scholarly journals Crowdsourcing Undone Science

2017 ◽  
Vol 3 ◽  
pp. 560 ◽  
Author(s):  
Gwen Ottinger
Keyword(s):  
Data Analysis ◽  
Case Studies ◽  
Local Effects ◽  
Grassroots Groups ◽  
New Knowledge ◽  
Natural Scientists ◽  
Informal Groups ◽  
Interpretive Frameworks

Could crowdsourcing be a way to get undone science done?  Could grassroots groups enlist volunteers to help make sense of large amounts of otherwise unanalyzed data—an approach that has been gaining popularity among natural scientists?  This paper assesses the viability of this technique for creating new knowledge about the local effects of petrochemicals, by examining three recent experiments in crowdsourcing led by non-profits and grassroots groups.  These case studies suggest that undertaking a crowdsourcing project requires significant resources, including technological infrastructures that smaller or more informal groups may find it difficult to provide.  They also indicate that crowdsourcing will be most successful when the questions of grassroots groups line up fairly well with existing scientific frameworks.  The paper concludes that further experimentation in crowdsourcing is warranted, at least in cases where adequate resources and interpretive frameworks are available, and that further investment in technological infrastructures for data analysis is needed. 

Case Studies in Data Analysis

1989 ◽  
Author(s):  
Andrew J. Mazzella ◽  
Delorey Jr. ◽  
Larson Dennis E. ◽  
Dickson Kevin P. ◽  
Jr Peter
Keyword(s):  
Data Analysis ◽  
Case Studies

Conceptualising a New Knowledge Management Logic Model for Global Health: A Case-Study Approach

2015 ◽  
Vol 14 (02) ◽  
pp. 1550015 ◽  
Author(s):  
Saori Ohkubo ◽  
Sarah V. Harlan ◽  
Naheed Ahmed ◽  
Ruwaida M. Salem
Keyword(s):  
Knowledge Management ◽  
Global Health ◽  
Case Studies ◽  
Health Professionals ◽  
Health Workers ◽  
Logic Model ◽  
Unique Contribution ◽  
Program Outcomes ◽  
New Knowledge ◽  
Health Field

Over the past few decades, knowledge management (KM) has become well-established in many fields, particularly in business. Several KM models have been at the forefront of promoting KM in businesses and organisations. However, the applicability of these traditional KM models to the global health field is limited by their focus on KM processes and activities with few linkages to intended outcomes. This paper presents the new Knowledge Management for Global Health (KM4GH) Logic Model, a practical tool that helps global health professionals plan ways in which resources and specific KM activities can work together to achieve desired health program outcomes. We test the validity of this model through three case studies of global and field-level health initiatives: an SMS-based mobile phone network among community health workers (CHWs) and their supervisors in Malawi, a global electronic Toolkits platform that provides health professionals access to health information resources, and a netbook-based eHealth pilot among CHWs and their clients in Bangladesh. The case studies demonstrate the flexibility of the KM4GH Logic Model in designing various KM activities while defining a common set of metrics to measure their outcomes, providing global health organisations with a tool to select the most appropriate KM activities to meet specific knowledge needs of an audience. The three levels of outcomes depicted in the model, which are grounded in behavioural theory, show the progression in the behaviour change process, or in this case, the knowledge use process, from raising awareness of and using the new knowledge to contributing to better health systems and behaviours of the public, and ultimately to improving the health status of communities and individuals. The KM4GH Logic Model makes a unique contribution to the global health field by helping health professionals plan KM activities with the end goal in mind.

Agents in bioinformatics

2005 ◽  
Vol 20 (2) ◽  
pp. 117-125 ◽  
Author(s):  
MICHAEL LUCK ◽  
EMANUELA MERELLI
Keyword(s):  
Data Analysis ◽  
Simulation Models ◽  
Biological Data ◽  
System Modelling ◽  
Agent Based Simulation ◽  
Huge Amount ◽  
Agent Based ◽  
Information Agents ◽  
New Knowledge ◽  
Definition Of

The scope of the Technical Forum Group (TFG) on Agents in Bioinformatics (BIOAGENTS) was to inspire collaboration between the agent and bioinformatics communities with the aim of creating an opportunity to propose a different (agent-based) approach to the development of computational frameworks both for data analysis in bioinformatics and for system modelling in computational biology. During the day, the participants examined the future of research on agents in bioinformatics primarily through 12 invited talks selected to cover the most relevant topics. From the discussions, it became clear that there are many perspectives to the field, ranging from bio-conceptual languages for agent-based simulation, to the definition of bio-ontology-based declarative languages for use by information agents, and to the use of Grid agents, each of which requires further exploration. The interactions between participants encouraged the development of applications that describe a way of creating agent-based simulation models of biological systems, starting from an hypothesis and inferring new knowledge (or relations) by mining and analysing the huge amount of public biological data. In this report we summarize and reflect on the presentations and discussions.

scholarly journals ELM Meets Urban Big Data Analysis: Case Studies

2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Ningyu Zhang ◽  
Huajun Chen ◽  
Xi Chen ◽  
Jiaoyan Chen
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Case Studies ◽  
Virtual Worlds ◽  
Health Hazard ◽  
Big Data Analysis ◽  
Application Framework ◽  
Rapid Progress ◽  
Urban Computing ◽  
General Application

In the latest years, the rapid progress of urban computing has engendered big issues, which creates both opportunities and challenges. The heterogeneous and big volume of data and the big difference between physical and virtual worlds have resulted in lots of problems in quickly solving practical problems in urban computing. In this paper, we propose a general application framework of ELM for urban computing. We present several real case studies of the framework like smog-related health hazard prediction and optimal retain store placement. Experiments involving urban data in China show the efficiency, accuracy, and flexibility of our proposed framework.

The Contribution of University Business Incubators to New Knowledge-Based Ventures

2001 ◽  
Vol 15 (4) ◽  
pp. 239-250 ◽  
Author(s):  
Rosa Grimaldi ◽  
Alessandro Grandi
Keyword(s):  
Case Studies ◽  
Empirical Analysis ◽  
Technological Knowledge ◽  
Business Incubators ◽  
Knowledge Based ◽  
New Knowledge ◽  
The Creation ◽  
The Empirical Analysis

This paper examines the role of university business incubators (UBIs) in supporting the creation of new knowledge-based ventures. UBIs are described as effective mechanisms for overcoming weaknesses of the more traditional public incubating institutions. They offer firms a range of university-related benefits, such as access to laboratories and equipment, to scientific and technological knowledge and to networks of key contacts, and the reputation that accrues from affiliation with a university. The empirical analysis is based on the Turin Polytechnic Incubator (TPI) and on case studies of six academic spin-offs hosted at TPI. While TPI does not effectively resolve such problems as inadequate access to funding capital and the lack of management and financial skills in its tenant companies, the networking capacity of incubating programmes is seen as a key characteristic that may help new knowledge-based ventures to overcome such difficulties.

Knowledge Management of Healthcare by Clinical-Pathways

2010 ◽  
Vol 09 (02) ◽  
pp. 119-125 ◽  
Author(s):  
Tomoyoshi Yamazaki ◽  
Katsuhiro Umemoto
Keyword(s):  
Knowledge Management ◽  
Theoretical Model ◽  
Case Studies ◽  
Knowledge Creation ◽  
Clinical Pathways ◽  
Medical Doctors ◽  
New Knowledge ◽  
Healthcare Process ◽  
Knowledge Intensive

Healthcare is a knowledge-intensive service provided by professionals, such as medical doctors, nurses, and pharmacists. Clinical-pathways are used by many healthcare organisations (HCOs) as a tool for performing the healthcare process, sharing and utilising knowledge from different professionals. In this paper, case studies were performed at two HCOs that use clinical-pathways actively in the healthcare process. Theoretical model construction, sharing, utilisation, and creation of the knowledge by different professionals, were tested by the case study of two HCOs which use clinical pathways actively. The theoretical model was a knowledge creation model which creates new knowledge continuously. In this theoretical model, clinical-pathways are suggested to be an effective tool for knowledge management in healthcare.

Evaluating the Impact of Waterfrac Technologies on Gas Recovery Efficiency: Case Studies Using Elliptical Flow Production Data Analysis

2007 ◽  
Author(s):  
Dilhan Ilk ◽  
Jay Alan Rushing ◽  
Richard Burl Sullivan ◽  
Thomas Alwin Blasingame
Keyword(s):  
Data Analysis ◽  
Case Studies ◽  
Production Data ◽  
Recovery Efficiency ◽  
Gas Recovery ◽  
Elliptical Flow ◽  
The Impact
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