Adding Discovery to Scholarly Search: Enhancing Institutional Repositories with OpenID and Connotea

By linking Connotea to institutional repositories through the use of open standards the value of the data on the repositories can be enriched by social data from the service. Conversely, registered users from linked repositories acquire automatic membership of Connotea, boosting the quantity and quality of Connotea’s member base with quality membership, I.E. academic authors. This integration would provide the basis for a new ‘discovery and evaluation’ layer to be added to institutional repositories globally. In this scenario, repositories register with Connotea as trusted providers of Identity (Using OpenID). Aims *Encourage growth of Connotea’s data by making it easy for users of Repositories (like EPrints) to join/use Connotea through an implementation of OpenID. *Host JavaScripts on Connotea.org that can insert social data directly into record pages in repositories. Objectives *Provide for ‘discovery’ and evaluation of information on top of regular search. *Increase the amount of quality data in the Connotea database (in support of the previous objective) Success Indicators *New members of Connotea begin to join through OpenID *Web visitor statistics begin to indicate new usage patterns suggesting they are ‘discovering’ information thanks to the use of social data

Mining for Social Serendipity

A common social problem at an event in which people do not personally know all of the other participants is the natural tendency for cliques to form and for discussions to mainly happen between people who already know each other. This limits the possibility for people to make interesting new acquaintances and acts as a retarding force in the creation of new links in the social web. Encouraging users to socialize with people they don't know by revealing to them hidden surprising links could help to improve the diversity of interactions at an event. The goal of this paper is to propose a method for detecting "surprising" relationships between people attending an event. By "surprising" relationship we mean those relationships that are not known a priori, and that imply shared information not directly related with the local context of the event (location, interests, contacts) at which the meeting takes place. To demonstrate and test our concept we used the Flickr community. We focused on a community of users associated with a social event (a computer science conference) and represented in Flickr by means of a photo pool devoted to the event. We use Flickr metadata (tags) to mine for user similarity not related to the context of the event, as represented in the corresponding Flickr group. For example, we look for two group members who have been in the same highly specific place (identified by means of geo-tagged photos), but are not friends of each other and share no other common interests or, social neighborhood

The Politics of Seed in Africa's Green Revolution: Alternative Narratives and Competing Pathways

As calls for a ‘Uniquely African Green Revolution’ gain momentum, a focus on seeds and seed systems is rising up the agricultural policy agenda. Much of the debate stresses the technological or market dimensions, with substantial investments being made in seed improvement and the development of both public and private sector delivery systems. But this misses out the political economy of policy processes behind this agenda: who wins, who loses, and whose interests are being served? Drawing on lessons from country case studies from Ethiopia, Ghana, Kenya, Malawi and Zimbabwe, as well as insights from a set of complementary studies of cross?cutting themes, this article assesses the evolution of seed system research and development programmes and processes across the region. By examining how the contrasting politics and different configurations of interests affect the way cereal seed systems operate, it highlights opportunities for reshaping the terms of the debate and opening up alternative pathways to more sustainable and socially just seed systems

Open Data Challenges

Calls in favour of Open Data in research are becoming overwhelming. They are at national [@RCKUOpen] and international levels [@Moedas2015, @RSOpen, @ams2016]. I will set out a working definition of Open Data and will discuss the key challenges preventing the publication of Open Data becoming standard practice. I will attempt to draw some general solutions to those challenges from field specific examples

citing-dataset-elements

<p>This repo contains a csv file that summarises guidance provided by a number of organisations around what features or information should be provided for citing data. By inferring across these sources we hope to find the most commonly suggested features that can be useful for citing data. We hope to use this resource to form the basis of a recommendation on how to use JATS to cite data, and there is a piece of work remaining This work was produced as a part of the NISO data-citation workshop that took place in London in June 2014.</p

08391 Group Summary -- Mining for Social Serendipity

A common social problem at an event in which people do not personally know all of the other participants is the natural tendency for cliques to form and for discussions to mainly happen between people who already know each other. This limits the possibility for people to make interesting new acquaintances and acts as a retarding force in the creation of new links in the social web. Encouraging users to socialize with people they don\u27t know by revealing to them hidden surprising links could help to improve the diversity of interactions at an event. The goal of this paper is to propose a method for detecting extit{"surprising"} relationships between people attending an event. By extit{"surprising"} relationship we mean those relationships that are not known a-priori, and that imply shared information not directly related with the local context of the event (location, interests, contacts) at which the meeting takes place. To demonstrate and test our concept we used the Flickr community. We focused on a community of users associated with a social event (a computer science conference) and represented in Flickr by means of a photo pool devoted to the event. We use Flickr metadata (tags) to mine for user similarity not related to the context of the event, as represented in the corresponding Flickr group. For example, we look for two group members who have been in the same highly specific place (identified by means of geo-tagged photos), but are not friends of each other and share no other common interests or, social neighborhood