Search Results
their data-intensive jobs. A recent report provided a list of some of the new jobs that may emerge from these changes, such as robot ethicist, machine-learning developer, behavior prediction analyst, and data farmer ( Tytler et al. 2019 ). For science data jobs, the future is here as workers help scientists find, access, and make interoperable machine-actionable data across domains and organizations to facilitate science. Presently, science students may be underprepared for these roles because
their data-intensive jobs. A recent report provided a list of some of the new jobs that may emerge from these changes, such as robot ethicist, machine-learning developer, behavior prediction analyst, and data farmer ( Tytler et al. 2019 ). For science data jobs, the future is here as workers help scientists find, access, and make interoperable machine-actionable data across domains and organizations to facilitate science. Presently, science students may be underprepared for these roles because
Scientific data sharing benefits establishing an honest academic environment by increasing replicability ( Carter et al. 2017 ; Nuijten 2019 ) and enhances the data value by reusing in further research ( Piwowar et al. 2007 ; Li et al. 2020a , 2021 ). The concept that “science is driven by data, data is a mirror of science” ( Hanson et al. 2011 ) has penetrated all aspects of scientific research. The essence of scientific data sharing is to provide scientific data to the public in an open
Scientific data sharing benefits establishing an honest academic environment by increasing replicability ( Carter et al. 2017 ; Nuijten 2019 ) and enhances the data value by reusing in further research ( Piwowar et al. 2007 ; Li et al. 2020a , 2021 ). The concept that “science is driven by data, data is a mirror of science” ( Hanson et al. 2011 ) has penetrated all aspects of scientific research. The essence of scientific data sharing is to provide scientific data to the public in an open
modern Earth scientists are often tasked with pioneering big-data analysis, of the top-10-ranked undergraduate programs in Earth science ( U.S. News and World Report 2022 ), the majority require one or fewer semesters of coursework in probability and statistics, and none have required coursework in computational methods such as databasing or computer programming. In the past several decades, the research community has proposed many guidelines to promote scientific rigor in observational
modern Earth scientists are often tasked with pioneering big-data analysis, of the top-10-ranked undergraduate programs in Earth science ( U.S. News and World Report 2022 ), the majority require one or fewer semesters of coursework in probability and statistics, and none have required coursework in computational methods such as databasing or computer programming. In the past several decades, the research community has proposed many guidelines to promote scientific rigor in observational
utilized to organize data outputs from ecological sampling, ocean profiles, snow samples, subsurface data, and beyond. A generalized example to the essential components schematized in Fig. 1 can be found in Fig. 2 , as a guideline to develop datagrams for any type of scientific data collected in the field. Fig. 2. Essential components of a datagram (A–J details are described above in text). Summary In recent years, data science publications in journals such as Earth System
utilized to organize data outputs from ecological sampling, ocean profiles, snow samples, subsurface data, and beyond. A generalized example to the essential components schematized in Fig. 1 can be found in Fig. 2 , as a guideline to develop datagrams for any type of scientific data collected in the field. Fig. 2. Essential components of a datagram (A–J details are described above in text). Summary In recent years, data science publications in journals such as Earth System
mode and spreading of the distribution. Overall, a proper set of chosen variables should have a mixture of smaller and higher moments. The common practice in data science to describe an unknown pdf is to use its mean μ and standard deviation σ together with the third and fourth standardized statistical moments: namely, skewness γ and kurtosis κ . For brevity, we will refer to all these parameters as statistical moments. Equation (1) of the supplemental material shows how the
mode and spreading of the distribution. Overall, a proper set of chosen variables should have a mixture of smaller and higher moments. The common practice in data science to describe an unknown pdf is to use its mean μ and standard deviation σ together with the third and fourth standardized statistical moments: namely, skewness γ and kurtosis κ . For brevity, we will refer to all these parameters as statistical moments. Equation (1) of the supplemental material shows how the
( Fig. 1 ). Fig . 1. A hybrid physics–AI model for improving hydrological forecast. Data science workflow and connections among different components are shown. A two-way workflow is shown here: Process-based climate and hydrological modeling provide machine identifiable and physically interpretable quantity to the deep learning model, which in turn provide an improved forecast, and its biophysical interpretation can develop a new scientific hypothesis. Each of these components is described in
( Fig. 1 ). Fig . 1. A hybrid physics–AI model for improving hydrological forecast. Data science workflow and connections among different components are shown. A two-way workflow is shown here: Process-based climate and hydrological modeling provide machine identifiable and physically interpretable quantity to the deep learning model, which in turn provide an improved forecast, and its biophysical interpretation can develop a new scientific hypothesis. Each of these components is described in
. 1999 ). To the best of our knowledge, it has not yet been applied in climate science. In practice, data scarcity, distribution shift, and varying regimes of the dynamical system complicate finding a suitable resampling or subsampling scheme that produces both sufficiently independent and identically distributed ensemble members without biases. In both bootstrap as well as subsampling techniques, a suitable choice of window size depends on the autocorrelation of the time series at hand. Highly
. 1999 ). To the best of our knowledge, it has not yet been applied in climate science. In practice, data scarcity, distribution shift, and varying regimes of the dynamical system complicate finding a suitable resampling or subsampling scheme that produces both sufficiently independent and identically distributed ensemble members without biases. In both bootstrap as well as subsampling techniques, a suitable choice of window size depends on the autocorrelation of the time series at hand. Highly
present. We then introduce our cases in terms of historical content and modern context. A description of cases allows us to derive a 19-element checklist that we hope can be applied to millions of historical weather observations remaining to be rescued. 2. Trust throughout the life cycle of historical data rescue The concept of trust is not new to weather and climate science. Trust has been raised numerous times, for example, with regard to climate models (e.g., Raäisaänen 2007 ; McGovern 2020
present. We then introduce our cases in terms of historical content and modern context. A description of cases allows us to derive a 19-element checklist that we hope can be applied to millions of historical weather observations remaining to be rescued. 2. Trust throughout the life cycle of historical data rescue The concept of trust is not new to weather and climate science. Trust has been raised numerous times, for example, with regard to climate models (e.g., Raäisaänen 2007 ; McGovern 2020
To Accurately and Lightly Downscale the Temperature Field by Deep Learninghistorical recalculation and its performance needs to be further validated and evaluated. We will perform experiments on larger areas and longer time range data in future work. Acknowledgments. We would like to thank the National Natural Science Foundation of China (Grants 91437105 and 92037000), the Fengyun Satellite Program (FY-APP-2021.0207), and the National key research and development program of China (Grant 2018YFC1506601) who supported this research. The authors thank National
historical recalculation and its performance needs to be further validated and evaluated. We will perform experiments on larger areas and longer time range data in future work. Acknowledgments. We would like to thank the National Natural Science Foundation of China (Grants 91437105 and 92037000), the Fengyun Satellite Program (FY-APP-2021.0207), and the National key research and development program of China (Grant 2018YFC1506601) who supported this research. The authors thank National
Improved Infilling of Missing Metadata from Expendable Bathythermographs (XBTs) Using Multiple Machine Learning Methodsscientists in fields of research beyond ocean science. 2. Data This project uses XBT temperature profiles and associated metadata from the WOD 2018 ( Boyer et al. 2018 ), a database of ocean variable measurements over more than a century. These data are freely available from the WOD, but to support this work we have published a preprocessed, analysis-ready version of these data, available on AWS S3 here: s3://xbt-data/csv_with_imeta/ . The source code which is available on GitHub ( https
scientists in fields of research beyond ocean science. 2. Data This project uses XBT temperature profiles and associated metadata from the WOD 2018 ( Boyer et al. 2018 ), a database of ocean variable measurements over more than a century. These data are freely available from the WOD, but to support this work we have published a preprocessed, analysis-ready version of these data, available on AWS S3 here: s3://xbt-data/csv_with_imeta/ . The source code which is available on GitHub ( https
