Difference between revisions of "Template:Article of the week"

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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig3 Schröder JofBioSem22 13.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Niszczota EconBusRev23 9-2.png|240px]]</div>
'''"[[Journal:Structure-based knowledge acquisition from electronic lab notebooks for research data provenance documentation|Structure-based knowledge acquisition from electronic lab notebooks for research data provenance documentation]]"'''
'''"[[Journal:Judgements of research co-created by generative AI: Experimental evidence|Judgements of research co-created by generative AI: Experimental evidence]]"'''


[[Electronic laboratory notebook]]s (ELNs) are used to document experiments and investigations in the wet [[Laboratory|lab]]. Protocols in ELNs contain a detailed description of the conducted steps, including the necessary [[information]] to understand the procedure and the raised research data, as well as to reproduce the research investigation. The purpose of this study is to investigate whether such ELN protocols can be used to create [[wikipedia:Semantics|semantic]] documentation of the provenance of research data by the use of [[Ontology (information science)|ontologies]] and linked data methodologies. Based on an ELN protocol of a biomedical wet lab experiment, a retrospective [[wikipedia:Provenance#Data provenance|provenance model]] of the raised research data describing the details of the experiment in a machine-interpretable way is manually engineered. Furthermore, an automated approach for knowledge acquisition from ELN protocols is derived from these results. This structure-based approach exploits the structure in the experiment’s description—such as headings, tables, and links—to translate the ELN protocol into a semantic knowledge representation ... ('''[[Journal:Structure-based knowledge acquisition from electronic lab notebooks for research data provenance documentation|Full article...]]''')<br />
The introduction of [[ChatGPT]] has fuelled a public debate on the appropriateness of using generative [[artificial intelligence]] (AI) ([[large language model]]s or LLMs) in work, including a debate on how they might be used (and abused) by researchers. In the current work, we test whether delegating parts of the research process to LLMs leads people to distrust researchers and devalues their scientific work. Participants (''N'' = 402) considered a researcher who delegates elements of the research process to a PhD student or LLM and rated three aspects of such delegation. Firstly, they rated whether it is morally appropriate to do so. Secondly, they judged whether—after deciding to delegate the research process—they would trust the scientist (who decided to delegate) to oversee future projects ... ('''[[Journal:Judgements of research co-created by generative AI: Experimental evidence|Full article...]]''')<br />
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Revision as of 15:26, 20 May 2024

Fig1 Niszczota EconBusRev23 9-2.png

"Judgements of research co-created by generative AI: Experimental evidence"

The introduction of ChatGPT has fuelled a public debate on the appropriateness of using generative artificial intelligence (AI) (large language models or LLMs) in work, including a debate on how they might be used (and abused) by researchers. In the current work, we test whether delegating parts of the research process to LLMs leads people to distrust researchers and devalues their scientific work. Participants (N = 402) considered a researcher who delegates elements of the research process to a PhD student or LLM and rated three aspects of such delegation. Firstly, they rated whether it is morally appropriate to do so. Secondly, they judged whether—after deciding to delegate the research process—they would trust the scientist (who decided to delegate) to oversee future projects ... (Full article...)
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