• Energy Research
• Open Access close
• Restricted close
• Open Source close
• Embargo close
• English close

Although GPS coordinates for current populations are not included due to the potential threat of poaching, the climate variables for each species are provided. The records for extant gecko and skinks mainly came from the New Zealand's Department of Conervation Herpetofauna Database. After updating the taxonomy and cleaning the data to reflect the taxonomy as at 2019 of 43 geckos speceis recognised across seven genera and 61 species in genus, we then thinned the occurrence records at a 1 km resolution for all species then predicted distributions for those with > 15 records using species distribution models. The climate variables for each species were selected among annual mean temperature (bio1), maximum temperature of the warmest month (bio5), minimum temperature of the coldest month (bio6), mean temperature of driest quarter (bio9), mean temperature of wettest quarter (bio10), and precipitation of the driest quarter (bio17). To reduce multicollinearity in species distribution models for each species, we only retained climate variables with a variable inflation factor < 10. The climate variables were from the CHELSA database (https://chelsa-climate.org/), which can be freely downloaded for current and future scenarios. We also provide MCC tree files for the geckos and skinks. The phylogenetic trees have been constructed for NZ geckos by (Nielsen et al., 2011) and for NZ skinks by (Chapple et al., 2009). For geckos we used a subset of the sequences used by Nielsen et al. (2011) for four genes, two nuclear (RAG 1, PDC) and two mitochondrial (16S, ND2 along with flanking tRNA sequences). For skinks, we used sequences from Chapple et al. (2009) for one nuclear (RAG 1) and five mitochondrial (ND2, ND4, Cyt b, 12S and 16S) genes, and additional ND2 sequences for taxa not included in the original phylogeny (Chapple et al., 2011, p. 201). In total we used sequences for all recognised extant taxa (Hitchmough et al., 2016) as at 2019 except for three species of skink (O. aff. inconspicuum “Okuru”, O. robinsoni, and O. aff. inconspicuum “North Otago”) and two species of gecko (M. “Cupola” and W. “Kaikouras”) for which genetic data were not available. Aim: The primary drivers of species and population extirpations have been habitat loss, overexploitation, and invasive species, but human-mediated climate change is expected to be a major driver in future. To minimise biodiversity loss, conservation managers should identify species vulnerable to climate change and prioritise their protection. Here, we estimate climatic suitability for two speciose taxonomic groups, then use phylogenetic analyses to assess vulnerability to climate change. Location: Aotearoa New Zealand (NZ) Taxa: NZ lizards: diplodactylid geckos and eugongylinae skinks Methods: We built correlative species distribution models (SDMs) for NZ geckos and skinks to estimate climatic suitability under current climate and 2070 future-climate scenarios. We then used Bayesian phylogenetic mixed models (BPMMs) to assess vulnerability for both groups with predictor variables for life history traits (body size and activity phase) and current distribution (elevation and latitude). We explored two scenarios: an unlimited dispersal scenario, where projections track climate, and a no-dispersal scenario, where projections are restricted to areas currently identified as suitable. Results: SDMs projected vulnerability to climate change for most modelled lizards. For species’ ranges projected to decline in climatically suitable areas, average decreases were between 42–45% for geckos and 33–91% for skinks, although area did increase or remain stable for a minority of species. For the no-dispersal scenario, the average decrease for geckos was 37–52% and for skinks was 33–52%. Our BPMMs showed phylogenetic signal in climate change vulnerability for both groups, with elevation increasing vulnerability for geckos, and body size reducing vulnerability for skinks. Main conclusions: NZ lizards showed variable vulnerability to climate change, with most species’ ranges predicted to decrease. For species whose suitable climatic space is projected to disappear from within their current range, managed relocation could be considered to establish populations in regions that will be suitable under future climates.

Ekvivalentinės juodosios anglies matavimai Isproje, Italijoje. Măsurători ale carbonului negru echivalent în Ispra, Italia. Вимірювання еквівалентного чорного вуглецю в Іспрі, Італія. Измервания на еквивалентен черен въглерод в Испра, Италия. Merania ekvivalentného čierneho uhlíka v Ispre, Taliansko. Tomhais de charbón dubh coibhéiseach in Ispra na hIodáile. Metingen van equivalente zwarte koolstof in Ispra, Italië. Mediciones de carbono negro equivalente en Ispra, Italia. Measurements of equivalent black carbon in Ispra, Italy. Pomiary równoważnego czarnego węgla w Ispra we Włoszech.

Wheat Biomass for Kherson and Poltava regions in Ukraine The dataset contains Dry Above Ground Biomass (DAM) estimates over the Kherson and Poltava regions in Ukraine for years 2020,2021 and 2022. - Processing:The processing is done using the AgriCarbon-EOv1.5 processing chain, using the TREX processing centre at CNES France.The input remote sensing data are L2A Sentinel-2 surface reflectances provided by the MAJA processing chain based on the Copernicus Sentinel-2 L1C data.The Landcover maps are provided using ML Deep learning based on the Copernicus L2A data.The daily weather data is extracted from ERA5Land products (C3S). -Geophysical variable:Dry Above ground biomass of winter wheat in g/m2. - Extents: * DAM estimates over the Copernicus Sentinel-2 tile 36TWT cover the Kherson region.* DAM estimates over the Copernicus Sentinel-2 tile 36UVA cover the Poltava region. - Spatial resolution:10m resolution estimlates over wheat plots identified in the landcover map. - Temporal coverage:Estimates are provided at the end of the wheat cycle for cycles:* The year 2020 correspond to cycle: 2019-2020* The year 2021 corresponds to cycle : 2020-2021* The year 2022 corresponds to cycle : 2021-2022 - Projection: EPSG:32636 - File content: Each Raster file has 2 bands containing respectively: * band1: mean value of DAM in g/m2. * band2: standard deviation of DAM in g/m2. - List of maps:* Dry_aboveground_biomass_2020_T36TWT_Kherson_Ukraine.tif* Dry_aboveground_biomass_2020_T36UVA_Poltava_Ukraine.tif* Dry_aboveground_biomass_2021_T36TWT_Kherson_Ukraine.tif* Dry_aboveground_biomass_2021_T36UVA_Poltava_Ukraine.tif* Dry_aboveground_biomass_2022_T36TWT_Kherson_Ukraine.tif* Dry_aboveground_biomass_2022_T36UVA_Poltava_Ukraine.tif

Dataset for the fresh and hardened properties of self-healing concrete, and the bond properties of steel reinforcement in self-healing concrete

Overview The following dataset presents the energy cycle characteristics for 5G/6G mobile systems supported by Renewable Energy Sources (RES) and/or Unmanned Aerial Vehicles (UAVs) and Reconfigurable Intelligent Surfaces (RISs). In addition, within the dataset, the energy gain related to the engagement of RES within the Radio Access Network (RAN) has also been distinguished. Scenario The considered network scenario includes 8 three- (_results_gcas.csv) or one-cell (_results_scas.csv & _results_kras.csv) base stations (BSs) placed within the Poznan city (surroundings of the old market) and supported by Renewable Energy Sources — photovoltaic panels (PVs) and/or wind turbines (WTs). The aforementioned base stations can be treated as stationary towers or mobile access points (e.g., drones/UAVs). Those latter have been additionally equipped with RIS devices, which are able to reflect and manipulate a radio signal to influence occurrences such as interferences, coverage, or human exposure. However, the use of RISs has been taken into account only to evaluate the impact of the engagement of such devices on the energy side of the mobile system, omitting the changes in radio characteristics. The network traffic has been assumed to be fixed (64 mobile users (UEs) with 100 Mbps downlink — DL, and 25 Mbps uplink — UL, per each), however, its density in specific parts of the city is modeled randomly for each simulation run. The simulation runs have been performed for 4 dates (vernal equinox, summer solstice, autumn equinox, winter solstice), each one from a different season of the year. The aim of such an approach was to highlight the impact of the time of the day and the year on the energy gain obtained thanks to enabling RES generators. The weather conditions assumed within the simulation are typical for the climate in Poland. Methodology The energy-cycle calculations (system's power consumption, renewable energy production, and excessive energy storage) have been based on the mathematical formulas from the scientific literature and performed within the digital simulation runs by using the Green Radio Access Network Design (GRAND) tool (developed by teams from the Ghent University & Poznan University of Technology). The UE-BS association process within the mobile system has been done by doing multi-objective optimization using the Gurobi software, which has taken into account parameters like path loss, predicted power consumption of BSs, and guaranteed DL & UL bit rates for UEs. Simulation setup The setup of the input parameters for used mathematical models (power consumption, energy generation, energy storage) has been done in accordance with the values attached within the delivered literature positions (cited within the publications included in the Related works section of the following dataset) and adjusted to the considered study. Furthermore, the data used to model the network environment (building distribution, coverage area, base stations' locations) as well as to predict weather conditions are the real data (for the year 2022) collected by the city hall of Poznan, one of the Polish mobile operators, and weather stations placed in Poznan, respectively. The number of simulation runs performed has been equal to 10 (each run has included energy-cycle calculations for 4 seasons of the year), with the time step of a single run set to 1 hour of the day. Results The results of the aforementioned investigations have been included in the attached files, which can be described as follows: File _results_gcas.csv The first column denotes the date (season of the year), for which the values have been obtained. The columns from second to fifth present observed values of the State of Charge (SoC) of a battery system (in %) for a single network cell on average in a time step. Those columns are the obtained values for the RAN, in which no RES, only PVs, only WTs, and both types of RES generators have been enabled, respectively. Files _results_scas.csv & _results_kras.csv The first column denotes the date (season of the year), for which the values have been obtained. The second and third columns denote the number of drone base station (DBS) exchanges within the wireless system on average in a particular time step, where no RES and only PVs are enabled, respectively. The fourth and fifth columns present the conventional (fossil-fuels-based) energy consumption (in kWh) for the whole system in a specific time step, in which no RES and only PVs are engaged for all the access nodes. The sixth column is the energy savings (in kWh) related to the use of RES generators within the mobile network. Furthermore, the seventh and eighth columns represent the amount of renewable energy harvested from the solar radiation in total and the peak value of this amount observed during the entire day, respectively. Acknowledgment More details about the conducted studies have been described within the attached papers (Related works section). The data has been collected within the COST CA10210 INTERACT. M. Deruyck is a Post-Doctoral Fellow of the FWO-V (Research Foundation – Flanders, ref: 12Z5621N). The work (including the following dataset preparation) by A. Samorzewski and A. Kliks was realized within project no. 2021/43/B/ST7/01365 funded by the National Science Center in Poland.

The Global Yield Gap Atlas project (GYGA - http://yieldgap.org ) has undertaken a yield gap assessment following the protocol recommended by van Ittersum et. al. (van Ittersum et. al., 2013). This datafile holds the results for rainfed rice.

This spreadsheet contains the results for the article, "Meeting the costs of decarbonising industry – the potential effects on prices and competitiveness (a case study of the UK)". These include projected impacts for industrial process decarbonisation (costs, fuel use, residual emissions), for key years (2030, 2040, 2050), distributed in the following ways: - Directly allocated to industrial sector in which they occur - Shared between sectors in proportion to the share of GVA of each supply chain - Embodied in final products - Embodied in final products, aggregated to consumption patterns The source of the projections and the method to perform the distribution are described in detail in the associated article. Further relevant documentation may be found in the following resources. Cooper, S. J.G., Allen, S. R., Gailani, A., Norman, J. B., Owen, A., Barrett, J., and Taylor, P., 2024. Meeting the costs of decarbonising industry – The potential effects on prices and competitiveness (a case study of the UK). Energy Policy, 184, 113904. Available from: https://doi.org/10.1016/j.enpol.2023.113904. For details of the methods used, please see the associated journal article.

Στοιχεία σχετικά με: (1) χωρική κατανομή της αστικής μελισσοκομίας (αριθμός κυψελών και αριθμός μελισσοκομικών τοποθεσιών) σε 14 ελβετικές πόλεις (Γενεύη, Λωζάνη, Biel, Neuchatel, Βασιλεία, Ζυρίχη, Chur, Luzern, St. Gallen, Winterthur, Βέρνη, Λουγκάνο, Bellinzona, Thun) για την περίοδο 2012-2018· (2) συγκεντρωτικά δεδομένα για τη μοντελοποίηση της βιωσιμότητας της αστικής μελισσοκομίας. Στοιχεία σχετικά με: (1) χωρική κατανομή της αστικής μελισσοκομίας (αριθμός κυψελών και αριθμός μελισσοκομικών τοποθεσιών) σε 14 ελβετικές πόλεις (Γενεύη, Λωζάνη, Biel, Neuchatel, Βασιλεία, Ζυρίχη, Chur, Luzern, St. Gallen, Winterthur, Βέρνη, Λουγκάνο, Bellinzona, Thun) για την περίοδο 2012-2018· (2) συγκεντρωτικά δεδομένα για τη μοντελοποίηση της βιωσιμότητας της αστικής μελισσοκομίας. Στοιχεία σχετικά με: (1) χωρική κατανομή της αστικής μελισσοκομίας (αριθμός κυψελών και αριθμός μελισσοκομικών τοποθεσιών) σε 14 ελβετικές πόλεις (Γενεύη, Λωζάνη, Biel, Neuchatel, Βασιλεία, Ζυρίχη, Chur, Luzern, St. Gallen, Winterthur, Βέρνη, Λουγκάνο, Bellinzona, Thun) για την περίοδο 2012-2018· (2) συγκεντρωτικά δεδομένα για τη μοντελοποίηση της βιωσιμότητας της αστικής μελισσοκομίας. Données sur: (1) répartition spatiale de l’apiculture urbaine (nombre de ruches et nombre de sites apicoles) dans 14 villes suisses (Genève, Lausanne, Biel, Neuchatel, Bâle, Zurich, Chur, Luzern, St Gallen, Winterthur, Berne, Lugano, Bellinzona, Thun) pour la période 2012-2018; (2) des données agrégées pour modéliser la durabilité de l’apiculture urbaine. Données sur: (1) répartition spatiale de l’apiculture urbaine (nombre de ruches et nombre de sites apicoles) dans 14 villes suisses (Genève, Lausanne, Biel, Neuchatel, Bâle, Zurich, Chur, Luzern, St Gallen, Winterthur, Berne, Lugano, Bellinzona, Thun) pour la période 2012-2018; (2) des données agrégées pour modéliser la durabilité de l’apiculture urbaine. Données sur: (1) répartition spatiale de l’apiculture urbaine (nombre de ruches et nombre de sites apicoles) dans 14 villes suisses (Genève, Lausanne, Biel, Neuchatel, Bâle, Zurich, Chur, Luzern, St Gallen, Winterthur, Berne, Lugano, Bellinzona, Thun) pour la période 2012-2018; (2) des données agrégées pour modéliser la durabilité de l’apiculture urbaine. Údaje o: (1) prostorové rozložení včelařství (počet úlů a počet včelařských lokalit) ve 14 švýcarských městech (Ženeva, Lausanne, Biel, Neuchatel, Basilej, Curych, Chur, Luzern, St. Gallen, Winterthur, Bern, Lugano, Bellinzona, Thun) na období 2012–2018; (2) souhrnné údaje pro modelování udržitelnosti včelařství ve městech. Údaje o: (1) prostorové rozložení včelařství (počet úlů a počet včelařských lokalit) ve 14 švýcarských městech (Ženeva, Lausanne, Biel, Neuchatel, Basilej, Curych, Chur, Luzern, St. Gallen, Winterthur, Bern, Lugano, Bellinzona, Thun) na období 2012–2018; (2) souhrnné údaje pro modelování udržitelnosti včelařství ve městech. Údaje o: (1) prostorové rozložení včelařství (počet úlů a počet včelařských lokalit) ve 14 švýcarských městech (Ženeva, Lausanne, Biel, Neuchatel, Basilej, Curych, Chur, Luzern, St. Gallen, Winterthur, Bern, Lugano, Bellinzona, Thun) na období 2012–2018; (2) souhrnné údaje pro modelování udržitelnosti včelařství ve městech. Data dwar: (1) id-distribuzzjoni spazjali tal-apikultura urbana (l-għadd ta’ doqqajs u n-numru ta’ postijiet tat-trobbija tan-naħal) f’14-il belt Svizzera (Ġinevra, Lausanne, Biel, Neuchatel, Basel, Zurich, Chur, Luzern, St. Gallen, Winterthur, Bern, Lugano, Bellinzona, Thun) għall-perjodu 2012–2018; (2) dejta aggregata biex timmudella s-sostenibbiltà tat-trobbija urbana tan-naħal. Data dwar: (1) id-distribuzzjoni spazjali tal-apikultura urbana (l-għadd ta’ doqqajs u n-numru ta’ postijiet tat-trobbija tan-naħal) f’14-il belt Svizzera (Ġinevra, Lausanne, Biel, Neuchatel, Basel, Zurich, Chur, Luzern, St. Gallen, Winterthur, Bern, Lugano, Bellinzona, Thun) għall-perjodu 2012–2018; (2) dejta aggregata biex timmudella s-sostenibbiltà tat-trobbija urbana tan-naħal. Daten zu: (1) räumliche Verteilung der Bienenzucht (Anzahl der Bienenstöcke und Anzahl der Imkereistandorte) in 14 Schweizer Städten (Genf, Lausanne, Biel, Neuchatel, Basel, Zürich, Chur, Luzern, St. Gallen, Winterthur, Bern, Lugano, Bellinzona, Thun) für den Zeitraum 2012-2018; (2) aggregierte Daten zur Modellierung der Nachhaltigkeit der städtischen Bienenzucht. Daten zu: (1) räumliche Verteilung der Bienenzucht (Anzahl der Bienenstöcke und Anzahl der Imkereistandorte) in 14 Schweizer Städten (Genf, Lausanne, Biel, Neuchatel, Basel, Zürich, Chur, Luzern, St. Gallen, Winterthur, Bern, Lugano, Bellinzona, Thun) für den Zeitraum 2012-2018; (2) aggregierte Daten zur Modellierung der Nachhaltigkeit der städtischen Bienenzucht. Sonraí maidir le: (1) dáileadh spásúil na beachaireachta uirbí (líon na gcoirceog agus líon na láithreacha beachaireachta) i 14 chathair na hEilvéise (an Ghinéiv, Lausanne, Biel, Neuchatel, Basel, Zurich, Chur, Luzern, St. Gallen, Winterthur, Bern, Lugano, Bellinzona, Thun) don tréimhse 2012-2018; (2) sonraí comhiomlánaithe chun inbhuanaitheacht na beachaireachta uirbí a shamhaltú. Gegevens over: (1) ruimtelijke verdeling van de stedelijke bijenteelt (aantal bijenkasten en aantal bijenteeltlocaties) in 14 Zwitserse steden (Geneva, Lausanne, Biel, Neuchatel, Bazel, Zürich, Chur, Luzern, St. Gallen, Winterthur, Bern, Lugano, Bellinzona, Thun) voor de periode 2012-2018; (2) geaggregeerde gegevens om de duurzaamheid van de stedelijke bijenteelt te modelleren. Dane dotyczące: 1) rozmieszczenie przestrzenne pszczelarstwa miejskiego (liczba uli i liczba miejsc pszczelarskich) w 14 miastach Szwajcarii (Genewa, Lozanna, Biel, Neuchatel, Bazylea, Zurych, Chur, Luzern, St. Gallen, Winterthur, Berno, Lugano, Bellinzona, Thun) w latach 2012–2018; 2) dane zagregowane w celu modelowania zrównoważonego rozwoju pszczelarstwa miejskiego. Tiedot seuraavista: 1) kaupunkien mehiläishoidon alueellinen jakautuminen (pesien lukumäärä ja mehiläishoitopaikkojen lukumäärä) 14 Sveitsin kaupungissa (Geneva, Lausanne, Biel, Neuchatel, Basel, Zürich, Chur, Luzern, St. Gallen, Winterthur, Bern, Lugano, Bellinzona, Thun) vuosina 2012–2018; (2) aggregoidut tiedot kaupunkien mehiläishoidon kestävyyden mallintamiseksi. Datos sobre: (1) distribución espacial de la apicultura urbana (número de colmenas y número de lugares de apicultura) en 14 ciudades suizas (Ginebra, Lausana, Biel, Neuchatel, Basilea, Zúrich, Chur, Luzern, St. Gallen, Winterthur, Bern, Lugano, Bellinzona, Thun) para el período 2012-2018; (2) datos agregados para modelar la sostenibilidad de la apicultura urbana.