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The immunology database and analysis portal (ImmPort) system is the archival repository and dissemination vehicle for clinical and molecular datasets created by research consortia funded by the National Institute of Allergy and Infectious Diseases Division of Allergy, Immunology, and Transplantation. With nearly 100 datasets now publicly available and hundreds of downloads per month, ImmPort is an important source for raw data and protocols from clinical trials, mechanistic studies, and novel methods for cellular and molecular measurements. To facilitate data transfer, templates for data representation and standard operating procedures have also been created and are also publicly available. ImmPort facilitates transparency and reproducibility in immunology research, serves as an important resource for education, and enables newly generated hypotheses and data-driven science.

Using user-generated content from open-access platforms such as Reddit for research raises ethical questions and challenges. Research projects involving publicly available data can qualify for an exemption from human research ethics review. However, when the exemption is granted, some scholars move to the data collection phase without attending further to ethical considerations. This does not always result from negligence but can be driven by the lack of coherent guidelines or limitations of procedural ethics. Despite receiving an exemption from ethics review, researchers can still engage with ethical concerns throughout the project. This article argues that a “situated ethics approach” to researching publicly available online data, which pays attention to flexibility, reflexivity, and complexity of research ethics, should be applied to projects working with data from user-led platforms—Reddit or others. Using a reflexive process and drawing iteratively on learnings, this article describes and analyses a situated ethics framework applied to a case study of doctoral research about youth health discussions on Reddit. Through a focus on three key areas: digital context, users’ views, and project specificity, the framework inspired a set of ethical questions that can assist with applying situated ethics to other studies. This paper advocates that a “situated ethics approach” to researching publicly available online data can usefully advance debates and practice in research on user-led platforms with public data, such as Reddit.

Background: Studies that identify children after acute trauma and prospectively track risk/protective factors and trauma responses over time are resource-intensive; small sample sizes often limit power and generalizability. The Prospective studies of Acute Child Trauma and Recovery (PACT/R) Data Archive was created to facilitate more robust integrative cross-study data analyses. Objectives: To (a) describe creation of this research resource, including harmonization of key variables; (b) describe key study- and participant-level variables; and (c) examine retention to follow-up across studies. Methods: For the first 30 studies in the Archive, we described study-level (design factors, retention rates) and participant-level (demographic, event, traumatic stress) variables. We used Chi square or ANOVA to examine study- and participant-level variables potentially associated with retention. Results: These 30 prospective studies (N per study = 50 to 568; overall N = 5499) conducted by 15 research teams in 5 countries enrolled children exposed to injury (46%), disaster (24%), violence (13%), traffic accidents (10%), or other acute events. Participants were school-age or adolescent (97%), 60% were male, and approximately half were of minority ethnicity. Using harmonized data from 22 measures, 24% reported significant traumatic stress ≥1 month post-event. Other commonly assessed outcomes included depression (19 studies), internalizing/externalizing symptoms (19), and parent mental health (19). Studies involved 2 to 5 research assessments; 80% of participants were retained for ≥2 assessments. At the study level, greater retention was associated with more planned assessments. At the participant level, adolescents, minority youth, and those of lower socioeconomic status had lower retention rates. Conclusion: This project demonstrates the feasibility and value of bringing together traumatic stress research data and making it available for re-use. As an ongoing research resource, the Archive can promote 'FAIR' data practices and facilitate integrated analyses to advance understanding of child traumatic stress.

Background Attacks on health care represent an area of growing international concern. Publicly available data are important in documenting attacks, and are often the only easily accessible data source. Data collection processes about attacks on health and their implications have received little attention, despite the fact that datasets and their collection processes may result in differing numbers. Comparing two separate datasets compiled using publicly-available data revealed minimal overlap. This article aims to explain the reasons for the lack of overlap, to better understand the gaps and their implications. Methods We compared the data collection processes for datasets comprised of publicly-reported attacks on health care from the World Health Organization (WHO) and Insecurity Insight’s Security in Numbers Database (SiND). We compared each individual event to compile a comparable dataset and identify unique and matched events in order to determine the overlap between them. We report descriptive statistics for this comparison. Results We identified a common dataset of 287 events from 2017, of which only 33 appeared in both datasets, resulting in a mere 12.9% (n = 254) overlap. Events affecting personnel and facilities appeared most often in both, and 22 of 31 countries lacked any overlap between datasets. Conclusions We conclude that the minimal overlap suggests significant underreporting of attacks on health care, and furthermore, that dataset definitions and parameters affect data collection. Source variation appears to best explain the discrepancies and closer comparison of the collection processes reveal weaknesses of both automated and manual data collection that rely on hidden curation processes. To generate more accurate datasets compiled from public sources requires systematic work to translate definitions into effective online search mechanisms to better capture the full range of events, and to increase the diversity of languages and local sources to better capture events across geographies.

Understanding past and current trends is crucial in the fashion industry to forecast future market demands. This study quantifies and reports the characteristics of the trendy walking styles of fashion models during real-world runway performances using three cutting-edge technologies: (a) publicly available video resources, (b) human pose detection technology, and (c) multivariate human-movement analysis techniques. The skeletal coordinates of the whole body during one gait cycle, extracted from publicly available video resources of 69 fashion models, underwent principal component analysis to reduce the dimensionality of the data. Then, hierarchical cluster analysis was used to classify the data. The results revealed that (1) the gaits of the fashion models analyzed in this study could be classified into five clusters, (2) there were significant differences in the median years in which the shows were held between the clusters, and (3) reconstructed stick-figure animations representing the walking styles of each cluster indicate that an exaggerated leg-crossing gait has become less common over recent years. Accordingly, we concluded that the level of leg crossing while walking is one of the major changes in trendy walking styles, from the past to the present, directed by the world’s leading brands.

Missing or unavailable data (NA) in multivariate data analysis is often treated with imputation methods and, in some cases, records containing NA are eliminated, leading to the loss of information. This paper addresses the problem of NA in multiple factor analysis (MFA) without resorting to eliminating records or using imputation techniques. For this purpose, the nonlinear iterative partial least squares (NIPALS) algorithm is proposed based on the principle of available data. NIPALS presents a good alternative when data imputation is not feasible. Our proposed method is called MFA-NIPALS and, based on simulation scenarios, we recommend its use until 15% of NAs of total observations. A case of groups of quantitative variables is studied and the proposed NIPALS algorithm is compared with the regularized iterative MFA algorithm for several percentages of NA.

Background E-cigarettes are the most-commonly used tobacco product by youth since 2014. To prevent youth access and use of e-cigarettes, many U.S. states and localities have enacted policies over a relatively short period of time. The adoption of these policies has necessitated timely data collection to evaluate impacts. Methods To assess the impact of flavored e-cigarette policies in select states and local jurisdictions across the United States, a multi-method, complementary approach was implemented from July 2019 to present, which includes analyses of cross-sectional online surveys of young people ages 13–24 years with retail sales data. Results From February 2020 through February 2023, cross-sectional surveys have been conducted in three cities, one county, and eight states where policy changes have been enacted or are likely to be enacted. Data collection occurred every six months to provide near real-time data and examine trends over time. Additionally, weekly retail sales data were aggregated to showcase monthly sales trends at the national level and for the selected states. Discussion This rapid and efficient method of coupling online survey data with retail sales data provides a timely and effective approach for monitoring a quickly changing tobacco product landscape, particularly for states and localities where rapidly-available data is often not available. This approach can also be used to monitor other health behaviors and relevant policy impacts.

Early detection of lung nodules is essential for preventing lung cancer. However, the number of radiologists who can diagnose lung nodules is limited, and considerable effort and time are required. To address this problem, researchers are investigating the automation of deep-learning-based lung nodule detection. However, deep learning requires large amounts of data, which can be difficult to collect. Therefore, data collection should be optimized to facilitate experiments at the beginning of lung nodule detection studies. We collected chest computed tomography scans from 515 patients with lung nodules from three hospitals and high-quality lung nodule annotations reviewed by radiologists. We conducted several experiments using the collected datasets and publicly available data from LUNA16. The object detection model, YOLOX was used in the lung nodule detection experiment. Similar or better performance was obtained when training the model with the collected data rather than LUNA16 with large amounts of data. We also show that weight transfer learning from pre-trained open data is very useful when it is difficult to collect large amounts of data. Good performance can otherwise be expected when reaching more than 100 patients. This study offers valuable insights for guiding data collection in lung nodules studies in the future.

Sports big data has been an emerging research area in recent years. The purpose of this study was to ascertain the most frequent research topics, application areas, data sources, and data usage characteristics in the existing literature, in order to understand the development of data-driven baseball research and the multidisciplinary participation in the big data era. A scoping review was conducted, focusing on the diversity of using publicly available major league baseball data. Next, the co-occurrence analysis in bibliometrics was used to present a knowledge map of the reviewed literature. Finally, we propose a comprehensive baseball data research domain framework to visualize the ecosystem of publicly available sports data applications mapped to the four application domains in the big data maturity model. After searching and screening process from the Web of Science, Science Direct, and SPORTDiscus database, 48 relevant papers with clearly indicated data sources and data fields used were finally selected and full reviewed for advanced analysis. The most relevant research hotspots for sports data are sequentially economics and finance, sports injury, and sports performance evaluation. Subjects studied ranged from pitchers, position players, catchers, umpires, batters, free agents, and attendees. The most popular data sources are PITCHf/x, the Lahman Baseball Database, and baseball-reference.com. This review can serve as a valuable starting point for researchers to plan research strategies, to discover opportunities for cross-disciplinary research innovations, and to categorize their work in the context of the state of research.