The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services, so this is an essential component of the FAIRification process.
FAIRdata.ai — FAIR Data Assessment
Assess your research data's FAIRness. Automated pipeline using F-UJI + Claude AI. Free to use.
FAIR Principles - GO FAIR
In 2016, the ‘FAIR Guiding Principles for scientific data management and stewardship’ were published in Scientific Data. The authors intended to provide guidelines to improve the Findability, Accessibility, Interoperability, and Reuse of digital assets. The principles emphasise machine-actionability (i.e., the capacity of… Continue reading →
FAIR Metadata Check (Extended): the FAIR rubric for any repository
Perspective Chapter: Fit for Purpose? Creative Commons Licensing for Research Data in the Age of Artificial Intelligence
Licensing is an important component of the re-usability of research data, itself part of the FAIR principles: without clear, machine-readable licensing, datasets risk becoming technically...
MyData
The human-centric approach to data is aimed at a fair, sustainable, and prosperous digital society. In such a society, people get value from their data and set the agenda on how it is used. And for organisations, the ethical use of data is always the most attractive option.
GO FAIR initiative: Make your data & services FAIR
A bottom-up international approach for the practical implementation of the European Open Science Cloud (EOSC) as part of a global Internet of FAIR Data & Services
(PDF) Understanding the Nature of Metadata: Systematic Review
PDF | Background Metadata are created to describe the corresponding data in a detailed and unambiguous way and is used for various applications in... | Find, read and cite all the research you need on ResearchGate
Science Live - The Platform for FAIR Research
Transform research into FAIR nanopublications. Create, discover, cite, and embed structured knowledge bricks.
Stanford quantifies the privacy-stripping power of metadata | TechCrunch
More proof, if proof were needed, of the privacy-stripping power of metadata. A multi-year crowdsourced study, conducted by Stanford scientists and published this week, underlines how much information can be inferred from basic phone logs cross-referenced with other public datasets.
(PDF) The Comparative Anatomy of Nanopublications and FAIR Digital Objects
PDF | Beginning in 1995, early Internet pioneers proposed Digital Objects as encapsulations of data and metadata made accessible through persistent... | Find, read and cite all the research you need on ResearchGate
Software Sharing for Modern Companies | Fair.io
Fair Source is an alternative to closed source, allowing you to safely share access to your core products. Fair Source Software (FSS) can be used with minimal restrictions, and becomes Open Source after a period of time.
Software Sharing for Modern Companies | Fair.io
Fair Source is an alternative to closed source, allowing you to safely share access to your core products. Fair Source Software (FSS) can be used with minimal restrictions, and becomes Open Source after a period of time.
Data Feminism
A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism.Today, data science is a form of power. I
Data Feminism
A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism.
dc: data provenance and source attribution
Part of the datacounterfactuals.org reading lists. Research on data provenance, dataset documentation, licensing and attribution audits, and technical source-attribution methods for understanding which data sources are available, permitted, or responsible for model behavior.
WASA: WAtermark-based Source Attribution for Large Language Model-Generated Data
SILO Language Models: Isolating Legal Risk In a Nonparametric Datastore
Datasheets for Datasets
Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus
A large-scale audit of dataset licensing and attribution in AI