In this paper, we propose a class of locally dependent latent trait models for responses to psychological and educational tests. Typically, item response models treat an individual's multiple response to stimuli as conditional independent given the individual's latent trait. In this paper, instead the focus is on models based on a family of conditional distributions, or kernel, that describes joint multiple item responses as a function of student latent trait, not assuming conditional independence. Specifically, we examine a hybrid kernel which comprises a component for one-way item response functions and a component for conditional associations between items given latent traits. The class of models allows the extension of item response theory to cover some new and innovative applications in psychological and educational research. An EM algorithm for marginal maximum likelihood of the hybrid kernel model is proposed. Furthermore, we delineate the relationship of the class of locally dependent models and the log-linear model by revisiting the Dutch identity (Holland, 1990).

When analyzing data, researchers make some choices that are either arbitrary, based on subjective beliefs about the data-generating process, or for which equally justifiable alternative choices could have been made. This wide range of data-analytic choices can be abused and has been one of the underlying causes of the replication crisis in several fields. Recently, the introduction of multiverse analysis provides researchers with a method to evaluate the stability of the results across reasonable choices that could be made when analyzing data. Multiverse analysis is confined to a descriptive role, lacking a proper and comprehensive inferential procedure. Recently, specification curve analysis adds an inferential procedure to multiverse analysis, but this approach is limited to simple cases related to the linear model, and only allows researchers to infer whether at least one specification rejects the null hypothesis, but not which specifications should be selected. In this paper, we present a Post-selection Inference approach to Multiverse Analysis (PIMA) which is a flexible and general inferential approach that considers for all possible models, i.e., the multiverse of reasonable analyses. The approach allows for a wide range of data specifications (i.e., preprocessing) and any generalized linear model; it allows testing the null hypothesis that a given predictor is not associated with the outcome, by combining information from all reasonable models of multiverse analysis, and provides strong control of the family-wise error rate allowing researchers to claim that the null hypothesis can be rejected for any specification that shows a significant effect. The inferential proposal is based on a conditional resampling procedure. We formally prove that the Type I error rate is controlled, and compute the statistical power of the test through a simulation study. Finally, we apply the PIMA procedure to the analysis of a real dataset on the self-reported hesitancy for the COronaVIrus Disease 2019 (COVID-19) vaccine before and after the 2020 lockdown in Italy. We conclude with practical recommendations to be considered when implementing the proposed procedure.

The use of programming languages in archaeological research has witnessed a notable surge in the last decade, particularly with R, a versatile statistical computing language that fosters the development of specialized packages. This article introduces the tesselle project (https://www.tesselle.org/), a comprehensive collection of R packages tailored for archaeological research and education. The tesselle packages are centered on quantitative analysis methods specifically crafted for archaeology. They are designed to complement both general-purpose and other specialized statistical packages. These packages serve as a versatile toolbox, facilitating the exploration and analysis of common data types in archaeology—such as count data, compositional data, or chronological data—and enabling the construction of reproducible workflows. Complementary packages for visualization, data preparation, and educational resources augment the tesselle ecosystem. This article outlines the project's inception, its objectives, design principles, and key components, along with reflections on future directions.

The Mediterranean Region registers an increasing prevalence of obesity. The region lacks a diet screener to assess obesogenic nutrients. This study aimed to evaluate the reproducibility and validity of the Modified Mediterranean Prime Screen (MMPS) in estimating obesogenic nutrients’ intake among women of reproductive age, as compared with a culturally validated Food Frequency Questionnaire (FFQ), in Lebanon. We developed the MMPS consisting of thirty-two food/beverage items specific to the Lebanese Mediterranean culture. The MMPS and FFQ were administered in two visits (2 weeks–6 months apart), face to face and via telephone during the coronavirus disease 2019 pandemic. The reproducibility and validity of the MMPS were assessed using intraclass correlation coefficients (ICC) and Pearson’s correlations, respectively. The study included 143 women, aged 31·5 (sd 4·6) years, BMI 24·2 (sd 4·0) kg/m2, 87 % with university education and 91 % food secure. The reproducibility of the MMPS was moderate for energy and all assessed nutrients except for SFA (ICC = 0·428). The agreement of the MMPS with the reference FFQ was adequate for energy and obesogenic nutrients. Yet, the Pearson correlations for energy-adjusted nutrient intake were low for trans-fatty acids (0·294) and PUFA (0·377). The MMPS can be a time-efficient tool for dietary assessment of energy and many obesogenic nutrients. Future studies should validate the MMPS across the lifespan and re-evaluate it after updating the fatty acid profiles in the culturally specific food composition tables.

With its promise of nondestructive processing, rapid low-cost sampling, and portability to any field site or museum in the world, portable X-ray fluorescence (pXRF) spectrometry is rapidly becoming a standard piece of equipment for archaeologists. Even though the use of pXRF is becoming standard, the publication of pXRF analytical methods and the resulting data remains widely variable. Despite validation studies that demonstrate the importance of sample preparation, data collection settings, and data processing, there remains no standard for how to report pXRF results. In this article, we address the need for best practices in publishing pXRF analyses. We outline information that should be published alongside interpretive results in any archaeological application of pXRF. By publishing this basic information, archaeologists will increase the transparency and replicability of their analyses on an inter-analyst/inter-analyzer basis and provide clarity for journal editors and peer reviewers on publications and grant proposals for studies that use pXRF. The use of these best practices will result in better science in the burgeoning use of pXRF in archaeology.

We assessed the rigor and reproducibility (R&R) activities of institutions funded by the National Center for Advancing Translational Sciences (NCTSA) through a survey and website search (N = 61). Of 50 institutional responses, 84% reported incorporating some form of R&R training, 68% reported devoted R&R training, 30% monitored R&R practices, and 10% incentivized them. Website searches revealed 9 (15%) freely available training curricula, and 7 (11%) institutional programs specifically created to enhance R&R. NCATS should formally integrate R&R principles into its translational science models and institutional requirements.

Over the past years, computational methods based on deep learning—that is, machine learning with multilayered neural networks—have become state-of-the-art in main research areas in computer-aided architectural design (CAAD). To understand current trends of CAAD with deep learning, to situate them in a broader historical context, and to identify future research challenges, this article presents a systematic review of publications that apply neural networks to CAAD problems. Research papers employing neural networks were collected, in particular, from CumInCad a major open-access repository of the CAAD community and categorized into different types of research problems. Upon analyzing the distribution of the papers in these categories, namely, the composition of research subjects, data types, and neural network models, this article suggests and discusses several historical and technical trends. Moreover, it identifies that the publications analyzed typically provide limited access to important research components used as part of their deep learning methods. The article points out the importance of sharing training experiments and data, of describing the dataset, dataset parameters, dataset samples, model, learning parameters, and learning results to support reproducibility. It proposes a guideline that aims at increasing the quality and availability of CAAD research with machine learning.

Motor unit number index of the upper trapezius (MUNIX-Trapezius) is a candidate biomarker for bulbar lower motor neuron function; however, reliability data is incomplete. To assess MUNIX-Trapezius reliability in controls, we conducted a systematic review, a cross-sectional study (n = 20), and a meta-analysis. We demonstrated a high inter- and intra-rater intraclass correlation (0.86 and 0.94, respectively), indicating that MUNIX-Trapezius is reliable with between-study variability moderated by age and MUNIX technique. With further validation, this measure can serve as a disease monitoring and response biomarker of bulbar function in the therapeutic development for amyotrophic lateral sclerosis.

The study was to evaluate the reproducibility and validity of the FFQ for residents of northeast China. A total of 131 participants completed two FFQ (FFQ1 and FFQ2) within a 3-month period, 125 participants completed 8-d weighed diet records (WDR) and 112 participants completed blood biomarker testing. Reproducibility was measured by comparing nutrient and food intake between FFQ1 and FFQ2. The validity of the FFQ was assessed by WDR and the triad method. The Spearman correlation coefficients (SCC) and intraclass correlation coefficients (ICC) for reproducibility ranged from 0·41 to 0·69 (median = 0·53) and from 0·18 to 0·68 (median = 0·53) for energy and nutrients and from 0·37 to 0·73 (median = 0·59) and from 0·33 to 0·86 (median = 0·60) for food groups, respectively. The classifications of same or adjacent quartiles ranged from 73·64 to 93·80 % for both FFQ. The crude SCC between the FFQ and WDR ranged from 0·27 to 0·55 (median = 0·46) for the energy and nutrients and from 0·26 to 0·70 (median = 0·52) for food groups, and classifications of the same or adjacent quartiles ranged from 65·32 to 86·29 %. The triad method indicated that validation coefficients for the FFQ were above 0·3 for most nutrients, which indicated a moderate or high level of validity. The FFQ that was developed for residents of northeast China for the Northeast Cohort Study of China is reliable and valid for assessing the intake of most foods and nutrients.

Bilingualism is hard to define, measure, and study. Sparked by the “replication crisis” in the social sciences, a recent discussion on the advantages of open science is gaining momentum. Here, we join this debate to argue that bilingualism research would greatly benefit from embracing open science. We do so in a unique way, by presenting six fictional stories that illustrate how open science practices – sharing preprints, materials, code, and data; pre-registering studies; and joining large-scale collaborations – can strengthen bilingualism research and further improve its quality.

Reproducibility of a deep-learning fully convolutional neural network is evaluated by training several times the same network on identical conditions (database, hyperparameters, and hardware) with nondeterministic graphics processing unit operations. The network is trained to model three typical time–space-evolving physical systems in two dimensions: heat, Burgers’, and wave equations. The behavior of the networks is evaluated on both recursive and nonrecursive tasks. Significant changes in models’ properties (weights and feature fields) are observed. When tested on various benchmarks, these models systematically return estimations with a high level of deviation, especially for the recurrent analysis which strongly amplifies variability due to the nondeterminism. Trainings performed with double floating-point precision provide slightly better estimations and a significant reduction of the variability of both the network parameters and its testing error range.

Foodborne and waterborne gastrointestinal infections and their associated outbreaks are preventable, yet still result in significant morbidity, mortality and revenue loss. Many enteric infections demonstrate seasonality, or annual systematic periodic fluctuations in incidence, associated with climatic and environmental factors. Public health professionals use statistical methods and time series models to describe, compare, explain and predict seasonal patterns. However, descriptions and estimates of seasonal features, such as peak timing, depend on how researchers define seasonality for research purposes and how they apply time series methods. In this review, we outline the advantages and limitations of common methods for estimating seasonal peak timing. We provide recommendations improving reporting requirements for disease surveillance systems. Greater attention to how seasonality is defined, modelled, interpreted and reported is necessary to promote reproducible research and strengthen proactive and targeted public health policies, intervention strategies and preparedness plans to dampen the intensity and impacts of seasonal illnesses.