scholarly journals Moderate resolution time series data management and analysis: automated large area mosaicking and quality control

2015 ◽  
Author(s):  
R Latifovic ◽  
D Pouliot ◽  
L Sun ◽  
J Schwarz ◽  
W Parkinson
Keyword(s):  
Time Series ◽  
Quality Control ◽  
Data Management ◽  
Time Series Data ◽  
Series Data ◽  
Resolution Time ◽  
Large Area ◽  
Moderate Resolution

scholarly journals System Performance Corresponding to Bacterial Community Succession after a Disturbance in an Autotrophic Nitrogen Removal Bioreactor

mSystems ◽  
2020 ◽  
Vol 5 (4) ◽  
Author(s):  
Hsiao-Pei Lu ◽  
Yung-Hsien Shao ◽  
Jer-Horng Wu ◽  
Chih-hao Hsieh
Keyword(s):  
Time Series ◽  
Microbial Community ◽  
System Performance ◽  
Time Series Data ◽  
Microbial Consortia ◽  
Series Data ◽  
Resolution Time ◽  
Physicochemical Variables ◽  
System Functioning ◽  
Fine Resolution

ABSTRACT Performance of a bioreactor is affected by complex microbial consortia that regulate system functional processes. Studies so far, however, have mainly emphasized the selective pressures imposed by operational conditions (i.e., deterministic external physicochemical variables) on the microbial community as well as system performance, but have overlooked direct effects of the microbial community on system functioning. Here, using a bioreactor with ammonium as the sole substrate under controlled operational settings as a model system, we investigated succession of the bacterial community after a disturbance and its impact on nitrification and anammox (anaerobic ammonium oxidation) processes with fine-resolution time series data. System performance was quantified as the ratio of the fed ammonium converted to anammox-derived nitrogen gas (N2) versus nitrification-derived nitrate (npNO3−). After the disturbance, the N2/npNO3− ratio first decreased, then recovered, and finally stabilized until the end. Importantly, the dynamics of N2/npNO3− could not be fully explained by physicochemical variables of the system. In comparison, the proportion of variation that could be explained substantially increased (tripled) when the changes in bacterial composition were taken into account. Specifically, distinct bacterial taxa tended to dominate at different successional stages, and their relative abundances could explain up to 46% of the variation in nitrogen removal efficiency. These findings add baseline knowledge of microbial succession and emphasize the importance of monitoring the dynamics of microbial consortia for understanding the variability of system performance. IMPORTANCE Dynamics of microbial communities are believed to be associated with system functional processes in bioreactors. However, few studies have provided quantitative evidence. The difficulty of evaluating direct microbe-system relationships arises from the fact that system performance is affected by convolved effects of microbiota and bioreactor operational parameters (i.e., deterministic external physicochemical forcing). Here, using fine-resolution time series data (daily sampling for 2 months) under controlled operational settings, we performed an in-depth analysis of system performance as a function of the microbial community in the context of bioreactor physicochemical conditions. We obtained statistically evaluated results supporting the idea that monitoring microbial community dynamics could improve the ability to predict system functioning, beyond what could be explained by operational physicochemical variables. Moreover, our results suggested that considering the succession of multiple bacterial taxa would account for more system variation than focusing on any particular taxon, highlighting the need to integrate microbial community ecology for understanding system functioning.

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>

Wetlands classification and assessment of Ramsar sites in China based on time series Moderate Resolution Imaging Spectroradiometer (MODIS) imagery

2018 ◽  
Vol 69 (5) ◽  
pp. 658 ◽  
Author(s):  
Liwei Xing ◽  
Zhenguo Niu ◽  
Panpan Xu ◽  
Dachong Li
Keyword(s):  
Time Series ◽  
Landscape Metrics ◽  
Time Series Data ◽  
Wetland Loss ◽  
Series Data ◽  
Landscape Disturbance ◽  
Landscape Integrity ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Globally, wetland loss and degradation have become serious environmental and ecological issues. Wetland monitoring of Ramsar sites in China is important for developing reasonable strategies to protect wetlands. Satellite image time series may be used for the long-term monitoring of wetland ecosystems. The present study used moderate-resolution imaging spectroradiometer (MODIS) time series data collected in 2001 and 2013 for 20 Ramsar sites in China and assessed the environmental status of these reserves using landscape metrics. The results showed that specific seasonal wetland classes, such as flooded mud, permanent water and seasonal marshes, can be identified using MODIS time series data with acceptable accuracy. In addition to wetland area, we suggest using other landscape metrics, including landscape integrity and landscape disturbance or degradation indices, to assess wetland environmental quality. The slight wetland loss (0.8%) noted in the 20 reserves evaluated herein could indicate the effectiveness of efforts of the Chinese government and local government agencies to protect Ramsar sites. The existing unfavourable environmental conditions, which were manifested by low landscape integrity and high landscape disturbance or degradation for some reserves, were caused primarily by increasing water requirements outside the reserves and by agricultural development within reserves. Therefore, determining how to balance relationships between economic development and ecological protection of the reserves will be important in the future.

scholarly journals Challenging problems of quality assurance and quality control (QA/QC) of meteorological time series data

2021 ◽  
Author(s):  
B. Faybishenko ◽  
R. Versteeg ◽  
G. Pastorello ◽  
D. Dwivedi ◽  
C. Varadharajan ◽  
...  
Keyword(s):  
Time Series ◽  
Quality Control ◽  
Quality Assurance ◽  
Time Series Data ◽  
Meteorological Station ◽  
Barro Colorado Island ◽  
Phase Iii ◽  
Series Data ◽  
Statistical Framework ◽  
East River

AbstractRepresentativeness and quality of collected meteorological data impact accuracy and precision of climate, hydrological, and biogeochemical analyses and predictions. We developed a comprehensive Quality Assurance (QA) and Quality Control (QC) statistical framework, consisting of three major phases: Phase I—Preliminary data exploration, i.e., processing of raw datasets, with the challenging problems of time formatting and combining datasets of different lengths and different time intervals; Phase II—QA of the datasets, including detecting and flagging of duplicates, outliers, and extreme data; and Phase III—the development of time series of a desired frequency, imputation of missing values, visualization and a final statistical summary. The paper includes two use cases based on the time series data collected at the Billy Barr meteorological station (East River Watershed, Colorado), and the Barro Colorado Island (BCI, Panama) meteorological station. The developed statistical framework is suitable for both real-time and post-data-collection QA/QC analysis of meteorological datasets.

scholarly journals An Analysis of Potential Structural Change in the Demand for South Atlantic Fresh Peaches

HortScience ◽  
1992 ◽  
Vol 27 (10) ◽  
pp. 1129-1131 ◽  
Author(s):  
J.E. Epperson ◽  
M.C. Chien ◽  
W.O. Mizelle
Keyword(s):  
Time Series ◽  
Quality Control ◽  
Structural Change ◽  
Time Series Data ◽  
South Atlantic ◽  
Capita Consumption ◽  
Series Data ◽  
Cultivar Development ◽  
Per Capita ◽  
Per Capita Consumption

An analysis was conducted using time-series data to identify possible structural change in the farm-gate demand for South Atlantic fresh peaches [Prunus persica (L.) Batsch.]. Structural change was not found in the price-quantity relationship. However, a failing per capita consumption of South Atlantic fresh peaches was found to be associated with an increase in the per capita consumption of fresh fruits in general. Thus, measures such as promotion and advertising, uniform quality control, and cultivar development may increase the demand for South Atlantic fresh peaches.

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