OpenML: networked science in machine learning

Many sciences have made significant breakthroughs by adopting online tools that help organize, structure and mine information that is too detailed to be printed in journals. In this paper, we introduce OpenML, a place for machine learning researchers to share and organize data in fine detail, so that they can work more effectively, be more visible, and collaborate with others to tackle harder problems. We discuss how OpenML relates to other examples of networked science and what benefits it brings for machine learning research, individual scientists, as well as students and practitioners.Comment: 12 pages, 10 figure

Theoretical Underpinnings and Practical Challenges of Crowdsourcing as a Mechanism for Academic Study

Researchers in a variety of fields are increasingly adopting crowdsourcing as a reliable instrument for performing tasks that are either complex for humans and computer algorithms. As a result, new forms of collective intelligence have emerged from the study of massive crowd-machine interactions in scientific work settings as a field for which there is no known theory or model able to explain how it really works. Such type of crowd work uses an open participation model that keeps the scientific activity (including datasets, methods, guidelines, and analysis results) widely available and mostly independent from institutions, which distinguishes crowd science from other crowd-assisted types of participation. In this paper, we build on the practical challenges of crowd-AI supported research and propose a conceptual framework for addressing the socio-technical aspects of crowd science from a CSCW viewpoint. Our study reinforces a manifested lack of systematic and empirical research of the symbiotic relation of AI with human computation and crowd computing in scientific endeavors

What the Crowd Sources: A Protocol for a Contribution-Centred Systematic Literature Review of Data Crowdsourcing Research

Data crowdsourcing is the mobilization of large groups of contributors—often volunteers via the Internet—to collect and/or analyze data. Research on data crowdsourcing often prioritizes the data consumer or project sponsor. Significant gaps remain in understanding how to address design issues from the perspective of data crowdsourcing contributors. A systematic literature review is an ideal method for identifying gaps in how researchers conceptualize contributions in data crowdsourcing. This project presents a protocol for such a systematic literature review of data crowdsourcing. We will use the protocol to guide a subsequent systematic literature review and the construction of a data-information-knowledge-wisdom chart that identifies critical gaps and opportunities for research in data crowdsourcing systems

Transition to 3D social networking

In this paper we analyse the theoretical under-pinnings underlying collaboration in virtual environments and propose a 3D Virtual World (VW) approach to the construction and facilitation of communities of practice within the context of social innovation. Although connected networks can emerge from 'flat' 2D Social Networks, while face-to-face meetings have been proven successful to undertake innovative entrepreneurial ventures, the 3D VW approach possesses affordances that can be exploited to augment the experience. We propose a model for a 3D Virtual World, as part of the Euro South Hub project, that facilitates the social innovation experience through collaboration and the setting up of communities of practice, using the Virtual Environment to move towards producing solutions for a better physical world.peer-reviewe

Food Waste Legislation Scholarship: A Mapping Study

The purpose of this study is to examine research activity on food waste legislation published in law journals to identify top sources and experts cited by recent scholarship. Searches for food loss and food waste were conducted in three legal research databases for law journal articles published between January 2013 and January 2018. The core list of selected articles consists of 13 law journal articles. The citations from each of the core articles were collected to form a database, which was analyzed to determine what kinds of resources legal scholars rely on when conducting research in food waste legislation. Government Sources and Primary Law contribute approximately 48% of the citations in the database. News, Nonprofit, and Law Reviews and Journals contribute approximately 31% of database citations. This study provides some insight into the complexity of food law and the facets of agriculture, industry, and society that affect the success of food waste reduction legislation

Volunteer participation in citizen science projects

The purpose of this research is to assess the current state of citizen science projects and reveal the role of volunteers in the research process. This is achieved by performing a literature review and content analysis of three international and one state-owned citizen science platforms (Wikipedia, SciStarter, CitSci and Precipita) that contain more than 800 research projects. Projects have been analyzed according to four categories: the academic disciplines, the way the project is designed, the phases of the research in which volunteers participate, and the tasks they perform. The results show that projects in the arts, humanities, and social sciences disciplines are almost non-existent. In addition, in the field of natural and physical sciences, projects are fostered with a top-down approach and volunteers participate primarily in the data collection phase in order to obtain a large volume of data, thereby receiving more financing from the European Union

Crowd Science: The Organization of Scientific Research in Open Collaborative Projects

A growing amount of scientific research is done in an open collaborative fashion, in projects sometimes referred to as "crowd science", "citizen science", or "networked science". This paper seeks to gain a more systematic understanding of crowd science and to provide scholars with a conceptual framework and an agenda for future research. First, we briefly present three case examples that span different fields of science and illustrate the heterogeneity concerning what crowd science projects do and how they are organized. Second, we identify two fundamental elements that characterize crowd science projects - open participation and open sharing of intermediate inputs - and distinguish crowd science from other knowledge production regimes such as innovation contests or traditional "Mertonian" science. Third, we explore potential knowledge-related and motivational benefits that crowd science offers over alternative organizational modes, and potential challenges it is likely to face. Drawing on prior research on the organization of problem solving, we also consider for what kinds of tasks particular benefits or challenges are likely to be most pronounced. We conclude by outlining an agenda for future research and by discussing implications for funding agencies and policy makers

The Effect of Scientific Collaboration on CSCW Research: A Scientometric Study

The structure and evolution of a scientific research community can be quantitatively assessed taking into account the interactions between scientific agents dispersed geographically. In the recent years, CSCW has stabilized as a cross-disciplinary field suffering significant changes in its core structure, and there is limited understanding about the factors influencing the nature and progress of collaborative computing research. In this paper, we measure the correlation between a set of features related to the influence of collaboration types on the number of citations as well as the geographical distribution of the accumulated contribution to the CSCW literature. Overall, our work can represent a starting point to demonstrate how the study of scientific collaboration can partly explain the variations in the number of citations, frequency of papers, and topics addressed

Making sense framework and assessment of participatory strategies

This report is a combined effort of Dundee University and the Joint Research Centre, based on the integration of D5.2 (Report and evaluation of the pilot approaches to ‘Making Sense campaigns’) and D4.3 (Report on assessment of participatory methods in the pilots and final recommendations). The document is structured as follows: Section 1 articulates the Making Sense approach to pilots and covers our campaign rationale, stakeholders and summarises the Making Sense pilots; Section 2 describes the design and iteration of the Making Sense Framework; Section 3 shows how the Making Sense Framework has been exemplified through the pilots and describes and illustrates each phase of the Framework with an example from a pilot; Section 4 focuses on ten key topics where we observed how citizen engagement and community building were addressed inside Making Sense and how the project participatory strategies developed from there on; Section 5 puts forward a new augmented version of previously devised recommendations for participatory or community driven sensing projects, with lessons learned from and for Making Sense.JRC.I.2-Foresight, Behavioural Insights and Design for Polic