Daniel E. Acuña

1.5k total citations
37 papers, 791 citations indexed

About

Daniel E. Acuña is a scholar working on Artificial Intelligence, Statistics, Probability and Uncertainty and Cognitive Neuroscience. According to data from OpenAlex, Daniel E. Acuña has authored 37 papers receiving a total of 791 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 10 papers in Statistics, Probability and Uncertainty and 7 papers in Cognitive Neuroscience. Recurrent topics in Daniel E. Acuña's work include scientometrics and bibliometrics research (10 papers), Topic Modeling (6 papers) and Health and Medical Research Impacts (3 papers). Daniel E. Acuña is often cited by papers focused on scientometrics and bibliometrics research (10 papers), Topic Modeling (6 papers) and Health and Medical Research Impacts (3 papers). Daniel E. Acuña collaborates with scholars based in United States, China and Australia. Daniel E. Acuña's co-authors include Konrad P. Körding, Stefano Allesina, Titipat Achakulvisut, Max Berniker, Scott T. Grafton, Stephen V. David, Jean Liénard, Robert S. Turner, Hugo L. Fernandes and Misha Teplitskiy and has published in prestigious journals such as Nature, Nature Communications and PLoS ONE.

In The Last Decade

Daniel E. Acuña

35 papers receiving 761 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Daniel E. Acuña United States 13 209 205 164 112 110 37 791
Cengiz Acartürk Türkiye 11 23 0.1× 51 0.2× 123 0.8× 153 1.4× 61 0.6× 56 650
Tessa Verhoef Netherlands 13 12 0.1× 79 0.4× 135 0.8× 110 1.0× 107 1.0× 36 687
Mohammed Saqr Finland 24 10 0.0× 34 0.2× 265 1.6× 205 1.8× 105 1.0× 117 1.9k
Frank Goldhammer Germany 25 22 0.1× 196 1.0× 320 2.0× 268 2.4× 143 1.3× 97 1.9k
Richard Tobin United Kingdom 20 13 0.1× 84 0.4× 653 4.0× 191 1.7× 37 0.3× 51 1.2k
Paul R. Smart United Kingdom 15 9 0.0× 142 0.7× 282 1.7× 120 1.1× 80 0.7× 90 678
David G. Dobolyi United States 15 10 0.0× 276 1.3× 120 0.7× 131 1.2× 185 1.7× 29 846
Stanley R. Trollip United States 10 13 0.1× 83 0.4× 177 1.1× 268 2.4× 115 1.0× 25 1.4k
John Kingston United States 22 23 0.1× 325 1.6× 808 4.9× 69 0.6× 19 0.2× 83 2.1k
Murat Perit Çakır Türkiye 12 20 0.1× 62 0.3× 72 0.4× 61 0.5× 42 0.4× 49 557

Countries citing papers authored by Daniel E. Acuña

Since Specialization
Citations

This map shows the geographic impact of Daniel E. Acuña'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 Daniel E. Acuña with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel E. Acuña more than expected).

Fields of papers citing papers by Daniel E. Acuña

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel E. Acuña. 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 Daniel E. Acuña. The network helps show where Daniel E. Acuña may publish in the future.

Co-authorship network of co-authors of Daniel E. Acuña

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel E. Acuña. A scholar is included among the top collaborators of Daniel E. Acuña 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 Daniel E. Acuña. Daniel E. Acuña is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Acuña, Daniel E., et al.. (2025). Estimating the predictability of questionable open-access journals. Science Advances. 11(35). eadt2792–eadt2792. 2 indexed citations
2.
Patterson, David A., et al.. (2024). Science Needs You: Mobilizing for Diversity in Award Recognition. Communications of the ACM. 67(8). 18–21. 1 indexed citations
3.
Ke, Qing, et al.. (2022). A dataset of mentorship in bioscience with semantic and demographic estimations. Scientific Data. 9(1). 467–467. 9 indexed citations
4.
Acuña, Daniel E., Misha Teplitskiy, James A. Evans, & Konrad P. Körding. (2022). Author-suggested reviewers rate manuscripts much more favorably: A cross-sectional analysis of the neuroscience section of PLOS ONE. PLoS ONE. 17(12). e0273994–e0273994. 2 indexed citations
5.
Qiu, Cheng, et al.. (2022). Paraphrase Identification with Deep Learning: A Review of Datasets and Methods. arXiv (Cornell University). 11 indexed citations
6.
7.
Acuña, Daniel E., et al.. (2021). Graphical integrity issues in open access publications: Detection and patterns of proportional ink violations. PLoS Computational Biology. 17(12). e1009650–e1009650. 7 indexed citations
8.
Achakulvisut, Titipat, Daniel E. Acuña, & Konrad P. Körding. (2020). Pubmed Parser: A Python Parser for PubMed Open-Access XML Subset and MEDLINE XML Dataset XML Dataset. The Journal of Open Source Software. 5(46). 1979–1979. 15 indexed citations
9.
Liénard, Jean, Titipat Achakulvisut, Daniel E. Acuña, & Stephen V. David. (2018). Intellectual synthesis in mentorship determines success in academic careers. Nature Communications. 9(1). 4840–4840. 70 indexed citations
10.
Lee, Taraz G., Daniel E. Acuña, Konrad P. Körding, & Scott T. Grafton. (2018). Limiting motor skill knowledge via incidental training protects against choking under pressure. Psychonomic Bulletin & Review. 26(1). 279–290. 12 indexed citations
11.
Éthier, Christian, Daniel E. Acuña, Sara A. Solla, & Lee E. Miller. (2016). Adaptive neuron-to-EMG decoder training for FES neuroprostheses. Journal of Neural Engineering. 13(4). 46009–46009. 8 indexed citations
12.
Ramkumar, Pavan, Daniel E. Acuña, Max Berniker, et al.. (2016). Chunking as the result of an efficiency computation trade-off. Nature Communications. 7(1). 12176–12176. 71 indexed citations
13.
Achakulvisut, Titipat, et al.. (2016). Science Concierge: A Fast Content-Based Recommendation System for Scientific Publications. PLoS ONE. 11(7). e0158423–e0158423. 73 indexed citations
14.
Acuña, Daniel E., Orion Penner, & Colin G. Orton. (2013). The future h‐index is an excellent way to predict scientistsˈ future impact. Medical Physics. 40(11). 110601–110601. 5 indexed citations
15.
Acuña, Daniel E., Stefano Allesina, & Konrad P. Körding. (2012). Predicting scientific success. Nature. 489(7415). 201–202. 151 indexed citations
16.
Schrater, Paul & Daniel E. Acuña. (2011). Rational bayesian analysis of sequential decision-making under uncertainty in humans and machines. 1 indexed citations
17.
Acuña, Daniel E. & Vı́ctor Parada. (2010). People Efficiently Explore the Solution Space of the Computationally Intractable Traveling Salesman Problem to Find Near-Optimal Tours. PLoS ONE. 5(7). e11685–e11685. 11 indexed citations
18.
Acuña, Daniel E., et al.. (2010). Structure Learning in Human Sequential Decision-Making. PLoS Computational Biology. 6(12). e1001003–e1001003. 45 indexed citations
19.
Acuña, Daniel E. & Paul Schrater. (2008). Structure Learning in Human Sequential Decision-Making. Neural Information Processing Systems. 21. 1–8. 3 indexed citations
20.
Acuña, Daniel E., et al.. (1990). Estudio de la insuficiencia vertebrobasilar mediante la compresión manual de las arterias vertebrales.

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.

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