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Food systems Emissions shares, 1990-2019
<p><strong>Greenhouse gas emissions from agri-food systems </strong></p> <p><strong>(1990-2019)</strong></p> <p> </p> <p><strong>Overview</strong></p> <p>We present results from the <a href="https://www.fao.org/faostat/en/#data/EM">FAOSTAT emissions shares</a> database which disseminates emissions from all economic sectors and from agri-food systems by gases (CO<sub>2</sub>, CH4, N<sub>2</sub>O, F-gases and their total in CO<sub>2</sub>eq) relative to 236 countries and territories over the period 1990 – 2019.</p> <p>In 2019, global greenhouse gas emissions from all economic sectors totaled about 54 billion tonnes CO<sub>2</sub>eq (54 Gt CO<sub>2</sub>eq), emissions from agri-food systems totaled 16.5 billion tonnes (Gt CO<sub>2</sub>eq) representing 31 percent of the total anthropogenic emissions from all economic sectors.</p> <p>This dataset focuses on emissions from agri-food systems which includes data on emissions from the farm-gate, land use change and pre- and post-production. The sum of these three sectors comprises the agri-food system emissions (16.5 Gt CO<sub>2</sub>eq).</p> <p>Pre- and post-production include emissions from: fertilizers manufacturing, on-farm electricity use, food processing, food transport, food retail, food waste disposal, food household consumption and food packaging.</p> <p><strong>Data Structure</strong></p> <p>The data is structured as a tabular data with attributes: AreaName, ISO3, ItemName, ElementName, Year, Value, Unit.</p> <p><strong>Attributes (Columns)</strong></p> <p>Attributes in the data are defined as below:</p> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Descriptions</strong></p> </td> </tr> <tr> <td> <p><strong>AreaName</strong></p> </td> <td> <p>characterizes all countries including world and regional aggregates</p> </td> </tr> <tr> <td> <p><strong>ISO3</strong></p> </td> <td> <p>represents three letter ISO3 country codes (not all regional aggregates have ISO3 country codes)</p> </td> </tr> <tr> <td> <p><strong>ItemName</strong></p> </td> <td> <p>represents all items covered in the data</p> </td> </tr> <tr> <td> <p><strong>ElementName</strong></p> </td> <td> <p>represents all gases covered in the data</p> </td> </tr> <tr> <td> <p><strong>Year</strong></p> </td> <td> <p>period covered by the data</p> </td> </tr> <tr> <td> <p><strong>Value</strong></p> </td> <td> <p>represents the emissions value</p> </td> </tr> <tr> <td> <p><strong>Unit</strong></p> </td> <td> <p>Unit of measurement (in this data emissions are measured in kilotonnes)</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p><strong>Files included in the dataset</strong></p> <p>We have included two files available for downloaded. The csv file contains the data in .csv format and a excel file contains data in .xlsx format.</p> <p><strong>Global warming potential (GWP)</strong></p> <p>In this data, the emissions total (CO<sub>2</sub>eq) is computed by applying the GWP values from the IPCC Fifth Assessment Report (AR5) as given below:</p> <p> </p> <table align="center"> <tbody> <tr> <td> <p> </p> </td> <td> <p><strong>Greenhouse gas</strong></p> </td> <td> <p><strong>GWPAR5 (IPCC, 2014)</strong></p> </td> </tr> <tr> <td> <p><em>Single</em></p> <p><em>gases</em></p> </td> <td> <p>N<sub>2</sub>O</p> </td> <td> <p>265</p> </td> </tr> <tr> <td> <p>CO<sub>2</sub></p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>CH<sub>4</sub></p> </td> <td> <p>28</p> </td> </tr> <tr> <td> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p><em>F-gases </em></p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> </td> <td> <p>HFC-23</p> </td> <td> <p>12,400</p> </td> </tr> <tr> <td> <p>HFC-32</p> </td> <td> <p>677</p> </td> </tr> <tr> <td> <p>HFC-41</p> </td> <td> <p>116</p> </td> </tr> <tr> <td> <p>HFC-125</p> </td> <td> <p>3,170</p> </td> </tr> <tr> <td> <p>HFC-134</p> </td> <td> <p>1,120</p> </td> </tr> <tr> <td> <p>HFC-134a</p> </td> <td> <p>1,300</p> </td> </tr> <tr> <td> <p>HFC-143</p> </td> <td> <p>328</p> </td> </tr> <tr> <td> <p>HFC-143a</p> </td> <td> <p>4,800</p> </td> </tr> <tr> <td> <p>HFC-152</p> </td> <td> <p>16</p> </td> </tr> <tr> <td> <p>HFC-152a</p> </td> <td> <p>138</p> </td> </tr> <tr> <td> <p>HFC-161</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>HFC-227ea</p> </td> <td> <p>3,350</p> </td> </tr> <tr> <td> <p>HFC-236cb</p> </td> <td> <p>1,210</p> </td> </tr> <tr> <td> <p>HFC-236ea</p> </td> <td> <p>1,330</p> </td> </tr> <tr> <td> <p>HFC-236fa</p> </td> <td> <p>8,060</p> </td> </tr> <tr> <td> <p>HFC-245ca</p> </td> <td> <p>716</p> </td> </tr> <tr> <td> <p>HFC-245fa</p> </td> <td> <p>858</p> </td> </tr> <tr> <td> <p>HFC-365mfc</p> </td> <td> <p>804</p> </td> </tr> <tr> <td> <p>HFC-43-10mee</p> </td> <td> <p>1,650</p> </td> </tr> <tr> <td> <p>Sulfur hexafluoride (SF<sub>6</sub>)</p> </td> <td> <p>23500</p> </td> </tr> <tr> <td> <p>Nitrogen trifluoride (NF<sub>3)</sub></p> </td> <td> <p>16,100</p> </td> </tr> <tr> <td> <p>PFC-14</p> </td> <td> <p>6,630</p> </td> </tr> <tr> <td> <p>PFC-116</p> </td> <td> <p>11,100</p> </td> </tr> <tr> <td> <p>PFC-218</p> </td> <td> <p>8,900</p> </td> </tr> <tr> <td> <p>PFC-318</p> </td> <td> <p>9,540</p> </td> </tr> <tr> <td> <p>PFC-31-10</p> </td> <td> <p>9,200</p> </td> </tr> <tr> <td> <p>PFC-41-12</p> </td> <td> <p>8,550</p> </td> </tr> <tr> <td> <p>PFC-51-14</p> </td> <td> <p>7,910</p> </td> </tr> <tr> <td> <p>PCF-91-18</p> </td> <td> <p>7,190</p> </td> </tr> <tr> <td> <p>µGWP</p> </td> <td> <p>5,195</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Data Sources</strong></p> <p>FAOSTAT climate change, emissions shares <a href="https://www.fao.org/faostat/en/#data/EM">data</a>, <a href="https://fenixservices.fao.org/faostat/static/documents/EM/cb7514en.pdf">analytical brief</a>.</p> <p>The primap-hist national historical emissions timeseries <a href="https://zenodo.org/record/5494497#.Yf18Cy8w2ic">data</a>, <a href="https://essd.copernicus.org/articles/8/571/2016/essd-8-571-2016.html">paper</a>.</p> <p> </p>
First Three-dimensional Quantification of Planktic Food Chain lower levels (Copepods) for the Ross Sea region Marine Protected Area (RSRMPA), Antarctica: Using FAIR-inspired legacy data with Machine Learning, and Open Source GIS
<p>This dataset is relative to the paper entitled: "First Three-dimensional Quantification of Planktic Food Chain lower levels (Copepods) for the Ross Sea region Marine Protected Area (RSRMPA), Antarctica: Using FAIR-inspired legacy data with Machine Learning, and Open Source GIS" publishing in journal Diversity (MPDI).</p> <p>Abstract:</p> <p>Zooplankton is a fundamental group in all aquatic ecosystems located the base of the food chain. It forms a link between the lower trophic levels with secondary consumers and shows marked fluctuations of populations with environmental change, especially reacting to heating and water acidification. At sea copepod crustaceans account for app. 70% in abundance of zooplankton and are a target of monitoring activities in key areas such as the Southern Ocean. In this study we have used FAIR-inspired legacy data (dating back to the ‘80s) collected in the Ross Sea by the Italian National Antarctic Program in GBIF.org. Together with other open-access GIS data sources and tools it allows generating, for the first time, three-dimensional predictive distribution maps for twenty-six copepod species. These predictive maps were obtained by applying machine learning techniques to grey literature data, which were visualized in open-source GIS platforms. In a Species Distribution Modeling (SDM) framework we used machine learning with three types of algorithms (TreeNet, RandomForest and Ensemble) to analyze the presence and absence of copepods at different areas and depth classes in function of environmental descriptors obtained from the Polar Macroscope Layers present in Quantartica. The models allow for the first time to map-predict the food chain in quantitative terms showing the relative index of occurrence (RIO) and identified the presence for each copepod species analyzed in the Ross Sea. Our results show marked geographical preferences that vary with species and trophic strategy. This study demonstrates that machine learning is a successful method in accurately predicting Antarctic copepod presence, also providing useful data to orient future sampling and management of wildlife and conservation.</p>
Assessing ambitious nature conservation strategies in a below 2-degree and food-secure world – supplementary spatial data
<p><strong>Assessing ambitious nature conservation strategies in a below 2-degree and food-secure world – supplementary spatial data</strong></p><p><strong>Authors: </strong>Marcel Kok, Johan Meijer, Willem-Jan van Zeist, Jelle Hilbers, Marco Immovilli, Jan Janse, Elke Stehfest, Michel Bakkenes, Andrzej Tabeau, Aafke Schipper, Rob Alkemade</p><p><strong>Point of contact:</strong> <a href="mailto:Marcel.Kok@pbl.nl">Marcel.Kok@pbl.nl</a></p><p><strong>Research paper summary:</strong> Global biodiversity is projected to further decline under a wide range of future socio-economic development pathways, even in sustainability-oriented scenarios. This raises the question how biodiversity can be put on a path to recovery, the core challenge for the implementation of the CBD Kunming-Montreal Global Biodiversity Framework. We designed two ambitious global conservation strategies, 'Half Earth' (HE) and 'Sharing the Planet' (SP), and evaluated their ability to restore terrestrial and freshwater biodiversity and to provide nature's contributions to people (NCP), while also limiting global warming below 2 degrees and ensuring food security. We applied the integrated assessment framework IMAGE with the GLOBIO biodiversity model, using the 'Middle of the Road' Shared Socio-economic Pathway (SSP2) with its projected human population growth as baseline. We found that the HE strategy performs generally better for terrestrial biodiversity (biodiversity intactness (MSA), Area of Habitat, Living Planet Index, Red List Index) in currently still natural regions. The SP strategy yields more improvements for biodiversity in human-used areas, for freshwater biodiversity and for regulating NCP (pest control, pollination, erosion control, water quality). However, both strategies were insufficient to restore biodiversity and corresponded with considerable increases in food security risks and global temperature. Only when we combined the conservation strategies with a portfolio of 'integrated sustainability measures', including climate change mitigation and reductions of food waste and animal product consumption, our scenarios resulted in a restoration of biodiversity and NCP while keeping global warming below two degrees and food security risks below the baseline projection.</p><p><strong>Contents:</strong> This repository contains the supplementary spatial data describing the specific prioritization of conservation areas under the Half Earth (HE) and Sharing the Planet (SP) scenarios, and the resulting scenario land use and MSA data sets for the year 2050, including also a baseline (BL) scenario. All spatial data is in geotiff format at a 10 arcsecond resolution in WGS84 coordinate system. Detailed description of the methodology is provided in the paper listed under "related identifiers".</p><p><strong>Keywords:</strong> Nature conservation, Half Earth, Sharing the Planet, Climate Change, Food Security, Solution-oriented scenarios, Biodiversity, Nature's Contribution to People, NCP</p>
Mapping of samples – Deliverables in WP3: Novel pyrolysis oil from non-food/feed biomass
<p>In the H2020-project BioMates (www.biomates.eu, see chapter 4 “Funding and disclaimer”), RISE produced samples from ablative fast pyrolysis of herbaceous biomass in a TRL 5-plant. A dedicated document provides identifiers for relevant liquid samples and their blends [1]. The document at hand maps it to the substances reported to be used in the public deliverables and public deliverable summaries connected to Work Package 3 “Technology scale up and validation” of the BioMates-project, as far as mapping is not provided in the documents themselves.</p>
Dehulling of pulses for food use
<p>Harri Laine from Arola farm talks about dehulling faba beans and peas for food use. Together with his wife Erja, they operate a dehulling line that has the capacity to dehull approximately 500 kg of faba beans or peas per hour. The video is an output of the Legumes Translated project and is available in Finnish with subtitles in Finnish and English.</p>
Good Practices and Success Cases for Short Food Supply Chains connecting consumers and producers
<p>The agroBRIDGES Good Practices (GPs) is a compilation of 51 Good Practices prepared by TEAGASC in collaboration with other agroBRIDGES project partners, where the Good Practices, success cases and opportunities related to Short Food Supply Chains (SFSCs) across Europe are highlighted. The Good Practices are identified and collated from SFSCs in Denmark, France, Greece, Italy, Latvia, Lithuania, the Netherlands, Poland, Turkey, Finland, Ireland, Spain and other international representations across Europe.</p> <p>The dataset contains a spreadsheet containing the 51 Good Practices collected. The included cases are presented in alphabetical order and numbered from 1 -51:</p> <p>1. "Bios Coop" Social Consumer Cooperative </p> <p>2. AGFORISE (AGroFOod clusters platform with common long-term Research and Innovation) </p> <p>3. Agriloops </p> <p>4. Airfield Estate </p> <p>5. Benedicto Turgus (Benedict’s Market) </p> <p>6. Bienvenue à la ferme </p> <p>7. Bioalverde </p> <p>8. Burgerboerderijen </p> <p>9. CASETTA ROSSA Solidarity Purchase Group / Gruppo di Acquisto Solidale (GAS) Casetta Rossa </p> <p>10. Castlegregory Market </p> <p>11. Cloughjordan Community Farm </p> <p>12. Conservas artesanales contigo </p> <p>13. Digital Agriculture Market </p> <p>14. Drumanilra </p> <p>15. Evidence of Public Procurement of local food in the EU </p> <p>16. Fru Møllers Mølleri </p> <p>17. Gastroteca </p> <p>18. Kindergarten "Duzgynelis" </p> <p>19. Københavns Fødevarefællesskab (KBHFF)/Copenhagen Food Co-operative (CPH) </p> <p>20. La Colmena que dice sí </p> <p>21. La OSA Coop </p> <p>22. La OSA Coop </p> <p>23. La Ruche qui dit Oui/Boeren en Buren/L'Alveare Che Dice SÍ </p> <p>24. Local food program </p> <p>25. M**bun </p> <p>26. Manna Organic Store </p> <p>27. Más Que Lechugas </p> <p>28. Mobilus Turgelis </p> <p>29. Naturecode </p> <p>30. Né d'une seule ferme </p> <p>31. Podkarpacki E-bazarek </p> <p>32. Podkarpackie Smaki (Subcarpathian Flavors Cluster) </p> <p>33. Pora Na Pola </p> <p>34. American Farm School of Thessaloniki </p> <p>35. Public Food Services </p> <p>36. QualityLowInputFood (QLIF) </p> <p>37. Rechtstreek </p> <p>38. REKO food collective </p> <p>39. School milk and fruit </p> <p>40. Self picking berries </p> <p>41. Streeckgenoten</p> <p>42. SVAIGI - online shop </p> <p>43. Telaraki.com </p> <p>44. The Agricultural Marketing Platform </p> <p>45. The Cold Chain of Raw Milk </p> <p>46. The Eco-Cabinet (Økoskabet) </p> <p>47. The Little Cheese Shop </p> <p>48. Tiešā pirkšana - Direct shopping group </p> <p>49. TomaTomate </p> <p>50. Traditional United Europe Food (TRUEFOOD) </p> <p>51. Via Amerina and Forre Bio-District</p>
Regional assessment of the current status of Short Food Supply Chains
<p>The assessment of the current status of the Short Food Supply Chains (SFSCs) in each of the 12 Beacon Regions (focal regions) of the agroBRIDGES project is one of the different key aspects that jointly conform to the overall project concept.</p> <p>It is important to study the existing knowledge and SFSCs in each region, to identify and later stimulate the wide adoption of sustainable SFSC-based business models already existing successfully in one or some of the regions. The traditional/current business models, that will be spotted here, like the new SFSCs business and marketing models to be developed later, be categorised, and analysed from an economic (with special attention on producer’s income), environmental and social point of view.</p> <p>The 12 Beacon Regions portrayed in this report are listed in the table below:</p> <table> <thead> <tr> <th> <p><strong>Country</strong></p> </th> <th> <p><strong>Region</strong></p> </th> <th> <p><strong>Beacon Region Leader </strong></p> </th> </tr> </thead> <tbody> <tr> <td> <p>Greece</p> </td> <td> <p>Central Macedonia</p> </td> <td> <p>Q-PLAN</p> </td> </tr> <tr> <td> <p>Finland</p> </td> <td> <p>All</p> </td> <td> <p>VTT</p> </td> </tr> <tr> <td> <p>Netherlands</p> </td> <td> <p>All</p> </td> <td> <p>WU</p> </td> </tr> <tr> <td> <p>Italy</p> </td> <td> <p>Lazio and Piemonte</p> </td> <td> <p>CREA</p> </td> </tr> <tr> <td> <p>Ireland</p> </td> <td> <p>All</p> </td> <td> <p>MTU</p> </td> </tr> <tr> <td> <p>Denmark</p> </td> <td> <p>All</p> </td> <td> <p>FBCD</p> </td> </tr> <tr> <td> <p>Spain</p> </td> <td> <p>Andalucia</p> </td> <td> <p>CTA</p> </td> </tr> <tr> <td> <p>Poland</p> </td> <td> <p>All</p> </td> <td> <p>UNMF</p> </td> </tr> <tr> <td> <p>Latvia</p> </td> <td> <p>All</p> </td> <td> <p>UNMF</p> </td> </tr> <tr> <td> <p>Lithuania</p> </td> <td> <p>All</p> </td> <td> <p>UNMF</p> </td> </tr> <tr> <td> <p>France</p> </td> <td> <p>Bretagne, Pays de la Loire, Auvergne – Rhône Alpes</p> </td> <td> <p>VGP</p> </td> </tr> <tr> <td> <p>Turkey</p> </td> <td> <p>All</p> </td> <td> <p>SUF</p> </td> </tr> </tbody> </table> <p>All Beacon Regions have contributed data and insights about their specific region collected through desk research. They have focused on 2 appointed sectors that is of importance for the region. The focal sectors have been settled in the application for this project. The knowledge generated in the desk research is qualified through interviews with members of the regional Multi Actor Platform (MAP). Some interviews are summarized in the document, while others are integrated into the description.</p> <p>For each Beacon Region the current status of SFSCs is described in these chapters:</p> <ul> <li>Overall status</li> <li>Main regional drivers</li> <li>Environmental aspects</li> <li>Economic aspects</li> <li>Social aspects</li> </ul> <p>Along this information you will find a description of 5-10 important business or marketing models for each region together with statistics on market sectors and size. The description of the models is based on Business Model Canvas.</p> <p>The dataset contains the 12 regional reports, outlining the core topics of the regional assessment described above. In some regional reports, the notes from interviews with local actors are available and provided in the respective Annex. </p>
CafeteriaSA corpus: Scientific abstracts annotated across different food semantic resources
<p>In the last decades, a great amount of work has been done in predictive modeling of issues related to human and environmental health. Resolution of issues related to healthcare is made possible by the existence of several biomedical vocabularies and standards, which play a crucial role in understanding health information, together with a large amount of health-related data. However, despite the large number of available resources and work done in the health and environmental domains, there is a lack of semantic resources that can be utilized in the food and nutrition domain, as well as their interconnections. For this purpose, in an European Food Safety Authority-funded project CAFETERIA, we have developed the first annotated corpus of 500 scientific abstracts that consists of 6,407 annotated food entities with regard to Hansard taxonomy, 4,299 for FoodOn, and 3,623 for SNOMED-CT. The CafeteriaSA corpus will enable further development of natural language processing methods for food information extraction from textual data that will allow extracting of food information from scientific textual data.</p>
CafeteriaFCD corpus: Food consumption data annotated with regard to different food semantic resources
<p>The FoodBase curated version which contains 1,000 manually evaluated recipes, annotated with the appropriate semantic tags from the Hansard Taxonomy, FoodON and SNOMED-CT.</p>
Tomato Classification using Mass Spectrometry-Machine Learning Technique: a Food Safety-enhancing Platform
<p>Food safety and quality assessment mechanisms are unmet needs that industries and countries have been continuously facing in recent years. Our study aimed at developing a platform using Machine Learning algorithms to analyze Mass Spectrometry data for classification of tomatoes on organic and non-organic. Tomato samples were analyzed using silica gel plates and direct-infusion electrospray-ionization mass spectrometry technique. Decision Tree algorithm was tailored for data analysis. This model achieved 92% accuracy, 94% sensitivity and 90% precision in determining to which group each fruit belonged. Potential biomarkers evidenced differences in treatment and production for each group.</p>
Complete datasets and code for "Hungry or angry? Experimental evidence for the effects of food availability on two measures of stress in developing wild raptor nestlings"
<p><strong>Abstract</strong></p> <p>Food shortage challenges the development of nestlings; yet, to cope with this stressor, nestlings can induce stress responses to adjust metabolism or behaviour. Food shortage also enhances the antagonism between siblings, but it remains unclear whether the stress response induced by food shortage operates via the individual nutritional state or via the social environment experienced. In addition, the understanding of these processes is hindered by the fact that effects of food availability often co-vary with other environmental factors. We used a food supplementation experiment to test the effect of food availability on two complementary stress measures, feather corticosterone (CORTf) and Heterophil/Lymphocyte-ratio (H/L) in developing red kite (Milvus milvus) nestlings, a species with competitive brood hierarchy. By statistically controlling for the effect of food supplementation on the nestlings’ body condition, we disentangled the effects of food and ambient temperature on nestlings during development. Experimental food supplementation increased body condition, and both CORTf and H/L were reduced in nestlings of high body condition. Additionally, CORTf decreased with age in non-supplemented nestlings. H/L decreased with age in all nestlings and was lower in supplemented last-hatched nestlings compared to non-supplemented ones. Ambient temperature showed a negative effect on H/L. Our results indicate that food shortage increases the nestlings’ stress levels through both, a reduced food intake affecting nutritional state and the nestlings’ social environment. Thus, food availability in conjunction with ambient temperature shape between- and within nest differences in stress load, which may have carry-over effects on behaviour and performance in further life-history stages.</p>
Supplementary Materials for "Food security in Roman Palmyra (Syria) in light of paleoclimatological evidence and its historical implications"
<p>Contained here are the SI files for the article "Food security in Roman Palmyra (Syria) in light of paleoclimatological evidence and its historical implications". With all the materials contained here, as well as the openly accessible datasets cited in S1_File, every step of the study can be reproduced. Detailed instructions are contained within. Includes code for Data Analysis.</p> <p>Article DOI: [forthcoming]</p>
Supporting data of: Hydrography and food distribution during a tidal cycle above a cold-water coral mound
<p>This file contains the raw data and data analyses scripts to:</p> <p>Hydrography and food distribution during a tidal cycle above a cold-water coral mound</p> <p>Evert de Froe, Sandra R. Maier, Henriette G. Horn, George A. Wolff, Sabena Blackbird, Christian Mohn, Mads Schultz, Anna-Selma van der Kaaden, Chiu H. Cheng, Evi Wubben, Britt van Haastregt, Eva Friis Moller, Marc Lavaleye, Karline Soetaert, Gert-Jan Reichart, Dick van Oevelen.</p> <p>Deep Sea Research Part I: Oceanographic Research Papers, 2022,<br> ISSN 0967-0637,<br> https://doi.org/10.1016/j.dsr.2022.103854.<br> <strong>Abstract: </strong>Cold-water corals (CWCs) are important ecosystem engineers in the deep sea that provide habitat for numerous species and can form large coral mounds. These mounds influence surrounding currents and induce distinct hydrodynamic features, such as internal waves and episodic downwelling events that accelerate transport of organic matter towards the mounds, supplying the corals with food. To date, research on organic matter distribution at coral mounds has focussed either on seasonal timescales or has provided single point snapshots. Data on food distribution at the timescale of a diurnal tidal cycle is currently limited. Here, we integrate physical, biogeochemical, and biological data throughout the water column and along a transect on the south-eastern slope of Rockall Bank, Northeast Atlantic Ocean. This transect consisted of 24-hour sampling stations at four locations: Bank, Upper slope, Lower slope, and the Oreo coral mound. We investigated how the organic matter distribution in the water column along the transect is affected by tidal activity. Repeated CTD casts indicated that the water column above Oreo mound was more dynamic than above other stations in multiple ways. First, the bottom water showed high variability in physical parameters and nutrient concentrations, possibly due to the interaction of the tide with the mound topography. Second, in the surface water a diurnal tidal wave replenished nutrients in the photic zone, supporting new primary production. Third, above the coral mound an internal wave (200 m amplitude) was recorded at 400 m depth after the turning of the barotropic tide. After this wave passed, high quality organic matter was recorded in bottom waters on the mound coinciding with shallow water physical characteristics such as high oxygen concentration and high temperature. Trophic markers in the benthic community suggest feeding on a variety of food sources, including phytodetritus and zooplankton. We suggest that there are three transport mechanisms that supply food to the CWC ecosystem. First, small phytodetritus particles are transported downwards to the seafloor by advection from internal waves, supplying high quality organic matter to the CWC reef community. Second, the shoaling of deeper nutrient-rich water into the surface water layer above the coral mound could stimulate diatom growth, which form fast-sinking aggregates. Third, evidence from lipid analysis indicates that zooplankton faecal pellets also enhance supply of organic matter to the reef communities. This study is the first to report organic matter quality and composition over a tidal cycle at a coral mound and provides evidence that fresh high-quality organic matter is transported towards a coral reef during a tidal cycle.</p> <p> </p>
Eurobarometer on Food Safety 2022 - Dataset
<p>This Special Eurobarometer, commissioned by EFSA, examines Europeans’ perceptions of and attitudes towards food safety and provides insights in terms of:</p> <p>• Europeans’ interest in food safety-related topics and factors affecting food-related decisions;</p> <p>• Awareness of and main concerns about food-safety topics, as well as attitudes towards healthy diet and food-related risks;</p> <p>• Main information channels on food-related risks;</p> <p>• Levels of trust in different actors from farm to fork;</p> <p>• Awareness of different aspects of the EU food safety system;</p> <p>• Behaviour in the area of food safety, using an example of a foodborne disease outbreak.</p> <p>The survey was carried out by the Kantar network in the 27 EU Member States between 21st March and 20th April 2022. A total of 26,509 respondents from different social and demographic groups were interviewed face-to-face at home in their mother tongue. In countries where using only face-to-face interviewing was not feasible due to the impact of COVID-19, online interviews were also used to supplement face-to-face ones.</p> <p>The methodology used is that of the Standard Eurobarometer surveys carried out by the Directorate-General for Communication. It is the same for all countries and territories covered in the survey.</p> <p>#------------------------------------------------------------------#</p> <p>The datasets published are distributed as follows:</p> <p>Volume A: Countries<br> Volume AA: Groups of countries<br> Volume B: EU/socio-demographics<br> Volume C: Country/socio-demographics<br> Microdata ( eb972_food_safety_v3.csv )<br> Data-Map: Variable Labels</p> <p>#------------------------------------------------------------------#</p> <p> </p>
S118 | PFASFCCMIGEX | 68 PFAS in Migrating & Extractable Food Contact Chemicals (FCCmigex)
<p>This is the collection associated with list S118 PFASFCCMIGEX 68 PFAS in Migrating & Extractable Food Contact Chemicals (FCCmigex) 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> <p>List of the 68 PFASs identified in migrating and extractable food contact chemicals (FCCmigex- see <a href="../records/10551195">S112</a>) by the Food Packaging Forum based on 47 studies as published in Phelps et al (2024) DOI:<a href="https://pubs.acs.org/doi/10.1021/acs.est.3c03702">10.1021/acs.est.3c03702</a>.</p>
Data supporting: Combined stress of an insecticide and heatwaves or elevated temperature induce community and food web effects in a Mediterranean freshwater ecosystem
<p>Data used to obtain the results of the research paper entitled: "Combined stress of an insecticide and heatwaves or elevated temperature induce community and food web effects in a Mediterranean freshwater ecosystem", published in the journal "Water Research". The data derives from an outdoor (meso-) cosm experiment in Spain (Imdea Water, Alcala de Henares) in which the transportable temperature and heatwave control device (TENTACLE) was used to investigate the multiple stressors effects of two different climate change scenarios related to temperature (i.e., elevated temperature and reoccurring heatwaves) in combination with the neonicotinoid insecticide imidacloprid.</p>
Survey data on behaviours and attitudes towards green food consumption of participants of the SmartFood Urban Living Lab in Warsaw, Poland
<p>In this dataset, we present raw data of a survey on behaviours and attitudes towards green food consumption, conducted between June 2023 and April 2024 among a group of 21 households from Warsaw, participating in a SmartFood Urban Living Lab (ULL). The dataset is complemented with results collected from two control groups. The SmartFood Urban Living Lab was an intervention aimed at providing residents of urban blocks of flats with a novel technology for growing their own food. The ULL served as an experimental ground for testing and refining innovations such as hydroponic cabins, rainwater management systems, solar energy systems, and insect farming units. Residents actively participated in the lab, providing valuable insights into the practical challenges and benefits of urban farming, which helped refine and adapt the technologies for broader application. After each month of the intervention, a survey was conducted to check participants' behaviours and attitudes towards green food consumption</p>
Food Tracking Device data
<p>This dataset includes data from the Food Tracking Device from several pilot cases within the URBAN-WASTE project. In particular, a few hotels and restaurants were supplied with a food waste tracking device where the kitchen could record their food waste data. The statistics would then be transformed into simple figures to give the kitchen feedback on their development in terms of food waste generation.</p>
Zambezi dataset to "WHAT-IF: an open-source decision support tool for water infrastructure investment planning within the Water-Energy-Food-Climate Nexus"
<p>This is the dataset used in the HESS publication "<a href="https://www.hydrol-earth-syst-sci-discuss.net/hess-2019-167/">WHAT-IF: an open-source decision support tool for water infrastructure investment planning within the Water-Energy-Food-Climate Nexus</a>"</p> <p>The dataset describes the water-energy-food nexus of the Zambezi River Basin used as input to the <a href="https://github.com/RaphaelPB/WHAT-IF">WHAT-IF model</a>.</p> <p>The file Data_Organization.pdf, summarizes the available data. For more info look at the <a href="https://www.hydrol-earth-syst-sci-discuss.net/hess-2019-167/">publication</a> and/or <a href="https://github.com/RaphaelPB/WHAT-IF">Github</a>.</p>
Dataset on consumer acceptance of policy measures for sustainable food consumption
<p>This data was obtained from an online survey conducted with Swiss participants from the German-speaking parts of Switzerland. The survey was conducted in February 2023. Participants were recruited through a professional panel provider and quotas were used for age and gender. The final sample contained 453 respondents. In the first part of the survey, respondents provided basic sociodemographic information and reported how often they consumed meat and dairy products. In the second part, their knowledge regarding the environmental impact of food was assessed using an existing scale by Hartmann et al. (2021). Next, respondents indicated for 19 different policy measures how much they agreed with each of them. Finally, respondents answered several questions assessing their health consciousness and environmental attitudes using existing scales. The survey can be used and adapted to different contents, aiming to investigate public acceptance of policy measures for sustainable food consumption.</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.