China’s Lunar and Planetary Data System: Preserve and Present Reliable Chang’e Project and Tianwen-1 Scientific Data Sets

Data infrastructure systems such as the National Aeronautics and Space Administration (NASA) Planetary Data System (PDS), European Space Agency (ESA) Planetary Data Archive (PSA)and Japan Aerospace Exploration Agency (JAXA) Data Archive and Transmission System (DARTS) archive large amounts of scientific data obtained through dozens of planetary exploration missions and have made great contributions to studies of lunar and planetary science. Since China started lunar exploration activities in 2007, the Ground Research and Application System (GRAS), one of the five systems developed as part of China’s Lunar Exploration Program (CLEP) and the Planetary Exploration of China (PEC), has gradually established China’s Lunar and Planetary Data System (CLPDS), which involves the archiving, management and long-term preservation of scientific data from China’s lunar and planetary missions; additionally, data are released according to the policies established by the China National Space Administration (CNSA). The scientific data archived by the CLPDS are among the most important achievements of the CLEP and PEC and provide a resource for the international planetary science community. The system plays a key and important role in helping scientists obtain fundamental and original research results, advancing studies of lunar and planetary science in China, and improving China’s international influence in the field of lunar and planetary exploration. This paper, starting from CLEP and PEC mission planning, explains the sources, classification, format and content of the lunar and Mars exploration data archived in the CLPDS. Additionally, the system framework and core functions of the system, such as data archiving, management and release, are described. The system can be used by the international planetary science community to comprehensively understand the data obtained in the CLEP and PEC, help scientists easily access and better use the available data resources, and contribute to fundamental studies of international lunar and planetary science. Moreover, since China has not yet systematically introduced the CLPDS, through this article, international data organizations could learn about this advanced system. Therefore, opportunities for international data cooperation can be created, and the data service capability of the CLPDS can be improved, thus promoting global data sharing and application for all humankind.

Preparing a Data Archive or Repository for Changing Research Data and Materials Retention Policies

Archival expectations and requirements for researchers’ data and code are changing rapidly, both among publishers and institutions, in response to what has been referred to as a “reproducibility crisis.” In an effort to address this crisis, a number of publishers have added requirements or recommendations to increase the availability of supporting information behind the research, and academic institutions have followed. Librarians should focus on ways to make it easier for researchers to effectively share their data and code with reproducibility in mind. At the Cornell Center for Social Sciences, we have instituted a Results Reproduction Service (R-Squared) for Cornell researchers. Part of this service includes archiving the R-Squared package in our CoreTrustSeal certified Data and Reproduction Archive, which has been rebuilt to accommodate both the unique requirements of those packages and the traditional role of our data archive. Librarians need to consider roles that archives and institutional repositories can play in supporting researchers with reproducibility initiatives. Our commentary closes with some suggestions for more information and training.

Analysis and implementation of realtime stock prediction using reinforcement frameworks

Real time stock prediction is interesting research topic due to the risk involved with volatile scenarios. Modelling of the stocks by reducing the overestimation in ANN model, due to rapid fluctuations in the market guide fund managers risky decisions while building stock portfolio. This paper builds real time framework for stock prediction using deep reinforcement learning to buy, sell or hold the stocks. This paper models the transformed stock tick data and technical indicators using Transformed Deep-Q Learning. Our framework is cost reduced and transaction time optimized to get real time stock prediction using GPU and Memory containers. Stock predictor is architected using GRPC based clean architecture which has the benefits of easy updates, addition of new services with reduced integration costs. Data archive features of the cloud will give benefit of reduced cost of the new stock predictor framework.

PREVISÃO DE UM CASO DE CICLOGÊNESE BOMBA NO ATLÂNTICO SUL, ATRAVÉS DO MODELO WRF COM ASSIMILAÇÃO VARIACIONAL E INICIALIZAÇÃO POR FILTRO DIGITAL

O presente trabalho tem como objetivo avaliar o impacto da assimilação de dados pelo método variacional, na simulação do Weather Research and Forescast (WRF), em um caso de ciclogênese explosivo, ocorrida no sul do Brasil em 30 de junho de 2020. Avalia-se também a capacidade do Filtro Digital (FD), com a janela de Dolph-Chebyshev (FDDC), em filtrar ondas de gravidade espúrias nas soluções do modelo. A condição inicial para integração do WRF é gerada pelo Global Forecast System (GFS) das 12 UTC, fornecida pelo National Center for Enviroment Prediction (NCEP). Os dados de assimilação foram obtidos através das redes de compartilhamento mundial de dados do Research data Archive (RDA), além dos dados das estações da região sul do Brasil acessadas pelo Banco de Dados Meteorológicos do Instituto Nacional de Meteorologia (INMET). Foram definidos 4 experimentos, EXP1 - previsões do WRF, EXP2 - WRF com o Filtro Digital (WRFDF), EXP3 - WRF com assimilação variacional tridimensional (WRFDA), EXP4 - WRF com assimilação variacional tridimensional e Filtro Digital (WRFDADF). O Filtro Digital mostrou-se uma importante metodologia para eliminação do ruído, gerado pelo desequilíbrio entre os campos de massa e vento, no começo da integração do modelo. Destaca-se a diminuição do erro nos experimentos com assimilação de dados, em especial no vento em superfície.

Data archiving and meta-data – saving the data for future use

This chapter covers the main aspects of data archiving, as the last phase of data handling in the process of urban drainage and stormwater management metrology. Data archiving is the process of preparing and storing the data for future use, usually not executed by the personnel who acquired the data. A data archive (also known as a data repository) can be defined as storage of a selected subset of raw, processed, validated and resampled data, with descriptions and other meta-data, linked to simulation results, if there are any. A data archive should be equipped with tools for search and data extraction along with procedures for data management, in order to maintain the database quality for an extended period of time. It is recommended, mostly for security reasons, to separate (both in a physical and in a digital sense) the archive database from the working database. This chapter provides the reader with relevant information about the most important issues related to data archive design, the archiving process and data characteristics regarding archiving. Also, the importance of good and comprehensive meta-data is underlined throughout the chapter. The management of a data archive is evaluated with a special focus on predicting future resources needed to keep the archive updated, secure, available, and in compliance with legal demands and limitations. At the end, a set of recommendations for creating and maintaining a data archive in the scope of urban drainage is given.

Molecular Dynamics Study on Graphene-Nanoflake Sensor Sandwiched Between Crossed Graphene-Nanoribbon Junctions

We present a design of a nanoscale inertial measurement unit or a data archive using a graphene-nanoflake (GNF) sandwiched between crossed graphene-nanoribbon (GNR) junctions. When an external force applied is below the retracting force, the inertial force exerted on the movable GNF can telescope it. Then, the self-restoring force increases as the attractive van der Waals force between the GNF and the GNRs, which enables the GNF to automatically and fully retract back into the sandwich position immediately after the externally applied force is released. When the external force exceeds the retracting force, the GNF escapes from the crossed GNR junctions, which enables the device to be used as non-volatile memory. The heterostructure of GNR/h-BN/GNR can be considered as an advanced structure in the proposed scheme.

Active Maintenance: A Proposal for the Long-Term Computational Reproducibility of Scientific Results

ABSTRACT Computational reproducibility, or the ability to reproduce analytic results of a scientific study on the basis of publicly available code and data, is a shared goal of many researchers, journals, and scientific communities. Researchers in many disciplines including political science have made strides toward realizing that goal. A new challenge, however, has arisen. Code too often becomes obsolete within only a few years. We document this problem with a random sample of studies posted to the Institution for Social and Policy Studies (ISPS) Data Archive; we encountered nontrivial errors in seven of 20 studies. In line with similar proposals for the long-term maintenance of data and commercial software, we propose that researchers dedicated to computational reproducibility should have a plan in place for “active maintenance” of their analysis code. We offer concrete suggestions for how data archives, journals, and research communities could encourage and reward the active maintenance of scientific code and data.