Welcome — The Turing Way

“The Turing Way is an open source community-driven guide to reproducible, ethical, inclusive and collaborative data science.

Our goal is to provide all the information that data scientists in academia, industry, government and in the third sector need at the start of their projects to ensure that they are easy to reproduce and reuse at the end.

The book started as a guide for reproducibility, covering version control, testing, and continuous integration. But technical skills are just one aspect of making data science research “open for all”.

In February 2020, The Turing Way expanded to a series of books covering reproducible research, project design, communication, collaboration, and ethical research.”

A Community Handbook for Open Data Science

“The Turing Way started in December 2018 and has quickly evolved into a collaborative, inclusive and international endeavor with the aim of uncovering gold standards to ensure reproducible, ethical, inclusive and collaborative data science. How did this happen? I think two ingredients were central to The Turing Way‘s success: extraordinary community building and a clear enticing vision….

Anyone can contribute is a central theme. And not only that: anyone can bring ideas to the table. And folks are doing just that. At the time of writing this post 168 people have contributed. So on average the project has gained 9 new contributors every month since it’s initiation….”

A community-maintained standard library of population genetic models | eLife

Abstract:  The explosion in population genomic data demands ever more complex modes of analysis, and increasingly these analyses depend on sophisticated simulations. Re-cent advances in population genetic simulation have made it possible to simulate large and complex models, but specifying such models for a particular simulation engine remains a difficult and error-prone task. Computational genetics researchers currently re-implement simulation models independently, leading to inconsistency and duplication of effort. This situation presents a major barrier to empirical researchers seeking to use simulations for power analyses of upcoming studies or sanity checks on existing genomic data. Population genetics, as a field, also lacks standard benchmarks by which new tools for inference might be measured. Here we describe a new resource, stdpopsim, that attempts to rectify this situation. Stdpopsim is a community-driven open source project, which provides easy access to a growing catalog of published simulation models from a range of organisms and supports multiple simulation engine backends. This resource is available as a well-documented python library with a simple command-line interface. We share some examples demonstrating how stdpopsim can be used to systematically compare demographic inference methods, and we encourage a broader community of developers to contribute to this growing resource.

 

A Quantitative Portrait of Wikipedia’s High-Tempo Collaborations during the 2020 Coronavirus Pandemic

Abstract:  The 2020 coronavirus pandemic was a historic social disruption with significant consequences felt around the globe. Wikipedia is a freely-available, peer-produced encyclopedia with a remarkable ability to create and revise content following current events. Using 973,940 revisions from 134,337 editors to 4,238 articles, this study examines the dynamics of the English Wikipedia’s response to the coronavirus pandemic through the first five months of 2020 as a “quantitative portrait” describing the emergent collaborative behavior at three levels of analysis: article revision, editor contributions, and network dynamics. Across multiple data sources, quantitative methods, and levels of analysis, we find four consistent themes characterizing Wikipedia’s unique large-scale, high-tempo, and temporary online collaborations: external events as drivers of activity, spillovers of activity, complex patterns of editor engagement, and the shadows of the future. In light of increasing concerns about online social platforms’ abilities to govern the conduct and content of their users, we identify implications from Wikipedia’s coronavirus collaborations for improving the resilience of socio-technical systems during a crisis.

 

A Quantitative Portrait of Wikipedia’s High-Tempo Collaborations during the 2020 Coronavirus Pandemic

Abstract:  The 2020 coronavirus pandemic was a historic social disruption with significant consequences felt around the globe. Wikipedia is a freely-available, peer-produced encyclopedia with a remarkable ability to create and revise content following current events. Using 973,940 revisions from 134,337 editors to 4,238 articles, this study examines the dynamics of the English Wikipedia’s response to the coronavirus pandemic through the first five months of 2020 as a “quantitative portrait” describing the emergent collaborative behavior at three levels of analysis: article revision, editor contributions, and network dynamics. Across multiple data sources, quantitative methods, and levels of analysis, we find four consistent themes characterizing Wikipedia’s unique large-scale, high-tempo, and temporary online collaborations: external events as drivers of activity, spillovers of activity, complex patterns of editor engagement, and the shadows of the future. In light of increasing concerns about online social platforms’ abilities to govern the conduct and content of their users, we identify implications from Wikipedia’s coronavirus collaborations for improving the resilience of socio-technical systems during a crisis.

 

Activists create public online spreadsheet of police violence videos.

“Police officers around the country have been responding with violence as demonstrators gather to protest the killing of George Floyd. Much of this violence has been caught on video and has been instrumental in pushing authorities to hold officers accountable. In Buffalo, for example, two police officers were charged after video went viral of officers shoving a 75-year-old protester to the ground. In New York, two police officers were suspended for violence that was caught on video: an officer violently pushing a woman to the ground and another pulling a protester’s face mask down before blasting pepper spray. The sheer volume of material coming out of the demonstrations though makes it difficult to keep track so two activists decided to start compiling the clips into a handy spreadsheet that is available online….

Lawyer T. Greg Doucette and mathematician Jason Miller have been working to compile the videos in the Google Sheet titled “GeorgeFloyd Protest – police brutality videos on Twitter.” The database currently has 428 videos. Doucette started the effort as a Twitter thread. Miller saw that and realized it was going to be long and unwieldy so he wanted to create a way for people to easily access and sort the videos. For those who aren’t obsessively scrolling through their timelines all day, the spreadsheet can help easily locate videos of police violence in their area because they can be sorted by city and state. The activists have also created a Google Drive with backups of all the videos.

Having all video evidence of police violence documented in one place helps counter the argument that these are just isolated incidents. “When they’re shared as one-offs, you see a familiar pattern,” Doucette tells Vice. “The victim ‘was no angel’ or ‘wasn’t perfect’ or ‘just should have complied,’ and the officer is ‘just one bad apple,’ or ‘we shouldn’t rush to judgment,’ or ‘you don’t know what happened before the video started rolling.’” 

iNaturalist – SciStarter

“iNaturalist is a place where you can record what you see in nature, meet other nature lovers, and learn about the natural world. It is also a crowdsourced species identification system and an organism occurrence recording tool. You can use it to record your own observations, get help with identifications, collaborate with others to collect this kind of information for a common purpose, or access the observational data collected by iNaturalist users.

From hikers to hunters, birders to beach-combers, the world is filled with naturalists, and many of us record what we find. What if all those observations could be shared online? You might discover someone who finds beautiful wildflowers at your favorite birding spot, or learn about the birds you see on the way to work. If enough people recorded their observations, it would be like a living record of life on Earth that scientists and land managers could use to monitor changes in biodiversity, and that anyone could use to learn more about nature.

That’s the vision behind iNaturalist.org. So if you like recording your findings from the outdoors, or if you just like learning about life, join us!”

Open Scholarship Knowledge Base

“A project

The Open Scholarship Knowledge Base is a collaborative initiative to curate and share knowledge about the what, why, and how of open scholarship. This includes reviewing, consolidating, organizing, and improving the discoverability of content to support the education and application of open practices for all aspects of the research lifecycle.

A community

Spearheaded by volunteers, the Open Scholarship Knowledge Base is a community of diverse individuals aligned by a shared goal to make learning and applying open research practices easier. It is being built by and for the community it aims to serve. Researchers, teachers, funders, librarians, and anyone wanting to open scholarship are welcome to edit, curate, and contribute to this community resource.

Join the community by contributing your favorite content to the OSKB through this content submission form!

A platform

Educational content (tutorials, workshop materials, videos, papers, and more) generated by the many contributors to open scholarship across disciplines and regions will be curated and maintained as openly accessible modules and trainings. For example, a user can discover content about data sharing that relates to their discipline, role, and data characteristics, and follow self-guided learning pathways on why and how to share their data….”