The C-BARQ (or Canine Behavioral Assessment and Research Questionnaire) is designed to provide dog owners and professionals with standardized evaluations of canine temperament and behavior

Abstract mathscout is a mathematica^1 1mathematica is a registered trademark of Wolfram Research Inc. package to postprocess the output of other programs for scientific calculations. We wrote mathscout to import data from a major program for ab initio computational chemistry into mathematica, so that we could postprocess the chemical results. It can be used to import the output of many other packages that are used, e.g. in molecular dynamics, crystallography, spectroscopic analysis, metabolic and phys... Title of program: msct.m Catalogue Id: ADZQ_v1_0 Nature of problem Import data from output files of scientific computing packages, such as Gaussian, into Mathematica for symbolic calculation and production of publication quality tables and plots; Versions of this program held in the CPC repository in Mendeley Data ADZQ_v1_0; msct.m; 10.1016/j.cpc.2007.07.009 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)

The included raw data was generated by evolutionary (Wright-Fisher) simulations. The used C++ simulation scripts are included as source codes and compiled executables.The raw data is processed, plotted and complemented with modeling results by Wolfram Mathematica scripts. The used code snippets are presented in Mathematica Notebooks.We provide all code and data to recreate the figures in the publication.

This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2018) Abstract In this paper the C++ version of FIRE is presented — a powerful program performing Feynman integral reduction to master integrals. All previous versions used only Wolfram Mathematica, the current version mostly uses Wolfram Mathematica as a front-end. However, the most complicated part, the reduction itself can now be done by C++, which significantly improves the performance and allows one to reduce Feynman integrals in previously impossible situations. Title of program: FIRE5 Catalogue Id: AEPW_v2_0 Nature of problem Reducing Feynman integrals to master integrals can be treated as a task to solve a huge system of sparse linear equations with polynomial coefficients. Versions of this program held in the CPC repository in Mendeley Data AEPW_v1_0; FIRE4; 10.1016/j.cpc.2013.06.016 AEPW_v2_0; FIRE5; 10.1016/j.cpc.2014.11.024

This repository contains replication data for "ERA5-Land and GMFD Uncover The Effect of Daily Temperature Extremes on Agricultural Yields"

This repository contains a detailed database of energy consumption and emissions data for Italian industrial sites in 2022. The database was developed through a novel top-down methodology that bridges national statistics and site-level data, as detailed in the associated data paper Bridging the industrial data gap: Top-down approach from national statistics to site-level energy consumption data - ScienceDirect

Key features of the database:

• Site-specific energy consumption data across seven energy carriers: solid fossil fuels, manufactured gases, oil and petroleum products, natural gas, biofuels, non-renewable wastes, and electricity
• Process heat categorization by temperature ranges (<100°C, 100°C-500°C, 500°C-1000°C, and >1000°C)
• Integration of process emissions from feedstock use in hard-to-abate sectors
• Geographical coordinates and regional information for each industrial site
• Industrial sector classification using both ATECO 2007 and EUROSTAT Energy Balance categories

Data sources integrated:

• EU Emissions Trading System (EU-ETS) database
• EUROSTAT Energy Balance statistics
• JRC-IDEES database
• Italian municipalities geographical database

The methodology presented can be replicated for other European countries, as it relies on commonly available data sources across the EU. This database is particularly valuable for:

• Energy system modelers
• Researchers studying industrial decarbonization
• Policymakers developing targeted climate strategies
• Industry stakeholders assessing technology transition pathways

This work was funded by the European Union - NextGenerationEU, in the framework of the consortium iNEST - Interconnected Nord-Est Innovation Ecosystem (PNRR, Missione 4 Componente 2, Investimento 1.5 D.D. 1058 23/06/2022, ECS_00000043 – Spoke1, RT3A, CUP I43C22000250006).

Agriculture is the largest global consumer of water. Irrigated areas constitute 40 % of the total area used for agricultural production (FAO, 2014a) Information on their spatial distribution is highly relevant for regional water management and food security. Spatial information on irrigation is highly important for policy and decision makers, who are facing the transition towards more efficient sustainable agriculture. However, the mapping of irrigated areas still represents a challenge for land use classifications, and existing global data sets differ strongly in their results. The following study tests an existing irrigation map based on statistics and extends the irrigated area using ancillary data. The approach processes and analyzes multi-temporal normalized difference vegetation index (NDVI) SPOT-VGT data and agricultural suitability data - both at a spatial resolution of 30arcsec - incrementally in a multiple decision tree. It covers the period from 1999 to 2012. The results globally show a 18 % larger irrigated area than existing approaches based on statistical data. The largest differences compared to the official national statistics are found in Asia and particularly in China and India. The additional areas are mainly identified within already known irrigated regions where irrigation is more dense than previously estimated. The validation with global and regional products shows the large divergence of existing data sets with respect to size and distribution of irrigated areas caused by spatial resolution, the considered time period and the input data and assumption made.