1: Uniform collection standards are in place.

2: When uniform measures are not used by hospital units or labs, the CRF provides conversion guidance.

3: Inter-site sampling differences are described in the data dictionary. 

1: Random audits of clinical source documents against data entered on hard copy and eCRFs.

2: Double data entry audits of selected forms that are known to be prone to errors.

3: Audit of eCRF and hard copy logs for biospecimens; audit of complex assessments by experts.

4: Training and on-line documentation of how to handle exceptions.

1: Manuals compiled by multidisciplinary team of stakeholders and involve data curation expertise. For example,   Imaging studies and biospecimen collection procedures are detailed for study personnel and end-users.

2: Manuals specify how eCRF data is collected, when, by what metrics and under what conditions.

3: CRF fields have links to relevant data manual sections. 

1: Scheduled queries are run for data checks across tables/CRFs for variables prone to conflict.

2: There should be rules to ensure that incompatible choices are excluded both within a data element (e.g. cannot be both male and female) and between related elements (e.g. male cannot be pregnant).

1: Specifications for floating point, integer, factor or free text

1: Data base fields conform as appropriate to FITBIR NINDS Common Data Elements for Traumatic Brain Injury; CDISC; CDASH; and Abbreviated Injury Scores.

2: Pre-specified data ontology at design-time.

3: Published data elements used as far as possible. However, when not possible, deviations from published specifications clearly documented and have robust scientific justification.