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.
1,648
datasets available to search
ShareScore release 0.9.0
Dataset results
1,648 results for “farm”
Dataset for "Best organic farming deployment scenarios for pest control: a modeling approach" V3
<p>Organic Farming (OF) has been expanding recently in response to growing consumer demand and as a response to environmental concerns. The area under OF is expected to further increase in the future. The effect of OF expansion on pest densities in organic and conventional crops remains difficult to predict because OF expansion impacts Conservation Biological Control (CBC), which depends on the surrounding landscape context. In order to understand and forecast how pests and their biological control may vary during OF expansion, we modeled the effect of spatial changes in farming practices on population dynamics of a pest and its natural enemy. We investigated the impact on pest density and on predator to pest ratio of three contrasted scenarios aiming at 50% organic fields through the progressive conversion of conventional fields. Scenarios were 1) conversion of Isolated conventional fields first (IP), 2) conversion of conventional fields within Groups of conventional fields first (GP), and 3) Random conversion of conventional field (RD). We coupled a neutral spatially explicit landscape model to a predator-prey model to simulate pest dynamics in interaction with natural enemy predators. The three OF expansion scenarios were applied to nine landscape types differing in their proportion and fragmentation of semi-natural habitat. We further investigated if the ranking of scenarios was robust to pest control methods in OF fields and pest and predator dispersal abilities.</p> <p>We found that organic farming expansion affected more predator densities than pest densities for most landscape types. The impact of OF expansion on final pest and predator densities was also stronger in organic than conventional fields and in landscapes with large proportions of highly fragmented semi-natural habitats. Based on pest densities and the predator to pest ratio, our results suggest that a progressive organic conversion with a focus on isolated conventional fields (scenario IP) could help promote CBC. Careful landscape planning of OF expansion appeared most necessary when pest management was substantially less efficient in organic than in conventional crops, and in landscapes with low proportion of semi-natural habitats.</p> <p><strong>This dataset contains simulation outputs and the R script that was used to describe, display and analyse data. The model itself can be found at <a href="https://doi.org/10.17605/OSF.IO/Z2QCX">https://doi.org/10.17605/OSF.IO/Z2QCX</a></strong></p> <p><strong>Please note that this is the third version of this dataset, following recommendations from the PCI Ecology reviewers and editor.</strong></p>
Youtube comments on Smart Farming
<p>Please cite the following paper, if you are working on or using the above dataset.</p> <p>https://www.mdpi.com/2624-7402/4/2/29</p> <p>Farming Dataset</p> <p>The datasets are taken from the following 16 YouTube videos related to farming:<br> 1. How Japan Is Reshaping Its Agriculture By Harnessing Smart Farming Technology-<br> Science Insider<br> 2. Europe has the best regenerative farmers in the world - Richard Perkins<br> 3. The Futuristic Farms That Will Feed the World - Freethink<br> 4. Vertical farms could take over the world - Freethink<br> 5. India’s largest Precision Farm - Simply Fresh Discover Agriculture<br> 6. Solar Panels Plus Farming? Agrivoltaics Explained Undecided with Matt Ferell<br> 7. 7 Israeli Agriculture Technologies Israel<br> 8. The CNH Industrial Autonomous Tractor Concept (Full Version CNH Industrial<br> 9. IoT Smart Plant Monitoring System Viral Science -the home of creativity<br> 10. Singapore’s Bold Plan to Build the Farms of the Future Tomorrow’s Build<br> 11. Smart Vertical Farms in Sharjah Episode Up<br> 12. Drones, robots, and super sperm – the future of farming DW Documentary<br> 13. Simply Fresh - India’s Largest State Of The Art Precision Farm<br> 14. RIPPA The Farm Robot Exterminates Pests And Weeds- ABC Science<br> 15. Top 10 Agritech Startups Empowering Indian Farmers Backstage With Millionaires<br> 16. This Farm of the Future Uses No Soil and 95% Less Water Stories<br> Total counts: 7334<br> The comments have been divided into the following labels:<br> 1. Praising (970 comments)<br> 2. Opinion (3224 comments),<br> 3. Suggestion (231 comments),<br> 4. Undefined (1386 comments),<br> 5. Queries (1007 comments)<br> 6. Hybrid (516 comments).</p> <p>The file includes:<br> 1. Comment<br> 2. Label</p>
European consumers ́ preference and willingness to pay for food products labelled as obtained by a circular farming system -in relation to environmental attitudes and consumption behaviours
<p>Data was collected with questionnaire-based research carried out in Belgium, Croatia, Hungary, Italy, Poland, and Spain as part of a European project. The survey questions were designed to obtain the Willingness to pay using 2 different methodologies the discrete choice experiment and the open-end choice experiment. The survey also included questions about consumers environmental attitudes, and consumption behavior (purchase, use and recycling), to identify if them have influence on preferences towards more sustainable food products. The 3 analyzed food products were pork meat, milk and bread, all of them obtained through different agricultural production systems (circular, conventional, and organic agriculture). The sample was stratified in terms of gender and age to be representative to the average population in each country. Furthermore, respondents included in this study were those that are mainly, or in part responsible for the household food shopping. The questionnaire was translated to the languages of the countries involved in the data collection and pre-launched using a pilot sample of 50 consumers in each case study country. Finally, a total of 5,362 validated questionnaires were obtained. Data was collected online using the Qualtrics market research company, and Net panel market company for Hungary from June 2021 to January 2022.</p>
PIE LTER 15-minute meteorological data from the Marshview Farm weather station located in Newbury, MA, year 2024
Meteorological measurements for 2024 at MBL Marshview Farm, Newbury, MA. Sensors conduct measurements every 5 seconds and measurements are reported as averages or totals for 15 minute intervals. 15 minute averages are reported for air temperature, humidity, solar radiation, PAR, wind speed and direction and barometric pressure. 15 minute totals are reported for precipitation.
PIE LTER 15-minute meteorological data from the Marshview Farm weather station located in Newbury, MA, year 2025
Meteorological measurements for 2025 at MBL Marshview Farm, Newbury, MA. Sensors conduct measurements every 5 seconds and measurements are reported as averages or totals for 15 minute intervals. 15 minute averages are reported for air temperature, humidity, solar radiation, PAR, wind speed and direction and barometric pressure. 15 minute totals are reported for precipitation.
Physiological parameters for three farm animal species (cattle, sheep, and swine) as the basis for the development of generic physiologically based kinetic models
<p><strong>IMPORTANT : PLEASE DISREGARD VERSION 1 OF THIS UPLOAD SINCE IT INCLUDES ERRONEOUS INFORMATION.</strong></p> <p>This excel file (DOI: 10.5281/zenodo.3433224) provides physiological parameters and their inter-individual variability (mean, coefficient of variation, sample size) for three farm animal species: cattle (<em>Bos taurus</em>), sheep (<em>Ovis aries</em>), and swine (<em>Sus scrofa domesticus</em>). These physiological parameters were estimated based on the results of extensive literature searches and specific experimental data described in Lautz et al., (2020). This file is associated with R codes (DOI: 10.5281/zenodo.3432796) for generic PBK models, partition coefficient Quantitative Structure Activity Relationship (QSAR) models for each farm animal species and parameterisation of the model.</p> <p>The full data collection and implementation of the models using case studies are described in Lautz et al., 2020 (10.1016/j.toxlet.2019.10.008).</p>
Carbon emissions and economic assessment of farm operations under different tillage practices in organic rainfed almond orchards under semiarid Mediterranean conditions
<p>This dataset corresponds to yield, price and fuel consumption from organic rainfed almond orchards in SE Spain under different diversification and tillage practices. The objective is to carry out an integrated environmental (focused on the CO<sub>2</sub> emissions) and economic assessment of farm operations under different diversification and tillage practices through a cradle-to-farm gate life cycle assessment (LCA) based on these data.</p> <p>These data correspond to the open-access article " Carbon emissions and economic assessment of farm operations under different tillage practices in organic rainfed almond orchards under semiarid Mediterranean conditions" published in Scientia Horticulturae. (https://doi.org/10.1016/j.scienta.2019.108978), funded by the European Commission Horizon 2020 project Diverfarming [grant agreement 728003].</p>
How to produce a farm video - Legumes Translated online seminar
<p>On September 1st, 2020 Legumes Translated and Thomas Alföldi of FiBL (Research Institute of Organic Agriculture) hosted an online seminar about the production of low-budget audio-visual material for research and innovation projects in the field of agriculture. In this webinar, affordable equipment for realising good quality videos are demonstrated and his experiences from planning, filming and editing videos are shared.</p>
Resilient farm demographics withstand, adapt, or transform in the face of competitive pressure, technological change, and the expected lifestyles of future generations
<p>Farm demographics has been recognized as an important driver of structural change in European agriculture. Focus groups and computer simulations on farm demographic change were used to better understand its role for the case study regions of the Altmark in the eastern part of Germany and Flanders in the northern part of Belgium. According to these analyses, many potential agricultural entrants are deterred by what they view as a poor quality of life that farming offers. This applies to farm successors as well as hired workers. For higher attractiveness of agriculture, policy objectives should address the social image of farming as well as revitalize rural areas. Increasingly critical is the demand for skilled hired labour. However, policies dealing with farm demographic change ignore these needs and focus almost exclusively on farm succession. Particularly, the direct payment system, including additional support for small farms and young farmers, must be re-evaluated for its effectiveness. The analyses provide evidence that this system constrains European agricultural development more than assists it; ultimately preventing farms from adapting and transforming.</p>
Agricultural land use and livestock composition by case study of the SURE-Farm project - Input data for a dynamic nitrogen flow model
<p>Dataset used as input to the model by Pinsard et al (2021) and results published in D5.5 of the SURE-Farm project.</p>
FoPIA-SURE-Farm 1 dataset
<p>This dataset contains the FoPIA-SURE-Farm data from workshops on assessing current sustainability and resilience of farming systems conducted within the context of SURE-Farm in the winter of 2018/2019.</p>
Data and Code for "Does Organic Farming Jeopardize Food Security of Farm Households in Benin?"
<p>This data and code archive provides all the data and code for replicating the empirical analysis that is presented in the journal article "<a href="https://doi.org/10.1016/j.foodpol.2024.102622" target="_blank" rel="noopener">Does Organic Farming Jeopardize Food Security of Farm Households in Benin?</a>" authored by Ghislain B.D. Aïhounton and Arne Henningsen and published in the journal Food Policy (Volume 124, April 2024, 102622, DOI: 10.1016/j.foodpol.2024.102622).</p> <p>We conducted the empirical analysis with the "R" statistical software (version 4.3.3) using the add-on packages "AER" (version 1.2.12), "DescTools" (version 0.99.54), "lmtest" (version 0.9.40), "moments" (version 0.14.1), "sandwich" (version 3.1.0), "stargazer" (version 5.2.3), and "xtable" (version 1.8.4) that are all available at CRAN.</p> <p>This replication package contains the following files:</p> <p>* README<br>This file.</p> <p>* R/dataBenin.csv<br>A CSV file that contains the (unprepared) data set. The variables in this file are described in file R/Variables.csv. This CSV file is imported by R script PrepareDataFoodNutrition.R.</p> <p>* R/Variables.csv<br>A CSV file that describes the variables in the (unprepared) data set (file R/dataBenin.csv).</p> <p>* R/PrepareData.R<br>An R script that imports the (unprepared) data set (file R/dataBenin.csv), calculates additional variables and add theses variables to the data set, removes observations that should not be used in the empirical analysis, and saves the prepared data set as CSV file (R/dataFoodNutrition.csv).</p> <p>* R/dataPrepared.csv<br>A CSV file that contains the (prepared) data set used in the empirical analysis. This CSV file is created by the R script R/PrepareDataFoodNutrition.R. It is imported by the R scripts R/DescriptiveTab.R, FoodNutritionImpact.R, and GridSearchFoodSecurity.R.</p> <p>* R/DescriptiveTab.R<br>An R script that imports the prepared data set (file R/dataFoodNutrition.R) and creates Table 1 of the paper ("Descriptive statistics", file paper/tables/DescriptiveStat.tex) as LaTeX file.</p> <p>* R/Estimations.R<br>An R script that imports the prepared data set (file R/dataFoodNutrition.R), conducts all the analyses presented in the paper, creates Tables 2 and 3 of the paper ("OLS and IV regression results of the conditional associations between organic farming and outcomes" and "OLS and IV regression results of the conditional associations between organic farming and mediating outcomes", LaTeX files paper/tables/estMainReg.tex and paper/tables/estMedReg.tex), creates Figures 1 and 2 of the paper ("Estimated conditional associations of organic farming with outcomes" and "Estimated conditional associations of organic farming with mediating outcomes", 12 PDF files paper/figures/*.pdf), and 45 Tables that are included in the Supplementary Information: 36 tables with detailed regression results (LaTeX files paper/tables/tabels/est*.tex), one table with results of the first-stage probit regression (LaTeX file paper/tables/tabels/estProbit.tex), 6 tables with detailed regression results of estimations for testing the exogeneity of the instrument as suggested by Di Falco et al. (2011) (LaTeX files paper/tables/tabels/estOLS*Falco.tex), and 2 tables with coefficient bounds obtained as suggested by Oster (2019) (LaTeX files paper/tables/tabels/Oster*.tex).</p> <p>* R/GridSearch.R<br>An R script that re-runs our regression analyses with different units of measurement of IHS-transformed variables and calculates various indicators that can can be used to assess the appropriateness of different units of measurement as suggested by Aihounton and Henningsen (2021) and that creates 28 Tables that are included in the Supplementary Information (LaTeX files paper/tables/tabels/grid*.tex).</p> <p>* R/functions/calcOsterBounds.R<br>An R script that defines the R function calcOsterBounds() that calculates coefficient bounds using the method suggested by Oster (2019). This function is used by the R script R/FoodNutritionImpact.R.</p> <p>* R/functions/calcSemiElaOrg.R<br>An R script that defines the R function calcSemiElaOrg() that calculates the semi-elasticity of various log-transformed or IHS-transformed variables with respect to the dummy variable for organic farming. This function is used by the R scripts R/FoodNutritionImpact.R and R/GridSearchFoodSecurity.R.</p> <p>* R/functions/createFormula.R<br>An R script that defines the R function createFormula() that creates the regression formulas for the various empirical analyses that are presented in the paper. This function is used by the R scripts R/FoodNutritionImpact.R and R/GridSearchFoodSecurity.R.</p> <p>* R/functions/functionsTables.R<br>An R script that defines various R functions that are used to create tables in LaTeX format. These functions are used by the R scripts R/FoodNutritionImpact.R and R/GridSearchFoodSecurity.R.</p> <p>* R/functions/predR2.R<br>An R script that defines the R function predR2() that calculates the predictive R-squared value. This R script has been obtained from the replication package of the article:<br>Aïhounton, G. B. D. and Henningsen, A. (2021). Units of measurement and the inverse hyperbolic sine transformation. The Econometrics Journal, 24(2):334–351. https://doi.org/10.1093/ectj/utaa032<br>The function consists of a slightly modified version of the code that is available at: https://tomhopper.me/2014/05/16/can-we-do-better-than-r-squared/ This function is used by the R script R/GridSearchFoodSecurity.R.</p> <p>* paper/figures/*.pdf<br>12 LaTeX files that are the (sub)figures in Figures 1 and 2 of the paper ("Estimated conditional associations of organic farming with outcomes" and "Estimated conditional associations of organic farming with mediating outcomes"). These 12 files are created by the R script R/FoodNutritionImpact.R.</p> <p>* paper/tables/DescriptiveStat.tex<br>A LaTeX file that creates Table 1 of the paper ("Descriptive statistics"). This file is created by the R script R/DescriptiveTab.R.</p> <p>* paper/tables/estMainReg.tex<br>A LaTeX file that creates Table 2 of the paper ("OLS and IV regression results of the conditional associations between organic farming and outcomes"). This file is created by the R script R/FoodNutritionImpact.R.</p> <p>* paper/tables/estMedReg.tex<br>A LaTeX file that creates Table 3 of the paper ("OLS and IV regression results of the conditional associations between organic farming and mediating outcomes"). This file is created by the R script R/FoodNutritionImpact.R.</p> <p>* paper/tables/tabels/est*.tex<br>36 LaTeX files that create 36 tables that are included in the Supplementary Information and present detailed regression results. These 36 files are created by the R script R/FoodNutritionImpact.R.</p> <p>* paper/tables/tabels/estProbit.tex<br>A LaTeX files that creates a table that is included in the Supplementary Information and presents the results of the first-stage probit regression. This file is created by the R script R/FoodNutritionImpact.R.</p> <p>* paper/tables/tabels/estOLS*Falco.tex<br>6 LaTeX files that create 6 tables that are included in the Supplementary Information and present detailed regression results for testing the exogeneity of the instrument as suggested by Di Falco et al. (2011). These 6 files are created by the R script R/FoodNutritionImpact.R.</p> <p>* paper/tables/tabels/Oster*.tex<br>2 LaTeX files that create 2 tables that are included in the Supplementary Information and present coefficient bounds obtined as suggested by Oster (2019). These 2 files are created by the R script R/FoodNutritionImpact.R.</p> <p>* paper/tables/tabels/grid*.tex<br>28 LaTeX files that create 28 tables that are included in the Supplementary Information and present various indicators for assessing the appropriateness of different units of measurement of IHS-transformed variables as suggested by Aihounton and Henningsen (2021). These 28 files are created by the R script R/GridSearchFoodSecurity.R</p>
Estimated field scale sediment loss on the North Wyke Farm Platform in typical and extreme wet winters
<p>Based on monitored runoff and turbidity at 15-minute intervals from the North Wyke Fam Platform - a UK National Bioscience Research Infrastructure (NBRI), sediment loss during both typical and more extreme wet winters (December - February, inclusive) over the past decade (2012-2013, 2013-2014, 2015-2016, 2019-2020 and 2023-2024) from 5 grassland field catchments and 5 recently converted arable field catchments was estimated, including uncertainty ranges. Daily rainfall totals for the corresponding winter periods are also included.</p>
The location of solar farms within England's ecological landscape: implications for biodiversity conservation
<p>Data associated to the article entitled 'The location of solar farms within England's ecological landscape: implications for biodiversity conservation'. </p> <p> </p>
EJPSOIL ARTEMIS on-farm monitoring of soil health and ecosystems services (meta)data
<p>This database includes the data and metadata from the initial on-farm monitoring od soil health and soil related ecosystem services of the EJPSOIL ARTEMIS project. </p>
2-round Delphi study on digital technologies in vegetable farming in Switzerland
<p>This dataset contains survey data including the codebook for a 2-round Delphi study we conducted in Switzerland in autumn 2020. Selected experts were asked to describe the future of digital technologies in vegetable farming in Switzerland.</p>
Dataset for the article "Barriers and Opportunities for Sustainable Farming Practices and Crop Diversification Strategies in Mediterranean Cereal-Based Systems"
<p>Datasets from the surveys applied for the article "Barriers and Opportunities for Sustainable Farming Practices and Crop Diversification Strategies in Mediterranean Cereal-Based Systems" <a href="https://doi.org/10.3389/fenvs.2022.861225">https://doi.org/10.3389/fenvs.2022.861225</a></p>
COMPREHENSIVE LIVESTOCK HEALTH PROGRAM: TARGETED TREATMENT AND HOLISTIC INTERVENTIONS FOR MAJOR PREVALENT DISEASES IN THE LIVESTOCK FARMING COMMUNITY OF DAYNILE DISTRICT, MOGADISHU, SOMALIA.
<p>The general objective of this project was to intervene with the most common livestock diseases in Dayniile district by carrying out a comprehensive campaign for treatment and control. The specific objectives consisted of a treatment campaign, improving infrastructure for establishing disinfectant foot dips and hand washing points, providing disinfectant tools, and finalising community engagement and education by doing training at the farm level.<br>The team visited different donors and added their contribution. After collecting sufficient funds from various sources, the team began the procurement of the necessary materials. This included purchasing veterinary drugs and supplies from local pharmacies and other essentials like stationery. The first activity was treatment campaigns, which were a central aspect of the project. Over 290 animals were treated for various diseases and conditions. The farm manager was informed of the diagnoses, and upon receiving their permission, the appropriate treatments were administered. The second intervention action was a vaccination campaign. The team vaccinated a total of 70 animals against clostridial bacteria, which is one of the most common camel diseases encountered in the area. The third intervention was the establishment of biosecurity facilities at select livestock farms. Among all the farms involved in the project, five were chosen for the provision of enhanced biosecurity measures. These measures included the installation of foot dips and teat dips. The fourth activity was educating livestock farmers on strategies for controlling and preventing livestock diseases. The training was held at Beder Camel Dairy Farm and attended by approximately 10 individuals, comprising 3 females and 7 males. The content of the training was three modules: the first was general farm biosecurity, the second was operational biosecurity, and the third was concern for vaccination. Recommendation: We recommend that each farm hire livestock health specialists to easily implement disease prevention steps and promptly solve each new case.<br> We recommend the livestock association, veterinary clinics, and other institutions working on livestock do routine campaigns that facilitate the determination of prevalent diseases and the treatment of those cases</p>
Farming Practices and Systems
<p>The Pesticides (use) database includes data on the use of major pesticide groups (Insecticides, Herbicides, Fungicides, Plant growth regulators and Rodenticides) and of relevant chemical families. Data report the quantities (in tonnes of active ingredients) of pesticides used in or sold to the agricultural sector for crops and seeds. Information on quantities applied to single crops is not available.</p>
Wind tunnel experiment of a micro wind farm model
<p>Simultaneous strain gage measurements of sixty porous disk models, in a scaled wind farm with one hundred models, and for fifty-six different layouts. </p> <p>For detailed information about the experimental setup and wind farm layouts see: </p> <p>Bossuyt, J., Meneveau, C., & Meyers, J. (2018). Effect of layout on asymptotic boundary layer regime in deep wind farms. <em>Physical Review Fluids. See also:</em> https://arxiv.org/abs/1808.09579 .</p> <p>For more information about the experimental design of the porous disk models, see also:</p> <p>Bossuyt, J., Howland, M. F., Meneveau, C., & Meyers, J. (2017). Measurement of unsteady loading and power output variability in a micro wind farm model in a wind tunnel. <em>Experiments in Fluids</em>, <em>58</em>(1), 1. http://doi.org/10.1007/s00348-016-2278-6</p> <p> Bossuyt, J., Meneveau, C., & Meyers, J. (2017). Wind farm power fluctuations and spatial sampling of turbulent boundary layers. <em>Journal of Fluid Mechanics</em>, <em>823</em>, 329-344. http://doi.org/10.1017/jfm.2017.328</p> <p> </p> <p>The data contains matrices 'WF_U', 'x', and 'y', and variable 'fs' for each layout. <br> The matrix 'WF_U' contains the reconstructed velocity signal in m/s measured by each porous disk, and has size ( 20 , 3 , number of time samples), with 20 the number of porous disk rows, and 3 the number of streamwise aligned porous disk columns in the wind farm. Matrices 'x', and 'y' have size (20,3) and contain the locations of each instrumented porous disk in units of disk diameter D = 0.03m. It is important to note that the wind farm has one extra column of non-instrumented porous disk models on each side, for a total of 20x5=100 porous disk models.The variable 'fs' contains the sampling frequency in Hz, at which all 60 porous disks are simultaneously sampled.</p> <p>--------------------------------------------------------<br> Example code to load data in Matlab :<br> --------------------------------------------------------<br> filename = 'U_C1_1.h5';<br> fileID = H5F.open(filename,'H5F_ACC_RDONLY','H5P_DEFAULT');</p> <p>datasetID = H5D.open(fileID,'WF_U');<br> WF_U = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br> H5D.close(datasetID);</p> <p>datasetID = H5D.open(fileID,'fs');<br> fs = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br> H5D.close(datasetID);</p> <p>datasetID = H5D.open(fileID,'x');<br> x = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br> H5D.close(datasetID);</p> <p>datasetID = H5D.open(fileID,'y');<br> y = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br> H5D.close(datasetID);</p> <p>H5F.close(fileID);</p> <p>--------------------------------------------------------<br> Example code to load data in Python:<br> --------------------------------------------------------<br> import h5py<br> filename = 'U_C1_1.h5'<br> f = h5py.File(filename, 'r')</p> <p>U = f['WF_U'][()]<br> x = f['x'][()]<br> y = f['y'][()]<br> fs = f['fs'][0][0]<br> f.close()</p> <p>--------------------------------------------------------<br> Example code to generate figures 15 and 16 of Bossuyt et al. (2018). Effect of layout on asymptotic boundary layer regime in deep wind farms. Physical Review Fluids, in Matlab<br> --------------------------------------------------------<br> WF_cases_selected = 1:7;</p> <p>folder = '/';% folder with files</p> <p>WF_cases_l = {'U_C1';'U_C2';'NU1_C1';'NU1_C2';'NU2_C1';'NU2_C2';'NU2_C3'};% name of layout variations<br> WF_cases_n = [6, 7, 11, 8, 11, 7, 6]; % 'number of layout variations for each case</p> <p>WF_data.x = cell( length(WF_cases_selected) , 1);% x - coordinates of porous disk locations<br> WF_data.y = cell( length(WF_cases_selected) , 1);% y - coordinates of porous disk locations<br> WF_data.shift = cell( length(WF_cases_selected) , 1);% spanwise shift of layout series<br> WF_data.fs = cell( length(WF_cases_selected) , 1);<br> WF_data.WF_Pm = cell( length(WF_cases_selected) , 1);<br> WF_data.WF_Um = cell( length(WF_cases_selected) , 1);<br> WF_data.WF_U_rms = cell( length(WF_cases_selected) , 1);</p> <p><br> for i = 1 : length(WF_cases_selected)<br> <br> WF_data_case = struct;<br> WF_data_case.x = cell( WF_cases_n(i) , 1);<br> WF_data_case.y = cell( WF_cases_n(i) , 1);<br> WF_data_case.shift = cell( WF_cases_n(i) , 1);<br> WF_data_case.fs = cell( WF_cases_n(i) , 1);<br> WF_data_case.WF_Pm = cell( WF_cases_n(i) , 1);<br> WF_data_case.WF_Um = cell( WF_cases_n(i) , 1);<br> WF_data_case.WF_U_rms = cell( WF_cases_n(i) , 1);<br> <br> for j = 1:WF_cases_n(i)<br> clc<br> i<br> j<br> <br> WF_data_var = struct;<br> <br> %read the file<br> filename = [folder WF_cases_l{i} '_' num2str(j) '.h5'];<br> fileID = H5F.open(filename,'H5F_ACC_RDONLY','H5P_DEFAULT');<br> <br> datasetID = H5D.open(fileID,'WF_U');<br> WF_data_var.WF_U = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br> H5D.close(datasetID);<br> <br> datasetID = H5D.open(fileID,'fs');<br> WF_data_case.fs{j} = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br> H5D.close(datasetID);<br> <br> datasetID = H5D.open(fileID,'x');<br> WF_data_case.x{j} = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br> H5D.close(datasetID);<br> <br> datasetID = H5D.open(fileID,'y');<br> WF_data_case.y{j} = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br> H5D.close(datasetID);<br> <br> H5F.close(fileID);<br> <br> WF_data_var.WF_P = WF_data_var.WF_U.^3;</p> <p> % Time averaged power<br> WF_data_case.WF_Pm{j} = mean(WF_data_var.WF_P,3);<br> <br> % normalize by power in first row: Pi/P1<br> WF_data_case.WF_Pm{j} = WF_data_case.WF_Pm{j}./mean(WF_data_case.WF_Pm{j}(1,:));<br> <br> % Time averaged velocity<br> WF_data_case.WF_Um{j} = mean(WF_data_var.WF_U,3);<br> <br> % u_rms --> TI<br> WF_data_case.WF_U_rms{j} = std(WF_data_var.WF_U,[],3);<br> end<br> <br> WF_data.x{i} = WF_data_case.x;<br> WF_data.y{i} = WF_data_case.y;<br> WF_data.fs{i} = WF_data_case.fs;<br> WF_data.WF_Pm{i} = WF_data_case.WF_Pm;<br> WF_data.WF_Um{i} = WF_data_case.WF_Um;<br> WF_data.WF_U_rms{i} = WF_data_case.WF_U_rms;<br> <br> %determine spanwise shift for plot legends<br> tmp1 = WF_data.y{i}{j-1};<br> tmp2 = WF_data.y{i}{j};<br> dy = diff( [tmp1(:,1) tmp2(:,1)] ,1,2);<br> dy = max(dy(abs(dy)>0));<br> WF_data.shift{i} = 0:dy:(WF_cases_n(i)-1)*dy;<br> <br> end</p> <p>%%<br> line_tick = {'o-','*-','+-','d-','s-','^-','v-','<-','>-','p-','h-'};<br> line_color = [51,160,44; 141,211,199; 31,120,180; 106,61,154; 227,26,28; 177,89,40; 255,127,0; 166,206,227]./255;</p> <p>legend_items = cell(size(WF_cases_selected));<br> for i = 1:length(legend_items)<br> legend_items{i} = strrep(WF_cases_l{i},'_','-');<br> end</p> <p>%% average power entire farm<br> row_start = 1;<br> row_end = 19;<br> f1 = figure;<br> set(gcf,'paperposition',[0,0,8.4,4.9])<br> hold on</p> <p>for i = 1 : length(WF_cases_selected)<br> tmp_P = zeros(size(WF_data.shift{i}));<br> for j = 1:WF_cases_n(i)<br> tmp_P(j) = mean(mean( WF_data.WF_Pm{i}{j}(row_start:row_end,:)));<br> end<br> plot( WF_data.shift{i} , tmp_P, line_tick{i} ,'Color', line_color(i,:) ,'MarkerFaceColor', line_color(i,:) )<br> end</p> <p>% manualy plot errorbars<br> for i = 1:length(WF_cases_selected)<br> tmp_P = zeros(size(WF_data.shift{i}));<br> for j = 1:WF_cases_n(i)<br> tmp_P(j) = mean(mean( WF_data.WF_Pm{i}{j}(row_start:row_end,:)));<br> end<br> px = WF_data.shift{i} ;<br> py = tmp_P;<br> pw = 0.05;<br> pe = zeros(size(px))+0.01;%for uncertainty value see Bossuyt et al. (2018) Physical Review Fluids. <br> for j = 1:WF_cases_n(i)<br> plot( [px(j)-pw/2 px(j)+pw/2] , [py(j)+pe(j) py(j)+pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)<br> plot( [px(j)-pw/2 px(j)+pw/2] , [py(j)-pe(j) py(j)-pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)<br> plot( [px(j) px(j)],[py(j)-pe(j) py(j)+pe(j)],':', 'Color', line_color(i,:),'LineWidth',0.5)<br> end<br> end<br> xlabel('\Delta_y [D]')<br> ylabel('$\langle P_i /P_1\rangle_{1}^{19}$','Interpreter','Latex')<br> box('on')<br> ylim([0.35 0.66])<br> xlim([-0.1 2.6])<br> legend1 = legend(legend_items');<br> set(legend1,'Location','southeast');<br> print(f1, 'WF_Pm_all','-dpng','-r300')</p> <p>%% average power end of farm<br> row_start = 16;<br> row_end = 19;<br> f2 = figure;<br> set(gcf,'paperposition',[0,0,8.4,4.9])<br> hold on</p> <p>for i = 1 : length(WF_cases_selected)<br> tmp_P = zeros(size(WF_data.shift{i}));<br> for j = 1:WF_cases_n(i)<br> tmp_P(j) = mean(mean( WF_data.WF_Pm{i}{j}(row_start:row_end,:)));<br> end<br> plot( WF_data.shift{i} , tmp_P, line_tick{i} ,'Color', line_color(i,:) ,'MarkerFaceColor', line_color(i,:) )<br> end</p> <p>% manualy plot errorbars<br> for i = 1:length(WF_cases_selected)<br> tmp_P = zeros(size(WF_data.shift{i}));<br> for j = 1:WF_cases_n(i)<br> tmp_P(j) = mean(mean( WF_data.WF_Pm{i}{j}(row_start:row_end,:)));<br> end<br> px = WF_data.shift{i} ;<br> py = tmp_P;<br> pw = 0.05;<br> pe = zeros(size(px))+0.02; %for uncertainty value see Bossuyt et al. (2018) Physical Review Fluids. <br> for j = 1:WF_cases_n(i)<br> plot( [px(j)-pw/2 px(j)+pw/2] , [py(j)+pe(j) py(j)+pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)<br> plot( [px(j)-pw/2 px(j)+pw/2] , [py(j)-pe(j) py(j)-pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)<br> plot( [px(j) px(j)],[py(j)-pe(j) py(j)+pe(j)],':', 'Color', line_color(i,:),'LineWidth',0.5)<br> end<br> end<br> xlabel('\Delta_y [D]')<br> ylabel('$\langle P_i /P_1\rangle_{16}^{19}$','Interpreter','Latex')<br> box('on')<br> ylim([0.27 0.52])<br> xlim([-0.1 2.6])<br> legend1 = legend(legend_items');<br> set(legend1,'Location','southeast');<br> print(f2, 'WF_Pm_end', '-dpng','-r300')</p> <p>%% plot average unsteady loading total farm<br> row_start = 1;<br> row_end = 19;<br> f3 = figure;<br> set(gcf,'paperposition',[0,0,8.4,4.9])<br> hold on<br> for i = 1 : length(WF_cases_selected)<br> tmp_TI = zeros(size(WF_data.shift{i}));<br> for j = 1:WF_cases_n(i)<br> tmp_TI(j) = mean(mean(WF_data.WF_U_rms{i}{j}(row_start:row_end,:)./WF_data.WF_Um{i}{j}(row_start:row_end,:)))*100;<br> end<br> plot( WF_data.shift{i} , tmp_TI , line_tick{i} ,'Color', line_color(i,:) ,'MarkerFaceColor', line_color(i,:))<br> end</p> <p>% manualy plot errorbars<br> for i = 1:length(WF_cases_selected)<br> tmp_TI = zeros(size(WF_data.shift{i}));<br> for j = 1:WF_cases_n(i)<br> tmp_TI(j) = mean(mean(WF_data.WF_U_rms{i}{j}(row_start:row_end,:)./WF_data.WF_Um{i}{j}(row_start:row_end,:)))*100;<br> end<br> px = WF_data.shift{i} ;<br> py = tmp_TI;<br> pw = 0.05;<br> pe = zeros(size(px))+ 0.004*tmp_TI;%for uncertainty value see Bossuyt et al. (2018) Physical Review Fluids. <br> for j = 1:WF_cases_n(i)<br> plot( [px(j)-pw/2 px(j)+pw/2] , [py(j)+pe(j) py(j)+pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)<br> plot( [px(j)-pw/2 px(j)+pw/2] , [py(j)-pe(j) py(j)-pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)<br> plot( [px(j) px(j)],[py(j)-pe(j) py(j)+pe(j)],':', 'Color', line_color(i,:),'LineWidth',0.5)<br> end<br> end<br> xlabel('\Delta_y [D]')<br> ylabel('$ \langle TI \rangle_{1}^{19} [\%]$','Interpreter','Latex')<br> box('on')<br> xlim([-0.1 2.6])<br> legend1 = legend(legend_items');<br> set(legend1,'Location','northeast');<br> print(f3, 'WF_TI_all','-dpng','-r300')</p> <p>%% plot average unsteady loading end of farm<br> row_start = 16;<br> row_end = 19;<br> f4 = figure;<br> set(gcf,'paperposition',[0,0,8.4,4.9])<br> hold on<br> for i = 1 : length(WF_cases_selected)<br> tmp_TI = zeros(size(WF_data.shift{i}));<br> for j = 1:WF_cases_n(i)<br> tmp_TI(j) = mean(mean(WF_data.WF_U_rms{i}{j}(row_start:row_end,:)./WF_data.WF_Um{i}{j}(row_start:row_end,:)))*100;<br> end<br> plot( WF_data.shift{i} , tmp_TI , line_tick{i} ,'Color', line_color(i,:) ,'MarkerFaceColor', line_color(i,:))<br> end</p> <p>% manualy plot errorbars<br> for i = 1:length(WF_cases_selected)<br> tmp_TI = zeros(size(WF_data.shift{i}));<br> for j = 1:WF_cases_n(i)<br> tmp_TI(j) = mean(mean(WF_data.WF_U_rms{i}{j}(row_start:row_end,:)./WF_data.WF_Um{i}{j}(row_start:row_end,:)))*100;<br> end<br> px = WF_data.shift{i} ;<br> py = tmp_TI;<br> pw = 0.05;<br> pe = zeros(size(px))+ 0.01*tmp_TI;%for uncertainty value see Bossuyt et al. (2018) Physical Review Fluids. <br> for j = 1:WF_cases_n(i)<br> plot( [px(j)-pw/2 px(j)+pw/2] , [py(j)+pe(j) py(j)+pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)<br> plot( [px(j)-pw/2 px(j)+pw/2] , [py(j)-pe(j) py(j)-pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)<br> plot( [px(j) px(j)],[py(j)-pe(j) py(j)+pe(j)],':', 'Color', line_color(i,:),'LineWidth',0.5)<br> end<br> end<br> xlabel('\Delta_y [D]')<br> ylabel('$ \langle TI \rangle_{16}^{19} [\%]$','Interpreter','Latex')<br> box('on')<br> xlim([-0.1 2.6])<br> legend1 = legend(legend_items');<br> set(legend1,'Location','northeast');<br> print(f4, 'WF_TI_end','-dpng','-r300')</p> <p> </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.