THE DATA:
Unfortunately, we are unable to share our data for this project. Since we were working with a vulnerable population (children), we were asked by Queens University Belfast’s IRB-equivalent to include language in the consent documents indicating that the data would not be shared outside of the research team. Thus, the datasets generated during and/or analyzed during the current study are not publicly available as participants were informed that no-one outside of the research team would have access to the research data when they signed their consent forms.
Thus, we provide Stata, R and Mplus scripts used to generate all tables and figures reported in the paper. Since we cannot share the raw study data, most of these files cannot be run, but in the interest of transparency we include the scripts so that our code can be checked. Since a major portion of the paper is the LTA modeling, we took an additional step there and generated simulated data that allows the R+Mplus scripts to be run. These runnable scripts and the simulated data are contained in the subfolder LTA_code_EXEC. For further information about the study datasets, please contact the authors (Emails: Jennifer.Murray@qub.ac.uk; ruth.hunter@qub.ac.uk)
Kimbrough, E., Krupka, E., Kumar, R., Murray, J., and Ramalingam, A. (conditional accept). On the Stability of Norms and Norm-Following Propensity: A Cross Cultural Panel Study with Adolescents. Experimental Economics
The International Committee of Medical Journal Editors (ICMJE) requires researchers to post individual participant data (IPD) plans for interventional clinical trials with registration in order to be eligible for publication in its member journals. This study looked at how researchers interpret the ICMJE requirements and the related prompts for information used by ClinicalTrials.gov. This data consists of the analyzed contents of the IPD plans that researchers at the University of Michigan (U-M) submitted with trial registrations for the first 27 months that the 2019 requirement was in effect.
Samuel, S. M. & Wilson, D. L. & Fleming, E., (2023) “Evaluating individual participant data plans for ICMJE compliance: A case study at University of Michigan”, Journal of the Society for Clinical Data Management 3(4). doi: https://doi.org/10.47912/jscdm.257
The data deposited here is as follows: The clinical shedding/symptom data, RNAseq, steroid, and wearable E4 data was partially presented in publications [1]-[3] and the cognitive lumos and VAFS data is presented in the paper [4], which is under review and embargoed. The data files are: subject.json, sample.json, and genematrix_TPM.csv. In addition, a copy of the blank consent form used to enroll volunteers in the study is included (17964_Adult Consent_2015Mar17-Mod 1_clean.pdf)., Clinical symptom and viral shedding data (in subject.json): reports each subject's accumulated and maximum self-reported symptom score (modified Jackson score) and shedding titrations from nasal-pharyngeal washes after inoculation. , RNAseq data (genematrix_TMP.csv): Whole blood was collected in PAXgene™ Blood RNA tubes (PreAnalytiX), and total RNA extracted using the PAXgene™ Blood miRNA Kit (QIAGEN) using the manufacturer’s recommended protocol. RNA quantity and quality were assessed using Nanodrop 2000 spectrophotometer (Thermo-Fisher) and Bioanalyzer 2100 with RNA 6000 Nano Chips (Agilent). RNA sequencing libraries were prepared using Illumina TruSeq mRNA Library Kit with RiboZero Globin depletion, and sequenced on an Illumina NextSeq sequencer with 50bp paired-end reads (target 40M reads per sample). After demultiplexing to FASTQ paired-end read counts files, the 396 samples were TPM transformed using HISAT2 software with the reference genome Homo_sapiens.GRCh38.84. Each sample corresponds to one of the 18 subjects at one of 22 time points. One of these samples was of insufficient quality to be mapped to read counts. In addition to the TPM normalized RNAseq data contained in this repository, the raw FASTQ data for the 395 samples are deposited in the GEO repository ( https://www.ncbi.nlm.nih.gov/geo), Accession # GSE215087. , Cognitive data (sample.json): Outcomes from a NeuroCognitive Performance Test (NCPT) that was taken approximately 3 time daily by all volunteers. The NCPT is a repeatable, web-based, computerized, cognitive assessment platform designed to measure subtle changes in performance across multiple cognitive domains. Subject scores along 18 cognitive variables data were collected at approximated 22 time points during the challenge study. The data structure sample.json contains the raw cognitive data and the extracted 18 cognitive scores over time for each subject. , The Visual Analog Fatigue Scale (sample.json): the VAFS is a measure of cognitive fatigue that was measured approximately 3 times per day at the same time as the NCPT and blood draw. , Wearable device data (sample.json): participants wore an Empatica E4 device for the duration of the challenge study. Summarized features are provided for each subject that include sleep duration (mean and std), sleep offset (mean and std), and temperature (mean and std). , Steroid data was also collected and is included in the sample.json. This steroid data was collected from the whole blood samples and consists of cortisol, melatonin, and DHEAS.
, and See README.txt for more specific details on the data structures contained in the sample.json, subject.json, and genematrix_TPM.csv files.
X She, Y Zhai, R Henao, CW Woods, C Chiu, Geoffrey S. Ginsburg, Peter X.K. Song, AO. Hero, “Adaptive multi-channel event segmentation and feature extraction for monitoring health outcomes,” IEEE Transactions on Biomedical Engineering, vol. 68, no. 8, pp. 2377-2388, Aug. 2021, doi: 10.1109/TBME.2020.3038652. Available on arxiv:2008.09215 , Emilia Grzesiak, Brinnae Bent, Micah T. McClain, Christopher W. Woods, Ephraim L. Tsalik, Bradly P. Nicholson, Timothy Veldman, Thomas W. Burke, Zoe Gardener, Emma Bergstrom, Ronald B. Turner, Christopher Chiu, P. Murali Doraiswamy, Alfred Hero, Ricardo Henao, Geoffrey S. Ginsburg, Jessilyn Dunn Assessment of the Feasibility of Using Noninvasive Wearable Biometric Monitoring Sensors to Detect Influenza and the Common Cold Before Symptom Onset. JAMA Netw Open. 2021;4(9):e2128534. doi:10.1001/jamanetworkopen.2021.28534 , E Sabeti, S Oh, PX Song, A Hero. “A Pattern Dictionary Method for Anomaly Detection,” Entropy, vol 24, pp. 1095 Aug 2022. doi: 10.3390/e24081095, and Yaya Zhai, P. Murali Doraiswamy, Christopher W. Woods, Ronald B. Turner, Thomas W. Burke, Geoffrey S. Ginsburg, Alfred O. Hero, "Pre-exposure cognitive performance variability is associated with severity of respiratory infection," manuscript under review.
Health status data includes data about the health of persons within a census tract in Metropolitan Detroit, measured at the census tract level. This includes data about 1) mortality by condition; 2) exposures to toxic substances; and 3) disability.
Coverage for all data: 10-county Detroit-Warren-Ann Arbor Combined Statistical Area.
This dataset includes census tract-level data concerning housing in Metropolitan Detroit. The data includes: 1) Total housing units and total mortgages in the tract; 2) Land use; 3) Real estate information (foreclosures, sales transactions, and home values); 4) Vacant housing; 5) Housing age and available facilities; 6) Housing condition; and 7) Spatial measures of subsidized housing in the tract.
Data coverage should say 2006 to 2015.
The information and education environment refers to: 1) the presence of information infrastructures such as broadband Internet access and public libraries in a location; 2) a person’s proximity to information infrastructures and sources; 3) the distribution of information infrastructures, sources and in a specific location; and 4) exposure to specific messages (information content) within a specific location.
Coverage for all data: 10-county Detroit-Warren-Ann Arbor Combined Statistical Area.
The food environment is: 1) The physical presence of food that affects a person’s diet; 2) A person’s proximity to food store locations; 3) The distribution of food stores, food service, and any physical entity by which food may be obtained; or 4) A connected system that allows access to food. (Source: https://www.cdc.gov/healthyplaces/healthtopics/healthyfood/general.htm) Data included here concern: 1) Food access; and 2) Liquor access. Spatial Coverage for most data: 10-county Detroit-Warren-Ann Arbor Combined Statistical Area, Michigan, USA. See exception for grocery store data below.
Active living resources include spaces and organizations that facilitate physical activity, including 1) park land, 2) recreation areas (including parks, golf courses, amusement parks, beaches and other recreational landmarks); and 3) recreation centers (including gyms, dancing instruction, martial arts instruction, bowling centers, yoga instruction, sports clubs, fitness programs, golf course, pilates instruction, personal trainers, swimming pools, skating rinks, etc.)
Coverage for all data: 10-county Detroit-Warren-Ann Arbor Combined Statistical Area.
The Social Environment refers to characteristics of the people and institutions in a census tract, including: 1)
Religious organizations (churches and places of worship); and 2) Voter turnout for the 2012 Presidential Election. Coverage for all data: 10-county Detroit-Warren-Ann Arbor Combined Statistical Area.