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+load data, boilerplate:
+
+```{python}
+#| echo: false
+from pathlib import Path
+import re
+## standard imports
+from IPython.core.display import Markdown as md
+import numpy as np
+import pandas as pd
+from matplotlib import pyplot as plt
+import seaborn as sns
+from tabulate import tabulate
+import bibtexparser
+
+sns.set_style("whitegrid")
+
+DATA_DIR=Path("./02-data")
+RAW_DATA=DATA_DIR.joinpath("raw")
+WORKING_DATA=DATA_DIR.joinpath("intermediate")
+PROCESSED_DATA=DATA_DIR.joinpath("processed")
+SUPPLEMENTARY_DATA=DATA_DIR.joinpath("supplementary")
+
+bib_string=""
+for partial_bib in RAW_DATA.glob("**/*.bib"):
+    with open(partial_bib) as f:
+        bib_string+="\n".join(f.readlines())
+bib_sample_raw_db = bibtexparser.parse_string(bib_string)
+
+bib_string=""
+for partial_bib in WORKING_DATA.glob("**/*.bib"):
+    with open(partial_bib) as f:
+        bib_string+="\n".join(f.readlines())
+bib_sample = bibtexparser.parse_string(bib_string)
+```
+
+```{python}
+# load relevant studies
+from src import load_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 = (load_data.from_yml(f"{PROCESSED_DATA}")
+    .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
+```
+
+```{python}
+df_country_groups = pd.concat([pd.read_excel(WB_COUNTRY_GROUPS_FILE), pd.DataFrame(data={'Economy':['global'],'Code':['WLD'],'Region':['Europe & Central Asia;South Asia;North America;East Asia & Pacific;Sub-Saharan Africa;Europe & Central Asia;Latin America & Caribbean'], 'Income group':[''], 'Lending category':['']})]).set_index("Economy")
+
+def countries_to_regions(countries:str):
+    res = set()
+    for c in countries.replace(" ;", ";").replace("; ",";").split(";"):
+        if c in df_country_groups.index:
+            region = df_country_groups.at[c,'Region']
+            res.add(region)
+    return ";".join(res)
+
+# countries_to_regions("India; Nicaragua")
+bib_df['region'] = bib_df['country'].map(countries_to_regions)
+bib_df['region'].value_counts().plot.bar()
+```
+
+prep data: 
+
+Map a 0-5 external validity score based on 'representativeness' to rows:
+
+```{python}
+df=bib_df
+vd=df[(df['design'] == 'quasi-experimental') | (df['design'] == 'experimental')]
+
+vd = vd.assign(valid_ext=0)
+vd["representativeness"] = vd["representativeness"].fillna("")
+
+mask_subnational = vd['representativeness'].str.contains("subnational")
+mask_national = vd['representativeness'].str.contains("national")
+mask_regional = vd['representativeness'].str.contains("regional")
+mask_local = vd['representativeness'].str.contains("local")
+
+vd.loc[mask_regional, 'valid_ext'] = 5
+vd.loc[mask_national, 'valid_ext'] = 4
+vd.loc[mask_subnational, 'valid_ext'] = 3
+vd.loc[mask_local, 'valid_ext'] = 2
+
+vd[['representativeness', 'valid_ext']]
+```
+
+Map an internal validity score based on study design/method:
+
+```{python}
+vd = vd.assign(valid_int=0)
+vd["method"] = vd["method"].fillna("")
+
+vd.loc[vd['method'].str.contains("RCT"), 'valid_int'] = 5.0
+vd.loc[vd['method'].str.contains("|".join(["RD","regression.vdiscontinuity"])), 'valid_int'] = 4.5
+vd.loc[vd['method'].str.contains("|".join(["IV","instrumental.variable"])), 'valid_int'] = 4.0
+vd.loc[vd['method'].str.contains("|".join(["PSM","propensity.score.matching"])), 'valid_int'] = 3.5
+vd.loc[vd['method'].str.contains("|".join(["DM","discontinuity.matching"])), 'valid_int'] = 3.0
+vd.loc[vd['method'].str.contains("|".join(["DID","difference.in.difference"])), 'valid_int'] = 3.0
+vd.loc[vd['method'].str.contains("|".join(["OLS","ordinary.least.square"])), 'valid_int'] = 2.0
+vd[['method', 'valid_int']]
+```
+