Enhancing Biomedical Research Data Management Challenges and Strategies

In the era of digital research, scientific progress relies on the sharing and reuse of research data. Technological advancements in the biomedical field, such as gene sequencing and proteomics, have accelerated data generation, posing challenges to data management and sharing. This study investigates data management practices of the top 20 biomedical journals by impact factor, exploring the significance and strategies of research data management in the biomedical domain. The paper highlights challenges in biomedical data management, including incomplete data, inconsistent standards, and sharing limitations, while proposing solutions to enhance data quality, standardization, and citation acknowledgment. Through these improvements, credibility and reproducibility of research data can be achieved, fostering enhanced research quality and efficiency in the biomedical field.

Introduction

Research data is a crucial component of scientific achievements, encompassing raw data, data archives, software, models, algorithms, program code, experimental guidelines, and more. Broadly defined, research data refers to the outcomes of observations or experiments conducted to validate scientific findings, excluding text, tables, and data within papers [1]. In the era of digital research, scientific progress heavily relies on the sharing and reutilization of research data.