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.
Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score
This map shows the geographic impact of D. L. Johnson'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 D. L. Johnson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites D. L. Johnson more than expected).
This network shows the impact of papers produced by D. L. Johnson. 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 D. L. Johnson. The network helps show where D. L. Johnson may publish in the future.
Co-authorship network of co-authors of D. L. Johnson
This figure shows the co-authorship network connecting the top 25 collaborators of D. L. Johnson.
A scholar is included among the top collaborators of D. L. Johnson 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 D. L. Johnson. D. L. Johnson 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.
Harris, B.L., A M Winkelman, & D. L. Johnson. (2012). Large-Scale Single-Step Genomic Evaluation for Milk Production Traits. Bulletin - International Bull Evaluation Service/Interbull bulletin. 20–24.10 indexed citations
2.
Spelman, Richard, J. Arias, Michael Keehan, et al.. (2010). Application of genomic selection in the New Zealand dairy cattle industry.. Proceedings of the World Congress on Genetics Applied to Livestock Production. 311.14 indexed citations
3.
Harris, B.L., et al.. (2009). National genomic evaluations without genotypes. Bulletin - International Bull Evaluation Service/Interbull bulletin. 189.1 indexed citations
Johnson, D. L., et al.. (2005). Moving from BLUP to marker-assisted BLUP for genetic evaluations. Bulletin - International Bull Evaluation Service/Interbull bulletin. 151.1 indexed citations
Jansen, Ritsert C., D. L. Johnson, & J.A.M. van Arendonk. (1998). A Mixture Model Approach to the Mapping of Quantitative Trait Loci in Complex Populations With an Application to Multiple Cattle Families. Socio-Environmental Systems Modeling.3 indexed citations
Baker, R. L., et al.. (1986). Heterosis retention for live weight in advanced generations of a Hereford and Angus crossbreeding experiment.. Proceedings of the World Congress on Genetics applied to Livestock Production. 301–307.8 indexed citations
Jury, K. E., D. L. Johnson, & J. N. Clarke. (1979). Ad justment factors for lamb weaning weight. New Zealand Journal of Agricultural Research. 22(3). 385–389.6 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.