Frequently Asked Questions

Knowledgr is a fully transparent, community driven platform designed to empower scientific content creators. Using blockchain technology not only can we create a completely open, fair, honest, reviewable network of all scientific workflows but also for the first time ever create a network where the contributors accrue value through the quality of their content contribution. This in itself is revolutionary and one of the many reasons we decided that there was no better approach for a sustainable, open science platform.

Why have a blockchain?

Knowledgr's central thesis is that use of bibliometrics as a measure of a quality is the biggest problem within academia today. The downstream result of this practice is an incentive structure which encourages academics to participate in unhealthy research behaviors out of self-preservation.

In theory, the solution to this problem would be a metric which 1.) accurately represented a research output's scientific worth and 2.) incentivized efficient and equitable behaviors among the stakeholders within academia. Unfortunately, a perfect metric will likely never exist and we are confident that any attempt by a centralized power to make an improved metric will end up resulting in similar unintended consequences.  

The next best option is to empower the scientific community to come to a consensus over how the quality of research outputs are measured. A blockchain is a powerful tool to accomplish this because it allows participants within a community to establish consensus in a decentralized, equitable manner.

Knowledgr uses an open-source, public blockchain powered by a social-consensus algorithm that is designed to distribute tokens to research outputs based upon the community's perception of any given output's intellectual value. Because the blockchain is public anyone will be able to access the entire history of user interactions that occur within Knowledgr. Our hope is that this information will give the scientific community the required dataset from which to create a dynamic, evidence-based metric that constantly evolves to incentivize healthy research behaviors.

What is an evidence-based metric?

In order to accomplish our mission of crowdsourcing scientific research there are aspects of the platform that must be evidence-based. 

The goal of the social-consensus algorithm is to set incentives that encourage healthy research behaviors (i.e. sharing experimental data as soon as it is produced). The Knowledgr team has suggested an initial format based on our own hypotheses, but we expect this to evolve as our community builds evidence in the form of user interaction data. Every six months we plan to encourage a hard-fork (code update) to improve the algorithm based on the existing evidence. Eventually, our community will be able to self-govern updates by observing our openly published user interaction data and hypothesizing improvements designed to increase the quality of science being produced. 

Won’t this new metric eventually be gamed too?

The goal of this evidence-based metric is not necessarily to “measure” the value of research outputs, but instead to incentivize healthy research behaviors. Our hope is that we will encourage users to game the system by publishing openly, collaborating, and building upon each other’s work.

In order to ensure this metric is maximally incentivizing these behaviors every six months the Knowledgr blockchain will undergo regularly scheduled “forks” (code changes) to update the social consensus algorithm. The goal of these forks will be to incorporate evidence-based alterations sourced from the community with the goal of more effectively encouraging healthy research behaviors. 

Level of Expertise

No formal academic STEM training

Undergraduate STEM degree

STEM publication – Any author

Graduate STEM degree

1st author STEM publication

STEM PhD Degree

>1 1st author STEM publications

STEM Post-Doctoral degree

STEM Principal Investigator

STEM 1st author publications with > 20 citations

Previous open-access journal publication or pre-print

Expertise Rank











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How is the expertise rank determined?

A user’s expertise is determined from their real-life academic contributions. When a user signs up for Knowledgr, they submit proof-of-identity documents such as a univverity transcript/email or example of a published paper. Once these documents are uploaded, new users are assigned expertise scores in specific fields of science according to the following chart. Eventually expertise will be dynamic based upon the value of the content generated by a user in any given field of science. 

We understand that it may appear hypocritical to claim that bibliometrics are the root of the systemic issues in academia while simultaneously incorporating a researcher’s publication history into their Knowledgr expertise rank. While we strive for a more accurate measure of scientific value, we recognize the role that citations play in today’s academic climate and we want to honor the hard work and achievement of researchers in the current system

What is the weighting procedure of the votes?

Each post is tagged with a field of science - a user’s vote weight on that post is dependent on their expertise in the field of science being discussed. Eventually Knowledgr will allow posts to be tagged by multiple fields of science. 

Upvote weight = ER^3 - A user with a graduate stem degree casts 64 votes, while a user with an undergrad degree casts 8 votes.

What are the licenses of the posts? 

The license on all of the posts will be a CC BY 4.0

How was Knowledgr funded?

Our investor is MouseBelt. They are a blockchain accelerator managed by No Rest Labs and NueValue Capital. In exchange for a 15% equity-share and 8% of the initial tokens (both subject to vesting) MouseBelt has provided Knowledgr with $250k of engineering time along with business development services. 

MouseBelt has been very supportive of our mission thus far and did not ask for a board seat in the terms of their investment and therefore have no direct influence over Knowledgr’s decision making as an entity. 

MouseBelt and any other future equity-based investors will be asked to agree to terms supporting Knowlegr’s for-science mission. These terms will be published for community criticism.  

What is your sustainability model for the business?

Within Knowledgr, patrons of science can use KNLG to increase the number of tokens awarded to a specific field of science, or demographic of scientist.

If I am interested in incentivizing early career oncology researchers in France – I could create a fund of KNLG that is rewarded to posts tagged with ‘Oncology’ authored by French users who are below the age of 35.


Our value proposition to funders is that we are able to reduce their administrative costs of grant-giving while increasing the total value of science produced per funding dollar. Knowledgr’s social consensus algorithm will be able to more efficiently and objectively distribute capital compared to the traditional research granting committee. Currently, funding institutions spend between 5-12% of their budgets administering grants. In comparison, Knowledgr will retain 5% of the tokens that are donated to scientists through our algorithm as revenue. 

As the platform grows, Knowledgr will reduce the 5% fee, but initially it is a necessity to cover all our overhead and growth plans. In the interest of transparency we will publish all of our financial information for our community to openly criticize and hypothesize improvements.

How is the information posted also archived?


Text post and user interactions are stored openly in our blockchain. Images will be hosted within an Amazon S3 server. We encourage researchers to take advantage of existing tools like Github, Figshare, Open Science Framework, or Zenodo to host their datasets and submit a reference URL within posts that will then be stored on the Knowledgr blockchain. 

Can users re-post articles from other open-access sources?

The best part about the open science community is how easy it is to interface with the existing tools in the space. We plan to plug into the APIs of Figshare, Open Science Framework, and to help seed observations. In addition, we are exploring the process of converting PDFs from various pre-print servers to import existing hypotheses. Finally, users are also welcome to find other sources of open-access content and share the science within Knowledgr. 

If you are a researcher who has previously authored an open-access publication and you find that someone else has already posted your article within Knowledgr, let us know. We have set aside 2% of our total token supply to compensate academics who have previously published open access content that has been posted by another user within Knowledgr.

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