Impact data is part of data collection and mining, and these are commonly used methods of collecting information for research and development in industries around the world. The Digital Era has brought advanced ways of tracking metadata when users don’t even realize they’re being tracked. Even though this is a widely accepted practice, the use of artificial intelligence tracking is very contested, making its way into legal arenas everywhere regularly.

Changing how research is performed with the Digital Era is a necessity, but reusing data in the field of science is always a balancing act of integrity combined with the importance of understanding the research in question. When it comes to collecting qualitative data, reusing shared impact data is sometimes the only way to get the necessary information to perform the study.

What is Impact Data?

There are two basic types of data that are collected through data mining processes: impact data and performance data. There is not a lot to be contested about the collection of performance data. It measures quantitative factors that are relevant but don’t cause a lot of ways. How many students are in a school, how many instructors teach a subject, and how many books are available in the resource library are all different types of performance data that could be used in research.

Impact data, on the other hand, looks for qualitative measures that demonstrate changes in knowledge and skills. The data collected here is often used to judge a person, place, group, or organization, and therefore it is more subjective and contested.

The Benefits to Reusing Qualitative Data

New knowledge is gained daily and then stored in repositories where it is archived until it is ready for use. All these resources and data are analyzed through evolving methods of data management and reuse so that future generations can access them.

As with all fieldwork, though, researchers must deal with answering questions before they can access the data, such as:

●      How will the samples be obtained?

●      How will the data accessed be managed once it is available, and then how will it be securely stored until it is ready to be disseminated to the public?

●      What methods will be used to determine if the data that is generated is sufficiently able to be used as legitimate evidence to inform the public without revealing sensitive information that was supposed to be anonymous or concealed when the data was collected

These concerns regarding repurposing and reusing data focus on qualitative, subjective thoughts about the context of the research, the reasons behind the data collected, and the method that was used to collect the information in the first place.

Researchers are expected to review all of these categories and look for methods of co-production so that their own insights can be used further without the need for the sharing of data beyond the immediate circle of use.

It’s a necessary step to make sure the data collected is used strategically, taking care to critically evaluate any further possible ways that the research obtained from the data could be shared extensively without compromising the information that was given to the original sourcing scholar. Sharing results is different than sharing the data itself, and impact data in particular, because of the qualitative nature of it, is questionable and sensitive. Reusing it for further research without the express permission of the owner of the data is never a good idea if you want your work to be transparent and considered to be reputable.