Deanna M. Macris

10 total papers · 457 total citations
7 papers, 276 citations indexed

About

Deanna M. Macris is a scholar working on Developmental and Educational Psychology, Cognitive Neuroscience and Education. According to data from OpenAlex, Deanna M. Macris has authored 7 papers receiving a total of 276 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Developmental and Educational Psychology, 3 papers in Cognitive Neuroscience and 3 papers in Education. Recurrent topics in Deanna M. Macris's work include Child and Animal Learning Development (4 papers), Child Development and Digital Technology (2 papers) and Educational Strategies and Epistemologies (2 papers). Deanna M. Macris is often cited by papers focused on Child and Animal Learning Development (4 papers), Child Development and Digital Technology (2 papers) and Educational Strategies and Epistemologies (2 papers). Deanna M. Macris collaborates with scholars based in United States. Deanna M. Macris's co-authors include Derek E. Lyons, Frank C. Keil, David M. Sobel, Katarzyna Chawarska, Kelly Powell, Suzanne Macari, Philip M. Fernbach, Jessa Reed, Frederick Shic and Abigail S. Greene and has published in prestigious journals such as Scientific Reports, Philosophical Transactions of the Royal Society B Biological Sciences and Developmental Psychology.

In The Last Decade

Deanna M. Macris

7 papers receiving 264 citations

Author Peers

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

Author Last Decade Papers Cites
Deanna M. Macris 181 112 105 55 51 7 276
Katalin Egyed 217 1.2× 102 0.9× 57 0.5× 48 0.9× 29 0.6× 12 293
Frances Buttelmann 167 0.9× 76 0.7× 134 1.3× 19 0.3× 31 0.6× 8 297
Lauren H. Howard 159 0.9× 155 1.4× 116 1.1× 23 0.4× 55 1.1× 16 307
Stipe Grgas 244 1.3× 113 1.0× 119 1.1× 15 0.3× 29 0.6× 18 327
Werner-Reimers-Stiftung 103 0.6× 98 0.9× 51 0.5× 29 0.5× 40 0.8× 12 292
Beate Priewasser 191 1.1× 100 0.9× 102 1.0× 27 0.5× 33 0.6× 9 216
佳世子 稲垣 185 1.0× 124 1.1× 46 0.4× 17 0.3× 30 0.6× 3 304
Adrienne Wente 113 0.6× 72 0.6× 74 0.7× 19 0.3× 47 0.9× 7 265
Donald Peterson 193 1.1× 50 0.4× 125 1.2× 14 0.3× 30 0.6× 7 272
Karin Strid 178 1.0× 63 0.6× 120 1.1× 7 0.1× 25 0.5× 12 236

Countries citing papers authored by Deanna M. Macris

Since Specialization
Citations

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

Fields of papers citing papers by Deanna M. Macris

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Deanna M. Macris. 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 Deanna M. Macris. The network helps show where Deanna M. Macris may publish in the future.

Co-authorship network of co-authors of Deanna M. Macris

This figure shows the co-authorship network connecting the top 25 collaborators of Deanna M. Macris. A scholar is included among the top collaborators of Deanna M. Macris 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 Deanna M. Macris. Deanna M. Macris 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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