Ipseity Daily Data

At what rate do individuals say they are X? Begining on 2025-07-08, I surveyed a small random sample of American adults on a daily cadence. They responded to many items of the form: Does <signifier> describe you today? The file ipseity-daily-aggregated.csv in this repository contains the aggregated results through 2025-07-28. The columns are as follows: signifier - a word, phrase or emoji yes_count - the number of respondents who answered yes (aggregated all-time). no_count - the number of respondents who answered no (aggregated all-time). prevalence_per_10k - an estimate of the prevalence per 10,000. If we were to survey 10,000 American adults, how many would we expect to respond yes to this signifier? The number is calculated as yes_count / (yes_count + no_count) * 10,000. earliest_obs_date - this date represents the first time this particular signifier was ever displayed to a respondent. Signifiers are chosen with stochasticity from a long list; new signifiers are sometimes added, old signifiers are sometimes deleted, and a signifier may be moved higher or lower in priority for sampling. latest_obs_date - the date closest to the present date on which signifier was most recently presented to a respondent. Version created on 2025-09-11 — cumulative data.个体自述符合特征X的比例为多少？ 本调研始于2025年7月8日，以每日频次对小型美国成年人随机样本开展调查。受访者需回答大量形如「<signifier>是否契合你今日的状态？」的问题。 本仓库中的ipseity-daily-aggregated.csv文件包含截至2025年7月28日的聚合调研结果，各字段说明如下： 1. signifier（标识词）：单词、短语或表情符号 2. yes_count（肯定应答数）：所有受访对象中选择肯定回答的总人次（累计全量统计） 3. no_count（否定应答数）：所有受访对象中选择否定回答的总人次（累计全量统计） 4. prevalence_per_10k（每万人流行度估算值）：针对每10000名美国成年人的肯定应答估算比例。即若调研10000名美国成年人，预计会有多少人对该标识词给出肯定回答。计算公式为 yes_count / (yes_count + no_count) × 10000。 5. earliest_obs_date（首次观测日期）：该标识词首次向受访对象展示的日期。标识词从长列表中随机选取；系统会不时新增、删除标识词，或调整标识词在抽样优先级中的排序。 6. latest_obs_date（末次观测日期）：该标识词最近一次向受访对象展示的日期，距当前时间最近。 本数据集版本创建于2025年9月11日，包含累计调研数据。