scholarly journals The Networked Naturalist: Mobile phone data collection for citizen science and education

2010 ◽  
Author(s):  
Eric Graham
Keyword(s):  
Data Collection ◽  
Mobile Phone ◽  
Citizen Science ◽  
Mobile Phone Data ◽  
Science And Education

The Emerging Role of Citizen Science and Geospatial Big Data in Supporting the SDGs

2020 ◽  
Author(s):  
Steffen Fritz
Keyword(s):  
Sustainable Development ◽  
Big Data ◽  
Data Collection ◽  
Mobile Phone ◽  
Citizen Science ◽  
National Level ◽  
Well Being ◽  
Data Sources ◽  
Mobile Phone Data ◽  
Development Goals

<p>In September 2015, the United Nations ratified the 17 Sustainable Development Goals (SDGs), which are comprised of a further 169 targets and 232 indicators for monitoring progress on poverty, well-being and major environmental and socio-economic problems, both nationally and globally. Much of the data used for SDG monitoring comes from censuses, surveys and other administrative data provided by national statistical offices, government agencies and international organizations. However, traditional data collection can be costly and infrequent, and the information can become outdated very quickly. Moreover, reporting is generally at the national level, so spatial variations of indicators within a country are not often available, yet this information is critical for effective spatial planning. Without knowing where issues are occurring in space, we cannot implement targeted solutions. Hence, there is currently a lack of data needed for effective monitoring and implementation of the SDGs.</p><p>Non-traditional data sources such as those arising from citizen science and geospatial big data, e.g., satellite imagery, mobile phone data, social media, etc. are part of the current ‘data revolution’, all of which have potential use in SDG monitoring and implementation. This lecture will provide an overview of these new and emerging non-traditional data sources in monitoring the SDGs, providing a range of examples from citizen science, Earth Observation (including the work of the Group on Earth Observations) and mobile phone data, among others. Where relevant, we will touch upon disaster risk reduction. Finally, actions will be presented that are currently happening to promote the data revolution for sustainable development and what is still needed to make tangible progress on SDG implementation using these new data sources as well as how the engagement of citizens in data collection can trigger transformative and behavioral change.</p>

scholarly journals EpiCollect+: linking smartphones to web applications for complex data collection projects

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 199 ◽  
Author(s):  
David M. Aanensen ◽  
Derek M. Huntley ◽  
Mirko Menegazzo ◽  
Chris I. Powell ◽  
Brian G. Spratt
Keyword(s):  
Data Collection ◽  
Mobile Phone ◽  
Web Application ◽  
Web Applications ◽  
Complex Structure ◽  
Sub Saharan Africa ◽  
Mobile Phone Data ◽  
Complex Data ◽  
Branching Hierarchy ◽  
Media Types

Previously, we have described the development of the generic mobile phone data gathering tool, EpiCollect, and an associated web application, providing two-way communication between multiple data gatherers and a project database. This software only allows data collection on the phone using a single questionnaire form that is tailored to the needs of the user (including a single GPS point and photo per entry), whereas many applications require a more complex structure, allowing users to link a series of forms in a linear or branching hierarchy, along with the addition of any number of media types accessible from smartphones and/or tablet devices (e.g., GPS, photos, videos, sound clips and barcode scanning). A much enhanced version of EpiCollect has been developed (EpiCollect+). The individual data collection forms in EpiCollect+ provide more design complexity than the single form used in EpiCollect, and the software allows the generation of complex data collection projects through the ability to link many forms together in a linear (or branching) hierarchy. Furthermore, EpiCollect+ allows the collection of multiple media types as well as standard text fields, increased data validation and form logic. The entire process of setting up a complex mobile phone data collection project to the specification of a user (project and form definitions) can be undertaken at the EpiCollect+ website using a simple ‘drag and drop’ procedure, with visualisation of the data gathered using Google Maps and charts at the project website. EpiCollect+ is suitable for situations where multiple users transmit complex data by mobile phone (or other Android devices) to a single project web database and is already being used for a range of field projects, particularly public health projects in sub-Saharan Africa. However, many uses can be envisaged from education, ecology and epidemiology to citizen science.

scholarly journals Implementation of Mobile Phone Data Collection in the Conduct EPI Comprehensive Review in East and Southern African Countries

2021 ◽  
Vol Special Issue (2) ◽  
pp. 55-62
Author(s):  
Isah Mohammed Bello ◽  
Abubakar Sadiq Umar ◽  
Godwin Ubong Akpan ◽  
Joseph Okeibunor ◽  
Chukwudi Shibeshi ◽  
...  
Keyword(s):  
Data Collection ◽  
Mobile Phone ◽  
Real Time ◽  
Mobile Phones ◽  
Health Sector ◽  
Mobile Phone Data ◽  
Data Detection ◽  
Time Data ◽  
African Countries ◽  
Comprehensive Review

Mobile phone data collection tools are increasingly becoming very usable collecting, collating and analysing data in the health sector. In this paper, we documented the experiences with mobile phone data collection, collation and analysis in 5 countries of the East and Southern African, using Open Data Kit (ODK), where questionnaires were designed and coded on an XML form, uploaded and data collected using Android-Based mobile phones, with a web-based system to monitor data in real-time during EPI comprehensive review. The ODK interface supports in real-time monitoring of the flow of data, detection of missing or incomplete data, coordinate location of all locations visited, embedded charts for basic analysis. It also minimized data quality errors at entry level with the use of validation codes and constraint developed into the checklist. These benefits, combined with the improvement that mobile phones offer over paper-based in terms of timeliness, data loss, collation, and real-time data collection, analysis and uploading difficulties, make mobile phone data collection a feasible method of data collection that needs to be further explored in the conduct of all surveys in the organization.

scholarly journals Knowledge and Skill Retention of a Mobile Phone Data Collection Protocol in Rural Liberia

2014 ◽  
Vol 59 (2) ◽  
pp. 176-183 ◽  
Author(s):  
Michelle L. Munro ◽  
Jody R. Lori ◽  
Carol J. Boyd ◽  
Pamela Andreatta
Keyword(s):  
Data Collection ◽  
Mobile Phone ◽  
Mobile Phone Data ◽  
Skill Retention

scholarly journals Using Mobile Phone Data Collection Tool, Surveda, for Noncommunicable Disease Surveillance in Five Low- and Middle-income Countries

2020 ◽  
Vol 12 (2) ◽  
Author(s):  
Yang Song ◽  
Rachael Phadnis ◽  
Jennifer Favaloro ◽  
Juliette Lee ◽  
Charles Q. Lau ◽  
...  
Keyword(s):  
Data Collection ◽  
Mobile Phone ◽  
Web Application ◽  
Interactive Voice Response ◽  
Survey Design ◽  
Noncommunicable Disease ◽  
Mobile Phone Data ◽  
Data Collection Methods ◽  
Phone Survey ◽  
Collection Methods

Objectives: The Noncommunicable Disease (NCD) Mobile Phone Survey, a component of the Bloomberg Philanthropies Data for Health Initiative, determines the prevalence of NCDs and their associated risk factors and demonstrates the use of mobile phone administered surveys to supplement periodic national household surveys. The NCD Mobile Phone Survey uses Surveda to administer the survey; Surveda is an open source, multi-modal software specifically developed for the project. The objective of the paper is to describe Surveda, review data collection methods used in participating countries and discuss how Surveda and similar approaches can improve public health surveillance. Methods: Surveda features full-service survey design and implementation through a web application and collects data via Short Messaging Service (SMS), Interactive Voice Response (IVR) or mobile web. Surveda’s survey design process employs five steps: creating a project, creating questionnaires, designing and starting a survey, monitoring survey progress, and exporting survey results. Results: The NCD Mobile Phone Survey has been successfully conducted in five countries, Zambia (2017), Philippines (2018), Morocco (2019), Malawi (2019), and Sri Lanka (2019), with a total of 23,682 interviews completed. Discussion: This approach to data collection demonstrates that mobile phone surveys can supplement face-to-face data collection methods. Furthermore, Surveda offers major advantages including automated mode-switch, question randomization and comparison features. Conclusion: Accurate and timely survey data informs a country’s abilities to make targeted policy decisions while prioritizing limited resources. The high acceptance of Surveda demonstrates that the use of mobile phones for surveillance can deliver accurate and timely data collection.

scholarly journals Patient Willingness to Consent to Mobile Phone Data Collection for Mental Health Apps: Structured Questionnaire (Preprint)

2017 ◽  
Author(s):  
Daniel Di Matteo ◽  
Alexa Fine ◽  
Kathryn Fotinos ◽  
Jonathan Rose ◽  
Martin Katzman
Keyword(s):  
Mental Health ◽  
Data Collection ◽  
Mobile Phone ◽  
Mobile Phones ◽  
Tertiary Care ◽  
Data Retrieval ◽  
Mental Health Disorder ◽  
Self Report ◽  
Mobile Phone Data ◽  
Health State

BACKGROUND It has become possible to use data from a patient’s mobile phone as an adjunct or alternative to the traditional self-report and interview methods of symptom assessment in psychiatry. Mobile data–based assessment is possible because of the large amounts of diverse information available from a modern mobile phone, including geolocation, screen activity, physical motion, and communication activity. This data may offer much more fine-grained insight into mental state than traditional methods, and so we are motivated to pursue research in this direction. However, passive data retrieval could be an unwelcome invasion of privacy, and some may not consent to such observation. It is therefore important to measure patients’ willingness to consent to such observation if this approach is to be considered for general use. OBJECTIVE The aim of this study was to measure the ownership rates of mobile phones within the patient population, measure the patient population’s willingness to have their mobile phone used as an experimental assessment tool for their mental health disorder, and, finally, to determine how likely patients would be to provide consent for each individual source of mobile phone–collectible data across the variety of potential data sources. METHODS New patients referred to a tertiary care mood and anxiety disorder clinic from August 2016 to October 2017 completed a survey designed to measure their mobile phone ownership, use, and willingness to install a mental health monitoring app and provide relevant data through the app. RESULTS Of the 82 respondents, 70 (85%) reported owning an internet-connected mobile phone. When asked about installing a hypothetical mobile phone app to assess their mental health disorder, 41% (33/80) responded with complete willingness to install with another 43% (34/80) indicating potential willingness to install such an app. Willingness to give permissions for specific types of data varied by data source, with respondents least willing to consent to audio recording and analysis (19% [15/80] willing respondents, 31% [25/80] potentially willing) and most willing to consent to observation of the mobile phone screen being on or off (46% [36/79] willing respondents and 23% [18/79] potentially willing). CONCLUSIONS The patients surveyed had a high incidence of ownership of internet-connected mobile phones, which suggests some plausibility for the general approach of mental health state inference through mobile phone data. Patients were also relatively willing to consent to data collection from sources that were less personal but expressed less willingness for the most personal communication and location data.

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