diff --git a/scoping_review.qmd b/scoping_review.qmd
index 8d0a2e7..48c21ee 100644
--- a/scoping_review.qmd
+++ b/scoping_review.qmd
@@ -28,6 +28,8 @@ zotero:
 ```{python}
 #| echo: false
 from pathlib import Path
+
+from bibtexparser.model import Field
 DATA_DIR=Path("./02-data")
 RAW_SAMPLE=DATA_DIR.joinpath("raw")
 WORKING_SAMPLE=DATA_DIR.joinpath("intermediate")
@@ -616,10 +618,19 @@ Keeping in mind that these results are not yet screened for their full relevance
 #| label: fig-publications-per-year
 #| fig-cap: Publications per year
 reformatted = []
-for e in sample_raw.entries:
-    reformatted.append([e["Year"], e["Author"], e["Title"], e["Type"], e["Times-Cited"], e["Usage-Count-Since-2013"]])
+for e in sample_relevant:
+    ed = e.fields_dict
+    reformatted.append([ed.get("year", "0000").value,
+                        ed.get("author").value,
+                        ed.get("title").value,
+                        ed.get("type", Field(key="type", 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,
+                        ])
+# FIXME do not just drop missing values
 bib_df = pd.DataFrame(reformatted, columns = ["Year", "Author", "Title", "Type", "Cited", "Usage"])
-bib_df["Date"] = pd.to_datetime(bib_df["Year"], format="%Y")
+bib_df = bib_df.dropna(how="any")
+bib_df["Date"] = pd.to_datetime(bib_df["Year"], format="mixed")
 bib_df["Year"] = bib_df["Date"].dt.year
 
 # only keep newer entries