The Yelp Open Dataset is a subset of Yelp data intended for educational use. It provides real-world business data, like reviews, photos, check-ins, and attributes.
How the Open Knowledge Format can improve data sharing | Google Cloud Blog
Learn how the Open Knowledge Format helps secure data sharing and improves collaboration across teams with standardized documentation.
How the Open Knowledge Format can improve data sharing | Google Cloud Blog
Learn how the Open Knowledge Format helps secure data sharing and improves collaboration across teams with standardized documentation.
Open North – Building trust in data, for the common good
Open North is a not-for-profit organization dedicated to advancing the common good. Working alongside governments and civic-minded organizations of all sizes, we provide data expertise to enhance decision-making, drive innovation, optimize public and civic services, and address society's most pressing challenges. Our work focuses on building the capacity of organizations to make better decisions about managing their data so that it is useful, actionable, secure, and trustworthy throughout its entire lifecycle.
The Consensus Trap: Dissecting Subjectivity and the “Ground Truth” Illusion in Data Annotation
As part of the Digital Library's transition to Open Access, new features for researchers are available in the Premium Edition. Click here to learn more.
Find Open Datasets for AI and Research | Kaggle
Browse and download hundreds of thousands of open datasets for AI research, model training, and analysis. Join a community of millions of researchers, developers, and builders to share and collaborate on Kaggle.
The Future of Open Source | datagubbe.se
A large-scale audit of dataset licensing and attribution in AI
The race to train language models on vast, diverse and inconsistently documented datasets raises pressing legal and ethical concerns. To improve data transparency and understanding, we convene a multi-disciplinary effort between legal and machine learning experts to systematically audit and trace more than 1,800 text datasets. We develop tools and standards to trace the lineage of these datasets, including their source, creators, licences and subsequent use. Our landscape analysis highlights sharp divides in the composition and focus of data licenced for commercial use. Important categories including low-resource languages, creative tasks and new synthetic data all tend to be restrictively licenced. We observe frequent miscategorization of licences on popular dataset hosting sites, with licence omission rates of more than 70% and error rates of more than 50%. This highlights a crisis in misattribution and informed use of popular datasets driving many recent breakthroughs. Our analysis of data sources also explains the application of copyright law and fair use to finetuning data. As a contribution to continuing improvements in dataset transparency and responsible use, we release our audit, with an interactive user interface, the Data Provenance Explorer, to enable practitioners to trace and filter on data provenance for the most popular finetuning data collections: www.dataprovenance.org.
Doing Data Science on the Shoulders of Giants: The Value of Open Source Software for the Data Science Community
Open source software is ubiquitous throughout data science, and enables the work of nearly every data scientist in some way or another. Open source projects, however, are disproportionately maintained by a small number of individuals, some of whom are institutionally supported, but many of whom do this maintenance on a purely volunteer basis. The health of the data science ecosystem depends on the support of open source projects, on an individual and institutional level.
Doing Data Science on the Shoulders of Giants: The Value of Open Source Software for the Data Science Community · Issue 2.2, Spring 2020
Open source software is ubiquitous throughout data science, and enables the work of nearly every data scientist in some way or another. Open source projects, however, are disproportionately maintained by a small number of individuals, some of whom are institutionally supported, but many of whom do this maintenance on a purely volunteer basis. The health of the data science ecosystem depends on the support of open source projects, on an individual and institutional level.
Building Bridges for Data Science Education
As part of the Digital Library's transition to Open Access, new features for researchers are available in the Premium Edition. Click here to learn more.
Linked Open Vocabularies (LOV)
Your entry point to high quality and reusable Vocabularies to describe Linked Data.
Local-first software: you own your data, in spite of the cloud
As part of the Digital Library's transition to Open Access, new features for researchers are available in the Premium Edition. Click here to learn more.
Introducing OpenAI
OpenAI is a non-profit artificial intelligence research company. Our goal is to advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return. Since our research is free from financial obligations, we can better focus on a positive human impact.
The Great Open Source Shake-up
The past year and a half has seen a number of open source and open source-ish companies like Elastic, Confluent, and MongoDB change licenses on certain products, moving away from traditional open s…
🚨Free data alert!! 🚨 Please share. Large new dataset of Amazon product reviews, including full text and photos and product characteristics, with individual *reviews labeled as fake reviews*. I believe this is the first publicly available data of this kind. github.com/bretthollenbeck/fake-reviews-…
Someone recently announced a “restaurant listings/menus on atproto so they can own their own data”, and as someone who used to do engineering for OpenTable, let me tell you that project is not solving a problem that restaurants actually have. Owning your own data is an anti-value prop for many orgs.
dame
hard pill to swallow for atproto developers/creators: 99% of potential users either do not care about, value, understand, or want to “own their own data” i see so many apps/projects lead with this “value prop”, but it’s not an important or meaningful consideration for most people
