This dataset was generated as part of the study aimed at profiling global scientific academies, which play a significant role in promoting scholarly communication and scientific progress. Below is a detailed description of the dataset:Data Generation Procedures and Tools: The dataset was compiled using a combination of web scraping, manual verification, and data integration from multiple sources, including Wikipedia categories,member of union of scientific organizations, and web searches using specific query phrases (e.g., "country name + (academy OR society) AND site:.country code"). The records were enriched by cross-referencing data from the Wikidata API, the VIAF API, and the Research Organisation Registry (ROR). Additional manual curation ensured accuracy and consistency.Temporal and Geographical Scopes: The dataset covers scientific academies from a wide temporal scope, ranging from the 15th century to the present. The geographical scope includes academies from all continents, with emphasis on both developed and post-developing countries. The dataset aims to capture the full spectrum of scientific academies across different periods of historical development.Tabular Data Description: The dataset comprises a total of 301 academy records and 14,008 website navigation sections. Each row in the dataset represents a single scientific academy, while the columns describe attributes such as the academy’s name, founding date, location (city and country), website URL, email, and address.Missing Data: Although the dataset offers comprehensive coverage, some entries may have missing or incomplete fields. For instance, section was not available for all records.Data Errors and Error Ranges: The data has been verified through manual curation, reducing the likelihood of errors. However, the use of crowd-sourced data from platforms like Wikipedia introduces potential risks of outdated or incomplete information. Any errors are likely minor and confined to fields such as navigation menu classifications, which may not fully reflect the breadth of an academy's activities.Data Files, Formats, and Sizes: The dataset is provided in CSV format and JSON format, ensuring compatibility with a wide range of software applications, including Microsoft Excel, Google Sheets, and programming languages such as Python (via libraries like pandas).This dataset provides a valuable resource for further research into the organizational behaviors, geographic distribution, and historical significance of scientific academies across the globe. It can be used for large-scale analyses, including comparative studies across different regions or time periods.Any feedback on the data is welcome! Please contact the maintaner of the dataset!If you use the data, please cite the following paper:Xiaoli Chen and Xuezhao Wang. 2024. Profiling Global Scientific Academies. In The 2024 ACM/IEEE Joint Conference on Digital Libraries (JCDL ’24), December 16–20, 2024, Hong Kong, China. ACM, New York, NY, USA, 5 pages. https://doi.org/10.1145/3677389.3702582

This repository provides access to five reconstruction files as well as the code and the static polygons and rotation files used to generate them. This set of palaeogeographic reconstruction files provide palaeocoordinates for three global grids at H3 resolutions 2, 3, and 4, which have an average cell spacing of ~316 km, ~119 km, and ~45 km. Grids were reconstructed at a temporal resolution of one million years throughout the entire Phanerozoic (540–0 Ma). The reconstruction files are stored as comma-separated-value (CSV) files which can be easily read by almost any spreadsheet program (e.g. Microsoft Excel and Google Sheets) or programming language (e.g. Python, Julia, and R). In addition, R Data Serialization (RDS) files—a common format for saving R objects—are also provided as lighter (and compressed) alternatives to the CSV files. The structure of the reconstruction files follows a wide-form data frame structure to ease indexing. Each file consists of three initial index columns relating to the H3 cell index (i.e. the 'H3 address'), present-day longitude of the cell centroid, and the present-day latitude of the cell centroid. The subsequent columns provide the reconstructed longitudinal and latitudinal coordinate pairs for their respective age of reconstruction in ascending order, indicated by a numerical suffix. Each row contains a unique spatial point on the Earth's continental surface reconstructed through time. NA values within the reconstruction files indicate points which are not defined in deeper time (i.e. either the static polygon does not exist at that time, or it is outside the temporal coverage as defined by the rotation file). The following five Global Plate Models are provided (abbreviation, temporal coverage, reference): WR13, 0–550 Ma, (Wright et al., 2013) MA16, 0–410 Ma, (Matthews et al., 2016) TC16, 0–540 Ma, (Torsvik and Cocks, 2016) SC16, 0–1100 Ma, (Scotese, 2016) ME21, 0–1000 Ma, (Merdith et al., 2021) In addition, the H3 grids for resolutions 2, 3, and 4 are provided. For more information, please refer to the article describing the data: Jones, L.A. and Domeier, M.M. 2023. Earth surface evolution: a Phanerozoic gridded dataset of Global Plate Model reconstructions. (TBC). For any additional queries, contact: Mathew M. Domeier (mathewd@uio.no) or Lewis A . Jones (lewisa.jones@outlook.com) If you use these files, please cite: Jones, L.A. and Domeier, M.M. 2023. Earth surface evolution: a Phanerozoic gridded dataset of Global Plate Model reconstructions. Zenodo data repository. DOI:10.5281/zenodo.10069222

This repository provides access to five pre-computed reconstruction files as well as the static polygons and rotation files used to generate them. This set of palaeogeographic reconstruction files provide palaeocoordinates for three global grids at H3 resolutions 2, 3, and 4, which have an average cell spacing of ~316 km, ~119 km, and ~45 km, respectively. Grids were reconstructed at a temporal resolution of one million years throughout the entire Phanerozoic (540–0 Ma). The reconstruction files are stored as comma-separated-value (CSV) files which can be easily read by almost any spreadsheet program (e.g. Microsoft Excel and Google Sheets) or programming language (e.g. Python, Julia, and R). In addition, R Data Serialization (RDS) files—a common format for saving R objects—are also provided as lighter (and compressed) alternatives to the CSV files. The structure of the reconstruction files follows a wide-form data frame structure to ease indexing. Each file consists of three initial index columns relating to the H3 cell index (i.e. the 'H3 address'), present-day longitude of the cell centroid, and the present-day latitude of the cell centroid. The subsequent columns provide the reconstructed longitudinal and latitudinal coordinate pairs for their respective age of reconstruction in ascending order, indicated by a numerical suffix. Each row contains a unique spatial point on the Earth's continental surface reconstructed through time. NA values within the reconstruction files indicate points which are not defined in deeper time (i.e. either the static polygon does not exist at that time, or it is outside the temporal coverage as defined by the rotation file).

The following five Global Plate Models are provided (abbreviation, temporal coverage, reference) within the GPMs folder:

WR13, 0–550 Ma, (Wright et al., 2013)

MA16, 0–410 Ma, (Matthews et al., 2016)

TC16, 0–540 Ma, (Torsvik and Cocks, 2016)

SC16, 0–1100 Ma, (Scotese, 2016)

ME21, 0–1000 Ma, (Merdith et al., 2021)

In addition, the H3 grids for resolutions 2, 3, and 4 are provided within the grids folder. Finally, we also provide two scripts (python and R) within the code folder which can be used to generate reconstructed coordinates for user data from the reconstruction files.

For access to the code used to generate these files:

https://github.com/LewisAJones/PhanGrids

For more information, please refer to the article describing the data:

Jones, L.A. and Domeier, M.M. 2024. A Phanerozoic gridded dataset for palaeogeographic reconstructions. (2024).

For any additional queries, contact:

Lewis A. Jones (lewisa.jones@outlook.com) or Mathew M. Domeier (mathewd@uio.no)

If you use these files, please cite:

Jones, L.A. and Domeier, M.M. 2024. A Phanerozoic gridded dataset for palaeogeographic reconstructions. DOI: 10.5281/zenodo.10069221

References

Matthews, K. J., Maloney, K. T., Zahirovic, S., Williams, S. E., Seton, M., & Müller, R. D. (2016). Global plate boundary evolution and kinematics since the late Paleozoic. Global and Planetary Change, 146, 226–250. https://doi.org/10.1016/j.gloplacha.2016.10.002.

Merdith, A. S., Williams, S. E., Collins, A. S., Tetley, M. G., Mulder, J. A., Blades, M. L., Young, A., Armistead, S. E., Cannon, J., Zahirovic, S., & Müller, R. D. (2021). Extending full-plate tectonic models into deep time: Linking the Neoproterozoic and the Phanerozoic. Earth-Science Reviews, 214, 103477. https://doi.org/10.1016/j.earscirev.2020.103477.

Scotese, C. R. (2016). Tutorial: PALEOMAP paleoAtlas for GPlates and the paleoData plotter program: PALEOMAP Project, Technical Report.

Torsvik, T. H., & Cocks, L. R. M. (2017). Earth history and palaeogeography. Cambridge University Press. https://doi.org/10.1017/9781316225523.

Wright, N., Zahirovic, S., Müller, R. D., & Seton, M. (2013). Towards community-driven paleogeographic reconstructions: Integrating open-access paleogeographic and paleobiology data with plate tectonics. Biogeosciences, 10, 1529–1541. https://doi.org/10.5194/bg-10-1529-2013.

Simple dataset with 2010-2019 population data from 42 countries in Latin American and the Caribbean in a wide format. Used as part of the Google Data Analysis Certification programme and useful to beginners in data analysis.