Selected Aspects of Carbon Stock Assessment in Aboveground Biomass

Given the significance of national carbon inventories, the importance of large-scale estimates of carbon stocks is increasing. Accurate biomass estimates are essential for tracking changes in the carbon stock through repeated assessment of carbon stock, widely used for both vegetation and soil, to estimate carbon sequestration. Objectives: The aim of our study was to determine the variability of several aspects of the carbon stock value when the input matrix was (1) expressed either as a vector or as a raster; (2) expressed as in local (1:10,000) or regional (1:100,000) scale data; and (3) rasterized with different pixel sizes of 1, 10, 100, and 1000 m. Method: The look-up table method, where expert carbon content values are attached to the mapped landscape matrix. Results: Different formats of input matrix did not show fundamental differences with exceptions of the biggest raster of size 1000 m for the local level. At the regional level, no differences were notable. Conclusions: The results contribute to the specification of best practices for the evaluation of carbon storage as a mitigation measure, as well as the implementation of national carbon inventories.

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Large-scale carbon stock assessment of woody vegetation in tropical dry deciduous forest of Sathanur reserve forest, Eastern Ghats, India

Environmental Monitoring and Assessment ◽

10.1007/s10661-017-5899-1 ◽

2017 ◽

Vol 189 (4) ◽

Cited By ~ 16

Author(s):

Durai Sanjay Gandhi ◽

Somaiah Sundarapandian

Keyword(s):

Carbon Stock ◽

Large Scale ◽

Deciduous Forest ◽

Stock Assessment ◽

Eastern Ghats ◽

Woody Vegetation ◽

Reserve Forest ◽

Dry Deciduous Forest

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Identifiers for the 21st century: How to design, provision, and reuse persistent identifiers to maximize utility and impact of life science data

10.1101/117812 ◽

2017 ◽

Cited By ~ 1

Author(s):

Julie A McMurry ◽

Nick Juty ◽

Niklas Blomberg ◽

Tony Burdett ◽

Tom Conlin ◽

...

Keyword(s):

Best Practices ◽

Large Scale ◽

Data Science ◽

Life Science ◽

Core Component ◽

Science Data ◽

Web Based ◽

Large Scale Data ◽

Online Databases ◽

Scale Data

AbstractIn many disciplines, data is highly decentralized across thousands of online databases (repositories, registries, and knowledgebases). Wringing value from such databases depends on the discipline of data science and on the humble bricks and mortar that make integration possible; identifiers are a core component of this integration infrastructure. Drawing on our experience and on work by other groups, we outline ten lessons we have learned about the identifier qualities and best practices that facilitate large-scale data integration. Specifically, we propose actions that identifier practitioners (database providers) should take in the design, provision and reuse of identifiers; we also outline important considerations for those referencing identifiers in various circumstances, including by authors and data generators. While the importance and relevance of each lesson will vary by context, there is a need for increased awareness about how to avoid and manage common identifier problems, especially those related to persistence and web-accessibility/resolvability. We focus strongly on web-based identifiers in the life sciences; however, the principles are broadly relevant to other disciplines.

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Best Practices and Lessons Learned from Deploying and Operating Large-Scale Data-Centric Parallel File Systems

SC14: International Conference for High Performance Computing, Networking, Storage and Analysis ◽

10.1109/sc.2014.23 ◽

2014 ◽

Cited By ~ 25

Author(s):

Sarp Oral ◽

James Simmons ◽

Jason Hill ◽

Dustin Leverman ◽

Feiyi Wang ◽

...

Keyword(s):

Best Practices ◽

Large Scale ◽

File Systems ◽

Lessons Learned ◽

Parallel File Systems ◽

Large Scale Data ◽

Parallel File ◽

Scale Data

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Large-Scale Data Learning Method for Anomaly Detection using Machine Learning for Monitoring Vibration in Vehicle Equipment

IEEJ Transactions on Industry Applications ◽

10.1541/ieejias.140.480 ◽

2020 ◽

Vol 140 (6) ◽

pp. 480-487

Author(s):

Minoru Kondo

Keyword(s):

Machine Learning ◽

Anomaly Detection ◽

Large Scale ◽

Learning Method ◽

Large Scale Data ◽

Scale Data

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Characterization of seawater reverse osmosis fouled membranes from large scale commercial desalination plant

10.31221/osf.io/96zj5 ◽

2019 ◽

Author(s):

Chem Int

Keyword(s):

Reverse Osmosis ◽

Large Scale ◽

Retention Rate ◽

Local Level ◽

Microscopic Analysis ◽

Transmembrane Pressure ◽

Hydraulic Permeability ◽

Seawater Reverse Osmosis ◽

A Chain

The objective of this work is to study the ageing state of a used reverse osmosis (RO) membrane taken in Algeria from the Benisaf Water Company seawater desalination unit. The study consists of an autopsy procedure used to perform a chain of analyses on a membrane sheet. Wear of the membrane is characterized by a degradation of its performance due to a significant increase in hydraulic permeability (25%) and pressure drop as well as a decrease in salt retention (10% to 30%). In most cases the effects of ageing are little or poorly known at the local level and global measurements such as (flux, transmembrane pressure, permeate flow, retention rate, etc.) do not allow characterization. Therefore, a used RO (reverse osmosis) membrane was selected at the site to perform the membrane autopsy tests. These tests make it possible to analyze and identify the cause as well as to understand the links between performance degradation observed at the macroscopic scale and at the scale at which ageing takes place. External and internal visual observations allow seeing the state of degradation. Microscopic analysis of the used membranes surface shows the importance of fouling. In addition, quantification and identification analyses determine a high fouling rate in the used membrane whose foulants is of inorganic and organic nature. Moreover, the analyses proved the presence of a biofilm composed of protein.

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