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Validation of Experimental Data Origins: A Swarm of DAQ devices able to Deliver Unique Experimental Data using Blockchain‐like Fingerprint ID to a Data Repository

Miguel Tomas Silva edited this page Aug 17, 2024 · 88 revisions

Navigation | AeonLabs Main Index >> Open Scientific Research >> Real-time validation of Experimental Data Origins: A Swarm of DAQ devices able to Deliver Unique Experimental Data using Blockchain-like Fingerprint ID to a Data Repository >> Wiki >> Validation of Experimental Data Origins

Miguel Tomás1*, Kiera Tabatha2

1,2 Department of Computer Sciences, Blekinge Tekniska Högskola, 371 79 Karlskrona

Abstract

This paper discusses an innovative experimental setup and procedure for the automation and management of collected experimental data in real-time, compatible with any open environment. It was conceptualized and prototyped as a Swarm Learning (SL) architecture (hardware and software) as a fully decentralized algorithm principle to improve data trustworthiness. Conceptually, SL is a decentralized approach to validate and maintain a trustworthy database of experimental data in real-time, publicly accessible, through data redundancy, validation, and authentication of datasets across multiple smart data acquisition devices (SDAD) connected locally or remotely. Every participating site is a node in the Swarm network with the purpose of performing data validation and authentication tasks by sharing local hardware resources. Data trustworthiness and sovereignty are ensured with the real-time generation of a Unique Data Fingerprint Identification (UDFID) for a single experimental data record. New SDADs can enter a Swarm Network via a blockchain smart contract, regulating access and operational conditions in a fully autonomous way. New Swarm nodes agree to the collaboration terms, obtain the model, and perform local validation and authentication until all tasks are completed. This allows the acquisition of much larger experimental datasets, validated and authenticated publicly, while at the same time making them available for analysis from sources outside the primary scientific research of a given specific site, and offers new opportunities to overcome the limitations of collaborative work in science by enabling any research site to easily connect and join a swarm network, increasing experimental data trustworthiness from unknown sources.


Keywords: Live Experimental testing; Open Environments; Data Authenticity; Data Repository

Suggested Citation Miguel T., Validation of Experimental Data Origins: A Swarm of DAQ devices able to Deliver Unique Experimental Data using Blockchain-like Fingerprint ID to a Data Repository (July 23, 2024). Available at http://dx.doi.org/10.2139/ssrn.4210504

Status: work in progress document revision: 11-05-2024 Original document: MS Word docX with embedded 360 videos
*Corresponding author: +32 471 632 520 (WhatsApp only)
short URL address to this document here on GitHub: https://tinyurl.com/ValidationDataOrigins


Index

  1. Introduction
  2. Open Scientific Data
  3. Experimental Data Authenticity and Trustworthiness
  4. Swarm Intelligence
  5. Proposed Smart Data Acquisition Device
  6. Conclusions
  7. References and Symbols used


CRediT Author Statement
Miguel Tomás (https://orcid.org/0000-0002-5910-5179) Conceptualization, Methodology, Hardware, Software, Data curation, Visualization, Investigation. Validation, Writing, Funding, Kiera Tabatha (https://orcid.org/0000-0003-2656-4798) Writing, Reviewing and Editing


Data Availability
Data used to support the findings of this study are available for anyone to download and use on the following dataverse: https://dataverse.harvard.edu/dataverse/MiguelTomasMainDataverse. PCB electronics hardware design KiCad files are available on GitHub with a Creative Commons License: https://github.com/aeonSolutions/openScienceResearch-Smart-DAQ-Device-able-to-Upload-Live-Experimental-Sensor-Data-to-a-Data-Repo


Declaration of generative AI and AI-assisted technologies in the writing process
During the preparation of this work, the author(s) used the Grammarly Extension for MS Word in order to Mitigate possible plagiarism on copy and editing from the internet. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.


Declaration of Competing Interest
To this date, the author declares to have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


About the Author
Miguel Tomás has been researching and implementing technology solutions for start-up businesses and public institutions for the past 20 years. To learn more about the author, connect to his LinkedIn profile page using the following web address: https://www.linkedin.com/in/migueltomas/


A final statement about Mental Health in Science
It is found recurrently on the internet news and comments about another scientific researcher, in particular junior and medior researchers, going through some kind of mental health difficulties in particular those associated with sensory overload events. Promoted by abusive usage of wireless devices, assembled or modified to actuate at a distance and invisible, causing harm to a victim’s neurobiology. This is an old area of knowledge, that now needs to be openly discussed and commented, in a peaceful constructive way towards the identification of solutions to all abusive usages of wireless frequency waves (radiation or vibration) in electronic devices. Open solutions are the correct path toward safety in the usage of all things related to such technologies. Be knowledgeable to stay safe.



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