Data publishing and research workflow solutions for the modern, open data ecosystem.

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.

Access to your data through modern RESTful or GraphQL APIs

The need for modernisation in the data ecosystem is met with a number of challenges. Data can be both dynamic, static, structured, unstructure and, well, vast. You'll therefore need modern tooling to interrogate it, and to access it. A RESTful API, application programming interface, or GraphQL, graph querying language, API will provide the flexibility needed to meet the demands of open data collectives.

Self-documenting and queryable RESTful and GraphQL API endpoints for your data, providing a platform for your collaborators to find your data in a standardised, queryable and searchable format that can be accessed in what ever flavour of programming language needed, meetign the needs of data interoperability for different skills and communities.

We do the **heavy lifting**. Our API holds data, with an Object Relational Mapper (ORM) system provided a link to your database's preferred SQL approach. This means you won't have to write complex SQL queries, we will handle that all for you. Providing documentation, and resources, for collaborators and researchers to gain access to your data, as soon as it is published. We understand the need for data to be accessible, not only open, and therefore we know that it is important that we provide this key functionality for your, your team or your institution.

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.