Recommendation 4.0
Status: Draft 20.5.2025
The International Generic Sample Number (IGSN) is a globally unique and persistent identifier designed specifically for physical samples and related objects. At Helmholtz, we recommend the use of IGSNs to ensure that samples—and other tangible sources from which data are derived—can be reliably identified, referenced, and linked across research workflows. The motivation to use IGSNs lies in their ability to improve traceability, reproducibility, and data integration across disciplines. By assigning a persistent identifier to a sample, researchers can unambiguously connect it to associated datasets, publications, instruments, and collection metadata, supporting FAIR principles and enabling long-term reuse and verification of research outcomes.
IGSN is used to identify samples in data infrastructures.
For organizations this means:
For data curators this means:
For researchers it means:
For data infrastructures:
| mandatory | conditional | optional | |
|---|---|---|---|
| Helmholtz FAIR Principle | X |
The institution needs to be a member of Data Cite or needs to partner with a member to be able to register IGSNs.
Parent: 0.1
Dependent: 4.1, 4.2, 4.3
Other: none
Names of contributors to this recommendation
Manu
[1] Plankytė, Vaida, Macneil, Rory, & Chen, Xiaoli. (2023). Guiding principles for implementing persistent identification and metadata features on research tools to boost interoperability of research data and support sample management workflows. Zenodo. https://doi.org/10.5281/zenodo.8284206
About
The International Generic Sample Number (IGSN) is a persistent, globally unique identifier designed to unambiguously reference physical samples and other material objects in the research lifecycle. It enables reliable citation, tracking, and linking of samples to related data, instruments, people, and publications, making them FAIR—findable, accessible, interoperable, and reusable.
History
Originally developed by the geoscience community in the early 2000s, IGSN emerged from the need to manage and cite geological samples across laboratories and institutions. It was formalized through the IGSN e.V. foundation in 2011 and has since evolved into a cross-disciplinary identifier supported by the global research infrastructure. Since 2021, IGSNs have been registered through DataCite, aligning their metadata with other research outputs.
Structure
IGSN records consist of a unique identifier (a prefix-suffix structure similar to DOIs) and a metadata record that captures core descriptive information about the sample: sample type, material, collection method, spatial and temporal context, and links to related entities (e.g., datasets, people, institutions). Metadata can be enhanced to fit domain-specific needs while maintaining a consistent structure for interoperability.
Motivation
Using IGSNs improves sample traceability, ensures reproducibility of results, and supports data integration across disciplines. It allows researchers to explicitly reference the physical basis of data analyses, which is critical for verification, reuse, and credit assignment.
Current Use of IGSN
IGSNs are currently used in a range of domains, including geosciences, environmental sciences, archaeology, and biodiversity research. For example, ocean drilling samples from IODP expeditions, sediment cores, rock specimens, water samples, and even archaeological artifacts have been assigned IGSNs. These identifiers help integrate sample-based research into digital infrastructures and link physical materials to datasets and publications, thus enabling transparent and connected science.
What: Lab- or institution-specific sample IDs.
Pros: Easy to implement, tailored to local needs.
Cons: Not globally unique, not resolvable, hard to track across systems or publications.
What: Identifiers assigned by domain-specific repositories or museums (e.g., GenBank accession numbers, museum catalog numbers).
Pros: Well-integrated in their domains.
Cons: Often not globally unique, not persistent outside their system, not interoperable across disciplines.
What: Using general-purpose persistent identifiers like DOIs or Handles for samples.
Pros: Technically viable; DOI infrastructure is mature.
Cons: Lack of community consensus or metadata model for samples unless built on top of IGSN or similar; harder to ensure consistency and semantic clarity.
What: A persistent identifier scheme designed for objects of any type.
Pros: Flexible, openly governed, used by some institutions (e.g., museums, archives).
Cons: Less widely adopted in science, lacks built-in metadata requirements for samples, limited interoperability in research workflows.
Why IGSN?
While alternatives exist, IGSN is currently the only PID system specifically designed to handle the complexities of referencing physical samples across scientific domains. It combines:
Therefore, for research workflows that require transparent, machine-readable, and citable links between samples and data, IGSN remains the most suitable and sustainable option.
(quality of content, limitations, interoperability, sustainability: expected future dissemination / technical availability / funding)
IGSN is used to identify samples in data infrastructures.
For organizations this means:
For data curators this means:
For researchers it means:
For data infrastructures:
Also see [3] Baldewein et al. (2023). FAIR WISH D7 -Standard Operating Procedure for automatic IGSN registration. Zenodo. https://doi.org/10.5281/zenodo.10401380
GFZ Data Services
Pangaea
Hereon HCDC (?)
Others?
IGSN is implemented within the Helmholtz association at AWI, GFZ, and Hereon through the FAIRWish Project [4]. See [3] for more information.
Another implementation is documented at the Kiel University (CAU) [6]
[1] Plankytė, Vaida, Macneil, Rory, & Chen, Xiaoli. (2023). Guiding principles for implementing persistent identification and metadata features on research tools to boost interoperability of research data and support sample management workflows. Zenodo. https://doi.org/10.5281/zenodo.8284206
[2] Klump, J., Lehnert, K., Ulbricht, D., Devaraju, A., Elger, K., Fleischer, D., Ramdeen, S., Wyborn, L. (2021): Towards Globally Unique Identification of Physical Samples: Governance and Technical Implementation of the IGSN Global Sample Number. - Data Science Journal, 20, 1, 1-16., DOI: https://doi.org/10.5334/dsj-2021-033
[3] Baldewein, L., Kleeberg, U., Brauser, A., Elger, K., Frenzel, S., Heim, B., & Wieczorek, M. (2023). FAIR WISH D7 - Standard Operating Procedure for automatic IGSN registration. Zenodo. https://doi.org/10.5281/zenodo.10401380
[4] The FAIR Wish Project: https://helmholtz-metadaten.de/de/inf-projects/fair-wish-fair-workflows-to-establish-igsn-for-samples-in-the-helmholtz-association
[5] IGSN Documentation on forschungsdaten.org https://www.forschungsdaten.org/index.php/IGSN
[6] IGSN Service and Documentation at the University Kiel https://igsn.uni-kiel.de/de