Modular designed Apps – an opportunity to standardize data collection methods and to encourage the reuse of software

Data collection strategies vary among different citizen science projects. This complicates the intercomparability of parameter values acquired in different studies (e.g., methodological and scale issues) and results in variable data quality. This creates problems regarding the merging of different data sets and hampers the reuse of data from different projects. Modular designed applications for mobile devices (Apps) represent a framework that helps to foster the standardisation of data collection methods. While they encourage the reuse of the software, they provide enough flexibility for an adjustment in accordance with the research question(s) of interest.The currently developed App &#8220;FieldMApp&#8221; offers such a framework running under Android and iOS. The related concept includes predefined frame functionalities, like settings for the user account and the user interface, and adaptable application-related functionalities. The latter comprise several modules that are categorized as sensor test, basic functionality, parameter collection and data quality collection modules. The interdependencies of these modules are documented in a wiki. This enables an individual and context-based selection of functionalities. The FieldMApp is based on open-source software libraries (Xamarin, Open Development Kit (ODK), SQLite, CoreCLR-NCalc, LusoV.YamarinUsbSerialForAndroid, Newtonsoft.Json, SharpZipLib) and will be published as open-source software. Hence, the existing catalogue of functionalities can be augmented in the future. The premise for such extensions is that modules are published together with smart, universally applicable data quality recording routines and a proper documentation in the wiki.In this contribution, we present the concept and the structure of the FieldMApp and some current fields of application that are related to the cultivation of arable land, soil mapping, forest monitoring, and Earth Observation. The extension of the functionality catalogue is exemplified by the newly implemented speech recognition module. A related quality recording routine will be introduced. With this contribution we would like to encourage citizens and scientists to elicit which requirements such an App should fulfil from their point of view.

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EXAMINING DATA COLLECTION METHODS AND DATA QUALITY IN RETROSPECTIVE RESEARCH ON ELDERLY POPULATIONS

The Gerontologist ◽

10.1093/geront/gnv209.01 ◽

2015 ◽

Vol 55 (Suppl_2) ◽

pp. 490-490

Keyword(s):

Data Collection ◽

Data Quality ◽

Data Collection Methods ◽

Elderly Populations ◽

Collection Methods

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Technological Innovations in Data Collection: Acceptance, Data Quality and Costs

Sociological Research Online ◽

10.5153/sro.50 ◽

1996 ◽

Vol 1 (4) ◽

pp. 23-37 ◽

Cited By ~ 25

Author(s):

Edith de Leeuw ◽

William Nicholls

Keyword(s):

Data Collection ◽

Data Quality ◽

Computer Assisted ◽

New Techniques ◽

Data Collection Methods ◽

Electronic Reporting ◽

Research Organizations ◽

Survey Costs ◽

Professional Research ◽

Collection Methods

Whether computer assisted data collection methods should be used for survey data collection is no longer an issue. Most professional research organizations, commercial, government and academic, are adopting these new methods with enthusiasm. Computer assisted telephone interviewing (CATI) is most prevalent, and computer assisted personal interviewing (CAPI) is rapidly gaining in popularity. Also, new forms of electronic reporting of data using computers, telephones and voice recognition technology are emerging. This paper begins with a taxonomy of current computer assisted data collection methods. It then reviews conceptual and theoretical arguments and empirical evidence on such topics as: (1) respondents and interviewer acceptance of new techniques, (2) effect of computer assisted interviewing on data quality, (3) consequences for survey costs and (4) centralized vs. decentralized deployment of CATI.

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Positional Accuracy Assessment for Updating Authoritative Geospatial Datasets Based on Open Source Data and Remotely Sensed Images

Journal of Engineering ◽

10.31026/j.eng.2020.02.06 ◽

2020 ◽

Vol 26 (2) ◽

pp. 70-84 ◽

Cited By ~ 1

Author(s):

Mervat S. Jasem ◽

Odey AL-Hamadani

Keyword(s):

High Resolution ◽

Data Collection ◽

Open Source ◽

Spatial Data ◽

Road Network ◽

Data Sources ◽

National Standard ◽

Positional Accuracy ◽

Data Collection Methods ◽

Collection Methods

OpenStreetMap (OSM) represents the most common example of online volunteered mapping applications. Most of these platforms are open source spatial data collected by non-experts volunteers using different data collection methods. OSM project aims to provide a free digital map for all the world. The heterogeneity in data collection methods made OSM project databases accuracy is unreliable and must be dealt with caution for any engineering application. This study aims to assess the horizontal positional accuracy of three spatial data sources are OSM road network database, high-resolution Satellite Image (SI), and high-resolution Aerial Photo (AP) of Baghdad city with respect to an analogue formal road network dataset obtained from the Mayoralty of Baghdad (MB). The methodology of, U.S. National Standard Spatial Data Accuracy (NSSDA) was applied to measure the degree of agreement between each data source and the formal dataset (MB) in terms of horizontal positional accuracy by computing RMSE and NSSDA values. The study concluded that each of the three data sources does not agree with the MB dataset in both study sites AL-Aadhamiyah and AL-Kadhumiyah in terms of positional accuracy.

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