ASLTL encourages, and in many cases expects, authors to make the data underlying their published findings openly available, in order to support transparency, reproducibility, and the cumulative development of knowledge in linguistics, translation studies, and literary studies.

What Counts as Data in This Field

In the journal's areas of scope, 'data' may include corpora and corpus annotations, interview or questionnaire transcripts, coded qualitative data, experimental or psycholinguistic datasets, translation-parallel texts, and the scripts or code used for data processing and analysis.

Data Availability Statement

Authors are required to include a Data Availability Statement at the end of the manuscript (before the reference list), indicating where the data supporting the findings can be accessed, or explaining clearly why the data cannot be shared.

Recommended Repositories

Where data can be shared, authors are encouraged to deposit it in a recognised open repository (e.g., Zenodo, the Open Science Framework (OSF), a institutional data repository, or a relevant linguistic corpus archive such as TalkBank or CLARIN), and to cite the dataset, ideally with its own DOI, in the manuscript's reference list.

Legitimate Exceptions

The journal recognises that open data is not always possible or appropriate. Data may be withheld, in full or in part, where sharing would compromise participant confidentiality or violate the terms of informed consent; where the data is subject to third-party intellectual-property or licensing restrictions (for example, commercially licensed corpora); or where the data concerns vulnerable populations or sensitive linguistic communities and sharing could cause harm. In such cases, the Data Availability Statement should explain the restriction and, where feasible, describe the conditions under which access might be granted (e.g., upon reasonable request and subject to a data-use agreement).

Code and Reproducibility

Authors using computational or statistical methods (for example, corpus-linguistic software, NLP pipelines, or statistical models) are encouraged to share the associated analysis code alongside the data, to allow other researchers to verify and build on the reported results.