Abstract The silicon solar cells achieved relatively low prices in the last years and to introduce a new structure in the PV industry, the amount of silicon per watt has to be reduced, requiring a cost-effective manufacturing process. The use of n-type solar grade silicon has the advantages of presenting higher minority carrier lifetime than p-type one and the absence of the boron-oxygen defects. The aim of this paper is to present the development of 100 μm thick n+np+ silicon solar cells with a selective p+ rear emitter formed by boron deposited by spin-on and an Al/Ag grid deposited by screen-printing. The firing temperature of Ag/Al (rear face) e Ag (front face) was optimized and the temperature of 840 °C produced the devices with higher efficiency. The solar cells presented efficiencies of 16%, achieving a low silicon consumption of 1.6 g/W, 40% lower than thick p-type devices produced by the same process.

Data is based upon exports from the data held in the EpiDoc TEI-XML files of the I.Sicily project which contains XML editions of the inscribed texts of ancient Sicily. The data was extracted after indexing of the files using an eXist database, piped into csv files through the use of export function of www.ag-grid.com employed on the ISicily website (http://sicily.classics.ox.ac.uk). The datasets are based on the data in I.Sicily as of 22.06.2016. That precise dataset at the time of export is no longer preserved. The current state of the data can be found at http://sicily.classics.ox.ac.uk or at https://github.com/JonPrag/ISicily/ where the commit history enables retrieval of past datastates back to January 2017. Quantitative study of the epigraphic culture of ancient (Greco-Roman) Sicily

This spatial dataset consists of 199 1-kilometer (km) resolution grids depicting estimated agricultural use of 199 pesticides in 1992 for the conterminous United States. Each grid cell value in the national grids of this dataset is the estimated total kilograms (kg) of a pesticide applied to row crops, small grain crops and fallow land, pasture and hay crops, and orchard and vineyard crops within the 1- by 1-km area. Nonagricultural uses of pesticides are not included in this dataset. Of the 199 pesticides represented in the grids, 92 are herbicides, 58 are insecticides, and 32 are fungicides. The remaining 17 grids are composed of the category "other pesticides", which consists of fumigants, growth regulators, and defoliants. Although this data set is referenced to 1992, it generally represents a composite of estimated pesticide use during the early 1990s.

110kV-, 220kV-, 380kV-Leitungen und -Umspannwerke, 110kV-Kabel

Recent approaches to the study of biological molecules employ manifold learning to single-particle cryo-EM data sets to map the continuum of states of a molecule into a low-dimensional space spanned by eigenvectors or “conformational coordinates”. This is done separately for each projection direction (PD) on an angular grid. One important step in deriving a consolidated map of occupancies, from which the free energy landscape of the molecule can be derived, is to propagate the conformational coordinates from a given choice of “anchor PD” across the entire angular space. Even when one eigenvector dominates, its sign might invert from one PD to the next. The propagation of the second eigenvector is particularly challenging when eigenvalues of the second and third eigenvector are closely matched, leading to occasional inversions in their ranking as we move across the angular grid. In the absence of a computational approach, this propagation across the angular space has been done thus far “by hand” using visual clues, thus greatly limiting the general use of the technique. In this work we have developed a method that is able to solve the propagation problem computationally, by using optical flow and a probabilistic graphical model. We demonstrate its utility by selected examples.

The current dataset includes 320 topsoil samples (0–20 cm depth) collected from four agricultural sites in the Czech Republic. The samples were gathered from Přestavlky, Klučov, Nová Ves nad Popelkou, and Udrnice (80 samples from each site) in June 2021. It contains sample coordinates and some soil parameters including SOC and texture, prepared and stored in MS Excel (.xlsx) format. The data were used in STEROPES WP1 (basic local model development), WP3 (effect of texture), and WP4 (effect of vegetation and plant residues).

A dataset contains 5,503 unique occurrences of ca. 500 vascular plant species from Pereslavsky and Rostovsky Districts of Yaroslavl Oblast (Russia). They are based on field records by Alena G. Frontova performed in 2014 using a grid scheme. Georeferences are based on WGS84 grid scheme with 31 grid squares. Field recording was performed within a small grid square (2,5′ lat. x 5′ long.). Only one small grid square (SW, SE, NW, or NE) per larger one (5′ lat. x 10′ long.) was sampled. Occurrence is placed in the small grid square centroid. Dataset contains only one occurrence per species per grid square.

The dataset is a part of the Bachelor's Paper "Flora of the southern tip of Yaroslavl Oblast" (Frontova, 2015) supervised by Dr. A.P. Seregin. Voucher specimens were trasfered to the Moscow University Herbarium and are available via the "Moscow University Herbarium (MW)" dataset (https://www.gbif.org/dataset/902c8fe7-8f38-45b0-854e-c324fed36303).

Bridging the gap between the predictions of coarse-scale climate models and the fine-scale climatic reality of species is a key issue of climate change biology research. While it is now well known that most organisms do not experience the climatic conditions recorded at weather stations, there is little information on the discrepancies between microclimates and global interpolated temperatures used in species distribution models, and their consequences for organisms’ performance. To address this issue, we examined the fine-scale spatiotemporal heterogeneity in air, crop canopy and soil temperatures of agricultural landscapes in the Ecuadorian Andes and compared them to predictions of global interpolated climatic grids. Temperature time-series were measured in air, canopy and soil for 108 localities at three altitudes and analysed using Fourier transform. Discrepancies between local temperatures vs. global interpolated grids and their implications for pest performance were then mapped and analysed using GIS statistical toolbox. Our results showed that global interpolated predictions over-estimate by 77.5±10% and under-estimate by 82.1±12% local minimum and maximum air temperatures recorded in the studied grid. Additional modifications of local air temperatures were due to the thermal buffering of plant canopies (from −2.7°K during daytime to 1.3°K during night-time) and soils (from −4.9°K during daytime to 6.7°K during night-time) with a significant effect of crop phenology on the buffer effect. This discrepancies between interpolated and local temperatures strongly affected predictions of the performance of an ectothermic crop pest as interpolated temperatures predicted pest growth rates 2.3–4.3 times lower than those predicted by local temperatures. This study provides quantitative information on the limitation of coarse-scale climate data to capture the reality of the climatic environment experienced by living organisms. In highly heterogeneous region such as tropical mountains, caution should therefore be taken when using global models to infer local-scale biological processes.

Soil data collected in an agricultural area with vegetable crops in Spain (Campo de Cartagena). The data refers to soil properties of 141 soil samples collected at a depth of 0-10 cm, considering a regular sampling grid, in different commercial fields with varying soil salinity. The samples were collected at a period when the soil was bare, during two consecutive summers, following the harvest of the annual crops, and pictures of the soil surface were taken for eventual correction of corresponding remote sensing imaging. The data includes: soil organic carbon (SOC) (Walkley-Black method), soil water content, electric conductivity of the saturated soil paste (ECe), EC1:5, soil texture, stone content and pH1:2.5.

The data may be representative of the soil conditions of the area, which is an intensive productive agricultural low land, potentially prone to the development of soil salinity as a result of the rise of saline groundwater and/or irrigation. The data can be used to establish relations between soil salinity (ECe) and other soil properties as well as build prediction models of the soil properties from remote sensing namely, for developing models for SOC prediction under the STEROPES project (WP3, WP5 and WP6).The aim of the collected dataset was to be able to analyze the influence of soil salinity in SOC prediction from remote sensing.

Data in the form of MS Excel file (xlsx).

Soil data collected in an agricultural area with annual crops in Portugal (Lezíria Grande). The data refers to soil properties of 63 soil samples collected at a depth of 0-20 cm, considering a regular sampling grid, in four fields with varying soil salinity (field areas between 2 and 34 ha). The samples were collected at a period when the soil was bare, following the harvest of the annual crops, and pictures of the soil surface were taken for eventual correction of corresponding remote sensing imaging. The data includes: soil organic carbon (SOC) (Walkley-Black method), soil water content, electric conductivity of the saturated soil paste (ECe), EC1:5, and pH1:5. The data may be representative of the soil conditions of the area, which is a highly productive agricultural low land, prone to the development of soil salinity as a result of the rise of saline groundwater and/or irrigation. The data can be used to establish relations between soil salinity (ECe) and other soil properties as well as build prediction models of the soil properties from remote sensing namely, for developing models for SOC prediction under the STEROPES project (WP5 (WP5-T3) and WP2 (WP2-T3)).The aim of the collected dataset was to be able to analyze the influence of soil salinity in SOC prediction from remote sensing. Data in the form of MS Excel files (xlsx), pictures of the soil surface in jpg. format.

As the “third pole” of the world, the Qinghai-Tibet Plateau (QTP) is extremely ecologically sensitive and fragile while facing increasing human activities and overgrazing. In this study, eight types of spatial data were firstly selected, including grazing intensity, Night-Time Light, population density, Gross Domestic Product (GDP) density, the ratio of cultivated land, the slope of the Normalized Difference Vegetation Index (NDVI), distance to road, and distance to town. Then, the entropy weight method was applied to determine the weight of each factor. Finally, the five-year interval human activity intensity data in 1990, 1995, 2000, 2005, 2010 and 2015 were made in the agricultural and pastoral areas of QTP through the spatial overlap method. By preparing the historical spatial datasets of human activity intensity, our study will help to explore the influence of human disturbance on the alpine ecosystems on the QTP and provide effective support for decision-making of government aiming at regional ecosystem management and sustainable development.

The C2 symmetric chiral ligand 4,6-bis[(4S,7R)-7,8,8′-trimethyl-4,5,6,7-tetrahydro-4,7-methaneindazol-2-yl]pyrimidine (bpzCampm) was synthesized and reacted with Cu(I) and Ag(I) salts to give four chiral [2 × 2] grid-type structures: [M4(bpzCampm)4]X4, M = Cu(I), Ag(I); X = BF4−, PF6−. The structures of the two BF4− derivatives were determined by X-ray diffraction. The grids contain facing ligands that are not parallel and, in contrast to the situation found in previous examples, the anions are not hosted within the cavities but toluene molecules are. In the case of the copper derivative, π-stacking interactions are established between the pyrimidine rings and these toluene molecules and others situated in the intercationic regions, giving rise to the formation of π-stacked columns with alternating pyrimidine and toluene molecules. Due to the chirality of bpzCampm, chiral cavities are generated in the crystalline structure. From NMR studies, it was concluded that the copper grids are maintained in solution but this is not the case for the silver derivatives. The UV−vis spectra of the copper complexes show an MLCT band, higher for the PF6− derivative. Some derivatives exhibit fluorescence with a high influence of the solvent and counteranion in the intensity of the emission.

This data set represents the estimated percentage of the 1-km grid cell that is covered by or subject to the agricultural conservation practice (CPIS02), Pond, Lake or Reservoir as an Irrigation Source (PLRIS) on agricultural land by county. Pond, Lake or Reservoir as an Irrigation Source are described as an "inland body of water (fresh or salt) of considerable size occupying a basin or hollow on the earth's surface, and which may or may not have a current or single direction of flow." (U.S. Department of Agriculture, 1995) This data set was created with geographic information systems (GIS) and database management tools. The acres on which PLRIS's are applied were totaled at the county level in the tabular NRI database and then apportioned to a raster coverage of agricultural land within the county based on the Enhanced National Land Cover Dataset (NLCDe) 1-kilometer resolution land cover grids (Nakagaki, 2003). Federal land is not considered in this analysis because NRI does not record information on those lands.

This data set represents the estimated percentage of the 1-km grid cell that is covered by or subject to the agricultural conservation practice (CPIS03), Stream, Ditch or Canal as an Irrigation Source (SDCIS) on agricultural land by county. Stream, Ditch or Canal as an Irrigation Source is described as: "Stream : A flow of water in a channel or bed, as a brook, rivulet, or small river. Ditch : A long, narrow trench or furrow dug in the ground, as for irrigation. Canal : An artificial waterway used for irrigation." (U.S. Department of Agriculture, 1995) This data set was created with geographic information systems (GIS) and database management tools. The acres on which SDCIS's are applied were totaled at the county level in the tabular NRI database and then apportioned to a raster coverage of agricultural land within the county based on the Enhanced National Land Cover Dataset (NLCDe) 1-kilometer resolution land cover grids (Nakagaki, 2003). Federal land is not considered in this analysis because NRI does not record information on those lands.

This data set represents the estimated percentage of the 1-km grid cell that is covered by or subject to the agricultural conservation practice (CPIS04), Stream, Lagoon or Other Waste Waster (not including tailwater recovery) as an Irrigation Source (LWWIS) on agricultural land by county. Stream, Lagoon or Other Waste Waster (not including tailwater recovery) as an Irrigation Source is described as an "impoundment made by excavation or earthfill for biological treatment of animal or other agricultural waste." (U.S. Department of Agriculture, 1995) This data set was created with geographic information systems (GIS) and database management tools. The acres on which LWWIS's are applied were totaled at the county level in the tabular NRI database and then apportioned to a raster coverage of agricultural land within the county based on the Enhanced National Land Cover Dataset (NLCDe) 1-kilometer resolution land cover grids (Nakagaki, 2003). Federal land is not considered in this analysis because NRI does not record information on those lands.

description: This data set represents the estimated percentage of the 1-km grid cell that is covered by or subject to the agricultural conservation practice (CPIS05), Combination of Irrigation Sources (CIS) on agricultural land by county. A combination of irrigation sources means one or more sources of irrigation, such as wells, ponds, or streams are used on agricultural land. (U.S. Department of Agriculture, 1995) This data set was created with geographic information systems (GIS) and database management tools. The acres on which CIS's are applied were totaled at the county level in the tabular NRI database and then apportioned to a raster coverage of agricultural land within the county based on the Enhanced National Land Cover Dataset (NLCDe) 1-kilometer resolution land cover grids (Nakagaki, 2003). Federal land is not considered in this analysis because NRI does not record information on those lands.; abstract: This data set represents the estimated percentage of the 1-km grid cell that is covered by or subject to the agricultural conservation practice (CPIS05), Combination of Irrigation Sources (CIS) on agricultural land by county. A combination of irrigation sources means one or more sources of irrigation, such as wells, ponds, or streams are used on agricultural land. (U.S. Department of Agriculture, 1995) This data set was created with geographic information systems (GIS) and database management tools. The acres on which CIS's are applied were totaled at the county level in the tabular NRI database and then apportioned to a raster coverage of agricultural land within the county based on the Enhanced National Land Cover Dataset (NLCDe) 1-kilometer resolution land cover grids (Nakagaki, 2003). Federal land is not considered in this analysis because NRI does not record information on those lands.

We constructed geographically gridded data global maps of maximum and mean root depth in agricultural land based upon the crop and pasture distribution maps in Monfreda et al. (2008) and Ramankutty et al. (2008). The maps are relative to year 2000 and have a resolution of 5 arc-min (i.e., approximately 10 km at the equator). Recommended CitationMaggi, F., la Cecilia, D., Tang, F. H.M, and McBratney, A. (2020). The global environmental hazard of glyphosate use, Science of the Total Environment,https://doi.org/10.1016/j.scitotenv.2020.137167References

Monfreda, C., N. Ramankutty, and J. A. Foley (2008), Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000, Global Biogeochem. Cycles, 22, GB1022, doi:10.1029/2007GB002947

Ramankutty, Navin, Evan, Amato T, Monfreda, Chad, & Foley, Jonathan A. 2008. Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000. Global Biogeochemical Cycles, 22(1).