ResearchRabbit's CSV export is missing crucial fields for my analysis.

I have been conducting an extensive performance and utility analysis of ResearchRabbit over the last quarter, integrating its output into my broader literature review workflows. My primary methodology involves exporting data for subsequent quantitative analysis, where I measure metrics such as publication velocity within a field, co-citation network density, and author influence trends over time.

The current CSV export functionality presents a significant bottleneck. While it provides basic fields like `Title`, `Authors`, and `Publication Date`, it omits several data points I have identified as crucial for reproducible, metric-driven research. This forces a manual reconciliation process that introduces error and destroys the efficiency gains the tool promises.

Based on my audit, the missing fields that critically impair analysis include:

* **Citation Counts:** The raw number of citations per paper is fundamental for calculating impact scores and identifying seminal works. Its absence requires cross-referencing with external APIs (e.g., Semantic Scholar, OpenAlex), adding latency and points of failure.
* **Digital Object Identifier (DOI):** This is the primary key for any systematic data pipeline. Without a reliable, machine-readable DOI for each entry, automated deduplication and enrichment against other bibliographic databases become nearly impossible.
* **Abstracts:** While the UI displays them, their exclusion from the export precludes any form of automated keyword extraction, topic modeling, or semantic similarity analysis on the corpus I've assembled.
* **Author Affiliations:** For mapping institutional collaboration networks and research hub influence, affiliation data is non-negotiable. Its omission flattens the geographic and organizational dimension of the literature map.
* **Publication Venue (Journal/Conference) Details:** The journal name, volume, issue, and page numbers are essential for venue-level analysis, such as tracking which publications are most influential for a given topic.

A trivial example of the resultant workflow degradation: to generate a simple timeline of citation velocity, I must now manually merge two datasets. The exported CSV lacks the necessary keys for a clean join.

```python
# Pseudo-code illustrating the unnecessary complexity
researchrabbit_df = pd.read_csv('export_from_rr.csv') # Has Title, Authors, Year
external_metadata_df = fetch_from_crossref(doi_list) # Has DOI, Citation_Count, Journal

# Cannot join directly; must use fuzzy matching on Title & Authors - error-prone
merged_df = fuzzy_merge(researchrabbit_df, external_metadata_df, on=['Title', 'Authors'])
```

This lack of granular, machine-friendly export transforms what should be a streamlined data collection phase into a protracted data-wrangling session. For a tool designed to accelerate discovery, this export schema represents a severe limitation for anyone attempting to move beyond qualitative browsing into quantitative, evidence-based literature analysis. I am interested to know if others in the community have developed workarounds or if there are plans to expose a more comprehensive data export API.