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- 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
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- 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
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- 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
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- Creator:
- Moser, Carol, Schoenebeck, Sarita , and Resnick, Paul
- Description:
- These data, survey instruments (including informed consent) and analysis scripts come from Carol Moser's dissertation titled, Impulse Buying: Designing for Self-Control with E-commerce.
- Keyword:
- Impulse Buying, Self-control, and Experimental Design
- Discipline:
- Social Sciences
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- Creator:
- Horsley, Timothy J. and Sampson, Christina P.
- Description:
- The data (raw data, composite files [processed], and some images) can be read by the program TerraSurveyor. Version 3.0.34.10 of the software was used to create the composite files in this deposit. and The magnetometer data was the second step in a geophysical survey program that began with magnetic susceptibility survey of a portion of the Weedon Island Preserve in St. Petersburg, Florida. Geophysical survey was used to map human occupation of the study area and to guide subsequent archaeological excavations.
- Keyword:
- magnetometry, geophysical survey, remote sensing, Florida archaeology, and coastal archaeology
- Citation to related publication:
- Sampson, C. P. (2019) Safety Harbor at the Weeden Island Site: Late Pre-Columbian Craft, Community, and Complexity on Florida's Gulf Coast. PhD Dissertation, University of Michigan. and Sampson, Christina Perry and Timothy J. Horsley. Using Multi-Staged Magnetic Survey and Excavation to Assess Community Settlement Organization: A Case Study from the Central Peninsular Gulf Coast of Florida. Advances in Archaeological Practice. Cambridge University Press: 18 December 2019. https://doi.org/10.1017/aap.2019.45
- Discipline:
- Science and Social Sciences
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- 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
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- Creator:
- Gainsburg, Izzy and Earl, Allison
- Description:
- This research explores trigger warnings as an interpersonal emotion-regulation strategy, introducing a temporal dimension to interpersonal emotion-regulation by regulating others' future, anticipated emotions. Across studies, believing that trigger warnings are protective (versus coddling) increased their effect on anticipated negative affect, but weakened their effect on experienced negative affect. Study 1 demonstrated that anticipated anxiety for warned-of content predicts intentions to avoid information. Furthermore, beliefs about trigger warnings as protective (versus coddling) best predicted anticipated anxiety for warned-of content and subsequent intentions to avoid. In Study 2, participants had higher anticipated negative affect for videos with trigger warnings, compared to those without, and this mediated increased avoidance for warned-of videos. In Study 3, trigger warnings preceding essays increased anticipated negative affect and attentional-regulation strategies, but reduced experiences of negative affect. For more information, please see the article Gainsburg, I., & Earl, A. (2018). Trigger warnings as an interpersonal emotion-regulation tool: Avoidance, attention, and affect depend on beliefs. Journal of Experimental Social Psychology, 79, 252–263. https://doi.org/10.1016/j.jesp.2018.08.006
- Citation to related publication:
- Gainsburg, I., & Earl, A. (2018). Trigger warnings as an interpersonal emotion-regulation tool: Avoidance, attention, and affect depend on beliefs. Journal of Experimental Social Psychology, 79, 252–263. https://doi.org/10.1016/j.jesp.2018.08.006
- Discipline:
- Social Sciences
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- Creator:
- Yan, Haoyang MI and Yates, J Frank
- Description:
- The research aims to demonstrate judgment accuracy about other individuals' socio-political opinions produced by prototype matching and base/shift heuristics or people's own strategies (control).
- Keyword:
- judgment accuracy and socio-political opinions
- Discipline:
- Social Sciences
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- Creator:
- Moser, Carol, Schoenebeck, Sarita Y., and Resnick, Paul
- Description:
- This work investigates what features e-commerce sites use to encourage impulse buying and what tools consumers desire to curb their online spending. We present supplementary material for two studies: (1) a systematic content analysis of 200 top e-commerce websites in the U.S. and (2) a survey of online impulse buyers (N=151). Files include: (1) Study 1 Code book for content analysis of websites (2) Study 1 CSV data file resulting from the content analysis (3) Study 1 PDFs (N=200) of e-commerce websites analyzed (4) Study 2 Online survey questionnaire (5) Study 2 Survey code book for free response questions
- Citation to related publication:
- Moser, C., Schoenebeck, S.Y., Resnick, P., 2019. Impulse Buying: Design Practices and Consumer Needs, in: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI ’19. Association for Computing Machinery, Glasgow, Scotland, UK, pp. 1–15. https://doi.org/10.1145/3290605.3300472
- Discipline:
- Social Sciences
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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