Methods

Finding reliable historical sources, whether for a research paper or personal interest, often requires more than a quick internet search. Effective research depends on understanding where sources originate, how to navigate different search methods, and when to use (or avoid) both traditional and digital tools.

Archives play a central role in historical research, but they are far from uniform. Some exist only as physical collections, others are fully digital, and many combine both formats. Access to archival materials can vary widely: some collections are easily searchable through keywords, while others require browsing detailed catalogues or relying on prior knowledge of the subject. Because of this, your research strategy must adapt to the type of archive you are working with.

This page introduces a range of search methods and offers an overview of digital approaches, such as digital methods, that can help you explore source material more deeply and effectively.

Updated last: 20/5 2026

Search Methods

A strong history paper begins with a well-developed search strategy. Instead of searching randomly, you should take a structured approach to finding relevant sources.

Start by clearly defining your topic and thinking about your information needs. What kind of sources are you looking for? Primary sources, academic articles, or books? It is also important to set limitations, such as language, time period, or geographical focus, to make your search more precise.

 

Search Approaches and strategies

When developing a search strategy, it is useful to combine different search approaches. Each approach serves a different purpose, and using them together will give you a more complete and reliable set of sources.

A quick search is often the best place to start. This involves simple searches in tools like library databases or search engines to get an overview of your topic. At this stage, you are not aiming for perfect results, but for orientation, identifying key themes, important authors, and useful keywords. A quick search helps you understand how your topic is discussed and gives you a foundation for more systematic searching.

Chain searching (also called reference searching) builds on sources you have already found. When you read a relevant book or article, you look at its reference list to see which sources the author has used. This allows you to trace the research backwards and discover important earlier works. Chain searching is particularly useful in history, where key texts and primary sources are often reused and discussed across multiple studies.

Closely related is citation searching, which works in the opposite direction. Instead of looking backwards at references, you look forward to see who has cited a particular source. Many academic databases allow you to track citations, helping you find newer research that builds on or responds to earlier work. This is a powerful way to follow academic debates and identify how interpretations of a topic have developed over time.

Block searching is a more structured and systematic approach. Here, you break your topic down into key concepts (or “blocks”) and then identify several keywords or synonyms for each concept. You then combine these using Boolean operators. For example, if your topic is about propaganda in World War I, one block might include “propaganda” OR “mass communication,” while another might include “World War I” OR “First World War.” These blocks are then combined using AND. This approach helps ensure that your search is both broad (through synonyms) and precise (through combination of concepts).

In practice, you will often move between these approaches. You might begin with a quick search, use chain and citation searching to expand your material, and then apply block searching to conduct a more thorough and systematic search. Together, these methods support the idea that academic information searching is a circular and evolving process, where each step helps refine the next.

woman looking through a book at the library bookcases

Key Words & Booleans

Keyword searching is a fundamental part of finding academic sources. It involves selecting the most important words from your topic or research question and using them to search in databases, archives, libraries and catalogues and more.

Choosing the right keywords is essential. As identifying key terms from your topic and research question, use synonyms, and continuously refine your keywords as your understanding develops. To improve your results, you can combine keywords using Boolean operators.

The three main operators are AND, OR, and NOT. Using AND narrows your search by requiring all terms to be present (e.g. “industrialisation AND Denmark”). OR broadens your search by including synonyms or related terms (e.g. “industrialisation OR industrialization”). NOT excludes unwanted results (e.g. “revolution NOT French”).

By combining keywords and Boolean operators, you can make your searches both more precise and more flexible. It is often necessary to adjust your keywords and combinations as your understanding of the topic develops. Though it is important to not that not all archives allow or have made it possible to use booleans in searches.

These are not the only Boolean operators you can use to refine your search and make it either broader or more precise. You can learn more about other booleans and searching with them here: https://www.scribbr.com/working-with-sources/boolean-operators/

Interested in learning more? Check out these AU resources about search methods and techniques:

Danish: https://studypedia.au.dk/informationssoegning/soegemetoder-og-teknikker

English: https://studypedia.au.dk/en/information-search/search-methods-and-techniques 

Here you can explore more subject-specific resources here:

Danish: https://library.au.dk/fagsider/

English: https://library.au.dk/en/subject-areas

Digital Methods

digital methods are the tools and techniques used to study humanities subjects through digital technology. These methods include text mining, data visualization, digital mapping, network analysis, and the creation of online archives. They allow researchers to analyze large amounts of information more quickly and identify patterns that may be difficult to see through traditional methods alone. Digital methods are useful because they combine critical thinking with technical skills such as data analysis and digital communication. They also make research more accessible to the public through interactive websites, databases, and visual projects. In addition, learning digital methods can improve career opportunities in fields like education, media, libraries, museums, and cultural heritage.

Examples of digital methods and their use include:

  • Text mining — analyzing thousands of books, newspapers, or social media posts for themes, word frequencies, or sentiment.
  • Data visualization — turning complex information into maps, graphs, timelines, or networks.
  • GIS mapping (Geographic Information Systems) — studying historical events, migration, literature, or cultural change through spatial data.
  • Network analysis — examining relationships between people, texts, organizations, or ideas.
  • Digital archives and databases — organizing and preserving historical or cultural materials online.
  • Corpus linguistics — studying language patterns across large collections of texts.
  • Machine learning and AI tools — identifying patterns that would be difficult to detect manually.
  • Digital storytelling and interactive media — presenting research through websites, games, VR, or multimedia projects.

The following sections contains a selection of different relevant digital methods for historians. If you are a student and interested in other tools and digital methods not mentioned on this site. Take a closer look at Ucloud:

https://cc.au.dk/en/cdmm/tools-and-tutorials/data-handling/ucloud

https://cloud.sdu.dk/app/login


R - Coding

R coding is used for statistical analysis, data visualization, and data science projects. It allows users to clean, organize, and analyze large datasets efficiently. With R, you can create graphs, run statistical models, and automate repetitive research tasks. It is widely used in academia, business analytics, and social sciences. R also supports reproducible research through scripts and reporting tools like R Markdown.

R coding can help history students analyze large historical datasets, visualize trends over time, and conduct quantitative research on topics such as population changes, elections, or migration patterns

More resources to start learning and exploring the possibilities with R: 

https://www.r-project.org/

https://education.rstudio.com/learn/beginner/

https://www.codecademy.com/learn/learn-r

API - Application Programming Interface

Application Programming Interface (APIs) allow different software systems to communicate and exchange data automatically. APIs are used to connect applications, retrieve online data, and automate workflows between platforms. Developers can use APIs to integrate services such as maps, social media, databases, or AI tools into their own applications. APIs improve efficiency by enabling systems to share information in real time. They are widely used in web development, research automation, and digital platforms.

APIs can help history students automatically collect and connect data from digital archives, museum collections, and online databases for research and digital history projects. An example of an API is from the Kgl. Bibliotek in relation to Mediestream, which allows users to download newspaper articles and related metadata. However, access is restricted to materials that are more than 140 years old due to copyright and access regulations.

Some archives allow the download of digitized materials from their collections through public APIs. These APIs can provide access to metadata, catalog information, images, manuscripts, maps, newspapers, audio recordings, and other historical sources. Researchers can use APIs to search large collections more efficiently, organize datasets, visualize historical information, or build digital tools and databases for analysis.

It is important to note that access policies vary between institutions. Some archives provide open and free API access, while others require registration, usage limits, or special permission for downloading materials. In some cases, only metadata is openly available, while the digitized files themselves may have copyright or usage restrictions.

When using archival APIs, both researchers and students should always review the archive’s terms of use, citation requirements, and licensing conditions before downloading or reusing materials.

Voyant

Voyant Tools is a web-based tool for text analysis and digital humanities research. It helps users explore word frequencies, trends, keywords, and patterns within large collections of texts. Researchers can visualize data through word clouds, graphs, and interactive charts. Voyant is especially useful for literary analysis, discourse analysis, and qualitative research. It requires little coding knowledge, making it accessible for beginners.

Voyant can help history students examine recurring themes, language patterns, and word frequencies in historical texts, speeches, newspapers, or archival collections.

Learn more about Voyant here: 

https://voyant-tools.org/

https://docs.cloud.sdu.dk/Apps/voyantserver.html

Openrefine

OpenRefine is a powerful tool that can be used for cleaning and organizing messy data. It helps users detect duplicates, standardize formats, and correct inconsistencies in datasets. OpenRefine can transform data into structured formats suitable for analysis or publication. It also supports linking datasets to external databases and APIs. The tool is especially valuable for researchers, librarians, and data journalists working with large datasets.

OpenRefine can help history students organize and clean historical datasets, such as census records or archival metadata, to improve accuracy in research projects.

Learn more and download the program yourself: https://openrefine.org/

Transkribus

Transkribus is an AI-powered platform for transcribing handwritten and historical documents. It uses Handwritten Text Recognition (HTR) technology to convert scanned texts into searchable digital text. Users can train custom models for specific handwriting styles and languages. Transkribus is commonly used in archives, museums, and historical research projects. It helps save time and improves access to historical documents and manuscripts.

Transkribus can help history students transcribe handwritten manuscripts and archival documents more quickly, making historical sources easier to search and analyze.

Learn more about Transkribus and try it yourself: https://www.transkribus.org/