biblioview

{biblioview} is an R package and interactive Shiny dashboard designed to streamline, enrich, and explore literature libraries directly from Zotero Group Libraries. It implements a resource-polite pipeline to pull base metadata, resolve missing abstracts, and track live citation metrics.

Key Features

• Selective Collection Mapping: Scan and select specific collections (sub-folders) within your Zotero group library before pulling data down.
• Batch Citation Metrics: Mass-queries the OpenAlex API in unified chunks of up to 50 items at once, populating up-to-date citation counts.
• Enrichment: Automatically check Crossref, OpenAlex and Europe PMC via a fallback loop to patch missing abstract text. Separate function to write retrieved abstracts back to your Zotero database.
• Sorting, Filtering and Export: Retrieved libraries can be sorted, filtered and exported.
• DOI-RIS conversion: Convenience functions to retrieve bibliographies from a vector of DOIs. The resulting RIS file can then be imported in Zotero.
• Shiny Apps: The package contains two shiny apps, one with the complete functionality and a restricted one for embedding via <iframe></iframe> in other websites.
• URL Parameters: Both apps support URL parameters for providing group ID, API key, sub-collection folder and an optional title.

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Installation

Install the development version of {biblioview} directly from GitHub:

# install.packages("remotes")
# remotes::install_github("tpetzoldt/biblioview")
install.packages("biblioview", repos = c('https://tpetzoldt.r-universe.dev', 'https://cloud.r-project.org'))

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Getting Started

1. Launching the Interactive Dashboard

You can start the bundled Shiny dashboard directly from the package environment using launch_dashboard().

The launcher function accepts optional arguments to pre-populate your credentials and dynamically customize the application window title at startup, removing the need to manage complex URL query strings manually.

library(biblioview)

# Option A: Launch a clean, empty dashboard
launch_dashboard()

# Option B: Launch pre-configured for a project team
launch_dashboard(
  group = "1234567",
  key   = "your_secret_zotero_key",
  title = "Water Quality Research Portal"
)

Deployment Note: If hosting the dashboard on a server infrastructure (such as Shiny Server or Posit Connect), credentials can be passed dynamically via standard web URL routing parameter rules instead (e.g., https://your-shiny-server.org/biblioview/?group=1234567&key=your_secret_zotero_key&title=Water%20Quality%20Research%20Portal).

2. Using Package Functions Independently

If you are building custom analysis pipelines, you can skip the UI and run the modular batch operations directly in your own R scripts:

library(biblioview)

# 1. Grab metadata from a specific collection sub-folder
raw_data <- fetch_all_zotero_data(
  group_id = "numeric_group_id", 
  api_key = "your_secret_zotero_key",
  collection_id = "collection_id"
)

# 2. Patch gaps in abstract listings
abstract_enriched <- enrich_missing_abstracts(raw_data, email_contact)

# 3. Pull live batch metrics from OpenAlex
final_dataset <- fetch_citation_counts(abstract_enriched, email_contact)

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The Dashboard Processing Pipeline

The layout operates as a progressive workflow loop to protect network resources and keep UI performance snappy:

1. Step 0 (Scan Folders): Authenticates your credentials against the Zotero API and dynamically generates a multi-select dropdown map of your target sub-collections.
2. Step 1 (Fetch Selected Library): Downloads reference items and isolates row entries to drop duplicate DOIs.
3. Step 2 (Fetch Citation Metrics): Triggers the fast-batch OpenAlex lookup to append citation records. This step leverages the optional API email input text box, defaulting to your system's POLITE_EMAIL variable if left blank.
4. Step 3 (Run Abstract Enrichment): Executes defensive, item-by-item fallback abstract search queries.

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Permanent Setting of Email Address

Instead of entering your email address into the dashboard every time, you can set a system environment variable.

Run the following command in your R console:

usethis::edit_r_environ()

Add your email address profile rule to the .Renviron file that opens up, then restart your R session:

POLITE_EMAIL="your.name@example.com"

Documentation

User and developer documentation is provided at Github Pages.

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License

This project is licensed under the GPL (>= 2) License.