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# System prepended metadata

title: Data strategy for Akvaplan-niva

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# Data strategy for Akvaplan-niva

## Introduction

### Vision
_Human users_ should be able to find Akvaplan-niva data using either a regular internet search, or through specialist data search platforms. Once found, a user should be able to access a landing page for each dataset. The landing page should reside on a persistent address (URL/URI/IRI) on Akvaplan-niva's secure (HTTPS) web space, enabling referencing and sharing datasets.

Great care should be given in presenting the data and including all relevant information needed to access, cite, and use the data. Whenever possible data should be previewable, preferably via interactive visualisations.

To signal a commitment to the long-term stability and permanence of a published dataset, each landing page should be connected to a DOI.

_Machines_, or programmable agents, should be able to access a dataset catalog in a standardised and machine-readable representation, like [DCAT v2](https://www.w3.org/TR/vocab-dcat-2/). Each dataset will have one or more distributions, ie. a set of files and/or data services. Integrity. Auth. Machine-parsability. Validatopn. ExportAutomation and machine-actionability.
Transperancy and immutability. provenance.
This means that not only should data be available online, it should be a
linked > knowledge graph

Data consumers should, whenever possible, receive data that is formatted using international standard formatting and vocabularies.


## Principles
Securing data and making it available and understandable for humans and machines, now and in the future, is a complex undertaking that requires a principled approach and a long-term commitment.

Both humans and machines need to access data via the web. This strategy is to a large extent based on work led by the [W3C](https://w3.org) that promotes an open and standardised web, in particular their recommendation: [Data on the Web Best Practices](https://www.w3.org/TR/dwbp/).

The [FAIR principles](https://doi.org/10.1038/sdata.2016.18) – that data should be findable, accessible, interoperable, and reusable, is currently the most widely used guidelines for professional data management. The FAIR principles are not limited to human data interaction, but specifically ["emphasise machine-actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention)"](https://www.go-fair.org/fair-principles/).
