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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>
National Checklists 2017: Benin Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>A list of species from Benin collected using effechecka and geonames polygons
National Checklists 2019: Benin Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>A list of species from Benin collected using effechecka and geonames polygons
qdgc Benin
<p>QDGC tables delivered in geopackage file<br> - - - - - - - - - - - - - - - - - - - - - -<br> QDGC represents a way of making (almost) equal area squares covering a specific area to represent specific qualities of the area covered. The squares themselves are based on the degree squares covering earth. Around the equator we have 360 longitudinal lines , and from the north to the south pole we have 180 latitudinal lines. Together this gives us 64800 segments or tiles covering earth.<br> <br> <br> <br> <br> Within each geopackage file you will find a number of tables with these names:<br> <br> <br> <br> <br> -tbl_qdgc_01<br> -tbl_qdgc_02<br> -tbl_qdgc_03<br> -tbl_qdgc_04<br> -tbl_qdgc_05<br> -etc<br> <br> <br> <br> <br> The attributes for each table are:<br> <br> <br> <br> <br> qdgc Unique Quarter Degree Grid Cell reference string<br> area_reference Country<br> level_qdgc QDGC level<br> cellsize degrees decimal degree for the longitudal and latitudal length of the cell<br> lon_center Longitude center of the cell<br> lat_center Latitudal center of the cell<br> area_km2 Calculated area for the cell<br> geom Geometry<br> <br> <br> <br> <br> Metadata<br> --------<br> Geodata GCS_WGS_1984<br> Datum: D_WGS_1984<br> Prime Meridian: 0<br> <br> <br> <br> <br> Areas are calculated with different versions of Albers Equal Area Conic using the PostGIS function st_area. For the African continent I have used Africa Albers Equal Area Conic which will look like this:<br> - st_area(st_transform(geom, 102022))/1000000)<br> <br> <br> <br> <br> Licensing<br> ---------<br> Creative Commons Attribution 4.0 International<br> <br> <br> <br> <br> Conditions<br> ----------<br> Delivered to the user as-is. No guarantees. If you find errors, please tell me and I will try to fix it. Suggestions for improvements can be addressed to the github repository: https://github.com/ragnvald/qdgc<br> <br> <br> <br> <br> Thankyou<br> --------<br> The work has over the years been supported and received advice and moral support from many organisations and stakeholders. Here are some of them:<br> - Tanzania Wildlife Research Institute<br> - Dept of Biology, NTNU, Norway<br> - Norwegian Environment Agency<br> - Eivin Røskaft, Steven Prager, Howard Frederick, Julian Blanc, Honori Maliti, Paul Ramsey<br> <br> <br> <br> <br> References<br> ----------<br> * http://en.wikipedia.org/wiki/QDGC<br> * http://www.mindland.com/wp/projects/quarter-degree-grid-cells/about-qdgc/<br> * http://en.wikipedia.org/wiki/Lambert_azimuthal_equal-area_projection<br> * http://www.safe.com<br> <br> <br> <br> <br> <br> <br> <br> <br> Ragnvald Larsen<br> Trondheim 21th of January, 2021<br> <br> <br> <br> <br> ragnvald@mindland.com<br> www.mindland.com</p>
Infrastructure Climate Resilience Assessment Data Starter Kit for Benin
<p> This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems. </p> <p> These extracts are derived from global datasets which have been clipped to the national scale (or subnational, in cases where national boundaries have been split, generally to separate outlying islands or non-contiguous regions), using Natural Earth (2023) boundaries, and is not meant to express an opinion about borders, territory or sovereignty. </p> <p> Human-induced climate change is increasing the frequency and severity of climate and weather extremes. This is causing widespread, adverse impacts to societies, economies and infrastructures. Climate risk analysis is essential to inform policy decisions aimed at reducing risk. Yet, access to data is often a barrier, particularly in low and middle-income countries. Data are often scattered, hard to find, in formats that are difficult to use or requiring considerable technical expertise. Nevertheless, there are global, open datasets which provide some information about climate hazards, society, infrastructure and the economy. This "data starter kit" aims to kickstart the process and act as a starting point for further model development and scenario analysis. </p> <p>Hazards:</p> <ul> <li>coastal and river flooding (Ward et al, 2020)</li> <li>extreme heat and drought (Russell et al 2023, derived from Lange et al, 2020)</li> <li>tropical cyclone wind speeds (Russell 2022, derived from Bloemendaal et al 2020 and Bloemendaal et al 2022)</li> </ul> <p>Exposure:</p> <ul> <li>population (Schiavina et al, 2023)</li> <li>built-up area (Pesaresi et al, 2023)</li> <li>roads (OpenStreetMap, 2023)</li> <li>railways (OpenStreetMap, 2023)</li> <li>power plants (Global Energy Observatory et al, 2018)</li> <li>power transmission lines (Arderne et al, 2020)</li> </ul> <p> The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people. </p> <p> To learn more about related concepts, there is a free short course available through the Open University on <a href="https://www.open.edu/openlearncreate/course/view.php?id=11539">Infrastructure and Climate Resilience</a>. This <a href="https://opsis.eci.ox.ac.uk/courses/2-infra-for-resil/">overview of the course</a> has more details. </p> <p> These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow: </p> <ul> <li> <a href="https://github.com/tomalrussell/snkit"><code>snkit</code></a> helps clean network data </li> <li> <a href="https://github.com/nismod/snail"><code>nismod-snail</code></a> is designed to help implement infrastructure exposure, damage and risk calculations </li> </ul> <p> The <a href="https://github.com/nismod/open-gira"><code>open-gira</code></a> repository contains a larger workflow for global-scale open-data infrastructure risk and resilience analysis. </p> <p> For a more developed example, some of these datasets were key inputs to a regional climate risk assessment of current and future flooding risks to transport networks in East Africa, which has a related online visualisation tool at <a href="https://east-africa.infrastructureresilience.org/">https://east-africa.infrastructureresilience.org/</a> and is described in detail in Hickford et al (2023). </p> <p><strong>References</strong></p> <ul> <li> Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the global power system using open data [Dataset]. In Nature Scientific Data (1.1.1, Vol. 7, Number Article 19). Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.3628142">10.5281/zenodo.3628142</a> </li> <li> Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2020): STORM tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/12705164.v3">10.4121/12705164.v3</a> </li> <li> Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan); Martinez, Andrew B.; et al. (2022): STORM climate change tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/14510817.v3">10.4121/14510817.v3</a> </li> <li> Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. (2018) Global Power Plant Database. Published on Resource Watch and Google Earth Engine; <a href="http://resourcewatch.org/">resourcewatch.org/</a> </li> <li> Hickford et al (2023) Decision support systems for resilient strategic transport networks in low-income countries – Final Report. Available online: <a href="https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries">https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries</a> </li> <li> Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI: <a href="https://doi.org/10.1029/2020EF001616">10.1029/2020EF001616</a> </li> <li> Natural Earth (2023) Admin 0 Map Units, v5.1.1. [Dataset] Available online: <a href="https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details/">www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details</a> </li> <li> OpenStreetMap contributors, Russell T., Thomas F., nismod/datapkg contributors (2023) Road and Rail networks derived from OpenStreetMap. [Dataset] Available at <a href="https://global.infrastructureresilience.org">global.infrastructureresilience.org</a> </li> <li> Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030) European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea">data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea</a>, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA </li> <li> Russell, T., Nicholas, C., & Bernhofen, M. (2023). Annual probability of extreme heat and drought events, derived from Lange et al 2020 (Version 2) [Dataset]. Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.8147088">10.5281/zenodo.8147088</a> </li> <li> Schiavina M., Freire S., Carioli A., MacManus K. (2023): GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe">data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe</a>, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE </li> <li> Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, et al. (2020) Aqueduct Floods Methodology. Technical Note. Washington, D.C.: World Resources Institute. Available online at: <a href="https://www.wri.org/publication/aqueduct-floods-methodology">www.wri.org/publication/aqueduct-floods-methodology</a>. </li> </ul>
Transport Starter Data Kit: Historical socio-transport data for Benin
<p>This Transport Starter Data Kit contains historical annual data (1990–2021) on passenger and freight activity, segregated by mode and fuel. Additionally, historical data on energy intensities, load factors, vehicle stock, population (total, urban, rural, growth), and GDP (total, agriculture, construction, mining, manufacturing, service, energy, growth) are included in the kit, within the 'Data' tab. The historical data can be used as a foundation for transport-energy modelling and/or to identify areas of improvement. This data was verified through consultation with relevant stakeholders before publishing. The definition used for each vehicle mode is found in the 'Definitions' tab, and the description of each data observation status is found in the 'Notes' tab. All data sources are linked where possible.</p>
National Checklists: Benin Species List
Data from: GBIF.org (23 January 2025) GBIF Occurrence Download <a href="https://doi.org/10.15468/dl.vd2ajk" target="_blank" rel="noopener">https://doi.org/10.15468/dl.vd2ajk</a>
Fig. 3 in Efficacy of Actellic 300 CS-based indoor residual spraying on key entomological indicators of malaria transmission in Alibori and Donga, two regions of northern Benin
Fig. 3 Seasonal variation of sibling species (An. coluzzii and An. gambiae) in the study area. Abbreviations: DS, dry season; RS, rainy season
Fig. 2 in Efficacy of Actellic 300 CS-based indoor residual spraying on key entomological indicators of malaria transmission in Alibori and Donga, two regions of northern Benin
Fig. 2 Mortality rate of Anopheles gambiae Kisumu (laboratory susceptible strain) after 30 min exposure to cement and mud walls treated with pirimiphos-methyl in 2017 (a) and 2018 (b). The red line indicates the WHO efficacy threshold (mortality of 80%) of an insecticide
Figure 13 in Spatial and temporal variations of fish communities in the longitudinal gradient of the Mono River (Benin and Togo: West Africa)
Figure 13. – Indicator species for each cluster of the dendrogram resulting from the self-organizing map procedure (n = 10). IndVal values (in %) are shown in brackets. Shown indicator values (p <0.05) are only those greater than 25%.
Figure 2 in Spatial and temporal variations of fish communities in the longitudinal gradient of the Mono River (Benin and Togo: West Africa)
Figure 2. – Variation (average and standard deviation) in species richness by sampling sites. Site order follows the upstream-downstream gradient.
Figure 12 in Spatial and temporal variations of fish communities in the longitudinal gradient of the Mono River (Benin and Togo: West Africa)
Figure 12. – Correlation circle of the environmental variable, which discriminate clusters (n = 3) defined by the self-organizing map for stations in the F1 x F2 design for the factorial discriminant analysis. Dis, distance from source; FoBa, forest area; CaHi, canopy height; Vol, flow velocity; Cond, conductivity; Trans, water transparency; Alt, altitude.
Figure 7 in Spatial and temporal variations of fish communities in the longitudinal gradient of the Mono River (Benin and Togo: West Africa)
Figure 7. – Hierarchical classification of the nodes of the Kohonen map based on the species richness of the sites (n = 10). A: Self-organizing map (SOM) (20 nodes); B: Hierarchical clustering of the SOM nodes with a Ward linkage method and a Euclidean distance: the numbers (i.e. ranging from 1 to 20) correspond to those assigned on each node of the SOM.
Fig. 3 in A new species of Quadrivisio (Amphipoda, Maeridae) from coastal tropical lagoons (Benin, West Africa)
Fig. 3. Quadrivisio laleyei sp. nov. A., C. Holotype, ♂, 7.5 mm, MNHN IU-2017-209, gnathopod. B., D. Paratype, ♀, 7.0 mm, MNHN IU-2017-211, gnathopod.
Fig. 2 in A new species of Quadrivisio (Amphipoda, Maeridae) from coastal tropical lagoons (Benin, West Africa)
Fig. 2. Quadrivisio laleyei sp. nov., ♂, holotype, 7.5 mm, MNHN IU-2017-209. A. Habitus (scale 1). B. Lower lip (scale 3). C. First antenna (scale 2). D. Second antenna (scale 2). E. Left mandible (scale 4). F. Maxilliped palp (scale 3).
Fig. 5 in A new species of Quadrivisio (Amphipoda, Maeridae) from coastal tropical lagoons (Benin, West Africa)
Fig. 5. Quadrivisio laleyi sp. nov. A–D., F. Holotype, ♂, 7.5 mm, MNHN IU-2017-209. E. Paratype, ♀, 7.0 mm, MNHN IU-2017-211. A. Male uropod 1 (scale 1). B. Male uropod 2 (scale 1). C. Male urosome (scale 1). D. Male uropod 3 (scale 1). E. Female uropod 3 (scale 1). F. Male telson (scale 2).
Fig. 4 in A new species of Quadrivisio (Amphipoda, Maeridae) from coastal tropical lagoons (Benin, West Africa)
Fig. 4. Quadrivisio laleyi sp. nov. A–B., D–E. Holotype, ♂, 7.5 mm, MNHN IU-2017-209. C. Paratype, ♂, 7.6 mm, MNHN IU-2017-210. A. Pereopod 3. B. Pereopod 4. C. Pereopod 5. D. Pereopod 6. E. Pereopod 7.
Fig. 1 in Severe coenurosis caused by larvae of Taenia serialis in an olive baboon (Papio anubis) in Benin
Fig. 1. Clinical presentation of the baboon with the clear presence of swellings in various areas of the body: ventral abdominal and thoracic parts, inner part of forearms, intermandibular region (arrow heads) and dorsal region also.
Fig 5 in Monitoring Spodoptera frugiperda in Benin: assessing the influence of trap type, pheromone blends, and habitat on pheromone trapping
Fig 5. Phylogenetic tree based on a portion of the COI barcoding segment showing the relationships of selected non-target moth specimens (g54xxx) isolated from fall armyworm pheromone traps relative to selected GenBank sequences. GenBank sequences are indicated by species name followed by accession number. Fall armyworm R-strain and fall armyworm C-strain are consensus sequences for the 2 fall armyworm host strains.
Fig 3 in Monitoring Spodoptera frugiperda in Benin: assessing the influence of trap type, pheromone blends, and habitat on pheromone trapping
Fig 3. Field screening of home-made trap design (Jar2 and Jar4) in comparison to Unitrap model using pheromone lures (all combined) over 2 maize cropping systems (maize monoculture and maize-cowpea intercrops) during the second planting season. The traps were installed on 30 Sep 2019 during the second maize growing season, and the moth collection period covered Oct to Dec. The data denotes average numbers per trap type for overall 3-d intervals moth collections with standard errors.
ScienceDex guides
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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.