An open access dataset could be defined as "digital, online, free of charge, and free of most copyright and licensing restrictions." The recommendations of the Budapest Open Access Declaration — including the use of liberal licensing (such as Creative Commons CC BY) — is widely recognised in the community as a means to make a dataset or work truly open access.

For a work to be open access, the copyright holder must consent in advance to let users "copy, use, distribute, transmit and display the work publicly and to make and distribute derivative works, in any digital medium for any responsible purpose, subject to proper attribution of authorship....".

Therefore, publishing your data with asencis is non-restrictive - meaning by default you are not able to restrict access to your data once it has obtained a persistent identifier. In our system we generate a uuidv4 and a Digital Object Identifier (DOI) which is registered externally with the leading DOI provider - Datacite.

As such, by publishing on asencis your data with be published with one of the following licenses of your choosing:

• 0BSD Zero Clause License
• Academic Free License v3.0
• Creative Commons Attribution 4.0 International
• Creative Commons Zero v1.0 Universal
• Common Public Attribution License 1.0
• Common Public Attribution License 1.0
• GNU General Public License v3.0 only
• MIT License
• Open Data Commons Attribution License v1.0
• Open Government Licence v3.0

Going further than open access

At asencis, we're deeply passionate about open access - that's why we believe that merely publishing your data with rich metadata is not enough. A dataset needs to be understood not just by a handful of researchers involved in its procurement, but by the wider community of scientists, students, authors and journalists. That's why we believe for a dataset to be truly open access it must be annotated, with these annotations available on demand. Furthermore, the datafiles within a dataset must be highly interoperable - therefore, where possible, datasets published onto our platform will come with browsable APIs for the data. Such that we have an open standard for accessing the raw datafiles, regardless of machine, operating system, preferred programming language or understand of tooling. An open API for the raw data, and not just the metadata, is crucial in brining data to people in a quicker, more interoperable and standardised way.

Open data philosophies and processes can not stop at the point of publication. An open API for the raw data, and not just the metadata, is crucial in brining data to people in a quicker, more interoperable and standardised way...

In essence, our open access policy focusing on ensuring that if you publish with asencis ensures, your research is findable, accessible, interoperable and re-usable. This adherenace to FAIR data publishing principles is key to our open access initiatives.

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.

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 potential solution.

Enforcing open data standardisation through dataset annotations

With the need for modernisation in the data ecosystem, we feel our purpose is to ensure the patterns of working with open data are consistent across borders, cultures and languages.

To ensure data can be accessed the world over, we’re enhancing datasets with structured datafile annotations. Datafile annotations allow data to be shared with structured information providing solid data interoperability, underpinning the foundation for the RESTful API and GraphQL APIs. Datafile anotations are a requirement of publishing datafiles within your datasets.

Datafile annotations will allow authors, creators and contributors to let their potential audiences know what columns the dataset contains, what datatypes each column is, the units (and prefixes) of each given column and detailed descriptions of each field/column within the dataset.

On top of these academically useful datafile contexts, we also promote dataset creators to list which columns are they most important to interrogate, providing useful public outreach to the nature of the dataset for people previously unreached by the academic data publishing sphere.