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Spatial distribution of cattle, sheep and goat density, and grazed areas for the European Union and the United Kingdom

To improve the sustainability of the European livestock sector we need improved knowledge on livestock density, and also on the grazing patterns. Here we provide spatially explicit data on the distribution of cattle, sheep and goats, developed by combining agricultural and veterinary statistics, in-situ data, expert surveys and machine learning. The data allow for the differentiation between livestock that are grazing on semi-natural areas and managed grasslands, versus those that do not graze and are kept indoors.  This dataset covers all European Union Member States and the United Kingdom, and presents the spatial distribution of cattle, sheep and goat density for approximately the year 2020. Livestock density was allocated on the Corine Land Cover data, resulting in a data-set with a 100 m resolution (EPSG: 3035 - ETRS89-extended / LAEA Europe). Together with the livestock density maps, we also provide spatial data on the probability for grazing, and allocated grazed and non grazed areas. File description: The data-set consists of the following files:   <ul> <li>clc_forage_mask.tif , forage areas mask for EU, based on selected Corine Land Cover classes (not including seminatural land cover areas such as natural grasslands...). This was developed by surveying grazing, grassland and livestock experts from all EU Member States and the United Kingdom. More info in the upcoming paper and in the linked paper below (Malek et al. 2024). Values are the same as in the Corine Land Cover data.</li> <li>grazing_probability.tif , grazing probability map, indicating how likely each location in the EU+UK is grazed</li> <li>allocated_grazing.tif , allocated grazing map, indicating which areas are grazed and which are not</li> </ul>   <ul> <li>cattle density maps: <ul> <li>cattle_grazing.tif , cattle grazing on managed forage areas</li> <li>cattle_other.tif , cattle kept indoors, receiving feed from managed forage areas</li> <li>cattle_seminatural.tif ,  cattle grazing in semi-natural areas</li> <li>cattle_mosaic_categorical.tif (with a legend file) , combined categorical map for all cattle types.</li> </ul> </li> </ul>   <ul> <li>sheep and goat density maps: <ul> <li>sheep_goat_other.tif , sheep and goat density</li> <li>sheep_goat_seminatural.tif , sheep and goat grazing in seminatural areas</li> </ul> </li> </ul>

opencc-by-4.0Aug 2024View details →

Colony forming unit (CFU) data accompanying publication: Bacterial microbiome dynamics in commercial integrated aquaculture systems growing Ulva in abalone effluent water.

Excel sheet with colony forming unit (CFU) data from an abalone farm growing the green seaweed Ulva in abalone effluent water. This dataset characterises the bacterial communities isolated from abalone effluent water entering and leaving the Ulva raceways, as well as from Ulva itself. Dataset is accompanied by two SigmaPlot files showing statistical analyses (statistical outcomes also available in publication).

Old silvoarable site at Etonbury Wood (Bedford) - United Kingdom

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Silvopastoral parkland at Clapham Park (Bedford) - United Kingdom

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Silvopastoral parkland at Howburry Hall (Bedford) - United Kingdom

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opencc-by-4.0Jan 2023View details →

Dataset: A review of methods to trace material flows into final products in dynamic material flow analysis - from industry shipments in physical units to monetary input-output tables (p

Dynamic material flow analysis (dMFA) is widely used to model stock-flow dynamics. To appropriately represent material lifetimes, recycling potentials, and service provision, dMFA requires data about the allocation of economy-wide material consumption to different end-use products or sectors, that is, the different product stocks, in which material consumption accumulates. Previous estimates of this allocation only cover few years, countries, and product groups. Recently, several new methods for estimating end-use product allocation in dMFA were proposed, which so far lack systematic comparison. We review and systematize five methods for tracing material consumption into end-use products in inflow-driven dMFA and discuss their strengths and limitations. Widely used data on industry shipments in physical units have low spatio-temporal coverage, which limits their applicability across countries and years. Monetary input–output tables (MIOTs) are widely available and their economy-wide coverage makes them a valuable source to approximate material end-uses. We find four distinct MIOT-based methods: consumption-based, waste input–output MFA (WIO-MFA), Ghosh absorbing Markov chain, and partial Ghosh. We show that when applied to a given MIOT, the methods’ underlying input–output models yield the same results, with the exception of the partial Ghosh method, which involves simplifications. For practical applications, the MIOT system boundary must be aligned to those of dMFA, which involves the removal of service flows, sector (dis)aggregation, and re-defining specific intermediate outputs as final demand. Theoretically, WIO-MFA, applied to a modified MIOT, produces the most accurate results as it excludes massless and waste transactions. In part 2 of this work, we compare methods empirically and suggest improvements for aligning MIOT-dMFA system boundaries.

Dataset for Detecting False Data Injection Attacks in GOOSE Protocol Communication between RTU and Bay Protection Unit

This dataset focuses on the detection and prevention of False Data Injection (FDI) attacks targeting the communication between a Remote Terminal Unit (RTU) and a Bay Protection Unit in a power substation, utilizing the Generic Object Oriented Substation Event (GOOSE) protocol. It includes both clean traffic events and recorded instances of FDI attacks.

Fig. 9. Agapostemon subtilior Cockerell, 1898 in Taxonomy of Agapostemon angelicus and the A. texanus species complex (Hymenoptera, Halictidae) in the United States

Fig. 9. Agapostemon subtilior Cockerell, 1898, neotype, female (UCMC0000005). A. Dorsal view of head and thorax. B. Propodeum. C. Lateral view with inset of labels. Images provided by Bekka Shupe and Virginia Scott (UCMC).

Fig. 8 in Taxonomy of Agapostemon angelicus and the A. texanus species complex (Hymenoptera, Halictidae) in the United States

Fig. 8. Range maps of specimens examined by the authors. A. Agapostemon texanus Cresson, 1872 s. s. B. Distribution of A. angelicus Cockerell, 1924 in Minnesota. C. Distribution of A. subtilior Cockerell, 1898 in Minnesota. D. Distribution of A. texanus s. s. in Minnesota.

Fig. 7 in Taxonomy of Agapostemon angelicus and the A. texanus species complex (Hymenoptera, Halictidae) in the United States

Fig. 7. Range maps of specimens examined by the authors. A. Agapostemon angelicus Cockerell, 1924. B. A. subtilior Cockerell, 1898.

Fig. 4. Female metasoma comparison. A. Agapostemon angelicus Cockerell, 1924. B. A. subtilior Cockerell, 1898. C. A. texanus Cresson, 1872 s. s. D. A. angelicus, T1 and T2 in Taxonomy of Agapostemon angelicus and the A. texanus species complex (Hymenoptera, Halictidae) in the United States

Fig. 4. Female metasoma comparison. A. Agapostemon angelicus Cockerell, 1924. B. A. subtilior Cockerell, 1898. C. A. texanus Cresson, 1872 s. s. D. A. angelicus, T1 and T2, with red arrow pointing to lateral areas basal to premarginal line with denser punctures. E. A. subtilior, T1 and T2, with red arrow pointing to area of sparser punctures. F. A. texanus s. s., T1 and T2, with red arrow pointing to area of denser punctures. Scale bars: 1 mm.

Fig. 2 in Taxonomy of Agapostemon angelicus and the A. texanus species complex (Hymenoptera, Halictidae) in the United States

Fig. 2. Comparison of Agapostemon angelicus Cockerell, 1924 and A. subtilior Cockerell, 1898, female head and clypeus. A. A. angelicus, face. B. A. angelicus, apex of clypeus. C. A. subtilior, face. D. A. subtilior, apex of clypeus. Scale bars: A, C = 1 mm; B, D = 500 µm.

Fig. 3 in Taxonomy of Agapostemon angelicus and the A. texanus species complex (Hymenoptera, Halictidae) in the United States

Fig. 3. Thorax of Agapostemon angelicus Cockerell, 1924 and A. subtilior Cockerell, 1898, females. A. A. angelicus, propodeum. B. A. subtilior, propodeum. C. A. angelicus, side. D. A. subtilior, side. Scale bars: 1 mm.

Fig. 1 in Taxonomy of Agapostemon angelicus and the A. texanus species complex (Hymenoptera, Halictidae) in the United States

Fig. 1. Genal striae of females. A. Agapostemon angelicus Cockerell, 1924. B. A. subtilior Cockerell, 1898. C. A. texanusCresson, 1872 s. s. D. Zoomed-in view of A. texanuss. s. striae, image of paralectotype (MCZENT548), provided by Zoe Flores and Crystal Maier of the MCZ. Scale bars: 1 mm.

Fig. 6 in Taxonomy of Agapostemon angelicus and the A. texanus species complex (Hymenoptera, Halictidae) in the United States

Fig. 6. Apical view of male genitalia. A. Agapostemon angelicus Cockerell, 1924. B. A. subtilior Cockerell, 1898. C. A. texanus Cresson, 1872 s. s. D. A. texanus s. s., zoomed in with black arrow pointing to more evenly curved lower stylus. E. A. subtilior zoomed in with black arrow pointing to more laterally extending lower stylus. Scale bars: 500 µm.

Fig. 5. Male hind legs. A. Agapostemon angelicus Cockerell, 1924, anterior view. B. A. angelicus, posterior view. C. A. subtilior Cockerell, 1898 in Taxonomy of Agapostemon angelicus and the A. texanus species complex (Hymenoptera, Halictidae) in the United States

Fig. 5. Male hind legs. A. Agapostemon angelicus Cockerell, 1924, anterior view. B. A. angelicus, posterior view. C. A. subtilior Cockerell, 1898, lightest color, anterior view. D. A. subtilior, lightest color, posterior view. E. A. subtilior, anterior view. F. A. subtilior, posterior view. G. A. subtilior, dark, anterior view. H. A. subtilior, dark, posterior view. I. A. texanus Cresson, 1872, anterior view. J. A. texanus, posterior view. K. A. angelicus, basitarsus. L. A. subtilior, basitarsus. M. A. texanus, basitarsus. Scale bars: A–J = 1 mm; K–M = 500 µm.

opencc-by-4.0Mar 2024View details →

A Dataset of the Operating Station Heat Rate for 806 Indian Coal Plant Units using Machine Learning

<div> <div> <div> <div> India aims to achieve net-zero emissions by 2070 and has set an ambitious target of 500 GW of renewable power generation capacity by 2030. Coal plants currently contribute to more than 60% of India’s electricity generation in 2022. Upgrading and decarbonizing high-emission coal plants became a pressing energy issue. A key technical parameter for coal plants is the operating station heat rate (SHR), which represents the thermal efficiency of a coal plant. Yet, the operating SHR of Indian coal plants varies and is not comprehensively documented. This study extends from several existing databases and creates an SHR dataset for 806 Indian coal plant units using machine learning (ML), presenting the most comprehensive coverage to date. Additionally, it incorporates environmental factors such as water stress risk and coal prices as prediction features to improve accuracy. This dataset, easily downloadable from our visualization platform, could inform energy and environmental policies for India’s coal power generation as the country transitions towards its renewable energy targets. </div> </div> </div> </div>

Crater Catalogue for Age Determination of the Chang'e-6 Mare Unit and Key Ejecta Source Craters

This page archives data used in the following article: Xu, L., Qiao, L., Xie., M., Wang, Y., Zhu, M-H., & Yan, J. (2024). Chronology, Local Stratigraphy, and Foreign Ejecta Materials at the Chang'e-6 Landing Site: Constraints on the Provenance of Samples Returned from the Moon's Farside. Geophysical Research Letters, 51. DOI: 10.1029/2024GL111311   They include the Crater Catalogue for Age Determination of the Chang'e-6 Mare Unit and Key Ejecta Source Craters: CE6_MareUnit.zip: ArcGIS shapefile for the CRATER and AREA of the mare unit; CE6_5kmCircle_NAC.zip: ArcGIS shapefile for the CRATER and AREA of a 5 km-sized circle across the Chang'e-6 landing site; ChaffeeS.zip, White.zip, Vavilov.zip, and White'.zip: ArcGIS shapefile for the CRATER and AREA of key ejecta source craters Chaffee S, White, Vavilov, and White', respectively.

opencc-zeroJan 2024View details →

Linked collectors and determiners for: Multivariate discrimination and description of a new species of Tapinoma from the Western United States..

Natural history specimen data linked to collectors and determiners held within, "Multivariate discrimination and description of a new species of Tapinoma from the Western United States.". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/7bae7513-ec11-4c9b-a570-3040c4734745">https://bionomia.net/dataset/7bae7513-ec11-4c9b-a570-3040c4734745</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/7bae7513-ec11-4c9b-a570-3040c4734745">https://gbif.org/dataset/7bae7513-ec11-4c9b-a570-3040c4734745</a>. Formatted as a Frictionless Data package.