Find research datasets worth reusing

opencc-by-4.0Nov 2024View details →

Historical and future water demand for households and industry for the STARS4Water river basins

<pre>This repository contains the data related to the deliverable D2.5 "Data sets on scenario narratives" prepared within the STARS4Water project ("Supporting STakeholders for Adaptive, Resilient and Sustainable Water Management").</pre> The data spans historical years (2000-2020) and projections under different Shared Socioeconomic Pathways (SSP1-5) scenarios for the years 2020-2050. The repository contains historical and future water demand for households and industry for the STARS4Water river basins divided into two items packed in zip file: 1. STARS4Water_Domestic_and_Industrial_Water_Demands_historical.zip  for years 2000-2020 2. STARS4Water_Domestic_and_Industrial_Water_Demands_projections.zip for years 2020-2050 (SSP1-SSP5) The data in the repository was prepared based on Python scripts developed by Stephanie E. Lips and described in Towards a global high resolution water demand dataset. Effect of data quality and downscaling techniques - the case for Europe, Utrecht University, 2020 as well as open source databases of WorldPop, WorldBank, UNCTADstat, EIA, Eurostat, Aquastat, UNEP an others.

opencc-by-4.0May 2021View details →

National Survey on the Effects of COVID-19 on the Wellbeing of Mexican Households (ENCOVID-19- DECEMBER 2020)

Amid the COVID-19 outbreak, the ENCOVID-19 provides information on the well-being of Mexican households in four main domains: labor, income, mental health, and food insecurity. It offers timely information to understand the social consequences of the pandemic and the lockdown measures. It is a project consisting of a series of cross-sectional telephone surveys collected in key moments of the COVID-19 pandemic. In addition to the four main domains and a set of COVID19-related questions, the survey includes new key indicators every month to capture the impact of the pandemic on issues like education, social programs, and crime. This is the sixth dataset of the project, corresponding to December 2020, collected nine months after the lockdown began in Mexico. Data collection was performed from November 27 to December 11, 2020.

opencc-by-4.0May 2022View details →

National Survey on the Effects of COVID-19 on the Wellbeing of Mexican Households (ENCOVID-19 - APRIL 2022)

Amid the COVID-19 outbreak, the ENCOVID-19 provides information on the well-being of Mexican households in four main domains: labor, income, mental health, and food insecurity. It offers timely information to understand the social consequences of the pandemic and the lockdown measures. It is a project consisting of a series of cross-sectional telephone surveys collected in key moments of the COVID-19 pandemic. In addition to the four main domains and a set of COVID19-related questions, the survey includes new key indicators every month to capture the impact of the pandemic on issues like education, social programs, and crime. This is the eleventh dataset of the project, corresponding to April 2022, collected 24 months after the lockdown began in Mexico. Data collection was performed from March 17 to May 2, 2022.

opencc-by-4.0Jul 2021View details →

National Survey on the Effects of COVID-19 on the Wellbeing of Mexican Households (ENCOVID-19- MAY 2021)

Amid the COVID-19 outbreak, the ENCOVID-19 provides information on the well-being of Mexican households in four main domains: labor, income, mental health, and food insecurity. It offers timely information to understand the social consequences of the pandemic and the lockdown measures. It is a project consisting of a series of cross-sectional telephone surveys collected in key moments of the COVID-19 pandemic. In addition to the four main domains and a set of COVID19-related questions, the survey includes new key indicators every month to capture the impact of the pandemic on issues like education, social programs, and crime. This is the ninth dataset of the project, corresponding to May 2021, collected thirteen months after the lockdown began in Mexico. Data collection was performed from May 21 to Jun 17, 2021.

opencc-by-4.0Dec 2021View details →

National Survey on the Effects of COVID-19 on the Wellbeing of Mexican Households (ENCOVID-19- OCTOBER 2021)

Amid the COVID-19 outbreak, the ENCOVID-19 provides information on the well-being of Mexican households in four main domains: labor, income, mental health, and food insecurity. It offers timely information to understand the social consequences of the pandemic and the lockdown measures. It is a project consisting of a series of cross-sectional telephone surveys collected in key moments of the COVID-19 pandemic. In addition to the four main domains and a set of COVID19-related questions, the survey includes new key indicators every month to capture the impact of the pandemic on issues like education, social programs, and crime. This is the tenth dataset of the project, corresponding to October 2021, collected 19 months after the lockdown began in Mexico. Data collection was performed from October 20 to November 13, 2021.

opencc-by-4.0May 2021View details →

National Survey on the Effects of COVID-19 on the Wellbeing of Mexican Households (ENCOVID-19 - MARCH 2021)

Amid the COVID-19 outbreak, the ENCOVID-19 provides information on the well-being of Mexican households in four main domains: labor, income, mental health, and food insecurity. It offers timely information to understand the social consequences of the pandemic and the lockdown measures. It is a project consisting of a series of cross-sectional telephone surveys collected in key moments of the COVID-19 pandemic. In addition to the four main domains and a set of COVID19-related questions, the survey includes new key indicators every month to capture the impact of the pandemic on issues like education, social programs, and crime. This is the sixth dataset of the project, corresponding to March 2021, collected one year after the lockdown began in Mexico. Data collection was performed from February 26 to March 27, 2021.

opencc-by-4.0Jul 2024View details →

Survey data on households' use of smart home technology and their time of use of electric appliances (eCAPE)

This survey data includes the responsed from a survey questionnaire which was used to collect information on smart home technologies and time of use of electric appliances in Danish households. The survey covers themes like adoption and use ofhousehold appliances, households’ division of everyday chores, timing of everyday activities, and everyday flexibility. The purpose of this survey is to gather information about Danish households and their everyday practices and flexibility related to electricity use. The intention is to combine questions from the survey with real time data of electricity consumption at household level with a time resolution of few minutes, and to do so for a large representative population. However, the electricity consumption is not allowed to share publicly, and therefore not included in this data upload.  The survey includes questions of socio-economic factors. The questionnaire was distributed in Danish but was developed in and translated from English because ofinternational cooperation. The survey was developed under the project eCAPE - New Energy Consumer Roles and Smart Technologies– Actors, Practices and Equality. The eCAPE project is financed by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program under the grant agreement number 786643 (https://www.ecape.aau.dk/). The project is led by Professor Kirsten Gram-Hanssen from Department of the Built Environment, Aalborg University.  See also  <a href="https://www.researchgate.net/publication/373453718_Survey_questionnaire_on_households'_use_of_smart_home_technology_and_their_time_of_use_of_electric_appliances">https://www.researchgate.net/publication/373453718_Survey_questionnaire_on_households'_use_of_smart_home_technology_and_their_time_of_use_of_electric_appliances</a>

opencc-by-4.0May 2022View details →

Survey on the Effects of COVID-19 on the Wellbeing of Mexico City Households (ENCOVID-19 CDMX – JULY 2021)

Amid the COVID-19 outbreak, the ENCOVID-19 CDMX provides information on the well-being of Mexico City households in four main domains: labor, income, mental health, and food insecurity. It offers timely information to understand the social consequences of the pandemic and the lockdown measures. It is a cross-sectional telephone survey that, in addition to the four main domains and a set of COVID19-related questions, includes key indicators to capture the impact of the pandemic on issues like education, social programs, and crime. This is the third dataset of the project, corresponding to July 2021, collected 15 months after the lockdown began in Mexico. Data collection was performed from July 19 to 31, 2021.

opencc-by-4.0Apr 2023View details →

Can household storage conditions reduce food waste and environmental impact ? A broccoli case study

The consumption stage has been identified as the largest producer of food waste (FW) across the food supply chain (FSC), with fruit and vegetables being the most affected product category. The present study aims to determine the op- timal storage scenario at household level to avoid food waste and which has the lowest environmental footprint. Broc- coli was stored under different storage conditions: unbagged or bagged (periodically opened) in bioplastic bags inside a domestic refrigerator at 5 or 7 °C for 34 days and then analysed for relative humidity (RH), sensory properties and bioactive compounds. A life cycle assessment (LCA) was conducted to evaluate the environmental profile of 1 kg of broccoli purchased by the consumer (cradle-to-grave). At day 0 (base scenario) the carbon footprint was 0.81 kg CO2 eq/kg, with the vegetable farming being the main contributor to this environmental impact, mostly driven by fertiliser (production and its emissions to air and water) and irrigation (due to electricity consumption for water pumping). Quality and food waste depended on time and storage conditions: For short storage times, within three days, the best quality combined with the lowest environmental footprint was for unbagged broccoli at 7 °C and no household food waste. However, this scenario had the highest food waste level from day 3 onwards, with increased resource loss and overall environmental footprint. For long-term storage, using a bag and storing at 5 °C helped to re- duce food waste with the lowest environmental footprint. For example, at 16 days, this scenario (bagged at 5 °C) could save 4.63 kg/FU ofbroccoli and 3.16 kg CO2 eq/FU compared to the worst scenario (unbagged at 7 °C). Consumers are the key to reducing household food waste and this research provides the knowledge for improvement.

opencc-by-4.0Jul 2024View details →

Socio - Economic Survey on Green Transition in Albania - Households

Socio-Economic Survey on Green Transition in Albania - Households The file contains the dataset (cleaned), the questionnaire in Albanian, the coding used for data processing in SPSS, and the detailed results for each question in the questionnaire (organised in sections).

opencc-by-nc-sa-4.0Sep 2024View details →

Household surveys in four informal settlements in Abidjan (Côte d'Ivoire) and Nairobi (Kenya)

Description: Latest release of data (anonymized) collected in informal settlements in Côte d'Ivoire and Kenya during my PhD thesis, with the respective metadata (questionnaire files). Please note that some data (geolocation, specific age of participant, and health facilities used) have been ommitted due to personal data protection concerns. Includes: Data (CSV), questonnaires (XLS), and Jupyter notebooks summarizing the data (using Python). Ethical clearance: We obtained ethical clearance in Switzerland from EPFL’s HREC (decision n° 068-2020), in Kenya from KEMRI (KEMRI/RES/7/3/1) and the National Commission for Science, Technology & Innovation (NACOSTI/P/21/10921), and in Côte d’Ivoire from the National Health and Life Sciences Ethics Committee (Comité National d’Éthique des Sciences de la Vie et de la Santé, ref. n° 005-22/MSHPCMU/CNESVS-km). Citation: Pessoa Colombo V. Relating health benefits of water, sanitation, and hygiene services with the context of urban informal settlements: lessons from Côte d'Ivoire and Kenya. PhD thesis. EPFL: Lausanne. 2023. https://doi.org/10.5075/epfl-thesis-10143

opencc-by-sa-4.0Oct 2024View details →

Unclean but affordable solid fuels effectively sustained household energy equity

This dataset contains the data used in preparation for the paper "Unclean but affordable solid fuels effectively sustained household energy equity" by Ke Jiang et al, describing the inequity of household energy consumption, cost and burden in mainland China in 2017.

opencc-by-4.0Oct 2022View details →

Base rates of food safety practices in European households: Summary data from the SafeConsume Household Survey

This data set contains estimates of the base rates of 550 food safety-relevant food handling practices in European households. The data are representative for the population of private households in the ten European countries in which the SafeConsume Household Survey was conducted (Denmark, France, Germany, Greece, Hungary, Norway, Portugal, Romania, Spain, UK). Sampling design In each of the ten EU and EEA countries where the survey was conducted (Denmark, France, Germany, Greece, Hungary, Norway, Portugal, Romania, Spain, UK), the population under study was defined as the private households in the country. Sampling was based on a stratified random design, with the NUTS2 statistical regions of Europe and the education level of the target respondent as stratum variables. The target sample size was 1000 households per country, with selection probability within each country proportional to stratum size. Fieldwork The fieldwork was conducted between December 2018 and April 2019 in ten EU and EEA countries (Denmark, France, Germany, Greece, Hungary, Norway, Portugal, Romania, Spain, United Kingdom). The target respondent in each household was the person with main or shared responsibility for food shopping in the household. The fieldwork was sub-contracted to a professional research provider (Dynata, formerly Research Now SSI). Complete responses were obtained from altogether 9996 households. Weights In addition to the SafeConsume Household Survey data, population data from Eurostat (2019) were used to calculate weights. These were calculated with NUTS2 region as the stratification variable and assigned an influence to each observation in each stratum that was proportional to how many households in the population stratum a household in the sample stratum represented. The weights were used in the estimation of all base rates included in the data set. Transformations All survey variables were normalised to the [0,1] range before the analysis. Responses to food frequency questions were transformed into the proportion of all meals consumed during a year where the meal contained the respective food item. Responses to questions with 11-point Juster probability scales as the response format were transformed into numerical probabilities. Responses to questions with time (hours, days, weeks) or temperature (C) as response formats were discretised using supervised binning. The thresholds best separating between the bins were chosen on the basis of five-fold cross-validated decision trees. The binned versions of these variables, and all other input variables with multiple categorical response options (either with a check-all-that-apply or forced-choice response format) were transformed into sets of binary features, with a value 1 assigned if the respective response option had been checked, 0 otherwise. Treatment of missing values In many cases, a missing value on a feature logically implies that the respective data point should have a value of zero. If, for example, a participant in the SafeConsume Household Survey had indicated that a particular food was not consumed in their household, the participant was not presented with any other questions related to that food, which automatically results in missing values on all features representing the responses to the skipped questions. However, zero consumption would also imply a zero probability that the respective food is consumed undercooked. In such cases, missing values were replaced with a value of 0.

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U.S. household food waste tracking data in support of Li et al. 2023

These data were used to generate the results in the article “Household Food Waste Trending Upwards in the United States: Insights from a National Tracking Survey,” by Ran Li, Yiheng Shu, Kathryn E. Bender & Brian E. Roe, which has been accepted for publication in the Journal of the Agricultural and Applied Economics Association (doi – https://doi.org/10.1002/jaa2.59). The Stata code used to generate results is available from the authors upon request. U.S. residents who participate in consumer panels managed by a commercial vendor were invited by email or text message to participate in a two-part online survey during four waves of data collection: February and March of 2021 (Feb 21 wave, 425 initiated, 361 completed), July and August of 2021 (Jul 21 wave, 606 initiated, 419 completed), December of 2021 and January of 2022 (Dec 21 wave, 760 initiated, 610 completed), and February, March and April of 2022 (Feb 22 wave, 607 initiated, 587 completed), July, August and Septemper of 2022 (Jul 22 wave, 1817 initiated, 1067 completed). We are not able to determine if any respondents participated in multiple waves, i.e., if any of the observations are repeat participants. All participants provided informed consent and received compensation. Inclusion criteria included age 18 years or older and performance of at least half of the household food preparation. No data was collected during major holidays, i.e., the weeks of the Fourth of July (Independence Day), Christmas, or New Years. Recruitment quotas were implemented to ensure sufficient representation by geographical region, race, and age group. Post-hoc sample weights were constructed to reflect population characteristics on age, income and household size. The protocol was approved by the local Internal Review Board. The approach begins with participants completing an initial survey that ends with an announcement that a follow-up survey will arrive in about one week, and that for the next 7 days, participants should pay clo

openCC (other)Oct 2023View details →

opencc-by-4.0Jun 2021View details →

Optimised household consumption profiles through a smart building energy mangement system TABEDE

In the context of the TABEDE project (<a href="https://www.tabede.eu/">https://www.tabede.eu/</a>) several synthetic profiles simulating the consumption and generation of residential buildings, whose appliances were controlled by our proposed Energy Management System (i.e., the TABEDE solution), were simulated. Their construction process was characterised by the following: <ul> <li>Consumption profiles were generated via a bottom-up approach capable of emulating the consumption of individual household appliances. These last ones correspond to the most used appliances in the UK, which were randomly distributed among the buildings based on their average utilisation rate and ownership observed in residential buildings in the country.</li> <li>The physics in terms of heat exchange between neighbouring buildings and the environment were considered, together with the size of the buildings and their physical characteristics. A total of 66 houses and apartments, according to 8 type or building archetypes were created.</li> <li>PV generation profiles were generated according to the meteorological condition of the simulated day.</li> </ul> Together with this, the profiles feature how the TABEDE solution optimised the flexible part of the consumption (i.e., appliances that were controllable by the solution and whose consumption could be shifted in time without sacrificing user comfort) to minimize the electricity bill of the buildings. The information contained in the actual database features the following variables: <ul> <li>TABEDE penetration: percentage of buildings owning the TABEDE solution. Buildings with TABEDE will observe their flexible consumption being optimised.</li> <li>PV penetration: percentage of buildings with a PV system installed on them.</li> <li>Simulation day: one day in summer (19/06/2019) featuring the highest solar radiation of the year, and a day in winter (19/12/2019) with the lowest.</li> <li>Batteries: whether the PV systems is installed alongside household batteries.</li> </ul> Details on the formulation can be found in: <a href="https://urldefense.com/v3/__https:/www.energy-proceedings.org/an-intelligent-infrastructure-for-enabling-demand-response-ready-buildings/__;!!La4veWw!khYBEaeJY85mX5yQUrp0PwoXcg5U10dEdgZ296hONYGyBS5xg91Z8MoDUQy34a4f9Lo$">https://www.energy-proceedings.org/an-intelligent-infrastructure-for-enabling-demand-response-ready-buildings/</a>

opencc-by-4.0Nov 2023View details →

Household information for houses in Kasungu district participating in the Maladrone study, 2021

Each row in the dataset contains information on each household participating in the Maladrone study, 2021. The information is as follows:uniqueid: Unique ID assigned to the household, comprising of two letters corresponding to the community (ML, CK, CP) and the study house number.under_5: Number of people under the age of 5 who live in the house at the time of asking.over_5:  Number of people over the age of 5 who live in the house at the time of asking.under_5_rwt: Number of people under the age of 5 who usually sleep in the room where the CDC light trap was set.over_5_rwt: Number of people over the age of 5 who usually sleep in the room where the CDC light trap was set.number_mosquito_nets: Number of mosquito nets in the household.mosquito_nets_rwt: Number of mosquito nets usually used in the room where the CDC light trap was set.under_5_nets: Number of under 5s in the household who usually slept under a net.over_5_nets: Numer of over 5s in the household who usually slept under a net.unde_5_nets_rwt: Number of under 5s in the household who usually slept under a net in the room where the CDC light trap was set.over_5_nets_rwt: Number of under 5s in the household who usually slept under a net in the room where the CDC light trap was set.roof_type: Main material used for the roof of the house. Choices were iron sheet, thatched or tiled.eaves: Whether the eaves of the house were open, partally open, or closed.windows: Whether the windows of the house were open, partally open, or closed.

opencc-by-4.0Mar 2024View details →

Data and Code for "Does Organic Farming Jeopardize Food Security of Farm Households in Benin?"

This data and code archive provides all the data and code for replicating the empirical analysis that is presented in the journal article "<a href="https://doi.org/10.1016/j.foodpol.2024.102622" target="_blank" rel="noopener">Does Organic Farming Jeopardize Food Security of Farm Households in Benin?</a>" authored by Ghislain B.D. Aïhounton and Arne Henningsen and published in the journal Food Policy (Volume 124, April 2024, 102622, DOI: 10.1016/j.foodpol.2024.102622). We conducted the empirical analysis with the "R" statistical software (version 4.3.3) using the add-on packages "AER" (version 1.2.12), "DescTools" (version 0.99.54), "lmtest" (version 0.9.40), "moments" (version 0.14.1), "sandwich" (version 3.1.0), "stargazer" (version 5.2.3), and "xtable" (version 1.8.4) that are all available at CRAN. This replication package contains the following files: * README This file. * R/dataBenin.csv A CSV file that contains the (unprepared) data set. The variables in this file are described in file R/Variables.csv. This CSV file is imported by R script PrepareDataFoodNutrition.R. * R/Variables.csv A CSV file that describes the variables in the (unprepared) data set (file R/dataBenin.csv). * R/PrepareData.R An R script that imports the (unprepared) data set (file R/dataBenin.csv), calculates additional variables and add theses variables to the data set, removes observations that should not be used in the empirical analysis, and saves the prepared data set as CSV file (R/dataFoodNutrition.csv). * R/dataPrepared.csv A CSV file that contains the (prepared) data set used in the empirical analysis. This CSV file is created by the R script R/PrepareDataFoodNutrition.R. It is imported by the R scripts R/DescriptiveTab.R, FoodNutritionImpact.R, and GridSearchFoodSecurity.R. * R/DescriptiveTab.R An R script that imports the prepared data set (file R/dataFoodNutrition.R) and creates Table 1 of the paper ("Descriptive statistics", file paper/tables/DescriptiveStat.tex) as LaTeX file. * R/Estimations.R An R script that imports the prepared data set (file R/dataFoodNutrition.R), conducts all the analyses presented in the paper, creates Tables 2 and 3 of the paper ("OLS and IV regression results of the conditional associations between organic farming and outcomes" and "OLS and IV regression results of the conditional associations between organic farming and mediating outcomes", LaTeX files paper/tables/estMainReg.tex and paper/tables/estMedReg.tex), creates Figures 1 and 2 of the paper ("Estimated conditional associations of organic farming with outcomes" and "Estimated conditional associations of organic farming with mediating outcomes", 12 PDF files paper/figures/*.pdf), and 45 Tables that are included in the Supplementary Information: 36 tables with detailed regression results (LaTeX files paper/tables/tabels/est*.tex), one table with results of the first-stage probit regression (LaTeX file paper/tables/tabels/estProbit.tex), 6 tables with detailed regression results of estimations for testing the exogeneity of the instrument as suggested by Di Falco et al. (2011) (LaTeX files paper/tables/tabels/estOLS*Falco.tex), and 2 tables with coefficient bounds obtained as suggested by Oster (2019) (LaTeX files paper/tables/tabels/Oster*.tex). * R/GridSearch.R An R script that re-runs our regression analyses with different units of measurement of IHS-transformed variables and calculates various indicators that can can be used to assess the appropriateness of different units of measurement as suggested by Aihounton and Henningsen (2021) and that creates 28 Tables that are included in the Supplementary Information (LaTeX files paper/tables/tabels/grid*.tex). * R/functions/calcOsterBounds.R An R script that defines the R function calcOsterBounds() that calculates coefficient bounds using the method suggested by Oster (2019). This function is used by the R script R/FoodNutritionImpact.R. * R/functions/calcSemiElaOrg.R An R script that defines the R function calcSemiElaOrg() that calculates the semi-elasticity of various log-transformed or IHS-transformed variables with respect to the dummy variable for organic farming. This function is used by the R scripts R/FoodNutritionImpact.R and R/GridSearchFoodSecurity.R. * R/functions/createFormula.R An R script that defines the R function createFormula() that creates the regression formulas for the various empirical analyses that are presented in the paper. This function is used by the R scripts R/FoodNutritionImpact.R and R/GridSearchFoodSecurity.R. * R/functions/functionsTables.R An R script that defines various R functions that are used to create tables in LaTeX format. These functions are used by the R scripts R/FoodNutritionImpact.R and R/GridSearchFoodSecurity.R. * R/functions/predR2.R An R script that defines the R function predR2() that calculates the predictive R-squared value. This R script has been obtained from the replication package of the article: Aïhounton, G. B. D. and Henningsen, A. (2021). Units of measurement and the inverse hyperbolic sine transformation. The Econometrics Journal, 24(2):334–351. https://doi.org/10.1093/ectj/utaa032 The function consists of a slightly modified version of the code that is available at: https://tomhopper.me/2014/05/16/can-we-do-better-than-r-squared/ This function is used by the R script R/GridSearchFoodSecurity.R. * paper/figures/*.pdf 12 LaTeX files that are the (sub)figures in Figures 1 and 2 of the paper ("Estimated conditional associations of organic farming with outcomes" and "Estimated conditional associations of organic farming with mediating outcomes"). These 12 files are created by the R script R/FoodNutritionImpact.R. * paper/tables/DescriptiveStat.tex A LaTeX file that creates Table 1 of the paper ("Descriptive statistics"). This file is created by the R script R/DescriptiveTab.R. * paper/tables/estMainReg.tex A LaTeX file that creates Table 2 of the paper ("OLS and IV regression results of the conditional associations between organic farming and outcomes"). This file is created by the R script R/FoodNutritionImpact.R. * paper/tables/estMedReg.tex A LaTeX file that creates Table 3 of the paper ("OLS and IV regression results of the conditional associations between organic farming and mediating outcomes"). This file is created by the R script R/FoodNutritionImpact.R. * paper/tables/tabels/est*.tex 36 LaTeX files that create 36 tables that are included in the Supplementary Information and present detailed regression results. These 36 files are created by the R script R/FoodNutritionImpact.R. * paper/tables/tabels/estProbit.tex A LaTeX files that creates a table that is included in the Supplementary Information and presents the results of the first-stage probit regression. This file is created by the R script R/FoodNutritionImpact.R. * paper/tables/tabels/estOLS*Falco.tex 6 LaTeX files that create 6 tables that are included in the Supplementary Information and present detailed regression results for testing the exogeneity of the instrument as suggested by Di Falco et al. (2011). These 6 files are created by the R script R/FoodNutritionImpact.R. * paper/tables/tabels/Oster*.tex 2 LaTeX files that create 2 tables that are included in the Supplementary Information and present coefficient bounds obtined as suggested by Oster (2019). These 2 files are created by the R script R/FoodNutritionImpact.R. * paper/tables/tabels/grid*.tex 28 LaTeX files that create 28 tables that are included in the Supplementary Information and present various indicators for assessing the appropriateness of different units of measurement of IHS-transformed variables as suggested by Aihounton and Henningsen (2021). These 28 files are created by the R script R/GridSearchFoodSecurity.R

opencc-by-4.0Apr 2020View details →

SCALIBUR video 1: From household food waste to bioplastics and biopesticides

This video is part of a 3 part series explaining the innovative technologies being developed in the SCALIBUR project. The script is as follows: Each of us throws away a whopping 200 kilograms of food and organic waste each year. More and more cities separately collect this bio-waste. But what to do with it all? The SCALIBUR project is developing innovative technologies to convert household organic waste into valuable products. Where we see waste, SCALIBUR partners see a resource. One approach uses novel biochemical conversion, combining enzymatic hydrolysis and fermentation processes, to transform organic waste into sustainable bio-based products... Like bio-pesticides for more ecological agriculture, or biodegradable and compostable biopolymers for sustainable bioplastics. These technologies will help cities manage waste in a more sustainable and cost efficient way. And contribute to the creation of a truly circular bioeconomy in Europe.

opencc-by-4.0Aug 2022View details →

Survey on the Effects of COVID-19 on the Wellbeing of Mexico City Households (ENCOVID- 19 CDMX – JULY 2020)

Amid the COVID-19 outbreak, the ENCOVID-19 CDMX provides information on the well-being of Mexico City households in four main domains: labor, income, mental health, and food insecurity. It offers timely information to understand the social consequences of the pandemic and the lockdown measures. It is a cross-sectional telephone survey that, in addition to the four main domains and a set of COVID-19 related questions, includes key indicators to capture the impact of the pandemic on issues like education, social programs, and crime. This is the first dataset of the project, corresponding to July 2020, collected four months after the lockdown began in Mexico. Data collection was performed between the 8th and the 17th of July.