​Department of Economics Management and Statistics

This dataset was generated as a result of the experiement described in the paper "Spillover effects of cooperative behaviour when switching tasks: the role of gender"

Reproduction Package for Knockin’ on H(e)aven’s door. Financial crises and offshore wealth by Silvia Marchesi and Giovanna Marcolongo. For further information see the README file

We provide data and code to replicate the results presented in "A Hodrick-Prescott Filter with automatically selected breaks" (Maranzano & Pelagatti, 2025). The subfolders allow replicating the following: 1. Simulation experiments discussed in Section 3 "Simulations"; 2. Application results discussed in Section 4 "Assessing structural breaks in the Italian labour market"; 3. Simulation experiments discussed in Section 5 "A comparison with other business cycle extraction methods". For each subfolder a readme file is provided. It contains information about the reproduction steps.

We present a dataset for fuel sales analysis at the Italian provincial (NUTS3) level from January 2015 to October 2023 (release V3, January 2024). Fuel sales data are collected at monthly frequency, and are organized by fuel type, usage, and point of sale (highway, municipal road, extra-network road). Fuels data are augmented by a set of socio-economic and geographical variables, which help explain the impact of economic phenomena and topography on fuel sales. The data is collected from the Monthly oil Bullettin of Italian Ministry of Environment and Energy Security (MITE), ISTAT (Istituto Nazionale di Statistica), Bank of Italy and Eurostat, and has been collected through both automated web scraping and manual downloads, then cleaned and reshaped to be suitable for analysis. The produced dataset may be useful for spatiotemporal fuel sales forecasting, air quality analysis, urban mobility, econometric research, as well as machine learning applications. To further assist the user in finding valuable insight, an R Shiny app (freely available at the webpage https://ale-ch.shinyapps.io/it-fuel-dashboard/) was developed for data exploration. App code and the data have been made fully available on the following Github repository (https://github.com/ale-ch/it-fuel-dashboard). The app consists of interactive plots that allow the user to visualize every variable in the dataset at different time ranges and locations, allowing full flexibility in data exploration.

Codes and database originated for the manuscript published in the DOI article: 10.1016/j.jdeveco.2023.103053

BayesANT is a package for the taxonomic classification of DNA sequences. It trains a taxonomic classifier on a dataset of DNA barcodes and returns probabilistic predictions for query DNA sequences. BayesANT explicitly accounts for potential taxonomic novelty of the query sequences by relying on Bayesian nonparametric species sampling priors to model the taxonomic tree.

This folder contains the data and the R code to reproduce the figures and tables in the paper Zito, Rigon and Dunson (2022) - "Inferring Taxonomic placement from DNA barcoding aiding in discovery of new taxa", accepted as open access publication in Methods in Ecology and Evolution. The file "main_FinBOL.R" reproduces the tables in the main document and in the Supporting information available online for the analysis of the FinBOL data, while "main_Simulation_Section4_SI.R" reproduces the simulation in Section 4 of the Supporting information. All data are saved in the folder "data". For replicability purposes, we added version 2.13 of the RDP classifier to the repository, in the folder "RDP/java". This has been downloaded from https://sourceforge.net/projects/rdp-classifier/. For questions, contact the author at alessandro.zito@duke.edu

Major changes: Changed httr dependency for httr2 (#272) Removed two authors of code formerly including for stubbing results; which is now done via httptest2 package. Minor changes: Moved jsonlite from Imports to Suggests (now only used in tests).

BayesANT is a package for the taxonomic classification of DNA sequences. It trains a taxonomic classifier on a dataset of DNA barcodes and returns probabilistic predictions for query DNA sequences. BayesANT explicitly accounts for potential taxonomic novelty of the query sequences by relying on Bayesian nonparametric species sampling priors to model the taxonomic tree. The package works with both aligned and not aligned sequences.

Fix for breaking change in dodgr (#494)