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1,425 results for “Agriculture”
High-Throughput Analysis of Lung Immune Cells in a Combined Murine Model of Agriculture Dust-Triggered Airway Inflammation with Rheumatoid Arthritis
GEO Series GSE155436. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Mycobacterium avium ssp. paratuberculosis Infection Induces an Immune Tolerance Phenotype in Primary Bovine Macrophages from Johne’s Disease Cows (Agriculture and Agri-Food Canada_N.Bissonnette_J00007
GEO Series GSE98363. Bos taurus. 72 samples. Type: Expression profiling by high throughput sequencing.
Figure 6 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052
Figure 6 Typical agricultural landscapes of BESTMAP demonstration case studies.
Waldock et al. 2020 Ecography: Insect occurrence in agricultural land-uses depends on realized niche and geographic range properties
<p>This file contains the site-level data from the PREDICTS database, code to develop environmental niche estimates for species from GBIF occurrences, and all statistical analysis investigating the role of species environmental niches in determining occupancy in agro-ecosystems used in the publication: Waldock et al. 2020 Ecography.</p>
Data suppository for 'The flower preference of beneficial insects in the agricultural area of Hollands Kroon in the Netherlands'
<p>Data suppository including management plan, R script, excel files and all data.</p>
Figure 4 in Diversity and life-history traits of wild bees (Insecta: Hymenoptera) in intensive agricultural landscapes in the Rolling Pampa, Argentina
Figure 4. Percentage composition of main bee taxa captured during the three sampling rounds.
Figure 1 in Noxious arthropods as potential prey of the venomous Javan slow loris (Nycticebus javanicus) in a West Javan volcanic agricultural system
Figure 1. Map of the study location Cipaganti near Garut, West Java.
Replication package for: The Equilibrium Impact of Agricultural Risk on Intermediate Inputs and Aggregate Productivity
<p>The package contains the necessary code and data to reproduce the figures and tables in Donovan (forthcoming), "The Equilibrium Impact of Agricultural Risk on Intermediate Inputs and Aggregate Productivity," Review of Economic Studies. Detailed instructions on accessing the raw data are available in the README file.</p>
Data from: Population structure of a global agricultural invasive pest, Bactrocera dorsalis (Diptera: Tephritidae)
Bactrocera dorsalis, the Oriental fruit fly, is one of the world's most destructive agricultural insect pests and a major impediment to international fresh commodity trade. The genetic structuring of the species across its entire geographic range has never been undertaken, because under a former taxonomy B. dorsalis was divided into four distinct taxonomic entities, each with their own, largely non-overlapping, distributions. Based on the extensive sampling of six a priori groups from 63 locations, genetic and geometric morphometric datasets were generated to detect macrogeographic population structure, and to determine prior and current invasion pathways of this species. Weak population structure and high genetic diversity were detected among Asian populations. Invasive populations in Africa and Hawaii are inferred to be the result of separate, single invasions from South Asia; while South Asia is also the likely source of other Asian populations. The current northward invasion of B. dorsalis into central China is the result of multiple, repeated dispersal events, most likely related to fruit trade. Results are discussed in the context of global quarantine, trade and management of this pest. The recent expansion of the fly into temperate China, with very few associated genetic changes, clearly demonstrates the threat posed by this pest to ecologically similar areas in Europe and North America.
Measuring exposure of agriculture to observed temperature change, 1961-2024
<p>The dataset disseminates regional indicators that link observed temperature change and agricultural statistics, connecting NASA-GISS and FAOSTAT data for mean annual temperature change on land with statistics of population, agricultural land use, crop and livestock production and gross economic value by country, over the time series 1961-2024.</p>
ASSESSING THE IMPACT OF YOUTH INVOLVEMENT ON NATIONAL DEVELOPMENT PROJECTS: A STUDY ON THE AGRICULTURAL DEVELOPMENT PROJECTS LED BY YALLA YALLA GROUP (YYG) UNDER RAB-RWANDA
<div> <p><strong><span> </span></strong></p> </div> <p><strong><span> </span></strong></p> <p><strong><span>ABSTRACT</span></strong></p> <p><span>The Study assessed he impact of Youth Involvement on National Development Projects: A Study on the Agricultural </span><span>development projects</span><span> Led by </span><span>Yalla Yalla Group (YYG) </span><span>under RAB-Rwanda</span><span> <span>a country which is experiencing rapid economic growth and characterized by a predominantly youthful population. Despite economic progress, Rwanda faces challenges such as insufficient job creation and inadequate economic infrastructure, leading to persistent issues of unemployment and underemployment, particularly among the youth. In response to these challenges, Yalla Yalla Group (YYG), comprising young professionals, directs its efforts toward agriculture development projects. The research objectives encompass evaluating the effectiveness of YYG's projects, examining socio-economic benefits, assessing the impact on community development, and exploring potential barriers to success. The study population comprises 198 individuals actively involved in YYG projects, with a sample of 132 determined using Slovin's formula through convenient sampling. Quantitative data is collected through a structured questionnaire and statistical analyses, including SPSS and multiple linear regression, are employed to scrutinize hypotheses. The findings reveal a highly significant positive correlation (r = 0.977, p < 0.01) between "country development" and "young people involvement," indicating a strong linear relationship. The F-statistic of 219.903 emphasizes that the predictors significantly explain variance in country development beyond chance. Specifically, the study highlights that increased involvement in agriculture is associated with higher levels of country development (coefficient: 0.506, t = 5.500, p < 0.001), underscoring a positive relationship. Livestock involvement, while statistically significant (coefficient: 0.017, t = 0.097, p = 0.003), exhibits a limited impact on country development. In contrast, higher engagement in business and entrepreneurship significantly correlates with increased country development (coefficient: 0.919, t = 4.963, p < 0.001), emphasizing a strong positive relationship. Additionally, heightened participation in environmental conservation is linked to elevated country development (coefficient: 0.157, t = 3.049, p = 0.003). </span></span></p> <p><strong><span>Keywords:</span></strong><span> </span><em><span>Youth Involvement;<span> </span>National Development Projects; Agricultural Development; Youth-Led Initiatives; Sustainable Development</span></em></p>
Data associated to the paper: "Plant species better adapted to climate change need agricultural extensification to persist"
<p>This dataset relates to the 500-ENI network, which is funded by the French Ministry of Agriculture, in order to monitor the unintended effects of agricultural practices on biodiversity. It contains compositional and functional floristic data, as well as climatic and agricultural data between 2013 and 2021 in 555 agricultural field margins in France. These data and the R script associated, allow to reproduce the analyses described in the paper “Plant species better adapted to climate change need agricultural extensification to persist” and its Appendix, in particular the PCA on the CWM of functional traits, the temporal models and the community trajectory in the CSR triangle of Grime. The “Data” folder includes floristic, functional and environmental data by observation and site, as well as the functional traits of species. Finally, the “Output” folder contains the output files obtained from the scripts. </p>
Data for: "The influence of urban and agricultural landscape context on forest diversity and structure across ecoregions "
<p>This is the data associated with "The influence of urban and agricultural landscape context on forest diversity and structure across ecoregions" by JP Schmit, LR Johnson, M Baker, L Darling, R Fahey, DH Locke, AT Morzillo, NF Sonti, TLE Trammell, MFJ Aronson, and ML Johnson, published in Ecosphere in 2025.</p>
Figure 2 from: Blanco-Gutiérrez I, Esteve P, Garrido A, Gómez-Ramos A, Arce A, Zubelzu S, Díaz-Ambrona CH, Sánchez R, Calatrava J, López-Correa JM (2021) RECLAMO: Unlocking the potential of wastewater reuse for agricultural irrigation in Spain . Research Ideas and Outcomes 7: e76793. https://doi.org/10.3897/rio.7.e76793
Figure 2 GANNT Diagram.
Figure 1 from: Blanco-Gutiérrez I, Esteve P, Garrido A, Gómez-Ramos A, Arce A, Zubelzu S, Díaz-Ambrona CH, Sánchez R, Calatrava J, López-Correa JM (2021) RECLAMO: Unlocking the potential of wastewater reuse for agricultural irrigation in Spain . Research Ideas and Outcomes 7: e76793. https://doi.org/10.3897/rio.7.e76793
Figure 1 PERT Diagram. Project structure and links between WPs.
Fig. 1 in Factors Affecting Avifaunal Diversity In Selected Agro-Ecosystems Of Himachal Pradesh Agricultural University, Palampur, Himachal Pradesh, India
Fig. 1. Location of the study area. Himachal Pradesh Agricultural University, Palampur, India.
smallSSD: The Small Robot Company's detection dataset for agriculture
<p>The Small Robot Company's detection dataset for agriculture, consisting of images collected by the Tom robot labelled with bounding boxes to delineate wheat and weeds.</p> <p>This data is shared under a <a href="https://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial-4.0 International</a> license.</p> <p>More information about this data is available at <a href="https://github.com/SmallRobotCompany/smallssd">https://github.com/SmallRobotCompany/smallssd</a>.</p>
Figure 1 from: Gonçalves MW, Gambale PG, Godoy FR, Alves AA, Rezende PH, Cruz AD, Maciel NM, Nomura F, Bastos RP, de Marco-Jr P, Silva DM (2017) The agricultural impact of pesticides on Physalaemus cuvieri tadpoles (Amphibia: Anura) ascertained by comet assay. Zoologia 34: 1-8. https://doi.org/10.3897/zoologia.34.e19865
Figure 1 - Spatial distribution of the ponds sampled at five municipalities in the state of Goiás.
High-Resolution RGB Images and Corresponding Masks of Agricultural Fields
<p>This dataset consists of <strong>325 high-resolution RGB images</strong> and their corresponding <strong>masks</strong>. The images were captured using a DJI AIR 2S drone from four distinct types of agricultural fields: <strong>orchards, olive groves, wheat (green), and vineyards</strong>.</p> <p>All images are provided in <strong>TIFF format</strong>, representing the raw outputs directly from the drone without any preprocessing or compression, thus preserving full sensor quality. The masks are also stored in <strong>TIFF format</strong>.</p> <p>The masks were generated using <strong>orthomosaic processing software</strong>.</p> <p>This dataset is intended for <strong>semantic segmentation tasks</strong> in the field of precision agriculture, supporting the development of models for <strong>automatic vegetation detection</strong> and <strong>crop analysis</strong>.</p>
Dataset MADFORWATER: WP2: Adaptation of wastewater treatment technologies for agricultural reuse: Task2.4: Industrial wastewater treatment: Subset 4
<p>This dataset contains the data underlying the following publication: Chouchane H, Mahjoubi M, Ettoumi B, Neifar, M. Cherif A (2017).</p> <p>A novel thermally stable heteropolysaccharide based bioflocculant from hydrocarbonoclastic strain <em>Kocuria rosea</em> BU22S and its application in dye removal, Environmental Technology, 39(7):859-872. <a href="https://doi.org/10.1080/09593330.2017.1313886">https://doi.org/10.1080/09593330.2017.1313886</a></p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.