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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Can big data solve a big problem? Reporting the obesity data landscape in line with the Foresight obesity system map
|
|---|---|
| Published in |
International Journal of Obesity, September 2018
|
| DOI | 10.1038/s41366-018-0184-0 |
| Pubmed ID | |
| Authors |
Michelle A. Morris, Emma Wilkins, Kate A. Timmins, Maria Bryant, Mark Birkin, Claire Griffiths |
| Abstract |
Obesity research at a population level is multifaceted and complex. This has been characterised in the UK by the Foresight obesity systems map, identifying over 100 variables, across seven domain areas which are thought to influence energy balance, and subsequent obesity. Availability of data to consider the whole obesity system is traditionally lacking. However, in an era of big data, new possibilities are emerging. Understanding what data are available can be the first challenge, followed by an inconsistency in data reporting to enable adequate use in the obesity context. In this study we map data sources against the Foresight obesity system map domains and nodes and develop a framework to report big data for obesity research. Opportunities and challenges associated with this new data approach to whole systems obesity research are discussed. Expert opinion from the ESRC Strategic Network for Obesity was harnessed in order to develop a data source reporting framework for obesity research. The framework was then tested on a range of data sources. In order to assess availability of data sources relevant to obesity research, a data mapping exercise against the Foresight obesity systems map domains and nodes was carried out. A reporting framework was developed to recommend the reporting of key information in line with these headings: Background; Elements; Exemplars; Content; Ownership; Aggregation; Sharing; Temporality (BEE-COAST). The new BEE-COAST framework was successfully applied to eight exemplar data sources from the UK. 80% coverage of the Foresight obesity systems map is possible using a wide range of big data sources. The remaining 20% were primarily biological measurements often captured by more traditional laboratory based research. Big data offer great potential across many domains of obesity research and need to be leveraged in conjunction with traditional data for societal benefit and health promotion. |
X Demographics
The data shown below were collected from the profiles of 27 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 8 | 30% |
| Australia | 3 | 11% |
| United States | 3 | 11% |
| Ireland | 1 | 4% |
| Unknown | 12 | 44% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 12 | 44% |
| Scientists | 12 | 44% |
| Practitioners (doctors, other healthcare professionals) | 3 | 11% |
Mendeley readers
The data shown below were compiled from readership statistics for 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 69 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 11 | 16% |
| Student > Master | 10 | 14% |
| Researcher | 7 | 10% |
| Student > Doctoral Student | 4 | 6% |
| Student > Bachelor | 4 | 6% |
| Other | 14 | 20% |
| Unknown | 19 | 28% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 7 | 10% |
| Nursing and Health Professions | 7 | 10% |
| Medicine and Dentistry | 6 | 9% |
| Social Sciences | 6 | 9% |
| Psychology | 4 | 6% |
| Other | 16 | 23% |
| Unknown | 23 | 33% |
Attention Score in Context
This research output has an Altmetric Attention Score of 16. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 November 2018.
All research outputs
#2,130,614
of 24,330,936 outputs
Outputs from International Journal of Obesity
#1,042
of 4,437 outputs
Outputs of similar age
#44,652
of 345,437 outputs
Outputs of similar age from International Journal of Obesity
#19
of 67 outputs
Altmetric has tracked 24,330,936 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,437 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 24.9. This one has done well, scoring higher than 76% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 345,437 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 67 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.