Adrienne M. Mendrik

Despite the increasing popularity and societal relevance of mobile instant messenger services (MIMSs) such as WhatsApp, Telegram and Signal, the vast majority of the research on social media is focused on the “traditional” social media platforms such as Twitter (X) and Facebook, mostly due to the difficulty of accessing MIMSs data. Consequently, little is known about even the most basic topological features of societal-scale instant messenger networks. To overcome this knowledge gap, we employ the innovative approach of data donation by respondents of a high-quality probability-based panel in the Netherlands (the LISS panel) to collect user data while preserving their privacy. Focusing on WhatsApp as the most popular MIMS, this study collects the first measurement of MIMS usage on a national probability sample, focusing on degree distributions as a key feature of network topology. We find that the degree distribution of contacts is best approximated by a log-normal distribution, while the distribution of group membership is best approximated by the exponential distribution. At the individual level, we find that predictors of degree derived from the literature on extended networks mostly replicate.

Social scientists aim to create explanations of the world. For each social phenomenon, scientists have proposed a myriad of theories to explain its working mechanisms. Traditionally, these theories are tested by generating hypotheses, translating them into a statistical model, and assessing the significance of the model’s coefficients. Such an approach, however, often leads to the specification of a large number of (at times contradictory) models, all asserting that they capture the same theory. As things currently stand, there is no framework that allows for a comparison of these models. In this article, we argue that benchmarks can serve as a standard frame of reference that can help to determine which models fit better with empirical observations in a specific context. A benchmark is a standardized validation framework that allows for a direct comparison of the prediction accuracy of various models that address the same research problem. We outline the potential of organizing benchmark challenges in the social sciences and provide recommendations for their utilization.

The social sciences have produced an impressive body of research on determinants of fertility outcomes, or whether and when people have children. However, the strength of these determinants and underlying theories are rarely evaluated on their predictive ability on new data. This prevents us from systematically comparing studies, hindering the evaluation and accumulation of knowledge. In this paper, we present two datasets which can be used to study the predictability of fertility outcomes in the Netherlands. One dataset is based on the LISS panel, a longitudinal survey which includes thousands of variables on a wide range of topics, including individual preferences and values. The other is based on the Dutch register data which lacks attitudinal data but includes detailed information about the life courses of millions of Dutch residents. We provide information about the datasets and the samples, and describe the fertility outcome of interest. We also introduce the fertility prediction data challenge PreFer which is based on these datasets and will start in Spring 2024. We outline the ways in which measuring the predictability of fertility outcomes using these datasets and combining their strengths in the data challenge can advance our understanding of fertility behaviour and computational social science. We further provide details for participants on how to take part in the data challenge.

Recently, a new workflow has been introduced that allows academic researchers to partner with individuals interested in donating their digital trace data for academic research purposes (Boeschoten, Ausloos, et al., 2022). In this workflow, the digital traces of participants are processed locally on their own devices in such a way that only the subset of participants' digital trace data that is of legitimate interest to a research project are shared with the researcher, which can only occur after the participant has provided their informed consent.

In this paper we aim to refine the concept of grand challenges in medical image analysis, based on statistical principles from quantitative and qualitative experimental research. We identify two types of challenges based on their generalization objective: 1) a deployment challenge and 2) an insight challenge. A deployment challenge's generalization objective is to find algorithms that solve a medical image analysis problem, which thereby requires the use of a quantitative experimental design. An insight challenge's generalization objective is to gain a broad understanding of what class of algorithms might be effective for a class of medical image analysis problems, in which case a qualitative experimental design is sufficient. Both challenge types are valuable, but problems arise when a challenge's design and objective are inconsistent, as is often the case when a challenge does not carefully consider these concepts. Therefore, in this paper, we propose a theoretical framework, based on statistical principles, to guide researchers in challenge design, documentation, and assessment. Experimental results are given that explore the factors that effect the practical implementation of this theoretical framework.

Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi)automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65–80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.

Background and Purposes. The 320-detector row CT scanner enables visualization of whole-brain hemodynamic information (dynamic CT angiography (CTA) derived from CT perfusion scans). However, arterial image quality in dynamic CTA (dCTA) is inferior to arterial image quality in standard CTA. This study evaluates whether the arterial image quality can be improved by using a total bolus extraction (ToBE) method. Materials and Methods. DCTAs of 15 patients, who presented with signs of acute cerebral ischemia, were derived from 320-slice CT perfusion scans using both the standard subtraction method and the proposed ToBE method. Two neurointerventionalists blinded to the scan type scored the arterial image quality on a 5-point scale in the 4D dCTAs in consensus. Arteries were divided into four categories: (I) large extradural, (II) intradural (large, medium, and small), (III) communicating arteries, and (IV) cerebellar and ophthalmic arteries. Results. Quality of extradural and intradural arteries was significantly higher in the ToBE dCTAs than in the standard dCTAs (extradural , large intradural , medium intradural , and small intradural ). Conclusion. The 4D dCTAs derived with the total bolus extraction (ToBE) method provide hemodynamic information combined with improved arterial image quality as compared to standard 4D dCTAs.