Daniela Gallo

837 total citations
36 papers, 690 citations indexed

About

Daniela Gallo is a scholar working on Genetics, Molecular Biology and Agronomy and Crop Science. According to data from OpenAlex, Daniela Gallo has authored 36 papers receiving a total of 690 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Genetics, 14 papers in Molecular Biology and 9 papers in Agronomy and Crop Science. Recurrent topics in Daniela Gallo's work include Animal Genetics and Reproduction (12 papers), Milk Quality and Mastitis in Dairy Cows (8 papers) and Genetic and phenotypic traits in livestock (5 papers). Daniela Gallo is often cited by papers focused on Animal Genetics and Reproduction (12 papers), Milk Quality and Mastitis in Dairy Cows (8 papers) and Genetic and phenotypic traits in livestock (5 papers). Daniela Gallo collaborates with scholars based in Italy, United States and Germany. Daniela Gallo's co-authors include G. Cosenza, A. Leonard Sheffner, K. Bailey, Alfredo Pauciullo, A. Rando, Herbert P. Sarett, P. Masina, Luigi Ramunno, D. Di Berardino and L. Ramunno and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Dairy Science and Gene.

In The Last Decade

Daniela Gallo

34 papers receiving 629 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniela Gallo Italy 16 275 250 199 112 67 36 690
Raymond Dils United Kingdom 16 78 0.3× 30 0.1× 231 1.2× 20 0.2× 38 0.6× 40 590
Kaïs Hussain Al-Gubory France 14 91 0.3× 171 0.7× 96 0.5× 7 0.1× 15 0.2× 29 533
A. R. Jones Australia 16 41 0.1× 24 0.1× 140 0.7× 43 0.4× 49 0.7× 37 698
MASUMI KADOWAKI Japan 11 51 0.2× 45 0.2× 66 0.3× 19 0.2× 97 1.4× 28 402
Kamil Borkowski United States 19 54 0.2× 28 0.1× 315 1.6× 22 0.2× 66 1.0× 39 873
Joshua W. Smith United States 13 33 0.1× 25 0.1× 165 0.8× 49 0.4× 33 0.5× 33 618
Nori G. Tolosa de Talamoni Argentina 12 37 0.1× 19 0.1× 119 0.6× 30 0.3× 49 0.7× 18 438
Marjorie G. Horning United States 13 36 0.1× 20 0.1× 324 1.6× 22 0.2× 45 0.7× 22 745
M Hirst United States 5 204 0.7× 12 0.0× 272 1.4× 12 0.1× 47 0.7× 8 768
T. C. Huang United States 9 27 0.1× 19 0.1× 210 1.1× 49 0.4× 202 3.0× 13 716

Countries citing papers authored by Daniela Gallo

Since Specialization
Citations

This map shows the geographic impact of Daniela Gallo'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 Daniela Gallo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniela Gallo more than expected).

Fields of papers citing papers by Daniela Gallo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniela Gallo. 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 Daniela Gallo. The network helps show where Daniela Gallo may publish in the future.

Co-authorship network of co-authors of Daniela Gallo

This figure shows the co-authorship network connecting the top 25 collaborators of Daniela Gallo. A scholar is included among the top collaborators of Daniela Gallo 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 Daniela Gallo. Daniela Gallo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Cosenza, G., Andrea Fulgione, Sara Albarella, et al.. (2025). Identification and Validation of Genus/Species-Specific Short InDels in Dairy Ruminants. BMC Veterinary Research. 21(1). 215–215.
2.
Gentile, Antonio, Andrea Fulgione, Valentina Iovane, et al.. (2024). In vivo biological validation of in silico analysis: A novel approach for predicting the effects of TLR4 exon 3 polymorphisms on brucellosis. Infection Genetics and Evolution. 118. 105552–105552. 1 indexed citations
3.
Cosenza, G., Patrice Martin, Giuseppina Garro, et al.. (2023). A novel allelic donkey β-lactoglobulin I protein isoform generated by a non-AUG translation initiation codon is associated with a nonsynonymous SNP. Journal of Dairy Science. 106(6). 4158–4170. 1 indexed citations
4.
Cosenza, G., et al.. (2021). Complete CSN1S2 Characterization, Novel Allele Identification and Association With Milk Fatty Acid Composition in River Buffalo. Frontiers in Genetics. 11. 622494–622494. 7 indexed citations
5.
6.
7.
Ramunno, L., et al.. (2011). A preliminary analysis of mRNA transcribed from the N allele at the CSN1S1 locus of the goat. SHILAP Revista de lepidopterología.
8.
Cosenza, G., Alfredo Pauciullo, A. Coletta, et al.. (2011). Short communication: Translational efficiency of casein transcripts in Mediterranean river buffalo. Journal of Dairy Science. 94(11). 5691–5694. 12 indexed citations
9.
Pauciullo, Alfredo, G. Cosenza, Daniel Nicodemo, et al.. (2009). Molecular cloning, promoter analysis and SNP identification of Italian Nicastrese and Saanen lactoferrin gene. Veterinary Immunology and Immunopathology. 134(3-4). 279–283. 1 indexed citations
10.
Cosenza, G., Alfredo Pauciullo, Daniela Gallo, D. Di Berardino, & Luigi Ramunno. (2005). A SspI PCR-RFLP detecting a silent allele at the goat CSN2 locus. Journal of Dairy Research. 72(4). 456–459. 21 indexed citations
11.
Ramunno, Luigi, G. Cosenza, A. Rando, et al.. (2005). Comparative analysis of gene sequence of goat CSN1S1 F and N alleles and characterization of CSN1S1 transcript variants in mammary gland. Gene. 345(2). 289–299. 79 indexed citations
12.
Ramunno, Luigi, G. Cosenza, A. Rando, et al.. (2004). The goat αs1-casein gene: gene structure and promoter analysis. Gene. 334. 105–111. 41 indexed citations
13.
Cosenza, G., et al.. (2003). A MvaI PCR-RFLP detecting a silent allele at the goat α-lactalbumin locus. Journal of Dairy Research. 70(3). 355–357. 21 indexed citations
14.
Ramunno, L., G. Cosenza, Morena Pappalardo, et al.. (2001). Characterization of two new alleles at the goat CSN1S2 locus. Animal Genetics. 32(5). 264–268. 65 indexed citations
15.
Ramunno, L., G. Cosenza, Morena Pappalardo, et al.. (2000). Identification of the goat CSN1S1F allele by means of PCR‐RFLP method. Animal Genetics. 31(5). 342–342. 49 indexed citations
16.
Tita, Patrizia, et al.. (1996). Clinical features and hormonal characteristics in a case of ovarian arrhenoblastoma. Journal of Endocrinological Investigation. 19(7). 484–487. 7 indexed citations
17.
Bartek, M.J., et al.. (1987). Liquid chromatographic determination of sotalol in plasma and urine employing solid-phase extraction and fluorescence detection. Journal of Chromatography B Biomedical Sciences and Applications. 421(2). 309–318. 23 indexed citations
18.
Gallo, Daniela, et al.. (1966). The Species Specificity of Cholestyramine in Its Effect on Synthesis Of Liver Lipids and Level of Serum Cholesterol. Experimental Biology and Medicine. 122(2). 328–334. 24 indexed citations
19.
Cornatzer, W. E. & Daniela Gallo. (1961). Phospholipid Analysis of Cellular Fractions of the Liver Following Administration of Single Dose of Choline.. Experimental Biology and Medicine. 107(2). 383–386. 2 indexed citations
20.
Cornatzer, W. E., et al.. (1953). Phospholipid and protein-bound phosphotus synthesis in the rabbit papilloma.. PubMed. 13(11). 795–7. 9 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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