1. Big science and big data in nephrology
Julio Saez-Rodriguez, Markus M. Rinschen, Jürgen Floege, Rafael Kramann 
Kidney International 
DOI: /j.kint
Copyright © 2019 International Society of Nephrology Terms and Conditions

2. Figure 1 Overview of data generation and analysis for nephrology.
Kidney International DOI: ( /j.kint )
Copyright © 2019 International Society of Nephrology Terms and Conditions

3. Figure 2
The most common omics technologies and the information they provide regarding molecular processes. scRNA, single-cell RNA; SNP, single-nucleotide polymorphism; Transc., transcription; WES, whole-exome sequencing; WGS, whole-genome sequencing.
Kidney International DOI: ( /j.kint )
Copyright © 2019 International Society of Nephrology Terms and Conditions

4. Figure 3
Computational strategies to apply statistics and machine learning to big data. Use of prior knowledge to extract molecular signatures can facilitate subsequent analyses.
Kidney International DOI: ( /j.kint )
Copyright © 2019 International Society of Nephrology Terms and Conditions

5. Figure 4
Timeline of technological developments and their incorporation into basic and translational research in the renal and oncology (“cancer”) fields. GWAS, Genome-Wide Association Study; KPMP, Kidney Precision Medicine Project; Nat Gen, Nature Genetics; scRNA, single-cell RNA; TCGA, The Cancer Genome Atlas.
Kidney International DOI: ( /j.kint )
Copyright © 2019 International Society of Nephrology Terms and Conditions