A Data-driven Approach for Core Biodiversity Ontology Development.
A deep learning-based approach for segmenting and counting reproductive organs from digitized herbarium specimen images using refined Mask Scoring R-CNN
A Test Collection for Dataset Retrieval in Biodiversity Research
BEXIS2: A FAIR-aligned data management system for biodiversity, ecology and environmental data
BiodivOnto: Towards a Core Ontology for Biodiversity
Building high-quality merged ontologies from multiple sources with requirements customization
Capturing and Semantically Describing Provenance to Tell the Story of R Scripts
Comprehensive leaf size traits dataset for seven plant species from digitised herbarium specimen images covering more than two centuries
Dataset search in biodiversity research: Do metadata in data repositories reflect scholarly information needs?
Deep leaf: Mask R-CNN based leaf detection and segmentation from digitized herbarium specimen images
ISTMINER: Interactive Spatiotemporal Co-occurrence Pattern Extraction: A Biodiversity case study
Machine Learning Pipelines: Provenance, Reproducibility and FAIR Data Principles.
PhenoDeep: A Deep Learning-Based Approach for Detecting Reproductive Organs from Digitized Herbarium Specimen Images
ReproduceMeGit: A Visualization Tool for Analyzing Reproducibility of Jupyter Notebooks
Results of the Ontology Alignment Evaluation Initiative 2021
Towards an Ontology Network for the reproducibility of scientific studies
Towards Scientific Data Synthesis Using Deep Learning and Semantic Web
Towards Tracking Provenance from Machine Learning Notebooks
Understanding experiments and research practices for reproducibility: an exploratory study
[Dai:Si] – A Modular Dataset Retrieval Framework with a Semantic Search for Biological Data
ReproduceMeGit: A Visualization Tool for Analyzing Reproducibility of Jupyter Notebooks
Title: | ReproduceMeGit: A Visualization Tool for Analyzing Reproducibility of Jupyter Notebooks |
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Authors: | Sheeba Samuel and Birgitta König-Ries |
Source: | Provenance Week 2021 |
Place: | Provenance and Annotation of Data and Processes - 8th and 9th International Provenance and Annotation Workshop, IPAW 2020 + IPAW 2021, Virtual Event, July 19-22, 2021 |
Date: | 2021-07-19 |
Type: | Publication |
Abstract: |
Computational notebooks have gained widespread adoption among researchers from academia and industry as they support reproducible science. These notebooks allow users to combine code, text, and visualizations for easy sharing of experiments and results. They are widely shared in GitHub, which currently has more than 100 million repositories, making it the world’s largest host of source code. Recent reproducibility studies have indicated that there exist good and bad practices in writing these notebooks, which can affect their overall reproducibility. We present ReproduceMeGit, a visualization tool for analyzing the reproducibility of Jupyter Notebooks. This will help repository users and owners to reproduce and directly analyze and assess the reproducibility of any GitHub repository containing Jupyter Notebooks. The tool provides information on the number of notebooks that were successfully reproducible, those that resulted in exceptions, those with different results from the original notebooks, etc. Each notebook in the repository, along with the provenance information of its execution, can also be exported in RDF with the integration of the ProvBook tool. |
URL: | https://doi.org/10.1007/978-3-030-80960-7_12 |
BibTex: |
@InProceedings{samuel2021ReproduceMeGit, author="Samuel, Sheeba and K{\"o}nig-Ries, Birgitta", editor="Glavic, Boris and Braganholo, Vanessa and Koop, David", title="ReproduceMeGit: A Visualization Tool for Analyzing Reproducibility of Jupyter Notebooks", booktitle="Provenance and Annotation of Data and Processes", year="2021", publisher="Springer International Publishing", address="Cham", pages="201--206", isbn="978-3-030-80960-7" } |