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  • Rural Digital Europe
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Hagedorn, Frank;

    Data on soil nutrients and charcteristics, tree and understory biomass, foliage nutrients, mineralogy, soil temperature and moisture along an elevation gradient from boreal forest to mountain tundra in teh Khibiny mountains (Kola Penninsula, Russia). Samples were taken along two gradients at 7 elevation level either under tree canopy (tree) and in open areas (open).

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ B2FINDarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    B2FIND
    Dataset . 2024
    Data sources: B2FIND
    Environmental Data Portal
    Dataset . 2024
    License: CC BY SA
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ B2FINDarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      B2FIND
      Dataset . 2024
      Data sources: B2FIND
      Environmental Data Portal
      Dataset . 2024
      License: CC BY SA
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Salak, Boris; Kienast, Felix;

    This study examines the siting scenarios for renewable energy infrastructure (REI) in Switzerland, incorporating the external costs of ecosystem services and, innovatively, social preferences. This approach challenges the prevalent techno-economic siting paradigm, which often overlooks these externalities. To minimize the external costs of the scenarios while maximizing energy yield, Marxan, an optimization software, was employed. MARXAN was run for 2 versions: a) without ground-mounted open space PV infrastructure (excl. OS) and b) with ground-mounted open space PV infrastructure (incl. OS). In each version optimization was done using ecological costs (ECUess) or social costs (ECUsoc) in a regular grid of 4x4km (planning unit). File: PU_data.shp: compressed Shapefile (ESRI) from the MARXAN optimization with 2216 rows (objects) and 18 columns (variables) with sf (simple feature) and data frame classes. Headers are described below: X01 = Planning_units (PUs) X02 = incl.OS. number of times pu was selected in MARXAN when optimized for ecological costs X03 = incl.OS. number of times pu was selected in MARXAN when optimized for social costs X04 = incl.OS. ecological costs (ECUess) of pu when realizing the total energy (normalized) X05 = incl.OS. social costs (ECUsoc) of pu when realizing the total energy (normalized) X06 = incl.OS. total energy (MWh/a) X07 = incl.OS. energy from roof-mounted PV infrastructure (MWh/a) X08 = incl.OS. energy from wind turbines (MWh/a) X09 = incl.OS. energy from ground-mounted PV infrastructure (MWh/a) X10 = excl.OS. number of times pu was selected in MARXAN when optimized for ecological costs X11 = excl.OS. number of times pu was selected in MARXAN when optimized for social costs X12 = excl.OS. ecological costs (ECUess) of pu when realizing the total energy (normalized) X13 = excl.OS. social costs (ECUsoc) of pu when realizing the total energy (normalized) X14 = excl.OS. total energy (MWh/a) X15 = excl.OS. energy from roof-mounted PV infrastructure (MWh/a) X16 = excl.OS. energy from wind turbines (MWh/a) X17 = excl.OS. energy from ground-mounted PV infrastructure (MWh/a) geometry = Simple feature XY geometry (SFC_POINT) each representing the center of a 4x4km planning unit (PU) in EPSG 21781 (CRS CH1903 / LV03). For MARXAN optimizations (for meaning of standard files in MARXAN see https://scholar.google.ch/scholar_url?url=https://courses.washington.edu/cfr590/software/Marxan1810/marxan_manual_1_8_2.pdf&hl=de&sa=X&ei=NAWHZI6JJ8PFmAGMo47oDQ&scisig=AGlGAw-DBrsV4kUzkR6GkN1jtG66&oi=scholarr), pu_data.shp was used to generate MARXAN files input.dat, pu.dat, puvfeat.dat, spec.dat. Coordinate Reference System: CH1903 / LV03. EPSG: 21781.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ B2FINDarrow_drop_down
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    B2FIND
    Dataset . 2024
    Data sources: B2FIND
    Environmental Data Portal
    Dataset . 2024
    License: CC BY SA
    Data sources: Datacite
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      B2FIND
      Dataset . 2024
      Data sources: B2FIND
      Environmental Data Portal
      Dataset . 2024
      License: CC BY SA
      Data sources: Datacite
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    Authors: Weber, Dominique; Schwieder, Marcel; Ritter, Lukas; Koch, Tiziana; +4 Authors

    A rule-based algorithm [(Schwieder et al., 2022)](https://doi.org/10.1016/j.rse.2021.112795) was used to produce annual maps for 2018–2021 of grassland-management events, i.e. mowing and/or grazing, for Switzerland using Sentinel-2 and Landsat 8 satellite time series. All satellite images were processed with the [FORCE](https://force-eo.readthedocs.io) framework. The resulting maps provide information on the number and timing of grassland-management events at a spatial resolution of 10 m × 10 m for the whole of Switzerland. For the final maps, permanent grasslands were masked using a variety of land-use layers, according to [Huber et al. (2022)](https://doi.org/10.1002/rse2.298) but replacing the crop mask with the agricultural-use data from the cantons. We assessed the detection of management events based on independent reference data, which we acquired from daily time series of publicly available webcams that are widely distributed across Switzerland. We further tested the ecological relevance of the generated intensity measures in relation to nationwide biodiversity data (see [Weber et al., 2023](https://doi.org/10.1002/rse2.372)). The webcam-based reference data used for verification was subsequently added on 14.02.2024.

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    B2FIND
    Dataset . 2023
    Data sources: B2FIND
    Environmental Data Portal
    Dataset . 2023
    License: CC BY SA
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ B2FINDarrow_drop_down
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      B2FIND
      Dataset . 2023
      Data sources: B2FIND
      Environmental Data Portal
      Dataset . 2023
      License: CC BY SA
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Paula, Ballikaya,; Wenqi, Song,; Marcel, Guillong,; Olivier, Bachmann,; +2 Authors

    This dataset contains the values of several chemical elements (Mg, Al, Si, S, K, Ca, Ti, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Sr, Tl, Pb, Bi) measured in the latewood of tree rings of Mongolian oak from Harbin, China, at a 5-year resolution. Due to the lack of a suitable reference material for wood, absolute concentration was not calculated, and the ratio between the chemical element and 13C was taken as proxy for the element signal. In Harbin, one of the largest cities and most important industrial centers in northeastern China, air quality monitoring systems were built only by the end of 2015 to meet the national requirements. Thus, dendrochemical analyses could be used as a tool to complement for the lack of air quality data over longer periods of time, allowing for the reconstruction of the temporal trend of trace metals. Our main scopes were to: (a) assess the chemical composition of Quercus mongolica Fisch. ex Ledeb. tree rings from Harbin using a recently developed system of laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS), (b) identify the main chemical elements which derived from air pollution and may be used as indicators over the period 1965–2020 in Harbin, while excluding those that were controlled by physiological processes in the tree, and (c) reconstruct the history of pollution in Harbin by comparing the tree-ring chemical composition of recent decades with that of previous decades, in trees growing in the highly polluted city of Harbin and in trees growing in a control site 90 km away from major pollution sources. Briefly, the temporal trend of some elements was influenced by physiological factors, by environmental factors such as pollution, or influenced by both. Mg, K, Zn, Cu, Ni, Pb, As, Sr and Tl showed changes in pollution levels over time.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ B2FINDarrow_drop_down
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    B2FIND
    Dataset . 2023
    Data sources: B2FIND
    Environmental Data Portal
    Dataset . 2023
    License: CC 0
    Data sources: Datacite
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      B2FIND
      Dataset . 2023
      Data sources: B2FIND
      Environmental Data Portal
      Dataset . 2023
      License: CC 0
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: He, Zhichao;

    Title: Closer to causality: How effective is spatial planning in governing built-up land expansion in Fujian Province, China? Research objective: The Major Function Oriented Zone (MFOZ), the first strategic spatial plan in China, is developed to achieve a coordinated regional development, through spatial regulation and zoning of development. The MFOZ he MFOZ divided land into four major function-oriented zones: The development-optimized zone, the development-prioritised zone, the development-restricted zone, and the development-prohibited zone. We used propensity score marching to evaluate the effect of the MFOZ on built-up land expansion in Fujian Province over three time intervals (2013–2015, 2013–2018 and 2013–2020). Data: Data.xlsx contains the variables of 954 towns in Fujian Province. Town_ID is the town unique ID; County_ID is the county unique ID; City_ID is the city unique ID; MFOZ is the the development-prioritised zone and the development-restricted zone (The development-optimized zone and the development-prohibited zone are excluded); Builtup_13_15 is the built-up land expansion from 2013 to 2015; Builtup_13_18 is the built-up land expansion from 2013 to 2018; Builtup_13_20 is the built-up land expansion from 2013 to 2020; Dis2water is the Euclidean distance from the town to the nearest waterbody; Slope is the the average slope within the town; GDP is the average GDP in 2010 within the town; Pop is the average population in 2010 within the town; Road is the average population in 2010 within the town; Dis2city is the Euclidean distance from the town to the nearest prefectural city centre; Nei_Arable, Nei_Forest, and Nei_Built.up are the area of arable land, forest land, and built-up land neighbouring town i in 2010. Method: we used the propensity score matching to compare the changes in the amount of built-up land in the towns of the development-prioritised zone with the matched towns of the development-restricted zone. Additionally, we used three evaluation intervals (2013–2015, 2013–2018 and 2013–2020) to evaluate temporal variation in the causal effect of the MFOZ on built-up land expansion.

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    B2FIND
    Dataset . 2022
    Data sources: B2FIND
    Environmental Data Portal
    Dataset . 2022
    License: CC 0
    Data sources: Datacite
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      B2FIND
      Dataset . 2022
      Data sources: B2FIND
      Environmental Data Portal
      Dataset . 2022
      License: CC 0
      Data sources: Datacite
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    Authors: Pierri Daunt, Ana Beatriz; Inostroza, Luis; Hersperger, Anna;

    The present dataset is part of the published scientific paper entitled “The role of spatial planning in land change: An assessment of urban planning and nature conservation efficiency at the southeastern coast of Brazil” (Pierri Daunt, Inostroza and Hersperger, 2021). In this work, we evaluated the conformance of stated spatial planning goals and the outcomes in terms of urban compactness, basic services and housing provision, and nature conservation for different land-use strategies. We evaluate the 2005 Ecological-Economic Zoning (EEZ) and two municipal master plans from 2006 in a coastal region in São Paulo State, Brazil. We used Partial Least Squares Path Modelling (PLS-PM) to explain the relationship between the plan strategies and land-use change ten years after implementation in terms of urban compactness, basic services and housing increase, and nature conservation. We acquired the data for the explanatory variables from different sources listed on Table 1. Since the model is spatially explicit, all input data were transformed to a 30 m resolution raster. Regarding the evaluated spatial plans, we acquired the zones limits from the São Paulo State Environmental Planning Division (CPLA-SP), Ilhabela and Ubatuba municipality. 1) Land use and cover data: Urban persistence, Urban axial, Urban infill, Urban Isolates, Forest cover persistence, Forest cover gain, NDVI increase We acquired two Landsat Collection 1 Higher-Level Surface Reflectance images distributed by the U.S. Geological Survey (USGS), covering the entire study area (paths 76 and 77, row 220, WRS-2 reference system, https://earthexplorer.usgs.gov/). We classified one image acquired by the Landsat 5 Thematic Mapper (TM) sensor on 2005-05-150, and one image from the Landsat 8 Operational Land Imager (OLI) sensor from 2015-08-15. We collected 100 samples for forest cover, 100 samples for built-up cover and 100 samples for other classes. We then classified these three classes of land cover at each image date using the Support Vector Machine (SVM) supervised algorithm (Hsu et al., 2003), using ENVI 5.0 software. Land-use and land-cover changes from 2005 to 2015 were quantified using map algebra, by mathematically adding them together in pairs (10*LULC2015 + LULC2005). We reclassified the LULC data into forest gain (conversion of any 2005 LULC to forest cover in 2015); forest persistence (2005 forested pixels that remained forested in 2015); new built-up area (conversion of any 2005 LULC to built-up in 2015); and urban maintenance (2005 built-up pixels that remained built-up in 2015). To describe the spatial configuration of the urban expansion, we classified the new built-up areas into axial, infill and isolated, following Inostroza et al. (2013) (For details, please refer to Supplementary Material I at the original publication). The NDVI was obtained from the same source used for the LULC data. With the Google Engine platform, we used an annual average for the best pixels (without clouds) for 2005 and 2015, and we calculated the changes between dates. We used increases of > 0.2 NDVI to represent an improvement in forest quality. 2) Federal Census data organization: Urban Basic Services and Housing indicator, socioeconomic and population: The data used to infer the values of basic services provision, socioeconomic and population drivers was derived from the Brazilian National Census data (IBGE, 2000 and 2010). Population density, permanent housing unit density, mean income, basic education, and the percentage of houses receiving waste collection, sanitation and water provision services, called basic services in the context of this study, were calculated per 30 m pixel. The Human Development Index is only available at the municipality level. We attributed the HDI for the vector file with the municipality border, and we rasterized (30 m resolution) this file in QGIS. Annual rates of change were then calculated to allow comparability between LULC periods. To infer the BSH, we used only areas with an increase in permanent housing density and basic services provision (See Supplementary Material I at the original publication). 3) Topographic drivers To infer the values of the topographic driver, we used the slope data and the Topographic Index Position (TPI) based on the digital elevation model from SRTM (30 m resolution) produced by ALOS (freely available at eorc.jaxa.jp/ALOS/en/about/about_index.htm), and both variables were considered constant from 2005 to 2015 (See Supplementary Material I at the original publication).

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    B2FIND
    Dataset . 2022
    Data sources: B2FIND
    Environmental Data Portal
    Dataset . 2022
    License: CC 0
    Data sources: Datacite
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      B2FIND
      Dataset . 2022
      Data sources: B2FIND
      Environmental Data Portal
      Dataset . 2022
      License: CC 0
      Data sources: Datacite
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    Authors: Jackson N.; Badole S.; Morgan J.; Chhetri R.; +68 Authors

    The Low-Frequency Array (LOFAR) Long-Baseline Calibrator Survey (LBCS) was conducted between 2014 and 2019 in order to provide a set of calibrators for the LOFAR array. Here we present the complete survey, for which a preliminary analysis was published in 2016 using 20% of the survey data. The final catalogue consists of 30007 observations of 24713 sources in the northern sky, selected for a combination of bright low-frequency radio flux density and flat spectral index using existing surveys (WENSS, VLSS and MSSS). Approximately 1 calibrator per square degree is identified by the detection of compact flux, for declinations north of 30{deg} and a considerably lower density south of this point. The catalogue contains indicators of degree of correlated flux on baselines between the Dutch core and each of the international stations for all of the observations. Use of the VLBA calibrator list, together with statistical arguments by comparison with flux densities from lower-resolution catalogues, allow us to establish a rough flux density scale for the LBCS observations, so that LBCS statistics can be used to estimate compact fluxes, on scales between 300mas and 2-arcseconds, for sources observed in the survey. Cone search capability for table J/A+A/658/A2/lbcsall (Positions and coherence measurements) Cone search capability for table J/A+A/658/A2/lbcscons (Positions and coherence measurements, with multiple observations of the same source consolidated and with the best coherence measurement reported)

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    B2FIND
    Dataset . 2022
    Data sources: B2FIND
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      B2FIND
      Dataset . 2022
      Data sources: B2FIND
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    Authors: Schneebeli, Martin; Jaggi, Matthias; Löwe, Henning;

    The dataset contains several snow models in the Standard Tesselated Geometry File Format (stl) for 3D visualization, printing and additive manufacturing. Different snow types are available (new snow, rounded snow, depth hoar, buried surface hoar, graupel).

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    B2FIND
    Dataset . 2022
    Data sources: B2FIND
    Environmental Data Portal
    Dataset . 2022
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    Data sources: Datacite
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      B2FIND
      Dataset . 2022
      Data sources: B2FIND
      Environmental Data Portal
      Dataset . 2022
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    Authors: Casanelles Abella, Joan; Moretti, Marco;

    Data on: (1) spatial distribution of urban beekeeping (number of hives and number of beekeeping locations) in 14 Swiss cities (Geneva, Lausanne, Biel, Neuchatel, Basel, Zurich, Chur, Luzern, St. Gallen, Winterthur, Bern, Lugano, Bellinzona, Thun) for the period 2012-2018; (2) aggregated data to model the sustainability of urban beekeeping.

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    B2FIND
    Dataset . 2021
    Data sources: B2FIND
    Environmental Data Portal
    Dataset . 2021
    License: CC 0
    Data sources: Datacite
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      B2FIND
      Dataset . 2021
      Data sources: B2FIND
      Environmental Data Portal
      Dataset . 2021
      License: CC 0
      Data sources: Datacite
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    Authors: Shimwell T.W.; Rottgering H.J.A.; Best P.N.; Williams W.L.; +68 Authors

    The LOFAR Two-metre Sky Survey (LoTSS) is a deep 120-168MHz imaging survey that will eventually cover the entire Northern sky. Each of the 3170 pointings will be observed for 8 hrs, which, at most declinations, is sufficient to produce ~5-arcsec resolution images with a sensitivity of ~0.1mJy/beam and accomplish the main scientific aims of the survey which are to explore the formation and evolution of massive black holes, galaxies, clusters of galaxies and large-scale structure. Due to the compact core and long baselines of LOFAR, the images provide excellent sensitivity to both highly extended and compact emission. For legacy value, the data are archived at high spectral and time resolution to facilitate subarcsecond imaging and spectral line studies. In this paper we provide an overview of the LoTSS. We outline the survey strategy, the observational status, the current calibration techniques, a preliminary data release, and the anticipated scientific impact. The preliminary images that we have released were created using a fully-automated but direction-independent calibration strategy and are significantly more sensitive than those produced by any existing large-area low-frequency survey. In excess of 44,000 sources are detected in the images that have a resolution of 25-arcsec, typical noise levels of less than 0.5mJy/beam, and cover an area of over 350 square degrees in the region of the HETDEX Spring Field (right ascension 10:45:00 to 15:30:00 and declination 45:00:00 to 57:00:00). Cone search capability for table J/A+A/598/A104/lotss (Full source catalogue of LoTSS PDR 120-168MHz sources)

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    NARCIS
    Dataset
    Data sources: NARCIS
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    NARCIS
    Dataset . 2017
    Data sources: NARCIS
    B2FIND
    Dataset . 2017
    Data sources: B2FIND
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      NARCIS
      Dataset
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      NARCIS
      Dataset . 2017
      Data sources: NARCIS
      B2FIND
      Dataset . 2017
      Data sources: B2FIND
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Hagedorn, Frank;

    Data on soil nutrients and charcteristics, tree and understory biomass, foliage nutrients, mineralogy, soil temperature and moisture along an elevation gradient from boreal forest to mountain tundra in teh Khibiny mountains (Kola Penninsula, Russia). Samples were taken along two gradients at 7 elevation level either under tree canopy (tree) and in open areas (open).

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    B2FIND
    Dataset . 2024
    Data sources: B2FIND
    Environmental Data Portal
    Dataset . 2024
    License: CC BY SA
    Data sources: Datacite
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      B2FIND
      Dataset . 2024
      Data sources: B2FIND
      Environmental Data Portal
      Dataset . 2024
      License: CC BY SA
      Data sources: Datacite
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    Authors: Salak, Boris; Kienast, Felix;

    This study examines the siting scenarios for renewable energy infrastructure (REI) in Switzerland, incorporating the external costs of ecosystem services and, innovatively, social preferences. This approach challenges the prevalent techno-economic siting paradigm, which often overlooks these externalities. To minimize the external costs of the scenarios while maximizing energy yield, Marxan, an optimization software, was employed. MARXAN was run for 2 versions: a) without ground-mounted open space PV infrastructure (excl. OS) and b) with ground-mounted open space PV infrastructure (incl. OS). In each version optimization was done using ecological costs (ECUess) or social costs (ECUsoc) in a regular grid of 4x4km (planning unit). File: PU_data.shp: compressed Shapefile (ESRI) from the MARXAN optimization with 2216 rows (objects) and 18 columns (variables) with sf (simple feature) and data frame classes. Headers are described below: X01 = Planning_units (PUs) X02 = incl.OS. number of times pu was selected in MARXAN when optimized for ecological costs X03 = incl.OS. number of times pu was selected in MARXAN when optimized for social costs X04 = incl.OS. ecological costs (ECUess) of pu when realizing the total energy (normalized) X05 = incl.OS. social costs (ECUsoc) of pu when realizing the total energy (normalized) X06 = incl.OS. total energy (MWh/a) X07 = incl.OS. energy from roof-mounted PV infrastructure (MWh/a) X08 = incl.OS. energy from wind turbines (MWh/a) X09 = incl.OS. energy from ground-mounted PV infrastructure (MWh/a) X10 = excl.OS. number of times pu was selected in MARXAN when optimized for ecological costs X11 = excl.OS. number of times pu was selected in MARXAN when optimized for social costs X12 = excl.OS. ecological costs (ECUess) of pu when realizing the total energy (normalized) X13 = excl.OS. social costs (ECUsoc) of pu when realizing the total energy (normalized) X14 = excl.OS. total energy (MWh/a) X15 = excl.OS. energy from roof-mounted PV infrastructure (MWh/a) X16 = excl.OS. energy from wind turbines (MWh/a) X17 = excl.OS. energy from ground-mounted PV infrastructure (MWh/a) geometry = Simple feature XY geometry (SFC_POINT) each representing the center of a 4x4km planning unit (PU) in EPSG 21781 (CRS CH1903 / LV03). For MARXAN optimizations (for meaning of standard files in MARXAN see https://scholar.google.ch/scholar_url?url=https://courses.washington.edu/cfr590/software/Marxan1810/marxan_manual_1_8_2.pdf&hl=de&sa=X&ei=NAWHZI6JJ8PFmAGMo47oDQ&scisig=AGlGAw-DBrsV4kUzkR6GkN1jtG66&oi=scholarr), pu_data.shp was used to generate MARXAN files input.dat, pu.dat, puvfeat.dat, spec.dat. Coordinate Reference System: CH1903 / LV03. EPSG: 21781.

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    B2FIND
    Dataset . 2024
    Data sources: B2FIND
    Environmental Data Portal
    Dataset . 2024
    License: CC BY SA
    Data sources: Datacite
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      B2FIND
      Dataset . 2024
      Data sources: B2FIND
      Environmental Data Portal
      Dataset . 2024
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    Authors: Weber, Dominique; Schwieder, Marcel; Ritter, Lukas; Koch, Tiziana; +4 Authors

    A rule-based algorithm [(Schwieder et al., 2022)](https://doi.org/10.1016/j.rse.2021.112795) was used to produce annual maps for 2018–2021 of grassland-management events, i.e. mowing and/or grazing, for Switzerland using Sentinel-2 and Landsat 8 satellite time series. All satellite images were processed with the [FORCE](https://force-eo.readthedocs.io) framework. The resulting maps provide information on the number and timing of grassland-management events at a spatial resolution of 10 m × 10 m for the whole of Switzerland. For the final maps, permanent grasslands were masked using a variety of land-use layers, according to [Huber et al. (2022)](https://doi.org/10.1002/rse2.298) but replacing the crop mask with the agricultural-use data from the cantons. We assessed the detection of management events based on independent reference data, which we acquired from daily time series of publicly available webcams that are widely distributed across Switzerland. We further tested the ecological relevance of the generated intensity measures in relation to nationwide biodiversity data (see [Weber et al., 2023](https://doi.org/10.1002/rse2.372)). The webcam-based reference data used for verification was subsequently added on 14.02.2024.

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    B2FIND
    Dataset . 2023
    Data sources: B2FIND
    Environmental Data Portal
    Dataset . 2023
    License: CC BY SA
    Data sources: Datacite
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      B2FIND
      Dataset . 2023
      Data sources: B2FIND
      Environmental Data Portal
      Dataset . 2023
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    Authors: Paula, Ballikaya,; Wenqi, Song,; Marcel, Guillong,; Olivier, Bachmann,; +2 Authors

    This dataset contains the values of several chemical elements (Mg, Al, Si, S, K, Ca, Ti, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Sr, Tl, Pb, Bi) measured in the latewood of tree rings of Mongolian oak from Harbin, China, at a 5-year resolution. Due to the lack of a suitable reference material for wood, absolute concentration was not calculated, and the ratio between the chemical element and 13C was taken as proxy for the element signal. In Harbin, one of the largest cities and most important industrial centers in northeastern China, air quality monitoring systems were built only by the end of 2015 to meet the national requirements. Thus, dendrochemical analyses could be used as a tool to complement for the lack of air quality data over longer periods of time, allowing for the reconstruction of the temporal trend of trace metals. Our main scopes were to: (a) assess the chemical composition of Quercus mongolica Fisch. ex Ledeb. tree rings from Harbin using a recently developed system of laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS), (b) identify the main chemical elements which derived from air pollution and may be used as indicators over the period 1965–2020 in Harbin, while excluding those that were controlled by physiological processes in the tree, and (c) reconstruct the history of pollution in Harbin by comparing the tree-ring chemical composition of recent decades with that of previous decades, in trees growing in the highly polluted city of Harbin and in trees growing in a control site 90 km away from major pollution sources. Briefly, the temporal trend of some elements was influenced by physiological factors, by environmental factors such as pollution, or influenced by both. Mg, K, Zn, Cu, Ni, Pb, As, Sr and Tl showed changes in pollution levels over time.

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    B2FIND
    Dataset . 2023
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    Environmental Data Portal
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      B2FIND
      Dataset . 2023
      Data sources: B2FIND
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    Authors: He, Zhichao;

    Title: Closer to causality: How effective is spatial planning in governing built-up land expansion in Fujian Province, China? Research objective: The Major Function Oriented Zone (MFOZ), the first strategic spatial plan in China, is developed to achieve a coordinated regional development, through spatial regulation and zoning of development. The MFOZ he MFOZ divided land into four major function-oriented zones: The development-optimized zone, the development-prioritised zone, the development-restricted zone, and the development-prohibited zone. We used propensity score marching to evaluate the effect of the MFOZ on built-up land expansion in Fujian Province over three time intervals (2013–2015, 2013–2018 and 2013–2020). Data: Data.xlsx contains the variables of 954 towns in Fujian Province. Town_ID is the town unique ID; County_ID is the county unique ID; City_ID is the city unique ID; MFOZ is the the development-prioritised zone and the development-restricted zone (The development-optimized zone and the development-prohibited zone are excluded); Builtup_13_15 is the built-up land expansion from 2013 to 2015; Builtup_13_18 is the built-up land expansion from 2013 to 2018; Builtup_13_20 is the built-up land expansion from 2013 to 2020; Dis2water is the Euclidean distance from the town to the nearest waterbody; Slope is the the average slope within the town; GDP is the average GDP in 2010 within the town; Pop is the average population in 2010 within the town; Road is the average population in 2010 within the town; Dis2city is the Euclidean distance from the town to the nearest prefectural city centre; Nei_Arable, Nei_Forest, and Nei_Built.up are the area of arable land, forest land, and built-up land neighbouring town i in 2010. Method: we used the propensity score matching to compare the changes in the amount of built-up land in the towns of the development-prioritised zone with the matched towns of the development-restricted zone. Additionally, we used three evaluation intervals (2013–2015, 2013–2018 and 2013–2020) to evaluate temporal variation in the causal effect of the MFOZ on built-up land expansion.

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    B2FIND
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      B2FIND
      Dataset . 2022
      Data sources: B2FIND
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      Dataset . 2022
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    Authors: Pierri Daunt, Ana Beatriz; Inostroza, Luis; Hersperger, Anna;

    The present dataset is part of the published scientific paper entitled “The role of spatial planning in land change: An assessment of urban planning and nature conservation efficiency at the southeastern coast of Brazil” (Pierri Daunt, Inostroza and Hersperger, 2021). In this work, we evaluated the conformance of stated spatial planning goals and the outcomes in terms of urban compactness, basic services and housing provision, and nature conservation for different land-use strategies. We evaluate the 2005 Ecological-Economic Zoning (EEZ) and two municipal master plans from 2006 in a coastal region in São Paulo State, Brazil. We used Partial Least Squares Path Modelling (PLS-PM) to explain the relationship between the plan strategies and land-use change ten years after implementation in terms of urban compactness, basic services and housing increase, and nature conservation. We acquired the data for the explanatory variables from different sources listed on Table 1. Since the model is spatially explicit, all input data were transformed to a 30 m resolution raster. Regarding the evaluated spatial plans, we acquired the zones limits from the São Paulo State Environmental Planning Division (CPLA-SP), Ilhabela and Ubatuba municipality. 1) Land use and cover data: Urban persistence, Urban axial, Urban infill, Urban Isolates, Forest cover persistence, Forest cover gain, NDVI increase We acquired two Landsat Collection 1 Higher-Level Surface Reflectance images distributed by the U.S. Geological Survey (USGS), covering the entire study area (paths 76 and 77, row 220, WRS-2 reference system, https://earthexplorer.usgs.gov/). We classified one image acquired by the Landsat 5 Thematic Mapper (TM) sensor on 2005-05-150, and one image from the Landsat 8 Operational Land Imager (OLI) sensor from 2015-08-15. We collected 100 samples for forest cover, 100 samples for built-up cover and 100 samples for other classes. We then classified these three classes of land cover at each image date using the Support Vector Machine (SVM) supervised algorithm (Hsu et al., 2003), using ENVI 5.0 software. Land-use and land-cover changes from 2005 to 2015 were quantified using map algebra, by mathematically adding them together in pairs (10*LULC2015 + LULC2005). We reclassified the LULC data into forest gain (conversion of any 2005 LULC to forest cover in 2015); forest persistence (2005 forested pixels that remained forested in 2015); new built-up area (conversion of any 2005 LULC to built-up in 2015); and urban maintenance (2005 built-up pixels that remained built-up in 2015). To describe the spatial configuration of the urban expansion, we classified the new built-up areas into axial, infill and isolated, following Inostroza et al. (2013) (For details, please refer to Supplementary Material I at the original publication). The NDVI was obtained from the same source used for the LULC data. With the Google Engine platform, we used an annual average for the best pixels (without clouds) for 2005 and 2015, and we calculated the changes between dates. We used increases of > 0.2 NDVI to represent an improvement in forest quality. 2) Federal Census data organization: Urban Basic Services and Housing indicator, socioeconomic and population: The data used to infer the values of basic services provision, socioeconomic and population drivers was derived from the Brazilian National Census data (IBGE, 2000 and 2010). Population density, permanent housing unit density, mean income, basic education, and the percentage of houses receiving waste collection, sanitation and water provision services, called basic services in the context of this study, were calculated per 30 m pixel. The Human Development Index is only available at the municipality level. We attributed the HDI for the vector file with the municipality border, and we rasterized (30 m resolution) this file in QGIS. Annual rates of change were then calculated to allow comparability between LULC periods. To infer the BSH, we used only areas with an increase in permanent housing density and basic services provision (See Supplementary Material I at the original publication). 3) Topographic drivers To infer the values of the topographic driver, we used the slope data and the Topographic Index Position (TPI) based on the digital elevation model from SRTM (30 m resolution) produced by ALOS (freely available at eorc.jaxa.jp/ALOS/en/about/about_index.htm), and both variables were considered constant from 2005 to 2015 (See Supplementary Material I at the original publication).

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    B2FIND
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    Dataset . 2022
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      B2FIND
      Dataset . 2022
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    Authors: Jackson N.; Badole S.; Morgan J.; Chhetri R.; +68 Authors

    The Low-Frequency Array (LOFAR) Long-Baseline Calibrator Survey (LBCS) was conducted between 2014 and 2019 in order to provide a set of calibrators for the LOFAR array. Here we present the complete survey, for which a preliminary analysis was published in 2016 using 20% of the survey data. The final catalogue consists of 30007 observations of 24713 sources in the northern sky, selected for a combination of bright low-frequency radio flux density and flat spectral index using existing surveys (WENSS, VLSS and MSSS). Approximately 1 calibrator per square degree is identified by the detection of compact flux, for declinations north of 30{deg} and a considerably lower density south of this point. The catalogue contains indicators of degree of correlated flux on baselines between the Dutch core and each of the international stations for all of the observations. Use of the VLBA calibrator list, together with statistical arguments by comparison with flux densities from lower-resolution catalogues, allow us to establish a rough flux density scale for the LBCS observations, so that LBCS statistics can be used to estimate compact fluxes, on scales between 300mas and 2-arcseconds, for sources observed in the survey. Cone search capability for table J/A+A/658/A2/lbcsall (Positions and coherence measurements) Cone search capability for table J/A+A/658/A2/lbcscons (Positions and coherence measurements, with multiple observations of the same source consolidated and with the best coherence measurement reported)

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    B2FIND
    Dataset . 2022
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      B2FIND
      Dataset . 2022
      Data sources: B2FIND
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    Authors: Schneebeli, Martin; Jaggi, Matthias; Löwe, Henning;

    The dataset contains several snow models in the Standard Tesselated Geometry File Format (stl) for 3D visualization, printing and additive manufacturing. Different snow types are available (new snow, rounded snow, depth hoar, buried surface hoar, graupel).

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    B2FIND
    Dataset . 2022
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    Environmental Data Portal
    Dataset . 2022
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      B2FIND
      Dataset . 2022
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    Authors: Casanelles Abella, Joan; Moretti, Marco;

    Data on: (1) spatial distribution of urban beekeeping (number of hives and number of beekeeping locations) in 14 Swiss cities (Geneva, Lausanne, Biel, Neuchatel, Basel, Zurich, Chur, Luzern, St. Gallen, Winterthur, Bern, Lugano, Bellinzona, Thun) for the period 2012-2018; (2) aggregated data to model the sustainability of urban beekeeping.

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    B2FIND
    Dataset . 2021
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    Dataset . 2021
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      B2FIND
      Dataset . 2021
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      Dataset . 2021
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    Authors: Shimwell T.W.; Rottgering H.J.A.; Best P.N.; Williams W.L.; +68 Authors

    The LOFAR Two-metre Sky Survey (LoTSS) is a deep 120-168MHz imaging survey that will eventually cover the entire Northern sky. Each of the 3170 pointings will be observed for 8 hrs, which, at most declinations, is sufficient to produce ~5-arcsec resolution images with a sensitivity of ~0.1mJy/beam and accomplish the main scientific aims of the survey which are to explore the formation and evolution of massive black holes, galaxies, clusters of galaxies and large-scale structure. Due to the compact core and long baselines of LOFAR, the images provide excellent sensitivity to both highly extended and compact emission. For legacy value, the data are archived at high spectral and time resolution to facilitate subarcsecond imaging and spectral line studies. In this paper we provide an overview of the LoTSS. We outline the survey strategy, the observational status, the current calibration techniques, a preliminary data release, and the anticipated scientific impact. The preliminary images that we have released were created using a fully-automated but direction-independent calibration strategy and are significantly more sensitive than those produced by any existing large-area low-frequency survey. In excess of 44,000 sources are detected in the images that have a resolution of 25-arcsec, typical noise levels of less than 0.5mJy/beam, and cover an area of over 350 square degrees in the region of the HETDEX Spring Field (right ascension 10:45:00 to 15:30:00 and declination 45:00:00 to 57:00:00). Cone search capability for table J/A+A/598/A104/lotss (Full source catalogue of LoTSS PDR 120-168MHz sources)

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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ NARCISarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      Dataset . 2017
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