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Building a multi-scaled geospatial temporal ecology database from disparate data sources: fostering open science and data reuse

Overview of attention for article published in Giga Science, July 2015
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)

Mentioned by

1 blog
25 tweeters
1 peer review site
4 Facebook pages
1 Google+ user


64 Dimensions

Readers on

131 Mendeley
1 CiteULike
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Building a multi-scaled geospatial temporal ecology database from disparate data sources: fostering open science and data reuse
Published in
Giga Science, July 2015
DOI 10.1186/s13742-015-0067-4
Pubmed ID

Patricia A. Soranno, Edward G. Bissell, Kendra S. Cheruvelil, Samuel T. Christel, Sarah M. Collins, C. Emi Fergus, Christopher T. Filstrup, Jean-Francois Lapierre, Noah R. Lottig, Samantha K. Oliver, Caren E. Scott, Nicole J. Smith, Scott Stopyak, Shuai Yuan, Mary Tate Bremigan, John A. Downing, Corinna Gries, Emily N. Henry, Nick K. Skaff, Emily H. Stanley, Craig A. Stow, Pang-Ning Tan, Tyler Wagner, Katherine E. Webster


Although there are considerable site-based data for individual or groups of ecosystems, these datasets are widely scattered, have different data formats and conventions, and often have limited accessibility. At the broader scale, national datasets exist for a large number of geospatial features of land, water, and air that are needed to fully understand variation among these ecosystems. However, such datasets originate from different sources and have different spatial and temporal resolutions. By taking an open-science perspective and by combining site-based ecosystem datasets and national geospatial datasets, science gains the ability to ask important research questions related to grand environmental challenges that operate at broad scales. Documentation of such complicated database integration efforts, through peer-reviewed papers, is recommended to foster reproducibility and future use of the integrated database. Here, we describe the major steps, challenges, and considerations in building an integrated database of lake ecosystems, called LAGOS (LAke multi-scaled GeOSpatial and temporal database), that was developed at the sub-continental study extent of 17 US states (1,800,000 km(2)). LAGOS includes two modules: LAGOSGEO, with geospatial data on every lake with surface area larger than 4 ha in the study extent (~50,000 lakes), including climate, atmospheric deposition, land use/cover, hydrology, geology, and topography measured across a range of spatial and temporal extents; and LAGOSLIMNO, with lake water quality data compiled from ~100 individual datasets for a subset of lakes in the study extent (~10,000 lakes). Procedures for the integration of datasets included: creating a flexible database design; authoring and integrating metadata; documenting data provenance; quantifying spatial measures of geographic data; quality-controlling integrated and derived data; and extensively documenting the database. Our procedures make a large, complex, and integrated database reproducible and extensible, allowing users to ask new research questions with the existing database or through the addition of new data. The largest challenge of this task was the heterogeneity of the data, formats, and metadata. Many steps of data integration need manual input from experts in diverse fields, requiring close collaboration.

Twitter Demographics

The data shown below were collected from the profiles of 25 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 131 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 3 2%
United States 2 2%
Canada 2 2%
Portugal 1 <1%
Unknown 123 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 32 24%
Student > Ph. D. Student 22 17%
Student > Master 17 13%
Professor > Associate Professor 12 9%
Other 8 6%
Other 31 24%
Unknown 9 7%
Readers by discipline Count As %
Environmental Science 36 27%
Agricultural and Biological Sciences 30 23%
Computer Science 14 11%
Earth and Planetary Sciences 11 8%
Engineering 6 5%
Other 11 8%
Unknown 23 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 26. 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 August 2017.
All research outputs
of 15,356,632 outputs
Outputs from Giga Science
of 740 outputs
Outputs of similar age
of 233,708 outputs
Outputs of similar age from Giga Science
of 1 outputs
Altmetric has tracked 15,356,632 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 740 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.2. This one has done well, scoring higher than 75% 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 233,708 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them