The following examples contain versatile research mapping tools for all abilities.

• The R package Bibliometrix and its graphical interface Biblioshiny is probably the most versatile tool, providing options for coders and non-coders.
• VOSViewer can load data from a variety of sources and contains some great visualisations.
• The open-source software SciMAT includes a data management module for pre-processing and de-duplication of the data, as well as an excellent science mapping and visualisation module.
• ResearchRabbit is a free online AI-based science mapping tool that can be linked with your citation manager (Zotero, Mendeley, EndNote) to start mapping instantly, based on 'seed papers'. In contrast to the above examples, however, it's algorithms are a blackbox, making its visualisations non-reproducible. Nevertheless, it supports an intuitive workflow and is easy to use.

In contrast to systematic bibliometric reviews, these science mapping approaches offer a quick way of gaining an overview over your research topic but are not designed for comprehensive literature reviews. Systematic bibliometric reviews aim to summarise the current state of research on a chosen subject by including all relevant evidence. To ensure an unbalanced account, a literature search protocol has to be established and at least two researchers collaboratively select and assess studies for inclusion - science mapping tools guarantee no such rigour.

Mapping your research topic using bibliometrics can also help you identify potential collaborators. Identifying prolific authors or institutions and who they collaborate with can be a starting point. The below examples are based on the keyword search 'altmetrics' on Web of Science for 2019-2022 (retrieved on 25/01/2023). The data was analysed with bibliometrix package in R. The below example shows that there only three main collaboration clusters in the chosen publications set with the strongest link between Leiden University and Stellenbosch University.

Furthermore, authors receiving a high number of citations from within your defined research area might be interested in future collaborations (see example below on right).

Identifying publications that founded an emerging new research area, are highly cited or frequently co-cited with publications relevant for your research topic can help you gain a more complete picture of the existing scientific literature. The example shows a co-citation network that forms three clusters based on how often publications are cited together. The publication set for this science map is based on a Web of Science keyword search for 'altmetrics' for 2019-2022 (retrieved on 25/01/2023). The data was analysed using the bibliometrix package in R.

• A highly-referenced paper likely shaped the research direction in its respective field but it is no indication of quality: Authors might refer to a manuscript to challenge or refute its conclusions or criticise its methods.

Searching the literature (further info in separate subjectguide) is an important part of academic work. In brief, research databases (Scopus, Web of Science, PubMed, Lens.org etc.) have less sophisticated algorithms than we might be used to from google searches. However, far from being a drawback, this allows us to gain tight control of our search definition.

To develop a keyword combination that accurately reflects your research topic can take time. To find the right combination of phrases and keywords consider using boolean operators (AND, OR, NOT) and truncation (*). Further guidance on advanced search strategies are in a separate subjectguide.

Remember that if you choose to combine data, from separate database searches, a de-duplication step will be necessary. Reference management software (Zotero, EndNote, RefWorks, Mendeley etc.) can be useful in pre-processing your publication set.