Julich-Brain: A 3D probabilistic atlas of the human brain’s cytoarchitecture | Science

Abstract:  Cytoarchitecture is a basic principle of microstructural brain parcellation. Here we introduce Julich-Brain, a 3D atlas containing cytoarchitectonic maps of cortical areas and subcortical nuclei. The atlas is probabilistic to consider variations between individual brains. Building such an atlas was highly data- and labor-intensive and required to develop nested, interdependent workflows for detecting borders between brain areas, data processing, provenance tracking, and flexible execution of processing chains to handle large amounts of data at different spatial scales. Gap maps complement cortical maps to achieve full cortical coverage. The atlas concept is dynamic, i.e., continuously adapted with progress in mapping, openly available to support neuroimaging studies of healthy subjects and patients, as well as modeling and simulation, and interoperable, to link with other atlases and recourses.

 

Julich-Brain: A 3D probabilistic atlas of the human brain’s cytoarchitecture | Science

Abstract:  Cytoarchitecture is a basic principle of microstructural brain parcellation. Here we introduce Julich-Brain, a 3D atlas containing cytoarchitectonic maps of cortical areas and subcortical nuclei. The atlas is probabilistic to consider variations between individual brains. Building such an atlas was highly data- and labor-intensive and required to develop nested, interdependent workflows for detecting borders between brain areas, data processing, provenance tracking, and flexible execution of processing chains to handle large amounts of data at different spatial scales. Gap maps complement cortical maps to achieve full cortical coverage. The atlas concept is dynamic, i.e., continuously adapted with progress in mapping, openly available to support neuroimaging studies of healthy subjects and patients, as well as modeling and simulation, and interoperable, to link with other atlases and recourses.

 

Could this be the start of a new era in scholarly communication? – F1000 Blogs

“There are in fact a number of research publishing models in widespread use that are designed precisely to enable rapid publication of new findings (as a preprint does) while assuring expert and transparent peer review to support trust in, and decision-making around, an article’s potential use. F1000Research [13] developed such a publishing model for the life sciences in 2013, with a mandatory requirement that the underlying data and code are made FAIR (Finable, Accessible, Interoperable and Reusable) to support reproducibility of the findings and their use and reuse.  In addition, publications can be updated as new data comes in or new understanding is developed, thereby enabling the publication to track the ongoing research workflow – like a ‘living article’.

This model is now being extended out to all research disciplines, and major funders around the world also now have their own publishing platforms for their grantees utilising this same rapid and transparent publishing model, including Wellcome [14], the Bill & Melinda Gates Foundation [15], the Irish Health Research Board [16], and later this year, the European Commission [17]. Indeed these platforms have seen a big upsurge in submissions on COVID-19 during this time due to the obvious benefits of this approach during such an emergency [for examples see 18, 19 and 20]. Furthermore, this model can bring considerable cost and efficiency gains: average article processing charges on Wellcome Open Research are 67% cheaper than the average Wellcome pays to other venues for Open Access [21], and the model enables the publication of a much broader range of outputs, helping to reduce research waste….”

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….”

Open Repositories Conference Handbook – OR Steering Committee – LYRASIS Wiki

“This document is intended to serve both as an aide-memoire for the Open Repositories organization and as a loose guide for each year’s conference organisers. We attempt to keep this as up to date as possible, but there may be areas which are inaccurate. If you have questions, please contact the Chair of the Open Repositories Steering Committee..

Following the overview, the middle sections of the document are essentially a walk through of the conference “creation” process whilst the Appendices contain related documents that will be useful in the process….”

Open-Access Data and Computational Resources to Address COVID-19 | Data Science at NIH

“COVID-19 open-access data and computational resources are being provided by federal agencies, including NIH, public consortia, and private entities. These resources are freely available to researchers, and this page will be updated as more information becomes available. 

The Office of Data Science Strategy seeks to provide the research community with links to open-access data, computational, and supporting resources. These resources are being aggregated and posted for scientific and public health interests. Inclusion of a resource on this list does not mean it has been evaluated or endorsed by NIH….”

Open Science since Covid-19: Open Access + Open Data

Abstract:  The coronavirus crisis has created different initiatives that promote access to open publications and open data, solve collaboratively and from different places, being an example of the benefits of open science. From the initial version of the Compilation on Open Science from COVID-19: Open Access + Open Data (Version I: April 3, 2020) published by Alejandro Uribe-Tirado (http://eprints.rclis.org/39864/), it seemed to us, a good practice to update this first input openly and collaboratively, using the platform: https://etherpad.wikimedia.org/p/covid19. This new version (Version II: June 3, 2020), is the result of this joint work.

Journal Best Practices Checklist: LPC – Google Docs

“This document organizes LPC’s resources related to journal publishing into a best practices ‘checklist.’ It isn’t comprehensive or authoritative, but will hopefully provide a starting point. With the exception of the Shared Documentation, all resources listed are freely available. New resources will be added to this list as they are created. In the meantime, we suggest also keeping an eye on the Library Publishing Workflows project, which is investigating and documenting journal publishing workflows in libraries. …”

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.