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Articles de revues sur le sujet « JupyterLab »

Auteur : Grafiati

Publié le 2 juin 2025

Mis à jour le 31 juillet 2025

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1

Erokhin, Andrey, and Andrey Zarochentsev. "JupyterLab at the Saint Petersburg State University." Journal of Physics: Conference Series 1525 (April 2020): 012062. http://dx.doi.org/10.1088/1742-6596/1525/1/012062.

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Erdmann, Martin, Benjamin Fischer, Lukas Geiger, et al. "Evolution of the VISPA-project." EPJ Web of Conferences 214 (2019): 05021. http://dx.doi.org/10.1051/epjconf/201921405021.

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VISPA (Visual Physics Analysis) is a web-platform that enables users to work on any secure shell (SSH) reachable resource using just their webbrowser. It is used successfully in research and education for HEP data analysis. The emerging JupyterLab is an ideal choice for a comprehensive, browser-based, and extensible work environment and we seek to unify it with the efforts of the VISPA-project. The primary objective is to provide the user with the freedom to access any external resources at their disposal, while maintaining a smooth integration of preconfigured ones including their access perm

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Thanuja Nishadi, A. S. "Text Analysis: Naïve Bayes Algorithm using Python JupyterLab." International Journal of Scientific and Research Publications (IJSRP) 9, no. 11 (2019): p9515. http://dx.doi.org/10.29322/ijsrp.9.11.2019.p9515.

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Cervera, Enric, and Angel P. Del Pobil. "ROSLab: Sharing ROS Code Interactively With Docker and JupyterLab." IEEE Robotics & Automation Magazine 26, no. 3 (2019): 64–69. http://dx.doi.org/10.1109/mra.2019.2916286.

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Niimura, Masaaki, Kazuki Kobayashi та Shinpei Ogata. "JupyterLab によるPython 学習のはじめかた". IEICE Communications Society Magazine 15, № 4 (2022): 333–38. http://dx.doi.org/10.1587/bplus.15.333.

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Cruz, Vanderson da Silva, Mateus Silva Rêgo, Iure da Silva Carvalho, et al. "ESTUDO TEÓRICO DA EQUAÇÃO DE SCHRODINGER PARA OSCILADORES: HARMÔNICO E ANARMÔNICOS QUÁRTICO, SÊXTUPLO E ÓCTUPLO QUÂNTICOS." REVISTA FOCO 17, no. 3 (2024): e3316. http://dx.doi.org/10.54751/revistafoco.v17n3-103.

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Este artigo explora o potencial da simulação computacional como uma ferramenta para superar limitações experimentais e teóricas na física, com um foco específico na mecânica quântica. O objetivo é resolver numericamente a equação de Schrodinger, utilizando o método de elementos finitos (MDF), para diversos potenciais, incluindo dos osciladores harmônico e anarmônico quártico, sêxtuplo e óctuplo. O estudo emprega o MDF, com apoio das plataformas JupyterLab e wxMaxima, para a diagonalização e discretização dos sistemas. São investigados osciladores harmônicos e anarmônicos, com resultados que in

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Zhou, Chenliang, Dominic Kuang, Jingru Liu, et al. "AISpace2: An Interactive Visualization Tool for Learning and Teaching Artificial Intelligence." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 09 (2020): 13436–43. http://dx.doi.org/10.1609/aaai.v34i09.7068.

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AIspace is a set of tools used to learn and teach fundamental AI algorithms. The original version of AIspace was written in Java. There was not a clean separation of the algorithms and visualization; it was too complicated for students to modify the underlying algorithms. Its next generation, AIspace2, is built on AIPython, open source Python code that is designed to be as close as possible to pseudocode. AISpace2, visualized in JupyterLab, keeps the simple Python code, and uses hooks in AIPython to allow visualization of the algorithms. This allows students to see and modify the high-level al

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Beer, Max, Niclas Eich, Martin Erdmann, et al. "Knowledge sharing on deep learning in physics research using VISPA." EPJ Web of Conferences 245 (2020): 05040. http://dx.doi.org/10.1051/epjconf/202024505040.

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The VISPA (VISual Physics Analysis) project provides a streamlined work environment for physics analyses and hands-on teaching experiences with a focus on deep learning. VISPA has already been successfully used in HEP analyses and teaching and is now being further developed into an interactive deep learning platform. One specific example is to meet knowledge sharing needs in deep learning by combining paper, code and data at a central place. Additionally the possibility to run it directly from the web browser is a key feature of this development. Any SSH reachable resource can be accessed via

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Fawzi, Ahmad, Joni Fat, and Meirista Wulandari. "Evaluation Method of Real-Time Face Detection." International Journal of Application on Sciences, Technology and Engineering 1, no. 2 (2023): 643–50. http://dx.doi.org/10.24912/ijaste.v1.i2.643-650.

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Object detection is one of computer technology using image or video. Face detection is closely related Image processing and computer vision are used to detect several objects including human faces, landscapes, cars, etc. Face detection algorithm aims to confirm if an image has a face as the object in it. In this study, face detection uses several methods, namely the Eigenface method, the Fisherface method, and the Local Binary Pattern Histogram (LBPH) method. This study used 10 different subjects. The test was carried out 15 times using each face detection method with constant distance. The fa

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Trujillo-Franco, Luis Gerardo, Julio Alexis Guízar-García, Jaime González-Sierra, Josué Daniel Rivera-Fernández, Hugo Francisco Abundis-Fong, and Erik López-Vázquez. "Jupyter notebook como auxiliar en la enseñanza de ecuaciones diferenciales con experimentos." Pädi Boletín Científico de Ciencias Básicas e Ingenierías del ICBI 12 (November 30, 2024): 120–27. https://doi.org/10.29057/icbi.v12iespecial4.13281.

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Las ecuaciones diferenciales ordinarias tienen una considerable presencia en las diversas ramas de la ingeniería, dada su utilidad en el modelado y descripción de los fenómenos y procesos con los que el ingeniero tiene contacto en su quehacer creativo y de solución de problemas. Es por esta razón que, en los programas de estudio enfocados a la ingeniería, existe al menos un curso dedicado al tema de las ecuaciones diferenciales, donde se abordan los métodos analíticos y numéricos utilizados para su solución. Hoy en día existen muchas herramientas tecnológicas que permiten ilustrar de manera mu

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Bertini, Marco, and Mathias Lux. "nteract." ACM SIGMultimedia Records 12, no. 2 (2020): 1. http://dx.doi.org/10.1145/3548562.3548569.

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Writing source code for programs with lightweight text editors or fully featured integrated development environments is considered the main method of programming. Notebooks, however, are an extremely practical tool. In contrast to IDEs, projects are set up more easily and they allow for running programs in a read-eval-print loop (REPL) environment. The Jupyter Notebooks Quick Start Guide [1] describes notebook documents as "… both human-readable documents containing the analysis description and the results (figures, tables, etc..) as well as executable documents which can be run to perform dat

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Chamorro-Atalaya, Omar, Dora Arce-Santillan, José Antonio Arévalo-Tuesta, et al. "Text mining and sentiment analysis of teacher performance satisfaction in the virtual learning environment." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 1 (2022): 525. http://dx.doi.org/10.11591/ijeecs.v28.i1.pp525-534.

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Although it is true that artificial intelligence and data science have become key tools that contribute to the improvement of many processes, identifying patterns and contributing to decision making, however, there are environments in which they are not yet being using it relevantly and effectively. The objective of this study is to identify the relevant factors, based on the opinions expressed by the students through the social network Twitter regarding the perception of satisfaction with the teaching performance during the virtual learning environment. For which sentiment analysis and text m

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Chamorro-Atalaya, Omar, Dora Arce-Santillan, José Antonio Arévalo-Tuesta, et al. "Text mining and sentiment analysis of teacher performance satisfaction in the virtual learning environment." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 1 (2022): 525–35. https://doi.org/10.11591/ijeecs.v28.i1.pp525-535.

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Although it is true that artificial intelligence and data science have become key tools that contribute to the improvement of many processes, identifying patterns and contributing to decision making, however, there are environments in which they are not yet being using it relevantly and effectively. The objective of this study is to identify the relevant factors, based on the opinions expressed by the students through the social network Twitter regarding the perception of satisfaction with the teaching performance during the virtual learning environment. For which sentiment analysis and text m

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Edwin Santos de Guzman, Isagani Mirador Tano, Keno Piad, et al. "Unlocking insights from academic library data using clustering and recommender dashboard analytics for enhanced book collection management." World Journal of Advanced Research and Reviews 25, no. 3 (2025): 645–57. https://doi.org/10.30574/wjarr.2025.25.3.0779.

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This study, titled "Unlocking Insights from Academic Library Data using Clustering and Recommender Dashboard Analytics for Enhanced Book Collection Management: UST Perspective," explores data-driven strategies to enhance the University of Santo Tomas (UST) library’s collection management. The K-Means clustering algorithm was used to analyze library collection data, identifying patterns based on book titles, publication years, authors, and categories. The clustering results revealed high-demand clusters, including Social Sciences, Health Sciences, Humanities, and Science and Technology, while l

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Chevez, Carlos R., Freydell Pinell, and Álvaro Antonio Mejía Quiroz. "Simulación del proceso de recarga para Aguas Subterráneas utilizando Redes Neuronales Artificiales como Método de Aproximación en el Acuífero Las Sierras, Nicaragua." Revista Torreón Universitario 12, no. 33 (2023): 112–25. http://dx.doi.org/10.5377/rtu.v12i33.15896.

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El conocimiento de la funcionalidad de un sistema hidrogeológico es de vital importancia para su gestión y conservación. Una de las variables y entrada principal que alimenta al sistema es la Recarga Potencial (Rp) producto de la precipitación (P). La finalidad del presente trabajo es el diseño de un modelo regresor no lineal usando Redes Neuronales Artificiales (RNA). Para lo anterior, se utilizó los datos recolectados por el INETER para estimar la Rp a través de las variables de entrada como: precipitación (P), texturas de suelo y demás variables ambientales en el Acuífero ubicado en Managua

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Sánchez, Arteaga José Roberto. "Análisis comparativo de IDEs enfocados a Machine Learning." Esprint Investigación 2, no. 2 (2023): 5–13. https://doi.org/10.61347/ei.v2i2.53.

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El creciente uso de modelos de Machine Learning (ML) para gestionar grandes cantidades de datos, impulsado por la crisis de la Pandemia COVID-19, ha posibilitado el desarrollo productivo de los Entornos de Desarrollo Integrado (IDEs). Sin embargo, existen varios IDEs de desarrollo disponibles y elegir el más adecuado para tareas específicas puede ser un desafío. El objetivo de este estudio es proporcionar una revisión exhaustiva de las diversas herramientas de desarrollo integrado enfocadas para el campo de ML. Para ello, se diseñó una investigación comparativa donde se utilizaron fuentes cien

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Noguere, G., S. Xu, L. Desgrange, et al. "Generation of thermal scattering laws with the CINEL code." EPJ Web of Conferences 284 (2023): 17002. http://dx.doi.org/10.1051/epjconf/202328417002.

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The thermal scattering laws (TSL) take into account the crystalline structure and atomic motions of isotopes bound in materials. This paper presents the CINEL code, which was developed to generate temperature-dependent TSL for solid, liquid and free gas materials of interest for nuclear reactors. CINEL is able to calculate TSL from the phonon density of states (PDOS) of materials under the Gaussian-Incoherent approximations. The PDOS can be obtained by using theoretical approaches (e.g., ab initio density functional theory and molecular dynamics) or experimental results. In this work, the PDOS

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Sánchez Arteaga, José Roberto. "Análisis comparativo de IDEs enfocados a Machine Learning." Esprint Investigación 2, no. 2 (2023): 5–13. http://dx.doi.org/10.61347/ei.v2i2.53.

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El creciente uso de modelos de Machine Learning(ML) para gestionar grandes cantidades de datos que existen actualmente, impulsado por la crisis de la Pandemia COVID-19, ha llevado al desarrollo productivo de estos modelos gracias a los Entornos de Desarrollo Integrado (IDEs). Sin embargo, existen varios IDEs de desarrollo disponibles y elegir el más adecuado para tareas específicas puede ser un desafío. El objetivo de este estudio es proporcionar una revisión exhaustiva de las diversas herramientas de desarrollo integrado enfocadas para el campo de ML, para ello, este estudio se basa en una in

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Kaloshina, T. Yu, and D. A. Sevostyanov. "Digital competencies as the basis of professional competitiveness (on the example of training management specialists)." Professional education in the modern world 12, no. 1 (2022): 105–13. http://dx.doi.org/10.20913/2618-7515-2022-1-13.

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Introduction. The digitalization of the economy and the digitalization of society are occurring at a rapid pace. The Russian Federation has approved a strategy for the development of the information society for the period up to 2030. The implementation of this strategy provides for improving the quality of life, creating conditions for the development of knowledge, the use of digital technologies, increasing the degree of digital literacy, digital security, etc. The formation of a global digital environment has affected all spheres of life, and especially education. Currently, the digitalizati

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Koc, Soner, Jovana Babic, Nevena Nikolic, et al. "Abstract 6394: The SB Image Processing Toolkit: Machine learning for cancer research on the CGC." Cancer Research 82, no. 12_Supplement (2022): 6394. http://dx.doi.org/10.1158/1538-7445.am2022-6394.

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Abstract Applying machine learning algorithms to analysis of multi-omics datasets has provided a wealth of analysis and interpretations of data not easily achievable by conventional methods. The Cancer Genomics Cloud (CGC), powered by Seven Bridges, offers many helpful features to simplify and streamline the performance of machine learning (ML) tasks on the platform. The CGC platform contains features and tools for all of the steps of a ML project, from data exploration, to model generation, to production, so that all key steps occur on the platform. The Data Cruncher interactive analysis tool

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Rind, Ofer, William Strecker-Kellogg, Daniel Allan, Douglas Benjamin, Mizuki Karasawa, and Kristy Li. "Integrating Interactive Jupyter Notebooks at the BNL SDCC." EPJ Web of Conferences 245 (2020): 07054. http://dx.doi.org/10.1051/epjconf/202024507054.

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At the SDCC we are deploying a Jupyterhub infrastructure to enable scientists from multiple disciplines to access our diverse compute and storage resources. One major design goal was to avoid rolling out yet another compute backend, but rather to leverage our pre-existing resources via our batch systems (HTCondor and Slurm). Challenges faced include creating a frontend that allows users to choose what HPC resources they have access to as well as selecting containers or environments, delegating authentication to a MFA-enabled proxy, and automating deployment of multiple hub instances. This pape

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Reich, Michael M., Thorin Tabor, John Liefeld, et al. "Abstract 2073: Genomics to Notebook (g2nb): Extending the electronic notebook to address the needs of cancer bioinformatics." Cancer Research 83, no. 7_Supplement (2023): 2073. http://dx.doi.org/10.1158/1538-7445.am2023-2073.

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Abstract As the availability of genomic data and analysis tools from large-scale cancer initiatives continues to increase, with single-cell studies adding new dimensions to the potential scientific insights, the need has become more urgent for a software environment that supports the rapid pace of cancer data science. The The Jupyter Notebook environment has become the de facto medium for this purpose due to its ease in combining scientific exposition with executable code to form a single reproducible “research narrative” document. However, analyses are often compute-intensive, requiring more

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Brown, Jeff. "Using Jupyterhub in the Classroom : Setup and Lessons Learned." International Journal of Software Engineering & Applications 9, no. 2 (2018): 01–08. http://dx.doi.org/10.5121/ijsea.2018.9201.

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Munk, Rasmus, David Marchant, and Brian Vinter. "Cloud enabling educational platforms with corc." CTE Workshop Proceedings 8 (March 19, 2021): 438–57. http://dx.doi.org/10.55056/cte.299.

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In this paper, it is shown how teaching platforms at educational institutions can utilize cloud platforms to scale a particular service, or gain access to compute instances with accelerator capability such as GPUs. Specifically at the University of Copenhagen (UCPH), it is demonstrated how the internal JupyterHub service, named Data Analysis Gateway (DAG), could utilize compute resources in the Oracle Cloud Infrastructure (OCI). This is achieved by utilizing the introduced Cloud Orchestrator (corc) framework, in conjunction with the novel JupyterHub spawner named MultipleSpawner. Through this

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Karasawa, Mizuki, John Hover, and Shigeki Misawa. "Federated User Account Management." EPJ Web of Conferences 245 (2020): 07058. http://dx.doi.org/10.1051/epjconf/202024507058.

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BNL SDCC (Scientific Data and Computing Center) recently deployed a centralized identity management solution to support Single Sign On (SSO) authentication across multiple IT systems. The system supports federated login access via CILogon and InCommon and multi-factor authentication (MFA) to meet security standards for various application and services such as Jupyterhub / Invenio that are provided to the SDCC user community. CoManage (cloud-based) and FreeIPA / Keycloak (local) are utilized to provided complex authorization for authenticated users. This talk will focus on technical overviews a

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Miao, Kehua, Jie Li, Wenxing Hong, and Mingtao Chen. "A Microservice-Based Big Data Analysis Platform for Online Educational Applications." Scientific Programming 2020 (June 3, 2020): 1–13. http://dx.doi.org/10.1155/2020/6929750.

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The booming development of data science and big data technology stacks has inspired continuous iterative updates of data science research or working methods. At present, the granularity of the labor division between data science and big data is more refined. Traditional work methods, from work infrastructure environment construction to data modelling and analysis of working methods, will greatly delay work and research efficiency. In this paper, we focus on the purpose of the current friendly collaboration of the data science team to build data science and big data analysis application platfor

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Lyu, Mengye, Yuan Zhang, Shaojun Liu, and Lingling Chen. "Adapting Jupyter for C++ Programming Education: An Empirical Study on Lab Instruction Strategies and Student Perspectives." Contemporary Education and Teaching Research 4, no. 11 (2023): 556–61. http://dx.doi.org/10.61360/bonicetr232015151101.

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This paper explores the use of the Jupyter platform in teaching C++ programming at the higher education level. It includes a comparative analysis between traditional integrated development environments and online judge systems. The study discusses a teaching approach that leverages JupyterHub and Xeus-Cling to create an interactive learning setting. This approach was pilot-tested during a C++ lab session at Shenzhen Technology University, and student feedback was subsequently gathered and analyzed. A substantial number of students found value in Jupyter's interactive capabilities and the seaml

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Bhatt, Asti, Todd Valentic, Ashton Reimer, Leslie Lamarche, Pablo Reyes, and Russell Cosgrove. "Reproducible Software Environment: a tool enabling computational reproducibility in geospace sciences and facilitating collaboration." Journal of Space Weather and Space Climate 10 (2020): 12. http://dx.doi.org/10.1051/swsc/2020011.

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The Reproducible Software Environment (Resen) is an open-source software tool enabling computationally reproducible scientific results in the geospace science community. Resen was developed as part of a larger project called the Integrated Geoscience Observatory (InGeO), which aims to help geospace researchers bring together diverse datasets from disparate instruments and data repositories, with software tools contributed by instrument providers and community members. The main goals of InGeO are to remove barriers in accessing, processing, and visualizing geospatially resolved data from multip

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Stetzler, Steven, Mario Jurić, Kyle Boone, Andrew Connolly, Colin T. Slater, and Petar Zečević. "The Astronomy Commons Platform: A Deployable Cloud-based Analysis Platform for Astronomy." Astronomical Journal 164, no. 2 (2022): 68. http://dx.doi.org/10.3847/1538-3881/ac77fb.

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Abstract We present a scalable, cloud-based science platform solution designed to enable next-to-the-data analyses of terabyte-scale astronomical tabular data sets. The presented platform is built on Amazon Web Services (over Kubernetes and S3 abstraction layers), utilizes Apache Spark and the Astronomy eXtensions for Spark for parallel data analysis and manipulation, and provides the familiar JupyterHub web-accessible front end for user access. We outline the architecture of the analysis platform, provide implementation details and rationale for (and against) technology choices, verify scalab

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Signell, Richard P., and Dharhas Pothina. "Analysis and Visualization of Coastal Ocean Model Data in the Cloud." Journal of Marine Science and Engineering 7, no. 4 (2019): 110. http://dx.doi.org/10.3390/jmse7040110.

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The traditional flow of coastal ocean model data is from High-Performance Computing (HPC) centers to the local desktop, or to a file server where just the needed data can be extracted via services such as OPeNDAP. Analysis and visualization are then conducted using local hardware and software. This requires moving large amounts of data across the internet as well as acquiring and maintaining local hardware, software, and support personnel. Further, as data sets increase in size, the traditional workflow may not be scalable. Alternatively, recent advances make it possible to move data from HPC

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Drabent, Alexander, Oliver Freyermuth, Manuel Giffels, et al. "Federated Heterogeneous Compute and Storage Infrastructure for the PUNCH4NFDI Consortium." EPJ Web of Conferences 295 (2024): 07020. http://dx.doi.org/10.1051/epjconf/202429507020.

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PUNCH4NFDI, funded by the Germany Research Foundation initially for five years, is a diverse consortium of particle, astro-, astroparticle, hadron and nuclear physics embedded in the National Research Data Infrastructure initiative. In order to provide seamless and federated access to the huge variety of compute and storage systems provided by the participating communities covering their very diverse needs, the Compute4PUNCH and Storage4PUNCH concepts have been developed. Both concepts comprise state-of-the-art technologies such as a token-based AAI for standardized access to compute and stora

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Rakitin, Maksim, Stuart Campbell, Daniel Allan, et al. "Next generation experimental data access at NSLS-II." Journal of Physics: Conference Series 2380, no. 1 (2022): 012100. http://dx.doi.org/10.1088/1742-6596/2380/1/012100.

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Abstract The NSLS-II network and computing infrastructure has been significantly updated recently. The re-IP process in 2020-2021 enabled the NSLS-II network to be routable to the rest of the BNL campus. Then, standardization of the operating systems and deployment procedures helped to deliver a consistent environment to workstations and servers used by all NSLS-II beamlines. In particular, the RedHat Enterprise Linux 8 was deployed to 700+ machines using the RedHat Satellite infrastructure management product, and all critical services (IOCs, databases, etc.) were migrated to the new OS. NFS u

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Ciangottini, Diego, Tommaso Boccali, Andrea Ceccanti, et al. "First experiences with a portable analysis infrastructure for LHC at INFN." EPJ Web of Conferences 251 (2021): 02045. http://dx.doi.org/10.1051/epjconf/202125102045.

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The challenges proposed by the HL-LHC era are not limited to the sheer amount of data to be processed: the capability of optimizing the analyser's experience will also bring important benefits for the LHC communities, in terms of total resource needs, user satisfaction and in the reduction of end time to publication. At the Italian National Institute for Nuclear Physics (INFN) a portable software stack for analysis has been proposed, based on cloud-native tools and capable of providing users with a fully integrated analysis environment for the CMS experiment. The main characterizing traits of

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Kumar, Anup, Gianmauro Cuccuru, Björn Grüning, and Rolf Backofen. "An accessible infrastructure for artificial intelligence using a Docker-based JupyterLab in Galaxy." GigaScience 12 (December 28, 2022). http://dx.doi.org/10.1093/gigascience/giad028.

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Abstract Background Artificial intelligence (AI) programs that train on large datasets require powerful compute infrastructure consisting of several CPU cores and GPUs. JupyterLab provides an excellent framework for developing AI programs, but it needs to be hosted on such an infrastructure to enable faster training of AI programs using parallel computing. Findings An open-source, docker-based, and GPU-enabled JupyterLab infrastructure is developed that runs on the public compute infrastructure of Galaxy Europe consisting of thousands of CPU cores, many GPUs, and several petabytes of storage t

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Hanwell, Marcus D., Chris Harris, Alessandro Genova, et al. "Open Chemistry, JupyterLab , REST , and quantum chemistry." International Journal of Quantum Chemistry 121, no. 1 (2020). http://dx.doi.org/10.1002/qua.26472.

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Büschenfeldt, Maika. "Einführung in die Datenvisualisierung mit Python." API Magazin 2, no. 2 (2021). http://dx.doi.org/10.15460/apimagazin.2021.2.2.77.

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Durch die Aufbereitung und grafische Darstellung von Daten (Datenvisualisierung) lassen sich Zusammenhänge und Trends besser erkennen und verstehen. Das kompakte Tutorial bietet einen Einstieg in die Datenvisualisierung mit der Programmiersprache Python und dem Tool JupyterLab.

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Marcotti, Stefania, Martin L. Jones, Thomas J. A. Slater, and David J. Barry. "Enhancing Research Through Image Analysis Workshops: Experiences and Best Practices." Microscopy Research and Technique, December 17, 2024. https://doi.org/10.1002/jemt.24769.

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ABSTRACTModern microscopy systems allow researchers to generate large volumes of image data with relative ease. However, the challenge of analyzing these data effectively is often hindered by a lack of computational skills. This bottleneck negatively impacts both research reproducibility and efficiency, as researchers frequently rely on manual or semi‐automated analysis methods. Interactive image analysis workshops offer a valuable solution, equipping researchers with the skills and tools needed to automate image processing tasks. In this paper, we share our experiences and best practices from

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Liu, Eric S., Dylan A. Lukes, and William G. Griswold. "Refactoring in Computational Notebooks." ACM Transactions on Software Engineering and Methodology, December 13, 2022. http://dx.doi.org/10.1145/3576036.

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Due to the exploratory nature of computational notebook development, a notebook can be extensively evolved even though it is small, potentially incurring substantial technical debt. Indeed, in interview studies notebook authors have attested to performing on-going tidying and big cleanups. However, many notebook authors are not trained as software developers, and environments like JupyterLab possess few features to aid notebook maintenance. As software refactoring is traditionally a critical tool for reducing technical debt, we sought to better understand the unique and growing ecology of comp

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Gadhave, K., Z. Cutler, and A. Lex. "Persist: Persistent and Reusable Interactions in Computational Notebooks." Computer Graphics Forum 43, no. 3 (2024). http://dx.doi.org/10.1111/cgf.15092.

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AbstractComputational notebooks, such as Jupyter, support rich data visualization. However, even when visualizations in notebooks are interactive, they are a dead end: Interactive data manipulations, such as selections, applying labels, filters, categorizations, or fixes to column or cell values, could be efficiently applied in interactive visual components, but interactive components typically cannot manipulate Python data structures. Furthermore, actions performed in interactive plots are lost as soon as the cell is re‐run, prohibiting reusability and reproducibility. To remedy this problem,

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Zöller, Marc-André, Waldemar Titov, Thomas Schlegel, and Marco F. Huber. "XAutoML: A Visual Analytics Tool for Understanding and Validating Automated Machine Learning." ACM Transactions on Interactive Intelligent Systems, September 28, 2023. http://dx.doi.org/10.1145/3625240.

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In the last ten years, various automated machine learning (AutoML) systems have been proposed to build end-to-end machine learning (ML) pipelines with minimal human interaction. Even though such automatically synthesized ML pipelines are able to achieve competitive performance, recent studies have shown that users do not trust models constructed by AutoML due to missing transparency of AutoML systems and missing explanations for the constructed ML pipelines. In a requirements analysis study with 36 domain experts, data scientists, and AutoML researchers from different professions with vastly d

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Garibay-Rodriguez, Jaime, Chaofan Chen, Haibing Shao, et al. "Computational Framework for Radionuclide Migration Assessment in Clay Rocks." Frontiers in Nuclear Engineering 1 (June 24, 2022). http://dx.doi.org/10.3389/fnuen.2022.919541.

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In the context of nuclear waste disposal, a pre-requisite to assure their long term safety is the need for safety assessment studies aided by computational simulations, in particular, radionuclide migration from the waste to the geosphere. It is established that underground repositories for nuclear waste will provide retardation barriers for radionuclides. However, the understanding of the sorption mechanisms of radionuclides onto mineral surfaces (i.e., illite, montmorillonite) is essential for modelling their migration. On the other hand, mechanistic-based radionuclide migration simulations,

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Mahé, Isabelle, Yara Skaff, Hélène Helfer, et al. "Care pathway for patients hospitalized with venous thromboembolism." European Journal of Clinical Investigation, January 16, 2025. https://doi.org/10.1111/eci.14383.

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AbstractBackgroundVenous thromboembolism (VTE), including pulmonary embolism (PE) and deep vein thrombosis (DVT), is a potentially fatal disease with a multifactorial nature, impacting different medical and surgical specialties. Recently, new guidelines and direct oral anticoagulants facilitated early discharge for most DVT patients and non‐severe PE patients.ObjectiveThe aim of this study is to illustrate the distribution of VTE patients throughout the hospital and map their care pathway from Emergency Department (ED) to hospital discharge.MethodsThis multicenter retrospective cross‐sectional

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Durelli, Vinicius. "The Most In-Demand Soft Skills for QA Professionals in Brazil." May 13, 2024. https://doi.org/10.5281/zenodo.11186887.

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# Replication Package for "The Most In-Demand Soft Skills for QA Professionals in Brazil" This repository contains the replication package for the paper titled "The Most In-Demand Soft Skills for QA Professionals in Brazil", including a Jupyter notebook and a CSV file containing the data used in our research. The Jupyter notebook was created using Google Colab and contains the code for the analysis presented in the paper. ## Contents - `jserd_soft_skills_data_analysis.ipynb`: The Jupyter notebook containing all data analysis and visualization code. - `soft_skills_data_br.csv`: The data file us

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Peter, Devine, Sing Koh Yun, and Blincoe Kelly. "Evaluating Software User Feedback Classifiers on Unseen Apps, Datasets, and Metadata." November 29, 2021. https://doi.org/10.5281/zenodo.5733504.

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Replication package for running the experiments from "Evaluating Software User Feedback Classifiers on Unseen Apps, Datasets, and Metadata". For the most up-to-date version of this code, please see https://github.com/Peter-Devine/user_feedback_machine_learning_cls_experiments # About This repository is supplementary to the paper "Evaluating Software User Feedback Classifiers on Unseen Apps, Datasets, and Metadata" from Peter Devine, Yun Sing Koh, and Kelly Blincoe. The code for this paper with written by Peter Devine. If you would like to run this code yourself, or would li

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Van den Brand, Jan, Karl Quint, and Karen Naranjo. "MO1035KIDNEY KNOWLEDGE AMONG USERS OF AN ONLINE SELF MANAGEMENT PLATFORM FOR PEOPLE WITH CKD." Nephrology Dialysis Transplantation 36, Supplement_1 (2021). http://dx.doi.org/10.1093/ndt/gfab109.002.

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Abstract Background and Aims Renal Tracker is a blended on- and offline self management platform for people with CKD. One of the goals of the program is to increase user’s knowledge about their kidney disease in order to facilitate communication with their health care providers. In the present study we aim to a baseline data for kidney knowledge to which the program could be compared. Method We recruited a cohort of 74 users from the United States of America via social media channels, including Facebook, Pinterest and Google Adwords. The users were offered a 12 week online course program in ad

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Bandai, Toshiyuki, Teamrat Ghezzehei, Peishi Jiang, Patrick Kidger, Xingyuan Chen, and Carl Steefel. "Dataset for Learning constitutive relations from soil moisture data via physically constrained neural networks." May 22, 2024. https://doi.org/10.5281/zenodo.11248011.

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In this repository, we provide all the datasets and codes that are needed to reproduce the analysis conducted in the paper entitled "Learning constitutive relations from soil moisture data via physically constrained neural networks." Each folder contains the following items:codes: Python Source codes for the JAX-Richards and inverse modeling. The version of Python and JAX is 3.9.18 and 0.4.19. Because these Python codes are not compatible with the latest version of JAX (0.4.28 as of 5/22/2024), users need to install older version of JAX and several other packages. We recommend the following in

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Keçeci, Mehmet. "Künneth Teoremi Bağlamında Özdevinimli ve Evrişimli Kuantum Algoritmalarında Yapay Zekâ Entegrasyonu ile Hata Minimizasyonu." May 29, 2025. https://doi.org/10.5281/zenodo.15540876.

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Künneth Teoremi Bağlamında Özdevinimli ve Evrişimli Kuantum Algoritmalarında Yapay Zekâ Entegrasyonu ile Hata Minimizasyonu <strong>Mehmet Keçeci<sup>1</sup></strong> <strong><sup>1</sup></strong><strong>ORCID </strong> : https://orcid.org/0000-0001-9937-9839, İstanbul, Türkiye <strong> </strong> <strong>Received</strong>: 29.05.2025   <strong>Özet/Abstract:</strong><strong> </strong> <strong> </strong> Kuantum algoritmalarının güvenilirliği ve etkinliği, özellikle gürültüye duyarlı günümüz NISQ cihazlarınd

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Jeff, Brown. "USING JUPYTERHUB IN THE CLASSROOM: SETUP AND LESSONS LEARNED." March 31, 2018. https://doi.org/10.5281/zenodo.1213525.

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Jupyter notebooks, formerly known as iPython notebooks, are widely used for data analysis and other areas of scientific computing. Notebooks can contain formatted text, images, LaTeX formulas, as well as code that can be executed, edited and executed again. A jupyter hub is a multi-user server for jupyter notebooks, and setting up a jupyter hub is a complex endeavour that involves many steps. The instructions found online for setup often have to be customized for different operating systems, and there is not one source that covers all aspects of setup. This paper describes the details of setti

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Freyermuth, Oliver, Katrin Kohl, and Peter Wienemann. "Unleashing JupyterHub: Exploiting Resources Without Inbound Network Connectivity Using HTCondor." Computing and Software for Big Science 5, no. 1 (2021). http://dx.doi.org/10.1007/s41781-021-00063-1.

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AbstractIn recent years Jupyter notebooks have conquered class rooms and some scientists also enjoy their convenience to quickly evaluate ideas and check whether a more detailed study is justified. To lower the threshold for getting started with Jupyter notebooks and to ease sharing and collaborative use, offering a JupyterHub service is tempting. However, offering such a service for a larger science class also requires a compute backend with sufficient resources such that hundreds of notebooks can be run simultaneously. Since resource usage for teaching activities typically fluctuates signifi

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Georg, Gottlob, Lanzinger Matthias, Mario Longo Davide, and Okulmus Cem. "Experimental Data for GHD Computation under Updates." April 24, 2022. https://doi.org/10.5281/zenodo.6481125.

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<strong>Full raw data and other data on the experiments</strong> The following is an overview on the contents of the data that is provided here to allow third parties to analyze our experimental data as well as give them all the tools needed to replicate them. <strong>Naming of the update operations:</strong> We initially worked with the assumption of being in a database context, with conjunctive queries as the example application. Thus the names of updates was different when we ran the experiments. Instead of speaking about adding or removing constraints, the term 'relation' was used.

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