A cryopreservation experiment produces a chain of linked facts. A sample of a given type is suspended in a cryoprotectant solution of known composition, cooled along a recorded temperature path, stored, warmed, and then assessed with one or more viability assays. A published result usually keeps only the last link, a viability figure. The cooling rate, the cryoprotectant lot, the thaw conditions and the assay used to produce that figure are often left in a methods paragraph that cannot be read by machine. WIA-CRYO-010 exists to keep the whole chain in structured form, so that another laboratory can reproduce the run, compare it, or pool it with its own.
The standard covers basic research and clinical applications. Its main features are:
The standard describes how data are recorded, exchanged and analyzed. It does not prescribe which cryoprotectant to use, which cooling rate is best, or which viability threshold counts as success. Those remain scientific judgments. The standard only requires that the judgments be stated in a form others can inspect.
The book follows the order in which a research program meets the problem, from vocabulary through data capture, clinical use, measurement, protocols, collaboration and implementation, and ends with ethics. This chapter is the orientation; each later chapter takes one slice of the standard.
| Chapter | Subject |
|---|---|
| 1 | Introduction to cryopreservation research |
| 2 | Experimental data formats |
| 3 | Clinical trial data management |
| 4 | Tissue viability metrics |
| 5 | Revival success protocols |
| 6 | Research collaboration standards |
| 7 | Implementation guide |
| 8 | Future directions and ethics |
The English and Korean editions use the same eight-chapter layout, so a team working in both languages can cite chapter numbers without translation.
The standard does not publish adoption statistics, so this section does not claim any. What it defines are the quantities that adoption makes comparable. Without shared definitions, "viability" can mean a trypan blue count in one laboratory and an ATP readout in another. Phase 2 of the specification fixes the arithmetic:
Viability (%) = (Viable Cells / Total Cells) × 100
Recovery (%) = (Cells Post-Thaw / Cells Pre-Freeze) × 100
Effective Viability = Viability × Recovery / 100
CI (95%) = p ± 1.96 × √(p(1-p)/n)Effective viability matters because a sample can show high viability among the cells that survive while most of the cells were lost. A high percentage of live cells among a small surviving fraction is not a good outcome. Reporting viability and recovery together prevents that misreading.
For binary success rates the standard specifies the Wilson score interval instead of the simple normal approximation. The Wilson interval behaves better with small samples and with proportions near 0 or 1, and both are common in cryopreservation studies. Phase 2 also gives sample-size formulas for proportions and for continuous outcomes, using Zα = 1.96 and Zβ = 0.84 (α = 0.05 two-tailed, 80% power), so that a study's size is justified before cells are frozen. The same phase covers t-tests, Cohen's d, ANOVA, inverse-variance meta-analysis and the I² heterogeneity statistic, where values above 75% indicate high heterogeneity.
The gain from adoption is therefore comparability. Two laboratories that both report a 95% interval computed the same way, with units attached, can be pooled in a meta-analysis. Two that do not, usually cannot.
The standard is organized as four phases, shown as four cards on the landing page. This book's source material details Phase 1 (data formats and schemas) and Phase 2 (research algorithms and calculations). The later chapters treat the remaining phases where the specification does.
Phase 1: data formats. All data must be represented as JSON-LD, so each record carries a context that gives its terms stable meanings. The base context reads:
{
"@context": "https://wia.org/standards/cryo-research/v1",
"@vocab": "https://wia.org/schemas/cryo-research#",
"schema": "http://schema.org/",
"xsd": "http://www.w3.org/2001/XMLSchema#",
"unit": "http://qudt.org/schema/qudt/"
}Phase 1 defines five record types: CryoResearchExperiment, CryoprotectantFormulation, ViabilityAssessment, LongTermOutcomeStudy and the generic CryoResearchData envelope. Every record may carry quality metadata (completeness, consistency, accuracy, timeliness, validation) and provenance (creator, timestamps, changeLog). All numeric values must carry units, and enumerations must use the controlled vocabularies.
Phase 2: algorithms. The formulas of section 1.3, together with two composite scores. The Vitrification Quality Index weights viability, metabolic activity, functional score, ice crystal score and apoptosis rate, with default weights of 0.4, 0.3, 0.2, 0.05 and 0.05, and scales the result to 0-10. The last two terms are subtracted, since lower ice and apoptosis scores are better. The Data Quality Score is the mean of four 0-1 components: completeness, consistency, accuracy and timeliness.
Putting the algorithms in the standard means the same raw counts give the same derived numbers wherever they are processed.
| Term | Meaning in this standard |
|---|---|
| CPA (cryoprotectant agent) | A compound added to limit freezing injury. Formulations separate permeatingCPAs, nonPermeatingCPAs, iceBlockers and additives. |
| Permeating CPA | A small molecule that enters the cell, for example DMSO in the SDK example. Recorded with CAS number, concentration, unit, manufacturer, catalog and lot number. |
| Non-permeating CPA | A solute that stays outside the cell. Concentrations are given in mM, mg/mL or %w/v. |
| Vitrification | Solidification into a glass without ice crystals. The formulation records glassTransitionTemp, and the VQI scores vitrification outcomes. |
| Slow freeze | Controlled-rate cooling, the method value in the SDK example, with a recorded temperature profile and mean cooling rate. |
| Viability | The fraction of viable cells among total cells, assessed at several levels (section 1.6 and Chapter 4). |
| Recovery | Cells after thaw divided by cells before freezing. |
| Effective viability | Viability multiplied by recovery. |
| Osmolality | Solute concentration of the base solution and the final mix, recorded in mOsm/kg. |
| Lifecycle status | One of PROPOSED, APPROVED, PREPARING, IN_PROGRESS, ANALYZING, COMPLETED, SUSPENDED, FAILED or ARCHIVED. |
The standard assigns identifiers in two styles. The experiment ID in the README format is CRYO-YYYY-NNN, for example CRYO-2025-001. The Phase 1 schema describes experimentId as a UUID or other unique identifier and uses urn:uuid: for record @id values.
The repository includes a five-tab interactive simulator at simulator/index.html. It lets a reader work through the standard without writing code: format experimental data, calculate viability metrics, build research protocols, test API integration and run compliance tests. A sensible way to use it with this book is to read a chapter, then rebuild the chapter's example in the matching tab.
The README states the rules the simulator exercises, and they are worth knowing now:
Phase 1's controlled vocabulary of assay methods lets the same measurement be recorded unambiguously: TRYPAN_BLUE, PROPIDIUM_IODIDE, 7AAD, CALCEIN_AM, MTT, ALAMAR_BLUE, ATP_ASSAY, ANNEXIN_V_PI, FLOW_CYTOMETRY, LDH_RELEASE, TUNEL and OTHER.
WIA-CRYO-010 reuses established identifiers and vocabularies instead of defining new ones:
relatedPublications and citations.registration block of a long-term outcome study.The standard also has a version history: v1.0 was the initial release, v1.1 added enhanced statistics, v1.2 added clinical extensions, and v2.0 is current. The README's SDK example and the Phase 1 schema differ in field style, with the SDK payload using names such as temperature_profile and sample_temperature and the JSON-LD schema using camelCase names such as experimentId. Chapter 2 compares them side by side so that implementers know which one governs which interface.
npm install @wia/cryo-010The source material does not name Korean institutions, so this section describes where a Korean laboratory's existing records fit the schema and does not assert any formal mapping. A Korean research group can fill the identity fields in the usual way: the investigator's ORCID, the institution's ROR ID, and a DOI for related publications. The metadata.irbApproval field holds the institutional review board approval for studies involving human material, and fundingSource records the funder. Clinical studies that fall under local regulation can use the registration block of the outcome-study schema to record whichever registry the study is listed in. The same privacy-by-design reasoning that applies under HIPAA and GDPR applies to patient-linked data here.
Because the framework is built on international identifiers, a Korean record is readable abroad without conversion, and the Korean-language eBook gives local teams the same chapters in their own language. Anyone preparing a submission to a national regulator should confirm that regulator's own requirements. The FDA and EMA formats are the ones the standard names.
This chapter set out the problem, which is that a viability number is only meaningful with the cooling path, cryoprotectant composition and assay method behind it. It also set out the answer: JSON-LD records with controlled vocabularies and mandatory units, plus fixed formulas for viability, recovery, effective viability, Wilson intervals, power analysis and the VQI. You now have the vocabulary of CPAs, vitrification, recovery and lifecycle status, and a way to try each rule in the simulator.
Chapter 2 moves from orientation to the record itself. It walks through the experiment record field by field (lifecycle, scientific design, technical protocol, results and metadata), shows how a temperature profile and a cryoprotectant formulation are written, and reconciles the SDK payload with the JSON-LD schema.
spec/WIA-CRYO-010-v2.0.md, and the project README.