Mary E. Haas

11.5k total citations · 4 hit papers
53 papers, 3.7k citations indexed

About

Mary E. Haas is a scholar working on Sociology and Political Science, Education and Surgery. According to data from OpenAlex, Mary E. Haas has authored 53 papers receiving a total of 3.7k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Sociology and Political Science, 11 papers in Education and 8 papers in Surgery. Recurrent topics in Mary E. Haas's work include Educator Training and Historical Pedagogy (14 papers), Genetic Associations and Epidemiology (6 papers) and Galaxies: Formation, Evolution, Phenomena (5 papers). Mary E. Haas is often cited by papers focused on Educator Training and Historical Pedagogy (14 papers), Genetic Associations and Epidemiology (6 papers) and Galaxies: Formation, Evolution, Phenomena (5 papers). Mary E. Haas collaborates with scholars based in United States, Germany and United Kingdom. Mary E. Haas's co-authors include Krishna G. Aragam, Sekar Kathiresan, Amit V. Khera, Patrick T. Ellinor, Eric S. Lander, Mark Chaffin, Seung Hoan Choi, Pradeep Natarajan, Steven A. Lubitz and Carolina Roselli and has published in prestigious journals such as Cell, Proceedings of the National Academy of Sciences and Journal of Biological Chemistry.

In The Last Decade

Mary E. Haas

42 papers receiving 3.6k citations

Hit Papers

Genome-wide polygenic scores for common diseases identify... 2018 2026 2020 2023 2018 2019 2021 2022 500 1000 1.5k

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Mary E. Haas United States 19 1.4k 948 594 562 493 53 3.7k
Kenneth Rice United States 37 1.2k 0.9× 1.2k 1.2× 434 0.7× 539 1.0× 587 1.2× 123 4.4k
Tom G. Richardson United Kingdom 29 1.3k 0.9× 786 0.8× 427 0.7× 485 0.9× 249 0.5× 92 2.8k
Allan Motyer Australia 7 2.2k 1.6× 1.2k 1.3× 331 0.6× 463 0.8× 368 0.7× 17 4.5k
N. Charlotte Onland‐Moret Netherlands 30 847 0.6× 813 0.9× 337 0.6× 414 0.7× 416 0.8× 114 3.5k
Clare Bycroft United Kingdom 4 2.5k 1.8× 1.7k 1.8× 344 0.6× 478 0.9× 395 0.8× 4 5.2k
Alisa K. Manning United States 22 1.3k 1.0× 1.4k 1.5× 506 0.9× 337 0.6× 272 0.6× 58 4.3k
Dana C. Crawford United States 35 2.9k 2.1× 2.1k 2.2× 350 0.6× 429 0.8× 504 1.0× 151 5.9k
Muin J. Khoury United States 39 2.4k 1.7× 1.8k 1.9× 442 0.7× 521 0.9× 265 0.5× 95 7.0k
Joshua A. Bell United Kingdom 35 549 0.4× 1.1k 1.2× 408 0.7× 437 0.8× 280 0.6× 90 4.2k
Laure El ghormli United States 23 585 0.4× 682 0.7× 444 0.7× 264 0.5× 251 0.5× 58 2.7k

Countries citing papers authored by Mary E. Haas

Since Specialization
Citations

This map shows the geographic impact of Mary E. Haas'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 Mary E. Haas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mary E. Haas more than expected).

Fields of papers citing papers by Mary E. Haas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mary E. Haas. 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 Mary E. Haas. The network helps show where Mary E. Haas may publish in the future.

Co-authorship network of co-authors of Mary E. Haas

This figure shows the co-authorship network connecting the top 25 collaborators of Mary E. Haas. A scholar is included among the top collaborators of Mary E. Haas 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 Mary E. Haas. Mary E. Haas 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.
Mirshahi, Uyenlinh L., Guoli Chen, Bryn S. Moore, et al.. (2022). Framework From a Multidisciplinary Approach for Transitioning Variants of Unknown Significance From Clinical Genetic Testing in Kidney Disease to a Definitive Classification. Kidney International Reports. 7(9). 2047–2058. 2 indexed citations
2.
Haas, Mary E., James P. Pirruccello, Sam Friedman, et al.. (2021). Machine learning enables new insights into genetic contributions to liver fat accumulation. Cell Genomics. 1(3). 100066–100066. 55 indexed citations
3.
Khera, Amit V., Mark Chaffin, Kaitlin H. Wade, et al.. (2019). Polygenic Prediction of Weight and Obesity Trajectories from Birth to Adulthood. Cell. 177(3). 587–596.e9. 432 indexed citations breakdown →
4.
Khera, Amit V., Mark Chaffin, Krishna G. Aragam, et al.. (2018). Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nature Genetics. 50(9). 1219–1224. 1631 indexed citations breakdown →
5.
Meakin, Paul J., Mary E. Haas, Bernadette Bonardo, et al.. (2018). The beta secretase BACE1 regulates the expression of insulin receptor in the liver. Nature Communications. 9(1). 1306–1306. 56 indexed citations
6.
Sun, Xiaowei, Mary E. Haas, Ji Miao, et al.. (2015). Insulin Dissociates the Effects of Liver X Receptor on Lipogenesis, Endoplasmic Reticulum Stress, and Inflammation. Journal of Biological Chemistry. 291(3). 1115–1122. 16 indexed citations
7.
Ramolla, M., Mary E. Haas, R. Chini, et al.. (2014). PHOTOMETRIC REVERBERATION MAPPING OF ACTIVE GALACTIC NUCLEI. Redalyc (Universidad Autónoma del Estado de México). 45. 79–82. 1 indexed citations
8.
Haas, Mary E., Alan Attie, & Sudha B. Biddinger. (2013). The regulation of ApoB metabolism by insulin. Trends in Endocrinology and Metabolism. 24(8). 391–397. 132 indexed citations
9.
Drouart, G., C. De Breuck, J. Vernet, et al.. (2012). Jet and torus orientations in high redshift radio galaxies. Astronomy and Astrophysics. 548. A45–A45. 29 indexed citations
10.
Haas, Joel T., Ji Miao, Dipanjan Chanda, et al.. (2012). Hepatic Insulin Signaling Is Required for Obesity-Dependent Expression of SREBP-1c mRNA but Not for Feeding-Dependent Expression. Cell Metabolism. 15(6). 873–884. 155 indexed citations
11.
Haas, Mary E.. (2008). Conducting Interviews to Learn about World War II. Social Education. 72(5). 264.
12.
Siebenmorgen, R., Mary E. Haas, E. Pantin, et al.. (2008). Nuclear activity in nearby galaxies. Astronomy and Astrophysics. 488(1). 83–90. 19 indexed citations
13.
Müller, S., Mary E. Haas, R. Siebenmorgen, et al.. (2004). Dust in 3CR radio galaxies: On the FR 1 – FR 2 difference. Springer Link (Chiba Institute of Technology). 18 indexed citations
14.
Haas, Mary E.. (2004). The Presidency and Presidential Elections in the Elementary Classroom.. Social Education. 68(5). 340. 2 indexed citations
15.
Haas, Mary E., et al.. (2002). Multiple Strategies for Teaching Current Events..
16.
Haas, Mary E., et al.. (2001). A Profile of Elementary Social Studies Teachers and Their Classrooms.. Social Education. 65(2). 122. 14 indexed citations
17.
Haas, Mary E., et al.. (1994). Studying World War II in the Elementary School. Curriculum Concerns.. Social studies and the young learner. 7(2). 18. 1 indexed citations
18.
Haas, Mary E.. (1993). "The Great Solar System Rescue": A Highly Usable Videodisc Program.. Social Education. 57(1). 11–12. 1 indexed citations
19.
Haas, Mary E.. (1988). What Is the Name of the Mystery Nation. Social studies and the young learner. 1(2). 19–20. 2 indexed citations
20.
Haas, Mary E.. (1986). War and Peace: The Students' Views.. Journal of research and development in education. 19(3). 84–89. 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.

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