diff --git a/docs/pages/worked-examples/demo_set3only.R b/docs/pages/worked-examples/demo_set3only.R
new file mode 100755
index 0000000..0203c1f
--- /dev/null
+++ b/docs/pages/worked-examples/demo_set3only.R
@@ -0,0 +1,249 @@
+library(BatchCorrMetabolomics)
+data(BC)
+
+set.3.lod <- min(set.3[!is.na(set.3)])
+minBatchOccurrence.Ave <- 2
+minBatchOccurrence.Line <- 4
+
+
+TOF.ngenotypes <- nlevels(set.3.Y$SCode)-1 ## take away the ref samples
+TOF.nNA <- apply(set.3, 2, function(x) sum(is.na(x)))
+TOF.nref <- sum(set.3.Y$SCode == "ref")
+
+
+conditions <- c("", "0", "1", "2", "c")
+experiments <- c(t(outer(c("Q", "S"), conditions, paste, sep = "")))
+methods <- rep("lm", length(experiments))
+methods[grep("c", experiments)] <- "tobit"
+imputeValues <- rep(NA, length(experiments))
+imputeValues[grep("0", experiments)] <- 0
+imputeValues[grep("1", experiments)] <- set.3.lod / 2
+imputeValues[grep("2", experiments)] <- set.3.lod
+imputeValues[grep("c", experiments)] <- set.3.lod - .01
+refSamples <- list("Q" = which(set.3.Y$SCode == "ref"),
+                   "S" = which(set.3.Y$SCode != "ref"))
+strategies <- rep(c("Q", "S"), each = length(conditions))
+
+## leave out the censored regressions for A
+exp.idx <- (1:length(experiments))[-grep("c", experiments)]
+suppressMessages(allResultsAve <-
+                   lapply(exp.idx,
+                          function(ii)
+                            apply(set.3, 2, doBC,
+                                  ref.idx = refSamples[[ strategies[[ii]] ]],
+                                  batch.idx = set.3.Y$Batch,
+                                  minBsamp = minBatchOccurrence.Ave,
+                                  correctionFormula = formula("X ~ B"),
+                                  seq.idx = set.3.Y$SeqNr,
+                                  method = methods[ii],
+                                  imputeVal = imputeValues[ii])))
+names(allResultsAve) <- experiments[exp.idx]
+
+suppressMessages(allResultsLine <-
+                   lapply(seq(along = experiments),
+                          function(ii)
+                            apply(set.3, 2, doBC,
+                                  ref.idx = refSamples[[ strategies[[ii]] ]],
+                                  batch.idx = set.3.Y$Batch,
+                                  minBsamp = minBatchOccurrence.Line,
+                                  seq.idx = set.3.Y$SeqNr,
+                                  method = methods[ii],
+                                  imputeVal = imputeValues[ii])))
+names(allResultsLine) <- experiments
+
+library("RUVSeq")
+
+idx <- which(set.3.Y$SCode == "ref")
+replicates.ind <- matrix(-1, nrow(set.3) - length(idx) + 1, length(idx))
+replicates.ind[1,] <- idx
+replicates.ind[-1,1] <- (1:nrow(set.3))[-idx]
+
+allResultsRUV <-
+  lapply(0:2,
+         function(ImpVal) {
+           huhn <- set.3
+           huhn[is.na(huhn)] <- ImpVal * set.3.lod / 2
+           woppa <- t(RUVs(t(huhn),
+                           1:ncol(huhn),
+                           k = 3,
+                           replicates.ind,
+                           round = FALSE, isLog = TRUE)$normalizedCounts)
+           woppa[is.na(set.3)] <- NA
+           woppa
+         })
+
+
+# figure 2
+
+par(mfrow = c(1,1))
+huhnPCA <- evaluateCorrection(set.3, set.3.Y, what = "PCA",
+                              plot = TRUE, legend.loc = "bottomright")
+title(main = paste("Uncorrected Interbatch Distance:", round(huhnPCA, 3)))
+
+huhnPCA.A <- evaluateCorrection(allResultsAve[["Q"]], set.3.Y, what = "PCA",
+                                plot = TRUE, legend.loc = "bottomright")
+title(main = paste("Corrected Interbatch Distance:", round(huhnPCA.A, 3)))
+
+# huhnPCA.A <- evaluateCorrection(allResultsAve[["Q0"]], set.3.Y, what = "PCA",
+#                                 plot = TRUE, legend.loc = "bottomright")
+# title(main = paste("Q0: Interbatch distance:", round(huhnPCA.A, 3)))
+
+
+
+results.ave <- results.line <- matrix(NA, length(experiments) + 1, 2)
+dimnames(results.line) <- dimnames(results.ave) <-
+  list(c("No correction", experiments), c("PCA", "duplo"))
+
+results.line[1,1] <- results.ave[1,1] <-
+  evaluateCorrection(set.3, set.3.Y, what = "PCA", plot = FALSE)
+results.line[1,2] <- results.ave[1,2] <-
+  evaluateCorrection(set.3, set.3.Y, what = "duplo", plot = FALSE)
+
+for (exp in experiments[exp.idx]) {
+  x <- allResultsAve[[exp]]
+  woppa <- set.3
+  woppa[!is.na(x)] <- x[!is.na(x)]
+  results.ave[exp, 1] <-
+    evaluateCorrection(woppa, set.3.Y, "PCA", plot = FALSE)
+  results.ave[exp, 2] <-
+    evaluateCorrection(woppa, set.3.Y, "duplo", plot = FALSE)
+}
+
+for (exp in experiments) {
+  x <- allResultsLine[[exp]]
+  woppa <- set.3
+  woppa[!is.na(x)] <- x[!is.na(x)]
+  results.line[exp, 1] <-
+    evaluateCorrection(woppa, set.3.Y, "PCA", plot = FALSE)
+  results.line[exp, 2] <-
+    evaluateCorrection(woppa, set.3.Y, "duplo", plot = FALSE)
+}
+
+results.ruv <-
+  cbind(sapply(allResultsRUV,
+               evaluateCorrection, set.3.Y,
+               what = "PCA", plot = FALSE),
+        sapply(allResultsRUV,
+               evaluateCorrection, set.3.Y,
+               what = "duplo", plot = FALSE))
+dimnames(results.ruv) <- list(paste("R", 0:2, sep = ""),
+                              c("PCA", "duplo"))
+
+# figure 6 
+floodresults.ave <- rbind(results.ave, results.ruv)
+floodresults.ave.label <- factor(substr(rownames(floodresults.ave), 1, 1))
+floodresults.line <- rbind(results.line, results.ruv)
+floodresults.line.label <- factor(substr(rownames(floodresults.line), 1, 1))
+PCA.range <- range(c(floodresults.ave[,1], floodresults.line[,1]), 
+                   na.rm = TRUE)
+duplo.range <- range(c(floodresults.ave[,2], floodresults.line[,2]),
+                     na.rm = TRUE)
+par(mfrow = c(1,1))
+plot(floodresults.ave[,1], floodresults.ave[,2],
+     main = "Data set III - only batch correction",
+     ylab= "Repeatability", xlab = "Interbatch distance",
+     xlim = PCA.range, ylim = duplo.range, 
+     col = as.integer(floodresults.ave.label))
+text(floodresults.ave[,1], floodresults.ave[,2],
+     pos = ifelse(floodresults.ave.label == "N", 2, 4),
+     col = as.integer(floodresults.ave.label),
+     labels = rownames(floodresults.ave))
+
+plot(floodresults.line[,1], floodresults.line[,2], 
+     main = "Data set III - batch and order correction", 
+     xlim = PCA.range, ylim = duplo.range, 
+     ylab= "Repeatability", xlab = "Interbatch distance",
+     col = as.integer(floodresults.line.label))
+text(floodresults.line[,1], floodresults.line[,2],
+     pos = ifelse(floodresults.line.label %in% c("N", "Q"), 2, 4),
+     col = as.integer(floodresults.line.label),
+     labels = rownames(floodresults.line))
+
+
+# log peak area mean
+library("dplyr")
+library("ggplot2")
+
+raw <- data.frame(set.3)
+#corrected <- data.frame(allResultsAve[["Q"]])
+corrected <- data.frame(allResultsRUV[[0]])
+row.names(corrected) <- row.names(raw)
+
+raw[is.na(raw)] <- 0
+corrected[is.na(corrected)] <- 0
+
+raw['mean'] <- apply(raw, 1, mean)
+corrected['mean'] <- apply(corrected, 1, mean)
+raw_plus_meta <- merge(raw, set.3.Y, by="row.names", all=TRUE) 
+corrected_plus_meta <- merge(corrected, set.3.Y, by="row.names", all=TRUE) 
+
+plot_areas_batchwise<- function(df,title) {
+  gm = mean(df$mean)
+  ggplot() + 
+    geom_point(data=df, mapping=aes(x=SeqNr, y=mean, color=Batch)) +
+    xlab("injection order") + ylab("log mean peak area") + ggtitle(title) +
+    geom_hline(yintercept=gm, linetype="dashed", color = "black") +
+    stat_smooth(method="lm", formula=y~1, se=FALSE)
+}
+
+plot_areas_batchwise(raw_plus_meta,"Raw")
+plot_areas_batchwise(corrected_plus_meta,"Corrected")
+
+raw_refs <- raw_plus_meta[ which(raw_plus_meta$SCode=='ref'),]
+raw_samples <- raw_plus_meta[ which(raw_plus_meta$SCode!='ref'),]
+corrected_refs <- corrected_plus_meta[ which(corrected_plus_meta$SCode=='ref'),]
+corrected_samples <- corrected_plus_meta[ which(corrected_plus_meta$SCode!='ref'),]
+
+plot_areas_batchwise(raw_refs,"raw refs")
+plot_areas_batchwise(corrected_refs,"corrected refs")
+plot_areas_batchwise(raw_samples,"raw samples")
+plot_areas_batchwise(corrected_samples,"corrected samples")
+
+
+
+raw_plus_meta$Batch <- as.character(raw_plus_meta$Batch)
+raw_plus_meta[ which(raw_plus_meta$SCode=='ref'),]$Batch = "QC"
+raw_plus_meta$Batch <- as.factor(raw_plus_meta$Batch)
+
+corrected_plus_meta$Batch <- as.character(corrected_plus_meta$Batch)
+corrected_plus_meta[ which(corrected_plus_meta$SCode=='ref'),]$Batch = "QC"
+corrected_plus_meta$Batch <- as.factor(corrected_plus_meta$Batch)
+
+plot_areas_batchwise(raw_plus_meta,"raw")
+plot_areas_batchwise(corrected_plus_meta,"corrected")
+
+
+getPCA <- function(X, Y, GRAPH)
+{			
+  nbatches <- nlevels(Y$Batch)
+  noref.idx <- which(Y$SCode != "ref")
+  Xsample <- X[noref.idx,]
+  YSample <- Y[noref.idx,]
+  
+  Xsample <- Xsample[, apply(Xsample, 2, function(x) !all(is.na(x)))]
+  
+  ## replace NA values with column means
+  for (i in 1:ncol(Xsample))
+    Xsample[is.na(Xsample[,i]),i] <- mean(Xsample[,i], na.rm = TRUE)
+  
+  Xsample <- Xsample[, apply(Xsample, 2, sd, na.rm = TRUE) > 0]
+  ## Original is using ChemometricsWithR which reports an unintelligible result object, hence switching to something well maintained to get the PCA scores
+  X.PCA <- FactoMineR::PCA(Xsample, scale.unit = TRUE, graph=GRAPH)
+  
+  return (X.PCA)
+  
+}
+
+
+
+set3.uncorrected.PCA <- getPCA(set.3, set.3.Y,GRAPH = FALSE)
+set3.uncorrected.PCAscores <- data.frame(set3.uncorrected.PCA$ind$coord)
+set3.uncorrected.PCAscores <- merge(set3.uncorrected.PCAscores, set.3.Y, by="row.names", all=TRUE) 
+write.csv(set3.uncorrected.PCAscores,"set3.uncorrected.PCA.csv", row.names = TRUE)
+
+set3.corrected.PCA <- getPCA(corrected, set.3.Y,GRAPH = FALSE)
+set3.corrected.PCAscores <- data.frame(set3.corrected.PCA$ind$coord)
+set3.corrected.PCAscores <- merge(set3.corrected.PCAscores, set.3.Y, by="row.names", all=TRUE) 
+write.csv(set3.corrected.PCAscores,"set3.corrected.PCA.csv", row.names = TRUE)
+
+
diff --git a/docs/pages/worked-examples/metabo-batches.mzQC.md b/docs/pages/worked-examples/metabo-batches.mzQC.md
index dae65be..f48398b 100644
--- a/docs/pages/worked-examples/metabo-batches.mzQC.md
+++ b/docs/pages/worked-examples/metabo-batches.mzQC.md
@@ -7,12 +7,19 @@ permalink: /examples/metabo-batches/
 Here, we describe details of a metabolomics mzQC JSON document used to describe a Studies' quality before and after batch correction methods are applied. 
 For description of the general structure of mzQC, see the Single-Run Example of mzQC.
 Find the complete file at the bottom of this document or in the example folder.
-The mzQC file is made from the acquisions of GC-ToF-MS polar metabolite data of an Arabidopsis nucleotype-plasmotype diallel study as described in [Improved batch correction in untargeted MS-based metabolomics](https://dx.doi.org/10.1007%2Fs11306-016-1015-8). 
+The mzQC file is made from the acquisions of GC-ToF-MS polar metabolite data of an Arabidopsis nucleotype-plasmotype diallel study as described in [Improved batch correction in untargeted MS-based metabolomics](https://dx.doi.org/10.1007%2Fs11306-016-1015-8) by Wehrens, et al.(2016). 
 
 ```
-    "description": "This is a metabolomics batch-correction use-case study for mzQC using the method described in 'Improved batch correction in untargeted MS-based metabolomics' by Wehrens, et al.(2016) [https://doi.org/10.1007%2Fs11306-016-1015-8]. The data comes from set3 of the supplementary material.",
+"description": "This is a metabolomics batch-correction use-case study for mzQC using the method described in 'Improved batch correction in untargeted MS-based metabolomics' by Wehrens, et al.(2016) [https://doi.org/10.1007%2Fs11306-016-1015-8]. The data comes from set3 of the supplementary material.",
 ```
+## Data
 This example has 240 runs, for which each a `qualityMetric` object is present in `qualityMetrics`, for sake of brevity only containing the number of successfully identified primary metabolites.
+The data is taken from *set3* of the publication only, extracted from `BC.RData` and stored as `set3.peakarea.csv` and `set3.uncorrected.PCA.csv` (same data).
+All data from [Wehrens, et al.(2016) can be found on GitHub](https://github.com/rwehrens/BatchCorrMetabolomics).
+## Batch correction
+Batch correction was applied as described in the supplementary with BatchCorrMetabolomics (see github repo) scripted in [`demo_set3only.R`](demo_set3only.R). Output is `set3.corrected.PCA.csv`.
+## mzQC
+From the above information and data, we can formulate a mzQC file.
 ```
 {
   "metadata": {
@@ -61,7 +68,7 @@ This example has 240 runs, for which each a `qualityMetric` object is present in
   },
 ...
 ```
-To be able to estimate the effect of the batch correction, we first describe all runs that are batch corrected by a principle component analysis of their peak area features. Hence, we use a `setQuality` composed of all involved `inputFiles`, and add related metrics in the qualityMetric section.
+To be able to estimate the effect of the batch correction, we first describe all runs that are batch corrected by a principal component analysis of their peak area features. Hence, we use a `setQuality` composed of all involved `inputFiles`, and add related metrics in the qualityMetric section.
 ```
 {
   "accession":"MS:4000092",
@@ -106,4 +113,4 @@ before | after
 
 
 ### This is the mzQC file once again, in full:
-**[metabo-batches.mzQC](https://github.com/HUPO-PSI/mzQC/tree/main/specification_documents/examples/metabo-batches.mzQC)**
\ No newline at end of file
+**[metabo-batches.mzQC](https://github.com/HUPO-PSI/mzQC/tree/main/specification_documents/examples/metabo-batches.mzQC)**