The GLOBALISE project’s digitalisation of the Dutch East India Company (VOC) archives raises questions about representing gender and marginalised identities. This paper outlines the challenges of accurately conveying gender information in the archives, highlighting issues such as the lack of self-identified gender descriptions, low representation of marginalised groups, colonial context, and multilingualism in the collection. Machine learning (ML) and machine translation (MT) used in the digitalisation process may amplify existing biases and under-representation. To address these issues, the paper proposes a gender policy for GLOBALISE, offering guidelines and methodologies for handling gender information and increasing the visibility of marginalised identities. The policy contributes to discussions about representing gender and diversity in digital historical research, ML, and MT.

Cultural heritage data is a rich source of information about the history and culture development in the past. When used with due understanding of its intrinsic complexity it can both support research in social sciences and humanities, and become input for machine learning and artificial intelligence algorithms. In all cases ethical and contextual considerations can be encouraged when the relevant information is provided in a clear and well structured form to potential users before they begin to interact with the data. Proposed data-envelopes, basing on the existing documentation frameworks, address the particular needs and challenges of the cultural heritage field while combining machine-readability and user-friendliness. We develop and test data-envelopes usability on the data from the Huygens Institute for History and Culture of the Netherlands. This paper presents the following contributions: i) we highlight the complexity of CH data, featuring the unique ethical and contextual considerations they entail; ii) we evaluate and compare existing dataset documentation frameworks, examining their suitability for CH datasets; iii) we introduce the “data-envelope”–a machine readable adaptation of existing dataset documentation frameworks, to tackle the specificities of CH datasets. Its modular form is designed to serve not only the needs of machine learning (ML), but also and especially broader user groups varying from humanities scholars, governmental monitoring authorities to citizen scientists and the general public. Importantly, the data-envelope framework emphasises the legal and ethical dimensions of dataset documentation, facilitating compliance with evolving data protection regulations and enhancing the accountability of data stewardship in the cultural heritage sector. We discuss and invite the readers for further conversation on the topic of ethical considerations, and how the different audiences should be informed about the importance of datasets documentation management and their context.