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

scholarly journals Topological Data Analysis Approaches to Uncovering the Timing of Ring Structure Onset in Filamentous Networks

2021 ◽  
Vol 83 (3) ◽  
Author(s):  
Maria-Veronica Ciocanel ◽  
Riley Juenemann ◽  
Adriana T. Dawes ◽  
Scott A. McKinley
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Actin Filaments ◽  
Time Series Data ◽  
Polymer Networks ◽  
Topological Data Analysis ◽  
Series Data ◽  
Topological Features ◽  
Topological Data

AbstractIn developmental biology as well as in other biological systems, emerging structure and organization can be captured using time-series data of protein locations. In analyzing this time-dependent data, it is a common challenge not only to determine whether topological features emerge, but also to identify the timing of their formation. For instance, in most cells, actin filaments interact with myosin motor proteins and organize into polymer networks and higher-order structures. Ring channels are examples of such structures that maintain constant diameters over time and play key roles in processes such as cell division, development, and wound healing. Given the limitations in studying interactions of actin with myosin in vivo, we generate time-series data of protein polymer interactions in cells using complex agent-based models. Since the data has a filamentous structure, we propose sampling along the actin filaments and analyzing the topological structure of the resulting point cloud at each time. Building on existing tools from persistent homology, we develop a topological data analysis (TDA) method that assesses effective ring generation in this dynamic data. This method connects topological features through time in a path that corresponds to emergence of organization in the data. In this work, we also propose methods for assessing whether the topological features of interest are significant and thus whether they contribute to the formation of an emerging hole (ring channel) in the simulated protein interactions. In particular, we use the MEDYAN simulation platform to show that this technique can distinguish between the actin cytoskeleton organization resulting from distinct motor protein binding parameters.

scholarly journals Using and acquiring time-series data with the EMSO ERIC DataLab

2020 ◽  
Author(s):  
Raul Bardaji ◽  
Jaume Piera ◽  
Juanjo Dañobeitia ◽  
Ivan Rodero
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Web Application ◽  
Time Series Data ◽  
Interdisciplinary Teams ◽  
Series Data ◽  
Published Data ◽  
Oceanographic Data ◽  
Engineering Best Practices ◽  
Parameters Of Interest

<p>In marine sciences, the way in which many research groups work is changing as scientists use published data to complement their field campaign data online, thanks to the large increase in the number of open access observations. Many institutions are making great efforts to provide the data following FAIR principles (findability, accessibility, interoperability, and reusability) and are bringing together interdisciplinary teams of data scientists and data engineers.</p><p>There are different platforms for downloading marine and oceanographic data and many libraries to analyze data. However, the reality is that scientists continue to have difficulty finding the data they need. On many occasions, data platforms provide information about the metadata, but they do not show any underlying graph of the data that can be downloaded. Sometimes, scientists cannot download only the data parameters of interest and have to download huge amounts of data with other not useful parameters for their studies. On other occasions, the platform allows to download the data parameters of interest but offers the time-series data as many files, and it is the scientist who has to join the pieces of data into a single dataset to be analyzed correctly. EMSO ERIC is developing a data service that helps reduce the burden of scientists to search and acquire data as much as possible.</p><p> </p><p>We present the EMSO ERIC DataLab web application, which provides users with capabilities to preview harmonized data from the EMSO ERIC observatories, perform some basic data analyses, create or modify datasets, and download them. Use case scenarios of the DataLab include the creation of a NetCDF file with time-series information across EMSO ERIC observatories.</p><p>The DataLab has been developed using engineering best practices and trend technologies for big data management, including specialized Python libraries for web environments and oceanographic data analysis, such as Plotly, Dash, Flask, and the Module for Ocean Observatory Data Analysis (MOODA).</p>

Examining the Long Run Effects of Export, Import and FDI Inflows on the FDI Outflows from India: A Causality Analysis

2009 ◽  
Vol 10 (1) ◽  
pp. 65-88
Author(s):  
Nandita Dasgupta
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Driving Force ◽  
Time Series Data ◽  
Series Data ◽  
Causality Analysis ◽  
Fdi Inflows ◽  
Long Run ◽  
Time Series Data Analysis ◽  
Trade And Investment

The objective of this paper is to examine the effects of international trade and investment related macro economic variables, namely, exports, imports and FDI inflows on the outflows of FDI from India over 1970 through 2005. Using time series data analysis, the empirical part of the paper finds unidirectional Granger Causality from export and import to FDI outflows but no such causality exists from FDI inflows to the corresponding outflows from India. Results confirm the assumption that lagged imports and exports are a driving force of ing front.

scholarly journals Periodic Time Series Data Analysis by Deep Learning Methodology

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 223078-223088
Author(s):  
Haolong Zhang ◽  
Haoye Lu ◽  
Amiya Nayak
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Periodic Time Series ◽  
Time Series Data Analysis

Robust Extrema Features for Time-Series Data Analysis

2013 ◽  
Vol 35 (6) ◽  
pp. 1464-1479 ◽  
Author(s):  
Pramod K. Vemulapalli ◽  
Vishal Monga ◽  
Sean N. Brennan
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Time Series Data Analysis
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