5. Increase data re-use (through clarifying licences)

Owned by Varmo Pilt
06 January 2022
2 min read


How will you provide documentation needed to validate data analysis and facilitate data re-use (e.g. readme files with information on methodology, codebooks, data cleaning, analyses, variable definitions, units of measurement, etc.)?

Will your data be made freely available in the public domain to permit the widest re-use possible? Will your data be licensed using standard reuse licenses, in line with the obligations set out in the Grant Agreement?

Will the data produced in the project be useable by third parties, in particular after the end of the project?

Will the provenance of the data be thoroughly documented using the appropriate standards?

Describe all relevant data quality assurance processes.



Sample:

5. Increase data re‐use


 We plan to make our datasets reusable by assuring high data quality, by providing all documentation needed to support data interpretation and reuse and by clearly licensing the data via the repository so that others know what kinds of reuse are permitted.


Tools needed: 

  • The data can be read by any software compatible with .jpeg files
  • The data can be read by any software compatible with .csv files
  • A software licence for SPSS will be required to read the data file which has been analysed.
  • Code necessary to process and interpret the data will be deposited on TalTechData.
  • Data Transfer/Processing agreements will be signed prior to any data sharing.
  • Data will be deposited at a repository/database (please provide name) immediately and without embargo, using a license (please specify license type, e.g CC‐BY).


Data quality: 

  • Data will be quality‐checked at collection/generation by validation against controls or publicly available databases.
  • RNA seq data will be quality controlled in terms of sequence quality, sequencing depth, reads duplication rates (clonal reads), alignment quality, nucleotide composition bias, PCR bias, GC bias, rRNA and mitochondria contamination, coverage uniformity. Only high‐quality data will be included in the subsequent analysis.
  • The register holder assures data quality in terms of completeness and correctness of registration.
  • The transcribed interview material will be coded independently by two researchers.
  • Images will be inspected for artifacts and the results will be recorded in a spreadsheet file.
  • Mass spectrometry results will be quality‐checked for contamination and mass accuracy.
  • Register data will be quality controlled according to a procedure established in our group (REF).
  • Data will be checked at the point of entry in REDCap or SMART‐TRIAL for double entries, completeness, missing data and unreasonable values.  
  • To assure data quality, the study will be conducted according to the COREQ guidelines for qualitative research.