JSON, XML, and YAML for Scientists: Data Formats Explained Simply

Scientists deal with information in a variety of formats. Whether you are exchanging data between software tools, storing experiment results, or visualizing molecular properties, understanding how data is structured and shared is essential. Three of the most commonly used formats across scientific computing are JSON, XML, and YAML. Each has its strengths, weaknesses, and ideal use cases. This article explains them in simple, practical terms, focusing on how researchers can choose and use them effectively in laboratory informatics, bioinformatics, and computational research workflows.

Understanding Data Formats in Modern Science

Every scientific dataset has to be stored, transferred, or analyzed in a structured way. For example, when you export simulation results from a molecular dynamics software, the metadata and parameters might be stored in an XML file. When you use a web API to access a database like PubChem or UniProt, the response is often in JSON. YAML, on the other hand, is becoming increasingly popular in configuration files for reproducible workflows such as Nextflow, Snakemake, or Docker-based bioinformatics pipelines.

In essence, these formats act as “languages” for machines to understand and communicate scientific data. They provide structure, hierarchy, and meaning to raw information so that both humans and computers can interpret it consistently.

What is JSON?

JavaScript Object Notation (JSON) is a lightweight data-interchange format that is easy for both humans and computers to read and write. Despite its origins in web development, JSON has become the preferred format in scientific APIs, cloud computing, and data analytics due to its simplicity.

A JSON document consists of key-value pairs, organized into objects {} and arrays [].

Example of JSON in a molecular context:

{
  "molecule": "water",
  "formula": "H2O",
  "atoms": [
    {"element": "H", "x": 0.0, "y": 0.757, "z": 0.586},
    {"element": "H", "x": 0.0, "y": -0.757, "z": 0.586},
    {"element": "O", "x": 0.0, "y": 0.0, "z": 0.0}
  ]
}

This structure is concise and ideal for data transmission between tools such as REST APIs, machine learning scripts, and visualization programs. Many databases including PubChem, ChEMBL, and the Protein Data Bank (PDB) now support JSON as a native output option.

Advantages of JSON:

• Very compact and human-readable
• Natively supported by most programming languages including Python, R, and Java
• Excellent for web-based tools and data exchange via APIs
• Easy to parse and convert into native data structures like Python dictionaries or JavaScript objects

Limitations of JSON:

• Lacks support for comments, which makes it harder to annotate metadata directly
• Only supports text encoding, so binary data must be encoded separately
• Does not preserve data order or attributes as strictly as XML

JSON works best for lightweight communication, such as sharing molecular properties, experiment parameters, or computational results across web services.

What is XML?

Extensible Markup Language (XML) has been a cornerstone of structured data in scientific computing for more than two decades. It is verbose but powerful, especially when a high level of hierarchy, schema validation, and metadata integration is needed. XML represents data using tags similar to HTML, which define both structure and meaning.

Example of XML in a molecular context:

<molecule name="water">
  <formula>H2O</formula>
  <atoms>
    <atom element="H" x="0.0" y="0.757" z="0.586"/>
    <atom element="H" x="0.0" y="-0.757" z="0.586"/>
    <atom element="O" x="0.0" y="0.0" z="0.0"/>
  </atoms>
</molecule>

Here, each piece of data is clearly defined by its tags and attributes. This makes XML excellent for storing detailed scientific records and ensuring compatibility across platforms.

Advantages of XML:

• Extremely well-defined structure with support for schemas (XSD)
• Ideal for metadata-rich data exchange (for example, Chemical Markup Language, CML)
• Highly extensible and self-descriptive
• Supported by countless scientific applications

Limitations of XML:

• More verbose than JSON or YAML
• Harder to read manually for large datasets
• Parsing can be slower for big files

XML shines in scenarios where rigorous data validation, versioning, and documentation are critical. In molecular sciences, XML-based standards such as CML (Chemical Markup Language) and SBML (Systems Biology Markup Language) are widely used for storing chemical and biological network models.

What is YAML?

YAML Ain’t Markup Language (YAML) is a human-friendly data serialization format designed to be readable and intuitive. It is frequently used for configuration files, workflow definitions, and metadata storage in computational pipelines. YAML’s indentation-based syntax makes it clean and easy to follow.

Example of YAML in a molecular context:

molecule: water
formula: H2O
atoms:
  - element: H
    x: 0.0
    y: 0.757
    z: 0.586
  - element: H
    x: 0.0
    y: -0.757
    z: 0.586
  - element: O
    x: 0.0
    y: 0.0
    z: 0.0

Advantages of YAML:

• Extremely readable, almost like natural text
• Supports comments and complex data types
• Excellent for configuration and parameter files
• Supported by most modern scientific workflow tools

Limitations of YAML:

• Sensitive to indentation, which can cause parsing errors
• Parsing libraries are not as standardized as those for JSON or XML
• Not ideal for extremely large datasets

YAML is best suited for configuration, documentation, and small metadata files that accompany computational workflows. For example, a YAML file can define input datasets, computational steps, and output formats for a Nextflow pipeline used in genomics or proteomics.

Choosing the Right Format

Choosing between JSON, XML, and YAML depends on the scientific task and the environment in which the data will be used.

For instance, if you are designing a bioinformatics tool that exchanges data with a web server, JSON is the simplest choice. If you are publishing a chemical database where strict schema and metadata control are needed, XML or CML would be ideal. For researchers configuring complex workflows in cloud or HPC environments, YAML provides clarity and simplicity.

Converting Between Formats

Scientific projects often require interoperability between these formats. Fortunately, many libraries exist for converting data.

• Python: json, xml.etree.ElementTree, pyyaml
• Command-line tools: yq, xmlstarlet, jq
• Cross-platform utilities: OpenBabel and RDKit for molecular data formats

A simple example in Python for converting JSON to YAML:

import json
import yaml

with open("data.json", "r") as jfile:
    data = json.load(jfile)

with open("data.yaml", "w") as yfile:
    yaml.dump(data, yfile)

Such conversions make it possible to unify workflows where one tool outputs JSON while another expects YAML or XML input.

Practical Tips for Scientists

1. Use JSON for fast communication: Ideal for web APIs, mobile apps, and cloud-based systems.
2. Use XML for archival and standardization: When you need validation, schema definition, or compliance with existing bioinformatics standards.
3. Use YAML for configuration: Simplifies reproducible workflows and version control in projects using Git or cloud deployments.
4. Validate your data: Use schema validators and linters to catch format errors early.
5. Document your structure: Even simple formats benefit from clear documentation explaining each field’s meaning and unit.

Conclusion

For scientists, understanding JSON, XML, and YAML is more than a matter of syntax. It is a foundation for interoperability, reproducibility, and data integrity in modern research. Each format offers unique strengths—JSON for simplicity, XML for structure, and YAML for readability. Knowing when and how to use them will make your data pipelines more efficient, your publications more reproducible, and your collaborations more seamless.

By mastering these formats, you build a bridge between the scientific ideas in your experiments and the computational systems that bring them to life.