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The data contains three supporting datasets: 1. Mid-intertidal data 2. Vertical transect data 3. GPS coordinates for all sites

Tiny molecules (hormones) are released from the bowels and other organs to the bloodstream in response to meals in order to orchestrate the metabolism. Oxyntomodulin, GLP-1 and glucagon are all known to regulate parameters such as appetite, food intake and induce weight loss. Therefore we want to investigate mechanistic relations between the three hormones.

The complete data set that was the basis of the article: Stratulat, C.; Ginghina, R.E.; Bratu, A.E.; Isleyen, A.; Tunc, M.; Hafner-Vuk, K.; Frey, A.M.; Kjeldsen, H.; Vogl, J. Development- and Validation-Improved Metrological Methods for the Determination of Inorganic Impurities and Ash Content from Biofuels. Energies 2023, 16, 5221. https://doi.org/10.3390/en16135221 This work is part of the 19ENG09 BIOFMET project. This project has received funding from the EMPIR programme co-financed by the Participating States and from the European Union's Horizon 2020 re-search and innovation programme.

Author: Daniel Vázquez Pombo (dvapo@elektro.dtu.dk) ------------------------------------------------------------------------------- This dataset corresponds to the results of the paper titled: "Multi-Horizon Data-Driven Wind Power Forecast: From Nowcast to 2 Days-Ahead" 4th International Conference on Smart Energy Systems and Technologies (SEST) - 2021 -> https://sites.univaasa.fi/sest2021/ Submmited: Dec 2020 Accepted: Feb 2021 Published: Sep 2021 ------------------------------------------------------------------------------- The folder contains all the results presented in the paper, for clarity. Additional resources might be supplied under request. -------------------------------------------------------------------------------

To quantify the seasonal transition dates, we used NDVI derived from Sentinel-2 MultiSpectral Instrument (Level-1C) images during 2016–2020 based on Google Earth Engine (https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2). We performed an atmospheric correction (Yin et al., 2019) on the images before calculating NDVI. The months from May to October were set as the study period each year. The quality control process includes 3 steps: (i) the cloud was masked according to the QA60 band; (ii) images were removed if the number of pixels with NDVI values outside the range of -1–1 exceeds 30% of the total pixels while extracting the median value of each date; (iii) NDVI outliers resulting from cloud mask errors (Coluzzi et al., 2018) and sporadic snow were deleted pixel by pixel. NDVI outliers mentioned here appear as a sudden drop to almost zero in the growing season and do not form a sequence in this study (Komisarenko et al., 2022). To identify outliers, we iterated through every two consecutive NDVI values in the time series and calculated the difference between the second and first values for each pixel every year. We defined anomalous NDVI differences as points outside of the percentiles threshold [10 90], and if the NDVI difference is positive, then the first NDVI value used to calculate the difference will be the outlier, otherwise, the second one will be the outlier. Finally, 215 images were used to reflect seasonal transition dates in all 5 study periods of 2016–2020 after the quality control. Each image was resampled with 32 m spatial resolution to match the resolution of the ArcticDEM data and SnowModel outputs. To detect seasonal transition dates, we used a double sigmoid model to fit the NDVI changes on time series, and points where the curvature changes most rapidly on the fitted curve, appear at the beginning, middle, and end of each season (Klosterman et al., 2014). The applicability of this phenology method in the Arctic has been demonstrated (Ma et al., 2022; Westergaard-Nielsen et al., 2013; Westergaard-Nielsen et al., 2017). We focused on 3 seasonal transition dates, i.e., SOS, NDVImax day, and EOF. The NDVI values for some pixels are still below zero in spring and summer due to topographical shadow. We, therefore, set a quality control rule before calculating seasonal transition dates for each pixel, i.e., if the number of days with positive NDVI values from June to September is less than 60% of the total number of observed days, the pixel will not be considered for subsequent calculations. As verification of fitted dates, the seasonal transition dates in dry heaths and corresponding time-lapse photos acquired from the snow fence area are shown in Fig. 2. Snow cover extent is greatly reduced and vegetation is exposed with lower NDVI values on the SOS. All visible vegetation is green on the NDVImax day. On EOF, snow cover distributes partly, and NDVI decreases to a value close to zero. # Data from: Drivers of contemporary and future changes in Arctic seasonal transition dates for a tundra site in coastal Greenland The dataset includes all original images used in this study to extract seasonal transition dates and corresponding results. ## Description of the data and file structure Datasets included: (1) The spatial distribution of NDVI values for this study region (168 rows and 166 columns). Each file is named in the form of '' year-month-day''. For example, a file named "2016-05-02'' represents the data for 2nd, May of 2016. The normal NDVI values in each file range from -1 to 1, and NaN represents no valid value. The folder named 'unique_date_NDVI' refers to the spatial distribution of NDVI for all available dates, directly acquired from satellite images. The folder named 'unique_date_NDVI_rm_outlier' refers to the spatial distribution of NDVI after quality correction for each date using the described method. (2) The extracted phenology indicators for each pixel in this study region. Five tables named 'Phe_pixel_XXXX.xlsx' include the extracted seasonal transition dates during 2016–2020, pixel by pixel. There are 9 columns in each table, they are row number and column number (used to describe the specific location of pixel), year, start of spring, middle of spring, end of spring, start of fall, middle of fall, and end of fall. ## Sharing/Access information All functions regarding the extraction of seasonal transition dates can be found here: * All parameters and associated functions regarding the SnowModel can be found here: * All original meteorological data in this study is from: * Climate change has had a significant impact on the seasonal transition dates of Arctic tundra ecosystems, causing diverse variations between distinct land surface classes. However, the combined effect of multiple controls as well as their individual effects on these dates remains unclear at various scales and across diverse land surface classes. Here we quantified spatiotemporal variations of three seasonal transition dates (start of spring, maximum Normalized Difference Vegetation Index (NDVImax) day, end of fall) for five dominant land surface classes in the ice-free Greenland and analyzed their drivers for current and future climate scenarios, respectively.

It has previously been shown that a combination of tyrosine, capsaicin, catechines, and caffeine may stimulate the sympathetic nervous system and promote satiety, lipolysis and thermogenesis. In addition, dietary calcium may increase fecal fat excretion. Method: 80 healthy Danish overweight to obese (BMI between 28 and 35 kg/m2), 18 to 70 years of age subjects of both gender. All subjects was be weight stable (within 3-kg) 2 months prior to study inclusion, non-smoking, non-athletic and have no daily use of medication except for anticonception and antihypertensive compounds. All subjects gave their written consent after verbal and written information about the study. The study protocol was approved by The Municipal Ethical Committee of Copenhagen and Frederiksberg as being in accordance with the Helsinki II Declaration. The total study period was 12 weeks. The intervention design was an 8-weeks randomized, 3-arm parallel, placebo-controlled and double-blind intervention. Prior to the randomized intervention a weight loss was initiated by 4 week treatment on a 3.4 MJ/d low caloric diet (LCD). Only subject who lost more than 4% of their initial body weight after 4 weeks LCD treatment was randomized and continued in the study. The subjects were randomized into 3 balanced groups i.e. placebo, simple or enterocoated release. All subjects received dietary instruction to a slightly hypo caloric diet of -300 kcal/day after the isoenergetic educational system. The dietary advice was reinforced by dietetic consultations every fortnight. Body weight and composition were assessed by DEXA before and after the LCD period, four weeks into the randomized period and at completion i.e. the last day of the study. All participants underwent assessment of energy expenditure by indirect calorimetry in a ventilated hood. The first respiratory measurement was on the first day of the intervention and will last for 5 hours. All subjects collected all feces excreted three days prior to respiratory measurement and one day of urine excreted. The objective of the present study was to investigate the acute and sub-chronic effect of a supplement containing green tea extract, capsaisin, caffeine, tyrosine and calcium or placebo taken t.i.d on thermogenesis, body fat loss, and fecal fat excretion. Furthermore, if a 8-week supplementation could prevent weight-regain after initial 4-week weight loss.

This is the ERA5 dataset that can be used to calculate air density for wind energy purposes using the equations presented in https://doi.org/10.3390/en12112038. The NetCDF file contains mean fields of monthly means from 2010 to 2020 of the variable temperature, surface pressure, specific humidity and the lapse rate. The horizontal grid resolution is 0.25 degrees and covers the whole globe. These fields are used in the WAsP software version 12.6 and above to calculate the air density at specified heights above mean sea level. The WAsP software (www.wasp.dk) is the industry standard method to calculate the annual energy production of wind farms. The ERA5 data are generated using Copernicus Climate Change Service information [2020]

Understanding N budgets of tundra ecosystems is crucial for projecting future changes in plant community composition, greenhouse gas balances and soil N stocks. Winter warming can lead to higher tundra winter nitrogen (N) mineralization rates, while summer warming may increase both growing season N mineralization and plant N demand. The undulating tundra landscape is inter-connected through water and solute movement on top of and within near-surface soil, but the importance of lateral N fluxes for tundra N budgets is not well known. We studied the size of lateral N fluxes and the fate of lateral N input in the snowmelt period with a shallow thaw layer, and in the late growing season with a deeper thaw layer. We used 15N to trace inorganic lateral N movement in a Low-arctic mesic tundra heath slope in West Greenland and to quantify the fate of N in the receiving area. We found that half of the early-season lateral N input was retained by the receiving ecosystem, whereas half was transported downslope. Plants appear as poor utilizers of early-season N, indicating that higher winter N mineralization may influence plant growth and carbon (C) sequestration less than expected. Still, evergreen plants were better at utilizing early-season N, highlighting how changes in N availability may impact plant community composition. In contrast, later growing season lateral N input was deeper and offered an advantage to deeper-rooted deciduous plants. The measurements suggest that N input driven by future warming at the study site will have no significant impact on the overall N2O emissions. Our work underlines how tundra ecosystem N allocation, C budgets and plant community composition vary in their response to lateral N inputs, which may help us understand future responses in a warmer Arctic. (Less)