propublica/data-institute-2019

Materials for the ProPublica Data Institute 2019

The Future of Open Source | datagubbe.se

IPFS (@ipfs.tech)

🪩 Introducing matadisco.org! — a way to discover open datasets via #atproto that doesn't depend on any single institution staying online. 🕺🏼 We have more public data than ever (satellite imagery, climate models, genomic sequences), but finding it is still painfully hard. https://matadisco.org

We Need A New Kind Of Data Rights

Associational data rights would empower communities to reclaim power from Big Tech

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.

Matadisco — Decentralized Data Discovery

An open, decentralized network for data discovery built on ATProto. Publish metadata, build portals, find data.

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.

DataX Research

DataX Research has 3 repositories available. Follow their code on GitHub.

Democratizing Data

Democratizing Data builds a community-driven data ecosystem by identifying how datasets are used and reducing barriers to accessing high-quality public data. The initiative enhances the discoverability, usability, and relevance of data for researchers, policymakers, and stakeholder communities. A suite of tools and strategic partnerships supports this work by connecting users to the data, insights, and networks needed to inform decisions and generate impact.

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.

Spacedrive — A local-first data engine for everything you own

Index any data source. Search everything from one place. Keep it on your machine.

Wikidata Launches Free Vector Database as Open Alternative to Closed AI Systems

Wikidata has launched something big.

Collaboratory at Columbia: An Aspen Grove of Data Science Education · Issue 3.4, Fall 2021

Many universities recognize the rapidly growing impact of data science in all fields of study and the professions and seek to embed this expertise widely across their educational offerings. There is often broad interest in developing new data science curricula, with some universities even allocating funds toward this purpose. Yet, it is often unclear what resources are needed for effective data science education, and how resources ought to be prioritized. Although university leadership might be aware of a growing number of successful data science acceleration programs and pedagogical models, many of which are either general purpose or specific to a particular discipline, there remains a lack of clarity about how these models might address their own specific needs. This article presents the Collaboratory Program at Columbia University (termed “the Collaboratory”), which is both a set of “data science in context” educational approaches, as well as a meta-model for an accelerator program that allows different institutions to respond flexibly to their own disciplinary heterogeneity in terms of data science educational needs. The novelty of the Collaboratory lies in its crowd-sourcing approach to creating new data science pedagogy and its ability to kindle transdisciplinary collaboration in doing so. By offering seed funding, it fosters proactive efforts to embed data science “in context” into more traditional domains through a cohort of compelling, transdisciplinary, crowd-sourced data science education proposals each year. Collaboratory educational offerings are required to be developed through a partnership between two faculty members, a data scientist and a domain expert from another field, or a larger team with complementary expertise. Over the past 5 years, the Collaboratory has supported the development of a wide spectrum of data science pedagogical models spread across more than 40 academic departments, centers, institutes, and professional schools at Columbia University. As a result, the Collaboratory has to date served the learning needs of more than 4,000 students. Furthermore, it has cultivated a thriving ecosystem that includes a funding mechanism and a community-support structure that all contribute to its agility and success. Here, we offer our experience and best practices in developing and managing the Collaboratory, which, we hope, will contribute to a blueprint for data science education leaders everywhere.

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.

A Data Utopia for Science-of-Science

Here I want to briefly sketch out a vision for how to solve a key set of problems facing science-of-science researchers, using the relatively new idea of a ‘data trust.’ In my ideal wor…