diff --git a/DESCRIPTION b/DESCRIPTION
index 1a011cd..ccd716c 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: miaTime
 Type: Package
 Title: Microbiome Time Series Analysis
-Version: 0.99.10
+Version: 0.99.11
 Authors@R:
      c(person(given = "Leo", family = "Lahti", role = c("aut"),
               email = "leo.lahti@iki.fi",
diff --git a/NEWS b/NEWS
index ca47b03..37fae73 100755
--- a/NEWS
+++ b/NEWS
@@ -1,3 +1,7 @@
+Changes in version 0.99.11
+Date: 2025-09-22
++ Added Kumaraswamy2024 data
+
 Changes in version 0.99.10
 Date: 2025-09-06
 + Added survival data on Crohn's disease (crohn_survival)
diff --git a/R/miaTime.R b/R/miaTime.R
index 818478e..588f3d0 100755
--- a/R/miaTime.R
+++ b/R/miaTime.R
@@ -185,3 +185,46 @@ NULL
 #' @keywords
 #' data
 NULL
+
+#' @title Kumaraswamy2024
+#' @description
+#' The Kumaraswamy2024 includes microbiota and metabolite profiling data 
+#' from 78 Indian individuals (40 males, 38 females).
+#' 
+#' The Indian subjects were grouped into four diet groups (~20 subjects per group), 
+#' and fecal samples were collected across three seasonal time points.
+#'
+#' The microbiota profiling was performed using HITChip microarray analysis 
+#' (in duplicate), qPCR (in triplicate with eight-point standard curves), and 
+#' LC-HRMS and HPLC metabolite profiling with internal standards.
+#'
+#' Column metadata includes diet group assignment, sampling season, sex, BMI, 
+#' age, and questionnaire-based lifestyle metadata.
+#'
+#' Quality control metrics include Pearson correlation (>0.98) for HITChip, 
+#' qPCR assay efficiency (>0.99), and technical replicates for HPLC and qPCR.
+#'
+#' Data sources:
+#' - Microbiota HITChip microarray data
+#' - qPCR absolute abundance data
+#' - Chemical profiling data (HPLC, LC-HRMS)
+#' - Sample metadata (diet, lifestyle)
+#'
+#' Processed and raw data are available via:
+#' - Zenodo (DOI: https://doi.org/10.5281/zenodo.14424024)
+#' - NCBI-SRA (fermented foods 16S rRNA sequencing, accession: PRJNA1191989)
+#'
+#' @name Kumaraswamy2024
+#' @docType data
+#' @author Jeyaram, K., Lahti, L., Tims, S. et al
+#' @return Loads the data set in R.
+#' @references
+#' Jeyaram, K., Lahti, L., Tims, S. et al. Fermented foods affect the seasonal 
+#' stability of gut bacteria in an Indian rural population.
+#' Nat Commun 16, 771 \url{https://doi.org/10.1038/s41467-025-56014-6}
+#' @usage data(Kumaraswamy2024)
+#' @format The data set in
+#' \code{\link[TreeSummarizedExperiment:TreeSummarizedExperiment-class]{TreeSummarizedExperiment}}
+#' format.
+#' @keywords data
+NULL
diff --git a/data/Kumaraswamy2024.rda b/data/Kumaraswamy2024.rda
new file mode 100644
index 0000000..9ab0423
Binary files /dev/null and b/data/Kumaraswamy2024.rda differ
diff --git a/inst/scripts/Kumaraswamy2024.R b/inst/scripts/Kumaraswamy2024.R
new file mode 100644
index 0000000..b50b7da
--- /dev/null
+++ b/inst/scripts/Kumaraswamy2024.R
@@ -0,0 +1,105 @@
+# Load required libraries
+library(TreeSummarizedExperiment)
+library(Cairo)
+library(dplyr)
+library(readxl)
+
+# Function to load Excel data
+read_data <- function (f) {
+    x <- read_excel(f)
+    rownams <- unname(unlist(x[,1]))
+    x <- x[, -1]
+    x <- as.matrix(x)
+    rownames(x) <- rownams
+    return(x)
+}
+# Get data from : https://zenodo.org/records/14424024
+# Abundance profiles
+gen <- read_data(file.path("..", "data", "Genus_hitchip.xlsx"))
+phy <- read_data(file.path("..", "data", "Phylum_hitchip.xlsx"))
+oli <- read_data(file.path("..", "data", "Oligo_hitchip.xlsx"))
+
+# Metadata
+md <- read_data(file.path("..", "data", "modified_file.xlsx"))
+rownames(md) <- unname(md[, "sample"])
+md <- as.data.frame(md)
+md[14:61] <- lapply(md[14:61], as.logical)
+# Group-A: never consumed Hawaijar and Dahi (n=20, control)
+# Group-B: consume Hawaijar and Dahi (n=21)
+# Group-C: consume Hawaijar, not Dahi (n=23)
+# Group-D: consume Dahi, not Hawaijar (n=14)
+md[, "timepoint"] <- as.numeric(unlist(md[, "timepoint"]))
+md[, "season"] <- factor(unlist(md[, "season"]),
+                         evels=c("summer", "autumn", "winter"))
+factors <- c("age", "sex", "bmi", "clan", "nature_of_birth",
+             "marital_status", "residence", "subject", "group")
+for(f in factors) {
+  md[, f] <- factor(unlist(md[, f]), levels=sort(unique(md[, f])))
+}
+
+# Create tse data object
+tse <-TreeSummarizedExperiment(
+    assays=SimpleList(signal=gen), colData=DataFrame(md))
+# Add altExps
+altExp(tse, "phylum")  <- TreeSummarizedExperiment(
+    assays=SimpleList(signal=phy))
+
+altExp(tse, "oligo")   <- TreeSummarizedExperiment(
+    assays=SimpleList(signal=oli))
+# There is one NA, replace it with min value
+assay(altExp(tse, "oligo"), "signal")[is.na(assay(altExp(tse, "oligo"), "signal"))] <- min(assay(altExp(tse, "oligo"), "signal"), na.rm=TRUE)
+
+# -------------------------------------------
+
+# Total load in LOG10_16S _RNA_gene copies_per_g
+# tabs 6 and 8 have different sample names
+tabs <- list()
+for (i in 1:11) {
+  tabs[[i]] <- read_excel(file.path("..", "data", "AbsoluteloadTaxaspecificqPCRdata.xlsx"), sheet = i)
+}
+tabs <- tabs[-c(6,8)]
+d <- Reduce(function(dtf1,dtf2) dplyr::full_join(dtf1,dtf2,by="sample"), tabs)
+d <- data.frame(d)
+rownams <- unname(unlist(d[, "sample"]))
+d <- d[, -1]
+d[d %in% c("missing data", "NA")] <- NA
+d <- apply(d, 2, as.numeric)
+rownames(d) <- rownams
+altExp(tse, "total_loads")  <- TreeSummarizedExperiment(assays=SimpleList(signal=t(d)))
+
+# 'Fecal metabolite profile_LC-HRMS Data.xlsx'
+x <- read_excel(file.path("..", "data", "Fecal\ metabolite\ profile_LC-HRMS Data.xlsx"), sheet = 1)
+colnams <- as.character(x[3,])
+x <- x[-c(1,2,3),]
+colnames(x) <- colnams
+xr <- x[, 1:5]
+rownames(xr) <- paste0("feature_", 1:nrow(xr))
+xd <- apply(as.matrix(x[, 6:ncol(x)]), 2, as.numeric)
+M <- matrix(NA, nrow=nrow(xd), ncol=ncol(tse))
+colnames(M) <- colnames(tse)
+# Match samples
+M[, colnames(xd)] <- xd
+rownames(M) <- rownames(xr)
+altExp(tse, "metabolites")  <- TreeSummarizedExperiment(
+    assays=SimpleList(signal=M), rowData=xr)
+
+# 'SCFA data-HPLC.xlsx'
+x <- read_excel(file.path("..", "data", "SCFA\ data-HPLC.xlsx"))
+colnams <- unname(unlist(x[1,]))
+x <- x[-1, ]
+colnames(x) <- colnams
+rownams <- x$sample
+x <- x[,-1]
+x <- as.matrix(x)
+x <- apply(x,2,as.numeric)
+scfa <- t(x)
+colnames(scfa) <- rownams
+M <- matrix(NA, nrow=nrow(scfa), ncol=ncol(tse))
+colnames(M) <- colnames(tse)
+M[, colnames(scfa)] <- scfa
+rownames(M) <- colnams[-1]
+altExp(tse, "scfa")  <- TreeSummarizedExperiment(assays=SimpleList(signal=M))
+
+# -----------------------------------------------------------------------------
+
+save(tse, file="Kumaraswamy2024.rda")
diff --git a/man/Kumaraswamy2024.Rd b/man/Kumaraswamy2024.Rd
new file mode 100644
index 0000000..c5a423e
--- /dev/null
+++ b/man/Kumaraswamy2024.Rd
@@ -0,0 +1,57 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/miaTime.R
+\docType{data}
+\name{Kumaraswamy2024}
+\alias{Kumaraswamy2024}
+\title{Kumaraswamy2024}
+\format{
+The data set in
+\code{\link[TreeSummarizedExperiment:TreeSummarizedExperiment-class]{TreeSummarizedExperiment}}
+format.
+}
+\usage{
+data(Kumaraswamy2024)
+}
+\value{
+Loads the data set in R.
+}
+\description{
+The Kumaraswamy2024 includes microbiota and metabolite profiling data
+from 78 Indian individuals (40 males, 38 females).
+
+The Indian subjects were grouped into four diet groups (~20 subjects per group),
+and fecal samples were collected across three seasonal time points.
+
+The microbiota profiling was performed using HITChip microarray analysis
+(in duplicate), qPCR (in triplicate with eight-point standard curves), and
+LC-HRMS and HPLC metabolite profiling with internal standards.
+
+Column metadata includes diet group assignment, sampling season, sex, BMI,
+age, and questionnaire-based lifestyle metadata.
+
+Quality control metrics include Pearson correlation (>0.98) for HITChip,
+qPCR assay efficiency (>0.99), and technical replicates for HPLC and qPCR.
+
+Data sources:
+\itemize{
+\item Microbiota HITChip microarray data
+\item qPCR absolute abundance data
+\item Chemical profiling data (HPLC, LC-HRMS)
+\item Sample metadata (diet, lifestyle)
+}
+
+Processed and raw data are available via:
+\itemize{
+\item Zenodo (DOI: https://doi.org/10.5281/zenodo.14424024)
+\item NCBI-SRA (fermented foods 16S rRNA sequencing, accession: PRJNA1191989)
+}
+}
+\references{
+Jeyaram, K., Lahti, L., Tims, S. et al. Fermented foods affect the seasonal
+stability of gut bacteria in an Indian rural population.
+Nat Commun 16, 771 \url{https://doi.org/10.1038/s41467-025-56014-6}
+}
+\author{
+Jeyaram, K., Lahti, L., Tims, S. et al
+}
+\keyword{data}