Leveraging citation networks to generate narrative visualizations of scholars’ careers

We present an approach for generating dynamic narrative visualizations of scholars' careers. This approach includes an animated node-link diagram which shows the citation network accumulated around the researcher over the course of the career, with nodes and links appearing as the representation of the career progresses. Additional data provide more richness to the narrative, including timelines of key indicators, career milestones, and excerpts from qualitative interviews with the scholars. The intended audiences for this work include the scholars, who can enjoy and gain insight from a new way of looking back on their careers, and funding agencies, who have an interest in finding ways to evaluate the impact that their scholars have had

Leveraging Citation Networks to Visualize Scholarly Influence Over Time

Assessing the influence of a scholar's work is an important task for funding organizations, academic departments, and researchers. Common methods, such as measures of citation counts, can ignore much of the nuance and multidimensionality of scholarly influence. We present an approach for generating dynamic visualizations of scholars' careers. This approach uses an animated node-link diagram showing the citation network accumulated around the researcher over the course of the career in concert with key indicators, highlighting influence both within and across fields. We developed our design in collaboration with one funding organization---the Pew Biomedical Scholars program---but the methods are generalizable to visualizations of scholarly influence. We applied the design method to the Microsoft Academic Graph, which includes more than 120 million publications. We validate our abstractions throughout the process through collaboration with the Pew Biomedical Scholars program officers and summative evaluations with their scholars

Harnessing Scholarly Literature as Data to Curate, Explore, and Evaluate Scientific Research

Thesis (Ph.D.)--University of Washington, 2021There currently exist hundreds of millions of scientific publications, with more being created at an ever-increasing rate. This is leading to information overload: the scale and complexity of this body of knowledge is increasing well beyond the capacity of any individual to make sense of it all, overwhelming traditional, manual methods of curation and synthesis. At the same time, the availability of this literature and surrounding metadata in structured, digital form, along with the proliferation of computing power and techniques to take advantage of large-scale and complex data, represents an opportunity to develop new tools and techniques to help people make connections, synthesize, and pose new hypotheses. This dissertation consists of several contributions of data, methods, and tools aimed at addressing information overload in science. My central contribution to this space is Autoreview, a framework for building and evaluating systems to automatically select relevant publications for literature reviews, starting from small sets of seed papers. These automated methods have the potential to help researchers save time and effort when keeping up with relevant literature, as well as surfacing papers that more manual methods may miss. I show that this approach can work to recommend relevant literature, and can also be used to systematically compare different features used in the recommendations. I also present the design, implementation, and evaluation of several visualization tools. One of these is an animated network visualization showing the influence of a scholar over time. Another is SciSight, an interactive system for recommending new authors and research by finding similarities along different dimensions. Additionally, I discuss the current state of available scholarly data sets; my work curating, linking, and building upon these data sets; and methods I developed to scale graph clustering techniques to very large networks

Leveraging citation networks to generate narrative visualizations of scholars’ careers

We present an approach for generating dynamic narrative visualizations of scholars' careers. This approach includes an animated node-link diagram which shows the citation network accumulated around the researcher over the course of the career, with nodes and links appearing as the representation of the career progresses. Additional data provide more richness to the narrative, including timelines of key indicators, career milestones, and excerpts from qualitative interviews with the scholars. The intended audiences for this work include the scholars, who can enjoy and gain insight from a new way of looking back on their careers, and funding agencies, who have an interest in finding ways to evaluate the impact that their scholars have had

Is There a Role for Postmastectomy Radiation Therapy in Ductal Carcinoma In Situ?

Background. DCIS treated by mastectomy ensures high local control rates. There is limited data on risk for relapse and lack of clear indication for adjuvant radiation therapy (RT). We report a retrospective review on a population of DCIS patients treated with mastectomy. The objective was to identify the overall incidence of relapse, risk factors for local recurrence, and accordingly for whom adjuvant postmastectomy RT may be considered. Methods. This is an IRB-approved retrospective study on a prospective breast cancer database. From 1997 to 2007, we identified 969 patients with diagnoses of DCIS, among them 211 breasts in 207 patients were treated with mastectomy and comprise the study group. Results. With a median followup of 55 months (4.6 years) the 10-year relapse-free survival is 97%. Two of 211 breasts (0.9%) treated with mastectomy developed a local-regional recurrence. Both the relapses were among patients defined as having <1 mm final mastectomy margin. Conclusions. The rare local relapse after mastectomy limits our ability to reliably identify risk factors for relapse. The consideration for postmastectomy RT should be based on an individualized risk evaluating surgical technique used, presence of BRCA mutation, grade and extent of tumor, and proximity of lesion to the margin of resection