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Dataset results
15 results for “public_food”
Swiss public's acceptance and sustainability perceptions of food produced with chemical, digital and mechanical weed control measures and the influence of information source on technology perception in agriculture
<p><span>This data was obtained from an online survey conducted with the Swiss public from the two biggest language regions (German and French) in Switzerland. The survey was conducted in February 2023. Participants were recruited through a professional panel provider and quotas were used for age, gender and language region. The final sample contained </span><span>542 respondents. </span><span>In the first part of the survey, respondents provided basic sociodemographic information. In the second part, their sustainability perceptions regarding four different weed management practices (full-surface spraying, hoeing machine, spot spraying and precise spraying) were investigated. Respondents were then assigned to one of five information source groups, in which information on a hoeing and a milking robot was presented, using 5 different information sources (male/female farmer, male/female scientist, no source). Technology perception was assessed using several questions and aspects. Finally, respondents answered several questions assessing their attitudes towards the perception of farmers, food technology neophobia, chemophobia and the importance of naturalness. The survey can be used and adapted to different contents, aiming to investigate public perception of smart farming technologies and the influence of information sources on technology perception. </span></p>
Topical Classification of Food Safety Publications with a Knowledge Base – materials
<p>Supplementary material for the article titled "<strong><a href="https://doi.org/10.1007/978-981-19-4364-5_48">Topical Classification of Food Safety Publications with a Knowledge Base</a></strong>".</p> <p><strong>Citation</strong></p> <p>If you use this data in research work, please cite this paper:</p> <p>Sowinski, P., Wasielewska-Michniewska, K., Ganzha, M., & Paprzycki, M. (2022). Topical Classification of Food Safety Publications with a Knowledge Base. In <em>Sustainable Technology and Advanced Computing in Electrical Engineering</em> (pp. 673-693). Springer, Singapore.</p> <p>BibTeX:</p> <pre><code>@incollection{sowinski2022topical, title={Topical Classification of Food Safety Publications with a Knowledge Base}, author={Sowinski, Piotr and Wasielewska-Michniewska, Katarzyna and Ganzha, Maria and Paprzycki, Marcin}, booktitle={Sustainable Technology and Advanced Computing in Electrical Engineering}, pages={673--693}, year={2022}, publisher={Springer}, doi={10.1007/978-981-19-4364-5_48} }</code></pre> <p> </p>
EUROMALT Briefing note No. 2 Ochratoxin A - Additional document from EUROMALT supporting the comments submitted during the public consultation on the Risk assessment of ochratoxin A in food
<p>This document has been submitted by EUROMALT as additional document supporting the comments submitted during the public consultation organised by the European Food Safety Authority in relation to the draft scientific opinion on the Risk assessment of ochratoxin A in food. The text of the comments is published in the Technical report of the public consultation - see related identifiers section, http://doi.org/10.2903/sp.efsa.2020.1845</p> <p> </p> <p>EUROMALT has agreed by email submitted to EFSA to disclose this confidential document and to have it published on Zenodo.</p> <p>.</p>
Data for publication: A food system transformation can enhance global health, environmental conditions and social inclusion
<p>Data related to the publication "A food system transformation can enhance global health, environmental conditions and social inclusion"</p>
Scientific Webinar on Sustainable Public Food Procurement (SPFP) in the European Union
<p>On 23 April 2024, <a href="https://sapiensnetwork.eu/">SAPIENS Network</a> <a href="https://sapiensnetwork.eu/research/early-stage-researcher-projects/sustainability-to-collective-table/">Early Stage Researcher Chiara Falvo</a> held a scientific webinar focusing on Sustainable Public Food Procurement (SPFP) in the European Union. The event took place in hybrid form and was hosted within the Master’s Course in Food Systems Law at the Department of Law of the University of Turin, also in collaboration with the Department of Agricultural, Forestry and Food Sciences (DISAFA). After a brief introduction on public procurement law and practice, Chiara delved into the legal strategies and mechanisms for integrating social and environmental considerations into the procurement of food and catering services. She also highlighted some national and local experiences that are leading the way in the field. During the event, <a href="https://www.giurisprudenza.unito.it/do/docenti.pl/Alias?silvia.mirate#tab-profilo">Professor Silvia Mirate</a>, who also acted as a discussant, provided an overview on the new EU Deforestation Regulation (EUDR), followed by Chiara's exploration of its relevance for public procurement. Contributing to bridging the gap between the scientific domains of law and agricultural and forestry sciences, this webinar may be relevant for students and newcomers to public procurement, especially in the food sector, as well as for anyone interested in understanding deforestation issues and the latest legal mechanisms to combat them. </p>
Supplementary Material to the Publication: Clonal relation between Salmonella enterica subspecies enterica serovar Dublin strains of bovine and food origin in Germany
<p>OHEJP Project: BeOne</p> <p><em>Salmonella enterica </em>serovar Dublin (<em>S</em>. Dublin) is a host-adapted serovar that causes enteritis and/or systemic diseases in cattle. Because the serovar is not host-specific, it can infect other species, including human beings, causing severe disease and a higher mortality rate than other non-typhoidal serovars. Given that human illnesses are primarily caused by contaminated milk, milk products, and beef, data on the genetic connection between <em>S</em>. Dublin strains from livestock and food should be analyzed. </p> <p>Whole genome sequencing (WGS) was performed on 144 <em>S</em>. Dublin strains from cattle and 30 strains from food. Multilocus sequence typing (MLST) found that the majority of livestock and food isolates were of the sequence type ST-10. As discovered by core-genome Single-Nucleotide Polymorphisms Typing and core-genome MLST, 14 of 30 strains from food origin were clonally related to at least one strain from cattle. Without outliers, the remaining 16 food-borne strains fit into the genomic structure of <em>S</em>. Dublin in Germany. WGS demonstrated to be an effective method not only for learning about the epidemiology of Salmonella strains, but also for detecting clonal relationships between organisms isolated at different stages of production. This study discovered a strong genetic link between <em>S</em>. Dublin strains from cattle and food, and thus the potential to cause human infections. <em>S</em>. Dublin strains from both origins have a nearly comparable collection of virulence factors, emphasizing their ability to produce severe clinical symptoms in animals as well as humans, emphasizing the importance of effective <em>S</em>. Dublin management in a farm to fork strategy.</p>
Contribution submitted by Antonella Garzelli for the Public consultation on the draft risk assessment of glycoalkaloids in feed and food, in particular in potatoes and potato-derived products
<p>The European Food Safety Authority (EFSA) carried out a public consultation, from 27 February 2020 until 15 April 2020, to receive input from interested parties – organizations or private citizens - on a draft scientific opinion on the risks for animal and human health related to the presence of glycoalkaloids in feed and food, in particular in potatoes and potato-derived products. The draft scientific opinion was prepared by the EFSA Panel on Contaminants in the Food Chain (CONTAM Panel), supported by the Working Group on Glycoalkaloids in feed and food. EFSA used the EU survey tool to receive comments on the draft scientific opinion, and the way these comments were considered for the finalisation of the opinion is described in the Technical Report available at: <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fefsa.onlinelibrary.wiley.com%2Fdoi%2F10.2903%2Fsp.efsa.2020.EN-1905&data=02%7C01%7C%7C242adcee21744b01890408d82cba24b4%7C406a174be31548bdaa0acdaddc44250b%7C1%7C0%7C637308525272584775&sdata=97Tc3%2FrT1SXtzIN14PCCvUv0OYyaiSO1H76iezwqvnQ%3D&reserved=0">https://efsa.onlinelibrary.wiley.com/doi/10.2903/sp.efsa.2020.EN-1905</a>. Some contributors also attached files to the comments submitted via the EU survey tool. For transparency reasons these files are made available in the public domain as Zenodo uploads.</p>
Contribution submitted by STARCH EU for the Public consultation on the draft risk assessment of glycoalkaloids in feed and food, in particular in potatoes and potato-derived products
<p>The European Food Safety Authority (EFSA) carried out a public consultation, from 27 February 2020 until 15 April 2020, to receive input from interested parties – organizations or private citizens - on a draft scientific opinion on the risks for animal and human health related to the presence of glycoalkaloids in feed and food, in particular in potatoes and potato-derived products. The draft scientific opinion was prepared by the EFSA Panel on Contaminants in the Food Chain (CONTAM Panel), supported by the Working Group on Glycoalkaloids in feed and food. EFSA used the EU survey tool to receive comments on the draft scientific opinion, and the way these comments were considered for the finalisation of the opinion is described in the Technical Report available at: <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fefsa.onlinelibrary.wiley.com%2Fdoi%2F10.2903%2Fsp.efsa.2020.EN-1905&data=02%7C01%7C%7C242adcee21744b01890408d82cba24b4%7C406a174be31548bdaa0acdaddc44250b%7C1%7C0%7C637308525272584775&sdata=97Tc3%2FrT1SXtzIN14PCCvUv0OYyaiSO1H76iezwqvnQ%3D&reserved=0">https://efsa.onlinelibrary.wiley.com/doi/10.2903/sp.efsa.2020.EN-1905</a>. Some contributors also attached files to the comments submitted via the EU survey tool. For transparency reasons these files are made available in the public domain as Zenodo uploads.</p>
Contribution submitted by Matthew Walker for the Public consultation on the draft risk assessment of glycoalkaloids in feed and food, in particular in potatoes and potato-derived products
<p>The European Food Safety Authority (EFSA) carried out a public consultation, from 27 February 2020 until 15 April 2020, to receive input from interested parties – organizations or private citizens - on a draft scientific opinion on the risks for animal and human health related to the presence of glycoalkaloids in feed and food, in particular in potatoes and potato-derived products. The draft scientific opinion was prepared by the EFSA Panel on Contaminants in the Food Chain (CONTAM Panel), supported by the Working Group on Glycoalkaloids in feed and food. EFSA used the EU survey tool to receive comments on the draft scientific opinion, and the way these comments were considered for the finalisation of the opinion is described in the Technical Report available at: <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fefsa.onlinelibrary.wiley.com%2Fdoi%2F10.2903%2Fsp.efsa.2020.EN-1905&data=02%7C01%7C%7C242adcee21744b01890408d82cba24b4%7C406a174be31548bdaa0acdaddc44250b%7C1%7C0%7C637308525272584775&sdata=97Tc3%2FrT1SXtzIN14PCCvUv0OYyaiSO1H76iezwqvnQ%3D&reserved=0">https://efsa.onlinelibrary.wiley.com/doi/10.2903/sp.efsa.2020.EN-1905</a>. Some contributors also attached files to the comments submitted via the EU survey tool. For transparency reasons these files are made available in the public domain as Zenodo uploads.</p>
Data from: Using risk of bias domains to identify opportunities for improvement in food- and nutrition-related research: an evaluation of research type and design, year of publication, and source of funding
Purpose: This retrospective cross-sectional study aimed to identify opportunities for improvement in food and nutrition research by examining risk of bias (ROB) domains. Methods: Rating were extracted from critical appraisal records for 5675 studies used in systematic reviews conducted by three organizations. Variables were as follows: ROB domains defined by the Cochrane Collaboration (Selection, Performance, Detection, Attrition, and Reporting), publication year, research type (intervention or observation) and specific design, funder, and overall quality rating (positive, neutral, or negative). Appraisal instrument questions were mapped to ROB domains. The kappa statistic was used to determine consistency when multiple ROB ratings were available. Binary logistic regression and multinomial logistic regression were used to predict overall quality and ROB domains. Findings: Studies represented a wide variety of research topics (clinical nutrition, food safety, dietary patterns, and dietary supplements) among 15 different research designs with a balance of intervention (49%) and observation (51%) types, published between 1930 and 2015 (64% between 2000-2009). Duplicate ratings (10%) were consistent (k=0.86-0.94). Selection and Performance domain criteria were least likely to be met (57.9% to 60.1%). Selection, Detection, and Performance ROB ratings predicted neutral or negative quality compared to positive quality (p<0.001). Funder, year, and research design were significant predictors of ROB. Some sources of funding predicted increased ROB (p<0.001) for Selection (Interventional: industry only and none/not reported; Observational: other only and none/not reported) and Reporting (Observational: university only and other only). Reduced ROB was predicted by combined and other-only funding for intervention research (p<0.005). Performance ROB domain ratings started significantly improving in 2000; others improved after 1990 (p<0.001). Research designs with higher ROB were nonrandomized intervention and time series designs compared to RCT and prospective cohort designs respectively (p<0.001). Conclusions: Opportunities for improvement in food and nutrition research are in the Selection, Performance, and Detection ROB domains.
Citizen's willingness to pay for public goods resulting from the implementation of innovation by food companies.
<p><span>The data consists of the results of the choice experiment conducted across six European countries in order to assess citizens' willingness to pay for public goods resulting from the implementation of innovation by food companies. </span></p>
Public food forest opportunities and challenges in small municipalities
Open the record for dataset details and reuse information.
Data from: Using risk of bias domains to identify opportunities for improvement in food- and nutrition-related research: an evaluation of research type and design, year of publication, and source of funding
Open the record for dataset details and reuse information.
Data supporting publication: MiFoDB, a workflow for microbial food metagenomic characterization, enables high-resolution analysis of fermented food microbial dynamics
<p>MiFoDB (Microbial Foods Database) is a workflow and primary reference database which includes 675 assembled MAGs and RefSeq bacterial, yeast, fungal, and substrate genomes from fermented foods.</p>
Datasets for publication "Post-mining effects on fish communities and food web dynamics"
<p>Raw data and R scripts of all analyses of the paper.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.