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Netscity workshop - Geocollab Project

March 10, 2022

Netscity workshop - Geocollab Project


March 10, 2022

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  1. GEOCOLLAB project Two main research areas: oMarine science (research on

    seaweeds and algae) o Gene editing o Else? Possible sources (what do we already have access to?): o Bibliographic databases (WoS, Scopus, …) o Conference data (to be identified) oResearch Projects data (ERC, ANR, Research in Svalbard…) o Contracts (science-industry partnerships); Patents… o Phd theses (Theses.fr, etc.)
  2. Spatial information • Postal address: 5 cours des Humanités, 93000,

    Aubervilliers, FR • Organisation (without a city field): University of Edinburgh, Scotland, UK (It can be in Roslin or Edinburgh) • Organisation + ROR/Grid ID (Dimensions.ai; OpenAlex) Caltech, https://ror.org/05dxps055 Max Planck Society, https://ror.org/01hhn8329 (with many child institutes)
  3. Existing softwares for bibliometric mapping CiteSpace, Leydesdorff’s programs and Sci2

    Tool o geocoding data at the street level o mapping network data using Google Earth Maps and Yahoo! Maps using KML files From Chen, 2016 A practical guide for mapping Scientific litterature
  4. Bibliographic data – what is the row material we need?

    « CITY, PROVINCE, COUNTRY » city province country Monterotondo RM Italy Milan Italy Rome Italy Khania Greece POSTAL ADDRESSES
  5. Data processing : 1) Extraction of addresses 1) Geocoding 1)

    Clustering at the urban area/country levels File with bibliographic metadata Sources: Web Of Science, Scopus, or personal files in .csv Input Netscity Outputs • Cartes • Tables • Fichier d’export Context
  6. Why should we agregate the geocoded data at the urban

    area level? The output of the geocoding process for 2012 Web of Science publications – Québec area
  7. Why should we agregate the geocoded data at the urban

    area level? The output of the geocoding process for 2012 Web of Science publications – London area
  8. Methods: grouping into agglomerations Issues ▪ Group together publication sites

    that are in the same urban area. ▪ Globally comparable urban areas, despite very different urban realities ➢ Search for a delimitation adapted to the urban phenomenon ➢ Delimitation by spatial crossing between urban population density and scientific publications’ spatial distribution Maisonobe, Jégou & Eckert, 2018, Delineating urban agglomerations across the world: a dataset for studying the spatial distribution of academic research at city level DOI : 10.4000/cybergeo.29637
  9. Counting methods: arbitrating bet. Full & fractional countings References: Van

    Hooydonk, 1997; Gauffriau et al., 2008, Leydesdorff & Park, 2017 • Full: the total number of addresses/urban areas/countries per publications • Fractional: the sum of each fractioned credit totals one (avoiding double counts) → With NETSCITY the reference unit for normalization can be the address, the urban area or the country
  10. Counting methods: arbitrating bet. Full & fractional countings 2 variables

    can be normalised and mapped with NETSCITY 1. Number of publications/projects per geographical entity (the total number geographical entities involved in a publication/project) 1. Intensity of scientific collaboration between geographical entities (the total number of links between the geographical entities involved in a publication/project) For instance, if a given publication stems from three different urban areas, each inter-urban link receives 1/3 as a weight for this publication. More generally, if a publication is co-signed from 𝑛 urban areas, each pair of urban areas (A, B), with A < B, is assigned a value 𝑙 equals to: 1/𝑛(𝑛 − 1)/2 = 2/(𝑛(𝑛 − 1))
  11. Weighted projection method This method is the normalised counting method

    used in the web application NETSCITY (Maisonobe et al. 2019) A variant: « Newman » projection method (2001). See https://toreopsahl.com/tnet/two-mode-networks/ 2-mode 1-mode
  12. Seaweeds and algae research Topic query: TS = (seaweed* OR

    alga OR kelp* OR algae* OR algal* OR seagrass* OR sea plant* OR phyco* OR Chlorell* OR Protothec* OR Charophy* OR Chlorophyt* OR Rhodophy* OR Cryptophy* OR Haptophy* OR Charophy* OR Chlorarachniophy* OR Glaucophy* OR rockweed* OR dulse* OR dillisk* OR dilsk* OR carragheen moss* OR sea lettuce* OR Chondrus) Should we add: microalga, macroalga, phytoplankton, cyanobacteria? Else? See: https://www.sciencedirect.com/science/article/pii/B9780128170762000135
  13. Prospects • Collaborative space: internal repository for the project with

    useful datasets and scripts • R workshop and tutorial (tidyverse + cartigraph) • Issue of corpus delineation
  14. Reference Marion Maisonobe, Laurent Jégou, Nikita Yakimovich, Guillaume Cabanac (2019).

    NETSCITY: a geospatial application to analyse and map world scale production and collaboration data between cities. In ISSI’19: Proceedings of the 17th International Conference on Scientometrics and Informetrics, Tome 1, p. 631-642, Rome: Edizioni Efesto. [PDF]