diff --git a/_quarto.yml b/_quarto.yml
index 0c713d9..c94da1e 100644
--- a/_quarto.yml
+++ b/_quarto.yml
@@ -16,7 +16,7 @@ book:
     - index.qmd
     - book/introduction.qmd
     - book/usecase/index.qmd
-    - book/inmemory/in_memory_interoperability.qmd
+    - book/in_memory/index.qmd
     - book/on_disk_interoperability.qmd
     - text: Workflow frameworks
       part: book/workflow_frameworks/index.qmd
diff --git a/book/in_memory/.gitignore b/book/in_memory/.gitignore
new file mode 100644
index 0000000..249cda9
--- /dev/null
+++ b/book/in_memory/.gitignore
@@ -0,0 +1 @@
+/data
\ No newline at end of file
diff --git a/book/inmemory/images/inmemorymatrix.png b/book/in_memory/images/inmemorymatrix.png
similarity index 100%
rename from book/inmemory/images/inmemorymatrix.png
rename to book/in_memory/images/inmemorymatrix.png
diff --git a/book/inmemory/in_memory_interoperability.qmd b/book/in_memory/index.qmd
similarity index 95%
rename from book/inmemory/in_memory_interoperability.qmd
rename to book/in_memory/index.qmd
index fce4c6c..a300adb 100644
--- a/book/inmemory/in_memory_interoperability.qmd
+++ b/book/in_memory/index.qmd
@@ -86,7 +86,8 @@ with (default_converter + pandas2ri.converter).context():
 One big limitation of rpy2 is the inability to convert sparse matrices: there is no built-in conversion module for scipy.
 The `anndata2ri` package provides, apart from functionality to convert SingleCellExperiment objects to an anndata objects, functions to convert sparse matrices.
 
-```{r include=FALSE}
+```{r import_sce, include=FALSE}
+# this is added so renv knows that singlecellexperiment needs to be installed
 library(SingleCellExperiment)
 ```
 
@@ -146,7 +147,7 @@ The pseudobulked data is read in:
 ```{python load_data}
 import anndata as ad
 
-pd_adata = ad.read_h5ad("usecase/data/pseudobulk.h5ad")
+pd_adata = ad.read_h5ad("../usecase/data/pseudobulk.h5ad")
 ```
 
 Select small molecule and control:
@@ -216,11 +217,11 @@ res = utils.head(dplyr.arrange(res, 'padj'), 10)
 ```
 
 Write to disk: this again requires the pandas2ri converter to convert the results to a pandas dataframe.
-```{python write_results}
+```{python write_results, eval=FALSE}
 with (robjects.default_converter + pandas2ri.converter).context():
     res_pd = robjects.conversion.get_conversion().rpy2py(res)
 
-    res_pd.to_csv("usecase/data/de_contrasts.csv")
+    res_pd.to_csv("../usecase/data/de_contrasts.csv")
 ```
 
 ### Usecase: ran in R
@@ -228,7 +229,7 @@ with (robjects.default_converter + pandas2ri.converter).context():
 ```{r read_in}
 library(anndata)
 
-adata_path <- "usecase/data/sc_counts_subset.h5ad"
+adata_path <- "../usecase/data/sc_counts_subset.h5ad"
 adata <- anndata::read_h5ad(adata_path)
 ```
 
@@ -280,12 +281,12 @@ pb_X <- group_by(cr, celltype) %>% summarise(across(where(is.numeric), sum))
 
 Construct obs for pseudobulk. Use 'plate_well_celltype_reannotated' as index and make sure to retain the columns 'sm_name', 'cell_type', and 'plate_name':
 
-```{r pb_obs, eval = FALSE}
+```{r pb_obs_r, eval = FALSE}
 pb_obs <- adata$obs[c("sm_name", "cell_type", "plate_name", "well", "plate_well_celltype_reannotated")]
 pb_obs <- pb_obs[!duplicated(pb_obs), ]
 ```
 
-```{python pb_obs, eval = FALSE}
+```{python pb_obs_py, eval = FALSE}
 pb_obs = adata.obs[["sm_name", "cell_type", "plate_name", "well"]].copy()
 pb_obs.index = adata.obs["plate_well_celltype_reannotated"]
 pb_obs = pb_obs.drop_duplicates()
@@ -309,6 +310,6 @@ Store to disk:
 
 <!-- note: don't remove the `eval=FALSE` for this one, as not to overwrite the usecase data -->
 ```{r store_pseudobulk, eval=FALSE}
-write_h5ad(pb_adata, "notebooks/usecase_data/pseudobulk.h5ad")
+write_h5ad(pb_adata, "../usecase/data/pseudobulk.h5ad")
 
 ```
\ No newline at end of file