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1,316 results for “trade”
United States LEMIS wildlife trade data curated by EcoHealth Alliance
<p>Shared here are United States Fish and Wildlife Service (USFWS) Law Enforcement Management Information System (LEMIS) data on wildlife and wildlife product imports into the United States. This data was obtained via Freedom of Information Act (FOIA) requests by EcoHealth Alliance.</p> <p>Data were curated, cleaned, and made accessible via an R package interface: <a href="https://github.com/ecohealthalliance/lemis">https://github.com/ecohealthalliance/lemis</a>.</p> <p>Additionally, a summary of a portion of the data can be found in Smith et al. 2017, <em>EcoHealth </em>(<a href="https://doi.org/10.1007/s10393-017-1211-7">https://doi.org/10.1007/s10393-017-1211-7</a>).</p> <p>l<strong>emis_2000_2014_cleaned.csv</strong>: This file represents the compiled, cleaned LEMIS data from 2000-2014. This data is identical to the version 1.1.0 dataset available through the <strong>lemis </strong>R package.</p> <p><strong>lemis_codes.csv</strong>: Full values for all coded values used in the LEMIS data. Identical to the output from the <strong>lemis </strong>R package function "lemis_codes()".</p> <p><strong>lemis_metadata.csv</strong>: Data fields and field descriptions for all variables in the LEMIS data. Identical to the output from the <strong>lemis </strong>R package function "lemis_metadata()".</p> <p><strong>raw_data.zip</strong>: This archive contains all of the raw LEMIS data files that are processed and cleaned with the code contained in the 'data-raw' subdirectory of the <strong>lemis </strong>R package repository.</p>
Data in support of 'Mechanistic insights into plant community responses to environmental variables: genome size, cellular nutrient investments, and metabolic trade-offs.'
Data was collected to examine whether and how the plant genome size (GS) influences traits (stomata size, stomata density, cellular and tissue level carbon (C), nitrogen (N), and phosphorus (P) contents) and metabolic-tradeoffs (of photosynthesis, evapotranspiration, water-use, efficiency) of plants in treatment plots in which nothing, N, P, or NP had been annually added. Data was collected from ~500 plants from seven grassland sites that are all part of the Nutrient Network (https://nutnet.org), a globally distributed experiment in which plots have different nutrient amendment treatments that are administered identically to allow cross-site comparisons of the effects of nutrients on biodiversity patterning. The sites chosen varied along a North-South latitude, longitude, mean annual precipitation (MAP) and mean annual temperature (MAT) gradient.
MCR LTER: Coral Reef: Growth-predation risk trade-offs constrain the local distribution of a thicket-forming staghorn coral to marginal reef habitats; Data for Ladd et al., 2025, Scientific Reports.
This dataset is in support of the manuscript: Growth-predation risk tradeoffs constrain the local distribution of a thicket-forming staghorn coral to marginal reef habitats. These data were collected to 1) document how Acropora pulchra is distributed around the island of Moorea, and 2) to better understand the ecological processes that shape that distribution. Data include 1) results from surveys around the island of Moorea documenting the presence and size distribution of Acropora pulchra thickets, 2) results from an experiment measuring the growth and survivorship of Acropora pulchra fragments in the presence and absence of fish predators at nearshore fringing reef sites and adjacent sites in the mid lagoon (n = 20 sites in total), and 3) ancillary data on nitrogen content and dN15 in the tissue of the macroalgae Turbinaria ornata, sediment accumulation, and corallivore biomass at the experimental sites. All data were collected in 2016 and 2017.
Working time, energy throughput and value added embodied in production, consumption and trade by subsectors for the US, the EU, China and rest of the world (2011)
<p>This repository contains the data needed to reproduce the results in:</p> <p>Pérez-Sánchez, L., Velasco-Fernández, R., Giampietro, M., The international division of labor and embodied working time in trade for the US, the EU and China, Ecological Economics. <a href="http://doi.org/10.1016/j.ecolecon.2020.106909">https://doi.org/10.1016/j.ecolecon.2020.1069097</a></p> <p>Sources of data are specified in the dataset (under tab "references")</p> <p> </p>
Commodity-driven deforestation, associated carbon emissions and trade 2001-2022
<p><span>This dataset contains estimates of commodity-driven deforestation and associated carbon emissions for the period 2001-2022, estimated by the Deforestation Driver and Carbon Emission (DeDuCE) model (Singh & Persson 2024), which combines remote sensing data on forest loss and land-use with agricultural statistics to identify and attribute deforestation across the world to expansion of cropland, pastures and forest plantation, and the commodities produced on this land. This also contains data on deforestation embodied in the production, exports, imports, and consumption of agricultural and forestry commodities by country, year, and commodity for the time period 2005-2022 derived using physical and monetary trade models. The data is an update of the results presented in Pendrill et al. (2022) and the differences between the two datasets are detailed in the explainer available here.</span></p>
Knowledge gaps on trade-offs of soil carbon sequestration related to soil management strategies
<p>The database contains 87 unique literature items (29 reviews, 42 meta-analyses, 16 original papers) describing the effect of a soil management strategy (tillage management, cropping systems, water management, cover crops, crop residues, livestock manure, slurry, compost, biochar, liming) on the trade-offs between soil carbon sequestration or SOC change and N2O emission, CH4 emission and nitrogen leaching. Since some literature items describe effects of several SMS categories, the database_summary tab comprises a total of 112 unique inputs. For each input it is indicated in the Database_summary tab if it was used as input for the "Soil management effect assessment" in Maenhout et al. (2024) [Maenhout, P., Di Bene, C., Cayuela, M. L., Diaz-Pines, E., Govednik, A., Keuper, F., Mavsar, S., Mihelic, R., O'Toole, A., Schwarzmann, A., Suhadolc, M., Syp, A., & Valkama, E. (2024). Trade-offs and synergies of soil carbon sequestration: Addressing knowledge gaps related to soil management strategies. European Journal of Soil Science, 75(3), e13515. https://doi.org/10.1111/ejss.13515] and/or to define knowledge gaps ("Knowledge gap in tab"-column). Knowledge gaps and research recommendations are gouped per soil management strategy in different tabs in this database. Per soil management strategy, knowledge gaps are clustered per theme in groups. These themes include: the specific soil management strategy, pedoclimatic conditions, establishment of experiments, other soil management strategies, meta-analysis, modelling and other</p>
Data from: Trade-offs in Coordination Strategies for Duet Jazz Performances Subject to Network Delay and Jitter
<p>This dataset is associated with the paper “Trade-offs in Coordination Strategies for Duet Jazz Performances Subject to Network Delay and Jitter” and includes recordings of improvisations by jazz duos over a network. The related paper is published in <em>Music Perception</em> and is accessible at <a href="https://doi.org/10.1525/mp.2024.42.1.48">doi:10.1525/mp.2024.42.1.48</a> </p> <p><strong>Introduction:</strong></p> <p>This dataset includes data from approximately four hours of live, improvised musical duo performances over a simulated network environment collected in Cambridge, United Kingdom between April-July 2022 as part of a doctoral research project. Data includes audio and video recordings of 130 individual performances, biometric data, and subjective evaluations and comments from the musicians. The primary aim of the project was to collect data via a novel performance capture and manipulation system for use in the empirical modelling of ensemble coordination strategies during networked music-making. This analysis is reported in Cheston, Cross, and Harrison (2023), "Trade-offs in Coordination Strategies for Networked Jazz Performances". Please refer to this publication for full details on the data collection procedure. Our codebook is <a href="https://github.com/HuwCheston/Jazz-Jitter-Analysis">hosted on GitHub</a> and, in conjunction with this dataset, can be used to reproduce the analysis contained in the article.</p> <p>The ten musicians shown in these recordings were recruited for their expertise in jazz improvisation. They were grouped into five duos consisting each of one pianist and drummer, with no musician performing in more than one duo. Participants were instructed to improvise together over a standard twelve-bar blues musical structure, but following a formula which required them to provide a clear and unambiguous pulse of continuous quarter notes. Varying amounts of network latency and jitter were simulated for each performance, consisting respectively of the minimum amount of delay applied to the live feedback a musician heard from their partner and the degree that this delay varied. The amount of latency and jitter applied to the performance is summarised in the file or directory name for each performance and is described in detail in the above publication. Note that latency and jitter conditions were presented in a random order for each duo.</p> <p><strong>Data collected includes:</strong></p> <ul> <li>audio recordings for each performance, with and without delay, collected via direct line-in (MIDI, WAV).</li> <li>video recordings, collected via high-quality webcams (MKV, AVI).</li> <li>streams of the quarter note pulse provided by each musician in a performance (MIDI).</li> <li>muxed audio-visual recordings of both participants in each performance (MP4)</li> <li>accelerometer and photoplethysmography streams, collected from arm-worn devices (TXT, duos 3-5 only)</li> <li>questionnaire responses from performers, evaluating each condition (XLSX)</li> <li>ratings of performance quality from an unbiased sample of listeners, collected during an online perceptual study (CSV)</li> </ul> <p><strong>Repository structure:</strong></p> <p><strong><em>NB: please see <a href="https://huwcheston.github.io/Jazz-Jitter-Analysis/getting-started.html">this section of the code documentation website</a> for a full description of how to recreate the analyses and models created in the paper.</em></strong></p> <p>The files <em>data.zip </em>and <em>data.z0*</em> contain all data collected from the study, APART from the perceptual study stimuli & results. To open these files, download the <em>data.zip</em> file and <em><strong>all the corresponding volumes ending in .z0 </strong></em>and open the <em>data.zip</em> file using a tool for opening multi-part zip files, such as WinRAR. <em>Do not try to open the files ending in .z0</em>, otherwise you may get a message about the data being corrupted. Inside <em>data.zip</em>, you'll see the following folders and files:</p> <ul> <li><em>avmanip_output</em>: the raw MIDI, audio, and video output from each performance <ul> <li>the subfolders are organised with a single folder per participant duo, experimental block, and condition.</li> <li>avmanip_output\trial_1\Block 1\Condition 1 - 23 05 relates to the performance of the first duo of participants in the first session of the experiment, in the first condition they encountered, with 23ms of latency and 0.5x jitter.</li> </ul> </li> <li><em>midi_bpm_cleaning</em>: the cleaned MIDI files (quarter note onset positions) <ul> <li>the subfolders are organised similarly to the <em>avmanip_output</em> folder, using the same conventions.</li> </ul> </li> <li><em>muxed_performances</em>: the combined audio-video .mp4 files from each performance <ul> <li>these files are labelled in the format: duo_session_latency_jitter_keysfmt_drumsfmt.</li> <li>muxed_performances\kdelay_ddelay\d1_s1_l23_j00_kdelay_ddelay.mp4 relates to the performance of the first duo of participants in the first session of the experiment, in the first condition they encountered, with 23ms of latency and 0.5x jitter, and with latency and jitter applied to both keys and drummer.</li> <li>for more information on recreating these videos, <a href="https://huwcheston.github.io/Jazz-Jitter-Analysis/getting-started.html#reproduce-combined-audio-visual-stimuli">see the linked section of the code documentation website.</a></li> </ul> </li> <li><em>questionnaire_anonymized</em>: the anonymized questionnaire responses given by participants, also contained in the supplementary material of the associated paper (see preprint).</li> </ul> <p>Alongside <em>data.zip </em>and the <em>data.z0*</em> archives, there are two further loose files, <em>Database View Participant - Dashboard.csv, Database View SuccessTrial - Dashboard.csv, </em>which are the anonymized demographic and response data from the perceptual experiment, and one loose archive folder <em>perceptual_study_videos.rar</em>, which contains the stimuli used in the perceptual experiment.</p> <p>To reproduce the analysis from the paper, all files should be unzipped into the \data\raw directory of the code repository created after <a href="https://github.com/HuwCheston/Jazz-Jitter-Analysis">cloning this from GitHub</a>. For more detail and instructions on installation, <a href="https://huwcheston.github.io/Jazz-Jitter-Analysis/getting-started.html">see the section of the code documentation website linked here</a>.</p> <p><strong>Usage:</strong></p> <p>These recordings of live, improvised duo performances are unattributed and anonymised as agreed with participants at the point of data collection. The musicians involved received a one-off, fixed payment for their time and had their travel expenses reimbursed, with funding provided by Cambridge Digital Humanities (<a href="https://www.cdh.cam.ac.uk/research/projects/newmusicsoftwareplatform/">project page</a>). All participants consented to the use of their recordings for projects by the current authors and for these recordings to be shared with interested members of the music psychology community, with the intention of furthering academic research. The musicians did not intend that the recordings be used for commercial, artistic, or entertainment purposes, and such use is not permitted.</p> <p><strong>Citation:</strong></p> <p>If you use this dataset in your research, please cite the paper it relates to:</p> <pre><code>@article{10.1525/mp.2024.42.1.48, author = {Cheston, Huw and Cross, Ian and Harrison, Peter M. C.}, title = "{Trade-offs in Coordination Strategies for Duet Jazz Performances Subject to Network Delay and Jitter}", journal = {Music Perception}, volume = {42}, number = {1}, pages = {48-72}, year = {2024}, month = {09}, issn = {0730-7829}, doi = {10.1525/mp.2024.42.1.48}, url = {https://doi.org/10.1525/mp.2024.42.1.48}, eprint = {https://online.ucpress.edu/mp/article-pdf/42/1/48/833292/mp.2024.42.1.48.pdf}, }</code></pre> <p><strong>Contact:</strong></p> <p>Huw Cheston - <a href="http://twitter.com/huwcheston/">@huwcheston</a> - hwc31@cam.ac.uk</p>
Results files for Land-free Bioenergy From Circular Agroecology -- A Diverse Option Space and Trade-offs
<p>This is the open data repository to support and reproduce results in the paper "<em>Land-free Bioenergy From Circular Agroecology -- A Diverse Option Space and Trade-offs</em>." There are <strong>three types </strong>of files here:</p> <p> </p> <p> </p> <p><strong>1. Ready-to-use final results files of all strategies and scenarios referred to in the paper. </strong>They can be downloaded and used directly without running any codes. They all have the same naming format for strategies/scenarios: `Org` = organic share, `ConcRed` = concentrate feeding reduction share, `WasteRed` = waste reduction share, and numbers refer to the share. E.g., `Org0_ConcRed50_WasteRed75` is a strategy with 0% organic share, 50% concentrate feeding reduction, and 75% waste reduction.</p> <p> </p> <ul> <li>`NationalAncillaryBioenergyPotential_EJ.csv`: The national potential of ancillary bioenergy in 2050 from all scenarios. (Units: EJ). Same in both pathways.</li> <li>`GlobalPotentialEnvironmentalImpacts_NutrientFirst.csv`: Environmental impacts of all scenarios from the pathway `<em>NutrientFirst</em>.` The first three rows refer to the combination of agroecological practices in places, which allow you to explore environmental impacts grouped by, e.g., different organic shares.</li> <li>`GlobalPotentialEnvironmentalImpacts_NegFirst.csv`: Same structure as the file above, but from another pathway, `<em>NegativeFirst</em>`.</li> </ul> <p> </p> <p><strong>2. `SOLmOutputs` contains all original output files from our model <a href="https://orgprints.org/id/eprint/38778/">SOLmV6</a>. </strong></p> <p> </p> <p><strong>3. `DataCleaningKit` has the Python codes and additional dataset of heat values to process 2. `SOLmOutputs` and spit 1. </strong>(Tip: One should adjust the `input_path` and `output_path` before running `DataCleaning.py.`)</p> <p> </p> <p> </p> <p>Fei Wu (fei.wu@usys.ethz.ch)</p> <p>Delft, August, 2023</p> <p> </p>
Data from: Trade-off between standing biomass and productivity in species-rich tropical forest: evidence, explanations and implications
<p>These files are the R code and plot data files used for calculating species population turnover of biomass and abundance in a tropical forest plot.</p> <p>This dataset is a processed subset of the original dataset used in our analysis of biomass turnover across tree populations as demonstrated in <a href="https://doi.org/10.1111/1365-2745.13485">the main paper</a>. Readers interested in using the Pasoh 50-ha plot data for purposes other than reviewing our analysis are advised to contact the <a href="https://www.frim.gov.my/">Forest Research Institute Malaysia (FRIM)</a> and the <a href="https://forestgeo.si.edu/">Center for Tropical Forest Science-Forest Global Earth Observatory (CTFS-Forest GEO)</a>, Smithsonian Tropical Research Institute.</p>
Result data related to "Tröndle et al (2020) -- Trade-offs between geographic scale, cost, and infrastructure requirements for fully renewable electricity in Europe"
<p>The dataset contains aggregated result data of our study. See `README.md` for more information.</p> <p>If you use this data in an academic publication, please cite the following article:</p> <blockquote> <p>Tröndle, T., Lilliestam, J., Marelli, S., Pfenninger, S., 2020. Trade-offs between geographic scale, cost, and infrastructure requirements for fully renewable electricity in Europe. Joule.</p> </blockquote> <p>CHANGELOG:</p> <p>Version 1.2 (2020-09-28)</p> <p>* Add location name to scenario results.<br> * Add scenario results in CSV format, next to already existing NetCDF format.<br> * Remove capacity factors from scenario results.</p> <p>Version 1.1 (2020-07-17)</p> <p>* Remove macOS resource forks cluttering the zip file.</p> <p> </p>
Data for: Application Performance Monitoring: Trade-Off between Overhead Reduction and Maintainability
<p>Monitoring of a software system provides insights into its runtime behavior, improving system analysis and comprehension. System-level monitoring approaches focus, e.g., on network monitoring, providing information on externally visible system behavior. Application-level performance monitoring frameworks, such as Kieker or Dapper, allow to observe the internal application behavior, but introduce runtime overhead depending on the number of instrumentation probes.<br /> We report on how we were able to significantly reduce the runtime overhead of the Kieker monitoring framework. For achieving this optimization, we employed micro-benchmarks with a structured performance engineering approach. During optimization, we kept track of the impact on maintainability of the framework. In this paper, we discuss the emerged trade-off between performance and maintainability in this context.<br /> To the best of our knowledge, publications on monitoring frameworks provide none or only weak performance evaluations, making comparisons cumbersome. However, our micro-benchmark, presented in this paper, provides a basis for such comparisons. Our experiment code and data are available as open source software such that interested researchers may repeat or extend our experiments for comparison on other hardware platforms or with other monitoring frameworks.</p> <p>This dataset supplements the paper and contains the raw experimental data as well as several generated diagrams for each experiment.</p>
General CCU (Product) Acceptance & Trade-Off Decisions
<p>The dataset obtained for WP6 of the CO2SMOS project contains anonymized data including demographic and attitudinal information, perceptions of benefits and barriers regarding CCU adoption and acceptance data obtained from a choice-based conjoint experiment on trade-off decisions in CCU product purchase situations, obtained through an online survey conducted with participants from Germany, Norway, Poland, and Spain.</p>
Retrieval results for optically thin clouds in the trades
<p>ASTER satellite observations at 15 m pixel resolution are used to extract the signal of optically thin clouds during the EUREC4A field campaign (https://doi.org/10.5194/essd-2021-18). The signal of optically thin clouds is derived as a residual from the all-sky minus the simulated clear-sky (https://doi.org/10.5281/zenodo.4842675) and minus the known cloudy signal according to following a common cloud masking scheme. The paper describing the method, dataset, and results is intended for publication in the journal of Atmospheric Chemistry and Physics (ACP) under Mieslinger et al., 2021.</p> <p>The dataset includes basic information of the relevant input variables to clear-sky radiative transfer simulations as well as the resulting probability density function over reflectance values and for discrete flag values (clear-sky, optically thin clouds, clouds) given an ASTER observation. This data builds the basis for any derived quantities such as the area fraction or the expected reflectance corresponding to a certain flag value.</p>
Wine trade - Export/Import - Intra-EU/Extra-EU
<p>Import and export between intra-EU and extra-EU states per HS-/CN-code (product ID), reporting and partner country, value in EUR€, and quantity in kg and litres. The dataset is based on data gathered from Eurostat. </p> <p>The dataset underpins the report "Mapping the local-global wine chain from Europe to China: Towards shared standards and benchmarks in wine traceability and authenticity" (Nofima report 10/2021 - Link: https://hdl.handle.net/11250/2734677). </p>
Code and data associated with: Searching the web builds fuller picture of arachnid trade
<p>Data and code used in the paper: Searching the web builds fuller picture of arachnid trade. Throughout the methods we have indicated the stage of analysis each data component was used and the code script connected. We have numbered to code and data supplements to reflect as closely as possible the order in which data generation and summary was undertaken. The following provide additional details linked to each of the data files.</p> <p>Data S1 - Website data: lang = language of the search engine used, ad hoc websites had language described after discovery; engine = the search engine used; page = the page on which the website appeared from the search engine; searchdate = search date in YYYY-mm-dd HH:MM:SS; link = link to the webpage, redacted to protect website identity; reviewdate = date revewied for arachnids being sold and search strategy; sells = whether the website sells arachnids (1 == sells); allow = whether the site explcicilt forbids automated searching (1 == allows, NA when search method was not fully automated, e.g., single page); type = the type of the website (e.g., trade, classified ads); order = whether arachnids where organised in a particular ways; target = a refined target URL to start search; method = the search method chosen, see methods for details; refine = any refinement or filter than could constrain the scope of the website to be searched; spages = the number of pages required to cycle through to cover the entire stock (also separated by ; if multiple cycles where needed or multiple single pages could be easily collected); prelimCheck = whether the website passed initial checks for arachnid selling; notes = any details that might need special attention during searches; webID = code used for subsequent data summary.</p> <p>Data S2 - Raw keyword searches outputs: species keywords. sp = the modern species or genus that a keyword is associated with; page = the number of the page the keyword was detected on; keyw = the exact keyword that was detected; spORgen = whether the keyword was a species binomial or just genus; termsSurrounding = the words surrounding a genus keyword detection (only applies to Data S3); webID = the website ID.</p> <p>Data S3 – Raw keyword searches outputs: genus keywords. sp = the modern species or genus that a keyword is associated with; page = the number of the page the keyword was detected on; keyw = the exact keyword that was detected; spORgen = whether the keyword was a species binomial or just genus; termsSurrounding = the words surrounding a genus keyword detection (multiple detections separated by ;); webID = the website ID.</p> <p>Data S4 - Raw keyword search outputs: temporal sample. sp = the modern species or genus that a keyword is associated with; page = the number of the page the keyword was detected on; keyw = the exact keyword that was detected; spORgen = whether the keyword was a species binomial or just genus; termsSurrounding = the words surrounding a genus keyword detection (multiple detections separated by ;); webID = the website ID; timestamp.parse = the timestamp extracted from the archived web page; year = a simplified timestamp including only the year.</p> <p>Data S5 - LEMIS data used. An arachnid filtered version of <sup>74,75</sup>.</p> <p>Data S6 - CITES trade database data used <sup>76</sup>.</p> <p>Data S7 - CITES appendices data used <sup>77</sup>.</p> <p>Data S8 - IUCN Redlist data used <sup>78</sup>.</p> <p>Data S9 - Compiled final dataset, with data deriving from WSC, Scorpion files, ITIS, WAM and the data collection process. speciesId = a numeric code, one per species; clade = the clade the species belongs to; family = the family the species belongs to; genus = the genus of the species; species = the species epithet; author = the species authority name; year = the species authority year; parentheses = whether parentheses are needed with the authority; distribution = WSC original distribution descriptions; invalid = whether the species is considered valid; source = the species source, either World Spider Catalogue, Scorpion files, ITIS or WAM; accName = the species binomial being used as our accepted name; allNames = the accepted species binomial and all synonyms; allGenera = the accepted genus, and all other genera the species has belonged to at one point; onlineTradeSnap = whether the species was detected via a match to the accName in the snapshot data; onlineTradeSnap_Any = whether the species was detected via any synonym in the snapshot data; onlineTradeSnap_genus = whether the genus was detected via a match to the genus in the snapshot data; onlineTradeSnap_genusAny = whether the genus was detected via any synonym in the snapshot data; onlineTradeTemp = whether the species was detected via a match to the accName in the temporal data; onlineTradeTemp_Any = whether the species was detected via any synonym in the temporal data; onlineTradeTemp_genus = whether the genus was detected via a match to the genus in the temporal data; onlineTradeTemp_genusAny = whether the genus was detected via any synonym in the temporal data; onlineTradeEither = whether the species was detected via a match to the accName in the temporal data or snapshot data; onlineTradeEither_Any = whether the species was detected via any synonym in the temporal data or snapshot data; LEMIStrade = whether the species was detected via a match to the accName in the LEMIS data; LEMIStrade_Any = whether the species was detected via any synonym in the LEMIS data; LEMIStrade_genus = whether the genus was detected via any synonym in the LEMIS data; LEMIStrade_genusAny = whether the genus was detected via any synonym in the LEMIS data; CITEStrade = whether the species was detected via a match to the accName in the CITES trade database data; CITEStrade_Any = whether the species was detected via any synonym in the CITES trade database data; CITEStrade_genus = whether the genus was detected via any synonym in the CITES trade database data; CITEStrade_genusAny = whether the genus was detected via any synonym in the CITES trade database data; CITESapp = the CITES appendix the species is listed under using an exact match to the accName; CITESapp_Any = the CITES appendix the species is listed under using any match to any of the species’ synonyms; redlist = the IUCN Redlist category the species is listed under using an exact match to the accName; redlist_Any = the IUCN Redlist category the species is listed under using any match to any of the species’ synonyms; extactMatchTraded = the species is detected in any of the trade sources via a match to the accName; anyMatchTraded = the species is detected in any of the trade sources via a match to any species’ synonym.</p> <p>Data S10 - Forum listings of “What species are you currently keeping” from an online fora posted between 9th September 2021 and 9th October 2021, to provide an idea of online discussions. Each user with a separate list is provided in a separate tab. Morph_collector is the same as poster1, but the potential cryptic species or morphs are noted separately to make them clearer.</p> <p>Data S11 – Distribution information for spiders. Only two columns used in summaries: accName = the accepted name used throughout summaries; NAME = the country name the spider occurs in.</p> <p>Data S12 - Distribution information for scorpions. species = the accepted name used throughout summaries; NAME = the country name the scorpions occurs in.</p> <p>Code S1 - Search URL Extract.R</p> <p>Code S2 - Retrieve web data.R</p> <p>Code S3 - Temporal Classified Ads.R</p> <p>Code S4 - Keyword Generation.R</p> <p>Code S5 - Keyword Search.R</p> <p>Code S6 - LEMIS filter and summary.R</p> <p>Code S7 - Compiling results.R</p> <p>Code S8 - Summary Figures.R</p> <p>Code S9 - Temporal Figures.R</p> <p>Code S10 - New description figure.R</p> <p>Code S11 - Term exploration.R</p> <p>Code S12 - LEMIS summary and mapping.R</p>
Insider trading regulation and shorting constraints. Evaluating the joint effects of two market interventions.
<p>This dataset contains the raw experimental data and the analysis script for the paper Merl, R., Stöckl, T., Palan, S., 2022. "Insider trading regulation and shorting constraints. Evaluating the joint effects of two market interventions", Journal of Banking and Finance 106490, https://doi.org/10.1016/j.jbankfin.2022.106490.</p> <p>Instructions:</p> <p>1. Unpack all files into one folder.<br> 2. Open R version 4.1.2 and set the working directory to the folder with all the files.<br> 3. Run Script.R.</p> <p>In case the SPTools package is not available from GitHub anymore, you can also find it included in this dataset so you can install it from here.</p>
"Agricultural trade and its impacts on cropland use and the global loss of species habitat." - Supplementary data
<p>This dataset and code is part of the following publication:<br> Schwarzmueller, F. & Kastner, T (2022), Agricultural trade and its impact on cropland use<br> and the global loss of species' habitats. Sustainability Science, doi: 10.1007/s11625-022-01138-7<br> </p> <p>There are three zip-folders accompanying this publication:</p> <p>Code.zip contains all the R-Scripts and input files neccessary for the calculation that were written by the authors.</p> <p>Data.zip contains the FAO-input data (as dowloaded in 2021). This exact data is not available anymore from the FAOSTAT website, which is why we included it in this repository.</p> <p>TradeMatrixFeed_import_dry_matter_1986-2013.zip contains the results from the calculation as shown in the paper.</p>
A Dataset of French Trade Directories from the 19th Century (FTD)
<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 Optical Character Recognition (OCR) and Named Entity Recognition (NER) on 19th century French documents.</p> <p><br> This dataset is divided into two parts:</p> <ol> <li>A <strong>labeled dataset</strong>, which contains 8765 manually corrected entries from 78 pages (18 different directories), and which is designed for supervised training.</li> <li>An <strong>unlabeled dataset</strong>, containing 1058196 raw entries from 6887 pages (13 different directories), and which is designed for self-supervised pre-training.</li> </ol> <p>For the <strong>labeled dataset</strong>, we provide:</p> <ul> <li>Original pages and cropped images</li> <li>Human-corrected positions, transcriptions and entity tagging for each entry</li> <li>OCR prediction from 3 systems (Tesseract v4, PERO OCR v2020 and Kraken)</li> <li>Projected NER reference from clean text to OCR predictions, making it suitable to evaluate the performance of NER systems on real, noisy OCR predictions</li> </ul> <p>For the <strong>unlabeled dataset</strong>, we provide:</p> <ul> <li>Automatically detected positions for each entry (lot of noise)</li> <li>OCR predictions for each entry (PERO OCR engine)</li> </ul> <p> </p> <p><strong>How to cite this dataset</strong><br> Please cite this dataset as:</p> <blockquote> <p>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.</p> </blockquote> <pre><code>@dataset{abadie_dataset_22, author = {Abadie, Nathalie and Bacciochi, St{\'e}phane and Carlinet, Edwin and Chazalon, Joseph and Cristofoli, Pascal and Dum{\'e}nieu, Bertrand and Perret, Julien}, title = {{A} {D}ataset of {F}rench {T}rade {D}irectories from the 19th {C}entury ({FTD})}, month = mar, year = 2022, publisher = {Zenodo}, version = {v1.0.0}, doi = {10.5281/zenodo.6394464}, url = {https://doi.org/10.5281/zenodo.6394464} }</code></pre> <p><br> You may also be interested in <strong>our paper presented at DAS 2022</strong> (15th IAPR International Workshop on Document Analysis Systems), which <strong>compares the performance of OCR and NER</strong> systems <strong>on this dataset</strong>:</p> <blockquote> <p>N. Abadie, E. Carlinet, J. Chazalon and B. Duménieu, A Benchmark of Named Entity Recognition Approaches in Historical Documents — Application to 19th Century French Directories, May 2022, La Rochelle, France, Springer.</p> </blockquote> <pre><code>@inproceedings{abadie_das_22, author = {Abadie, Nathalie and Carlinet, Edwin and Chazalon, Joseph and Dum{\'e}nieu, Bertrand}, title = {{A} {B}enchmark of {N}amed {E}ntity {R}ecognition {A}pproaches in {H}istorical {D}ocuments — {A}pplication to 19th {C}entury {F}rench {D}irectories}, month = may, year = 2022, publisher = {Springer}, place = {La Rochelle, France} }</code></pre> <p><br> <strong>Copyright and License</strong><br> The images were extracted from the original source <a href="https://gallica.bnf.fr">https://gallica.bnf.fr</a>, owned by the <em>Bibliothèque nationale de France</em> (French national library).<br> 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. <br> <strong>Researchers do not have to pay any fee for reusing the original contents in research publications or academic works. </strong> <br> <em>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.</em></p> <p>The original contents were significantly transformed before being included in this dataset.<br> All derived content is licensed under the permissive <strong>Creative Commons Attribution 4.0 International</strong> license.</p>
The SPIN covid19 RMRIO dataset: Global trade network data for the years 2016-2026 reflecting macroeconomic effects of the covid19 pandemic - B. Data for 2020 - 2026 - Covid 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>covid</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>counterfactual</em> scenario are stored in the following repository: <a href="https://doi.org/10.5281/zenodo.5713839">10.5281/zenodo.5713839</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>covid</em> scenario is in line with April 2021 WEO's data and includes the macroeconomic effects of Covid 19.</p> <p>All tables are labelled in 2015 US$ and valued in basic prices.</p>
The SPIN covid19 RMRIO dataset: Global trade network data for the years 2016-2026 reflecting macroeconomic effects of the covid19 pandemic - A. Code and data for 2016-2019
<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 2016 to 2019 (<em>hist</em> scenario) and the corresponding labels.<br> Data for years 2020 to 2026 are stored in the corresponding repositories:</p> <ul> <li><em>covid</em>: <a href="https://doi.org/10.5281/zenodo.5713825">10.5281/zenodo.5713825</a></li> <li><em>counterfactual: </em><a href="https://doi.org/10.5281/zenodo.5713839">10.5281/zenodo.5713839</a></li> </ul> <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>From 2020 to 2026, the dataset includes two diverging scenarios. The <em>covid</em> scenario is in line with April 2021 WEO's data and includes the macroeconomic effects of Covid 19. The<em> counterfactual</em> scenario is in line with October 2019 WEO's data and simulates the global economy without Covid 19. Tables from 2016 to 2019 are labelled as <em>hist</em>.</p> <p>The <em>Projections</em> folder includes the generated tables for years from 2016 to 2019 (<em>hist</em> scenario) and the corresponding labels.<br> The <em>Sources </em>folder contains the data records from the IFS and WEO databases. The <em>Method data</em> contains the data files used to generate the tables with the SPIN method and the following Python scripts:</p> <ul> <li><em>SPIN_covid19_MRIO_files_preparation.py</em> generates the data files from the source data.</li> <li><em>SPIN_covid19_RMRIO runs.py</em> is the command to run the SPIN method and generate the dataset.</li> <li><em>figures.py</em> is a script to produce figures reflecting the consistency of the projected tables and the evolution of macroeconomic figures in the 2016-2026 period for a selection of countries.</li> </ul> <p>All tables are labelled in 2015 US$ and valued in basic prices.</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)
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DANDI Archive for NWB datasets
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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.