Find research datasets worth reusing

FULFILL dataset round 1 Delhi and Mumbai (India)

This dataset and codebook correspond to the initial round of survey data gathered in Delhi and Mumbai (India) in 2023, within the project FULFILL - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes.  As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from six countries: Denmark, France, Germany, Italy, Latvia, and India. In the first round of the survey, we recruited a representative sample of approximately 2000 households in each country, taking into account both the individual and household perspectives. In order to consider sufficiency-oriented lifestyles not only in Europe but also in the Global South, we conducted a similar survey in India. More specifically, we adjusted the survey to fit the context (e.g., including cooling) and, due to the large size and diversity within India, we focused data collection on two Mega Cities (>10Mio inhabitants), namely Mumbai and Delhi. Due to the different cultural context and in exchange with Indian researchers and the supporting market research institute, we decided to change the methodology for data collection from an online survey to face-to-face interviews. The survey includes a quantitative assessment of the carbon footprint in various domains of life, such as housing, mobility, and diet. In addition to this, the survey also measures socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. Furthermore, the survey includes measures of quality of life, encompassing aspects such as health and well-being, environmental quality, financial security, and comfort.

FULFILL dataset round 1 Latvia

<table> <tbody> <tr> <td>This dataset and codebook correspond to the initial round of survey data gathered in Latvia in 2022, within the project FULFILL - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes.  As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from six countries: Denmark, France, Germany, Italy, Latvia, and India. In the first round of the survey, we recruited a representative sample of approximately 2000 households in each country, taking into account both the individual and household perspectives. The survey includes a quantitative assessment of the carbon footprint in various domains of life, such as housing, mobility, and diet. In addition to this, the survey also measures socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. Furthermore, the survey includes measures of quality of life, encompassing aspects such as health and well-being, environmental quality, financial security, and comfort.</td> </tr> </tbody> </table>

FULFILL dataset round 1 Italy

<table> <tbody> <tr> <td>This dataset and codebook correspond to the initial round of survey data gathered in Italy in 2022, within the project FULFILL - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes.  As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from six countries: Denmark, France, Germany, Italy, Latvia, and India. In the first round of the survey, we recruited a representative sample of approximately 2000 households in each country, taking into account both the individual and household perspectives. The survey includes a quantitative assessment of the carbon footprint in various domains of life, such as housing, mobility, and diet. In addition to this, the survey also measures socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. Furthermore, the survey includes measures of quality of life, encompassing aspects such as health and well-being, environmental quality, financial security, and comfort.</td> </tr> </tbody> </table>

FULFILL dataset round 1 France

<table> <tbody> <tr> <td>This dataset and codebook correspond to the initial round of survey data gathered in France in 2022, within the project FULFILL - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes.  As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from six countries: Denmark, France, Germany, Italy, Latvia, and India. In the first round of the survey, we recruited a representative sample of approximately 2000 households in each country, taking into account both the individual and household perspectives. The survey includes a quantitative assessment of the carbon footprint in various domains of life, such as housing, mobility, and diet. In addition to this, the survey also measures socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. Furthermore, the survey includes measures of quality of life, encompassing aspects such as health and well-being, environmental quality, financial security, and comfort.</td> </tr> </tbody> </table>

FULFILL dataset round 1 Denmark

This dataset and codebook correspond to the initial round of survey data gathered in Denmark in 2022, within the project FULFILL - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes.  As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from six countries: Denmark, France, Germany, Italy, Latvia, and India. In the first round of the survey, we recruited a representative sample of approximately 2000 households in each country, taking into account both the individual and household perspectives. The survey includes a quantitative assessment of the carbon footprint in various domains of life, such as housing, mobility, and diet. In addition to this, the survey also measures socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. Furthermore, the survey includes measures of quality of life, encompassing aspects such as health and well-being, environmental quality, financial security, and comfort.

opencc-by-4.0Jul 2024View details →

DAS Control over the spatial correlation of silica perforations in thin films as a function of solution conditions

Dataset production context : A perforated silica layer with structural correlation is engineered using sol-gel chemistry, applied to large-scale flat and curved sur-faces. The anion(s) used in the preparation give tailored spatial correlation, and control over perforation size and density. Surface structuration is rapidly and reproducibly created using water and salts as inexpensive and ecofriendly reagents.

Nairobi and Istanbul Multi-Hazard Interrelationships Database

Nairobi and Istanbul Multi-Hazard Interrelationships Database 10.5281/zenodo.13220740 This Nairobi and Istanbul Multi-Hazard Interrelationships Database uses a critical review of 135 sources (academic and grey literature, databases, online and social media), to identify the breadth of natural hazard types that might influence Nairobi (19 possible natural hazard types) and Istanbul (23 hazard types). We further identified hazard interrelationship pairs (e.g., an earthquake triggering landslides) in Nairobi (88 potential hazard interrelationship pairs) and Istanbul (105 hazard pairs) out of a possible 576 interrelationships. This extensive Excel (140 kb) database accompanies the paper &Scaron;akić Trogrlić et al. (2024). The Nairobi and Istanbul Multi-Hazard Interrelationships Database consists of the following eight tabs (in brackets the number of rows [R] × columns [C] of information): <ul> <li>Excel Tab A.          Single Hazard Evidence Nairobi (87R×16C)</li> <li>Excel Tab B.          Single Hazard Evidence Istanbul (68R×11C)</li> <li>Excel Tab C.          Hazard Interrelationships Nairobi (118R×14C)</li> <li>Excel Tab D.          Hazard Interrelationships Istanbul (122R×13C)</li> <li>Excel Tab E.          Definitions (Evidence Types) (7 definitions)</li> <li>Excel Tab F.          Definitions (Hazards) (31R×5C)</li> <li>Excel Tab G.         Definitions (Hazard Relations) (3 definitions)</li> <li>Excel Tab H.          References</li> </ul>  For Nairobi (Tab A) and Istanbul (Tab B), each row in the database presents a source of evidence of a single hazard type influencing Nairobi or Istanbul. We compiled multiple evidence sources for many of the hazard types, each on its own row. In columns, we describe the evidence through various qualifiers, including the following: <ul> <li>identifying the hazard type (24 possible hazard types)</li> <li>source information and URL link</li> <li>source content</li> <li>hazard interrelationships</li> <li>anthropogenic influences</li> <li>video evidence</li> <li>source reflections</li> </ul>  For Nairobi (Tab C) and Istanbul (Tab D), each row in the databases presents a source of evidence of a hazard interrelationship in Nairobi or Istanbul. In columns, we describe the evidence through various qualifiers, including the following: <ul> <li>primary hazard (24 hazard types)</li> <li>secondary hazard (where applicable, the same 24 hazards as for the primary hazard)</li> <li>the generic description of hazard interrelationship mechanisms</li> <li>whether the relationship is triggered or increased probability or both</li> <li>source information and URL link</li> <li>source content (e.g., interrelationship type, description, and hazard sequence)</li> </ul> The reader is referred to &Scaron;akić Trogrlić et al. (2024) for a detailed description of the methodology by which this database was constructed. References <a name="_Hlk154927256"></a>&Scaron;akić Trogrlić, R., Thompson, H. E., Yahya Menteşe, E., Hussain, E., Gill, J. C., Taylor, F. E., Mwangi, E., Öner, E., Bukachi, V. G., & Malamud, B. D. (2024). Multi-hazard interrelationships and risk scenarios in urban areas: A case of Nairobi and Istanbul. Earth’s Future. 12, e2023EF004413. https://doi.org/10.1029/2023EF004413

Dataset of "Strain-Engineered Ir Shell Enhances Activity and Stability of Ir-Ru Catalysts for Water Electrolysis: An Operando Wide-Angle X-Ray Scattering Study"

Ir-Ru alloys with high Ru content serve as stable and highly active catalysts for the oxygen evolution reaction (OER) in Proton Exchange Membrane Water Electrolyzers (PEM-WEs), enabling efficient operation with remarkably low Ir loadings (150 µg cm-²). Despite this, the mechanisms behind their enhanced stability remain unclear. In this study, we employ operando Wide-Angle X-ray Scattering (WAXS) and complementary ex-situ techniques to investigate the structural evolution of these magnetron-sputtered alloys within a PEM-WE cell. Our results reveal that, upon potential application, Ru is leached from the surface, leading to the formation of a bimetallic Ir-Ru@IrOx core-shell structure. The Ir shell, significantly strained by the underlying Ir-Ru core, exhibits substantially higher catalytic activity than pure Ir. Notably, the Ir-Ru 25:75 catalyst shows superior stability over Ir-Ru 50:50, despite its higher Ru content, due to a more robust Ir shell that protects subsurface Ir and Ru from oxidation and dissolution. This study not only clarifies the performance-enhancing mechanisms of Ir-Ru catalysts but also suggests that other, more economical materials such as Co, Os, or Ti could serve as effective cores in Ir-M systems, offering a pathway to more cost-effective catalysts for PEM-WE applications.

opencc-by-4.0Jul 2024View details →

Exploring Vocatives in Folk Songs of the Podillia Region

This dataset is based on the folklore collection Pisni Podillia: zapysy Nasti Prysiazhniuk v seli Pohrebyshche. 1920-1970 rr.  (Myshanych 1976). The collection consists of 850 songs, encompassing 13,005 lines and 78,888 tokens. Vocatives were manually distinguished and recorded in a separate column in the corpus without the assistance of RStudio, due to the complexity of distinguishing vocatives in Ukrainian. Vocatives in Ukrainian folk songs were analysed using the R programming language along with RStudio.  Code written for text analysis in Estonian Literary Museum.    This dataset consists of the following files: 1. vocatives_Podillia_folk_songs.R: R script used for analyzing the corpus, including vocative counting, song length analysis, POS-tag analysis,  semantic group and structural types analysis.  2. corpus_vocatives.csv: Contains the text data of Podillia folk songs with manually distinguished vocatives.  3. corpus_POS_tokens.csv: Contains verified the POS-tagged tokens of the corpus.

opencc-by-4.0Jan 2023View details →

Mappings for "Developing a Scalable Annotation Method for Large Datasets That Enhances Alarms With Actionability Data to Increase Informativeness: Mixed Methods Approach"

Studies identified false and non-actionnable alarms as a factor for alarm fatigue in intensive care units. To annotate patient alarms, and analyse the alarm situation in intensive care units, we conceptualized and performed data mappings related to airway management and medication interventions. The mappings were based on information retrieved from the patient data management system (PDMS) and clinical expertise. For the airway management mappings, we used additional resources such as ISO 19223:2019 or ventilator instruction manuals. The mappings do not include patient data. As the mappings are generic, they could be used in other contexts than alarm annotation and research. 1. Respiratory Management Mappings: <ul> <li>General tables summarizing the 1) categories based on ISO 19223:2019 to describe respiratory support therapies (RSTs), 2) defining the invasiveness level of a RST and 3) listing the abbreviations used in the mappings</li> <li> Tables including PDMS entries for airway devices (ADs), ventilation devices (VDs), and ventilation modes (VMs) </li> <li> Mapping of AD entries (from the PDMS) to defined categories </li> <li> Mapping of VDs, VMs, and ADs to defined RSTs, including information on invasiveness </li> <li> Table specifying suitable ventilation parameters in the context of each RST </li> </ul> 2. Medication Mappings: <ul> <li> General tables providing information on physiological alarm conditions (PACs), interventions, routes, and techniques of administration of interest </li> <li> Mapping of routes of administration to techniques of administration including PDMS entries </li> <li> Mapping of active ingredients (including SNOMED CT Fully Specified Names and Identifiers), related PDMS information, and routes and techniques of administration to defined PAC and interventions </li> </ul>

opencc-by-4.0Sep 2024View details →

Vegetation survey (BACI and Paired-plots) from arid central Australia for impacts of buffel grass on resident native plant communities

The data set accompanies the accepted paper in Ecosphere. The data set includes two experimental appraoches to assess the spread and impacts of buffel grass, Cenchrus cilairis, in the Aṉangu Pitjantjatjara Yankunytjatjara (APY) Lands of arid central Australia: a Before-After-Control-Impact (BACI) experiment over 25 years at 15 sites (surveyed in 1994-95 and 2018-19), and a spatially paired-plot (randomised-block) experiment at 18 sites (surveyed in 2018-19). Both experiments spanned two geographic regions (~ 300 km apart) and multiple vegetation communities amongst flat plains and rocky hills landforms. Each experimental design has a plant species data set, and a data set that includes site variables and summed relative cover of plant functional groups. Data collection methodology is described in the accompanying paper, and summarised here. Each site was one hectare in size. The ecological data was collected in accordance with standard biological survey methods in South Australia (Heard and Channon 1997), including recording of plant species and cover abundance, life form, height class and habitat variables including percent bare earth, litter, rock/strew and soil type (clay percent). Fire history for the previous 25 years was also available from fire scar mapping. Species cover-abundance was estimated in the field using a modified Braun-Blanquet scale and later converted to a raw continuous variable based on the mid-point of the cover class: 1% (1-10 plants, <5% cover); 2% (sparsely present, <5% cover; 3% (plentiful but <5% cover); 15% (5 to 25% cover class); 37% (25 to 50% cover class); 63% (50 to 75% cover class).  Buffel grass was recorded on the same scale. Plant species were vouchered and identification checked post-field by the South Australian Hebarium. Plant taxonomy reflects current names (as of 2015) in the Biological Databases of South Australia and taxonomy was aligned between the 1990s and 2020s decades. Recently some species have been split into multiple species (e.g. Acacia aneura, Mulga) but this latest taxonomy was not adopted to retain taxonomic alignment within the dataset. The raw mid-point percent cover was converted to relative percent cover by dividing each species’ (or groups’) raw cover by the summed cover of all species at that site (including buffel grass + understorey + overstorey species). Classification of plants into functional groups was based on field assessed (1) height class + (2) life form, and literature-derived (3) life strategy (perennial or annual) + (4) Native status to South Australia. Height classes were grouped into overstorey (>1m in height) and understorey (≤1m). Summed relative cover for each functional group per site is included in the site and cover data sets to facilitate modelling of cover with site variables. The plant species data sets is the full list of species and cover abundance recorded at each site which can be used for analysis of community composition, diversity, turnover or individual species change. Sensitive species (one species in this dataset) has had the coordinates denatured by 10km due according to the requirements of the Biological Database of South Australia for sensitive species. All coordinates provided in MGA 52 Eastings and Northings (UTM, Australian National Grid).  The authors wish to acknowledge Traditional Owners and Aṉangu Pitjantjatjara Yankunytjatjara (APY) Lands Organisation who gave permission for collaboration, data collection, photographs and reporting on and about their Traditional Lands. Data is jointly the Intellectual Property of Aṉangu as the Traditional Owners and the author team, and approval has been granted for research and publication use with appropriate acknowledgment of Aṉangu and the author team. The 1990s baseline data is also the Intellectual Property of the South Australian Government and is made publicly available under a licencing agreement with the Biological Databases of South Australia (licence number 2412). Many people assisted in the field during the 1990s and 2020s vegetation surveys and are wholly acknowledged. APY Land Management, Alinytjara Wilurara Landscape Board, Central Land Council, Ten Deserts Project, Charles Darwin University, South Australian Department for Environment and Water, State Herbarium of South Australia, Holsworth Wildlife Research Endowment, Jill Landsberg Trust and Ecological Society of Australia all provided either funding and/or in-kind support of the project. Study conducted with APY Executive Board approval, South Australian Scientific Permit Q26782 and Northern Territory Wildlife Permit 63104.

BioReCer Main biological feedstock flows database

BioReCer aims at assessing and complementing current certification schemes for biological resources according to the new EU sustainability goals to enhance bio-based circular systems. This will be achieved by including new criteria that align with EU taxonomy and EU corporate due diligence regulations into guidelines for certifying biological resources’ sustainability, origin, tracking and traceability (T&T), and by ensuring applicability at EU and global scale. By promoting the sustainability and trade of biological resources, BioReCer will increase the added value, use, as well as social acceptance of bio-based products. Part of the specific objectives of the project is to map the current European biomass flows in 4 main sectors: <ol> <li>Fishery</li> <li>Urban waste and wastewater</li> <li>Agriculture</li> <li>Forestry</li> </ol> This database presents information on over 30 biomass feedstocks from across this sectors and is estimated to cover approx. 90% of the available secondary biological feedstocks in the EU.  Table 1 shows the amount of secondary biomass produced by feedstock and their fates. Table 2 shows the amount of primary biomass prodused, imported and exported to and from Europe. This database is based on the work done for derivable 2.1 - Main biological feedstocks flows.

opencc-by-4.0Apr 2020View details →

Market Power / Import demand elasticity faced by an exporter at 6-digit HS level from Solleder (2020)

Description This dataset contains the market power of exporters at the country level for more than 4000 6-digit HS codes (HS 1992 / H0) from Solleder (2020). Market power is proxied by the inverse of the import demand elasticity faced by the exporting country. Elasticities are estimated following the method developed by Kee et al. (2008). For more information, please refer to Solleder (2020). The dta file can be opened with STATA 14 or above. The csv file is a comma-separated value file. The separator is ',', and the first row is variable names. The content is the same in both files. Variables are: <ul> <li>exporter: ISO 3166 3-character country codes, string; </li> <li>commoditycode: product  6-digit HS codes in HS revision 1992 (H0), string;</li> <li>epsilon: import demand elasticity faced by the exporter, numeric;</li> <li>epsilon_se: standard error of epsilon, numeric;</li> <li>marketpower: market power, inverse of the absolute value of the import demand elasticity faced by the exporter, numeric.</li> </ul>   Reference <div> <div>Kee H.L., A. Nicita, M. Olarreaga 2008 'Import demand elasticities and trade distortions' Rev. Econ. Stat., 90 (4), pp. 666-682</div> <div> </div> <div>Solleder J.M. 2020 'Market power and export taxes' European Economic Review, Volume 125, 103425, ISSN 0014-2921, <a href="https://doi.org/10.1016/j.euroecorev.2020.103425">https://doi.org/10.1016/j.euroecorev.2020.103425</a>.</div> </div>

opencc-by-4.0Jul 2024View details →

Datasets for testing the robustness of LiDAR vegetation metrics to varying point densities

The calculation of vegetation metrics from LiDAR point clouds might be affected by the available point density of a dataset. Testing how the same LiDAR vegetation metrics differ with different point densities can therefore inform about their robustness for upscaling metrics to other areas or other LiDAR point clouds. The datasets made available here were generated to test the robustness of LiDAR vegetation metrics to varying point densities and spatial resolutions (i.e., plots of 1 × 1 m, 2 × 2 m, 5 × 5 m and 10 × 10 m size). A total of 25 LiDAR vegetation metrics representing different aspects of vegetation height, vegetation cover and structural complexity were tested (see metric definition in Kissling et al. 2023, <a href="https://doi.org/10.1016/j.dib.2022.108798">https://doi.org/10.1016/j.dib.2022.108798</a>). The metric calculation was similar to the metric calculation in the Laserchicken software (Meijer et al. 2020, <a href="https://doi.org/10.1016/j.softx.2020.100626">https://doi.org/10.1016/j.softx.2020.100626</a>) and the Laserfarm workflow (Kissling et al. 2022, https://doi.org/10.1016/j.ecoinf.2022.101836). The Dutch AHN4 dataset from the years 2020–2022 with a point density of 20–30 points/m2 was used. Initially, 100 plots (i.e., squared polygons around centre points) were randomly placed across the Netherlands in Dutch Natura 2000 sites that predominantly contain woodland habitats (using shapefiles from the European Environmental Agency). For each centre point, square polygons of the desired resolutions (i.e., 1 × 1 m, 2 × 2 m, 5 × 5 m or 10 × 10 m plot size) were generated. The square polygons were subsequently used to clip the LiDAR point clouds from the Dutch AHN4 point cloud dataset. Since not all locations of the 100 randomly placed plots contained points, the actual sample sizes were slightly smaller than 100, i.e., 94 plots for the 1 × 1 m, 2 × 2 m and 5 × 5 m resolution and 95 plots for the 10 × 10 m resolution. Metrics were calculated with the original point density of the Dutch AHN4 dataset (20–30 points/m2) and with six systematically down-sampled point clouds for the same plots (i.e., keeping 5%, 10%, 20%, 40%, 60% and 80% of the points in the original point clouds). For each clipped point cloud of a plot at a given resolution, the points were first sorted according to their GPS acquisition time (from earliest to latest). Points were then systematically discarded and only 5%, 10%, 20%, 40%, 60% and 80% of the points in the original point clouds were kept. The kept points were used for calculating the 25 LiDAR vegetation metrics.

opencc-by-4.0Sep 2024View details →

Labeled Time Series Data of Force/Torque for Monitoring Assembly Processes with a Delta Robot

This dataset comprises 524 recordings of 6-dimensional time series data, capturing forces in three directions and torques in three directions during the assembly of small car model wheels. The data was collected using an equidistant sampling method with a sampling period of 0.004 seconds. Each time series represents the process of assembling one wheel, specifically the placement of a tire onto a rim, and includes a label indicating whether the assembly was successful (OK). The wheels were assembled in batches of four, and the recordings were obtained over six different days. The labels of recordings from two (days 3 and 4) of the six days are invalid as described in [1].  The labels presented in this data set are only binary (they do not describe the reason of the failure). The labels of recordings from days 5 and 6 are created by human while the other labels came from a convolutional neural network based computer vision classifier and can be inaccurate as described in section 5.4 of [1].    <h4>Dataset Structure:</h4> <ul> <li>File: <code>ForceTorqueTimeSeries.csv</code> <ul> <li>Columns: <ul> <li><code>idx (1-524)</code>: Index of the recording corresponding to the assembly of one wheel.</li> <li><code>label (true/false)</code>: Indicates whether the assembly was successful (TRUE = product is OK).</li> <li><code>meas_id (1-6)</code>: Identifier for the day on which the recording was made (refer to Table 2.1 in [1]).</li> <li><code>force_x</code>: X-component of the force measured by the sensor mounted on the delta robot's end effector.</li> <li><code>force_y</code>: Y-component of the force.</li> <li><code>force_z</code>: Z-component of the force.</li> <li><code>torque_x</code>: X-component of the torque.</li> <li><code>torque_y</code>: Y-component of the torque.</li> <li><code>torque_z</code>: Z-component of the torque.</li> </ul> </li> </ul> </li> </ul> <h4>Additional Files:</h4> <ul> <li><code>IMG_3351.MOV</code>: A video demonstrating the assembly process for one batch of four wheels.</li> <li><code>F3-BP-2024-Trna-Ales-Ales Trna - 2024 - Anomaly detection in robotic assembly process using force and torque sensors.pdf</code>: Bachelor thesis [1] detailing the dataset and preliminary experiments on fault detection.</li> <li><code>F3-BP-2024-Hanzlik-Vojtech-Anomaly_Detection_Bachelors_Thesis.pdf</code>: Bachelor thesis [2] describing the data acquisition process.</li> </ul> <h3>References:</h3> <ol> <li>Trna, A. (2024). Anomaly detection in robotic assembly process using force and torque sensors [Bachelor’s thesis, Czech Technical University in Prague].</li> <li>Hanzlik, V. (2024). Edge AI integration for anomaly detection in assembly using Delta robot [Bachelor’s thesis, Czech Technical University in Prague].</li> </ol>

FULFILL dataset - diet policy acceptability - health information provision France

This dataset represents survey data on sufficiency-oriented policy acceptability in regard to dietary consumption. The study was part of the second round surveys in France in 2023 within the FULFILL project - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes. As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from three countries: France, Italy, and Latvia, with representative sampling (age, income, gender, current region). In this survey on the acceptability of sufficiency-oriented diet policies we recruited a representative sample with approximately 800 participants from France, taking into account primarily the individual perspective, added by some questions on the household level. The central part of the survey includes the randomised provision of information on the health-risks associated with meat consumption. We were interested in peoples' acceptability on three majorly discussed and sufficiency-relevant policies, i.e. meat tax, carbon label or meat-free day at public canteens. We investigated if the information provision impacted people's acceptability (overall, self vs. others perspective). We measured several control variables (socio-economics such as age, gender, income, education, household size, life stage, ideological measures such as political orientation or attitudinal measures such as sufficiency orientation and climate change denial). A quantitative assessment of the carbon footprint in the food consumption domain was also included.

FULFILL dataset - diet policy acceptability - health information provision Latvia

This dataset represents survey data on sufficiency-oriented policy acceptability in regard to dietary consumption. The study was part of the second round surveys in Latvia in 2023 within the FULFILL project - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes. As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from two countries: France, Italy, and Latvia, with representative sampling (age, income, gender, current region). In this survey on the acceptability of sufficiency-oriented diet policies we recruited a representative sample with approximately 500 participants from Latvia, taking into account primarily the individual perspective, added by some questions on the household level. The central part of the survey includes the randomised provision of information on the health-risks associated with meat consumption. We were interested in peoples' acceptability on three majorly discussed and sufficiency-relevant policies, i.e. meat tax, carbon label or meat-free day at public canteens. We investigated if the information provision impacted people's acceptability (overall, self vs. others perspective). We measured several control variables (socio-economics such as age, gender, income, education, household size, life stage, ideological measures such as political orientation or attitudinal measures such as sufficiency orientation and climate change denial). A quantitative assessment of the carbon footprint in the food consumption domain was also included.

FULFILL dataset - diet policy acceptability - health information provision Italy

This dataset represents survey data on sufficiency-oriented policy acceptability in regard to dietary consumption. The study was part of the second round surveys in Italy in 2023 within the FULFILL project - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes. As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from three countries: France, Italy, and Latvia, with representative sampling (age, income, gender, current region). In this survey on the acceptability of sufficiency-oriented diet policies we recruited a representative sample with approximately 800 participants from each country, taking into account primarily the individual perspective, added by some questions on the household level. The central part of the survey includes the randomised provision of information on the health-risks associated with meat consumption. We were interested in peoples' acceptability on three majorly discussed and sufficiency-relevant policies, i.e. meat tax, carbon label or meat-free day at public canteens. We investigated if the information provision impacted people's acceptability (overall, self vs. others perspective). We measured several control variables (socio-economics such as age, gender, income, education, household size, life stage, ideological measures such as political orientation or attitudinal measures such as sufficiency orientation and climate change denial). A quantitative assessment of the carbon footprint in the food consumption domain was also included.

FULFILL dataset - diet policy acceptability - efficacy and acceptability framing Germany

<div> This dataset represents survey data on sufficiency-oriented policy acceptability in regard to dietary consumption. The study was part of the second round surveys in Germany in 2023 within the FULFILL project - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes. As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from two countries: Denmark and Germany, with representative sampling (age, income, gender, current region). In this survey on the acceptability of sufficiency-oriented diet policies we recruited a representative sample with approximately 800 participants from Denmark and Germany, taking into account primarily the individual perspective, added by some questions on the household level. The central part of the survey includes a framing experiment including three groups with participants being randomly assigned to. We were interested in peoples' acceptability on three majorly discussed and sufficiency-relevant policies, i.e. meat tax, carbon label or meat-free day at public canteens. We investigated if an information on either the efficacy of the measures or a combination of information with acceptance information or none of these information could influence people's acceptability (overall, self vs. others perspective). We measured several control variables (socio-economics such as age, gender, income, education, household size, life stage, ideological measures such as political orientation or attitudinal measures such as sufficiency orientation and climate change denial). A quantitative assessment of the carbon footprint in the food consumption domain was also included. </div>