Skills Data Science Open Science And Transparency Standards Preparation

Open Science And Transparency Standards Preparation

v20260724
joap-open-science-and-transparency
This guide helps researchers meet rigorous open science and transparency requirements, such as those mandated by journals like JAP. It details the process of depositing data, analysis code, and materials with persistent identifiers (DOIs), drafting comprehensive data-availability statements, and correctly managing prior-use disclosures and pre-registrations. Essential for ensuring research reproducibility and publication compliance.
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Overview

Open Science & Transparency (joap-open-science-and-transparency)

JAP is a TOP-aligned APA journal. Empirical submissions (including meta-analyses) are held to the Transparency and Openness Promotion (TOP) standards: share data, materials, and analysis code with persistent identifiers, provide a data-availability / transparency statement, and report preregistration where applicable — with transparency weighed in evaluation. Prepare this early, not at acceptance. Verify the current TOP level and exact statement wording on the official APA page (待核实).

When to trigger

  • Building the data/materials/code deposits and the data-availability statement
  • Deciding whether (and how) to claim an exemption from sharing
  • Linking a preregistration and reporting its status
  • A reviewer or editor flagged transparency, data access, or reproducibility

What is expected (verify current wording on the official page)

  1. Data, materials, and code availability. Make the dataset, study materials (scales, manipulations, instructions), and analysis scripts available, or state precisely why not. Sensitive personnel / organizational data often need a documented exemption with an access path.
  2. Data-transparency / data-availability statement. A statement describing what data exist, where they live, how they can be accessed, and whether any portion has appeared in other papers (a prior-use / overlapping-data disclosure is expected when datasets are reused across articles).
  3. Persistent identifiers (DOIs). Provide DOIs for shared data/materials/code (OSF, ICPSR, Dataverse, Zenodo) — not transient personal links.
  4. Preregistration. Report preregistration honestly and link it (anonymized for masked review); its presence and quality are weighed, and Registered Reports may be available (confirm on the official page; 待核实).

Build-it-right checklist

  • Data deposited with a DOI; a codebook/data dictionary included (or a justified exemption)
  • Analysis code deposited; results regenerate in a fresh session
  • Materials (scales, manipulations, instructions) deposited with a DOI
  • Data-availability / transparency statement drafted per current TOP requirements
  • Prior-use disclosure: any overlap of this dataset with other papers stated explicitly
  • Preregistration linked (anonymized for review); confirmatory vs. exploratory consistent with it
  • Shared links anonymized for masked submission (no author-identifying repository info)

Exemptions (do them honestly)

  • Personnel and organizational data are often legally or contractually constrained. State exactly what is withheld, why (confidentiality, proprietary, IRB), and how others can access or approximate it (synthetic/aggregated data, application path, full code release). Unjustified opacity counts against the paper.

Data-availability statement — worked draft (illustrative)

A model statement for the servant-leadership package; confirm the exact required headings against the journal's current TOP/author requirements.

Data availability: Team- and individual-level data for the field study and
  trial-level data for the experiment are available at OSF
  (DOI 10.XXXX/osf.io/abcde), with a codebook.
Materials: All scales, the leadership manipulation, and instructions are
  deposited (DOI 10.XXXX/osf.io/fghij).
Code: Mplus/R scripts reproduce all reported values in a fresh session; a run
  log is included (DOI 10.XXXX/osf.io/klmno).
Preregistration: The experiment was preregistered (osf.io/pqrst) before data
  collection; one field-study serial-mediation analysis is reported as exploratory.
Prior use: The field dataset has not been used in any other manuscript.
  (If applicable, list overlapping papers and the constructs each uses.)
Exemptions: None. (If applicable: what is withheld, why, and the access path.)

Transparency-readiness decision table

Situation Editorial read What to deposit / state
Fully shareable data + materials + code expected baseline DOIs for data, materials, code + run log
Proprietary organizational data exemption considered if justified synthetic/aggregated data + access path + full code
Archival / third-party dataset acceptable with provenance link source, share derivation scripts, document version
Dataset reused across papers requires disclosure state overlap, which constructs each paper uses
"Available on request" reads as non-compliant replace with a persistent DOI before submission

Reviewer / editor pushback and the venue fix

  • "No DOI, just a personal link" → swap every transient link for a persistent identifier before the masked submission goes out.
  • "Statement says open but the repo is empty" → deposit first; draft the statement from the live DOIs.
  • "Is this the same data as your other paper?" → add a prior-use/overlap disclosure; JAP expects it.
  • "Preregistration deviates from the analysis" → add a deviations note; relabel post hoc work exploratory.
  • "OSF page reveals author identity" → use the anonymized view link for masked review.

Anti-patterns

  • Treating data/materials/code sharing as optional or "available on request"
  • Omitting the data-availability/transparency statement or a required prior-use disclosure
  • Personal/transient links instead of DOIs
  • Claiming an exemption without a justification or access path
  • Identifying author info in repository links during masked review

Output format

【Data】deposited + DOI + codebook (or justified exemption)? [Y/N]
【Materials】deposited + DOI? [Y/N]
【Code】deposited + fresh-session reproducible? [Y/N]
【Transparency statement】drafted per current TOP wording (待核实)? [Y/N]
【Prior-use disclosure】dataset overlap stated? [Y/N/NA]
【Preregistration】linked (anonymized) + consistent with reporting? [Y/N/NA]
【Next】joap-review-process

Supplementary resources

Info
Category Data Science
Name joap-open-science-and-transparency
Version v20260724
Size 6.69KB
Updated At 2026-07-28
Language