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Ge Peng
Earth System Science Center/NASA MSFC IMPACT, The University of Alabama in Huntsville, Huntsville, AL

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Journal article
Published: 04 May 2021 in Data Science Journal
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ACS Style

Ge Peng; Robert R. Downs; Carlo Lacagnina; Hampapuram Ramapriyan; Ivana Ivánová; David Moroni; Yaxing Wei; Gilles Larnicol; Lesley Wyborn; Mitch Goldberg; Jörg Schulz; Irina Bastrakova; Anette Ganske; Lucy Bastin; Siri Jodha S. Khalsa; Mingfang Wu; Chung-Lin Shie; Nancy Ritchey; Dave Jones; Ted Habermann; Christina Lief; Iolanda Maggio; Mirko Albani; Shelley Stall; Lihang Zhou; Marie Drévillon; Sarah Champion; C. Sophie Hou; Francisco Doblas-Reyes; Kerstin Lehnert; Erin Robinson; Kaylin Bugbee. Call to Action for Global Access to and Harmonization of Quality Information of Individual Earth Science Datasets. Data Science Journal 2021, 20, 1 .

AMA Style

Ge Peng, Robert R. Downs, Carlo Lacagnina, Hampapuram Ramapriyan, Ivana Ivánová, David Moroni, Yaxing Wei, Gilles Larnicol, Lesley Wyborn, Mitch Goldberg, Jörg Schulz, Irina Bastrakova, Anette Ganske, Lucy Bastin, Siri Jodha S. Khalsa, Mingfang Wu, Chung-Lin Shie, Nancy Ritchey, Dave Jones, Ted Habermann, Christina Lief, Iolanda Maggio, Mirko Albani, Shelley Stall, Lihang Zhou, Marie Drévillon, Sarah Champion, C. Sophie Hou, Francisco Doblas-Reyes, Kerstin Lehnert, Erin Robinson, Kaylin Bugbee. Call to Action for Global Access to and Harmonization of Quality Information of Individual Earth Science Datasets. Data Science Journal. 2021; 20 (1):1.

Chicago/Turabian Style

Ge Peng; Robert R. Downs; Carlo Lacagnina; Hampapuram Ramapriyan; Ivana Ivánová; David Moroni; Yaxing Wei; Gilles Larnicol; Lesley Wyborn; Mitch Goldberg; Jörg Schulz; Irina Bastrakova; Anette Ganske; Lucy Bastin; Siri Jodha S. Khalsa; Mingfang Wu; Chung-Lin Shie; Nancy Ritchey; Dave Jones; Ted Habermann; Christina Lief; Iolanda Maggio; Mirko Albani; Shelley Stall; Lihang Zhou; Marie Drévillon; Sarah Champion; C. Sophie Hou; Francisco Doblas-Reyes; Kerstin Lehnert; Erin Robinson; Kaylin Bugbee. 2021. "Call to Action for Global Access to and Harmonization of Quality Information of Individual Earth Science Datasets." Data Science Journal 20, no. 1: 1.

Preprint content
Published: 16 April 2021
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Under the auspices of the Earth Science Information Partners (ESIP) and with collaboration among the ESIP Information Quality Cluster (IQC), the Barcelona Supercomputing Center (BSC) Evaluation and Quality Control (EQC) team, and the Australia/New Zealand Data Quality Interest Group (AU/NZ DQIG), a community effort has been undertaken by international Earth Science domain experts. The objective of this effort is to develop global community guidelines with practical recommendations to promote sharing and reusing of quality information at the dataset level, leveraging the experiences and expertise of a team of interdisciplinary domain experts and community best practices. The community guidelines aim to help stakeholders such as science data centers, repositories, data producers and publishers, data managers and stewards, etc., i) to capture and represent quality information of their datasets in a way that is in line with the FAIR guiding principles; ii) to allow for the maximum trust, sharing, reuse and value of their datasets; and iii) to enable global access to and integration of dataset quality information. The vision of developing these guidelines is to promote the creation and use of freely and openly shared dataset quality information that is consistently described, readily available in community standardized formats, and capable of being integrated across commonly-used Earth science systems and tools for search and access with explicitly expressed usage licenses.

ACS Style

Ge Peng; Carlo Lacagnina; Robert R. Downs; Hampapuram Ramapriyan; Ivana Ivánová; Anette Ganske; Dave Jones; Lucy Bastin; Lesley Wyborn; Irina Bastrakova; Mingfang Wu; Chung-Lin Shie; David F. Moroni; Gilles Larnicol; Yaxing Wei; Nancy Ritchey; Chung-Yi Hou; Ted Habermann; Sarah Champion; Gary Berg-Cross; Kaylin Bugbee; Jeanné le Roux. International Community Guidelines for Sharing and Reusing Quality Information of Individual Earth Science Datasets. 2021, 1 .

AMA Style

Ge Peng, Carlo Lacagnina, Robert R. Downs, Hampapuram Ramapriyan, Ivana Ivánová, Anette Ganske, Dave Jones, Lucy Bastin, Lesley Wyborn, Irina Bastrakova, Mingfang Wu, Chung-Lin Shie, David F. Moroni, Gilles Larnicol, Yaxing Wei, Nancy Ritchey, Chung-Yi Hou, Ted Habermann, Sarah Champion, Gary Berg-Cross, Kaylin Bugbee, Jeanné le Roux. International Community Guidelines for Sharing and Reusing Quality Information of Individual Earth Science Datasets. . 2021; ():1.

Chicago/Turabian Style

Ge Peng; Carlo Lacagnina; Robert R. Downs; Hampapuram Ramapriyan; Ivana Ivánová; Anette Ganske; Dave Jones; Lucy Bastin; Lesley Wyborn; Irina Bastrakova; Mingfang Wu; Chung-Lin Shie; David F. Moroni; Gilles Larnicol; Yaxing Wei; Nancy Ritchey; Chung-Yi Hou; Ted Habermann; Sarah Champion; Gary Berg-Cross; Kaylin Bugbee; Jeanné le Roux. 2021. "International Community Guidelines for Sharing and Reusing Quality Information of Individual Earth Science Datasets." , no. : 1.

Journal article
Published: 09 February 2021 in Data Science Journal
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Article: Stewardship Maturity Assessment Tools for Modernization of Climate Data Management

ACS Style

Robert Dunn; Christina Lief; Ge Peng; William Wright; Omar Baddour; Markus Donat; Brigitte Dubuisson; Jean-François Legeais; Peter Siegmund; Reinaldo Silveira; Xiaolan L. Wang; Markus Ziese. Stewardship Maturity Assessment Tools for Modernization of Climate Data Management. Data Science Journal 2021, 20, 1 .

AMA Style

Robert Dunn, Christina Lief, Ge Peng, William Wright, Omar Baddour, Markus Donat, Brigitte Dubuisson, Jean-François Legeais, Peter Siegmund, Reinaldo Silveira, Xiaolan L. Wang, Markus Ziese. Stewardship Maturity Assessment Tools for Modernization of Climate Data Management. Data Science Journal. 2021; 20 (1):1.

Chicago/Turabian Style

Robert Dunn; Christina Lief; Ge Peng; William Wright; Omar Baddour; Markus Donat; Brigitte Dubuisson; Jean-François Legeais; Peter Siegmund; Reinaldo Silveira; Xiaolan L. Wang; Markus Ziese. 2021. "Stewardship Maturity Assessment Tools for Modernization of Climate Data Management." Data Science Journal 20, no. 1: 1.

Preprint content
Published: 15 December 2020
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In less than one decade the open-access data journal Earth System Science Data (ESSD, a member of the Copernicus Open Access Publisher family) grew from a start-up venture into one of the highest-rated journals in global environmental science. Stimulated by data needs of the International Polar Year 2007-2008, ESSD now serves a very broad community of data providers and users, ensuring that users get free and easy access to quality data products and that providers gain full public credit for preparing, describing and sharing those products. Adopting technology and practices from research journals, ESSD moved data publication from an abstract concept to a working enterprise; several publishers now support similar data-sharing journals. As it confronts increasing challenges and barriers, ESSD serves as a prominent voice for and an example of emphatic fully-free fully-open global data access. Data journals such as ESSD clearly meet a strong community need.

ACS Style

David Carlson; Kirsten Elger; Ge Peng; Johannes Wagner; Jens Klump. Promoting Global Sharing of Earth System Science Data Through Free and Open Access Data Publication. 2020, 1 .

AMA Style

David Carlson, Kirsten Elger, Ge Peng, Johannes Wagner, Jens Klump. Promoting Global Sharing of Earth System Science Data Through Free and Open Access Data Publication. . 2020; ():1.

Chicago/Turabian Style

David Carlson; Kirsten Elger; Ge Peng; Johannes Wagner; Jens Klump. 2020. "Promoting Global Sharing of Earth System Science Data Through Free and Open Access Data Publication." , no. : 1.

Preprint content
Published: 15 December 2020
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Knowledge about the quality of data and metadata is important to support informed decisions on the (re)use of individual datasets and is an essential part of the ecosystem that supports open science. Quality assessments reflect the reliability and usability of data and need to be consistently curated, fully traceable, and adequately documented, as these are crucial for sound decision- and policy-making efforts that rely on data. Quality assessments also need to be consistently represented and readily integrated across systems and tools to allow for improved sharing of information on quality at the dataset level for individual quality attribute or dimension. Although the need for assessing the quality of data and associated information is well recognized, methodologies for an evaluation framework and presentation of resultant quality information to end users may not have been comprehensively addressed within and across disciplines. Global interdisciplinary domain experts have come together to systematically explore needs, challenges and impacts of consistently curating and representing quality information through the entire lifecycle of a dataset. This paper describes the findings, calls for community action to develop practical guidelines, and outlines community recommendations for developing such guidelines. Community practical guidelines will allow for global access and harmonization of quality information at the level of individual Earth science datasets and support open science.

ACS Style

Ge Peng; Robert R. Downs; Carlo Lacagnina; Hampapuram Ramapriyan; Ivana Ivánová; David F. Moroni; Yaxing Wei; Larnicol Gilles; Lesley Wyborn; Mitchell Goldberg; Jörg Schulz; Irina Bastrakova; Anette Ganske; Lucy Bastin; Siri Jodha Singh Khalsa; Mingfang Wu; Chung-Lin Shie; Nancy Ritchey; Dave Jones; Ted Habermann; Christina Lief; Iolanda Maggio; Mirko Albani; Shelley Stall; Lihang Zhou; Marie Drévillon; Sarah Champion; Chung-Yi Hou; Francisco Doblas-Reyes; Kerstin Annette Lehnert; Erin Robinson; Kaylin Bugbee. Call to Action for Global Access to and Harmonization of Quality Information of Individual Earth Science Datasets. 2020, 1 .

AMA Style

Ge Peng, Robert R. Downs, Carlo Lacagnina, Hampapuram Ramapriyan, Ivana Ivánová, David F. Moroni, Yaxing Wei, Larnicol Gilles, Lesley Wyborn, Mitchell Goldberg, Jörg Schulz, Irina Bastrakova, Anette Ganske, Lucy Bastin, Siri Jodha Singh Khalsa, Mingfang Wu, Chung-Lin Shie, Nancy Ritchey, Dave Jones, Ted Habermann, Christina Lief, Iolanda Maggio, Mirko Albani, Shelley Stall, Lihang Zhou, Marie Drévillon, Sarah Champion, Chung-Yi Hou, Francisco Doblas-Reyes, Kerstin Annette Lehnert, Erin Robinson, Kaylin Bugbee. Call to Action for Global Access to and Harmonization of Quality Information of Individual Earth Science Datasets. . 2020; ():1.

Chicago/Turabian Style

Ge Peng; Robert R. Downs; Carlo Lacagnina; Hampapuram Ramapriyan; Ivana Ivánová; David F. Moroni; Yaxing Wei; Larnicol Gilles; Lesley Wyborn; Mitchell Goldberg; Jörg Schulz; Irina Bastrakova; Anette Ganske; Lucy Bastin; Siri Jodha Singh Khalsa; Mingfang Wu; Chung-Lin Shie; Nancy Ritchey; Dave Jones; Ted Habermann; Christina Lief; Iolanda Maggio; Mirko Albani; Shelley Stall; Lihang Zhou; Marie Drévillon; Sarah Champion; Chung-Yi Hou; Francisco Doblas-Reyes; Kerstin Annette Lehnert; Erin Robinson; Kaylin Bugbee. 2020. "Call to Action for Global Access to and Harmonization of Quality Information of Individual Earth Science Datasets." , no. : 1.

Preprint content
Published: 29 August 2020
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This document provides background for and summarizes main takeaways of a workshop held virtually to kick off the development of community guidelines for consistently curating and representing dataset quality information in a way that is in line with the FAIR principles.

ACS Style

Ge Peng; Carlo Lacagnina; Robert R. Downs; Ivana Ivanova; David F. Moroni; H.K. Ramapriyan; Yaxing Wei; Gilles Larnicol. Laying the Groundwork for Developing International Community Guidelines to Effectively Share and Reuse Digital Data Quality Information – Case Statement, Workshop Summary Report, and Path Forward. 2020, 1 .

AMA Style

Ge Peng, Carlo Lacagnina, Robert R. Downs, Ivana Ivanova, David F. Moroni, H.K. Ramapriyan, Yaxing Wei, Gilles Larnicol. Laying the Groundwork for Developing International Community Guidelines to Effectively Share and Reuse Digital Data Quality Information – Case Statement, Workshop Summary Report, and Path Forward. . 2020; ():1.

Chicago/Turabian Style

Ge Peng; Carlo Lacagnina; Robert R. Downs; Ivana Ivanova; David F. Moroni; H.K. Ramapriyan; Yaxing Wei; Gilles Larnicol. 2020. "Laying the Groundwork for Developing International Community Guidelines to Effectively Share and Reuse Digital Data Quality Information – Case Statement, Workshop Summary Report, and Path Forward." , no. : 1.

Journal article
Published: 03 March 2020 in Remote Sensing
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Arctic sea ice extent has been utilized to monitor sea ice changes since the late 1970s using remotely sensed sea ice data derived from passive microwave (PM) sensors. A 15% sea ice concentration threshold value has been used traditionally when computing sea ice extent (SIE), although other threshold values have been employed. Does the rapid depletion of Arctic sea ice potentially alter the basic characteristics of Arctic ice extent? In this paper, we explore whether and how the statistical characteristics of Arctic sea ice have changed during the satellite data record period of 1979–2017 and examine the sensitivity of sea ice extents and their decadal trends to sea ice concentration threshold values. Threshold choice can affect the timing of annual SIE minimums: a threshold choice as low as 30% can change the timing to August instead of September. Threshold choice impacts the value of annual SIE minimums: in particular, changing the threshold from 15% to 35% can change the annual SIE by more than 10% in magnitude. Monthly SIE data distributions are seasonally dependent. Although little impact was seen for threshold choice on data distributions during annual minimum times (August and September), there is a strong impact in May. Threshold choices were not found to impact the choice of optimal statistical models characterizing annual minimum SIE time series. However, the first ice-free Arctic summer year (FIASY) estimates are impacted; higher threshold values produce earlier FIASY estimates and, more notably, FIASY estimates amongst all considered models are more consistent. This analysis suggests that some of the threshold choice impacts to SIE trends may actually be the result of biased data due to surface melt. Given that the rapid Arctic sea ice depletion appears to have statistically changed SIE characteristics, particularly in the summer months, a more extensive investigation to verify surface melt impacts on this data set is warranted.

ACS Style

Jessica L. Matthews; Ge Peng; Walter N. Meier; Otis Brown. Sensitivity of Arctic Sea Ice Extent to Sea Ice Concentration Threshold Choice and Its Implication to Ice Coverage Decadal Trends and Statistical Projections. Remote Sensing 2020, 12, 807 .

AMA Style

Jessica L. Matthews, Ge Peng, Walter N. Meier, Otis Brown. Sensitivity of Arctic Sea Ice Extent to Sea Ice Concentration Threshold Choice and Its Implication to Ice Coverage Decadal Trends and Statistical Projections. Remote Sensing. 2020; 12 (5):807.

Chicago/Turabian Style

Jessica L. Matthews; Ge Peng; Walter N. Meier; Otis Brown. 2020. "Sensitivity of Arctic Sea Ice Extent to Sea Ice Concentration Threshold Choice and Its Implication to Ice Coverage Decadal Trends and Statistical Projections." Remote Sensing 12, no. 5: 807.

Journal article
Published: 17 January 2020 in Climate
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The prospect of an ice-free Arctic in our near future due to the rapid and accelerated Arctic sea ice decline has brought about the urgent need for reliable projections of the first ice-free Arctic summer year (FIASY). Together with up-to-date observations and characterizations of Arctic ice state, they are essential to business strategic planning, climate adaptation, and risk mitigation. In this study, the monthly Arctic sea ice extents from 12 global climate models are utilized to obtain projected FIASYs and their dependency on different emission scenarios, as well as to examine the nature of the ice retreat projections. The average value of model-projected FIASYs is 2054/2042, with a spread of 74/42 years for the medium/high emission scenarios, respectively. The earliest FIASY is projected to occur in year 2023, which may not be realistic, for both scenarios. The sensitivity of individual climate models to scenarios in projecting FIASYs is very model-dependent. The nature of model-projected Arctic sea ice coverage changes is shown to be primarily linear. FIASY values predicted by six commonly used statistical models that were curve-fitted with the first 30 years of climate projections (2006–2035), on other hand, show a preferred range of 2030–2040, with a distinct peak at 2034 for both scenarios, which is more comparable with those from previous studies.

ACS Style

Ge Peng; Jessica L. Matthews; Muyin Wang; Russell Vose; Liqiang Sun. What Do Global Climate Models Tell Us about Future Arctic Sea Ice Coverage Changes? Climate 2020, 8, 15 .

AMA Style

Ge Peng, Jessica L. Matthews, Muyin Wang, Russell Vose, Liqiang Sun. What Do Global Climate Models Tell Us about Future Arctic Sea Ice Coverage Changes? Climate. 2020; 8 (1):15.

Chicago/Turabian Style

Ge Peng; Jessica L. Matthews; Muyin Wang; Russell Vose; Liqiang Sun. 2020. "What Do Global Climate Models Tell Us about Future Arctic Sea Ice Coverage Changes?" Climate 8, no. 1: 15.

Data descriptor
Published: 10 August 2019 in Data
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The climate normal, that is, the latest three full-decade average, of Arctic sea ice parameters is useful for baselining the sea ice state. A baseline ice state on both regional and local scales is important for monitoring how the current regional and local states depart from their normal to understand the vulnerability of marine and sea ice-based ecosystems to the changing climate conditions. Combined with up-to-date observations and reliable projections, normals are essential to business strategic planning, climate adaptation and risk mitigation. In this paper, monthly and annual climate normals of sea ice parameters (concentration, area, and extent) of the whole Arctic Ocean and 15 regional divisions are derived for the period of 1981–2010 using monthly satellite sea ice concentration estimates from a climate data record (CDR) produced by NOAA and the National Snow and Ice Data Center (NSIDC). Basic descriptions and characteristics of the normals are provided. Empirical Orthogonal Function (EOF) analysis has been utilized to describe spatial modes of sea ice concentration variability and how the corresponding principal components change over time. To provide users with basic information on data product accuracy and uncertainty, the climate normal values of Arctic sea ice extents (SIE) are compared with that of other products, including a product from NSIDC and two products from the Copernicus Climate Change Service (C3S). The SIE differences between different products are in the range of 2.3–4.5% of the CDR SIE mean. Additionally, data uncertainty estimates are represented by using the range (the difference between the maximum and minimum), standard deviation, 10th and 90th percentiles, and the first, second, and third quartile distribution of all monthly values, a distinct feature of these sea ice normal products.

ACS Style

Ge Peng; Anthony Arguez; Walter N. Meier; Freja Vamborg; Jake Crouch; Philip Jones. Sea Ice Climate Normals for Seasonal Ice Monitoring of Arctic and Sub-Regions. Data 2019, 4, 122 .

AMA Style

Ge Peng, Anthony Arguez, Walter N. Meier, Freja Vamborg, Jake Crouch, Philip Jones. Sea Ice Climate Normals for Seasonal Ice Monitoring of Arctic and Sub-Regions. Data. 2019; 4 (3):122.

Chicago/Turabian Style

Ge Peng; Anthony Arguez; Walter N. Meier; Freja Vamborg; Jake Crouch; Philip Jones. 2019. "Sea Ice Climate Normals for Seasonal Ice Monitoring of Arctic and Sub-Regions." Data 4, no. 3: 122.

Journal article
Published: 11 February 2019 in Eos
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Bruce R. Barkstrom, principal investigator for NASA missions involved with understanding Earth’s radiation budget, committed his life to analyzing, interpreting, and stewarding Earth science data.

ACS Style

Hampapuram K. Ramapriyan; Robert Downs; Jeff Dozier; Ruth Duerr; Mike Folk; James Frew; Nancy Hoebelheinrich; Chris A. Mattmann; Ge Peng. Bruce Barkstrom (1944–2018). Eos 2019, 100, 1 .

AMA Style

Hampapuram K. Ramapriyan, Robert Downs, Jeff Dozier, Ruth Duerr, Mike Folk, James Frew, Nancy Hoebelheinrich, Chris A. Mattmann, Ge Peng. Bruce Barkstrom (1944–2018). Eos. 2019; 100 ():1.

Chicago/Turabian Style

Hampapuram K. Ramapriyan; Robert Downs; Jeff Dozier; Ruth Duerr; Mike Folk; James Frew; Nancy Hoebelheinrich; Chris A. Mattmann; Ge Peng. 2019. "Bruce Barkstrom (1944–2018)." Eos 100, no. : 1.

Journal article
Published: 03 January 2019 in Data Science Journal
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ACS Style

Ge Peng; Anna Milan; Nancy A. Ritchey; Robert P. Partee Ii; Sonny Zinn; Evan McQuinn; Kenneth S. Casey; Paul Lemieux Iii; Raisa Ionin; Philip Jones; Arianna Jakositz; Donald Collins. Practical Application of a Data Stewardship Maturity Matrix for the NOAA OneStop Project. Data Science Journal 2019, 18, 1 .

AMA Style

Ge Peng, Anna Milan, Nancy A. Ritchey, Robert P. Partee Ii, Sonny Zinn, Evan McQuinn, Kenneth S. Casey, Paul Lemieux Iii, Raisa Ionin, Philip Jones, Arianna Jakositz, Donald Collins. Practical Application of a Data Stewardship Maturity Matrix for the NOAA OneStop Project. Data Science Journal. 2019; 18 (1):1.

Chicago/Turabian Style

Ge Peng; Anna Milan; Nancy A. Ritchey; Robert P. Partee Ii; Sonny Zinn; Evan McQuinn; Kenneth S. Casey; Paul Lemieux Iii; Raisa Ionin; Philip Jones; Arianna Jakositz; Donald Collins. 2019. "Practical Application of a Data Stewardship Maturity Matrix for the NOAA OneStop Project." Data Science Journal 18, no. 1: 1.

Accepted manuscript
Published: 02 January 2019 in Environmental Research Letters
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The seasonal evolution of Arctic sea ice can be described by the timing of key dates of sea ice concentration (SIC) change during its annual retreat and advance cycle. Here, we use SICs from a satellite passive microwave climate data record to identify the sea ice dates of opening (DOO), retreat (DOR), advance (DOA), and closing (DOC) and the periods of time between these events. Regional variability in these key dates, periods, and sea ice melt onset and freeze-up dates for 12 Arctic regions during the melt seasons of 1979 – 2016 is investigated. We find statistically significant positive trends in the length of the melt season (outer ice-free period) for most of the eastern Arctic, the Bering Sea, and Hudson and Baffin Bays with trends as large as 11.9 days decade-1 observed in the Kara Sea. Trends in the DOR and DOA contribute to statistically significant increases in the length of the open water period for all regions within the Arctic Ocean ranging from 3.9 to 13.8 days decade-1. The length of the ice retreat period (DOR - DOO) ranges from 17.1 days in the Sea of Okhotsk to 41 days in the Greenland Sea. The length of the ice advance period (DOC - DOA) is generally much shorter and ranges from 17.9 days to 25.3 days in the Sea of Okhotsk and Greenland Sea, respectively. Additionally, we derive the extent of the seasonal ice zone (SIZ) and find statistically significant negative trends (SIZ is shrinking) in the Sea of Okhotsk, Baffin Bay, Greenland Sea, and Barents Sea regions, which are geographically open to the oceans and influenced by reduced winter sea ice extent. Within regions of the Arctic Ocean, statistically significant positive trends indicate that the extent of the SIZ is expanding as Arctic summer sea ice declines.

ACS Style

Angela C Bliss; Michael Steele; Ge Peng; Walter N Meier; Suzanne Dickinson. Regional variability of Arctic sea ice seasonal change climate indicators from a passive microwave climate data record. Environmental Research Letters 2019, 14, 045003 .

AMA Style

Angela C Bliss, Michael Steele, Ge Peng, Walter N Meier, Suzanne Dickinson. Regional variability of Arctic sea ice seasonal change climate indicators from a passive microwave climate data record. Environmental Research Letters. 2019; 14 (4):045003.

Chicago/Turabian Style

Angela C Bliss; Michael Steele; Ge Peng; Walter N Meier; Suzanne Dickinson. 2019. "Regional variability of Arctic sea ice seasonal change climate indicators from a passive microwave climate data record." Environmental Research Letters 14, no. 4: 045003.

Preprint
Published: 18 October 2018
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Assessing the stewardship maturity of individual datasets is an essential part of ensuring and improving the way datasets are documented, preserved, and disseminated to users. It is a critical step towards meeting U.S. federal regulations, organizational requirements, and user needs. However, it is challenging to do so consistently and quantifiably. The Data Stewardship Maturity Matrix (DSMM), developed jointly by NOAA’s National Centers for Environmental Information (NCEI) and the Cooperative Institute for Climate and Satellites–North Carolina (CICS-NC), provides a uniform framework for consistently rating stewardship maturity of individual datasets in nine key components: preservability, accessibility, usability, production sustainability, data quality assurance, data quality control/monitoring, data quality assessment, transparency/traceability, and data integrity. So far, the DSMM has been applied to over 900 individual datasets that are archived and/or managed by NCEI, in support of the NOAA’s OneStop Data Discovery and Access Framework Project. As a part of the OneStop-ready process, tools, implementation guidance, workflows, and best practices are developed to assist the application of the DSMM and described in this paper. The DSMM ratings are also consistently captured in the ISO standard-based dataset-level quality metadata and citable quality descriptive information documents, which serve as interoperable quality information to both machine and human end-users. These DSMM implementation and integration workflows and best practices could be adopted by other data management and stewardship projects or adapted for applications of other maturity assessment models.

ACS Style

Ge Peng; Anna Milan; Nancy A. Ritchey; Robert P. Partee; Sonny Zinn; Evan McQuinn; Kenneth S. Casey; Paul Lemieux; Raisa Ionin; Philip Jones; Arianna Jakositz; Donald Collins. Practical Application of a Data Stewardship Maturity Matrix for the NOAA OneStop Project. 2018, 1 .

AMA Style

Ge Peng, Anna Milan, Nancy A. Ritchey, Robert P. Partee, Sonny Zinn, Evan McQuinn, Kenneth S. Casey, Paul Lemieux, Raisa Ionin, Philip Jones, Arianna Jakositz, Donald Collins. Practical Application of a Data Stewardship Maturity Matrix for the NOAA OneStop Project. . 2018; ():1.

Chicago/Turabian Style

Ge Peng; Anna Milan; Nancy A. Ritchey; Robert P. Partee; Sonny Zinn; Evan McQuinn; Kenneth S. Casey; Paul Lemieux; Raisa Ionin; Philip Jones; Arianna Jakositz; Donald Collins. 2018. "Practical Application of a Data Stewardship Maturity Matrix for the NOAA OneStop Project." , no. : 1.

Journal article
Published: 21 August 2018 in Remote Sensing
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Information on the timing of Arctic snow and ice melt onset, sea ice opening, retreat, advance, and closing, can be beneficial to a variety of stakeholders. Sea ice modelers can use information on the evolution of the ice cover through the rest of the summer to improve their seasonal sea ice forecasts. The length of the open water season (as derived from retreat/advance dates) is important for human activities and for wildlife. Long-term averages and variability of these dates as climate indicators are beneficial to business strategic planning and climate monitoring. In this study, basic characteristics of temporal means and variability of Arctic sea ice climate indicators derived from a satellite-based climate data record from March 1979 to February 2017 melt and freeze seasons are described. Our results show that, over the Arctic region, anomalies of snow and ice melt onset, ice opening and retreat dates are getting earlier in the year at a rate of more than 5 days per decade, while that of ice advance and closing dates are getting later at a rate of more than 5 days per decade. These significant trends resulted in significant upward trends for anomalies of inner and outer ice-free periods at a rate of nearly 12 days per decade. Small but significant downward trends of seasonal ice loss and gain period anomalies were also observed at a rate of −1.48 and −0.53 days per decade, respectively. Our analyses also demonstrated that the means of these indicators and their trends are sensitive to valid data masks and regional averaging methods.

ACS Style

Ge Peng; Michael Steele; Angela C. Bliss; Walter N. Meier; Suzanne Dickinson. Temporal Means and Variability of Arctic Sea Ice Melt and Freeze Season Climate Indicators Using a Satellite Climate Data Record. Remote Sensing 2018, 10, 1328 .

AMA Style

Ge Peng, Michael Steele, Angela C. Bliss, Walter N. Meier, Suzanne Dickinson. Temporal Means and Variability of Arctic Sea Ice Melt and Freeze Season Climate Indicators Using a Satellite Climate Data Record. Remote Sensing. 2018; 10 (9):1328.

Chicago/Turabian Style

Ge Peng; Michael Steele; Angela C. Bliss; Walter N. Meier; Suzanne Dickinson. 2018. "Temporal Means and Variability of Arctic Sea Ice Melt and Freeze Season Climate Indicators Using a Satellite Climate Data Record." Remote Sensing 10, no. 9: 1328.

Journal article
Published: 26 March 2018 in Data Science Journal
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ACS Style

Ge Peng. The State of Assessing Data Stewardship Maturity – An Overview. Data Science Journal 2018, 17, 1 .

AMA Style

Ge Peng. The State of Assessing Data Stewardship Maturity – An Overview. Data Science Journal. 2018; 17 ():1.

Chicago/Turabian Style

Ge Peng. 2018. "The State of Assessing Data Stewardship Maturity – An Overview." Data Science Journal 17, no. : 1.

Journal article
Published: 02 February 2018 in Remote Sensing
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An ice-free Arctic summer would have pronounced impacts on global climate, coastal habitats, national security, and the shipping industry. Rapid and accelerated Arctic sea ice loss has placed the reality of an ice-free Arctic summer even closer to the present day. Accurate projection of the first Arctic ice-free summer year is extremely important for business planning and climate change mitigation, but the projection can be affected by many factors. Using an inter-calibrated satellite sea ice product, this article examines the sensitivity of decadal trends of Arctic sea ice extent and statistical projections of the first occurrence of an ice-free Arctic summer. The projection based on the linear trend of the last 20 years of data places the first Arctic ice-free summer year at 2036, 12 years earlier compared to that of the trend over the last 30 years. The results from a sensitivity analysis of six commonly used curve-fitting models show that the projected timings of the first Arctic ice-free summer year tend to be earlier for exponential, Gompertz, quadratic, and linear with lag fittings, and later for linear and log fittings. Projections of the first Arctic ice-free summer year by all six statistical models appear to converge to the 2037 ± 6 timeframe, with a spread of 17 years, and the earliest first ice-free Arctic summer year at 2031.

ACS Style

Ge Peng; Jessica L. Matthews; Jason T. Yu. Sensitivity Analysis of Arctic Sea Ice Extent Trends and Statistical Projections Using Satellite Data. Remote Sensing 2018, 10, 230 .

AMA Style

Ge Peng, Jessica L. Matthews, Jason T. Yu. Sensitivity Analysis of Arctic Sea Ice Extent Trends and Statistical Projections Using Satellite Data. Remote Sensing. 2018; 10 (2):230.

Chicago/Turabian Style

Ge Peng; Jessica L. Matthews; Jason T. Yu. 2018. "Sensitivity Analysis of Arctic Sea Ice Extent Trends and Statistical Projections Using Satellite Data." Remote Sensing 10, no. 2: 230.

Journal article
Published: 24 January 2018 in Data Science Journal
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Scientific data stewardship is an important part of long-term preservation and the use/reuse of digital research data. It is critical for ensuring trustworthiness of data, products, and services, which is important for decision-making. Recent U.S. federal government directives and scientific organization guidelines have levied specific requirements, increasing the need for a more formal approach to ensuring that stewardship activities support compliance verification and reporting. However, many science data centers lack an integrated, systematic, and holistic framework to support such efforts. The current business- and process-oriented stewardship frameworks are too costly and lengthy for most data centers to implement. They often do not explicitly address the federal stewardship requirements and/or the uniqueness of geospatial data. This work proposes a data-centric conceptual enterprise framework for managing stewardship activities, based on the philosophy behind the Plan-Do-Check-Act (PDCA) cycle, a proven industrial concept. This framework, which includes the application of maturity assessment models, allows for quantitative evaluation of how organizations manage their stewardship activities and supports informed decision-making for continual improvement towards full compliance with federal, agency, and user requirements.

ACS Style

Ge Peng; Jeffrey L. Privette; Curt Tilmes; Robert Bristol; Tom Maycock; John J. Bates; Scott Hausman; Otis Brown; Edward J. Kearns. A Conceptual Enterprise Framework for Managing Scientific Data Stewardship. Data Science Journal 2018, 17, 15 -15.

AMA Style

Ge Peng, Jeffrey L. Privette, Curt Tilmes, Robert Bristol, Tom Maycock, John J. Bates, Scott Hausman, Otis Brown, Edward J. Kearns. A Conceptual Enterprise Framework for Managing Scientific Data Stewardship. Data Science Journal. 2018; 17 ():15-15.

Chicago/Turabian Style

Ge Peng; Jeffrey L. Privette; Curt Tilmes; Robert Bristol; Tom Maycock; John J. Bates; Scott Hausman; Otis Brown; Edward J. Kearns. 2018. "A Conceptual Enterprise Framework for Managing Scientific Data Stewardship." Data Science Journal 17, no. : 15-15.

Preprint
Published: 19 December 2017
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Data stewardship encompasses all activities that preserve and improve the information content, accessibility, and usability of data and metadata. Recent regulations, mandates, policies and guidelines set forth by the U.S. government, federal and funding agencies, scientific societies and scholarly publishers, have levied stewardship requirements on digital scientific data. This raised level of requirements has increased the need for a formal approach to stewardship activities that they support compliance verification. For any entity to meet or verify the compliance with the stewardship requirements, it is necessary to assess the current stage, identify gaps, and define a roadmap forward for improvement if necessary. This, however, touches on standards and best practices in multiple knowledge domains. Therefore, data stewardship practitioners, especially these at data repositories, data service centers or associated with data stewardship programs, can benefit from the knowledge of existing maturity assessment models. This article provides an overview of the current stage of assessing stewardship maturity for federally funded digital scientific data. A brief description of existing maturity assessment models and related application(s) is provided. This helps stewardship practitioners to readily obtain basic information about these models. It allows them to evaluate each model’s suitability for their unique verification and improvement needs.

ACS Style

Ge Peng. The State of Assessing Data Stewardship Maturity – An Overview. 2017, 1 .

AMA Style

Ge Peng. The State of Assessing Data Stewardship Maturity – An Overview. . 2017; ():1.

Chicago/Turabian Style

Ge Peng. 2017. "The State of Assessing Data Stewardship Maturity – An Overview." , no. : 1.

Papers
Published: 02 November 2017 in Annals of Glaciology
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With rapid and accelerated Arctic sea-ice loss, it is beneficial to update and baseline historical change on the regional scales from a consistent, intercalibrated, long-term time series of sea-ice data for understanding regional vulnerability and monitoring ice state for climate adaptation and risk mitigation. In this paper, monthly sea-ice extents (SIEs) derived from a passive microwave sea-ice concentration climate data record for the period of 1979–2015, are used to examine Arctic-wide and regional temporal variability of sea-ice cover and their decadal trends for 15 regions of the Arctic. Three unique types of SIE annual cycles are described. Regions of vulnerability within each of three types to further warming are identified. For the Arctic as a whole, the analysis has found significant changes in both annual SIE maximum and minimum, with −2.41 ± 0.56% per decade and −13.5 ± 2.93% per decade change relative to the 1979–2015 climate average, respectively. On the regional scale, the calculated trends for the annual SIE maximum range from +2.48 to −10.8% decade−1, while the trends for the annual SIE minimum range from 0 to up to −42% decade−1.

ACS Style

Ge Peng; Walter N. Meier. Temporal and regional variability of Arctic sea-ice coverage from satellite data. Annals of Glaciology 2017, 59, 191 -200.

AMA Style

Ge Peng, Walter N. Meier. Temporal and regional variability of Arctic sea-ice coverage from satellite data. Annals of Glaciology. 2017; 59 (76pt2):191-200.

Chicago/Turabian Style

Ge Peng; Walter N. Meier. 2017. "Temporal and regional variability of Arctic sea-ice coverage from satellite data." Annals of Glaciology 59, no. 76pt2: 191-200.

Journal article
Published: 01 July 2017 in D-Lib Magazine
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ACS Style

Hampapuram Ramapriyan; Ge Peng; David Moroni; Chung-Lin Shie. Ensuring and Improving Information Quality for Earth Science Data and Products. D-Lib Magazine 2017, 23, 1 .

AMA Style

Hampapuram Ramapriyan, Ge Peng, David Moroni, Chung-Lin Shie. Ensuring and Improving Information Quality for Earth Science Data and Products. D-Lib Magazine. 2017; 23 (7):1.

Chicago/Turabian Style

Hampapuram Ramapriyan; Ge Peng; David Moroni; Chung-Lin Shie. 2017. "Ensuring and Improving Information Quality for Earth Science Data and Products." D-Lib Magazine 23, no. 7: 1.