seaborn: statistical data visualization
Open accessAbstract
seaborn is a library for making statistical graphics in Python.It provides a high-level interface to matplotlib and integrates closely with pandas data structures.Functions in the seaborn library expose a declarative, dataset-oriented API that makes it easy to translate questions about data into graphics that can answer them.When given a dataset and a specification of the plot to make, seaborn automatically maps the data values to visual attributes such as color, size, or style, internally computes statistical transformations, and decorates the plot with informative axis labels and a legend.Many seaborn functions can generate figures with multiple panels that elicit comparisons between conditional subsets of data or across different pairings of variables in a dataset.seaborn is designed to be useful throughout the lifecycle of a scientific project.By producing complete graphics from a single function call with minimal arguments, seaborn facilitates rapid prototyping and exploratory data analysis.And by offering extensive options for customization, along with exposing the underlying matplotlib objects, it can be used to create polished, publication-quality figures.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
All graphs of experimental results plot the mean and standard deviation over 3 runs as computed by Seaborn.
“All graphs of our experimental results plot the mean and standard deviation over 3 runs as computed by Seaborn (Waskom, 2021).”
From The Power of Scale for Parameter-Efficient Prompt Tuning (2021) · cites this paper as Waskom, 2021
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Matplotlib: a 2D graphics environment
Hunter, J. D. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.55 · Cited by 39,249 · see citing works
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., et al. (2020) · Nature · doi:10.1038/s41586-020-2649-2 · Cited by 23,826 · see citing works
Data Structures for Statistical Computing in Python
McKinney, W. (2010) · Proceedings of the Python in Science Conferences · doi:10.25080/majora-92bf1922-00a · Cited by 11,511 · see citing works
Inference by Eye: Confidence Intervals and How to Read Pictures of Data.
Cumming, G., & Finch, S. (2005) · American Psychologist · doi:10.1037/0003-066x.60.2.170 · Cited by 1,449 · cited 1× · see citing works
“The confidence intervals are computed with a bootstrap algorithm, allowing them to generalize over many different statistics, and the default level allows the user to perform “inference by eye” (Cumming & Finch, 2005).”
MethodWickham, H. (2014) · Journal of Statistical Software · doi:10.18637/jss.v059.i10 · Cited by 937 · see citing works
Distributed representation of context by intrinsic subnetworks in prefrontal cortex
Waskom, M., & Wagner, A. D. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1615269114 · Cited by 26 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 2
- Tukey, J. W. (1977). Exploratory data analysis. Addison-Wesley. ISBN: 978-0201076165
- Wickham, H. (2016). ggplot2: Elegant graphics for data analysis. Springer-Verlag. ISBN: 978-3-319-24277-4
Cited by
Sorted by citations
gmx_MMPBSA: A New Tool to Perform End-State Free Energy Calculations with GROMACS
Tresanco, M. S. V., Valdés-Tresanco, M. E., Valiente, P. A., & Moreno, E. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c00645 · Cited by 3,468 · see citing works
The Power of Scale for Parameter-Efficient Prompt Tuning
Lester, B., Al-Rfou, R., & Constant, N. (2021) · arXiv · doi:10.48550/arxiv.2104.08691 · Cited by 3,092
The Power of Scale for Parameter-Efficient Prompt Tuning
Lester, B., Al-Rfou, R., & Constant, N. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.243 · Cited by 3,096 · see citing works
Definitions, methods, and applications in interpretable machine learning
Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900654116 · Cited by 2,167 · see citing works
Abbott, R., Abbott, T. D., Acernese, F., Ackley, K., Adams, C., Adhikari, N., Adhikari, R. X., Adya, V. B., Affeldt, C., Agarwal, D., et al. (2023) · Physical Review X · doi:10.1103/physrevx.13.041039 · Cited by 1,768 · see citing works
Chklovski, A., Parks, D. H., Woodcroft, B. J., & Tyson, G. W. (2023) · Nature Methods · doi:10.1038/s41592-023-01940-w · Cited by 1,705 · see citing works
A universal graph deep learning interatomic potential for the periodic table
Chen, C., & Ong, S. P. (2022) · Nature Computational Science · doi:10.1038/s43588-022-00349-3 · Cited by 1,035 · see citing works
Generalized biomolecular modeling and design with RoseTTAFold All-Atom
Krishna, R., Wang, J., Ahern, W., Sturmfels, P., Venkatesh, P., Kalvet, I., Lee, G. R., Morey-Burrows, F. S., Anishchenko, I., Humphreys, I. R., et al. (2024) · Science · doi:10.1126/science.adl2528 · Cited by 978 · see citing works
High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections
Beck, H. E., McVicar, T. R., Vergopolan, N., Berg, A., Lutsko, N. J., Dufour, A., Zeng, Z., Jiang, X. S., van Dijk, A. I. J. M., & Miralles, D. G. (2023) · Scientific Data · doi:10.1038/s41597-023-02549-6 · Cited by 1,036 · see citing works
The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans
The Tabula Sapiens Consortium*, Jones, R. C., Karkanias, J., Krasnow, M. A., Pisco, A. O., Quake, S. R., Salzman, J., Yosef, N., Bulthaup, B., Brown, P., et al. (2022) · Science · doi:10.1126/science.abl4896 · Cited by 1,056 · see citing works
Gao, C. A., Howard, F. M., Markov, N. S., Dyer, E. C., Ramesh, S., Luo, Y., & Pearson, A. T. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00819-6 · Cited by 1,099 · see citing works
SARS-CoV-2 infection triggers profibrotic macrophage responses and lung fibrosis
Wendisch, D., Dietrich, O., Mari, T., von Stillfried, S., Ibarra, I. L., Mittermaier, M., Mache, C., Chua, R. L., Knoll, R., Timm, S., et al. (2021) · Cell · doi:10.1016/j.cell.2021.11.033 · Cited by 528 · see citing works
Sarker, I. H. (2021) · SN Computer Science · doi:10.1007/s42979-021-00765-8 · Cited by 570 · see citing works
A high-performance neuroprosthesis for speech decoding and avatar control
Metzger, S. L., Littlejohn, K. T., Silva, A. B., Moses, D. A., Seaton, M. P., Wang, R., Dougherty, M. E., Liu, J. R., Wu, P., Berger, M. A., et al. (2023) · Nature · doi:10.1038/s41586-023-06443-4 · Cited by 522 · see citing works
AlphaFill: enriching AlphaFold models with ligands and cofactors
Hekkelman, M. L., de Vries, I., Joosten, R. P., & Perrakis, A. (2023) · Nature Methods · doi:10.1038/s41592-022-01685-y · Cited by 511 · see citing works
Multiple pathways for SARS-CoV-2 resistance to nirmatrelvir
Iketani, S., Mohri, H., Culbertson, B., Hong, S. J., Duan, Y., Luck, M. I., Annavajhala, M. K., Guo, Y., Sheng, Z., Uhlemann, A., et al. (2022) · Nature · doi:10.1038/s41586-022-05514-2 · Cited by 498 · see citing works
Abbott, R., Abbott, T. D., Acernese, F., Ackley, K., Adams, C., Adhikari, N., Adhikari, R. X., Adya, V. B., Affeldt, C., Agarwal, D., et al. (2024) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.109.022001 · Cited by 499 · see citing works
The connectome of an insect brain
Winding, M., Pedigo, B. D., Barnes, C. L., Patsolic, H. G., Park, Y., Kazimiers, T., Fushiki, A., Andrade, I. V., Khandelwal, A., Valdes-Aleman, J., et al. (2023) · Science · doi:10.1126/science.add9330 · Cited by 435 · see citing works
Gao, C. A., Howard, F. M., Markov, N. S., Dyer, E. C., Ramesh, S., Luo, Y., & Pearson, A. T. (2022) · doi:10.1101/2022.12.23.521610 · Cited by 1,099 · see citing works
A neutrophil response linked to tumor control in immunotherapy
Gungabeesoon, J., Gort-Freitas, N. A., Kiss, M., Bolli, E., Messemaker, M., Siwicki, M. R., Hicham, M., Bill, R., Koch, P. D., Cianciaruso, C., et al. (2023) · Cell · doi:10.1016/j.cell.2023.02.032 · Cited by 416 · see citing works
Argentieri, M. A., Xiao, S., Bennett, D., Winchester, L. M., Nevado‐Holgado, A. J., Ghose, U., Albukhari, A., Yao, P., Mazidi, M., Lv, J., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03164-7 · Cited by 378 · see citing works
Quantitative high-confidence human mitochondrial proteome and its dynamics in cellular context
Morgenstern, M., Peikert, C. D., Lübbert, P., Suppanz, I., Klemm, C., Alka, O., Steiert, C., Naumenko, N., Schendzielorz, A. B., Melchionda, L., et al. (2021) · Cell Metabolism · doi:10.1016/j.cmet.2021.11.001 · Cited by 318 · see citing works
Néron, B., Littner, E., Haudiquet, M., Perrin, A., Cury, J., & Rocha, E. P. C. (2022) · Microorganisms · doi:10.3390/microorganisms10040700 · Cited by 326 · see citing works
Open Data from the Third Observing Run of LIGO, Virgo, KAGRA, and GEO
Abbott, R., Abe, H., Acernese, F., Ackley, K., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Adya, V. B., Affeldt, C., et al. (2023) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/acdc9f · Cited by 333 · see citing works
Wills, R. C. J., Dong, Y., Proistosescu, C., Armour, K. C., & Battisti, D. S. (2022) · Geophysical Research Letters · doi:10.1029/2022gl100011 · Cited by 305 · see citing works
Vaccination induces HIV broadly neutralizing antibody precursors in humans
Leggat, D. J., Cohen, K. W., Willis, J. R., Fulp, W. J., deCamp, A. C., Kalyuzhniy, O., Cottrell, C. A., Menis, S., Finak, G., Ballweber-Fleming, L., et al. (2022) · Science · doi:10.1126/science.add6502 · Cited by 285 · see citing works
Tropical deforestation causes large reductions in observed precipitation
Smith, C., Baker, J. C. A., & Spracklen, D. V. (2023) · Nature · doi:10.1038/s41586-022-05690-1 · Cited by 300 · see citing works
Stevens, L. J., Pruijssers, A. J. P., Lee, H. W., Gordon, C. J., Tchesnokov, E. P., Gribble, J., George, A. S., Hughes, T. M., Lu, X., Li, J., et al. (2022) · Science Translational Medicine · doi:10.1126/scitranslmed.abo0718 · Cited by 249 · see citing works
DeepLC can predict retention times for peptides that carry as-yet unseen modifications
Bouwmeester, R., Gabriels, R., Hulstaert, N., Martens, L., & Degroeve, S. (2021) · Nature Methods · doi:10.1038/s41592-021-01301-5 · Cited by 232 · see citing works
MUON: multimodal omics analysis framework
Bredikhin, D., Kats, I., & Stegle, O. (2022) · Genome Biology · doi:10.1186/s13059-021-02577-8 · Cited by 216 · see citing works
Thölke, P., Mantilla-Ramos, Y.-J., Abdelhedi, H., Maschke, C., Dehgan, A., Harel, Y., Kemtur, A., Berrada, L. M., Sahraoui, M., Young, T., et al. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120253 · Cited by 234 · see citing works
Pan-cancer proteomic map of 949 human cell lines
Gonçalves, E., Poulos, R. C., Cai, Z., Barthorpe, S., Manda, S. S., Lucas, N., Beck, A., Bucio-Noble, D., Dausmann, M., Hall, C., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.06.010 · Cited by 228 · see citing works
SARS-CoV-2 infection generates tissue-localized immunological memory in humans
Poon, M. M. L., Rybkina, K., Kato, Y., Kubota, M., Matsumoto, R., Bloom, N. I., Zhang, Z., Hastie, K. M., Grifoni, A., Weiskopf, D., et al. (2021) · Science Immunology · doi:10.1126/sciimmunol.abl9105 · Cited by 214 · see citing works
Ultra-deep sequencing of Hadza hunter-gatherers recovers vanishing gut microbes
Carter, M. M., Olm, M. R., Merrill, B. D., Dahan, D., Tripathi, S. M., Spencer, S. P., Yu, F. B., Jain, S., Neff, N. F., Jha, A. R., et al. (2023) · Cell · doi:10.1016/j.cell.2023.05.046 · Cited by 230 · see citing works
Spontaneous behaviour is structured by reinforcement without explicit reward
Markowitz, J. E., Gillis, W. F., Jay, M., Wood, J. J., Harris, R., Cieszkowski, R., Scott, R., Brann, D. H., Koveal, D., Kula, T., et al. (2023) · Nature · doi:10.1038/s41586-022-05611-2 · Cited by 214 · see citing works
Abac, A., Abbott, R., Abouelfettouh, I., Acernese, F., Ackley, K., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Agarwal, D., et al. (2024) · The Astrophysical Journal Letters · doi:10.3847/2041-8213/ad5beb · Cited by 208 · see citing works
Nasarian, E., Alizadehsani, R., Acharya, U. R., & Tsui, K.-L. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102412 · Cited by 207 · see citing works
Reconstruction of neocortex: Organelles, compartments, cells, circuits, and activity
Turner, N. L., Macrina, T., Bae, J. A., Yang, R., Wilson, A. M., Schneider-Mizell, C., Lee, K., Lu, R., Wu, J., Bodor, A. L., et al. (2022) · Cell · doi:10.1016/j.cell.2022.01.023 · Cited by 206 · see citing works
<scp>AlphaFold2</scp> fails to predict protein fold switching
Chakravarty, D., & Porter, L. L. (2022) · Protein Science · doi:10.1002/pro.4353 · Cited by 193 · see citing works
Whitfield, Z. J., Dolan, P., Kunitomi, M., Tassetto, M., Seetin, M. G., Oh, S. D., Heiner, C. R., Paxinos, E. E., & Andino, R. (2017) · Current Biology · doi:10.1016/j.cub.2017.09.067 · Cited by 198 · see citing works
Benchmarking spatial clustering methods with spatially resolved transcriptomics data
Yuan, Z., Zhao, F., Lin, S., Zhao, Y. S., Yao, J., Cui, Y., Zhang, X.-Y., & Zhao, Y. C. (2024) · Nature Methods · doi:10.1038/s41592-024-02215-8 · Cited by 199 · see citing works
Standardized and reproducible measurement of decision-making in mice
Aguillon-Rodriguez, V., Angelaki, D. E., Bayer, H. M., Bonacchi, N., Carandini, M., Cazettes, F., Chapuis, G. A., Churchland, A. K., Dan, Y., DeWitt, E. E. J., et al. (2021) · eLife · doi:10.7554/elife.63711 · Cited by 229 · see citing works
Protein structure generation via folding diffusion
Wu, K., Yang, K., van den Berg, R., Alamdari, S., Zou, J., Lu, A. X., & Amini, A. P. (2024) · Nature Communications · doi:10.1038/s41467-024-45051-2 · Cited by 204 · see citing works
Quality assessment of gene repertoire annotations with OMArk
Nevers, Y., Warwick Vesztrocy, A., Rossier, V., Train, C.-M., Altenhoff, A., Dessimoz, C., & Glover, N. M. (2025) · Nature Biotechnology · doi:10.1038/s41587-024-02147-w · Cited by 186 · see citing works
AlphaFold2: A Role for Disordered Protein/Region Prediction?
Wilson, C. J., Choy, W.-Y., & Karttunen, M. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23094591 · Cited by 173 · see citing works
Py-Feat: Python Facial Expression Analysis Toolbox
Cheong, J. H., Jolly, E., Xie, T.-K., Byrne, S., Kenney, M., & Chang, L. (2021) · Affective Science · doi:10.1007/s42761-023-00191-4 · Cited by 201 · see citing works
Smart City Data Science: Towards data-driven smart cities with open research issues
Sarker, I. H. (2022) · Internet of Things · doi:10.1016/j.iot.2022.100528 · Cited by 180 · see citing works
Accurate de novo design of membrane-traversing macrocycles
Bhardwaj, G., O’Connor, J., Rettie, S., Huang, Y., Ramelot, T. A., Mulligan, V. K., Alpkilic, G. G., Palmer, J. M., Bera, A. K., Bick, M. J., et al. (2022) · Cell · doi:10.1016/j.cell.2022.07.019 · Cited by 177 · see citing works
Pacifici, C., Iyer, K. G., Mobasher, B., da Cunha, E., Acquaviva, V., Burgarella, D., Rivera, G. C., Carnall, A. C., Chang, Y.-Y., Chartab, N., et al. (2023) · The Astrophysical Journal · doi:10.3847/1538-4357/acacff · Cited by 175 · see citing works
Key features and guidelines for the application of microbial alpha diversity metrics
Cassol, I., Ibañez, M., & Bustamante, J. P. (2025) · Scientific Reports · doi:10.1038/s41598-024-77864-y · Cited by 194 · see citing works
Wearable movement-tracking data identify Parkinson’s disease years before clinical diagnosis
Schalkamp, A., Peall, K. J., Harrison, N. A., & Sandor, C. (2023) · Nature Medicine · doi:10.1038/s41591-023-02440-2 · Cited by 172 · see citing works
Chang, L. J., Jolly, E., Cheong, J. H., Rapuano, K. M., Greenstein, N., Chen, P.-H. A., & Manning, J. R. (2021) · Science Advances · doi:10.1126/sciadv.abf7129 · Cited by 174 · see citing works
Madagascar’s extraordinary biodiversity: Threats and opportunities
Ralimanana, H., Perrigo, A., Smith, R. J., Borrell, J. S., Faurby, S., Rajaonah, M. T., Randriamboavonjy, T., Vorontsova, M. S., Cooke, R., Phelps, L. N., et al. (2022) · Science · doi:10.1126/science.adf1466 · Cited by 162 · see citing works
Mouse spontaneous behavior reflects individual variation rather than estrous state
Levy, D. R., Hunter, N., Lin, S., Robinson, E. M., Gillis, W. F., Conlin, E., Anyoha, R., Shansky, R. M., & Datta, S. R. (2023) · Current Biology · doi:10.1016/j.cub.2023.02.035 · Cited by 163 · see citing works
Spatial transcriptomic landscape unveils immunoglobin-associated senescence as a hallmark of aging
Ma, S., Ji, Z., Zhang, B., Geng, L., Cai, Y., Nie, C., Li, J., Zuo, Y., Sun, Y., Xu, G., et al. (2024) · Cell · doi:10.1016/j.cell.2024.10.019 · Cited by 162 · see citing works
A concerted neuron–astrocyte program declines in ageing and schizophrenia
Ling, E., Nemesh, J. C., Goldman, M., Kamitaki, N., Reed, N. M., Handsaker, R. E., Genovese, G., Vogelgsang, J. S., Gerges, S., Kashin, S., et al. (2024) · Nature · doi:10.1038/s41586-024-07109-5 · Cited by 161 · see citing works
Explainable machine learning model for liquefaction potential assessment of soils using XGBoost-SHAP
Jas, K., & Dodagoudar, G. R. (2022) · Soil Dynamics and Earthquake Engineering · doi:10.1016/j.soildyn.2022.107662 · Cited by 164 · see citing works
Convolutional networks for supervised mining of molecular patterns within cellular context
de Teresa, I., Goetz, S. K., Mattausch, A., Stojanovska, F., Zimmerli, C. E., Toro‐Nahuelpan, M., Cheng, D., Tollervey, F., Pape, C., Beck, M., et al. (2023) · Nature Methods · doi:10.1038/s41592-022-01746-2 · Cited by 159 · see citing works
Chandrasekaran, S. N., Cimini, B. A., Goodale, A. B., Miller, L., Kost‐Alimova, M., Jamali, N., Doench, J. G., Fritchman, B., Skepner, A. P., Melanson, M., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02241-6 · Cited by 159 · see citing works
AlphaFold predictions of fold-switched conformations are driven by structure memorization
Chakravarty, D., Schafer, J. W., Chen, E. A., Thole, J. F., Ronish, L. A., Lee, M., & Porter, L. L. (2024) · Nature Communications · doi:10.1038/s41467-024-51801-z · Cited by 159 · see citing works
Neural fragility as an EEG marker of the seizure onset zone
Li, A., Huynh, C., Fitzgerald, Z. B., Cajigas, I., Brusko, G. D., Jagid, J. R., Claudio, A. O., Kanner, A. M., Hopp, J. L., Chen, S. H., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00901-w · Cited by 157 · see citing works
Compatibility rules of human enhancer and promoter sequences
Bergman, D. T., Jones, T. R., Liu, V., Ray, J., Jagoda, E., Siraj, L., Kang, H. Y., Nasser, J., Kane, M., Rios, A., et al. (2022) · Nature · doi:10.1038/s41586-022-04877-w · Cited by 155 · see citing works
Blebs promote cell survival by assembling oncogenic signalling hubs
Weems, A. D., Welf, E. S., Driscoll, M., Zhou, F., Mazloom-Farsibaf, H., Chang, B.-J., Murali, V. S., Gihana, G. M., Weiss, B. G., Chi, J., et al. (2023) · Nature · doi:10.1038/s41586-023-05758-6 · Cited by 154 · see citing works
Protein complexes in cells by AI‐assisted structural proteomics
(2023) · doi:10.15252/msb.202311544 · Cited by 147 · see citing works
Integrated intracellular organization and its variations in human iPS cells
Viana, M. P., Chen, J., Knijnenburg, T. A., Vasan, R., Yan, C., Arakaki, J. E., Bailey, M., Berry, B., Borensztejn, A., Brown, E. M., et al. (2023) · Nature · doi:10.1038/s41586-022-05563-7 · Cited by 132 · see citing works
Xist nucleates local protein gradients to propagate silencing across the X chromosome
Markaki, Y., Chong, J. G., Wang, Y., Jacobson, E. C., Luong, C., Tan, S. Y. X., Jachowicz, J. W., Strehle, M. M., Maestrini, D., Banerjee, A. K., et al. (2021) · Cell · doi:10.1016/j.cell.2021.10.022 · Cited by 144 · see citing works
The Redshift Evolution of the Binary Black Hole Merger Rate: A Weighty Matter
van Son, L. V., de Mink, S. D., Callister, T., Justham, S., Renzo, M., Wagg, T., Broekgaarden, F., Kummer, F., Pakmor, R., & Mandel, I. (2021) · Astrophysical Journal · doi:10.3847/1538-4357/ac64a3 · Cited by 112 · see citing works
INSTANCE – the Italian seismic dataset for machine learning
Michelini, A., Cianetti, S., Gaviano, S., Giunchi, C., Jozinović, D., & Lauciani, V. (2021) · Earth system science data · doi:10.5194/essd-13-5509-2021 · Cited by 145 · see citing works
Diverse silent chromatin states modulate genome compartmentalization and loop extrusion barriers
Spracklin, G., Abdennur, N. A., Imakaev, M., Chowdhury, N., Pradhan, S., Mirny, L. A., & Dekker, J. (2022) · Nature Structural & Molecular Biology · doi:10.1038/s41594-022-00892-7 · Cited by 139 · see citing works
Srivastava, K., Carreño, J. M., Gleason, C., Monahan, B., Singh, G., Abbad, A., Tcheou, J., Raskin, A. J., Kleiner, G., van Bakel, H., et al. (2024) · Immunity · doi:10.1016/j.immuni.2024.01.017 · Cited by 144 · see citing works
Ahmed, A. A. M., Deo, R. C., Feng, Q., Ghahramani, A., Raj, N., Yin, Z., & Yang, L. (2021) · Journal of Hydrology · doi:10.1016/j.jhydrol.2021.126350 · Cited by 143 · see citing works
The LIGO Scientific Collaboration, The Virgo Collaboration, Abbott, R., Abbott, T. D., Acernese, F., Ackley, K., Adams, C., Adhikari, N., Adhikari, R. X., Adya, V. B., et al. (2021) · Strathprints: The University of Strathclyde institutional repository (University of Strathclyde) · doi:10.48550/arxiv.2108.01045 · Cited by 142 · see citing works
Artificial intelligence in structural health management of existing bridges
Mucci, V. M. D., Cardellicchio, A., Ruggieri, S., Nettis, A., Renò, V., & Uva, G. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105719 · Cited by 140 · see citing works
Long, H., Jia, Q., Wang, L., Fang, W., Wang, Z., Jiang, T., Zhou, F., Zheng, J., Huang, J., Zhou, L., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.04.018 · Cited by 137 · see citing works
AI is a viable alternative to high throughput screening: a 318-target study
Program, T. A. A., Wallach, I., Bernard, D., Nguyen, K. T., Ho, G., Morrison, A., Stecuła, A., Rosnik, A. M., O’Sullivan, A. M., Davtyan, A., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-54655-z · Cited by 137 · see citing works
Bifarin, O. O. (2023) · PLoS ONE · doi:10.1371/journal.pone.0284315 · Cited by 143 · see citing works
Tumour immune rejection triggered by activation of α2-adrenergic receptors
Zhu, J., Naulaerts, S., Boudhan, L., Martin, M., Gatto, L., & den Eynde, B. J. V. (2023) · Nature · doi:10.1038/s41586-023-06110-8 · Cited by 134 · see citing works
Evolution of Plasmid Mobility: Origin and Fate of Conjugative and Nonconjugative Plasmids
Coluzzi, C., Garcillán-Barcia, M. P., de la Cruz, F., & Rocha, E. P. C. (2022) · Molecular Biology and Evolution · doi:10.1093/molbev/msac115 · Cited by 132 · see citing works
An organotypic atlas of human vascular cells
Barnett, S. N., Cujba, A.-M., Yang, L., Maceiras, A. R., Li, S., Kedlian, V. R., Pett, J. P., Polański, K., Miranda, A. M. A., Xu, C., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03376-x · Cited by 132 · see citing works
Davoudian, P. A., Shao, L.-X., & Kwan, A. C. (2023) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.2c00637 · Cited by 128 · see citing works
Prototype global analysis of LISA data with multiple source types
Littenberg, T. B., & Cornish, N. J. (2023) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.107.063004 · Cited by 131 · see citing works
Kazemi, N., Abdolrazzaghi, M., & Musı́lek, P. (2021) · IEEE Transactions on Microwave Theory and Techniques · doi:10.1109/tmtt.2021.3081119 · Cited by 130 · see citing works
Masanneck, L., Schmidt, L., Seifert, A., Kölsche, T., Huntemann, N., Jansen, R., Mehsin, M., Bernhard, M., Meuth, S. G., Böhm, L., et al. (2024) · Journal of Medical Internet Research · doi:10.2196/53297 · Cited by 130 · see citing works
The California-Kepler Survey. X. The Radius Gap as a Function of Stellar Mass, Metallicity, and Age
Petigura, E. A., Rogers, J. G., Isaacson, H. T., Owen, J. E., Kraus, A. L., Winn, J. N., MacDougall, M. G., Howard, A. W., Fulton, B. J., Kosiarek, M., et al. (2022) · The Astronomical Journal · doi:10.3847/1538-3881/ac51e3 · Cited by 129 · see citing works
Aging clocks based on accumulating stochastic variation
Meyer, D. H., & Schumacher, B. (2024) · Nature Aging · doi:10.1038/s43587-024-00619-x · Cited by 128 · see citing works
vRhyme enables binning of viral genomes from metagenomes
Kieft, K., Adams, A., Salamzade, R., Kalan, L., & Anantharaman, K. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac341 · Cited by 119 · see citing works
Wang, J., Liu, J., Wang, H., Zhou, M., Ke, G., Zhang, L., Wu, J., Gao, Z., & Lu, D. (2024) · Nature Communications · doi:10.1038/s41467-024-46276-x · Cited by 127 · see citing works
Tang, Z., Bai, Y., Fang, Q., Yuan, Y., Zeng, Q., Chen, S., Xu, T., Chen, J., Tan, L., Wang, C., et al. (2025) · Cancer Cell · doi:10.1016/j.ccell.2025.03.011 · Cited by 127 · see citing works
Assembly of 43 human Y chromosomes reveals extensive complexity and variation
Hallast, P., Ebert, P., Loftus, M. G., Yilmaz, F., Audano, P. A., Logsdon, G. A., Bonder, M. J., Zhou, W., Höps, W., Kim, K., et al. (2023) · Nature · doi:10.1038/s41586-023-06425-6 · Cited by 123 · see citing works
Oh, H. S., Urey, D. Y., Karlsson, L., Zhu, Z., Shen, Y., Farinas, A., Timsina, J., Duggan, M. R., Chen, J., Guldner, I. H., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03565-2 · Cited by 122 · see citing works
Sequence-to-sequence translation from mass spectra to peptides with a transformer model
Yilmaz, M., Fondrie, W. E., Bittremieux, W., Melendez, C., Nelson, R., Ananth, V., Oh, S., & Noble, W. S. (2024) · Nature Communications · doi:10.1038/s41467-024-49731-x · Cited by 118 · see citing works
Architecture of the linker-scaffold in the nuclear pore
Petrovic, S., Samanta, D., Perriches, T., Bley, C. J., Thierbach, K., Brown, B., Nie, S., Mobbs, G. W., Stevens, T. A., Liu, X., et al. (2022) · Science · doi:10.1126/science.abm9798 · Cited by 114 · see citing works
Franklin, D., Tzavelis, A., Lee, J. Y., Chung, H. U., Trueb, J. T., Arafa, H., Kwak, S. S., Huang, I. A., Liu, Y., Rathod, M., et al. (2023) · Nature Biomedical Engineering · doi:10.1038/s41551-023-01098-y · Cited by 119 · see citing works
Bubis, J. A., Arrey, T. N., Damoc, E., Delanghe, B., Slováková, J., Sommer, T. M., Kagawa, H., Pichler, P., Rivron, N. C., Mechtler, K., et al. (2025) · Nature Methods · doi:10.1038/s41592-024-02559-1 · Cited by 118 · see citing works
de Miguel, F. J., Gentile, C., Feng, W. W., Silva, S. J., Sankar, A., Expósito, F., Cai, W. L., Melnick, M. A., Robles-Oteíza, C., Hinkley, M. M., et al. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.07.005 · Cited by 117 · see citing works
Alneberg, J., Karlsson, C. M. G., Divne, A., Bergin, C., Homa, F., Lindh, M. V., Hugerth, L. W., Ettema, T. J. G., Bertilsson, S., Andersson, A. F., et al. (2018) · Microbiome · doi:10.1186/s40168-018-0550-0 · Cited by 113 · see citing works
Fast evaluation of the adsorption energy of organic molecules on metals via graph neural networks
Pablo‐García, S., Morandi, S., Vargas–Hernández, R. A., Jorner, K., Ivković, Ž., López, N., & Aspuru‐Guzik, A. (2023) · Nature Computational Science · doi:10.1038/s43588-023-00437-y · Cited by 118 · see citing works
Ly, H. H., Daniel, S., Soriano, S. K. V., Kis, Z., & Blakney, A. K. (2022) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.2c00032 · Cited by 116 · see citing works
Luo, Y., Xia, Y., Liu, D., Li, X., Li, H., Liu, J., Zhou, D., Dong, Y. J., Li, X., Qian, Y., et al. (2024) · Cell · doi:10.1016/j.cell.2024.06.013 · Cited by 115 · see citing works
Protonotarios, A., Bariani, R., Cappelletto, C., Pavlou, M., García-García, A., Cipriani, A., Protonotarios, I., Rivas, A., Wittenberg, R., Graziosi, M., et al. (2022) · European Heart Journal · doi:10.1093/eurheartj/ehac235 · Cited by 111 · see citing works
- Authors
- Waskom, M.
- Affiliations
- New York University (US)
- Corresponding author
- Waskom, Michael
- ORCID
- Waskom, Michael
- Venue
- Journal of Open Source Software
- Publisher
- The Open Journal
- Published
- 2021-04-06
- Volume
- 6
- Issue
- 60
- Pages
- 3021
- ISSN
- 2475-9066 (ISSN-L)
- OpenAlex
- W3128261063
- Cited by
- 7,310
- References
- 6
- Citations by year
- 2026: 1498 · 2025: 1881 · 2024: 1517 · 2023: 1172 · 2022: 805 · 2021: 380 · 2020: 34 · 2019: 8 · 2018: 6 · 2017: 5 · 2012: 2
- FWCI
- 349.32
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- The Journal of Open Source Software (publishedVersion, cc-by)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, other-oa)
- Astrophysics Source Code Library
- Zenodo (CERN European Organization for Nuclear Research) (other-oa)
- Funders
- National Science Foundation (0801700)
- Simons Foundation (527794)
- Simons Society of Fellows
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Computational Physics and Python Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Data Analysis with R
- Keywords
- seaborn, statistical graphics, Python library, exploratory data analysis, data visualization, publication-quality figures
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review