Data management is vital to making data discoverable, accessible, and understandable, and making things discoverable, accessible, and understandable is a key part of what researchers do. Let us dive a little deeper into understanding Data and why proper data management is needed.

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How important is Data Management for Qualitative Research?

Data Management The management of research data is a service area that is getting more attention from libraries. Many librarians have begun to provide a variety of services in this area and now impart knowledge on data management to researchers to improve the following things:

• Data Management Practices

• Create Data Management Subject Guides

• Assist in Supporting Funding Agency

• Publisher Data Requirements

What is Data, and how do I understand it?

To understand, you must first understand what Data Management is. To put it simply, data can be defined as facts and statistics organized for reference and analysis. If you look at it from an Information Science perspective, data can be defined more in relations to the scope of research, meaning that data is:

• Collected

• Observed

• Created

For the purposes of examination to produce original research results. It is essential to recognize that data goes beyond worksheets of numbers and can take countless forms: Biospecimens, video/audio recordings, pictures, and procedures. It is perhaps most useful to think of data as everything necessary to replicate a given scientific output.

Good Data Management

Let us dive a little deeper into understanding Data and why proper data management is needed. The first step is that we must recognize that data is neither static nor isolated. Data is processed and analyzed by combining different measurements or different data types, which makes a story.

A simple example is a researcher gathering of magnetic resonance imaging (MRI) data from many patients in a clinical trial focusing on the before and after treatment when using a specific drug. The MRI images would then be handled in some way, which could be done by measuring tumor size to producing a set of numbers.

The breakdown of the analysis would involve linking information about the change in tumor size, the dosage, and length of treatment. Then this information could be used to produce a figure in a published article, in which that figure communicates how well the drug therapy worked. If the researcher were also gathering blood samples, recording vital signs, or testing for biomarkers, the story would become more complex. Using many subjects with multiple data types in the research process creates a considerable amount and range of data that needs to be accounted for and organized

Meaning and Need for Research Data Management

To give you an idea of the purpose and need for Research Data Management. Imagine the data from such an experiment could be composed of:

• A folder filled with MRI image files or perhaps multiple folders filled with MRI images for an individual study participant

• Data regarding treatment timing and dosage stored in spreadsheets or possibly in paper forms

• Spreadsheets of Processed Data

• Final analyzed data used to create a figure for publication.

Say a researcher happened to be called upon to produce raw data that was used to create a published figure with the information above, this task would be challenging without proper data management, if not impossible. By using descriptive names for variables, for example, Tumor Size, putting the weight in kg. That communicates what they represent, descriptive titles for files and folders, which makes it clear what is contained therein, and a saved study workflow that describes the analysis methodology. All of these seemingly small details are types of data management.

Data management ensures that the story of a researcher’s data collection process is organized, understandable, and clear. The notion of the data lifecycle is often used to help researchers understand the scope and meaning of data management. Data management needs to fall within the first three stages of this lifecycle: creating or collecting data, processing data from its rawest form to another form for examination, and analyzing the data so that the results can be passed on as some form of academic output, such as a journal article.

All three of these stages require data management to ensure that the researchers document how they collected their data and how they transformed raw data into analyzed data, and to guarantee that the data is described in a clear way. If the data is clear, it can be used by other researchers to test the strength of the original results or to reanalyze the original data differently.

Data management is vital to making data discoverable, accessible, and understandable, and making things discoverable, accessible, and understandable is a key part of what researchers do.