scholarly journals Connect to Protect Research-Community Partnerships: Assessing Change in Successful Collaboration Factors over Time

2010 ◽  
Vol 1 (1) ◽  
Author(s):  
Mauri Ziff
Keyword(s):  
Community Partnerships ◽  
Research Community ◽  
Successful Collaboration ◽  
Over Time

scholarly journals A Semi-Supervised Learning Approach for Tackling Twitter Spam Drift

2019 ◽  
Vol 18 (02) ◽  
pp. 1950010 ◽  
Author(s):  
Niddal Imam ◽  
Biju Issac ◽  
Seibu Mary Jacob
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Research Community ◽  
Machine Learning Techniques ◽  
Spam Detection ◽  
Learning Approach ◽  
New Approach ◽  
Detection Systems ◽  
Learning Techniques ◽  
Over Time

Twitter has changed the way people get information by allowing them to express their opinion and comments on the daily tweets. Unfortunately, due to the high popularity of Twitter, it has become very attractive to spammers. Unlike other types of spam, Twitter spam has become a serious issue in the last few years. The large number of users and the high amount of information being shared on Twitter play an important role in accelerating the spread of spam. In order to protect the users, Twitter and the research community have been developing different spam detection systems by applying different machine-learning techniques. However, a recent study showed that the current machine learning-based detection systems are not able to detect spam accurately because spam tweet characteristics vary over time. This issue is called “Twitter Spam Drift”. In this paper, a semi-supervised learning approach (SSLA) has been proposed to tackle this. The new approach uses the unlabeled data to learn the structure of the domain. Different experiments were performed on English and Arabic datasets to test and evaluate the proposed approach and the results show that the proposed SSLA can reduce the effect of Twitter spam drift and outperform the existing techniques.

scholarly journals Leaf-GP: An Open and Automated Software Application for Measuring Growth Phenotypes for Arabidopsis and Wheat

2017 ◽  
Author(s):  
Ji Zhou ◽  
Christopher Applegate ◽  
Albor Dobon Alonso ◽  
Daniel Reynolds ◽  
Simon Orford ◽  
...  
Keyword(s):  
Computer Vision ◽  
High Performance ◽  
Growth Traits ◽  
Source Code ◽  
Research Community ◽  
Phenotypic Analysis ◽  
Software Application ◽  
Dynamic Growth ◽  
Scientific Libraries ◽  
Over Time

AbstractBackgroundPlants demonstrate dynamic growth phenotypes that are determined by genetic and environmental factors. Phenotypic analysis of growth features over time is a key approach to understand how plants interact with environmental change as well as respond to different treatments. Although the importance of measuring dynamic growth traits is widely recognised, available open software tools are limited in terms of batch processing of image datasets, multiple trait analysis, software usability and cross-referencing results between experiments, making automated phenotypic analysis problematic.ResultsHere, we present Leaf-GP (Growth Phenotypes), an easy-to-use and open software application that can be executed on different platforms. To facilitate diverse scientific user communities, we provide three versions of the software, including a graphic user interface (GUI) for personal computer (PC) users, a command-line interface for high-performance computer (HPC) users, and an interactive Jupyter Notebook (also known as the iPython Notebook) for computational biologists and computer scientists. The software is capable of extracting multiple growth traits automatically from large image datasets. We have utilised it in Arabidopsis thaliana and wheat (Triticum aestivum) growth studies at the Norwich Research Park (NRP, UK). By quantifying growth phenotypes over time, we are able to identify diverse plant growth patterns based on a variety of key growth-related phenotypes under varied experimental conditions.As Leaf-GP has been evaluated with noisy image series acquired by different imaging devices and still produced reliable biologically relevant outputs, we believe that our automated analysis workflow and customised computer vision based feature extraction algorithms can facilitate a broader plant research community for their growth and development studies. Furthermore, because we implemented Leaf-GP based on open Python-based computer vision, image analysis and machine learning libraries, our software can not only contribute to biological research, but also exhibit how to utilise existing open numeric and scientific libraries (including Scikit-image, OpenCV, SciPy and Scikit-learn) to build sound plant phenomics analytic solutions, efficiently and effectively.ConclusionsLeaf-GP is a comprehensive software application that provides three approaches to quantify multiple growth phenotypes from large image series. We demonstrate its usefulness and high accuracy based on two biological applications: (1) the quantification of growth traits for Arabidopsis genotypes under two temperature conditions; and (2) measuring wheat growth in the glasshouse over time. The software is easy-to-use and cross-platform, which can be executed on Mac OS, Windows and high-performance computing clusters (HPC), with open Python-based scientific libraries preinstalled. We share our modulated source code and executables (.exe for Windows; .app for Mac) together with this paper to serve the plant research community. The software, source code and experimental results are freely available at https://github.com/Crop-Phenomics-Group/Leaf-GP/releases.

scholarly journals Lessons Learned From Food & Fitness About Building Successful Partnerships: Focus, Capacity, and Sustainability

2018 ◽  
Vol 19 (1_suppl) ◽  
pp. 115S-124S ◽  
Author(s):  
Laurie Lachance ◽  
Martha Quinn ◽  
Theresa Kowalski-Dobson
Keyword(s):  
The United States ◽  
Community Partnerships ◽  
Lessons Learned ◽  
Children And Families ◽  
Sustainable Change ◽  
Community Residents ◽  
Implementation Period ◽  
Changes Over Time ◽  
Locally Grown ◽  
Over Time

Approaches undertaken by the Food & Fitness (F&F) community partnerships demonstrate that engaging community residents in the process of creating systems change strengthens the ability of neighborhoods, organizations, and institutions to foster and sustain those changes over time. The F&F partnerships were established to increase access to locally grown food and safe places for physical activity for children and families in communities with inequities across the United States. A critical focus of this initiative has been to use community-determined approaches to create changes in policies, infrastructures, and systems that will lead not only to change but also to sustainable change that positively influences health equity. During the 9 years of the initiative, lessons were learned about the fundamental elements that built the foundation for success across all partnership work. Data were extracted from the systems and policy change tracking forms related to efforts for all F&F sites over the entire implementation period (2009-2016). Documentation related to both the process and outcomes of the efforts were qualitatively analyzed to determine factors related to success. The following factors have emerged from our analyses and uncover a deeper understanding of what actions and factors were critical for the work: focus of the work over time, capacity built in the partnerships, and sustainability of the work and outcomes.

Coauthorship in Regional Science

2017 ◽  
Vol 41 (4) ◽  
pp. 387-409
Author(s):  
Jing Chen ◽  
Randall Jackson
Keyword(s):  
Cluster Analysis ◽  
Scientific Collaboration ◽  
Science Research ◽  
Research Community ◽  
Regional Science ◽  
Collaboration Networks ◽  
Regional Research ◽  
Coauthorship Network ◽  
And Cluster Analysis ◽  
Over Time

The year 2015 marked the fiftieth anniversary of West Virginia University’s (WVU) Regional Research Institute (RRI), which has played an important role in many scientific collaboration networks. Through social network analysis (SNA) focusing on the RRI research community since its inception in 1965, this article illustrates the role that organizations and the networks they promote can play in scientific problem domains, promoting scholarly collaborations and coauthorship in the field of regional science. We analyzed an evolving WVU RRI coauthorship network that has grown and gained in complexity over time in terms of (1) global metrics, (2) components and cluster analysis, (3) centrality, and (4) PageRank and AuthorRank. The results of these analyses depict a well-developed and influential scientific collaboration structure within both WVU and the regional science research community.

Work Observer

2014 ◽  
Vol 22 (1) ◽  
pp. 4-7
Author(s):  
Jeffrey T. Hansberger
Keyword(s):  
Data Collection ◽  
Observational Data ◽  
Research Laboratory ◽  
Research Community ◽  
Interaction Data ◽  
Data Collection Tool ◽  
Dynamic Information ◽  
Army Research Laboratory ◽  
Face To Face ◽  
Over Time

Longitudinal communication data are needed to understand the dynamics, adaptation, and evolution of networks over time, particularly when the mode of interaction is face-to-face communication. Traditional means of collecting communication and interaction data that include questionnaires are often not appropriate for collecting this type of dynamic information. To push the field beyond the analysis of discrete snapshots of interactions and speculation about what occurs between those snapshots, an observational data-collection tool called Work Observer was designed by the Army Research Laboratory and is freely available to the research community.

scholarly journals Exploring How White-Faced Sakis Control Digital Visual Enrichment Systems

Animals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 557
Author(s):  
Ilyena Hirskyj-Douglas ◽  
Vilma Kankaanpää
Keyword(s):  
Visual Stimuli ◽  
Research Community ◽  
Abstract Art ◽  
Confounding Factors ◽  
Bell Curve ◽  
Visual Elements ◽  
Open Question ◽  
Over Time

Computer-enabled screen systems containing visual elements have long been employed with captive primates for assessing preference, reactions and for husbandry reasons. These screen systems typically play visual enrichment to primates without them choosing to trigger the system and without their consent. Yet, what videos primates, especially monkeys, would prefer to watch of their own volition and how to design computers and methods that allow choice is an open question. In this study, we designed and tested, over several weeks, an enrichment system that facilitates white-faced saki monkeys to trigger different visual stimuli in their regular zoo habitat while automatically logging and recording their interaction. By analysing this data, we show that the sakis triggered underwater and worm videos over the forest, abstract art, and animal videos, and a control condition of no-stimuli. We also note that the sakis used the device significantly less when playing animal videos compared to other conditions. Yet, plotting the data over time revealed an engagement bell curve suggesting confounding factors of novelty and habituation. As such, it is unknown if the stimuli or device usage curve caused the changes in the sakis interactions over time. Looking at the sakis’ behaviours and working with zoo personnel, we noted that the stimuli conditions resulted in significantly decreasing the sakis’ scratching behaviour. For the research community, this study builds on methods that allow animals to control computers in a zoo environment highlighting problems in quantifying animal interactions with computer devices.

scholarly journals Asset Mapping as a Tool for Identifying Resources in Community Health: A Methodological Overview

2019 ◽  
Vol 2 (1) ◽  
pp. 13-25
Author(s):  
Tanvir C Turin ◽  
Maaz Shahid ◽  
Marcua Vaska
Keyword(s):  
Community Health ◽  
Health Research ◽  
Community Partnerships ◽  
Research Community ◽  
Research Projects ◽  
Intended Audience ◽  
Asset Mapping ◽  
Surface Level ◽  
Health Related ◽  
The Creation

Background: By focusing on a community’s strengths instead of its’ weaknesses, the process of asset mapping provides researchers a new way to assess community health. This process is also a useful tool for assessing health-related needs, disparities, and inequities within the communities. This paper aims to serve as a basic and surface level guide to understanding and planning for creating an asset map. Methods: A step-by-step guideline is provided in this paper as an introduction to those interested in creating an asset map using organizational outlines and previous application in research projects. Results: To help readers better grasp asset maps, a few examples are first provided that show the application of asset maps in health research, community engagement, and community partnerships. This is followed by elaboration of the six steps involved in the creation of an asset map. Conclusion: This paper introduces researchers to the steps required to create an asset map, with examples from published literature. The intended audience includes students and researchers new to the creation of asset maps.

scholarly journals Research Community Analytic Tool Based on Topic Modeling and Network Analysis

2020 ◽  
pp. 13-21
Author(s):  
И. Н. Девицын ◽  
И. В. Савин
Keyword(s):  
Graph Theory ◽  
Topic Modeling ◽  
Research Community ◽  
Analytical Tool ◽  
State University ◽  
Research Teams ◽  
Research Areas ◽  
Research Organizations ◽  
New Research ◽  
Over Time

В статье рассматривается новый инструмент анализа научных сообществ с использованием методов моделирования тем и теории графов. Результаты применения предложенного нами подхода представлены для публикаций авторов, аффилированных с Сургутским государственным университетом в Scopus за период 1995–2021 гг. Разработанный инструмент позволяет определять основные направления научных исследований, выявлять передовые коллективы научных работников по отдельным направлениям, а также анализировать взаимосвязи научных коллективов. Представлены результаты распределения публикаций по времени, девяти основным темам, расчет метрик графов соавторства, построенных на основе исследуемого набора данных. В будущем разработанный подход можно применить для оценки научно-исследовательского потенциала научных организаций, для оперативного определения направлений научных исследований, выявления передовых коллективов и научных работников по перспективным направлениям. The study presents a new research community analytical tool based on topic modeling and methods from graph theory. The results of the proposed approach are presented for Scopusindexed publications by the authors affiliated with Surgut State University in 1995–2021. The tool makes it possible to determine the key research areas, identify the leading research teams in certain areas and analyze the relationships between these teams. The paper includes the distribution of publications over time, nine main areas of publications, and a range of metrics for the coauthorship graphs of the studied dataset. In the future, the tool can be applied to assess the potential of research organizations, select the research areas, and identify the leading research teams and researchers in promising areas.

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