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FUnctionality Sharing In Open eNvironments
Heinz Nixdorf Chair for Distributed Information Systems
 
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Dr. Sheeba Samuel

PostDoc Researcher

Ernst-Abbe-Platz 2 , 07743 Jena , Room: 3211

0000-0002-7981-8504

Curriculum Vitae

Past Positions:

Member of Technical Staff II – Aruba, a Hewlett Packard Enterprise Company (July 2013-December 2015)

Graduate Technical Intern – Aruba, a Hewlett Packard Enterprise Company (January 2013-June 2013)

Education:

PhD, Computer Science, Friedrich Schiller University Jena, Germany (2016-2019)

Master of Technology (M Tech), Information Technology, International Institute of Information Technology, Bangalore, India (2011-2013)

Bachelor of Technology (B Tech), Computer Science and Engineering, Cochin University of Science and Technology (CUSAT), India (2007-2011)

Grants

  • ProChance 2017 Grant, Friedrich Schiller University Jena
    Promotion of the scientific interaction of young female researchers.
  • IMPULSE project 2020, Friedrich Schiller University Jena
    Support Programme for early and advanced postdocs to apply for own third-party funds. Funding code: IP 2020-10
  • Start-up funding from MSCJ for the project “Integrating Knowledge Graphs for DL Interpretability”
  • Freistaats Thüringen funding for the research project ‘Explainability and Reproducibility for AI’

Work

Reproducibility work website:  https://w3id.org/reproduceme/research

Personal website: https://sheeba-samuel.github.io/

Professional Activities

Conferences and Journals

Invited Speaker Talks

  • Invited Speaker Talk on “Provenance and Reproducibility: a look into Jupyter Notebooks” in Thuringian RDM-Days 2021 “Data documentation: A love note to the future!” [Slides]
  • Invited Speaker Talk on “Contributions to Open Science for Reproducible Research” in QPTData Open Science Workshop at FIZ Karlsruhe, 2020. [Slides]
  • Invited Speaker Talk on “The Story of an Open Science Experiment” in Open Science Days at Max Planck Society, Berlin, 2020.
  • Speaker Talk at JupyterCon 2020

Research Area

My research area includes:

  • Scientific Data Management and processing
  • Reproducibility of Scientific Experiments
  • Semantic Web
  • Data provenance
  • Machine Learning

Publications

2021

Towards Tracking Provenance from Machine Learning Notebooks
Dominik Kerzel, Sheeba Samuel, Birgitta König-Ries
13th International Conference on Knowledge Discovery and Information Retrieval (KDIR)
25.10.2021
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.
ReWords, co-located with 17th IEEE e-Science 2021 27.9.2021
A Data-driven Approach for Core Biodiversity Ontology Development.
Nora Abdelmageed, Alsayed Algergawy, Sheeba Samuel, Birgitta König-Ries
Third International Workshop on Semantics for Biodiversity, co-located with ICBO, 2021 15.9.2021
Towards an Ontology Network for the reproducibility of scientific studies
Sheeba Samuel, Alsayed Algergawy, Birgitta König-Ries
JOWO Workshops 2021 13.9.2021
ReproduceMeGit: A Visualization Tool for Analyzing Reproducibility of Jupyter Notebooks
Sheeba Samuel and Birgitta König-Ries
Provenance Week 2021
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 19.7.2021
Machine Learning Pipelines: Provenance, Reproducibility and FAIR Data Principles.
Sheeba Samuel, Frank Löffler, Birgitta König-Rie
Provenance Week 2021
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 19.7.2021
BiodivOnto: Towards a Core Ontology for Biodiversity
Nora Abdelmageed, Alsayed Algergawy, Sheeba Samuel and Birgitta Koenig-Ries
The Semantic Web: ESWC 2021 Satellite Events: poster and demo
4.6.2021
Understanding experiments and research practices for reproducibility: an exploratory study
Sheeba Samuel, Birgitta König-Ries
PeerJ 21.4.2021

2020

ReproduceMeGit: A Visualization Tool for Analyzing Reproducibility of Jupyter Notebooks
Sheeba Samuel and Birgitta König-Ries
Provenance Week 2020
Charlotte, North Carolina, USA 22.6.2020
Machine Learning Pipelines: Provenance, Reproducibility and FAIR Data Principles
Sheeba Samuel, Frank Löffler and Birgitta König-Ries
Provenance Week 2020
Charlotte, North Carolina, USA 22.6.2020
A virtual “Werkstatt” for digitization in the sciences
Sheeba Samuel, Maha Shadaydeh, Sebastian Böcker, Bernd Brügmann, Solveig Franziska Bucher, Volker Deckert, Joachim Denzler, Peter Dittrich, Ferdinand von Eggeling, Daniel Güllmar, Orlando Guntinas-Lichius, Birgitta König-Ries, Frank Löffler, Lutz Maicher, Manja Marz, Mirco Migliavacca, Jürgen R. Reichenbach, Markus Reichstein, Christine Römermann, Andrea Wittig
Research Ideas and Outcomes
11.5.2020

2019

A Provenance-based Semantic Approach to Support Understandability, Reproducibility, and Reuse of Scientific Experiments
Sheeba Samuel
Friedrich Schiller University Jena 20.12.2019

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 and Christoph Biskup
11th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences (SWAT4HCLS 2018)
Antwerp, Belgium 5.12.2018
ProvBook: Provenance-based Semantic Enrichment of Interactive Notebooks for Reproducibility
Sheeba Samuel and Birgitta König-Ries
The 17th International Semantic Web Conference (ISWC) Demo Track 2018
Monterey, California, USA 10.10.2018
Combining P-Plan and the REPRODUCE-ME Ontology to Achieve Semantic Enrichment of Scientific Experiments using Interactive Notebooks
Sheeba Samuel and Birgitta König-Ries
15th Extended Semantic Web Conference (ESWC) Posters & Demo 2018
Crete, Greece 6.6.2018

2017

Integrative Data Management for Reproducibility of Microscopy Experiments
Sheeba Samuel
14th Extended Semantic Web Conference (ESWC) 2017
Portoroz, Slovenia 28.5.2017
REPRODUCE-ME: Ontology-based Data Access for Reproducibility of Microscopy Experiments
Sheeba Samuel, Birgitta König-Ries
14th Extended Semantic Web Conference (ESWC) 2017
Portoroz, Slovenia 28.5.2017

2016

Towards Reproducibility of Microscopy Experiments
Sheeba Samuel, Frank Taubert, Daniel Walther, Birgitta König-Ries and H. Martin Bücker
First International Workshop on Reproducible Open Science, 2016 co-located with TPDL
Hannover, Germany 9.9.2016

Talks

2021

Provenance and Reproducibility: A Look into Jupyter Notebooks
Thuringian RDM Days 2021 'Data documentation: A love note to the future!'
Germany 22.6.2021

2020

ProvBook: Capturing and Visualizing Provenance in Jupyter Notebooks for Reproducibility
JupyterCon 2020
JupyterCon 2020 13.10.2020
Analyzing the use and reproducibility of Jupyter Notebooks using ReproduceMeGit
JupyterCon 2020
JupyterCon 2020 13.10.2020
ReproduceMeGit: A Visualization Tool for Analyzing Reproducibility of Jupyter Notebooks
Provenance Week 2020
Virtual Provenance Week 2020 22.6.2020
Machine Learning Pipelines: Provenance, Reproducibility and FAIR Data Principles
Virtual Provenance Week
Provenance Week 2020 22.6.2020
Contributions to Open Science for Reproducible Research
QPTData Open Science Workshop
FIZ Karlsruhe 23.1.2020

2019

A Provenance-based Semantic Approach to Support Understandability, Reproducibility, and Reuse of Scientific Experiments
PhD Dissertation Defense
Friedrich Schiller University Jena 20.12.2019

2018

The Story of an Experiment: A Provenance-based Semantic Approach towards Research Reproducibility
11th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences (SWAT4HCLS 2018)
Antwerp, Belgium 4.12.2018
Provenance-based Semantic Approach for the Reproducibility of Scientific Experiments
ICEI2018 - The 10th International Conference on Ecological Informatics
Jena, Germany 27.9.2018

2017

Integrative Data Management for Reproducibility of Microscopy Experiments
14th Extended Semantic Web Conference (ESWC) 2017 PhD Symposium
Portoroz, Slovenia 29.5.2017

2016

Towards Reproducibility of Microscopy Experiments
First International Workshop on Reproducible Open Science (RepScience 2016)
Hannover, Germany 9.9.2016

Supervised Theses

2021

Reproducibility of Machine Learning Experiments given the provenance data (taken)
Tarek Al Mustafa - Bachelor
Friedrich-Schiller-Universität Jena
17.5.2021
Tracking Provenance in Machine Learning Scripts (taken)
Dominik Kerzel - Bachelor
Friedrich-Schiller-Universität Jena
1.4.2021

Teaching

Current Projects

A virtual “Werkstatt” for digitization in the sciences
Data is central in almost all scientific disciplines nowadays. Furthermore, intelligent systems have developed rapidly in recent years, so that in many disciplines the expectation is emerging that with the help of intelligent systems, significant challenges can be overcome and science can be done in completely new ways. In order for this to succeed, however, first, fundamental research in computer science is still required, and, second, generic tools must be developed on which specialized solutions can be built. A virtual manufactory for digitization in the sciences, the “Werkstatt”, which is being established at the Michael Stifel Center Jena (MSCJ) for data-driven and simulation science is a collaborative project funded by the Carl Zeiss Foundation to address fundamental questions in computer science and applications. The Werkstatt focuses on three key areas, which include generic tools for machine learning, knowledge generation using machine learning processes, and semantic methods for the data life cycle, as well as the application of these topics in different disciplines. Core and pilot projects address the key aspects of the topics and form the basis for sustainable work in the Werkstatt.
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Semantic Annotations for Building a Reproducible and Interoperable Solution for End-to-End Machine Learning Pipelines
Machine learning (ML) is becoming an increasingly important scientific area supporting decision making and knowledge generation and is creating a high impact on several fields including healthcare, education, and many more. With this, it also becomes more and more important that the results of ML experiments are reproducible. The goal of this proposed project is to enable reproducibility and interoperability of end-to-end ML pipelines via the development and exploitation of semantic annotations. The provenance information along with the semantic annotations will be used to make ML experiments more comparable, thus having a basis to judge progress of the field. The aim of the overall project is the application of semantic technologies to ML pipelines for efficient provenance capture, representation, query, comparison, and visualization to enable reproducibility and interoperability. The project is funded through the IMPULSE program of Friedrich-Schiller University Jena.
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ThurAI (Thuringian Artificial Intelligence)
The projects focus on the reproducibility and explainability of Deep Learning Pipelines. This is a collaborative project between TU Ilmenau, FSU Jena and IMMS GmbH.
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Completed Projects

Project Z2 of CRC ReceptorLight: Integrative Data Management and Processing
Project Z2 of CRC ReceptorLight is jointly carried out by the Jena University Hospital and Friedrich Schiller University Jena within the framework of the Collaborative Research Centre (CRC) "High-end light microscopy elucidates membrane receptor function (Receptor Light)" established in 2015. CRC ReceptorLight is funded by the German Research Foundation (DFG) and consists of around 20 research projects from various institutions located in Jena and Würzburg. It will apply and develop light microscopy techniques with highest resolutions in space and time. The volume of the resulting datasets will be large, expected to be in multi-petabyte data. The overall goal of project…
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