Gabriele Scalia

2.1k total citations · 1 hit paper
16 papers, 509 citations indexed

About

Gabriele Scalia is a scholar working on Molecular Biology, Communication and Geography, Planning and Development. According to data from OpenAlex, Gabriele Scalia has authored 16 papers receiving a total of 509 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Molecular Biology, 5 papers in Communication and 4 papers in Geography, Planning and Development. Recurrent topics in Gabriele Scalia's work include Public Relations and Crisis Communication (5 papers), Geographic Information Systems Studies (4 papers) and Single-cell and spatial transcriptomics (3 papers). Gabriele Scalia is often cited by papers focused on Public Relations and Crisis Communication (5 papers), Geographic Information Systems Studies (4 papers) and Single-cell and spatial transcriptomics (3 papers). Gabriele Scalia collaborates with scholars based in Italy, United States and Switzerland. Gabriele Scalia's co-authors include Barbara Pernici, Colin A. Grambow, William H. Green, Yi‐Pei Li, Aviv Regev, Tommaso Biancalani, Daniela J. Di Bella, Sung Min Yang, Evan Z. Macosko and Robert R. Stickels and has published in prestigious journals such as Nature, SHILAP Revista de lepidopterología and Nature Biotechnology.

In The Last Decade

Gabriele Scalia

14 papers receiving 501 citations

Hit Papers

Molecular logic of cellul... 2021 2026 2022 2024 2021 50 100 150 200
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Molecular logic of cellular diversification in the mouse cerebral cortex
    2021 204 citations Daniela J. Di Bella, Ehsan Habibi et al. Nature profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Gabriele Scalia Italy 9 240 109 98 50 49 16 509
Christophe Trefois Luxembourg 9 322 1.3× 12 0.1× 31 0.3× 12 0.2× 14 0.3× 16 651
Daniel Himmelstein United States 14 989 4.1× 17 0.2× 281 2.9× 12 0.2× 9 0.2× 49 1.5k
Anirban Majumder United States 13 127 0.5× 9 0.1× 15 0.2× 11 0.2× 31 0.6× 30 520
Gregorio Alanis‐Lobato Germany 16 1.0k 4.3× 18 0.2× 168 1.7× 4 0.1× 11 0.2× 33 1.4k
Xiaoqing Wang China 13 89 0.4× 9 0.1× 27 0.3× 101 2.0× 3 0.1× 45 577
Chunying Wu United States 14 204 0.8× 35 0.3× 16 0.2× 75 1.5× 34 635
Kalliopi Tsafou Denmark 10 876 3.6× 17 0.2× 123 1.3× 2 0.0× 8 0.2× 12 1.1k
Kristen M. Naegle United States 16 537 2.2× 11 0.1× 41 0.4× 83 1.7× 27 819
Xiaoqi Ma China 15 130 0.5× 68 0.6× 11 0.1× 11 0.2× 1 0.0× 91 768
Shujia Zhou United States 13 40 0.2× 110 1.0× 7 0.1× 27 0.5× 1 0.0× 43 553

Countries citing papers authored by Gabriele Scalia

Since Specialization
Citations

This map shows the geographic impact of Gabriele Scalia's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Gabriele Scalia with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gabriele Scalia more than expected).

Fields of papers citing papers by Gabriele Scalia

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gabriele Scalia. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Gabriele Scalia. The network helps show where Gabriele Scalia may publish in the future.

Co-authorship network of co-authors of Gabriele Scalia

This figure shows the co-authorship network connecting the top 25 collaborators of Gabriele Scalia. A scholar is included among the top collaborators of Gabriele Scalia based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Gabriele Scalia. Gabriele Scalia is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

16 of 16 papers shown
1.
Scalia, Gabriele, Steven T. Rutherford, Kerry R. Buchholz, et al.. (2025). Deep-learning-based virtual screening of antibacterial compounds. Nature Biotechnology.
2.
Heimberg, Graham, Tony Kuo, Daryle J. DePianto, et al.. (2024). A cell atlas foundation model for scalable search of similar human cells. Nature. 638(8052). 1085–1094. 30 indexed citations
3.
Miller, Jeremy A., Jeongbin Park, Boudewijn P. F. Lelieveldt, et al.. (2023). Reference-based cell type matching of in situ image-based spatial transcriptomics data on primary visual cortex of mouse brain. Scientific Reports. 13(1). 9567–9567. 9 indexed citations
4.
Ratti, Francesca, et al.. (2022). Hybrid deep learning-based feature-augmented detection for molecular communication systems. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 1–6. 2 indexed citations
5.
Bella, Daniela J. Di, Ehsan Habibi, Robert R. Stickels, et al.. (2021). Molecular logic of cellular diversification in the mouse cerebral cortex. Nature. 595(7868). 554–559. 204 indexed citations breakdown →
6.
Scalia, Gabriele, et al.. (2021). Data Ecosystems for Scientific Experiments: Managing Combustion Experiments and Simulation Analyses in Chemical Engineering. Frontiers in Big Data. 4. 663410–663410. 15 indexed citations
7.
Scalia, Gabriele, Chiara Francalanci, & Barbara Pernici. (2021). CIME: Context-aware geolocation of emergency-related posts. GeoInformatica. 26(1). 125–157. 17 indexed citations
8.
Scalia, Gabriele, Colin A. Grambow, Barbara Pernici, Yi‐Pei Li, & William H. Green. (2020). Evaluating Scalable Uncertainty Estimation Methods for Deep Learning-Based Molecular Property Prediction. Journal of Chemical Information and Modeling. 60(6). 2697–2717. 144 indexed citations
9.
Scalia, Gabriele, et al.. (2020). A Data-driven Approach to Optimize Bounds on the Capacity of the Molecular Channel. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 97. 1–7.
10.
Scalia, Gabriele, Matteo Pelucchi, Alessandro Stagni, et al.. (2019). Towards a scientific data framework to support scientific model development. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 2(1-2). 245–273. 5 indexed citations
11.
Pernici, Barbara, et al.. (2019). Spatio-temporal mining of keywords for social media cross-social crawling of emergency events. GeoInformatica. 23(3). 425–447. 7 indexed citations
12.
Fernandez-Marquez, Jose Luis, et al.. (2019). CROWD4EMS: A CROWDSOURCING PLATFORM FOR GATHERING ANDGEOLOCATING SOCIAL MEDIA CONTENT IN DISASTER RESPONSE. SHILAP Revista de lepidopterología. XLII-3/W8. 331–340. 9 indexed citations
13.
Francalanci, Chiara, et al.. (2018). Talking about Places:Considering Context for the Geolocation of Images Extracted from Tweets. GI_Forum. 1. 243–250. 4 indexed citations
14.
Havas, Clemens, Bernd Resch, Chiara Francalanci, et al.. (2017). E2mC: Improving Emergency Management Service Practice through Social Media and Crowdsourcing Analysis in Near Real Time. Sensors. 17(12). 2766–2766. 49 indexed citations
15.
Scalia, Gabriele. (2017). Network-based content geolocation on social media for emergency management. 4 indexed citations
16.
Francalanci, Chiara, et al.. (2017). IMEXT: A method and system to extract geolocated images from Tweets — Analysis of a case study. 382–390. 10 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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