Daniele Pinna

30 total papers · 822 total citations
15 papers, 510 citations indexed

About

Daniele Pinna is a scholar working on Atomic and Molecular Physics, and Optics, Condensed Matter Physics and Electrical and Electronic Engineering. According to data from OpenAlex, Daniele Pinna has authored 15 papers receiving a total of 510 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Atomic and Molecular Physics, and Optics, 7 papers in Condensed Matter Physics and 7 papers in Electrical and Electronic Engineering. Recurrent topics in Daniele Pinna's work include Magnetic properties of thin films (9 papers), Advanced Memory and Neural Computing (6 papers) and Physics of Superconductivity and Magnetism (5 papers). Daniele Pinna is often cited by papers focused on Magnetic properties of thin films (9 papers), Advanced Memory and Neural Computing (6 papers) and Physics of Superconductivity and Magnetism (5 papers). Daniele Pinna collaborates with scholars based in United States, Germany and Czechia. Daniele Pinna's co-authors include Karin Everschor‐Sitte, George I. Bourianoff, Andrew D. Kent, D. L. Stein, U. Nowak, Daniel Heinze, Samridh Jaiswal, Andreas Donges, Niklas Keil and Levente Rózsa and has published in prestigious journals such as Journal of Applied Physics, Nature Nanotechnology and Physical Review B.

In The Last Decade

Daniele Pinna

14 papers receiving 501 citations

Author Peers

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

Author						Papers	Cites
Daniele Pinna	403	202	186	144	85	15	510
A. Giordano	445 1.1×	232 1.1×	177 1.0×	154 1.1×	36 0.4×	35	550
Takayuki Seki	434 1.1×	228 1.1×	97 0.5×	223 1.5×	57 0.7×	16	577
Christopher Safranski	400 1.0×	210 1.0×	172 0.9×	153 1.1×	50 0.6×	21	496
A. Hamadeh	392 1.0×	213 1.1×	114 0.6×	102 0.7×	56 0.7×	32	447
Himanshu Fulara	449 1.1×	303 1.5×	108 0.6×	117 0.8×	133 1.6×	23	567
Vasyl Tyberkevych	392 1.0×	217 1.1×	136 0.7×	71 0.5×	77 0.9×	30	469
Matthias Pernpeintner	460 1.1×	220 1.1×	112 0.6×	149 1.0×	30 0.4×	10	518
Benjamin Pigeau	487 1.2×	180 0.9×	126 0.7×	90 0.6×	42 0.5×	15	544
Eric R. J. Edwards	538 1.3×	308 1.5×	116 0.6×	167 1.2×	47 0.6×	23	615
D. Gusakova	500 1.2×	180 0.9×	276 1.5×	188 1.3×	23 0.3×	30	559

Countries citing papers authored by Daniele Pinna

Since Specialization

Citations

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

Fields of papers citing papers by Daniele Pinna

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniele Pinna. 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 Daniele Pinna. The network helps show where Daniele Pinna may publish in the future.

Co-authorship network of co-authors of Daniele Pinna

This figure shows the co-authorship network connecting the top 25 collaborators of Daniele Pinna. A scholar is included among the top collaborators of Daniele Pinna 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 Daniele Pinna. Daniele Pinna 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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