Bubble Nebula (NASA, ESA, and the Hubble Heritage Team (STScI/AURA)) | asencis
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Data publishing and research workflow solutions for the modern, open data ecosystem.

Our solutions are industry leading. Let us explain why we can help both publish your data but help boost your research efficiency.


A better way to publish your data

Publishing your data as part of your workflow is the solution top expanding the capabilities of open data collectives.

  • 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.

  • Citable datasets with a Digital Object Identifier (DOI)

    Publishing your data has never been easier. We provide the neccessary connection to create a unique auto-generated Digital Object Identifier, DOI, code via the DataCite Registration Agency, our DOI registration partner. This allows you to publish your research data with confidence, and receive proper credit when your work is re-used.

    The asencis platform, along with our DOI partner, will ensure you work is both accessible and searchable by equipping your dataset/(s) with the neccessary metadata, descriptions of and facts and figures about the data, that meets the standards of, and adheres to, uniform, consistent DOI meta schema.

    The **auto-registration** of DOIs when you publish your data with us will also allow your data to be crawlable, where data libraries, such as Google Dataset Search, rely on exposed crawlable structured meta data in a standard format. This allows your data to be search engine findable; anywhere.

  • Security of your published data

    Our data warehousing strategy ensures your data is your data. You own your data. asencis just provides both the infrastructure, hosting and ability to access your data.

    Your data is encrypted as a standard, where we also provide further masking (the details of which we shouldn't disclose). This allows your data to be safe and secure in our hands, and only disclosed when you're ready.

  • Best in class integrations support

    Do you want to integrate the RESTful API or GraphQL for your data with a particular tool or software? Our API driven approach will be enough, as they can be integrated with many programming languages, whether that be R, Python, Go, Java, Scala, Tensorflow or MATLAB, to name but a few. This approach abstracts away the neccessary understanding of the language of the database layer.

    With the out-of-the-box RESTful API or GraphQL approach, you and your collaborators can start interrogating your data, connecting with your iPython Jupiter notebook, your MATLAB scripts, Octave online or implement your statiscal analysis within your R environment.

Are you ready to publish your scientific data?

Mint your data with a doi.org indexed Digital Object identifier whilst making your data globally accessible and open with structured data support*, a browsable API and accompanying meta data.

*(JSON linked data Google Scholar verified)

    N.B. The above datasets are purely for demonstrative purposes only.