{"part":"task","text":"# Datasets\nPass a dataset's id as the `dataset` argument.\n{\n \"id\": \"garcia_colored_perceptions\",\n \"rows\": 5600,\n \"description\": \"The present study is based on an original survey experiment that tests whether assessments of others\u2019 skin color are affected by a subtle racial cue, a name. Random assignment achieved a fairly uniform distribution of subjects across conditions, with 2,783 unique ratings of faces assigned Hispanic names and 2,817 unique ratings of faces assigned non-Hispanic names. Because each subject rated pictures with both Hispanic and non-Hispanic names, subject characteristics were necessarily balanced across experimental conditions.\\n\\nVariables: \\n- id: Respondent\u2019s unique ID number\\n- imageno: Unique image number, from the FEI face database\\n- hispanicimage: Was the image presented with a distinctively Hispanic name or a distinctively non-Hispanic name? (1 = Distinctively Hispanic name, NOT INCLUDED IN DATASET)\\n- femaleimage: Does the person in the image present as male or female? (1 = Female, 0 = Male)\\n- hispanicimagexfemaleimage: Interaction between hispanicimage and femaleimage\\n- namepair: To which pair of names was the image attached? (\\\"\\\"a\\\"\\\" = Catalina/Catherine, \\\"\\\"b\\\"\\\" = Juana/Joann, \\\"\\\"c\\\"\\\" = Margarita/Margaret. \\\"\\\"d\\\"\\\" = Carlota/Charlotte, \\\"\\\"e\\\"\\\" = Daniela/Daniella, \\\"\\\"f\\\"\\\" = Juan/John, \\\"\\\"g\\\"\\\" = Pedro/Peter, \\\"\\\"h\\\"\\\" = Ricardo/Richard, \\\"\\\"i\\\"\\\" = Pablo/Paul, \\\"\\\"j\\\"\\\" = Jose/Joe)\\n- color: Skin color rating of image (1 = Lightest; 11 = Darkest)\\n- gender: Respondent's gender (1 = Male; 2 = Female)\\n- age: Respondent's age\\n- hispanic: Respondent's identification as Hispanic/Latino(a) (1 = Yes; 2 = No)\\n- race: Respondent's racial identification (1 = White, 2 = Black/African-American; 3 = American Indian or Alaska Native; 4 = Asian or Pacific Islander; 5 = Some other race)\\n- educ: Respondent's educational attainment (1 = Less than high school; 2 = High school/GED; 3 = Some college; 4 = 2-year college degree (Associate\u2019s); 5 = 4-year college degree (BA, BS); 6 = Graduate or professional degree)\\n- hhinc: Respondent's household inome from all sources (1 = Less than $10,000; 2 = $10,000- $19,999; 3 = $20,000-$29,999; 4 = $30,000-$39,999; 5 = $40,000-$49,999; 6 = $50,000-$59,999; 7 = $60,000- $69,999; 8 = $70,000-$79,999; 9 = $80,000-$89,999; 10 = $90,000-$99,999; 11 = $100,000-$149,999; 12 = More than $150,000; 13 = Rather not say)\\n- born: Respondent's country of birth\\n- region: Respondent's US Census region of residence (1 = Northeast; 2 = Midwest; 3 = South; 4 = West; 5 = Rather not say)\\n- rs_color: Respondent's self-rated skin color (1 = Lightest; 11 = Darkest)\",\n \"columns\": {\n  \"id\": \"int64\",\n  \"imageno\": \"int64\",\n  \"hispanicimagexfemaleimage\": \"int64\",\n  \"femaleimage\": \"int64\",\n  \"namepair\": \"str\",\n  \"color\": \"int64\",\n  \"gender\": \"int64\",\n  \"age\": \"int64\",\n  \"hispanic\": \"int64\",\n  \"race\": \"int64\",\n  \"educ\": \"int64\",\n  \"hhinc\": \"int64\",\n  \"born\": \"int64\",\n  \"region\": \"int64\",\n  \"rs_color\": \"int64\",\n  \"hispanicname\": \"int64\"\n }\n}\n{\n \"id\": \"garcia_colored_perceptions_ref\",\n \"rows\": 5600,\n \"description\": \"Reference frame of causcibench_metadata_rule/1: the CSV with the columns the metadata names derived.\",\n \"columns\": {\n  \"id\": \"int64\",\n  \"imageno\": \"int64\",\n  \"hispanicimagexfemaleimage\": \"int64\",\n  \"femaleimage\": \"int64\",\n  \"namepair\": \"str\",\n  \"color\": \"int64\",\n  \"gender\": \"int64\",\n  \"age\": \"int64\",\n  \"hispanic\": \"int64\",\n  \"race\": \"int64\",\n  \"educ\": \"int64\",\n  \"hhinc\": \"int64\",\n  \"born\": \"int64\",\n  \"region\": \"int64\",\n  \"rs_color\": \"int64\",\n  \"hispanicname\": \"int64\",\n  \"femaleimage_x_hispanicname\": \"int64\"\n }\n}"}