We strive to answer the most common questions about the QDPX data format.

FAQ - Frequently Asked Questions

What is 'QDPX XML'?

QDPX is sometime referred to as QDPX XML; the two terms are often used synonymously/interchangeably. Strictly speaking, this is not fully correct.

A QDPX bundle contains many different files of different types. But at the heart of each QDPX bundle is a single XML file that contains the all-important metadata for your project: A list of all associated documents, memos, codes and, most importantly, a list of all segmented sections ("quotations"), i.e. the portions of each document that you have selected to be coded. This file can be considered the central map of your project. It allows other applications to make sense of how your project is organized and to reconstitute it if necessary in another environment.

Is QDPX the same thing as a project file or project bundle?

The short answer is no. A QDPX bundle includes many core elements of a QDA project (from platforms like Maxqda, Nvivo, ATLAS, Quirkos, etc.) but is not a complete substitute for native project files.

Since QDPX serves, in its original intent, as an interchange format between different platforms, it is, by necessity, a compromise. As such, it contains those elements of a QDA project that make up the core of that project and that are therefore common to all platforms. However, each platform follows different approaches to organizing and storing its data internally, and they also all offer different features that may or may not be available in other programs. These types of data are excluded from the QDPX bundle and must be added back manually, if so desired, after a project transfer from platform A to platform B.

Thus, if you are transferring projects between different installations of the same platform, you should always give preference to that platform's native file format. QDPX serves as a lowest common denominator between multiple platforms, but is not a substitute for their own proprietary file/project bundle formats.

What is in a QDPX Bundle?

  1. Project Metadata: Information about the project itself, such as the title, description, authors, and other metadata that provides context for the qualitative data.

  2. Data Sources: This includes the actual qualitative data, which could be text, images, audio, video, or other types of data that have been analyzed or coded.

  3. Codes: These are the tags or labels that have been applied to segments of data during the analysis process. Codes help to categorize and interpret the data.

  4. Annotations and Memos: Any notes, comments, or extended pieces of text that provide additional context or interpretation for the data or codes.

  5. Variables: These could be demographic data or other types of variables that have been assigned to data sources or cases.

  6. Case Data: Information about the specific cases or units of analysis in the project. This could include individual participants, focus groups, or other entities that are the subject of the research.

  7. Relations: Information about the relationships between different codes, between codes and data segments, or between different data sources.

  8. Sets or Groups: Collections of codes, data sources, or cases that are grouped together for some analytical purpose.

  9. Queries: Saved search queries that can be used to retrieve specific subsets of the data.

  10. Visual Models: Some QDA software allows for the creation of visual models or diagrams that represent relationships between codes, themes, or concepts. These may also be included in a QDPX bundle.

  11. Additional Files: Any supplementary files that are part of the project, such as bibliographies, spreadsheets, or external references.

  12. Version Information: Data about the software used to create the project, as well as any version history or change logs for the project itself.

It's worth noting that not all QDA software will include all of these components in a QDPX bundle, and some may include additional components that are specific to that software.

The QDPX bundle aims to be a comprehensive package that includes all the elements needed to fully understand and replicate a qualitative research project.

What information sources about this format exist?

Here are the most accessible information sources.

We are in process of building a larger knowledge base about QDPX with samples, detailed descriptions and processing tips and instructions right here on this website.

How do I open a qdpx file?

.qdpx bundles are ordinary zip files, i.e. files of the MIME type application/zip. Here is how you can open them on various platforms to see what is contained in them:

  1. Windows: Right-click on the .qdpx file and choose "Extract All..." using built-in utility or use software like 7-Zip or WinRAR.

  2. macOS: Double-click the .qdpx file to automatically extract it using the built-in Archive Utility or use third-party software like The Unarchiver.

  3. Linux: Use the unzip command in the terminal or graphical tools like Archive Manager.

  4. Online Tools: Websites like "" can unzip .qdpx files if you upload them.

Remember, once unzipped, you'll find XML files and possibly other data types that make up the qualitative research project. Use appropriate software to interact with these files.

What is Qualitative Research, and who uses it?

Qualitative research is a methodological approach that focuses on understanding human behavior, experiences, and social phenomena through the collection and analysis of non-numerical data such as text, audio, or visual material. It is commonly employed in a variety of fields, typcially in the social sciences, humanities, in healthcare, and even market research. The users of qualitative research range from academic researchers and market analysts to healthcare professionals, journalists, consultants, and government agencies. They leverage qualitative research predominantly to gain in-depth insights into complex issues, understand social dynamics, and explore the nuances of human experience.

Types of Users:

  1. Academic Researchers: To analyze textual, audio, or visual data for social sciences, humanities, and other academic disciplines.
  2. Market Researchers: For consumer behavior studies, focus groups, and sentiment analysis.
  3. Healthcare Professionals: To analyze patient interviews, medical histories, and other qualitative data.
  4. Journalists: For content analysis, interviews, and investigative research.
  5. Consultants and Analysts: To provide in-depth insights into organizational behavior, employee sentiments, and more.
  6. Government Agencies: For policy analysis, public opinion studies, and community assessments.

Common Uses:

  • Data Coding: Tagging or labeling segments of data for analysis.
  • Thematic Analysis: Identifying patterns or themes within qualitative data.
  • Content Analysis: Quantitatively summarizing qualitative data.
  • Narrative Analysis: Studying the stories or accounts of individuals.
  • Discourse Analysis: Analyzing written or spoken language within its social context.

Can the limitations of qualitative research work product be transcended?

The inability to directly utilize results of qualitative research in classic quantitative research, data analysis, and Business Intelligence (BI) is often considered a limitation. Qualitative and quantitative research serve different purposes and offer different kinds of insights: Qualitative research excels in providing context, understanding human behavior, and exploring complex social phenomena. It gives depth and nuance to data that quantitative research often cannot. On the other hand, quantitative research is excellent for generalizing results and making predictions, which is often the focus in data analysis and BI.

However, there exists a strong desire to more directly integrate and utilize the work product of (relatively narrow and methodically narrowly circumscribed) qualitative research in other, more mainstream analytical fields. The gap between qualitative and quantitative data is increasingly being bridged through mixed-methods research and advanced analytics techniques. Tools are emerging that can quantify qualitative data, and qualitative insights are being integrated into BI systems to provide a more comprehensive understanding.

But it is also now possible to at least indirectly transform actual qualitative research result data into formats that can be used in data analytics and BI. Services like and appliQ specialize in transforming such data, predominantly by creating bridges between the common QACDAS format .qdpx and standard data formats like SQL, CSV, and JSON. These transformed data can then be utilized in all common data analytics tools, such as Tableau, and structured data storage processes like databases.

How does QDPX contribute to the long-term archival of qualitative research projects?

The QDPX format provides a standardized, XML-based structure that encapsulates all the essential components of a research project. This includes not just the raw data, but also the metadata, codes, annotations, and other elements that give context and meaning to the data. Because it is inherently software-agnostic, the QDPX format ensures that research data can be accessed, understood, and repurposed in the future, regardless of the software tools available at that time. This makes it an ideal choice for researchers who are looking to preserve the integrity and utility of their qualitative research projects for the long term.

How are the complex relationships between codes, themes, and data sources handled?

The QDPX format is designed to handle complex relationships between codes, themes, and data sources by providing a structured, XML-based framework that can encapsulate these relationships. Within a QDPX bundle, relationships are defined to indicate how different codes are related to each other, how codes are applied to specific segments of primary data, and how themes or concepts are interconnected. This allows for a nuanced representation of the qualitative analysis, capturing not just the coded data but also the analytical thought process and contextual relationships that are crucial in qualitative research.

By standardizing the way these relationships are stored and exchanged, the QDPX format ensures that the richness and complexity of qualitative data are preserved when migrating between different qualitative data analysis software packages.

Are there case studies that demonstrate the effective use of the QDPX format?

As of this writing, specific case studies demonstrating the effectiveness of the QDPX format in qualitative research were not readily available in public documentation. However, given its capabilities and the growing need for interoperability in qualitative research, it is likely that the QDPX format will be featured in future case studies or research papers that explore its practical applications and benefits. We are planning to undertake a proper study at the earliest possible time and publish it here.

If you become aware of any such studies, please kindly share this information with us.

How secure is the QDPX format for storing sensitive or confidential data?

QDPX itself does not have inherent security features to protect sensitive or confidential research data. It functions primarily as a standardized, XML-based structure for the exchange and archival of qualitative research projects.

However, external security measures can be applied to QDPX bundles like any other data files. For instance, the ZIP archive containing the QDPX bundle can be encrypted and password-protected to restrict unauthorized access. Additionally, secure transfer protocols can be used when sharing QDPX files, and secure storage solutions can be employed to safeguard the data. Therefore, while the QDPX format doesn't provide built-in security, the data can still be adequately protected using external security tools and protocols.

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