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# Data, Volume 2, Issue 3 (September 2017) – 11 articles

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700 KiB
Article
An Improved Power Law for Nonlinear Least-Squares Fitting?
by Benjamin Helyer and Michael Courtney
Data 2017, 2(3), 31; https://doi.org/10.3390/data2030031 - 19 Sep 2017
Cited by 1 | Viewed by 4637
Abstract
Models based on a power law are prevalent in many areas of study. When regression analysis is performed on data sets modeled by a power law, the traditional model uses a lead coefficient. However, the proposed model replaces the lead coefficient with a [...] Read more.
Models based on a power law are prevalent in many areas of study. When regression analysis is performed on data sets modeled by a power law, the traditional model uses a lead coefficient. However, the proposed model replaces the lead coefficient with a scaling parameter and reduces uncertainties in best-fit parameters for data sets with exponents close to 3. This study extends previous work by testing each model for a range of parameters. Data sets with known values of scaling parameter and exponent were generated by adding normally distributed random errors with controlled mean and standard deviations to underlying power laws. These data sets were then analyzed for both forms of the power law. For the scaling parameter, the proposed model provided smaller errors in 96/180 cases and smaller uncertainties in 88/180 cases. In most remaining cases, the traditional model provided smaller errors or uncertainties. Examination of conditions indicates that the proposed law has potential in select cases, but due to ambiguity in the conditions which favor one model over the other, an approach similar to the one in this study is encouraged for determining which model will offer reduced errors and uncertainties in data sets where additional accuracy is desired. Full article
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251 KiB
Article
Estimating Cost Savings from Early Cancer Diagnosis
by Zura Kakushadze, Rakesh Raghubanshi and Willie Yu
Data 2017, 2(3), 30; https://doi.org/10.3390/data2030030 - 4 Sep 2017
Cited by 65 | Viewed by 14829
Abstract
We estimate treatment cost-savings from early cancer diagnosis. For breast, lung, prostate and colorectal cancers and melanoma, which account for more than 50% of new incidences projected in 2017, we combine published cancer treatment cost estimates by stage with incidence rates by stage [...] Read more.
We estimate treatment cost-savings from early cancer diagnosis. For breast, lung, prostate and colorectal cancers and melanoma, which account for more than 50% of new incidences projected in 2017, we combine published cancer treatment cost estimates by stage with incidence rates by stage at diagnosis. We extrapolate to other cancer sites by using estimated national expenditures and incidence rates. A rough estimate for the U.S. national annual treatment cost-savings from early cancer diagnosis is in 11 digits. Using this estimate and cost-neutrality, we also estimate a rough upper bound on the cost of a routine early cancer screening test. Full article
501 KiB
Article
Adjustable Robust Singular Value Decomposition: Design, Analysis and Application to Finance
by Deshen Wang
Data 2017, 2(3), 29; https://doi.org/10.3390/data2030029 - 30 Aug 2017
Cited by 8 | Viewed by 5190
Abstract
The Singular Value Decomposition (SVD) is a fundamental algorithm used to understand the structure of data by providing insight into the relationship between the row and column factors. SVD aims to approximate a rectangular data matrix, given some rank restriction, especially lower rank [...] Read more.
The Singular Value Decomposition (SVD) is a fundamental algorithm used to understand the structure of data by providing insight into the relationship between the row and column factors. SVD aims to approximate a rectangular data matrix, given some rank restriction, especially lower rank approximation. In practical data analysis, however, outliers and missing values maybe exist that restrict the performance of SVD, because SVD is a least squares method that is sensitive to errors in the data matrix. This paper proposes a robust SVD algorithm by applying an adjustable robust estimator. Through adjusting the tuning parameter in the algorithm, the method can be both robust and efficient. Moreover, a sequential robust SVD algorithm is proposed in order to decrease the computation volume in sequential and streaming data. The advantages of the proposed algorithms are proved with a financial application. Full article
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944 KiB
Data Descriptor
Development of a Data Set of Pesticide Dissipation Rates in/on Various Plant Matrices for the Pesticide Properties Database (PPDB)
by Kathleen Lewis and John Tzilivakis
Data 2017, 2(3), 28; https://doi.org/10.3390/data2030028 - 29 Aug 2017
Cited by 34 | Viewed by 8013
Abstract
Data relating to the rate at which pesticide active substances dissipate on or within various plant matrices are important for a range of different risk assessments; however, despite the importance of this data, dissipation rates are not included in the most common online [...] Read more.
Data relating to the rate at which pesticide active substances dissipate on or within various plant matrices are important for a range of different risk assessments; however, despite the importance of this data, dissipation rates are not included in the most common online data resources. Databases have been collated in the past, but these tend not to be maintained or regularly updated. The purpose of the exercise described herein was to collate a new database in a format compatible with the main online pesticide database resource (the Pesticide Properties Database, PPDB), to validate this database in line with the Pesticide Properties Database protocols and thus ensure that the data is maintained and updated in future. Data was collated using a systematic review approach using several scientific databases. Collated literature was subjected to a quality assessment, and then data was extracted into an MS Excel spreadsheet. The outcome of the study is a database based on data collated from 1390 published articles covering over 400 pesticides and over 200 crops across a wide variety of different matrices (leaves, fruits, seeds etc.) for pesticide residues on the crop surface, as well as residues absorbed within the plant material. This data is now fully incorporated into the PPDB. Full article
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2175 KiB
Data Descriptor
A 2001–2015 Archive of Fractional Cover of Photosynthetic and Non-Photosynthetic Vegetation for Beijing and Tianjin Sandstorm Source Region
by Xiaosong Li, Zengyuan Li, Cuicui Ji, Hongyan Wang, Bin Sun, Bo Wu and Zhihai Gao
Data 2017, 2(3), 27; https://doi.org/10.3390/data2030027 - 25 Aug 2017
Cited by 3 | Viewed by 3792
Abstract
Fractional covers of photosynthetic and non-photosynthetic vegetation are key indicators for land degradation surveillance in the dryland of China. However, there are no available, well validated, and multispectral-based products. Aiming for this, we selected the Beijing and Tianjin Sandstorm Source Region as the [...] Read more.
Fractional covers of photosynthetic and non-photosynthetic vegetation are key indicators for land degradation surveillance in the dryland of China. However, there are no available, well validated, and multispectral-based products. Aiming for this, we selected the Beijing and Tianjin Sandstorm Source Region as the study area, and utilized the linear spectral mixture model for generating the fractional cover of PV, NPV, and bare soil, with endmember spectra retrieved from the field measured endmember spectral library, based on the MODIS NBAR data from 2001 to 2015. The unmixing results were validated through comparison with the field samples. The results show the method adopted could acquire rational and accurate estimation of fractional cover of photosynthetic vegetation (R2 = 0.6297, RMSE = 0.2443) and non-photosynthetic vegetation (R2 = 0.3747, RMSE = 0.2568). The dataset could provide key data support for the users in land degradation surveillance fields. Full article
(This article belongs to the Special Issue Geomatic Data for Land Degradation Surveillance)
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662 KiB
Data Descriptor
Chlamydospore Specific Proteins of Candida albicans
by Sujata Ingle, Santosh Kodgire, Asha Shiradhone, Rajendra Patil and Gajanan Zore
Data 2017, 2(3), 26; https://doi.org/10.3390/data2030026 - 22 Aug 2017
Cited by 2 | Viewed by 5424
Abstract
Polymorphic yeast, Candida albicans, forms thick-walled structures called chlamydospores in order to survive under adverse conditions. We present proteomic profile changes occurring during chlamydospore formation. Chlamydospores were induced by inoculating C. albicans cells (grown for 48 h) on rice extract and semisolid [...] Read more.
Polymorphic yeast, Candida albicans, forms thick-walled structures called chlamydospores in order to survive under adverse conditions. We present proteomic profile changes occurring during chlamydospore formation. Chlamydospores were induced by inoculating C. albicans cells (grown for 48 h) on rice extract and semisolid agar containing tween 80 (1%), and were overlaid by a polyethene sheet to induce microaerophilic conditions at 30 °C. Proteins extracted from chlamydospores and hyphae (producing chlamydospores) were identified by LC-MS/MS analysis. Present datasets include proteomic data (Swath spectral libraries) of chlamydospores and yeast phase cells, as well as methodologies and tools used for the data generation. Further analysis is expected to provide an opportunity to understand modulations in metabolic processes, molecular architecture (i.e., cell wall, membrane, and cytoskeleton) and stress response pathways leading to chlamydospore formation and thus facilitating survival of C. albicans under adverse conditions. Full article
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702 KiB
Data Descriptor
A Database of Weekly Sea Ice Parcel Tracks Derived from Lagrangian Motion Data with Ancillary Data Products
by Matthew Tooth and Mark Tschudi
Data 2017, 2(3), 25; https://doi.org/10.3390/data2030025 - 15 Aug 2017
Cited by 2 | Viewed by 3517
Abstract
Arctic sea ice has been on the decline over the past several decades, and multi-year sea ice has decreased significantly in its areal share of the overall sea ice cover. Changes in several key variables such as radiative balances, albedo, ice surface temperature, [...] Read more.
Arctic sea ice has been on the decline over the past several decades, and multi-year sea ice has decreased significantly in its areal share of the overall sea ice cover. Changes in several key variables such as radiative balances, albedo, ice surface temperature, and ice thickness have driven much of the decline, but the motion of sea ice makes studying the effects of these variables on individual parcels difficult. Previous studies have observed changes in the means of these variables and their impacts on sea ice concentration, but an accessible database of Lagrangian tracked data is not yet available for study. In order to address this, a database has been developed at the University of Colorado Boulder that performs Lagrangian tracking on individual sea ice parcels and saves coincident ancillary thermodynamic and dynamic variables for each parcel on a weekly timescale. Full article
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187 KiB
Data Descriptor
Thermodynamic Data of Fusarium oxysporum Grown on Different Substrates in Gold Mine Wastewater
by Enoch A. Akinpelu, Seteno K. O. Ntwampe, Lukhanyo Mekuto and Tunde V. Ojumu
Data 2017, 2(3), 24; https://doi.org/10.3390/data2030024 - 15 Aug 2017
Cited by 1 | Viewed by 3380
Abstract
The necessity for sustainable process development has led to an upsurge in bio-based processes, thereby placing a higher demand on the use of suitable microorganisms. Similarly, thermodynamics is a veritable tool that can predict the behavior of any material under well-defined conditions. Thermodynamic [...] Read more.
The necessity for sustainable process development has led to an upsurge in bio-based processes, thereby placing a higher demand on the use of suitable microorganisms. Similarly, thermodynamics is a veritable tool that can predict the behavior of any material under well-defined conditions. Thermodynamic data of Fusarium oxysporum used in the bioremediation of gold mine wastewater, for a process supported with different carbon sources, was investigated. The data were obtained using a Discovery DSC® (TA Instruments, Inc. New Castle, DE, USA) equipped with modulated Differential Scanning Calorimeter (MDSCTM) software. The data revealed minimal differences in the physical properties of the F. oxysporum used, indicating that the utilisation of agro-waste for microbial proliferation in wastewater treatment is as feasible as when refined carbon sources are used. The data will be helpful for the development of environmentally benign process development strategies, especially for environmental engineering applications. Full article
928 KiB
Data Descriptor
Overview of German Additive Manufacturing Companies
by Felix W. Baumann and Dieter Roller
Data 2017, 2(3), 23; https://doi.org/10.3390/data2030023 - 31 Jul 2017
Cited by 4 | Viewed by 13429
Abstract
This dataset is the description of a curated list of companies involved in additive manufacturing in Germany. The companies included are of various categories, such as 3D printing providers, hardware manufacturers, software developers and vendors. The list was compiled through literature and Internet-based [...] Read more.
This dataset is the description of a curated list of companies involved in additive manufacturing in Germany. The companies included are of various categories, such as 3D printing providers, hardware manufacturers, software developers and vendors. The list was compiled through literature and Internet-based research, resulting in the compilation of information from a number of resources, such as the Bundesanzeiger (Federal Gazette), the Registergerichte (Register Courts), the respective websites themselves and a B2B marketplace (Wer liefert Was?). The aim of compiling this list is to provide information to researchers on the current situation of 3D printing in Germany. Full article
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2205 KiB
Data Descriptor
A High Resolution Dataset of Drought Indices for Spain
by Sergio M. Vicente-Serrano, Miquel Tomas-Burguera, Santiago Beguería, Fergus Reig, Borja Latorre, Marina Peña-Gallardo, M. Yolanda Luna, Ana Morata and José C. González-Hidalgo
Data 2017, 2(3), 22; https://doi.org/10.3390/data2030022 - 28 Jun 2017
Cited by 127 | Viewed by 12963
Abstract
Drought indices are essential metrics for quantifying drought severity and identifying possible changes in the frequency and duration of drought hazards. In this study, we developed a new high spatial resolution dataset of drought indices covering all of Spain. The dataset includes seven [...] Read more.
Drought indices are essential metrics for quantifying drought severity and identifying possible changes in the frequency and duration of drought hazards. In this study, we developed a new high spatial resolution dataset of drought indices covering all of Spain. The dataset includes seven drought indices, spans the period 1961–2014, and has a spatial resolution of 1.1 km and a weekly temporal resolution. A web portal has been created to enable download and visualization of the data. The data can be downloaded as single gridded points for each drought index, but the entire drought index dataset can also be downloaded in netCDF4 format. The dataset will be updated for complete years as the raw meteorological data become available. Full article
(This article belongs to the Special Issue Geomatic Data for Land Degradation Surveillance)
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920 KiB
Article
Using Semantic Web Technologies to Query and Manage Information within Federated Cyber-Infrastructures
by Alexander Willner, Mary Giatili, Paola Grosso, Chrysa Papagianni, Mohamed Morsey and Ilya Baldin
Data 2017, 2(3), 21; https://doi.org/10.3390/data2030021 - 23 Jun 2017
Cited by 6 | Viewed by 5813
Abstract
A standardized descriptive ontology supports efficient querying and manipulation of data from heterogeneous sources across boundaries of distributed infrastructures, particularly in federated environments. In this article, we present the Open-Multinet (OMN) set of ontologies, which were designed specifically for this purpose as well [...] Read more.
A standardized descriptive ontology supports efficient querying and manipulation of data from heterogeneous sources across boundaries of distributed infrastructures, particularly in federated environments. In this article, we present the Open-Multinet (OMN) set of ontologies, which were designed specifically for this purpose as well as to support management of life-cycles of infrastructure resources. We present their initial application in Future Internet testbeds, their use for representing and requesting available resources, and our experimental performance evaluation of the ontologies in terms of querying and translation times. Our results highlight the value and applicability of Semantic Web technologies in managing resources of federated cyber-infrastructures. Full article
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