Abstract
Linear programming is a mathematical optimization technique used in numerous fields including mathematics, economics, and computer science, with numerous industrial contexts, including solving optimization problems such as planning routes, allocating resources, and creating schedules. As a result of its wide breadth of applications, a considerable amount of its user base is lacking in terms of programming knowledge and experience and thus often resorts to using graphical software such as Microsoft Excel. However, despite its popularity amongst less technical users, the methodologies used by these tools are often ad-hoc and prone to errors. To counteract this problem we propose creating a block-based language that allows users to create linear programming models using data contained inside spreadsheets. This language will guide the users to write syntactically and semantically correct programs and thus aid them in a way that current languages do not.

Abstract
Portugal is one of the European countries that implemented early protective measures in the context of the COVID-19 pandemic. Portugal declared a state of emergency on 18 March, and a set of regional and national preventive public health measures was progressively implemented. Studies on the psychological impact of pandemics show evidence of the negative impact on mental health. Of particular concern are individuals with previous fragility (e.g. personal, family or occupational) and those undergoing life transitions. In this paper, we present a telephone-based psychological crisis intervention that was implemented to provide brief, appropriate, and timely psychological help. This intervention follows standard models of crisis intervention and is structured in five phases and five different intervention modules to take into account the impact of the pandemic on the mental health of specific risk groups. With these support services, we hope to help our community better cope with the immediate impact of the pandemic and to contribute to preventing serious mental health problems in the medium and long term.

Abstract
Data collection is pervasively bound to our digital lifestyle. A recent study reports that the growth of the data created and replicated in 2020 was even higher than in the previous years to an astonishing global amount of 64.2 zettabytes of data. There are numerous companies whose services/products rely heavily on data analysis, and mining the produced data has already revealed great value for businesses in different sectors. In order to be able to support the professionals that do this job, typically known as data scientists, we first need to characterize them. To contribute towards this characterization, we conducted a public survey and in this work we present the results about a particular aspects of their life: the tools they use and need.

Abstract
We conducted a workshop on ''Addressing Challenges in Recruiting Participants for Human-Centric Computing Research Studies'' at the IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)'24 Conference. In the workshop, we conducted a brainstorming session on ''roadmap development of making participant recruitment easier for human-centric computing studies in both industry and academia''. This article presents 7 stages of participant recruitment and key strategies identified by the authors (workshop participants) during the brainstorming session.

Abstract
Computational reproducibility-the ability to reexecute a scientific experiment using the same code, data, and configuration-should be straightforward. However, researchers often struggle with inconsistencies in documentation, missing dependencies, and environment setup, which undermines the credibility of scientific results. To address this, we propose a conversational, text-based tool that aids researchers in reproducing and packaging computational experiments into a single file. This file can be re-executed with a double-click on any machine, requiring only a single tool. SciConv is designed to support two key scenarios: (i) enabling researchers to prepare their own experiments in a reproducible, shareable format, and (ii) helping other researchers reproduce existing experiments from shared code repositories. In both cases, the tool reduces technical overhead and simplifies environment configuration through conversational interaction. We evaluated the tool through two studies. In the first, we reproduced 15 of 18 published experiments, with most requiring little or no user interaction. In the second, we conducted a user study comparing our tool with a professional platform, using the System Usability Scale (SUS) and NASA Task Load Index (TLX). The results show a statistically significant advantage for our tool in both usability and workload, demonstrating its effectiveness in supporting reproducibility. © 2025 IEEE.

Abstract
Reproducibility in computational science is increasingly dependent on the ability to faithfully re-execute experiments involving code, data, and software environments. However, assessing the effectiveness of reproducibility tools is difficult due to the lack of standardized benchmarks. To address this, we collected 38 computational experiments from diverse scientific domains and attempted to reproduce each using 8 different reproducibility tools. From this initial pool, we identified 18 experiments that could be successfully reproduced using at least one tool. These experiments form our curated benchmark dataset, which we release along with reproducibility packages to support ongoing evaluation efforts. This article introduces the curated dataset, incorporating details about software dependencies, execution steps, and configurations necessary for accurate reproduction. The dataset is structured to reflect diverse computational requirements and methodologies, ranging from simple scripts to complex, multi-language workflows, ensuring it presents the wide range of challenges researchers face in reproducing computational studies. It provides a universal benchmark by establishing a standardized dataset for objectively evaluating and comparing the effectiveness of reproducibility tools. Each experiment included in the dataset is carefully documented to ensure ease of use. We added clear instructions following a standard, so each experiment has the same kind of instructions, making it easier for researchers to run each of them with their own reproducibility tool.The utility of the dataset is demonstrated through extensive evaluations using multiple reproducibility tools.