Achieving Data Synergy: The Socio-Technical Process of Handling Data

Higginson, Sarah; Topouzi, Marina; Andrade-Cabrera, Carlos; O’Dwyer, Ciara; Darby, Sarah; Finn, Donal

doi:10.1007/978-3-319-99097-2_5

Cited by 1 publication

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We call this effect data synergy , borrowed from Higginson et al. (2018). We hypothesize that deep learning networks use their internal representations to automatically form multilevel models that learn inter‐regional homogeneities and heterogeneities (commonalities and differences between regions).…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we systematically study the interesting phenomenon with DL models where a large training set leads to a unified model that tends to be statistically stronger than a collection of stratified, locally trained models (i.e., the whole is greater than the sum of its parts). We call this effect data synergy, borrowed from Higginson et al (2018). We hypothesize that deep learning networks use their internal representations to automatically form multilevel models that learn inter-regional homogeneities and heterogeneities (commonalities and differences between regions).…”

mentioning

confidence: 99%

See 1 more Smart Citation

The Data Synergy Effects of Time‐Series Deep Learning Models in Hydrology

Fang

Kifer

Lawson

et al. 2022

Water Resources Research

View full text Add to dashboard Cite

When fitting statistical models to variables in geoscientific disciplines such as hydrology, it is a customary practice to stratify a large domain into multiple regions (or regimes) and study each region separately. Traditional wisdom suggests that models built for each region separately will have higher performance because of homogeneity within each region. However, each stratified model has access to fewer and less diverse data points. Here, through two hydrologic examples (soil moisture and streamflow), we show that conventional wisdom may no longer hold in the era of big data and deep learning (DL). We systematically examined an effect we call data synergy, where the results of the DL models improved when data were pooled together from characteristically different regions. The performance of the DL models benefited from modest diversity in the training data compared to a homogeneous training set, even with similar data quantity. Moreover, allowing heterogeneous training data makes eligible much larger training datasets, which is an inherent advantage of DL. A large, diverse data set is advantageous in terms of representing extreme events and future scenarios, which has strong implications for climate change impact assessment. The results here suggest the research community should place greater emphasis on data sharing.

show abstract

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%