SPE Database

This project was inspired by the Confidence Database. We aimed at curating a database that include trial-level data and other meta-data from as many empirical studies that used self-matching task from Sui, He, & Humphreys (2012). <!-- OSF, preprint, and publication links will be directly added to this paragraph -->

Currently, the SPE database includes trial-level data from 50 studies (48 curated on disk + 2 deferred entries) / 104 rows per the master index 1_Data/Dataset_inf.csv (see PROJ_STATE.md). . Each dataset includes information on reaction times (RTs), accuracy (ACC), and other information reported in papers. Participants in these included studies come from diverse cultural backgrounds and age range, facilitating cross-study comparisons and meta-analytic investigations.

The SPE Database is continuously updated as new studies and datasets become available. We welcome contributions from researchers who wish to share their data and help expand this resource. If you are interested in contributing or collaborating, please feel free to reach out!

This project is in parallel with an on-going preregistered meta-analysis leading by Hu Chuan-Peng and Zheng Liu (see registry here).

Data & metadata

• Master index: 1_Data/Dataset_inf.csv (UTF-8 with BOM) — one row per experiment-sample, keyed by Folder_Name (the project-wide ID for papers/preprints, == study folder) + Exp + subj_Group. Key columns: FirstAuthor, Year (official print year), Journal, DOI (paper DOI), Country/City, Stim_Type, Stim_language, License, numTrials, Sample_Size/Male/Female, Repo_Link (data repository link). Current inventory: 48 curated studies on disk + 1 deferred entry (Hu_YQ_2026_ChinaSciData, ex Hu_2023_SDB) + 1 deferred (Scheller_2026_elife) (authoritative counts live in the CSV).
• Data readability (since 2026-08-30): Shape/Label columns hold actual stimulus values (shape names / label words); original numeric codes are kept in the raw ShapeCode/LabelCode columns and the Clean *_Origin_Identity columns.
• Cleaned data: *_ExpN_Clean.csv uses standardized columns Subject, Shape, Label, Matching, ACC, RT_ms, plus 3-level Identity columns (Origin → English → Standardized: NonPerson/Self/Close/Acquaintance/Celebrity/Stranger). Cleaning is minimal preprocessing — invalid values (e.g., ACC = -1) are kept and documented in the codebook; users preprocess per their own analysis goals.
• Per-study metadata: paper-level <Study>.json + experiment-level <Study>_Exp<N>.json (v2 schema) + Codebook_<Study>_Exp<N>_Clean.xlsx.
• Contributing / adding data: follow the curation conventions in .opencode/skills/spe-database-curation/SKILL.md (folder naming, JSON schemas, codebook rules, DOI/year verification workflow) — load it via skill(name="spe-database-curation") for any data-curation task.
• For agents: repository conventions and efficiency rules live in AGENTS.md; project state lives in PROJ_STATE.md (see also the Document map in AGENTS.md).
• For collaborators (合作者): how to provide raw data / full texts for the four pending studies and for new ingestions, then let the agent finish the rest — see For_COLLABORATORS.md (中文).

Leading Team

• Zhenxin Cai (School of Psychology, Nanjing Normal University,email:czx@nnu.edu.cn)
• Wang Qihui(School of Psychology, Nanjing Normal University)
• Xinru Sun (School of Psychology, Nanjing Normal University)
• Wanke Pan (School of Psychology, Nanjing Normal University)
• Mengzheng Hu (School of Psychology, Nanjing Normal University)
• Zheng Liu (Division of Applied Psychology, School of Humanities and Social Science, CUHK-Shenzhen)
• Jie Sui (School of Psychology, University of Aberdeen)
• Hu Chuan-Peng (Corresponding author, School of Psychology, Nanjing Normal University, email: hcp4715@hotmail.com)

Data contributors

Authors of original studies were invited and listed here, if accepted, as contributors of the future manuscript. We will adhere to general authorship criteria for authors in our future data descriptor paper. That is, authors of our data descriptor paper must have been responsible for at least one of the following see CRediT roles:

• Conceptualization
• Methodology
• Formal Analysis
• Investigation

AND at least one of the following:

• Writing - Original Draft Preparation
• Writing - Review & Editing

Confirmed contributors

• Amodeo, Letizia (Department of Experimental Clinical and Health Psychology, Ghent University)
• Bertamini, Marco (Department of General Psychology, University of Padova)
• Constable, Merryn D. (Department of Psychology, Northumbria University)
• Dalmaso, Mario (Department of Developmental and Social Psychology, University of Padova)
• Feldborg, Michella (University of Aberdeen)
• Frings, Christian (University of Trier)
• Fuentes, Luis J. (Departamento de Psicología Básica y Metodología, Facultad de Psicología y Logopedia, Universidad de Murcia)
• Gao, Xiangping (Department of Psychology, Shanghai Normal University)
• Goris, Judith (Department of Experimental Psychology, Ghent University)
• Guan, Lili (School of Psychology, Northeast Normal University)
• Haciahmet, Céline (University of Trier)
• Hung, Kalai (Tsinghua University)
• Kolvoort, Ivar (Department of Psychology, Programme Group Psychological Methods, University of Amsterdam)
• Liang, Qiongdan (Tsinghua University)
• Liu, Tuo (Goethe University Frankfurt)
• Makovski, Tal (Department of Psychology, Tel-Hai Academic College)
• Martínez-Pérez, Víctor (University of Castilla-La Mancha Albacete Campus, Faculty of Medicine (UCLM - Albacete))
• Navon, Mayan (Department of Education and Psychology, the Open University of Israel)
• Nijhof, Annabel D. (Department of Experimental Clinical and Health Psychology, Ghent University)
• Northoff, Georg (Institute of Mental Health Research, University of Ottawa)
• Palmero, Lucía B. (Departamento de Psicología Básica y Metodología, Facultad de Psicología y Logopedia, Universidad de Murcia)
• Pastötter, Bernhard (University of Trier)
• Qian, Haoyue (School of Physics and Shanghai Key Laboratory of Magnetic Resonance, East China Normal University; Department of Psychology, Shanghai Normal University)
• Schäfer, Sarah (University of Trier)
• Vicovaro, Michele (Department of General Psychology, University of Padova)
• Wang, Fei (Tsinghua University)
• Wiersema, Jan R. (Department of Experimental Clinical and Health Psychology, Ghent University)
• Wozniak, Mateusz (Social Cognition in Human-Robot Interaction Group, Italian Institute of Technology; Social Mind Center, Department of Cognitive Science, Central European University; Cognition and Philosophy Lab, Department of Philosophy, Monash University; Institute of Psychology, Jagiellonian University)
• Zhang, Yongfa (Tsinghua University)

Data Version

Version v0.1.5 — 2026-06-28

New features/changes

• [Reproducible Analyses]: Added three self-contained R Markdown analyses — Identity-level SPE with mixed-model bootstrap (1_Identity_Analysis), bootstrap estimation of SPE under mismatch conditions (2_Mismatch_Analysis), and exploratory Spearman bootstrap moderator analysis (3_Exploratory_Analysis).
• [Updated Data Fold Structure]: Updated the data folder structure for better organization and clarity.It helps to organize the data and analyses in a more structured and logical manner, making it easier to navigate and understand the contents of the repository.
• [Updated Metadata in JSON Format]: Updated the JSON file to include more detailed information about the data, such as the number of participants, the number of trials, and the number of variables. This makes it easier to understand the structure and content of the data.

Version 0.1.4 — 2026-04-13

New features/changes

• [Visualization Tool]: Added Shiny-based interactive data cleaning tool with batch processing capabilities
• [Batch Processing]: Support for processing multiple *_raw.csv files in a directory automatically
• [Interactive Interface]: Web-based UI for variable mapping, Identity standardization, and data preview
• [Flexible Input]: Support for various path formats (with/without quotes, forward/backward slashes)
• [Identity Mapping]: Manual Shape and Label Identity mapping with auto-detection suggestions
• [Batch Download]: Results packaged as ZIP archive for easy distribution
• [Progress Tracking]: Real-time batch processing progress and detailed results summary

Version 0.1.3 — 2025-10-20

New features/changes

• [Data Filtering]: Using R, retaining behavioral variables required for calculating the Self-Prioritization Effect (SPE), including Matching, Shape/Face/Voice, Label, Identity (Shape_Origin_Identity,Shape_English_Identity,Shape_Standardized_Identity,Label_Origin_Identity,Label_English_Identity,Label_Standardized_Identity standardized as: NonPerson, Self, Close, Acquaintance, Celebrity, Stranger), RT_ms, and ACC. Demographic variables (e.g., gender, age, handedness) were also retained when available.
• [Floder Structure]: The database is bifurcated into two primary folders: "Clean_Data" and "Raw_Data." The "Clean_Data" folder encompasses micpreprocessed data files, whereas the "Raw_Data" folder houses the original data files sourced from the articles. Within the Clean_Data folder, a JSON file has been added to document the paper's infromation, and a codebook has been included to provide a detailed account of the dataset's contents. Additionally, a codebook is present to meticulously log the data descriptions of the dataset.

Version 0.1.2 — 2025-06-16

New features/changes

• [Data Filtering]: Performed initial data filtering using R, retaining behavioral variables required for calculating the Self-Prioritization Effect (SPE), including Matching, Shape/Face/Voice, Label, Identity (Shape_Identity standardized as: NonPerson, Self, Close, Acquaintance, Celebrity, Stranger), RT_ms, and ACC. Demographic variables (e.g., gender, age, handedness) were also retained when available.
• [SPE Analysis]: Conducted exploratory analysis of SPE using Clean_Data, calculating sequential dependency effects and analyzing the impact of different Identity categories on RT and ACC.
• [Visualization]: Visualized the distribution of SPE for each participant, providing a clear view of SPE performance across different Identity categories.

Bugs/glitches discovered after the release

• [Insufficient Preprocessing]: Data filtering was performed rather than full preprocessing, which may lead to invalid values during data exploration (e.g., ACC values may include -1 for no response, 2 for incorrect key press). Users must perform their own preprocessing based on their analysis goals. Details of each article's Clean_Data are available in the Codebook within the Clean_Data folder.

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Version 0.1.0 — 2025-05-16

New features/changes

• [Data Structure Setup]: Established the initial data structure of the SPE database, including behavioral and demographic data.
• [Data Integration]: Integrated raw data from multiple published articles, including behavioral variables (e.g., RT, ACC) and demographic variables.
• [README File]: Provided a basic README file explaining the database structure and usage guidelines.

Bugs/glitches discovered after the release

• [Inconsistent Variable Names]: Some raw data files contained inconsistent variable names, causing issues during data integration.
• [Missing Demographic Variables]: Certain articles lacked demographic variables, resulting in incomplete metadata.

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Unreleased

Planned

• [Metadata in JSON Format]: Transition metadata storage from .md to .json format for each article, providing a more structured and machine-readable format.

Folder structure

root
│  .gitignore
│  README.md
├─1_Data 
│   └─ Dataset_inf.csv  # master index (UTF-8 with BOM); legacy Dataset_inf.xlsx pending removal
│   └─ <Author>_<Year>_<Suffix>   # Suffix = readable journal/database abbreviation (e.g. JEPHPP, ActaPsych, ConsciousCog, QJEP), full short journal name (Cognition, Cortex), psyarxiv/unpub tag, or data-repo abbreviation (SDB)
│       └─ <Author>_<Year>_<Suffix>_<Exp-id>_Clean.csv
│       └─ <Author>_<Year>_<Suffix>_<Exp-id>_subj_info.csv
│       └─ Codebook_<Author>_<Year>_<Suffix>_<Exp-id>_Clean.xlsx
│       └─ <Author>_<Year>_<Suffix>.json  # Including Meta data for each paper.
│       └─ <Author>_<Year>_<Suffix>_<Exp-id>.json  # Including methodological information for the specific experiment.
│       └─ <Author>_<Year>_<Suffix>_<Exp-id>_raw.csv
├─2_Code
│   └─ Clean_Data.Rproj
│   └─ Clean_Data.Rmd
│   └─ README.md
└─3_Reports
     │
     └─ README.md