Understanding the mechanical behavior of heterogeneous materials is becoming increasingly crucial across various fields, including aerospace engineering, composite materials development, geology, and biomechanics. While substantial literature exists on this topic, conventional methods often rely on commercial software packages. This study presents a framework for computed tomography (CT) scan-based finite element (FE) analysis of such materials using open-source software in most of the workflow. Our work focuses on three key aspects:1.Mesh generation that incorporates spatially varying mechanical properties and well-defined boundary conditions.2.Validation of the FE results through comparison with digital image correlation (DIC) system measurements.3.Open-source software utilization throughout the entire process, making it more accessible and cost-effective.This work aims to demonstrate the effectiveness of this framework for analyzing heterogeneous materials in various fields, offering a more accessible and affordable approach.

Formal infectious disease surveillance in Ukraine has been disrupted by Russia\'s 2022 invasion, leading to challenges with tracking and containing epidemics. To analyze the effects of the war on infectious disease epidemiology, we used open-source data from EPIWATCH, an artificial intelligence early-warning system. We analyzed patterns of infectious diseases and syndromes before (November 1, 2021-February 23, 2022) and during (February 24-July 31, 2022) the conflict. We compared case numbers for the most frequently reported diseases with numbers from formal sources and found increases in overall infectious disease reports and in case numbers of cholera, botulism, tuberculosis, HIV/AIDS, rabies, and salmonellosis during compared with before the invasion. During the conflict, although open-source intelligence captured case numbers for epidemics, such data (except for diphtheria) were unavailable/underestimated by formal surveillance. In the absence of formal surveillance during military conflicts, open-source data provide epidemic intelligence useful for infectious disease control.

In an era marked by scientific stagnation, Decentralized Science (DeSci) challenges the inefficiencies of traditional funding and publishing systems. DeSci employs blockchain technology to address the misalignment of incentives in academic research, emphasizing transparency, rapid funding, and open-source principles. Centralized institutions have been linked to a deceleration of progress, which is acutely felt in the field of longevity science-a critical discipline as aging is the #1 risk factor for most diseases. DeSci proposes a transformative model where decentralized autonomous organizations (DAOs) facilitate community-driven funding, promoting high-risk, high-reward research. DeSci, particularly within longevity research, could catalyze a paradigm shift towards an equitable, efficient, and progressive scientific future.

Water bodies\' bathymetry is a crucial information for understanding and sustainably managing water resources. Bathymetric surveys can be expensive due to sonar equipment cost, but low-cost alternatives options exist. We present a methodology that standardize the bathymetric data collection and processing of recreational-grade sonar data. The sonar data postprocessing if fully implemented in R, with ready to use functions able to produce bathymetric maps or extract river cross sections\' metrics with minimal computing efforts. The method robustly produces a variety of outputs; the performance of the equipment adopted and of the interpolation technique allow for high accuracy and low-cost bathymetric reconstruction.•The method implemented allow for a robust and consistent processing of recreational-grade sonar water depth measures.•Through R-based functions the data are postprocessed to obtain bathymetry maps also for complex shape waterbodies.•Further metrics of rivers/channel cross sections can be extracted.

BACKGROUND: Low- and lower-middle-income countries account for a higher percentage of global epidemics and chronic diseases. In most low- and lower-middle-income countries, there is limited access to health care. The implementation of open-source electronic health records (EHRs) can be understood as a powerful enabler for low- and lower-middle-income countries because it can transform the way health care technology is delivered. Open-source EHRs can enhance health care delivery in low- and lower-middle-income countries by improving the collection, management, and analysis of health data needed to inform health care delivery, policy, and planning. While open-source EHR systems are cost-effective and adaptable, they have not proliferated rapidly in low- and lower-middle-income countries. Implementation barriers slow adoption, with existing research focusing predominantly on technical issues preventing successful implementation.
OBJECTIVE: This interdisciplinary scoping review aims to provide an overview of contextual barriers affecting the adaptation and implementation of open-source EHR systems in low- and lower-middle-income countries and to identify areas for future research.
METHODS: We conducted a scoping literature review following a systematic methodological framework. A total of 7 databases were selected from 3 disciplines: medicine and health sciences, computing, and social sciences. The findings were reported in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. The Mixed Methods Appraisal Tool and the Critical Appraisal Skills Programme checklists were used to assess the quality of relevant studies. Data were collated and summarized, and results were reported qualitatively, adopting a narrative synthesis approach.
RESULTS: This review included 13 studies that examined open-source EHRs\' adaptation and implementation in low- and lower-middle-income countries from 3 interrelated perspectives: socioenvironmental, technological, and organizational barriers. The studies identified key issues such as limited funding, sustainability, organizational and management challenges, infrastructure, data privacy and protection, and ownership. Data protection, confidentiality, ownership, and ethics emerged as important issues, often overshadowed by technical processes.
CONCLUSIONS: While open-source EHRs have the potential to enhance health care delivery in low- and lower-middle-income-country settings, implementation is fraught with difficulty. This scoping review shows that depending on the adopted perspective to implementation, different implementation barriers come into view. A dominant focus on technology distracts from socioenvironmental and organizational barriers impacting the proliferation of open-source EHRs. The role of local implementing organizations in addressing implementation barriers in low- and lower-middle-income countries remains unclear. A holistic understanding of implementers\' experiences of implementation processes is needed. This could help characterize and solve implementation problems, including those related to ethics and the management of data protection. Nevertheless, this scoping review provides a meaningful contribution to the global health informatics discipline.

The community structure and ecological function of marine ecosystems are critically dependent on phytoplankton. However, our understanding of phytoplankton is limited due to the lack of detailed information on their morphology. To address this gap, we developed a framework that combines scanning electron microscopy (SEM) with photogrammetry to create realistic 3D (three-dimensional) models of phytoplankton. The workflow of this framework is demonstrated using two marine algal species, one dinoflagellate Prorocentrum micans and one diatom Halamphora sp. The resulting 3D models are made openly available and allow users to interact with phytoplankton and their complex structures virtually (digitally) and tangibly (3D printing). They also allow for surface area and biovolume calculations of phytoplankton, as well as the exploration of their light scattering properties, which are both important for ecosystem modeling. Additionally, by presenting these models to the public, it bridges the gap between scientific inquiry and education, promoting broader awareness on the importance of phytoplankton.

Quantifying and analyzing licking behavior can offer valuable insights into fundamental neurobiological mechanisms controlling animal consummatory behaviors. Lickometers are typically based on electrical properties, a strategy that comes with limitations, including susceptibility to electrical interference and generation of electrical disturbances in electrophysiological measurements. While optical lickometers offer an alternative method to measure licks and quantify fluid intake in animals, they are prone to false readings and susceptibility to outside light sources. To overcome this problem, we propose a low-cost open-source lickometer that combines a restricted infrared beam defined by optical fibers, with a poke design that allows easy access to the tongue while limiting access of other body parts and external light sources. This device also includes features for detecting nose pokes and presenting visual cues during behavioral tasks. We provide validation experiments that demonstrate the optical lickometer\'s reliability, high-sensitivity and precision, and its application in a behavioral task, showcasing the potential of this tool to study lick microstructure in combination with other techniques, such as imaging of neural activity, in freely moving mice.

Enclosed public spaces are hotspots for airborne disease transmission. To measure and maintain indoor air quality in terms of airborne transmission, an open source, low cost and distributed array of particulate matter sensors was developed and named Dynamic Aerosol Transport for Indoor Ventilation, or DATIV, system. This system can use multiple particulate matter sensors (PMSs) simultaneously and can be remotely controlled using a Raspberry Pi-based operating system. The data acquisition system can be easily operated using the GUI within any common browser installed on a remote device such as a PC or smartphone with a corresponding IP address. The software architecture and validation measurements are presented together with possible future developments.

The tree-like morphology of neurons and glia is a key cellular determinant of circuit connectivity and metabolic function in the nervous system of essentially all animals. To elucidate the contribution of specific cell types to both physiological and pathological brain states, it is important to access detailed neuroanatomy data for quantitative analysis and computational modeling. NeuroMorpho.Org is the largest online collection of freely available digital neural reconstructions and related metadata and is continuously updated with new uploads. Earlier in the project, we released multiple datasets together yearly, but this process caused an average delay of several months in making the data public. Moreover, in the past 5 years, >80% of invited authors agreed to share their data with the community via NeuroMorpho.Org, up from <20% in the first 5 years of the project. In the same period, the average number of reconstructions per publication increased 600%, creating the need for automatic processing to release more reconstructions in less time. The progressive automation of our pipeline enabled the transition to agile releases of individual datasets as soon as they are ready. The overall time from data identification to public sharing decreased by 63.7%; 78% of the datasets are now released in less than 3 months with an average workflow duration below 40 days. Furthermore, the mean processing time per reconstruction dropped from 3 h to 2 min. With these continuous improvements, NeuroMorpho.Org strives to forge a positive culture of open data. Most importantly, the new, original research enabled through reuse of datasets across the world has a multiplicative effect on science discovery, benefiting both authors and users.

In September 2021 I made a collection of interview transcripts available for public use under a CreativeCommons license through the Princeton DataSpace. The interviews include 39 conversations I had with gig workers at AmazonFlex, Uber, and Lyft in 2019 as part of a study on automation efforts within these organizations. I made this decision because (1) I was required to contribute to a publicly available data set as a requirement of my funding and (2) I saw it as an opportunity to engage in the collaborative qualitative science experiments emerging in Science and Technology studies. This article documents my thought process and step-by-step design decisions for designing a study, gathering data, masking it, and publishing it in a public archive. Importantly, once I decided to publish these data, I determined that each choice about how the study would be designed and implemented had to be assessed for risk to the interviewee in a very deliberate way. It is not meant to be comprehensive and cover every possible condition a researcher may face while producing qualitative data. I aimed to be transparent both in my interview data and the process it took to gather and publish these data. I use this article to illustrate my thought process as I made each design decision for this study in hopes that it could be useful to a future researcher considering their own data publishing process.