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5,510 results for “Foods”
Scripts and data for: Integrating different facets of diversity into food web models: how adaptation among and within functional groups shape ecosystem functioning
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Data from: Parasite-mediated changes in host traits alter food web dynamics
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Data from: Topography of inputs into the hippocampal formation of a food-caching bird
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Lake food webs: Species invasion progressively disrupts the trophic structure of native food webs
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Data for: Generalist invasion in a complex lake food web
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Long-term data reveal widespread phenological change across major U.S. estuarine food webs
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Sampling a pika’s pantry: Temporal shifts in nutritional quality & over-winter preservation of American pika food caches
Climate change is increasing temperature, decreasing precipitation, and increasing atmospheric CO2 concentrations in many ecosystems. As atmospheric carbon rises, plants may increase carbon-based defenses such as phenolics, thereby potentially affecting food quality, foraging habits, and habitat suitability for mammalian herbivores. In alpine habitats, the American pika (Ochotona princeps) is a model species for studying effects of changing plant chemistry on mammals. To survive between growing seasons, pikas cache “haypiles” of plants rich in phenolics. Although they are acutely toxic to pikas, phenolic compounds help plants retain biomass and nutrition during storage, and they break down over time. Alpine avens (Geum rossii, Rosales: Rosaceae) is a high-phenolic plant species that comprises up to 75% of pika winter diet in Colorado. Here, we tested the hypothesis that contemporary climate change has affected the nutritional value of Alpine avens to pikas in the last 30 years. Specifically, we compared phenolic activity, nutritional quality, and overwinter preservation of plants collected at Niwot Ridge, Colorado (USA) in 1992 to those collected between 2010 – 2018, spanning nearly three decades of climate change. Phenolic activity increased in alpine avens since 1992, while fiber and nitrogen content decreased. Importantly, overwinter preservation of plant biomass also increased, particularly on windblown slopes without long-lasting snow cover. Previous studies indicate that pikas at this site still depend on alpine avens in their winter food caches. Increasing phenolic content in alpine avens could therefore enhance the preservation of haypiles over winter; however, if pikas must further delay consuming these plants to avoid acute toxicity, then he nutritional gains from enhanced preservation may not be beneficial. This study provides important insights into how changing plant chemistry will affect mammalian herbivores in the future.
Quantification of Food Waste Disposal in the United States: A Meta-Analysis
This data set is the result of a systematic review of studies on food waste disposed in the United States, an issue which major consequences for social, nutritional, economic, and environmental issues. It was created to determine how much food is discarded in the U.S., and to determine if specific factors drive increased disposal. By applying meta-analytic tools on it this dataset, it was found that the aggregate proportion of food waste in U.S. municipal solid waste from 1995 to 2013 was 0.147 (95% CI 0.137–0.157) of total disposed waste, which is lower than that estimated by U.S. Environmental Protection Agency for the same period (0.176). Further, that the proportion of food waste increased significantly with time, and there were no significant differences in food waste between rural and urban samples, or between commercial/institutional and residential samples. These results are published in the study titled Quantification of Food Waste Disposal in the United States: A Meta-Analysis (Thyberg et al., 2015).
Food and waterborne outbreaks data complementing the European Union One Health 2018 Zoonoses Report
<p>Food and waterborne outbreaks data reported under the framework of Directive 2003/99/EC and in accordance with the update of the technical specifications for harmonised reporting of FBOs through the EU reporting system in accordance with Directive 2003/99/EC. This dataset includes the number of outbreaks, as well as the number of human cases, hospitalisations and deaths, per causative agent. In addition, other information can include data on causative agents, food vehicles, and the factors in food preparation and handling that contributed to the food-borne outbreaks. Reporting countries can also provide information on the nature of the evidence supporting the suspicion of the food vehicle. This evidence can be epidemiological, microbiological, descriptive environmental, or based on product tracing investigations. </p>
FoodOntoMap version 2: Linking Food Concepts across different Food Ontologies
<p>A dataset providing food concept normalization using different food ontologies. There are eight data sets which describe the unique food concepts from each ontology, and one main file which provides the mapping between the different ontologies. All files are in two formats (.csv, .xlsx).</p> <p>The prefixes for each dataset are as follows:</p> <ol> <li>"A" - Hansard Corpus</li> <li>"B" - FOODON</li> <li>"C" - SNOMEDCT</li> <li>"D" - OF</li> <li>"E" - RCD</li> <li>"F" - MESH</li> <li>"G" - SNMI</li> <li>"H" - NDDF<br> </li> </ol>
multiple food patch collective foraging experiments
<p>This dataset contains .mp4 video files of experimental recordings for the following publication: https://doi.org/10.1101/744649.</p> <p>The .mp4 files (100x sped up) are contained within the zipped folder, and the .csv file contains the metadata for the dataset. </p> <ul> <li><strong>strains</strong> : DA609 and N2</li> <li><strong>timestamp</strong> : from 20170615 to 20170626</li> <li><strong>strain_description</strong> : DA609: npr-1(ad609)X. N2: lab reference strain.</li> <li><strong>sex</strong> : hermaphrodite</li> <li><strong>stage</strong> : adult</li> <li><strong>number_worms: </strong>40</li> <li><strong>media</strong> : NGM agar low peptone</li> <li><strong>arena</strong> : <ul> <li><strong>style</strong> : petri</li> <li><strong>size</strong> : 35 mm</li> </ul> </li> <li><strong>food</strong> : OP50, diluted OD = 0.75, freshly seeded</li> <li><strong>who</strong> : S. Serena Ding</li> <li><strong>protocol</strong> : Method in Ding, Schumacher et al. (2019) <a href="https://doi.org/10.7554/eLife.43318">https://doi.org/10.7554/eLife.43318</a>. Alternatively, visit dx.doi.org/10.17504/protocols.io.vybe7sn for a step-by-step protocol.</li> <li><strong>lab</strong> : <ul> <li><strong>name</strong> : Behavioural Genomics</li> <li><strong>location</strong> : London, United Kingdom</li> </ul> </li> <li><strong>days_of_adulthood</strong> : 1</li> <li><strong>total time (s)</strong> : 3600 per file</li> <li><strong>playback speed: </strong>100x</li> </ul>
2019 Eurobarometer on Food Safety in the EU_Datasets
<p>This Special Eurobarometer, commissioned by EFSA, provides insights in terms of:</p> <ul> <li>Europeans’ overall interest in food safety, including factors affecting food-related decisions, main information channels on food-related risks, changes in behaviour and views on complexity of communication about food-related risks.</li> <li>Awareness and risk perceptions when it comes to food safety topics, trust in different sources of information and understanding of the EU food safety system</li> </ul> <p>The survey was carried out by the Kantar network in the 28 EU Member States between the 9<sup>th</sup> and 26<sup>th</sup> of April 2019. Some 27,655 respondents from different social and demographic groups were interviewed face-to-face at home in their mother tongue. </p> <p>Additional data was collected during November 2019 in the following pre-accession countries: Albania, Montenegro, Serbia, North Macedonia and Turkey.</p> <p>The methodology used is that of the <a href="http://ec.europa.eu/commfrontoffice/publicopinion/index.cfm">Standard Eurobarometer surveys</a> carried out by the Directorate-General for Communication. It is the same for all countries and territories covered in the survey.</p> <p>The datasets published are distributed as follows:</p> <ul> <li>Volume A: Countries</li> <li>Volume AA: Groups of countries</li> <li>Volume B: EU/socio-demographics</li> <li>Volume C: Country/socio-demographics</li> <li>Microdata</li> </ul> <p><em>Disclaimer > In accordance with Regulation (EU) 2018/1725 on the protection of natural persons with regard to the processing of personal data applicable to EFSA, some data elements were excluded from the eb_91.3_EFSA_Microdata file published hereby, in order to prevent that individual respondents may be identifiable. The excluded data elements include occupation and the region of respondent’s residence within the country. </em></p>
Quebec Ministry of Agriculture, Fisheries and Food from 2012-2018 web archive collection derivatives
<p>Web archive derivatives of the Quebec Ministry of Agriculture, Fisheries and Food from 2012-2018 collection from the <a href="https://www.banq.qc.ca/accueil/">Bibliothèque et Archives nationales du Québec</a>. The derivatives were created with the <a href="https://github.com/archivesunleashed/aut/">Archives Unleashed Toolkit</a>. Merci beaucoup BAnQ!</p> <p>These derivatives are in the <a href="https://parquet.apache.org/">Apache Parquet format</a>, which is a <a href="http://en.wikipedia.org/wiki/Column-oriented_DBMS">columnar storage</a> format. These derivatives are generally small enough to work with on your local machine, and can be easily converted to Pandas DataFrames. See <a href="https://github.com/archivesunleashed/notebooks/blob/master/parquet_pandas_example.ipynb">this</a> notebook for examples.</p> <p><strong>Domains</strong></p> <pre><code class="language-java">.webpages().groupBy(ExtractDomainDF($"url").alias("url")).count().sort($"count".desc)</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>domain</li> <li>count</li> </ul> <p><strong>Web Pages</strong></p> <pre><code class="language-java">.webpages().select($"crawl_date", $"url", $"mime_type_web_server", $"mime_type_tika", RemoveHTMLDF(RemoveHTTPHeaderDF(($"content"))).alias("content"))</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>crawl_date</li> <li>url</li> <li>mime_type_web_server</li> <li>mime_type_tika</li> <li>content</li> </ul> <p><strong>Web Graph</strong></p> <pre><code class="language-java">.webgraph()</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>crawl_date</li> <li>src</li> <li>dest</li> <li>anchor</li> </ul> <p><strong>Image Links</strong></p> <pre><code class="language-java">.imageLinks()</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>src</li> <li>image_url</li> </ul> <p><a href="https://github.com/archivesunleashed/aut-docs/blob/master/current/binary-analysis.md#binary-analysis"><strong>Binary Analysis</strong></a></p> <ul> <li>Audio</li> <li>Images</li> <li>PDFs</li> <li>Presentation program files</li> <li>Spreadsheets</li> <li>Text files</li> <li>Videos</li> <li>Word processor files</li> </ul>
Antimicrobial resistance monitoring results complementing the EU Overview Summary Report on AMR in zoonotic and indicator bacteria from humans, animals and food in 2017/2018 - Ireland
<p>This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision 2013/652/EU. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision 2013/652/EU. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included. REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: AMR_2018_IE_20200204: >> The Food Safety Authority of Ireland</p>
Antimicrobial resistance monitoring results complementing the EU Overview Summary Report on AMR in zoonotic and indicator bacteria from humans, animals and food in 2017/2018 - Sweden
<p>This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision 2013/652/EU. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision 2013/652/EU. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included. REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: AMR_2018_SE_20200204: >> National Veterinary Institute, Swedish Zoonosis Centre</p>
Antimicrobial resistance monitoring results complementing the EU Overview Summary Report on AMR in zoonotic and indicator bacteria from humans, animals and food in 2017/2018 - Latvia
<p>This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision 2013/652/EU. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision 2013/652/EU. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included. REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: AMR_2018_LV_20200204: >> Assessment and Registration Agency of Food and Veterinary Service of Latvia</p>
Antimicrobial resistance monitoring results complementing the EU Overview Summary Report on AMR in zoonotic and indicator bacteria from humans, animals and food in 2017/2018 - Spain
<p>This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision 2013/652/EU. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision 2013/652/EU. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included. REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: AMR_2018_ES_20200204: >> Agencia Espaola de Consumo, Seguridad Alimentaria y Nutricin >> Ministerio de Agricultura, Pesca y Alimentacin</p>
Specific monitoring results of ESBL-/AmpC-/carbapenemase-producing bacteria and specific monitoring of carbapenemase-producing bacteria, in the absence of isolates detected complementing the European Union Overview Summary Report on Antimicrobial Resistance in zoonotic and indicator bacteria from humans, animals and food in 2017/2018
<p>This dataset derives from the specific monitoring of E. coli producers of ESBLs/AmpC/carbapenemases, as well as the specific monitoring of carbapenemase-producers (voluntary reporting), in the absence of any isolates detected. This dataset contains only data when Total units positive equals zero '0'.</p>
Antimicrobial resistance monitoring results complementing the EU Overview Summary Report on AMR in zoonotic and indicator bacteria from humans, animals and food in 2017/2018 - Cyprus
<p>This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision 2013/652/EU. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision 2013/652/EU. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included. REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: AMR_2018_CY_20200204: >> Ministry of Agriculture, Natural Resources and Evironment - Veterinary Services</p>
Antimicrobial resistance monitoring results complementing the EU Overview Summary Report on AMR in zoonotic and indicator bacteria from humans, animals and food in 2017/2018 - Iceland
<p>This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision 2013/652/EU. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision 2013/652/EU. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included. REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: AMR_2018_IS_20200204: >> Icelandic Food and Veterinary Authority</p>
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.