Research Data Management

Where can I get support?

Contact researchdata@zu.ac.ae for any questions or comments regarding data services.

What is research data?

Research data is defined here as any information that has been collected, observed, generated or created to validate original research findings. Data may be numerical, descriptive, visual or tactile. It may be raw, cleaned or processed, and may be held in any format or media. Data can be, for example:

• Instrument measurements
• Experimental observations
• Images, video and audio
• Text documents, spreadsheets, databases
• Survey results and interview transcripts
• Simulation data, models and software
• Slides, artifacts, specimens, samples
• Sketches, diaries, lab notebooks

What is Research Data Management?

Research Data Management (RDM) is a process that includes all activity surrounding your research data, e.g. planning, collecting, analyzing, organizing, describing, sharing, and preserving data.

Why Should I Manage my Data?

• It's good scientific practice: you ensure research integrity and reproducibility
• You increase your own research efficiency
• You save time and resources in the long run
• You enhance data security and minimize the risk of data loss
• You prevent duplication of effort by enabling others to use your data
• You meet funding body grant requirements (if applicable)
• Publishing datasets is an academic merit of its own!

What are the FAIR principles?

FAIR principles are the general guidelines for making your data Findable, Accessible, Interoperable and Reusable. You should always try to follow these principles during the entire research data management cycle.

To make your data FAIR...

• Findable: Use rich metadata and a persistent identifiers. Submit your data in a searchable source
• Accessible: Use open protocols. Make metadata accessible even if the actual data is not. Include authentication steps if necessary
• Interoperable: Use open formats and follow community standards (keywords, controlled vocabularies and ontologies)
• Reusable: Provide clear documentation and a usage license (e.g. Creative Commons)

How do I start planning my Data Management?

You should create a Data Management Plan (DMP), which is a document that contains all the information related to managing the data for your project. Many funders and institutions require a data management plan to be attached with the funding application. The most commonly used tools for DMPs are DMPTool and DMPonline. Both are free tools that include a variety of templates and guidelines based on funder and institutional requirements. You can also view published plans and share your plans with others. See Plan your Data Management for more information.

What tools can I use to process and analyze my data?

This depends on the goals of your project, the nature of your data and available resources. See Process and Analyze Data for a list of commonly used tools, recommended reading and useful support materials.

How do I name, organize and backup my files?

See Manage and Store: Top 5 Tips for Working with your Files

How do I manage and store my active data during the research project?

You can use Open Science Framework (OSF) to facilitate your research data management during the project. OSF is a free collaboration tool for managing data, files, code and other research information. Features include:

• Centralized location for all content
• Access and version control
• Integrations with third party services (e.g. Google Drive, Dropbox, Mendeley, Zotero, GitHub)
• Repository with persistent identifiers

FigShare, Mendeley Data, Zenodo can also be used to manage active data. All of these are free to use but the free versions have some limitations in file sizes and other features. They are also repositories that you can use to publish your final dataset after the project.

What do I have to publish after the research project?

This depends on funder and institutional requirements. Many funders require you to publish at least the part of your data that is necessary to verify the results of your research. Usually this means the microdata (individual level responses, measurements or observations) before the analysis has been applied. This dataset should then be described with metadata to enable other researchers to access and utilize it in their own research.

You are usually not required to publish all your raw data or the final aggregated results (macrodata, e.g. tables, figures) that you have already published in your research article, but sometimes this may be required as well. See Discover and Reuse Data: Data Types for more information.

When publishing data, you should consider two things:

1. What data is required to reproduce or validate the results?
2. What data could be useful to others?

What about sensitive data?

Always share your data responsibly! You should make your data "as open as possible, as closed as necessary" (EU Horizon 2020 Manual). Please keep in mind that all data (e.g. personal data, proprietary data and other restricted or confidential data) is not meant to be published, but you should always try make at least a description of your dataset available. This way other researchers and other interested parties know that such a dataset exists and can contact you for more information. You can make the description available in ZU Scholars.

Where can I publish my data?

Some funders may have specific repository requirements. Otherwise you can publish your data in a general repository (OSF, FigShare, Zenodo, Mendeley Data, Harvard Dataverse) or ZU Scholars. If you have a larger dataset, please contact scholars@zu.ac.ae before submitting files to ZU Scholars. See also Discover and Reuse Data for other repositories.

You should also consider publishing a data article of your dataset and data collection processes. Data articles are peer-reviewed, citable papers that describe research data without analysis or conclusions. Data in Brief, Scientific Data and BMC Research Notes are well known examples of data journals that publish data articles.

How do I cite a dataset?

If you use a dataset published by others, you should cite the dataset just like you would cite an article. If you have published datasets yourself, you can even cite your own dataset to enable readers to find and reuse your data. Use the citation style specified by your publisher. If no style is specified, include at least Authors, Publication year, Title of the data, Version ID, Publisher, Digital Object Identifier, Access date and time.