Description
Sniffing Emergence and Trajectories in Academic Papers and Patents.
Description
Provides a unified set of methods to detect scientific emergence and technological trajectories in academic papers and patents. The package combines citation network analysis with community detection and attribute extraction, also applying natural language processing (NLP) and structural topic modeling (STM) to uncover the contents of research communities. It implements metrics and visualizations of community trajectories, including novelty indicators, citation cycle time, and main path analysis, allowing researchers to map and interpret the dynamics of emerging knowledge fields. Applications of the method include: Souza et al. (2022) <doi:10.1002/bbb.2441>, Souza et al. (2022) <doi:10.14211/ibjesb.e1742>, Matos et al. (2023) <doi:10.1007/s43938-023-00036-3>, Maria et al. (2023) <doi:10.3390/su15020967>, Biazatti et al. (2024) <doi:10.1016/j.envdev.2024.101074>, Felizardo et al. (2025) <doi:10.1007/s12649-025-03136-z>, and Miranda et al. (2025) <doi:10.1016/j.ijhydene.2025.01.089>.
README.md
birddog 
The goal of birddog is sniffing out emergence and trajectories in scientific and patent literature.
Installation
Install the stable version from CRAN:
install.packages("birddog")
library(birddog)
Or the development version from GitHub:
# install.packages("remotes")
remotes::install_github("roneyfraga/birddog")
library(birddog)
Features
Data import
read_openalex()– OpenAlex API or CSV exportsread_wos()– Web of Science BibTeX, RIS, plain-text, tab-delimited
Citation network and community detection
sniff_network()– direct citation or bibliographic coupling networkssniff_components()– identify connected componentssniff_groups()– community detection (fast greedy, Louvain, Leiden, walktrap, edge betweenness)
Group analysis
sniff_groups_attributes()– group-level summary statistics and horizon plotssniff_groups_keywords()– keyword frequency per groupsniff_groups_terms()– NLP-based phrase extractionsniff_groups_hubs()– hub classification (Zi-Pi, Guimera and Amaral 2005)sniff_groups_cumulative_citations()– per-document citation growth
Indexes
sniff_citations_cycle_time()– measures the pace of change (Kayal 1999)sniff_entropy()– normalized Shannon entropy for keyword diversity (Shannon 1948; Pielou 1966)
Trajectories
sniff_groups_cumulative()– cumulative clusterization over timesniff_groups_trajectories()– Jaccard similarity DAG across yearsplot_group_trajectories_2d()/plot_group_trajectories_3d()– node-based trajectory plotsdetect_main_trajectories()– top-N disjoint paths via dynamic programmingfilter_trajectories()– filter and rank detected trajectoriesplot_group_trajectories_lines_2d()/plot_group_trajectories_lines_3d()– variable-width line plots
Main path analysis
sniff_key_route()– key-route search (Liu and Lu 2012) with SPC weights
Topic modeling
sniff_groups_stm_prepare()/sniff_groups_stm_run()– structural topic modeling within groups
Vignettes
The vignettes are available online here:
Methodological workflow
Main publications
- Miranda et al. (2025) The Landscape of Green and Biohydrogen Technology: A Data-Driven Exploration Using Non-Supervised Methods
- Felizardo et al. (2025) Transforming Wastes into Resources: Innovations in Cotton Biorefineries for a Sustainable Future
- Biazatti et al. (2024) Soybean biorefinery and technological forecasts based on a bibliometric analysis and network mapping
- Maria et al. (2023) Evolution of Green Finance: A Bibliometric Analysis through Complex Networks and Machine Learning
- Matos et al. (2023) Building and evaluating prospective scenarios for corn-based biorefineries
- Souza et al. (2022) Is entrepreneurship an emerging area of research? A computational response
- Souza et al. (2022) Bioenergy research in Brazil: A bibliometric evaluation of the BIOEN Research Program.