BACKGROUND: Data dashboards are published tools that present visualizations; they are increasingly used to display data about behavioral health, social determinants of health, and chronic and infectious disease risks to inform or support public health endeavors. Dashboards can be an evidence-based approach used by communities to influence decision-making in health care for specific populations. Despite widespread use, evidence on how to best design and use dashboards in the public health realm is limited. There is also a notable dearth of studies that examine and document the complexity and heterogeneity of dashboards in community settings.
OBJECTIVE: Community stakeholders engaged in the community response to the opioid overdose crisis could benefit from the use of data dashboards for decision-making. As part of the Communities That HEAL (CTH) intervention, community data dashboards were created for stakeholders to support decision-making. We assessed stakeholders\' perceptions of the usability and use of the CTH dashboards for decision-making.
METHODS: We conducted a mixed methods assessment between June and July 2021 on the use of CTH dashboards. We administered the System Usability Scale (SUS) and conducted semistructured group interviews with users in 33 communities across 4 states of the United States. The SUS comprises 10 five-point Likert-scale questions measuring usability, each scored from 0 to 4. The interview guides were informed by the technology adoption model (TAM) and focused on perceived usefulness, perceived ease of use, intention to use, and contextual factors.
RESULTS: Overall, 62 users of the CTH dashboards completed the SUS and interviews. SUS scores (grand mean 73, SD 4.6) indicated that CTH dashboards were within the acceptable range for usability. From the qualitative interview data, we inductively created subthemes within the 4 dimensions of the TAM to contextualize stakeholders\' perceptions of the dashboard\'s usefulness and ease of use, their intention to use, and contextual factors. These data also highlighted gaps in knowledge, design, and use, which could help focus efforts to improve the use and comprehension of dashboards by stakeholders.
CONCLUSIONS: We present a set of prioritized gaps identified by our national group and list a set of lessons learned for improved data dashboard design and use for community stakeholders. Findings from our novel application of both the SUS and TAM provide insights and highlight important gaps and lessons learned to inform the design of data dashboards for use by decision-making community stakeholders.
BACKGROUND: ClinicalTrials.gov NCT04111939; https://clinicaltrials.gov/study/NCT04111939.

We analyze leading journals in behavioral finance to identify the most-used keywords in the area and how they have evolved. Using keyword analysis of data between 2000 and 2020 as well as data mapping and visualization tools, a dynamic map of the discipline was constructed. This study assesses the state-of-the-art of the field, main topics of discussion, relationships that arise between the concepts discussed, and emerging issues of interest. The sample comprises 3876 pieces, including 15859 keywords from journals responsible for the growth of the discipline, namely the Journal of Behavioral and Experimental Economics, Journal of Behavioral and Experimental Finance, Journal of Economic Psychology, Journal of Behavioral Finance, and Review of Behavioral Finance. During the period analyzed, our results depict a lively area and highlight the prominent role that experiments play in the field. Two related but different streams of behavioral finance research are revealed.

BACKGROUND: User-centered data visualizations can reduce physician cognitive load and support clinical decision making. To facilitate the selection of appropriate visualizations for single patient health data summaries, this scoping review provides a literature overview of possible visualization techniques and the corresponding reported user-centered design phases.
METHODS: The publication databases PubMed, Web of Science, IEEE Xplore and ACM Digital Library were searched for relevant articles from 2017 to 2022.
RESULTS: Of the 777 articles screened, 78 articles were included in the final analysis. The most commonly used visualization techniques are table, scatterplot-line timeline, text and event timelines, with 24 other visualization techniques identified. The testing phase of the user centered design process is reported most frequently.
CONCLUSIONS: This scoping review can support developers in the selection of suitable visualizations for single patient health data by revealing the design space of possible visualization techniques.

The analysis of large sets of spatio-temporal data is a fundamental challenge in epidemiological research. As the quantity and the complexity of such kind of data increases, automatic analysis approaches, such as statistics, data mining, machine learning, etc., can be used to extract useful information. While these approaches have proven effective, they require a priori knowledge of the information being sought, and some interesting insights into the data may be missed. To bridge this gap, information visualization offers a set of techniques for not only presenting known information, but also exploring data without having a hypothesis formulated beforehand. In this paper, we introduce Epid Data Explorer (EDE), a visualization tool that enables exploration of spatio-temporal epidemiological data. EDE allows easy comparisons of indicators and trends across different geographical areas and times. It facilitates this exploration through ready-to-use pre-loaded datasets as well as user-chosen datasets. The tool also provides a secure architecture for easily importing new datasets while ensuring confidentiality. In two use cases using data associated with the COVID-19 epidemic, we demonstrate the substantial impact of implemented lockdown measures on mobility and how EDE allows assessing correlations between the spread of COVID-19 and weather conditions.

Extensive research has made significant progress in exploring the potential application of extracellular vesicles (EV) in the diagnosis and treatment of osteoarthritis (OA). However, there is current a lack of study on bibliometrics. In this study, we completed a novel bibliometric analysis of EV research in OA over the past two decades. Specifically, we identified a total of 354 relevant publications obtained between January 1, 2003 and December 31, 2022. We also provided a description of the distribution information regarding the countries or regions of publication, institutions involved, journals, authors, citations, and keywords. The primary research focuses encompassed the role of extracellular vesicles in the diagnosis of OA, delivery of active ingredients, treatment strategies, and cartilage repair. These findings highlight the latest research frontiers and emerging areas, providing valuable insights for further investigations on the application of extracellular vesicles in the context of osteoarthritis.

A central goal of biological anthropology is connecting environmental variation to differences in host physiology, biology, health, and evolution. The microbiome represents a valuable pathway for studying how variation in host environments impacts health outcomes. While there are many resources for learning about methods related to microbiome sample collection, laboratory analyses, and genetic sequencing, there are fewer dedicated to helping researchers navigate the dense portfolio of bioinformatics and statistical approaches for analyzing microbiome data. Those that do exist are rarely related to questions in biological anthropology and instead are often focused on human biomedicine. To address this gap, we expand on existing tutorials and provide a \"road map\" to aid biological anthropologists in understanding, selecting, and deploying the data analysis and visualization methods that are most appropriate for their specific research questions. Leveraging an existing dataset of fecal samples and survey data collected from wild geladas living in Simien Mountains National Park in Ethiopia (Baniel et al., 2021), this paper guides researchers toward answering three questions related to variation in the gut microbiome across host and environmental factors. By providing explanations, examples, and a reproducible workflow for different analytic methods, we move beyond the theoretical benefits of considering the microbiome within anthropological research and instead present researchers with a guide for applying microbiome science to their work. This paper makes microbiome science more accessible to biological anthropologists and paves the way for continued research into the microbiome\'s role in the ecology, evolution, and health of human and non-human primates.

Mass spectrometry imaging (MSI) provides information about the spatial localization of molecules in complex samples with high sensitivity and molecular selectivity. Although point-wise data acquisition, in which mass spectra are acquired at predefined points in a grid pattern, is common in MSI, several MSI techniques use line-wise data acquisition. In line-wise mode, the imaged surface is continuously sampled along consecutive parallel lines and MSI data are acquired as a collection of line scans across the sample. Furthermore, aside from the standard imaging mode in which full mass spectra are acquired, other acquisition modes have been developed to enhance molecular specificity, enable separation of isobaric and isomeric species, and improve sensitivity to facilitate the imaging of low abundance species. These methods, including MS/MS-MSI in both MS2 and MS3 modes, multiple-reaction monitoring (MRM)-MSI, and ion mobility spectrometry (IMS)-MSI have all demonstrated their capabilities, but their broader implementation is limited by the existing MSI analysis software. Here, we present MSIGen, an open-source Python package for the visualization of MSI experiments performed in line-wise acquisition mode containing MS1, MS2, MRM, and IMS data, which is available at https://github.com/LabLaskin/MSIGen. The package supports multiple vendor-specific and open-source data formats and contains tools for targeted extraction of ion images, normalization, and exportation as images, arrays, or publication-style images. MSIGen offers multiple interfaces, allowing for accessibility and easy integration with other workflows. Considering its support for a wide variety of MSI imaging modes and vendor formats, MSIGen is a valuable tool for the visualization and analysis of MSI data.

As digital phenotyping, the capture of active and passive data from consumer devices such as smartphones, becomes more common, the need to properly process the data and derive replicable features from it has become paramount. Cortex is an open-source data processing pipeline for digital phenotyping data, optimized for use with the mindLAMP apps, which is used by nearly 100 research teams across the world. Cortex is designed to help teams (1) assess digital phenotyping data quality in real time, (2) derive replicable clinical features from the data, and (3) enable easy-to-share data visualizations. Cortex offers many options to work with digital phenotyping data, although some common approaches are likely of value to all teams using it. This paper highlights the reasoning, code, and example steps necessary to fully work with digital phenotyping data in a streamlined manner. Covering how to work with the data, assess its quality, derive features, and visualize findings, this paper is designed to offer the reader the knowledge and skills to apply toward analyzing any digital phenotyping data set. More specifically, the paper will teach the reader the ins and outs of the Cortex Python package. This includes background information on its interaction with the mindLAMP platform, some basic commands to learn what data can be pulled and how, and more advanced use of the package mixed with basic Python with the goal of creating a correlation matrix. After the tutorial, different use cases of Cortex are discussed, along with limitations. Toward highlighting clinical applications, this paper also provides 3 easy ways to implement examples of Cortex use in real-world settings. By understanding how to work with digital phenotyping data and providing ready-to-deploy code with Cortex, the paper aims to show how the new field of digital phenotyping can be both accessible to all and rigorous in methodology.

This paper presents an implementation of an architecture based on open-source solutions using ELK Stack - Elasticsearch, Logstash, and Kibana - for real-time data analysis and visualizations in the Medical Data Integration Center, University Hospital Cologne, Germany. The architecture addresses challenges in handling diverse data sources, ensuring standardized access, and facilitating seamless analysis in real-time, ultimately enhancing the precision, speed, and quality of monitoring processes within the medical informatics domain.

We present a data visualization tool for tumor boards, merging clinical with molecular and multi-omics data to refine precision oncology decisions. The tool offers a holistic patient perspective, facilitating personalized treatment strategies. By integrating clinical and laboratory datasets, it enables intuitive navigation through complex information. Clinicians are supported in their decision-making by user-friendly visualizations. Future studies are needed to evaluate its real-world impact and usability in precision oncology settings.