Obviously, the data collection process is the initial stage of every intelligent system which intends to perform behavioral analysis and consequently expect the deterministic or non-deterministic outcome. Here, a major challenge will be the tediousness of the data collection process itself. As an example, if a system requires data to be collected from multiple subjects based on different parameters and in multiple trials, the time needed to be allocated for data collection will be considerably enormous. We will consider this as a challenge of time.
Data collection is one of the most expensive stages of any project or system. To elucidate more, a known type of cost will be transportation cost of the subject from her origin to the center of data collection during observational data collection. During experimental data collection, researchers need to be involved and perform measurements during the procedure. This also requires arrangement and involvement of the researcher that will not be free in most cases plus the cost of measurement equipment. During simulation, the cost of the simulator is applied, and it is variable depending on the context of the study or experiment. There will be a major cost of participation payable to the participant to motivate them to involve into the process of data collection. This is classified as a significant cost.
In practice, the data collection process, regardless of in which context the data being collected, involves interaction with subjects (either human subject or nonhuman). In the medical field, for instance, this interaction needs some sort of medical-related skill sets, expertise of clinicians and equipment while data is being collected. But a computer science researcher who is actively doing research in the medical domain and trying to create an intelligent system based on collected data does not necessarily need to hold that expertise or access to some special equipment like EEG devices, etc. Therefore, either cost of collaboration between clinician expert and researcher should be affordable or the project might have been affected by time delay due to lack of expertise and equipment. In fact, this is a matter of both time and cost.
Solution for Time and Cost challenges
Based on above mentioned time-related challenges, what would be the potential solution? Zarela has implemented a system to enable Angels (volunteer participants) and former researchers (or research institutions) to act as participant entities and contribute to supply bio-signal data sets. They can share their bio-signal data sets through Zarela’s platform (by Zarela’s smart contract) and significantly reduce the time of the bio-signals collection in projects with homogeneous data modality or similar context.
For example, Mages (a researcher or any research institute) could be connected to the Hubs and acquire the bio-signal data sets (center or labs that are equipped with facilities and devices to record bio-signals) regardless of any geographical limitation. This capability of the Zarela platform can help the Mages diminish the time of bio-signal data collection while ensuring secure exchange of the bio-signal data sets.
Another advantage would be cost reduction of the data collection process by providing capability to exchange data sets (bio-signals) by Zarela’s token (BioBit) on the blockchain. It could better motivate the Angles to participate in the data collection process and let them invest in BioBit digitized assets. In terms of cost, the borderless feature of Zarela’s platform for data collection would be an advantage to gather bio-signals from Angels (those will receive their contribution rewards by BioBit tokens) who will provide bio-signals at a cheaper price.