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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Zakutayev SciData2018 5.jpg|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Soto-Perdomo SoftwareX2023 24.jpg|240px]]</div>
'''"[[Journal:An open experimental database for exploring inorganic materials|An open experimental database for exploring inorganic materials]]"'''
'''"[[Journal:OptiGUI DataCollector: A graphical user interface for automating the data collecting process in optical and photonics labs|OptiGUI DataCollector: A graphical user interface for automating the data collecting process in optical and photonics labs]]"'''


The use of advanced machine learning algorithms in experimental [[Materials informatics|materials science]] is limited by the lack of sufficiently large and diverse datasets amenable to data mining. If publicly open, such data resources would also enable materials research by scientists without access to expensive experimental equipment. Here, we report on our progress towards a publicly open High Throughput Experimental Materials (HTEM) Database (htem.nrel.gov). This database currently contains 140,000 sample entries, characterized by structural (100,000), synthetic (80,000), chemical (70,000), and optoelectronic (50,000) properties of inorganic thin film materials, grouped in >4,000 sample entries across >100 materials systems; more than a half of these data are publicly available. This article shows how the HTEM database may enable scientists to explore materials by browsing web-based user interface and an application programming interface. This paper also describes a HTE approach to generating materials data and discusses the [[laboratory information management system]] (LIMS) that underpins the HTEM database. Finally, this manuscript illustrates how advanced machine learning algorithms can be adopted to materials science problems using this open data resource. ('''[[Journal:An open experimental database for exploring inorganic materials|Full article...]]''')<br />
OptiGUI DataCollector is a Python 3.8-based graphical user interface (GUI) that facilitates automated data collection in optics and photonics research and development equipment. It provides an intuitive and easy-to-use platform for controlling a wide range of optical instruments, including [[spectrometer]]s and lasers. OptiGUI DataCollector is a flexible and modular framework that enables simple integration with different types of devices. It simplifies experimental workflow and reduces human error by automating parameter control, data acquisition, and [[Data analysis|analysis]]. OptiGUI DataCollector is currently focused on optical mode conversion utilizing fiber optic technologies ... ('''[[Journal:OptiGUI DataCollector: A graphical user interface for automating the data collecting process in optical and photonics labs|Full article...]]''')<br />
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Latest revision as of 15:05, 17 June 2024

Fig1 Soto-Perdomo SoftwareX2023 24.jpg

"OptiGUI DataCollector: A graphical user interface for automating the data collecting process in optical and photonics labs"

OptiGUI DataCollector is a Python 3.8-based graphical user interface (GUI) that facilitates automated data collection in optics and photonics research and development equipment. It provides an intuitive and easy-to-use platform for controlling a wide range of optical instruments, including spectrometers and lasers. OptiGUI DataCollector is a flexible and modular framework that enables simple integration with different types of devices. It simplifies experimental workflow and reduces human error by automating parameter control, data acquisition, and analysis. OptiGUI DataCollector is currently focused on optical mode conversion utilizing fiber optic technologies ... (Full article...)

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