2019
DOI: 10.1038/s41597-018-0005-2
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Author Correction: A compendium of geochemical information from the Saanich Inlet water column

Abstract: In Table 3 of this Data Descriptor the units of Mean_N 2 O and Mean_CH 4 are incorrectly stated as "Nanomolar (μM)". This should instead read "Nanomolar (nM)".

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Cited by 258 publications
(237 citation statements)
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“…To gain further insight into the expression of genes underlying root trait QTLs we used the publicly available differential gene expression data of Williams 82, a parental line used in developing this inter-specific mapping population. Gene expressions across diverse soybean genetic backgrounds were obtained from Array express ( ) and RNA-sequencing datasets ( Ithal et al, 2007 ; Libault et al, 2010 ; Gong et al, 2014 ; Kour et al, 2014 ; Leisner et al, 2014 ; Lin et al, 2014 ; Shen et al, 2014 ; Valdés-López et al, 2014 ; Wu et al, 2014 ; Zabala and Vodkin, 2014 ; Aghamirzaie et al, 2015 ; Brown and Hudson, 2015 ; Devi et al, 2015 ; Huang and Schiefelbein, 2015 ; Jones et al, 2015 ; Lambirth et al, 2015 ; Lanubile et al, 2015 ; Okamoto et al, 2015 ; Shin et al, 2015 ; Whaley et al, 2015 ; Bellieny-Rabelo et al, 2016 ; Song et al, 2016 ; Cho et al, 2017 ; Dastmalchi et al, 2017 ; Huang et al, 2017 ; Waters et al, 2018 ; Adhikari et al, 2019 ; Li et al, 2019 ; Liu et al, 2019 ; Neupane et al, 2019 ; Wang et al, 2019 ) integrated into Genevestigator database, ( Zimmermann et al, 2014 ). The transcriptome data of the wild soybean parental line, PI 483460B used in this study is not available.…”
Section: Methodsmentioning
confidence: 99%
“…To gain further insight into the expression of genes underlying root trait QTLs we used the publicly available differential gene expression data of Williams 82, a parental line used in developing this inter-specific mapping population. Gene expressions across diverse soybean genetic backgrounds were obtained from Array express ( ) and RNA-sequencing datasets ( Ithal et al, 2007 ; Libault et al, 2010 ; Gong et al, 2014 ; Kour et al, 2014 ; Leisner et al, 2014 ; Lin et al, 2014 ; Shen et al, 2014 ; Valdés-López et al, 2014 ; Wu et al, 2014 ; Zabala and Vodkin, 2014 ; Aghamirzaie et al, 2015 ; Brown and Hudson, 2015 ; Devi et al, 2015 ; Huang and Schiefelbein, 2015 ; Jones et al, 2015 ; Lambirth et al, 2015 ; Lanubile et al, 2015 ; Okamoto et al, 2015 ; Shin et al, 2015 ; Whaley et al, 2015 ; Bellieny-Rabelo et al, 2016 ; Song et al, 2016 ; Cho et al, 2017 ; Dastmalchi et al, 2017 ; Huang et al, 2017 ; Waters et al, 2018 ; Adhikari et al, 2019 ; Li et al, 2019 ; Liu et al, 2019 ; Neupane et al, 2019 ; Wang et al, 2019 ) integrated into Genevestigator database, ( Zimmermann et al, 2014 ). The transcriptome data of the wild soybean parental line, PI 483460B used in this study is not available.…”
Section: Methodsmentioning
confidence: 99%
“…A salient example is the fact that specialized communities typically encompass unimodal areas, whereas high-participation rich club areas are located in polysensory association cortex. We envision that these findings will help to inform future work on structure-function relationships, including studies of hierarchical organization (Buckner & Margulies, 2019 ; Theodoni et al, 2020 ), comparisons of structural and functional connectivity (Hori et al, 2020 ), and ultimately, large-scale computational models (Shen et al, 2019 ). Indeed, the marmoset connectome possesses a prominent hierarchical architecture defined by the laminar dependence of connections, which is reflected by the cytological attributes of individual areas (Theodoni et al, 2020 ).…”
Section: Discussionmentioning
confidence: 99%
“…determined the longitude and latitude of a location using google maps (www.google.com/ maps) [23]. When administrative districts or provinces were reported as occurrence locations, we extracted longitude and latitude of the center of the region [23]. We further classified all occurrence data into three levels according to their administrative regions (i.e.…”
Section: Plos Onementioning
confidence: 99%
“…The online databases focusing on invertebrates, especially insects, are lagging far behind [3]. However, in response to growing needs, online invertebrate databases are gradually a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 increasing in number as more insect data are being digitized in online repositories such as FreshWaterBiodiversity (http://data.freshwaterbiodiversity.eu/), Global Biodiversity Information Facility (http://www.gbif.org/), OdonataCentral [4], and the Odonate Phenotypic Database [5]. Along with these online databases containing worldwide information, regional databases like Butterflies of India (https://www.ifoundbutterflies.org/), Butterflies of Belgium [6], and Odonata of India (https://www.indianodonata.org) are currently emerging, providing more detailed insights on the extant species with their spatial and temporal information.…”
Section: Introductionmentioning
confidence: 99%
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