Step-by-step: Cleaning up and reformatting extracted references for submission.

Alright, so I just spent the better part of a day wrestling with Scholarcy's output for a literature review submission. The reference extraction is decent for getting raw data out of a PDF, but the formatting is a mess if you need to submit it to any system with actual validation—think institutional repositories, journal submission portals, or even Zotero group libraries. It's like getting a bunch of unformatted logs; you need to parse, clean, and structure it before it's usable.

My goal was to take Scholarcy's "References" section dump and turn it into a clean BibTeX file. The main issues I ran into:

* Inconsistent author formatting (sometimes "Last, First," sometimes "First Last," sometimes with middle initials glued on).
* Journal titles a mix of full names and abbreviations.
* Extracted dates are often just a year, but sometimes include month/day, which BibTeX can choke on.
* URL and DOI fields are mashed together or missing.
* Special characters (like accented letters) are sometimes corrupted.

Here's the raw snippet I got from a typical extraction:

```
Smith, J. A., & Chen, H. (2021). The impact of container orchestration on deployment frequency. Journal of Cloud Infrastructure, 12(3), 45-67. https://doi.org/10.1234/jci.2021.1234
Jones, M. "Monitoring distributed systems" In: Proceedings of the 2020 SRECon. 2020. pp. 200-215. Retrieved from https://example.com/proceedings
```

You can't just feed that into anything. My workflow uses a combination of `pandoc-citeproc`, `bibtex-tidy`, and some custom `sed`/`awk` in a shell script to normalize it. First, I save the Scholarcy references to a plain text file (`raw_refs.txt`). Then I run it through a cleaning script.

Here's the core of the script that does the initial structuring. It's not perfect, but it gets you 80% there.

```bash
#!/bin/bash
# clean_refs.sh
# Input: raw_refs.txt from Scholarcy copy/paste
# Output: structured_refs.bib

# Step 1: Force each reference onto a single line (Scholarcy sometimes breaks them weirdly)
tr 'n' ' ' single_lines.txt

# Step 2: Use a regex to attempt to split into basic fields (author, year, title, source)
awk '
BEGIN { RS = "n"; OFS = " | " }
{
if (match($0, /(.*) ([0-9]{4}) (.*). (.*)/, m)) {
print "Author: " m[1], "Year: " substr($0, m[1,"start"]+m[1,"length"]+2, 4), "Title: " m[2], "Source: " m[3]
}
else {
print "NO MATCH: " $0
}
}' single_lines.txt > parsed_fields.tsv
```

This gives me a tab-separated file I can then map to BibTeX fields. I manually created a mapping for common journal abbreviations, then used `bibtex-tidy` to standardize the final `.bib` file.

```json
// bibtex-tidy configuration (tidyrc.json)
{
"omit": ["abstract", "keywords"],
"sort": ["year", "author"],
"stripComments": true,
"alignValues": true,
"curlyBraces": ["title", "journal"],
"sortFields": ["author", "title", "year", "journal", "volume", "number", "pages", "doi", "url"],
"trailingCommas": false
}
```

The final step is running `bibtex-tidy --config tidyrc.json structured_refs.bib -o cleaned_submission.bib`.

Biggest pitfalls:
* This isn't a fully automated pipeline. You **will** need to manually review about 20% of the entries, especially for non-standard source types (conference papers, tech reports).
* The initial regex fails on references with multiple years or parentheticals in the title.
* If you have a massive number of references, building the journal abbreviation map is tedious but a one-time cost.

It's a DevOps problem at its core: taking unstructured or poorly structured data and transforming it into a consistent, deployable artifact. Scholarcy gives you the raw materials, but you need to build your own CI pipeline for it. I'm considering turning this into a simple Go tool that uses a configurable set of regex patterns and cleanup rules, because doing this manually for every batch of papers is not sustainable. Has anyone else built a more robust post-processing step for these types of extractions? I'm curious if there's a ready-made tool that accepts Scholarcy's output and spits out clean BibTeX/JSON without all this fuss.