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12,237 results for “2019”
Molino San Vincenzo US 1900/S5/2018 (2019)
Source: Objaverse 1.0 / Sketchfab
Calatrava La Vieja 2019 - Optimized
3D model of the south-east sector of the medina de Calatrava la Vieja, in which were discovered the remains of several houses from the Islamic and Christian periods, with continued occupation between the 10th and 15th centuries. In the southwestern corner, a large landfill from the Almohad period (1195-1212 CE) has also been documented, which intentionally filled in the old Islamic defensive moat that stood between the alcázar and the medina. The 3D model shows the appearance of the whole excavated area of the medina at the end of 2019 campaign. ID CALATRAVA LA VIEJA 2019 FINAL EXCAVATION Hervás Herrera, Miguel Ángel (2016): Calatrava la Vieja. Conservación y Restauración (1975-2010), Universidad de Castilla-La Mancha, Repositorio Digital RUIDERA, Albacete, 2016, 958 págs. URI: http://hdl.handle.net/10578/8711. 882 Photos P4 Pro V2. Reality Capture Source: Objaverse 1.0 / Sketchfab
TARP 2019: SU 1044
Photogrammetric capture of stratigraphic unit 1044 Source: Objaverse 1.0 / Sketchfab
Sagalassos 2019 Restored Arch
2019 excavations The data were collected by Global Digital Heritage in August 2019, as part of the University of Leuven Sagalassos Archaeological Research Project and supported by the Global Heritage Fund. Source: Objaverse 1.0 / Sketchfab
TARP 2019: SU 2059 - SU 2061
Photogrammetric capture of stratigraphic units 2059-2061 Source: Objaverse 1.0 / Sketchfab
UFLUX 100m half-yearly carbon, water, and energy fluxes in Europe in 2019
<div> <div> <h3>UFLUX Ensemble Europe100m6monthly (European 100 6-monthly) in 2019</h3> <p><strong>Overview</strong><br>The <strong>UFLUX ensemble dataset</strong> offers <strong>European fluxes at 100 m spatial resolution</strong>, generated using <strong>Deep Forest machine learning models</strong>. It integrates <strong>satellite-based Sentinel-2 vegetation proxies NIRv</strong> with <strong>ERA5 climate reanalysis</strong>, and is trained against <strong>ICOS eddy covariance observations</strong>. The UFLUX project includes five core flux components:</p> <ul> <li> <p>Gross Primary Production (<strong>GPP</strong>)</p> </li> <li> <p>Ecosystem Respiration (<strong>RECO</strong>)</p> </li> <li> <p>Net Ecosystem Exchange (<strong>NEE</strong>)</p> </li> <li> <p>Sensible Heat Flux (<strong>H</strong>)</p> </li> <li> <p>Latent Energy Flux (<strong>LE</strong>)</p> </li> </ul> <p><strong>Background and Methodology</strong><br>The <strong>Unified FLUXes (UFLUX)</strong> initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.</p> <p>Key innovations of UFLUX include:</p> <ol> <li> <p><strong>Consistent Upscaling Framework</strong>: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.</p> </li> <li> <p><strong>Hybrid Explainable ML</strong>: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).</p> </li> <li> <p><strong>Uncertainty Quantification</strong>: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.</p> </li> <li> <p><strong>Multisource Integration</strong>: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.</p> </li> <li> <p><strong>Superior Gap-Filling</strong>: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.</p> </li> <li> <p><strong>High Performance</strong>: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.</p> </li> <li> <p><strong>Community Adoption</strong>: Already used by other global upscaling projects, highlighting its reliability and impact.</p> </li> </ol> <p><strong>Applications</strong><br>UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.</p> <p><strong>Resources</strong></p> <ul> <li><strong>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></strong></li> <li> <p><strong>Code Repository</strong>: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a></p> </li> <li> <p><strong>Technical & Descriptive Publication</strong>: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a></p> </li> </ul> </div> </div>
Deaths and work related occupational injuries detailed at the country, gender, and NACE Rev.2 sector levels from 2008 to 2019
<p>Ensuring social data's reliability is essential in accurately evaluating social and economic impacts across geographical locations, economic sectors and stakeholder categories. Yet, the MRIO model utilized in our research (EXIOBASE) was hindered by out-of-date or significantly proxy fatality statistics, causing potential inaccuracies in our findings. We have comprehensively revised EXIOBASE fatality data to address this shortcoming, incorporating detailed, nation-specific, and up-to-date data. The update includes work-related fatal occupational injuries as well as fatalities associated with occupational exposure to a variety of 17 hazardous substances and conditions such as asbestos, arsenic, benzene, beryllium, cadmium, chromium, diesel engine exhaust, formaldehyde, nickel, polycyclic aromatic hydrocarbons, silica, sulfuric acid, trichloroethylene, asthmagens, particulate matter, gases and fumes, noise and ergonomic factors. Our methodological process is built on three pillars: data acquisition, raw data processing, and computation of fatal injuries by country, gender, year, and EXIOBASE economic sector.</p> <p>Data were sourced from the World Health Organization (WHO) (Pega et al., 2021) and Eurostat databases (Publications Office of the European Union, 2013). The WHO data was carefully screened based on specific criteria such as age above 15 years, gender, and fatal injuries only. Eurostat data provided granular information on work-related fatalities, classified by economic activities in the European Community (or NACE Rev.2 (Eurostat, 2008)). The WHO provided aggregate fatality data for 2010 and 2016. The strategy for allocating these deaths across Eurostat categories depended on the countries' geographical location, with different methods applied to European and non-European nations.</p> <p>For European nations, fluctuations in fatality numbers within a NACE Rev.2 sector mirrored the changes registered by Eurostat. For non-European countries, fatality figures were proportionally allocated across economic sectors split according to the NACE Rev.2 classification, reflecting the workforce size associated with each economic sector. Due to the scarcity of data for nations within Asia, America, or Africa, we adopted a regional approach, computing fatality ratios over each NACE Rev.2 category for each region by integrating data for available countries over a reference year. For 2010 and 2016, the aggregate fatality figures for nations within these three zones were established. Due to the temporal proximity of both reference years, we postulated a linear trend in the fatality count between these two years. The number of fatalities for a specific country, year, and per NACE Rev.2 activity was then calculated by applying the previously mentioned fatality ratio to the total number of deaths for that nation. Last, we applied the European annual ratios to their total mortality figures for the few countries that could not be classified as European or belonging to one of the aforementioned zones.</p> <p>The result is a comprehensive database that includes the number of fatalities (expressed in the number of deaths for work-related fatal occupational injuries and in Disability-adjusted life years (DALYs), for fatalities associated with occupational exposure to a specific risk factor), detailed at the country, gender, and NACE Rev.2 sector levels from 2008 to 2019, providing insights into work-related fatal injuries across different health effects and geographical regions.</p> <p> </p> <p><strong>Nomenclature</strong></p> <p>Archives:</p> <ul> <li><em>Concordance_ISIC_Exiobase.xlsx : </em>Concordance between the International Standard Industrial Classification (ISIC) and the exiobase sectors</li> <li><em>Concordance_ISO3_EXIO3.xlsx - </em>Concordance between the ISO3 code and the Exiobase regions</li> <li><em>Workforce_by_ISO3.csv - </em>Number of active persons per Country (ISO3 code), per Statistical Classification of Economic Activities in the European Community (NACE), Sex, Year (from 1991 to 2021)</li> <li><em>Workforce_by_EXIO3.csv - </em>Number of active persons per Exiobase region (EXIO3 code), per Statistical Classification of Economic Activities in the European Community (NACE), Sex, Year (from 1991 to 2021)</li> <li><em>Death_ISO3.csv - </em>Number of death per Country (ISO3 code), per Statistical Classification of Economic Activities in the European Community (NACE), Sex, Estimate (point, lower, upper), Year (from 2009 to 2019)</li> <li><em>Death_EXIO3.csv - </em>Number of death per Exiobase Region (EXIO3 code), per Statistical Classification of Economic Activities in the European Community (NACE), Sex, Estimate (point, lower, upper), Year (from 2009 to 2019)</li> <li><em>Death_EXIO3_region_exiobase_sector.csv - </em>Number of death per Exiobase Region (EXIO3 code), exiobase sector, Sex, Estimate (point, lower, upper), Year (from 2009 to 2019)</li> <li>Injuries_ISO3.zip - Archive of DALY per Country (ISO3 code), Exiobase Sector, Sex, Estimate (point, lower, upper), Type of Exposure, Year (from 2009 to 2019)</li> <li>Injuries_EXIO3.zip - Archive of DALY per Exiobase Region (EXIO3 code), Exiobase Sector, Sex, Estimate (point, lower, upper), Type of Exposure, Year (from 2009 to 2019)</li> <li><em>Workforce_EXIO3_sector_exiobase.xlsx - </em>Number of active persons per Exiobase region (EXIO3 code), per exiobase sector, Sex, Year (from 1991 to 2021).<br>This file is available here : Berthet, Etienne and Lavalley, Julien and Anquetil-Deck, Candy and Ballesteros, Fernanda and Stadler, Konstantin and Soytas, Ugur and Hauschild, Michael and Laurent, Alexis, Assessing the Social and Environmental Impacts of Critical Mineral Supply Chains for the Energy Transition in Europe. Available at SSRN: <a href="https://ssrn.com/abstract=4610350" target="_blank" rel="noopener noreferrer">https://ssrn.com/abstract=4610350</a> or <a href="http://dx.doi.org/10.2139/ssrn.4610350" target="_blank" rel="noopener noreferrer">http://dx.doi.org/10.2139/ssrn.4610350</a> </li> </ul> <p><strong>Content of Injuries_*.zip:</strong></p> <ul> <li>arsenic_*.csv</li> <li>asbestos_*.csv</li> <li>asthmagens_*.csv</li> <li>benzene_*.csv</li> <li>beryllium_*.csv</li> <li>cadmium_*.csv</li> <li>chromium_*.csv</li> <li>diesel_*.csv</li> <li>ergonomic_*.csv</li> <li>formaldehyde_*.csv</li> <li>gases_*.csv</li> <li>nickel_*.csv</li> <li>noise_*.csv</li> <li>polycyclic_*.csv</li> <li>silica_*.csv</li> <li>sulfuric_*.csv</li> <li>trichloro_*.csv</li> </ul>
Global Photovoltaic Solar Panel Dataset from 2019 to 2022
<p>Using Google Earth imagery and 2019-2022 Sentinel-2 datasets, we developed a two-stage classification framework to obtain the annual global dataset of solar photovoltaic panels at 20-meter resolution from 2019 to 2022.</p> <p>To classify the global solar photovoltaic panels, we applied the global zoning method of IPCC AR6 WGI to define the main-zoning, and used 4 degree × 4 degree grids to create the sub-zoning. Then, we extracted the solar photovoltaic panels in each sub-zoning, and stored the result data in TIFF format.</p> <p>The number of each file corresponds to the ID in the attribute table of the sub-zoning-ID file, and users can download and use the corresponding file based on the sub-zoning-ID. After the ID, 2019, 2020, 2021, and 2022 respectively represent four years.</p> <p>The folder of the annual global PV dataset is named after the year, and each file is named as "sub zoning ID_year".</p>
FIGURE 14. Chisosa caquetio Huber, 2019 in Notes on Chisosa (Araneae, Pholcidae), with the description of a new species from Mexico
FIGURE 14. Chisosa caquetio Huber, 2019; male from Landhuis San Pedro (ZFMK Ar 24207), female from Kueba di Brua (ZFMK Cur43). A, B. Tips of female right tarsi 1 and 2, prolateral views. C. Tip of male right tarsus 4, prolateral view. D. Tarsal organ and slit sensillum on female right tarsus 1. E. Tarsal organ and slit sensillum on male right tarsus 2. F. Tarsal organ and slit sensillum on female right tarsus 3. Scale lines: 10 µm (A–C, F); 2 µm (D, E).
FIGURE 11. Chisosa caquetio Huber, 2019 in Notes on Chisosa (Araneae, Pholcidae), with the description of a new species from Mexico
FIGURE 11. Chisosa caquetio Huber, 2019; male from Landhuis San Pedro (ZFMK Ar 24207), female from Kueba di Brua (ZFMK Cur43). A. Female prosoma, frontal dorsal view. B. Male gonopore. C. Male anterior lateral spinneret (ALS). D. Female ALS and posterior median spinnerets (PMS). E. Cone-shaped processes on left male chelicera, frontal view. F. Stridulatory file on left male chelicera. Scale lines: 100 µm (A); 10 µm (B, D–F); 2 µm (C).
FIGURE 13. Chisosa caquetio Huber, 2019 in Notes on Chisosa (Araneae, Pholcidae), with the description of a new species from Mexico
FIGURE 13. Chisosa caquetio Huber, 2019; male from Landhuis San Pedro (ZFMK Ar 24207), female from Kueba di Brua (ZFMK Cur43). A. Male tibia 1, showing trichobothrium and sexually dimorphic 'vertical' hairs. B. Sexually monomorphic short vertical hair (putative chemoreceptor). C. Cuticular plate (arrow) on female left metatarsus 3. D. Trichobothrium on female metatarsus 4. E. Slender metatarsal hair on male left metatarsus 4, retrolateral view. F. Slender metatarsal hairs (arrows) on female right metatarsus 3, prolateral view. G. Slender metatarsal hairs (arrows) on female left metatarsus 4, retrolateral view. H. Distinctively widened tip of slender metatarsal hair on female left metatarsus 4. Scale lines: 10 µm (A, D–G); 1 µm (B); 2 µm (C, H).
FIGURE 12. Chisosa caquetio Huber, 2019 in Notes on Chisosa (Araneae, Pholcidae), with the description of a new species from Mexico
FIGURE 12. Chisosa caquetio Huber, 2019; male from Landhuis San Pedro (ZFMK Ar 24207), female from Kueba di Brua (ZFMK Cur43). A. Male palpal tarsal organ. B. Male bulbal processes, showing sperm duct opening (arrow) and possible glandular pores. C. Male bulbal processes; arrow: sperm duct opening. D. Right female chelicera, showing absence of stidulatory ridges. E. Tip of female left palpal tarsus. F. Left female palp. Scale lines: 2 µm (A); 10 µm (B, C, E, F); 20 µm (D).
FIGURE 15. Chisosa caquetio Huber, 2019 in Notes on Chisosa (Araneae, Pholcidae), with the description of a new species from Mexico
FIGURE 15. Chisosa caquetio Huber, 2019; two females from Kueba di Brua; ZFMK Cur43. A, B. Abdomens, ventral views. C, E. Cleared genitalia, ventral views. D, F. Cleared genitalia, dorsal views. Abbreviations: aep, anterior (main) epigynal plate; pep, posterior epigynal plate; pp, pore plate. Scale lines: 0.2 mm (A, B), 0.1 mm (C–F).
Quantities of antibiotics for veterinary use imported into Rwanda between 2019 and 2021
<p>Estimating antibiotic consumption in animals is fundamental to guiding decision-making and research on controlling the emergence and spread of antibiotic-resistant bacteria in humans, animals, and the environment. This study aimed to establish importation trends of antibiotics for veterinary use in Rwanda between 2019 and 2021. Data was collected from the Rwanda Food and Drugs Authority's database. Quantities of imported antibiotic active ingredients were computed using the information extracted from the issued import licenses. These quantities were subsequently adjusted per animal biomass. In total, 35,291.39 kg of antibiotics were imported into Rwanda between 2019 and 2021, with an annual mean of 11,763.80 ± 1,486.91 kg. The adjustment of imported quantities of antibiotics per animal biomass revealed that 29.11, 24.33, and 30.28 mg/kg were imported in 2019, 2020, and 2021 respectively. A slight but not statistically significant decline in antibiotic importation was noted in 2020 (p-value>0.05). Most of the imported antibiotics were indicated to be used in food-producing animals (35,253.76 kg or 99.99% of the imported antibiotics). Tetracyclines (17,768.65 kg), followed by sulfonamides (7,864.99 kg) and aminoglycosides (4,071.12 kg), were the most imported antibiotics over the studied period. It was noted that 78.93% of the imported antibiotics were categorized as highly important antimicrobials for human medicine. This study established a generalized overview of the importation of antibiotics for veterinary use in Rwanda. These results can serve as guidance for the control of antibiotic misuse. They can be used to make a correlation between antibiotic importation, antibiotic consumption, and the occurrence of antibiotic resistance in the country.</p>
FIGURE 4. Leptochilus spp. A–D, L. leleji Fateryga, 2019 in On some new and little-known Palaearctic species of the genus Leptochilus de Saussure, 1853 (Hymenoptera: Vespidae: Eumeninae)
FIGURE 4. Leptochilus spp. A–D, L. leleji Fateryga, 2019, ♂, Russia: Altai Republic; E–H, L. nacrocephalus (Kostylev, 1940), ♀, holotype. A, F, habitus in lateral view; B, E, head in frontal view; C, G, mesosoma in dorsal view; D, apex of flagellum; H, labels. Scale bars 1 mm.
FIGURE 5. Leptochilus leleji Fateryga, 2019. A in On some new and little-known Palaearctic species of the genus Leptochilus de Saussure, 1853 (Hymenoptera: Vespidae: Eumeninae)
FIGURE 5. Leptochilus leleji Fateryga, 2019. A, habitat in the type locality; B, female feeding at a fading flower of Sibbaldianthe bifurca (L.) Kurtto & T. Erikss.; C, female licking the handle of an entomological net.
Figure S4. Presence of Japanese Kanji ideograms on rubber bales found in 2021 along the Brazilian coastline. This historical evidence from 2021 supports the hypothesis of a new wreck as the 2018 and 2019 event had different language entries from the former French Indo-China (Teixeira et al., 2021).
Open the record for dataset details and reuse information.
Figs 1–6 in Two new species of the genus Carpelimus Leach, 1819 from New Guinea, related to Carpelimus (Trogophloeus) notumus Gildenkov, 2019 (Coleoptera: Staphylinidae: Oxytelinae)
Figs 1–6. Carpelimus spp., holotypes, males: 1, 3–4 — C. (Trogophloeus) kwauensis, sp.n.; 2, 5–6 — C. (Trogophloeus) nabireensis, sp.n.; 1–2 — dorsal view; 3, 5 — aedeagus, ventral view; 4, 6 — aedeagus, lateral view. Scale bar: 0.25 mm. Рис. 1–6. Carpelimus spp., голотипы, самцы,: 1, 3–4 — Carpelimus (Trogophloeus) kwauensis, sp.n.; 2, 5–6 — Carpelimus (Trogophloeus) nabireensis, sp.n.; 1–2 — вид сверху; 3, 5 — Эдеагус, сниЗу; 4, 6 — Эдеагус, сбоку. МасШтаб: 0,25 мм.
High-resolution wall-to-wall time series predictions of seasonal maize area and yield for Rwanda over 2019-2023
<p>This is the companion dataset to publication {TBD}. It contains 1) seasonal composites of predicted maize cover and yield at 10 m resolution in Rwanda for two annual agricultural seasons over five years, 2) scripts for the end-to-end machine learning pipeline that produces these data products, and 3) data or references needed as inputs to the pipeline. </p> <h2>1) Maize cover and yield seasonal composites</h2> <p>The data are provided here as netCDF4 files with four dimensions for x, y, band, and season. They can also be accessed as Google Earth ImageCollections at: </p> <ul> <li>https://code.earthengine.google.com/?asset=projects/b2p-geospatial/assets/lulc_classifier_composite</li> <li>https://code.earthengine.google.com/?asset=projects/b2p-geospatial/assets/maize_yield_composite </li> </ul> <h3>Land cover and maize classification</h3> <p>The land cover classification file is found at <code>data/composites/lulc_classifier_Rwanda_2019to2023.nc</code>.</p> <p>The land cover classification images contain 3 bands/variables: <em>maizeProb</em>, the raw predicted probability of the pixel being maize given by the gradient boosted tree model; <em>majorityClass</em>, the categorical land cover class with the highest predicted probability among any of the nine classes in the respective pixel; and <em>optimalClass</em>, the categorical land cover class adjusted to agree with national statistics for expected maize area.</p> <p>The land cover classes map to the raster values as follows: </p> <div> <div> <pre>{<br> 1: 'maize',<br> 2: 'nonmaize_annual',<br> 3: 'nonmaize_perennial',<br> 4: 'scrub_shrub_land',<br> 5: 'forest',<br> 6: 'flooded_vegetation',<br> 7: 'water',<br> 8: 'structure',<br> 9: 'bare'<br>}</pre> </div> </div> <p>The dataset includes 5 years (2019-2023) and 10 seasons - the available time period at time of publication. In Rwanda, maize is typically planted and harvested during two distinct agricultural seasons per year: Season A from September to February and Season B from March to June. Therefore the seasons in the data are: 2019_Season_A, 2019_Season_B, 2020_Season_A, 2020_Season_B, 2021_Season_A, 2021_Season_B, 2022_Season_A, 2022_Season_B, 2023_Season_A, 2023_Season_B.</p> <h3>Maize yield</h3> <p>The maize yield file is found at <code>data/composites/maize_yield_Rwanda_2019to2023.nc</code>.</p> <p>Each of the images in the yield composites has 3 bands/variables also: <em>maizeYield</em>, the model's output of continuous predicted yield (kg/ha) in each pixel regardless of land class; <em>maizeYield_majorityClass</em>, predicted maize yield masked to the majority class land classification; and <em>maizeYieldAdj_optimalClass</em>, where the raw predicted yields were masked to the optimal maize classification land cover layer and normalized to national statistics. </p> <p>The dataset includes the same seasons as the classification product; see above for a description.</p> <h2>2) End-to-end machine learning pipeline</h2> <p>All earth observation imagery, analysis, and outputs unless otherwise stated were hosted in the Google Earth Engine (GEE) environment and developed with the Earth Engine Python API in Python v3.10. To set up a local conda environment use the <code>scripts/environment.yml</code> file. The user must have <a href="https://cloud.google.com/storage">Google Cloud Storage (GCS)</a> and <a href="https://cloud.google.com/earth-engine">Google Earth Engine (GEE)</a> accounts. The pipeline, at this scale, will incur some processing and storage fees, although Google offers a free trial to all new users and the total cost of the high-resolution wall-to-wall predictions is nominal (~$20 for one season). </p> <p>The scripts needed to perform the pipeline are located in the <code>scripts</code> folder. </p> <p>The files contained in the <code>scripts/helpers</code> directory will be called by various subsequent scripts and do not to be run interactively by the user. </p> <p>Follow the script in the order described below. The user should pause after running each script and confirm that all outputs were created and loaded to GCS before continuing the pipeline; for some steps this may take hours to days depending on processing speed. </p> <h3>Google Cloud Storage and Earth Engine set-up</h3> <p>Users should specify the names of the bucket and asset project that were chosen during set up of their GCS and GEE environments in the <em>Objects</em> section of <code>scripts/helpers/maize_pipeline_0_workspace.py</code>.</p> <h3>Pipeline set-up</h3> <p>In <code>scripts/pipeline_setup</code>, you will find the following scripts to perform data preparation of inputs into model building and prediction. </p> <ul> <li><code>maize_pipeline_1_clean_training_data.py</code> - Cleans and merges all available crop label and yield data for model training and validation</li> <li><code>maize_pipeline_2_dwnld_data_training.py</code> - Downloads satellite-derived and auxiliary features at training data points for model building</li> <li><code>maize_pipeline_3_dwnld_data_inference.py</code> - Downloads satellite-derived and auxiliary features at every 10 m pixel in Rwanda on a district-wise basis for prediction</li> </ul> <h3>Land cover and maize classification</h3> <p>In <code>scripts/maize_classification</code>, you will find the following scripts to perform model building, prediction, and post-processing for the classificaton of land cover type and maize cover.</p> <ul> <li><code>maize_classifier_1_feature_selection.py</code> - Selects features subset for land cover classification with mutual information score or variable importance</li> <li><code>maize_classifier_2_build_model.py</code> - Builds gradient boosted tree model for land cover classification from training data</li> <li><code>maize_classifier_3_prediction.py</code> - Applies model for land cover classification to every 10 m pixel in Rwanda by season and district</li> <li><code>maize_classifier_4_postprocess.py</code> - Mosaics district-wise predictions and normalizes maize cover predictions to national agricultural statistics</li> </ul> <h3>Maize yield</h3> <p>In <code>scripts/maize_yield</code>, you will find the following scripts to perform modeling building, prediction, and post-processing for maize yield estimation. </p> <ul> <li><code>maize_yield_1_build_model.py</code> - Builds gradient boosted tree model and performs bias correction for maize yield estimation from training data</li> <li><code>maize_yield_2_prediction.py</code> - Applies model for maize yield estimation to every 10 m pixel in Rwanda by season and district</li> <li><code>maize_yield_3_postprocess.py</code> - Mosaics district-wise predictions and normalizes maize yield predictions to national agricultural statistics</li> </ul> <p>If you are running the entire pipeline with refreshed training data and model building, run each of these scripts, in order. By default, the script will run all A and B seasons from 2019A to current. Otherwise, if you just wish to re-run or update seasonal predictions from the existing classification or yield model run <code>maize_pipeline_3_dwnld_data_inference.py</code> to download the seasonal feature data across Rwanda and <code>maize_classifier_3_prediction.py</code>and <code>maize_classifier_4_postprocess.py</code> for classification predictions or <code>maize_yield_2_prediction.py</code> and <code>maize_yield_3_postprocess.py</code> for yield predictions, making sure to specify which season(s) are of interest in each script. However to do this, you also need to have a copy of the previously built models in your GCS (provided at <code>data/models</code>). </p> <h2>3) Input data into machine learning pipeline</h2> <p>A description of datasets that must be sourced outside of the GEE platform is provided below. When available, the primary data source is also included in the directory <code>data/baselayers</code>. All other data, including Sentinel-2 imagery, auxiliary data, and other existing global land cover classificaiton products are hosted on GEE and called by the scripts directly. All datasets last accessed on 12 March 2024.</p> <h3>Administrative and geological boundaries</h3> <ul> <li>World Countries - Downloaded from <a href="https://datacatalog.worldbank.org/search/dataset/0038272/World-Bank-Official-Boundaries">The World Bank Official Boundaries</a> and included here at <code>data/baselayers/World_Countries</code>.</li> <li>Rwanda district boundaries - Downloaded from <a href="https://datacatalog.worldbank.org/search/dataset/0041453/Rwanda-Admin-Boundaries-and-Villages">The World Bank Rwanda Admin Boundaries And Villages</a> and included here at <code>data/baselayers/WB_NISR_2018</code>. This should be loaded into a FeatureCollection GEE asset named <em>districts_fc</em> for use in the pipeline. </li> <li>Rwanda agro-ecological zones - Downloaded from <a href="https://doi.org/10.1371/journal.pone.0149239">Nzeyimana, Hartemink & Geissen (2016)</a> and included here at <code>data/baselayers/MINAGRI_AEZ_1980</code>. This should be loaded into a FeatureCollection GEE asset named <em>aez_rwanda</em> for use in the pipeline. </li> </ul> <h3>Global land cover classification product</h3> <ul> <li>Microsoft/Impact Observatory LULC - Although the <a href="https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class">10m Annual Land Use Land Cover (9-class) V1</a> product contains data from 2017-2022, only the LULC map from the year 2021 was used, provided here at <code>data/baselayers/impactobs_lulc_rwa_2021.tif</code>. This should be loaded into an ImageCollection GEE asset named <em>impact_obs_lulc</em> for use in the pipeline.</li> </ul> <p>(The others - Dynamic World and ESA's WorldCover - are hosted on GEE directly.)</p> <h3>Land cover labels and maize yield crop cuttings</h3> <ul> <li>One Acre Fund - Contact authors to request access as this dataset is not hosted publicly. </li> <li>RTI International - The original source of this data (Radiant MLHub) has been discontinued, but users may be able to access it via <a href="https://beta.source.coop/repositories/rti/rwanda-crop-type/">Source Cooperative</a>. The data is also included here at <code>data/baselayers/rti_rwanda_crop_type_labels</code>. </li> <li>Crop Harvest - Downloaded from <a href="../records/7257688">Tseng et al. (2021, v13)</a> and included here at <code>data/baselayers/CropHarvest</code>. These data points were ultimately not used in the training data, but are provided here for others that may find this dataset useful in their context.</li> </ul> <h3>Rwanda national agricultural surveys</h3> <ul> <li>National Institute of Statisitcs Rwanda (NISR) - Downloaded from <a href="https://statistics.gov.rw/datasource/seasonal-agricultural-survey">NISR Seasonal Agricultural Survey</a> and existing seasons included here at <code>data/baselayers/NISR_Seasonal_Ag_Surveys</code>. For each subsequent season, the user will have to download the spreadsheet of survey results from the NISR webpage (linked) and add the respective season to the <code>get_nisr_data</code> function in the <code>helpers/maize_pipeline_0_helpers_postprocess.py</code> script to clean and read in the data for use in the pipeline. </li> </ul> <p> </p>
FIGURE 4 in Taxonomic revision of the Neotropical skipper genus Incisus Grishin, 2019 (Lepidoptera, Hesperiidae, Pyrginae), with the description of a new species
FIGURE 4. Male genitalia of Incisus species in lateral, internal, dorsal and ventral views, respectively. A–D. Incisus angulata (Bell, 1937) (Brazil, Minas Gerais, Virgínia, Fazenda dos Campos, DZ 46.339). E–H. Incisus fasciatus (Hayward, 1933) (Brazil, Paraná, Guaíra, Parque Nacional de Sete Quedas, DZ 44.840). I–L. Incisus incisus (Mabille, 1878) (Brazil, Santa Catarina, Joinville, DZ 46.353). M–P. I. mineirus sp. nov. (Brazil, Minas Gerais, São Francisco, DZ 46.559). Scale bar = 500 µm.
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.