One of the many challenges facing the academic community with respect to open data and open access to research, is the peer review process. The basic premise of the peer review process is to ensure integrity of research and to provide much needed interrogation of ideas, theories and conclusions. However, it can also become subject to a black hole of bias and professional rivalry, leading to unfair and unreasonable rejections. An editor might have turned it down summarily without review. A referee might have demanded a futile and time consuming extra analysis. Or a rival might have sat on a manuscript for months, consigning it to limbo under the cloak of anonymity.

Here at asencis, we believe in removing this cloak of anonimity - allowing researchers to request reviews, as well as allowing their reviewers comments to be publicaly available and attributable. Peer review underpins the academic publication process, but in recent years has been much criticised. We believe that by providing open peer review of datasets through publicly posted dataset interpretations is the solution. This is what we are refering to as "Open Peer Review".

It is therefore out policy to ensure that all interpretations provided on asencis are open and attributable to a researcher, via their ORCID. Interpretations are made instantly public. With this transparency, we truly believe open data and the open publishing of data can be made much more robust, as well as provided a much needed facet of openness to the peer review process. A sign of how things can be done.

Open peer review with dataset interpretations

For us, open data means more than just the data being made public. Fundamentally, we believe that peer review must not be anonymous. As well as provide good data interoperability, we’ve provided researchers with the ability for researchers to add public interpretations alongside any dataset on our platform (not just their own).

We believe the key to asencis is to bring datasets to the attention of other researchers, the public, the press and other interested readers, and to frame these advances in an appropriate scientific, clinical, cultural or historical context. Taking into account the opinions of experts who may be closer to the actual topic of the submitted work.

This approach to **public** dataset interpretations will provide the critical means by which we can safeguard the integrity of the datasets submitted to asencis. Peer review can call attention to details within datasets as seemingly minor as misplaced units or, to those as grave as data fabrication. Careful peer reviewers can improve data integrity in countless ways.