Your research project will be easier to perform and your data will be easier to use if you have a clear strategy for collecting and storing your data. The OHSU library offers services in the form of one-on-one consultations or small group training and workshops, and self-guided data management education.
Each of the boxes above represents a stage in the research lifecycle. The lifecycle model is used to provide context in describing data stewardship activities that should take place over the course of a research project.
- 1. Data planning is an important during the project conception and proposal writing. This includes preparing a Data Management Plan (DMP) and deciding where to archive your data at the end of the project. Plan consent for data sharing, if needed. Determine costs related to data storage & archiving.
- - Additional information about data management plans, or try the Data Management Plan Tool (DMPTool)
- - For advanced computing and storage needs, please contact the OHSU Advanced Computing Center
- 2. Finalize plans for data documentation procedures. Revise DMP if necessary. Communicate data responsibilities to project members. Establish, document and communicate protocols and methods.
Project data lifecycle
(Note: all stages may occur simultaneously)
- 3.i. Organize files; use consistent, thoughtful file-naming conventions; carry out regular backups; consider access control and security.
- 3.ii. Assign clear roles for quality assurance (QA)/quality control (QC); check instrument precision, bias, and scale; replicates where possible; use controlled vocabulary on log/data sheets; check for out-of-range values, empty cells, etc.
- 3.iii. Document the context of data collection: project history, aims, and hypotheses; data collection methods: sampling, process, instruments used, hardware and software used, scale and resolution, temporal and geographic coverage and secondary data sources used; dataset structure of data files, study cases, relationships between files; data validation, checking, proofing, cleaning and quality assurance procedures carried out; changes made to data over time since their original creation and identification of different versions of data files; information on access and use conditions or data confidentiality.
Data-level documentation: names, labels, units and descriptions for variables, records and their values; explanation or definition of codes and classification schemes used; definitions of specialist terminology or acronyms used; codes of, and reasons for, missing values; derived data created after collection, with code, algorithm or command file; weighting and grossing variables created; data listing of annotations for cases, individuals or items.
- 3.iv. Document and manage file versions; document file manipulations; document analysis procedures; write software codes with future sharing in mind.
- 3.v. Determine appropriate file formats; finalize data documentation; share data within project team for analysis and interpretation.
- 4. Write paper(s)
- 5. Refer to to the Share and Archive page for further details. Contact archive for up-to-date requirements; deposit data in archive or repository; establish links between dataset(s) and paper(s) via citation and the use of identifers (DOI, ORCID, etc.); perform data management audit against DMP; submit final report to funder.
- 6. Data are available for discovery and reuse, by you and others.