Reflecting Complete Research workflow and Best Research Practices in the form of Badges

by Team Profeza

CREDIT is a cloud-enabled SaaS tool for data management to provide an opportunity to authors to register their Additional Research Outputs(AROs) reflecting RAW, REPEAT & NULL/NEGATIVE entities generated at various stages of research workflow to ensure their reusability & gaining credit. Hence contributing towards enriching research articles & reproducible science. CREDIT framework & interface is developed on FAIR data principles. The generated AROs are segregated into the following categories:

Categories:

  • RAW Data

Includes detailed methods, protocols, troubleshooting steps and also the unprocessed preliminary raw data files underlying the results that were shown in the main article. Reflecting the efforts/practices put in by authors to derive the results and analysis leading to seminal findings - this would ensure that the data are Re-usable and reproducible.

  • REPEAT Data

Includes the unprocessed data/observations retrieved while repeating the experiments(replications) - This would ensure that the preliminary data points are reproducible and aids in enhancing the article’s authenticity and also ensures to rule-out any Scientific frauds/misconducts. It also reflects best research practices put forward by authors.

  • NULL/NEGATIVE Data

It includes the experiments & observations that author has obtained during the course of research study not complementing the main hypothesis. Gives authors an opportunity to disclose any null/negative data points which they have generated - Which might save resources/time of other researchers, also provides a way to recognise additional scholarly outputs and gives funders an insight on broader spectrum of the study.

Complete Freedom to Authors for Sharing the Data

Authors’ have full freedom & control over sharing AROs as they wish in context to the research article under communication via CREDIT tool in aforementioned categories.

How it Works for Publisher's

  • Pre-registered DOI’s will be assigned to the shared AROs (individual preliminary data points, troubleshooting and methods),
  • These AROs can be used by Peer-review managers to authenticate the claims made by the author in the research article under communication.
  • Editor conveys the message of acceptance of research article & AROs(which is inbuilt into the tool)
  • The respective badges with underlying AROs would go live alongside the research article on the publisher’s website
  • If the Author submits only Raw data and Repeat data only these badges would appear and if he also does share any Null data, Null/Negative results badge would appear. PS: Also if Authors are not willing to share their data on Credit no badges would appear. 
  • A Single line magic-Script code that will be placed on the Publisher’s website & would take care all these possibilities without impacting the infrastructure of Publisher’s portal & workflow.

CREDIT Badges are Dynamic!

The appearance of these badges happens dynamically, hence creates a possibility that the metrics around the data, when readers engage with it would be fed back to the main published article in real-time (accessible via the badge - Enhancing Discoverability and also giving credits to Authors). And in the near-future we also have plans to roll out Badges that can be embedded in PDF articles. 

USP's of CREDIT Badges & it's proposed workflow

In contrast to other open-data inititatives CREDIT supports the improvement of the quality and completeness of the data (In the form of a data-article) associated along with the main article rather than simply acknowledging that the data have been uploaded to a repository. This ensures that the published articles are more reusable and reproducible in a more sustainable way. 

Exploring Pilot Partnerships

We are actively looking forward to partner with Publishers, Academic Societies, Institutes, Funders and other Startups who are willing engage in pilot with CREDIT to explore the opportunity of enriching the research articles with AROs & giving granular scholarly credit to their authors. Hence paving a way towards more re-usable & reproducible research outputs.