scholarly journals ImageJ and 3D Slicer: open source 2/3D morphometric software

2019 ◽  
Author(s):  
Fiona Pye ◽  
Nussaȉbah B Raja ◽  
Bryan Shirley ◽  
Ádám T Kocsis ◽  
Niklas Hohmann ◽  
...  
Keyword(s):  
Data Collection ◽  
Open Source ◽  
Programming Languages ◽  
Quality Data ◽  
Software Projects ◽  
High Quality Data ◽  
3D Slicer ◽  
Data Collection And Analysis ◽  
Digital Methods ◽  
Imagej Plugin

In a world where an increasing number of resources are hidden behind paywalls and monthly subscriptions, it is becoming crucial for the scientific community to invest energy into freely available, community-maintained systems. Open-source software projects offer a solution, with freely available code which users can utilise and modify, under an open source licence. In addition to software accessibility and methodological repeatability, this also enables and encourages the development of new tools. As palaeontology moves towards data driven methodologies, it is becoming more important to acquire and provide high quality data through reproducible systematic procedures. Within the field of morphometrics, it is vital to adopt digital methods that help mitigate human bias from data collection. In addition,m mathematically founded approaches can reduce subjective decisions which plague classical data. This can be further developed through automation, which increases the efficiency of data collection and analysis. With these concepts in mind, we introduce two open-source shape analysis software, that arose from projects within the medical imaging field. These are ImageJ, an image processing program with batch processing features, and 3DSlicer which focuses on 3D informatics and visualisation. They are easily extensible using common programming languages, with 3DSlicer containing an internal python interactor, and ImageJ allowing the incorporation of several programming languages within its interface alongside its own simplified macro language. Additional features created by other users are readily available, on GitHub or through the software itself. In the examples presented, an ImageJ plugin “FossilJ” has been developed which provides semi-automated morphometric bivalve data collection. 3DSlicer is used with the extension SPHARM-PDM, applied to synchrotron scans of coniform conodonts for comparative morphometrics, for which small assistant tools have been created.

scholarly journals ImageJ and 3D Slicer: open source 2/3D morphometric software

2019 ◽  
Author(s):  
Fiona Pye ◽  
Nussaȉbah B Raja ◽  
Bryan Shirley ◽  
Ádám T Kocsis ◽  
Niklas Hohmann ◽  
...  
Keyword(s):  
Data Collection ◽  
Open Source ◽  
Programming Languages ◽  
Quality Data ◽  
Software Projects ◽  
High Quality Data ◽  
3D Slicer ◽  
Data Collection And Analysis ◽  
Digital Methods ◽  
Imagej Plugin

In a world where an increasing number of resources are hidden behind paywalls and monthly subscriptions, it is becoming crucial for the scientific community to invest energy into freely available, community-maintained systems. Open-source software projects offer a solution, with freely available code which users can utilise and modify, under an open source licence. In addition to software accessibility and methodological repeatability, this also enables and encourages the development of new tools. As palaeontology moves towards data driven methodologies, it is becoming more important to acquire and provide high quality data through reproducible systematic procedures. Within the field of morphometrics, it is vital to adopt digital methods that help mitigate human bias from data collection. In addition, mathematically founded approaches can reduce subjective decisions which plague classical data. This can be further developed through automation, which increases the efficiency of data collection and analysis. With these concepts in mind, we introduce two open-source shape analysis software, that arose from projects within the medical imaging field. These are ImageJ, an image processing program with batch processing features, and 3D Slicer which focuses on 3D informatics and visualisation. They are easily extensible using common programming languages, with 3D Slicer containing an internal python interactor, and ImageJ allowing the incorporation of several programming languages within its interface alongside its own simplified macro language. Additional features created by other users are readily available, on GitHub or through the software itself. In the examples presented, an ImageJ plugin “FossilJ” has been developed which provides semi-automated morphometric bivalve data collection. 3D Slicer is used with the extension SPHARM-PDM, applied to synchrotron scans of coniform conodonts for comparative morphometrics, for which small assistant tools have been created in Python.

scholarly journals ImageJ and 3D Slicer: open source 2/3D morphometric software

2019 ◽  
Author(s):  
Fiona Pye ◽  
Nussaȉbah B Raja ◽  
Bryan Shirley ◽  
Ádám T Kocsis ◽  
Niklas Hohmann ◽  
...  
Keyword(s):  
Data Collection ◽  
Open Source ◽  
Programming Languages ◽  
Quality Data ◽  
Software Projects ◽  
High Quality Data ◽  
3D Slicer ◽  
Data Collection And Analysis ◽  
Digital Methods ◽  
Imagej Plugin

In a world where an increasing number of resources are hidden behind paywalls and monthly subscriptions, it is becoming crucial for the scientific community to invest energy into freely available, community-maintained systems. Open-source software projects offer a solution, with freely available code which users can utilise and modify, under an open source licence. In addition to software accessibility and methodological repeatability, this also enables and encourages the development of new tools. As palaeontology moves towards data driven methodologies, it is becoming more important to acquire and provide high quality data through reproducible systematic procedures. Within the field of morphometrics, it is vital to adopt digital methods that help mitigate human bias from data collection. In addition, mathematically founded approaches can reduce subjective decisions which plague classical data. This can be further developed through automation, which increases the efficiency of data collection and analysis. With these concepts in mind, we introduce two open-source shape analysis software, that arose from projects within the medical imaging field. These are ImageJ, an image processing program with batch processing features, and 3D Slicer which focuses on 3D informatics and visualisation. They are easily extensible using common programming languages, with 3D Slicer containing an internal python interactor, and ImageJ allowing the incorporation of several programming languages within its interface alongside its own simplified macro language. Additional features created by other users are readily available, on GitHub or through the software itself. In the examples presented, an ImageJ plugin “FossilJ” has been developed which provides semi-automated morphometric bivalve data collection. 3D Slicer is used with the extension SPHARM-PDM, applied to synchrotron scans of coniform conodonts for comparative morphometrics, for which small assistant tools have been created in Python.

scholarly journals Low-cost double pendulum for high-quality data collection with open-source video tracking and analysis

HardwareX ◽  
2020 ◽  
Vol 8 ◽  
pp. e00138
Author(s):  
Audun D. Myers ◽  
Joshua R. Tempelman ◽  
David Petrushenko ◽  
Firas A. Khasawneh
Keyword(s):  
Data Collection ◽  
Open Source ◽  
Low Cost ◽  
Video Tracking ◽  
Quality Data ◽  
Double Pendulum ◽  
High Quality ◽  
High Quality Data

Collecting High-Quality Data

2018 ◽  
Author(s):  
Mary Kay Gugerty ◽  
Dean Karlan
Keyword(s):  
Data Collection ◽  
Social Desirability ◽  
Measurement Errors ◽  
Monitoring And Evaluation ◽  
Quality Data ◽  
Social Desirability Bias ◽  
High Quality ◽  
High Quality Data ◽  
Evaluation Systems ◽  
Demand Effect

Without high-quality data, even the best-designed monitoring and evaluation systems will collapse. Chapter 7 introduces some the basics of collecting high-quality data and discusses how to address challenges that frequently arise. High-quality data must be clearly defined and have an indicator that validly and reliably measures the intended concept. The chapter then explains how to avoid common biases and measurement errors like anchoring, social desirability bias, the experimenter demand effect, unclear wording, long recall periods, and translation context. It then guides organizations on how to find indicators, test data collection instruments, manage surveys, and train staff appropriately for data collection and entry.

EDS and WDS Automation: Past Development and Future Technology

1999 ◽  
Vol 5 (S2) ◽  
pp. 556-557
Author(s):  
J. J. McCarthy ◽  
J. J. Frief
Keyword(s):  
Data Collection ◽  
Programming Languages ◽  
Electron Probe ◽  
Light Element ◽  
Automated Analysis ◽  
Spectral Processing ◽  
Electron Probe Analysis ◽  
Beam Position ◽  
Data Collection And Analysis ◽  
Automation Systems

Early Development Automation of electron probe analysis began to flourish in the early 1970s spurred on by advances in computer technology and the availability of operating systems and programming languages that the individual researcher could afford to dedicate to a single instrument. By the end of the decade, most researchers and vendors in the microanalysis field had adopted the PDP-11 minicomputer, and languages such as FOCAL, FORTRAN and BASIC that ran on these computers. A good summary of these early efforts was given by Hatfield. The first use of the energy dispersive detector on the electron probe in 1968 added the need to control the acquisition, display and processing of EDS spectra. As a result, the 70’s were also a time when much attention was focussed on development of software for on-line data reduction and analysis. These efforts produced a suite of programs to provide matrix corrections and spectral processing, and automation of WDS data collection. The culmination of these development efforts was first reported in 1977 with the analysis of a lunar whitlockite mineral by simultaneous EDS/WDS measurement. This analysis determined the concentration of 23 elements, 8 by EDS and took a total of 37 minutes for data collection and analysis. In this paper, the authors noted the complementary use of the EDS and WDS (WDS for trace elements and severe peak overlaps, EDS for other elements and rapid qualitative analysis) in their automated instrument, a convention that remains common on the electron probe even today. Toward the end of the decade the analytical accuracy and precision achieved by automated analysis of bulk samples approached the limits of the instrumentation, with the exception of analysis of light element concentrations.Two Decades of Improvements The explosive growth in digital electronics and microprocessors for data processing and control functions during the 80’s was rapidly applied to electron probe automation. Second and third generation automation systems included direct control of many microscope functions, beam position and imaging conditions. Motor positioning was more precise and far faster. As a result, the data collection and analysis of 23 elements reported in 1977 could be accomplished at least three times faster on a modern instrument.

scholarly journals Information Explanatory Applications in Street Activity Security – A Survey

2020 ◽  
pp. 81-83
Author(s):  
Samsudeen S ◽  
Salomi M
Keyword(s):  
Data Collection ◽  
Path Selection ◽  
Data Sources ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Start Up ◽  
Improvement Strategies ◽  
Paper Survey

The paper survey helps to diminish the start-up complex of knowledge assortment and clear analytics for factual modeling & course improvement for probability connected by engine vehicles. We tend to seem that the writing is isolated into 2 totally different inquire concerning areas: (a) discerning/illustrative methods which endeavor in order to urge it and assess clatter hazard supported distinctive powerful conditions, and (b) improvement strategies which center by minimizing clatter probability by route, path-selection and break design. Interpretation based on inquire concerning results of the 2 streams are restricted to beat the problem that tends to show freely accessible high-quality data sources (diverse take into account plans, result factors, and indicator factors) and communicative instructive strategies (information summarization, visualization, and measuring decrease) which are used for understanding safer-routing and provides code to encourage data collection/exploration by practitioners/res

Rapidly Deployable MTConnect-Based Machine Tool Monitoring Systems

2017 ◽  
Author(s):  
Roby Lynn ◽  
Wafa Louhichi ◽  
Mahmoud Parto ◽  
Ethan Wescoat ◽  
Thomas Kurfess
Keyword(s):  
Data Collection ◽  
Open Source ◽  
Machine Tools ◽  
Large Scale ◽  
Machining Process ◽  
Medium Size ◽  
Time Data ◽  
Machine Monitoring ◽  
Tool Monitoring ◽  
Data Collection And Analysis

The amount of data that can be gathered from a machining process is often misunderstood, and even if these data are collected, they are frequently underutilized. Intelligent uses of data collected from a manufacturing operation can lead to increased productivity and lower costs. While some large-scale manufacturers have developed custom solutions for data collection from their machine tools, small- and medium-size enterprises need efficient and easily deployable methods for data collection and analysis. This paper presents three broad solutions to data collection from machine tools, all of which rely on the open-source and royalty-free MTConnect protocol: the first is a machine monitoring dashboard based on Microsoft Excel; the second is an open source solution using Python and MTConnect; and the third is a cloud-based system using Google Sheets. Time studies are performed on these systems to determine their capability to gather near real-time data from a machining process.

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