In this work, we survey a breadth of literature that has revealed the limitations of
predominant practices for dataset collection and use in the field of machine learning. We …

Openness and sharing of information are fundamental to the progress of science and to the
effective functioning of the research enterprise. The advent of scientific journals in the 17th …

Data is a crucial component of machine learning. The field is reliant on data to train, validate,
and test models. With increased technical capabilities, machine learning research has …

Datasets that power machine learning are often used, shared, and reused with little visibility
into the processes of deliberation that led to their creation. As artificial intelligence systems …

Machine learning, artificial intelligence, and other modern statistical methods are providing
new opportunities to operationalise previously untapped and rapidly growing sources of …

Generating value from data requires the ability to find, access and make sense of datasets.
There are many efforts underway to encourage data sharing and reuse, from scientific …

Author summary How we communicate research is changing because of new (especially
digital) possibilities. This article sets out 10 easy steps researchers can take to disseminate …

The reproducibility of experiments has been a long standing impediment for further scientific
progress. Computational methods have been instrumental in drug discovery efforts owing to …

What would data science look like if its key critics were engaged to help improve it, and how
might critiques of data science improve with an approach that considers the day-to-day …

Social media commentary, satellite imagery and GPS data are a part of 'alternative data', that
is, data that originate outside of the standard repertoire of market data but are considered …