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Supporting data for publication 'Performance analysis of a high-efficiency multi-bed active magnetic regenerator device" submitted to the Applied Thermal Engineering (DOI 10.1016/j.applthermaleng.2021.117569)The Excel sheet summarizes the experimental output parameters for the performance data presented in the publication. All data were measured continuously after reaching steady-state conditions, and the data were averaged over a time span of 600 s.

Abstract District heating (DH) enables the utilisation and distribution of heating from sources unfeasible for stand-alone applications and combined with cogeneration of heat and power (CHP), has been the cornerstone of Denmark’s realisation of a steady national primary energy supply over the last four decades. However, progressively more energy-efficient houses and a steadily improving heat pump (HP) performance for individual dwellings is straining the competitive advantage of the CHP–DH combination as DH grid losses are growing in relative terms due to decreasing heating demands of buildings and relatively high DH supply temperatures. A main driver for the DH water temperature is the requirements for domestic hot water (DHW) production. This article investigates two alternatives for DHW supply: (a) DH based on central HPs combined with a heat exchanger, and (b) a combination of DH based on central HPs and a small booster HP using DH water as low-temperature source for DHW production. The analyses are conducted using the energyPRO simulation model and are conducted with hourly varying factors; heating demands, DH grid losses, HP coefficient of performance (COP) and spot market prices in order to be able to analyse the relative performance of the two options and their performance over the year. Results are also compared to individual boilers and individual HPs. The results indicate that applying booster HPs enables the DH system to operate at substantially lower temperature levels, improving the COP of central DH HPs while simultaneously lowering DH grid losses significantly. Thus, DH performance is increased significantly. Additionally, performance for the DH HP with booster combination is considerably better than individual boiler or HP solutions.

AbstractWe devise a methodology to predict failures in wind turbine drive‐train components and quantify its utility. The methodology consists of two main steps. The first step is the set up of a predictive model for shutdown events, which is able to raise an alarm in advance of the fault‐induced shutdown. The model is trained on data for shutdown events retrieved from the alarm log of an offshore wind farm. Here, it is assumed that the timely prediction of low‐severity events, typically caused by abnormal component operation, allows for an intervention that can prevent premature component failures. The prediction models are based on statistical classification using only supervisory control and data acquisition (SCADA) data. In the second step, the shutdown prediction model is combined with a cost model to provide an estimate of the benefits associated with implementing the predictive maintenance system. This is achieved by computing the maximum net utility attainable as a function of the model performance and efficiency of intervention carried out by the user. Results show that the system can be expected to be cost‐effective under specific conditions. A discussion about potential improvements of the approach is provided, along with suggestions for further research in this area.

The three datasets contain the spectral data acquired on waste wood samples using a handheld spectrophotometer (MicroNIR™ OnSite instrument). The waste wood samples have been collected in a panel board company located in the Northern part of Italy during two days of sampling (February 18-19, 2020). In detail, 24 randomly distributed increments have been collected from 16 static lots, resulting in a total of 384 samples (we note these DT-SamTot). All the samples have been analyzed by Near-Infrared (NIR) spectrophotometer directly on site. In addition, four of the 24 increments for each lot - resulting in a total of 64 samples - have been sent to the lab for further analysis (DT-Lab). Additionally, another dataset has been created based on a reduced DT-SamTot dataset, where we only consider the four of 24 increments for each lot that were sent to the lab (DT-SamRed). It is important for having more accurate indications about the differences in variability between DT-Lab and DT-SamTot samples. We provide three CSV files: DT-Sam_Tot_270521_v01.csv: spectral data and information of DT-SamTot.; DT-Sam_Red_270521_v01.csv: spectral data and information of DT-SamRed. DT-Lab_270521_v01.csv: spectral data and information of DT-Lab. The three CSV files contain similar information in the columns: Sample code: it is reporting the sample code where S1 is the number of lot, the successive number is the number of sample (from 1 to 24) and the last number the NIR replicate. E.g. S4-13-1.sam: lot number 4, sample number 13, NIR replicate number 1. Please note that for DT-Lab dataset we have a different coding where labA and labB are the two sample replicates for the moisture content analysis. Rep: number indicating the NIR replicates for each sample. Please note that for DT-Lab dataset we have also rep2 column reporting the sample replicates for the moisture content analysis. Lot: number of lot to which the sample belongs (from 1 to 16). Day: day in which the sample has been collected (1 = 18/02/2020; 2 = 19/02/2020). Mois: moisture content of the sample (%). PCN: net calorific value of the sample (J/g). Spectral data: absorbance values for each sample from 908.1 nm to 1676.2 nm. The aim behind this dataset is to investigate the variability of the waste wood (WP1 of WoodSpec project) and this information is essential for increasing the reuse of the material and guarantee an accurate and successful use of a NIR sensor into real industrial applications. A second aim is the development of regression models for predicting the moisture content and net calorific value of the samples (WP3 of WoodSpec project). First indications about the variability and the chemical-physical characteristics of the material are essential for determining the suitability in energy applications. If you would like know more about the data, or to use these data, please refer to our article in Renewable Energy, doi: https://doi.org/10.1016/j.renene.2021.05.137 Funding: The project leading to this application has received funding from theEuropean Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 838560. Terms of use: These data are provided "as is", without any warranties of any kind. The data are provided under the Creative Commons Attribution 4.0 International license.

# Bird\_breeding\_productivity\_data [https://doi.org/10.5061/dryad.fxpnvx0zt](https://doi.org/10.5061/dryad.fxpnvx0zt) This folder contains data sets (**Bird_prod_data.csv, Clim_mean_prod_lin.csv, Clim_mean_prod_poly.csv, Clim_trend_PCA_prod_lin.csv, Clim_trend_PCA_prod_poly.csv**), models (.rds files; see below for their naming scheme) and code (**R-script_bird_prod.R**) related to the article: *Climatic predictors of long-distance migratory birds’ breeding productivity across Europe* ## Description of the data and file structure The data is stored in subfolder "Data" **Bird_prod_data.csv** * *Reg*: breeding region; CZP = the Czech Republic, DEG-DKC = Germany and Denmark, ESP = Spain, FRP_N = northern part of France, FRP_S = central & southern part of France, GBT_N = northern parts of Great Britain – Wales and England, Scotland, Northern Ireland – and Ireland, GBT_S = southern parts of Great Britain – England and Wales, HGB = Hungary, NLA = the Netherlands, SFH = Finland, SVS = Sweden - *EURING*: species code * *Year*: year corresponding to breeding season - *Species*: species name (see also Table 3 in the article) * *Site*: site code - *Ad*: number of adults * *Juv*: number of juveniles - *TotalEPR*: water availability in wintering grounds (called ETr in the article) * *Ad_scaled*: Number of adults standardized to mean = 0 and SD = 1 for each species and site - *T3, T4, T5, T6*: temperature in March, April, May, June * *GDD10_3, GDD10_4, GDD10_5, GDD10_6*: growing degree-days in March, April, May, June - *GOD*: green-up onset date * *Rain_anom_3, Rain_anom_4, Rain_anom_5, Rain_anom_6*: precipitation anomaly in March, April, May, June, abbreviated as ΔR in the article - *R10_5, R10_6*: number of heavy rain days in May, June * *R20_5, R20_6*: number of very heavy rain days in May, June - *R1c_5, R1c_6*: number of consecutive rain days 1mm in May, June * *R2c_5, R2c_6*: number of consecutive rain days 2mm in May, June **Clim_mean_prod_lin.csv** * *reg*: breeding region - *clim_var*: abbreviation of climate variable * *mean_val*: mean value of the climate variable - *Est_prod_lin*: estimate of the linear term in the relationship between breeding productivity and climate variable * *SE_prod_lin*: standard error of the estimate of the linear term in the relationship between breeding productivity and climate variable **Clim_mean_prod_poly.csv** * *reg*: breeding region - *clim_var*: abbreviation of climate variable * *mean_val*: mean value of the climate variable - *Est_prod_poly*: estimate of the quadratic term in the relationship between breeding productivity and climate variable * *SE_prod_poly*: standard error of the estimate of the quadratic term in the relationship between breeding productivity and climate variable **Clim_trend_PCA_prod_lin.csv** * *reg*: breeding region - *clim_change*: climate warming variable derived from the first axis of PCA (Principal Component Analysis), for months of March, April, May, June * *Est_trend*: slope of the linear temporal trend of climate warming variable over the study period **Clim_trend_PCA_prod_poly.csv** * reg: breeding region - clim_change: climate warming variable derived from the first axis of PCA (Principal Component Analysis), for months of March, April, May, June * Est_trend: slope of the quadratic temporal trend of climate warming variable over the study period Fitted models (88 files) are stored in subfolder "Models" Naming scheme of the models is: **Hyp2 or Hyp3**: models for testing Hypothesis 2 or Hypothesis 3, respectively **resp1 or resp2**: response variable of the model was derived from the relationship between breeding productivity and the linear term of the climate variable (i.e. *Est_prod_lin*, see above in Clim_mean_prod_lin.csv) or the quadratic term of the climate variable (i.e. *Est_prod_poly*, see above in Clim_mean_prod_poly.csv), respectively **lin or poly**: models employ linear or polynomial (quadratic) terms of climate variables, respectively **T, GDD10, ΔR, GOD**: climate variables used in testing Hypothesis 2 or Hypothesis 3, i.e. temperature, growing degree-days, precipitation anomaly, and green-up onset date, respectively **3, 4, 5, 6**: months of March, April, May, or June **warm_PCA1** (for Hypothesis 3 only): climate warming variable was derived from the first axis of PCA (Principal Component Analysis), suffixes 3, 4, 5 or 6 means months of March, April, May, and June ## Code/Software The code file "R-script_bird_prod.R" is an R script created by version 4.3.1, allowing to run all our analyses. It consists of the following parts: * loading the libraries * loading the data set Bird_prod_data.csv and preparing the variables for testing Hypothesis 1 * fitting the models for testing Hypothesis 1 * performing the model averaging * extraction of the marginal effects of climate variables * calculation of the temporal variance explained by climate variables * loading the data sets Clim_mean_prod_lin.csv and Clim_mean_prod_poly.csv and preparing the variables for testing Hypothesis 2 * fitting the models for testing Hypothesis 2 * extraction of parameters from the fitted models * loading the data sets Clim_trend_PCA_prod_lin.csv and Clim_trend_PCA_prod_poly.csv and preparing the variables for testing Hypothesis 3 * fitting the models for testing Hypothesis 3 * extraction of parameters from the fitted models Ongoing climate changes represent a major determinant of demographic processes in many organisms worldwide. Birds, and especially long-distance migrants, are particularly sensitive to such changes. To better understand these impacts on long-distance migrants’ breeding productivity, we tested three hypotheses focused on (i) the shape of the relationships with different climate variables, including previously rarely tested quadratic responses, and on regional differences in these relationships predicted by (ii) mean climatic conditions and (iii) by the rate of climate change in respective regions ranging from Spain to Finland. We calculated breeding productivity from constant effort ringing sites from 11 European countries covering 34 degrees of latitude, and extracted temperature- and precipitation-related climate variables from E-OBS and NASA MODIS datasets. To test our hypotheses, we fitted GLMM and Bayesian meta-analytic models. We revealed hump-shaped responses of productivity to temperature, growing degree-days, green-up onset date, and precipitation anomaly, and negative responses to intense and prolonged rains across the regions. The effects of March temperature and April growing degree-days were more negative in cold than in warm regions, except that one with the highest accumulated heat, whereas increasing June precipitation anomalies were associated with higher productivity in both dry and wet regions. The rate of climate warming was unrelated to productivity responses to climate. The influence of climate on bird productivity proved to be frequently non-linear, as expected by ecological theory. To explain the differences between regions, the rate of climate change is less important than regional interannual variability in climate (which is predicted to increase), but this may change with the progression of climate change in the future. Productivity declines in long-distance migratory songbirds are particularly expected if out-of-norm water excess increases in frequency or strength.

Abstract Despite the looming land scarcity for agriculture, cropland abandonment is widespread globally. Abandoned cropland can be reused to support food security and climate change mitigation. Here, we investigate the potentials and trade-offs of using global abandoned cropland for recultivation and restoring forests by natural regrowth, with spatially-explicit modelling and scenario analysis. We identify 101 Mha of abandoned cropland between 1992 and 2020, with a capability of concurrently delivering 29 to 363 Peta-calories yr− 1 of food production potential and 290 to 1,066 MtCO2 yr− 1 of net climate change mitigation potential, depending on land-use suitability and land allocation strategies. We also show that applying spatial prioritization is key to maximizing the achievable potentials of abandoned cropland and demonstrate other possible approaches to further increase these potentials. Our findings offer timely insights into the potentials of abandoned cropland and can inform sustainable land management to buttress food security and climate goals.

Removal of Reactive Black 5 (RB5) dye from an aqueous solution was studied by its adsorption on banana peel biochars (BPBs). The factors affecting RB5 dye adsorption such as pH, exposure time, RB5 dye concentration, adsorbent dose, particle size and temperature were investigated. Maximum 97% RB5 dye removal was obtained at pH 3 with 75 mg/L adsorbate concentration by banana peel biochars. Fourier transform infrared (FTIR) and scanning electron microscopy (SEM) were used to characterize the adsorbent material. The data of equilibrium were analyzed by Langmuir and Freundlich isotherm models. The experimental results were best reflected by Langmuir isotherm with maximum 7.58 mg/g adsorption capacity. Kinetic parameters were explored and pseudo-second order was found suitable which reflected that rate of adsorption was controlled by physisorption. Thermodynamic variables exhibited that the sorption process was feasible, spontaneous, and exothermic in nature. Banana peel biochar showed excellent regeneration efficiency up to five cycles of successive adsorption-desorption. Banana peel biochar maintained >38% sorption potential of RB5 dye even after five cycles of adsorption-desorption. The phytotoxic study exhibited the benign nature of BPB-treated RB5 dye on tomato seeds.

This paper presents a new method, based on the Smart Energy Systems concept. The aim is to increase the share of renewable energy penetration on islands. The method is applied to the island of Gran Canaria (Spain), considering the entire energy system of the island. Several smart renewable energy strategies are proposed following a cross-sectoral approach between the electricity, heating/cooling, desalination, transport and gas sectors. The different smart renewable energy strategies were applied in a series of steps, while looking for a transition from the current energy system to a nearly 100% renewable energy system. Based on the results, the study concludes that the suggested method is applicable for increasing renewable integration on islands and can potentially be used in helping energy planners to take decisions about priorities in development of the sector to improve such integration. The results indicate that, for the case of Gran Canaria, a 75.9% renewable energy system could be attained with technologies that can be implemented at present. Furthermore, it is shown that a nearly 100% renewable energy system in Gran Canaria is technically feasible and could be achieved if certain technologies acquire greater maturity. © 2018 Elsevier Ltd 443 421 2,048 5,537 Q1 Q1 SCIE