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ZIP herunterladen Herunterladen... name research-impact description Research impact measurement with altmetrics, citation normalization, field-weighted indicators, and journal ranking analysis for evaluation studies. tags ["research-impact","altmetrics","citation-normalization","journal-ranking","research-evaluation"] version 1.0.0 authors ["@xjtulyc"] license MIT platforms ["claude-code","codex","gemini-cli","cursor"] dependencies {"python":["pandas>=2.0","numpy>=1.24","scipy>=1.11","statsmodels>=0.14","matplotlib>=3.7","requests>=2.31"]} last_updated 2026-03-17 status stable
Research Impact Measurement
When to Use This Skill
Use this skill when you need to:
Compute field-normalized citation indicators (MNCS, FWCI, PP-top10%)
Calculate journal impact metrics (JIF, SJR, SNIP, h5-index)
Measure altmetric attention (Altmetric score, PlumX)
Evaluate institutional research performance (CWTS Leiden methodology)
Assess open access impact and citation advantages
Compare research profiles across departments or universities
Compute co-citation and bibliographic coupling similarity
Trigger keywords : research impact, citation normalization, field-weighted citation impact, FWCI, MNCS, journal impact factor, Eigenfactor, h5-index, altmetrics, Altmetric score, open access advantage, institutional benchmarking, CWTS Leiden, CERN, REF, ERA, percentile-based indicators, PP-top10, research performance evaluation.
Background & Key Concepts
Field-Weighted Citation Impact (FWCI)
$$\text{FWCI}p = \frac{c_p}{\langle c \rangle {\text{field, year}}}$$
where $c_p$ is citation count of paper $p$ and $\langle c \rangle$ is the mean citation count of all papers in the same field and publication year. FWCI = 1.0 means world average; FWCI > 1.0 means above average.
Mean Normalized Citation Score (MNCS)
$$\text{MNCS} = \frac{1}{|S|} \sum_{p \in S} \text{FWCI}_p$$
for a set $S$ of papers. Fractional counting assigns weight $1/k$ to multi-authored papers with $k$ authors.
Percentage in Top 10% (PP-top10%)
$$\text{PP-top10%} = \frac{|{p \in S : \text{FWCI}p \geq \text{P} {90}(\text{field, year})}|}{|S|} \times 100$$
Journal Impact Factor (JIF)
$$\text{JIF}{y} = \frac{\sum {y-2}^{y-1} \text{citations to journal articles}}{\sum_{y-2}^{y-1} \text{citable items}}$$
SCImago Journal Rank (SJR)
SJR is a prestige-weighted citation metric based on eigenvector centrality of the journal citation network:
$$\text{SJR}j = \alpha \sum_k \frac{\text{cit} {kj}}{A_k \cdot \text{cit}_k} \text{SJR}_k + (1 - \alpha) \frac{1}{N}$$
Altmetric Attention Score
Weighted sum of online attention signals:
News articles: +8 pts
Blog posts: +5 pts
Twitter/X mentions: +1 pt (capped at 16)
Wikipedia: +3 pts
Policy documents: +3 pts
Mendeley saves: proportional
Environment Setup
pip install pandas>=2.0 numpy>=1.24 scipy>=1.11 statsmodels>=0.14 \
matplotlib>=3.7 requests>=2.31
import pandas as pd
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
print ("Research impact analysis environment ready" )
Core Workflow
Step 1: Field-Normalized Citation Indicators import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
np.random.seed(42 )
n_papers = 500
fields = ["Computer Science" , "Medicine" , "Physics" , "Chemistry" , "Social Science" ]
field_baselines = {
"Computer Science" : 12 ,
"Medicine" : 25 ,
"Physics" : 18 ,
"Chemistry" : 20 ,
"Social Science" : 6 ,
}
years = np.random.randint(2015 , 2023 , n_papers)
paper_fields = np.random.choice(fields, n_papers)
citations = np.array([
int (np.random.lognormal(
np.log(field_baselines[f]) + 0.1 * (yr - 2015 ), 1.0
))
for f, yr in zip (paper_fields, years)
])
oa_status = np.random.binomial(1 , 0.40 , n_papers)
citations = (citations * (1 + 0.25 * oa_status)).astype(int )
df = pd.DataFrame({
"paper_id" : [f"P{i:05d} " for i in range (n_papers)],
"field" : paper_fields,
"year" : years,
"citations" : citations,
"oa" : oa_status,
"n_authors" : np.random.randint(1 , 10 , n_papers),
})
field_year_means = df.groupby(["field" , "year" ])["citations" ].mean()
field_year_p90 = df.groupby(["field" , "year" ])["citations" ].quantile(0.90 )
def compute_fwci (row ):
"""Compute Field-Weighted Citation Impact."""
baseline = field_year_means.get((row["field" ], row["year" ]), 1.0 )
return row["citations" ] / max (baseline, 0.1 )
df["fwci" ] = df.apply(compute_fwci, axis=1 )
def is_top10 (row ):
"""Check if paper is in top 10% of its field-year."""
p90 = field_year_p90.get((row["field" ], row["year" ]), 0 )
return int (row["citations" ] >= p90)
df["top10" ] = df.apply(is_top10, axis=1 )
df["frac_fwci" ] = df["fwci" ] / df["n_authors" ]
field_metrics = df.groupby("field" ).agg(
n_papers=("paper_id" , "count" ),
mncs=("fwci" , "mean" ),
mncs_frac=("frac_fwci" , "mean" ),
pp_top10=("top10" , "mean" ),
total_cit=("citations" , "sum" ),
).reset_index()
field_metrics["pp_top10_pct" ] = field_metrics["pp_top10" ] * 100
print ("=== Field-Normalized Research Impact ===" )
print (field_metrics.round (3 ).to_string(index=False ))
from scipy.stats import mannwhitneyu
oa_fwci = df[df["oa" ] == 1 ]["fwci" ]
closed_fwci = df[df["oa" ] == 0 ]["fwci" ]
stat, p_val = mannwhitneyu(oa_fwci, closed_fwci, alternative="greater" )
print (f"\n=== Open Access Citation Advantage ===" )
print (f"OA MNCS: {oa_fwci.mean():.3 f} " )
print (f"Non-OA MNCS: {closed_fwci.mean():.3 f} " )
print (f"OA advantage: {(oa_fwci.mean()/closed_fwci.mean() - 1 )*100 :.1 f} %" )
print (f"Mann-Whitney U test: p = {p_val:.4 f} ({'significant' if p_val < 0.05 else 'not significant' } )" )
fig, axes = plt.subplots(1 , 3 , figsize=(15 , 5 ))
for field in fields:
mask = df["field" ] == field
axes[0 ].hist(df.loc[mask, "fwci" ].clip(0 , 10 ), bins=20 , alpha=0.4 ,
label=field, density=True )
axes[0 ].axvline(1.0 , color="red" , ls="--" , label="World average" )
axes[0 ].set_xlabel("FWCI" ); axes[0 ].set_ylabel("Density" )
axes[0 ].set_title("FWCI Distribution by Field" )
axes[0 ].legend(fontsize=7 )
colors = ["steelblue" if mncs >= 1.0 else "orange"
for mncs in field_metrics["mncs" ]]
axes[1 ].bar(field_metrics["field" ], field_metrics["pp_top10_pct" ],
color=colors, edgecolor="black" )
axes[1 ].axhline(10 , color="red" , ls="--" , label="World avg (10%)" )
axes[1 ].set_xticklabels(field_metrics["field" ], rotation=15 , ha="right" )
axes[1 ].set_ylabel("PP-top10% (%)" )
axes[1 ].set_title("Percentage in Top 10% by Field" )
axes[1 ].legend()
oa_labels = ["Open Access" , "Closed Access" ]
oa_data = [oa_fwci.clip(0 , 10 ).values, closed_fwci.clip(0 , 10 ).values]
bp = axes[2 ].boxplot(oa_data, labels=oa_labels, patch_artist=True ,
showfliers=False )
bp["boxes" ][0 ].set_facecolor("lightgreen" )
bp["boxes" ][1 ].set_facecolor("lightyellow" )
axes[2 ].axhline(1.0 , color="red" , ls="--" , label="FWCI=1 (world avg)" )
axes[2 ].set_ylabel("FWCI" )
axes[2 ].set_title("OA vs. Non-OA Citation Impact" )
axes[2 ].legend()
plt.tight_layout()
plt.savefig("research_impact.png" , dpi=150 , bbox_inches="tight" )
plt.close()
print ("\nFigure saved: research_impact.png" )
Step 2: Journal Impact Metrics import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
np.random.seed(42 )
n_journals = 50
journal_names = [f"Journal_{i:02d} " for i in range (n_journals)]
years = [2020 , 2021 , 2022 , 2023 ]
prestige = np.random.uniform(0.1 , 2.0 , n_journals)
annual_articles = np.random.randint(50 , 500 , n_journals)
records = []
for j_idx, jname in enumerate (journal_names):
for yr in years:
n_articles = int (annual_articles[j_idx] * (1 + 0.05 * (yr - 2020 )))
cit_yr1 = np.random.poisson(prestige[j_idx] * 8 , n_articles).sum ()
cit_yr2 = np.random.poisson(prestige[j_idx] * 12 , n_articles).sum ()
records.append({
"journal" : jname,
"pub_year" : yr,
"n_articles" : n_articles,
"cit_yr1" : cit_yr1,
"cit_yr2" : cit_yr2,
})
jdf = pd.DataFrame(records)
def compute_jif (jdf, impact_year=2022 , window=2 ):
"""Compute Journal Impact Factor."""
results = []
for jname in jdf["journal" ].unique():
j = jdf[jdf["journal" ] == jname]
window_df = j[j["pub_year" ].between(impact_year - window, impact_year - 1 )]
n_citable = window_df["n_articles" ].sum ()
citations_received = window_df["cit_yr2" ].sum ()
if n_citable > 0 :
jif = citations_received / n_citable
else :
jif = 0
results.append({"journal" : jname, "JIF_2022" : jif,
"n_citable_items" : n_citable})
return pd.DataFrame(results).sort_values("JIF_2022" , ascending=False )
jif_df = compute_jif(jdf)
print ("=== Journal Impact Factors (Top 10) ===" )
print (jif_df.head(10 ).round (3 ).to_string(index=False ))
print (f"\nMedian JIF: {jif_df['JIF_2022' ].median():.2 f} " )
print (f"JIF distribution: {jif_df['JIF_2022' ].describe().round (2 ).to_dict()} " )
def eigenfactor_score (jif_df, n_iter=100 ):
"""Simplified Eigenfactor via iterative citation-weighted prestige.
Note: True Eigenfactor uses full citation matrix; this is illustrative.
"""
n = len (jif_df)
jif_values = jif_df["JIF_2022" ].values
P = np.full((n, n), 1 /n)
col_sums = jif_values.sum ()
if col_sums > 0 :
P += jif_values[:, None ] / col_sums * 0.5
P = P / P.sum (axis=1 , keepdims=True )
v = np.ones(n) / n
for _ in range (n_iter):
v = v @ P
v = v / v.sum ()
jif_df = jif_df.copy()
jif_df["eigenfactor_score" ] = v * 100
return jif_df
jif_df = eigenfactor_score(jif_df)
print ("\nTop 5 by Eigenfactor Score:" )
print (jif_df.nlargest(5 , "eigenfactor_score" )[
["journal" , "JIF_2022" , "eigenfactor_score" ]].round (3 ).to_string(index=False ))
fig, axes = plt.subplots(1 , 2 , figsize=(12 , 5 ))
axes[0 ].hist(jif_df["JIF_2022" ], bins=15 , color="steelblue" , edgecolor="black" )
axes[0 ].axvline(jif_df["JIF_2022" ].median(), color="red" , ls="--" ,
label=f"Median JIF={jif_df['JIF_2022' ].median():.2 f} " )
axes[0 ].set_xlabel("Journal Impact Factor" ); axes[0 ].set_ylabel("Frequency" )
axes[0 ].set_title("JIF Distribution" )
axes[0 ].legend()
top15 = jif_df.head(15 )
axes[1 ].barh(top15["journal" ], top15["JIF_2022" ],
color="steelblue" , edgecolor="black" )
axes[1 ].set_xlabel("JIF 2022" ); axes[1 ].set_title("Top 15 Journals by JIF" )
axes[1 ].invert_yaxis()
plt.tight_layout()
plt.savefig("journal_impact.png" , dpi=150 , bbox_inches="tight" )
plt.close()
print ("\nFigure saved: journal_impact.png" )
Step 3: Altmetrics and Online Attention Analysis import numpy as np
import pandas as pd
import requests
import matplotlib.pyplot as plt
import os
def get_altmetric_score (doi, api_key=None ):
"""Fetch Altmetric score for a paper DOI.
Args:
doi: DOI string (e.g., "10.1038/nature12373")
api_key: Altmetric API key from environment variable
Returns:
dict with score and attention breakdown, or simulated data
"""
if api_key is None :
api_key = os.getenv("ALTMETRIC_API_KEY" , "" )
url = f"https://api.altmetric.com/v1/doi/{doi} "
params = {}
if api_key:
params["key" ] = api_key
try :
response = requests.get(url, params=params, timeout=10 )
if response.status_code == 200 :
data = response.json()
return {
"doi" : doi,
"altmetric_score" : data.get("score" , 0 ),
"news_mentions" : data.get("cited_by_feeds_count" , 0 ),
"twitter_mentions" : data.get("cited_by_tweeters_count" , 0 ),
"blog_mentions" : data.get("cited_by_posts_count" , 0 ),
"policy_mentions" : data.get("cited_by_policies_count" , 0 ),
"mendeley_readers" : data.get("readers" , {}).get("mendeley" , 0 ),
}
except Exception:
pass
return None
np.random.seed(42 )
n_papers_am = 200
altmetric_scores = np.random.exponential(2 , n_papers_am)
altmetric_scores = np.where(np.random.random(n_papers_am) < 0.7 , 0 ,
altmetric_scores * 5 )
df_am = pd.DataFrame({
"paper_id" : [f"P{i:05d} " for i in range (n_papers_am)],
"field" : np.random.choice(["Medicine" , "Physics" , "CS" , "Social Science" ],
n_papers_am),
"citations" : np.random.negative_binomial(2 , 0.1 , n_papers_am),
"altmetric_score" : altmetric_scores,
"twitter_mentions" : (altmetric_scores * np.random.uniform(0.5 , 2 , n_papers_am)).astype(int ),
"news_mentions" : (altmetric_scores * np.random.uniform(0 , 0.3 , n_papers_am)).astype(int ),
"mendeley_readers" : np.random.randint(0 , 500 , n_papers_am),
"oa" : np.random.binomial(1 , 0.4 , n_papers_am),
})
from scipy.stats import spearmanr
corr, p = spearmanr(df_am["altmetric_score" ], df_am["citations" ])
print (f"Spearman correlation (altmetric vs. citations): r={corr:.3 f} , p={p:.4 f} " )
field_attention = df_am.groupby("field" ).agg(
mean_altmetric=("altmetric_score" , "mean" ),
mean_twitter=("twitter_mentions" , "mean" ),
mean_mendeley=("mendeley_readers" , "mean" ),
n_viral=(
"altmetric_score" ,
lambda x: (x > np.percentile(df_am["altmetric_score" ], 90 )).mean() * 100
),
).reset_index()
print ("\n=== Altmetric Attention by Field ===" )
print (field_attention.round (2 ).to_string(index=False ))
WEIGHTS = {"news_mentions" : 8 , "twitter_mentions" : 1 ,
"mendeley_readers" : 0.1 }
def compute_altmetric_composite (row ):
"""Simplified Altmetric-like composite score."""
return sum (row.get(k, 0 ) * w for k, w in WEIGHTS.items())
df_am["composite_altmetric" ] = df_am.apply(compute_altmetric_composite, axis=1 )
fig, axes = plt.subplots(1 , 3 , figsize=(15 , 5 ))
axes[0 ].scatter(np.log1p(df_am["citations" ]),
np.log1p(df_am["altmetric_score" ]),
alpha=0.4 , s=15 , color="steelblue" )
axes[0 ].set_xlabel("ln(Citations + 1)" )
axes[0 ].set_ylabel("ln(Altmetric Score + 1)" )
axes[0 ].set_title(f"Altmetrics vs. Citations\n(Spearman r={corr:.2 f} )" )
x = np.arange(len (field_attention))
w = 0.25
axes[1 ].bar(x - w, field_attention["mean_altmetric" ], w,
label="Altmetric" , color="steelblue" )
axes[1 ].bar(x, field_attention["mean_twitter" ], w,
label="Twitter" , color="orange" )
axes[1 ].bar(x + w, field_attention["mean_mendeley" ] / 10 , w,
label="Mendeley/10" , color="green" )
axes[1 ].set_xticks(x)
axes[1 ].set_xticklabels(field_attention["field" ], rotation=15 , ha="right" )
axes[1 ].set_title("Attention Breakdown by Field" )
axes[1 ].legend(fontsize=8 )
axes[2 ].hist(df_am["altmetric_score" ][df_am["altmetric_score" ] > 0 ],
bins=30 , log=True , color="coral" , edgecolor="black" )
axes[2 ].set_xlabel("Altmetric Score (> 0)" )
axes[2 ].set_ylabel("Frequency (log)" )
axes[2 ].set_title("Altmetric Score Distribution" )
plt.tight_layout()
plt.savefig("altmetrics_analysis.png" , dpi=150 , bbox_inches="tight" )
plt.close()
print ("\nFigure saved: altmetrics_analysis.png" )
Advanced Usage
Institutional Research Profile Dashboard import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
def institutional_profile (df, institution_col="institution" , field_col="field" ,
fwci_col="fwci" , pp10_col="top10" ):
"""Compute full institutional research profile.
Args:
df: paper-level DataFrame
Returns:
profile DataFrame indexed by institution
"""
profile = df.groupby(institution_col).agg(
n_papers=(fwci_col, "count" ),
mncs=(fwci_col, "mean" ),
pp_top10=(pp10_col, lambda x: x.mean() * 100 ),
n_fields=(field_col, "nunique" ),
citation_share=("citations" , lambda x: x.sum ()),
).reset_index()
total_cit = profile["citation_share" ].sum ()
profile["citation_share_pct" ] = profile["citation_share" ] / total_cit * 100
def hhi (grp ):
shares = grp[field_col].value_counts(normalize=True )
return (shares**2 ).sum ()
profile["specialization_hhi" ] = [
hhi(df[df[institution_col] == inst])
for inst in profile[institution_col]
]
return profile.sort_values("mncs" , ascending=False )
np.random.seed(42 )
n_paps = 400
institutions = ["Univ_A" , "Univ_B" , "Univ_C" , "Institute_D" , "Center_E" ]
inst_prestige = {"Univ_A" : 1.5 , "Univ_B" : 1.2 , "Univ_C" : 0.9 ,
"Institute_D" : 1.8 , "Center_E" : 1.1 }
df_inst = df.copy().head(n_paps)
df_inst["institution" ] = np.random.choice(institutions, n_paps,
p=np.array([0.30 , 0.25 , 0.20 , 0.15 , 0.10 ]))
df_inst["fwci" ] = df_inst.apply(
lambda r: r["fwci" ] * inst_prestige.get(r["institution" ], 1.0 ), axis=1 )
df_inst["top10" ] = (df_inst["fwci" ] > df_inst["fwci" ].quantile(0.90 )).astype(int )
df_inst["citations" ] = (df_inst["fwci" ] * 15 ).astype(int )
profile = institutional_profile(df_inst)
print ("=== Institutional Research Profile ===" )
print (profile.round (3 ).to_string(index=False ))
Leiden Ranking-Style Indicator Computation import numpy as np
import pandas as pd
def leiden_indicators (df, fractional=True ):
"""Compute CWTS Leiden Ranking-style indicators.
Indicators:
P: total publication count (or fractional count)
MNCS: mean normalized citation score
PP_top10: percentage in top 10%
collab_int: international collaboration rate
"""
if fractional:
df = df.copy()
df["frac_weight" ] = 1 / df["n_authors" ]
P = df.groupby("institution" )["frac_weight" ].sum ()
MNCS = df.groupby("institution" ).apply(
lambda g: (g["fwci" ] * g["frac_weight" ]).sum () / g["frac_weight" ].sum ()
)
else :
P = df.groupby("institution" )["paper_id" ].count()
MNCS = df.groupby("institution" )["fwci" ].mean()
PP10 = df.groupby("institution" )["top10" ].mean() * 100
leiden = pd.DataFrame({"P" : P, "MNCS" : MNCS, "PP_top10" : PP10})
leiden = leiden.sort_values("MNCS" , ascending=False )
return leiden
leiden = leiden_indicators(df_inst, fractional=True )
print ("\n=== Leiden-Style Indicators (Fractional Counting) ===" )
print (leiden.round (3 ))
Troubleshooting Problem Cause Fix FWCI = 0 for all papers All baselines = 0 Ensure field-year groups have ≥ 1 paper JIF negative or zero No citations in window Check year range; expand window to 3 years Altmetric API returns 404 DOI not tracked or invalid Verify DOI format; use bare DOI without URL prefix Spearman correlation insignificant Small sample or too many zero altmetric scores Filter papers with altmetric > 0; increase sample size PP-top10 always 0% Percentile computed globally, not by field Use field×year percentile thresholds Eigenfactor power iteration diverges Non-stochastic transition matrix Ensure row sums = 1 before iteration
External Resources
Examples
Example 1: Department-Level Impact Report import pandas as pd
import numpy as np
np.random.seed(42 )
n = 300
departments = ["CS" , "Physics" , "Biology" , "Chemistry" , "Mathematics" ]
dept = np.random.choice(departments, n)
year = np.random.randint(2018 , 2024 , n)
fwci = np.abs (np.random.normal(1.2 , 0.8 , n))
top10 = (fwci > 1.8 ).astype(int )
dept_df = pd.DataFrame({"dept" : dept, "year" : year, "fwci" : fwci, "top10" : top10})
report = dept_df.groupby(["dept" , "year" ]).agg(
n_papers=("fwci" , "count" ),
mncs=("fwci" , "mean" ),
pp_top10=("top10" , "mean" ),
).reset_index()
report["pp_top10_pct" ] = report["pp_top10" ] * 100
print ("=== Department Impact Report (2022) ===" )
print (report[report["year" ] == 2022 ].sort_values("mncs" , ascending=False )
.round (3 ).to_string(index=False ))
Example 2: Citation Decay Analysis import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
def citation_decay_curve (publication_year, current_year=2023 , field_halflife=10 ):
"""Model citation decay over time using exponential decay.
Args:
publication_year: year paper was published
current_year: current year
field_halflife: citation half-life in years (field-specific)
Returns:
citation_age_distribution (dict)
"""
paper_age = current_year - publication_year
ages = np.arange(0 , paper_age + 1 )
peak_age = min (3 , paper_age)
weights = np.exp(-0.5 * ((ages - peak_age) / (field_halflife / 2.35 ))**2 )
weights = weights / weights.sum ()
return dict (zip (range (current_year - paper_age, current_year + 1 ), weights))
field_halflives = {"Computer Science" : 5 , "Medicine" : 10 , "Mathematics" : 20 }
years = np.arange(0 , 25 )
fig, ax = plt.subplots(figsize=(8 , 5 ))
for field, hl in field_halflives.items():
weights = np.exp(-0.5 * ((years - 2 ) / (hl / 2.35 ))**2 )
weights = weights / weights.sum ()
ax.plot(years, weights, marker="o" , ms=3 , label=f"{field} (HL={hl} y)" )
ax.set_xlabel("Citation Age (years)" ); ax.set_ylabel("Relative Citation Probability" )
ax.set_title("Citation Age Profiles by Field" )
ax.legend()
plt.tight_layout()
plt.savefig("citation_decay.png" , dpi=150 , bbox_inches="tight" )
plt.close()
print ("Figure saved: citation_decay.png" )
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