Training citizen scientists through an online game developed for data quality control

Abstract. Some form of training is often necessary in citizen science projects. While in many citizen science projects it is possible to keep tasks simple so that training requirements are minimal, some projects include more challenging tasks and, thus, require more extensive training. Training can hinder joining a project, and therefore most citizen science projects prefer to keep training requirements low. However, training may be needed to ensure good data quality. In this study, we evaluated if an online game that was originally developed for data quality control in a citizen science project, can be used for training for that project. More specifically, we investigated whether the CrowdWater game can be used to train new participants on how to use the virtual staff gauge in the CrowdWater smartphone app for the collection of water level class data. Within this app, the task of placing a virtual staff gauge to start measurements at a new location has proven to be challenging; however this is a crucial task for all subsequent measurements at this location. We analysed the performance of 52 participants in the placement of the virtual staff gauge before and after playing the online CrowdWater game as a form of training. After playing the game, the performance improved for most participants. This suggests that players learned project related tasks intuitively by observing actual gauge placements by other citizen scientists and thus acquired knowledge about how to best use the app instinctively. Interestingly, self-assessment was not a good proxy for the participants’ performance or performance increase. These results demonstrate the value of an online game for training, particularly when compared to other information materials, which are often not used extensively by citizen scientist. These findings are useful for the development of training strategies for other citizen science projects because they indicate that gamified approaches might provide valuable alternative training methods.

Download Full-text

Training citizen scientists through an online game developed for data quality control

Geoscience Communication ◽

10.5194/gc-3-109-2020 ◽

2020 ◽

Vol 3 (1) ◽

pp. 109-126 ◽

Cited By ~ 1

Author(s):

Barbara Strobl ◽

Simon Etter ◽

H. J. Ilja van Meerveld ◽

Jan Seibert

Keyword(s):

Quality Control ◽

Data Quality ◽

Citizen Science ◽

Training Methods ◽

Online Game ◽

Data Quality Control ◽

Training Requirements ◽

Science Projects ◽

Other Information ◽

Before And After

Abstract. Some form of training is often necessary for citizen science projects. While in some citizen science projects, it is possible to keep tasks simple so that training requirements are minimal, other projects include more challenging tasks and, thus, require more extensive training. Training can be a hurdle to joining a project, and therefore most citizen science projects prefer to keep training requirements low. However, training may be needed to ensure good data quality. In this study, we evaluated whether an online game that was originally developed for data quality control in a citizen science project can be used for training for that project. More specifically, we investigated whether the CrowdWater game can be used to train new participants on how to place the virtual staff gauge in the CrowdWater smartphone app for the collection of water level class data. Within this app, the task of placing a virtual staff gauge to start measurements at a new location has proven to be challenging; however, this is a crucial task for all subsequent measurements at this location. We analysed the performance of 52 participants in the placement of the virtual staff gauge before and after playing the online CrowdWater game as a form of training. After playing the game, the performance improved for most participants. This suggests that players learned project-related tasks intuitively by observing actual gauge placements by other citizen scientists in the game and thus acquired knowledge about how to best use the app instinctively. Interestingly, self-assessment was not a good proxy for the participants' performance or the performance increase through the training. These results demonstrate the value of an online game for training. These findings are useful for the development of training strategies for other citizen science projects because they indicate that gamified approaches might provide valuable alternative training methods, particularly when other information materials are not used extensively by citizen scientists.

Download Full-text

Supplementary material to "Training citizen scientists through an online game developed for data quality control"

10.5194/gc-2019-26-supplement ◽

2019 ◽

Author(s):

Barbara Strobl ◽

Simon Etter ◽

H. J. Ilja van Meerveld ◽

Jan Seibert

Keyword(s):

Quality Control ◽

Data Quality ◽

Online Game ◽

Data Quality Control ◽

Supplementary Material

Download Full-text

An improved comprehensive strategy for deep and quantitative N-glycomics based on optimization of sample preparation, isotope-based data quality control and quantification, new N-glycan libraries and new algorithms

10.1101/2020.10.15.340638 ◽

2020 ◽

Author(s):

Yudong Guan ◽

Jiaxiang Hu ◽

Weiqian Cao ◽

Wencong Cui ◽

Fan Yang ◽

...

Keyword(s):

Quality Control ◽

Sample Preparation ◽

Data Quality ◽

Biomarker Discovery ◽

Data Quality Control ◽

Post Translational Modification ◽

Comprehensive Strategy ◽

All Trans Retinoic Acid ◽

Depth Analysis ◽

Before And After

ABSTRACTGlobal in-depth analysis of N-glycosylation, as the most complex post-translational modification of proteins, is requiring methods being as sensitive, selective and reliable as possible. Here, an enhanced strategy for N-glycomics is presented comprising optimized sample preparation yielding enhanced glycoprotein recovery and permethylation efficiency, isotopic labelling for data quality control and relative quantification, integration of new N-glycan libraries (human and mouse), newly developed R-scripts matching experimental MS1 data to theoretical N-glycan compositions and bundled sequencing algorithms for MS2-based structural identification to ultimately enhance the coverage and accuracy of N-glycans. With this strategy the numbers of identified N-glycans are more than doubled compared with previous studies, exemplified by etanercept (more than 3-fold) and chicken ovalbumin (more than 2-fold) at nanogram level. The power of this strategy and applicability to biological samples is further demonstrated by comparative N-glycomics of human acute promyelocytic leukemia cells before and after treatment with all-trans retinoic acid, showing that N-glycan biosynthesis is slowed down and 57 species are significantly altered in response to the treatment. This improved analytical platform enables deep and accurate N-glycomics for glycobiological research and biomarker discovery.

Download Full-text

Data Assimilation and Ocean Data Quality Control Upgrades in SWAFS

10.21236/ada626595 ◽

2002 ◽

Author(s):

Clark Rowley ◽

James Cummings

Keyword(s):

Quality Control ◽

Data Assimilation ◽

Data Quality ◽

Data Quality Control

Download Full-text

Automated Data Quality Control in FDOPA brain PET Imaging using Deep Learning

Computer Methods and Programs in Biomedicine ◽

10.1016/j.cmpb.2021.106239 ◽

2021 ◽

pp. 106239

Author(s):

Antonella D. Pontoriero ◽

Giovanna Nordio ◽

Rubaida Easmin ◽

Alessio Giacomel ◽

Barbara Santangelo ◽

...

Keyword(s):

Quality Control ◽

Deep Learning ◽

Data Quality ◽

Pet Imaging ◽

Data Quality Control ◽

Brain Pet

Download Full-text

A Machine Learning Approach for Data Quality Control of Earth Observation Data Management System

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss39084.2020.9323615 ◽

2020 ◽

Author(s):

Weiguo Han ◽

Matthew Jochum

Keyword(s):

Machine Learning ◽

Quality Control ◽

Data Quality ◽

Earth Observation ◽

Data Management System ◽

Learning Approach ◽

Observation Data ◽

Data Quality Control ◽

Machine Learning Approach ◽

Earth Observation Data

Download Full-text

An interactive graphical system for XBT data quality control and visualization

Computers & Geosciences ◽

10.1016/s0098-3004(00)00172-2 ◽

2001 ◽

Vol 27 (7) ◽

pp. 867-876 ◽

Cited By ~ 1

Author(s):

Pankajakshan Thadathil ◽

Aravind K Ghosh ◽

J.S Sarupria ◽

V.V Gopalakrishna

Keyword(s):

Quality Control ◽

Data Quality ◽

Data Quality Control ◽

Graphical System

Download Full-text

Implementation of Marine Environment Monitoring Data Quality Control System

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.926-930.4254 ◽

2014 ◽

Vol 926-930 ◽

pp. 4254-4257 ◽

Cited By ~ 2

Author(s):

Jin Xu ◽

Da Tao Yu ◽

Zhong Jie Yuan ◽

Bo Li ◽

Zi Zhou Xu

Keyword(s):

Quality Control ◽

Control System ◽

Data Quality ◽

Marine Environment ◽

Quality Control System ◽

Monitoring Data ◽

Environment Monitoring ◽

Data Quality Control ◽

Labor Costs ◽

Marine Environment Monitoring

Traditional artificial perception quality control methods of marine environment monitoring data have many disadvantages, including high labor costs and mistakes of data review. Based on GIS spatial analysis technology, Marine Environment Monitoring Data Quality Control System is established according to the Bohai Sea monitoring regulation. In the practical application process, it plays the role of improving efficiency of quality control, saving the manpower and financial resources. It also provides an important guarantee for the comprehensive analysis and management of marine environment data.

Download Full-text