BibSonomy

Efforts towards internationalization have become increasingly important in scientific environments. As for content-based indexing of scientific research data, however, standards leading to internationally coherent indexing which is vital for retrieval purposes are not yet sufficiently developed. Even concerning the concrete use of indexing instruments, launched by initiatives on an international scale, there are still no binding policies and guidelines. Against this backdrop, essential criteria which internationally applicable indexing systems should meet will be outlined. These will be illustrated through the multilingual European Language Social Science Thesaurus (ELSST ), originally based on the UK Data Archive’s (UKDA) Humanities and Social Science Electronic Thesaurus (HASSET ) and ultimately developed by the Council of European Social Science Data Archives (CESSDA). Additionally, the general pros and cons of using international versus national indexing languages will be weighed using the ELSST and the Thesaurus for the Social Sciences (TSS) developed by GESIS – Leibniz-Institute for the Social Sciences. In this light, the benefit of vocabulary crosswalks for supporting a combined use of international and national indexing systems will be discussed.

Language and indexing biases may exist among Chinese-sponsored randomized clinical trials (CS-RCTs). Such biases may threaten the validity of systematic reviews.

This paper presents a preliminary exploration of an actor-based model for subject indexing, which considers four types of actors: professional indexers, domain experts, casual indexers, and machine algorithms. The paper describes each of the four actors, enumerating differences in approach, training, methodology, priorities, and tools, as well as similarities and historical collaborations between actors. The paper then explores how the actor-based model for subject indexing might serve as a complement to existing models that focus on processes, methods, disciplinary norms, and cultural biases by defining and exploring the following key properties of an actor-based model for subject indexing: 1) actors are the primary drivers of subject indexing work, 2) observing and understanding many types of actors’ processes in real-life situations is as valuable as prescribing correct methods for professional subject indexing, and 3) multiple and different types of actors can perform subject analysis work and subject representation work on the same information objects, and these hybrid (multi-actor) approaches to subject indexing are explicitly supported. These key properties suggest that an actor-based model for subject indexing might open new research opportunities and encourage new hybrid and collaborative approaches to knowledge organization.

PubMed is a primary global open-access literature research database. Articles on PubMed are indexed manually with medical subject headings (MeSH®) to facilitate more complete literature searches. We aimed to determine the length of delay from publication to MeSH® indexing for key respiratory journals and to investigate whether delays are increasing over time and whether there are country or impact-factor specific biases in indexing.