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A land data assimilation system for sub-Saharan Africa food and water security applications

Overview of attention for article published in Scientific Data, February 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (58th percentile)

Mentioned by

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1 news outlet
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6 X users
facebook
1 Facebook page

Citations

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309 Dimensions

Readers on

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300 Mendeley
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Title
A land data assimilation system for sub-Saharan Africa food and water security applications
Published in
Scientific Data, February 2017
DOI 10.1038/sdata.2017.12
Pubmed ID
Authors

Amy McNally, Kristi Arsenault, Sujay Kumar, Shraddhanand Shukla, Pete Peterson, Shugong Wang, Chris Funk, Christa D. Peters-Lidard, James P. Verdin

Abstract

Seasonal agricultural drought monitoring systems, which rely on satellite remote sensing and land surface models (LSMs), are important for disaster risk reduction and famine early warning. These systems require the best available weather inputs, as well as a long-term historical record to contextualize current observations. This article introduces the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), a custom instance of the NASA Land Information System (LIS) framework. The FLDAS is routinely used to produce multi-model and multi-forcing estimates of hydro-climate states and fluxes over semi-arid, food insecure regions of Africa. These modeled data and derived products, like soil moisture percentiles and water availability, were designed and are currently used to complement FEWS NET's operational remotely sensed rainfall, evapotranspiration, and vegetation observations. The 30+ years of monthly outputs from the FLDAS simulations are publicly available from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) and recommended for use in hydroclimate studies, early warning applications, and by agro-meteorological scientists in Eastern, Southern, and Western Africa.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 300 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 300 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 64 21%
Researcher 51 17%
Student > Master 33 11%
Student > Bachelor 17 6%
Student > Doctoral Student 14 5%
Other 38 13%
Unknown 83 28%
Readers by discipline Count As %
Earth and Planetary Sciences 50 17%
Environmental Science 45 15%
Engineering 30 10%
Agricultural and Biological Sciences 24 8%
Social Sciences 8 3%
Other 34 11%
Unknown 109 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 25 January 2021.
All research outputs
#2,719,471
of 25,541,640 outputs
Outputs from Scientific Data
#1,054
of 3,371 outputs
Outputs of similar age
#55,967
of 434,615 outputs
Outputs of similar age from Scientific Data
#22
of 51 outputs
Altmetric has tracked 25,541,640 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,371 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 22.0. This one has gotten more attention than average, scoring higher than 68% 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 434,615 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 51 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 58% of its contemporaries.