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16 results for “newcomers”
How to choose a task? Mismatches in perspectives of newcomers and existing contributors
<p>Dataset for the paper: How to choose a task? Mismatches in perspectives of newcomers and existing contributors</p>
Data and material for: "Code review for newcomers: is it different?"
<p>Data and material for the paper "Code review for newcomers: is it different?", published in the Proceedings of the 11th International Workshop on Cooperative and Human Aspects of Software Engineering (CHASE 2018).</p>
Empowering Newcomer Voices: A Trauma-Informed, Gender-Responsive Group Intervention in Schools (VOCES-NUEVAS)
ClinicalTrials.gov study NCT07041931. IPD Sharing: NO. Countries: 1. Publications: 6.
A Surveillance and Azithromycin Treatment for Newcomers and Travelers Evaluation: The ASANTE Trial
ClinicalTrials.gov study NCT01767506. IPD Sharing: YES. Countries: 1. Publications: 1.
FIGURES 1–8. Cithaeron praedonius. 1 in Vagabond but elusive: two newcomers to the Eastern Amazon (Araneae: Cithaeronidae; Prodidomidae)
FIGURES 1–8. Cithaeron praedonius. 1 female from Pará, Brazil, MPEG 20618, dorsal view; 2 same, detail of eyes; 3 male from Pará, Brazil, MPEG 20618, ventral view, showing pseudosegmented tarsi; 4 left male palp, ventral view; 5 same, retrolateral view; 6 same female, epigyne, ventral view; 7 same, cleared, ventral view; 8 same, dorsal view. Scale bars: Figs 1, 3 = 1 mm; Figs 2, 4–8 = 0.25 mm.
FIGURE 16 in Vagabond but elusive: two newcomers to the Eastern Amazon (Araneae: Cithaeronidae; Prodidomidae)
FIGURE 16. Distribution records of C. praedonius (green circles) and Z. doriai (red squares) in Northern/Northeastern Brazil. Lines connecting localities show distance in kilometers.
FIGURES 9–15. Zimiris doriai. 9 in Vagabond but elusive: two newcomers to the Eastern Amazon (Araneae: Cithaeronidae; Prodidomidae)
FIGURES 9–15. Zimiris doriai. 9 female from Pará, Brazil, MPEG 20620, dorsal view; 10 same, detail of eyes; 11 same, spinnerets, ventral view; 12 male from Pará, Brazil, MPEG 20620, left palp, ventral view; 13 same, retrolateral view; 14 same female, epigyne, ventral view; 15 same, cleared, dorsal view. Scale bars: Fig. 9 = 1 mm; Figs 10–15 = 0.25 mm.
Replication Package for ASE 2023 Paper "Personalized First Issue Recommender for Newcomers in Open Source Projects"
<p>This replication package contains a replication package for ASE 2023 paper titled "Personalized First Issue Recommender for Newcomers in Open Source Projects." This package includes a dataset of 68,858 issues from 100 GitHub projects, records of 123 manually labeled issue samples, and Python scripts for analyzing the data and evaluating models. The package is also stored in the GitHub repository <a href="https://github.com/mcxwx123/PFIRec">https://github.com/mcxwx123/PFIRec</a>.</p> <p>Required Environment</p> <p>We recommend setting up the required environment on a commodity Linux machine with at least 1 CPU Core, 8GB Memory, and 100GB empty storage space. Our experiments were conducted on an Ubuntu 20.04 server with two Intel Xeon Gold CPUs, 320GB memory, and 36TB RAID 5 Storage.</p> <p>Files and Replicating Results</p> <p>We used the GFI-bot database and the GitHub GraphQL API to collect features of 68,858 candidate issues and restore historical states of resolvers of 11,615 FIs (first issues).</p> <p>The followings are the files and replicating results:</p> <p>Dataset:</p> <p>The raw data of newcomer-issue pairs' features are stored in <code>ReplicationPackage/data/dataset_{bertmodel}_{num}.pkl</code>, where {bertmodel} is one of the four BERT-based language models: SIMCSE, RoBERTa, CodeBERT, and BERTOverflow, corresponding to the dataset whose textual features are extracted by one of the four language models. And {num} is 0 to 19, corresponding to the 20 chronological folds. The training sets of the GFI-Bot approach are contained in <code>ReplicationPackage/data/training_set_recgfi_simcse_{num}.pkl</code>. <code>ReplicationPackage/data/newcomerdata.json</code> contains first issues' title and description and their resolvers' total commit number and number of commits in the latest month, and <code>ReplicationPackage/data/processeddata.pkl</code> contains the 37 developers' features for the empirical study. <code>ReplicationPackage/data/isstexts.json</code> contains issues titles and descriptions for Stanik et al.'s approach.</p> <p>Python scripts:</p> <p><code>ReplicationPackage/empirical.py</code> is the script for reproducing all the results in Section III of the paper. <code>ReplicationPackage/model.py</code> is the script for reproducing all the results in Section IV of the paper.</p> <p>Records:</p> <p><code>ReplicationPackage/PFIs.csv</code> records the manually labeled issues for the empirical study.</p> <p>Figures:</p> <p>By running <code>ReplicationPackage/empirical.py</code> and <code>ReplicationPackage/model.py</code>, you can get all the figures in the fold <code>ReplicationPackage/figures/</code>. Besides the figures in the paper, <code>ReplicationPackage/figures/</code> also contains <code>typedis_{num}.png</code>, and <code>domaindis_{num}.png</code>, {num} is 1 to 4, representing additional results of newcomer features for Figure 4 in the paper.</p>
FIGURE 1 in Palaeoentomology (2018-2022): A newcomer on the rise to high impact
FIGURE 1. The numbers of papers, pages, and citations in the first five years of Palaeoentomology. Citation data from Web of Science Core Collection™ (10 April 2023).
Fuerte Program for Newcomer Immigrant Youth
ClinicalTrials.gov study NCT04156373. IPD Sharing: NO. Countries: 1. Publications: 4.
Asthma and Health Literacy in Newcomer Communities in the Greater Vancouver Area
ClinicalTrials.gov study NCT01474928. IPD Sharing: Not stated. Countries: 1. Publications: 4.
A storm of relevant project-related terms to support newcomers' onboarding
<p>Both distributed and collocated software teams use collaborative communication channels, such as instant messaging (IM) tools, to support software development and management tasks. Furthermore, when teams use IM tools, relevant software-related discussions end up stored in these tools' log files. This research aims to investigate how we can extract and use projects' knowledge from developers' IM logs to support newcomers' onboarding. We propose an approach based on data-mining techniques to automatically obtain frequent software-related discussions from the developer's IM logs, extracting what we call the Project's Frequent Knowledge (PFK). We assessed the proposed approach evaluating three newcomers' knowledge acquisition regarding a software project. The results demonstrated that, on average, 70% of the frequent software-related discussions identified in this study were useful to determine the PFK, and newcomers were able to comprehend software related issues by analyzing the PFK. Our findings indicate the usefulness of the proposed approach to extract software knowledge from developers' IM logs and support newcomers' onboarding. Moreover, a conducted follow-up interview involving newcomers and the team's project manager revealed the feasibility of using PFK on knowledge transfer and acquisition.</p>
Software Solutions for Newcomers in Software Projects: a Systematic Literature Review
<p>Systematic Literature Review - Supplementary material </p>
How do CONTRIBUTING files support newcomers in Open Source projects?
<p> </p> <p>Welcome to the replication package for the paper entitled "Do CONTRIBUTING files provide information about OSS newcomers' onboarding barriers?"</p> <p><strong>Repository Structure </strong><br> Our package is divided in folders and it is organized as follows:</p> <ul> <li><strong>app</strong>: This folder contains the implementation of our streamlit application, used to demonstrate the capabilities of our classification model. </li> <li><strong>data</strong>: If you are looking for the data we have used for classification, this is the folder where it is located. This folder also contains the raw contributing files of each project analyzed, and the spreadsheets qualitatively analyzed by the authors. </li> <li><strong>results</strong>: This folder contains all the files related to our results section. From the analysis of our classification model to the results of our online questionnaire.</li> <li><strong>scripts</strong>: If you want to see the code we wrote during the whole classification process, including but not limited to the classification process, this is the folder you are looking for.</li> <li><strong>survey</strong>: instrument used in the survey, raw answers and a spreadsheet with analyzed data.</li> <li><strong>misc</strong>: This folder contains miscellaneous files that were not used in this paper, but support the statements of it (e.g. a screenshot of the top ten languages used on GitHub from the Octoverse website).</li> </ul> <p><em>Each folder has its specific README.md with more specific details about their content.</em></p> <p><strong>I want to use your model!</strong><br> We are glad you are interested in our classification model. The final model is available as a `classification_model.sav` file inside the `app` folder that you can load using Pickle. If you are not familiar with Pickle or don't know how to load a model, we recommend you to take a look at our code implementation inside the `app` folder (`classify_content.py` is a good starting point).<br> </p>
Intervention to Address Disparate Mental Health Consequences of COVID-19 Pandemic on Latinx and African Newcomers
ClinicalTrials.gov study NCT05092542. IPD Sharing: YES. Countries: 1. Publications: 0.
Data for the COVID newcomers project
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