DOI: 10.5281/zenodo.15706531
title: Spatial Attributes Extracted from Datasets in General-Purpose Repositories

description: This dataset contains results from research conducted for the bachelor's thesis Analysis of Many Records from General Repositories for Their Spatial Attributes.

It is organized into several folders, reflecting the different stages of data processing, from raw metadata harvesting to refined geodata:



1_openaire graph dataset/



dataset.json_auszug_dryad.json_identifiers.pickle,dataset.json_auszug_figshare.json_identifiers.pickle,dataset.json_auszug_zenodo.json_identifiers.pickle:Each contain the identifiers extracted from the OpenAIRE Graph Dataset.(pickle)

dataset.json_auszug_dryad.json_identifiers.pickle_shuffled.pickle,dataset.json_auszug_figshare.json_identifiers.pickle_shuffled.pickle,dataset.json_auszug_zenodo.json_identifiers.pickle_shuffled.pickle:Each contains the randomly shuffled identifiers.(pickle)

statistics/1_statistics.json:Contains statistics that were created as part of the execution of the Jupyter notebook. (JSON)



2_result of threaded_metadata_harvester/



metadata_db.json:The original metadata records downloaded via REST API from Dryad, Figshare and Zenodo between May 13, 2025, and May 18, 2025. (TinyDB)

checkpoint.pickle:A checkpoint file containing the progress and statistics of the metadata harvesting process. (pickle)



3_result of threaded_dataset_analysis/



metadata_db_vacuum.sqlite3:The primary, cleaned database. It is a converted version of the TinyDB, enriched with the results of the spatial analysis performed by geoextent. (SQLite3)

metadata_db_vacuum.csv:A complete export of the SQLite database, containing all normalized metadata from the harvested datasets. (CSV)

metadata_db_vacuum_processed.csv:A partial export, containing only the metadata of datasets that were processed and analyzed.

metadata_db_vacuum_processed_with_bbox.csv:A partial export, containing only the metadata of datasets that were processed and analyzed and contain a bounding box. (CSV)

metadata_db_vacuum.sqlite3_statistics.csv:Contains statistics of the datasets. (CSV)



4_geopackages/



metadata_db_vacuum.gpkg:Contains all datasets with their respective bounding box as polygon features. (GeoPackage)

metadata_db_vacuum_center.gpkg:Contains all datasets with their geographical centroids as point features. (GeoPackage)



5_poster/



This folder contains the accompanying maps as DIN A3 posters. These images correspond to the illustrations referenced in the thesis. 

01_poster_frame.pdfRepresentation of all bounding boxes with a black outline. (PDF)

02_poster_f_a(x)=x.pdfRepresentation of all bounding boxes with values scaled by $f_a(x) = x$. (PDF)

03_poster_g_a(x)=x^2.pdfRepresentation of all bounding boxes with values scaled by $g_a(x) = x^2$. (PDF)

04_poster_g_b(x)=1-x^2.pdfRepresentation of all bounding boxes with values scaled by $g_b(x) = 1 - x^2$. (PDF)

05_poster_f_b(x)=1-x.pdfRepresentation of all bounding boxes with values scaled by $f_b(x) = 1 - x$. (PDF)

06_poster_g_c(x)=(1-x)^2.pdfRepresentation of all bounding boxes with values scaled by $g_c(x) = (1 - x)^2$. (PDF)

07_poster_h_{50}(x)=1frac(50x).pdfRepresentation of all bounding boxes with values scaled by $h_{50}(x) = 1/(50x)$. (PDF)

08_poster_h_{100}(x)=1frac(100x).pdfRepresentation of all bounding boxes with values scaled by $h_{100}(x) = 1/(100x)$. (PDF)

09_poster_h_{200}(x)=1frac(200x).pdfRepresentation of all bounding boxes with values scaled by $h_{200}(x) = 1/(200x)$. (PDF)

10_poster_circles_white.pdfRepresentation of the centroids of all bounding boxes. (PDF)

11_poster_circles_diff_sizes.pdfRepresentation of all bounding boxes by colored circles of variable size at the centroids. (PDF)

12_poster_deg30.pdfRepresentation of the counted bounding boxes with grid intervals of 30°. (PDF)

13_poster_deg15.pdfRepresentation of the counted bounding boxes with grid intervals of 15°. (PDF)

14_poster_deg10.pdfRepresentation of the counted bounding boxes with grid intervals of 10°. (PDF)

15_poster_countries.pdfNumber of bounding boxes per country. (PDF)

16_poster_countries_area.pdfNumber of bounding boxes per area of the countries. (PDF)

17_poster_countries_pop.pdfNumber of bounding boxes per population of the countries. (PDF)

18_poster_countries_gdp.pdfNumber of bounding boxes per gross domestic product of the countries. (PDF)




 
description: In research publication, there is a clear trend towards the co-publication of more and more research data. The long-term accessibility and citability of the data are key objectives, but the actual subsequent use depends to a large extent on the findability and usability of the data. The spatial reference of many data sets is particularly relevant in the natural sciences, but is often not adequately represented in general repositories. This study examines how the download and analysis of spatial datasets from general repositories can be made effective and representative. In a second step, the regional distribution of this data is analyzed in order to identify the over- and underrepresentation of certain regions of the world. The study is based on a representative sample of datasets from selected repositories, which are analyzed with regard to their spatial metadata. The results indicate that obtaining a representative sample is possible, with only minor limitations resulting from the filtering of the datasets to be analyzed. The datasets are globally distributed, with a particularly high concentration of bounding boxes over continental areas. The highest density is found at the coordinate origin and the poles, while the Pacific region is significantly underrepresented.