Open source telecommunications framework for time-series data from smart grid sensors

AbstractBackground and aimsMinirhizotrons are commonly used to study root turnover which is essential for understanding ecosystem carbon and nutrient cycling. Yet, extracting data from minirhizotron images requires intensive annotation effort. Existing annotation tools often lack flexibility and provide only a subset of the required functionality. To facilitate efficient root annotation in minirhizotrons, we present the user-friendly open source tool rhizoTrak.Methods and resultsrhizoTrak builds on TrakEM2 and is publically available as Fiji plugin. It uses treelines to represent branching structures in roots and assigns customizable status labels per root segment. rhizoTrak offers configuration options for visualization and various functions for root annotation mostly accessible via keyboard shortcuts. rhizoTrak allows time-series data import and particularly supports easy handling and annotation of time series images. This is facilitated via explicit temporal links (connectors) between roots which are automatically generated when copying annotations from one image to the next. rhizoTrak includes automatic consistency checks and guided procedures for resolving conflicts. It facilitates easy data exchange with other software by supporting open data formats.ConclusionsrhizoTrak covers the full range of functions required for user-friendly and efficient annotation of time-series images. Its flexibility and open source nature will foster efficient data acquisition procedures in root studies using minirhizotrons.

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PhilDB - The time series database with built-in change logging

10.7287/peerj.preprints.1488v1 ◽

2015 ◽

Author(s):

Andrew MacDonald

Keyword(s):

Time Series ◽

Big Data ◽

Open Source ◽

High Performance ◽

Time Series Data ◽

Handling Time ◽

Series Data ◽

Meta Data ◽

Static Data ◽

Data Tracking

PhilDB is an open-source time series database. It supports storage of time series datasets that are dynamic, that is recording updates to existing values in a log as they occur. Recent open-source systems, such as InfluxDB and OpenTSDB, have been developed to indefinitely store long-period, high-resolution time series data. Unfortunately they require a large initial installation investment before use because they are designed to operate over a cluster of servers to achieve high-performance writing of static data in real time. In essence, they have a ‘big data’ approach to storage and access. Other open-source projects for handling time series data that don’t take the ‘big data’ approach are also relatively new and are complex or incomplete. None of these systems gracefully handle revision of existing data while tracking values that changed. Unlike ‘big data’ solutions, PhilDB has been designed for single machine deployment on commodity hardware, reducing the barrier to deployment. PhilDB eases loading of data for the user by utilising an intelligent data write method. It preserves existing values during updates and abstracts the update complexity required to achieve logging of data value changes. PhilDB improves accessing datasets by two methods. Firstly, it uses fast reads which make it practical to select data for analysis. Secondly, it uses simple read methods to minimise effort required to extract data. PhilDB takes a unique approach to meta-data tracking; optional attribute attachment. This facilitates scaling the complexities of storing a wide variety of data. That is, it allows time series data to be loaded as time series instances with minimal initial meta-data, yet additional attributes can be created and attached to differentiate the time series instances as a wider variety of data is needed. PhilDB was written in Python, leveraging existing libraries. This paper describes the general approach, architecture, and philosophy of the PhilDB software.

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A Comparison of ArcGIS and QGIS for Animation

Cartographic Perspectives ◽

10.14714/cp85.1405 ◽

2017 ◽

pp. 23-32

Author(s):

Owen Stuckey

Keyword(s):

Time Series ◽

Data Processing ◽

Open Source ◽

Time Series Data ◽

Series Data ◽

Data Input ◽

Input Processing ◽

Similarities And Differences ◽

Baseline Cost ◽

Criteria For Evaluation

I compare two GIS programs which can be used to create cartographic animations—the commercial Esri ArcGIS and the free and open-source QGIS. ArcGIS implements animation through the “Time Slider” while QGIS uses a plugin called “TimeManager.” There are some key similarities and differences as well as functions unique to each plugin. This analysis examines each program’s capabilities in mapping time series data. Criteria for evaluation include the number of steps, the number of output formats, input of data, processing, output of a finished animation, and cost. The comparison indicates that ArcGIS has more control in input, processing, and output of animations than QGIS, but has a baseline cost of $100 per year for a personal license. In contrast, QGIS is free, uses fewer steps, and enables more output formats. The QGIS interface can make data input, processing, and output of an animation slower.

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Automatic offset detection using R open source libraries

10.5194/egusphere-egu21-8678 ◽

2021 ◽

Author(s):

Shambo Bhattacharjee ◽

Alvaro Santamaría-Gómez

Keyword(s):

Time Series ◽

Cluster Analysis ◽

Open Source ◽

Change Point ◽

Time Series Data ◽

Detection Method ◽

Series Data ◽

Position Time Series ◽

Gnss Time Series ◽

Gnss Position Time Series

Long GNSS position time series contain offsets typically at rates between 1 and 3 offsets per decade. We may classify the offsets whether their epoch is precisely known, from GNSS station log files or Earthquake databases, or unknown. Very often, GNSS position time series contain offsets for which the epoch is not known a priori and, therefore, an offset detection/removal operation needs to be done in order to produce continuous position time series needed for many applications in geodesy and geophysics. A further classification of the offsets corresponds to those having a physical origin related to the instantaneous displacement of the GNSS antenna phase center (from Earthquakes, antenna changes or even changes of the environment of the antenna) and those spurious originated from the offset detection method being used (manual/supervised or automatic/unsupervised). Offsets due to changes of the antenna and its environment must be avoided by the station operators as much as possible. Spurious offsets due to the detection method must be avoided by the time series analyst and are the focus of this work. Even if manual offset detection by expert analysis is likely to perform better, automatic offset detection algorithms are extremely useful when using massive (thousands) GNSS time series sets. Change point detection and cluster analysis algorithms can be used for detecting offsets in a GNSS time series data and R offers a number of libraries related to performing these two. For example, the &#8220;Bayesian Analysis of Change Point Problems&#8221; or the &#8220;bcp&#8221; helps to detect change points in a time series data. Similarly, the &#8220;dtwclust&#8221; (Dynamic Time Warping algorithm) is used for the time series cluster analysis. Our objective is to assess various open-source R libraries for the automatic offset detection.

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RhythmicAlly: Your R and Shiny–Based Open-Source Ally for the Analysis of Biological Rhythms

Journal of Biological Rhythms ◽

10.1177/0748730419862474 ◽

2019 ◽

Vol 34 (5) ◽

pp. 551-561 ◽

Cited By ~ 7

Author(s):

Lakshman Abhilash ◽

Vasu Sheeba

Keyword(s):

Time Series ◽

Open Source ◽

Time Series Data ◽

Biological Rhythms ◽

Series Data ◽

Biological Time ◽

Source Program ◽

Wide Range ◽

Infradian Rhythms ◽

User Friendly

Research on circadian rhythms often requires researchers to estimate period, robustness/power, and phase of the rhythm. These are important to estimate, owing to the fact that they act as readouts of different features of the underlying clock. The commonly used tools, to this end, suffer from being very expensive, having very limited interactivity, being very cumbersome to use, or a combination of these. As a step toward remedying the inaccessibility to users who may not be able to afford them and to ease the analysis of biological time-series data, we have written RhythmicAlly, an open-source program using R and Shiny that has the following advantages: (1) it is free, (2) it allows subjective marking of phases on actograms, (3) it provides high interactivity with graphs, (4) it allows visualization and storing of data for a batch of individuals simultaneously, and (5) it does what other free programs do but with fewer mouse clicks, thereby being more efficient and user-friendly. Moreover, our program can be used for a wide range of ultradian, circadian, and infradian rhythms from a variety of organisms, some examples of which are described here. The first version of RhythmicAlly is available on Github, and we aim to maintain the program with subsequent versions having updated methods of visualizing and analyzing time-series data.

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TATSSI: A Free and Open-Source Platform for Analyzing Earth Observation Products with Quality Data Assessment

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10040267 ◽

2021 ◽

Vol 10 (4) ◽

pp. 267

Author(s):

Inder Tecuapetla-Gómez ◽

Gerardo López-Saldaña ◽

María Isabel Cruz-López ◽

Rainer Ressl

Keyword(s):

Time Series ◽

Open Source ◽

Satellite Data ◽

Time Series Data ◽

Earth Observation ◽

Quality Analysis ◽

Quality Data ◽

Series Data ◽

Data Handling ◽

Geographical Regions

Earth observation (EO) data play a crucial role in monitoring ecosystems and environmental processes. Time series of satellite data are essential for long-term studies in this context. Working with large volumes of satellite data, however, can still be a challenge, as the computational environment with respect to storage, processing and data handling can be demanding, which sometimes can be perceived as a barrier when using EO data for scientific purposes. In particular, open-source developments which comprise all components of EO data handling and analysis are still scarce. To overcome this difficulty, we present Tools for Analyzing Time Series of Satellite Imagery (TATSSI), an open-source platform written in Python that provides routines for downloading, generating, gap-filling, smoothing, analyzing and exporting EO time series. Since TATSSI integrates quality assessment and quality control flags when generating time series, data quality analysis is the backbone of any analysis made with the platform. We discuss TATSSI’s 3-layered architecture (data handling, engine and three application programming interfaces (API)); by allowing three APIs (a native graphical user interface, some Jupyter Notebooks and the Python command line) this development is exceptionally user-friendly. Furthermore, to demonstrate the application potential of TATSSI, we evaluated MODIS time series data for three case studies (irrigation area changes, evaluation of moisture dynamics in a wetland ecosystem and vegetation monitoring in a burned area) in different geographical regions of Mexico. Our analyses were based on methods such as the spatio-temporal distribution of maxima over time, statistical trend analysis and change-point decomposition, all of which were implemented in TATSSI. Our results are consistent with other scientific studies and results in these areas and with related in-situ data.

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CauseMap: Fast inference of causality from complex time series

10.7287/peerj.preprints.583 ◽

2015 ◽

Author(s):

M. Cyrus Maher ◽

Ryan D. Hernandez

Keyword(s):

Time Series ◽

Personalized Medicine ◽

Open Source ◽

Biomedical Research ◽

Time Series Data ◽

Series Data ◽

Dimensional System ◽

Single Individual ◽

Causal Relationships ◽

Causal System

Background: Establishing health-related causal relationships is a central pursuit in biomedical research. Yet, the interdependent non-linearity of biological systems renders causal dynamics laborious and at times impractical to disentangle. This pursuit is further impeded by the dearth of time series that are sufficiently long to observe and understand recurrent patterns of flux. However, as data generation costs plummet and technologies like wearable devices democratize data collection, we anticipate a coming surge in the availability of biomedically-relevant time series data. Given the life-saving potential of these burgeoning resources, it is critical to invest in the development of open source software tools that are capable of drawing meaningful insight from vast amounts of time series data. Results: Here we present CauseMap, the first open source implementation of convergent cross mapping (CCM), a method for establishing causality from long time series data (> ~25 observations). Compared to existing time series methods, CCM has the advantage of being model-free and robust to unmeasured confounding that could otherwise induce spurious associations. CCM builds on Takens’ Theorem, a well-established result from dynamical systems theory that requires only mild assumptions. This theorem allows us to reconstruct high dimensional system dynamics using a time series of only a single variable. These reconstructions can be thought of as shadows of the true causal system. If the reconstructed shadows can predict points from the opposing time series, we can infer that the corresponding variables are providing views of the same causal system, and so are causally related. Unlike traditional metrics, this test can establish the directionality of causation, even in the presence of feedback loops. Furthermore, since CCM can extract causal relationships from times series of, e.g. a single individual, it may be a valuable tool to personalized medicine. We implement CCM in Julia, a high-performance programming language designed for facile technical computing. Our software package, CauseMap, is platform-independent and freely available as an official Julia package. Conclusions: CauseMap is an efficient implementation of a state-of-the-art algorithm for detecting causality from time series data. We believe this tool will be a valuable resource for biomedical research and personalized medicine.

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Eliciting Disease Data from Wikipedia Articles

Online Journal of Public Health Informatics ◽

10.5210/ojphi.v8i1.6526 ◽

2016 ◽

Vol 8 (1) ◽

Author(s):

Geoffrey Fairchild ◽

Lalindra De Silva ◽

Sara Y. Del Valle ◽

Alberto M. Segre

Keyword(s):

Time Series ◽

Open Source ◽

Disease Surveillance ◽

Time Series Data ◽

Ground Truth ◽

Series Data ◽

Disease Detection ◽

Surveillance Systems ◽

Ground Truth Data ◽

Named Entity

Traditional disease surveillance systems suffer from several disadvantages, including reporting lags and antiquated technology, that have caused a movement towards internet-based disease surveillance systems. This study presents the use of Wikipedia article content in this sphere. We demonstrate how a named-entity recognizer can be trained to tag case, death, and hospitalization counts in the article text. We also show that there are detailed time series data that are consistently updated that closely align with ground truth data. We argue that Wikipedia can be used to create the first community-driven open-source emerging disease detection, monitoring, and repository system.

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