Sheeba Samuel

About Sheeba Samuel

Sheeba Samuel, With an exceptional h-index of 9 and a recent h-index of 8 (since 2020), a distinguished researcher at Friedrich-Schiller-Universität Jena, specializes in the field of Provenance, Reproducibility, FAIR, Research Data Management, Semantic Web.

His recent articles reflect a diverse array of research interests and contributions to the field:

From human experts to machines: An LLM supported approach to ontology and knowledge graph construction

Computational reproducibility of Jupyter notebooks from biomedical publications

FAIR Jupyter: a knowledge graph approach to semantic sharing and granular exploration of a computational notebook reproducibility dataset

MLProvLab: provenance management for data science notebooks

MLProvCodeGen: A Tool for Provenance Data Input and Capture of Customizable Machine Learning Scripts

Reproducible Domain-Specific Knowledge Graphs in the Life Sciences: a Systematic Literature Review

The role of Ontology Matching in Ontology Network Development

How Reproducible are the Results Gained with the Help of Deep Learning Methods in Biodiversity Research?

Sheeba Samuel Information

University

Position

Postdoctoral Researcher Germany

Citations(all)

226

Citations(since 2020)

210

Cited By

43

hIndex(all)

9

hIndex(since 2020)

8

i10Index(all)

9

i10Index(since 2020)

6

Email

University Profile Page

Google Scholar

Sheeba Samuel Skills & Research Interests

Provenance

Reproducibility

FAIR

Research Data Management

Semantic Web

Top articles of Sheeba Samuel

Title

Journal

Author(s)

Publication Date

From human experts to machines: An LLM supported approach to ontology and knowledge graph construction

arXiv preprint arXiv:2403.08345

Vamsi Krishna Kommineni

Birgitta König-Ries

Sheeba Samuel

2024/3/13

Computational reproducibility of Jupyter notebooks from biomedical publications

GigaScience

Sheeba Samuel

Daniel Mietchen

2024

FAIR Jupyter: a knowledge graph approach to semantic sharing and granular exploration of a computational notebook reproducibility dataset

arXiv preprint arXiv:2404.12935

Sheeba Samuel

Daniel Mietchen

2024/4/19

MLProvLab: provenance management for data science notebooks

Dominik Kerzel

Birgitta König-Ries

Samuel Sheeba

2023

MLProvCodeGen: A Tool for Provenance Data Input and Capture of Customizable Machine Learning Scripts

Tarek Al Mustafa

Birgitta König-Ries

Sheeba Samuel

2023

Reproducible Domain-Specific Knowledge Graphs in the Life Sciences: a Systematic Literature Review

Samira Babalou

Sheeba Samuel

Birgitta König-Ries

2023/9/15

The role of Ontology Matching in Ontology Network Development

Sheeba Samuel

Birgitta König-Ries

Alsayed Algergawy

2023

How Reproducible are the Results Gained with the Help of Deep Learning Methods in Biodiversity Research?

Biodiversity Information Science and Standards

Waqas Ahmed

Vamsi Krishna Kommineni

Birgitta Koenig-ries

Sheeba Samuel

2023

Concept Explainability for Plant Diseases Classification

arXiv preprint arXiv:2309.08739

Jihen Amara

Birgitta König-Ries

Sheeba Samuel

2023/9/15

BiodivNERE: Gold standard corpora for named entity recognition and relation extraction in the biodiversity domain

Biodiversity Data Journal

Nora Abdelmageed

Felicitas Löffler

Leila Feddoul

Alsayed Algergawy

Sheeba Samuel

...

2022

[RE] Nondeterminism and Instability in Neural Network Optimization

Waqas Ahmed

Sheeba Samuel

2022

Toward a framework for integrative, FAIR, and reproducible management of data on the dynamic balance of microbial communities

Luiz Gadelha

Martin Hohmuth

Mahnoor Zulfiqar

David Schöne

Sheeba Samuel

...

2022/10/11

A collaborative semantic-based provenance management platform for reproducibility

PeerJ Computer Science

Sheeba Samuel

Birgitta König-Ries

2022/3/10

End-to-End provenance representation for the understandability and reproducibility of scientific experiments using a semantic approach

Journal of biomedical semantics

Sheeba Samuel

Birgitta König-Ries

2022/1/6

Understanding experiments and research practices for reproducibility: an exploratory study

PeerJ

Sheeba Samuel

Birgitta König-Ries

2021/4/21

Towards Tracking Provenance from Machine Learning Notebooks.

Dominik Kerzel

Sheeba Samuel

Birgitta König-Ries

2021

The Story of an Open Science Experiment

Sheeba Samuel

2021

Capturing and Semantically Describing Provenance to Tell the Story of R Scripts

Maria Luiza Mondelli

Sheeba Samuel

Birgitta König-Ries

Luiz MR Gadelha

2021/9/20

A Data-driven Approach for Core Biodiversity Ontology Development.

Nora Abdelmageed

Alsayed Algergawy

Sheeba Samuel

Birgitta König-Ries

2021

Towards an Ontology Network for the Reproducibility of Scientific Studies.

Sheeba Samuel

Alsayed Algergawy

Birgitta König-Ries

2021/9/13

See List of Professors in Sheeba Samuel University(Friedrich-Schiller-Universität Jena)

Co-Authors

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