Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
91
datasets available to search
ShareScore release 0.9.0
Dataset results
91 results for “food systems”
Figure 7 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103
Figure 7 Results of implementing different scouting and foraging strategies on the performance of model colonies in pollen collection. Three different foraging strategies (i.e. distance, quality or random) were tested for each scouting strategy (i.e. distance, quantity and random). The total amount of collected pollen, the mean number of daily foraging flights, the number of foraging flights and their success were evaluated for all combinations of scouting and foraging strategies.
Figure 5 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103
Figure 5 Scout, recruit and foragers behaviour rules. Without private and social information, model bees become scout bees. When there is no private information because they never performed a foraging flight or because the flight was unsuccessful, model bees become recruits and will search for social information. If model bees have private information, they are considered forager bees even if no social information is available in the colony. In the presence of social information, scout and forager bees can change foraging locations (50% chance).
Figure 4 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103
Figure 4 Available foraging hours and weather variables (temperature and solar radiation) for each simulation day throughout the year. Rain and wind variables are not shown, but were used to calculate the number of available foraging hours.
Figure 3 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103
Figure 3 Example of nectar (in yellow on the left side) and pollen (in blue on the right side) spatial and temporal distribution through the season. In each snapshot, a brighter colour indicates a higher amount of the resource in the polygon. A total of 12 snapshots were taken every 30 days, starting on day 15 of the simulation.
Figure 2 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103
Figure 2 The total mass of floral resources (i.e. sugar and pollen) in the studied landscape available to bees in all the simulations. The mass of floral resources was calculated, based on the production and phenology of the individual plant species comprising the habitats present in the studied landscape and the landscape composition. Pollen availability started on simulation day 20 and nectar was available from day 39.
Figure 1 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103
Figure 1 Components in ALMaSS landscape model. The blue arrow represents the access to landscape information at a 1 m2 resolution. In this example, one element has woody habitats, while the other is an arable field. The information about each element depends on its type and the temporal factors described in the green boxes. The orange box shows some of the factors derived from the landscape element type, its management and the weather.
Figure 6 from: Basak S, Christy J, Guillier L, Audiat-Perrin F, Sanaa M, Tenenhaus-Aziza F, Bect J, Vazquez E (2024) Quantitative risk assessment of Haemolytic and Uremic Syndrome (HUS) from consumption of raw milk soft cheese. Food and Ecological Systems Modelling Journal 5: e109502. https://doi.org/10.3897/fmj.5.109502
Figure 6 The relative batch risk (with respect to a baseline risk value) is plotted as a function of the initial STEC (main pathogenic serotypes MPS-STEC) concentration (CFU/ml).
Figure 6 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103
Figure 6 Results of the implementation of different scouting and foraging strategies on the performance of model colonies in terms of nectar collection. For each scouting strategy (i.e. distance, quality or random), four different foraging strategies (i.e. distance, energy efficiency, quality and random) were tested. The total amount of sugar collected, the mean number of daily foraging flights and their success were evaluated for all combinations of scouting and foraging strategies.
Figure 5 from: Basak S, Christy J, Guillier L, Audiat-Perrin F, Sanaa M, Tenenhaus-Aziza F, Bect J, Vazquez E (2024) Quantitative risk assessment of Haemolytic and Uremic Syndrome (HUS) from consumption of raw milk soft cheese. Food and Ecological Systems Modelling Journal 5: e109502. https://doi.org/10.3897/fmj.5.109502
Figure 5 Batch rejection probability as a function of the initial STEC (main pathogenic serotypes MPS-STEC) concentration (CFU/ml).
Figure 4 from: Basak S, Christy J, Guillier L, Audiat-Perrin F, Sanaa M, Tenenhaus-Aziza F, Bect J, Vazquez E (2024) Quantitative risk assessment of Haemolytic and Uremic Syndrome (HUS) from consumption of raw milk soft cheese. Food and Ecological Systems Modelling Journal 5: e109502. https://doi.org/10.3897/fmj.5.109502
Figure 4 Evolution of STEC colony size during draining, salting and ripening of cheese fabrication. The decline rate for the MPS O157:H7 strain and non-MPS strains are equal (orange line) and significantly higher than the decline rate of MPS non-O157:H7 strain (red line). The three phases, namely, draining, salting and ripening are separated by vertical blue dotted lines.
Figure 3 from: Basak S, Christy J, Guillier L, Audiat-Perrin F, Sanaa M, Tenenhaus-Aziza F, Bect J, Vazquez E (2024) Quantitative risk assessment of Haemolytic and Uremic Syndrome (HUS) from consumption of raw milk soft cheese. Food and Ecological Systems Modelling Journal 5: e109502. https://doi.org/10.3897/fmj.5.109502
Figure 3 Evolution of STEC (main pathogenic serotypes MPS-STEC) in log10 CFU/ml during the storage and moulding step. The blue vertical line shows the end of the storage phase.
Figure 2 from: Basak S, Christy J, Guillier L, Audiat-Perrin F, Sanaa M, Tenenhaus-Aziza F, Bect J, Vazquez E (2024) Quantitative risk assessment of Haemolytic and Uremic Syndrome (HUS) from consumption of raw milk soft cheese. Food and Ecological Systems Modelling Journal 5: e109502. https://doi.org/10.3897/fmj.5.109502
Figure 2 Histogram of STEC (main pathogenic serotypes MPS-STEC) concentration (log10 (CFU/ml)) in milk put into production.
Figure 1 from: Basak S, Christy J, Guillier L, Audiat-Perrin F, Sanaa M, Tenenhaus-Aziza F, Bect J, Vazquez E (2024) Quantitative risk assessment of Haemolytic and Uremic Syndrome (HUS) from consumption of raw milk soft cheese. Food and Ecological Systems Modelling Journal 5: e109502. https://doi.org/10.3897/fmj.5.109502
Figure 1 Schematic diagram of the batch level simulator of the risk assessment model. Modules are denoted by pink coloured boxes with the blue boxes denoting the set of corresponding input parameters \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} \theta = \{\theta^{\rm farm}, \theta^{\rm cheese}, \theta^{\rm con}, \theta^{\rm post}\} \end{equation*} \end{varwidth} \end{document} and the orange boxes denoting the outputs, namely, milk loss per batch \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} M^{\rm batch} \end{equation*} \end{varwidth} \end{document} , probability of rejecting a particular batch \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} P^{\rm batch} \end{equation*} \end{varwidth} \end{document} and batch risk \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} R^{\rm batch} \end{equation*} \end{varwidth} \end{document} .
Figure 2 from: Topping CJ, Duan X (2024) Managing large and complex population operations with agent-based models: The ALMaSS Population_Manager. Food and Ecological Systems Modelling Journal 5: e117593. https://doi.org/10.3897/fmj.5.117593
Figure 2 The time step processes. The three parts of the time step (BeginStep, Step, EndStep) process can run in multithreaded mode for each object 1 to n, extant at that time and are separated by customisable methods for reporting or list management by the Population_Manager class.
Figure 1 from: Topping CJ, Duan X (2024) Managing large and complex population operations with agent-based models: The ALMaSS Population_Manager. Food and Ecological Systems Modelling Journal 5: e117593. https://doi.org/10.3897/fmj.5.117593
Figure 1 The current class hierarchy for beetle population managers, starting with the parent class Population_Manager_Base.
Figure 3 from: Topping CJ, Duan X (2024) Managing large and complex population operations with agent-based models: The ALMaSS Population_Manager. Food and Ecological Systems Modelling Journal 5: e117593. https://doi.org/10.3897/fmj.5.117593
Figure 3 Change in the maximum and minimum sizes and population numbers for two scenarios using the Theoretical1 species, N = do nothing, Rand = randomise the execution order.
Planetary boundaries for food systems
<p>Global transfers of the national-scale transgression of planetary boundaries for food systems</p>
Building Access to Food Through Systems and Solidarity (BASIS): A Subsidized and Culturally-adapted Produce Box Program for Immigrant Communities of Brooklyn, NY
ClinicalTrials.gov study NCT07076251. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Aboveground mammal and invertebrate exclusions cause consistent changes in soil food webs of two subalpine grassland types, but mechanisms are system-specific
Open the record for dataset details and reuse information.
Supporting data for Loik et al. 2017 Wavelength-Selective Solar Photovoltaic Systems: Powering greenhouses for plant growth at the food-energy-water nexus. Earth's Future
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
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.