Analysis of (Non-)retracted Academic Papers in OpenAlex Platform

Основні поняття

OpenAlex's misclassification of publication status, particularly retractions, impacts data accuracy and user trust.

Анотація

Abstract:

OpenAlex consolidates academic data but misclassifies retractions due to a flawed boolean field.
Users should verify data from Dec 22, 2023, to Mar 19, 2024, for accuracy.
Introduction:

OpenAlex aids bibliometric analyses with broad academic coverage.
Shift towards open science favors platforms like OpenAlex over closed databases.
Results and Discussion:

Crossref provides granular publication status while OpenAlex uses a binary retraction indicator.
Mislabeling retractions can misinform professionals and erode trust in research quality.
Method:

Initial study retrieved 47,720 retraction records from the OpenAlex API.
Enriched metadata using Crossref's "update-nature" field but faced limitations.
Data Availability Statement:

Data and scripts for figures available at a GitHub repository.
Conclusion:

Issue reported to OurResearch team; incorrect records identified and corrected.
Stakeholders advised to verify critical metadata using alternative tools.

Статистика

The issue affects data provided by OpenAlex in the period between 22 Dec 2023 and 19 Mar 2024.
We enriched of 47,018 entries (excluding 704 records lacking DOIs) OpenAlex records with the “update-nature” from Crossref using a Python script.
This resulted in a subset of 20486 records.

Цитати

"Inaccurate representation of retractions in OpenAlex results in the misclassification of papers within institutional repositories." - Herb (2024)
"Mislabelling influential publications as retractions risks undermining public trust in scientific research." - Content Analysis

Ключові висновки, отримані з

(Non-)retracted academic papers in OpenAlex

by Christian Ha... о arxiv.org 03-21-2024

https://arxiv.org/pdf/2403.13339.pdf

Глибші Запити

How can platforms like OpenAlex ensure accurate representation of publication statuses beyond retractions?

To ensure accurate representation of publication statuses beyond retractions, platforms like OpenAlex can implement several strategies. Firstly, they should enhance their metadata processing algorithms to differentiate between various types of updates such as corrections, expressions of concern, and retractions. By incorporating more nuanced classification systems similar to Crossref's granular approach, platforms can provide users with a clearer understanding of the status of publications.
Secondly, collaboration with authoritative databases like Retraction Watch and other reputable sources can help in cross-verifying retraction information. By integrating data from multiple reliable sources into their database, platforms like OpenAlex can validate the accuracy of publication statuses and reduce the likelihood of misclassifications.
Moreover, establishing clear communication channels with users and researchers to report inaccuracies or inconsistencies in metadata is crucial. Platforms should encourage feedback mechanisms that allow stakeholders to flag potential errors in publication statuses for prompt correction.
Regular audits and quality checks on metadata entries are essential to maintain data integrity. Implementing automated checks combined with manual verification processes can help identify discrepancies in publication statuses early on and rectify them swiftly.
By adopting these measures proactively, platforms like OpenAlex can uphold the credibility and reliability of their data while ensuring that users have access to accurate information about the status of academic publications.

What are the potential consequences of mislabelled retractions on the credibility of academic research?

Mislabelled retractions pose significant risks to the credibility and trustworthiness of academic research across various dimensions:

Impact on Research Integrity: Mislabelling a paper as retracted when it has not been retracted undermines the integrity of scholarly work by spreading misinformation about its validity. This could lead researchers down erroneous paths based on flawed or misrepresented findings.

Professional Reputations: Authors associated with falsely labelled retracted papers may suffer reputational damage due to perceived involvement in misconduct or flawed research practices. Such associations could impact career prospects within academia negatively.

Patient Care & Public Trust: In fields such as healthcare where research findings directly influence patient care decisions, mislabelled retractions could jeopardize treatment outcomes if healthcare professionals rely on inaccurate information for clinical practice guidelines.

Erosion Of Trust In Academic Institutions: Continued instances of mislabelled retractions may erode public trust in academic institutions' ability to uphold rigorous standards for scientific inquiry and dissemination.

How can stakeholders collaborate to improve metadata accuracy across diverse academic sources?

Collaboration among stakeholders is pivotal for enhancing metadata accuracy across diverse academic sources:

Standardization Efforts: Stakeholders should work towards standardizing metadata formats and classifications across different databases and repositories within academia.

Data Sharing Agreements: Establishing agreements for sharing verified metadata between databases ensures consistency in information presentation.

3 .Cross-Verification Mechanisms: Introducing mechanisms for cross-verifying data through collaborative efforts between different entities helps validate accuracy.
4 .Community Feedback Loops: Encouraging active participation from researchers, publishers, librarians, and database administrators fosters a culture where errors are reported promptly leading to improved data quality.
5 .Training And Education Programs: Providing training programs focused on best practices related to maintaining high-quality metadata enhances awareness among stakeholders regarding the importance of accurate data representation.
By fostering collaboration through these avenues,stakeholders collectively contribute towards improving overallmetadataaccuracyacrossdiverseacademicresourcesandensuringtheintegrityofscholarlycommunicationflows