Automated Author ProfileBaldini, Franscesco
University of Glasgow0000-0002-5904-4070
Baldini, Franscesco
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 8.2 (sum of 14 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Mid-infrared spectral data from laboratory reared tsetse flies Glossina spp. used for sex and age prediction with MIRS toolbox.
Authors
- Pazmino Betancourth, Mauro ;
- Casas Gomez-Uribarri, Ivan ;
- Mondragon-Shem, Karina ;
- Babayan, Simon ;
- Baldini, Francesco ;
- Lee Rafuse, Haines
The malaria parasite, which is transmitted by several Anopheles mosquito species, requires more time to reach its human-transmissible stage than the average lifespan of mosquito vectors. Monitoring the species-specific age structure of mosquito populations is critical to evaluating the impact of vector control interventions on malaria risk. We developed a rapid, cost-effective surveillance method based on deep learning of mid-infrared spectra of mosquito cuticle that simultaneously identifies the species and age class of three main malaria vectors in natural populations. Using spectra from over 40,000 ecologically and genetically diverse An. gambiae, An. arabiensis, and An. coluzzii females, we developed a deep transfer learning model that learned and predicted the age of new wild populations in Tanzania and Burkina Faso with minimal sampling effort. Additionally, the model was able to detect the impact of simulated control interventions on mosquito populations, measured as a shift in their age structures. In the future, we anticipate our method can be applied to other arthropod vector-borne diseases.
Authors
- Siria, Doreen ;
- Sanou, Roger ;
- Mitton, Joshua ;
- Mwanga, Emmanuel ;
- Niang, Abdoulaye ;
- Saré, Issiaka ;
- Johnson, Paul ;
- Wynne, Klaas ;
- Murray-Smith, Roderick ;
- Ferguson, Heather ;
- Gonzalez Jimenez, Mario ;
- Babayan, Simon ;
- Diabaté, Abdoulaye ;
- Okumu, Fredros ;
- Baldini, Francesco
GRAMS IQ file - Training dataset of the mosquitoes used to generate the calibration for oocyst infection detection through PLS. All the spectra included in this file are listed in the calibration STATA file.
Authors
- Maia, Marta F. ;
- Kapulu, Melissa ;
- Muthui, Michelle ;
- Wagah, Martin G. ;
- Fergusson, Heather M. ;
- Dowell, Floyd E. ;
- Baldini, Franscesco ;
- Ranford-Cartwright, Lisa
Ms Excel dataset (.xlsx) - Prediction outputs from IQ Predict for samples used to validate the oocyst calibration (mosquitoes maintained for 7 days) and used to validate the sporozoite calibration (mosquitoes maintained for 14 days). PLS score is highlighted in red.
Authors
- Maia, Marta F. ;
- Kapulu, Melissa ;
- Muthui, Michelle ;
- Wagah, Martin G. ;
- Fergusson, Heather M. ;
- Dowell, Floyd E. ;
- Baldini, Franscesco ;
- Ranford-Cartwright, Lisa
IQ Predict - Calibration file generated on GRAMS IQ through PLS regression using 12 latent regression factors. This file is used to upload on IQ Predict to predict the presence of sporozoite infection in independents samples (not contained in the training set) of unknown infections status.
Authors
- Maia, Marta F. ;
- Kapulu, Melissa ;
- Muthui, Michelle ;
- Wagah, Martin G. ;
- Fergusson, Heather M. ;
- Dowell, Floyd E. ;
- Baldini, Franscesco ;
- Ranford-Cartwright, Lisa
GRAMS IQ file - Training dataset of the mosquitoes used to generate the calibration for sporozoite infection detection through PLS. All the spectra included in this file are listed in the calibration STATA file.
Authors
- Maia, Marta F. ;
- Kapulu, Melissa ;
- Muthui, Michelle ;
- Wagah, Martin G. ;
- Fergusson, Heather M. ;
- Dowell, Floyd E. ;
- Baldini, Franscesco ;
- Ranford-Cartwright, Lisa
(.spc) files - Zip folder containing all the NIRS spectra used to in the training and test datasets to generate the calibrations and validation.
Authors
- Maia, Marta F. ;
- Kapulu, Melissa ;
- Muthui, Michelle ;
- Wagah, Martin G. ;
- Fergusson, Heather M. ;
- Dowell, Floyd E. ;
- Baldini, Franscesco ;
- Ranford-Cartwright, Lisa
STATA dataset (.dta) List of all mosquito samples used to generate the calibrations, including information on mosquito age, infection load, SMFA round and their partial least square (PLS) regression scores from self-predictions (leave-one-out cross validations (LOOCV).
Authors
- Maia, Marta F. ;
- Kapulu, Melissa ;
- Muthui, Michelle ;
- Wagah, Martin G. ;
- Fergusson, Heather M. ;
- Dowell, Floyd E. ;
- Baldini, Franscesco ;
- Ranford-Cartwright, Lisa
Readme File
Authors
- Maia, Marta F. ;
- Kapulu, Melissa ;
- Muthui, Michelle ;
- Wagah, Martin G. ;
- Fergusson, Heather M. ;
- Dowell, Floyd E. ;
- Baldini, Franscesco ;
- Ranford-Cartwright, Lisa