Is Google Trends a quality data source?

Background: Performance Monitoring for Action Ethiopia (PMA-Ethiopia) is a survey project that builds on the PMA2020 and PMA Maternal and Newborn Health projects to generate timely and actionable data on a range of reproductive, maternal, and newborn health (RMNH) indicators using a combination of cross-sectional and longitudinal data collection. Objectives: This manuscript 1) describes the protocol for PMA- Ethiopia, and 2) describes the measures included in PMA Ethiopia and research areas that may be of interest to RMNH stakeholders. Methods: Annual data on family planning are gathered from a nationally representative, cross-sectional survey of women age 15-49. Data on maternal and newborn health are gathered from a cohort of women who were pregnant or recently postpartum at the time of enrollment. Women are followed at 6-weeks, 6-months, and 1-year to understand health seeking behavior, utilization, and quality. Data from service delivery points (SDPs) are gathered annually to assess service quality and availability. Households and SDPs can be linked at the enumeration area level to improve estimates of effective coverage. Discussion: Data from PMA-Ethiopia will be available at www.pmadata.org. PMA-Ethiopia is a unique data source that includes multiple, simultaneously fielded data collection activities. Data are available partner dynamics, experience with contraceptive use, unintended pregnancy, empowerment, and detailed information on components of services that are not available from other large-scale surveys. Additionally, we highlight the unique contribution of PMA Ethiopia data in assessing the impact of coronavirus disease 2019 (COVID-19) on RMNH.

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Changes in air-pollution related behaviour measured by Google Trends Search Volume Index in response to reported air quality in Poland

10.21203/rs.3.rs-234681/v1 ◽

2021 ◽

Author(s):

Wojciech Nazar ◽

Katarzyna Plata-Nazar

Keyword(s):

Air Pollution ◽

Air Quality ◽

Winter Season ◽

Google Trends ◽

Volume Index ◽

Quality Data ◽

Coefficient Of Determination ◽

Pollution Levels ◽

Search Volume ◽

Air Quality Data

Abstract Background Decreased air quality is connected to a higher number of hospital admissions and an increase in daily mortality rates. Thus, Poles’ behavioural response to sometimes elevated air pollution levels is vital. The aim of this study was to carry out analysis of changes in air-pollution related information seeking behaviour in response to nationwide reported air quality in Poland. Methods Google Trends Search Volume Index data was used to investigate Poles’ interest in air pollution-related keywords. PM10 and PM2.5 concentrations measured across Poland between 2016 and 2019 were collected from the Chief Inspectorate of Environmental Protection databases. Pearson Product-Moment Correlation and the R2 correlation coefficient of determination were used to measure spatial and seasonal correlations between reported air pollution levels and the popularity of search queries. Results The highest PM10 and PM2.5 concentrations were observed in southern voivodeships and during the winter season. Similar trends were observed for Poles’ interest in air-pollution related keywords. All R2 coefficient of determination values were > 0.5 and all correlations were statistically significant. Conclusion Poland’s air quality does not meet the World Health Organisation guidelines. Also, the air quality is lower in southern Poland and during the winter season. It appears that Poles are aware of this issue and search for daily air quality data in their location. Greater interest in air quality data in Poland strongly correlates with both higher regional and higher seasonal air pollution levels.

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Data Fusion for Correcting Measurement Errors

Journal of Survey Statistics and Methodology ◽

10.1093/jssam/smy010 ◽

2018 ◽

Vol 7 (2) ◽

pp. 175-200

Author(s):

Tracy Schifeling ◽

Jerome P Reiter ◽

Maria Deyoreo

Keyword(s):

Data Fusion ◽

National Survey ◽

Measurement Errors ◽

College Graduates ◽

Quality Data ◽

High Quality ◽

High Quality Data ◽

Unrealistic Assumption ◽

Data Source ◽

Fusion Framework

AbstractOften in surveys, key items are subject to measurement errors. Given just the data, it can be difficult to determine the extent and distribution of this error process and, hence, to obtain accurate inferences that involve the error-prone variables. In some settings, however, analysts have access to a data source on different individuals with high-quality measurements of the error-prone survey items. We present a data fusion framework for leveraging this information to improve inferences in the error-prone survey. The basic idea is to posit models about the rates at which individuals make errors, coupled with models for the values reported when errors are made. This can avoid the unrealistic assumption of conditional independence typically used in data fusion. We apply the approach on the reported values of educational attainments in the American Community Survey, using the National Survey of College Graduates as the high-quality data source. In doing so, we account for the sampling design used to select the National Survey of College Graduates. We also present a process for assessing the sensitivity of various analyses to different choices for the measurement error models. Supplemental material is available online.

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Simulation-driven machine learning for robotics and automation

tm - Technisches Messen ◽

10.1515/teme-2019-0072 ◽

2019 ◽

Vol 86 (11) ◽

pp. 673-684 ◽

Cited By ~ 4

Author(s):

Mohamed El-Shamouty ◽

Kilian Kleeberger ◽

Arik Lämmle ◽

Marco Huber

Keyword(s):

Machine Learning ◽

Quality Data ◽

High Quality Data ◽

Industrial Manufacturing ◽

Physics Simulation ◽

Individualized Production ◽

Data Source ◽

Lot Sizes ◽

Industrial Use ◽

Mass Personalization

AbstractMass personalization—a megatrend in industrial manufacturing and production—requires fast adaptations of robotics and automation solutions to continually decreasing lot sizes. In this paper, the challenges of applying robot-based automation in a highly individualized production are highlighted. To face these challenges, a framework is proposed that combines latest machine learning (ML) techniques, like deep learning, with high-end physics simulation environments. ML is used for programming and parameterizing machines for a given production task with minimal human intervention. If the simulation environment realistically captures physical properties like forces or elasticity of the real world, it provides a high-quality data source for ML. In doing so, new tasks are mastered in simulation faster than in real-time, while at the same time existing tasks are executed. The functionality of the simulation-driven ML framework is demonstrated on an industrial use case.

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Empirical Research on Business Intelligence Success

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.842.754 ◽

2013 ◽

Vol 842 ◽

pp. 754-758

Author(s):

Sergey Kokin ◽

Tien An Wang

Keyword(s):

Information System ◽

Business Intelligence ◽

Structural Equation ◽

Quality Data ◽

Intelligence System ◽

Information System Success ◽

Equation Modelling ◽

User Access ◽

Data Source ◽

System Success

The present paper is a research on problems of measuring Business Intelligence (BI) System Success. The well-known system of Information System Success by DeLone and McLean and Business Intelligence Capabilities Framework were reviewed to develop a framework for evaluating Business Intelligence (BI) Success. We hypothesize that BI Capabilities (Data Type Quality, Data Source Quality, Flexibility, Interaction with other Systems, User Access Quality) are positively connected with BI Success. Our hypotheses are tested with survey data. The respondents are CEOs, CIOs and BI specialists of Russian-based companies. Structural equation modelling exhibits a good fit with the observed data. Our results show, that BI Capabilities have a strong influence on BI System Success. However, there is more work to be done, because some of the hypotheses are not supported. The present paper contributes theoretically to Information System Success domain by expanding research about Business Intelligence System Success.

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Where and When Can We Use Google Trends to Measure Issue Salience?

Political Science and Politics ◽

10.1017/s1049096513000279 ◽

2013 ◽

Vol 46 (02) ◽

pp. 280-290 ◽

Cited By ~ 25

Author(s):

Jonathan Mellon

Keyword(s):

Google Trends ◽

Issue Salience ◽

Social Scientists ◽

Download Time ◽

The World ◽

Search Data ◽

Data Source ◽

Almost All ◽

Google Search ◽

Over Time

Google search data have several major advantages over traditional survey data. First, the high costs of running frequent surveys mean that most survey questions are only asked occasionally making comparisons over time difficult. By contrast, Google Trends provides information on search trends measured weekly. Second, there are many countries where surveys are only conducted sporadically, whereas Google search data are available anywhere in the world where sufficient numbers of people use its search engine. The Google Trends website allows researchers to download data for almost all countries at no cost and to download time series of any search term's popularity over time (provided enough people have searched for it). For these reasons, Google Trends is an attractive data source for social scientists.

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Availability and quality of publicly available health workforce data sources in Australia: a scoping review protocol

BMJ Open ◽

10.1136/bmjopen-2019-034400 ◽

2020 ◽

Vol 10 (1) ◽

pp. e034400

Author(s):

Marianne Gillam ◽

Matthew Leach ◽

Jessica Muller ◽

David Gonzalez-Chica ◽

Martin Jones ◽

...

Keyword(s):

Health Services ◽

Scoping Review ◽

Health Workforce ◽

Data Sources ◽

Quality Data ◽

Process Data ◽

Australian Health ◽

Health Services Planning ◽

Data Source

IntroductionThe health workforce is an integral component of the healthcare system. Comprehensive, high-quality data on the health workforce are essential to identifying gaps in health service provision, as well as informing future health workforce and health services planning, and health policy. While many data sources are used in Australia for these purposes, the quality of the data sources with respect to relevance, accessibility and accuracy is not clear.Methods and analysisThis scoping review aims to identify and appraise publicly available data sources describing the Australian health workforce. The review will include any data source (eg, registry, administrative database and survey) or document reporting a data source (eg, journal article, report) on the Australian health workforce, which is publicly available and describes the characteristics of the workforce. The search will be conducted in 10 bibliographic databases and the grey literature using an iterative process. Screening of titles and abstracts will be undertaken by two investigators, independently, using Covidence software. Any disagreement between investigators will be resolved by a third investigator. Documents/data sources identified as potentially eligible will be retrieved in full text and reviewed following the same process. Data will be extracted using a customised data extraction tool. A customised appraisal tool will be used to assess the relevance, accessibility and accuracy of included data sources.Ethics and disseminationThe scoping review is a secondary analysis of existing, publicly available data sources and does not require ethics approval. The findings of this scoping review will further our understanding of the quality and availability of data sources used for health workforce and health services planning in Australia. The results will be submitted for publication in peer-reviewed journals and presented at conferences targeted at health workforce and public health topics.

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g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update)

Nucleic Acids Research ◽

10.1093/nar/gkz369 ◽

2019 ◽

Vol 47 (W1) ◽

pp. W191-W198 ◽

Cited By ~ 434

Author(s):

Uku Raudvere ◽

Liis Kolberg ◽

Ivan Kuzmin ◽

Tambet Arak ◽

Priit Adler ◽

...

Keyword(s):

Web Design ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Biological Data ◽

Functional Enrichment ◽

Quality Data ◽

Primary Data ◽

Web Interface ◽

Biological Data Analysis ◽

Data Source

Abstract Biological data analysis often deals with lists of genes arising from various studies. The g:Profiler toolset is widely used for finding biological categories enriched in gene lists, conversions between gene identifiers and mappings to their orthologs. The mission of g:Profiler is to provide a reliable service based on up-to-date high quality data in a convenient manner across many evidence types, identifier spaces and organisms. g:Profiler relies on Ensembl as a primary data source and follows their quarterly release cycle while updating the other data sources simultaneously. The current update provides a better user experience due to a modern responsive web interface, standardised API and libraries. The results are delivered through an interactive and configurable web design. Results can be downloaded as publication ready visualisations or delimited text files. In the current update we have extended the support to 467 species and strains, including vertebrates, plants, fungi, insects and parasites. By supporting user uploaded custom GMT files, g:Profiler is now capable of analysing data from any organism. All past releases are maintained for reproducibility and transparency. The 2019 update introduces an extensive technical rewrite making the services faster and more flexible. g:Profiler is freely available at https://biit.cs.ut.ee/gprofiler.

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Validation: The Use of Google Trends as an Alternative Data Source for COVID-19 Surveillance in Indonesia

Asia Pacific Journal of Public Health ◽

10.1177/1010539520940896 ◽

2020 ◽

Vol 32 (6-7) ◽

pp. 368-369

Author(s):

Ricvan Dana Nindrea ◽

Nissa Prima Sari ◽

Lutfan Lazuardi ◽

Teguh Aryandono

Keyword(s):

Google Trends ◽

Data Source

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Monitoring of Drought Awareness from Google Trends: A Case Study of the 2011–17 California Drought

Weather Climate and Society ◽

10.1175/wcas-d-18-0085.1 ◽

2019 ◽

Vol 11 (2) ◽

pp. 419-429 ◽

Cited By ~ 14

Author(s):

Jonghun Kam ◽

Kimberly Stowers ◽

Sungyoon Kim

Keyword(s):

Power Law ◽

Google Trends ◽

Search Term ◽

Power Law Model ◽

Power Law Decay ◽

The Social ◽

Social Dynamic ◽

Data Source ◽

Google Search

Abstract This study introduces “Google Trends” as a social data source in monitoring and modeling the dynamics of drought awareness during the 2011–17 California drought. In this study, drought awareness is defined and operationalized as the relative search interest activities within California, using the search term “drought” from Google Trends. First, the 2011–17 California drought is characterized in the duration–intensity curve with other historical California droughts for comparative purposes, using the 12-month standard precipitation index data (1895–2017). Second, the potential triggers of the peaks of drought awareness during the 2011–17 California drought are investigated through Google Trends and Google Search. The Google Trends data show that the first peak of drought awareness occurred when the drought condition reached its peak and the governor declared the drought emergency (January 2014). The other peaks in August 2014, April 2015, and January 2017 are related to public interest in drought recovery driven by the forecast of the strong El Niño winter of 2014/15, the governor’s issue of water use rules, and California floods in early 2017, respectively. Last, a power-law decay model of drought awareness is fitted to the Google Trends data. According to the fitted power-law model, Californians remained interested in drought after the social trigger–related peaks longer than they did after the natural trigger–related peaks. The findings of this study suggest that it is necessary to develop a more realistic social dynamic modeling for communities that can respond to natural and human triggers and capture interactions with awareness of related disasters.

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