Use this Skill to structure research grant proposals: NSF/NIH Specific Aims, budget justification, ERC narrative sections, structured abstracts, and biosketch formatting.
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name
grant-writing
description
Use this Skill to structure research grant proposals: NSF/NIH Specific Aims, budget justification, ERC narrative sections, structured abstracts, and biosketch formatting.
TL;DR — Produce well-structured grant proposal sections using Jinja2
templates (NIH Specific Aims, ERC B1/B2 narratives), auto-generate budget
tables with pandas, and format reference lists with pybtex. Covers NIH, NSF,
and ERC Starting Grant conventions.
When to Use This Skill
Use this Skill whenever you need to:
Draft or refine a NIH Specific Aims page following the standard one-page format
Structure an NSF Project Description with explicit intellectual merit and broader impacts
Outline ERC Starting Grant B1 (extended synopsis) and B2 (scientific proposal) sections
Generate a personnel budget justification table with fringe, indirect, and total costs
Format an NIH Biosketch (personal statement, positions, contributions to science)
Write a 250-word structured abstract with labeled sections for any funding agency
Format a BibTeX reference list using pybtex for grant submissions
Task
When to apply
Specific Aims page
NIH R01, R21, K-series, SBIR/STTR submissions
NSF Project Description
NSF CAREER, standard grants, RAPID, EAGER
ERC B1/B2 narrative
ERC StG, CoG, AdG applications
Budget justification
Any sponsored research with modular or detailed budget
Biosketch
NIH applications; NSF equivalent (Facilities & Other Resources)
The Specific Aims page is the single most important page in an NIH application.
Reviewers read it first and it determines whether the full application receives
detailed review. The canonical one-page structure is:
Opening paragraph — Establishes the research problem, knowledge gap, and
why the problem matters (~100 words).
Long-term goal — The overarching scientific objective beyond this grant.
Objective — What this specific project will accomplish.
Central hypothesis — A testable, falsifiable statement derived from
preliminary data.
Specific Aims list — Typically 2–4 aims, each with a brief rationale and
expected outcome.
More from this repository
Innovation — What is new about the approach or the scientific question.
Impact — Expected contribution to the field and public health relevance.
NSF Project Description
NSF requires explicit separation of intellectual merit (advancing knowledge
within a field) and broader impacts (benefits to society). Page limits vary
by program (typically 15 pages for standard grants, 20 for CAREER). Word count
guidance:
Section
Typical length
Introduction & significance
1–2 pages
Preliminary results
2–3 pages
Research plan (each aim)
2–3 pages per aim
Intellectual merit summary
0.5–1 page
Broader impacts
1–2 pages
References cited
Does not count toward page limit
ERC Starting Grant B1/B2 Structure
Section
Description
Page limit
B1 — Extended synopsis
Short self-contained overview of the project
5 pages
B2 — Scientific proposal
Full research plan: state of the art, methodology, resources
15 pages
ERC reviewers assess scientific excellence (novelty, ambition, feasibility)
and principal investigator quality (track record, independence).
Budget Categories
A typical NIH R01 modular budget ($250 K/year direct costs) or detailed budget
includes:
All NIH applications require a structured abstract with labeled sections:
Background, Objective, Methods, Expected Results, and Significance. The total
must not exceed 250 words.
Step 1 — Render NIH Specific Aims Page with Jinja2
Define your grant content as a Python dictionary and render a structured
Specific Aims page using a Jinja2 template.
import os
from jinja2 import Environment, BaseLoader
from dotenv import load_dotenv
load_dotenv()
SPECIFIC_AIMS_TEMPLATE = """
SPECIFIC AIMS
{{ background }}
Long-term goal: {{ long_term_goal }}
Objective: {{ objective }}
Central hypothesis: {{ central_hypothesis }}
Rationale: {{ rationale }}
SPECIFIC AIMS:
{% for aim in aims %}
Aim {{ loop.index }}: {{ aim.title }}
{{ aim.description }}
Expected outcome: {{ aim.expected_outcome }}
{% endfor %}
Innovation: {{ innovation }}
Impact: {{ impact }}
""".strip()
defrender_specific_aims(content: dict) -> str:
"""
Render a NIH Specific Aims page from a structured content dictionary.
Args:
content: Dictionary with keys: background, long_term_goal, objective,
central_hypothesis, rationale, aims (list of dicts with
title/description/expected_outcome), innovation, impact.
Returns:
Rendered Specific Aims text as a string.
Example:
>>> text = render_specific_aims(EXAMPLE_AIMS_CONTENT)
>>> print(text[:200])
"""
env = Environment(loader=BaseLoader())
template = env.from_string(SPECIFIC_AIMS_TEMPLATE)
return template.render(**content)
EXAMPLE_AIMS_CONTENT = {
"background": (
"Alzheimer's disease (AD) affects 6.7 million Americans and currently ""lacks disease-modifying treatments. Neuroinflammation driven by ""microglial activation is increasingly recognized as a key pathological ""mechanism, yet the molecular switches that convert microglia from ""homeostatic to disease-associated states remain poorly understood."
),
"long_term_goal": (
"Elucidate microglial state transitions in AD to identify novel ""therapeutic targets for neuroinflammation."
),
"objective": (
"Characterize the transcriptional and epigenetic regulators governing ""microglial activation in human AD brain tissue and validated mouse models."
),
"central_hypothesis": (
"TREM2 signaling coordinates with APOE-mediated lipid metabolism to drive ""disease-associated microglial (DAM) state transitions, and disrupting ""this axis will attenuate amyloid pathology."
),
"rationale": (
"Preliminary data from our lab demonstrate that TREM2-deficient microglia ""fail to upregulate DAM markers in 5xFAD mice, supporting the central ""hypothesis. Identifying the upstream regulators will open new therapeutic ""windows."
),
"aims": [
{
"title": "Define the TREM2-dependent transcriptional network in human AD microglia.",
"description": (
"We will perform single-nucleus RNA-seq and ATAC-seq on ""post-mortem prefrontal cortex from 40 AD and 20 control donors ""stratified by TREM2 genotype."
),
"expected_outcome": (
"A high-resolution atlas of microglial states linked to TREM2 ""variant status and amyloid burden."
),
},
{
"title": "Determine how APOE isoforms modulate TREM2-driven microglial lipid metabolism.",
"description": (
"Using APOE knock-in mice crossed with 5xFAD, we will apply ""lipidomics and CRISPR-interference screens to identify lipid ""mediators downstream of TREM2 signaling."
),
"expected_outcome": (
"Identification of 3–5 lipid species that serve as rheostat ""switches for DAM induction."
),
},
{
"title": "Test whether pharmacological modulation of the TREM2-APOE axis reduces AD pathology.",
"description": (
"Lead compounds identified in Aim 2 will be administered to ""5xFAD mice; amyloid plaque load, synaptic density, and ""cognitive performance will be quantified."
),
"expected_outcome": (
"At least one compound that reduces plaque burden by ≥30 % and ""rescues novel-object recognition deficits."
),
},
],
"innovation": (
"This project is innovative because it integrates multi-omic single-cell ""profiling with functional CRISPR screens in a genotype-stratified human ""cohort — a combination not previously applied to TREM2-APOE interactions."
),
"impact": (
"Success will produce a mechanistic framework for microglial reprogramming ""in AD and candidate therapeutic targets ready for preclinical IND-enabling ""studies, directly advancing NIH's goal of developing disease-modifying ""AD therapies."
),
}
if __name__ == "__main__":
text = render_specific_aims(EXAMPLE_AIMS_CONTENT)
output_path = "specific_aims.txt"withopen(output_path, "w", encoding="utf-8") as fh:
fh.write(text)
print(f"Specific Aims page written to {output_path}")
print(f"Character count: {len(text)} (target ≤ 3500 for one page)")
Step 2 — Generate Budget Justification Table with pandas
import pandas as pd
import numpy as np
defbuild_budget_table(
personnel: list[dict],
equipment: list[dict] | None = None,
travel_domestic: float = 2000.0,
travel_international: float = 3000.0,
materials: float = 15000.0,
other_direct: float = 5000.0,
indirect_rate: float = 0.52,
years: int = 4,
) -> pd.DataFrame:
"""
Build a multi-year NIH detailed budget justification table.
Args:
personnel: List of dicts with keys: role, name, effort_pct,
annual_salary, fringe_rate.
equipment: List of dicts with keys: item, cost, year (1-indexed).
travel_domestic: Annual domestic travel per year (USD).
travel_international: Annual international travel per year (USD).
materials: Annual materials & supplies (USD).
other_direct: Annual other direct costs (USD).
indirect_rate: Facilities & administrative rate (fraction, e.g. 0.52).
years: Project duration in years.
Returns:
DataFrame with one row per line item per year plus totals row.
"""
rows = []
for yr inrange(1, years + 1):
# Personnel + fringefor p in personnel:
salary_cost = p["annual_salary"] * p["effort_pct"] / 100# Apply 3 % annual escalation from year 2 onward
salary_cost *= (1.03 ** (yr - 1))
fringe_cost = salary_cost * p["fringe_rate"]
rows.append({
"year": yr,
"category": "Personnel",
"line_item": f"{p['role']} — {p['name']}",
"direct_cost": round(salary_cost, 2),
"notes": f"{p['effort_pct']}% effort; fringe ${fringe_cost:,.0f}",
})
# Equipment (year-specific)if equipment:
for eq in equipment:
if eq["year"] == yr:
rows.append({
"year": yr,
"category": "Equipment",
"line_item": eq["item"],
"direct_cost": eq["cost"],
"notes": "One-time purchase ≥ $5,000",
})
# Travel
rows.append({
"year": yr,
"category": "Travel",
"line_item": "Domestic conference travel",
"direct_cost": travel_domestic,
"notes": "1 PI + 1 trainee × 1 conference",
})
rows.append({
"year": yr,
"category": "Travel",
"line_item": "International conference travel",
"direct_cost": travel_international,
"notes": "1 PI × 1 international meeting",
})
# Materials
rows.append({
"year": yr,
"category": "Materials & Supplies",
"line_item": "Lab consumables & reagents",
"direct_cost": materials,
"notes": "Antibodies, cell culture, sequencing reagents",
})
# Other direct
rows.append({
"year": yr,
"category": "Other Direct Costs",
"line_item": "Publication fees & software",
"direct_cost": other_direct,
"notes": "Open-access fees, statistical software licenses",
})
df = pd.DataFrame(rows)
# Compute indirect costs per year
indirect_rows = []
for yr, grp in df.groupby("year"):
mtdc = grp["direct_cost"].sum()
indirect = round(mtdc * indirect_rate, 2)
indirect_rows.append({
"year": yr,
"category": "Indirect (F&A)",
"line_item": f"Indirect costs @ {indirect_rate*100:.0f}% MTDC",
"direct_cost": indirect,
"notes": f"Applied to MTDC of ${mtdc:,.0f}",
})
df = pd.concat([df, pd.DataFrame(indirect_rows)], ignore_index=True)
# Grand totals row
total_cost = df["direct_cost"].sum()
total_row = pd.DataFrame([{
"year": "ALL",
"category": "TOTAL",
"line_item": "Total Project Cost",
"direct_cost": round(total_cost, 2),
"notes": "",
}])
df = pd.concat([df, total_row], ignore_index=True)
return df
SAMPLE_PERSONNEL = [
{"role": "Principal Investigator", "name": "Dr. J. Smith",
"effort_pct": 20, "annual_salary": 120000, "fringe_rate": 0.30},
{"role": "Postdoctoral Researcher", "name": "Dr. A. Lee",
"effort_pct": 100, "annual_salary": 58000, "fringe_rate": 0.28},
{"role": "Graduate Research Assistant", "name": "M. Chen",
"effort_pct": 50, "annual_salary": 32000, "fringe_rate": 0.10},
]
SAMPLE_EQUIPMENT = [
{"item": "High-content fluorescence microscope", "cost": 85000, "year": 1},
{"item": "Ultra-low temperature freezer (-80 °C)", "cost": 12000, "year": 2},
]
if __name__ == "__main__":
budget_df = build_budget_table(
personnel=SAMPLE_PERSONNEL,
equipment=SAMPLE_EQUIPMENT,
travel_domestic=2500,
travel_international=3500,
materials=18000,
other_direct=6000,
indirect_rate=0.52,
years=4,
)
budget_df.to_csv("budget_justification.csv", index=False)
print(budget_df.to_string(index=False))
# Summary by year
yearly = budget_df[budget_df["year"] != "ALL"].copy()
yearly["direct_cost"] = pd.to_numeric(yearly["direct_cost"])
print("\nAnnual totals:")
print(yearly.groupby("year")["direct_cost"].sum().apply(lambda x: f"${x:,.0f}"))
Step 3 — Format References with pybtex and Write Structured Abstract
import textwrap
from pybtex.database import BibliographyData, Entry, Person
from pybtex.database.output.bibtex import Writer as BibTexWriter
import io
defcreate_bibtex_entry(
key: str,
authors: list[str],
title: str,
journal: str,
year: int,
volume: str = "",
pages: str = "",
doi: str = "",
) -> Entry:
"""
Create a pybtex BibTeX Entry object for a journal article.
Args:
key: BibTeX citation key (e.g., 'Smith2024').
authors: List of author names in 'Last, First' format.
title: Article title.
journal: Journal name.
year: Publication year.
volume: Journal volume number.
pages: Page range (e.g., '123-145').
doi: Digital object identifier.
Returns:
pybtex Entry object of type 'article'.
"""
persons = {"author": [Person(a) for a in authors]}
fields = {
"title": title,
"journal": journal,
"year": str(year),
}
if volume:
fields["volume"] = volume
if pages:
fields["pages"] = pages
if doi:
fields["doi"] = doi
return Entry("article", persons=persons, fields=fields)
defrender_bibliography(entries: dict[str, Entry]) -> str:
"""Render a set of pybtex entries as a BibTeX string."""
bib_data = BibliographyData(entries=entries)
writer = BibTexWriter()
stream = io.StringIO()
writer.write_stream(bib_data, stream)
return stream.getvalue()
defwrite_structured_abstract(
background: str,
objective: str,
methods: str,
expected_results: str,
significance: str,
word_limit: int = 250,
) -> str:
"""
Compose and validate a structured 250-word abstract.
Args:
background: 1–2 sentences on the problem and knowledge gap.
objective: 1 sentence stating the study objective.
methods: 2–3 sentences describing design, participants, measures.
expected_results: 2–3 sentences on anticipated findings.
significance: 1–2 sentences on importance and next steps.
word_limit: Maximum word count (default 250 for NIH).
Returns:
Formatted abstract string with word count report.
"""
sections = [
("Background", background),
("Objective", objective),
("Methods", methods),
("Expected Results", expected_results),
("Significance", significance),
]
lines = []
for label, text in sections:
wrapped = textwrap.fill(f"{label}: {text}", width=80)
lines.append(wrapped)
abstract = "\n\n".join(lines)
word_count = len(abstract.split())
status = "OK"if word_count <= word_limit elsef"OVER LIMIT by {word_count - word_limit} words"
footer = f"\n\n[Word count: {word_count}/{word_limit} — {status}]"return abstract + footer
# ── Usage ─────────────────────────────────────────────────────────────────────if __name__ == "__main__":
# Bibliography
entries = {
"Smith2023": create_bibtex_entry(
key="Smith2023",
authors=["Smith, Jane", "Lee, Andrew"],
title="TREM2 variants and microglial activation in Alzheimer disease",
journal="Nature Neuroscience",
year=2023,
volume="26",
pages="1234-1245",
doi="10.1038/s41593-023-XXXX-X",
),
"Jones2022": create_bibtex_entry(
key="Jones2022",
authors=["Jones, Robert", "Patel, Sunita", "Kim, David"],
title="APOE4 modulates lipid metabolism in disease-associated microglia",
journal="Cell",
year=2022,
volume="185",
pages="3456-3471",
doi="10.1016/j.cell.2022.XX.XXX",
),
}
bib_str = render_bibliography(entries)
withopen("references.bib", "w", encoding="utf-8") as fh:
fh.write(bib_str)
print("BibTeX references written to references.bib")
# Structured abstract
abstract = write_structured_abstract(
background=(
"Alzheimer's disease (AD) lacks disease-modifying treatments; ""microglial neuroinflammation is a mechanistically plausible target ""but the upstream regulators of microglial state transitions remain ""unknown."
),
objective=(
"To identify TREM2- and APOE-dependent transcriptional regulators ""of disease-associated microglia (DAM) in human AD brain tissue."
),
methods=(
"We will perform single-nucleus RNA-seq and ATAC-seq on 60 ""post-mortem prefrontal cortex samples stratified by TREM2 genotype ""and AD status. Hits will be validated in APOE knock-in/5xFAD mice ""using CRISPR-interference screens and lipidomics."
),
expected_results=(
"We expect to identify 3–5 transcription factors that gate DAM ""induction in a TREM2-dependent manner and lipid mediators that ""link APOE isoforms to TREM2 signaling. At least one compound will ""reduce plaque burden by ≥30 % in vivo."
),
significance=(
"These findings will establish a mechanistic basis for targeting ""the TREM2-APOE axis in AD and will nominate preclinical candidates ""for IND-enabling studies, accelerating the pipeline of ""neuroinflammation-targeted AD therapies."
),
)
print(abstract)
Advanced Usage
NIH Biosketch Formatting
The NIH Biosketch (Form PHS 2590 / SciENcv format) contains four sections:
Personal Statement — 4 sentences maximum. State why you are well-suited
for this project. Reference up to 4 publications.
Positions, Scientific Appointments, and Honors — Reverse-chronological
list with institution, role, and dates.
Contributions to Science — Up to 5 paragraphs, each with a brief
narrative and up to 4 publications. Describe the historical context, your
contribution, and significance.
Additional Information — Research support (current and pending).
BIOSKETCH_TEMPLATE = """
BIOGRAPHICAL SKETCH
NAME: {{ name }}
eRA COMMONS USER NAME: {{ era_commons }}
POSITION TITLE: {{ position_title }}
EDUCATION/TRAINING:
{% for edu in education %}
{{ edu.institution }} | {{ edu.degree }} | {{ edu.field }} | {{ edu.year }}
{% endfor %}
A. Personal Statement
{{ personal_statement }}
Key publications:
{% for pub in personal_pubs %}
{{ loop.index }}. {{ pub }}
{% endfor %}
B. Positions, Scientific Appointments, and Honors
{% for pos in positions %}
{{ pos.dates }}: {{ pos.role }}, {{ pos.institution }}
{% endfor %}
C. Contributions to Science
{% for contrib in contributions %}
{{ loop.index }}. {{ contrib.narrative }}
Publications:
{% for pub in contrib.publications %}
{{ loop.index }}. {{ pub }}
{% endfor %}
{% endfor %}
""".strip()
defrender_biosketch(data: dict) -> str:
"""Render NIH Biosketch from structured dictionary using Jinja2."""from jinja2 import Environment, BaseLoader
env = Environment(loader=BaseLoader())
template = env.from_string(BIOSKETCH_TEMPLATE)
return template.render(**data)
ERC Starting Grant B1 / B2 Outline
ERC proposals require explicit evidence of scientific excellence and
PI independence. Structure B1 (5 pages) as follows:
Subsection
Content
B1.1 Overview
Project title, keywords, short abstract (10 lines)
B1.2 State of the art
Current knowledge and open question
B1.3 Objectives
3–5 numbered objectives aligned with work packages
B1.4 Methodology
Key experimental/computational approaches
B1.5 Originality
How the proposal goes beyond the state of the art
B1.6 Resources
Team composition, key infrastructure
For B2 (15 pages), expand each subsection and add:
Work package (WP) table with lead, duration, deliverables, milestones
Example 1 — Full NIH Specific Aims Page Rendered via Jinja2
# Run from the project root; requires jinja2 and python-dotenv installedimport os
from dotenv import load_dotenv
load_dotenv()
# Re-use functions defined in Step 1
text = render_specific_aims(EXAMPLE_AIMS_CONTENT)
word_count = len(text.split())
char_count = len(text)
print(f"=== NIH Specific Aims Page ({'~' + str(word_count)} words) ===\n")
print(text)
print(f"\n[Characters: {char_count} | Target: ≤ 3500 for one page at 11pt Arial]")
# Save to file for reviewwithopen("specific_aims_final.txt", "w", encoding="utf-8") as fh:
fh.write(text)
print("\nSaved to specific_aims_final.txt")
Example 2 — NSF Budget Table and Broader Impacts Template
import pandas as pd
# --- Budget Table for NSF CAREER (5 years) ---
nsf_personnel = [
{"role": "Principal Investigator", "name": "Dr. M. Rivera",
"effort_pct": 25, "annual_salary": 110000, "fringe_rate": 0.32},
{"role": "Graduate Research Assistant (0.5 FTE)", "name": "TBD",
"effort_pct": 50, "annual_salary": 34000, "fringe_rate": 0.08},
{"role": "Undergraduate Researcher (summer)", "name": "TBD",
"effort_pct": 100, "annual_salary": 10000, "fringe_rate": 0.08},
]
nsf_equipment = [
{"item": "Confocal laser scanning microscope (shared)", "cost": 45000, "year": 1},
]
nsf_budget = build_budget_table(
personnel=nsf_personnel,
equipment=nsf_equipment,
travel_domestic=3000,
travel_international=4000,
materials=12000,
other_direct=4000,
indirect_rate=0.56,
years=5,
)
nsf_budget.to_csv("nsf_career_budget.csv", index=False)
# Year 1 direct cost summary
yr1 = nsf_budget[
(nsf_budget["year"] == 1) & (nsf_budget["category"] != "Indirect (F&A)")
]
yr1_direct = pd.to_numeric(yr1["direct_cost"]).sum()
print(f"NSF CAREER Year 1 direct costs: ${yr1_direct:,.0f}")
# --- Broader Impacts template ---
BROADER_IMPACTS = """
BROADER IMPACTS
Intellectual Merit: [Summarize contribution to fundamental knowledge]
Broader Impacts:
1. Graduate and postdoctoral training: One PhD student and one postdoc will
receive training in [field], preparing them for careers in academia and
industry. Mentoring will follow the Individual Development Plan (IDP)
framework.
2. Broadening participation: We will partner with [HBCU/MSI partner] to
host two REU undergraduates per summer from underrepresented groups.
Travel support will be provided.
3. K-12 outreach: We will develop two inquiry-based modules for local high
schools in partnership with [School District], reaching ~200 students
per year.
4. Open science: All datasets will be deposited in [repository] under CC-BY
license within 12 months of collection. Analysis code will be released on
GitHub under MIT license.
5. Policy engagement: Findings will be communicated to [agency] via annual
stakeholder briefings and one policy brief per project year.
"""print(BROADER_IMPACTS)
withopen("nsf_broader_impacts.txt", "w", encoding="utf-8") as fh:
fh.write(BROADER_IMPACTS)
print("Broader impacts template saved to nsf_broader_impacts.txt")
