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

Abstract

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 km2). 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s13742-015-0067-4) contains supplementary material, which is available to authorized users.

Skills and Knowledge for Data-Intensive Environmental Research

“…A large-scale study may call for the integration of many different types of data, creating philosophical, logistical, and analytical challenges (Jones et al 2006, Soranno et al 2015). …”

Fine-scale wetland features mediate vector and climate-dependent macroscale patterns in human West Nile virus incidence

“…1) that includes the midwestern and northeastern United States. The data were partially acquired from LAGOS, an existing integrated geospatial database assembled by our research team at Michigan State University (Soranno et al 2015) that includes wetland data from the National Wetlands Inventory (NWI), stream data from the National Hydrography Dataset (NHD), as well as the novel wetland metrics that we derived from these datasets (National Wetland Inventory 2014; National Hydrography Dataset 2007). We independently obtained Palmer hydrological drought indices (PHDI) from National Oceanic and Atmospheric Administration (NOAA) and human WNV incidence data from the Centers for Disease Control and Prevention (CDC) and combined these data with the LAGOS-derived data (ArboNET Database 2013; National Climate Data Center 2014).…”

“…We also used the LAGOS-GIS Toolbox to calculate wetland hydrological connectivity, which we define as wetland connections to streams (Soranno et al 2015). Stream locational data were acquired from the high-resolution, 1:24,000 scale NHD that includes several different feature sets including NHDArea features and NHDFlowline features (National Hydrography Dataset 2007).…”

Hash-Based Feature Learning for Incomplete Continuous-Valued Data

“…To illustrate the advantages of using SVR, we have compared its performance to other regression methods on four lake water quality data sets obtained from the LAGOS database [14]. Details about the data sets can be found in Section 5.…”

“…We used several lake water quality data sets from LAGOS [14], which is a geo-spatial database that contains landscape characterization features and lake water quality data measured at multiple scales covering 17 states in the United States. We used four lake water quality variables-total phosphorus (TP), total nitrogen (TN), total chlorophyll-a (chla) and Secchi depth (Secchi)-as response variables, creating 4 distinct data sets for our experiments.…”