scholarly journals Keys to successful scientific VGI projects

2016 ◽  
Author(s):  
Jens Ingensand ◽  
Sarah Composto ◽  
Olivier Ertz ◽  
Daniel Rappo ◽  
Marion Nappez ◽  
...  
Keyword(s):  
Volunteered Geographic Information ◽  
Geographic Information ◽  
Quality Data ◽  
Research Projects ◽  
High Quality ◽  
High Quality Data ◽  
Geographic Data

Scientific projects are increasingly using volunteered geographic information (VGI) in order to collect and validate geographic data. This concept relies on the three challenges that first of all users can be found and second be convinced to collaborate and contribute and that scientists finally are able to gather high quality data for their projects. In this paper these three challenges are discussed using the experience with three different research projects: Urbangene, Signalez-nous and BioSentiers.

scholarly journals Keys to successful scientific VGI projects

2016 ◽  
Author(s):  
Jens Ingensand ◽  
Sarah Composto ◽  
Olivier Ertz ◽  
Daniel Rappo ◽  
Marion Nappez ◽  
...  
Keyword(s):  
Volunteered Geographic Information ◽  
Geographic Information ◽  
Quality Data ◽  
Research Projects ◽  
High Quality ◽  
High Quality Data ◽  
Geographic Data

Scientific projects are increasingly using volunteered geographic information (VGI) in order to collect and validate geographic data. This concept relies on the three challenges that first of all users can be found and second be convinced to collaborate and contribute and that scientists finally are able to gather high quality data for their projects. In this paper these three challenges are discussed using the experience with three different research projects: Urbangene, Signalez-nous and BioSentiers.

scholarly journals Keys to successful scientific VGI projects

2016 ◽  
Author(s):  
Jens Ingensand ◽  
Sarah Composto ◽  
Olivier Ertz ◽  
Daniel Rappo ◽  
Marion Nappez ◽  
...  
Keyword(s):  
Volunteered Geographic Information ◽  
Geographic Information ◽  
Quality Data ◽  
Research Projects ◽  
High Quality ◽  
High Quality Data ◽  
Geographic Data

Scientific projects are increasingly using volunteered geographic information (VGI) in order to collect and validate geographic data. This concept relies on the three challenges that first of all users can be found and second be convinced to collaborate and contribute and that scientists finally are able to gather high quality data for their projects. In this paper these three challenges are discussed using the experience with three different research projects: Urbangene, Signalez-nous and BioSentiers.

scholarly journals Challenges in VGI for scientific projects

2016 ◽  
Author(s):  
Jens Ingensand ◽  
Sarah Composto ◽  
Marion Nappez ◽  
Timothée Produit ◽  
Olivier Ertz ◽  
...  
Keyword(s):  
Recent Trend ◽  
Scientific Research ◽  
Volunteered Geographic Information ◽  
Geographic Information ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Final Goal ◽  
Challenges And Opportunities

Volunteered Geographic Information (VGI) is a recent trend that has been successfully used in order to collect and share geographic information. This method is of interest for scientists who are in need of data and who want to get people involved in their cause. In this paper we discuss the challenges and opportunities that scientists may face when using the concept. An initial challenge is to find users who are willing to contribute. Second, scientist must get these users to interact with the application and with each other. The final goal is to end up with high quality data that can be used for scientific research.

scholarly journals Challenges in VGI for scientific projects

2016 ◽  
Author(s):  
Jens Ingensand ◽  
Sarah Composto ◽  
Marion Nappez ◽  
Timothée Produit ◽  
Olivier Ertz ◽  
...  
Keyword(s):  
Recent Trend ◽  
Scientific Research ◽  
Volunteered Geographic Information ◽  
Geographic Information ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Final Goal ◽  
Challenges And Opportunities

Volunteered Geographic Information (VGI) is a recent trend that has been successfully used in order to collect and share geographic information. This method is of interest for scientists who are in need of data and who want to get people involved in their cause. In this paper we discuss the challenges and opportunities that scientists may face when using the concept. An initial challenge is to find users who are willing to contribute. Second, scientist must get these users to interact with the application and with each other. The final goal is to end up with high quality data that can be used for scientific research.

scholarly journals Challenges in VGI for scientific projects

2016 ◽  
Author(s):  
Jens Ingensand ◽  
Sarah Composto ◽  
Marion Nappez ◽  
Timothée Produit ◽  
Olivier Ertz ◽  
...  
Keyword(s):  
Recent Trend ◽  
Scientific Research ◽  
Volunteered Geographic Information ◽  
Geographic Information ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Final Goal ◽  
Challenges And Opportunities

Volunteered Geographic Information (VGI) is a recent trend that has been successfully used in order to collect and share geographic information. This method is of interest for scientists who are in need of data and who want to get people involved in their cause. In this paper we discuss the challenges and opportunities that scientists may face when using the concept. An initial challenge is to find users who are willing to contribute. Second, scientist must get these users to interact with the application and with each other. The final goal is to end up with high quality data that can be used for scientific research.

Collecting High-Quality Data

2018 ◽  
Author(s):  
Mary Kay Gugerty ◽  
Dean Karlan
Keyword(s):  
Data Collection ◽  
Social Desirability ◽  
Measurement Errors ◽  
Monitoring And Evaluation ◽  
Quality Data ◽  
Social Desirability Bias ◽  
High Quality ◽  
High Quality Data ◽  
Evaluation Systems ◽  
Demand Effect

Without high-quality data, even the best-designed monitoring and evaluation systems will collapse. Chapter 7 introduces some the basics of collecting high-quality data and discusses how to address challenges that frequently arise. High-quality data must be clearly defined and have an indicator that validly and reliably measures the intended concept. The chapter then explains how to avoid common biases and measurement errors like anchoring, social desirability bias, the experimenter demand effect, unclear wording, long recall periods, and translation context. It then guides organizations on how to find indicators, test data collection instruments, manage surveys, and train staff appropriately for data collection and entry.

Developmental Impacts of Remittances on Migrant-Sending Households: Micro-Level Evidence from Punjab, Pakistan

2019 ◽  
Vol 14 (3) ◽  
pp. 338-366
Author(s):  
Kashif Imran ◽  
Evelyn S. Devadason ◽  
Cheong Kee Cheok
Keyword(s):  
Human Development ◽  
Human Development Index ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Development Index ◽  
Policy Interventions ◽  
Education And Health ◽  
Micro Level ◽  
The Government

This article analyzes the overall and type of developmental impacts of remittances for migrant-sending households (HHs) in districts of Punjab, Pakistan. For this purpose, an HH-based human development index is constructed based on the dimensions of education, health and housing, with a view to enrich insights into interactions between remittances and HH development. Using high-quality data from a HH micro-survey for Punjab, the study finds that most migrant-sending HHs are better off than the HHs without this stream of income. More importantly, migrant HHs have significantly higher development in terms of housing in most districts of Punjab relative to non-migrant HHs. Thus, the government would need policy interventions focusing on housing to address inequalities in human development at the district-HH level, and subsequently balance its current focus on the provision of education and health.

Capturing sexual assault data: An information system designed by forensic clinicians and healthcare researchers

2017 ◽  
Vol 47 (1) ◽  
pp. 46-55 ◽  
Author(s):  
S Aqif Mukhtar ◽  
Debbie A Smith ◽  
Maureen A Phillips ◽  
Maire C Kelly ◽  
Renate R Zilkens ◽  
...  
Keyword(s):  
Information System ◽  
Sexual Assault ◽  
Data Management ◽  
Sexual Violence ◽  
Western Australia ◽  
Management System ◽  
Data Management System ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data

Background: The Sexual Assault Resource Center (SARC) in Perth, Western Australia provides free 24-hour medical, forensic, and counseling services to persons aged over 13 years following sexual assault. Objective: The aim of this research was to design a data management system that maintains accurate quality information on all sexual assault cases referred to SARC, facilitating audit and peer-reviewed research. Methods: The work to develop SARC Medical Services Clinical Information System (SARC-MSCIS) took place during 2007–2009 as a collaboration between SARC and Curtin University, Perth, Western Australia. Patient demographics, assault details, including injury documentation, and counseling sessions were identified as core data sections. A user authentication system was set up for data security. Data quality checks were incorporated to ensure high-quality data. Results: An SARC-MSCIS was developed containing three core data sections having 427 data elements to capture patient’s data. Development of the SARC-MSCIS has resulted in comprehensive capacity to support sexual assault research. Four additional projects are underway to explore both the public health and criminal justice considerations in responding to sexual violence. The data showed that 1,933 sexual assault episodes had occurred among 1881 patients between January 1, 2009 and December 31, 2015. Sexual assault patients knew the assailant as a friend, carer, acquaintance, relative, partner, or ex-partner in 70% of cases, with 16% assailants being a stranger to the patient. Conclusion: This project has resulted in the development of a high-quality data management system to maintain information for medical and forensic services offered by SARC. This system has also proven to be a reliable resource enabling research in the area of sexual violence.

scholarly journals Fooled by Performance Randomness: Overrewarding Luck

2019 ◽  
Vol 101 (4) ◽  
pp. 658-666 ◽  
Author(s):  
Romain Gauriot ◽  
Lionel Page
Keyword(s):  
Causal Effect ◽  
Experimental Situation ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Economic Organizations ◽  
Quasi Experimental ◽  
Similar Location ◽  
Do So

We provide evidence of a violation of the informativeness principle whereby lucky successes are overly rewarded. We isolate a quasi-experimental situation where the success of an agent is as good as random. To do so, we use high-quality data on football (soccer) matches and select shots on goal that landed on the goal posts. Using nonscoring shots, taken from a similar location on the pitch, as counterfactuals to scoring shots, we estimate the causal effect of a lucky success (goal) on the evaluation of the player's performance. We find clear evidence that luck is overly influencing managers' decisions and evaluators' ratings. Our results suggest that this phenomenon is likely to be widespread in economic organizations.

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