endnote-utils 1.0.3

Convert EndNote XML to CSV/JSON/XLSX with streaming parse and TXT report.

EndNote Utils

Convert EndNote XML and RIS files into clean CSV / JSON / XLSX with automatic TXT reports. Includes an LLM screening tool (via Ollama) to label include / exclude / uncertain from title + abstract. Supports both Python API and command-line interface (CLI).

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Table of Contents

• EndNote Utils
  • Table of Contents
  • ✨ Features
  • 📦 Installation
• 🔹 A) Exporter (XML/RIS → CSV/JSON/XLSX)
  • Quick examples
  • Exporter CLI options
  • Output Snippet
    • Export report snippet
    • CSV export snippet
• 🔹 B) LLM Screening
  • 1. Install Ollama + models
    • Why local models?
  • 2. Write criteria (criteria.txt)
  • 3. Run the screener
  • LLM CLI options
  • Output snippets
    • Screener output snippet
    • Screening report snippet
• 🔹 C) One-Shot Pipeline
  • Examples
  • Full-screen CLI options
  • Output snippet
• 🧪 Python API
  • Import surface
  • export
  • export_folder
  • export_files_to_csv_with_report (low-level)
  • screen_csv_with_ollama (LLM)
  • End-to-end example (pure Python)
  • Make your own LLM stats report
• ❓ FAQ
• ⚠️ Disclaimer
• 📜 License

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✨ Features

• ✅ Parse one file (--xml or --ris) or a folder of mixed *.xml / *.ris
• ✅ Streaming parsers (low memory usage)
• ✅ Extract fields: database, ref_type, title, journal, authors, year, volume, number, abstract, doi, urls, keywords, publisher, isbn, language, extracted_date
• ✅ Add database column from filename (IEEE.xml → IEEE, PubMed.ris → PubMed)
• ✅ Normalize DOI (10.xxxx → https://doi.org/...)
• ✅ Always generate a TXT report (counts, duplicates, stats)
• ✅ Deduplicate by doi or title+year (--dedupe)
• ✅ Export to CSV, JSON, XLSX
• ✅ Auto-create output folders if missing
• ✅ Python API for integration
• ✅ LLM screening with Qwen or Mistral via Ollama
• ✅ One-shot pipeline: endnote-full-screen = export → screen in one command

How to think about it: use the Exporter to normalize your sources into a single table, then (optionally) run LLM Screening to triage papers. If you want both in one go, use endnote-full-screen.

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📦 Installation

pip install endnote-utils

Requires Python 3.8+.

If you see Excel export errors, reinstall:

pip install --upgrade openpyxl

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🔹 A) Exporter (XML/RIS → CSV/JSON/XLSX)

The exporter turns EndNote XML/RIS into tidy tables. This is the best place to deduplicate, filter, and summarize your corpus before further work (i.e., extract data for reading grid).

Quick examples

# Single XML → CSV
endnote-utils --xml data/IEEE.xml --out output/ieee.csv

# Single RIS → JSON
endnote-utils --ris data/PubMed.ris --out output/pubmed.json

# Folder (mixed XML/RIS) → XLSX with stats & DOI dedupe
endnote-utils --folder data/refs --out output/all.xlsx --stats --dedupe doi

Exporter CLI options

Option	Description	Example
--xml FILE.xml	Parse one EndNote XML file	--xml data/IEEE.xml
--ris FILE.ris	Parse one RIS file	--ris data/PubMed.ris
--folder DIR	Parse all *.xml / *.ris in folder	--folder data/refs
--out PATH	Output path; format inferred from extension	--out output/all.csv
--format FMT	Force format: csv, json, xlsx	--format json
--report PATH	Save TXT report	--report reports/run1.txt
--no-report	Disable TXT report	--no-report
--delimiter CH	CSV delimiter	--delimiter ';'
--quoting MODE	CSV quoting: minimal, all, nonnumeric, none	--quoting all
--no-header	Suppress CSV header	--no-header
--encoding ENC	Output encoding	--encoding utf-8
--ref-type STR	Filter records by reference type	--ref-type "Conference Proceedings"
--year YYYY	Filter records by year	--year 2024
--max-records N	Stop after N records per file	--max-records 100
--dedupe MODE	Deduplicate: none, doi, title-year	--dedupe doi
--dedupe-keep K	Keep first or last duplicate	--dedupe-keep last
--stats	Add summary stats to report	--stats
--stats-json P	Save stats + duplicates as JSON	--stats-json output/stats.json
--verbose	Verbose logging	--verbose

Tip: use --stats early to sanity-check your dataset (years, ref types, top journals) before screening.

Output Snippet

Export report snippet

========================================
EndNote Export Report
========================================
Run started : 2025-09-12 12:42:20
Files       : 4
Duration    : 0.47 seconds

Per-file results
----------------------------------------
IEEE.xml       : 2147 exported, 0 skipped
PubMed.ris     : 504 exported, 0 skipped
TOTAL exported : 2651

Duplicates table (by database)
----------------------------------------
Database    Origin  Retractions  Duplicates  Remaining
------------------------------------------------------
IEEE          2200            0         53        2147
PubMed         520            2         14         504

CSV export snippet

database	ref_type	title	journal	authors	year	volume	number	abstract	doi	urls	keywords	publisher	isbn	language	extracted_date
IEEE	Conference Proceedings	Automating Detection of Papilledema in Pediatric Fundus Images with Explainable Machine Learning	2022 IEEE International Conference on Image Processing (ICIP)	K. Avramidis; M. Rostami; M. Chang; S. Narayanan	2022			Papilledema is an ophthalmic neurologic disorder in which increased intracranial pressure leads to swelling of the optic nerves. Undiagnosed papilledema in...	https://doi.org/10.1109/ICIP46576.2022.9897529		Integrated optics; Deep learning; Training; Location awareness; Optical imaging; Feature extraction; Robustness; human-centered AI; model explainability; papilledema; pseudopapilledema; multi-view learning		2381-8549		2025-09-11

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🔹 B) LLM Screening

Once you have a clean CSV, you can ask a local LLM to label each row as include / exclude / uncertain based on title + abstract. The tool also records reasons.

1. Install Ollama + models

# Install Ollama
https://ollama.ai/download

# Pull models
ollama pull qwen2.5:7b-instruct
ollama pull mistral-nemo:12b

Model pages: Qwen 2.5, Mistral-Nemo

Why local models?

• Data stays on your machine (good for sensitive corpora)
• No API costs or rate limits
• Works offline once models are pulled

2. Write criteria (criteria.txt)

Keep criteria short and concrete. The LLM uses this to decide whether a paper belongs in your review.

Inclusion:
- English, peer-reviewed journals or conferences (2022–Sep 2025).
- Human participants or clinical datasets related to neurological disorders.
- Empirical AI/ML with an explainability/interpretability component.
- Clinical relevance: diagnosis, prognosis, monitoring, risk prediction, decision support.

Exclusion:
- Non-English; pre-2022; grey literature.
- Non-human only or simulated without validation.
- No neurology/clinical data.
- Secondary research without new empirical results.
- Pure algorithm papers without XAI/evaluation.

3. Run the screener

# Using Qwen
endnote-screen output/all.csv output/screened.csv criteria.txt \
  --preset qwen --log-file logs/screen.log --verbose

# Using Mistral
endnote-screen output/all.csv output/screened.csv criteria.txt \
  --preset mistral --log-file logs/screen.log

How it parses answers: models can respond in a strict 3-line template or a compact single-line format; both are supported and robust to light markdown/noise.

LLM CLI options

Option	Description	Example
input_csv	Input CSV (must have title, abstract)	output/all.csv
output_csv	Output CSV (new columns appended)	output/screened.csv
criteria_txt	Screening criteria file	criteria.txt
--preset NAME	Model preset (qwen, mistral)	--preset qwen
--model TAG	Override model tag	--model mistral-nemo:12b
--title-col COL	Title column name	--title-col Title
--abstract-col C	Abstract column name	--abstract-col Abstract
--temperature X	Sampling temperature	--temperature 0.2
--max-tokens N	Max tokens per row	--max-tokens 256
--num-ctx N	Context window	--num-ctx 4096
--retry N	Retries per row on errors	--retry 3
--max-records N	Limit to first N rows (test mode)	--max-records 50
--log-every N	Log every N rows	--log-every 25
--log-file PATH	Save log file	--log-file logs/screen.log
--verbose	Verbose logs	--verbose

What gets added: exclude (yes/no/maybe), reason (≤2 short sentences with a required prefix), confidence (if present), and quality flags for title/abstract truncation.

Output snippets

Screener output snippet

title	abstract	exclude	reason
Review of Personalized Semantic Secure Communications Based on the DIKWP Model	(empty)	yes	no relevance because no clinical neurology/XAI scope or empirical evaluation.
Performance Analysis of Deep-Learning and Explainable AI Techniques for Detecting and Predicting Epileptic Seizures	We benchmark…	no
Insights into the Potential of Fuzzy Systems for Medical AI Interpretability	We discuss…	yes	low relevance because conceptual discussion lacks empirical study on neurological data.

Screening report snippet

========================================
LLM Screening Report
========================================
Started    : 2025-09-16 14:43:25
Finished   : 2025-09-16 14:43:29
Input CSV  : output/all.csv
Output CSV : output/screened.csv
Model      : qwen2.5:7b-instruct
Rows       : 50
Duration   : 22.8 seconds
Throughput : 2.2 rows/s

Decisions
----------------------------------------
include               : 18 (36.0%)
exclude_no_relevance  : 28 (56.0%)
exclude_low_relevance :  2 ( 4.0%)
exclude_review        :  1 ( 2.0%)
uncertain             :  1 ( 2.0%)
avg confidence        : 0.83  (n=50)

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🔹 C) One-Shot Pipeline

The full-screen runner combines both steps: it first exports (from XML/RIS into your chosen format) and then screens the CSV output. This approach is well-suited for batch processing and fully reproducible pipelines.

Examples

# Export folder → CSV → Screen with Qwen
endnote-full-screen \
  --folder data/refs \
  --out output/all.csv \
  --criteria criteria.txt \
  --preset qwen \
  --dedupe doi \
  --stats \
  --log-file logs/screen.log

# Screen-only (use existing CSV)
endnote-full-screen \
  --csv-in output/all.csv \
  --out output/screened.csv \
  --criteria criteria.txt \
  --preset mistral \
  --log-file logs/screen.log \
  --max-records 20

Full-screen CLI options

Option	Description	Example
--xml FILE.xml	Input: one EndNote XML file	--xml data/IEEE.xml
--ris FILE.ris	Input: one RIS file	--ris data/PubMed.ris
--folder DIR	Input: folder with mixed XML/RIS files	--folder data/refs
--csv-in FILE	Input: existing CSV (skip export, screen only)	--csv-in output/all.csv
--out PATH	Output file path (format inferred)	--out output/all.csv
--format FMT	Output format: csv, json, xlsx	--format csv
--report PATH	TXT report for export stage	--report reports/export.txt
--dedupe MODE	Deduplicate: none, doi, title-year	--dedupe doi
--dedupe-keep K	Keep first or last duplicate	--dedupe-keep last
--stats	Add summary stats	--stats
--stats-json P	Save stats as JSON	--stats-json output/stats.json
--criteria FILE	Screening criteria file (required)	--criteria criteria.txt
--preset NAME	Screening preset (qwen, mistral)	--preset qwen
--model TAG	Override model tag	--model mistral-nemo:12b
--max-records N	Limit rows for screening (test mode)	--max-records 20
--log-file PATH	Save screening log	--log-file logs/screen.log
--log-every N	Progress logging frequency	--log-every 25
--verbose	Verbose logging	--verbose

Output snippet

INFO: Export stage: 2 input file(s)
INFO: Exported 2150 record(s).
INFO: Screened output → output/all.csv
INFO: Export report → output/all_report.txt
INFO: LLM report → output/all_report_screen.txt
INFO: Screen log → logs/screen.log

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🧪 Python API

The Python API provides the same functionality as the CLI, allowing you to build custom workflows. Use the high-level helpers for fast results, or the low-level functions for maximum control and flexibility.

Import surface

from pathlib import Path

# Exporter APIs
from endnote_utils import export, export_folder, export_files_to_csv_with_report
from endnote_utils import DEFAULT_FIELDNAMES, CSV_QUOTING_MAP

# LLM screener (Ollama)
from endnote_utils import screen_csv_with_ollama

# Presets (optional): {"qwen": {...}, "mistral": {...}}
from endnote_utils.screen import MODEL_PRESETS

---

export

total, out_path, report_path = export(
    input_path: Path,
    out_path: Path,
    *,
    format: str | None = None,        # inferred from out_path if None
    delimiter: str = ",",
    quoting: str = "minimal",         # one of CSV_QUOTING_MAP keys
    include_header: bool = True,
    encoding: str = "utf-8",
    ref_type: str | None = None,      # filter
    year: int | None = None,          # filter
    max_records: int | None = None,   # per-file limit (testing)
    dedupe: str = "none",             # "none" | "doi" | "title-year"
    dedupe_keep: str = "first",       # "first" | "last"
    stats: bool = False,              # add summary stats to TXT report
    stats_json: Path | None = None,   # save stats/dupes as JSON
)

Returns: total (int), out_path (Path), report_path (Path | None)

---

export_folder

total, out_path, report_path = export_folder(
    folder: Path,
    out_path: Path,
    *,
    format: str | None = None,
    delimiter: str = ",",
    quoting: str = "minimal",
    include_header: bool = True,
    encoding: str = "utf-8",
    ref_type: str | None = None,
    year: int | None = None,
    max_records: int | None = None,   # per-file limit
    dedupe: str = "none",
    dedupe_keep: str = "first",
    stats: bool = False,
    stats_json: Path | None = None,
)

---

export_files_to_csv_with_report (low-level)

Use this if you want to pass a curated list of files and always emit a single CSV plus a report.

total, out_path, report_path = export_files_to_csv_with_report(
    inputs: list[Path],
    out_path: Path,                   # single CSV
    *,
    fieldnames: list[str] = DEFAULT_FIELDNAMES,
    delimiter: str = ",",
    quoting: str = "minimal",
    include_header: bool = True,
    encoding: str = "utf-8",
    ref_type: str | None = None,
    year: int | None = None,
    max_records_per_file: int | None = None,
    report_path: Path | None = None,
    dedupe: str = "none",
    dedupe_keep: str = "first",
    stats: bool = False,
    stats_json: Path | None = None,
)

---

screen_csv_with_ollama (LLM)

processed, wrote = screen_csv_with_ollama(
    input_csv: Path,
    output_csv: Path,
    criteria_txt: Path,
    *,
    model: str = "qwen2.5:7b-instruct",  # or "mistral-nemo:12b"
    title_col: str = "title",
    abstract_col: str = "abstract",
    temperature: float = 0.2,
    max_tokens: int = 256,
    num_ctx: int = 4096,
    retry: int = 3,
    max_records: int | None = None,     # test first N rows
    log_every: int = 25,
)

Effect: appends exclude, reason, confidence (if present), truncated_title, abstract_chunks, abstract_truncated to output_csv.

Tip (presets):

cfg = MODEL_PRESETS["qwen"]  # or "mistral"
processed, wrote = screen_csv_with_ollama(
    input_csv=Path("output/all.csv"),
    output_csv=Path("output/screened.csv"),
    criteria_txt=Path("criteria.txt"),
    model=cfg["model"],
    temperature=cfg["temperature"],
    max_tokens=cfg["max_tokens"],
    num_ctx=cfg["num_ctx"],
)

---

End-to-end example (pure Python)

from pathlib import Path
import csv, logging
from endnote_utils import export_folder, screen_csv_with_ollama
from endnote_utils.screen import MODEL_PRESETS

logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")

# 1) Export
total, csv_path, report = export_folder(
    Path("data/refs"),
    Path("output/all.csv"),
    dedupe="doi",
    stats=True
)

# 2) Screen
cfg = MODEL_PRESETS["qwen"]
processed, wrote = screen_csv_with_ollama(
    input_csv=csv_path,
    output_csv=Path("output/screened.csv"),
    criteria_txt=Path("criteria.txt"),
    model=cfg["model"],
    temperature=cfg["temperature"],
    max_tokens=cfg["max_tokens"],
    num_ctx=cfg["num_ctx"],
    log_every=50,
)

# 3) Keep only include=no
src = Path("output/screened.csv")
dst = Path("output/included.csv")
with src.open(newline="", encoding="utf-8") as fi, dst.open("w", newline="", encoding="utf-8") as fo:
    r = csv.DictReader(fi)
    w = csv.DictWriter(fo, fieldnames=r.fieldnames)
    w.writeheader()
    for row in r:
        if (row.get("exclude") or "").lower() == "no":
            w.writerow(row)

---

Make your own LLM stats report

This helper shows how to compute simple aggregates if you want to augment the built-in TXT report.

from collections import Counter
import csv, statistics
from pathlib import Path

def summarize_screen(csv_path: Path) -> dict:
    dec = Counter()
    confs = []
    reasons = Counter()
    with csv_path.open(newline="", encoding="utf-8") as f:
        r = csv.DictReader(f)
        for row in r:
            d = (row.get("exclude") or "").lower()
            dec[d] += 1
            try:
                c = float(row.get("confidence") or "")
                if 0 <= c <= 1:
                    confs.append(c)
            except Exception:
                pass
            rs = (row.get("reason") or "").strip()
            if rs:
                reasons[rs] += 1
    return {
        "rows": sum(dec.values()),
        "decisions": dec,
        "avg_conf": (statistics.mean(confs) if confs else None),
        "top_reasons": reasons.most_common(10),
    }

print(summarize_screen(Path("output/screened.csv")))

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❓ FAQ

Q: Which columns are required for screening? A: title and abstract. Rename via --title-col / --abstract-col if needed.

Q: Can I screen a CSV produced by other tools? A: Yes—any CSV with those two columns works.

Q: How does deduplication work? A: --dedupe doi removes repeated DOIs; --dedupe title-year removes identical (title, year) pairs. Reports show totals and duplicates by database.

Q: Where are reports saved? A: Export report: <out>_report.txt. Screening report: <out>_report_screen.txt.

Q: Is data sent anywhere? A: No. LLM screening runs locally via Ollama (no API keys, no cloud calls).

Q: Which model should I pick? A: qwen2.5:7b-instruct is fast and follows instructions well (good laptop default). mistral-nemo:12b is stronger but heavier (more RAM/VRAM).

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⚠️ Disclaimer

• LLM screening is an assistive tool, not a substitute for expert judgment — always manually review included/excluded results before relying on them for research or publication.
• Performance varies with hardware — smaller models (e.g., Qwen) generally run smoothly on standard laptops; larger ones (e.g., Mistral) may require more memory and computing power.
• Local execution only — all processing happens on your machine via Ollama. No API keys, cloud services, or external data transfers are involved.
• Reproducibility — results may vary slightly between runs due to model sampling. For consistency, record the model tag, preset, and parameters (e.g., temperature, max tokens) in your workflow.

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📜 License

MIT License © 2025 Minh Quach