scholarly journals Design and Validation of the Scientific Data Products for China’s Tianwen-1 Mission

2021 ◽  
Vol 217 (5) ◽  
Author(s):  
Xu Tan ◽  
Jianjun Liu ◽  
Xiaoxia Zhang ◽  
Wei Yan ◽  
Wangli Chen ◽  
...  
Keyword(s):  
Information Technologies ◽  
Meteorological Data ◽  
Flexible Structures ◽  
Image Data ◽  
Scientific Data ◽  
Data Validation ◽  
Ancillary Data ◽  
Data Application ◽  
Data Products ◽  
Spectrum Data

AbstractThe Tianwen-1 mission is China’s first Mars exploration mission. To complete the scientific objectives of “orbiting, landing, and patrolling” in one mission, thirteen instruments for acquiring multi-disciplinary data are configured. The data products with rich ancillary data and flexible structures will facilitate the data application. To integrate the requirements of experts from disparate disciplines and leverage new information technologies, a complete redesign of data products is promoted in the Tianwen-1 mission. We discuss the whole process of data product generation from the data pipeline design to data validation. We design the various data products according to the application requirements of each data type, including radar echo data, spectrum data, image data, energy spectrum data, magnetic field data, meteorological data, and acoustic data. Since various error sources will exist in the generation chain of the data products, data validation is essential before release to the public; the validation activities are discussed at the end. We deliver these scientific products to the community in a timely manner, with ancillary information and quality information. This paper can provide practical reference for Tianwen-1 data application.

The Rise of Primary Research Data

2017 ◽  
Vol 39 (3) ◽  
Author(s):  
Leah McEwen ◽  
David Martinsen
Keyword(s):  
Information Technologies ◽  
Measurement Data ◽  
Basic Research ◽  
Scientific Data ◽  
Digital Information ◽  
Primary Research ◽  
Hard Drives ◽  
Use Of Data ◽  
Global Commerce ◽  
Cloud Technologies

AbstractAs the scale of global commerce and opportunities for multidisciplinary collaboration increase, there is greater pressure on basic research to supply a quick return on investment (ROI). The emergence and development of digital information technologies in the new millennium have inspired a new look at how research outputs are managed and disseminated. The driving question in the minds of many research funders is this—will lowering the barriers for access increase the value of research for the greater society? This is a particularly interesting question to consider for measurement data, the greater amount of which are scattered across millions of separate, fixed publications (not to mention those never published and lingering in file drawers and on hard drives). Can the advent of cloud technologies, exchange standards, and provenance tracking facilitate improved access, evaluation, and use of data for both research and commerce? Can new value and discovery be realized through the greater aggregation of measured scientific data as “Big Data”?

Multi-Timescale LSTM for Rainfall–Runoff Forecasting

2021 ◽  
Author(s):  
Martin Gauch ◽  
Frederik Kratzert ◽  
Grey Nearing ◽  
Jimmy Lin ◽  
Sepp Hochreiter ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Meteorological Data ◽  
Multiple Timescales ◽  
Fine Grained ◽  
Additional Layer ◽  
Runoff Forecasting ◽  
Data Products ◽  
Future Data ◽  
Past Data ◽  
Branched Structure

<p>Rainfall–runoff predictions are generally evaluated on reanalysis datasets such as the DayMet, Maurer, or NLDAS forcings in the CAMELS dataset. While useful for benchmarking, this does not fully reflect real-world applications. There, meteorological information is much coarser, and fine-grained predictions are at best available until the present. For any prediction of future discharge, we must rely on forecasts, which introduce an additional layer of uncertainty. Thus, the model inputs need to switch from past data to forecast data at some point, which raises several questions: How can we design models that support this transition? How can we design tests that evaluate the performance of the model? Aggravating the challenge, the past and future data products may include different variables or have different temporal resolutions.</p><p>We demonstrate how to seamlessly integrate past and future meteorological data in one deep learning model, using the recently proposed Multi-Timescale LSTM (MTS-LSTM, [1]). MTS-LSTMs are based on LSTMs but can generate rainfall–runoff predictions at multiple timescales more efficiently. One MTS-LSTM consists of several LSTMs that are organized in a branched structure. Each LSTM branch processes a part of the input time series at a certain temporal resolution. Then it passes its states to the next LSTM branch—thus sharing information across branches. We generalize this layout to handovers across data products (rather than just timescales) through an additional branch. This way, we can integrate past and future data in one prediction pipeline, yielding more accurate predictions.</p><p> </p><p>[1] M. Gauch, F. Kratzert, D. Klotz, G. Nearing, J. Lin, and S. Hochreiter. “Rainfall–Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network.” Hydrology and Earth System Sciences Discussions, in review, 2020.</p>

scholarly journals Mediating Corporeality: Re-Interpretations at the Art/Science Interface

Somatechnics ◽  
2012 ◽  
Vol 2 (2) ◽  
pp. 284-304
Author(s):  
Patricia Adams
Keyword(s):  
Digital Imaging ◽  
Scientific Research ◽  
Digital Technologies ◽  
Adult Stem Cell ◽  
Image Data ◽  
Scientific Data ◽  
Research Data ◽  
The Self ◽  
First Century ◽  
Individual Perspective

Contemporary scientific discoveries are rapidly modifying established concepts of embodiment and corporeality. For example, developing techniques in adult stem cell research can actively remodel the human body; whilst neuroscientists are shedding increasing light on the functioning of our brains. My research at the art/science nexus draws upon recent media theories to investigate the ways twenty-first century constructs of ‘humanness’ and the ‘self’ are affected by both historical and contemporary scientific research and developments in digital imaging technologies. In this article, examples from my artworks: “machina carnis” and “HOST” illustrate how my use of innovative digital technologies and collaborative methodologies has enabled me to immerse myself in the scientific experience at first hand. I demonstrate how my reinterpretations of what is commonly termed ‘hard’ scientific research data does not seek to emulate ‘objective’ readings of the experimental digital image data but rather recontextualises it in the context of my artworks. These artworks acknowledge the personal and visceral content in the scientific data and enable viewer/participants to reflect upon the issues raised from an emotive and individual perspective.

scholarly journals Research Progress and Advantage Analysis of Big Data Application in Chinese Academic Journals

2020 ◽  
Author(s):  
Qin Wu ◽  
Hui Wu
Keyword(s):  
Big Data ◽  
Scientific Data ◽  
Research Progress ◽  
Academic Journals ◽  
Bibliometric Method ◽  
Data Application ◽  
Quantitative Visualization ◽  
Big Data Application ◽  
Social Units ◽  
Relationship Of

The mining and application of big data in academic journals can accelerate the construction of data journals, enhance journal’s influence, and promote the sharing and dissemination of scientific data in academic journals worldwide. This paper uses bibliometric method to retrieve published articles with the theme of big data and journal in CNKI database, analyzes the academic achievements of the development of academic journals with the application of big data in the recent five years using quantitative visualization analysis, expounds the research progress of academic journals in big data field, and discusses the advantages of big data application in periodical industry. The results show that: study on the application of big data in academic journals are gradually deepening and scientific, and the relevant research still needs more financial fund from the state and social units, big data has prominent advantages such as accuracy, scientificity and value maximization in the workflows of academic journals. The mining and application of massive data is very important for promoting the development of high-quality academic journals and optimizing the supply-demand relationship of knowledge services of academic journals.

Streamlining Oceanic Biogeochemical Dataset Assembly in Support of Global Data Products

2020 ◽  
Author(s):  
Eugene Burger ◽  
Benjamin Pfeil ◽  
Kevin O'Brien ◽  
Linus Kamb ◽  
Steve Jones ◽  
...  
Keyword(s):  
Quality Control ◽  
Data Integration ◽  
Scientific Data ◽  
Climate Data ◽  
Science Data ◽  
Metadata Record ◽  
Data Products ◽  
Analysis And Synthesis ◽  
Data Integration System ◽  
Global Data

<p>Data assembly in support of global data products, such as GLODAP, and submission of data to national data centers to support long-term preservation, demands significant effort. This is in addition to the effort required to perform quality control on the data prior to submission. Delays in data assembly can negatively affect the timely production of scientific indicators that are dependent upon these datasets, including products such as GLODAP. What if data submission, metadata assembly and quality control can all be rolled into a single application? To support more streamlined data management processes in the NOAA Ocean Acidification Program (OAP) we are developing such an application.This application has the potential for application towards a broader community.</p><p>This application addresses the need that data contributing to analysis and synthesis products are high quality, well documented, and accessible from the applications scientists prefer to use. The Scientific Data Integration System (SDIS) application developed by the PMEL Science Data Integration Group, allows scientists to submit their data in a number of formats. Submitted data are checked for common errors. Metadata are extracted from the data that can then be complemented with a complete metadata record using the integrated metadata entry tool that collects rich metadata that meets the Carbon science community requirements. Still being developed, quality control for standard biogeochemical parameters will be integrated into the application. The quality control routines will be implemented in close collaboration with colleagues from the Bjerknes Climate Data Centre (BCDC) within the Bjerknes Centre for Climate Research (BCCR).  This presentation will highlight the capabilities that are now available as well as the implementation of the archive automation workflow, and it’s potential use in support of GLODAP data assembly efforts.</p>

scholarly journals Experiences with making diffraction image data available: what metadata do we need to archive?

2014 ◽  
Vol 70 (10) ◽  
pp. 2502-2509 ◽  
Author(s):  
Loes M. J. Kroon-Batenburg ◽  
John R. Helliwell
Keyword(s):  
Diffraction Data ◽  
Image Data ◽  
Scientific Data ◽  
Data Sets ◽  
Data Archive ◽  
Resolution Limit ◽  
X Ray Diffraction ◽  
Raw Data ◽  
Data Processing Software ◽  
Lysozyme Crystals

Recently, the IUCr (International Union of Crystallography) initiated the formation of a Diffraction Data Deposition Working Group with the aim of developing standards for the representation of raw diffraction data associated with the publication of structural papers. Archiving of raw data serves several goals: to improve the record of science, to verify the reproducibility and to allow detailed checks of scientific data, safeguarding against fraud and to allow reanalysis with future improved techniques. A means of studying this issue is to submit exemplar publications with associated raw data and metadata. In a recent study of the binding of cisplatin and carboplatin to histidine in lysozyme crystals under several conditions, the possible effects of the equipment and X-ray diffraction data-processing software on the occupancies andBfactors of the bound Pt compounds were compared. Initially, 35.3 GB of data were transferred from Manchester to Utrecht to be processed withEVAL. A detailed description and discussion of the availability of metadata was published in a paper that was linked to a local raw data archive at Utrecht University and also mirrored at the TARDIS raw diffraction data archive in Australia. By making these raw diffraction data sets available with the article, it is possible for the diffraction community to make their own evaluation. This led to one of the authors ofXDS(K. Diederichs) to re-integrate the data from crystals that supposedly solely contained bound carboplatin, resulting in the analysis of partially occupied chlorine anomalous electron densities near the Pt-binding sites and the use of several criteria to more carefully assess the diffraction resolution limit. General arguments for archiving raw data, the possibilities of doing so and the requirement of resources are discussed. The problems associated with a partially unknown experimental setup, which preferably should be available as metadata, is discussed. Current thoughts on data compression are summarized, which could be a solution especially for pixel-device data sets with fine slicing that may otherwise present an unmanageable amount of data.

scholarly journals Mangrove Distribution in Riau Islands Using Remote Sensing Technology

2017 ◽  
Vol 1 (2) ◽  
pp. 58-62 ◽  
Author(s):  
Sudra Irawan ◽  
Dwi Ely Kurniawan ◽  
Wenang Anurogo ◽  
Muhammad Zainuddin Lubis
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Data ◽  
Research Data ◽  
Resolution Image ◽  
Mangrove Area ◽  
High Resolution Image ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Data Application

Mangrove mapping is done with remote sensing technology using high-resolution image data. Application and information are then presented in web form. This study aims to map the mangrove distribution in Riau Islands, Indonesia. Based on the analysis, from the research data obtained the total area of mangrove in Riau Islands in 2011 and 2017 amounted to 71,504.83 Ha and 64,218.90 Ha, decreased by 7,285, 93 Ha or decreased by 10.19%. Based on the regency, the largest mangrove area in 2017 is located in Batam City of 22,964.77 Ha, then Karimun Regency (13,659,58 Ha), Lingga Regency (11,881.61 Ha), Regency of Bintan (9,701.49) Ha, Natuna Regency (2,477.16 Ha), Tanjungpinang City (1,847.65 Ha), and Anambas Regency (1,686.61 Ha). The magnitude of the widespread change (widespread reduction) occurring over the years between 2011 and 2017 by district, Natuna Regency experienced the largest reduction of 1,949.69 Ha or around 41.39%, followed by Lingga Regency of 1,947.15 Ha (14.08%), Tanjungpinang Municipality of 284.13 Ha (13.33%), Karimun Regency 1,920.93 Ha (12.33%), Anambas Regency of 195.90 Ha (10.40%), Batam City 1,094.83 Ha (4.55%) and Bintan Regency with 93.29 Ha (0, 95%). Opportunities that the pixels classified on the mangrove image are truly mangrove on the facts in the field.

scholarly journals Comparison of Various Reanalyses Gridded Data with Observed Data from Meteorological Stations over India

10.29007/c1sf ◽  
2018 ◽  
Author(s):  
Arnab Bandyopadhyay ◽  
Grace Nengzouzam ◽  
W. Rahul Singh ◽  
Nemtinkim Hangsing ◽  
Aditi Bhadra
Keyword(s):  
Observational Data ◽  
General Circulation ◽  
Promising Result ◽  
Meteorological Data ◽  
Circulation Model ◽  
India Meteorological Department ◽  
Gridded Data ◽  
Statistical Parameters ◽  
Data Product ◽  
Data Products

Meteorological data such as precipitation and temperature are important for hydrological modelling. In areas where there is sparse observational data, an alternate means for obtaining information for different impact modelling and monitoring activities is provided by reanalysis products. Evaluating their behaviour is crucial to know their uncertainties. Therefore, we evaluated two reanalyses gridded data products, viz., Coordinated Regional Climate Downscaling Experiment (CORDEX) and National Centers for Environment Predictors and GCM (General Circulation Model) predictor variables (NCEP); two station based gridded data products, viz., Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) and India Meteorological Department (IMD) gridded data; one satellite based gridded data product i.e., Tropical Rainfall Measuring Mission (TRMM); and one merged data product, i.e., Global Precipitation Climatology Project (GPCP). These products were compared with IMD observed station data for 1971 to 2010 to evaluate their behaviour in terms of fitness by using statistical parameters such as NSE, CRM and R2. APHRODITE and TRMM gridded data showed overall good results for precipitation followed by IMD, GPCP, CORDEX and NCEP. APHRODITE also showed good agreement for mean temperature. CORDEX and NCEP gave a promising result for minimum and maximum temperatures with NCEP better than CORDEX.

Sign in / Sign up

Export Citation Format

Share Document