Integrative Data Management for Reproducibility of Microscopy Experiments. - Sheeba Samuel (ESWC (2), 2017)

REPRODUCE-ME: Ontology-Based Data Access for Reproducibility of Microscopy Experiments. - Sheeba Samuel; Birgitta König-Ries (ESWC (Satellite Events), 2017)

Towards Reproducibility of Microscopy Experiments. - Sheeba Samuel; Frank Taubert; Daniel Walther; Birgitta König-Ries; H. Martin Bücker (D Lib Mag., 2017)

Combining P-Plan and the REPRODUCE-ME Ontology to Achieve Semantic Enrichment of Scientific Experiments Using Interactive Notebooks. - Sheeba Samuel; Birgitta König-Ries (ESWC (Satellite Events), 2018)

ProvBook: Provenance-based Semantic Enrichment of Interactive Notebooks for Reproducibility. - Sheeba Samuel; Birgitta König-Ries (ISWC (P&D/Industry/BlueSky), 2018)

The Story of an experiment: a provenance-based semantic approach towards research reproducibility. - Sheeba Samuel; Kathrin Groeneveld; Frank Taubert; Daniel Walther; Tom Kache; Teresa Langenstück; Birgitta König-Ries; H. Martin Bücker; Christoph Biskup (SWAT4LS, 2018)

A provenance-based semantic approach to support understandability, reproducibility, and reuse of scientific experiments. - Sheeba Samuel (2019)

fusion-jena/ReproducibilitySurvey: ReproducibilitySurvey 0.1. - Sheeba Samuel; Birgitta König-Ries (Zenodo, 2020)

Capturing and Semantically Describing Provenance to Tell the Story of R Scripts. - Maria Luiza Mondelli; Sheeba Samuel; Birgitta König-Ries; Luiz M. R. Gadelha Jr. (e-Science, 2021)

BiodivOnto: Towards a Core Ontology for Biodiversity. - Nora Abdelmageed; Alsayed Algergawy; Sheeba Samuel; Birgitta König-Ries (ESWC (Satellite Events), 2021)

Towards Tracking Provenance from Machine Learning Notebooks. - Dominik Kerzel; Sheeba Samuel; Birgitta König-Ries (KDIR, 2021)

ReproduceMeGit: A Visualization Tool for Analyzing Reproducibility of Jupyter Notebooks. - Sheeba Samuel; Birgitta König-Ries (IPAW, 2021)

Machine Learning Pipelines: Provenance, Reproducibility and FAIR Data Principles. - Sheeba Samuel; Frank Löffler 001; Birgitta König-Ries (IPAW, 2021)

A Data-driven Approach for Core Biodiversity Ontology Development. - Nora Abdelmageed; Alsayed Algergawy; Sheeba Samuel; Birgitta König-Ries (JOWO, 2021)

Towards an Ontology Network for the Reproducibility of Scientific Studies. - Sheeba Samuel; Alsayed Algergawy; Birgitta König-Ries (JOWO, 2021)

fusion-jena/BiodivOnto: P&D_ESWC2021. - Nora Abdelmageed; Alsayed Algergawy; Sheeba Samuel; Birgitta König-Ries (Zenodo, 2021)

Toward a Framework for Integrative, FAIR, and Reproducible Management of Data on the Dynamic Balance of Microbial Communities. - Luiz M. R. Gadelha Jr.; Martin Hohmuth; Mahnoor Zulfiqar; David Schöne; Sheeba Samuel; Maria Sorokina; Christoph Steinbeck; Birgitta König-Ries (e-Science, 2022)

End-to-End provenance representation for the understandability and reproducibility of scientific experiments using a semantic approach. - Sheeba Samuel; Birgitta König-Ries (J. Biomed. Semant., 2022)

Computational reproducibility of Jupyter notebooks from biomedical publications. - Sheeba Samuel; Daniel Mietchen (CoRR, 2022)

A collaborative semantic-based provenance management platform for reproducibility. - Sheeba Samuel; Birgitta König-Ries (PeerJ Comput. Sci., 2022)

BiodivNERE: Gold Standard Corpora for Named Entity Recognition and Relation Extraction in Biodiversity Domain. - Nora Abdelmageed; Felicitas Löffler; Leila Feddoul; Alsayed Algergawy; Sheeba Samuel; Jitendra Gaikwad; Anahita Kazem; Birgitta König-Ries (Zenodo, 2022)

BiodivNERE: Gold Standard Corpora for Named Entity Recognition and Relation Extraction in Biodiversity Domain. - Nora Abdelmageed; Felicitas Löffler; Leila Feddoul; Alsayed Algergawy; Sheeba Samuel; Jitendra Gaikwad; Anahita Kazem; Birgitta König-Ries (Zenodo, 2022)

MLProvLab: Provenance Management for Data Science Notebooks. - Dominik Kerzel; Birgitta König-Ries; Sheeba Samuel (BTW, 2023)

MLProvCodeGen: A Tool for Provenance Data Input and Capture of Customizable Machine Learning Scripts. - Tarek Al Mustafa; Birgitta König-Ries; Sheeba Samuel (BTW, 2023)

The role of ontology matching in ontology network development. - Sheeba Samuel; Birgitta König-Ries; Alsayed Algergawy (OM@ISWC, 2023)

Concept Explainability for Plant Diseases Classification. - Jihen Amara; Birgitta König-Ries; Sheeba Samuel (VISIGRAPP (4: VISAPP), 2023)

Reproducible Domain-Specific Knowledge Graphs in the Life Sciences: a Systematic Literature Review. - Samira Babalou; Sheeba Samuel; Birgitta König-Ries (CoRR, 2023)

Integrating Domain Knowledge for Enhanced Concept Model Explainability in Plant Disease Classification. - Jihen Amara; Sheeba Samuel; Birgitta König-Ries (ESWC (1), 2024)

A Method for Integrating Heterogeneous Data into a Knowledge Graph. - Christoph Göpfert; Sheeba Samuel; Martin Gaedke (iiWAS (1), 2024)

Facilitating End-User Development of Websites with Micro-Frontends. - Jan-Ingo Haas; Sheeba Samuel; Martin Gaedke (iiWAS (2), 2024)

Towards the Automation of Knowledge Graph Construction using Large Language Models. - Vamsi Krishna Kommineni; Birgitta König-Ries; Sheeba Samuel (NLP4KGC@SEMANTiCS, 2024)

From human experts to machines: An LLM supported approach to ontology and knowledge graph construction. - Vamsi Krishna Kommineni; Birgitta König-Ries; Sheeba Samuel (CoRR, 2024)

Evaluating the method reproducibility of deep learning models in the biodiversity domain. - Waqas Ahmed; Vamsi Krishna Kommineni; Birgitta König-Ries; Jitendra Gaikwad; Luiz M. R. Gadelha Jr.; Sheeba Samuel (CoRR, 2024)

Enhancing Explainability in Multimodal Large Language Models Using Ontological Context. - Jihen Amara; Birgitta König-Ries; Sheeba Samuel (CoRR, 2024)

Harnessing multiple LLMs for Information Retrieval: A case study on Deep Learning methodologies in Biodiversity publications. - Vamsi Krishna Kommineni; Birgitta König-Ries; Sheeba Samuel (CoRR, 2024)

Explainability of Deep Learning-Based Plant Disease Classifiers Through Automated Concept Identification. - Jihen Amara; Birgitta König-Ries; Sheeba Samuel (CoRR, 2024)

FAIR Jupyter: A Knowledge Graph Approach to Semantic Sharing and Granular Exploration of a Computational Notebook Reproducibility Dataset. - Sheeba Samuel; Daniel Mietchen (TGDK, 2024)

Evaluating the method reproducibility of deep learning models in the biodiversity research. - Vamsi Krishna Kommineni; Waqas Ahmed; Birgitta König-Ries; Jitendra Gaikwad; Luiz M. R. Gadelha Jr.; Sheeba Samuel (Zenodo, 2024)

FAIR Jupyter. - Sheeba Samuel; Daniel Mietchen (DROPS Artifacts, 2024)

Vorwort. - Marina Tropmann-Frick; Tanja Auge; Sheeba Samuel (BTW Workshops, 2025)

Analyzing the reproducibility of research-related Jupyter notebooks at scale. - Daniel Mietchen; Sheeba Samuel (Software Engineering, 2025)

Evaluating the method reproducibility of deep learning models in biodiversity research. - Waqas Ahmed; Vamsi Krishna Kommineni; Birgitta König-Ries; Jitendra Gaikwad; Luiz M. R. Gadelha Jr.; Sheeba Samuel (PeerJ Comput. Sci., 2025)

Supplementary Data for "Taxonomy-based Keyword Annotation of Research Data". - Christoph Göpfert; Jan-Ingo Haas; Sheeba Samuel; Lucas Schröder; Martin Gaedke (Zenodo, 2025)

ShEx2SPARQL Evaluation Results. - Christoph Göpfert; Sheeba Samuel; Martin Gaedke (Zenodo, 2025)