About

Jane G. Adams, DNP, APRN, FNP-BC, is the Director of the Family Nurse Practitioner Program and an Assistant Clinical Professor in the School of Nursing at Northeastern University. Her role involves leadership in nursing education, particularly in preparing family nurse practitioners. Her professional background includes advanced practice nursing, with a focus on family health, and she contributes to the academic and clinical training of nursing students. Her work supports the development of healthcare professionals equipped to meet diverse patient needs, emphasizing both clinical expertise and educational excellence.

Research topics

• Political Science
• Computer Science
• Sociology
• Natural Language Processing
• World Wide Web
• Demography
• Biology
• Archaeology
• Media studies
• Geography
• Law
• Data science
• Linguistics
• Medicine

Selected publications

• Gridded Visualization of Statistical Trees for High‐Dimensional Multipartite Data in Systems Genetics

  Computer Graphics Forum · 2025-05-23

  articleOpen access1st authorCorresponding

  Abstract In systems genetics and other multi‐omics research, exploring high‐dimensional relationships among molecular and physiological variables across individuals poses significant challenges. We present the Gridded Trees interface, a novel interactive visualization tool designed to facilitate the exploration of conditional inference trees, which are hierarchical models of relationships in these complex datasets. Traditional static tools struggle to reveal patterns in tree‐structured data, but the Gridded Trees interface provides interactive, coordinated views, allowing users to navigate between overview and detail, filter data dynamically, and compare molecular‐physiological relationships across subgroups. By combining filtering techniques, strip plots, Sankey diagrams, and small multiples, the Gridded Trees interface enhances exploratory data analysis and supports hypothesis generation. In our systems genetics research use case, this tool has revealed significant associations among microbial populations and addiction‐related behavioral traits in genetically diverse mice. The Gridded Trees interface suggests broad potential for visualizing hierarchical and multipartite data across domains. A preprint of this paper as well as Supplemental Materials are available on OSF at https://osf.io/9emn5/ .

  PublisherOA PDFDOI

• Long-term (17-year) dynamics of herbaceous plant communities after shelterwood regeneration harvests in southern Appalachian cove- and upland hardwood forests

  Canadian Journal of Forest Research · 2025-01-01

  articleSenior author

  The southern Appalachians are a “hotspot” of plant diversity. Herbaceous communities are especially rich in mesic cove hardwood forests, compared to drier upland hardwood forests. We evaluated changes in forest structure and herbaceous plant communities over 17 years in mature cove- (CHM) and upland hardwood (UHM) forests, and young 2-age cove- (CHSW) and upland hardwood (UHSW) stands created by shelterwood-with-reserves regeneration harvests (SW). Structure of mature forests was relatively static. In contrast, reduced canopy cover after harvests initiated rapid increases in small tree stem density and blackberry ( Rubus) cover, followed by dense shade as young trees gained height. We identified 201 herbaceous species including 156 forbs. Species richness was about double in CHM and CHSW than in UHM and UHSW; composition changed little over time within treatments, even as forest structure changed in SW. Among the 79 herbaceous species analyzed, relative abundance of 26 showed a response; most were more abundant in CHM, CHSW, or both compared to UHM, UHSW, or both. Our results indicated that shelterwood harvests had a neutral or positive effect on herbaceous plant richness, diversity, and abundance of most species, and suggested that environmental gradients associated with forest type influenced herbaceous communities much more than SW alone.

  PublisherDOI

• Doom or Deliciousness: Challenges and Opportunities for Visualization in the Age of Generative Models

  2023-01-07 · 12 citations

  preprintOpen accessSenior author

  Generative text-to-image models (as exemplified by DALL-E, MidJourney, and Stable Diffusion) have recently made enormous technological leaps, demonstrating impressive results in many graphical domains—from logo design to digital painting and photographic composition. However, the quality of these results has led to existential crises in some fields of art, leading to questions about the role of human agency in the production of meaning in a graphical context. Such issues are central to visualization, and while these generative models have yet to be widely applied to visualization, it seems only a matter of time until their integration is manifest. Seeking to circumvent similar ponderous dilemmas, we attempt to understand the roles that generative models might play across visualization. We do so by constructing a framework that characterizes what these technologies offer at various stages of the visualization workflow, augmented and analyzed through semi-structured interviews with 21 experts from related domains. Throughthis work, we map the space of opportunities and risks that might arise in this intersection, identifying doomsday prophecies and delicious low-hanging fruits that are ripe for research.

  PublisherOA PDFDOI

• Ready Player Viz: Player-Created Data Visualizations for Video Games

  2023-08-15

  preprintOpen access1st authorCorresponding

  We present a survey of the design space of player-created strategic visualizations for video games, to better understand how players make sense of complex game mechanics and incorporate feedback from fellow players. We present several examples of these visualizations, and contrast them to developer-created visualizations, both in information presentation and purpose. We find that there is a rich community-building aspect to visualization development within game 'fandoms', facilitated by cross-platform exchange and iterative development, including: social media, informational 'wikis', and in-game modifications ('modding'). Finally, we consider player-created visualizations in the context of a broader imagining about the future of visualization development for non-game but analogous strategic applications. We maintain a collection of tagged and categorized examples of player-created video game visualizations.

  PublisherOA PDFDOI

• Doom or Deliciousness: Challenges and Opportunities for Visualization in the Age of Generative Models

  Computer Graphics Forum · 2023-06-01 · 48 citations

  articleOpen accessSenior author

  Generative text-to-image models (as exemplified by DALL-E, MidJourney, and Stable Diffusion) have recently made enormous technological leaps, demonstrating impressive results in many graphical domains-from logo design to digital painting to photographic composition. However, the quality of these results has led to existential crises in some fields of art, leading to questions about the role of human agency in the production of meaning in a graphical context. Such issues are central to visualization, and while these generative models have yet to be widely applied in visualization, it seems only a matter of time until their integration is manifest. Seeking to circumvent similar ponderous dilemmas, we attempt to understand the roles that generative models might play across visualization. We do so by constructing a framework that characterizes what these technologies offer at various stages of the visualization workflow, augmented and analyzed through semi-structured interviews with 21 experts from related domains. Through this work, we map the space of opportunities and risks that might arise in this intersection, identifying doomsday prophecies and delicious low-hanging fruits that are ripe for research.

  PublisherOA PDFDOI

• Designing Visualization Dashboards for Aligning Experimental and Analytical Results in Systems Genetics Addiction Research

  2023-10-17

  preprintOpen access1st authorCorresponding

  We introduce three data visualization projects related to systems genetics and addiction research. Historically, the challenge of biology research has been one of data collection. However, as measurement becomes easier, the challenge shifts towards data sensemaking. Here, researchers are asking questions about the relationships between genes, traits, and the microbiome, using visualization for data exploration. This work has implications for visualization of high-dimensional data in other domains, as it shows how statistical methods can support visualization filtering, aggregation, and information hierarchies to explore dense data.

  PublisherOA PDFDOI

• Allotaxonometry and rank-turbulence divergence: a universal instrument for comparing complex systems

  EPJ Data Science · 2023-09-19 · 7 citations

  articleOpen access

  Abstract Complex systems often comprise many kinds of components which vary over many orders of magnitude in size: Populations of cities in countries, individual and corporate wealth in economies, species abundance in ecologies, word frequency in natural language, and node degree in complex networks. Here, we introduce ‘allotaxonometry’ along with ‘rank-turbulence divergence’ (RTD), a tunable instrument for comparing any two ranked lists of components. We analytically develop our rank-based divergence in a series of steps, and then establish a rank-based allotaxonograph which pairs a map-like histogram for rank-rank pairs with an ordered list of components according to divergence contribution. We explore the performance of rank-turbulence divergence, which we view as an instrument of ‘type calculus’, for a series of distinct settings including: Language use on Twitter and in books, species abundance, baby name popularity, market capitalization, performance in sports, mortality causes, and job titles. We provide a series of supplementary flipbooks which demonstrate the tunability and storytelling power of rank-based allotaxonometry.

  PublisherOA PDFDOI

• Investigating the Visual Utility of Differentially Private Scatterplots

  IEEE Transactions on Visualization and Computer Graphics · 2023-07-05 · 13 citations

  articleOpen access

  Increasingly, visualization practitioners are working with, using, and studying private and sensitive data. There can be many stakeholders interested in the resulting analyses-but widespread sharing of the data can cause harm to individuals, companies, and organizations. Practitioners are increasingly turning to differential privacy to enable public data sharing with a guaranteed amount of privacy. Differential privacy algorithms do this by aggregating data statistics with noise, and this now-private data can be released visually with differentially private scatterplots. While the private visual output is affected by the algorithm choice, privacy level, bin number, data distribution, and user task, there is little guidance on how to choose and balance the effect of these parameters. To address this gap, we had experts examine 1,200 differentially private scatterplots created with a variety of parameter choices and tested their ability to see aggregate patterns in the private output (i.e. the visual utility of the chart). We synthesized these results to provide easy-to-use guidance for visualization practitioners releasing private data through scatterplots. Our findings also provide a ground truth for visual utility, which we use to benchmark automated utility metrics from various fields. We demonstrate how multi-scale structural similarity (MS-SSIM), the metric most strongly correlated with our study's utility results, can be used to optimize parameter selection.

  PublisherOA PDFDOI

• Investigating the Visual Utility of Differentially Private Scatterplots

  2023-03-26 · 2 citations

  preprintOpen access

  Increasingly, visualization practitioners are working with, using, and studying private and sensitive data. There can be many stakeholders interested in the resulting analyses—but widespread sharing of the data can cause harm to individuals, companies, and organizations. Practitioners are increasingly turning to differential privacy to enable public sharing of data with a guaranteed amount of privacy. Differential privacy algorithms do this by aggregating data statistics with noise, and this now-private data can be released visually with differentially private scatterplots. While the private visual output is affected by the algorithm choice, privacy level, bin number, data distribution, and user task, there is little guidance on how to choose and balance the effect of these parameters. To address this gap, we had experts examine 1,200 differentially private scatterplots created with a variety of parameter choices and tested their ability to see aggregate patterns in the private output (i.e. the visual utility of the chart). We synthesized these results to provide easy-to-use guidance for visualization practitioners releasing private data through scatterplots. Our findings also provide a ground truth for visual utility, which we use to benchmark automated utility metrics from a variety of fields. We demonstrate how multi-scale structural similarity (MS-SSIM), the metric most strongly correlated with our study’s utility results, can be used to optimize parameter selection. A free copy of this paper along with all supplemental materials is available at https://osf.io/wej4s/.

  PublisherOA PDFDOI

• Doom or Deliciousness: Challenges and Opportunities for Visualization in the Age of Generative Models

  2023-01-07

  preprintOpen accessSenior author

  Generative text-to-image models (as exemplified by DALL-E, MidJourney, and Stable Diffusion) have recently made enormous technological leaps, demonstrating impressive results in many graphical domains—from logo design to digital painting and photographic composition. However, the quality of these results has led to existential crises in some fields of art, leading to questions about the role of human agency in the production of meaning in a graphical context. Such issues are central to visualization, and while these generative models have yet to be widely applied to visualization, it seems only a matter of time until their integration is manifest. Seeking to circumvent similar ponderous dilemmas, we attempt to understand the roles that generative models might play across visualization. We do so by constructing a framework that characterizes what these technologies offer at various stages of the visualization workflow, augmented and analyzed through semi-structured interviews with 21 experts from related domains. Throughthis work, we map the space of opportunities and risks that might arise in this intersection, identifying doomsday prophecies and delicious low-hanging fruits that are ripe for research.

  PublisherOA PDFDOI

Frequent coauthors

• Peter Sheridan Dodds

  28 shared

• Christopher M. Danforth

  University of Vermont

  25 shared

• Thayer Alshaabi

  University of California, Berkeley

  23 shared

• Joshua R. Minot

  University of Vermont

  21 shared

• David Rushing Dewhurst

  Charles River Analytics (United States)

  15 shared

• Martha E. Shenton

  Harvard University

  14 shared

• Paul G. Nestor

  University of Massachusetts Boston

  14 shared

• Robert W. McCarley

  Harvard University

  13 shared

Labs

• Family Nurse Practitioner ProgramPI

Education

• Current PhD Student: Data Visualization, Khoury College of Computer Sciences

  Northeastern University

• B.F.A. Graphic Design and Digital Media, Communications and Creative Media

  Champlain College

  2015