FDSP (CASRAI)
FAIR Data Stewardship Certification — Professional
What you earn
The credential itself
Pass the examination and CASRAI issues this certificate and the post-nominal FDSP (CASRAI), signed by the Programme Director and verifiable by anyone, permanently, from the code on its face.
CASRAI Certification
Certificate of Professional Competence
This is to certify that
Dr. Amelia R. Hartley
has completed the prescribed programme of study and passed the examination in
FAIR Data Stewardship — Professional
and is admitted to the designationFDSP (CASRAI)
Certifications
FDSP (CASRAI) — FAIR Data Stewardship, Professional
CASRAI·Issued Mar 2026·Credential ID CASRAI-FDS-2026-TT18M5
casrai.org/verify/CASRAI-FDS-2026-TT18M5
Every certificate is issued under the signature of Dr. Diana Nieves Castro, MD, Programme Director of CASRAI Certification, who sets the syllabus, blueprint and pass mark for this credential.
It certifies that its holder passed the CASRAI examination for this course on the date shown, scored against the blueprint and pass mark published on this page — and anyone can confirm it from the verification code, without an account and without contacting us.
About this credential
What it covers
Body of knowledge
Exam blueprint
The exam is assembled to these weights on every attempt. They are published before purchase because a blueprint you cannot see is not a blueprint.
| Domain | Proportion of exam | Weight | Items |
|---|---|---|---|
| The FAIR Principles: provenance, structure, scope and limits1 | 12%≈ 1 item | ≈ 1 | |
| Findability: persistent identifiers, registration and indexing2 | 14%≈ 1 item | ≈ 1 | |
| Metadata: richness, schema selection and machine-actionability3 | 18%≈ 1 item | ≈ 1 | |
| Interoperability: vocabularies, ontologies and linked data4 | 14%≈ 1 item | ≈ 1 | |
| Reusability: licensing, provenance and community standards5 | 14%≈ 1 item | ≈ 1 | |
| FAIR assessment, maturity measurement and implementation planning6 | 12%≈ 1 item | ≈ 1 | |
| FAIR beyond datasets: software, digital objects and emerging practice7 | 8%≈ 1 item | ≈ 1 | |
| CARE, sensitive data, and the limits of FAIR8 | 8%≈ 1 item | ≈ 1 | |
| Total | 100% | 7 |
Item counts are approximate. Each attempt draws a fresh form to the weights above, so the exact number of items per domain varies between attempts.
Syllabus
12 modules, 12 lessons
Every lesson and every learning outcome is listed. The lesson bodies open on enrolment; nothing else about the course is withheld.
12 modules · 12 lessons · 90 h
Lesson material opens once you are enrolled. The full syllabus and every learning outcome are shown here so you can judge the coverage before you pay.
Module 1
1
1 lesson · 5 h
- Locked.FAIR in context: origins, structure, and what FAIR is not5 h · Locked
On completion you will be able to
- On completion the candidate can:
- State the origin, authorship and publication of the FAIR Guiding Principles, and describe how they moved from a workshop output to a funding condition.
- Distinguish FAIR principles, FAIR indicators, FAIR assessment tools, and FAIR implementation frameworks, and explain why conformance to a tool is not conformance to the principles.
- Refute, with a cited authority, the proposition that FAIR data must be open.
- Identify four questions FAIR does not answer and name the framework that answers each.
Module 2
2
1 lesson · 7 h
- Locked.Reading the fifteen sub-principles7 h · Locked
On completion you will be able to
- On completion the candidate can:
- Recite and interpret each of the fifteen sub-principles in its published wording.
- Classify a described practice against the sub-principle it serves, and identify practices that serve none.
- Explain the data/metadata asymmetry — why A2 binds metadata alone, and what follows for withdrawn or embargoed data.
- Analyse a real dataset landing page and produce a sub-principle-level gap list.
Module 3
3
1 lesson · 9 h
- Locked.Persistent identifiers in practice9 h · Locked
On completion you will be able to
- On completion the candidate can:
- Select an identifier type for a given entity and justify the selection against granularity and persistence requirements.
- Design a versioning and granularity policy for a repeatedly released dataset.
- Evaluate whether a resolution service delivers persistence, separating the technical mechanism from the organisational commitment.
- Diagnose why a correctly deposited dataset is not discoverable.
- Specify a tombstone strategy satisfying A2.
Module 4
4
1 lesson · 9 h
- Locked.Metadata I: layers, crosswalks, and the DataCite floor9 h · Locked
On completion you will be able to
- On completion the candidate can:
- Distinguish the three coexisting metadata layers — repository administrative/discovery, disciplinary descriptive, human-readable documentation — and allocate a given metadata element to the right layer.
- Produce a compliant DataCite record for a real dataset.
- Perform a crosswalk between two schemas and document the properties that do not map.
- Differentiate machine-readable from machine-actionable and justify the distinction with an example.
Module 5
5
1 lesson · 10 h
- Locked.Metadata II: disciplinary standards and the selection decision10 h · Locked
On completion you will be able to
- On completion the candidate can:
- Select a metadata standard for a given dataset by reasoning from discipline, intended reuse, target repository and harvesting requirements.
- Defend that selection against a named plausible alternative.
- Locate the domain-relevant community standard required by R1.3 using a register rather than local invention.
- Recognise the case where no community standard exists and specify a defensible local approach that remains crosswalkable.
Module 6
6
1 lesson · 6 h
- Locked.Documentation: codebooks, data dictionaries, datasheets6 h · Locked
On completion you will be able to
- On completion the candidate can:
- Produce a data dictionary defining every variable with label, type, permitted values, units, missing-value codes and derivation.
- Write a README that supports reuse by a person with no contact with the creators.
- Evaluate a documentation set against R1 and identify what a reuser could not determine.
- Apply the datasheet genre to a research dataset and state what it adds beyond a schema record.
Module 7
7
1 lesson · 11 h
- Locked.Semantics: vocabularies, ontologies and qualified references11 h · Locked
On completion you will be able to
- On completion the candidate can:
- Distinguish a controlled list, taxonomy, thesaurus (SKOS) and formal ontology (OWL), and select the least complex structure meeting a stated requirement.
- Assess whether a candidate vocabulary is itself FAIR, against I2.
- Express a qualified reference using a published relation vocabulary.
- Diagnose an interoperability failure at the technical, syntactic, structural or semantic level and identify which level is broken.
- Encode units of measurement machine-actionably.
Module 8
8
1 lesson · 10 h
- Locked.Licensing and the legal foundations of reuse10 h · Locked
On completion you will be able to
- On completion the candidate can:
- Determine whether copyright subsists in a given dataset and explain what a licence is doing when it does not.
- Determine whether sui generis database rights are engaged for a given dataset and jurisdiction, and select an instrument accordingly.
- Resolve a licence-compatibility problem in a derived dataset combining differently licensed sources.
- Justify a choice between CC0 and CC BY for a specific dataset and community, naming the trade-off honestly.
- Apply a machine-readable licence assertion satisfying R1.1.
Module 9
9
1 lesson · 6 h
- Locked.Provenance, data citation and availability statements6 h · Locked
On completion you will be able to
- On completion the candidate can:
- Record provenance sufficient to satisfy R1.2 — origin, processing steps, software and versions, responsible agents, time.
- Construct a complete data citation and explain each element's function against the Joint Declaration principles.
- Draft an accurate, specific and actionable data availability statement.
- Critique a non-statement such as "available from the authors on reasonable request" and state which FAIR sub-principles it fails.
Module 10
10
1 lesson · 7 h
- Locked.Assessing FAIRness and planning FAIRification7 h · Locked
On completion you will be able to
- On completion the candidate can:
- Assess a dataset against a published indicator set and report the result with its limitations stated.
- Select an appropriate assessment instrument for a stated purpose.
- Interpret an automated FAIR score correctly, identifying what was actually measured.
- Construct a prioritised, resourced FAIRification plan distinguishing producer-side from repository-side changes.
- Detect and refuse FAIR-washing.
Module 11
11
1 lesson · 7 h
- Locked.CARE, sensitive data, and the limits of FAIR7 h · Locked
On completion you will be able to
- On completion the candidate can:
- Apply the CARE Principles alongside FAIR and identify where the two frameworks pull in different directions.
- Distinguish anonymisation from pseudonymisation and state the legal consequence in a named jurisdiction.
- Assess re-identification risk and recommend proportionate disclosure control.
- Design a controlled-access arrangement satisfying A1.2.
- Refuse, with reasons, a FAIRness improvement that is ethically or legally impermissible.
Module 12
12
1 lesson · 3 h
- Locked.The boundary: FAIR4RS, FAIR Digital Objects, and knowing when to refer3 h · Locked
On completion you will be able to
- On completion the candidate can:
- Explain why data FAIR does not transfer unmodified to software, naming executability, composite nature, and continuous evolution and versioning.
- Identify the FAIR4RS sub-principles with no data counterpart and state the problem each solves.
- Recognise a software stewardship question and refer it rather than applying data practice to code.
- Describe the FAIR Digital Object concept and state honestly the maturity of its specification landscape.
Assessment
How the exam works
The exam is closed-book and multiple choice. Each attempt draws a fresh form to the domain weights above, so no two attempts are the same paper and no answer key circulates. The pass mark is 70% and is fixed — there is no curve, no quota and no adjustment by cohort.
Scoring is immediate. You are shown your overall result and, for every item, the option you chose, the correct option and the reasoning behind it — whether you passed or not. A failed attempt may be retaken after 90 days. The wait exists so a retake is a second attempt at the material rather than a second attempt at remembering the paper.
Forms are assembled to the blueprint above or not at all: where a domain cannot yet be sampled to that standard, the exam declines to start rather than issue an unbalanced paper. Your material and progress are never affected, and there is no time limit on when you sit.
Passing candidates are issued a certificate with a verification code. Anyone can check that code on our public verification page without an account and without contacting us.
Certifying authority
Signed by Dr. Diana Nieves Castro, MD
CASRAI’s certification programme is academically and medically directed by Dr. Diana Nieves Castro, MD, Programme Director, who sets the syllabus and blueprint for this credential and signs every certificate issued under it.
CASRAI has maintained the terminology and reporting standards of research administration for over a decade. It assumed leadership of the CRediT contributor-roles taxonomy in 2014 and carried it through to adoption as ANSI/NISO Z39.104-2022, now in use by publishers, funders and institutions worldwide. This examination is drawn from that body of work.
What the credential certifies: that its holder passed the CASRAI examination for this course on the date shown, scored against the blueprint and pass mark published above — verifiable by anyone, permanently, from the code on its face.
