1 to 10 of 248 Results
Feb 5, 2026
Frize, Monique; Deschênes, Claire, 2026, "Supporting Data for: Women’s Contribution to Science and Technology through ICWES Conferences", https://doi.org/10.5683/SP3/LZJJLV, Borealis, V1, UNF:6:6sHlyQvXXqWMhYBDJYVKtA== [fileUNF]
This dataset was created by Monique Frize, Claire Deschênes and Ruby Heap from data collected on each International Conference of Women Engineers and Scientists (ICWES), from ICWES-I to ICWES-XII. The dataset was first archived with the University of Ottawa Archives and Special Collections and has since been transformed into a dataset for analyses... |
Conway, Kyle; Gramaccia, Julie Alice; Scholz, Nikita; Averbeck, Téana, 2025, "Replication data for: Measuring Lexical Distance between Parallel Corpora: The Case of AI-Generated News Translation", https://doi.org/10.5683/SP3/MFZTWZ, Borealis, V1, UNF:6:v93vHhRpGD/NphylJBCVww== [fileUNF]
Data and code (in R) corresponding to the article "Measuring Lexical Distance between Parallel Corpora: The Case of AI-Generated News Translation" The purpose of this project was to develop tools to measure statistical distance between corpora of translated news articles in English in French. Included here are the meta-data for the articles, gather... |
Liang, Lisha; Han, Lingfang; Dhuper, Misha; Lutek, Keegan; Standen, Emily, 2025, "Replication data for: Liang et al 2025. Walking elicits muscle functional changes in the pectoral fin of Polypterus senegalus. JEB", https://doi.org/10.5683/SP3/IGEDUL, Borealis, V1, UNF:6:bsw1eA5ZhnkN4yfTD0KUWw== [fileUNF]
This dataset contains all numerical data (electromyogrpahy and kinematics) and r-code required to replicate the analysis discussed in Liang et al. 2025. Walking elicits muscle functional change in the pectoral fin of Polypterus senegalus. doi:10.1242/jeb.250474. The r-code (Liang_etal_2025_RProject.Rproj and tidyLinearAnalysis.R) works in conjuncti... |
Abdoulkader, Nasteho; Archibald, Jennifer; Lignereux, Morgane; Lehoux, Eric A.; Catelas, Isabelle, 2025, "Replication Data for: NLRP3 inflammasome-dependent and -independent interleukin-1β release by macrophages exposed to wear and corrosion products from CoCrMo implants", https://doi.org/10.5683/SP3/IEBNOE, Borealis, V1
Wear particles and metal ions released from cobalt–chromium–molybdenum (CoCrMo) alloy implants present a significant clinical concern. Particles such as Cr2O3 and CoCrMo and metal ions, including Co2+ and Cr3+, have the potential to induce adverse local tissue reactions (ALTR) that can lead to implant failure. The pro-inflammatory response of macro... |
Sep 2, 2025
Zafar, Huma, 2025, "Replication data for: Examining the OpenAlex Concepts: A Detailed Case Study of Machine-Derived Classification", https://doi.org/10.5683/SP3/29QGMP, Borealis, V1
Machine-learning techniques are becoming increasingly popular in metadata and clas- sification work due to their ability to operate at scale, but insufficient consideration has been given to how effective such techniques truly are against traditional prac- tice. This dataset was generated as part of a thesis project to analyze the machine-generated... |
Jun 26, 2025
van Walsum, Saskia; Butler, Leigh-Ann; Hare, Madelaine; Ripp, Chantal; Haustein, Stefanie, 2025, "Environmental scan of Open science policies - Canada, globally and U15 institutions", https://doi.org/10.5683/SP3/NTFQPS, Borealis, V2, UNF:6:Xt46LQPw22NgyBpEcivHcA== [fileUNF]
This dataset includes the results of two environmental scanning activities to better understand existing open science practices across Canada and globally. The file titled "Scan of open science policies - Canada and globally" includes results of a scan of OS practices that were manually surveyed across 40 national and global institutions, organizat... |
Van der Kolk, Jarno; Darveau, Peter; Tayler, Felicity; Cheung, Melissa, 2025, "Plus proches voisins | K-nearest neighbours", https://doi.org/10.5683/SP3/BPE2VO, Borealis, V3, UNF:6:obcfTbACzjK4pOPXcosJjw== [fileUNF]
K-nearest neighbours (KNN) is a machine learning algorithm that is good a classifying moderately-sized datasets. It does so by looking at the n-dimensional space, where n is the number of features, and looking at nearby points to the data point you want to classify. It is a fairly simple technique but it can be very effective for data with low nois... |
Van der Kolk, Jarno; Darveau, Peter; Tayler, Felicity; Cheung, Melissa, 2025, "Traitement du langage naturel | Natural language processing", https://doi.org/10.5683/SP3/CER6YQ, Borealis, V4
Natural language processing (NLP) is a machine learning technique to analyze large amounts of text to extract information. Some examples are sentiment analysis, translation, transcription, summarizing, tagging, but NLP is a very broad term and can apply to anything text related. This tutorial consists of two notebooks. The first notebooks delves in... |
Van der Kolk, Jarno; Darveau, Peter; Tayler, Felicity, 2025, "Analyse de jeux de données, présélection et détermination de modèles | Dataset analysis, model shortlisting, and model determination", https://doi.org/10.5683/SP3/5WPWJT, Borealis, V2
This tutorial will present some guidelines you can use. Additionally, we will show what resources are available once the model start to become bigger than what your computer can handle. |
Van der Kolk, Jarno; Darveau, Peter; Tayler, Felicity, 2024, "Aperçu de l’ensemble du contenu sur l’apprentissage-machine | Overview of all the machine learning content", https://doi.org/10.5683/SP3/YQ5SN2, Borealis, V3
Cette série de tutoriels entend combler trois lacunes au niveau de la compréhension de l’IA et des méthodologies d’apprentissage-machine : Proposer une introduction aux modèles d’intelligence artificielle et d’apprentissage-machine. Préparer les données requises par ces modèles. Intégrer les pratiques de gestion des données de recherche (GDR) aux m... |
