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62 results for “Food chain”
Short Food Supply Chains Business and Marketing Models Categorisation
<p>The present dataset contains information about the categorisation of Business Models for Short Food Supply Chains, in the context of the <a href="http://agrobridges.eu">agroBRIDGES project</a> (Horizon 2020, GA No. 101000788). Regional information about existing and new business models for Short Food Supply Chains were collected from 12 regions and countries of focus for the project (Beacon Regions) through desk research, interviews and co-creation workshops.</p> <p>The datasets collected information provided by Beacon Region Leaders in the co-creation workshops about models applied and newly or potentially developed ones and available in the literature, to categorise them into stylized models with specific and distinguishable features and attributes, enabling a systematic approach in the development of the sustainability assessment framework. An initial ranking of local business models was provided at a regional / country level. </p> <p>Through analysis of the aggregate ranking results of business models at local level, a final list of business models for SFSCs was derived. Five different Business Model types have been addressed and developed ( i. Community Supported Agriculture - CSA, ii. Face to face trade, iii. Online Food Trade, iv. Local Food Trade, and v. Improved Logistics). The final categorization list provided valuable insights in order to represent all regional contexts and eventually offer value-added information about the main factors for developing a successful business model that can enhance market success in the long term; as well as to strengthen farmer´s strategy that eventually enhances their position within the whole value chain and improve their customer experience.</p> <p>The Value Proposition Canvas and Business Model Canvas was defined for each of the most common SFSC represented in each of the targeted regions.</p> <p><br> The dataset contains:<br> • <strong>agroBRIDGES_SFSCs-BMModels-Categorisation_2021.12.01_v1 [zip file]: </strong>The initial list of business models collected from the co-creation workshops<br> • <strong>agroBRIDGES_SFSCs-BMModels-Categorisation-Tool_2021.12.01_v1 [.xlsx file]: </strong>Internal Assessment tool for Business models regional data collection: A data collection tool was built in-house and shared to all Beacon Region Leaders in order to enable them to fill it regarding their assumptions and conclusions after the co-creation participants discussed and presented their insights from regional activities.<br> • <strong>agroBRIDGES_SFSCs-BModels-CategorisationResults_2021.12.01_v1 [.xlsx file]:</strong> SFSCs BM type results in each of the regions.<br> • <strong>agroBRIDGES_BusinessModels_Canvas_2021.12.01_v1 [zip file]: </strong>The Business Model and Value Proposition Canvas of the 5 BM categories</p>
Food-chain length in desert streams of central and southern Arizona, USA
## overview Food chain length (FCL) is a key measure of the vertical structure of food webs that determines energy flow through ecosystems, carbon exchange between freshwater ecosystems and the atmosphere, and rates of nutrient cycling. FCL also has a strong bearing on the biomass of green plants in ecosystems and hence on water quality in aquatic ecosystems. Broad-scale syntheses of controls on FCL in stream ecosystems indicate that FCL declines with discharge variation but, counter to theory, does not vary significantly with energy supply. The mechanisms linking discharge and energy to FCL are largely unresolved in streams. We propose that lack of a relationship between energy supply and FCL may be due to variation in efficiency of energy transfer caused by constraints of food quality, or to a temporal mismatch between measures of energy inputs and FCL. Alternatively, the effects of flow variation on FCL may simply be paramount to energy supply, but potential mechanisms linking flow to FCL remain untested. Regime shifts—punctuated change between strings of high- and low-flow events—may cause comprehensive faunal replacement across trophic levels and collapse of the vertical structure of food webs. FCL may change as a result of loss (or gain) of an apex predator, or as a result of changes in feeding relationships leading to apex predators that eat higher on the food chain. Finally, flow variation may indirectly influence FCL through inputs of limiting nutrients during floods. In desert streams, algae typically provide the primary source of energy, and algal production is limited by nitrogen (N). N loading from terrestrial ecosystems is strongly related to flow variation, particularly to the inter-flood interval (IFI) or duration of baseflow between floods. Long IFI leads to larger N pulses and potentially greater net ecosystem production (NEP), thereby providing an indirect effect of flow variation on FCL. Specific aims of the research include: 1) Quantify the effe
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.
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>
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>
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>
Mapping the existing food systems, value chains and markets for agroecological products
<table> <tbody> <tr> <td>The food systems and markets of our focal farming systems are mapped as well as communities across the value chain, including operations from production over to food disposal after consumption, all along with the contribution of these operations to socio-economic and environmental outcomes. The datasets are the results of survey/interviews on the above topic in the CANALLS project ALLs.<span> </span></td> </tr> </tbody> </table>
Opinions and data on short food supply chains related policy analysis in 9 EU countries generated in the H2020 SMARTCHAIN Project grant number: 773785
<p>Questionnaires: Opinions and data on short food supply chains related policy analysis in 9 EU countries generated in the H2020 SMARTCHAIN Project grant number: <strong>773785</strong></p>
Case study for Sustainable agri-food supply chain planning through multi-objective optimisation
<p>Case study used to solve the multi-objective model proposed in: Sustainable agri-food supply chain planning through multi-objective optimisation</p>
Animated video presenting the advancements from AI4DI Supply Chain for Food and Beverage industries
<p>This animated video presents the dvancements from AI4DI Supply Chain for Food and Beverage industries.</p>
Food chain without giants: Modelling the trophic impact of bowhead whaling on little auk populations in the Atlantic Arctic
Open the record for dataset details and reuse information.
Forest conversion to oil palm compresses food chain length in tropical streams
<b>Description: </b><p>Fish stable isotope data associated with the paper in Ecology. This includes bulk and compound specific stable isotope raw data for fish at 17 streams over 2 sampling years, across a land use gradient</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/57"><b>Composition and abundance of tropical freshwater vertebrate communities across a land use gradient</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>Sime Darby Foundation (NA, NA)</li><li>Royal Geographic Society (RGS-IBG , PRA 01/16)</li><li>Royal Society (NA, NAF\R2\180791)</li><li>Singapore Ministry of Education (AcRF Tier 1 grant, R-154-000-A32-114)</li><li>Landmark Trust (Landmark Trust Futures Scheme , NA)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3974971">here</a></p><p><b>Files: </b>This consists of 1 file: Fish_SIA_data2.xlsx</p><p><b>Fish_SIA_data2.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>SIA_data</b> (described in worksheet Data)</p><p>Description: raw stable isotope fish data </p><p>Number of fields: 13</p><p>Number of data rows: 202</p><p>Fields: </p><ul><li><b>Date</b>: Date of sampling at each stream (Field type: date)</li><li><b>Stream</b>: Which stream was sampled (Field type: location)</li><li><b>Landuse</b>: Landuse of the stream sampled (Field type: categorical)</li><li><b>Species</b>: Species sampled (Field type: taxa)</li><li><b>SIA</b>: Was bulk or compound specific SIA used (Field type: categorical)</li><li><b>d15N_GLU</b>: d15N value of glutamic acid (Field type: numeric)</li><li><b>d15N_PHE</b>: d15N value of phenylalanine (Field type: numeric)</li><li><b>d13C_ILE</b>: d13C value of Isoleucine (Field type: numeric)</li><li><b>d13C_LEU</b>: d13C value of Leucine (Field type: numeric)</li><li><b>d13C_PHE</b>: d13C value of phenylalanine (Field type: numeric)</li><li><b>d13C_VAL</b>: d13C value of Valine (Field type: numeric)</li><li><b>D15N</b>: D15N (Field type: numeric)</li><li><b>D13C</b>: D13C (Field type: numeric)</li></ul></li></ol><p><b>Date range: </b>2015-03-01 to 2017-05-30</p><p><b>Latitudinal extent: </b>4.5770 to 4.9613</p><p><b>Longitudinal extent: </b>117.4441 to 117.8053</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Chordata <br> -  -  -  Actinopterygii <br> -  -  -  -  Cypriniformes <br> -  -  -  -  -  Cyprinidae <br> -  -  -  -  -  -  <i>Luciosoma</i> <br> -  -  -  -  -  -  -  <i>Luciosoma pelligrinii</i> <br> -  -  -  -  -  -  <i>Rasbora</i> <br> -  -  -  -  -  -  -  <i>Rasbora pycnopeza</i> <br> -  -  -  -  -  -  <i>Nematabramis</i> <br> -  -  -  -  -  -  -  <i>Nematabramis everetti</i> <br> -  -  -  -  -  -  <i>Hampala</i> <br> -  -  -  -  -  -  -  <i>Hampala sabana</i> <br> -  -  -  -  -  -  <i>Puntius</i> <br> -  -  -  -  -  -  -  <i>Puntius sealei</i> (as synonym: <i>Barbodes sealei</i>)<br> -  -  -  -  Siluriformes <br> -  -  -  -  -  Clariidae <br> -  -  -  -  -  -  <i>Clarias</i> <br> -  -  -  -  -  -  -  <i>Clarias anfractus</i> <br> -  -  -  -  -  Bagridae <br> -  -  -  -  -  -  <i>Hemibagrus</i> <br> -  -  -  -  -  -  -  <i>Hemibagrus baramensis</i> <br> -  -  -  -  Perciformes <br> -  -  -  -  -  Channidae <br> -  -  -  -  -  -  <i>Channa</i> <br> -  -  -  -  -  -  -  <i>Channa striata</i> <br></div><p></p>
Isotope analyses of amino acids in fungi and fungal feeding Diptera larvae allow differentiating ectomycorrhizal and saprotrophic fungi-based food chains
1- Both ectomycorrhizal (ECM) and saprotrophic fungi are fundamental to carbon and nutrient dynamics in forest ecosystems; however, the relative importance of these different fungal functional groups for higher trophic levels of the soil food web is virtually unknown. 2- To explore differences between fungal functional groups and their importance for higher trophic levels, we analysed isotopic composition of nitrogen and carbon in amino acids (AAs) and bulk tissue of leaf litter, fungi, and fungal-feeding Diptera larvae. 3- By accounting for isotopic variability of utilized substrates, compound-specific isotope analyses of nitrogen in AAs yielded more realistic results for the trophic position of fungi than bulk isotope analyses, with converging trophic positions of saprotrophic and ECM fungi. 4- Saprotrophic and ECM fungi possessed different AA δ<sup>13</sup>C signatures separating fungal functional groups and their consumers in fingerprinting approaches, thereby allowing to trace energy fluxes from these basal resources to higher trophic levels. 5- A pronounced isotopic fractionation even in essential/source AAs of fungal-feeding Diptera larvae necessitates further studies on tissue-/compound-specific isotopic differences in fungi and on potential supplementation by gut microorganisms. 6- The results highlight the potential of compound-specific isotope analysis of amino acids to identify and integrate contributions of different fungal functional groups to higher trophic levels in soil food webs.
Hunters versus Hunted: New perspectives on the physiological costs of survival at the top of the food chain
<ol> <li>Global biotic and abiotic threats,<a name="_Hlk41730390">particularly from pervasive human activities</a>, are progressively pushing large, apex carnivorous mammals into the functional role of mesopredator. Hunters are now becoming the hunted. Despite marked impacts on these animals and the ecosystems in which they live, little is known about the physiological repercussions of this role downgrading from ultimate to penultimate predator.</li> <li>Here we examine how such ecological role reversals alter the physiological processes associated with energy expenditure, and ultimately the cost of survival during peak performance.</li> <li>Taxonomic group, preferred habitat, and domestication affected the capacity of the oxygen pathway to support high levels of aerobic performance by carnivorous mammals. Fear responses associated with anthropogenic threats also impacted aerobic capacity. </li> <li>Allometric trends for three energetic metrics (maximum oxygen consumption, field metabolic rates, and the cost per stride or step), showed distinct trends in aerobic capacity for different evolutionary lineages of mammalian predators. Cursorial canids that chase down prey demonstrated the highest relative maximum oxygen consumption rates (10-25 times resting levels) and field metabolic rates, while ambush predators (i.e., felids) and marine mammals had aerobic capacities that were similar to or lower than sedentary domestic mammals of comparable size.</li> <li>The maximum energetic cost of performance for apex predators depended on whether the animals were hunters or the hunted. Escape responses were exceptionally costly for marine (narwhal, <i>Monodon monoceros</i>) and terrestrial (mountain lion, <i>Puma concolor) </i>locomotor specialists, as well as semi-aquatic (polar bear, <i>Ursus maritimus</i>) species; all showed a nearly two-fold increase in peak energy expenditure when avoiding threats.</li> <li>As the duration and frequency of threats to wild species continue to grow, cumulative energetic costs are becoming more apparent. In view of this, attention to the energy demands of apex predators will provide vital predictive power to anticipate mismatches between a species functional design and human-induced pressures, and allow for the development of conservation strategies based on how species are built to survive.</li> </ol>
Food Chain pilot raw data - Farm, Transportation, Warehouse
<p>Daily GDD (Growing Degree-day) data collected by a weather station involved in Food Chain Pilot in SOFIE EU project (GA n.779984).</p> <p>Handover, temperature, presence data collected in a transportation vehicle (farm to warehouse, warehouse to supermarket) involved in Food Chain Pilot in SOFIE EU project (GA n.779984).</p> <p>Temperature, humidity data collected in a warehouse involved in Food Chain Pilot in SOFIE EU project (GA n.779984).</p>
Food Chain pilot raw data - Farm, Transportation, Warehouse
<p>Daily GDD (Growing Degree-day) data collected by a weather station involved in Food Chain Pilot in SOFIE EU project (GA n.779984).</p> <p>Handover, temperature, presence data collected in a transportation vehicle (farm to warehouse, warehouse to supermarket) involved in Food Chain Pilot in SOFIE EU project (GA n.779984).</p> <p>Temperature, humidity data collected in a warehouse involved in Food Chain Pilot in SOFIE EU project (GA n.779984).</p>
Annex - Information Technology as Enabler of Transparency in Food Supply Chains
<p>This is the Annex of the paper: Information Technology as Enabler of Transparency in Food Supply Chains</p>
Data From: Shortened food chain length in a fished versus unfished coral reef
<p>Direct exploitation through fishing is driving dramatic declines in wildlife populations in ocean environments, particularly for predatory and large-bodied taxa. Despite wide recognition of this pattern and well-established consequences of such trophic downgrading on ecosystem function, there have been few empirical studies examining the effects of fishing on whole system trophic architecture. Understanding these kinds of structural impacts is especially important in coral reef ecosystems - often heavily fished and facing multiple stressors. Given often high dietary flexibility and numerous functional redundancies, especially in diverse ecosystems such as coral reefs, it is important to establish if web architecture is strongly impacted by fishing pressure, or if it might be resilient, at least to moderate intensity pressure. To examine this question, we used a combination of bulk and compound-specific stable isotope analyses measured across a range of predatory and low trophic level consumers between two coral reef ecosystems that differed with respect to fishing pressure but otherwise remained largely similar. We find that even in a high diversity system with relatively modest fishing pressure, there are strong reductions in the trophic position of the three highest trophic position consumers examined in the fished system but no effects on the trophic position of lower-level consumers. We see no evidence that this shortening of these affected food webs is being driven by changes in basal resource consumption, e.g., through changes in spatial location of foraging by consumers. Instead, this likely reflects internal changes in food web architecture suggesting that even in diverse systems, and with relatively modest pressure, human harvest causes significant compressions in food chain length. This observed shortening of these food webs may have many important emergent ecological consequences for the functioning of ecosystems impacted by fishing or hunting. Such important structural shifts may be widespread but unnoticed by traditional surveys. This insight may also be useful for applied ecosystems managers grappling with choices about the relative importance of protection for remote and pristine areas and the value of strict no-take areas to protect not just the raw constituents of systems affected by fishing and hunting but also the health and functionality of whole systems.</p>
Innovafrica project data on agricultural food value chains
<p>A dataset was generated using the baseline survey data of the project entitled "Innovations in Technology, Institutional and Extension Approaches towards Sustainable Agriculture and enhanced Food and Nutrition Security in Africa (Acronym - Innovafrica)", which involved 16 istitutions from Europe and Africa and was implemented in six countries namely Ethiopia, Kenya, Malawi, Rwanda, South Africa and Tanzania. It includes the following information: smallholders' socio-demographic and economic characteristics; improved agriculture practices and seed systems adopted; climate change related aspects; membership in agricultural associations, and access to subsidies, agricultural inputs and credit. </p>
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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.