This dataset contains the microdata file and the technical documentation of the UNTWIST survey. The survey was conducted between 21 October 2024 and 9 January 2025 in six European countries: Denmark, Germany, Hungary, Switzerland, and the UK. The technical documentation includes the fieldwork description, the variable harmonization and weighting report, the annotated description of the appended country-variables, and the unified codebook.

The dataset consists of the following files:

DATASET
* Appended_Main+Boost_wgt_pooled.csv

TECHNICAL SHEET

* Technical Sheet.pdf
This document provides a summary of the survey technical information and highlight the key aspects of the fieldwork, which are relevant to understand how the collection of data has been out in practice.

WEIGHTING APPENDIX

* Weighting Appendix.pdf
This appendix expands the description of the weighing procedure, that was followed to ensure the maximum quality of representativeness of the target population.

HARMONIZATION APPENDIX

* Harmonization Appendix.pdf
This document describes in detail the harmonization procedure of the country specific categories of two variables: the education level and religious belonging.

CODEBOOK

* UNTWIST Survey Codebook.pdf
This document presents the unified dataset codebook of both individual and country-level variables.

COUNTRY-VARIABLE DATA APPENDIX

* Country-Variable Data Appendix.pdf
This document provides further details of the appended country-level variables, such as concepts, measurement scales, codes and sources.

This dataset contains records of research articles extracted from the Web of Science (WoS) from 1980 to 2019---in total, 15,642 journals, 28,241,100 articles and 111,980,858 authorships across 153 research areas.

The main dataset (author_address_article_gend_v2.parquet), in Parquet format, contains all the authorships, where an authorship is defined as the tuple article-author. There are 12 variables per authorship (row):

• ut: unique article identifier.
• daisng_id: unique author identifier.
• country: author country (two-letter ISO code).
• date: publication date.
• gender: gender of the author ("male" or "female"), as provided by the Genderize.io API.
• probability: probability of the gender attribute, as provided by the Genderize.io API.
• count: number of entries for the author first name, as provided by the Genderize.io API.
• jsc: journal subject category.
• field: field of research.
• research_area: area of research.
• n_aut: number of authors in this publication.
• journal: journal name.

With the previous dataset, a resampler was applied to generate null homophily values for each year. There are 4 datasets in R Data Serialization (RDS) format:

• null_field.rds: null homophily values per country, year and field of research.
• null_field_comp.rds: null homophily values per year and field of research (only for complete authorships).
• null_research.rds: null homophily values per year and area of research.
• null_research_comp.rds: null homophily values per year and area of research (only for complete authorships).

All these datasets have the same structure:

• country: country (two-letter ISO code).
• year: year.
• variable: either field or research area name.
• m: average homophily.
• s: homophily std. error.

Finally, some supplementary files used in the descriptive analysis and methods:

• File null_research_l2019.rds is an example of the output from the resampling algorithm for year 2019.
• File wos_category_to_field.csv is a mapping from WoS categories to more general fields.
• File jcr_if_2020.csv contains the percentiles of the journal impact factor for the JCR 2020.

Cost of Living - Country Rankings Dataset

Context:

The "Cost of Living - Country Rankings Dataset" provides comprehensive information on the cost of living in various countries around the world. Understanding the cost of living is crucial for individuals, businesses, and policymakers alike, as it impacts decisions related to travel, relocation, investment, and economic analysis. This dataset is intended to serve as a valuable resource for researchers, data analysts, and anyone interested in exploring and comparing the cost of living across different nations.

Content:

This dataset comprises four primary columns:

1. Countries: This column contains the names of various countries included in the dataset. Each country is identified by its official name.

2. Cost of Living: The "Cost of Living" column represents the cost of living index or score for each country. This index is typically calculated by considering various factors, such as housing, food, transportation, healthcare, and other essential expenses. A higher index value indicates a higher cost of living in that particular country, while a lower value suggests a more affordable cost of living.

3. 2017 Global Rank: This column provides the global ranking of each country's cost of living in the year 2017. The ranking is based on the cost of living index mentioned earlier. A lower rank indicates a lower cost of living relative to other countries, while a higher rank suggests a higher cost of living position.

4. Available Data: The "Available Data" column indicates whether or not data for a specific country and year is available.

This dataset is designed to support various data analysis and visualization tasks. Users can explore trends in the cost of living, identify countries with high or low cost of living, and analyze how rankings have changed over time. Researchers can use this dataset to conduct in-depth studies on the factors influencing the cost of living in different regions and the economic implications of such variations.

Please note that the dataset includes information for the year 2017, and users are encouraged to consider this when interpreting the data, as economic conditions and the cost of living may have changed since then. Additionally, this dataset aims to provide a snapshot of cost of living rankings for countries in 2017 and may not cover every country in the world.

Link: https://www.theglobaleconomy.com/rankings/cost_of_living_wb/

Disclaimer: The accuracy and completeness of the data provided in this dataset are subject to the source from which it was obtained. Users are advised to cross-reference this data with authoritative sources and exercise discretion when making decisions based on it. The dataset creator and Kaggle assume no responsibility for any actions taken based on the information provided herein.

This data repository provides the Food and Agriculture Biomass Input Output (FABIO) database, a global set of multi-regional physical supply-use and input-output tables covering global agriculture and forestry.

The work is based on mostly freely available data from FAOSTAT, IEA, EIA, and UN Comtrade/BACI. FABIO currently covers 191 countries + RoW, 118 processes and 125 commodities (raw and processed agricultural and food products) for 1986-2013. All R codes and auxilliary data are available on GitHub. For more information please refer to https://fabio.fineprint.global.

The database consists of the following main components, in compressed .rds format:

Z: the inter-commodity input-output matrix, displaying the relationships of intermediate use of each commodity in the production of each commodity, in physical units (tons). The matrix has 24000 rows and columns (125 commodities x 192 regions), and is available in two versions, based on the method to allocate inputs to outputs in production processes: Z_mass (mass allocation) and Z_value (value allocation). Note that the row sums of the Z matrix (= total intermediate use by commodity) are identical in both versions.

Y: the final demand matrix, denoting the consumption of all 24000 commodities by destination country and final use category. There are six final use categories (yielding 192 x 6 = 1152 columns): 1) food use, 2) other use (non-food), 3) losses, 4) stock addition, 5) balancing, and 6) unspecified.

X: the total output vector of all 24000 commodities. Total output is equal to the sum of intermediate and final use by commodity.

L: the Leontief inverse, computed as (I – A)-1, where A is the matrix of input coefficients derived from Z and x. Again, there are two versions, depending on the underlying version of Z (L_mass and L_value).

E: environmental extensions for each of the 24000 commodities, including four resource categories: 1) primary biomass extraction (in tons), 2) land use (in hectares), 3) blue water use (in m3)., and 4) green water use (in m3).

mr_sup_mass/mr_sup_value: For each allocation method (mass/value), the supply table gives the physical supply quantity of each commodity by producing process, with processes in the rows (118 processes x 192 regions = 22656 rows) and commodities in columns (24000 columns).

mr_use: the use table capture the quantities of each commodity (rows) used as an input in each process (columns).

A description of the included countries and commodities (i.e. the rows and columns of the Z matrix) can be found in the auxiliary file io_codes.csv. Separate lists of the country sample (including ISO3 codes and continental grouping) and commodities (including moisture content) are given in the files regions.csv and items.csv, respectively. For information on the individual processes, see auxiliary file su_codes.csv. RDS files can be opened in R. Information on how to read these files can be obtained here: https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/readRDS

Except of X.rds, which contains a matrix, all variables are organized as lists, where each element contains a sparse matrix. Please note that values are always given in physical units, i.e. tonnes or head, as specified in items.csv. The suffixes value and mass only indicate the form of allocation chosen for the construction of the symmetric IO tables (for more details see Bruckner et al. 2019). Product, process and country classifications can be found in the file fabio_classifications.xlsx.

Footprint results are not contained in the database but can be calculated, e.g. by using this script: https://github.com/martinbruckner/fabio_comparison/blob/master/R/fabio_footprints.R

How to cite:

To cite FABIO work please refer to this paper:

Bruckner, M., Wood, R., Moran, D., Kuschnig, N., Wieland, H., Maus, V., Börner, J. 2019. FABIO – The Construction of the Food and Agriculture Input–Output Model. Environmental Science & Technology 53(19), 11302–11312. DOI: 10.1021/acs.est.9b03554

License:

This data repository is distributed under the CC BY-NC-SA 4.0 License. You are free to share and adapt the material for non-commercial purposes using proper citation. If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original. In case you are interested in a collaboration, I am happy to receive enquiries at martin.bruckner@wu.ac.at.

Known issues:

The underlying FAO data have been manipulated to the minimum extent necessary. Data filling and supply-use balancing, yet, required some adaptations. These are documented in the code and are also reflected in the balancing item in the final demand matrices. For a proper use of the database, I recommend to distribute the balancing item over all other uses proportionally and to do analyses with and without balancing to illustrate uncertainties.

Introduction

This dataset contains information on how to fairly distribute the mitigation efforts that countries need to undertake to together achieve certain climate goals. There is no single answer to this question, but we explore this topic by looking at various global emissions pathways, and subsequently allocate these emissions to countries using different effort-sharing rules. This data is applied in a preprint of a scientific article where we explore implications of justice on NDCs and international mitigation finance.

The research behind this dataset is still under development and therefore this dataset is not final. Our scientific work is still under revision so the data is subject to potential changes upon peer review of this publication. Nevertheless, because (a version of) this data is already used in the Carbon Budget Explorer and in scientific projects, we feel it should be available and versioned. Hence these releases of a preliminary version.

Carbon Budget Explorer

We also published this work on a website called the Carbon Budget Explorer: an online interactive tool that allows users to navigate through these results, without having to download and plot the data themselves. It is free and publicly available at www.carbonbudgetexplorer.eu. Currently, the Carbon Budget Explorer relies on a previous version of this dataset (version 0.1, unpublished, but available upon request). The Explorer will be updated with new data early 2025 (i.e., with the version presented in this data repository).

Data description

Default (DefaultAllocations.zip and DefaultReductions.zip)

For many users, these are the main datafiles. Per country and region, allocations and reduction targets are shown for two trajectories, which are associated with 1.5 (with slight overshoot: peak temperature 1.6) and 2.0 degree pathways, and default settings across all other dimensions. The exact parameters used in these precooked pathways are shown in Table 1 (see "Dimensions"). The reductions_default_*.csv files show data along the same structure, also using the default pathways, but contain the emission reductions with respect to 2015 rather than absolute allocations.

Global pathways (GlobalPathways.zip)

Allocating emissions to countries starts with determining global emissions pathways. The files in GlobalPathways.zip contain projected global emissions on GHG, CO2 and non-CO2 levels, constrained by various global settings (see below) such as temperature targets and derived CO2 budgets. The pathway shapes are informed by mitigation scenarios from the IPCC AR6 database. The starting values are all harmonized with 2021 historical datapoints. For convenience, the emissionspathways_default.csv datafile provides the pathways with default settings (see Table 1, column 'Default'). The complete dataset can be found in emissionspathways_all.csv.

Emission allocations (Allocations.zip -> allocations_*.nc)

The emissions from the global pathways can be divided among countries according to different allocation rules (see 'Allocation rules' for more information). Files of the format allocations_region.nc indicate allocations according to all allocation rules, parameters and global choices, for a single region. Because of the high number of parameters and dimensions, these files are shared in NetCDF (.nc) format. NetCDF files are commonly used for storing multidimensional scientific data and can be displayed, analyzed and read/written using GIS systems (such as ArcGIS, QGIS), MATLAB funcions (such as nccreate, ncread), R (e.g. using the ncdf4 package) and Python (e.g. using the xarray package).

Input data (Inputdata.zip)

Additional input data coming from third parties, such as population and GDP data, is stored in Inputdata.zip. We prepared these input data sources in the exact same format as the rest for convenience of the user, but we would like to emphasize that the appropriate references should be cited. For further information, please check 'Input data sources'.

CO2 budgets

A file has been added in the version 0.3.1, including cumulative CO2 budgets. How they are calculated, is slightly different for each rule (only PC, AP and ECPC are included here), because of the varying nature of these allocation rules. The PC budget is simply the fraction of the remaining carbon budget determined by a country's 2021 population share. The AP budget is computed by adding all positive CO2 allocations according to the AP rule. The ECPC budget is the full-century budget: that is, historical leftover (or debt) plus a country's fair per capita share between 2021-2100. Note that there is not necessarily a one-to-one relation between these budgets and the CO2 part of the allocation files (Allocations.zip). For example, the PC budget uses 2021 population, while the allocation files use year-to-year population numbers (also if they change in the future). We have the ambition to, in next versions, expand this dataset to account for and vary the choices one can make in this regard.

Allocation rules

Below you can find a summarized description of all allocation rules. More detailed information can be found in Van den Berg et al. (2020), as well as in a scientific paper (preprint) expected in summer 2024. The rules have a variety of parameters, each included as dimensions in the data. See Table 1, in "Dimensions", for details.

• The (immediate) 'Per Capita' method (PC) uses a country's population share in the global population and allocates future emissions accordingly. Naturally, socio-economic conditions affect this method. Therefore, all five SSPs are used in our analysis.
• 'Grandfathering' (GF) is a method that preserves current emission fractions. In other words, all countries reduce their emissions proportional to their current share. Note that this rule is controversial and is commonly not regarded as fair (see Rajamani et al. 2021). It is include here for reference only.
• The 'Per Capita Convergence' (PCC) method starts as 'Grandfathering', but converges over time to a 'Per Capita' basis. An additional important parameter here is the year at which this convergence completes.
• The 'Per Capita via Budget' (PCB_lin) method is a specific implementation of distributing the total CO2 budget on a per capita basis, and then drawing a linear line from current emissions down to net-zero CO2. A median non-CO2 path is added to end up with a total greenhouse gas emissions line. This is similar to, for example, Fekete et al. (2022).
• The 'Ability to Pay' (AP) method allocates emissions inversely related to the GDP per capita of countries. Also this method is dependent on the socio-economic scenario.
• The 'Equal Cumulative Per Capita' (ECPC) method builds on the per-capita convergence method, also accounts for historical responsibility: throughout the convergence period, countries resolve historical 'debt' or 'leftover' from what countries would have emitted if it had emissions according to a per capita share in the past. Note: this method has been significantly revised in version 0.4. In earlier versions, resolving of historical responsibility was only achieved by 2100, postponing most debt.
• The 'Greenhouse Development Rights' (GDR) method is, in the short run, based on a Responsibility-Capability Index, and in the long run based on GDP per capita (similar to 'Ability to Pay').

Dimensions

Table 1 - Data dimensions