Demo Data from EDBT 2026 / reviewers in the wild / expert
Tomasz J. Nowakowski
dblp:294/9166
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2021
0000-0003-2345-4964ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021
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% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
single-cell analysis |
0.5 | 1 | 2021 | UCSC Cell Browser: visualize your single-cell data · Bioinform. 2021 |
Bioinformatics and computational biology › single-cell analysis
single-cell visualization |
0.5 | 1 | 2021 | UCSC Cell Browser: visualize your single-cell data · Bioinform. 2021 |
Bioinformatics and computational biology › gene expression analysis
gene expression visualization |
0.1 | 1 | 2021 | UCSC Cell Browser: visualize your single-cell data · Bioinform. 2021 |
Methods — techniques the papers use, named apart from their topics
web-based visualization · 0.5python package · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | UCSC Cell Browser: visualize your single-cell dataabstractSUMMARY: As the use of single-cell technologies has grown, so has the need for tools to explore these large, complicated datasets. The UCSC Cell Browser is a tool that allows scientists to visualize gene expression and metadata annotation distribution throughout a single-cell dataset or multiple datasets. AVAILABILITY AND IMPLEMENTATION: We provide the UCSC Cell Browser as a free website where scientists can explore a growing collection of single-cell datasets and a freely available python package for scientists to create stable, self-contained visualizations for their own single-cell datasets. Learn more at https://cells.ucsc.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Matthew L. Speir, Aparna Bhaduri, Nikolay S. Markov, Pablo A. Moreno, Tomasz J. Nowakowski, Irene Papatheodorou, Alex A. Pollen, Brian J. Raney, Lucas Seninge, W. James Kent, Maximilian Haeussler |
Bioinform. | 5 |