Focus and Distraction 2025 (Dataset, n = 790)

Version v2.1 – Canonical Upgrade This version includes: • Canonical snake_case file naming• Upgraded README aligned to HCI standards• HRL and Construct Registry alignment• Standardised data dictionary structure No changes have been made to the raw response data.This update improves interoperability, machine-readability, and longitudinal compatibility within the HCI dataset library. This dataset is part of the Human Clarity Institute’s AI–Human Experience 2025 data series. It examines digital focus, distraction triggers, and the emotional experience of losing focus, including task-sustainability duration, primary sources of digital distraction, and self-reported emotional states linked to focus loss. The dataset includes:• validated 1–7 Likert-scale items• categorical measures of single-task focus duration and perceived focus quality• behavioural indicators such as distraction sources, coping strategies, and focus-maintenance habits• multi-select variables stored as canonical semicolon-delimited snake_case tokens• open-text reflections with minimal safe cleaning (trim + newline removal only)• demographic variables across six English-speaking countries• digital life exposure (daily hours online) and AI-tool usage frequency Data were collected on 3 September 2025 via Prolific from adults in the UK, US, Australia, Canada, New Zealand, and Ireland.All data were cleaned, anonymised, and processed under the Human Clarity Institute’s machine-readable dataset protocol, which includes: • canonical snake_case variable naming• validated numeric and categorical ranges• standardised multi-select formats• minimal safe text cleaning• full alignment with the accompanying data dictionary• removal of Prolific IDs, timestamps, and indirect identifiers• SHA-256 checksums for all files• removal of 6 duplicate submissions (final n = 790) This dataset contributes to understanding how digital environments shape human focus, attention sustainability, and subjective distraction experience, supporting longitudinal tracking of how cognitive load, distraction patterns, and self-regulation behaviours evolve as AI and digital tools become more embedded in everyday life.