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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig3 Husen DataSciJourn2017 16-1.png|240px]]</div>
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'''"[[Journal:Recommended versus certified repositories: Mind the gap|Recommended versus certified repositories: Mind the gap]]"'''
'''"[[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]]"'''


Researchers are increasingly required to make research data publicly available in data repositories. Although several organizations propose criteria to recommend and evaluate the quality of data repositories, there is no consensus of what constitutes a good data repository. In this paper, we investigate, first, which data repositories are recommended by various stakeholders (publishers, funders, and community organizations) and second, which repositories are certified by a number of organizations. We then compare these two lists of repositories, and the criteria for recommendation and certification. We find that criteria used by organizations recommending and certifying repositories are similar, although the certification criteria are generally more detailed. We distill the lists of criteria into seven main categories: “Mission,” “Community/Recognition,” “Legal and Contractual Compliance,” “Access/Accessibility,” “Technical Structure/Interface,” “Retrievability,” and “Preservation.” Although the criteria are similar, the lists of repositories that are recommended by the various agencies are very different. Out of all of the recommended repositories, less than six percent obtained certification. As certification is becoming more important, steps should be taken to decrease this gap between recommended and certified repositories, and ensure that certification standards become applicable, and applied, to the repositories which researchers are currently using. ('''[[Journal:Recommended versus certified repositories: Mind the gap|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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