What are some innovative approaches to managing academic research data?

Managing academic research data is a complex and challenging task that requires effective leadership, planning, and collaboration. Data is the lifeblood of research, but it can also pose ethical, legal, and technical issues that need to be addressed. In this article, we will explore some innovative approaches to managing academic research data that can help you enhance the quality, efficiency, and impact of your research projects.

1 Data management plans

A data management plan (DMP) is a document that outlines how you will collect, store, access, share, and preserve your research data throughout the research lifecycle. A DMP can help you comply with the requirements of your funders, publishers, and institutions, as well as ensure the integrity, security, and reproducibility of your data. A DMP should be created at the beginning of your research project and updated as needed. There are many tools and resources available online that can help you create and review your DMP, such as DMPTool, DMPonline, and DMPTuuli.

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Rasheed Adeoti

Data Scientist | Data Analyst | MTN Foundation Scholar | Educationist
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Effective Data Management Plans are crucial in the early phases of research, particularly when data collection is involved. They serve as a navigational map, simplifying the research journey. Think of a Data Management Plan as a map for your research journey. Just as a map provides a detailed route, landmarks, and precautions for a successful trip, a Data Management Plan outlines the path for collecting, storing, and securing data in your research, ensuring you reach your destination smoothly and securely

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Romoke Lasisi

Data-Driven Change Maker | Social Impact & Tech Influencer | AI & ML Enthusiast
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A data management plan (DMP) is a written document that outlines the handling of data during a project's lifecycle. The time and effort spent in setting up a good data management is never a waste, it usually pays off when the time comes to replicate your analysis and findings. It enables you to easily locate and understand your data, it increases data resuablility potential. Data management plans are also very beneficial for new data projects in corporate contexts, helping to clarify the significance of new data sources and their relationship to business objectives for all involved parties.

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2 Data sharing platforms

Data sharing platforms are online services that allow you to publish, discover, and reuse research data. Data sharing platforms can help you increase the visibility, citation, and impact of your research, as well as foster collaboration and innovation among researchers. Data sharing platforms can vary in their scope, features, and policies, so you should choose the one that best suits your research needs and goals. Some examples of data sharing platforms are Zenodo, Figshare, Dryad, and Mendeley Data.

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Romoke Lasisi

Data-Driven Change Maker | Social Impact & Tech Influencer | AI & ML Enthusiast
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Data sharing platforms are online that facilitate the exchange of data between different parties. They bridge the gap between data owner and data users who benefits from accessing the data. Data sharing platforms enhanced efficiency and the decision making. For instance organisation and businesses can utilise shared data to make strategic decisions about their targeted market and audience, streamline process and cut down expenses

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3 Data analysis tools

Data analysis tools are software applications that help you process, analyze, and visualize your research data. Data analysis tools can help you perform various tasks, such as cleaning, transforming, modeling, and reporting your data, as well as generating insights and conclusions from your data. Data analysis tools can range from simple to complex, depending on your research questions and methods. Some examples of data analysis tools are SPSS, R, Python, and Tableau.

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Romoke Lasisi

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Data analysis tools are software and programs that are capable of collecting and analyzing data about a business, its customers, it's market and its competition in order to improve processes and help uncover insights to make data-driven decisions. Examples of data analysis tools include: Python, Sas, Qilk, Power bi, Microsoft excel, KNIME, Rstudio e.t.c Data analysis tools enhance customer experience, mitigate risk, increase efficiency, helps deliver personalised experience etc

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4 Data repositories

Data repositories are online databases that store and preserve research data for long-term access and reuse. Data repositories can help you ensure the availability, reliability, and sustainability of your research data, as well as comply with the standards and best practices of your discipline and community. Data repositories can be general or specific, depending on their scope and content. Some examples of data repositories are Dataverse, ICPSR, GenBank, and PANGAEA.

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5 Data literacy skills

Data literacy skills are the abilities and competencies that enable you to understand, use, and communicate with data. Data literacy skills can help you improve the quality, efficiency, and impact of your research data management, as well as enhance your professional development and career opportunities. Data literacy skills can be acquired through formal or informal learning, such as courses, workshops, webinars, and online resources. Some examples of data literacy skills are data ethics, data quality, data visualization, and data storytelling.

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6 Data governance frameworks

Data governance frameworks are the policies, procedures, roles, and responsibilities that guide and regulate the management of research data. Data governance frameworks can help you ensure the compliance, accountability, and transparency of your research data management, as well as align your data strategy with your organizational goals and values. Data governance frameworks should be developed and implemented in consultation and collaboration with your stakeholders, such as researchers, funders, publishers, and institutions. Some examples of data governance frameworks are FAIR principles, RDA recommendations, and GDPR regulations.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are some innovative approaches to managing academic research data?

1

2

3

4

5

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1 Data management plans

2 Data sharing platforms

3 Data analysis tools

4 Data repositories

5 Data literacy skills

6 Data governance frameworks

7 Here’s what else to consider

Educational Leadership

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What are some innovative approaches to managing academic research data?

1

2

3

4

5

6

7

1 Data management plans

2 Data sharing platforms

3 Data analysis tools

4 Data repositories

5 Data literacy skills

6 Data governance frameworks

7 Here’s what else to consider

Educational Leadership

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