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5,510 results for “Foods”
Dataset - paper: Child eating behaviors, parental feeding practices and food shopping motivations during the COVID-19 lockdown in France
<p>Dataset corresponding to a paper that has been published in Appetite (Philippe K, Chabanet C, Issanchou S, Monnery-Patris S. <em>Child eating behaviors, parental feeding practices and food shopping motivations during the COVID-19 lockdown in France: (How) did they change? </em>Appetite. 2021 Jun 1;161:105132. doi: <strong>10.1016/j.appet.2021.105132</strong>. Epub 2021 Jan 23. PMID: 33493611; PMCID: PMC7825985).</p> <p>The objective of the study was to evaluate possible changes in eating behaviors in children aged 3–12 years, in parental eating and cooking behaviors, in parental feeding practices, and also in parental motivations when shopping for food during the lockdown, compared to the period before the lockdown.</p> <p>Information about the dataset and the corresponding documents can be found in the document "Metadata-paper-COVID.docx".</p>
Dataset - paper: Are food parenting practices gendered?
<p>Dataset corresponding to a paper that has been published in Appetite (Philippe K, Chabanet C, Issanchou S, Monnery-Patris S. <em>Are food parenting practices gendered? Impact of mothers' and fathers' practices on their child's eating behaviors</em>. Appetite. 2021 Nov 1;166:105433. doi: <strong>10.1016/j.appet.2021.105433</strong>. Epub 2021 Jun 1. PMID: 34087257).</p> <p>The objectives of the study were (1) to identify possible gender differences regarding parental feeding practices and styles and parental perceptions of the child’s eating behaviours, and (2) to assess the associations between maternal and paternal feeding practices and styles and child eating behaviours, and to study possible effects of concordant/discordant feeding practices in families.</p> <p>Information about the dataset and the corresponding documents can be found in the document "Metadata-paper-couples.docx".</p>
Food fraud vulnerability assessment data (on spice/ginger and wine)
<p>The dataset includes the results of food fraud vulnerability assessments (on spice/ginger and wine) of various companies based in China and Europe. The data form part of WP3 (Task 3.2): <em>Implementation of innovations in food authenticity. </em>The data is generated to better understand the food fraud vulnerability within selected food chains. The data is useful for anyone working in the field of food authentication.</p>
Fostering safe food handling among consumers: Data from an online survey experiment with 1,973 consumers from Norway and the UK
<p>Data and replication codes for the article "Fostering safe food handling among consumers: Causal evidence on game- and video-based online interventions". 1,973 participants from the UK and Norway, aged 18- 89 years, were assigned to (i) a control condition, or (ii) exposed to a brief information video, or (iii) in addition played an online game (two different conditions). In all conditions, participants answered a pre-survey and seven days later a post-survey. In the survey, next to collecting some information on sociodemographic background and certain preferences, subjects reported some recent food safety behaviors and we elicited beliefs in the efficacy of certain food safety actions, as well as beliefs in myths related to food and hygiene.</p> <p>We use this data set in our publication <br> Koch, A. K., Mønster, D., Nafziger, J., & Veflen, N. (2022). Fostering safe food handling among consumers: Causal evidence on game-and video-based online interventions. <em>Food Control</em>, 108825.</p>
Short Food Supply Chains Business and Marketing Models Categorisation
<p>The present dataset contains information about the categorisation of Business Models for Short Food Supply Chains, in the context of the <a href="http://agrobridges.eu">agroBRIDGES project</a> (Horizon 2020, GA No. 101000788). Regional information about existing and new business models for Short Food Supply Chains were collected from 12 regions and countries of focus for the project (Beacon Regions) through desk research, interviews and co-creation workshops.</p> <p>The datasets collected information provided by Beacon Region Leaders in the co-creation workshops about models applied and newly or potentially developed ones and available in the literature, to categorise them into stylized models with specific and distinguishable features and attributes, enabling a systematic approach in the development of the sustainability assessment framework. An initial ranking of local business models was provided at a regional / country level. </p> <p>Through analysis of the aggregate ranking results of business models at local level, a final list of business models for SFSCs was derived. Five different Business Model types have been addressed and developed ( i. Community Supported Agriculture - CSA, ii. Face to face trade, iii. Online Food Trade, iv. Local Food Trade, and v. Improved Logistics). The final categorization list provided valuable insights in order to represent all regional contexts and eventually offer value-added information about the main factors for developing a successful business model that can enhance market success in the long term; as well as to strengthen farmer´s strategy that eventually enhances their position within the whole value chain and improve their customer experience.</p> <p>The Value Proposition Canvas and Business Model Canvas was defined for each of the most common SFSC represented in each of the targeted regions.</p> <p><br> The dataset contains:<br> • <strong>agroBRIDGES_SFSCs-BMModels-Categorisation_2021.12.01_v1 [zip file]: </strong>The initial list of business models collected from the co-creation workshops<br> • <strong>agroBRIDGES_SFSCs-BMModels-Categorisation-Tool_2021.12.01_v1 [.xlsx file]: </strong>Internal Assessment tool for Business models regional data collection: A data collection tool was built in-house and shared to all Beacon Region Leaders in order to enable them to fill it regarding their assumptions and conclusions after the co-creation participants discussed and presented their insights from regional activities.<br> • <strong>agroBRIDGES_SFSCs-BModels-CategorisationResults_2021.12.01_v1 [.xlsx file]:</strong> SFSCs BM type results in each of the regions.<br> • <strong>agroBRIDGES_BusinessModels_Canvas_2021.12.01_v1 [zip file]: </strong>The Business Model and Value Proposition Canvas of the 5 BM categories</p>
Food and Agriculture Biomass Input–Output (FABIO) database
<p>This data repository provides the Food and Agriculture Biomass Input Output (FABIO) database, a global set of multi-regional physical supply-use and input-output tables covering global agriculture and forestry. </p> <p>The work is based on mostly freely available data from FAOSTAT, IEA, EIA, and UN Comtrade/BACI. FABIO currently covers <strong>191 countries</strong> + RoW, <strong>118 processes</strong> and <strong>125 commodities</strong> (raw and processed agricultural and food products) for 1986-2013. All R codes and auxilliary data are available on <a href="https://github.com/fineprint-global/fabio">GitHub</a>. For more information please refer to <a href="https://fabio.fineprint.global">https://fabio.fineprint.global</a>.</p> <p>The database consists of the following main components, in compressed .rds format:</p> <ul> <li>Z: the inter-commodity input-output matrix, displaying the relationships of intermediate use of each commodity in the production of each commodity, in physical units (tons). The matrix has 24000 rows and columns (125 commodities x 192 regions), and is available in two versions, based on the method to allocate inputs to outputs in production processes: Z_mass (mass allocation) and Z_value (value allocation). Note that the row sums of the Z matrix (= total intermediate use by commodity) are identical in both versions.</li> <li>Y: the final demand matrix, denoting the consumption of all 24000 commodities by destination country and final use category. There are six final use categories (yielding 192 x 6 = 1152 columns): 1) food use, 2) other use (non-food), 3) losses, 4) stock addition, 5) balancing, and 6) unspecified.</li> <li>X: the total output vector of all 24000 commodities. Total output is equal to the sum of intermediate and final use by commodity.</li> <li>L: the Leontief inverse, computed as (I – A)<sup>-1</sup>, where A is the matrix of input coefficients derived from Z and x. Again, there are two versions, depending on the underlying version of Z (L_mass and L_value).</li> <li>E: environmental extensions for each of the 24000 commodities, including four resource categories: 1) primary biomass extraction (in tons), 2) land use (in hectares), 3) blue water use (in m3)., and 4) green water use (in m3).</li> <li>mr_sup_mass/mr_sup_value: For each allocation method (mass/value), the supply table gives the physical supply quantity of each commodity by producing process, with processes in the rows (118 processes x 192 regions = 22656 rows) and commodities in columns (24000 columns).</li> <li>mr_use: the use table capture the quantities of each commodity (rows) used as an input in each process (columns).</li> </ul> <p>A description of the included countries and commodities (i.e. the rows and columns of the Z matrix) can be found in the auxiliary file io_codes.csv. Separate lists of the country sample (including ISO3 codes and continental grouping) and commodities (including moisture content) are given in the files regions.csv and items.csv, respectively. For information on the individual processes, see auxiliary file su_codes.csv. RDS files can be opened in R. Information on how to read these files can be obtained here: https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/readRDS</p> <p>Except of <em>X.rds</em>, which contains a matrix, all variables are organized as lists, where each element contains a sparse matrix. Please note that values are always given in physical units, i.e. tonnes or head, as specified in items.csv. The suffixes <em>value </em>and <em>mass </em>only indicate the form of allocation chosen for the construction of the symmetric IO tables (for more details see <a href="http://doi.org/10.1021/acs.est.9b03554">Bruckner et al. 2019</a>). Product, process and country classifications can be found in the file <em>fabio_classifications.xlsx</em>.</p> <p>Footprint results are not contained in the database but can be calculated, e.g. by using this script: <a href="https://github.com/martinbruckner/fabio_comparison/blob/master/R/fabio_footprints.R">https://github.com/martinbruckner/fabio_comparison/blob/master/R/fabio_footprints.R</a></p> <p> </p> <p><strong><em>How to cite: </em></strong></p> <p>To cite FABIO work please refer to this paper:</p> <p>Bruckner, M., Wood, R., Moran, D., Kuschnig, N., Wieland, H., Maus, V., Börner, J. 2019. FABIO – The Construction of the Food and Agriculture Input–Output Model. <em>Environmental Science & Technology</em> 53(19), 11302–11312. DOI: <a href="https://doi.org/10.1021/acs.est.9b03554">10.1021/acs.est.9b03554</a></p> <p> </p> <p><em><strong>License:</strong></em></p> <p>This data repository is distributed under the CC BY-NC-SA 4.0 License. You are free to share and adapt the material for non-commercial purposes using proper citation. If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original. In case you are interested in a collaboration, I am happy to receive enquiries at <a href="mailto:martin.bruckner@wu.ac.at">martin.bruckner@wu.ac.at</a>.</p> <p> </p> <p><strong><em>Known issues:</em></strong></p> <p>The underlying FAO data have been manipulated to the minimum extent necessary. Data filling and supply-use balancing, yet, required some adaptations. These are documented in the code and are also reflected in the balancing item in the final demand matrices. For a proper use of the database, I recommend to distribute the balancing item over all other uses proportionally and to do analyses with and without balancing to illustrate uncertainties.</p>
Can household storage conditions reduce food waste and environmental impact ? A broccoli case study
<p>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.</p>
Statistical analysis and dataset for: Invasive ants fed spinosad collectively recruit to known food faster yet individually abandon food earlier
<p>Linked to the journal article published in bioRxiv (https://doi.org/10.1101/2024.06.20.599949).</p> <p><em><strong>Abstract</strong></em></p> <p>Current management strategies applied to invasive ants rely on slow-acting insecticides which aim to delay the ant’s ability to detect the poison until its effects are noticeable. Despite this, most control efforts are unsuccessful, likely due to bait abandonment and insufficient sustained consumption. Conditioned taste aversion, a learned avoidance of a particular taste, is a crucial survival mechanism which prevents animals from repeatedly ingesting toxic substances. However, whether ants are capable of this delayed association between food taste and subsequent illness remains largely unexplored. Here, we exposed colonies of the highly invasive Argentine ant, <em>Linepithema humile</em>, to a sublethal dose of the slow-acting insecticide spinosad. We combined measurements of individual-level feeding patterns with quantification of collective preferences and foraging dynamics to investigate the potential effects of the toxicant on behaviour. Collectively, ants preferred an odour associated with a previously experienced food, even if this contained spinosad, over a novel one. However, at the individual-level, previous exposure to spinosad resulted in reduced food consumption, as a consequence of earlier food abandonment. Moreover, while control-treated colonies recruited slower to a food source which tasted like a previously experienced one, spinosad-exposed colonies recruited equally fast to both novel and familiar foods. Although it appears that ants are unable to develop a conditioned taste aversion to sublethal doses of spinosad, ingestion of even small amounts of the toxicant strongly influences foraging behaviour. Understanding the subtle effects of slow-acting pesticides on ant cognition and behaviour can ultimately inspire the development of more efficient control methodologies.</p>
Compilation of Digital Tools on Food Green House Gas Mitigation (CHOICE Project)
<p>A compilation of digital tools to support behaviour change and action to food mitigation measures. The compilation was created for the CHOICE Horizon Europe project (Grant Agreement -101081617).</p>
Data and script for Van Berkel et al: Can starlings use a reliable cue of future food deprivation to adaptively modify foraging and fat reserves?
<p>Supporting materials for:</p> <p><strong>Can starlings use a reliable cue of future food deprivation to adaptively modify foraging and fat reserves?</strong></p> <p>Menno van Berkel<sup>a</sup>, Melissa Bateson<sup>a</sup>, Daniel Nettle<sup>a</sup> and Jonathon Dunn<sup>a</sup>*</p> <p><sup>a</sup>Centre for Behaviour and Evolution & Institute of Neuroscience, Newcastle University, Newcastle, UK</p> <p>*Author for correspondence (email: jonathon.dunn@newcastle.ac.uk; telephone: (+44)7730015855; postal address: Institute of Neuroscience, Henry Wellcome Building, The Medical School, Framlington Place, Newcastle University, Newcastle upon Tyne, UK, NE2 4HH).</p> <p>R script and 3 .csv files.</p>
S54 | EFSAPRI | European Food Safety Authority Priority Substances
<p>This is the dataset associated with list S54 EFSAPRI on the NORMAN Suspect List Exchange:</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p>
Data in: Reduced predation and energy flux in soil food webs by introduced tree species
<p>The introduction of non-native tree species has become a global concern and may disruptnative communities and related ecosystem functions. Soil food webs regulate organic matter decomposition and nutrient cycling in forests with their feeding activities, butevaluating consequences of tree species introduction on soil invertebrates is challengingdue to the complex trophic structure and wide range in body size of soil invertebrates. Here, we employed an energetic food web approach, and estimated the energy flux in soil food webs using a four-node model including soil meso- and macrofauna decomposers and predators. We examined pure and mixed stands of native European beech (<em>Fagus sylvatica</em>), introduced Douglas fir (<em>Pseudotsuga menziesii</em>) and native range-expanding Norway spruce (<em>Picea abies</em>) across site conditions. Compared to native forests, introduced tree species reduced total mass of macrofauna predators by 92% at sandy sites but not that of decomposers, suggesting trophic downgrading in soil food webs by Douglas fir. The energy flux in mixed forests was intermediate between respective monocultures, suggesting that tree mixtures mitigate potential negative impacts of introduced tree species on food web functioning. Across size classes, soil macrofauna responded more sensitively to changes in environmental conditions than soil mesofauna. Despite the lower total mass, the energy flux through mesofauna outweighed that through macrofauna when consideringenergy loss to predators, highlighting the importance of mesofauna for decomposition processes in forest soil food webs. Additionally, total energy flux positively correlated with species richness, pointing to the significance of soil biodiversity for trophic functionality. Overall, the study emphasizes the critical role of tree species composition, site conditionsand soil biodiversity in driving energy flux through soil food webs and maintaining forest ecosystem functions.</p>
Base rates of food safety practices in European households: Summary data from the SafeConsume Household Survey
<p>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).</p> <p><em>Sampling design</em></p> <p>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.</p> <p><em>Fieldwork</em></p> <p>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.</p> <p><em>Weights</em></p> <p>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.</p> <p><em>Transformations</em></p> <p>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.</p> <p><em>Treatment of missing values</em></p> <p>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.</p>
Dataset and R script for the analysis in the article "Food waste between environmental education, peers, and family influence. Insights from primary school students in Northern Italy", Journal of Cleaner Production
<p>We hereby publish the dataset (with metadata) and the R script (R Core team, 2018) used for implementing the analysis presented in the paper "Food waste between environmental education, peers, and family influence. Insights from primary school students in Northern Italy", <em>Journal of Cleaner Production </em>(Piras et al., 2023). The dataset is provided in csv format with semicolons as separators and "NA" for missing data. The dataset includes all the variables used in at least one of the models presented in the paper, either in the main text or in the Supplementary Material. Other variables gathered by means of the questionnaires included as Supplementary Material of the paper have been removed. The dataset includes inputted values for missing data on independent variables. These were inputted using two approaches: last observation carried forward (LOCF) - preferred when possible - and last observation carried backward (LOCB). The metadata are presented as a PDF file.</p>
LoCard Food Classification
<p>How the data was compiled:<br> The grocery purchase data including 3574 product groups was received from the retailer for research purposes. The data was reclassified into appropriate categories suitable for the use of nutrition and health research.</p> <p>How the data has been handled:<br> A four-level hierarchical classification of product groups was used. Each class on the broadest level of hierarchy (Class 1) was subsequently divided into a reasonable number of finer sub-classes starting with Class level 2, followed by Class 3, and finally, Class 4, which was the most detailed level of hierarchy. The main ingredient of the product group, type of the food and purpose of use, nutritional content, and carbon footprint were considered in the reclassification process. The classified food groups were linked with Finnish food composition database.</p> <p>How the data can be used for research:<br> The re-classified data can be used to measure can be used to infer and describe food purchase patterns, monitor of the nutrition composition of food purchases, monitoring and evaluating dietary environmental and economic sustainability, and deriving consumer segments based on the re-classification.</p> <p>The authors strongly recommend reading the detailed description of the classification is published in <a href="https://doi.org/10.21203/rs.3.rs-2826970/v1">https://doi.org/10.21203/rs.3.rs-2826970/v1</a>.</p>
European consumers ́ preference and willingness to pay for food products labelled as obtained by a circular farming system -in relation to environmental attitudes and consumption behaviours
<p>Data was collected with questionnaire-based research carried out in Belgium, Croatia, Hungary, Italy, Poland, and Spain as part of a European project. The survey questions were designed to obtain the Willingness to pay using 2 different methodologies the discrete choice experiment and the open-end choice experiment. The survey also included questions about consumers environmental attitudes, and consumption behavior (purchase, use and recycling), to identify if them have influence on preferences towards more sustainable food products. The 3 analyzed food products were pork meat, milk and bread, all of them obtained through different agricultural production systems (circular, conventional, and organic agriculture). The sample was stratified in terms of gender and age to be representative to the average population in each country. Furthermore, respondents included in this study were those that are mainly, or in part responsible for the household food shopping. The questionnaire was translated to the languages of the countries involved in the data collection and pre-launched using a pilot sample of 50 consumers in each case study country. Finally, a total of 5,362 validated questionnaires were obtained. Data was collected online using the Qualtrics market research company, and Net panel market company for Hungary from June 2021 to January 2022.</p>
Harmonised branded foods dataset for FNS Cloud case studies (2017-2020)
<p>The dataset was compiled by merging and harmonisation of different datasets, which were compiled in four countries (Netherlands, Germany, Slovenia, Switzerland) in period 2017-2020 with the use of different methodologies. Dataset was prepared to support conduction of case studies within the HORIZON2020 FNS Cloud project. The dataset includes selected mandatory food labelling information about nutrition composition of foods/drinks within three categories, that are important for reformulation strategies: soft drinks, breakfast cereals and yoghurts. For one of the included datasets, data aslo include category of fruit and vegetable juices (including nectars).</p>
Food-chain length in desert streams of central and southern Arizona, USA
## overview Food chain length (FCL) is a key measure of the vertical structure of food webs that determines energy flow through ecosystems, carbon exchange between freshwater ecosystems and the atmosphere, and rates of nutrient cycling. FCL also has a strong bearing on the biomass of green plants in ecosystems and hence on water quality in aquatic ecosystems. Broad-scale syntheses of controls on FCL in stream ecosystems indicate that FCL declines with discharge variation but, counter to theory, does not vary significantly with energy supply. The mechanisms linking discharge and energy to FCL are largely unresolved in streams. We propose that lack of a relationship between energy supply and FCL may be due to variation in efficiency of energy transfer caused by constraints of food quality, or to a temporal mismatch between measures of energy inputs and FCL. Alternatively, the effects of flow variation on FCL may simply be paramount to energy supply, but potential mechanisms linking flow to FCL remain untested. Regime shifts—punctuated change between strings of high- and low-flow events—may cause comprehensive faunal replacement across trophic levels and collapse of the vertical structure of food webs. FCL may change as a result of loss (or gain) of an apex predator, or as a result of changes in feeding relationships leading to apex predators that eat higher on the food chain. Finally, flow variation may indirectly influence FCL through inputs of limiting nutrients during floods. In desert streams, algae typically provide the primary source of energy, and algal production is limited by nitrogen (N). N loading from terrestrial ecosystems is strongly related to flow variation, particularly to the inter-flood interval (IFI) or duration of baseflow between floods. Long IFI leads to larger N pulses and potentially greater net ecosystem production (NEP), thereby providing an indirect effect of flow variation on FCL. Specific aims of the research include: 1) Quantify the effe
Data from "Evaluating top-down, bottom-up, and environmental drivers of pelagic food web dynamics along an estuarine gradient"
Synthesized fish, benthic invertebrate, and water quality dataset used for analysis in: Rogers, T., S. Bashevkin, C. Burdi, D. Colombano, P. Dudley, B. Mahardja, L. Mitchell, S. Perry, and P. Saffarinia. 2022. Evaluating top-down, bottom-up, and environmental drivers of pelagic food web dynamics along an estuarine gradient. preprint, EcoEvoRxiv. https://doi.org/10.32942/X2MK5Z
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
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.