Evidence map›Paper›PMID 30497363›Full record

ArticleBMC bioinformatics2018

SEDE-GPS: socio-economic data enrichment based on GPS information.

Theodor Sperlea, Stefan Füser, Jens Boenigk, Dominik Heider

Abstract read
In one paragraph

Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Theodor SperleaFaculty of Mathematics and Computer Science, University of Marburg, Hans-Meerwein-Str. 6, Marburg (Lahn), D-35032, Germany.
Stefan FüserFaculty of Mathematics and Computer Science, University of Marburg, Hans-Meerwein-Str. 6, Marburg (Lahn), D-35032, Germany.
Jens BoenigkBiodiversity Department, Center for Water and Environmental Research, University of Duisburg-Essen, Essen, D-45141, Germany.
Dominik HeiderFaculty of Mathematics and Computer Science, University of Marburg, Hans-Meerwein-Str. 6, Marburg (Lahn), D-35032, Germany. dominik.heider@uni-marburg.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMicrobes are essentail components of all ecosystems because they drive many biochemical processes and act as primary producers. In freshwater ecosystems, the biodiversity in and the composition of microbial communities can be used as indicators for environmental quality. Recently, some environmental features have been identified that influence microbial ecosystems. However, the impact of human action on lake microbiomes is not well understood. This is, in part, due to the fact that environmental data is, albeit theoretically accessible, not easily available.

resultsIn this work, we present SEDE-GPS, a tool that gathers data that are relevant to the environment of an user-provided GPS coordinate. To this end, it accesses a list of public and corporate databases and aggregates the information in a single file, which can be used for further analysis. To showcase the use of SEDE-GPS, we enriched a lake microbial ecology sequencing dataset with around 18,000 socio-economic, climate, and geographic features. The sources of SEDE-GPS are public databases such as Eurostat, the Climate Data Center, and OpenStreetMap, as well as corporate sources such as Twitter. Using machine learning and feature selection methods, we were able to identify features in the data provided by SEDE-GPS that can be used to predict lake microbiome alpha diversity.

conclusionThe results presented in this study show that SEDE-GPS is a handy and easy-to-use tool for comprehensive data enrichment for studies of ecology and other processes that are affected by environmental features. Furthermore, we present lists of environmental, socio-economic, and climate features that are predictive for microbial biodiversity in lake ecosystems. These lists indicate that human action has a major impact on lake microbiomes. SEDE-GPS and its source code is available for download at http://SEDE-GPS.heiderlab.de.

Indexed as

Geographic Information SystemsAlgorithmsBiodiversityLakesMachine LearningMicrobiotaSocioeconomic FactorsDatabaseData enrichmentEcologyGPSMicrobial ecology

Identifiers

PMID30497363
PMCPMC6266930

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.