arXiv’s membership program is now based on submissions | arXiv.org blog

“arXiv’s members have provided approximately 25% of our operating budget for the past ten years, supporting arXiv’s mission to provide a reliable open platform for sharing research. By becoming arXiv members, more than 230 institutions around the world have made a strong statement in favor of open access, open science, and sustainable academic publishing. Thank you, members!

We are happy to announce our updated membership program, which was developed in collaboration with the Membership Advisory Board. This program is part of our sustainability model, complements arXiv’s diverse funding sources, including societies and other organizations, and ensures that arXiv will have the funding required to continue meeting researchers’ evolving needs.

arXiv membership is inclusive, flexible, and offers your institution a high value, low-risk, budget-conscious option to serve your scholarly community. Members receive public recognition, institutional usage statistics, eligibility to serve in arXiv’s governance, and more….

Universities, libraries, research institutes, and laboratories are invited to join or renew. For standard memberships, annual fees are based on submissions by institution, averaged over three years….”

Datasets on arXiv. We’re excited to announce our… | by Robert Stojnic | PapersWithCode | May, 2021 | Medium

“We’re excited to announce our partnership with arXiv to support links to datasets on arXiv!

Machine learning articles on arXiv now have a Code & Data tab to link to datasets that are used or introduced in a paper….

This makes it much easier to track dataset usage across the community and quickly find other papers using the same dataset. From Papers with Code you can discover other papers using the same dataset, track usage over time, compare models and find similar datasets….”

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….”