Spatial and temporal trend of Chinese manure nutrient pollution and assimilation capacity of cropland and grassland
In: Environmental science and pollution research: ESPR, Band 20, Heft 7, S. 5036-5046
ISSN: 1614-7499
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In: Environmental science and pollution research: ESPR, Band 20, Heft 7, S. 5036-5046
ISSN: 1614-7499
In: Ecotoxicology and environmental safety: EES ; official journal of the International Society of Ecotoxicology and Environmental safety, Band 233, S. 113317
ISSN: 1090-2414
In: Environmental science and pollution research: ESPR, Band 23, Heft 3, S. 2279-2287
ISSN: 1614-7499
In: STOTEN-D-22-08901
SSRN
In: Environmental management: an international journal for decision makers, scientists, and environmental auditors, Band 41, Heft 1, S. 79-89
ISSN: 1432-1009
In: Environmental management: an international journal for decision makers, scientists, and environmental auditors, Band 50, Heft 5, S. 888-899
ISSN: 1432-1009
In: STOTEN-D-22-02373
SSRN
In: HAZMAT-D-22-01390
SSRN
In: JEMA-D-22-09315
SSRN
In: ENVPOL-D-22-00229
SSRN
In: ENVPOL-D-22-00660
SSRN
In: STOTEN-D-22-15616
SSRN
In: ENVPOL-D-22-06807
SSRN
In: ENVPOL-D-21-08463
SSRN
The number of samples in biological experiments is continuously increasing, but complex protocols and human error in many cases lead to suboptimal data quality and hence difficulties in reproducing scientific findings. Laboratory automation can alleviate many of these problems by precisely reproducing machine-readable protocols. These instruments generally require high up-front investments, and due to the lack of open application programming interfaces (APIs), they are notoriously difficult for scientists to customize and control outside of the vendor-supplied software. Here, automated, high-throughput experiments are demonstrated for interdisciplinary research in life science that can be replicated on a modest budget, using open tools to ensure reproducibility by combining the tools OpenFlexure, Opentrons, ImJoy, and UC2. This automated sample preparation and imaging pipeline can easily be replicated and established in many laboratories as well as in educational contexts through easy-to-understand algorithms and easy-to-build microscopes. Additionally, the creation of feedback loops, with later pipetting or imaging steps depending on the analysis of previously acquired images, enables the realization of fully autonomous "smart" microscopy experiments. All documents and source files are publicly available to prove the concept of smart lab automation using inexpensive, open tools. It is believed this democratizes access to the power and repeatability of automated experiments.
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