14,869 research outputs found
A NeISS collaboration to develop and use e-infrastructure for large-scale social simulation
The National e-Infrastructure for Social Simulation (NeISS) project is focused on
developing e-Infrastructure to support social simulation research. Part of NeISS aims to
provide an interface for running contemporary dynamic demographic social simulation
models as developed in the GENESIS project. These GENESIS models operate at the
individual person level and are stochastic. This paper focuses on support for a simplistic
demographic change model that has a daily time steps, and is typically run for a number
of years.
A portal based Graphical User Interface (GUI) has been developed as a set
of standard portlets. One portlet is for specifying model parameters and setting a
simulation running. Another is for comparing the results of different simulation runs.
Other portlets are for monitoring submitted jobs and for interfacing with an archive of
results. A layer of programs enacted by the portlets stage data in and submit jobs to a
Grid computer which then runs a specific GENESIS model program executable. Once a
job is submitted, some details are communicated back to a job monitoring portlet. Once
the job is completed, results are stored and made available for download and further
processing. Collectively we call the system the Genesis Simulator.
Progress in the development of the Genesis Simulator was presented at the UK e-
Science All Hands Meeting in September 2011 by way of a video based demonstration
of the GUI, and an oral presentation of a working paper. Since then, an automated
framework has been developed to run simulations for a number of years in yearly time
steps. The demographic models have also been improved in a number of ways. This
paper summarises the work to date, presents some of the latest results and considers the
next steps we are planning in this work
Where are your Manners? Sharing Best Community Practices in the Web 2.0
The Web 2.0 fosters the creation of communities by offering users a wide
array of social software tools. While the success of these tools is based on
their ability to support different interaction patterns among users by imposing
as few limitations as possible, the communities they support are not free of
rules (just think about the posting rules in a community forum or the editing
rules in a thematic wiki). In this paper we propose a framework for the sharing
of best community practices in the form of a (potentially rule-based)
annotation layer that can be integrated with existing Web 2.0 community tools
(with specific focus on wikis). This solution is characterized by minimal
intrusiveness and plays nicely within the open spirit of the Web 2.0 by
providing users with behavioral hints rather than by enforcing the strict
adherence to a set of rules.Comment: ACM symposium on Applied Computing, Honolulu : \'Etats-Unis
d'Am\'erique (2009
A framework for design engineering education in a global context
This paper presents a framework for teaching design engineering in a global context using innovative technologies to enable distributed teams to work together effectively across international and cultural boundaries. The DIDET Framework represents the findings of a 5-year project conducted by the University of Strathclyde, Stanford University and Olin College which enhanced student learning opportunities by enabling them to partake in global, team based design engineering projects, directly experiencing different cultural contexts and accessing a variety of digital information sources via a range of innovative technology. The use of innovative technology enabled the formalization of design knowledge within international student teams as did the methods that were developed for students to store, share and reuse information. Coaching methods were used by teaching staff to support distributed teams and evaluation work on relevant classes was carried out regularly to allow ongoing improvement of learning and teaching and show improvements in student learning. Major findings of the 5 year project include the requirement to overcome technological, pedagogical and cultural issues for successful eLearning implementations. The DIDET Framework encapsulates all the conclusions relating to design engineering in a global context. Each of the principles for effective distributed design learning is shown along with relevant findings and suggested metrics. The findings detailed in the paper were reached through a series of interventions in design engineering education at the collaborating institutions. Evaluation was carried out on an ongoing basis and fed back into project development, both on the pedagogical and the technological approaches
The Research Object Suite of Ontologies: Sharing and Exchanging Research Data and Methods on the Open Web
Research in life sciences is increasingly being conducted in a digital and
online environment. In particular, life scientists have been pioneers in
embracing new computational tools to conduct their investigations. To support
the sharing of digital objects produced during such research investigations, we
have witnessed in the last few years the emergence of specialized repositories,
e.g., DataVerse and FigShare. Such repositories provide users with the means to
share and publish datasets that were used or generated in research
investigations. While these repositories have proven their usefulness,
interpreting and reusing evidence for most research results is a challenging
task. Additional contextual descriptions are needed to understand how those
results were generated and/or the circumstances under which they were
concluded. Because of this, scientists are calling for models that go beyond
the publication of datasets to systematically capture the life cycle of
scientific investigations and provide a single entry point to access the
information about the hypothesis investigated, the datasets used, the
experiments carried out, the results of the experiments, the people involved in
the research, etc. In this paper we present the Research Object (RO) suite of
ontologies, which provide a structured container to encapsulate research data
and methods along with essential metadata descriptions. Research Objects are
portable units that enable the sharing, preservation, interpretation and reuse
of research investigation results. The ontologies we present have been designed
in the light of requirements that we gathered from life scientists. They have
been built upon existing popular vocabularies to facilitate interoperability.
Furthermore, we have developed tools to support the creation and sharing of
Research Objects, thereby promoting and facilitating their adoption.Comment: 20 page
Wikis in scholarly publishing
Scientific research is a process concerned with the creation, collective accumulation, contextualization, updating and maintenance of knowledge. Wikis provide an environment that allows to collectively accumulate, contextualize, update and maintain knowledge in a coherent and transparent fashion. Here, we examine the potential of wikis as platforms for scholarly publishing. In the hope to stimulate further discussion, the article itself was drafted on "Species-ID":http://species-id.net/w/index.php?title=Wikis_in_scholarly_publishing&oldid=3815 - a wiki that hosts a prototype for wiki-based scholarly publishing - where it can be updated, expanded or otherwise improved
Beyond opening up the black box: Investigating the role of algorithmic systems in Wikipedian organizational culture
Scholars and practitioners across domains are increasingly concerned with
algorithmic transparency and opacity, interrogating the values and assumptions
embedded in automated, black-boxed systems, particularly in user-generated
content platforms. I report from an ethnography of infrastructure in Wikipedia
to discuss an often understudied aspect of this topic: the local, contextual,
learned expertise involved in participating in a highly automated
social-technical environment. Today, the organizational culture of Wikipedia is
deeply intertwined with various data-driven algorithmic systems, which
Wikipedians rely on to help manage and govern the "anyone can edit"
encyclopedia at a massive scale. These bots, scripts, tools, plugins, and
dashboards make Wikipedia more efficient for those who know how to work with
them, but like all organizational culture, newcomers must learn them if they
want to fully participate. I illustrate how cultural and organizational
expertise is enacted around algorithmic agents by discussing two
autoethnographic vignettes, which relate my personal experience as a veteran in
Wikipedia. I present thick descriptions of how governance and gatekeeping
practices are articulated through and in alignment with these automated
infrastructures. Over the past 15 years, Wikipedian veterans and administrators
have made specific decisions to support administrative and editorial workflows
with automation in particular ways and not others. I use these cases of
Wikipedia's bot-supported bureaucracy to discuss several issues in the fields
of critical algorithms studies, critical data studies, and fairness,
accountability, and transparency in machine learning -- most principally
arguing that scholarship and practice must go beyond trying to "open up the
black box" of such systems and also examine sociocultural processes like
newcomer socialization.Comment: 14 pages, typo fixed in v
A Linked Data Approach to Sharing Workflows and Workflow Results
A bioinformatics analysis pipeline is often highly elaborate, due to the inherent complexity of biological systems and the variety and size of datasets. A digital equivalent of the âMaterials and Methodsâ section in wet laboratory publications would be highly beneficial to bioinformatics, for evaluating evidence and examining data across related experiments, while introducing the potential to find associated resources and integrate them as data and services. We present initial steps towards preserving bioinformatics âmaterials and methodsâ by exploiting the workflow paradigm for capturing the design of a data analysis pipeline, and RDF to link the workflow, its component services, run-time provenance, and a personalized biological interpretation of the results. An example shows the reproduction of the unique graph of an analysis procedure, its results, provenance, and personal interpretation of a text mining experiment. It links data from Taverna, myExperiment.org, BioCatalogue.org, and ConceptWiki.org. The approach is relatively âlight-weightâ and unobtrusive to bioinformatics users
Bots in Wikipedia: Unfolding their duties
The success of crowdsourcing systems such as Wikipedia relies on people participating in these systems. However, in this research we reveal to what extent human and machine intelligence is combined to carry out semi-automatic workflows of complex tasks. In Wikipedia, bots are used to realize such combination of human-machine intelligence. We provide an extensive overview on various edit types bots carry out in this regard through the analysis of 1,639 approved task requests. We classify existing tasks by an action-object-pair structure and reveal existing differences in their probability of occurrence depending on the investigated work context. In the context of community services, bots mainly create reports, whereas in the area of guidelines or policies bots are mostly responsible for adding templates to pages. Moreover, the analysis of existing bot tasks revealed insights that suggest general reasons, why Wikipediaâs editor community uses bots as well as approaches, how they organize machine tasks to provide a sustainable service. We conclude by discussing how these insights can prepare the foundation for further research
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