Resources by Stefan

Airbnb Listing Puffery: Multi-City Inside Airbnb Data, ChatGPT Labels, and Analysis Code

Airbnb Listing Puffery: Multi-City Inside Airbnb Data, ChatGPT Labels, and Analysis Code

Stefan
Open Dataset
Creators: Thomas, Michael
Publication Date: 2026-04-21
Creators: Thomas, Michael
Important features / special characteristics: Large replication archive studying whether promotional puffery in Airbnb listing language affects outcomes. It includes original multi-city Inside Airbnb downloads, ChatGPT-generated assignment/classification outputs, and preparation/analysis code.

Data size: 69.722 GB (69,721,605,344 bytes).

Number of observations: Not reported in repository metadata.

Temporal coverage: Not reported in repository metadata; coverage depends on the included Inside Airbnb city snapshots.

Structure: 5 restricted files: a very large compressed city-data archive, two compressed ChatGPT-output archives, a compressed code archive, and a README.

Classification note: ChatGPT-derived labels are synthetic/derived, but the underlying listing data are scraped empirical Airbnb records; the requested category is retained.
Consumer–Spokesperson Vocal Similarity: Audio, Extracted Voice Features, Experiment Data, and Models

Consumer–Spokesperson Vocal Similarity: Audio, Extracted Voice Features, Experiment Data, and Models

Stefan
Open Dataset
Creators: Hyun, Kimberly; Lowe, Michael; Krishna, Aradhna
Publication Date: 2026-04-21
Creators: Hyun, Kimberly; Lowe, Michael; Krishna, Aradhna
Important features / special characteristics: Large multimodal replication collection supporting research on vocal similarity/timbre, trust, and persuasion. It includes audio files, structured files, feature-extraction/preprocessing resources, and modeling materials. The repository description distinguishes restricted Studies 1–2 from publicly shared Studies 3–6 resources hosted on OSF.

Data size: 44.081 GB (44,081,476,587 bytes).

Number of observations: The Dataverse record contains 4,230 files; the number of study participants/analytical observations is not reported in repository metadata.

Temporal coverage: Not reported in repository metadata.

Structure: 4,230 Dataverse files, predominantly WAV audio plus spreadsheets and analysis resources. The Dataverse metadata marks all files restricted; additional public code/data for Studies 3–6 are referenced at https://osf.io/gsx32/.

Classification note: The archive includes real audio/experimental material and derived features, not solely synthetic data; the requested category is retained.
Generative Agents: Interviews, Surveys, Behavioral Tasks, and Digital Representations of 1,052 U.S. Adults

Generative Agents: Interviews, Surveys, Behavioral Tasks, and Digital Representations of 1,052 U.S. Adults

Stefan
Open Dataset
Creators: Joon Sung Park; Carolyn Q. Zou; Jonne Kamphorst; Niles Egan; Aaron Shaw; Benjamin Mako Hill; Carrie Cai; Meredith Ringel Morris; Percy Liang; Robb Willer; Michael S. Bernstein
Publication Date: 2024-11-15
Creators: Joon Sung Park; Carolyn Q. Zou; Jonne Kamphorst; Niles Egan; Aaron Shaw; Benjamin Mako Hill; Carrie Cai; Meredith Ringel Morris; Percy Liang; Robb Willer; Michael S. Bernstein
Important features / special characteristics: Digital representations of a diverse national sample of 1,052 U.S. adults, grounded in sensitive real-person self-reports. Each participant completed an approximately two-hour AI-conducted semi-structured interview (about 6,500 words per person), structured surveys, and behavioral tasks. Agents were evaluated using General Social Survey items, the 44-item Big Five inventory, five behavioral economic games, and five social-science experiments; participants repeated tasks two weeks later for a test–retest benchmark.

Data size: Not reported in GB.

Number of observations: 1,052 participants / agent representations. The exact number of row-level records is not reported.

Temporal coverage: Study collection dates are not reported in the cited public materials; evaluation includes a two-week repeat-measurement interval.

Structure: Three principal input/configuration groupings are described: (1) two-hour interview transcripts, (2) structured survey responses, and (3) combined interview-plus-survey inputs. Evaluation data cover GSS responses, Big Five personality items, economic games, and social-science experiments.

Important classification note: The digital agents and their generated predictions are synthetic, but the grounding interviews, surveys, and task responses are real individual-level self-report data.
SYNTHIA — Synthetic Urban Driving Images with Segmentation, Depth, and Instance Labels

SYNTHIA — Synthetic Urban Driving Images with Segmentation, Depth, and Instance Labels

Stefan
Open Dataset
Creators: German Ros; Laura Sellart; Joanna Materzynska; David Vazquez; Antonio M. Lopez
Publication Date: 2016
Creators: German Ros; Laura Sellart; Joanna Materzynska; David Vazquez; Antonio M. Lopez
Synthetic urban driving scenes with RGB images, semantic and instance segmentation, depth, camera information, and varied seasons, weather, and viewpoints. Organized into multiple rendered sequences and image subsets.
Synthetic Country Populations built from Household Surveys and Auxiliary Data

Synthetic Country Populations built from Household Surveys and Auxiliary Data

Stefan
Open Dataset
Creators: International Household Survey Network (IHSN); World Bank contributors
Publication Date: n/a
Creators: International Household Survey Network (IHSN); World Bank contributors
Country-specific synthetic persons and households created from survey microdata and auxiliary geographic totals. Training datasets and variable structures differ by country; intended for population modelling and methodological training.
Synthetic Canadian People and Households

Synthetic Canadian People and Households

Stefan
Open Dataset
Creators: Manon Prédhumeau, Ed Manley
Publication Date: 2023
Creators: Manon Prédhumeau, Ed Manley
National synthetic population of Canadian persons and households for agent-based models. Individuals are nested within households and calibrated against census distributions; geographic and demographic attributes support population simulation.
RTI National Synthetic Population (USA) — Synthetic People and Households for Every U.S. State at Block-Group Level

RTI National Synthetic Population (USA) — Synthetic People and Households for Every U.S. State at Block-Group Level

Stefan
Open Dataset
Creators: RTI International research team
Publication Date: 2025
Creators: RTI International research team
National synthetic population covering all U.S. states at block-group resolution. Contains persons nested in households with demographic and household attributes. Technical validation reports very high agreement with aggregate controls, including household correlation of 0.999.

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