Volume
OASIcs, Volume 124
Commit2Data
- Part of: Series: Open Access Series in Informatics (OASIcs)
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Publication Details
- published at: 2024-10-28
- Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik
- ISBN: 978-3-95977-351-5
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Complete Volume
OASIcs, Volume 124, Commit2Data, Complete Volume
Abstract
OASIcs, Volume 124, Commit2Data, Complete Volume
Cite as
Commit2Data. Open Access Series in Informatics (OASIcs), Volume 124, pp. 1-134, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@Proceedings{haverkort_et_al:OASIcs.Commit2Data,
title = {{OASIcs, Volume 124, Commit2Data, Complete Volume}},
booktitle = {Commit2Data},
pages = {1--134},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-351-5},
ISSN = {2190-6807},
year = {2024},
volume = {124},
editor = {Haverkort, Boudewijn R. and de Jongste, Aldert and van Kuilenburg, Pieter and Vromans, Ruben D.},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Commit2Data},
URN = {urn:nbn:de:0030-drops-220873},
doi = {10.4230/OASIcs.Commit2Data},
annote = {Keywords: OASIcs, Volume 124, Commit2Data, Complete Volume}
}
Document
Front Matter
Front Matter, Table of Contents, Preface, Conference Organization
Abstract
Front Matter, Table of Contents, Preface, Conference Organization
Cite as
Commit2Data. Open Access Series in Informatics (OASIcs), Volume 124, pp. 0:i-0:x, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@InProceedings{haverkort_et_al:OASIcs.Commit2Data.0,
author = {Haverkort, Boudewijn R. and de Jongste, Aldert and van Kuilenburg, Pieter and Vromans, Ruben D.},
title = {{Front Matter, Table of Contents, Preface, Conference Organization}},
booktitle = {Commit2Data},
pages = {0:i--0:x},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-351-5},
ISSN = {2190-6807},
year = {2024},
volume = {124},
editor = {Haverkort, Boudewijn R. and de Jongste, Aldert and van Kuilenburg, Pieter and Vromans, Ruben D.},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Commit2Data.0},
URN = {urn:nbn:de:0030-drops-220868},
doi = {10.4230/OASIcs.Commit2Data.0},
annote = {Keywords: Front Matter, Table of Contents, Preface, Conference Organization}
}
Document
Use-Inspired Research on Big Data and Applications in the Public-Private Research and Innovation Program Commit2Data
Abstract
In this paper we give an overview of the public-private research and innovation program known as Commit2Data, which was executed throughout the years 2016 - 2024 in the Netherlands. We outline the set-up of the program, with special attention for its valorisation activities, and provide a future outlook.
Cite as
Boudewijn R. Haverkort, Aldert de Jongste, and Pieter van Kuilenburg. Use-Inspired Research on Big Data and Applications in the Public-Private Research and Innovation Program Commit2Data. In Commit2Data. Open Access Series in Informatics (OASIcs), Volume 124, pp. 1:1-1:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@InProceedings{haverkort_et_al:OASIcs.Commit2Data.1,
author = {Haverkort, Boudewijn R. and de Jongste, Aldert and van Kuilenburg, Pieter},
title = {{Use-Inspired Research on Big Data and Applications in the Public-Private Research and Innovation Program Commit2Data}},
booktitle = {Commit2Data},
pages = {1:1--1:8},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-351-5},
ISSN = {2190-6807},
year = {2024},
volume = {124},
editor = {Haverkort, Boudewijn R. and de Jongste, Aldert and van Kuilenburg, Pieter and Vromans, Ruben D.},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Commit2Data.1},
URN = {urn:nbn:de:0030-drops-213587},
doi = {10.4230/OASIcs.Commit2Data.1},
annote = {Keywords: Big data, public-private partnership (PPP)}
}
Document
Building a Digital Health Twin for Personalized Intervention: The EPI Project
Abstract
The Enabling Personalized Interventions (EPI) project, part of the COMMIT2DATA top sector initiative, brings together research on data science, software-defined network infrastructure, and secure and trustworthy data sharing, executed within the healthcare domain. The project applies the digital twin paradigm, in which data science-driven algorithms monitor and perform functions on a digital counterpart of a real-world entity, to enable proactive responses based on predicted outcomes. The EPI project applies this paradigm in the healthcare context by developing and testing applications that can act as personalized digital health twins for self/-joint management.
The EPI project addresses several challenges to digital twin applications in the healthcare domain, such as: 1) strict health data sharing policies often lead to data being locked in silos, 2) legal, policy and privacy requirements make data processing increasingly more complex, and 3) significant limitations on infrastructure resources may apply.
In this paper, we report on the use cases the EPI used as the basis to develop possible solutions to these challenges. In particular, we describe algorithms and tools for algorithmic real-time response and analysis of distributed data at scale. We discuss the automatic enforcement of legal interpretations and data-sharing conditions as executable policies. Finally, we investigate infrastructural challenges by implementing and experimenting with the EPI Framework - consisting of a distributed analysis infrastructure and BRANE for orchestrating multi-site applications. We conclude by describing our Proof of Concept (PoC) and showing its application to one of the EPI use cases.
Cite as
Jamila Alsayed Kassem, Corinne Allaart, Saba Amiri, Milen Kebede, Tim Müller, Rosanne Turner, Adam Belloum, L. Thomas van Binsbergen, Peter Grunwald, Aart van Halteren, Paola Grosso, Cees de Laat, and Sander Klous. Building a Digital Health Twin for Personalized Intervention: The EPI Project. In Commit2Data. Open Access Series in Informatics (OASIcs), Volume 124, pp. 2:1-2:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@InProceedings{alsayedkassem_et_al:OASIcs.Commit2Data.2,
author = {Alsayed Kassem, Jamila and Allaart, Corinne and Amiri, Saba and Kebede, Milen and M\"{u}ller, Tim and Turner, Rosanne and Belloum, Adam and van Binsbergen, L. Thomas and Grunwald, Peter and van Halteren, Aart and Grosso, Paola and de Laat, Cees and Klous, Sander},
title = {{Building a Digital Health Twin for Personalized Intervention: The EPI Project}},
booktitle = {Commit2Data},
pages = {2:1--2:18},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-351-5},
ISSN = {2190-6807},
year = {2024},
volume = {124},
editor = {Haverkort, Boudewijn R. and de Jongste, Aldert and van Kuilenburg, Pieter and Vromans, Ruben D.},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Commit2Data.2},
URN = {urn:nbn:de:0030-drops-213596},
doi = {10.4230/OASIcs.Commit2Data.2},
annote = {Keywords: Healthcare, Data Sharing, Personalised Medicine, Real-time Data Analysis, Digital Health Twin, Data Policies}
}
Document
Helping Cancer Patients to Choose the Best Treatment: Towards Automated Data-Driven and Personalized Information Presentation of Cancer Treatment Options
Abstract
When a person is diagnosed with cancer, difficult decisions about treatments need to be made. In this chapter, we describe an interdisciplinary research project which aims to automatically generate personalized descriptions of treatment options for patients. We relied on two large databases provided by the Netherlands Comprehensive Cancer Organisation (IKNL): The Netherlands Cancer Registry and the PROFILES dataset. Combining these datasets allowed us to extract personalized information about treatment options for different types of cancer. In a next step we provided personalized context to these numbers, both in verbal statements and in narratives, with the aim to facilitate shared decision making about treatments. We discuss strengths and limitations of our approach, illustrate how it generalizes to other health domains, and reflect on the overall research project.
Cite as
Emiel Krahmer, Felix Clouth, Saar Hommes, Ruben Vromans, Steffen Pauws, Jeroen Vermunt, Lonneke van de Poll-Franse, and Xander Verbeek. Helping Cancer Patients to Choose the Best Treatment: Towards Automated Data-Driven and Personalized Information Presentation of Cancer Treatment Options. In Commit2Data. Open Access Series in Informatics (OASIcs), Volume 124, pp. 3:1-3:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@InProceedings{krahmer_et_al:OASIcs.Commit2Data.3,
author = {Krahmer, Emiel and Clouth, Felix and Hommes, Saar and Vromans, Ruben and Pauws, Steffen and Vermunt, Jeroen and van de Poll-Franse, Lonneke and Verbeek, Xander},
title = {{Helping Cancer Patients to Choose the Best Treatment: Towards Automated Data-Driven and Personalized Information Presentation of Cancer Treatment Options}},
booktitle = {Commit2Data},
pages = {3:1--3:20},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-351-5},
ISSN = {2190-6807},
year = {2024},
volume = {124},
editor = {Haverkort, Boudewijn R. and de Jongste, Aldert and van Kuilenburg, Pieter and Vromans, Ruben D.},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Commit2Data.3},
URN = {urn:nbn:de:0030-drops-213609},
doi = {10.4230/OASIcs.Commit2Data.3},
annote = {Keywords: Oncology, Data-driven shared decision making, Latent class analysis, Risk communication, Narratives, Personalization}
}
Document
SeNiors empOWered via Big Data to Joint-Manage Their Medication-Related Risk of Falling in Primary Care: The SNOWDROP Project
Abstract
In older persons, falls are the leading cause of injuries, often resulting in emergency room visits, serious injuries, and possibly even death. Medications are a major risk factor for falls. Because we lack tools to assess individualized risks, general practitioners (GPs) struggle with fall related medication management for seniors, and senior patients are not properly equipped to engage in the joint management of their medications. Our aim in this project is to develop and evaluate a comprehensive data-driven science approach for valid prediction of personalized risk of falling that effectively supports joint medication management between seniors and GPs. The project has two objectives. First, we aim to develop and validate prediction models from electronic health records for assessing individualized risk of medication-related falls. Data science challenges include free text analysis; accounting for missing values; searching medication hierarchies; engineering new predictors, and understanding limitations of our approach. Second, we aim to develop and evaluate a joint medication management strategy for older patients and GPs, consisting of a clinical decision support system (CDSS) and a patient portal. We evaluate the effects of this strategy on changes in the quality of shared decision-making during a medication review consultation, medication management, and patient outcomes. The learnings from this project and the architecture underpinned by predictive modelling to support both GPs and patients can also be applied to other major health problems in the future.
Cite as
Leonie Westerbeek, Noman Dormosh, André Blom, Martijn Heymans, Meefa Hogenes, Annemiek Linn, Stephanie Medlock, Martijn Schut, Nathalie van der Velde, Henk van Weert, Julia van Weert, and Ameen Abu-Hanna. SeNiors empOWered via Big Data to Joint-Manage Their Medication-Related Risk of Falling in Primary Care: The SNOWDROP Project. In Commit2Data. Open Access Series in Informatics (OASIcs), Volume 124, pp. 4:1-4:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@InProceedings{westerbeek_et_al:OASIcs.Commit2Data.4,
author = {Westerbeek, Leonie and Dormosh, Noman and Blom, Andr\'{e} and Heymans, Martijn and Hogenes, Meefa and Linn, Annemiek and Medlock, Stephanie and Schut, Martijn and van der Velde, Nathalie and van Weert, Henk and van Weert, Julia and Abu-Hanna, Ameen},
title = {{SeNiors empOWered via Big Data to Joint-Manage Their Medication-Related Risk of Falling in Primary Care: The SNOWDROP Project}},
booktitle = {Commit2Data},
pages = {4:1--4:12},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-351-5},
ISSN = {2190-6807},
year = {2024},
volume = {124},
editor = {Haverkort, Boudewijn R. and de Jongste, Aldert and van Kuilenburg, Pieter and Vromans, Ruben D.},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Commit2Data.4},
URN = {urn:nbn:de:0030-drops-213617},
doi = {10.4230/OASIcs.Commit2Data.4},
annote = {Keywords: accidental falls, fall risk, medication, prediction model, clinical decision support, patient portal, shared decision-making}
}
Document
Real-Time Data-Driven Maintenance Logistics: A Public-Private Collaboration
Abstract
The project "Real-time data-driven maintenance logistics" was initiated with the purpose of bringing innovations in data-driven decision making to maintenance logistics, by bringing problem owners in the form of three innovative companies together with researchers at two leading knowledge institutions. This paper reviews innovations in three related areas: How the innovations were inspired by practice, how they materialized, and how the results impact practice.
Cite as
Willem van Jaarsveld, Alp Akçay, Laurens Bliek, Paulo da Costa, Mathijs de Weerdt, Rik Eshuis, Stella Kapodistria, Uzay Kaymak, Verus Pronk, Geert-Jan van Houtum, Peter Verleijsdonk, Sicco Verwer, Simon Voorberg, and Yingqian Zhang. Real-Time Data-Driven Maintenance Logistics: A Public-Private Collaboration. In Commit2Data. Open Access Series in Informatics (OASIcs), Volume 124, pp. 5:1-5:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@InProceedings{vanjaarsveld_et_al:OASIcs.Commit2Data.5,
author = {van Jaarsveld, Willem and Ak\c{c}ay, Alp and Bliek, Laurens and da Costa, Paulo and de Weerdt, Mathijs and Eshuis, Rik and Kapodistria, Stella and Kaymak, Uzay and Pronk, Verus and van Houtum, Geert-Jan and Verleijsdonk, Peter and Verwer, Sicco and Voorberg, Simon and Zhang, Yingqian},
title = {{Real-Time Data-Driven Maintenance Logistics: A Public-Private Collaboration}},
booktitle = {Commit2Data},
pages = {5:1--5:13},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-351-5},
ISSN = {2190-6807},
year = {2024},
volume = {124},
editor = {Haverkort, Boudewijn R. and de Jongste, Aldert and van Kuilenburg, Pieter and Vromans, Ruben D.},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Commit2Data.5},
URN = {urn:nbn:de:0030-drops-213626},
doi = {10.4230/OASIcs.Commit2Data.5},
annote = {Keywords: Data, Maintenance, Logistics, Optimization, Research, Project}
}
Document
WheelPower: Wheelchair Sports and Data Science Push It to the Limit
Abstract
Paralympic wheelchair athletes solely depend on the power of their upper-body for their on- court wheeled mobility as well as for performing sport-specific actions in ball sports, like a basketball shot or a tennis serve. The objective of WheelPower is to improve the power output of athletes in their sport-specific wheelchair to perform better in competition. To achieve this objective the current project systematically combines the three Dutch measurement innovations (WMPM, Esseda wheelchair ergometer, PitchPerfect system) to monitor a large population of athletes from different wheelchair sports resulting in optimal power production by wheelchair athletes during competition. The data will be directly implemented in feedback tools accessible to athletes, trainers and coaches which gives them the unique opportunity to adapt their training and wheelchair settings for optimal performance. Hence, the current consortium facilitates mass and focus by uniting scientists and all major Paralympic wheelchair sports to monitor the power output of many wheelchair athletes under field and lab conditions, which will be assisted by the best data science approach to this challenge.
Cite as
Riemer J. K. Vegter, Rowie J. F. Janssen, Marit P. van Dijk, Marco J. M. Hoozemans, Dirkjan H. E. J. Veeger, Han J. H. P. Houdijk, Luc H. V. van der Woude, Monique A. M. Berger, Rienk M. A. van der Slikke, and Sonja de Groot. WheelPower: Wheelchair Sports and Data Science Push It to the Limit. In Commit2Data. Open Access Series in Informatics (OASIcs), Volume 124, pp. 6:1-6:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@InProceedings{vegter_et_al:OASIcs.Commit2Data.6,
author = {Vegter, Riemer J. K. and Janssen, Rowie J. F. and van Dijk, Marit P. and Hoozemans, Marco J. M. and Veeger, Dirkjan H. E. J. and Houdijk, Han J. H. P. and van der Woude, Luc H. V. and Berger, Monique A. M. and van der Slikke, Rienk M. A. and de Groot, Sonja},
title = {{WheelPower: Wheelchair Sports and Data Science Push It to the Limit}},
booktitle = {Commit2Data},
pages = {6:1--6:10},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-351-5},
ISSN = {2190-6807},
year = {2024},
volume = {124},
editor = {Haverkort, Boudewijn R. and de Jongste, Aldert and van Kuilenburg, Pieter and Vromans, Ruben D.},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Commit2Data.6},
URN = {urn:nbn:de:0030-drops-213631},
doi = {10.4230/OASIcs.Commit2Data.6},
annote = {Keywords: Paralympic sports, Wheelchair sports, Power}
}
Document
Improving Power System Resilience with Enhanced Monitoring, Control, and Protection Algorithms
Abstract
This paper deals with the essentials of synchrophasor’s applications for future power systems to increase system reliability and resilience, which have been investigated within a four-year research project. The project has several applications, covering real-time disturbance detection and blackout prevention distributed across multiple work-packages. Firstly, an advanced big-data management platform built in a real-time digital simulation (RTDS) environment is described to support measurement data collection, processing, and sharing among stakeholders. This platform further presents and demonstrates a network-splitting methodology to avoid cascading failures. Online generator coherency identification is another synchrophasor application implemented on the platform, the use of which is demonstrated in the context of controlled network splitting. Using synchrophasors, data-analytics techniques can also identify and classify disturbances in real time with minor human intervention. Therefore, a novel centralized artificial intelligence (AI) based expert system is outlined to detect and classify critical events. Finally, the paper elaborates on developing advanced system resilience metrics for real-time vulnerability assessment of power systems with a high penetration of renewable energy, focusing on increasingly relevant dynamic interactions and system instability risks.
Cite as
Nidarshan Veerakumar, Aleksandar Boričić, Ilya Tyuryukanov, Marko Tealane, Matija Naglič, Maarten Van Riet, Danny Klaar, Arjen Jongepier, Jorrit Bos, Mohammad Golshani, Gert Rietveld, Mart van der Meijden, and Marjan Popov. Improving Power System Resilience with Enhanced Monitoring, Control, and Protection Algorithms. In Commit2Data. Open Access Series in Informatics (OASIcs), Volume 124, pp. 7:1-7:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@InProceedings{veerakumar_et_al:OASIcs.Commit2Data.7,
author = {Veerakumar, Nidarshan and Bori\v{c}i\'{c}, Aleksandar and Tyuryukanov, Ilya and Tealane, Marko and Nagli\v{c}, Matija and Van Riet, Maarten and Klaar, Danny and Jongepier, Arjen and Bos, Jorrit and Golshani, Mohammad and Rietveld, Gert and van der Meijden, Mart and Popov, Marjan},
title = {{Improving Power System Resilience with Enhanced Monitoring, Control, and Protection Algorithms}},
booktitle = {Commit2Data},
pages = {7:1--7:18},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-351-5},
ISSN = {2190-6807},
year = {2024},
volume = {124},
editor = {Haverkort, Boudewijn R. and de Jongste, Aldert and van Kuilenburg, Pieter and Vromans, Ruben D.},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Commit2Data.7},
URN = {urn:nbn:de:0030-drops-213647},
doi = {10.4230/OASIcs.Commit2Data.7},
annote = {Keywords: Grid Resilience, Synchrophasors, Real-time Cyber-Physical Experimental Testbed, Real-Time Monitoring, Protection, and Control, Event Detection Classification, Artificial Intelligence, Adaptive Incremental Learning, Controlled Islanding, Vulnerability, State Estimation, Dynamic Line and Cable Rating}
}
Document
RATE-Analytics: Next Generation Predictive Analytics for Data-Driven Banking and Insurance
Abstract
We conducted the RATE-Analytics project: a unique collaboration between Rabobank, Achmea, Tilburg and Eindhoven University. We aimed to develop foundations and techniques for next generation big data analytics. The main challenge of existing approaches is the lack of reliability and trustworthiness: if experts do not trust a model or its predictions they are much less likely to use and rely on that model. Hence, we focused on solutions to bring the human-in-the-loop, enabling the diagnostics and refinement of models, and support in decision making and justification. This chapter zooms in on the part of the project focused on developing explainable and trustworthy machine learning techniques.
Cite as
Dennis Collaris, Mykola Pechenizkiy, and Jarke J. van Wijk. RATE-Analytics: Next Generation Predictive Analytics for Data-Driven Banking and Insurance. In Commit2Data. Open Access Series in Informatics (OASIcs), Volume 124, pp. 8:1-8:11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@InProceedings{collaris_et_al:OASIcs.Commit2Data.8,
author = {Collaris, Dennis and Pechenizkiy, Mykola and van Wijk, Jarke J.},
title = {{RATE-Analytics: Next Generation Predictive Analytics for Data-Driven Banking and Insurance}},
booktitle = {Commit2Data},
pages = {8:1--8:11},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-351-5},
ISSN = {2190-6807},
year = {2024},
volume = {124},
editor = {Haverkort, Boudewijn R. and de Jongste, Aldert and van Kuilenburg, Pieter and Vromans, Ruben D.},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Commit2Data.8},
URN = {urn:nbn:de:0030-drops-213655},
doi = {10.4230/OASIcs.Commit2Data.8},
annote = {Keywords: Visualization, Visual Analytics, Machine Learning, Interpretability, Explainability, XAI}
}
Document
Digital Art Technical Sources for the Netherlands: Integration and Improvement of Sources on Glass for a Sustainable Future – Art DATIS
Abstract
Art DATIS (Digital Art Technical sources for the Netherlands: Integration and improvement of sources on glass for a Sustainable future) is a five-year research project (2018-2023) within the Netherlands Organisation for Scientific Research’s (NWO) Big Data / Digital Humanities program. The project is a collaboration between the Universities of Utrecht and Amsterdam, RKD Netherlands Institute for Art History, the Vrij Glas Foundation, and Picturae. The project investigates how to approach the automatic transcription and documentation of heterogeneous archival resources. The central object of the project is the archive of the Dutch glass artist Sybren Valkema (1916–96). Documents were digitised, and their content was made searchable through the processes of OCR and HTR. Through digitisation and the analysis of archival documents, the project aims to understand how traditional knowledge and practices of glassmaking were innovated during the twentieth century.
Cite as
Carlotta Capurro, Vera Provatorova, Marieke Hendriksen, Evangelos Kanoulas, and Sven Dupré. Digital Art Technical Sources for the Netherlands: Integration and Improvement of Sources on Glass for a Sustainable Future – Art DATIS. In Commit2Data. Open Access Series in Informatics (OASIcs), Volume 124, pp. 9:1-9:11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@InProceedings{capurro_et_al:OASIcs.Commit2Data.9,
author = {Capurro, Carlotta and Provatorova, Vera and Hendriksen, Marieke and Kanoulas, Evangelos and Dupr\'{e}, Sven},
title = {{Digital Art Technical Sources for the Netherlands: Integration and Improvement of Sources on Glass for a Sustainable Future – Art DATIS}},
booktitle = {Commit2Data},
pages = {9:1--9:11},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-351-5},
ISSN = {2190-6807},
year = {2024},
volume = {124},
editor = {Haverkort, Boudewijn R. and de Jongste, Aldert and van Kuilenburg, Pieter and Vromans, Ruben D.},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Commit2Data.9},
URN = {urn:nbn:de:0030-drops-213668},
doi = {10.4230/OASIcs.Commit2Data.9},
annote = {Keywords: Digital Humanities, Archives, Digitisation, Datafication, Digital Art History, Technical Art History}
}