arXiv’s Giving Week is May 2 – 8, 2021

“arXiv is free to read and submit research, so why are we asking for donations?

arXiv is not free to operate, and, as a nonprofit, we depend on the generosity of foundations, members, donors, volunteers, and individuals like you to survive and thrive. If arXiv matters to you and you have the means to contribute, we humbly ask you to join arXiv’s global community of supporters with a donation during arXiv’s Giving Week, May 2 – 8, 2021.

Less than one percent of the five million visitors to arXiv this month will donate. If everyone contributed just $1 each, we would be able to meet our annual operating budget and save for future financial stability.

Would you like to know more about our operations and how arXiv’s funds are spent? Check out our annual report for more information….”

Images of the arXiv: Reconfiguring large scientific image datasets | Published in Journal of Cultural Analytics

Abstract:  In an ongoing research project on the ascendancy of statistical visual forms, we have been concerned with the transforma­tions wrought by such images and their organisation as datasets in ‘re­drawing’ knowledge about empirical phenomena.Historians and science studies researchers have long established the generative rather than simply illustrative role of im­ages and figures within scientific practice. More recently, the deployment and generation of images by scientific researchand its communication via publication has been impacted by the tools, techniques, and practices of working with large(image) datasets. Against this background, we built a dataset of 10 million­plus images drawn from all preprint articles deposited in the open access repository arXiv from 1991 (its inception) until the end of 2018. In this article, we suggest ways – including algorithms drawn from machine learning that facilitate visually ’slicing’ through the image data and metadata – for exploring large datasets of statistical scientific images. By treating all forms of visual material found inscientific publications – whether diagrams, photographs, or instrument data – as bare images, we developed methods for tracking their movements across a range of scientific research. We suggest that such methods allow us different entry points into large scientific image datasets and that they initiate a new set of questions about how scientific representatio nmight be operating at more­-than-­human scale.

Images of the arXiv: Reconfiguring large scientific image datasets | Published in Journal of Cultural Analytics

Abstract:  In an ongoing research project on the ascendancy of statistical visual forms, we have been concerned with the transforma­tions wrought by such images and their organisation as datasets in ‘re­drawing’ knowledge about empirical phenomena.Historians and science studies researchers have long established the generative rather than simply illustrative role of im­ages and figures within scientific practice. More recently, the deployment and generation of images by scientific researchand its communication via publication has been impacted by the tools, techniques, and practices of working with large(image) datasets. Against this background, we built a dataset of 10 million­plus images drawn from all preprint articles deposited in the open access repository arXiv from 1991 (its inception) until the end of 2018. In this article, we suggest ways – including algorithms drawn from machine learning that facilitate visually ’slicing’ through the image data and metadata – for exploring large datasets of statistical scientific images. By treating all forms of visual material found inscientific publications – whether diagrams, photographs, or instrument data – as bare images, we developed methods for tracking their movements across a range of scientific research. We suggest that such methods allow us different entry points into large scientific image datasets and that they initiate a new set of questions about how scientific representatio nmight be operating at more­-than-­human scale.

Instant access to code, for any arXiv paper | arXiv.org blog

“In October, arXiv released a new feature empowering arXiv authors to link their Machine Learning articles to associated code. Developed in an arXivLabs collaboration with Papers with Code, the tool was met with great enthusiasm from arXiv’s ML community.

Now, we’re expanding the capability beyond Machine Learning to arXiv papers in every category. And, to better align with our arXiv communities, PwC is launching new sites in computer science, physics, mathematics, astronomy and statistics to help researchers explore code in these fields….”

Papers with Code is Expanding to More Sciences! | by Ross Taylor | PapersWithCode | Dec, 2020 | Medium

“Today we are launching new sites for computer science, physics, mathematics, astronomy and statistics. Partnering with arXiv, you can use these sites to sync code to show on arXiv paper pages. These sites are live today, and the code tab is now enabled for arXiv papers from all fields! Explore (and add code to) our new portal here: https://portal.paperswithcode.com

As a result of this expansion, we are now tracking artifacts for over 600k research papers. This is just the beginning, and we are deepening our coverage in the weeks and months ahead. We hope our efforts increase code availability for all fields of science — making it a research norm — so the entire research community can progress more quickly together!…”

New arXivLabs feature provides instant access to code | arXiv.org blog

“Today, arXivLabs launched a new Code tab, a shortcut linking Machine Learning articles with their associated code. arXivLabs provides a conduit for collaboration that invites community participation while allowing arXiv developers to focus on core services. This Code feature was developed by Papers with Code, a free resource for researchers and practitioners to find and follow the latest Machine Learning papers and code.

When a reader activates the Code tool on the arXiv abstract record page, the author’s implementation of the code will be displayed in the tab, if available, as well as links to any community implementations. This instant access allows researchers to use and build upon the work quickly and easily, increasing code accessibility and accelerating the speed of research….”