86 research outputs found
Sheeba-Samuel/ReOpen: ReOpen
No full text<p>The REPRODUCE-ME ontology, the mapping files and the SPARQL queries for reproducibility of scientific experiments.</p>
Reproducible Research: Responding to 6W and 1H Questions of Data Provenance
No full textSlides presented by Sheeba Samuel for the invited speaker talk on "Reproducible Research: Responding to 6W and 1H Questions of Data Provenance" in the HEIBRiDS Lecture Series at Einstein Center Digital Future, Berlin, Germany on 5th January 2022
PhD Dissertation Defense: A Provenance-based Semantic Approach to Support Understandability, Reproducibility, and Reuse of Scientific Experiments
No full textSlides presented for the PhD Dissertation Defense by Sheeba Samuel. This is based on the research work done as part of PhD Thesis "A Provenance-based Semantic Approach to Support Understandability, Reproducibility, and Reuse of Scientific Experiments"
FAIR Jupyter: a knowledge graph approach to semantic sharing and granular exploration of a computational notebook reproducibility dataset
No full textThe way in which data are shared can affect their utility and reusability.
Here, we demonstrate how data that we had previously shared in bulk can be
mobilized further through a knowledge graph that allows for much more granular
exploration and interrogation. The original dataset is about the computational
reproducibility of GitHub-hosted Jupyter notebooks associated with biomedical
publications. It contains rich metadata about the publications, associated
GitHub repositories and Jupyter notebooks, and the notebooks' reproducibility.
We took this dataset, converted it into semantic triples and loaded these into
a triple store to create a knowledge graph, FAIR Jupyter, that we made
accessible via a web service. This enables granular data exploration and
analysis through queries that can be tailored to specific use cases. Such
queries may provide details about any of the variables from the original
dataset, highlight relationships between them or combine some of the graph's
content with materials from corresponding external resources. We provide a
collection of example queries addressing a range of use cases in research and
education. We also outline how sets of such queries can be used to profile
specific content types, either individually or by class. We conclude by
discussing how such a semantically enhanced sharing of complex datasets can
both enhance their FAIRness, i.e., their findability, accessibility,
interoperability, and reusability, and help identify and communicate best
practices, particularly with regards to data quality, standardization,
automation and reproducibility
Survey on Understanding Experiments and Research Practices for Reproducibility: Material and Results
No full textThis dataset contains the results from the survey 'Understanding Experiments and Research Practices for Reproducibility' conducted in the context of DFG CRC/TRR ReceptorLight. This survey was conducted as part of the dissertation of Sheeba Samuel. The purpose of this study was to gain a better understanding of what is needed to achieve reproducibility of experiments in science. The online survey consisted of 26 questions grouped in 6 sections. The six sections are: (1) Privacy policy, (2) Research context of the participant, (3) Reproducibility, (4) Measures to ensure reproducibility, (5) Important factors to understand a scientific experiment to enable reproducibility and (6) Experiment Workflow/Research Practices. The survey was completely anonymous. The survey was made available online on 24th January 2019. Out of 150 respondents, 101 responses were considered eligible for the analysis of the results. This dataset contains the survey questionnaire with the results. The results contain the raw and processed data along with the graphs for each question.</div
The Story of an Experiment: A Provenance-based Semantic Approach towards Research Reproducibility
No full textSlides for the paper "The Story of an Experiment:A Provenance-based Semantic Approach towardsResearch Reproducibility" presented at SWAT4HCLS 2018, Antwerp, Belgium on 4th December 2018 by Sheeba Samuel
A provenance-based semantic approach to support understandability, reproducibility, and reuse of scientific experiments
No full textUnderstandability and reproducibility of scientific results are vital in every field of science. Several reproducibility measures are being taken to make the data used in the publications findable and accessible. However, there are many challenges faced by scientists from the beginning of an experiment to the end in particular for data management. The explosive growth of heterogeneous research data and understanding how this data has been derived is one of the research problems faced in this context. Interlinking the data, the steps and the results from the computational and non-computational processes of a scientific experiment is important for the reproducibility. We introduce the notion of end-to-end provenance management'' of scientific experiments to help scientists understand and reproduce the experimental results. The main contributions of this thesis are: (1) We propose a provenance modelREPRODUCE-ME'' to describe the scientific experiments using semantic web technologies by extending existing standards. (2) We study computational reproducibility and important aspects required to achieve it. (3) Taking into account the REPRODUCE-ME provenance model and the study on computational reproducibility, we introduce our tool, ProvBook, which is designed and developed to demonstrate computational reproducibility. It provides features to capture and store provenance of Jupyter notebooks and helps scientists to compare and track their results of different executions. (4) We provide a framework, CAESAR (CollAborative Environment for Scientific Analysis with Reproducibility) for the end-to-end provenance management. This collaborative framework allows scientists to capture, manage, query and visualize the complete path of a scientific experiment consisting of computational and non-computational steps in an interoperable way. We apply our contributions to a set of scientific experiments in microscopy research projects
Analyzing the reproducibility of research-related Jupyter notebooks at scale
No full textWe address computational reproducibility of publication-associated Jupyter notebooks at 3 levels: (i) using fully automated workflows, we analyzed the computational reproducibility of Jupyter notebooks associated with publications indexed in the biomedical literature repository PubMed Central. We identified such notebooks by mining the article’s full text, trying to locate them on GitHub, and attempting to rerun them in an environment as close to the original as possible. We documented reproduction success and exceptions and explored relationships between notebook reproducibility and variables related to the notebooks or publications. (ii) This study represents a reproducibility attempt in and of itself, using essentially the same methodology twice on PubMed Central over the course of 2 years, during which the corpus of Jupyter notebooks from articles indexed in PubMed Central has grown in a highly dynamic fashion. (iii) We imported the corpus into a knowledge graph with a public SPARQL endpoint that allows for fine-grained exploration of notebooks individually or in aggregation (e.g. by topic, by journal or by error type). In this talk, we zoom in on common problems and practices, highlight trends, and discuss potential improvements to Jupyter-related workflows associated with biomedical publications
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