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NETSCITY tutorial – GEOCOLLAB Workshop 2022 Unveiling world scale scientific production and collaborations between cities

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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 (, etc.)

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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 (; OpenAlex) Caltech, Max Planck Society, (with many child institutes)

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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

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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

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Other types of sources: e.g. conference attendance Netconf project With B. Bernela & F. Briatte

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Map by M. Maisonobe, CNRS. Data: LORD & TAI-NUI

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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

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Many heterogeneities, transliteration issues and data entry errors

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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

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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

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The case of Rome

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The variable administrative fragmentation of the territory at the world level

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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

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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

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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))

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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 2-mode 1-mode

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Query on the Web of Science Core Collection

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Export format (Tab-delimited)

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Slide 21 text The example of scientific production about Ectocarpus indexed in the WoS CC between 1920 & 2022

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Select the source format

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Wait while the geocoding

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Geocoding report

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Manual correction

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Export and/or check the address table

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Normalised nb of publications per country

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Normalised nb of publications per urban area

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Normalised nb of collab. bet. countries

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Normalised nb of collab. bet. urban areas

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Diagrammes & histogrammes (.csv et .jpg can be exported)

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Stock map at the country level

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Stock map at the urban area level

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Flow map bet. countries

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Carte des collaborations fractionnées entre aires urbaines

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Network of collab. bet. countries

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Network of collab. bet. urban areas

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Application case Go on page: ectocarpus-corpus-march-2022/

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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:

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The scientific production on algae and seaweeds in Scotland

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Distribution of Scotland’s publications on algae per scientific specialities

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Prospects • Collaborative space: internal repository for the project with useful datasets and scripts • R workshop and tutorial (tidyverse + cartigraph) • Issue of corpus delineation

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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]