arXiv subjects

Katy Boerner

Publications and source records attributed to Katy Boerner.

4 recordsLinked to original sources

arXiv2024

Enabling Global Image Data Sharing in the Life Sciences

Coordinated collaboration is essential to realize the added value of and infrastructure requirements for global image data sharing in the life sciences. In this White Paper, we take a first step at presenting some of the most common use cases as well as critical/emerging use cases of (including the use of artificial intelligence for) biological and medical image data, which would benefit tremendously from better frameworks for sharing (including technical, resourcing, legal, and ethical aspects). In the second half of this paper, we paint an ideal world scenario for how global image data sharing could work and benefit all life sciences and beyond. As this is still a long way off, we conclude by suggesting several concrete measures directed toward our institutions, existing imaging communities and data initiatives, and national funders, as well as publishers. Our vision is that within the next ten years, most researchers in the world will be able to make their datasets openly available and use quality image data of interest to them for their research and benefit. This paper is published in parallel with a companion White Paper entitled Harmonizing the Generation and Pre-publication Stewardship of FAIR Image Data, which addresses challenges and opportunities related to producing well-documented and high-quality image data that is ready to be shared. The driving goal is to address remaining challenges and democratize access to everyday practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location.

q-bio.OT↗

arXiv2013

"Seed+Expand": A validated methodology for creating high quality publication oeuvres of individual researchers

The study of science at the individual micro-level frequently requires the disambiguation of author names. The creation of author's publication oeuvres involves matching the list of unique author names to names used in publication databases. Despite recent progress in the development of unique author identifiers, e.g., ORCID, VIVO, or DAI, author disambiguation remains a key problem when it comes to large-scale bibliometric analysis using data from multiple databases. This study introduces and validates a new methodology called seed+expand for semi-automatic bibliographic data collection for a given set of individual authors. Specifically, we identify the oeuvre of a set of Dutch full professors during the period 1980-2011. In particular, we combine author records from the National Research Information System (NARCIS) with publication records from the Web of Science. Starting with an initial list of 8,378 names, we identify "seed publications" for each author using five different approaches. Subsequently, we "expand" the set of publication in three different approaches. The different approaches are compared and resulting oeuvres are evaluated on precision and recall using a "gold standard" dataset of authors for which verified publications in the period 2001-2010 are available.

cs.DL↗

arXiv2013

Collective allocation of science funding: from funding agencies to scientific agency

Public agencies like the U.S. National Science Foundation (NSF) and the National Institutes of Health (NIH) award tens of billions of dollars in annual science funding. How can this money be distributed as efficiently as possible to best promote scientific innovation and productivity? The present system relies primarily on peer review of project proposals. In 2010 alone, NSF convened more than 15,000 scientists to review 55,542 proposals. Although considered the scientific gold standard, peer review requires significant overhead costs, and may be subject to biases, inconsistencies, and oversights. We investigate a class of funding models in which all participants receive an equal portion of yearly funding, but are then required to anonymously donate a fraction of their funding to peers. The funding thus flows from one participant to the next, each acting as if he or she were a funding agency themselves. Here we show through a simulation conducted over large-scale citation data (37M articles, 770M citations) that such a distributed system for science may yield funding patterns similar to existing NIH and NSF distributions, but may do so at much lower overhead while exhibiting a range of other desirable features. Self-correcting mechanisms in scientific peer evaluation can yield an efficient and fair distribution of funding. The proposed model can be applied in many situations in which top-down or bottom-up allocation of public resources is either impractical or undesirable, e.g. public investments, distribution chains, and shared resource management.

physics.soc-ph↗

arXiv2009

Visual Conceptualizations and Models of Science

This Journal of Informetrics special issue aims to improve our understanding of the structure and dynamics of science by reviewing and advancing existing conceptualizations and models of scholarly activity. Several of these conceptualizations and models have visual manifestations supporting the combination and comparison of theories and approaches developed in different disciplines of science. Subsequently, we discuss challenges towards a theoretically grounded and practically useful science of science and provide a brief chronological review of relevant work. Then, we exemplarily present three conceptualizations of science that attempt to provide frameworks for the comparison and combination of existing approaches, theories, laws, and measurements. Finally, we discuss the contributions of and interlinkages among the eight papers included in this issue. Each paper makes a unique contribution towards conceptualizations and models of science and roots this contribution in a review and comparison with existing work.

cs.DL↗