Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Neural mechanisms for the recognition of biological movements
Countries citing papers authored by Martin A. Giese
Since
Specialization
Citations
This map shows the geographic impact of Martin A. Giese'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 Martin A. Giese with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Martin A. Giese more than expected).
This network shows the impact of papers produced by Martin A. Giese. 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 Martin A. Giese. The network helps show where Martin A. Giese may publish in the future.
Co-authorship network of co-authors of Martin A. Giese
This figure shows the co-authorship network connecting the top 25 collaborators of Martin A. Giese.
A scholar is included among the top collaborators of Martin A. Giese 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 Martin A. Giese. Martin A. Giese is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kharlamov, Evgeny, Sebastian Brandt, Martin A. Giese, et al.. (2016). Scalable Semantic Access to Siemens Static and Streaming Distributed Data. International Semantic Web Conference.2 indexed citations
Soylu, Ahmet, Evgeny Kharlamov, Dmitriy Zheleznyakov, et al.. (2014). OptiqueVQS: visual query formulation for OBDA. Oxford University Research Archive (ORA) (University of Oxford). 725–728.2 indexed citations
8.
Layher, Georg, Martin A. Giese, & Heiko Neumann. (2013). Learning Representations of Animated Motion Sequences - A Neural Model. Cognitive Science. 35(35).1 indexed citations
9.
Curio, Cristóbal, Enrico Chiovetto, & Martin A. Giese. (2013). Integration of kinematic components in the perception of emotional facial expressions. Perception. 42. 242–243.1 indexed citations
Giese, Martin A., Enrico Chiovetto, & Cristóbal Curio. (2012). Perceptual relevance of kinematic components of facial movements extracted by unsupervised learning. Max Planck Digital Library.1 indexed citations
12.
Giese, Martin A.. (2011). Der Inklusionsdiskurs in der Heil- und Sonderpädagogik – Ein anthropologisches Niemandsland.1 indexed citations
13.
Alama, Jesse, et al.. (2011). Dialogues games for classical logic (short paper). Data Archiving and Networked Services (DANS). 82–86.2 indexed citations
Giese, Martin A.. (2006). Die Textfassungen der Lebensbeschreibung Bischof Bernwards von Hildesheim.1 indexed citations
16.
Giese, Martin A.. (2004). Die Annales Quedlinburgenses. Bayerische Staatsbibliothek.3 indexed citations
17.
Ilg, Winfried, et al.. (2003). On the Representation, Learning and Transfer of Spatio-Temporal Movement Characteristics. Max Planck Institute for Plasma Physics. 0–0.4 indexed citations
18.
Giese, Martin A., et al.. (2000). Neural Model for the Recognition of Biological Motion.3 indexed citations
19.
Giese, Martin A., et al.. (1985). A revision of Campylopodium (C. Mull.) Besch.. 11. 125–133.4 indexed citations
20.
Giese, Martin A., et al.. (1985). A revision of Microcampylopus (C. Mull.) Fleisch.. 11. 114–124.6 indexed citations
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.