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.
A review of AI teaching and learning from 2000 to 2020
2022186 citationsDavy Tsz Kit Ng, Min Lee et al.Education and Information Technologiesprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by J. Stephen Downie
Since
Specialization
Citations
This map shows the geographic impact of J. Stephen Downie'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 J. Stephen Downie with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites J. Stephen Downie more than expected).
Fields of papers citing papers by J. Stephen Downie
This network shows the impact of papers produced by J. Stephen Downie. 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 J. Stephen Downie. The network helps show where J. Stephen Downie may publish in the future.
Co-authorship network of co-authors of J. Stephen Downie
This figure shows the co-authorship network connecting the top 25 collaborators of J. Stephen Downie.
A scholar is included among the top collaborators of J. Stephen Downie 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 J. Stephen Downie. J. Stephen Downie is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Ng, Davy Tsz Kit, et al.. (2022). A review of AI teaching and learning from 2000 to 2020. Education and Information Technologies. 28(7). 8445–8501.186 indexed citations breakdown →
Hu, Xiao, et al.. (2017). Exploring the Music Library Association Mailing List: A Text Mining Approach. Research Commons (University of Waikato). 302–308.3 indexed citations
5.
Glushko, Robert J., et al.. (2017). Creating a Policy Framework for Analytic Access to In-Copyright Works for Non-Consumptive Research.. DH.1 indexed citations
6.
Cole, Timothy W., et al.. (2016). A Comparative Analysis of Bibliographic Ontologies: Implications for Digital Humanities.. DH. 639–642.4 indexed citations
Downie, J. Stephen, et al.. (2014). Ten years of MIREX: reflections, challenges and opportunities. Research Commons (University of Waikato). 657–662.3 indexed citations
9.
Downie, J. Stephen, Robert H. McDonald, Timothy W. Cole, Robert Sanderson, & Frank Shipman. (2013). Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries.4 indexed citations
10.
Hu, Xiao, J. Stephen Downie, & Andreas F. Ehmann. (2009). LYRIC TEXT MINING IN MUSIC MOOD CLASSIFICATION. International Symposium/Conference on Music Information Retrieval. 411–416.94 indexed citations
11.
Hu, Xiao, J. Stephen Downie, Cyril Laurier, Mert Bay, & Andreas F. Ehmann. (2008). The 2007 MIREX Audio Mood Classification Task: Lessons Learned. International Symposium/Conference on Music Information Retrieval. 462–467.110 indexed citations
Hu, Xiao & J. Stephen Downie. (2007). Exploring Mood Metadata: Relationships with Genre, Artist and Usage Metadata.. International Symposium/Conference on Music Information Retrieval. 67–72.76 indexed citations
14.
Hu, Xiao, Mert Bay, & J. Stephen Downie. (2007). CREATING A SIMPLIFIED MUSIC MOOD CLASSIFICATION GROUND-TRUTH SET. International Symposium/Conference on Music Information Retrieval. 309–310.28 indexed citations
Hu, Xiao, J. Stephen Downie, Kris West, & Andreas F. Ehmann. (2005). MINING MUSIC REVIEWS: PROMISING PRELIMINARY RESULTS. International Symposium/Conference on Music Information Retrieval. 536–539.27 indexed citations
17.
Bainbridge, David, Sally Jo Cunningham, & J. Stephen Downie. (2004). Visual collaging of music in a digital library. Research Commons (University of Waikato). 397–402.15 indexed citations
18.
Bainbridge, David, Sally Jo Cunningham, & J. Stephen Downie. (2004). GREENSTONE as a Music Digital Library Toolkit.. Research Commons (University of Waikato). 42–43.3 indexed citations
19.
Downie, J. Stephen. (2001). Whither MIR Research: Thoughts about the Future.. International Symposium/Conference on Music Information Retrieval.1 indexed citations
20.
Downie, J. Stephen. (1999). Evaluating a simple approach to music information retrieval : conceiving melodic n-grams as text. Library and Archives Canada (Government of Canada).79 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.