Automated Author ProfileTrisovic, Ana
Harvard University0000-0003-1991-0533
Trisovic, Ana
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 3.4 (sum of 3 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This is the accompanying dataset for the article "A large-scale study on research code quality and execution" by Ana Trisovic, Matthew K. Lau, Thomas Pasquier, and Mercè Crosas.</br><b>Abstract</b>: The article presents a study on the quality and execution of research code from publicly-available replication datasets at the Harvard Dataverse repository. Research code is typically created by a group of scientists and published together with academic papers to facilitate research transparency and reproducibility. For this study, we define ten questions to address aspects impacting research reproducibility and reuse. First, we retrieve and analyze more than 2000 replication datasets with over 9000 unique R files published from 2010 to 2020. Second, we execute the code in a clean runtime environment to assess its ease of reuse. Common coding errors were identified, and some of them were solved with automatic code cleaning to aid code execution. We find that 74% of R files failed to complete without error in the initial execution, while 56% failed when code cleaning was applied, showing that many errors can be prevented with good coding practices. We also analyze the replication datasets from journals' collections and discuss the impact of the journal policy strictness on the code re-execution rate. Finally, based on our results, we propose a set of recommendations for code dissemination aimed at researchers, journals, and repositories.
Authors
- Trisovic, Ana
This is supplementary data to the article "Repository approaches to improving quality of shared data and code," and in particular, its first section on completeness of research code.</br>Run this code on Jupyter Binder here: <a href="https://mybinder.org/v2/dataverse/10.7910/DVN/EA3LC5/"><img src="https://mybinder.org/badge_logo.svg"></a>
Authors
- Trisovic, Ana
This dataset contains data and code that created a figure for the article Toward Reproducible and Extensible Research: from Values to Action.</br>Run this code on Jupyter Binder here: <a href="https://mybinder.org/v2/dataverse/10.7910/DVN/HOLVXA/"><img src="https://mybinder.org/badge_logo.svg"></a>
Authors
- Trisovic, Ana