5 Search Results for "Nüst, Daniel"

Document
DOI: 10.4230/LIPIcs.GIScience.2025.13
Assessing Map Reproducibility with Visual Question-Answering: An Empirical Evaluation

Authors: Eftychia Koukouraki, Auriol Degbelo, and Christian Kray

Published in: LIPIcs, Volume 346, 13th International Conference on Geographic Information Science (GIScience 2025)

Abstract
Reproducibility is a key principle of the modern scientific method. Maps, as an important means of communicating scientific results in GIScience and across disciplines, should be reproducible. Currently, map reproducibility assessment is done manually, which makes the assessment process tedious and time-consuming, ultimately limiting its efficiency. Hence, this work explores the extent to which Visual Question-Answering (VQA) can be used to automate some tasks relevant to map reproducibility assessment. We selected five state-of-the-art vision language models (VLMs) and followed a three-step approach to evaluate their ability to discriminate between maps and other images, interpret map content, and compare two map images using VQA. Our results show that current VLMs already possess map-reading capabilities and demonstrate understanding of spatial concepts, such as cardinal directions, geographic scope, and legend interpretation. Our paper demonstrates the potential of using VQA to support reproducibility assessment and highlights the outstanding issues that need to be addressed to achieve accurate, trustworthy map descriptions, thereby reducing the time and effort required by human evaluators.
Cite as

Eftychia Koukouraki, Auriol Degbelo, and Christian Kray. Assessing Map Reproducibility with Visual Question-Answering: An Empirical Evaluation. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 13:1-13:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{koukouraki_et_al:LIPIcs.GIScience.2025.13,
  author =	{Koukouraki, Eftychia and Degbelo, Auriol and Kray, Christian},
  title =	{{Assessing Map Reproducibility with Visual Question-Answering: An Empirical Evaluation}},
  booktitle =	{13th International Conference on Geographic Information Science (GIScience 2025)},
  pages =	{13:1--13:12},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-378-2},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{346},
  editor =	{Sila-Nowicka, Katarzyna and Moore, Antoni and O'Sullivan, David and Adams, Benjamin and Gahegan, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2025.13},
  URN =		{urn:nbn:de:0030-drops-238426},
  doi =		{10.4230/LIPIcs.GIScience.2025.13},
  annote =	{Keywords: map comparison, computational reproducibility, visual question answering, large language models, GeoAI}
}
Document
Resource Paper
DOI: 10.4230/TGDK.2.2.4
FAIR Jupyter: A Knowledge Graph Approach to Semantic Sharing and Granular Exploration of a Computational Notebook Reproducibility Dataset

Authors: Sheeba Samuel and Daniel Mietchen

Published in: TGDK, Volume 2, Issue 2 (2024): Special Issue on Resources for Graph Data and Knowledge. Transactions on Graph Data and Knowledge, Volume 2, Issue 2

Abstract
The 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.
Cite as

Sheeba Samuel and Daniel Mietchen. FAIR Jupyter: A Knowledge Graph Approach to Semantic Sharing and Granular Exploration of a Computational Notebook Reproducibility Dataset. In Special Issue on Resources for Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 2, Issue 2, pp. 4:1-4:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Article{samuel_et_al:TGDK.2.2.4,
  author =	{Samuel, Sheeba and Mietchen, Daniel},
  title =	{{FAIR Jupyter: A Knowledge Graph Approach to Semantic Sharing and Granular Exploration of a Computational Notebook Reproducibility Dataset}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{4:1--4:24},
  ISSN =	{2942-7517},
  year =	{2024},
  volume =	{2},
  number =	{2},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.2.2.4},
  URN =		{urn:nbn:de:0030-drops-225886},
  doi =		{10.4230/TGDK.2.2.4},
  annote =	{Keywords: Knowledge Graph, Computational reproducibility, Jupyter notebooks, FAIR data, PubMed Central, GitHub, Python, SPARQL}
}
Document
DOI: 10.4230/LIPIcs.GIScience.2021.II.2
Reproducible Research and GIScience: An Evaluation Using GIScience Conference Papers

Authors: Frank O. Ostermann, Daniel Nüst, Carlos Granell, Barbara Hofer, and Markus Konkol

Published in: LIPIcs, Volume 208, 11th International Conference on Geographic Information Science (GIScience 2021) - Part II

Abstract
GIScience conference authors and researchers face the same computational reproducibility challenges as authors and researchers from other disciplines who use computers to analyse data. Here, to assess the reproducibility of GIScience research, we apply a rubric for assessing the reproducibility of 75 conference papers published at the GIScience conference series in the years 2012-2018. Since the rubric and process were previously applied to the publications of the AGILE conference series, this paper itself is an attempt to replicate that analysis, however going beyond the previous work by evaluating and discussing proposed measures to improve reproducibility in the specific context of the GIScience conference series. The results of the GIScience paper assessment are in line with previous findings: although descriptions of workflows and the inclusion of the data and software suffice to explain the presented work, in most published papers they do not allow a third party to reproduce the results and findings with a reasonable effort. We summarise and adapt previous recommendations for improving this situation and propose the GIScience community to start a broad discussion on the reusability, quality, and openness of its research. Further, we critically reflect on the process of assessing paper reproducibility, and provide suggestions for improving future assessments. The code and data for this article are published at https://doi.org/10.5281/zenodo.4032875.
Cite as

Frank O. Ostermann, Daniel Nüst, Carlos Granell, Barbara Hofer, and Markus Konkol. Reproducible Research and GIScience: An Evaluation Using GIScience Conference Papers. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part II. Leibniz International Proceedings in Informatics (LIPIcs), Volume 208, pp. 2:1-2:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{ostermann_et_al:LIPIcs.GIScience.2021.II.2,
  author =	{Ostermann, Frank O. and N\"{u}st, Daniel and Granell, Carlos and Hofer, Barbara and Konkol, Markus},
  title =	{{Reproducible Research and GIScience: An Evaluation Using GIScience Conference Papers}},
  booktitle =	{11th International Conference on Geographic Information Science (GIScience 2021) - Part II},
  pages =	{2:1--2:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-208-2},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{208},
  editor =	{Janowicz, Krzysztof and Verstegen, Judith A.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.II.2},
  URN =		{urn:nbn:de:0030-drops-147615},
  doi =		{10.4230/LIPIcs.GIScience.2021.II.2},
  annote =	{Keywords: reproducible research, open science, reproducibility, GIScience}
}
Document
DOI: 10.4230/LIPIcs.GIScience.2021.I.4
Serverless GEO Labels for the Semantic Sensor Web

Authors: Anika Graupner and Daniel Nüst

Published in: LIPIcs, Volume 177, 11th International Conference on Geographic Information Science (GIScience 2021) - Part I (2020)

Abstract
With the increasing amount of sensor data available online, it is becoming more difficult for users to identify useful datasets. Semantic Web technologies can improve such discovery via meaningful ontologies, but the decision of whether a dataset is suitable remains with the users. Users can be aided in this process through the GEO label, which provides a visual summary of the standardised metadata. However, the GEO label is not yet available for the Semantic Sensor Web. This work presents novel rules for deriving the information for the GEO label’s multiple facets, such as user feedback or quality information, based on the Semantic Sensor Network Ontology and related ontologies. Thereby, this work enhances an existing implementation of the GEO label API to generate labels for resources of the Semantic Sensor Web. Further, the prototype is deployed to serverless cloud infrastructures. We find that serverless GEO label generation is capable of handling two evaluation scenarios for concurrent users and burst generation. Nonetheless, more real-world semantic sensor descriptions, an analysis of requirements for GEO label facets specific to the Semantic Sensor Web, and an integration into large-scale discovery platforms are needed.
Cite as

Anika Graupner and Daniel Nüst. Serverless GEO Labels for the Semantic Sensor Web. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part I. Leibniz International Proceedings in Informatics (LIPIcs), Volume 177, pp. 4:1-4:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{graupner_et_al:LIPIcs.GIScience.2021.I.4,
  author =	{Graupner, Anika and N\"{u}st, Daniel},
  title =	{{Serverless GEO Labels for the Semantic Sensor Web}},
  booktitle =	{11th International Conference on Geographic Information Science (GIScience 2021) - Part I},
  pages =	{4:1--4:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-166-5},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{177},
  editor =	{Janowicz, Krzysztof and Verstegen, Judith A.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.I.4},
  URN =		{urn:nbn:de:0030-drops-130392},
  doi =		{10.4230/LIPIcs.GIScience.2021.I.4},
  annote =	{Keywords: GEO label, geospatial metadata, data discovery, Semantic Sensor Web, serverless}
}
Document
Vision Paper
DOI: 10.4230/LIPIcs.COSIT.2019.8
Reproducible Research in Geoinformatics: Concepts, Challenges and Benefits (Vision Paper)

Authors: Christian Kray, Edzer Pebesma, Markus Konkol, and Daniel Nüst

Published in: LIPIcs, Volume 142, 14th International Conference on Spatial Information Theory (COSIT 2019)

Abstract
Geoinformatics deals with spatial and temporal information and its analysis. Research in this field often follows established practices of first developing computational solutions for specific spatiotemporal problems and then publishing the results and insights in a (static) paper, e.g. as a PDF. Not every detail can be included in such a paper, and particularly, the complete set of computational steps are frequently left out. While this approach conveys key knowledge to other researchers it makes it difficult to effectively re-use and reproduce the reported results. In this vision paper, we propose an alternative approach to carry out and report research in Geoinformatics. It is based on (computational) reproducibility, promises to make re-use and reproduction more effective, and creates new opportunities for further research. We report on experiences with executable research compendia (ERCs) as alternatives to classic publications in Geoinformatics, and we discuss how ERCs combined with a supporting research infrastructure can transform how we do research in Geoinformatics. We point out which challenges this idea entails and what new research opportunities emerge, in particular for the COSIT community.
Cite as

Christian Kray, Edzer Pebesma, Markus Konkol, and Daniel Nüst. Reproducible Research in Geoinformatics: Concepts, Challenges and Benefits (Vision Paper). In 14th International Conference on Spatial Information Theory (COSIT 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 142, pp. 8:1-8:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@InProceedings{kray_et_al:LIPIcs.COSIT.2019.8,
  author =	{Kray, Christian and Pebesma, Edzer and Konkol, Markus and N\"{u}st, Daniel},
  title =	{{Reproducible Research in Geoinformatics: Concepts, Challenges and Benefits}},
  booktitle =	{14th International Conference on Spatial Information Theory (COSIT 2019)},
  pages =	{8:1--8:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-115-3},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{142},
  editor =	{Timpf, Sabine and Schlieder, Christoph and Kattenbeck, Markus and Ludwig, Bernd and Stewart, Kathleen},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2019.8},
  URN =		{urn:nbn:de:0030-drops-111008},
  doi =		{10.4230/LIPIcs.COSIT.2019.8},
  annote =	{Keywords: vision paper, Geoinformatics, reproducibility, computational, spatial and temporal information, spatial data science, GI Science}
}
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