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.
11
datasets available to search
ShareScore release 0.9.0
Dataset results
11 results for “agricultural trade”
"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>
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>
Data from: Riparian reforestation on the landscape scale – Navigating trade-offs among agricultural production, ecosystem functioning and biodiversity
<p> </p> <p><strong>Short description</strong></p> <p>This repository contains the relevant data and code used for the analyses of the scientific publication: "<em>Riparian reforestation on the landscape scale – Navigating trade-offs among agricultural production, ecosystem functioning and biodiversity</em>", published in the Journal of Applied Ecology.</p> <p>For further details please see the original article and its supplementary materials.</p> <p> </p> <p><strong>Organization of the data</strong></p> <p>The repository contains two main folders:</p> <p> 1. Target indicators & spatial analysis</p> <p><em>‘target indicators.csv’</em>: Measured variables that have been quantified at the CROSSLINK field sampling campaign in the Zwalm catchment (EPT taxa richness, diatoms functional evenness, cotton-strip assay).</p> <p><em>‘bio-suitability segments.csv’</em>: Biophysical suitability for food production of the arable land for each riparian segment of the Zwalm.</p> <p><em>‘spatial analysis.xlsx’</em>: Results of the Zwalm spatial analyses addressing land-use and physiographic properties of the (1) local riparian corridors; (2) full riparian corridors within in the upstream catchments and (3) total upstream catchment areas for each sampling site.</p> <p><em>‘Summary model development Zwalm.pptx’</em>: Additional information on the models that have been used in the CoMOLA optimization framework.</p> <p> 2. CoMOLA input & parameterisation</p> <p>The files in this folder can be used for the parameterisation of the Python tool CoMOLA (Strauch et al., 2019). Source for CoMOLA, including user manual: https://github.com/michstrauch/CoMOLA</p> <p><em>‘config.ini’</em>: Basic configuration file of CoMOLA (needs to be adjusted to local settings)</p> <p><em>‘input’ folder</em>: Includes the CoMOLA input files that have been used in our study. See CoMOLA manual for more details on each file.</p> <p><em>‘models’ folder</em>: Includes the Python code of the models that are used for the calculation of all target indicators within the optimization framework (‘Zwalm_4_Models_v1_utf8.py’). The sub-folders ‘GIS_temp_files’ and ‘Input’ contain all files that are needed and have been used to run the Python code.</p> <p> </p>
Data supplement to "Aromatics and agriculture: A spatial approach to long-distance trade and the local economy of the Nabataeans"
<p>Data used to analyze the role of long-distance trade in the local agricultural economy of the Nabataeans, as described in Weaverdyck, E. J. S. forthcoming. "Aromatics and agriculture: A spatial approach to long-distance trade and the local economy of the Nabataeans." In S. von Reden (ed.). <em>Handbook of Ancient Afro-Eurasian Economies</em>, vol. 3. Oldenbourg: De Gruyter.</p> <p>Data sources are provided in the chapter.</p> <p>With the exception of runoff, environmental variables are rasters created using the focal statistics tool to sum the number of cells within a radius around each cell. They are named according to the following convention: f{radius}_{factor abbreviation}_{variable}.tif</p> <p>Factor abbreviations:</p> <table> <tbody> <tr> <td>A</td> <td>Aspect</td> </tr> <tr> <td>D</td> <td>DEV geomorphological land form</td> </tr> <tr> <td>H</td> <td>Hydrology</td> </tr> <tr> <td>S</td> <td>Slope</td> </tr> <tr> <td>T</td> <td>TPI geomorphological land form</td> </tr> </tbody> </table> <p>Of these environmental variables, the following were used as background variables in the MaxEnt models:</p> <table> <thead> <tr> <th scope="col">ASKP-LA</th> <th scope="col">WHS</th> </tr> </thead> <tbody> <tr> <td>f500_H_prec_soak</td> <td>f500_H_Runoff</td> </tr> <tr> <td>f500_T_Valley</td> <td>f5k_D_Ridge</td> </tr> <tr> <td>f5k_T_Ridge</td> <td>f5k_H_Runoff</td> </tr> <tr> <td>f5k_H_Runoff</td> <td>f500_D_UpperSlope_or_low_rise</td> </tr> <tr> <td>f5k_A_Northeast</td> <td>f5k_A_Southwest</td> </tr> <tr> <td>f500_D_UpperSlope_or_low_rise</td> <td>f500_D_Valley</td> </tr> <tr> <td>f5k_H_springs</td> <td> </td> </tr> <tr> <td>f5k_D_Ridge</td> <td> </td> </tr> <tr> <td>f5k_A_South</td> <td> </td> </tr> <tr> <td>f5k_A_Southwest</td> <td> </td> </tr> <tr> <td>f5k_A_Northwest</td> <td> </td> </tr> <tr> <td>f500_T_UpperSlope_or_low_rise</td> <td> </td> </tr> <tr> <td>f500_D_Lower_slope_or_shallow_valley</td> <td> </td> </tr> <tr> <td>f5k_A_North</td> <td> </td> </tr> </tbody> </table> <p> </p>
Data from: Quantifying trade-offs between butterfly abundance and movement in the management of agricultural set-aside strips
Open the record for dataset details and reuse information.
Trade-offs between biodiversity and agriculture are moving targets in dynamic landscapes
<ol> <li>Understanding how biodiversity responds to intensifying agriculture is critical to mitigating the trade-offs between them. These trade-offs are particularly strong in tropical and subtropical deforestation frontiers, yet it remains unclear how changing landscape context in such frontiers alters agriculture-biodiversity trade-offs.</li> <li>We focus on the Argentinean Chaco, a global deforestation hotspot, to explore how landscape context shapes trade-off curves between agricultural intensity and avian biodiversity. We use a space-for-time approach and integrate a large field dataset of bird communities (197 species, 234 survey plots), three agricultural intensity metrics (meat yield, energy yield and profit), and a range of environmental covariates in a hierarchical Bayesian occupancy framework.</li> <li>Woodland extent in the landscape consistently determines how individual bird species, and the bird community as a whole, respond to agricultural intensity. Many species switch in their fundamental response, from decreasing occupancy with increased agricultural intensity when woodland extent in the landscape is low (loser species), to increasing occupancy with increased agricultural intensity when woodland extent is high (winner species).</li> <li>This suggests that landscape context strongly mediates who wins and loses along agricultural intensity gradients. Likewise, where landscapes change, such as in deforestation frontiers, the very nature of the agriculture-biodiversity trade-offs can change as landscapes transformation progresses.</li> <li> <i>Synthesis and applications</i>. Schemes to mitigate agriculture-biodiversity trade-offs, such as land sparing or sharing, must consider landscape context. Strategies that are identified based on a snapshot of data risk failure in dynamic landscapes, particularly where agricultural expansion continues to reduce natural habitats. Rather than a single, fixed strategy, adaptive management of agriculture-biodiversity trade-offs is needed in such situations. Here we provide a toolset for considering changing landscape contexts when exploring such trade-offs. This can help to better align agriculture and biodiversity in tropical and subtropical deforestation <a>frontiers.</a> </li> </ol>
Exploring Tools for Sharing and Trading in Agricultural Machinery Rental: Insights from Gray Literature Review
<p>This spreadsheet contains data from the analysis of platforms for renting and selling agricultural machinery identified during the research. It includes overall evaluations as well as individual assessments from three evaluators, based on predefined criteria for qualitative analysis. The recorded information includes scores assigned to each evaluated site, reflecting the implementation or lack thereof of specific features, such as filter-based search, review systems, and other functionalities related to the sharing economy model in the agricultural sector. The data is organized to facilitate comparison among evaluators and overall conclusions.</p>
Supporting data and code for: "Global unsustainable virtual water flows in agricultural trade"
<p>Supporting data and code for: "<strong>Global unsustainable virtual water flows in agricultural trade"</strong></p> <p>This file contains:</p> <p>-The code used to process trade and production data.</p> <p>- Crop- and country-specific unsustainable virtual water flows for years 2000 and 2015.</p> <p>-Crop- and country-specific sustainable and unsustainable irrigation water consumption.</p> <p> </p>
Trade-offs between biodiversity and agriculture are moving targets in dynamic landscapes
Open the record for dataset details and reuse information.
Data from: Soil management shapes ecosystem service provision and trade-offs in agricultural landscapes
Agroecosystems are principally managed to maximize food provisioning even if they receive a large array of supporting and regulating ecosystem services (ESs). Hence, comprehensive studies investigating the effects of local management and landscape composition on the provision of and trade-offs between multiple ESs are urgently needed. We explored the effects of conservation tillage, nitrogen fertilization and landscape composition on six ESs (crop production, disease control, soil fertility, water quality regulation, weed and pest control) in winter cereals. Conservation tillage enhanced soil fertility and pest control, decreased water quality regulation and weed control, without affecting crop production and disease control. Fertilization only influenced crop production by increasing grain yield. Landscape intensification reduced the provision of disease and pest control. We also found tillage and landscape composition to interactively affect water quality regulation and weed control. Under N fertilization, conventional tillage resulted in more trade-offs between ESs than conservation tillage. Our results demonstrate that soil management and landscape composition affect the provision of several ESs and that soil management potentially shapes the trade-offs between them
Data from: Soil management shapes ecosystem service provision and trade-offs in agricultural landscapes
Open the record for dataset details and reuse information.
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.