scholarly journals An installation guide to the PC-based time-series data-management and plotting program BOB

1990 ◽  
Author(s):  
Thomas L. Murray
Keyword(s):  
Time Series ◽  
Data Management ◽  
Time Series Data ◽  
Series Data

Exploiting SeaDataCloud Temperature and Salinity time series data collections and comparing with Copernicus - a novel approach with SOURCE tool

2020 ◽  
Author(s):  
Paolo Oliveri ◽  
SImona Simoncelli ◽  
Pierluigi DI Pietro ◽  
Sara Durante
Keyword(s):  
Time Series ◽  
Best Practices ◽  
Data Management ◽  
Time Series Data ◽  
Management Plan ◽  
Global Ocean ◽  
Series Data ◽  
Data Repository ◽  
Log File ◽  
Data Collections

<p>One of the main challenges for the present and future in ocean observations is to find best practices for data management: infrastructures like Copernicus and SeaDataCloud already take responsibility for assembly, archive, update and publish data. Here we present the strengths and weaknesses in a SeaDataCloud Temperature and Salinity time series data collections, in particular a tool able to recognize the different devices and platforms and to merge them with processed Copernicus platforms.</p><p>While Copernicus has the main target to quickly acquire and publish data, SeaDataNet aims to publish data with the best quality available. This two data repository should be considered together, since the originator can ingest the data in both the infrastructures or only in one, or partially in both. This results sometimes in data partially available in Copernicus or SeaDataCloud, with great impact for the researcher who wants to access as much data as possible. The data reprocessing should not be loaded on researchers' shoulders, since only skilled users in all data management plan know how merge the data.</p><p>The SeaDataCloud time series data collections is a Global Ocean soon-to-be-published dataset that will represent a reference for ocean researchers, released in binary, user friendly Ocean Data View format. The database management plan was originally for profiles, but had been adapted for time series, resolving several issues like the uniqueness of the identifiers (ID).</p><p>Here we present an extension of the SOURCE (Sea Observations Utility for Reprocessing. Calibration and Evaluation) Python package, able to enhance the data quality with redundant sophisticated methods and simplify their usage. </p><p>SOURCE increases quality control (Q/C) performances on observations using statistical quality check procedures that follows the ocean best practices guidelines, exploiting the following  issues:</p><ol><li>Find and aggregate all broken time series using likeness in ID parameter strings;</li> <li>Find and organize in a dictionary all different metadata variables;</li> <li>Correct time series time to match simpler measure units;</li> <li>Filter devices that are outside of a selected horizontal rectangle;</li> <li>Give some information on original Q/C scheme by SeaDataCloud infrastructure;</li> <li>Give information tables on platforms and on the merged ID string duplicates together with an errors log file (missing time, depth, data, wrong Q/C variables, etc.).</li> </ol><p>In particular, the duplicates table and the log file may be helpful to SeaDataCloud partners in order to update the data collection and make it finally available for the users.</p><p>The reconstructed SeaDataCloud time series data, divided by parameter and stored in a more flexible dataset, give the possibility to ingest it in the main part of the software, allowing to compare it with Copernicus time series, find the same platform using horizontal and vertical surroundings (without looking to ID) find and cleanup  duplicated data, merge the two databases to extend the data coverage.</p><p>This allow researchers to have the most wide and the best quality possible data for the final users release and to to use these data to calibrate and validate models, in order to reach an idea of a whole area sea conditions.</p>

scholarly journals Extending HydroShare to enable hydrologic time series data as social media

2015 ◽  
Vol 18 (2) ◽  
pp. 198-209 ◽  
Author(s):  
Jeffrey M. Sadler ◽  
Daniel P. Ames ◽  
Shaun J. Livingston
Keyword(s):  
Social Media ◽  
Time Series ◽  
Web Services ◽  
Data Management ◽  
Data Storage ◽  
Time Series Data ◽  
Series Data ◽  
Design Development ◽  
Scientific Discussion ◽  
Hydrologic Time Series

The Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI) hydrologic information system (HIS) is a widely used service oriented system for time series data management. While this system is intended to empower the hydrologic sciences community with better data storage and distribution, it lacks support for the kind of ‘Web 2.0’ collaboration and social-networking capabilities being used in other fields. This paper presents the design, development, and testing of a software extension of CUAHSI's newest product, HydroShare. The extension integrates the existing CUAHSI HIS into HydroShare's social hydrology architecture. With this extension, HydroShare provides integrated HIS time series with efficient archiving, discovery, and retrieval of the data, extensive creator and science metadata, scientific discussion and collaboration around the data and other basic social media features. HydroShare provides functionality for online social interaction and collaboration while the existing HIS provides the distributed data management and web services framework. The extension is expected to enable scientists to access and share both national- and laboratory-scale hydrologic time series datasets in a standards-based web services architecture combined with social media functionality developed specifically for the hydrologic sciences.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

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