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How can you ensure your research data is accessible and usable for your audience?

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Research data is the foundation of any research project, but it can also be a source of frustration and confusion if it is not well managed, documented, and shared. How can you ensure your research data is accessible and usable for your audience, whether they are your collaborators, reviewers, funders, or the public? Here are some tips to help you improve your research data quality and usability.

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1 Plan ahead

Before you collect or generate any data, you should have a clear plan for how you will manage, store, back up, and document your data throughout the research lifecycle. This will help you avoid common pitfalls such as data loss, duplication, inconsistency, or incompleteness. You can use a data management plan (DMP) template or tool to guide you through the key aspects of data management, such as data formats, metadata standards, ethical and legal issues, and data sharing options.

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Piyush Maheshwari

Head of Ecosystem @Bima | Previously worked with Protocol Labs, Polygon etc
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While planning it's important to consider not only the creation of a data management plan but also to incorporate methods for executing that plan. This ensures that all the necessary elements for audience access to the data are readily available in an easily accessible manner.

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2 Document your data

Good documentation is essential for making your data understandable and reusable by yourself and others. Documentation can include information such as the purpose, methods, sources, definitions, codes, units, and quality of your data. You can document your data at different levels, such as the dataset, the file, the variable, or the value. You can use different methods, such as readme files, codebooks, data dictionaries, comments in code, or metadata schemas, to document your data.

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Piyush Maheshwari

Head of Ecosystem @Bima | Previously worked with Protocol Labs, Polygon etc
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Documenting research data is a crucial aspect of the research process that ensures the integrity, transparency, and usability of your data. Proper documentation not only benefits your own work but also facilitates collaboration and data sharing with others in the scientific community. There can be many more things involved like citation, version control, especially Data preservation etc.

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3 Organize your data

A well-organized data structure can make your data easier to find, access, and manipulate. You can organize your data by using consistent and meaningful names for your files, folders, and variables. You can also use standard and open formats for your data, such as CSV, XML, or JSON, to ensure compatibility and interoperability. You can also use version control systems, such as Git or SVN, to track and manage changes to your data over time.

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Piyush Maheshwari

Head of Ecosystem @Bima | Previously worked with Protocol Labs, Polygon etc
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Effective data organization ensures that your data remains accessible, comprehensible, and manageable throughout your research project. It supports collaboration, reproducibility, and sharing with others, promoting the integrity of your research. Adapt your organization structure to your project's specific needs and complexity, and document your strategy for future reference.

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4 Share your data

Sharing your data can increase the visibility, impact, and credibility of your research, as well as foster collaboration and innovation. You can share your data through various platforms, such as online repositories, journals, or websites. You should choose a platform that suits the type, size, and sensitivity of your data, as well as the expectations and requirements of your audience. You should also consider the license and terms of use of your data, and provide clear and accurate citation information for your data.

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5 Validate your data

Validating your data can help you ensure the accuracy, completeness, and consistency of your data, as well as identify and correct any errors or anomalies. You can validate your data by using various techniques, such as checking the range, format, and logic of your data values, comparing your data with other sources, or performing statistical tests and analyses. You can also use software tools, such as OpenRefine or R, to automate and simplify the data validation process.

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6 Review your data

Reviewing your data can help you assess the quality and usability of your data, as well as identify and address any gaps or issues. You can review your data by using various criteria, such as the relevance, reliability, timeliness, completeness, and clarity of your data. You can also solicit feedback from your peers, stakeholders, or users, and incorporate their suggestions and comments into your data improvement actions.

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7 Hereâ€™s what else to consider

This is a space to share examples, stories, or insights that donâ€™t fit into any of the previous sections. What else would you like to add?

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Research Management

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How can you ensure your research data is accessible and usable for your audience?

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1 Plan ahead

2 Document your data

3 Organize your data

4 Share your data

5 Validate your data

6 Review your data

7 Hereâ€™s what else to consider

Research Management

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How can you ensure your research data is accessible and usable for your audience?

1

2

3

4

5

6

7

1 Plan ahead

2 Document your data

3 Organize your data

4 Share your data

5 Validate your data

6 Review your data

7 Hereâ€™s what else to consider

Research Management

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