document

Unsupervised Learning for Public Transport Delay Pattern Analysis

Cheng, Y. (author), Krishnakumari, P.K. (author)

To analyze inherent and diverse patterns within line-based public transport daily delay occurrences, we introduce a data-driven exploratory analysis focused on the spatial-temporal distribution of these delays. Our approach relies on the utilization of the image pattern recognition technique and k-means clustering algorithm. We extract daily...

journal article 2024

document

Data-driven ballast layer degradation identification and maintenance decision based on track geometry irregularities

Zhao, Wenbo (author), Qiang, Weile (author), Yang, Fei (author), Jing, Guoqing (author), Guo, Y. (author)

Ballast layer defects are the primary cause for rapid track geometry degradation. Detecting these defects in real-time during track inspections is urgently needed to ensure safe train operations. To achieve this, an indicator, the track degradation rate (TDR) was proposed. This rate is calculated using track geometry inspection data to locate...

journal article 2023

document

Diagnosing and Addressing Emergent Harms in the Design Process of Public AI and Algorithmic Systems

Nouws, S.J.J. (author), Martinez de Rituerto de Troya, I. (author), Dobbe, R.I.J. (author), Janssen, M.F.W.H.A. (author)

Algorithmic and data-driven systems are increasingly used in the public sector to improve the efficiency of existing services or to provide new services through the newfound capacity to process vast volumes of data. Unfortunately, certain instances also have negative consequences for citizens, in the form of discriminatory outcomes, arbitrary...

conference paper 2023

document

Data science for service design: An introductory overview of methods and opportunities

Kunneman, Youetta (author), Alves da Motta-Filho, Mauricy (author), van der Waa, J.S. (author)

To support effective and successful projects, Service Design practitioners rely on insights that mainly build on qualitative research methodology. The literature on data science promises to help transform how design research is done, adding sophisticated quantitative analyses, complementing existing methods with the power of machines. Due to...

journal article 2022

document

Deep Learning for Object Detection and Segmentation in Videos: Toward an Integration With Domain Knowledge

Ilioudi, A. (author), Dabiri, A. (author), Wolf, B.J. (author), De Schutter, B.H.K. (author)

Deep learning has enabled the rapid expansion of computer vision tasks from image frames to video segments. This paper focuses on the review of the latest research in the field of computer vision tasks in general and on object localization and identification of their associated pixels in video frames in particular. After performing a systematic...

journal article 2022

document

Data science and advanced analytics for shipping energy systems

Coraddu, A. (author), Kalikatzarakis, Miltiadis (author), Walker, J.M. (author), Ilardi, Davide (author), Oneto, Luca (author)

The purpose of this chapter is to provide an overview of the state-of-the-art and future perspectives of Data Science and Advanced Analytics for Shipping Energy Systems. Specifically, we will start by listing the different static and dynamic data sources and knowledge base available in this particular context. Then we will review the Data...

book chapter 2022

document

Estimating Route Choice Characteristics of Truck Drivers from Sparse Automated Vehicle Identification Data through Data Fusion and Bi-Objective Optimization

Sharma, Salil (author), van Lint, J.W.C. (author), Tavasszy, Lorant (author), Snelder, M. (author)

Optimizing route choices for truck drivers is a key element in achieving reliable road freight operations. For commercial reasons, it is often difficult to collect freight activity data through traditional surveys. Automated vehicle identification (AVI) data on fixed locations (e.g., Bluetooth or camera) are low-cost alternatives that may...

book chapter 2022

document

Editorial: Frontiers in water: Rising stars 2021

Ganguly, Auroop R. (author), van Breukelen, B.M. (author), Evers, Mariele (author), Hendricks Franssen, Harrie Jan (author), Illangasekare, Tissa (author), Kumar, Praveen (author), Steefel, Carl (author), Taylor, Richard G. (author)

contribution to periodical 2022

document

Data science as knowledge creation a framework for synergies between data analysts and domain professionals

van der Voort, H.G. (author), van Bulderen, Sabine (author), Cunningham, Scott (author), Janssen, M.F.W.H.A. (author)

The road from data generation to data use is commonly approached as a data-driven, functional process in which domain expertise is integrated as an afterthought. In this contribution we complement this functional view with an institutional view, that takes data analysis and domain professionalism as complementary (yet fallible) knowledge...

journal article 2021

document

Identification of diseases and physiological disorders in potato via multispectral drone imagery using machine learning tools

León-Rueda, William A. (author), León Sánchez, C.A. (author), Caro, Sandra Gómez (author), Ramírez-Gil, Joaquín Guillermo (author)

The rapid and precise detection of diseases and plant disorders is the basis for the adequate and timely design of management strategies. Currently, there are several non-destructive alternatives that allow early detection, highlighting the use of spectral cameras attached to unmanned aerial vehicles (UAVs). The objective of this research was...

journal article 2021

document

Editorial: Data Science Applications to Inverse and Optimization Problems in Earth Science

Leeuwenburgh, O. (author), Emerick, Alexandre A. (author), Jafarpour, Behnam (author), Zhang, Dongxiao (author), Luo, Xiaodong (author)

contribution to periodical 2021

document

A survey on machine learning-based performance improvement of wireless networks: PHY, MAC and network layer

Kulin, Merima (author), Kazaz, T. (author), De Poorter, Eli (author), Moerman, Ingrid (author)

This paper presents a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack: PHY,MAC and network. First, the related work and paper contributions are discussed, followed by providing...

journal article 2021

document

Safe Blues: The case for virtual safe virus spread in the long-term fight against epidemics

Dandekar, Raj (author), Henderson, Shane G. (author), Jansen, H.M. (author), McDonald, Joshua (author), Moka, Sarat (author), Nazarathy, Yoni (author), Rackauckas, Christopher (author), Taylor, Peter G. (author), Vuorinen, Aapeli (author)

Viral spread is a complicated function of biological properties, the environment, preventative measures such as sanitation and masks, and the rate at which individuals come within physical proximity. It is these last two elements that governments can control through social-distancing directives. However, infection measurements are almost...

journal article 2021

document

Artificial intelligence in the water domain: Opportunities for responsible use

Doorn, N. (author)

Recent years have seen a rise of techniques based on artificial intelligence (AI). With that have also come initiatives for guidance on how to develop “responsible AI” aligned with human and ethical values. Compared to sectors like energy, healthcare, or transportation, the use of AI-based techniques in the water domain is relatively modest....

journal article 2021

document

Facilitating transmuters' acquisition of data scientist knowledge based on their educational backgrounds: state-of-the-practice and challenges

Ramzan, Muhammad Javed (author), Khan, Saif Ur Rehman (author), ur-Rehman, Inayat (author), Rehman, Muhammad Habib Ur (author), Al-Khanak, Ehab Nabiel (author)

Purpose: In recent years, data science has become a high-demand profession, thereby attracting transmuters (individuals who want to change their profession due to industry trends) to this field. The primary purpose of this paper is to guide transmuters in becoming data scientists. Design/methodology/approach: An exploratory study was conducted...

journal article 2021

document

AutoStepfinder: A fast and automated step detection method for single-molecule analysis

Loeff, L. (author), Kerssemakers, J.W.J. (author), Joo, C. (author), Dekker, C. (author)

Single-molecule techniques allow the visualization of the molecular dynamics of nucleic acids and proteins with high spatiotemporal resolution. Valuable kinetic information of biomolecules can be obtained when the discrete states within single-molecule time trajectories are determined. Here, we present a fast, automated, and bias-free step...

journal article 2021

document

Traditional vs. Machine-learning methods for forecasting sandy shoreline evolution using historic satellite-derived shorelines

Calkoen, Floris (author), Luijendijk, Arjen (author), Rivero, Cristian Rodriguez (author), Kras, Etienne (author), Baart, F. (author)

Forecasting shoreline evolution for sandy coasts is important for sustainable coastal management, given the present-day increasing anthropogenic pressures and a changing future climate. Here, we evaluate eight different time-series forecasting methods for predicting future shorelines derived from historic satellite-derived shorelines....

journal article 2021

document

An ethico-legal framework for social data science

Forgó, Nikolaus (author), Hänold, Stefanie (author), van den Hoven, M.J. (author), Krügel, Tina (author), Lishchuk, Iryna (author), Mahieu, R.L.P. (author), Monreale, Anna (author), Pedreschi, Dino (author), Pratesi, Francesca (author), van Putten, D.C. (author)

This paper presents a framework for research infrastructures enabling ethically sensitive and legally compliant data science in Europe. Our goal is to describe how to design and implement an open platform for big data social science, including, in particular, personal data. To this end, we discuss a number of infrastructural, organizational...

journal article 2020

document

Sensing the city through new forms of urban data

Psyllidis, A. (author)

book chapter 2020

document

Questions for Data Scientists in Software Engineering: A Replication

Huijgens, H.K.M. (author), Rastogi, A. (author), Mulders, E.A. (author), Gousios, G. (author), van Deursen, A. (author)

In 2014, a Microsoft study investigated the sort of questions that data science applied to software engineering should answer. This resulted in 145 questions that developers considered relevant for data scientists to answer, thus providing a research agenda to the community. Fast forward to five years, no further studies investigated whether the...

conference paper 2020