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5,510 results for “Foods”
UCSB SONGS Mitigation Monitoring: Wetland Performance Standard - Bird Food Chain Support
These data describe annual estimates of the density of feeding birds at four coastal wetlands as part of the SONGS San Dieguito Wetland Restoration monitoring program to track long-term patterns in food chain support provided to birds. This study began in 2012 in the San Dieguito Wetlands and Tijuana Estuary in San Diego County, CA, Carpinteria Salt Marsh in Santa Barbara County, CA, and Point Mugu Lagoon in Ventura County. Beginning in 2024, Tijuana Estuary was replaced with Los Penasquitos Lagoon in San Diego County, CA.
Factorial experiment to test effects of food availability and temperature on slimy sculpin (Cottus cognatus) at Toolik Field Station, 2018
We used a fully factorial experiment to test effects of food availability and temperature (7.6, 12.7 and 17.4 degrees C; 50 days) on growth, consumption, respiration, and excretion of slimy sculpin (Cottus cognatus).
Alligator pond food-web sampling in Shark River Slough and Taylor Slough, Everglades National Park, Florida, USA, 2018–2019
These datasets were used to investigate if American Alligators engineer differences in nutrient availability and changes to community structure by their creation of “alligator ponds” compared to the surrounding phosphorus (P)-limited oligotrophic marsh in the Everglades. We used a halo sampling design of three distinct habitats extending outward from ten active alligator ponds across a hydrological gradient. We performed nutrient analysis on basal food-web resources and quantitative community analyses, and stoichiometric analyses on plants and animals. These data underly the work in Strickland et al. (2023). An apex predator engineers wetland food-web heterogeneity through nutrient enrichment and habitat modification. Journal of Animal Ecology.
Stomach contents and stable isotopes from the aquatic food web in Everglades National Park, Florida, USA, 2018-2019
Stable isotope samples were collected among habitats and between seasons at multiple sites in both Shark River Slough and Taylor Slough of Everglades National Park to represent as much of the aquatic food web as possible. Stomach contents were also recorded for many of the collected vertebrate samples. Sampling occurred between October 2018 and April 2019. These data are a follow up study to the FCE1263 data package (https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-fce&identifier=1263) following the invasion of several fishes, most notably the African Jewelfish (Rubricatochromis letourneuxi), although there are some differences in habitats sampled and overall spatial coverage. R code for analyses associated with these data are available at https://github.com/pjflood/diet_and_pond_enrichment. Data collection is complete.
Data and code from "No evidence of sex ratio manipulation by black-throated blue warblers in response to food availability" Kaiser et al. 2023 Behavioral Ecology and Sociobiology
This dataset is published in support of "No evidence of sex ratio manipulation by black-throated blue warblers in response to food availability" by Kaiser et al. 2023 in Behavioral Ecology and Sociobiology. Data and code to test the assumptions and key predictions of the Trivers-Willard hypothesis, which proposes that females produce more sons or daughters depending on food availability, in the black-throated blue warbler at the Hubbard Brook Experimental Forest, NH, 2007-2012. Datasets support analyses of sex ratio bias at both the nest and nestling levels. Data tables support the comparison of the ratio of variances in the scaled pre-fledging mass of male and female nestlings using an F test and reproduction of Figures 2a and 2b. Figures are those used in the published manuscript. Code supports the calculation of offspring sex ratio bias at the population level, and considering separately both low- and high-quality habitats, using the Neuhäuser test, statistical models testing the assumptions of the Trivers-Willard hypothesis, effects of food availability and parental provisioning on offspring sex ratio, and effects of food availability on pre-fledging nestling mass of sons and daughters, and a power analysis to determine the power to detect an effect of food supplementation on sex ratio. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the US Forest Service, Northern Research Station.
Experimental Understory Food Web data in the El Verde area of the Luquillo Experimental Forest
These data include date, treatment, block number, number of coquies, number of anoles, number of insects collected on two sticky traps, number of insects counted on 4 Piper glabrescens and 4 Manilkara bidentata seedlings, percent herbivory on the aforementioned plants and number of spiders. All these measurements were taken within exclosures for closed controls, anole exclusions, coqui exclusions and total exclusions. Open controls were sampled from an area of similar dimension not enclosed in an exclosure. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
PIE LTER, stable isotope chemistry (carbon, nitrogen and sulfur) for food web analysis of functional groups in the Plum Island Sound Estuary, Massachusetts.
Stable isotopes of primary producers will be compared to stable isotopes of functional groups of organisms at primarily three sites within the estuary that have different dominant sources of organic matter. The three sites are: Lower (IBYC, SO-3, mouth of Plum Island Sound, 2-3 km upstream of the mouth of the estuary), Middle (OTL, PR-10.1, upper Sound, lower Parker, 8-11 km upstream of the mouth of the estuary) and Upper (P2, PR-21.9, upper Parker, above Middle Rd Bridge (22 km upstream of the mouth of the estuary). The Lower site is dominated by marine phytoplankton, the Middle site is dominated by a mixture of salt marsh and phytoplankton and the Upper site is dominated by oligohaline phytoplankton and fresh marsh. Ten functional groups will be sampled at each site (Surface sediment, benthic diatoms, Nereis, mummichog, ribbed mussels, POM, blue mussels, pelagic copepods (Acartia), silversides and soft shell clams (Mya). Marsh, benthic algae and phytoplankton inputs or benthic vs. pelagic pathways will be evident in the isotopic signals of these functional groups. Samples will be collected between the middle and end of August to reflect a growing season using recently produced OM. Samples of 15 – 20 individuals will be pooled for analysis. Some silverside samplings will have 3 different pooled samples for determination of variance. Often times the same species are not collected at each site due to habitat differences (salinity/discharge) so additional species are collected to try to accomplish task of getting functional groups collected.
SBC LTER: Santa Cruz Island: Abundance and Biomass of Benthic Organisms (food resource collection)
These data describe the abundance of benthic organisms as determined by random quadrat scrapings. These data represent the availability of food resources for fish at various depths, and are part of a long term investigation of temporal patterns in reef community composition. The sampling locations in this dataset include three sites along the north shore of Santa Cruz Island. Data collection began in 1982 and this dataset is updated annually.
Data and R Code from "A novel approach to sustainability assessment of food supply chains using networks of ecosystem services"
<p>Data and R code from this paper applying network analysis (iGraph) to two case studies pre and post agroecological transitions in Central America and Tanzania, Africa from the IPES-Food report. Further descriptions of this data and code can be found within the extended manuscript. R Code relies on the data from the scenarios (e.g., Nodes and Relations CSVs) and creates the output network metrics (e.g., Node Metric CSVs). </p>
Data for 'Local food crop production can fulfil demand for less than one-third of the population'
<p><strong>This dataset is supplement to the following publication (<em>please cite that when using the data</em>):</strong></p> <p>Kinnunen et al. 2020. Local food crop production can fulfil demand for less than one-third of the population. Nature Food 1: 229–237. http://doi.org/10.1038/s43016-020-0060-7</p> <p> </p> <p><strong>Data description</strong></p> <p><strong><em>Distance to food:</em></strong> Globally optimized distance between crop production and consumption. The optimization creates a theoretical food allocation set-up that minimizes travel time cost from crop production to consumption. Data is in two formats: NetCDF (dist_food_netcdf.zip) and multi-band geotiff (dist_food_tif.zip).</p> <p>The data includes:</p> <ul> <li>baseline scenario (dist_food_baseline.nc / .tif)</li> </ul> <p>and three other scenarios where food availability is changed by</p> <ul> <li>decreasing food waste by half (dist_food_halfLoss.nc / .tif)</li> <li>halving the yield gap (dist_food_halfYieldGap.nc / .tif)</li> <li>both of these measures together (dist_food_halfLoss_halfYielGap.nc / .tif)</li> </ul> <p><em>The data covers six crop functional types</em>: maize, pulses, rice, temperate cereals, tropical cereals and tropical roots </p> <p><em>Dataset specifications:</em></p> <p>spatial extent: -180, 180, -90, 90 (xmin, xmax, ymin, ymax)</p> <p>spatial resolution: 0.5 degrees</p> <p>projection: long/lat WGS84</p> <p>layers: 1: maize, 2: pulses, 3: rice, 4: temp_cereals, 5: trop_cereals, 6: trop_roots </p> <p>no data value: -999</p> <p>unit: km</p> <p> </p> <p><em><strong>Foodsheds:</strong></em> The data contains global foodsheds which are areas that are connected by food flows between raster cells. The food flows are from a theoretical food allocation set-up that minimizes travel time cost from crop production to consumption. In addition to normal foodsheds (values>0), there are two special cases: ridge-cells (value: -99) and unconnected single cells (value: -50). Ridge-cells are raster cells connected to multiple foodsheds, while being able to satisfy their own demand locally. Unconnected single cells are not connected to any other foodshed. Each positivie value is a crop specific id, signifying a connected foodshed area. </p> <p>Data is in two formats: NetCDF (foodsheds_netcdf.zip) and multi-band geotiff (foodsheds_tif.zip).</p> <p>The data includes:</p> <ul> <li>baseline scenario (foodsheds_baseline.nc / .tif)</li> </ul> <p>and three other scenarios where food availability is changed by</p> <ul> <li>decreasing food waste by half (foodsheds_halfLoss.nc / .tif)</li> <li>halving the yield gap (foodsheds_halfYieldGap.nc / .tif)</li> <li>both of these measures together (foodsheds_halfLoss_halfYielGap.nc / .tif)</li> </ul> <p><em>The data covers six crop functional types</em>: maize, pulses, rice, temperate cereals, tropical cereals and tropical roots </p> <p><em>Dataset specifications:</em></p> <p>spatial extent: -180, 180, -90, 90 (xmin, xmax, ymin, ymax)</p> <p>spatial resolution: 0.5 degrees</p> <p>projection: long/lat WGS84</p> <p>layers: 1: maize, 2: pulses, 3: rice, 4: temp_cereals, 5: trop_cereals, 6: trop_roots </p> <p>no data value: -999</p> <p>unit: -</p> <p> </p>
Risk assessment on Glycoalkaloids in feed and food: Occurrence data in food and feed submitted to EFSA and dietary exposure assessment for humans
<p><strong>UPDATE to version 2 of this upload:</strong></p> <p>Also the raw (no data cleaning applied to it) occurrence dataset as extracted from EFSA DWH is provided <em>in csv format</em>. This dataset is compliant with EFSA SSD model and contains two additional columns documenting issues identified in the cleaning process (column: issue) and the action taken (column: action) to address the issue (e.g. delete record or update values in specific fields).</p> <p><strong>Description - Version 1</strong></p> <p><strong>Annex: Tables on GAs on occurrence data in food and feed, and dietary exposure assessment for humans</strong></p> <p>Table A.1. Dietary surveys used for the estimation of acute dietary exposure to GA</p> <p>Table A.2. Number of results and samples per food category submitted to EFSA through the continuous call for data</p> <p>Table A.3. Analytical results excluded from the final dataset used to estimate dietary exposure and the criteria applied for exclusion</p> <p>Table A.4. Occurrence of alpha-chaconine and alpha-solanine (UB mg/kg) in the samples included in the final dataset (left censored results highlighted in yellow)</p> <p>Table A.5. European Starch Association data on feed and potatoes for starch</p> <p>Table A.6. Details acute assessment across surveys (consumption days only)</p> <p>Table A.7. Comparison of exposure summary results obtained using the uniform vs the normal distribution for reduction factors</p>
Mapping of FoodEx2 Exposure Hierarchy with the food categories of Annex II (part D) of Regulation (EC) No 1333/2008 on food additives
<p>FoodEx2 is a comprehensive food classification and description system aimed at covering the need to describe food in data collections across different food safety domains. All foods, including beverages and food supplements reported in the EFSA Comprehensive Food Consumption Database are coded with the FoodEx2 Exposure Hierarchy. EFSA developed a mapping of all FoodEx2 basic terms reported in the Comprehensive Database with the food categories of Annex II (part D) of Regulation (EC) No 1333/2008 on food additives in order to facilitate the assessment of exposure to food additives. In particular, this mapping has been used in the Food Additives Intake Model 2.0 (FAIM), which allows the estimation of chronic dietary exposure to food additives based on use levels proposed for food categories as presented in Annex II (part D) of Regulation (EC) No 1333/2008 on food additives.</p> <p>Some of the food categories, restrictions and/or exceptions presented in the Regulation could have not been mapped with FoodEx2 basic terms and the original food descriptors and/or FoodEx2 facets might have been used to correctly map all eating events. This information might not be available in this table.</p>
Montenegro results from the monitoring of pesticide residues in food
<p>This dataset contains the analytical results of pesticide residues measured in the food products analysed by the national competent authorities. Pesticide residues resulting from the use of plant protection products on crops that are used for food or feed production may pose a risk factor for public health. For this reason, a comprehensive legislative framework has been established in the European Union (EU), which defines rules for the approval of active substances used in plant protection products, the use of plant protection products and for pesticide residues in food. In order to ensure a high level of consumer protection, legal limits, so called “maximum residue levels” or briefly “MRLs”, are established in Regulation (EC) No 396/2005. EU-harmonised MRLs are set for all pesticides covering all types of food products. A default MRL of 0.01 mg/kg is applicable for pesticides not explicitly mentioned in the MRL legislation. Regulation (EC) No 396/2005 imposes on Member States the obligation to carry out controls to ensure that food placed on the market is compliant with the legal limits. The chemical monitoring data collected and published by EFSA include the analytical results provided by EU Member States, Iceland, Norway and three pre-accession countries: Bosnia-Herzegovina, Montenegro and North Macedonia. </p> <p>A sample is considered <strong>free of quantifiable residues</strong> if the analytes were not present in concentrations at or above the limit of quantification (LOQ). The LOQ is the smallest concentration of an analyte that can be quantified with the analytical method used to analyse the sample. It is commonly defined as the minimum concentration of the analyte in the test sample that can be determined with acceptable precision and accuracy.</p> <p>If a sample <strong>contains quantifiable residues</strong> but within the legally permitted limit (maximum residue level, MRL), it is described as a sample with quantified residue levels within the legal limits (below or at the MRL)</p> <p>A sample is considered <strong>non-compliant</strong> with the legal limit (MRL), if the measured residue concentrations clearly exceed the legal limits, taking into account the measurement uncertainty. It is current practice that the uncertainty of the analytical measurement is taken into account before legal or administrative sanctions are imposed on food business operators for infringement of the MRL legislation.</p> <p> </p> <p><strong>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION:</strong></p> <p>MOPER_2023 - Center for Eco-Toxicological Research - Administration for Food safety, Veterinary and Phytosanitary Affairs,</p> <p>MOPER_2022 - Center for Eco-Toxicological Research - Administration for Food safety, Veterinary and Phytosanitary Affairs,</p> <p>MOPER_2021 - Center for Eco-Toxicological Research - Administration for Food safety, Veterinary and Phytosanitary Affairs,</p> <p>MOPER_2020 - Center for Eco-Toxicological Research - Administration for Food safety, Veterinary and Phytosanitary Affairs,</p> <p>MOPER_2019 - Center for Eco-Toxicological Research - Administration for Food safety, Veterinary and Phytosanitary Affairs,</p> <p>MOPER_2018 - Center for Eco-Toxicological Research - Administration for Food safety, Veterinary and Phytosanitary Affairs,</p> <p>MOPER_2017 - Center for Eco-Toxicological Research - Administration for Food safety, Veterinary and Phytosanitary Affairs,</p> <p> </p> <p><strong>We are seeking feedback on our open data please complete the survey at the link below:<br>https://ec.europa.eu/eusurvey/runner/9344dfa0-f384-cb72-65f6-6c187a6d0f14</strong></p>
Towards sustainable food consumption: reference list
<p>This dataset includes all the bibliographic references from the SAPEA evidence review report 'Towards a sustainable food system'.</p>
Data and Code for "Does Organic Farming Jeopardize Food Security of Farm Households in Benin?"
<p>This data and code archive provides all the data and code for replicating the empirical analysis that is presented in the journal article "<a href="https://doi.org/10.1016/j.foodpol.2024.102622" target="_blank" rel="noopener">Does Organic Farming Jeopardize Food Security of Farm Households in Benin?</a>" authored by Ghislain B.D. Aïhounton and Arne Henningsen and published in the journal Food Policy (Volume 124, April 2024, 102622, DOI: 10.1016/j.foodpol.2024.102622).</p> <p>We conducted the empirical analysis with the "R" statistical software (version 4.3.3) using the add-on packages "AER" (version 1.2.12), "DescTools" (version 0.99.54), "lmtest" (version 0.9.40), "moments" (version 0.14.1), "sandwich" (version 3.1.0), "stargazer" (version 5.2.3), and "xtable" (version 1.8.4) that are all available at CRAN.</p> <p>This replication package contains the following files:</p> <p>* README<br>This file.</p> <p>* R/dataBenin.csv<br>A CSV file that contains the (unprepared) data set. The variables in this file are described in file R/Variables.csv. This CSV file is imported by R script PrepareDataFoodNutrition.R.</p> <p>* R/Variables.csv<br>A CSV file that describes the variables in the (unprepared) data set (file R/dataBenin.csv).</p> <p>* R/PrepareData.R<br>An R script that imports the (unprepared) data set (file R/dataBenin.csv), calculates additional variables and add theses variables to the data set, removes observations that should not be used in the empirical analysis, and saves the prepared data set as CSV file (R/dataFoodNutrition.csv).</p> <p>* R/dataPrepared.csv<br>A CSV file that contains the (prepared) data set used in the empirical analysis. This CSV file is created by the R script R/PrepareDataFoodNutrition.R. It is imported by the R scripts R/DescriptiveTab.R, FoodNutritionImpact.R, and GridSearchFoodSecurity.R.</p> <p>* R/DescriptiveTab.R<br>An R script that imports the prepared data set (file R/dataFoodNutrition.R) and creates Table 1 of the paper ("Descriptive statistics", file paper/tables/DescriptiveStat.tex) as LaTeX file.</p> <p>* R/Estimations.R<br>An R script that imports the prepared data set (file R/dataFoodNutrition.R), conducts all the analyses presented in the paper, creates Tables 2 and 3 of the paper ("OLS and IV regression results of the conditional associations between organic farming and outcomes" and "OLS and IV regression results of the conditional associations between organic farming and mediating outcomes", LaTeX files paper/tables/estMainReg.tex and paper/tables/estMedReg.tex), creates Figures 1 and 2 of the paper ("Estimated conditional associations of organic farming with outcomes" and "Estimated conditional associations of organic farming with mediating outcomes", 12 PDF files paper/figures/*.pdf), and 45 Tables that are included in the Supplementary Information: 36 tables with detailed regression results (LaTeX files paper/tables/tabels/est*.tex), one table with results of the first-stage probit regression (LaTeX file paper/tables/tabels/estProbit.tex), 6 tables with detailed regression results of estimations for testing the exogeneity of the instrument as suggested by Di Falco et al. (2011) (LaTeX files paper/tables/tabels/estOLS*Falco.tex), and 2 tables with coefficient bounds obtained as suggested by Oster (2019) (LaTeX files paper/tables/tabels/Oster*.tex).</p> <p>* R/GridSearch.R<br>An R script that re-runs our regression analyses with different units of measurement of IHS-transformed variables and calculates various indicators that can can be used to assess the appropriateness of different units of measurement as suggested by Aihounton and Henningsen (2021) and that creates 28 Tables that are included in the Supplementary Information (LaTeX files paper/tables/tabels/grid*.tex).</p> <p>* R/functions/calcOsterBounds.R<br>An R script that defines the R function calcOsterBounds() that calculates coefficient bounds using the method suggested by Oster (2019). This function is used by the R script R/FoodNutritionImpact.R.</p> <p>* R/functions/calcSemiElaOrg.R<br>An R script that defines the R function calcSemiElaOrg() that calculates the semi-elasticity of various log-transformed or IHS-transformed variables with respect to the dummy variable for organic farming. This function is used by the R scripts R/FoodNutritionImpact.R and R/GridSearchFoodSecurity.R.</p> <p>* R/functions/createFormula.R<br>An R script that defines the R function createFormula() that creates the regression formulas for the various empirical analyses that are presented in the paper. This function is used by the R scripts R/FoodNutritionImpact.R and R/GridSearchFoodSecurity.R.</p> <p>* R/functions/functionsTables.R<br>An R script that defines various R functions that are used to create tables in LaTeX format. These functions are used by the R scripts R/FoodNutritionImpact.R and R/GridSearchFoodSecurity.R.</p> <p>* R/functions/predR2.R<br>An R script that defines the R function predR2() that calculates the predictive R-squared value. This R script has been obtained from the replication package of the article:<br>Aïhounton, G. B. D. and Henningsen, A. (2021). Units of measurement and the inverse hyperbolic sine transformation. The Econometrics Journal, 24(2):334–351. https://doi.org/10.1093/ectj/utaa032<br>The function consists of a slightly modified version of the code that is available at: https://tomhopper.me/2014/05/16/can-we-do-better-than-r-squared/ This function is used by the R script R/GridSearchFoodSecurity.R.</p> <p>* paper/figures/*.pdf<br>12 LaTeX files that are the (sub)figures in Figures 1 and 2 of the paper ("Estimated conditional associations of organic farming with outcomes" and "Estimated conditional associations of organic farming with mediating outcomes"). These 12 files are created by the R script R/FoodNutritionImpact.R.</p> <p>* paper/tables/DescriptiveStat.tex<br>A LaTeX file that creates Table 1 of the paper ("Descriptive statistics"). This file is created by the R script R/DescriptiveTab.R.</p> <p>* paper/tables/estMainReg.tex<br>A LaTeX file that creates Table 2 of the paper ("OLS and IV regression results of the conditional associations between organic farming and outcomes"). This file is created by the R script R/FoodNutritionImpact.R.</p> <p>* paper/tables/estMedReg.tex<br>A LaTeX file that creates Table 3 of the paper ("OLS and IV regression results of the conditional associations between organic farming and mediating outcomes"). This file is created by the R script R/FoodNutritionImpact.R.</p> <p>* paper/tables/tabels/est*.tex<br>36 LaTeX files that create 36 tables that are included in the Supplementary Information and present detailed regression results. These 36 files are created by the R script R/FoodNutritionImpact.R.</p> <p>* paper/tables/tabels/estProbit.tex<br>A LaTeX files that creates a table that is included in the Supplementary Information and presents the results of the first-stage probit regression. This file is created by the R script R/FoodNutritionImpact.R.</p> <p>* paper/tables/tabels/estOLS*Falco.tex<br>6 LaTeX files that create 6 tables that are included in the Supplementary Information and present detailed regression results for testing the exogeneity of the instrument as suggested by Di Falco et al. (2011). These 6 files are created by the R script R/FoodNutritionImpact.R.</p> <p>* paper/tables/tabels/Oster*.tex<br>2 LaTeX files that create 2 tables that are included in the Supplementary Information and present coefficient bounds obtined as suggested by Oster (2019). These 2 files are created by the R script R/FoodNutritionImpact.R.</p> <p>* paper/tables/tabels/grid*.tex<br>28 LaTeX files that create 28 tables that are included in the Supplementary Information and present various indicators for assessing the appropriateness of different units of measurement of IHS-transformed variables as suggested by Aihounton and Henningsen (2021). These 28 files are created by the R script R/GridSearchFoodSecurity.R</p>
Supplementary Datafile for "A reform of value-added taxes on foods can have health, environmental and economic benefits in Europe"
<p>The dataset contains the results of the study "A reform of value-added taxes on foods can have health, environmental and economic benefits in Europe" by Marco Springmann, Eugenia Dinivitzer, Florian Freund, Jørgen Dejgård Jensen, and Clara G Bouyssou. </p> <p>It contains VAT rates on foods across Europe, as well as the results of reforming VAT rates for foods, including increasing rates for meat and dairy and reducing rates for fruits and vegetables. The set of results include changes in food demand, changes in environmental impacts (greenhouse gas emissions, land use, water use, and eutrophication potential), changes in diet-related mortality, changes in costs (revenues, climate change costs, costs of illness). </p>
FoodSky: A Food-oriented Large Language Model, and FoodEarth: A Foundamental Food Corpus and Instruction Dataset
<p>Food is the cornerstone of both survival and social life. With the increasing complexity of global dietary needs and preferences, there is a growing demand for food intelligence to enable tasks like recipe recommendation and diet-disease correlation discovery. To address this, we introduce the Food-oriented Large Language Model (LLM) FoodSky, which offers fine-grained perception and reasoning of food data. We constructed a food corpus, FoodEarth, from various authoritative sources to enhance FoodSky's knowledge. We also developed the Topic-based Selective State Space Model and Hierarchical Topic Retrieval Augmented Generation algorithms to improve FoodSky's ability to capture fine-grained food semantics and generate context-aware food-relevant text. Extensive experiments show that FoodSky outperforms general-purpose LLMs on the Chinese National Chef Exam and Dietetic Exam, achieving accuracies of 67.2% and 66.4%, respectively. FoodSky not only enhances culinary creativity and promotes healthier eating patterns but also establishes a new standard for domain-specific LLMs tackling real-world food-related issues.</p>
Data & R-Code for "Weather and food availability additively affect reproductive output in an expanding raptor population"
<p><strong>Abstract</strong></p> <p>The joint effects of interacting environmental factors on key demographic parameters can exacerbate or mitigate the separate factors’ effects on population dynamics. Given ongoing changes in climate and land use, assessing interactions between weather and food availability on reproductive performance is crucial to understand and forecast population dynamics. By conducting a feeding experiment in 4 years with different weather conditions, we were able to disentangle the effects of weather, food availability and their interactions on reproductive parameters in an expanding population of the red kite (<em>Milvus milvus</em>), a conservation-relevant raptor known to be supported by anthropogenic feeding. Brood loss occurred mainly during the incubation phase, and was associated with rainfall and low food availability. In contrast, brood loss during the nestling phase occurred mostly due to low temperatures. Survival of last-hatched nestlings and nestling development was enhanced by food supplementation and reduced by adverse weather conditions. However, we found no support for interactive effects of weather and food availability, suggesting that these factors affect reproduction of red kites additively. The results not only suggest that food-weather interactions are prevented by parental life-history trade-offs, but that food availability and weather conditions are crucial separate determinants of reproductive output, and thus population productivity. Overall, our results suggest that the observed increase in spring temperatures and enhanced anthropogenic food resources have contributed to the elevational expansion and the growth of the study population during the last decades.</p>
SCALIBUR video 1: From household food waste to bioplastics and biopesticides
<p>This video is part of a 3 part series explaining the innovative technologies being developed in the SCALIBUR project.</p> <p>The script is as follows:</p> <p>Each of us throws away a whopping 200 kilograms of food and organic waste each year. More and more cities separately collect this bio-waste. But what to do with it all? The SCALIBUR project is developing innovative technologies to convert household organic waste into valuable products. Where we see waste, SCALIBUR partners see a resource. One approach uses novel biochemical conversion, combining enzymatic hydrolysis and fermentation processes, to transform organic waste into sustainable bio-based products... Like bio-pesticides for more ecological agriculture, or biodegradable and compostable biopolymers for sustainable bioplastics. These technologies will help cities manage waste in a more sustainable and cost efficient way. And contribute to the creation of a truly circular bioeconomy in Europe.</p>
SCALIBUR video 2: From retail food waste to protein, lipids, and chitin
<p>This video is part of a 3 part series explaining the innovative technologies being developed in the SCALIBUR project.</p> <p>The script is as follows: </p> <p>Eyes bigger than your stomach? Hotels and restaurants make a big contribution to the 100 million tonnes of organic waste produced each year in the EU. The SCALIBUR project is developing innovative technologies to convert waste from the food service industry into valuable products. Where we see waste SCALIBUR partners see a resource. Insects like black soldier flies love leftovers, efficiently converting food scraps into a rich biomass. New processes are being developed to extract the valuable materials like proteins, lipids and chitin: raw materials for bioplastics, and food and feed products. These technologies will help cities manage waste in a more sustainable and cost efficient way. And contribute to the creation of a truly circular bio-economy in Europe.</p>
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.