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1,316 results for “Trade”
The SPIN covid19 RMRIO dataset: Global trade network data for the years 2016-2026 reflecting macroeconomic effects of the covid19 pandemic - C. Data for 2020 - 2026 - Counterfactual scenario
<p>The SPIN covid19 RMRIO dataset is a time series of MRIO tables covering years from 2016-2026 on a yearly basis. The dataset covers 163 sectors in 155 countries.</p> <p>This repository includes data for years from 2020 to 2026 (<em>counterfactual</em> scenario).<br> Code, method material and data for years 2016-2019 are stored in the following repository: <a href="http://doi.org/10.5281/zenodo.5713811">10.5281/zenodo.5713811</a><br> Data for the <em>covid</em> scenario are stored in the following repository: <a href="https://doi.org/10.5281/zenodo.5713825">10.5281/zenodo.5713825</a></p> <p>Tables are generated using the <a href="https://github.com/TBeaufils/SPIN">SPIN method</a>, based on the <a href="https://doi.org/10.5281/ZENODO.3993659">RMRIO tables</a> for the year 2015, GDP, imports and exports data from the <a href="https://data.imf.org/?sk=4c514d48-b6ba-49ed-8ab9-52b0c1a0179b">International Financial Statistics</a> (IFS) and the World Economic Outlooks (WEO) of <a href="https://www.imf.org/en/Publications/WEO/weo-database/2019/October">October 2019</a> and <a href="https://www.imf.org/en/Publications/WEO/weo-database/2021/April">April 2021</a>.</p> <p>The<em> counterfactual</em> scenario is in line with October 2019 WEO's data and simulates the global economy without Covid 19.</p> <p>All tables are labelled in 2015 US$ and valued in basic prices.</p>
Data from: Macro-evolutionary trade-offs as the basis for the distribution of European bats
<p>We have compiled a dataset of life history traits and distribution characteristics of 30 European bat species, based on a literature study of a total of 56 primary and secondary sources. These life history traits are grouped into morphological, physiological and ecological adaptations. </p> <p><em>Physiological adaptations:</em></p> <p>Neonatal mass: the average weight (g) of a newborn pup, measured within five days after birth.</p> <p>Average litter size: the average size of a full-term litter (including stillborn pups) per female.</p> <p>Weaning mass: the weight (g) of a juvenile during its first flight outside the roost.</p> <p>Adult body mass: the average weight (g) of adult bats during the summer (between 1 May and 1 July), excluding pregnant females.</p> <p>Litter mass: neonatal mass * average litter size.</p> <p>Relative mass of neonatal to adult: neonatal mass*100 / adult weight</p> <p>Relative mass of litter to adult: litter mass*100 / adult weight</p> <p>Gestation: the length of gestation period (in days), from fertilisation to birth. When mated during autumn or winter, the sperm (or fertilised egg in <em>M. schreibersii</em>) is stored throughout the winter. On arousal from hibernation in the spring, around mid March, female bats ovulate and gestation begins. In accordance with other researchers (e.g. Altringham 1996, Entwisle <em>et al</em>. 1998), 15 March was used as the start of the gestation period, for statistical reasons we also included <em>M. schreibersii</em>.</p> <p>Weaning: the length of the lactation period (in days) until offspring are fully independent. After the juveniles are capable of flight, mothers continue to give their young nourishment until they are fully independent. Only when no extra nourishment is provided are the offspring considered fully weaned.</p> <p>Reproductive period: gestation + weaning (in days).</p> <p>Average age at first reproduction: The age (in days) at which 75% of the female population becomes sexual mature. Many species reproduce just before or during their first winter (at approximately 80 days old), but in some species the majority of the population postpone their sexual development. Individuals are stated to have become sexual mature if they participate in mating, have been found to be pregnant or inseminated.</p> <p>Observed average age: observed average age of adults in a population at a given time (in years).</p> <p>Longevity: the age (in years) of the oldest observed individual. The longevity can only be obtained by marking and later recapturing individuals. Most recapture data are collected in summer roosts or hibernacula. As not all species show the same fidelity to summer roost sites or can be found in hibernacula that are accessible to humans, this measure is sensitive to the chance of recapture.</p> <p>Minimum hibernation temperature: the minimum temperature (degrees Celsius) at which each species is observed.</p> <p> </p> <p><em>Morphological adaptations</em></p> <p>Length of forearm at birth: the length of the forearm (mm) of a newborn bat, measured between the elbow to the wrist of a folded wing. This is widely accepted as a measurement of size. Although it is not the best reflection of the length of an individual, it can be measured rapidly and accurately under field conditions.</p> <p>Length of forearm adult: The length of the forearm (mm) of an adult bat.</p> <p>Relative length of forearm of a newborn to an adult: (length of the forearm at birth*100)/ Length of forearm adult.</p> <p>Wing span: the length of the wings (m). The distance between the wingtips of a bat with wings extended so the leading edge is straight (including body width).</p> <p>Wing area: The combined area of the two wings (m<sup>2</sup>) including the entire tail membrane and the portion of the body between the wings.</p> <p>Wing loading: the relation between body weight, wing size and gravity (Nm<sup>-2</sup>). This measurement is related to the mean pressure on the wings. Wing loading is the weight (mass, in kg, times gravitational acceleration) divided by the wing area, i.e Wing loading = (weight adult*9.81)/ wing area. The wing load can vary significantly between geometrically similar bats. Because of such allometry, large bats have a higher wing load than smaller bats.</p> <p>Wing aspect ratio: the square of the wingspan divided by the wing area, i.e. Wing aspect ratio = (wingspan)<sup>2</sup> / wing area. This ratio can be interpreted as a measure of the aerodynamic efficiency of flight. A higher aspect ratio usually corresponds with greater aerodynamic efficiency (i.e. a streamlined body) and lower energy use in flight.</p> <p>Flight speed: The speed of flight (m/s). The speed of flight is usually measured in wind tunnel experiments or during radio-tracking.</p> <p> </p> <p><em>Ecological adaptations </em></p> <p>Maximum migration distance: the maximum observed distance (km) between the summer and winter habitat. In contrast to birds, the direction of migration in bats is not determined by the change of the seasons, but by the locations of the hibernacula. This migration distance can only be obtained by capturing, marking and later recapturing individuals. Bats often migrate across national boundaries and gathering recapture data requires international cooperation. The chance of recapture is sensitive to sample effort and local observation methods.</p> <p>Average migration distance: the average distance (km) between the summer and winter habitat. Most species of bats migrate both short and long distances. The same restrictions described for maximum migration distance also apply to this parameter.</p> <p>Echolocation type: the predominant echolocation type used by each bat species. European bats use one or sometimes a combination of the following four types of echolocations: fm-CF-fm, fm-QCF (with the QCF part dominant), FM-qcf (with the FM part dominant) and FM. For statistical reasons both FM-qcf and FM are clustered in the group FM. The FM-qcf and fm-QCF echolocations are both often loud and used to detect distant prey. FM and fm-CF-fm echolocations are softer and bats using these types of echolocation receive more detailed knowledge of their surroundings. Bats primarily use only one type of echolocation, although many can make some slight adjustments to this.</p> <p>Echolocation range: the maximum distance that an echolocating bat can detect a structure or object.</p> <p>Echolocation minimum frequency: the minimum echolocation frequency (MHz) used by each bat species.</p> <p>Echolocation maximum frequency: the maximum echolocation frequency (MHz) used by each bat species.</p> <p>Duration call (ms): the average duration (in ms) of one complete call cycle.</p> <p> </p> <p><em>Distribution parameters</em></p> <p>Northern limit of range: the most northerly observation (in latitude) of each bat species. This measurement includes anecdotal observations and observations of male bats.</p> <p>Northern limit of reproduction range: the most northerly observation (in latitude) of a maternity group. Note: confusion is possible between summer roosts and maternity roosts. Summer roosts are often inhabited by both males and females and less than 70% of the adult females participate in reproduction. Maternity roosts are predominantly occupied by females, and more than 70% of the adult females participate in reproduction.</p> <p>Southern limit of range: the most southerly observation (in latitude) of each bat species. This measurement includes anecdotal observations and observations of male bats.</p> <p>Southern limit of reproduction range: the most southerly observation (in latitude) of a maternity group. The same restrictions described for northern limit of reproduction range also apply to this parameter.</p> <p>Western limit of range: the most western observation (in longitude) of each bat species</p> <p>Eastern limit of range: the most eastern observation (in longitude) of each bat species</p> <p>Night length: The average night length (in hours) during midsummer (21<sup>st</sup> June) at the northern limit of the reproduction range.</p> <p> </p> <p>Sources: 1. Jones et al. 2009, 2. Krapp 2011, 3. Schober & Grimmberger 1997, 4. Norberg & Rayner 1987, 5. Hutterer et al. 2005, 6. Dietz et al. 2009, 7. Supplementary data from Barclay et al. 2004, 8. Wilkinson & South 2002, 9 Jones & Rydell 1994, 10. Norberg 1986, 11. Jones 1994, 12. Baagøe 1987, 13.Fleming & Eby 2003, 14. Neuweiler 2000, 15. Hayssen et al. 1993, 16. Kunz & Kurta 1987, 17. Russo & Jones 2002, 18. Brunet-Rossinni & Austad 2004, 19. Aldridge 1987, 20. Urbańczyk 1991, 21. Nagel & Nagel 1991, 22. Masing & Lutsar 2007, 23. Masing 1983, 24. Gaisler 1970, 25. Norberg 1987, 26. Baydemür & Albayrak 2006, 27. Dietz et al. 2006, 28. Sharifi 2004, 29. Kerth et al. 2001, 30. Schmidt 2005, 31. Smirnov et al. 2008, 32. Verbeek 1998, 33. Pandurkska & Beshkov 1998, 34. Harmata 1969, 35. Sachanowicz & Zub 2002, 36. Arlettaz et al. 2001, 36. Ibáñez et al. 2001, 37. Estók 2007, 38. Lohrl 1936, 39. Kunz & Hood 2000, 40. Happold & Happold 1990, 41. Rydell 1990, 42. Reiter 2004, 43. Ransome 1990, 44. Zahn 1999, 45. Deanesly & Warwick 1939, 46. Racey 1969, 47. Racey & Swift 1981, 48. Racey 1974, 49. Masing 1982, 50. Boyd & Stebbings 1989, 51. Lesiñski 1986, 52. Barak & Yom-tov 1991, 53. Arlettaz et al. 2000, 54. Gaisler et al. 1997, 55, Heise 1989, 56. Papadatou et al. 2009, 57. Unpublished data: own measurements.</p> <p> </p>
Supporting Online Data for 'Timber trade in the United States of America 1870 to 2017. A socio-metabolic analysis'
<p>This data file (.xlsx) contains all data used to create tables and figures of the study "Timber trade in the United States of America 1870 to 2017. A socio-metabolic analysis". Main article is available under: https://doi.org/10.1080/01615440.2024.2316039</p>
Production and trade data on cocoa value chain in Ghana
<p>This dataset covers upstream, midstream and downstream behavioural patterns that were used to identify precursors of vulnerabilities in Ghana’s cocoa value chain. Data were obtained via focus group discussions and individual interviews with cocoa farmers in Ghana and extracted from annual reports of the global cocoa industry published by the International Cocoa Organisation. Behavioural patterns were established from transcripts and reports using NVivo as the computer-assisted qualitative data analysis software.</p>
Dataset of FEUTURE Online Paper No. 8 "Understanding the EU-Turkey Sectoral Trade Flows During 1990-2016: a Trade Gravity Approach"
<p>The dataset provides the following variables for 1990-2016 for Austria Belgium, Bulgaria, China, Denmark, France, Germany, Greece, Hungary, Iran, Ireland, Italy, Japan, Netherlands, Poland, Russia, Spain, Sweden, UK, and USA: </p> <p>- Total export and import (in USD and %)</p> <p>- Intermediate goods exports and imports (in USD and %)</p> <p>- Household goods exports and imports (in USD and %)</p> <p>- Capital goods exports and imports (in USD and %)</p> <p>- Mixed-end exports and imports (in USD and %)</p> <p>- Miscellaneous exports and imports (in USD and %)</p> <p> </p>
Data for Publication - Synergies and Trade-offs between Robusta Yield, Carbon Stocks and Biodiversity across Coffee Systems in the DR Congo
<p>Data used for the publication:</p> <p>"Synergies and Trade-offs between Robusta Yield, Carbon Stocks and Biodiversity across Coffee Systems in the DR Congo" - Ieben Broeckhoven, Jonas Depecker, Trésor Kasereka Muliwambene, Olivier Honnay, Roel Merckx and Bruno Verbist</p>
Simulation of integrated speed-accuracy measures when speed-accuracy trade-off is present
<p>The uploaded files contain simulation results and some software to generate these results obtained in monte carlo simulations that were designed to test whether and under which conditions integrated speed-accuracy measures are sensitive to speed accuracy trade-off. These simulations are extensively described and discussed in the following publication:</p> <p>Vandierendonck, A. (2021). On the Utility of Integrated Speed-Accuracy Measures when Speed-Accuracy Trade-off is present. Journal of Cognition. DOI: https://doi.org/10.5334/joc.154</p> <p>The added README contains detailed information on how to use the simulation data and the included software. This version corrects for errors in the scripts used to calculate the Balanced Integration Score (BIS).</p>
Data supplement for "Global agricultural trade and land system sustainability: implications for ecosystem carbon storage, biodiversity and human nutrition"
<p>This data supplements the publication "Global agricultural trade and land system sustainability: implications for ecosystem carbon storage, biodiversity and human nutrition" by Thomas Kastner, Abhishek Chaudhary, Simone Gingrich, Alexandra Marques, U. Martin Persson, Giorgio Bidoglio, Gaëtane Le Provost, Florian Schwarzmüller, available here:</p> <p><a href="https://doi.org/10.1016/j.oneear.2021.09.006">https://doi.org/10.1016/j.oneear.2021.09.006</a></p> <p>For details, please refer to that publication.</p>
PLANET4B coding of 29 interviews Trade & GVCs case study Brazil & EU 20250708 v2 data
<p>This file contains the coding of 29 interviews collected between 2023 and 2025, in the context of the case study "Trade & global value chains", part of the Horizon Europe Project PLANET4B. The interviews include participants from academia, environmental NGOs, Indigenous peoples and local communities, government and businesses in Brazil and in the Netherlands, connected to the global value chains of soy and beef between Brazil-Netherlands. Some of these interviews discuss the recent European Union Regulation on Deforestation-Free Products (EUDR). </p> <table> <tbody> <tr> <td> </td> </tr> </tbody> </table>
An Innovative Scheme to Confront the Trade‐Off Between Water Conservation and Heat Alleviation With Environmental Justice for Urban Sustainability: The Case of Phoenix, Arizona
<p><em><strong>The manuscript for this dataset is accepted by AGU Advances and can be accessed here: <a href="https://doi.org/10.1029/2022AV000816">link</a>. Please cite the literature when using the datasets.</strong></em></p> <p><strong>How to cite this article: Yuanhui Zhu, Soe Myint, Xin Feng, Yubin Li. An Innovative Scheme to Confront the Trade‐Off Between Water Conservation and Heat Alleviation With Environmental Justice for Urban Sustainability: The Case of Phoenix, Arizona. AGU Advances, 4, e2022AV000816. <a href="https://doi.org/10.1029/2022AV000816">https://doi.org/10.1029/2022AV000816</a></strong></p> <p>This study aims to develop a practical and integrated framework to tackle the tradeoff between land surface temperature (LST) reduction and water conservation for heat mitigation and resilience planning in Phoenix, Arizona. We developed a multi-objective framework of spatial optimization for priority areas that considers environmental justice. We employed the priority areas (i.e., residential districts, socio-economically disadvantaged neighborhoods, hotspot regions, and opportunity areas), ECOSTRESS-based LST, actual evapotranspiration (ETa, as a proxy to water use), Landsat-based LST and ETa changes (2000–2020), and the evaporative stress index (ESI). These datasets are used to identify the priority areas in which environmental conditions need to be improved seriously and (2) spatially optimize the placement of new green space (tree %, grass %) in the priority areas to realize the most significant LST reduction and minimum OWU. We provide the results of the new green space configurations with the scenarios for the percentage of new vegetation coverage (including trees and grass) overall increased to 25%, 35%, and 45% within the entire study areas, residential districts, socio-economically disadvantaged neighborhoods, and hotspot regions.</p> <table> <caption>The dataset summarization</caption> <tbody> <tr> <td>Category</td> <td>Dataset</td> <td>Resolution</td> <td>Source/method</td> <td>Time</td> </tr> <tr> <td>Environmental database</td> <td>Summer daytime LST</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental database</td> <td>Summer nighttime LST</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental database</td> <td>Summer ETa</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental database</td> <td>Summer ESI</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental change database</td> <td>Trends of summer LST changes</td> <td>30m</td> <td>Landsat-based Statistical Mono-Window algorithm</td> <td>2000-2020</td> </tr> <tr> <td>Environmental change database</td> <td>Trends of summer ETa changes</td> <td>30m</td> <td>Landsat-based Simplified Surface Energy Balance</td> <td>2000-2020</td> </tr> <tr> <td>The results of new green space configurations</td> <td>The spatial distributions of new green space</td> <td>--</td> <td>Spatial optimization</td> <td>--</td> </tr> </tbody> </table> <p>note: LULC: Land use and land cover; LST: Land Surface Temperature; ETa: Actual Evapotranspiration; ESI: Evaporative Stress Index</p> <p>We provide the different scenarios in shapefile format for spatial distributions of new space configurations. The naming convention for attribute tables in shapefile is :</p> <p>VV_new_perNN_LSTWW</p> <p>where:</p> <ul> <li>VV = New vegetation for tree or grass</li> <li>NN = The scenarios with new vegetation increased to 25%, 35%, or 45% (unit: %)</li> <li>WW = The weight values of land surface temperature range from 0 to 1 (unit: %) when executing spatial optimization for the tradeoff between land surface temperature reduction and outdoor water use conservation with vegetation coverage. The weight of 0 represents that our spatial optimization models only focus on outdoor water use conservation, and the weight of 1 denotes that we only consider land surface temperature reduction. </li> </ul> <p>Example: grass_new_per25_LST65 means -- new vegetation for grass; the scenario is set up by new vegetation increased to 25%; the weight of land surface temperature is 0.65. </p> <p> </p>
Trade policy announcements can increase price volatility in global food commodity markets (Replication Data)
<p>Replication data for "Trade policy announcements can increase price volatility in global food commodity markets":</p> <ul> <li>Original dataset on trade policy announcements from 2005 to 2017 for wheat and maize (corn) (details in codebook)</li> <li>Daily price ranges based on the highest and lowest price recorded for wheat and corn futures (traded at the Chicago Board of Trade, CBOT)</li> <li>Stocks-to-use data for the United States, which is compiled by the United States Department for Agriculture (USDA) and available at monthly frequency from their World Supply and Demand Estimates report</li> </ul>
Dataset of Linkability Networks of Ethereum Accounts Involved in NFT trading of Top 15 NFT Collections
<p>The data is organized in 32 files:</p> <ul> <li> <p>collection_metadata.txt stores basic information about each collection that was analyzed. The graph data of the collection is stored in a file named as the nickname of the collection (slug column in collection_metadata.txt). The most important columns/properties are: rank, slug – nickname, creation date, address, and volume data, ...</p> </li> <li> <p>15 files reporting NFT ownership transfer, the names corresponding to the nicknames of the collections in collection_metadata.txt.</p> </li> <li> <p>15 files with graph data, the names correspond to the nicknames of the collections in collection_metadata.txt. Each file gives the address linkage graph of one of the top 15 collections according to monetary volume on Opensea marketplace, computed as presented in methodology, exclusively on Ethereum blockchain.</p> </li> </ul>
A Dataset of French Trade Directories from the 19th Century for Nested NER task
<p>This dataset is composed of pages and entries extracted from French directories published between 1798 and 1861.</p> <p>The purpose of this dataset is to evaluate the performance of Nested Named Entity Recognition approaches on 19th century French documents, regarding both clean and noisy texts (due to the OCR engine).</p> <p><strong>Source dataset</strong></p> <p>This dataset has been built from this source dataset :</p> <pre><code class="language-markdown">N. Abadie, S. Baciocchi, E. Carlinet, J. Chazalon, P. Cristofoli, B. Duménieu and J. Perret, A Dataset of French Trade Directories from the 19th Century (FTD), version 1.0.0, May 2022, online at https://doi.org/10.5281/zenodo.6394464.</code></pre> <p><strong>Our experiments // Paper</strong></p> <p>Details about our experiments on nested NER approaches are given in our paper (<a href="https://hal.science/hal-03994759v2">the pre-print version is available here</a>).</p> <pre><code class="language-markdown">Tual, S., Abadie, N., Chazalon, J., Duménieu, B., & Carlinet, E. (2023). A Benchmark of Nested NER Approaches in Historical Structured Documents. Proceedings of the 17th International Conference on Document Analysis and Recognition, San José, California, USA. 2023. Springer. https://hal.science/hal-03994759v2</code></pre> <p>Our code is available on <a href="https://github.com/soduco/paper-nestedner-icdar23-code">Git-Hub</a>.</p> <p><strong>Dataset overview</strong></p> <p>The following list describes the <strong>keys of the .JSON</strong> file which contain the complete materials of our experiments.</p> <p>- id : Entry unique ID in a given page</p> <p>- box : Bounding box of the entry in the scanned directory page</p> <p>- book : Source directory of the entry (*see more information bellow*)</p> <p>- page : Page ID in a given directory</p> <p>- valid_box : Is the bbox of the entry valid ? (*all bbox are valid here*)</p> <p>- text_ocr_ref` : OCR extracted and manually corrected text of the entry</p> <p>- nested_ner_xml_ref : <em> text_ocr_ref</em> with nested ner entities</p> <p>- text_ocr_pero : OCR extracted text of the entry with PERO-OCR engine (best engine according to Abadie et al. experiment)</p> <p>- has_valid_ner_xml_pero : Is entities mapping between nested-ner entities annotated by hand on the ref text and pero ocr text correct ? (in our experiments, we only use entries with True value)</p> <p>- nested_ner_xml_pero : Annotated noisy entries produced with PERO OCR</p> <p>- text_ocr_tess : OCR extracted text of the entry with Tesseract engine (*not used in our expriments*)</p> <p>- nested_ner_xml_tess : Is entities mapping between nested-ner entities annotated by hand on the ref text and tesseract text correct? (not used in our experiments)</p> <p>- has_valid_ner_xml_tess : Annotated noisy entries produced with Tesseract. (not used in our experiments)</p> <p>Nested entities are annotated using XML tags. Our hierachy of entities is a *Part Of* a two-levels hierarchy. It means that bottom entities are contained in a top level entity.</p> <p> </p> <p><strong>Source documents // Copyright and licence</strong></p> <p><em>This section has been copied from the <a href="https://zenodo.org/record/6394464">original dataset description</a>.</em></p> <p>The images were extracted from the original source https://gallica.bnf.fr, owned by the *Bibliothèque nationale de France* (French national library).</p> <p>Original contents from the <em>Bibliothèque nationale de France</em> can be reused non-commercially, provided the mention "Source gallica.bnf.fr / Bibliothèque nationale de France" is kept. </p> <p>=> <strong>Researchers do not have to pay any fee for reusing the original contents in research publications or academic works.</strong></p> <p>Original copyright mentions extracted from <a href="https://gallica.bnf.fr/edit/und/conditions-dutilisation-des-contenus-de-gallica">https://gallica.bnf.fr/edit/und/conditions-dutilisation-des-contenus-de-gallica</a> on March 29, 2022.</p> <p>The original contents were significantly transformed before being included in this dataset.</p> <p>All derived content is licensed under the permissive *Creative Commons Attribution 4.0 International* license.</p> <p>Links to original contents are given in the window bellow :</p>
Predator- and competitor-induced plasticity: How changes in foraging morphology affect phenotypic trade-offs.
Studies of phenotypic plasticity frequently demonstrate functional trade-offs between alternative phenotypes by documenting environment-specific costs and benefits. However, the functional mechanisms underlying these trade-offs are often unknown. For example, predator-induced traits typically provide superior predator resistance but slower growth, while competitor-induced traits provide better growth but inferior predator resistance. While the mechanisms underlying predator resistance have been identified, the mechanisms underlying differential growth have remained elusive. To determine whether competitor and predator environments affect individual growth by induced changes in foraging morphology, we raised wood frog tadpoles (Rana sylvatica) under a factorial combination of competitors and predators and assessed changes in mouthparts that might affect growth. In general, competitors induced relatively larger oral discs, wider beaks, and longer tooth rows, while predators induced relatively smaller oral discs, narrower beaks, and shorter tooth rows. These effects were interactive; the largest competitor-induced responses occurred under high predator density and the largest predator-induced responses occurred under low competition. Further, one of the tooth rows that commonly appeared under low predation risk was frequently absent under high predation risk. These discoveries suggest that predator and competitor environments can have profound effects on prey foraging structures and that these effects set up growth trade-offs between phenotypes that favor the evolution of phenotypically plastic responses.
FIG. 15 in The exploitation of molluscs and other invertebrates in Alexandria (Egypt) from the Hellenistic period to Late Antiquity: food, usage, and trade
FIG. 15. — Spider conch (Lambis sp. Röding, 1798) shell from: A, Fouad site; and B, Theater Diana, probably used as a container. Inner side at the top and outer side at the bottom. Scale bar: 10 mm.
FIG. 14 in The exploitation of molluscs and other invertebrates in Alexandria (Egypt) from the Hellenistic period to Late Antiquity: food, usage, and trade
FIG. 14. — Indo-Pacificmolluscshells: A, Chicoreusramosus Linnaeus, 1758; B, Tridacna maxima (Röding, 1798). Scale bars: 10 mm.
Repository: Quantifying environmental impacts of primary aluminum ingot production and consumption: A trade-linked multilevel life cycle assessment
<p>This repository contains the input data, codes and results of the model developed in the paper "Quantifying environmental impacts of primary aluminum ingot production and consumption: A trade-linked multilevel life cycle assessment" published in the Journal of Industrial Ecology (2020) by Alexandre Milovanoff, I. Daniel Posen, Heather L. MacLean.</p>
Database for Market uptake of concentrating solar power in Europe: model-based analysis of drivers and policy trade-offs. MUSTEC project.
<p>This dataset contains the data underlying the modelling activities of the MUSTEC (<em>Market Uptake of Solar Thermal Electricity through Cooperation</em>) project used in the models Green-X (TU Wien) and Enertile (Fraunhofer ISI).</p> <p>For description of the modelled scenarios, results and findings, see: Resch, G., Schöniger, F., Kleinschmitt, C., Franke, K., Sensfuß, F., Thonig, R., and Lilliestam, J.:<em> </em><em> Market uptake of concentrating solar power in Europe: model-based analysis of drivers and policy trade-offs. </em>Deliverable 8.2 MUSTEC project, TU Wien, Wien.</p> <p>For information on the project see: https://www.mustec.eu/</p> <p>For data descriptions, licence, and further information, see README.md.</p> <p> </p>
FIG. 12 in When ivory came from the seas. On some traits of the trade of raw and carved sea-mammal ivories in the Middle Ages
FIG. 12. — Olaus Magnus, Carta Marina, 1539, detail. Uppsala universitetsbibliotek (Photo Uppsala universitetsbibliotek).
FIG. 8 in When ivory came from the seas. On some traits of the trade of raw and carved sea-mammal ivories in the Middle Ages
FIG. 8. — Elder of the Apocalypse, Saint-Omer, c. 075-1100. Saint-Omer, musée de l'Hôtel Sandelin: Inv. 2484. Height: 12 cm. © Musées de Saint-Omer.
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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.