Washington University School of Medicine in St. Louis Showcase

This dataset comprises a total of 26,990 magnetic resonance image files capturing the lower spine, pelvis, and femur. The images are sourced from individuals diagnosed with developmental dysplasia of the hip (DDH) as well as control subjects who share similar demographic characteristics. The dataset is organized into 35 folders: 15 folders contain data from control subjects, while 20 folders contain data from patients with DDH. All identifying information has been removed from the images, and they are saved in DICOM format. Each folder has been labeled with a code that begins with "DDH" and is accompanied by either _ctl (indicating a non-dysplastic control) or _pt (indicating a patient with DDH). Within the folder for each participant, there is a 'MRI_TagEdit' folder that contains the DICOM files. The number of files in each folder ranges from 688 to 934. Additionally, a README.txt file is included in each folder, providing details on the number of DICOM files and the file naming convention. To help interpret the data, MASTER-README.txt as well as codesheet.csv are included in this dataset. To facilitate data interpretation, this dataset includes a 'MASTER-README.txt' file which outlines the methodology employed for data collection and generation, data processing methods, and the de-identification procedure used for data sharing. Additionally, a 'codesheet.csv' is provided, containing subject IDs, sex, age, height, weight, and information on whether the subjects were controls or patients with developmental dysplasia of the hip. The collection of this data was conducted with the approval of the Washington University in St. Louis Institutional Review Board (ID# 201612053). Participants provided informed consent for the sharing of de-identified data.

This dataset contains raw data supporting all figures and supplemental figures, except for single cell RNA sequencing data and serum cytokine profiling data, used in PLOS Biology paper, Tumor-derived interleukin-1α and leukemia inhibitory factor promote extramedullary hematopoiesis (DOI: https://doi.org/10.1371/journal.pbio.3001746). This dataset includes 31 folders organized by Figure number and description of data type (flow cytometry, RT qPCR, ELISA, Immunofluorescence) and each folder contains a data dictionary file to describe variables.

Data collected from participants at during a clinical trial in Hohoe, Ghana where participants are identified by a unique participant ID. There are two datasets. The Safety dataset includes demographics, skin snip results, adverse event results, and ophthalmological data from baseline, day of treatment, Day 1-7, Month 3, Month 12, and Month 18. The Nodulectomy data set contains data pertaining to sectioned nodules read by scientists at 18 months which indicate embryogenesis in female worms, number of male and female worms, and worm calcification.

Trust can be defined as “a willingness to be vulnerable to another for a given set of tasks” and thus, trust and public health are inextricably linked. State actors are key participants in population health, organizing, among other things, mandates and guidelines that target health behaviors and encourage the uptake of medicines, screenings, diagnostics, and control of health conditions. Effective implementation of these crucial, government-sponsored health efforts is conditional on the public’s belief that the state is trustworthy and has one's best interest in mind – positioning trust in government as a central determinant of public health. Trusting relationships between patients, health systems, and health care providers are also essential, as high-quality, safe care and adherence with healthcare professionals’ recommendations heavily depend upon trust. In many countries, trust in government and health care providers are inseparable, as governments are the primary providers of healthcare. Despite these critical relationships, existing studies that link trust and public health outcomes often focus on contemporaneous factors, many of which are endogenous to public health outcomes (e.g., support for the incumbent political party). For example, Sopory and colleagues reported a comprehensive examination of the phenomenon of trust during public health emergency events among 68 studies from 28 countries that included individuals who were directly affected by a public health emergency. Importantly, no studies from South America or Africa were included. The shortage of research on the sociostructural, historical, economic, and political sources of low trust limits our understanding of how trust deficits might be remedied so as to improve population health. Understanding why trust is low as well as how to repair trust are thus of critical importance.

The aim of this qualitative research was to understand decision-making for policies impacting obesity and community health, and perspectives on obesity and related health disparities among local policymakers from municipalities experiencing obesity disparities. The dataset includes transcripts from 20 semi-structured interviews with local elected and top-appointed officials from municipalities across the United States, conducted between December 2020 and April 2021. The Institutional Review Board of Washington University in St. Louis approved this study (#202001197).

Data collected from participants at four separate timepoints in Cote d'Ivoire and Liberia identified by a unique participant ID. There are four timepoints collected from each country: Baseline, Follow-up Year 1, Follow-Up Year 2, and Follow-up Year 3. Each dataset includes the participant ID, gender, age, the microfilarae (MF) count collected through blood smear, and circulating filarial antigen (CFA) result from a strip test.

This dataset contains measures of loneliness, anxiety, and depression from a cross-sectional study conducted during the COVID-19 pandemic. Older adults responded by either phone or email surveys. Variables include self-report measures of psychosocial symptoms - loneliness, anxiety, and depression.

De-identified electronic health record data for patients hospitalized with pneumonia due to SARS-CoV-2 (2020) or influenza (2015-2019). The data contain certain demographics, vital sign values, laboratory values, and patient outcomes from hospitalization.