Comparative Cities is a teaching package designed to introduce students to analysis of manuscript schedules of the nineteenth century census for social, urban, family, and demographic history. The files are designed for use with SPSS. It was initially developed at Brown University with assistance of a project grant from the National Endowment for the Humanities. The file is organized to illustrate contrasts among cities at different stages of industrialization and the demographic transition in Europe and America: Pisa, Italy (1841), Amiens, France (1851), Stockport, England (1841 and 1851), and Providence, R.I. (1850, 1865, and 1880). The rural district around Pisa and part of Providence County are also included. There are approximately 1400 cases with information for individuals in each of eleven subfiles. These are random samples from the original 1:10 house samples for the four places made to permit flexible and economical student use. Summaries imbedded in the file permit analysis at the individual, household, or nuclear unit level. There are 142 variables for each individual. The package also contains a coursebook with explanation of each variable, a dictionary with occupational titles that appear in the censuses, course syllabus, and other instructions for use. The files are being used in the separate ongoing research of the two principal investigators and should be used for instructional purposes only. This teaching package can be supplied as two card-image data files, two files of SPSS instruction cards, and associated printed documentation. The package has also been updated with several files designed to be used with microcomputers. Included in the updated materials are four text files (Contents of Tape, Coursebook, Explanatory Materials, and Dictionary of Occupational Titles and Codes), a file of SPSSx data definition statements for use with PC-SPSSx, and a file of data definition statements for use with the Consortium's ABC statistical analysis package. Nine separate sub-files, each derived from the original census data and designed for analysis on micro-computers which are equipped with PC-SPSSx or ABC, are also provided. Finally, the package includes two mainframe SPSSx "Export" files which contain all of the data collected for each city. While these latter files duplicate the SPSS files contained in the earlier Comparative Cities package, they have been modified for use with SPSSx. The original Comparative Cities Teaching Package files can still be supplied as well. These files are oriented towards use of SPSS Version 9 on mainframe computers.

The multi-step method here applied in studying the genetic structure of a low dispersal and philopatric species, like the Fire Salamander Salamandra salamandra, was proved to be effective in identifying the hierarchical structure of population living in broadleaved forest ecosystems in Northern Italy. In this study 477 salamander larvae, collected in 28 sampling populations (SPs) in the Prealpine and in the foothill areas of Northern Italy, were genotyped at 16 specie-specific microsatellites. SPs showed a significant overall genetic variation (Global FST=0.032, p<0.001). The genetic population structure was assessed by using STRUCTURE 2.3.4. We found two main genetic groups, one represented by populations inhabiting the Prealpine belt, which maintain connections with those of the Eastern foothill lowland (PEF), and a second group with the populations of the Western foothill lowland (WF). The two groups were significantly distinct with a Global FST of 0.010 (p<0.001). While the first group showed a moderate structure, with only one divergent sampling population (Global FST =0.006, p<0.001), the second group proved more structured being divided in four clusters (Global FST=0.017, p=0.058). This genetic population structure should be due to the large conurbations and main roads that separate the WF group from the Prealpine belt and the Eastern foothill lowland. The adopted methods allowed the analysis of the genetic population structure of Fire Salamander from wide to local scale, identifying different degrees of genetic divergence of their populations derived from forest fragmentation induced by urban and infrastructure sprawl.

Supplementary material 1 from: Astuti G, Roma-Marzio F, D'Antraccoli M, Bedini G, Carta A, Sebastiani F, Bruschi P, Peruzzi L (2017) Conservation biology of the last Italian population of Cistus laurifolius (Cistaceae): demographic structure, reproductive success and population genetics. Nature Conservation 22: 169-190. https://doi.org/10.3897/natureconservation.22.19809

The dataset

The Pest Sticky Traps (PST) dataset is a collection of yellow chromotropic sticky trap pictures specifically designed for training/testing deep learning models to automatically count insects and estimate pest populations.

Images were manually annotated by some experts of the Department of Agriculture, Food and Environment of the University of Pisa (Italy) by putting a dot over the centroids of each identified insect. Specifically, we labeled insects as belonging to the category “whitefly” considering two different species, i.e., the sweet potato whitefly (Bemisia tabaci) (Gennadius) and the greenhouse whitefly (Trialeurodes vaporariorum) (Westwood).

The dataset comprises two subsets:
- a subset we suggest using for the training/validation phases (contained in the `train/` folder)
- a subset we suggest using for the test phase (contained in the `test/` folder)

Annotations of the two subsets are contained in `train/annotations.csv` and `test/annotations.csv`, respectively. They have the following columns:
- *imageName* - filename of the image containing the whiteflies,
- *X,Y* - 2D coordinates of the whitefly in the image space,
- *class* - class index of the insect (always 0 in this dataset).

Citing our work

If you found this dataset useful, please cite the following paper

@inproceedings{CIAMPI2023102384,
  title = {A deep learning-based pipeline for whitefly pest abundance estimation on chromotropic sticky traps},
  journal = {Ecological Informatics},
  volume = {78},
  pages = {102384},
  year = {2023},
  issn = {1574-9541},
  doi = {10.1016/j.ecoinf.2023.102384},
  url = {https://www.sciencedirect.com/science/article/pii/S1574954123004132},
  year = 2023,
  author = {Luca Ciampi and Valeria Zeni and Luca Incrocci and Angelo Canale and Giovanni Benelli and Fabrizio Falchi and Giuseppe Amato and Stefano Chessa},
}

and this Zenodo Dataset

@dataset{ciampi_2023_7801239,
  author = {Luca Ciampi and Valeria Zeni and Luca Incrocci and Angelo Canale and Giovanni Benelli and Fabrizio Falchi and Giuseppe Amato and Stefano Chessa},
  title = {Pest Sticky Traps: a dataset for Whitefly Pest Population Density Estimation in Chromotropic Sticky Traps}},
  month = apr,
  year = 2023,
  publisher = {Zenodo},
  version = {1.0.0},
  doi = {10.5281/zenodo.7801239},
  url = {https://doi.org/10.5281/zenodo.6560823}
}

Contact Information

If you would like further information about the dataset or if you experience any issues downloading files, please contact us at luca.ciampi@isti.cnr.it