The future of ar X iv and knowledge discovery in open science (2020)

Abstract: arXiv, the preprint server for the physical and mathematical sciences, is in its third decade of operation. As the flow of new, open access research increases inexorably, the challenges to keep up with and discover research content also become greater. I will discuss the status and future of arXiv, and possibilities and plans to make more effective use of the research database to enhance ongoing research efforts.

## classification papers submitted to arχiv are run through a natural language processing classifier @xcite . currently three classifiers are operational: the original full text classifier from ginsparg; a beta version of a more general broad classification package, including full text, run asynchronously; and a new fast metadata classifier developed by papers with code. note that arχiv submissions come with very sparse metadata. demanding large amounts of metadata provided by the author puts a burden on the author during submission and discourages use of arχiv for rapid distribution of research. moderators see the classifier score, and a paper with a high score in a particular category not selected by the author may be queued up for consideration by the moderators of that category, for selection either as a primary category or as a secondary choice of category. a few percent of submissions typically get some category changes, often the addition of one or more secondary categories for listing. category changes also lead to significant fraction of holds of submissions, both when a moderator deems a submission unsuitable for the choice of category, or when moderators disagree amongst themselves about the choice of category. orphaned submissions, those rejected from all choices of primary categories, may be rejected as out of scope. moderators will often recommend alternative possible choices of primary, or recommend secondary categories for submission. the classifiers are imperfect, in particular when trying to determine a fit for the smaller categories, even after balancing, and some broad categories are treated as exception cases. training the classifier is an iterative process, and more work is needed. ## knowledge discovery ultimately researchers and other readers want to discover the latest research that is relevant to their interests, and to find other relevant results, novel methods, complementary insights or other useful or interesting knowledge. a lot of discovery comes from finding your lane and staying in it, the categories provide useful silos for a significant fraction of researchers and push most of the directly relevant science to that community. beyond the silo, searches of the literature are useful for discovery, but are often constrained by what can be indexed for searching and how the search algorithm keys in on search terms. improved search algorithms and federated cross platform searches generally improve prospects for discovery. arχiv currently has formal relations with several entities to expedite cross platform discovery, including inspire, ads, dblp, semantic scholar and google scholar. we are working to improve discovery including author disambiguation. author id services such as orcid and institutional identifiers like ror also expedite searches. an ambition of arχiv is to provide custom delivery of new submissions beyond the current categories, to include among other options, author selection, types of content, and inclusion and exclusion by keywords and relevance. balancing this impetus is the danger of loss of discovery by browsing, the serendipitous discovery that came when browsing a physical journal and finding a surprise article adjacent to the one you were seeking, or a topical book you were not aware of shelved next to those you browsed. refined and narrow searches limit surprises. sometimes what you are looking for is not the "known unknowns" but rather the "unknown unknowns". it is tempting to consider providing a small fraction of random or semi-random search results in searches in the hope of triggering the rare discovery of an unknown. a more formal process may be more efficient and likelier to succeed, and arχiv is interested in pursuing knowledge discovery techniques, including knowledge graphs and novel techniques for finding relevant results that are not adjacent to the research area being searched. there are very large benefits to finding an existing solution to an experimental problem, a new computational technique making your modeling tractable, a statistical or mathematical method making your problem solvable, or the novel theoretical insight from a different subfield. more broadly we want to find emerging new directions of research, even before those doing the research realize there is an emergent effort which is headed in a new direction, to see disparate subfields converge into new synergistic research opportunities, and adjacent subfields diverge to nucleate new areas of research. these are hard problems, but exciting and with very high potential for discovery and speeding up research. arχiv core functionality is to get the paper to the reader, but quantity has a quality all of its own @xcite . bulk downloads of content for natural language processing, machine learning and other aggregate exploration has been enabled for some time through amazon's s3, with the user paying. arχiv has now partnered with kaggle to provide bulk access to arχiv contents, providing both aggregate metadata, and access to processing full text. the kaggle dataset is updated periodically and is free to use. text retrieval of any particular paper still goes through arxiv.org.