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  • What metadata should I include with my dataset?

    Metadata describes your dataset to others in a standardised way. Having good quality metadata helps people discover and use your dataset. This guidance provides a description and examples of good practice metadata when releasing on data.govt.nz.

  • Algorithm review underway to increase transparency and accountability

    A cross-government review is underway to increase the transparency and accountability of how government uses algorithms – to help improve the lives of New Zealanders.

  • Group guidance and commissioned reports

    Guidance provided by the Data Ethics Advisory Group.

  • CoP terms of reference

    The initial terms of reference for the New Zealand Government-Te Kāwanatanga o Aotearoa Algorithm Charter Community of Practice outlines the purpose, role, and membership of the CoP in its establishment phase.

  • Māori Data and AI – guidance for business

    About this guidance
    This guidance is intended to be high-level, introducing basic concepts to stimulate further investigation and thinking. There are various pieces of work underway to…

  • Implementation plan: Comparable and interoperable data 

    This implementation plan provides a timeline for activities that support meeting our obligations as a signatory to the international Open Data Charter.

  • Guidance note 5

    Introduction1 When NZGOAL was first released in August 2010, it supported use of the Creative Commons 3.0 New Zealand licences. At that time, this made obvious…

  • Terms of use

    Agreement to terms1. Welcome to the data.govt.nz site and the catalogue.data.govt.nz site hosted and operated by the Department of Internal Affairs (”we”, “our”, “DIA”) (for the purpose of…

  • Guidance note 6

    SummaryNZGOAL guidance notes1. NZGOAL is the New Zealand Government Open Access and Licensing framework. NZGOAL guidance notes either explore issues raised in NZGOAL or address operational…

  • Navigating ethical dilemma in education data: The story of the EQI

    Data driven technology seems to unlock opportunities. Yet it poses new types of risks, from bias and transparency challenges to issues of consent and data sovereignty.

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