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Early Career Researcher Data Management Training

Date/Time/Duration
Monday, April 4, 2022 - 12:30pm
Venue/Location
Virtual/Zoom
Organized By
DRIC
Speaker(s)
Prof. Frederick Ato Armah - Director and Professor of Environmental and Sustainability Science
Prof. Dorcas Obiri-Yeboah - Deputy Director and Associate Professor of Clinical Microbiology
Prof. David Teye Doku - Deputy Director and Associate Professor of Public Health

Background and Rationale

Irrespective of tenure, researchers worldwide work in highly competitive environments. Early career researchers compete within this environment with seasoned and world-acclaimed scholars.  Their survival in the scholarly world is therefore highly dependent on acquiring the needed knowledge, skills and competencies. A key soft skill that is fundamental to scholarly growth is research data management. Data management is very crucial as it prevents errors and increases the quality of analysis, allows for validation and replications, and increases the overall credibility of a researcher. The data life cycle provides a high-level overview of the stages involved in the successful management and preservation of data for use and reuse.

Training Goal

The goal of the training is to build and/or strengthen the capacity of participants in research data management through the provision of the knowledge, skills and competencies needed.

Expected Benefits/Outcomes

By the end of the workshop, participants will: 

  1. Increase their knowledge on the importance of data and data management
  2. Be able to describe the data life cycle and discuss the various components in details
  3. Improve their knowledge on the relationship between good data management and research impact
  4. Be able to identify the components of good data management plans
  5. Prepare a comprehensive data management plan for a given research project

Scope of the Training

Facilitation/presentation will be interactive and interspersed with hands-on training and/or personal/professional experiences and examples, where applicable. The overall agenda will be to cover the details of each aspect of the data life cycle.

Mode of Delivery

The two training sessions will be completely in virtual mode using Zoom.

Participants will first have an interactive discussion on the value of data and importance of data management. They will then be introduced to the data life cycle. Then the main contents will involve interactive presentations and discussion of the essential concepts and principles of proper research data management as it pertains to each of the steps in the data life cycle. For the practical aspect of the training, the participants will be divided into 5 groups each with 5 participants. Each group will be given pre-drafted research concepts based on which they will have to prepare a comprehensive data management plan under the guidance of a facilitator. These developed data management plans will be presented by the groups for discussion.

Each workshop session will include verbal and online evaluations. All participants will receive a digital certificate of participation.

Resource Persons

The main facilitators are drawn from the Directorate of Research, Innovation and Consultancy and are:

The core facilitators will be supported by:

Sponsor: Association of Commonwealth Universities

Target group/eligibility

Participants will be selected from University of Cape Coast and ACU institutions and will include:

  1. PhD Candidates
  2. Academic senior members who completed their PhD in less than 6 years ago
  3. Female applicants are particularly encouraged

Training Schedules

  1. Deadline for applications: 04th March 2022
  2. Selected participants notified: 15th March 2022
  3. First workshop: 04-06th April 2022
  4. Second workshop: 19-21th April 2022

 

Time: The workshops will take place at 12.30pm – 4.30pm UTC on each day

How to apply

Kindly follow the instructions, fill and submit this application form.

Key Features / Side Attractions
Data & Data Management
Describe the data life cycle
Identify the components of good data management plans