Acrobatic squirrels learn to leap and land on tree branches without falling is a research paper published in Science (2021). On theSindex it has a DataRank of 2.0. It has been cited 73 times, with 67 citing works in its 1-hop citation network.
Scored on demand from live citation data
Linked data & code
DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
We only score data papers we can read in full — never from an abstract alone.
Base Score Contribution
0.646
From this paper's citation signal
Citation Network Contribution
1.3
From 38 citing papers with measurable signal
Ranked by each citer's contribution to N(p) — log1p(Cq) divided by its reference count — out of 67 citers.
National Science Foundation
Grant: DGE-0903711
National Science Foundation
Grant: 1028319
CDI-Type II: Collaborative Research: Cyber-Amplified Bioinspiration in Robotics
National Institutes of Health
Grant: R15AG063103
National Institutes of Health
Grant: P20GM109090
Army Research Office
Grant: W911NF1810327
NIA NIH HHS
Grant: R15 AG063106
National Science Foundation
Grant: 0903711
IGERT: Biological and Bio-inspired Motion Systems Operating in Complex Environments
National Institutes of Health
Grant: 1R15AG063106-01
MECHANISMS OF FALL RESISTANCE TO DIVERSE SLIPPING CONDITIONS
National Institutes of Health
Grant: 5P20GM109090-08
Harnessing Movement Variability to Treat and Prevent Motor Related Disorders
Graduate Fellowship, National Science Foundation
Stochastic Labs Seed Funding
Chancellor’s Fellowship, University of California Berkeley
Influential Citations
1
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals
Data from: Acrobatic squirrels learn to leap and land on tree branches without falling