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.
Countries citing papers authored by Henriette Cramer
Since
Specialization
Citations
This map shows the geographic impact of Henriette Cramer'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 Henriette Cramer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Henriette Cramer more than expected).
Fields of papers citing papers by Henriette Cramer
This network shows the impact of papers produced by Henriette Cramer. 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 Henriette Cramer. The network helps show where Henriette Cramer may publish in the future.
Co-authorship network of co-authors of Henriette Cramer
This figure shows the co-authorship network connecting the top 25 collaborators of Henriette Cramer.
A scholar is included among the top collaborators of Henriette Cramer 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 Henriette Cramer. Henriette Cramer is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Garcia-Gathright, Jean, Aaron Springer, & Henriette Cramer. (2018). Assessing and Addressing Algorithmic Bias - But Before We Get There. National Conference on Artificial Intelligence.9 indexed citations
7.
Mennicken, Sarah, et al.. (2018). Challenges and Methods in Design of Domain-specific Voice Assistants.. National Conference on Artificial Intelligence.3 indexed citations
8.
Cramer, Henriette & Jennifer S. Thom. (2017). Not-so-Autonomous, Very Human Decisions in Machine Learning: Questions When Designing for ML.. National Conference on Artificial Intelligence.4 indexed citations
Aylett, Ruth, et al.. (2012). Can I trust you? Sharing information with artificial companions (Extended Abstract).2 indexed citations
12.
Ljungblad, Sara, et al.. (2012). Hospital robot at work. KTH Publication Database DiVA (KTH Royal Institute of Technology). 177–186.54 indexed citations
Cramer, Henriette, Mattias Rost, & Frank Bentley. (2011). An introduction to Research in the Large.5 indexed citations
15.
Tsui, Katherine M., et al.. (2011). Measuring Attitudes Towards Telepresence Robots. 16(2). 113–123.16 indexed citations
16.
Cramer, Henriette, Helena M. Mentis, & Ylva Fernaeus. (2010). Serious work on playful experiences : a preliminary set of challenges. Computer Supported Cooperative Work (CSCW).5 indexed citations
Lee, Min Kyung, Kristen Stubbs, Henriette Cramer, et al.. (2010). HRI pioneers workshop 2010. 9–9.1 indexed citations
19.
Cramer, Henriette, Vanessa Evers, Elena Zudilova‐Seinstra, & P.M.A. Sloot. (2004). Heuristic evaluation and context analysis to aid the development of a simulated vascular reconstruction system. UvA-DARE (University of Amsterdam).1 indexed citations
20.
Zimmer, Robert, et al.. (1980). BIOCHEMICAL INVESTIGATIONS INTO THE MODE OF ACTION OF NA-VALPROATE AND VALPROINIC ACID, RESPECTIVELY. Data Archiving and Networked Services (DANS).2 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.