This article provides a comprehensive overview of the fairness issues in artificial intelligence
(AI) systems, delving into its background, definition, and development process. The article …

In January 2023, a new NIH policy on data sharing went into effect. The policy applies to
both quantitative and qualitative research (QR) data such as data from interviews or focus …

A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles
were proposed in 2016 as prerequisites for proper data management and stewardship, with …

Neuroscience has made significant strides over the past decade in moving from a largely
closed science characterized by anemic data sharing, to a largely open science where the …

Effective data management and sharing have become increasingly crucial in biomedical
research; however, many laboratory researchers lack the necessary tools and knowledge to …

In the past decade, microbiome research-ers have produced a wealth of sequence data and
associated microbial outputs and activity 1. Most microbiome studies to date have analysed …

Contemporary advancements in low-cost automation and computation, reduced barrier to
entry in developing artificial intelligence/machine learning (AI/ML), and increased ability to …

The value of research data has grown as the emphasis on research transparency and data-
intensive research has increased. Data sharing is now required by funders and publishers …

Science has transformed our existence and has generated many advances improving
human life and productivity. The digital revolution in science provides increasing …

Proper application of the FAIR data principles is what separates a vibrant data ecosystem, in
which research data are frequently shared and reused, from a lifeless data graveyard …