Edoardo Carnesecchi

1.0k total citations · 1 hit paper
24 papers, 618 citations indexed

About

Edoardo Carnesecchi is a scholar working on Insect Science, Food Science and Health, Toxicology and Mutagenesis. According to data from OpenAlex, Edoardo Carnesecchi has authored 24 papers receiving a total of 618 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Insect Science, 10 papers in Food Science and 8 papers in Health, Toxicology and Mutagenesis. Recurrent topics in Edoardo Carnesecchi's work include Insect and Pesticide Research (11 papers), Pesticide Residue Analysis and Safety (8 papers) and Computational Drug Discovery Methods (7 papers). Edoardo Carnesecchi is often cited by papers focused on Insect and Pesticide Research (11 papers), Pesticide Residue Analysis and Safety (8 papers) and Computational Drug Discovery Methods (7 papers). Edoardo Carnesecchi collaborates with scholars based in Italy, Netherlands and France. Edoardo Carnesecchi's co-authors include Emilio Benfenati, Simone Tosi, J.L.C.M. Dorne, Marie‐Pierre Chauzat, Dennis vanEngelsdorp, Andrey A. Toropov, Alla P. Toropova, Nynke I. Kramer, Kunal Roy and Anna Lombardo and has published in prestigious journals such as The Science of The Total Environment, Journal of Hazardous Materials and Chemosphere.

In The Last Decade

Edoardo Carnesecchi

24 papers receiving 595 citations

Hit Papers

Lethal, sublethal, and combined effects of pesticides on ... 2022 2026 2023 2024 2022 25 50 75 100
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. Lethal, sublethal, and combined effects of pesticides on bees: A meta-analysis and new risk assessment tools
    2022 113 citations Simone Tosi, Edoardo Carnesecchi et al. The Science of The Total Environment profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Edoardo Carnesecchi Italy 15 273 194 154 152 129 24 618
Gaëlle Danièle France 13 292 1.1× 102 0.5× 7 0.0× 55 0.4× 144 1.1× 29 636
Changxing Wu China 16 320 1.2× 65 0.3× 5 0.0× 59 0.4× 224 1.7× 41 832
Diana Larisa Roman Romania 14 29 0.1× 27 0.1× 41 0.3× 10 0.1× 31 0.2× 25 435
Xuehua An China 11 155 0.6× 28 0.1× 6 0.0× 15 0.1× 213 1.7× 18 481
Keshav Singh India 14 66 0.2× 30 0.2× 19 0.1× 7 0.0× 41 0.3× 56 511
J. Cotterill United Kingdom 12 18 0.1× 16 0.1× 44 0.3× 16 0.1× 45 0.3× 18 403
Pierre‐Yves Communal France 12 123 0.5× 39 0.2× 2 0.0× 39 0.3× 105 0.8× 19 538
Wenjun Zhang China 15 112 0.4× 66 0.3× 2 0.0× 22 0.1× 130 1.0× 26 463
Selim Çetiner Türkiye 14 25 0.1× 46 0.2× 20 0.1× 47 0.3× 22 0.2× 45 765
Lucile Sage France 15 72 0.3× 133 0.7× 4 0.0× 9 0.1× 82 0.6× 27 853

Countries citing papers authored by Edoardo Carnesecchi

Since Specialization
Citations

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

Fields of papers citing papers by Edoardo Carnesecchi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Edoardo Carnesecchi

This figure shows the co-authorship network connecting the top 25 collaborators of Edoardo Carnesecchi. A scholar is included among the top collaborators of Edoardo Carnesecchi 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 Edoardo Carnesecchi. Edoardo Carnesecchi 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.
Vieira, Diana, Laure‐Alix Clerbaux, Alberto Orgiazzi, et al.. (2024). Evaluation of the ecological risk of pesticide residues from the European LUCAS Soil monitoring 2018 survey. Integrated Environmental Assessment and Management. 20(5). 1639–1653. 8 indexed citations
2.
Chatterjee, Mainak, Arkaprava Banerjee, Simone Tosi, et al.. (2023). Machine learning - based q-RASAR modeling to predict acute contact toxicity of binary organic pesticide mixtures in honey bees. Journal of Hazardous Materials. 460. 132358–132358. 29 indexed citations
3.
Raitano, Giuseppa, Anna Lombardo, Alessandra Roncaglioni, et al.. (2023). The VEGA Tool to Check the Applicability Domain Gives Greater Confidence in the Prediction of In Silico Models. International Journal of Molecular Sciences. 24(12). 9894–9894. 15 indexed citations
4.
Carnesecchi, Edoardo, Patience Browne, Sofia Batista Leite, et al.. (2023). OECD harmonised template 201: Structuring and reporting mechanistic information to foster the integration of new approach methodologies for hazard and risk assessment of chemicals. Regulatory Toxicology and Pharmacology. 142. 105426–105426. 15 indexed citations
5.
Nicola, Matteo Riccardo Di, Alexis V. Nathanail, Edoardo Carnesecchi, et al.. (2022). The use of new approach methodologies for the environmental risk assessment of food and feed chemicals. Current Opinion in Environmental Science & Health. 31. 100416–100416. 5 indexed citations
6.
Nicola, Matteo Riccardo Di, José Tarazona, Agnès Rortais, et al.. (2022). In Silico Methods for Environmental Risk Assessment: Principles, Tiered Approaches, Applications, and Future Perspectives. Methods in molecular biology. 2425. 589–636. 9 indexed citations
7.
Toropov, Andrey A., Matteo Riccardo Di Nicola, Alla P. Toropova, et al.. (2022). A regression-based QSAR-model to predict acute toxicity of aromatic chemicals in tadpoles of the Japanese brown frog (Rana japonica): Calibration, validation, and future developments to support risk assessment of chemicals in amphibians. The Science of The Total Environment. 830. 154795–154795. 14 indexed citations
8.
Tosi, Simone, et al.. (2022). Lethal, sublethal, and combined effects of pesticides on bees: A meta-analysis and new risk assessment tools. The Science of The Total Environment. 844. 156857–156857. 113 indexed citations breakdown →
9.
Benfenati, Emilio, Alessandra Roncaglioni, Edoardo Carnesecchi, et al.. (2021). Maintenance, update and further development of EFSA's Chemical Hazards: OpenFoodTox 2.0. EFSA Supporting Publications. 18(3). 3 indexed citations
10.
Lavado, Giovanna J., Diego Baderna, Edoardo Carnesecchi, et al.. (2021). QSAR models for soil ecotoxicity: Development and validation of models to predict reproductive toxicity of organic chemicals in the collembola Folsomia candida. Journal of Hazardous Materials. 423(Pt B). 127236–127236. 31 indexed citations
11.
Carnesecchi, Edoardo, Cosimo Toma, Alessandra Roncaglioni, et al.. (2020). Integrating QSAR models predicting acute contact toxicity and mode of action profiling in honey bees (A. mellifera): Data curation using open source databases, performance testing and validation. The Science of The Total Environment. 735. 139243–139243. 32 indexed citations
12.
Benfenati, Emilio, Edoardo Carnesecchi, Alessandra Roncaglioni, et al.. (2020). Maintenance,update and further development of EFSA's Chemical Hazards: OpenFoodTox 2.0. EFSA Supporting Publications. 17(3). 7 indexed citations
13.
Toropova, Alla P., Andrey A. Toropov, Edoardo Carnesecchi, Emilio Benfenati, & J.L.C.M. Dorne. (2020). The using of the Index of Ideality of Correlation (IIC) to improve predictive potential of models of water solubility for pesticides. Environmental Science and Pollution Research. 27(12). 13339–13347. 19 indexed citations
14.
Toropov, Andrey A., et al.. (2020). Pesticides, cosmetics, drugs: identical and opposite influences of various molecular features as measures of endpoints similarity and dissimilarity. Molecular Diversity. 25(2). 1137–1144. 3 indexed citations
15.
Carnesecchi, Edoardo, Andrey A. Toropov, Alla P. Toropova, et al.. (2019). Predicting acute contact toxicity of organic binary mixtures in honey bees (A. mellifera) through innovative QSAR models. The Science of The Total Environment. 704. 135302–135302. 46 indexed citations
16.
Carnesecchi, Edoardo, Claus Svendsen, Nadia Quignot, et al.. (2019). Investigating combined toxicity of binary mixtures in bees: Meta-analysis of laboratory tests, modelling, mechanistic basis and implications for risk assessment. Environment International. 133(Pt B). 105256–105256. 70 indexed citations
17.
Roy, Joyita, Probir Kumar Ojha, Edoardo Carnesecchi, et al.. (2019). First report on a classification-based QSAR model for chemical toxicity to earthworm. Journal of Hazardous Materials. 386. 121660–121660. 36 indexed citations
18.
Ojha, Probir Kumar, et al.. (2019). Exploring QSAR modeling of toxicity of chemicals on earthworm. Ecotoxicology and Environmental Safety. 190. 110067–110067. 33 indexed citations
19.
Baláž, Vojtěch, Christian Gortázar, Kris A. Murray, et al.. (2017). Scientific and technical assistance concerning the survival, establishment and spread of Batrachochytrium salamandrivorans (Bsal) in the EU. EFSA Journal. 15(2). e04739–e04739. 7 indexed citations
20.
Carnesecchi, Edoardo, J.L.C.M. Dorne, Jane Richardson, et al.. (2016). Predicting acute contact toxicity of pesticides in honeybees (Apis mellifera) through a k-nearest neighbor model. Chemosphere. 166. 438–444. 49 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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