Dataset: Scientometric Analysis of Molecular Techniques in Partial Nitritation/Anammox Reactors

Description

This dataset supports a scientometric study that maps how researchers have used molecular techniques to profile microbial communities in partial nitritation/anammox (PN/A) deammonification reactors from 2001 to 2025. The study tests the hypothesis that the spread and refinement of molecular tools (PCR, 16S rRNA gene amplicon sequencing, qPCR, FISH, metagenomics) have significantly shaped both our understanding of PN/A microbiomes and the operational development of these reactors. The dataset includes 207 primary articles that the authors retrieved from the Web of Science Core Collection using a Boolean Topic search that combines PN/A‑related terms (“partial nitritation/anammox”, “deammonification”, “ammonium removal”, “PN/A”) with molecular‑method and reactor descriptors. The authors screened records using the PRISMA 2020 framework, excluded review papers and non‑PN/A studies, resolved ambiguous cases through full‑text review, and exported metadata (authors, affiliations, journals, countries, references, citations, keywords) in plain‑text format for processing. They then used RStudio (Bibliometrix package) and Excel to calculate productivity, citation impact, collaboration networks, keyword co‑occurrence, and journal dispersion with Bradford’s Law, while manually classifying the molecular techniques in each article into 12 categories specifically linked to PN/A microbiome characterization. The data reveal sustained growth of PN/A molecular‑microbiology publications (12.25% per year), a strong concentration of output and citations in a small core of environmental engineering journals (especially Bioresource Technology, Chemical Engineering Journal, Science of the Total Environment, Water Research), and highly collaborative authorship with broad international co‑authorship. Thematic indicators show a core around “anammox”, “nitrogen removal” and “partial nitritation‑anammox”, with a shift from exploratory studies to reactor optimization after 2015. PCR and 16S rRNA amplicon sequencing dominate as standard methods (157 occurrences), shotgun metagenomics is emerging as a strategy (19 occurrences), and China serves as the main hub for publications and funding in this field. Researchers can use this dataset to replicate and extend the scientometric analyses (for example, by re‑running R/Bibliometrix scripts, redefining technique classes, or exploring new co‑authorship and keyword networks) and to support methodological or strategic decisions about where, how, and with which molecular tools the community investigates PN/A microbiomes worldwide.

Steps to reproduce

1. Query the Web of Science Core Collection (WoS) using the Topic search string reported in the article, combining PN/A terms (“partial nitritation/anammox”, “deammonification”, “ammonium removal”, “PN/A”) with molecular‑technique and reactor descriptors (“molecular techniques”, “16S rRNA”, “FISH”, “metagenomics”, “reactor*”). 2. Limit results to primary research articles, export all records (including full metadata and cited references) in WoS plain‑text format, and save the file locally. 3. Apply the PRISMA 2020 workflow to this file: remove review papers, exclude records not related to PN/A processes, and resolve borderline cases by reading the full text of articles that mention PN/A but not molecular techniques in the Topic field. 4. Open RStudio (v4.1.5 or later), install and load the Bibliometrix package (v4.3.5 and 5.0), and convert the WoS plain‑text file into a bibliometric data frame using `convert2df`, following the “Bibliometric Data Processing in R Studio” procedure described in the supplementary materials. 5. Use Bibliometrix and, if desired, the Biblioshiny web interface to calculate the main indicators (annual scientific production, journal‑level frequency data, citation metrics, author and country productivity, collaboration networks, keyword co‑occurrence, thematic maps) and export the resulting tables and figures; optionally refine and format outputs in Microsoft Excel. 6. For the molecular‑technique categorization, read each included article and assign the techniques used to one of the 12 predefined categories (e.g. “PCR and 16S rRNA gene amplicon sequencing”, “FISH”, “qPCR”, “shotgun metagenomics”) using the criteria listed in Supplementary Table S1, then compute cumulative and annual frequencies for each category over the 2001–2025 period. 7. To reproduce Figure 8, refine the data from the file "dadosbiblio09122025 - notes" by author, by the number of articles produced by each author, and by the molecular techniques employed in those articles, organizing the information into a matrix format and into another spreadsheet saved as a .csv file. Then, use and run the "Script - Heatmap of Author–Technique Associations.R" file, adjusting the file name and location according to the folder where it was saved.

Institutions

• Universidade Tecnológica Federal do Paraná

  Paraná, Curitiba

Categories

Funders

• Coordenação de Aperfeicoamento de Pessoal de Nível Superior

  Federal District, Brasília

  Grant ID: CAPES – Finance Code 001

Licence