Tesi etd-02142023-223310
Link copiato negli appunti
Tipo di tesi
Corso Ordinario Secondo Livello
Autore
TESTA, LORENZO
URN
etd-02142023-223310
Titolo
Unveiling research patterns of institutions via networks and functional data: evidence from genome research
Struttura
Cl. Sc. Sociali - Scienze Economiche
Corso di studi
SCIENZE ECONOMICHE E MANAGERIALI - SCIENZE ECONOMICHE E MANAGERIALI
Commissione
relatore Prof. MINA, ANDREA
Relatore Prof.ssa CHIAROMONTE, FRANCESCA
Presidente Prof. IRALDO, FABIO
Membro Prof.ssa ANNUNZIATA, ELEONORA
Membro Prof. PICCALUGA, ANDREA MARIO CUORE
Membro Prof.ssa MAGAZZINI, LAURA
Membro Prof. MOSCHELLA, DANIELE
Membro Prof.ssa ROMANO, MARIA FRANCESCA
Membro Dott. GIACHINI, DANIELE
Relatore Prof.ssa CHIAROMONTE, FRANCESCA
Presidente Prof. IRALDO, FABIO
Membro Prof.ssa ANNUNZIATA, ELEONORA
Membro Prof. PICCALUGA, ANDREA MARIO CUORE
Membro Prof.ssa MAGAZZINI, LAURA
Membro Prof. MOSCHELLA, DANIELE
Membro Prof.ssa ROMANO, MARIA FRANCESCA
Membro Dott. GIACHINI, DANIELE
Parole chiave
- bibliometrics
- functional data analysis
- genome research
- network science
- Science of science
Data inizio appello
20/03/2023;
Disponibilità
parziale
Riassunto analitico
Scientific productivity follows trajectories, shaped by heterogeneous factors that are not always easy to capture. In this work, we analyze bibliographic data on genome research and introduce state-of-the-art methodologies which shed new light on scientific productivity, describing some of its patterns. We retrieve data on publications from OpenAlex, collecting -- among the others -- information on authors' affiliations. Then, by exploiting our data, we build a collaboration network among institutions. We dynamically extract from it numerous measures of centrality, which heterogeneously embed the underlying network topology. We treat these dynamic observations as curves, and we exploit tools from functional data analysis to analyze them. We perform functional clustering on the institutional productivity trajectories, highlighting the strong geographic patterns behind them. Then, we run fully functional function-on-function regression models in order to link collaboration measures to productivity, and to quantitatively measure their impacts. We discover that institutions collaborating more (in particular in terms of acting as bridges among collaborators -- a feature measured through the betweenness centrality) are also more productive, in terms of number of papers published across time.
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