Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo
2018530 citationsDaniel E. Wagner, Caleb Weinreb et al.Scienceprofile →
Lineage tracing on transcriptional landscapes links state to fate during differentiation
2020400 citationsCaleb Weinreb, Alejo Rodriguez-Fraticelli et al.Scienceprofile →
Clonal analysis of lineage fate in native haematopoiesis
2018391 citationsAlejo Rodriguez-Fraticelli, Samuel L. Wolock et al.Natureprofile →
The dynamics of gene expression in vertebrate embryogenesis at single-cell resolution
2018365 citationsJames Briggs, Caleb Weinreb et al.Scienceprofile →
Spontaneous behaviour is structured by reinforcement without explicit reward
202390 citationsJeffrey E. Markowitz, Winthrop F. Gillis et al.Natureprofile →
Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics
202448 citationsCaleb Weinreb, Sherry Lin et al.Nature Methodsprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Caleb Weinreb's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Caleb Weinreb with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Caleb Weinreb more than expected).
This network shows the impact of papers produced by Caleb Weinreb. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Caleb Weinreb. The network helps show where Caleb Weinreb may publish in the future.
Co-authorship network of co-authors of Caleb Weinreb
This figure shows the co-authorship network connecting the top 25 collaborators of Caleb Weinreb.
A scholar is included among the top collaborators of Caleb Weinreb based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Caleb Weinreb. Caleb Weinreb is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Markowitz, Jeffrey E., Winthrop F. Gillis, Maya Jay, et al.. (2023). Spontaneous behaviour is structured by reinforcement without explicit reward. Nature. 614(7946). 108–117.90 indexed citations breakdown →
Weinreb, Caleb, Alejo Rodriguez-Fraticelli, Fernando D. Camargo, & Allon M. Klein. (2020). Lineage tracing on transcriptional landscapes links state to fate during differentiation. Science. 367(6479).400 indexed citations breakdown →
Briggs, James, Caleb Weinreb, Daniel E. Wagner, et al.. (2018). The dynamics of gene expression in vertebrate embryogenesis at single-cell resolution. Science. 360(6392).365 indexed citations breakdown →
12.
Wagner, Daniel E., Caleb Weinreb, Zach M. Collins, et al.. (2018). Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo. Science. 360(6392). 981–987.530 indexed citations breakdown →
Rodriguez-Fraticelli, Alejo, Samuel L. Wolock, Caleb Weinreb, et al.. (2018). Clonal analysis of lineage fate in native haematopoiesis. Nature. 553(7687). 212–216.391 indexed citations breakdown →
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.