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sborda@umich.edu
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- Creator:
- Galaty, Michael
- Description:
- CU (survey) database – The full CU (Collection Unit, i.e. “tract”) database, which includes all tract-survey data from all teams together in one place. This file is a .CSV export from FileMaker. Each entry includes data about each tract surveyed (see data dictionary). Tract locations are available via accompanying GIS shape files. NOTE: some tract database entries lack complete location data, e.g., a UTM Northing is present but not the Easting. These are available via the spatial data files work: https://deepblue.lib.umich.edu/data/concern/data_sets/k0698807d?locale=en. and CU (survey) database, by team – A copy of each team’s (A-K) Collection Unit (CU; i.e. “tract”) database is also included. These files are .CSV exports from the original FileMaker database.
- Keyword:
- archaeology
- Discipline:
- Science
-
- Creator:
- Galaty, Michael
- Description:
- Field notebooks include sketches and other notes about the site. NOTE: Many of these are poorly scanned and difficult to read.
- Discipline:
- Science
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- Creator:
- Galaty, Michael
- Description:
- GIS shape files for each tract along with additional, generic spatial data, including files for tract visibility, vegetation, overall pottery density, and overall tile density. The latter two are not chronologically specific; they include all pottery and tile counts by tract, regardless of age.
- Keyword:
- archaeology
- Discipline:
- Science
-
- Creator:
- Galaty, Michael
- Description:
- Each tract has a photo, a few have associated feature photos. Each photo is labelled with the date it was taken, and a consecutive number: ex. “A-150610-001”. Photos are in folders by team, and by date: Team A (362 megs), Team B (963 megs), Team C (638 megs), Team D (1.45 GB), Team E ( 1.41 GB), Team F (619 megs), Team G (461 megs), Team H (233 megs), Team I (817 megs), Team J (903 megs), and Team K (226 megs). Each folder is accompanied by an Excel photo log, exported to CSV, that provides captions.
- Keyword:
- archaeology
- Discipline:
- Science
-
- Creator:
- Galaty, Michael
- Description:
- PDFs of the reports written by survey team leaders at the end of the season, including the report as submitted and a final edited version. There are two reports for each team. [NOTE: in some cases, only the final edited version of a report is included.]
- Keyword:
- archaeology
- Discipline:
- Science
-
Survey Data
User Collection
- Creator:
- Galaty, Michael
- Description:
- All databases, field notebooks, paper maps, GIS files, photographs, and photo descriptions related to the intensive survey, of tracts and tumuli, and the collection of sites have been made available in PASH Deep Blue Data Realm 1. The data are broadly organized by team (A-K). The surveyed land was divided up into “tracts”. Tracts are labeled with team letter and a consecutive number: e.g., A-001, A-002, B-003, C-122, D-035.
- Keyword:
- Archaeology
- Discipline:
- Science
6Works -
- Creator:
- Galaty, Michael
- Description:
- This work is composed of PDFs of scans of miscellaneous documents related to a particular site, including maps, wall drawings, original notes, etc. For those sites that were systematically surface collected (Sites 001, 002, 003, and 011), scans of the site collection grid and raw counts of collected artifacts (on a “Site Collection Form”) are also included.
- Keyword:
- archaeology
- Discipline:
- Science
-
- Creator:
- Galaty, Michael
- Description:
- .CSV file that includes descriptions of each site.
- Discipline:
- Science
-
- 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:
- Sick, Volker , Reuss, David L, and Greene, Mark L
- Description:
- This archive contains data files from spark-ignited homogeneous combustion internal combustion engine experiments. Included are high-resolution two-dimensional two-component velocity fields acquired at two 5 x 6 mm regions, one near the head and one near the piston. Crank angle resolved heat flux measurements were made at a third location in the head. The engine was operated at 40 kPa, 500 and 1300 RPM, motor and fired. Included are in-cylinder pressure measurements, external pressure and temperature data, as well as details on the geometry of the optical engine to enable setups of simulation configurations.
- Keyword:
- combustion, internal combustion engine, heat Transfer, particle image velocimetry, in-cylinder flow, TCC III engine , optical engine, CFD validation, PIV, boundary layer, and turbulence
- Discipline:
- Engineering