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Scientific Data
GCN250, the first high resolution global data set for runoff curve numbers
In 2018, Scientific Data published the first global soils data set (HYSOGs250m) that can be directly used for curve-number (CN)-based runoff modeling. While the CN method was developed in the United States by the Department of Agriculture (USDA), it’s international adaptation has quickly grown.
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Scientific Data
Bridging scales in neuroscience
Bridging scales in neuroscience, from the level of proteins and molecules to large-scale networks, is an important endeavour in broadening our understanding of how the brain works. In our newly published Data Descriptor in Scientific Data, we provide a dataset and tools to help achieve this goal.
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Springer Nature
Six things publishers can do to support open data and reproducible research
This post is based on a short talk I gave at the SpotOn London 2018 conference, and describes six different areas in which scholarly publishers can make practical changes to improve the reliability and reproducibility of published research.
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Grace Baynes
VP, Research Data & New Product Development, Magazines & Research Services, Springer Nature
Data Dialogues
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Springer Nature
SN SCIGRAPH
SN SciGraph Latest Release: Patents, Clinical Trials and many new features
We are pleased to announce the third release of SN SciGraph Linked Open Data, which includes two brand new datasets - Patents and Clinical Trials linked to Springer Nature publications - among a number of new features that makes it easier to work with the data.
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Springer Nature
Research Data Support
A new approach to supporting data sharing in breast cancer research
A partnership between Springer Nature and the Breast Cancer Research Foundation (BCRF) is enabling researchers to make their research data more accessible.
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Contributor
Scientific Data
GCN250, the first high resolution global data set for runoff curve numbers
In 2018, Scientific Data published the first global soils data set (HYSOGs250m) that can be directly used for curve-number (CN)-based runoff modeling. While the CN method was developed in the United States by the Department of Agriculture (USDA), it’s international adaptation has quickly grown.
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