Creating and Analyzing
(data collection, analysis, synthesis)
In this researcher-focused stage, observations are made and datasets are created. In some cases, previously collected data or code from external sources may be reused or repurposed. There should be a plan in place to maintain and assure data quality and data integrity throughout the research project. The steps and methods used to process and analyze raw data should be documented, and any changes to the raw data files should be noted.
Descriptive metadata and documentation are an important part of this stage, and curators, data librarians and research support staff may have a role to play in creating or enhancing documentation and description.
Relevant tools and resources:
- Concordia - Directory structure guidance
- DCC Disciplinary Metadata
- UBC File Naming Guidelines
- Smithsonian Data Management Best Practices for Naming and Organizing Files
- RDA Metadata Standards Directory Working Group
- NIST Electronic File Organization Tips
- Creating a README for your dataset
- De-identification Guidance
- DataOne Data Management Skillbuilding Hub
- Colectica for Microsoft Excel
- Sensitive Data Toolkit for Researchers Part 3 - Research Data Management Language for Informed Consent
- Sensitive Data Toolkit for Researchers Part 2 - Human Participant Research Data Risk Matrix
- Sensitive Data Toolkit for Researchers Part 1 - Glossary of Terms for Sensitive Data used for Research Purposes
- OSF How to Make a Data Dictionary
- Data Curation Network Data Curation Primers
- Delivering Research Data Management Services MOOC
- SIKU (Sea Ice) Atlas
- Queen's University Library Research Data Management (RD) Workflow
- Primer on Data Management - What You Always Wanted to Know
- OpenRefine
- Inuit Tapiriit Kanatami Negotiating Research Relationships With Inuit Communities, A Guide For Researchers
- ICPSR - What is a codebook?
- Electronic Theses and Dissertations (ETD) plus Toolkit
- DDC Glossary of frequently used terminology
- DCC - Version control and authenticity
- DCC Curation Lifecycle Model (with checklists)
- Data Tree
- Cornell README file template
- Cornell guide to writing readme style metadata
- BitCurator
- Apache Tika
- ANDS File Formats
- ANDS Curation Continuum