Data Management for Reproducibility

Content overview

Approaches to ensuring reproducible research are many and varied.  This workshop presents a forensic approach to identifying data, processes, equipment and environmental factors which influence the variability and outcome of research and therefore impact its reproducibility. Using this approach we will analyse the elements of traditional Data Management Plans. Participants will identify the content required to demonstrate that research methods are robustly documented, and that data and metadata, which shows the extent to which variability has been controlled, are available for external scrutiny. 

This workshop is designed for researchers using primarily quantitative methods and who can share data management best practice locally. It is ideal for those in life and biomedical sciences.  Familiarity with FAIR principles is advised. 

Learning objectives

• Describe data integrity in relation to research data, understand the concept of critical data as it relates to research data integrity 
• Learn the basics of process mapping 
• Articulate the benefits of process mapping in defining robust research protocols and identifying data which is critical to reproducibility 
• Conduct data integrity assessments on process maps to identify critical research data 
• Analyse the research process to identify risks to critical data e.g. sources of variability. 
• Categorise the critical research data and associated data which can demonstrate the extent to which different types of variability have been controlled. 
• Identify metadata for critical data and data associated with variables 
• Understand the elements of the data management plan which are key to documenting how critical research data has been robustly generated, recorded, checked and stored 
• Describe the key elements of metadata and be able to articulate methods for documenting these within a DMP 
• Take a generic DMP template and describe the modifications, annotations etc required to ensure that it captures critical research data, associated data on control of variability and metadata within your discipline 
• Dos and don’ts of working with data in MS Excel 

Completion criteria

The outcomes from this session build on the work that participants will have done in the Data Sharing: From Planning to Publishing sessions. Participants will be expected to extend that work to provide customised training in Sensitive Data Sharing for their local situation.

Audience

This workshop is designed for researchers using primarily quantitative methods and who can share data management best practice locally. It is ideal for those in life and biomedical sciences.  It may be less relevant to those working in qualitative and arts and humanities research

Level (Introduction, intermediate, advanced)

Introductory, intermediate.

Prerequisite skills, expertise and experience

• General familiarity with research processes and an interest in training colleagues on data management techniques. 
• Familiarity with FAIR principles 

Overall likely time commitment

• 2 x 2.5 training hours 

• 1 office hour 

• 6-8 hours creating and presenting deliverables 

Event date and time

3rd and 12th February 2025, 10:00-12.30 

Registration deadline

14th January 2025

Training partner

University of Bristol

Location

Online

Cost to institution per participant

£500

Number of attendees

12

If the course is oversubscribed, a panel will make decision on the applicants based on answers in the application form

Evaluation

Self-evaluation opportunities are built into the structure of the course; a UKRN evaluation will also be provided via the Community of Practice. 

Application

For information about how to apply, please contact your institution’s Open Research Coordinator and Administrator (linked via this page). 

For specific course content queries, or general information regarding the UKRN ORP training, please contact: elle.chilton-knight@bristol.ac.uk