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HUN-REN Data Repository Platform

István Gábor Hatvani

Hatvani István Gábor portré

István Gábor Hatvani is a senior research fellow of the Paleoclimate 2ka Momentum Research Group at the Institute for Geological and Geochemical Research of the Center for Astronomy and Earth Sciences, and a lecturer at the Faculty of Natural Sciences of the Eötvös Loránd University, Budapest. Focusing on the background of past climate change(s), his main research interests include geostatistical and -spectral analyses of geochemical and paleoclimate data series. He also performs spatial and temporal sampling frequency optimization and comprehensive condition assessment of surface and subsurface water bodies using state-of-the-art methods. Hatvani has been an editor of various professional publications, like Open Geosciences, Central European Geology and the International Journal on Geomathematics; board member of the Hungarian Young Academy (2023/2024); Secretary of the Geomathematical Section of the Hungarian Geological Society (2015-2023), of the Geomathematical Subcommittee of the Hungarian Academy of Sciences (2018-2023), and of the Scientific Committee of Earth Sciences of the Hungarian Academy of Sciences (2022-); and member of the International Association of Geomathematics (IAMG) (2014-).

István Hatvani is convinced that in the coming years there will be a growing need to make the ever-increasing amount of research data freely available in a uniform structure, not just as supplements to studies. In order to achieve this on a domestic platform (e.g. in the framework of the ARP project), it is necessary to communicate the expectations and requests of researchers to the developers also in the final stages of development, so that the repository can meet the needs of researchers as much as possible. Hatvani has proposed three data sets to be included in the ARP data repository: (1) data related to the isotope hydrology atlas of groundwater across Hungary; (2) a geostatistical database for Europe describing the predicted stable isotope composition of precipitation generated by machine learning methods; and (3) a database for the identification of raw and coloring materials used in the production of Early Prehistoric glass beads.