Easy Data Analysis Research Articles

Abstract 2756: Multiplexing done differently: flexible, sensitive, and accurate detection of miRNA and protein analytes using Firefly particle technology

Abstract Detection of molecular biomarkers in biological fluids is used as an indicator of normal biological processes, pathogenic processes, and response to therapeutic intervention1,2. Using a combined signature of multiple biomarkers can better account for patient and epidemiological heterogeneity, and provide a more accurate indication of patient health2. This has necessitated the development of technologies that can be used for multiplex biomarker profiling directly from biofluids such as plasma, serum, and urine. Conventional assays consume excessive quantities of precious clinical samples, time and budget when used for discovery and validation of multi-analyte biomarker signatures. To address this need, we developed the Firefly Technology platform, which allows for sensitive and accurate detection of up to 75 protein analytes or 68 miRNAs directly from a biological sample. The Firefly platform uses patented Firefly® hydrogel particles and a three-region encoding design that allows for true, in-well multiplexing, providing flexibility over which analytes can be quantified in the same well and allowing for custom panel design. For the detection of protein analytes, the Firefly immunoassays use high-performance matched antibody pairs that reduce cross reactivity between individual analytes, provide up to 5 logs dynamic range, and typically demonstrate single-digit pg/ml sensitivity, while requiring only 12.5 µl biofluid input. Similarly, the Firefly microRNA assay can reliably detect as few as 1000 microRNA copies per sample with a linear dynamic range of ~5 logs, and without the need of prior RNA purification. This assay utilizes single step RT-PCR signal amplification using universal primers, thus leveraging PCR sensitivity while eliminating the need for separate reverse transcription reactions and mitigating amplification biases introduced by target-specific qPCR. The Firefly assays have been validated in a wide range of biological samples including plasma, serum, urine and cell culture supernatant, providing experimental flexibility. The 96well plate assay format enables high-throughput screening of samples, with readout conducted on standard flow cytometers, thereby omitting the need for complex and expensive dedicated instrumentation. Finally, the integrated Firefly Analysis Workbench software enables easy and rapid data analysis, visualization, and export in under ten minutes, and includes key features such as standard curve analysis and publication-quality heatmaps and graphs. Here we present data from several studies investigating cytokine profiling in human and rodent samples, and circulating and tumor microRNA profiles, using the Firefly Platform. Together, this novel combination of bioinformatics tools and multiplex, high-sensitivity assays enables rapid discovery and validation of biomarker signatures from fluid samples. Citation Format: Elnaz Atabakhsh, Graeme Doran, Conor Rafferty, Jennifer Heath, Sarah Albertson, Peter Gillis, Jonathan Pinney, Russell Neuner, Michael Chipchase, James Murray, Daniel Pregibon. Multiplexing done differently: flexible, sensitive, and accurate detection of miRNA and protein analytes using Firefly particle technology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2756. doi:10.1158/1538-7445.AM2017-2756

A Framework for Conducting Numerical Experiments on Cost System Design

This paper aims to advance the use of numerical experiments to investigate issues that surround the design of cost systems. As with laboratory and field experiments, researchers must decide on the independent variables and their levels, the experimental design, and the dependent variables. Options for dependent and independent variables are ample, as are the ways in which we can model the relations among these variables. We provide a modular framework that provides structure to these variables, their definitions, and the modeling of the connections among them. Further, we offer some insights into the design and layout of output data files, which will allow for easier data analysis. We also present tips on how to report the results from such numerical experiments effectively. Finally, we furnish online the source code in C# for many of these modules. We hope that the framework and guidance provided in this paper will help spur and focus further meaningful work in this important area of management accounting.

Mikroskopie und Analyse der Wundheilungsassays

The scratch wound assay is a powerful method to analyze cell migration which plays a central role in the wound healing process. In this application, we tracked the ability of HT-1080 fibrosarcoma cells to move into a created gap and heal the wound. The obtained results show that the combination of an easy experimental setup, time-lapse imaging and precise data analysis facilitate the study of collective cell migration, crucial for the development of novel therapeutics.

Clinical Success versus Attrition of Investigational Pharmaceuticals: A Vignette.

The clinical success of investigational pharmaceuticals remains doubtful due to an unacceptable level of attrition during clinical drug development and declining clinical approval rates. Countless molecules are being withdrawn during drug discovery and clinical trial phases due to poor pharmacokinetics (ADME), lack of safety and efficacy, commercial interests, market competition, and patent protection expiry. Consequently, the novel drug pipelines are running dry. Because huge capital is involved in different clinical trial phases, the task of bringing a new drug or drug formulation to the market is a very costly venture. Running a clinical trial is a complex, expensive, and time-intensive project. It is often tedious and troublesome to change a study design after its inception; thus, it is necessary to design the study for easy and comprehensive data analysis and interpretation. Cohort, cross-sectional, and case-control studies, collectively touted as observational studies, provide an option for solving clinical problems when a randomized controlled trial (RCT) is not feasible due to time, money, or ethical restraints. This review article presents three prime observational research methodologies using relevant examples, common causes of spurious results, and ways to trivialize their impacts, including the most popular indices for measuring clinical relevance of the treatment effects. The article also explores attrition issues and issues related to the improvement of clinical approval success, with an emphasis on possible applications of drug delivery technologies to salvage drug molecules.

On business processes of computer-supported collaborative learning: A case of peer assessment system development

Andrei I. Kolomiets - Student, Faculty of Computer Science, National Research University Higher School of EconomicsAddress: 20, Myasnitskaya Street, Moscow, 101000, Russian FederationE-mail: aikolomiets@edu.hse.ruOlga V. Maksimenkova - Senior Lecturer, School of Software Engineering, Junior Researcher, International Laboratory for Intelligent Systems and Structural Analysis, National Research University Higher School of EconomicsAddress: 20, Myasnitskaya Street, Moscow, 101000, Russian FederationE-mail: omaksimenkova@hse.ruAlexey A. Neznanov - Associate Professor, School of Data Analysis and Artificial Intelligence, Senior Researcher, International Laboratory for Intelligent Systems and Structural Analysis, National Research University Higher School of EconomicsAddress: 20, Myasnitskaya Street, Moscow, 101000, Russian FederationE-mail: aneznanov@hse.ru Nowadays peer assessment is recognized as a crucial part of a wide range of active learning routines. Nevertheless, practitioners and educators speak of the complexity and high resource consumption for the implementation of this type of assessment. Undoubtedly, convenient software that supports peer assessment processes may substantially raise productivity of its participants. A review of educational literature and free software shows there are several bottlenecks in the business processes of peer assessment and key user roles. First, most of the programs examined are web-based and expand a set of tools for teachers and learners by extra interfaces. Moreover, this logically creates a new branch in the learning business process. Second, there is probably no peer assessment system which allows users to attach something other than the text to be reviewed. There is a gap in the market of free peer assessment software. This paper offers a peer assessment system specification that attempts to eliminate these disadvantages in order to improve user experience and thus increase the use of information technologies in peer assessment. The specification is based on a thorough description of the peer assessment process involving complex artifacts and double-blinded peer review. Software called PASCA (peer assessment system for complex artifacts) is introduced to illustrate the specification achieved. PASCA uses habitual e-mail services and does not affect other business processes. It supports standard features like blinding and randomization, and it provides a set of original features. They contain evaluation of arbitrary artifacts, creation of complex peer review forms with their validation and scoring, and easy analysis of data from peer assessment sessions. [1] The article was prepared within the framework of the Basic Research Program at the National Research University Higher School of Economics (HSE) and supported within the framework of a subsidy by the Russian Academic Excellence Project “5-100”.

Survival trees for left-truncated and right-censored data, with application to time-varying covariate data.

Tree methods (recursive partitioning) are a popular class of nonparametric methods for analyzing data. One extension of the basic tree methodology is the survival tree, which applies recursive partitioning to censored survival data. There are several existing survival tree methods in the literature, which are mainly designed for right-censored data. We propose two new survival trees for left-truncated and right-censored (LTRC) data, which can be seen as a generalization of the traditional survival tree for right-censored data. Further, we show that such trees can be used to analyze survival data with time-varying covariates, essentially building a time-varying covariates survival tree. Implementation of the methods is easy, and simulations and real data analysis results show that the proposed methods work well for LTRC data and survival data with time-varying covariates, respectively.

OSAnalyzer: A Bioinformatics Tool for the Analysis of Gene Polymorphisms Enriched with Clinical Outcomes.

Background: The identification of biomarkers for the estimation of cancer patients’ survival is a crucial problem in modern oncology. Recently, the Affymetrix DMET (Drug Metabolizing Enzymes and Transporters) microarray platform has offered the possibility to determine the ADME (absorption, distribution, metabolism, and excretion) gene variants of a patient and to correlate them with drug-dependent adverse events. Therefore, the analysis of survival distribution of patients starting from their profile obtained using DMET data may reveal important information to clinicians about possible correlations among drug response, survival rate, and gene variants. Methods: In order to provide support to this analysis we developed OSAnalyzer, a software tool able to compute the overall survival (OS) and progression-free survival (PFS) of cancer patients and evaluate their association with ADME gene variants. Results: The tool is able to perform an automatic analysis of DMET data enriched with survival events. Moreover, results are ranked according to statistical significance obtained by comparing the area under the curves that is computed by using the log-rank test, allowing a quick and easy analysis and visualization of high-throughput data. Conclusions: Finally, we present a case study to highlight the usefulness of OSAnalyzer when analyzing a large cohort of patients.

ExpVIP: a Customizable RNA-seq Data Analysis and Visualization Platform.

The majority of transcriptome sequencing (RNA-seq) expression studies in plants remain underutilized and inaccessible due to the use of disparate transcriptome references and the lack of skills and resources to analyze and visualize these data. We have developed expVIP, an expression visualization and integration platform, which allows easy analysis of RNA-seq data combined with an intuitive and interactive interface. Users can analyze public and user-specified data sets with minimal bioinformatics knowledge using the expVIP virtual machine. This generates a custom Web browser to visualize, sort, and filter the RNA-seq data and provides outputs for differential gene expression analysis. We demonstrate expVIP's suitability for polyploid crops and evaluate its performance across a range of biologically relevant scenarios. To exemplify its use in crop research, we developed a flexible wheat (Triticum aestivum) expression browser (www.wheat-expression.com) that can be expanded with user-generated data in a local virtual machine environment. The open-access expVIP platform will facilitate the analysis of gene expression data from a wide variety of species by enabling the easy integration, visualization, and comparison of RNA-seq data across experiments.

Dental Informatics tool "SOFPRO" for the study of oral submucous fibrosis.

Background:Dental informatics is an evolving branch widely used in dental education and practice. Numerous applications that support clinical care, education and research have been developed. However, very few such applications are developed and utilized in the epidemiological studies of oral submucous fibrosis (OSF) which is affecting a significant population of Asian countries.Aims and Objectives:To design and develop an user friendly software for the descriptive epidemiological study of OSF.Materials and Methods:With the help of a software engineer a computer program SOFPRO was designed and developed by using, Ms-Visual Basic 6.0 (VB), Ms-Access 2000, Crystal Report 7.0 and Ms-Paint in operating system XP. For the analysis purpose the available OSF data from the departmental precancer registry was fed into the SOFPRO.Results:Known data, not known and null data are successfully accepted in data entry and represented in data analysis of OSF. Smooth working of SOFPRO and its correct data flow was tested against real-time data of OSF.Conclusion:SOFPRO was found to be a user friendly automated tool for easy data collection, retrieval, management and analysis of OSF patients.

Developing a paired-target apparatus for quantitative testing of nest defense behavior by vespine wasps in response to con- or heterospecific nest defense pheromones

Social wasps commonly exhibit impressive, pheromone-mediated nest defenses with stinging attacks on potential vertebrate nest predators. Studying this type of nest defense and comparing results across studies is challenging because there is no standardized method for quantifying defense intensities. For that reason, we developed a simple, paired-target apparatus coupled with easy and inexpensive data recording and analysis technologies. Each target is formed by two conjoined black plastic weigh boats that generate distinct percussive sounds when struck by attacking wasps. A battery-powered microphone inside each target converts the sounds into electrical signals that are transferred to a digital audio recorder. These audio files are then split into left- and right-channel files, saved as 16-bit WAV files, and the strikes to each target are counted using the open-source software SoundRuler. Using this apparatus, we show that workers of Vespula pensylvanica, V. alascensis, and V. germanica strike targets that are treated with conspecific venom sac extract more frequently than paired control targets. We also show that workers of V. alascensis, V. pensylvanica and V. germanica strike targets that are treated with heterospecific extracts more frequently than paired control targets, indicating that the wasps recognize nest alarm pheromones from congeners. These data provide evidence for conserved nest defense pheromones among some Vespula wasps and proof of concept that our technology is capable of quantifying the intensity of pheromone-mediated nest defense behavior in Vespula and other large and formidable social wasps.

On the analysis of Rate-All-That-Apply (RATA) data

Rate-All-That-Apply (RATA) is a variation of the more widely used CATA question format. For a pre-specified list of terms, consumers indicate whether they apply to a given product, and if they do so, to rate their intensity. For example, a 3-pt scale may be used with anchors ‘low’, ‘medium’ and ‘high’ or a 5-pt scale anchored at 1=‘slightly applicable’ and 5=‘very applicable.’ Given the hierarchical nature of the task and the non-normal distribution of the intensity data, it is not obvious how to analyze RATA data appropriately. In the present work we suggest interpreting RATA data as 4- or 6-point scales, considering a missing check for any attribute as a score of 0. Based on that, randomization tests were applied to investigate potential product differences. We show that the null distribution of these tests for RATA data coincides in practice with the one from classical parametric tests derived from an ANOVA context. Consequently, using the common F- and t-tests provides a valid and easy analysis of RATA data. In four consumer studies, tests for product difference usually gave the same results when RATA data were analyzed with ANOVA and Cochran’s Q test. Graphical display of the data was found to be very similar based on Correspondence Analysis (CA) (treating RATA as CATA data) and Principal Component Analysis (PCA) (treating RATA as continuous data).

Genome sequence analysis with MonetDB

Next-generation sequencing (NGS) technology has led the life sciences into the big data era. Today, sequencing genomes takes little time and cost, but yields terabytes of data to be stored and analyzed. Biologists are often exposed to excessively time consuming and error-prone data management and analysis hurdles. In this paper, we propose a database management system (DBMS) based approach to accelerate and substantially simplify genome sequence analysis. We have extended MonetDB, an open-source column-based DBMS, with a BAM module, which enables easy, flexible, and rapid management and analysis of sequence alignment data stored as Sequence Alignment/Map (SAM/BAM) files. We describe the main features of MonetDB/BAM using a case study on Ebola virus genomes.

HPVbase--a knowledgebase of viral integrations, methylation patterns and microRNAs aberrant expression: As potential biomarkers for Human papillomaviruses mediated carcinomas.

Human papillomaviruses (HPVs) are extremely associated with different carcinomas. Despite consequential accomplishments, there is still need to establish more promising biomarkers to discriminate cancerous progressions. Therefore, we have developed HPVbase (http://crdd.osdd.net/servers/hpvbase/), a comprehensive resource for three major efficacious cancer biomarkers i.e. integration and breakpoint events, HPVs methylation patterns and HPV mediated aberrant expression of distinct host microRNAs (miRNAs). It includes clinically important 1257 integrants and integration sites from different HPV types i.e. 16, 18, 31, 33 and 45 associated with distinct histological conditions. An inclusive HPV integrant and breakpoints browser was designed to provide easy browsing and straightforward analysis. Our study also provides 719 major quantitative HPV DNA methylation observations distributed in 5 distinct HPV genotypes from higher to lower in numbers namely HPV 16 (495), HPV 18 (113), HPV45 (66), HPV 31 (34) and HPV 33 (11). Additionally, we have curated and compiled clinically significant aberrant expression profile of 341 miRNAs including their target genes in distinct carcinomas, which can be utilized for miRNA therapeutics. A user-friendly web interface has been developed for easy data retrieval and analysis. We foresee that HPVbase an integrated and multi-comparative platform would facilitate reliable cancer diagnostics and prognosis.

Oasis: online analysis of small RNA deep sequencing data.

Summary: Oasis is a web application that allows for the fast and flexible online analysis of small-RNA-seq (sRNA-seq) data. It was designed for the end user in the lab, providing an easy-to-use web frontend including video tutorials, demo data and best practice step-by-step guidelines on how to analyze sRNA-seq data. Oasis’ exclusive selling points are a differential expression module that allows for the multivariate analysis of samples, a classification module for robust biomarker detection and an advanced programming interface that supports the batch submission of jobs. Both modules include the analysis of novel miRNAs, miRNA targets and functional analyses including GO and pathway enrichment. Oasis generates downloadable interactive web reports for easy visualization, exploration and analysis of data on a local system. Finally, Oasis’ modular workflow enables for the rapid (re-) analysis of data.Availability and implementation: Oasis is implemented in Python, R, Java, PHP, C++ and JavaScript. It is freely available at http://oasis.dzne.de.Contact: stefan.bonn@dzne.deSupplementary information: Supplementary data are available at Bioinformatics online.

Potential and limitations of microscopy and Raman spectroscopy for live-cell analysis of 3D cell cultures

Today highly complex 3D cell culture formats that closely mimic the in vivo situation are increasingly available. Despite their wide use, the development of analytical methods and tools that can work within the depth of 3D-tissue constructs lags behind. In order to get the most information from a 3D cell sample, adequate and reliable assays are required. However, the majority of tools and methods used today have been originally designed for 2D cell cultures and translation to a 3D environment is in general not trivial. Ideally, an analytical method should be non-invasive and allow for repeated observation of living cells in order to detect dynamic changes in individual cells within the 3D cell culture. Although well-established laser confocal microscopy can be used for these purposes, this technique has serious limitations including penetration depth and availability. Focusing on two relevant analytical methods for live-cell monitoring, we discuss the current challenges of analyzing living 3D samples: microscopy, which is the most widely used technology to observe and examine cell cultures, has been successfully adapted for 3D samples by recording of so-called “z-stacks”. However the required equipment is generally very expensive and therefore access is often limited. Consequently alternative and less advanced approaches are often applied that cannot capture the full structural complexity of a 3D sample. Similarly, image analysis tools for quantification of microscopic images range from highly specialized and costly to simplified and inexpensive. Depending on the actual sample composition and scientific question the best approach needs to be assessed individually. Another more recently introduced technology for non-invasive cell analysis is Raman micro-spectroscopy. It enables label-free identification of cellular metabolic changes with high sensitivity and has already been successful applied to 2D and 3D cell cultures. However, its future significance for cell analysis will strongly depend on the availability of application oriented and user-friendly systems including specific tools for easy analysis and interpretation of spectral data focusing on biological relevant information.

Analysis of transmitted HIV-1 drug resistance using 454 ultra-deep-sequencing and the DeepChek(®)-HIV system.

IntroductionNext-generation sequencing (NGS) is capable of detecting resistance-associated mutations (RAMs) present at frequencies of 1% or below. Several studies have found that baseline low-frequency RAMs are associated with failure to first-line HAART. One major limitation to the expansion of this technology in routine diagnostics is the complexity and laboriousness integral to bioinformatics analysis. DeepChek (ABL, TherapyEdge) is a CE-marked software that allows automated analysis and resistance interpretation of NGS data.ObjectiveTo evaluate the use of 454 ultra-deep-sequencing (Roche® 454, Life Sciences; 454-UDS) and DeepChek for routine baseline resistance testing in a clinical diagnostic laboratory.Methods107 newly diagnosed HIV-1-infected patients (subtypes: A, n=9; B, n=52; C, n=21; D, n=2; F, n=3; G, n=1; CRF01, n=7; CRF02, n=7; CRF06, n=1; CRF07, n=1; CRF10, n=1 and unassigned complex, n=2) with a median plasma viral load of 88,727 copies/mL (range: 1380–2,143,543) were tested by 454-UDS and Sanger sequencing for the detection of protease and reverse transcriptase RAMs. In addition, integrase RAMs were investigated in 57 of them. Sequence analysis and resistance interpretation were performed using DeepChek applying 1% and 20% thresholds for variant detections; filters applied were comparison between Sanger and 454-UDS, and Stanford and IAS list for resistance interpretation.ResultsThe time elapsed from generation of raw 454 data (between 2,000–5,000 sequences/sample) to elaboration of a resistance report was approximately 10 minutes per sample, equivalent to the time required for the same process using Sanger sequencing. Four patients (3.7%) showed baseline resistance by Sanger and 454-UDS at frequencies above 20%, which affected both NRTIs (n=2) and NNRTIs (n=2). In addition, 12 patients (11.2%) showed transmitted drug resistance (TDR) by 454-UDS at frequencies below 20% affecting NRTIs (n=9), NNRTIs (n=7) and PIs (n=2). Integrase resistance was not detected at baseline by 454-UDS or Sanger sequencing.ConclusionsDeepChek allowed easy and rapid analysis and interpretation of NGS data, thus facilitating the incorporation of this technology in routine diagnostics. The use of NGS considerably increased the detection rates of TDR to NRTI, NNRTIs and PIs. No transmitted resistance to integrase inhibitors was found in our population by Sanger sequencing or UDS.

Use of deep sequencing data for routine analysis of HIV resistance in newly diagnosed patients.

IntroductionUse of deep sequencing is becoming a critical tool in clinical virology, with an important impact in the HIV field for routine diagnostic purposes. Here, we present the comparison of deep and Sanger sequencing in newly diagnosed HIV patients, and the use of DeepChek v1.3 & VisibleChek for their interpretation and integration with virological and clinical data.Patients and MethodsPlasma samples from 88 newly diagnosed HIV-1-infected patients were included in the study. Median age (IQR) was 37 (27–47), median CD4 count (IQR) was 387 (220–554), and 85% were males. Median Viral Load (Log, IQR) was 5.03 (4.51–5.53). Deep sequencing was obtained using a GS-Junior (Roche). Sequences were preprocessed with the 454 AVA software; aligned reads were uploaded into the DeepChek v1.3 system (ABL SA). Sanger sequences (Trugene), were uploaded in parallel. Stanford algorithm (version 7.0) resistance interpretation to first line drugs and all the mutations (score≥5) were analyzed. For deep sequencing, 1%, 5% and 10% thresholds were chosen for resistance interpretation.ResultsUsing VisibleChek for analysis, we were able to describe the detection of any mutation using Sanger in 37/88 patients, with a total number of 50 Stanford ≥5 mutations, K103N and E138A being the most prevalent (n=4). Using UDS-1%, we found 72/88 patients with at least one mutation (total of 206 Stanford ≥5 mutations). Using Sanger data, 9/88 patients (10.22%) showed any resistance to NNRTIs, while none showed resistance to NRTIs or PIs. Using UDS-10% increased resistance to NRTIs [3/88 (3.40%)], to NNRTIs 12/88 (13.63%), and to a lesser extent to PIs [1/88 (1.13%)]. Using UDS-5% increased resistance to NRTIs [4/88 (4.54%)] and to NNRTIs [12/88 (13.63%)], but not to PIs. Using UDS-1% increased resistance to all classes: NRTIs [14/88 (15.90%)], NNRTIs [26/88 (30.68%)], and PIs [6/88 (6.81].ConclusionsDeepChek and VisibleChek allow for an easy, reliable and rapid analysis of UDS data from HIV-1. Compared to Sanger data, UDS detected a higher number of resistance mutations. UDS with a 5 &10% threshold resulted in an increase in the number of patients with any degree of resistance mainly to NRTI, NNRTIs. Going down as low as 1% increased resistance to all classes. A correct definition of clinically relevant thresholds for the interpretation of minor variant detection for different classes of ARVs is needed.

Application of next-generation sequencing in clinical oncology to advance personalized treatment of cancer

With the development and improvement of new sequencing technology, next-generation sequencing (NGS) has been applied increasingly in cancer genomics research over the past decade. More recently, NGS has been adopted in clinical oncology to advance personalized treatment of cancer. NGS is used to identify novel and rare cancer mutations, detect familial cancer mutation carriers, and provide molecular rationale for appropriate targeted therapy. Compared to traditional sequencing, NGS holds many advantages, such as the ability to fully sequence all types of mutations for a large number of genes (hundreds to thousands) in a single test at a relatively low cost. However, significant challenges, particularly with respect to the requirement for simpler assays, more flexible throughput, shorter turnaround time, and most importantly, easier data analysis and interpretation, will have to be overcome to translate NGS to the bedside of cancer patients. Overall, continuous dedication to apply NGS in clinical oncology practice will enable us to be one step closer to personalized medicine.

DEVELOPMENT OF A NEW GEOSPATIAL DATA SHARING/OVERLAY SYSTEM FOR LAND ENVIRONMENTAL STUDIES – CERES GAIA –

Abstract. A new geospatial data sharing/overlay system, CEReS Gaia, has developed. The purpose of the system development is to promote land surface environmental studies. The system has developed to meet the requirements by academic users who wish easy data analysis by using his/her own data with existing other data. The system has the following features; a) internationally unlimited expansion of servers, b) multi-language capability (currently, only English), c) free access without user registration, d) data upload/download by registered users, e) capability to overlay user’s data on other registered data, f) option of open or selective data distribution.

Structure revealing techniques based on parallel coordinates plot

Parallel coordinates plot (PCP) is an excellent tool for multivariate visualization and analysis, but it may fail to reveal inherent structures for complex and large datasets. Therefore, polyline clustering and coordinate sorting are inevitable for the accurate data exploration and analysis. In this paper, we propose a suite of novel clustering and dimension sorting techniques in PCP, to reveal and highlight hidden trend and correlation information of polylines. Spectrum theory is first introduced to specifically design clustering and sorting techniques for a clear view of clusters in PCP. We also provide an efficient correlation based sorting technique to optimize the ordering of coordinates to reveal correlated relations, and show how our view-range metrics, generated based on the aggregation constraints, can be used to make a clear view for easy data perception and analysis. Experimental results generated using our framework visually represent meaningful structures to guide the user, and improve the efficiency of the analysis, especially for the complex and noisy data.