Users on Twitter, a microblogging service, started the phenomenon of adding tags to their messages sometime around February 2008. These tags are distinct from those in other Web 2.0 systems because users are less likely to index messages for later retrieval. We compare tagging patterns in Twitter with those in Delicious to show that tagging behavior in Twitter is different because of its conversational, rather than organizational nature. We use a mixed method of statistical analysis and an interpretive approach to study the phenomenon. We find that tagging in Twitter is more about filtering and directing content so that it appears in certain streams. The most illustrative example of how tagging in Twitter differs is the phenomenon of the Twitter micro-meme: emergent topics for which a tag is created, used widely for a few days, then disappears. We describe the micro-meme phenomenon and discuss the importance of this new tagging practice for the larger real-time search context.

Wikidata is a community-maintained knowledge base that has been assembled from repositories in the fields of genomics, proteomics, genetic variants, pathways, chemical compounds, and diseases, and that adheres to the FAIR principles of findability, accessibility, interoperability and reusability. Here we describe the breadth and depth of the biomedical knowledge contained within Wikidata, and discuss the open-source tools we have built to add information to Wikidata and to synchronize it with source databases. We also demonstrate several use cases for Wikidata, including the crowdsourced curation of biomedical ontologies, phenotype-based diagnosis of disease, and drug repurposing.

The language learning process is a complicated one, and although classroom study forms the backbone of this process for many learners, motivated students will also engage in their own language study independent of the teacher. This paper examines four aspects of the learning process that self-directed learners are generally understood to be able to navigate: planning, implementing, monitoring, and evaluating; it then suggests ways in which teachers can foster these abilities in the language learning classroom. By teaching specific strategies for different learning tasks, encouraging reflection and self-analysis, and raising learners' awareness of their own learning processes in addition to features of the language they are studying, teachers can help learners to assume more responsibility for their own learning, and thus to become more effective language learners.

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We discuss Shape Expressions (ShEx), a concise, formal, modeling and validation language for RDF structures. For instance, a Shape Expression could prescribe that subjects in a given RDF graph that fall into the shape "Paper" are expected to have a section called "Abstract", and any ShEx implementation can confirm whether that is indeed the case for all such subjects within a given graph or subgraph. There are currently five actively maintained ShEx implementations. We discuss how we use the JavaScript, Scala and Python implementations in RDF data validation workflows in distinct, applied contexts. We present examples of how ShEx can be used to model and validate data from two different sources, the domain-specific Fast Healthcare Interoperability Resources (FHIR) and the domain-generic Wikidata knowledge base, which is the linked database built and maintained by the Wikimedia Foundation as a sister project to Wikipedia. Example projects that are using Wikidata as a data curation platform are presented as well, along with ways in which they are using ShEx for modeling and validation. When reusing RDF graphs created by others, it is important to know how the data is represented. Current practices of using human-readable descriptions or ontologies to communicate data structures often lack sufficient precision for data consumers to quickly and easily understand data representation details. We provide concrete examples of how we The original version of this chapter was revised: By mistake the chapter was originally published non open access. The correction to this chapter is available at

INTRODUCTION Research software plays an increasingly vital role in the scholarly record. Academic research libraries are in the early stages of exploring strategies for curating and preserving research software, aiming to facilitate support and services for long-term access and use. DESCRIPTION OF PROGRAM In 2016, the Council on Library and Information Resources (CLIR) began offering postdoctoral fellowships in software curation. Four institutions hosted the initial cohort of software curation fellows. This article describes the work activities and research program of the cohort, highlighting the challenges and benefits of doing this exploratory work in research libraries. NEXT STEPS Academic research libraries are poised to play an important role in research and development around robust services for software curation. The next cohort of CLIR fellows is set to begin in fall 2018 and will likely shape and contribute substantially to an emergent research agenda.

In this paper, the researchers analyse written discursive devices that learning advisors (LAs) at their institution use in order to give input to learners on their self-directed work. The researchers analysed written advising approaches by seven LAs throughout an eight-week period and coded the discursive devices according to their degree of directiveness. The results of the research indicate that LAs draw on a range of discursive devices and use varying degrees of directiveness when addressing the needs and learning stage of the students. The results have implications for LA training at the authors’ institution.

As explained in the first installment of this report (Thornton, 2013), the learning advisor (LA) team at Kanda University of International Studies (KUIS) has engaged in redesigning a curriculum for the Self-Access Learning Centre (SALC) by following a framework adapted from the Nation and Macalister (2010) model. This framework, which is based on an investigation of student needs, aims to establish criteria in the shape of clear principles and goals. Following the Environment Analysis stage, detailed in the previous installment of this column (Thornton, 2013), this paper describes the needs analysis stage which was undertaken in 2012.