Scientific Integrity & Data Quality Policy
Purpose
The commercial value of IsoGentiX's datasets depends entirely on their scientific credibility. Equally, our licensees make decisions — including drug discovery investment decisions — based on our data. This policy sets out the standards of scientific integrity and data quality that apply to all data generation, processing, and reporting activities, and that cannot be compromised by commercial pressure or timelines.
Core integrity requirements
- No fabrication — data is never invented, fabricated, or entered without a corresponding real-world observation or measurement
- No falsification — data is not altered, manipulated, or selectively excluded to produce a more favourable or commercially convenient result
- No misrepresentation — analytical results, coverage metrics, quality scores, and taxonomic identifications are reported accurately and without inflation
- Full disclosure of limitations — known limitations in datasets — including gaps in coverage, low-confidence taxonomic assignments, or QC failures — are disclosed to licensees in the accompanying data documentation
Quality standards
IsoGentiX applies defined quality thresholds to each data type in its pipeline. Genomic assemblies, transcriptomic datasets, metabolite libraries, and microbiome profiles are each subject to QC checkpoints before release. Data that fails applicable QC thresholds is withheld from licensing, reprocessed, or released with explicit quality flags — it is not released as if it met standards it did not achieve.
Methods documentation
The methods used to generate, process, and annotate each dataset are documented in sufficient detail to permit independent assessment of the results. Methods documentation accompanies all licensed datasets. IsoGentiX does not withhold methods information that would be material to a licensee's assessment of data fitness for purpose.
Error correction
Where errors in released datasets are identified — whether by internal review or by a licensee — IsoGentiX will investigate promptly, issue a corrected dataset where feasible, and notify all affected licensees. Errors will not be concealed or minimised to avoid commercial consequences.
Conflicts between commercial and scientific standards
Where commercial pressure — from licensees, investors, or internal timelines — conflicts with the scientific integrity requirements of this policy, scientific integrity takes precedence. Personnel who believe they are being asked to compromise data quality or integrity must raise this through the Grievance Policy process. No person will be penalised for refusing to compromise scientific standards.
Responsibility
The Chief Scientific Officer has primary responsibility for maintaining this policy and for ensuring that QC processes, methods documentation standards, and error-correction procedures are implemented and followed across all data generation activities.