Systematic Literature Review for Field Phenotyping Datadoi:10.26165/JUELICH-DATA/OYQLN3Jülich DATA2025-10-063Grygosch, Lars; Kleppert, Ireneusz, 2025, "Systematic Literature Review for Field Phenotyping Data", https://doi.org/10.26165/JUELICH-DATA/OYQLN3, Jülich DATA, V3Systematic Literature Review for Field Phenotyping Datadoi:10.26165/JUELICH-DATA/OYQLN3Grygosch, LarsKleppert, IreneuszJülich DATAGrygosch, LarsGrygosch, Lars2025-09-14Agricultural SciencesWe conducted a Systematic Literature Review (SLR) investigating the trajectory of field phenotyping research, moving beyond a catalog of technological advances to critically evaluate data availability and management practices. Our analysis is guided by the following research questions (RQs): - RQ1: What are the prevailing trends in field phenotyping literature regarding experimental scales, mea- sured traits, and deployed technologies? - RQ2: How prevalent are data sharing practices within the domain, and can these practices be correlated with specific experimental settings or technologies? - RQ3: To what extent are efforts towards FAIR-compliant datasets identifiable in the literature since the principles’ formalization in 2016? By combining bibliometric analysis with a focused assessment of associated datasets and their management, we aim to provide a comprehensive overview of both scientific progress and the persisting data bottleneck. Our goal is to inform future directions for research, infrastructure development, and policy, ensuring that the data revolution in field phenotyping finally fulfills its promise for enhancing climate resilience and global food security.CC0 Waiverabbreviation_mapping.jsonapplication/jsonamude_report_old.txttext/plainanalysis_pipeline_notebook.ipynbapplication/x-ipynb+jsonATT-04_Thesaurus_Plant_Phenotyping.txttext/plaincluster.csvtext/csvclustering.pytext/x-pythonclustering_v2.pytext/x-pythoncluster_v2.csvtext/csvcm.pytext/x-pythonconfig.pytext/x-pythonconfiguration_ranking.csvtext/csvdata_unified.csvtext/csvdb_attributes.csvtext/csvembedding_mapping.jsonapplication/jsonextr_attr_desc.jsonapplication/jsonimg_flowchart.pngimage/pngkeyword_analysis.pytext/x-pythonkeyword_analysis_v2.pytext/x-pythonkeyword_trends_combined.pngimage/pngkeyword_trends_line.pngimage/pngkwmapping.csvtext/csvkwmapping_embedding.csvtext/csvkwmapping.pytext/x-pythonmanual_screening.xlsxapplication/vnd.openxmlformats-officedocument.spreadsheetml.sheetmetadata_analysis.pytext/x-pythonmetadata_datadict.csvtext/csvmetadata_keywords.csvtext/csvmetadata_normalized.csvtext/csvmetadata_results.pngimage/pngmost_common_keywords_data.pngimage/pngmost_common_keywords.pngimage/pngpreprocessed_v2.xlsxapplication/vnd.openxmlformats-officedocument.spreadsheetml.sheetpreprocessed.xlsxapplication/vnd.openxmlformats-officedocument.spreadsheetml.sheetpreproc_report.mdtext/markdownrequirements.txttext/plainsavedrecs.xlsapplication/vnd.ms-excelscopus_2_wos.jsonapplication/jsonscopus_attr_desc.jsonapplication/jsonscopus_export.csvtext/csvscreeningdata_unified.xlsxapplication/vnd.openxmlformats-officedocument.spreadsheetml.sheetscreening_table.xlsxapplication/vnd.openxmlformats-officedocument.spreadsheetml.sheettime_series_plot_002.pngimage/pngtop_keywords_comparison.pngimage/pngunification.pytext/x-pythonutils.pytext/x-pythonwos_attr_desc.jsonapplication/jsonwos_export.xlsapplication/vnd.ms-excel