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>
Open citations involving Computer Science publications listed in DBLP
<p>Data used in the presentation "Open citations in Informatics" held during ECSS 2021. It includes six different files obtained using the software available at <a href="https://github.com/essepuntato/ecss-2021">https://github.com/essepuntato/ecss-2021</a>.</p>
Data Table - Digital Scholarship, PhD project "Bridging Data Science and Intellectual History: Computing the Nodes and Edges in the Old University of Louvain (1425-1797)"
<p>How were academic networks configured in the premodern world, and how did they change? This doctoral project seeks to offer a data-driven answer by computing networks at and around the Old University of Louvain (1425-1797), a crucial hub for the transfer of knowledge in late medieval and early modern Europe. Drawing upon datasets under construction from the teams of the PIs at KU Leuven and UCLouvain, this project sets out to plot and visualize networks of students, scholars and their ‘books’ over almost four centuries, thus integrating data from demographic, prosopographical and book historical datasets. This will lead to a better understanding of how academic communities evolved in the past, and it will help to assess how their organization and structure promoted or hindered the creation and transfer of knowledge in premodern Europe. Hence, the doctoral research project offers an innovative test case to develop novel understandings of networks (e.g.. new tested ways to define nodes and edges) in datasets on scholars and ‘literati’, and it will be able to compare these new results to interpretations of human capital indices in the past. As such, the project creates a pioneering pilot for integrating data science into the field of intellectual and early modern history. This enhanced collaboration between KU Leuven and UCLouvain on a theme related to their common past is timely in view of 600 years Leuven/Louvain in 2025.</p>
Diversity of Expertise is Key to Scientific Impact: a Large Scale Analysis in the Field of Computer Science
<p><strong>This repository is companion to a research paper with the following abstract:</strong></p> <p>Understanding the relationship between the composition of a research team and the potential impact of their research papers is crucial as it can steer the development of new science policies for improving the research enterprise. Numerous studies assess how the characteristics and diversity of research teams can influence their performance across several dimensions: ethnicity, internationality, size, and others. In this paper, we explore the impact of diversity in terms of the authors’ expertise and skills. To this purpose, we retrieved 114K papers in the field of Computer Science and analysed how the diversity of research fields within a research team relates to the number of citations their papers received in the upcoming 5 years. The results show that two different metrics reflecting the diversity of expertise are directly associated with the number of citations. This suggests that, at least in Computer Science, diversity of expertise is key to scientific impact.</p>
Improving Pancreatic Cancer Care by the Use of Computational Science and Technology
ClinicalTrials.gov study NCT06055010. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Replication Data for "Grammar-based fuzzing of data integration parsers in computational materials science"
Open the record for dataset details and reuse information.
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.