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Related Concept Videos

Archival Research01:40

Archival Research

Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research. Archival research relies on looking at past records or data sets to look for interesting patterns or relationships. For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and...
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
Data Collection I01:30

Data Collection I

Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of data...
Data Collection by Observations01:08

Data Collection by Observations

Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
Data: Types and Distribution01:19

Data: Types and Distribution

In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
Distributions in...
Data Collection by Experiments01:13

Data Collection by Experiments

Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
An example of the experimental method is a public clinical trial...

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DataCastle: A Pragmatic Approach for Research and Real-World Data Management.

Jori Kern1,2,3,4,5, Markus Katharina Brechtel6,7, Tim Schumacher1,2,3,4,5

  • 1Federated Information Systems, German Cancer Research Center (DKFZ), Heidelberg, Germany.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

DataCastle is an open-source platform enhancing biomedical research data management. It integrates pseudonymization, metadata extraction, and analysis tools to improve data transparency, reproducibility, and collaboration.

Keywords:
FAIR PrinciplesInteroperabilityResearch Data Management (RDM)

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Area of Science:

  • Biomedical Research Data Management
  • Health Informatics
  • Open-Source Software Development

Background:

  • Effective research data management (RDM) is crucial for transparency and reproducibility in biomedical research.
  • Fragmented infrastructures and heterogeneous data present significant challenges to RDM.
  • Existing systems often struggle to bridge FAIR data acquisition with FAIR data utilization.

Purpose of the Study:

  • To introduce DataCastle, a modular open-source platform designed to address RDM challenges in biomedical research.
  • To facilitate FAIR data principles throughout the research lifecycle, from acquisition to analysis.
  • To enhance data transparency, reproducibility, and collaboration through integrated data management solutions.

Main Methods:

  • DataCastle integrates enrollment-time pseudonymization, metadata extraction, and background versioning.
  • Structured data are captured using an Electronic Data Capture (EDC) system.
  • Unstructured data are managed in a filesystem-based data lake, with metadata mapped to Health DCAT-AP for findability and EHDS alignment.

Main Results:

  • The platform provides a unified environment for managing heterogeneous biomedical data.
  • It connects managed data to analysis and visualization tools, enabling reproducible workflows.
  • Metadata mapping supports findability and alignment with the European Health Data Space (EHDS).

Conclusions:

  • DataCastle offers a modular, open-source solution to bridge FAIR data acquisition and use in biomedical research.
  • The platform enhances data management by integrating key features like pseudonymization, metadata extraction, and versioning.
  • By connecting data to analysis tools, DataCastle promotes reproducible and version-linked research workflows.