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This dataset contains terrestrial fluxes of nitrous oxide (N2O), methane (CH4) and ecosystem respiration (carbon dioxide (CO2)) calculated from static chamber measurements in riparian buffers of oil palm plantations on mineral soil, in Riau, Sumatra, Indonesia. Measurements were made monthly, from January 2019 until September 2021, with a break from April 2019 to October 2019 to allow for felling and replanting, and another break from January 2021 to June 2021 due to Covid-19 restrictions. To help to reduce the environmental impact of oil palm plantations, riparian buffers are now required by regulations in many Southeast Asian countries. The experiments were conducted to investigate the impact of greenhouse gas emissions from the riparian buffers. Research was funded through NERC grant NE/R000131/1 Sustainable Use of Natural Resources to Improve Human Health and Support Economic Development (SUNRISE) Greenhouse gas concentrations were measured using static chambers, enclosed for 45 minutes. Multiple regressions (including linear and hierarchical multiple regression) were fitted to calculate the best fit flux, using the RCflux R package, written by Dr Peter Levy (UKCEH).

computer-assisted telephone interview (CATI)Organization of Fieldwork The HFPS COVID-19 Baseline was administered between May 26 and June 14, 2020. Data were collected by trained NSO interviewers who individually made phone calls from the call center at the NSO. Since the country was not fully on lockdown during the preparation and data collection exercise, interviewers were allowed to be in the office after seeking permission from the local authorities and also taking measures to protect themselves like ensuring 2 meters space between individuals. Most interviews were conducted from the call center, some interviews that required call backs conducted from the enumerators' homes. Subsequent rounds also followed the same protocols. Dates on when each round was administered can be found in the Basic Information Document.Gift to Households As a show of appreciation for the households' participation, all households that gave consent to be interviewed were transferred 1000 Malawi Kwacha credit to their phones (even if their interviews are only partially completed).Pre-loaded Information Basic information on every household was pre-loaded in the CATI assignments for each interviewer. The information was pre-loaded to (1) assist interviewers in calling and identifying the household and (2) ensure that each pre-loaded person is properly addressed and easily matched to the most recent face-to-face visits. Basic household information (location, household head name, phone numbers of adult members and reference persons, etc.) was pre-loaded. The list of individuals from IHPS 2019 and their basic characteristics were uploaded.Respondents The HFPS COVID-19 had ONE RESPONDENT per household. The respondent was always the knowledgeable adult household member or for some rounds the person that was randomly selected. The respondent must be a member of the household.Additional information For additional information on the COVID-19 High Frequency Phone Survey of Households study, please visit the World Bank website. The Malawi Integrated Household Panel Survey (IHPS) conducted in 2019 served as the frame for the HFPS-COVID-19. This sample of households is representative nationally as well as by the urban/rural divide. In every visit of the IHPS, phone numbers are collected from interviewed households for all household members and 3 reference persons who are in close contact with the household in order to assist in locating and interviewing households who may have moved in subsequent waves of the survey. This comprehensive set of phone numbers as well as the already well-established relationship between NSO and the IHPS households made this an ideal frame from which to conduct the COVID-19 monitoring survey in Malawi. Among the 3,181 households interviewed during the IHPS in 2019, 2,337 (73%) provided at least one phone number. Around 85 percent of these households provided a phone number for at least one household member while the remaining 15 percent only provided a phone number for a reference person. Households with only the phone number of a reference person were expected to be more difficult to reach but were nonetheless included in the frame and deemed eligible for selection for the HFPS COVID-19. To obtain a nationally representative sample for the HFPS-COVID-19, the survey aimed to recontact the entire sample of households that had been interviewed during the Integrated Household Panel Survey (IHPS) 2019 round and that had phone numbers for at least one household member or a reference individual. Interviewers attempted to contact all 2,337 households that had either a contact for a household member or reference person in the baseline round of the phone survey. To obtain unbiased estimates from the sample, the information reported by households needs to be adjusted by a sampling weight (or raising factor) W_H. To construct the sampling weights, the following steps outlined in Weight Calculations for Panel Surveys with Subsampling and Split-off Tracking by Himelein, K. (2014) were considered. Himelein, K. (2014) outlines eight steps, of which six were followed to construct the sampling weights for the HFPS-HH: 1. Begin with base weights from the Malawi Integrated Household Panel Survey (IHPS) 2019 for each household. 2. Incorporate probability of sub-selection of round 1 unit for each of the phone survey households. 3. Pool the weights in Steps 1 and 2. 4. Derive attrition-adjusted weights for all individuals by running a logistic response propensity model based on characteristics of the household head (i.e. gender, primary language spoken, education, labor force status) and characteristics of the household (household size, food consumption score, assets, financial characteristics). 5. Trim weights by replacing the top three percent of observations with the 98th percentile cut-off point; and 6. Post-stratify weights to known population totals to correct for the imbalances across our sample. In doing so, it is ensured that the distribution in the survey matches the distribution in the IHPS. Malawi High-Frequency Phone Survey COVID-19 (HFPS COVID-19) was implemented by the National Statistical Office (NSO) on a monthly basis during the period of May 2020 and June 2021. The survey is part of a World Bank-supported global effort to support countries in their data collection efforts to monitor the impacts of COVID-19. The financing for data collection and technical assistance in support of the Malawi HFPS COVID-19 is provided by the United States Agency for International Development (USAID) and the World Bank. The households were drawn from the sample of households interviewed in 2019 as part of the Integrated Household Panel Survey (IHPS 2019). The 2019 IHPS data are representative at the national and urban/rural-levels and phone survey weights were calculated (1) to counteract selection bias associated with not being able to call IHPS households without phone numbers, and (2) to mitigate against non-response bias associated with not being able to interview all target IHPS households with phone numbers. Each month, the households were asked a set of core questions on the key channels through which individuals and households were expected to be affected by COVID-19-related restrictions. Food security, employment, access to basic services, coping strategies, and non-labor sources of income were channels thought likely to be impacted. The core questionnaire was complemented by questions on selected topics that rotated each month. The objective of HFPS COVID-19 is to monitor the socio-economic effects of the evolving COVID-19 pandemic in real time. These data are intended to be used by the Malawian government and stakeholders to help design policies to mitigate the negative impacts on its population. The HFPS COVID-19 in Malawi is designed to accommodate the evolving nature of the crises, including revision of the questionnaire on a monthly basis. Households in Malawi that are representative nationally, as well as by the country's urban/rural divide.. Smallest Geographic Unit: GPS coordinates Datasets: DS0: Study-Level Files DS1: Round 5 Data DS2: Round 8 Data DS3: Round 9 Data DS4: Round 10 Data

Related Publication: Ground vibrations recorded by fiber-optic cables reveal traffic response to COVID-19 lockdown measures in Pasadena, California Xin Wang, Zhongwen Zhan, Ethan Williams, Miguel Gonzalez Herraez, Hugo Fidalgo Martins, and Martin Karrenbach 2021-08-11 https://doi.org/10.1038/s43247-021-00234-3 eng Traffic data in Pasadena as monitored by the Pasadena Distributed Acoustic Sensing array. A MATLAB script is provided to read the data.

The BOP-F Scientific Use File 2022Q1 Version 01 consists of the Stata files BOPF.2022Q2.01_wave01.dta to BOPF.2022Q2.01_wave05.dta and BOPF.2022Q2.01_2021Q3.dta to BOPF.2022Q2.01_2022Q1.dta. For more details, see the BOP-F documentation on the website of the Deutsche Bundesbank. The sample for the survey is drawn from the universe of firms based in Germany with a taxable turnover of more than €22,000 or at least one employer subject to social security contributions which includes roughly 1 million firms. The drawing is a proportional random sample according to industry, region and size class, so that the selection probability is equal for all firms. Self-administered questionnaire: Web-based

This contribution was delivered on 5 May 2022 on the occasion of the hybrid 2022 edition of EUI State of the Union on ‘A Europe fit for the next generation?' EU Member States have adopted several initiatives to establish a legal and technical framework for digital identity. The European Commission has facilitated this development by offering guidance and promoting interoperable solutions through frameworks such as eIDAS and solutions developed within the European Interoperability Framework. At the same time, two years of COVID-19 pandemic have led at once to an acceleration of digital identity projects, and mounting concerns that widespread data collection and availability can lead to the risk of privacy violations, citizen profiling and mass surveillance. This session will explore the opportunities and challenges of emerging digital identity and digital payments, including the privacy, security concerns as well as the outstanding opportunities for inclusive growth, resilient and sustainable solutions for the society of the future. The discussion will also cover emerging attempts to develop joint European solutions for digital identity, including the recent joint declaration between the governments of Finland and Germany to support the progress of the proposed regulation on European digital identity, and to accelerate the development of joint European solutions based on digital identity.

Cross-Section Weights: For the Kenya National Bureau of Statistics (KNBS) and Random Digit Dialing (RDD) samples, to make the sample nationally representative of the current population of households with mobile phone access, the research team created weights in two steps. Step 1: Constructed raw weights combining the two national samples: The population consisted of (I) households that existed in 2015/16, and did not change phone numbers, (II) households that existed in 2015/16, but changed phone number, (III) households that did not exist in 2015/16. Abstracting from differential attrition, the weights from the 2015/16 Kenya Integrated Household Budget Survey (KIHBS) Computer-Assisted Personal Interviewing (CAPI) pilot made the KIHBS sample representative of type (I) households. RDD households were asked whether they existed in 2015/16, when they had acquired their phone number, and where they lived in 2015/16, allowing them to be classified into type (I), (II), and (III) households and assigned to KIHBS strata. The weights of each RDD household were adjusted to be inversely proportional to the number of mobile phone numbers used by the household, and scaled relative to the average number of mobile phone numbers used in the KIHBS within each stratum. RDD therefore gave a representative sample of type (II) and (III) households. The research team then combined RDD and KIHBS type (I) households by ex-post adding RDD households into the 2015/16 sampling frame and adjusting weights accordingly. Last, the research team combined the representative samples of type (I), type (II), and type (III), using the share of each type within each stratum from RDD (inversely weighted by number of mobile phone numbers). Variable: WEIGHT_RAW Step 2: Scaled the weights to population proportions in each county and urban/rural stratum: The research team used post stratification to adjust for differential attrition and response rates across counties and rural/urban strata. They scaled the raw weights from step 1 to reflect the population size in each county and rural/urban stratum as recorded in the 2019 Kenya Population and Housing Census conducted by the KNBS (2019 Kenya Population and Housing Census, Volume II: Distribution of Population by Administrative Units, December 2019, Kenya National Bureau of Statistics, https://www.knbs.or.ke/?wpdmpro=2019-kenya-population-and-housing-census-volume-ii-distribution-of-population-by-administrative-units). Variable: WEIGHT Panel Weights: To construct panel weights, the research team followed the approach outlined in Himelein (2014): "Weight Calculations for Panel Surveys with Subsampling and Split-off Tracking". One target respondent was followed in each household. Wherever households were split, only the current household of the target respondent was interviewed. The weights for the wave 1 and 2 balanced panel were constructed by applying the following steps to the full sample of Kenyan nationals: Wave 1 cross-sectional weights after post-stratification adjustment were used as a base. W_1 = W_wave1 Attrition adjustment through propensity score-based method: The predicted probability that a sample household was successfully re-interviewed in the second survey wave was estimated through a propensity score estimation. The propensity score (PS) was modeled with a linear logistic model at the level of the household. The dependent variable was a dummy indicating whether a household that completed the survey in wave 1 had also done so in wave. The following covariates were used in the linear logistic model: Urban/rural dummy; County dummies; Household head gender; Household head age; Household size; Dependency ratio; Dummy: Is anyone in the household working; Asset ownership: radio; Asset ownership: mattress; Asset ownership: charcoal jiko; Asset ownership: fridge; Wall material: 3 dummies; Floor materials: 3 dummies; Connection to electricity grid; Number of mobile phones numbers household uses; Number of phone numbers recorded for follow-up; and Sample dummy for estimation with national samples. Ranked households by PS and split into 10 equal groups Calculated attrition adjustment factor: ac (attrition correction) = the reciprocal of the mean empirical response rate for the propensity score decile Adjusted base weights for attrition: W_2 = W_1 * ac Trimmed top 1 percent of the weights distribution (), by replacing the weights among the top 1 percent of the distribution with the highest value of a weight below the cutoff. W_3 = trim(W_2) Applied post-stratification in the same way as for cross-sectional weights (step 2) Variable: WEIGHT_PANEL_W1_2. The balanced panel weights including waves 3, 4, 5, 6, and 7 were constructed using the same procedure. Variables: WEIGHT_PANEL_W1_2_3, WEIGHT_PANEL_W1_2_3_4, WEIGHT_PANEL_W1_2_3_4_5, WEIGHT_PANEL_W1_2_3_4_5_6, and WEIGHT_PANEL_W1_2_3_4_5_6_7. The World Bank in collaboration with the Kenya National Bureau of Statistics and the University of California, Berkeley conducted the Kenya COVID-19 Rapid Response Phone Survey (RRPS) to track the socioeconomic impacts of the COVID-19 pandemic and the recovery from it to provide timely data to inform policy. This collection contains information from seven waves of the COVID-19 RRPS, which was part of a panel survey that targeted Kenyan nationals and started in May 2020. The same households were interviewed every two months for five survey rounds in the first year of data collection and every four months thereafter, with interviews conducted using Computer Assisted Telephone Interviewing (CATI) techniques. Sampled households that were not reached in earlier waves were also contacted along with households that were interviewed before. The "WAVE" variable represents in which wave the households were interviewed in. All waves of this survey included information on household background, service access, employment, food security, income loss, transfers, health, and COVID-19 knowledge and vaccinations. The data contain information from two samples of Kenyan households. The first sample is a randomly drawn subset of all households that were part of the 2015/16 Kenya Integrated Household Budget Survey (KIHBS) Computer-Assisted Personal Interviewing (CAPI) pilot and provided a phone number. The second was obtained through the Random Digit Dialing method, by which active phone numbers created from the 2020 Numbering Frame produced by the Kenya Communications Authority were randomly selected. The samples covered urban and rural areas and were designed to be representative of the population of Kenya using cell phones. The sample size for each completed wave was: Wave 1: 4,061 Kenyan households Wave 2: 4,492 Kenyan households Wave 3: 4,979 Kenyan households Wave 4: 4,892 Kenyan households Wave 5: 5,854 Kenyan households Wave 6: 5,765 Kenyan households Wave 7: 5,633 Kenyan households The collection is organized into three levels. The first level is the Household Level Data, which contains household level information. The 'HHID' variable uniquely identifies all households. The second level is the Adult Level Data, which contains data at the level of adult household members. Each adult in a household is uniquely identified by the 'ADULT_ID' variable. The third level is the Child Level Data, which contains information for every child in the household. Each child in a household is uniquely identified by the 'CHILD_ID' variable. Pre-loaded Information: Basic household information was pre-loaded in the Computer Assisted Telephone Interviewing (CATI) assignments for each enumerator. The information, for example the household's location, household head name, phone numbers, etc., was used to help enumerators call and identify the target households. The list of individuals from the Kenya Integrated Household Budget Survey (KIHBS) Computer-Assisted Personal Interviewing (CAPI) pilot and their basic characteristics were uploaded as well as basic information from previous survey waves where available from wave 2 onward. Respondents: The COVID-19 Rapid Response Phone Survey (RRPS) had one respondent per household. For the sample from the 2015/16 KIHBS CAPI pilot, the target respondent was defined as the primary male or female adult household member. They were randomly chosen where both existed to maintain gender balance. If the target respondent was not available for a call, the field team spoke to any adult currently living in the household of the target respondent. If the target respondent was deceased, the field team spoke to any adults that lived with the target respondent in 2015/16. Finally, if the household from 2015/16 split up, the field team targeted anyone in the household of the target respondent but did not survey a household member that no longer lived with the target respondent. For the sample based on Random Digit Dialing, the target respondent was the owner the phone number that was randomly selected. Where the target respondent was not available for the interview, the research team spoke to any other adult household member of the target respondent. Series Information: The first five waves extended over a period of two months each, while waves 6 and 7 extended over a period of four months. Data collections were implemented between May 2020 and March 2022. The COVID-19 Rapid Response Phone Survey (RRPS) with Kenyan households included two samples. The first sample consisted of households that were part of the 2015/16 Kenya Integrated Household Budget Survey (KIHBS) Computer-Assisted Personal Interviewing (CAPI) pilot and provided a phone number. The 2015/16 KIHBS CAPI pilot was representative at the national level stratified by county and place of residence (urban and rural areas). At least one valid phone number was obtained for 9,007 households and all of them were included in the COVID-19 RRPS sample. The target respondent was the primary male or female household member from the 2015/16 KIHBS CAPI pilot. The second sample consisted of households selected using the Random Digit Dialing method. A list of random mobile phone numbers was created using a random number generator from the 2020 Numbering Frame produced by the Kenya Communications Authority. The initial sampling frame therefore consisted of 92,999,970 randomly ordered phone numbers assigned to three networks: Safaricom, Airtel, and Telkom. An introductory text message was sent to 5,000 randomly selected numbers to determine if numbers were in operation. Out of these, 4,075 were found to be active and formed the final sampling frame. There was no stratification and individuals that were called were asked about the households they lived in. The purpose of the Kenya COVID-19 Rapid Response Phone Survey (RRPS) was to track the socioeconomic impacts of the COVID-19 pandemic and the recovery from it to provide timely data to inform policy. The RRPS covers the following topics: household roster travel patterns and interactions employment food security income loss transfers subjective welfare (50 percent of sample) health COVID-19 knowledge and vaccinations household and social relations (50 percent of sample). computer-assisted personal interview (CAPI); computer-assisted telephone interview (CATI)To protect respondent privacy, some geographic variables were de-identified in the Household Level Public-Use Data (DS1) and Adult Level Public-Use Data (DS3). Please see the ICPSR Codebook processing notes for additional information.For additional information on the COVID-19 High Frequency Phone Survey of Households, Kenya, 2020-2021 study, please visit the World Bank website. Datasets: DS0: Study-Level Files DS1: Household Level Public-Use Data DS2: Household Level Restricted-Use Data DS3: Adult Level Public-Use Data DS4: Adult Level Restricted-Use Data DS5: Child Level Public-Use Data Households in Kenya that are representative of the population using cell phones. Smallest Geographic Unit: Town

Impact assessments for agriculture are partly based on projections delivered by models. Sectoral policies are becoming more and more interrelated. Hence, there is a need to improve the capacity of current models, connect them or redesign them to deliver on an increasing variety of policy objectives, and to explore future directions for agricultural modelling in Europe. SUPREMA (SUpport for Policy RElevant Modelling of Agriculture) is a project that has received funding from the European Union’s Horizon 2020 research and innovation programme (under grant agreement No 773499 SUPREMA) and that came to address this challenge by proposing a meta-platform that supports modelling groups linked already through various other platforms and networks. SUPREMA should help close the gaps between expectations of policy makers and the actual capacity of models to deliver relevant policy analysis. The SUPREMA model family includes a set of ‘core models’ that are already used in support of key European impact assessments in agriculture, trade, climate and bioenergy policies. One of the work-packages of the project ("Testing the SUPREMA model family") had the objective of testing the SUPREMA model family comparing model outcomes of three applications, including: (i) harmonize baseline assumptions and to the extent possible align baseline projections across models in the platform, and (ii) showcase the potential of the models in the meta-platform to respond to the upcoming and existing policy needs by means of two exploratory policy scenarios. This open dataset includes 3 components: 1 - (Baseline scenario) - the harmonized baselines (for 2030 and 2050). Please note that the baseline projections do not take into account the 2020 and possible future effects of the SARS-CoV-2 pandemic 2 - (Agricultural policy scenario) - medium-term horizon scenarios aiming comparing different models and/or model combinations, that have a large degree of ‘similarity’ such as joined indicator variables, i.e.: AGMEMOD-MITERRA (combined) modelling tool and the CAPRI model. The main focus was comparing model results in both agronomic and biophysical domains. Two variants of the agricultural policy scenario have been simulated and compared: (i) a CAP greening scenario; and (ii) a sustainable diet scenario. Both scenarios are hypothetical but have been chosen in such a way that the can provide insights in future policy issues as: (i) a further greening of the CAP fits in the policy implementation space as it is included in the ongoing policy reform of the CAP after 2020; and (ii) as increasing consumer awareness about healthy diets and their relation to meat consumption, as well as the footprint/climate consequences are highly relevant with respect to the Green Deal roadmap (December 2019) and the Farm to Fork Strategy (May 2020) documents that have been recently published. 3 - (Climate change mitigation scenario) - scenarios that quantifies the GHG mitigation potential of the EU’s agricultural sector and domestic and global impacts of the EU policy, conditional on different levels of GHG mitigation efforts in the rest of the world. These are obtained through the SUPREMA models CAPRI, GLOBIOM and MAGNET and include scenarios where the EU only takes ambitious unilateral climate action up to scenario where the 1.5 C target is pursued globally SUPREMA has been coordinated by Wageningen Research with the participation of EuroCARE, Thünen Institute, Swedish University of Agricultural Sciences (SLU), European Commission Joint Research Centre (JRC) and Research Executive Agency (REA), International Institute for Applied Systems Analysis (IIASA) and Universidad Politécnica de Madrid (UPM).

The sample of the HFPS-HH is a subsample of the 2018/19 Ethiopia Socioeconomic Survey (ESS). The ESS is built on a nationally and regionally representative sample of households in Ethiopia. ESS 2018/19 interviewed 6,770 households in urban and rural areas. In the ESS interview, households were asked to provide phone numbers either their own or that of a reference household (i.e. friends or neighbors) so that they can be contacted in the follow-up ESS surveys should they move from their sampled location. At least one valid phone number was obtained for 5,374 households (4,626 owning a phone and 995 with a reference phone number). These households established the sampling frame for the HFPS-HH. To obtain representative strata at the national, urban, and rural level, the target sample size for the HFPS-HH is 3,300 households; 1,300 in rural and 2,000 households in urban areas. In rural areas, we attempt to call all phone numbers included in the ESS as only 1,413 households owned phones and another 771 households provided reference phone numbers. In urban areas, 3,213 households owned a phone and 224 households provided reference phone numbers. To account for non-response and attrition all the 5,374 households were called in round 1 of the HFPS-HH. The total number of completed interviews in round one is 3,249 households (978 in rural areas, 2,271 in urban areas). The total number of completed interviews in round two is 3,107 households (940 in rural areas, 2,167 in urban areas). The total number of completed interviews in round three is 3,058 households (934 in rural areas, 2,124 in urban areas). The total number of completed interviews in round four is 2,878 households (838 in rural areas, 2,040 in urban areas). The total number of completed interviews in round five is 2,770 households (775 in rural areas, 1,995 in urban areas). The total number of completed interviews in round six is 2,704 households (760 in rural areas, 1,944 in urban areas). The total number of completed interviews in round seven is 2,537 households (716 in rural areas, 1,1821 in urban areas). The total number of completed interviews in round eight is 2,222 households (576 in rural areas, 1,646 in urban areas). The total number of completed interviews in round nine is 2,077 households (553 in rural areas, 1,524 in urban areas). The total number of completed interviews in round ten is 2,178 households (537 in rural areas, 1,641 in urban areas). The total number of completed interviews in round eleven is 1,982 households (442 in rural areas, 1,540 in urban areas). The total number of completed interviews in round twelve is 888 households (204 in rural areas, 684 in urban areas). To obtain unbiased estimates from the sample, the information reported by households needs to be adjusted by a sampling weight (or raising factor) W_H. To construct the sampling weights, we follow the steps outlined in Himelein, K. (2014), which outlines eight steps, of which we follow six, to construct the sampling weights for the High Frequency Phone Survey of Households (HFPS-HH): Begin with base weights from the Ethiopia Socioeconomic Survey (ESS) 2018/19 for each household Incorporate probability of sub-selection of round 1 unit for each of the phone survey households. We calculate the probability of selection for each of the 20 strata in the ESS (urban and rural in each of the 11 regions except for Addis Ababa where we only have an urban stratum) by creating the numerators as the number of completed phone interviews and the denominator as the number of households in the ESS for each stratum. Pool the weights in Steps 1 and 2. Derive attrition-adjusted weights for all individuals by running a logistic response propensity model based on characteristics of the household head (i.e. education, labor force status, demographic characteristics), characteristics of the household (consumption, assets, financial characteristics), and characteristics of the dwelling (house ownership, overcrowding). Trim weights by replacing the top two percent of observations with the 98th percentile cut-off point; and Post-stratify weights to known population totals to correct for the imbalances across our urban and rural sample. In doing so, we ensure that the distribution in the survey matches the distribution in the ESS. *Additional technical details and explanations on each of the steps briefly outlined above can be found in Himelein, K. (2014). The potential impacts of the COVID-19 pandemic in Ethiopia are expected to be severe on Ethiopian households' welfare. To monitor these impacts on households, the team selected a subsample of households that had been interviewed for the Living Standards Measurement Study (LSMS) in 2019, covering urban and rural areas in all regions of Ethiopia. The 15-minute questionnaire covers a series of topics, such as knowledge of COVID and mitigation measures, access to routine healthcare as public health systems are increasingly under stress, access to educational activities during school closures, employment dynamics, household income and livelihood, income loss and coping strategies, and external assistance. The survey is implemented using Computer Assisted Telephone Interviewing, using a modular approach, which allows for modules to be dropped and/or added in different waves of the survey. Survey data collection started at the end of April 2020 and households are called back every three to four weeks for a total of seven survey rounds to track the impact of the pandemic as it unfolds and inform government action. This provides data to the government and development partners in near real-time, supporting an evidence-based response to the crisis. The sample of households was drawn from the sample of households interviewed in the 2018/2019 round of the Ethiopia Socioeconomic Survey (ESS). The extensive information collected in the ESS, less than one year prior to the pandemic, provides a rich set of background information on the COVID-19 High Frequency Phone Survey of households which can be leveraged to assess the differential impacts of the pandemic in the country. The COVID-19 High Frequency Phone Survey of Households, Ethiopia covered the following topics: Household Roster (Rounds 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12) Knowledge Regarding the Spread of COVID-19 (Round 1) Behavior and Social Distancing (Rounds 1, 3, 6, 7) Access to Basic Services (Rounds 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11) Employment (Rounds 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11) Income Loss and Coping (Rounds 1, 2, 3, 4, 5, 6, 7) Food Security (Rounds 1, 2, 3, 4, 11) Aid and Support/ Social Safety Nets (Rounds 1, 2, 3, 4, 5, 6, 7, 9) Agriculture (Rounds 3, 4, 5, 6, 7, 9) Locusts (Rounds 4, 6, 7) WASH (Rounds 4, 9) Education and Childcaring (Rounds 8, 11) Credit (Round 8) Migration (Round 8) Return Migration (Round 8) Tax (Round 9) SWIFT (Round 11) Youth Aspirations and Employment (Round 12) Datasets: DS0: Study-Level Files DS1: Round 1 Microdata DS2: Round 2 Microdata DS3: Round 3 Microdata DS4: Round 4 Microdata DS5: Round 5 Microdata DS6: Round 6 Microdata DS7: Round 7 Microdata DS8: Round 8 Microdata DS9: Round 9 Microdata DS10: Round 10 Microdata DS11: Round 11 Microdata DS12: Round 11 Education Microdata DS13: Round 12 Microdata The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories. Smallest Geographic Unit: kebele computer-assisted telephone interview (CATI)

Impact assessments for agriculture are partly based on projections delivered by models. Sectoral policies are becoming more and more interrelated. Hence, there is a need to improve the capacity of current models, connect them or redesign them to deliver on an increasing variety of policy objectives, and to explore future directions for agricultural modelling in Europe. SUPREMA (SUpport for Policy RElevant Modelling of Agriculture) is a project that has received funding from the European Union’s Horizon 2020 research and innovation programme (under grant agreement No 773499 SUPREMA) and that came to address this challenge by proposing a meta-platform that supports modelling groups linked already through various other platforms and networks. SUPREMA should help close the gaps between expectations of policy makers and the actual capacity of models to deliver relevant policy analysis. The SUPREMA model family includes a set of ‘core models’ that are already used in support of key European impact assessments in agriculture, trade, climate and bioenergy policies. One of the work-packages of the project ("Testing the SUPREMA model family") had the objective of testing the SUPREMA model family comparing model outcomes of three applications, including: (i) harmonize baseline assumptions and to the extent possible align baseline projections across models in the platform, and (ii) showcase the potential of the models in the meta-platform to respond to the upcoming and existing policy needs by means of two exploratory policy scenarios. This open dataset includes 3 components: 1 - (Baseline scenario) - the harmonized baselines (for 2030 and 2050). Please note that the baseline projections do not take into account the 2020 and possible future effects of the SARS-CoV-2 pandemic 2 - (Agricultural policy scenario) - medium-term horizon scenarios aiming comparing different models and/or model combinations, that have a large degree of ‘similarity’ such as joined indicator variables, i.e.: AGMEMOD-MITERRA (combined) modelling tool and the CAPRI model. The main focus was comparing model results in both agronomic and biophysical domains. Two variants of the agricultural policy scenario have been simulated and compared: (i) a CAP greening scenario; and (ii) a sustainable diet scenario. Both scenarios are hypothetical but have been chosen in such a way that the can provide insights in future policy issues as: (i) a further greening of the CAP fits in the policy implementation space as it is included in the ongoing policy reform of the CAP after 2020; and (ii) as increasing consumer awareness about healthy diets and their relation to meat consumption, as well as the footprint/climate consequences are highly relevant with respect to the Green Deal roadmap (December 2019) and the Farm to Fork Strategy (May 2020) documents that have been recently published. 3 - (Climate change mitigation scenario) - scenarios that quantifies the GHG mitigation potential of the EU’s agricultural sector and domestic and global impacts of the EU policy, conditional on different levels of GHG mitigation efforts in the rest of the world. These are obtained through the SUPREMA models CAPRI, GLOBIOM and MAGNET and include scenarios where the EU only takes ambitious unilateral climate action up to scenario where the 1.5 C target is pursued globally SUPREMA has been coordinated by Wageningen Research with the participation of EuroCARE, Thünen Institute, Swedish University of Agricultural Sciences (SLU), European Commission Joint Research Centre (JRC) and Research Executive Agency (REA), International Institute for Applied Systems Analysis (IIASA) and Universidad Politécnica de Madrid (UPM).