Findable data

Scientific repositories are databases established to collect, disseminate, and preserve research outputs such as scientific articles, datasets, software, and documentation. By depositing research outputs in repositories, these materials become more easily findable and accessible to others. Depending on policies and regulations, authors can make their work available through Open Access or restricted access. Repositories are quite diverse in scope. They can be general, meaning domain‑agnostic, or focused on specific types of data and research domains, known as field-specific repositories. They may also be associated with international organisations, institutions, or specific departments. Repositories offer different levels of FAIRness and trustworthiness. It is therefore important to promote awareness of best practices and to guide the scientific community in selecting the most appropriate repository for each type of research output.

## Topic definition and scope: This lesson introduces **Persistent Identifiers (PIDs)** and their role in making research outputs more **findable, accessible, and reliably linked** within the research ecosystem. The session situates PIDs within the **FAIR principles**, focusing especially on their importance for the *Findable* principle. Participants are introduced to widely used PID systems and explore how these identifiers support discovery, citation, attribution, and interoperability in research workflows. Persistent identifiers (PIDs) are globally unique and long-lasting references assigned to digital research objects and entities, such as datasets, publications, software, researchers, and organizations. This lesson introduces the concept and practical use of PIDs within the context of **FAIR data and Open Science practices**. Using a problem-based scenario, participants examine typical issues that arise when persistent identifiers are missing, for example when datasets cannot be found, links no longer work, or authors cannot be uniquely identified. Participants reflect on how such situations affect research transparency, reproducibility, and reuse. The session then introduces commonly used PID systems and demonstrates how they enable reliable identification and linking of research outputs and contributors. The lesson follows an **interactive, problem-based learning approach**. It starts with a short scenario illustrating common issues researchers encounter when persistent identifiers are missing, for example: * When a dataset cited in a publication cannot be found * When the same dataset appears under multiple names or locations  * When a dataset link leads to a “404  Page Not Found” error.  Participants discuss what may have gone wrong, who is affected by these issues, and how they impact trust, reproducibility, and reuse of research outputs. Building on this discussion, the instructor introduces the concept of PIDs and their role in the FAIR framework. Participants then work in small groups on fictional research cases to identify missing identifiers and determine which PIDs should have been assigned. The session concludes with a short reflection linking PIDs to participants’ own research practices, institutional Open Science policies, and funder requirements.

This lesson plan has been created with the aim to educate PhD students and researchers on metadata standards using tangible examples and practical activities. It assumes a low level of prior knowledge regarding metadata but assumes experience in research and familiarity with the FAIR principles. Resources can be provided asynchronous to bring everyone up to the same level.  We recommend starting by building up a theoretical baseline of all participants before giving the opportunity for participants to practice and start working directly with metadata, metadata standards and general concepts.  Note that many activities can be done as an individual or a group. This can be changed based on what type of session you are giving. Working in pairs or small groups can increase peer-learning and reduce the threshold for asking questions when confused (as these participants can discuss with their peers rather than having to ask the instructor). Working individually can also be advantageous when doing asynchronous work, hybrid or online sessions or with self-paced study. #