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Divit P. Singh

dblp:209/7204 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2018
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%
Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%
Human-computer interaction and pervasive computing
2 papers
Collaborative and social computing · 100%

Topics — the 4 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visualization and visual analytics › biological data visualization
biological network visualization
0.312018
CrowdLayout: Crowdsourced Design and Evaluation of Biological Network Visualizations · CHI 2018
Bioinformatics and computational biology › biological network
network biology
0.312017
GraphSpace: stimulating interdisciplinary collaborations in network biology · Bioinform. 2017
Bioinformatics and computational biology › network bioinformatics › biological network analysis
network visualization
0.312017
GraphSpace: stimulating interdisciplinary collaborations in network biology · Bioinform. 2017
Collaborative and social computing
crowdsourcing
0.112018
CrowdLayout: Crowdsourced Design and Evaluation of Biological Network Visualizations · CHI 2018

Methods — techniques the papers use, named apart from their topics

graph layout algorithms · 0.7crowdsourcing · 0.7web-based visualization · 0.6
YearPublicationVenuePosition
2018 CrowdLayout: Crowdsourced Design and Evaluation of Biological Network Visualizations
abstract
Biologists often perform experiments whose results generate large quantities of data, such as interactions between molecules in a cell, that are best represented as networks (graphs). To visualize these networks and communicate them in publications, biologists must manually position the nodes and edges of each network to reflect their real-world physical structure. This process does not scale well, and graph layout algorithms lack the biological underpinnings to offer a viable alternative. In this paper, we present CrowdLayout, a crowdsourcing system that leverages human intelligence and creativity to design layouts of biological network visualizations. CrowdLayout provides design guidelines, abstractions, and editing tools to help novice workers perform like experts. We evaluated CrowdLayout in two experiments with paid crowd workers and real biological network data, finding that crowds could both create and evaluate meaningful, high-quality layouts. We also discuss implications for crowdsourced design and network visualizations in other domains.
Divit P. Singh, Lee Lisle, T. M. Murali 0001, Kurt Luther
CHI1
2017 GraphSpace: stimulating interdisciplinary collaborations in network biology
abstract
SUMMARY: Networks have become ubiquitous in systems biology. Visualization is a crucial component in their analysis. However, collaborations within research teams in network biology are hampered by software systems that are either specific to a computational algorithm, create visualizations that are not biologically meaningful, or have limited features for sharing networks and visualizations. We present GraphSpace, a web-based platform that fosters team science by allowing collaborating research groups to easily store, interact with, layout and share networks. AVAILABILITY AND IMPLEMENTATION: Anyone can upload and share networks at http://graphspace.org. In addition, the GraphSpace code is available at http://github.com/Murali-group/graphspace if a user wants to run his or her own server. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Aditya Bharadwaj, Divit P. Singh, Anna M. Ritz, Allison N. Tegge, Christopher L. Poirel, Pavel K. Brazhnik, Neil Adames, Kurt Luther, Shiv D. Kale, Jean Peccoud, John J. Tyson, T. M. Murali 0001
Bioinform.2