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Article . 2022
Data sources: DOAJ
https://doi.org/10.1162/99608f...
Article . 2022 . Peer-reviewed
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What Data Visualization Reveals: Elizabeth Palmer Peabody and the Work of Knowledge Production

Authors: Lauren Klein;

What Data Visualization Reveals: Elizabeth Palmer Peabody and the Work of Knowledge Production

Keywords

Electronic computers. Computer science, QA75.5-76.95

19 references, page 1 of 2

Akbaba, D., Wilburn, C., Nance, M., & Meyer, M. (2021). Manifesto for putting “Chartjunk” in the trash 2021! Alt.VIS Workshop, IEEE VIS 2021. https://altvis.github.io/papers/akbaba.pdf Barkley Brown, E. (1989). African-American women's quilting: A framework for conceptualizing and teaching African-American women's history. Signs, 14(4), 921-929. http://doi.org/10.1086/494553 Bateman, S., Mandryk, R. L., Gutwin, C., Genest, A., McDine, D., & Brooks, C. (2010). Useful junk? The effects of visual embellishment on comprehension and memorability of charts. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI) 2010. ACM.

http://doi.org/10.1145/1753326.1753716 Battle-Baptiste, W., & Rusert, B. (2018). W.E.B. Du Bois's data portraits: Visualizing Black America.

Baym, N. (1991). The Ann sisters: Elizabeth Peabody's millennial historicism. American Literary History, 3(1), 25-45. https://doi.org/10.1093/alh/3.1.27 Beall, A., Allen, C., Vujic, A., & Klein, L. (2018). Reimagining Elizabeth Palmer Peabody's lost “mural charts.” In Proceedings of Digital Humanities 2018 (pp. 607-608). ADHO.

Bertin, Jacques. (1967). Sémiologie graphique: Les diagrammes, les réseaux, les cartes. Gauthier-Villars.

Bertini, E., Correll, M., & Franconeri, S. (2020). Why shouldn't all charts be scatter plots? Beyond precisiondriven visualizations. In Proceedings of IEEE VIS 2020 (pp. 206-210). IEEE.

http://doi.org/10.1109/VIS47514.2020.00048 Borkin, M. A., Bylinskii, Z., Kim, N. W., Bainbridge, C. M., Yeh, C. S., Borkin, D., Pfister, H., & Oliva, A.

(2015). Beyond memorability: Visualization recognition and recall. IEEE Transactions on Visualization and Computer Graphics, 22(1), 519-528. http://doi.org/10.1109/TVCG.2015.2467732 boyd, d., & Crawford, K. (2012). Critical questions for big data. Information, Communication, and Society, 15(5), 662-679. https://psycnet.apa.org/doi/10.1080/1369118X.2012.678878 Bradley, A. J. Sawal, S., & Collins, C. (2019). Approaching humanities questions using slow visual search interfaces. VIS4DH: 4th Workshop on Visualization for the Digital Humanities, IEEE VIS 2019.

Cairo, A. (2013, April 3). Emotional data visualization: Periscopic's “U.S. Gun Deaths” and the challenge of uncertainty. Peachpit. https://www.peachpit.com/articles/article.aspx?p=2036558 Card, S, Mackinlay, J., & Schneiderman, B. (Eds.). (1999). Readings in information visualization: Using vision to think. Morgan Kaufmann. http://doi.org/10.5555/300679 Correll, M., & Gleicher, M. (2014). Bad for data, good for the brain: Knowledge-first axioms for visualization design. DECISIVe: Workshop on Dealing with Cognitive Biases in Visualisations, IEEE VIS 2014.

D'Ignazio, C., & Klein, L. F. (2016). Feminist data visualization. VIS4DH: 1st Workshop on Visualization for the Digital Humanities, IEEE VIS 2016.

D'Ignazio, C., & Klein, L. F. (2020). Data feminism. MIT Press.

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    3
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    3
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Top 10%
Average
Average