Daniel P. Mäki

31 total papers · 887 total citations
17 papers, 618 citations indexed

About

Daniel P. Mäki is a scholar working on Artificial Intelligence, Experimental and Cognitive Psychology and Applied Mathematics. According to data from OpenAlex, Daniel P. Mäki has authored 17 papers receiving a total of 618 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Artificial Intelligence, 3 papers in Experimental and Cognitive Psychology and 3 papers in Applied Mathematics. Recurrent topics in Daniel P. Mäki's work include Speech Recognition and Synthesis (3 papers), Mathematical functions and polynomials (3 papers) and Phonetics and Phonology Research (3 papers). Daniel P. Mäki is often cited by papers focused on Speech Recognition and Synthesis (3 papers), Mathematical functions and polynomials (3 papers) and Phonetics and Phonology Research (3 papers). Daniel P. Mäki collaborates with scholars based in United States. Daniel P. Mäki's co-authors include John L. Casti, Maynard Thompson, Diane Kewley-Port, Charles S. Watson, Daniel J. Reed, Robert F. Port, Lance C. Pérez, Catherine Watson, Gary R. Kidd and James D. Miller and has published in prestigious journals such as The Journal of the Acoustical Society of America, International Journal of Environmental Research and Public Health and Journal of Speech Language and Hearing Research.

In The Last Decade

Daniel P. Mäki

15 papers receiving 520 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Daniel P. Mäki	308	146	82	52	47	17	618
Ariel Cintrón-Arias	189 0.6×	122 0.8×	112 1.4×	14 0.3×	30 0.6×	11	510
Mitja Slavinec	296 1.0×	44 0.3×	42 0.5×	67 1.3×	19 0.4×	26	671
Gábor Lippner	124 0.4×	321 2.2×	107 1.3×	35 0.7×	50 1.1×	26	539
Marco Alberto Javarone	293 1.0×	437 3.0×	145 1.8×	29 0.6×	46 1.0×	37	673
Paul Nicholls	73 0.2×	30 0.2×	12 0.1×	78 1.5×	53 1.1×	32	554
Duy Vu	227 0.7×	162 1.1×	9 0.1×	160 3.1×	40 0.9×	16	553
Ahmed S. Elgazzar	218 0.7×	73 0.5×	170 2.1×	51 1.0×	29 0.6×	31	540
Edgar Fuller	164 0.5×	48 0.3×	10 0.1×	106 2.0×	11 0.2×	48	570
Yuyun Hidayat	200 0.6×	21 0.1×	11 0.1×	60 1.2×	36 0.8×	55	530
Utkarsh Upadhyay	65 0.2×	17 0.1×	28 0.3×	143 2.8×	36 0.8×	23	522

Countries citing papers authored by Daniel P. Mäki

Since Specialization

Citations

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

Fields of papers citing papers by Daniel P. Mäki

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel P. Mäki

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

All Works

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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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