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  1. 10.17605/OSF.IO/AT4XE Despite increased use of digital biodiversity data in research, reliable methods to identify datasets are not widely adopted. While commonly used location-based dataset identifiers such as URLs help to easily download data today, additional identification schemes are needed to ensure long term access to datasets. We propose to augment existing location- and DOI-based identification schemes with cryptographic content-based identifiers. These content-based identifiers can be calculated from the datasets themselves using available cryptographic hashing algorithms (e.g., sha256). These algorithms take only the digital content as input to generate a unique identifier without needing a centralized identification administration. The use of content-based identifiers is not new, but a re-application of change management techniques used in the popular version control system "git". We show how content-based identifiers can be used to version datasets, to track the dataset locations, to monitor their reliability, and to efficiently detect dataset changes. We discuss the results of using our approach on datasets registered in GBIF and iDigBio from Sept 2018 to May 2020. Also, we propose how reliable, decentralized, dataset indexing and archiving systems can be devised. Lastly, we outline a modification to existing data citation practices to help work towards more reproducible and reusable research workflows. 
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