Travel-Time Enriched Affiliation Pair Dataset for the Scopus Co-Affiliation Metadata (Austria, 2013–2023)
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| Format: | Recurso digital |
| Language: | English |
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Zenodo
2025
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| _version_ | 1866901683620020224 |
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| author | Schlager, Christoph |
| author_facet | Schlager, Christoph |
| contents | <p>This dataset complements the Scopus Dataset of Co-Affiliation Metadata for Research Publications in Austria (2013–2023) (10.5281/zenodo.17953805) by providing travel-time estimates between institutional affiliations.</p> <p>Travel times between affiliation pairs were calculated using a two-step approach:</p> <ol> <li> <p>Geocoding: All affiliation addresses were converted to geographic coordinates in the World Geodetic System 1984 (WGS 84) reference frame using the Python geopy library.</p> </li> <li> <p>Routing: A local instance of the open-source Valhalla routing engine, which leverages OpenStreetMap data, was used to compute travel times between each affiliation pair.</p> </li> </ol> <p>Dataset structure:</p> <ul> <li> <p><code>affiliation_id_from</code>: ID of the origin affiliation</p> </li> <li> <p><code>affiliation_id_to</code>: ID of the destination affiliation</p> </li> <li> <p><code>distance_m</code>: Geodesic distance between affiliations (meters)</p> </li> <li> <p><code>duration_s</code>: Estimated travel time between affiliations (seconds)</p> </li> </ul> <p> </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17954106 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Travel-Time Enriched Affiliation Pair Dataset for the Scopus Co-Affiliation Metadata (Austria, 2013–2023) Schlager, Christoph co-affiliation Valhalla geocoding travel time <p>This dataset complements the Scopus Dataset of Co-Affiliation Metadata for Research Publications in Austria (2013–2023) (10.5281/zenodo.17953805) by providing travel-time estimates between institutional affiliations.</p> <p>Travel times between affiliation pairs were calculated using a two-step approach:</p> <ol> <li> <p>Geocoding: All affiliation addresses were converted to geographic coordinates in the World Geodetic System 1984 (WGS 84) reference frame using the Python geopy library.</p> </li> <li> <p>Routing: A local instance of the open-source Valhalla routing engine, which leverages OpenStreetMap data, was used to compute travel times between each affiliation pair.</p> </li> </ol> <p>Dataset structure:</p> <ul> <li> <p><code>affiliation_id_from</code>: ID of the origin affiliation</p> </li> <li> <p><code>affiliation_id_to</code>: ID of the destination affiliation</p> </li> <li> <p><code>distance_m</code>: Geodesic distance between affiliations (meters)</p> </li> <li> <p><code>duration_s</code>: Estimated travel time between affiliations (seconds)</p> </li> </ul> <p> </p> |
| title | Travel-Time Enriched Affiliation Pair Dataset for the Scopus Co-Affiliation Metadata (Austria, 2013–2023) |
| topic | co-affiliation Valhalla geocoding travel time |
| url | https://doi.org/10.5281/zenodo.17954106 |