An investigation into the viability of LibraryThing for promotional and user engagement purposes in libraries

Structured abstract: Purpose: LibraryThing is a Web 2.0 tool allowing users to catalogue books using data drawn from sources such as Amazon and the Library of Congress and has facilities such as tagging and interest groups. This study evaluates whether LibraryThing is a valuable tool for libraries to use for promotional and user engagement purposes. Methodology: This study used a sequential mixed methods 3 phase design: (1) the identification of LibraryThing features for user engagement or promotional purposes, (2) exploratory semi-structured interviews (3) a questionnaire. Findings: Several uses of LibraryThing for promotional and user engagement purposes were identified. The most popular reason libraries used LibraryThing was to promote the library or library stock, with most respondents using it specifically to highlight collections of books. Monitoring of patron usage was low and many respondents had not received any feedback. LibraryThing was commonly reported as being easy to use, remotely accessible, and having low cost, whilst its main drawbacks were the 200 book limit for free accounts, and it being a third-party site. The majority of respondents felt LibraryThing was a useful tool for libraries. Practical implications: LibraryThing has most value as a promotional tool for libraries. Libraries should actively monitor patron usage of their LibraryThing account or request user feedback to ensure that LibraryThing provides a truly valuable service for their library. Orginality : There is little research on the value of LibraryThing for libraries, or librarians perceptions of LibraryThing as a Web 2.0 tool.

Collaboration model for SSMEnetUK: a service science perspective

This paper aims to design a collaboration model for a Knowledge Community - SSMEnetUK. The research identifies SSMEnetUK as a socio-technical system and uses the core concepts of Service Science to explore the subject domain. The paper is positioned within the concept of Knowledge Management (KM) and utilising Web 2.0 tools for collaboration. A qualitative case study method was adopted and multiple data sources were used. In achieving that, the degree of co-relation between knowledge management activities and Web 2.0 tools for collaboration in the scenario are pitted against the concept of value propositions offered by both customer/user and service provider. The proposed model provides a better understanding of how Knowledge Management and Web 2.0 tools can enable effective collaboration within SSMEnetUK. This research is relevant to the wider service design and innovation community because it provides a basis for building a service-centric collaboration platform for the benefit of both customer/user and service provider.

A survey of data mining techniques for social media analysis

Social network has gained remarkable attention in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. The heavy reliance on social network sites causes them to generate massive data characterised by three computational issues namely; size, noise and dynamism. These issues often make social network data very complex to analyse manually, resulting in the pertinent use of computational means of analysing them. Data mining provides a wide range of techniques for detecting useful knowledge from massive datasets like trends, patterns and rules [44]. Data mining techniques are used for information retrieval, statistical modelling and machine learning. These techniques employ data pre-processing, data analysis, and data interpretation processes in the course of data analysis. This survey discusses different data mining techniques used in mining diverse aspects of the social network over decades going from the historical techniques to the up-to-date models, including our novel technique named TRCM. All the techniques covered in this survey are listed in the Table.1 including the tools employed as well as names of their authors.

The Met Office Global Coupled model 2.0 (GC2) configuration

The latest coupled configuration of the Met Office Unified Model (Global Coupled configuration 2, GC2) is presented. This paper documents the model components which make up the configuration (although the scientific description of these components is detailed elsewhere) and provides a description of the coupling between the components. The performance of GC2 in terms of its systematic errors is assessed using a variety of diagnostic techniques. The configuration is intended to be used by the Met Office and collaborating institutes across a range of timescales, with the seasonal forecast system (GloSea5) and climate projection system (HadGEM) being the initial users. In this paper GC2 is compared against the model currently used operationally in those two systems. Overall GC2 is shown to be an improvement on the configurations used currently, particularly in terms of modes of variability (e.g. mid-latitude and tropical cyclone intensities, the Madden–Julian Oscillation and El Niño Southern Oscillation). A number of outstanding errors are identified with the most significant being a considerable warm bias over the Southern Ocean and a dry precipitation bias in the Indian and West African summer monsoons. Research to address these is ongoing.

Coordination modes of 3-aminosalicylic and 3-hydroxyanthranilic acids in palladium(II), platinum(II) and rhenium(V) complexes. The crystal structure of cis-[Pt(HsalNH)(PPh3)(2)] (.) 0.25C(2)H(5)OH

New Pd(II), Pt(II) and Re(V) complexes of 3-aminosalicylic acid (H(2)salNH(2)) and 3-hydroxyantranilic acid (HantOH) have been prepared, cis-[Pt (HsalNH)(PPh3)(2)] center dot 0.25C(2)H(5)OH (1), trans-[PdCl(salNH(2))(PPh3)(2)](2), trans-[ReOI2(HsalNH(2))(PPh3)] center dot (CH3)(2)CO (3), cis-[Pt(HantO)(PPh3)(2)] (4), trans-[PdCl(antOH)(PPh3)(2)] center dot 4H(2)O (5), [PdCl(antOH)(bipy)] center dot C2H5OH (6), [PdCl2(HantOH)(2)] (7) and trans-[ReOI(HantO)(PPh3)(2)] center dot (CH3)(2)CO (8). The crystal structure of complex I was determined showing chelation of HsalNH(2-) through the adjacent nitrogen and oxygen atoms of the amino and phenolate groups. Infrared and H-1 NMR spectroscopic data for the complexes are presented. (c) 2005 Elsevier Ltd. All rights reserved.

Automated organizational network analysis for enterprise 2.0

Social Networking Sites have recently become a mainstream communications technology for many people around the world. Major IT vendors are releasing social software designed for use in a business/commercial context. These Enterprise 2.0 technologies have impressive collaboration and information sharing functionality, but so far they do not have any organizational network analysis (ONA) features that reveal any patterns of connectivity within business units. This paper shows the impact of organizational network analysis techniques and social networks on organizational performance, we also give an overview on current enterprise social software, and most importantly, we highlight how Enterprise 2.0 can help automate an organizational network analysis.

Targeting medication non-adherence behavior in selected autoimmune diseases: a systematic approach to digital health program development

Background 29 autoimmune diseases, including Rheumatoid Arthritis, gout, Crohn’s Disease, and Systematic Lupus Erythematosus affect 7.6-9.4% of the population. While effective therapy is available, many patients do not follow treatment or use medications as directed. Digital health and Web 2.0 interventions have demonstrated much promise in increasing medication and treatment adherence, but to date many Internet tools have proven disappointing. In fact, most digital interventions continue to suffer from high attrition in patient populations, are burdensome for healthcare professionals, and have relatively short life spans. Objective Digital health tools have traditionally centered on the transformation of existing interventions (such as diaries, trackers, stage-based or cognitive behavioral therapy programs, coupons, or symptom checklists) to electronic format. Advanced digital interventions have also incorporated attributes of Web 2.0 such as social networking, text messaging, and the use of video. Despite these efforts, there has not been little measurable impact in non-adherence for illnesses that require medical interventions, and research must look to other strategies or development methodologies. As a first step in investigating the feasibility of developing such a tool, the objective of the current study is to systematically rate factors of non-adherence that have been reported in past research studies. Methods Grounded Theory, recognized as a rigorous method that facilitates the emergence of new themes through systematic analysis, data collection and coding, was used to analyze quantitative, qualitative and mixed method studies addressing the following autoimmune diseases: Rheumatoid Arthritis, gout, Crohn’s Disease, Systematic Lupus Erythematosus, and inflammatory bowel disease. Studies were only included if they contained primary data addressing the relationship with non-adherence. Results Out of the 27 studies, four non-modifiable and 11 modifiable risk factors were discovered. Over one third of articles identified the following risk factors as common contributors to medication non-adherence (percent of studies reporting): patients not understanding treatment (44%), side effects (41%), age (37%), dose regimen (33%), and perceived medication ineffectiveness (33%). An unanticipated finding that emerged was the need for risk stratification tools (81%) with patient-centric approaches (67%). Conclusions This study systematically identifies and categorizes medication non-adherence risk factors in select autoimmune diseases. Findings indicate that patients understanding of their disease and the role of medication are paramount. An unexpected finding was that the majority of research articles called for the creation of tailored, patient-centric interventions that dispel personal misconceptions about disease, pharmacotherapy, and how the body responds to treatment. To our knowledge, these interventions do not yet exist in digital format. Rather than adopting a systems level approach, digital health programs should focus on cohorts with heterogeneous needs, and develop tailored interventions based on individual non-adherence patterns.