This map shows the geographic impact of Ute Schmid'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 Ute Schmid with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ute Schmid more than expected).
This network shows the impact of papers produced by Ute Schmid. 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 Ute Schmid. The network helps show where Ute Schmid may publish in the future.
Co-authorship network of co-authors of Ute Schmid
This figure shows the co-authorship network connecting the top 25 collaborators of Ute Schmid.
A scholar is included among the top collaborators of Ute Schmid 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 Ute Schmid. Ute Schmid is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Schmid, Ute, et al.. (2021). Explaining Machine Learned Relational Concepts in Visual Domains - Effects of Perceived Accuracy on Joint Performance and Trust. eScholarship (California Digital Library). 43(43).6 indexed citations
Buchholz, Sandra, et al.. (2018). “Keep It Going, Girl!” An Empirical Analysis of Gender Differences and Inequalities in Computer Sciences. International Journal of Gender, Science, and Technology. 10(2). 265–286.3 indexed citations
13.
Schmid, Ute, et al.. (2017). A Human Like Incremental Decision Tree Algorithm: Combining Rule Learning, Pattern Induction, and Storing Examples.. 64.2 indexed citations
14.
Hernández‐Orallo, José, Fernando Martínez‐Plumed, Ute Schmid, Michael Siebers, & David L. Dowe. (2017). Computer models solving intelligence test problems: progress and implications. Monash University Research Portal (Monash University). 5005–5009.
15.
Tenbrink, Thora, et al.. (2012). Analogical Problem Solving: Insights from Verbal Reports. Cognitive Science. 34(34).3 indexed citations
16.
Wiese, Eva, et al.. (2008). Mapping and Inference in Analogical Problem Solving — As Much as Needed or as Much as Possible?. eScholarship (California Digital Library). 30(30).2 indexed citations
Schmid, Ute & Fritz Wysotzki. (2000). Applying inductive program synthesis to macro learning. 371–378.8 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.