Peer reviewing plays an important role in academic publishing process that scrutinizes and provides feedback of the scienti c work prior to publication. Peer-reviewers put their e orts in reviewing others research work voluntarily, without any expectations of incentives or rewards. The peer-review process has been criticized for its defects like slowness, bias and abuse of the process. In this paper, we present a model to address these issues by using the approach of recording peer-review data on the blockchain. By using semantic web and linked data technologies, this system would be able to expose its data, and interact with other systems. This system will be used to quantify, recognize and incentivize the peer-reviewing e orts by researchers.
Peer-reviewing is the activity when researchers assess the scienti c work of other
researchers before publishing. The process of peer-reviewing is questioned and
criticized for the identi ed defects in it [
Researchers spend their valuable time in peer-reviewing that somehow, is less recognized and quanti ed by the community. Such e orts of researchers should be recognized, quanti ed and incentivized at some public platform.
There is a need of de ning the actual and potential requirements of the
peerreview process, along with a model to address the defects of the process as well
as to incentivize peer-reviewers.
The modern form of peer-review process used by journals, conferences and
publishers is in uenced by the review process adopted by the Royal Society of
Edinburgh in 1731, when publications were inspected by a selected group of
knowledgeable members [13]. Peer-review is at the heart of the processes of science
[
Researchers should get incentives for their peer-reviewing e orts, and these e orts should be quanti ed at some platform to acknowledge researchers' peerreviewing contributions. Such platform should ensure transparency and fairness in the process. By bringing improvements in the mechanism of peer-reviewing and making it transparent, fair and attractive for researchers, it will have a constructive impact on research and science. Researchers would be more motivated to do peer-reviewing actively. This linked data of peer-reviews, comments and incentives, can be exploited and used by other scholarly applications. 3
In this section peer-review patterns, their problems and incentive models for peer-reviewers are discussed. 3.1
Peer reviewing practice follows a number of patterns, for instance, in [
Closed Review: This process is categorized in two ways, single blind and double blind. Single blind process ensures that identities of reviewers remain unknown to the authors, while in Double blind process, in addition to the withholding of peer-reviewers names from authors, the names of the authors are not revealed to the reviewers. However, peer-reviewers can identify the articles after they are published.
Open Review: Open peer-review process consists of publishing the manuscripts along with the reviews they get, to ensure the transparency in peer-reviewing. In open peer-review, names of the authors and reviewers are mutually revealed, to avoid the potential bias. 3.2
Smith [
Slowness in the process: There are many journals take even more than a year
to review, accept and then publish a paper [
Prone to bias: Traditionally, peer-review scores are not made public [15] therefore, there are high chances of occurrence of bias. Wenneras nds strong evidence of gender bias against women in the process of peer-review while awarding nancial grants [15]. According to the study, peer-reviewers deemed women to be de cient in scienti c competence [15].
Abuse of the system: Peer-review process can be abused at many di erent
levels. From stealing research ideas to producing harsh review to block the
research progress of competitor, many researchers have been victim of the abuse
[
Fake peer-reviews: Haug, explains about the 64 retracted articles from 10
different journals by the publisher Springer. Haug further mentions about a South
Korean researcher Hyung-in Moon, who admitted to have email addresses so
that he could provide peer-reviews of his own articles using those email and fake
identities. Through such editorial checks, more than 250 articles were retracted
because of the fake-reviews, about 15% of the total number of retractions [
Publons1 is a platform for peer-reviewers where researchers can add their review records to their pro les by forwarding their review receipt email to Publons. It also allows journal integrations through a formal partnership of journals with Publons. This platform is exposed to the potential abuses listed above. 3.3
There have been several approaches to incentivising peer-reviewing, some of which are listed in table 1 below. To improve the peer-review process, punishing the peer-reviewers is also brought under discussion.
Gropp [
TheWinnower is a platform that publishes post-publication peer-reviews and
is exploring incentivizing peer-reviewers to highlight their work by elevating the
review report to the level of an original research publication [
ReviewerCredits 2 came up with an idea to give credit to peer-reviewers. Peerreviewers would have to contact ReviewerCredits which would contact the journal concerned for veri cation. After receiving veri cation, a peer-reviewer's pro le is credited with ReviewerCredits.
Punishing the peer-reviewer submitting after deadline, is suggested by Hauser
[
Q1 : What are the actual and potential requirements of peer-review processes? (e.g., open, closed, blind, double-blind, conferences, journals etc.)
Q2 : How are currently implemented processes addressing and failing to address these requirements?
Q3 : How does recording data and incentivizing peer-reviewers address and fail to address these requirements?
Q4 : How can currently implemented processes be compared with the blockchainbased processes in terms of addressing the requirements answered by Q1 ?
Q5 : By knowing that the peer-review data would be recorded permanently, does it a ect the researchers' behavior in terms of: i Papers they accept ii Papers they reject iii Papers they rate 5
By having above research questions, we derive hypotheses as:
H1 : Blockchain-based data-recording model for peer-reviewing process keeps the data tamper-free, traceable and immutable.
H2 : Blockchain-based incentivizing model for peer-reviewing process ensures fair and transparent distribution of rewards.
H3 : The features of immutability and transparency ensure tamper-free and traceable data. Therefore, researchers would be more careful in terms of accepting, rejecting and rating papers. 6
We are in a process to annotate the open-review data from ESWC. We have also deployed Ethereum4 based blockchain in our private network of the institute. We have written and implemented the SmartContracts for Authors, Reviews and Submissions from the ESWC-20185. Our next step is to implement customized ERC20 Token6 SmartContracts for researchers. 7
We plan to implement Ethereum [
The approach would be to annotate the peer-review data by reusing
Conference [
E1 : Are actual and potential requirements of peer-review process addressed by currently implemented peer-reviewing processes?
E2 : Does blockchain-based data-recording model ful ll these requirements and add the promising features (i.e., transparency, immutability, decentralization) to the peer-review process?
E3 : Does blockchain-based incentive model for peer-review process ensure the transparent and fair distribution of awards amongst researchers (peer-reviewers)?
E4 : Comparison between currently implemented peer-review processes and blockchain-based peer-review process in terms of the number of:
Existing approaches to incentivize peer-reviewers are based on centralized and manual systems requiring trust in third parties. Apart from the idea of a lottery fund for researchers, these models o er no real-world value incentives. While we mentioned about the defects in peer-review process and identi ed that we can address these issues by developing a system based on the linked-data and blockchains technologies that ensure data integration, re-usability of data, security and transparency.
Bene ts of peer-review process on Blockchain { Transparency: Blockchain o ers transparency in recording transactions in the network to make them accountable and audit-able. In peer-review system, the impact of transparency would strengthen the importance, reliability and trust in peer-reviews. With distributed database technology, each member in the network would be able to lookup, trace and validate any record of peer-review submissions and incentives given to peer-reviewers. The novel concept of immutability of data in blockchain would ensure the sanctity of records and prevent any fabrication or tampering of data. { Decentralized data: Blockchain follows peer-to-peer network infrastructure where the network peers(nodes) validate, store and reconcile data. Consensus is a concept of reviewing such transactions and taking decisions about their validity. By implementing peer-review system on blockchain, each node of the network would have the same copy of data which makes it persistent, incorruptible, secure and immutable, in the presence of Consensus. That safeguards the data from hacking attacks and attempts to manipulate it. { Trust in peer-review process: Features of blockchain ensure immutability, persistence, security and publicly availability of data. These features would enhance the trust in peer-review process. { Recognition: With the implementation of peer-review process on the blockchain, the contributions by peer-reviewers can be quanti ed as the recognition. Peer-reviewers would get recognition based on their reviews and rewards available publicly. Journals and conferences can also announce the best reviewers of the year title awards to the researchers. { Preventing possible abuse: Currently, peer-review systems are implemented by some centralized third-party entities that share the access to data with conference/journal program committee. The centralized-stored data can be abused potentially. Implementing peer-review system on blockchain can help in preventing any possible abuse of authority over a reviews database. Researchers can be veri ed through their o cial (institutional) emails, hence it would be very di cult to create multiple identity. In case they switch their institutes (they can add or amend their email addresses) system can detect the duplicity of a record based on name and date of birth of a researcher. { Less bias: Immutable reviews and comments are publicly available, so reviewers would want to come up with a valid and clear stance about the article, hence it would help in reducing the bias in the reviewing process if any. { Improved quality of the scienti c output: Encouraging timely and wellthought unbiased reviews will improve the quality of the scienti c output. Fair and transparent incentive model can help in increasing motivation for peer-reviewing. { Rewards: By participating in the public platform, authors, reviewers and commentators would be entitled to be awarded by ScienceMiles. These ScienceMiles in the form of a digital currency, are the acknowledgement by the research community, for the research community. { Large pool of peer-reviewers: This system would gather a large pool of well-reputed (veri ed) peer-reviewers, that would be helpful for journals to nd the suitable peer-reviewers for their publications.
I would like to thank Prof. John Domingue and Dr. Allan Third for supervising and guiding me through my PhD research. 13. R. Spier. The history of the peer-review process. TRENDS in Biotechnology, 20(8):357{358, 2002. 14. M. Ware. Peer review: An introduction and guide. Bristol: Publishing Research
Consortium, 2013. 15. C. Wenneras and A. Wold. Nepotism and sexism in peer-review. Women, sience and technology: A reader in feminist science studies, pages 46{52, 2001.