Updated documentation for shiny

Author: TNRiley

inst/shiny-app/CiteSource/www/about.md

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-<img src="https://user-images.githubusercontent.com/89118428/155393065-780381a0-ff77-45d3-b2ee-40332ef72064.png" width="200" align="right"/>
+## About CiteSource
 
-CiteSource was developed in order to provide researchers the ability to examine the utility and efficacy of literature resources and search methodologies. The idea behind CiteSource is simply allowing users to deduplicate citation records, while maintaining customizable metadata about the citation.
+<img src="https://user-images.githubusercontent.com/89118428/155393065-780381a0-ff77-45d3-b2ee-40332ef72064.png" width="200" style="float: right; margin-left: 20px; margin-bottom: 10px;"/>
 
-Customizable metadata can include anything from a resource name (ex.Web of Science, LENS.org, PubMed), a method (database search, handsearching, citation chasing/ snowballing), a variation used within a method (WoS string #1, Wos string #2, WoS string #3), a research phase (search, TI/AB screening, Full-text Screening), or a unique group of citations (benchmarking articles, articles from a previous review, articles with a specific author affiliation).
+CiteSource is an R package and accompanying Shiny web application designed to support evidence data-driven decision-making during search strategy development. CiteSource also allows users to analyze and report on the impact of information sources and search methods.
 
-The CiteSource deduplication process is better described as record merging process due to the fact that the customizable metadata from duplicate records is maintained through the creation of a single, primary record. Beyond the merging of customizable metadata, the primary record is created by using the most complete metadata available between duplicate records (currently DOI and Abstract fields).
+CiteSource was developed as part of the [Evidence Synthesis Hackathon](https://www.eshackathon.org/) initiative.
 
-Once records are deduplicated, users are able to easily create plots and tables in order to answer specific questions or to simply explore the data to an effort to develop new hypotheses. Examples of analysis may include how many unique records a specific source contributed or how traditional methods of searching fare against a new AI discovery tool in discovering relevant articles. Users may want to understand the overlap in records between two different search strings or evaluate the impact of including Google Scholar in a review. Before searching, a user may even develop a targeted search to better understand the topical coverage across databases that they intend to search, and once the search has been developed, how particular source, string, or method performed in discovering benchmarking articles.
+---
+
+### Key Features:
+
+<details>
+  <summary><strong>Flexible Metadata for Provenance Tracking:</strong></summary>
+
+> * A core strength of CiteSource is its ability to assign and retain custom metadata to track the *provenance* of each citation – precisely where and how it was found. Users can tag records using three key fields:
+>     * `cite_source`: Identify the origin database ('Web of Science', 'Scopus'), platform ('Lens.org'), or the specific search method used ('Citation Searching', 'String_1').
+>     * `cite_label`: Track citations through screening phases using standardized terms: `search` (for initial results, benchmarks), `screened` (for records passing title/abstract review), and `final` (for records included in the synthesis after full-text review).
+>     * `cite_string`: Add further detail, such as variations in search string syntax tested ('String_1a_truncation'), specific supplementary methods ('Handsearching_JournalX'), or other custom categories relevant to analysis.
+> * This detailed tagging enables rigorous analysis of the performance and contribution of each component of your overall search strategy.
+
+</details>
+
+<details>
+  <summary><strong>Advanced Deduplication & Intelligent Merging:</strong></summary>
+
+> * CiteSource employs the [`ASySD` (Automated Systematic Search Deduplicator) R package](https://github.com/camaradesuk/ASySD) to perform robust identification and merging of duplicate records.
+> * It conducts both *internal deduplication* (identifying duplicates within a single uploaded file/source, resulting in *distinct* records) and *external deduplication* (identifying duplicates across all uploaded files/sources, resulting in the set of *unique* citations).
+> * The process uses *intelligent merging*: custom metadata tags (`source`, `label`, `string`) from all identified duplicates are combined onto the primary record, preserving the full discovery history.
+> * The most complete bibliographic data (prioritizing DOI, Abstract) across duplicates is retained in the primary record.
+> * An optional *manual review* stage presents potential duplicates that fall below the automatic matching threshold, allowing users to confirm or reject merges for maximum accuracy.
+
+</details>
+
+<details>
+  <summary><strong>Data-Driven Analysis & Visualization:</strong></summary>
+
+> * Once deduplication is complete, CiteSource offers a suite of analysis and visualization tools designed specifically to speed up the *iterative process* of developing, testing, and validating search strategies:
+>     * Visualize Overlap: Use interactive **Heatmaps** (pairwise overlap) and **Upset Plots** (multi-set intersections) to understand shared and unique records across sources, labels, or strings.
+>     * Track Phase Progression: Employ the **Phase Analysis plot** (bar chart) to see the flow of unique and duplicate records through screening stages (`search` -> `screened` -> `final`).
+>     * Generate Summary Tables: Access quantitative insights via automated tables detailing:
+>         * Initial Record counts (showing the impact of internal deduplication).
+>         * Record Summaries (detailing unique/overlapping records contributed by each source/method).
+>         * Precision/Sensitivity calculations (evaluating source/method performance against the `final` included set).
+>         * A detailed, interactive **Record Level Table** for quickly examining and linking to citations .
+
+</details>
+
+<details>
+  <summary><strong>Enhanced Reporting & Transparent Export:</strong></summary>
+
+> * CiteSource facilitates *transparent reporting* of search methods and results, aligning with guidelines like PRISMA.
+> * Export your final, deduplicated dataset in standard bibliographic formats (`.csv`, `.ris`, `.bib`).
+> * The custom metadata is embedded directly into standard fields within the export files (e.g., using C1, C2, C7, C8, DB fields in `.ris` format), providing a clear and reproducible audit trail for your methodology.
+
+</details>
+
+---
+
+### Why use CiteSource for Evidence Synthesis?
+
+CiteSource is built for anyone involved in evidence synthesis. It helps you:
+
+* **Evaluate and optimize** information source selection based on unique record contributions.
+* **Refine and optimize** search strings by quickly testing variations.
+* **Analyze and report** the added value of different search methods, including supplementary searching techniques.
+* **Perform benchmark testing** to ensure key articles are captured by your strategy.
+* **Increase transparency and effectiveness** of your search strategy and processess through built-in tables for reporting.
+* **Save time** during the iterative search development.
+
+### What Other Applications does CiteSource Serve ?
+
+* Training in evidence synthesis search methods - MLIS classroom use for skill/knowledge development.
+* Methods research & development - large-scale methods testing, quick/live updates to analysis.
+* Library collection development - analyzing coverage of new databases compared to current subscriptions.
+
+---
+
+*CiteSource is available both as this interactive Shiny application and as a full R package with detailed vignettes. For more information on the R package, visit the [CiteSource Website](https://www.eshackathon.org/CiteSource/).*