Micro Archives as Rich Digital Object Representations

Abstract

Digital objects as well as real-world entities are commonly referred to in literature or on the Web by mentioning their name, linking to their website or citing unique identifiers, such as DOI and ORCID, which are backed by a set of meta information. All of these methods have severe disadvantages and are not always suitable though: They are not very precise, not guaranteed to be persistent or mean a big additional effort for the author, who needs to collect the metadata to describe the reference accurately. Especially for complex, evolving entities and objects like software, pre-defined metadata schemas are often not expressive enough to capture its temporal state comprehensively. We found in previous work that a lot of meaningful information about software, such as a description, rich metadata, its documentation and source code, is usually available online. However, all of this needs to be preserved coherently in order to constitute a rich digital representation of the entity. We show that this is currently not the case, as only 10% of the studied blog posts and roughly 30% of the analyzed software websites are archived completely, i.e., all linked resources are captured as well. Therefore, we propose Micro Archives as rich digital object representations, which semantically and logically connect archived resources and ensure a coherent state. With Micrawler we present a modular solution to create, cite and analyze such Micro Archives. In this paper, we show the need for this approach as well as discuss opportunities and implications for various applications also beyond scholarly writing.

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No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 1 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.