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Global Heat Flow Database Data Template

Since 1963, the International Heat Flow Commission (IHFC | www.ihfc-iugg.org) has been dedicated to providing standards for heat flow measurements and maintaining the Global Heat Flow Database (GHFDB) — a collection of heat flow data from around the world. The first quality framework for heat-flow-density data was proposed by Jessop et al. (1976), reflecting the state of knowledge, measurement techniques, and technical developments at that time. In 2019, the IHFC initiated a major revision of the GHFDB to develop an authenticated and quality-assessed database. This initiative involved multinational working groups and led to a comprehensive update of key parameters affecting heat-flow calculations. These updates included measurement methods for both temperature and thermal conductivity, as well as metadata structures. The new standard for a revised GHFDB structure was developed through a collaborative community approach and published in 2021 (Fuchs et al., 2021). This standard reflected changes in database technology and scientific documentation and served as a template for users submitting data to the GHFDB. It was further developed into the currently valid data and metadata standard in 2023, which also introduced an enhanced quality evaluation framework (Fuchs et al., 2023). The ongoing assessment work and the latest release of the GHFDB (Global Heat Flow Database Assessment Group et al., 2024), along with its frequent use, revealed the need for additional refinements. These refinements were particularly necessary in aspects related to metadata consistency, measurement techniques, and classification criteria. Consequently, further updates were implemented to improve the reliability and applicability of the dataset, ensuring a more robust evaluation of global heat-flow data. Here, we present the 2026.03 version of the GHFDB Data Template. The previous template introduced by Fuchs et al. (2023) has been improved based on the latest data ass6ssment process. The current version of the template incorporates the advancements in data collection methodologies, the IHFC quality evaluation framework, and metadata management, ensuring that data submitted to the GHFDB follows the IHFC standards for the GHFDB. A changelog is available and a summary of changes is also provided in the data descripton file (PDF). To promote open access, the template is also hosted on the official GitHub repository of the IHFC: https://github.com/ihfc-iugg. Users can download both the original version from 2023 and the revised templates. Version 2025.06 is also available in the previous-versions folder of this data publication. Maintaining the GHFDB Data Template in a version-controlled environment ensures transparency regarding changes over time and fosters a documentation style that sets high standards to support the reproducibility of research results. Moreover, it supports a smooth and fast integration of data from the research community into the Global Heat Flow Database of the IHFC.

The Global Heat Flow Database: Release 2024

The data publication contains the compilation of global heat-flow data by the International Heat Flow Commission (IHFC; www.ihfc-iugg.org) of the International Association of Seismology and Physics of the Earth's Interior (IASPEI). The presented data update release 2024 contains data generated between 1939 and 2024 and constitutes the second intermediate update benefiting from the global collaborative assessment and quality control of the Global Heat Flow Database running since May 2021 (http://assessment.ihfc-iugg.org). The data release comprises new original heat-flow data published since April 2023 (the update 2023). It contains 91,182 heat-flow data from 1,586 publications. 57% of the reported heat-flow values are from the continental domain (n ~ 54,553), while the remaining 43% are located in the oceanic domain (n ~ 36,692).

Heat Flow Quality Analysis Toolbox (hfqa_tool)

hfqa_tool is a python package for validating heat-flow data against the IHFC Global Heat Flow Database (GHFDB) schema and assessing data quality according to International Heat Flow Commission standards, including quality scoring based on the methodology described in Fuchs et al., 2024 (https://doi.org/10.1016/j.tecto.2023.229976). This is an updated and fully revised version of Chishti et al. (2025, https://doi.org/10.5880/fidgeo.2025.043)

The Global Heat Flow Database: Update 2023

The data publication contains the compilation of global heat-flow data by the International Heat Flow Commission (IHFC; www.ihfc-iugg.org) of the International Association of Seismology and Physics of the Earth's Interior (IASPEI). The presented data update 2023 contains data generated between 1939 and 2022 and constitutes the first intermediate update benefiting from the global collaborative assessment and quality control of the Global Heat Flow Database running since May 2021 (http://assessment.ihfc-iugg.org).

Heat Flow Quality Analysis Toolbox (hfqa_tool)

Heat Flow Quality Analysis Toolbox hfqa_tool is a Python package containing tools for validating and evaluating the quality of heat flow data. It is designed for researchers and professionals. hfqa_tool simplifies heat flow data analysis by providing standardized and reproducible quality checks. This is developed in compliance with the paper by Fuchs et al. (2023) titled "Quality-assurance of heat-flow data: The new structure and evaluation scheme of the IHFC Global Heat Flow Database," published in Tectonophysics 863: 229976. Also revised for the newer release 2024. There are mainly 2 functions defined in this tool with description as follows: vocabulary_check(): This set of code has been developed to check whether all the values entered in a Heatflow database adhere to a controlled vocabulary and proper structure described in the aforementioned scientific paper. It generates an error message for each entry where the value entered is out of bounds and does not meet the assigned criteria. The code also enables checking the vocabulary for multiple values entered in a single column for a particular Heatflow data entry. It's a recommended prerequisite before calculating 'Quality Scores' for a given Heatflow dataset. quality_scores(): This code has been developed to assess the quality of the Heatflow database in terms of U-score (Uncertainty quantification), M-Score (Methodological quality), and P-Flags (Perturbation effects) adhering to the data structure described in the aforementioned scientific paper.

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