Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,316
datasets available to search
ShareScore release 0.9.0
Dataset results
1,316 results for “trading”
FIG. 6 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. 6. — Tabernacle probably from Saint Pantaleon in Cologne, Cologne, c. 1180. London, Victoria and Albert Museum: 7650-1861. Height: 54.5 cm (Photo © Victoria and Albert Museum, London).
FIG. 2 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. 2. — Walrus tusk found in Skara Brae,Orkney, 3100-2400 BC. National Museums Scotland: X.HA 168. Height:45 cm (Photo © National Museums Scotland).
FIG. 5 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. 5. — Lewis Chessmen, Trondheim, third quarter of the 12th century. National Museums Scotland. Height: 6 to 10 cm (Photo © National Museums Scotland).
FIG. 1 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. 1. — The Symmachi Panel. Rome, late 4th-early 5th century. Victoria and Albert Museum: 212-1865. Height: 29.6 cm (Photo © Victoria and Albert Museum, London).
FIG. 7. — 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. 7. — 12th century seal matrix of a tax collector named Snorri. York Museums: YORYM 1973.5.29. Diameter: 3 cm (Photo York Museums, CC-BY SA 4.0).
FIG. 10 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. 10. — Pair of ceremonial staffs carved in narwhal tusks, England, 2nd quarter of the 12th century. A, Victoria and Albert Museum: A. 79-136; B, National Museums Liverpool: 1995.42. Lengths: A, 117 cm (Photo © Victoria and Albert Museum); B, 110 cm (Photo © National Museums Liverpool).
FIG. 6 in Socio-economic changes and their implication in the consumption and trade of meat during the La Tène period in Northern France: the cases of the Villeneuve-Saint-Germain and Condé-sur-Suippe (Aisne) oppida
FIG. 6. — Triangle plot presenting variations in the frequency of the three main species between the different archaeological features on the site of Villeneuve-Saint-Germain.
FIG. 7 in Socio-economic changes and their implication in the consumption and trade of meat during the La Tène period in Northern France: the cases of the Villeneuve-Saint-Germain and Condé-sur-Suippe (Aisne) oppida
FIG. 7. — Plan of the southern ditch sector on the site of Villeneuve-Saint-Germain: A, general plan of the site of Villeneuve-Saint-Germain (from Pion 1996); B, southern ditch sector on the site of Villeneuve-Saint-Germain (from Ruby & Auxiette 2010). Scale bar: 10 m.
FIG. 2 in Socio-economic changes and their implication in the consumption and trade of meat during the La Tène period in Northern France: the cases of the Villeneuve-Saint-Germain and Condé-sur-Suippe (Aisne) oppida
FIG. 2. — Development of herd composition during the Iron Age. BCP, Berry-au-Bac " le Chemin de la Pêcherie "; BGM, Bucy-le-Long " le Grand Marais "; LLF, Limé " les Fussis "; MDV = Menneville " Derrière le Village "; BFM, Bucy-le-Long " le Fond du Petit Marais "; LLP, Limé " La Prairie "; BAR, Baranton; BET, Bétheny " les Equiernolles "; BVC, Bazoches-sur-Vesles " les Chantraînes "; CLB, Ciry-Salsogne " le Bruy "; MNV, Mont-Notre-Dame " Vaudigny "; DRF, Damary " le Ruisseauds Fayau "; CSS, Condé-sur-Suippe; RD, Reims-Durocortorum; AC, Acy-Romance; SFR, Sermoise " les Fausses Rues "; VSE, Villeneuve-Saint-Germain " les Etomelles "; VSG, Villeneuve-Saint-Germain.
FIG. 8 in Socio-economic changes and their implication in the consumption and trade of meat during the La Tène period in Northern France: the cases of the Villeneuve-Saint-Germain and Condé-sur-Suippe (Aisne) oppida
FIG. 8. — Simplified chart of butchery cuts from bovinae according to observations carried out on the site of Villeneuve-Saint-Germain.
FIG. 2 in Socio-economic changes and their implication in the consumption and trade of meat during the La Tène period in Northern France: the cases of the Villeneuve-Saint-Germain and Condé-sur-Suippe (Aisne) oppida
FIG. 2. — Development of herd composition during the Iron Age. BCP, Berry-au-Bac " le Chemin de la Pêcherie "; BGM, Bucy-le-Long " le Grand Marais "; LLF, Limé " les Fussis "; MDV = Menneville " Derrière le Village "; BFM, Bucy-le-Long " le Fond du Petit Marais "; LLP, Limé " La Prairie "; BAR, Baranton; BET, Bétheny " les Equiernolles "; BVC, Bazoches-sur-Vesles " les Chantraînes "; CLB, Ciry-Salsogne " le Bruy "; MNV, Mont-Notre-Dame " Vaudigny "; DRF, Damary " le Ruisseauds Fayau "; CSS, Condé-sur-Suippe; RD, Reims-Durocortorum; AC, Acy-Romance; SFR, Sermoise " les Fausses Rues "; VSE, Villeneuve-Saint-Germain " les Etomelles "; VSG, Villeneuve-Saint-Germain.
FIG. 7 in Socio-economic changes and their implication in the consumption and trade of meat during the La Tène period in Northern France: the cases of the Villeneuve-Saint-Germain and Condé-sur-Suippe (Aisne) oppida
FIG. 7. — Plan of the southern ditch sector on the site of Villeneuve-Saint-Germain: A, general plan of the site of Villeneuve-Saint-Germain (from Pion 1996); B, southern ditch sector on the site of Villeneuve-Saint-Germain (from Ruby & Auxiette 2010). Scale bar: 10 m.
FIG. 5. — A in Socio-economic changes and their implication in the consumption and trade of meat during the La Tène period in Northern France: the cases of the Villeneuve-Saint-Germain and Condé-sur-Suippe (Aisne) oppida
FIG. 5. — A, Plan of one of the forge workshops on the site of Condé-sur-Suippe; B, General plan of the sites of Condé-sur-Suippe "la Sucrerie" (P. Pion 1987).
The target of selection matters: an established resistance – development-time negative genetic trade-off is not found when selecting on development time.
<p>Trade-offs are fundamental to evolutionary outcomes and play a central role in eco-evolutionary theory. They are often examined by experimentally selecting on one life-history trait and looking for negative correlations in other traits. For example, populations of the moth Plodia interpunctella selected to resist viral infection show a life-history cost with longer development times. However, we rarely examine whether the detection of such negative genetic correlations depends on the trait on which we select. Here we examine a well-characterised negative genotypic trade-off between development time and resistance to viral infection in the moth Plodia interpunctella and test whether selection on a phenotype known to be a cost of resistance (longer development time) leads to the predicted correlated increase in resistance. If there is tight pleiotropic relationship between genes that determine development time and resistance underpinning this trade-off, we might expect increased resistance when we select on longer development time. However, we show that selecting for longer development time in this system selects for reduced resistance when compared to selection for shorter development time. This shows how phenotypes typically characterised by a trade-off can deviate from that trade-off relationship, and suggests little genetic linkage between the genes governing viral resistance and those that determine response to selection on the key life-history trait. Our results are important for both selection strategies in applied biological systems and for evolutionary modelling of host-parasite interactions.</p>
Really a nontraded commodity? A look at the international potato trade network (dataset and R code)
<p>This archive contains code and data for a social network analysis of international potato trade that was published at https://perspectivesandforesight.wordpress.com/2012/11/08/really-a-nontraded-commodity-a-look-at-the-international-potato-trade-network/ on 08 November, 2012.</p> <p>It can serve as a reference for understanding the analysis, and as a basis for replication of the results as well as for carrying out more detailed analyses of international potato trade.</p> <p>The following information and data is included:</p> <p>- The R code used for the social network analysis of international potato trade.</p> <p>- Data files with bilateral matrices of global trade in fresh, frozen and seed potatoes.</p> <p>- Data files with supplementary data used for the analysis.</p> <p>- A copy of the original blog post that was written using the data and code provided herewith.</p>
Trade data from BACI to-be-used with regioinvent.
<p>The dataset contains import and export data of traded goods corresponding to ecoinvent products from the BACI database (https://www.cepii.fr/CEPII/en/bdd_modele/bdd_modele_item.asp?id=37), as well as production data from FAOSTAT and BGS/USGS and data used to estimation total production volumes from EXIOBASEv3.9.5.</p> <p>The only purpose of this dataset is to be used as an input to the Regioinvent Python package which can be found here: https://github.com/CIRAIG/Regioinvent</p> <p> </p> <p>v4 changes: Complete structure changed to accomodate for implementation of real production volume data, instead of only relying on the estimates from EXIOBASE. Also, now uses net exports to avoid problems wit re-exports. Imports were corrected in consequence to function be consistent with net exports.</p> <p> </p> <p> </p>
Phage selection drives resistance-virulence trade-offs in Ralstonia solanacearum plant pathogenic bacterium irrespective of the growth temperature
<p><span>While temperature has been shown to affect the survival and growth of bacteria and their phage parasites, it is unclear if trade-offs between phage resistance and other bacterial traits depend on the temperature. Here, we experimentally compared the evolution of phage resistance-virulence trade-offs and underlying molecular mechanisms in phytopathogenic <em>Ralstonia</em> <em>solanacearum</em> bacterium at 25 °C and 35 °C temperature environments. We found that experimental growth conditions selected for small colony variants (SCVs) with increased growth rate and mutations in the quorum-sensing (QS) signalling receptor gene, <em>phcS</em>. Interestingly, SCVs were also phage-resistant and reached higher frequencies in the presence of phages in both temperature environments. Evolving phage resistance was costly in terms of reduced carrying capacity, biofilm formation and reduced virulence i<em>n planta</em> possibly due to loss of QS-mediated expression of key virulence genes. We also observed mucoid phage-resistant colonies that showed loss of virulence and reduced twitching motility likely due to parallel mutations in prepilin peptidase gene pilD. Moreover, phage-resistant SCVs from 35 °C-phage treatment had parallel mutations in genes encoding type II secretion system (T2SS) genes (<em>gspE</em> and <em>gspF</em>), indicating that defects in pseudopilus made bacterium resistant to the phage. Additional transcriptomic analysis revealed upregulation of CBASS and type Ⅰ restriction-modification phage defence systems in response to phage exposure, which coincided with reduced expression of motility and virulence-associated genes, including <em>pilD</em> and type II and III secretion systems. Together, these results suggest that phage resistance-virulence trade-offs are not affected by the growth temperature but can be mediated through both pre- and post-infection phage resistance mechanisms.</span></p>
Replication data for An Empirical Approximation of the Effects of Trade Sanctions with an Application to Russia
<p>This is the dataset to replicate all the tables and figures in the paper <a href="https://doi.org/10.1093/epolic/eiad027">"An Empirical Approximation of the Effects of Trade Sanctions with an Application to Russia"</a>, published in <i>Economic Policy</i>, 2023, by Jean Imbs and Laurent Pauwels. All data manipulations and programming are detailed on the GitHub site:<a href="https://github.com/laurentpauwels/sanctionpaper"> https://github.com/laurentpauwels/sanctionpaper</a>. The raw and processed data are in this <i>sanctionpaperdata_v1/matlab/data folder. </i>For convenience the simulation output <i>(simulationoutput.txt) </i>required to build the scatter plots in Figure 1 with STATA is available in<i> sanctionpaperdata</i>_v1<i>/matlab/output</i>.</p><p><strong>Instructions</strong> </p><p> If you clone the GitHub repository:</p><p>1. Place the downloaded <i>data</i> folder (located in <i>sanctionpaperdata_v1/matlab/)</i> in the <i>matlab</i> folder of the GitHub repository. </p><p>2. Place the downloaded <i>simulation_output.txt</i> I(located in <i>sanctionpaperdata_v1/matlab/output/) </i>in the <i>matlab/output </i>folder of the GitHub repository if you do not want to run the simulations as detailed on GitHub.</p><p><strong>Description</strong></p><p>The <i>matlab/data/raw</i> folder contains an <i>ICIO21</i> folder with the ICIO21 data, and a <i>WIOD</i> folder with the SEA16 data (in <i>data/raw/WIOD/SEA16</i>) and the WIOT16 data in CSV format (in <i>data/raw/WIOD/WIOT16</i>).</p><p>NOTE: WIOD provides the data in XLSB format. The XLSB WIOD data is in the <i>WIOT_in_EXCEL.zip</i> located in the <i>matlab/data/raw/WIOD/</i>. Python is used to convert XLSB into CSV files. See python code in GitHub repository for unzipping and conversion to CSV. The converted CSV files are provided for convenience.</p><p>The parsed and pre-processed ICIO21, SEA16, and WIOT16 data are stored in the <i>/matlab/data/processed</i> folder into three separate .mat structure files:</p><p><i>icio21_strc.mat</i> contains:</p><ul><li>the meta data (<i>icio21_text</i>), i.e., the information about the structure of the numerical data such as lists of countrycode, countries, industrycode, industries, isic_rev4 codes, years covered, name of final categories, etc.</li><li>the numerical data (<i>icio21_data</i>):<ul><li>Z (<i>icio21_data.Z</i>), the intermediate IO data for the listed industries (R), countries (N), and years (T). Its structure is 3-dimensionsal: (NxR)x(NxR)xT.</li><li>F (<i>icio21_data.F</i>), the final demand data for the same countries, industries and years. Its structure is 3-dimension: (NxR)x(NxC)xT. The columns are NxC where C are the number of final demand categories.</li></ul></li></ul><p><br><i>wiod16_strc.mat</i> has the same structure as <i>icio21_strc.ma</i>t with the meta data in <i>wiot16_text</i> and the numerical data in <i>wiot16_data</i>.</p><p><i>sea16_strc.mat</i> has the meta data in <i>sea16_text</i> and the numerical data in <i>sea16_data</i>. SEA16 contains 16 variables instead of Input-Output type data. The country, industry, and year coverage is not the same as ICIO21.</p><p>NOTE: <i>matlab/scripts/convertMatlabStruc2data.m</i> in the GitHub repository converts <i>MATLAB v7.3 </i>format ("structure data") to an updated format without structure so that it is more easily compatible with other software. All data parsing and preprocessing are done with MATLAB, see GitHub repository for details.</p><p><strong>Sources</strong></p><p>The raw data come from these sources:</p><p>1. OECD Inter-Country Input-Output (ICIO) data November 2021 release (downloaded on 2 July 2023)</p><p>- Source: OECD-ICIO 2021 release data is available at <a href="http://oe.cd/icio">http://oe.cd/icio</a></p><p>2. WIOD Socio-Economic Accounts (SEA) data 2016 release (downloaded on 30 May 2023)</p><p>- Source: <a href="https://www.rug.nl/ggdc/valuechain/wiod/wiod-2016-release">https://www.rug.nl/ggdc/valuechain/wiod/wiod-2016-release</a></p><p>3. WIOD World Input-Output Tables (WIOT) data November 2016 (downloaded on 23 June 2023)</p><p>- Source: <a href="https://www.rug.nl/ggdc/valuechain/wiod/wiod-2016-release">https://www.rug.nl/ggdc/valuechain/wiod/wiod-2016-release</a> </p>
Genomic analyses reveal poaching hotspots and illegal trade in pangolins from Africa to Asia
<p>Reducing the illegal wildlife trade requires an understanding of its origins. Here we present a genomic approach for tracing confiscated scales from the world's most trafficked mammal, the white-bellied pangolin (<em>Phataginus tricuspis</em>), to their geographic origins. Analyzing scales seized in Hong Kong SAR, China from 2012–2018 revealed intense poaching along Cameroon's southern border. Poaching pressures shifted over time from West to Central Africa. Using data from seizures representing nearly one million African pangolins, we identified Nigeria as a significant hub for trafficking, where scales are amassed and shipped to Vietnam and Hong Kong SAR, China, with final transit to markets in Guangdong and Guangxi, China. This origin-to-destination approach offers new opportunities to disrupt the illegal wildlife trade and to guide anti-trafficking measures.</p>
Data for: Understanding consumers to inform market interventions for Singapore's shark fin trade
<ol> <li>Sharks, rays and their cartilaginous relatives (Class Chondricthyes, herein 'sharks') are amongst the world's most threatened species groups, primarily due to overfishing, which in turn is driven by complex market forces including demand for fins. Understanding the high-value shark fin market is a global priority for conserving shark and rays, yet the preferences of shark fin consumers are not well understood. This gap hinders the design of evidence-based consumer-focused conservation interventions. </li> <li>Using an online discrete choice experiment, we explored preferences for price, quality, size, menu types (as a proxy for exclusivity) and source of fins (with varying degrees of sustainability) among 300 shark fin consumers in Singapore: a global entrepot for shark fin trade. </li> <li>Overall, consumers preferred lower-priuced fins sourced from responsible fisheries or produced using novel lab-cultured techniques. We also identified four consumer segments, each with distinct psychographics characteristics and consumption behaviors. </li> <li>These preferences and profiles could be leveraged to inform new regulatory and market-based interventions regarding the sale and consumption of shark fins, and incentivize responsible fisheries and lab-cultured innovation for delivering conservation and sustainability goals. </li> <li>In addition, message framing around health benefits, shark endangerment and counterfeiting could reinforce existing beliefs amongst consumers in Singapore and drive behavioral shifts to ensure that market demand remains within the limits of sustainable supply. </li> </ol> <p>This dataset includes all the responses collected from the online discrete choice experiment which was implemented by a market survey company, as well as the goodness-of-fit chi-square analyses. These data were also used to plot the figures in the manuscript and the Supplemental Information. Password for excel sheet titled 'Final CEOE data' is 35433. Please refer to the published manuscript for more detailed information.</p> <p><strong>The authors received financial support from Silverstrand Capital awarded to Wildlife Conservation Society for the research, authorship, and publication of this work. </strong></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.