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- Creator:
- Yan, Xiang (Jacob), Clarke, Phillipa J., Okullo, Dolorence, Goodspeed, Robert, Data Driven Detroit, Gomez-Lopez, Iris N., and Veinot, Tiffany C
- Description:
- This collection was produced as part of the project, “A ‘Big Data’ Approach to Understanding Neighborhood Effects in Chronic Illness Disparities.” The Investigators for the project are Tiffany Veinot, Veronica Berrocal, Phillipa Clarke, Robert Goodspeed, Daniel Romero, and VG Vinod Vydiswaran from the University of Michigan. The study took place from 2015-2016, with funding from the University of Michigan’s Social Sciences Annual Institute, MCubed, and the Sloan and Moore Foundations. Contact: Tiffany Veinot, MLS, PhD Office: 3443 North Quad Phone: 734/615-8281 Email: tveinot@umich.edu MCubed project page: https://mcubed.umich.edu/projects/%E2%80%9Cbig-data%E2%80%9D-approach-understanding-neighborhood-effects-chronic-illness-disparities
- Keyword:
- Food Environment, Health Status, Employment, Health Care Resources, Neighborhood Safety, Healthcare Utilization, Transportation, Census tract level, Information and Education Environment, Spatial Measures, Detroit, Active Living Resources, Social Environment, Demographics, Community Health, Housing, and student-friendly
- Discipline:
- Social Sciences
6Works -
- Creator:
- Hero, Alfred O, Zhai, Yaya, Burke, Thomas, Doraiswamy, Murali, Ginsburg, Geoffrey S, Henao, Ricardo, Turner, Ronald B, and Woods, Christopher W
- Description:
- 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.
- Keyword:
- human challenge study and cognitive health and immunity
- Citation to related publication:
- 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.
- Discipline:
- Health Sciences and Social Sciences
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- Creator:
- Hemphill, Libby
- Description:
- Social media data offer a rich resource for researchers interested in public health, labor economics, politics, social behaviors, and other topics. However, scale and anonymity mean that researchers often cannot directly get permission from users to collect and analyze their social media data. This article applies the basic ethical principle of respect for persons to consider individuals’ perceptions of acceptable uses of data. We compare individuals' perceptions of acceptable uses of other types of sensitive data, such as health records and individual identifiers, with their perceptions of acceptable uses of social media data. Our survey of 1018 people shows that individuals think of their social media data as moderately sensitive and agree that it should be protected. Respondents are generally okay with researchers using their data in social research but prefer that researchers clearly articulate benefits and seek explicit consent before conducting research. We argue that researchers must ensure that their research provides social benefits worthy of individual risks and that they must address those risks throughout the research process.
- Keyword:
- social media, data ethics, and data reuse
- Discipline:
- Social Sciences
-
- Creator:
- Schöpke-Gonzalez, Angela M., Thomer, Andrea K., and Conway, Paul
- Description:
- This interview protocol was designed to investigate the research question: How do self-identified refugees in the receiving societies of Greece and Germany engage with information spaces to navigate identity during liminal and post-liminal portions of their refugee experiences?
- Keyword:
- information space, identity, liminality, and migration
- Citation to related publication:
- Schöpke-Gonzalez, A., Thomer, A., & Conway, P. (2020). Identity Navigation During Refugee Experiences: The International Journal of Information, Diversity, & Inclusion (IJIDI), 4(2), 36–67. https://doi.org/10.33137/ijidi.v4i2.33151
- Discipline:
- Social Sciences
-
- Creator:
- Budak, Ceren, Goel, Sharad, and Rao, Justin M
- Description:
- Our primary analysis is based on articles published in 2013 by the top thirteen US news outlets and two popular political blogs. To compile the set of articles published by these outlets, we first examined the complete web-browsing records for US-located users who installed the Bing Toolbar, an optional add-on application for the Internet Explorer web browser. For each of the fifteen news sites, we recorded all unique URLs that were visited by at least ten toolbar users, and we then crawled the news sites to obtain the full article title and text. This process resulted in a corpus of 803,146 articles published on the fifteen news sites over the course of a year, with each article annotated with its relative popularity. , Next, we built two binary classifiers using large-scale logistic regression. The first classifier—which we refer to as the news classifier —identifies “news” articles (i.e., articles that would typically appear in the front section of a traditional newspaper). The second classifier—the politics classifier —identifies political news from the subset of articles identified as news by the first classifier. 340,191 (42 percent) were classified as news. On the set of 340,191 news articles, 114,814 (34 percent) were classified as political. , Having identified approximately 115,000 political news articles, we next seek to categorize the articles by topic (e.g., gay rights, healthcare, etc.), and to quantify the political slant of the article. To do so, we turn to human judges recruited via Mechanical Turk to analyze the articles. For every day in 2013, we randomly selected two political articles, when available, from each of the 15 outlets we study, with sampling weights equal to the number of times the article was visited by our panel of toolbar users., Amazon Mechanical Turk Labeling task: To detect and control for possible preconceptions of an outlet’s ideological slant, workers, upon first entering the experiment, were randomly assigned to either a blinded or unblinded condition. In the blinded condition, workers were presented with only the article’s title and text, whereas in the unblinded condition, they were additionally shown the name of the outlet in which the article was published. Each article was then analyzed by two workers, one each from the sets of workers in the two conditions. For each article, each worker completed the following three tasks. First, they provided primary and secondary article classifications from a list of fifteen topics: (1) civil rights; (2) Democrat scandals; (3) drugs; (4) economy; (5) education; (6) elections; (7) environment; (8) gay rights; (9) gun-related crimes; (10) gun rights/regulation; (11) healthcare; (12) international news; (13) national security; (14) Republican scandals; and (15) other. , and Second, workers determined whether the article was descriptive news or opinion. Third, to measure ideological slant, workers were asked, “Is the article generally positive, neutral, or negative toward members of the Democratic Party?” and separately, “Is the article generally positive, neutral, or negative toward members of the Republican Party?” Choices for these last two questions were provided on a five-point scale: very positive, somewhat positive, neutral, somewhat negative, and very negative. To mitigate question-ordering effects, workers were initially randomly assigned to being asked either the Democratic or Republican party question first; the question order remained the same for any subsequent articles the worker rated. Finally, we assigned each article a partisanship score between –1 and 1, where a negative rating indicates that the article is net left-leaning and a positive rating indicates that it is net right-leaning. Specifically, for an article’s depiction of the Democratic Party, the five-point scale from very positive to very negative is encoded as –1, –0.5, 0, 0.5, 1. Analogously, for an article’s depiction of the Republican Party, the scale is encoded as 1, 0.5, 0, –.0.5, –1. The score for each article is defined as the average over these two ratings. Thus, an average score of –1, for example, indicates that the article is very positive toward Democrats and very negative toward Republicans. The result of this procedure is a large, representative sample of political news articles, with direct human judgments on partisanship and article topic.
- Keyword:
- news media, media bias, crowdsourcing, and machine learning
- Citation to related publication:
- https://academic.oup.com/poq/article-abstract/80/S1/250/2223443/?redirectedFrom=fulltext and Ceren Budak, Sharad Goel, Justin M. Rao, Fair and Balanced? Quantifying Media Bias through Crowdsourced Content Analysis, Public Opinion Quarterly, Volume 80, Issue S1, 2016, Pages 250–271, https://doi.org/10.1093/poq/nfw007
- Discipline:
- Social Sciences
-
- Creator:
- Benjamin Leffel
- Description:
- Time series dataset of adoption by year of climate action plans by 177 U.S. cities, 2010-2019, with links to plans included. This dataset is intended for use by researchers and practitioners investigating both individual climate action plans and time series patterns of adoption at the municipal level.
- Keyword:
- climate change, climate action plan, municipal, and Urban Sustainability Research Group
- Discipline:
- Social Sciences and Government, Politics and Law
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- Creator:
- Teplitskiy, Misha, Peng, Hao, Blasco, Andrea, and Lakhani, Karim R.
- Description:
- The data sources and methods used to process the raw data are described in the paper www.doi.org/10.1073/pnas.2118046119 and the associated Supplementary Information. These data are anonymized (see Methodology for details). Consequently, running the same code on these data vs. the data in the paper does not yield *identical* results but qualitatively similar ones.
- Citation to related publication:
- www.doi.org/10.1073/pnas.2118046119
- Discipline:
- Social Sciences
-
- Creator:
- Quarles, Christopher L.
- Description:
- Student capital is the set of skills, traits, and resources that an individual can draw upon to be successful in school. With dropout rates around 50%, community college students often don't have enough student capital to achieve their goals. The R code in this dataset estimates the average student capital of a group of community college students using data on their total credits and academic outcomes. It also contains R code to create figures, as found in the paper "The Shape of Educational Inequality" by Quarles, Budak & Resnick.
- Keyword:
- education, community college, and maximum likelihood estimation
- Citation to related publication:
- Quarles, C. L., Budak, C., Resnick, P. (2020). The shape of educational inequality. Science Advances. 6(29). https://doi.org/10.1126/sciadv.aaz5954
- Discipline:
- Social Sciences
-
- Creator:
- Gosner, Linda R., Nowlin, Jessica, and Smith, Alexander J.
- Description:
- Included here are 1) a detailed description of each of the dataset’s components, 2) a database of finds from the survey, 3) databases of the faunal bone studied by specialist Damià Ramis, 4) notes and documentation made in the field, 5) excavation photographs 6) artifact photographs.
- Keyword:
- Sardinia, Mediterranean archaeology, archaeological survey, excavation, and Classical archaeology
- Citation to related publication:
- Dommelen, Peter van, Enrique Díes Cusí, Linda R. Gosner, Jeremy Hayne, Guillem Pérez-Jordà, Damià Ramis, Andrea Roppa, and Alfonso Stiglitz. 2018. “Un millennio di storie: nuove notizie preliminari sul Progetto S’Urachi (San Vero Milis, OR), 2016-2018.” Quaderni. Rivista di Archeologia 29: 141–65. https://www.quaderniarcheocaor.beniculturali.it/index.php/qua/article/view/46, Gosner, Linda R., and Alexander J. Smith. 2018. “Landscape Use and Local Settlement at the Nuraghe S’Urachi (West-Central Sardinia): Results from the First Two Seasons of Site Survey.” Fasti Online Documents & Research: Survey Series, no. 7: 1–27. www.fastionline.org/docs/FOLDER-sur-2018-7.pdf., Gosner, Linda R., Jeremy Hayne, Emanuele Madrigali, Jessica Nowlin. 2020. New Evidence for Local Continuity and Phoenician Influence in the Ceramic Assemblage from Iron Age Su Padrigheddu (West-Central Sardinia). Proceedings of the IX Congreso de Estudios Fenicios y Púnicos. Myrta 5: 1649-1657. https://scholars.ttu.edu/en/publications/new-evidence-for-local-continuity-and-phoenician-influence-in-the, Madrigali, Emanuele, Linda R. Gosner, Jeremy Hayne, Jessica Nowlin, and Damià Ramis. 2019. “Tradizioni e interazioni nella quotidianità dell’età del ferro. nuove evidenze da Su Padrigheddu (San Vero Milis, OR).” Quaderni. Rivista di Archeologia 30: 107–26. https://scholars.ttu.edu/en/publications/tradizioni-e-interazioni-nella-quotidianit%C3%A0-dellet%C3%A0-del-ferro-nuo, Stiglitz, Alfonso, Enrique Díes Cusí, Damià Ramis, Andrea Roppa, and Peter van Dommelen. 2015. “Intorno al nuraghe: notizie preliminari sul Progetto S’Urachi (San Vero Milis, OR).” Quaderni. Rivista di Archeologia 26: 191–218., and Gosner, Linda R., Jessica Nowlin, and Alexander J. Smith. in preparation. Ground-truthing the Site-based Survey at S’Urachi and Su Padrigheddu (West-Central Sardinia): Results of the 2016 and 2017 Seasons.
- Discipline:
- Social Sciences
-
- Creator:
- Benjamin Leffel
- Description:
- Data were gathered to test three hypotheses on the impact economic growth has on environmental conditions in urban areas. The three hypotheses are: 1. Income will be associated with reductions in PM2.5, PM10, NO2 and SO2. 2. Public Administration GVA will be associated with reductions in PM2.5, PM10, NO2 and SO2. 3. Urban density will be associated with reductions in PM2.5, PM10, NO2 and SO2. More information about the research and the data can be found in: Benjamin Leffel, Nikki Tavasoli, Brantley Liddle, Kent Henderson & Sabrina Kiernan (2021) Metropolitan air pollution abatement and industrial growth: Global urban panel analysis of PM10, PM2.5, NO2 and SO2, Environmental Sociology, DOI: 10.1080/23251042.2021.1975349.
- Keyword:
- global cities, environment, urban, air pollution, income, Urban Sustainability Research Group, and student-friendly
- Citation to related publication:
- Benjamin Leffel, Nikki Tavasoli, Brantley Liddle, Kent Henderson & Sabrina Kiernan (2021) Metropolitan air pollution abatement and industrial growth: Global urban panel analysis of PM10, PM2.5, NO2 and SO2, Environmental Sociology, DOI: 10.1080/23251042.2021.1975349
- Discipline:
- Social Sciences