diff --git a/scoping_review.qmd b/scoping_review.qmd
index 8f27091..e2618f1 100644
--- a/scoping_review.qmd
+++ b/scoping_review.qmd
@@ -55,6 +55,42 @@ for partial_bib in WORKING_DATA.glob("**/*.bib"):
 bib_sample = bibtexparser.parse_string(bib_string)
 ```
 
+```{python}
+# load relevant studies
+from src import data
+
+# load zotero-based metadata: citations and uses
+zot_df = pd.DataFrame([
+    [
+        entry["doi"] if "doi" in entry.fields_dict else None,
+        entry["times-cited"] if "times-cited" in entry.fields_dict else None,
+        entry["usage"] if "usage" in entry.fields_dict else None,
+        entry["keywords"] if "keywords" in entry.fields_dict else None,
+    ]
+    for entry in bib_sample.entries
+], columns = ["doi", "cited", "usage", "keywords"]).drop_duplicates("doi").set_index("doi")
+
+# Add WB country grouping definitions (income group, world region)
+WB_COUNTRY_GROUPS_FILE = Path(f"{SUPPLEMENTARY_DATA}/wb-country-groupings.xlsx").resolve()
+df_country_groups = pd.read_excel(WB_COUNTRY_GROUPS_FILE).set_index("Economy")
+
+bib_df = (data.from_yml(f"{PROCESSED_DATA}/relevant")
+    .assign(
+        doi=lambda _df: _df["uri"].str.extract(r"https?://(?:dx\.)?doi\.org/(.*)", expand=False),
+        zot_cited=lambda _df: _df["doi"].map(zot_df["cited"]),
+        zot_usage=lambda _df: _df["doi"].map(zot_df["usage"]),
+        zot_keywords=lambda _df: _df["doi"].map(zot_df["keywords"]),
+        date = lambda _df: pd.to_datetime(_df["year"], format="%Y"),
+        year = lambda _df: _df["date"].dt.year,
+        region = lambda _df: _df["country"].map(df_country_groups["Region"]),
+        income_group = lambda _df: _df["country"].map(df_country_groups["Income group"]),
+    )
+    .query("year >= 2000")
+)
+zot_df = None
+df_country_groups = None
+```
+
 # Introduction
 
 This section will introduce the reader to the concern of inequality in the World of Work (WoW),
@@ -363,7 +399,6 @@ It restricts studies to those that comprise primary research published after 200
 with a focus on the narrowing criteria specified in @tbl-inclusion-criteria.
 
 ```{python}
-#| echo: false
 #| label: tbl-inclusion-criteria
 #| tbl-cap: Study inclusion and exclusion scoping criteria {#tbl-inclusion-criteria}
 
@@ -447,41 +482,6 @@ with small decreases between 2001 and 2008, as well as more significant ones in
 as can be seen in @fig-publications-per-year.
 Keeping in mind that these results are not yet screened for their full relevance to the topic at hand, so far only being *potentially* relevant in falling into the requirements of the search pattern, an increased results output does not necessarily mean a clearly rising amount of relevant literature.
 
-```{python}
-# load relevant studies
-from src import data
-bib_df = data.from_yml(f"{PROCESSED_DATA}/relevant")
-
-# load zotero-based metadata
-reformatted = []
-for e in sample_relevant:
-    ed = e.fields_dict
-    reformatted.append([
-                        ed.get("doi", Field(key="doi", value=None)).value,
-                        ed.get("times-cited", Field(key="times-cited", value=None)).value,
-                        ed.get("usage-count-since-2013", Field(key="usage-count-since-2013", value=None)).value,
-                        ed.get("keywords", Field(key="keywords", value=None)).value,
-                        ])
-zot_df = pd.DataFrame(reformatted, columns = ["doi", "cited", "usage", "keywords"])
-
-bib_df["doi"] = bib_df["uri"].str.extract(r"https?://(?:dx\.)?doi\.org/(.*)", expand=False)
-bib_df["zot_cited"] = bib_df["doi"].map(zot_df.drop_duplicates("doi").set_index("doi")["cited"])
-bib_df["zot_usage"] = bib_df["doi"].map(zot_df.drop_duplicates("doi").set_index("doi")["usage"])
-bib_df["zot_keywords"] = bib_df["doi"].map(zot_df.drop_duplicates("doi").set_index("doi")["keywords"])
-
-bib_df["date"] = pd.to_datetime(bib_df["year"], format="%Y")
-bib_df["year"] = bib_df["date"].dt.year
-
-# only keep newer entries
-bib_df = bib_df[bib_df["year"] >= 2000]
-
-# Add WB country grouping definitions (income group, world region)
-WB_COUNTRY_GROUPS_FILE = Path(f"{SUPPLEMENTARY_DATA}/wb-country-groupings.xlsx").resolve()
-df_country_groups = pd.read_excel(WB_COUNTRY_GROUPS_FILE)
-bib_df["income group"] = bib_df["country"].map(df_country_groups.set_index("Economy")["Income group"])
-bib_df["region"] = bib_df["country"].map(df_country_groups.set_index("Economy")["Region"])
-```
-
  <!-- TODO Should this be sub-divided by region or subdivision later per-section? -->
 ```{python}
 #| label: fig-publications-per-year