Indian Contribution to Open Access Scholarly Publishing: A Case Study of DOAJ

India has been a cradle of knowledge for thousands of years. Presently it has significant advantages in the 21st century knowledge race due to one of the largest higher education system in the world. It generates a lot of information in the form of research papers, project reports, books, conference papers, theses, dissertations, articles, and so on. Therefore, it is necessary to preserve, manage and make it accessible to the academic community in particular for sharing and visualizing their innovations for the betterment of society as a whole. The present study attempts to evaluate the initiatives taken by India to make this intellectual output accessible for all by publishing them in Open Access journals. The results revealed that India is continuously contributing in Open Access scholarly publishing as some of the premier institutions, particularly in the science and technology area, are providing open access to their research publications. The position of India in terms of number of journals in the Directory of Open Access Journals (DOAJ) is 7th in the world, well ahead of countries such as China, Australia, and Japan

ISPIDER Central: an integrated database web-server for proteomics

Despite the growing volumes of proteomic data, integration of the underlying results remains problematic owing to differences in formats, data captured, protein accessions and services available from the individual repositories. To address this, we present the ISPIDER Central Proteomic Database search (http://www.ispider.manchester.ac.uk/cgi-bin/ProteomicSearch.pl), an integration service offering novel search capabilities over leading, mature, proteomic repositories including PRoteomics IDEntifications database (PRIDE), PepSeeker, PeptideAtlas and the Global Proteome Machine. It enables users to search for proteins and peptides that have been characterised in mass spectrometry-based proteomics experiments from different groups, stored in different databases, and view the collated results with specialist viewers/clients. In order to overcome limitations imposed by the great variability in protein accessions used by individual laboratories, the European Bioinformatics Institute's Protein Identifier Cross-Reference (PICR) service is used to resolve accessions from different sequence repositories. Custom-built clients allow users to view peptide/protein identifications in different contexts from multiple experiments and repositories, as well as integration with the Dasty2 client supporting any annotations available from Distributed Annotation System servers. Further information on the protein hits may also be added via external web services able to take a protein as input. This web server offers the first truly integrated access to proteomics repositories and provides a unique service to biologists interested in mass spectrometry-based proteomics

Semantic learning webs

By 2020, microprocessors will likely be as cheap and plentiful as scrap paper,scattered by the millions into the environment, allowing us to place intelligent systems everywhere. This will change everything around us, including the nature of commerce, the wealth of nations, and the way we communicate, work, play, and live. This will give us smart homes, cars, TVs , jewellery, and money. We will speak to our appliances, and they will speak back. Scientists also expect the Internet will wire up the entire planet and evolve into a membrane consisting of millions of computer networks, creating an “intelligent planet.” The Internet will eventually become a “Magic Mirror” that appears in fairy tales, able to speak with the wisdom of the human race. Michio Kaku, Visions: How Science Will Revolutionize the Twenty - First Century, 1998 If the semantic web needed a symbol, a good one to use would be a Navaho dream-catcher: a small web, lovingly hand-crafted, [easy] to look at, and rumored to catch dreams; but really more of a symbol than a reality. Pat Hayes, Catching the Dreams, 2002 Though it is almost impossible to envisage what the Web will be like by the end of the next decade, we can say with some certainty that it will have continued its seemingly unstoppable growth. Given the investment of time and money in the Semantic Web (Berners-Lee et al., 2001), we can also be sure that some form of semanticization will have taken place. This might be superficial - accomplished simply through the addition of loose forms of meta-data mark-up, or more principled – grounded in ontologies and formalised by means of emerging semantic web standards, such as RDF (Lassila and Swick, 1999) or OWL (Mc Guinness and van Harmelen, 2003). Whatever the case, the addition of semantic mark-up will make at least part of the Web more readily accessible to humans and their software agents and will facilitate agent interoperability. If current research is successful there will also be a plethora of e-learning platforms making use of a varied menu of reusable educational material or learning objects. For the learner, the semanticized Web will, in addition, offer rich seams of diverse learning resources over and above the course materials (or learning objects) specified by course designers. For instance, the annotation registries, which provide access to marked up resources, will enable more focussed, ontologically-guided (or semantic) search. This much is already in development. But we can go much further. Semantic technologies make it possible not only to reason about the Web as if it is one extended knowledge base but also to provide a range of additional educational semantic web services such as summarization, interpretation or sense-making, structure-visualization, and support for argumentation

Multi Visualization and Dynamic Query for Effective Exploration of Semantic Data

Semantic formalisms represent content in a uniform way according to ontologies. This enables manipulation and reasoning via automated means (e.g. Semantic Web services), but limits the user’s ability to explore the semantic data from a point of view that originates from knowledge representation motivations. We show how, for user consumption, a visualization of semantic data according to some easily graspable dimensions (e.g. space and time) provides effective sense-making of data. In this paper, we look holistically at the interaction between users and semantic data, and propose multiple visualization strategies and dynamic filters to support the exploration of semantic-rich data. We discuss a user evaluation and how interaction challenges could be overcome to create an effective user-centred framework for the visualization and manipulation of semantic data. The approach has been implemented and evaluated on a real company archive

Exploring user and system requirements of linked data visualization through a visual dashboard approach

One of the open problems in SemanticWeb research is which tools should be provided to users to explore linked data. This is even more urgent now that massive amount of linked data is being released by governments worldwide. The development of single dedicated visualization applications is increasing, but the problem of exploring unknown linked data to gain a good understanding of what is contained is still open. An effective generic solution must take into account the user’s point of view, their tasks and interaction, as well as the system’s capabilities and the technical constraints the technology imposes. This paper is a first step in understanding the implications of both, user and system by evaluating our dashboard-based approach. Though we observe a high user acceptance of the dashboard approach, our paper also highlights technical challenges arising out of complexities involving current infrastructure that need to be addressed while visualising linked data. In light of the findings, guidelines for the development of linked data visualization (and manipulation) are provided

The Ubiquity of Large Graphs and Surprising Challenges of Graph Processing: Extended Survey

Graph processing is becoming increasingly prevalent across many application domains. In spite of this prevalence, there is little research about how graphs are actually used in practice. We performed an extensive study that consisted of an online survey of 89 users, a review of the mailing lists, source repositories, and whitepapers of a large suite of graph software products, and in-person interviews with 6 users and 2 developers of these products. Our online survey aimed at understanding: (i) the types of graphs users have; (ii) the graph computations users run; (iii) the types of graph software users use; and (iv) the major challenges users face when processing their graphs. We describe the participants' responses to our questions highlighting common patterns and challenges. Based on our interviews and survey of the rest of our sources, we were able to answer some new questions that were raised by participants' responses to our online survey and understand the specific applications that use graph data and software. Our study revealed surprising facts about graph processing in practice. In particular, real-world graphs represent a very diverse range of entities and are often very large, scalability and visualization are undeniably the most pressing challenges faced by participants, and data integration, recommendations, and fraud detection are very popular applications supported by existing graph software. We hope these findings can guide future research

Developing a curriculum of open educational resources for Linked Data

The EUCLID project is developing an educational curriculum about Linked Data, supported by multimodal Open Educational Resources (OERs) tailored to the real needs of data practitioners. The EUCLID OERs facilitate professional training for data practitioners, who aim to use Linked Data in their daily work. The EUCLID OERs are implemented as a combination of living learning materials and activities (eBook, online courses, webinars, face-to-face training), produced via a rigorous process and validated by the user community through continuous feedback