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Last update: 20-09-2024
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🔸 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
current project Total: > 8100 work.h
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Current scientific methods use traditional dataloggers (DAQ) to collect and measure experimental data. This means collected data many times is stored in a paper format, and most of the time in a conventional CSV Excel data file. This is prone to errors and even worse, forgery of experimental data. To this date, no dataloggers can automate experimental data acquisition in a scientific experiment in such a way that is possible to do remote validation of its origins, making it less transparent and less trustworthy.
The overall objective for this open project is to provide open hardware electronics for sensor data measurement acquisition capable of being used in a controlled environment, such as a scientific research laboratory and also outside, on-site. These smart DAQs have the capability to generate a Unique Fingerprint ID on all sensor data measurements collected for a more Trustworthy and authentic collection of experimental data. Dubbed S.D.A.D., these devices have the required specifications and capabilities to enable real-time validation of Experimental Data Origins. For a more detailed explanation of how this smart device is used please navigate to the document (paper) currently being written "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 Repositor".
This project also highlights the importance and requirement for open hardware electronics to have minimum hardware specifications to allow advertising and identification by unknown third-party individuals and authorities, in real time and during the operation of a device. This minimum hardware specification for microcontrollers is one that will enforce improved safety and security of hardware electronics in society in everyday usage, in particular smart and intelligent devices capable of interacting with the environment and/or capable of interacting with machinery. An example easier to understand this need, and safety requirement, is autonomous vehicles.
In short, the open hardware electronics being prototyped and the open firmware code being programmed by this project allow...
- real-time authentication of a sci. researcher using an official researcher ID (for instance ORCID), before starting to collect sensor data on an experiment;
- a scientific researcher to remotely audit experimental data records directly from an Excel workbook using Excel's Power Query functionality;
- third-party sci. researchers and teams to remotely audit experimental data records directly from an Excel workbook using Execel's Power Query functionality;
- Editors for sci. Journals to follow an ongoing project while at the same time audit, remotely, experimental data records produced;
The major advantages of sci. researchers using this smart DAQ on their experimental campaigns are:
- publish in higher-ranked scientific journals;
- scientific researchers from outside academia and sci. research made by enterprise businesses to publish in high-ranked scientific journals;
- Enable businesses and institutions to use collected data in cases of legal dispute and court of law;
The hardware electronics being prototyped on this project have the main objective to research safer smart hardware electronics, with the ability to self-identify to other nearby devices and also when a human queries the surrounding environment for IoE devices nearby. The main objective focuses on the requirement for microcontrollers to have part of the IC a minimum number of sensors, to allow automatic self-identification when queried to do so, and at the same time, to allow validation of sensor data and exchange with other devices in more trustworthy type of way. When a built-in sensor into the microcontroller IC starts to malfunction, and self-identification is no longer possible, it will automatically disable the malfunctioning hardware electronics where it is installed.
Regarding regulations and legality in science, the minimum hardware specifications of the smart data acquisition devices prototyped "ensure that cybersecurity elements of research US security programs meet the objectives" as defined in the NSPM-33 guidelines for scientific research[2], page 20, by requiring the researcher ID of the person responsible to oversight before starting an experiment, for instance, ORCID [1], and during collection of sensor data values from samples\specimens on each data record creation appended with a "Unique Data Fingerprint Identification" token string. Since these devices are open hardware electronics and also open firmware it allows developers in the IT department easy and simpler integration with existing systems and network architectures in academic institutions and research departments of enterprise business.
- Expected impact of this project on Scientific Research and Businesses
- How this project contributes to advance open technologies and open solutions
- Operational mode of the Openness of Processes, and Foreseen Contribution to Open Scientific Research
- Workplan with the envisioned timeline and milestones ( deliverables / reports / participations etc.)
- Assembly Costs and Hardware Testing
- Measurable KPIs
-
Open Hardware Electronics
- Smart DAQ Specifications
- Setup and Configure the Smart DAQ
- Data Measurements
- MS Excel Power Query Tutorials on YouTube by Leila Gharani
- Jupiter Notebooks tutorials on YouTube by Roshan
- How to code in Python on YouTube by Programming with Mosh
- 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
- Research and prototype Hardware Electronics with Built‐in Unique Data Fingerprint Identification for Safer Internet of Everything Solutions
- Open & Trustworthy Publishing: Open Data Acquisition Devices Able to Deliver Unique Experimental Data to a Scientific Journal in real-time
One of the grand challenges of artificial intelligence is developing agents capable of conducting scientific research and discovering new knowledge. While frontier models have already been used to aid human scientists, e.g. for brainstorming ideas or writing code, they still require extensive manual supervision or are heavily constrained to a specific task[1]. When S.D.A.D. DAQ devices are connected with AI-Scientist type of algorithms will allow further automation with automated inclusion of experimental data into the document being written by AI. In short, it will be possible to automate the analysis and discussion of experimental results, and in the end, a scientific researcher will only need to perform document revision before submitting to a journal.
Research Laboratories currently scheduled to use these open software and open hardware technologies:
- ⌛ Civil Engineering Department of Federal University of Santa Maria, Brazil;
- ⌛ Civil Engineering Department of Federal University of Santa Catarina, Brazil;
- ⌛ Civil Engineering Department of Beira Interior University, Portugal;
Want to join this open science project and start using these technologies?
Send an email to [email protected] or a WhatsApp message to +32 471 632 520.
Please include the link to your LinkedIn profile in your message. Thank you.
[1] Case Study: Stanford University Integrates ORCID into a Centralized Identity Management System
[2] NSPM-33 guidelines for scientific research
[3] The European Code of Conduct for Research Integrity
[4] Integrity at the Heart of Healthy and Effective Research Cultures
[5] Practical Guide to Supporting Diversity in Research Environments
[6] Australian Code for the Responsible Conduct of Research
[7] Guidelines and codes of conduct in Japan
- Data Repositories
- Open Science Networks
- Open Science Projects Worth Mention
- https://www.scienceeurope.org/
- Japan Science and Technology Agency - research Integrity Portal
- Chinese Academy of Sciences
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