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The data consists of annual measurements of standing aboveground plant biomass, annual aboveground net primary productivity and annual soil respiration between 1998 and 2012. Data were collected from seven European shrublands that were subject to the climate manipulations drought and warming. Sites were located in the United Kingdom (UK), the Netherlands (NL), Denmark ( two sites, DK-B and DK-M), Hungary (HU), Spain (SP) and Italy (IT). All field sites consisted of untreated control plots, plots where the plant canopy air is artificially warmed during night time hours, and plots where rainfall is excluded from the plots at least during the plants growing season. Standing aboveground plant biomass (grams biomass per square metre) was measured in two undisturbed areas within the plots using the pin-point method (UK, DK-M, DK-B), or along a transect (IT, SP, HU, NL). Aboveground net primary productivity was calculated from measurements of standing aboveground plant biomass estimates and litterfall measurements. Soil respiration was measured in pre-installed opaque soil collars bi-weekly, monthly, or in measurement campaigns (SP only). The datasets provided are the basis for the data analysis presented in Reinsch et al. (2017) Shrubland primary production and soil respiration diverge along European climate gradient. Scientific Reports 7:43952 https://doi.org/10.1038/srep43952 Standing biomass was measured using the non-destructive pin-point method to assess aboveground biomass. Measurements were conducted at the state of peak biomass specific for each site. Litterfall was measured annually using litterfall traps. Litter collected in the traps was dried and the weight was measured. Aboveground biomass productivity was estimated as the difference between the measured standing biomass in year x minus the standing biomass measured the previous year. Soil respiration was measured bi-weekly or monthly, or in campaigns (Spain only). It was measured on permanently installed soil collars in treatment plots. The Gaussen Index of Aridity (an index that combines information on rainfall and temperature) was calculated using mean annual precipitation, mean annual temperature. The reduction in precipitation and increase in temperature for each site was used to calculate the Gaussen Index for the climate treatments for each site. Data of standing biomass and soil respiration was provided by the site responsible. Data from all sites were collated into one data file for data analysis. A summary data set was combined with information on the Gaussen Index of Aridity Data were then exported from these Excel spreadsheet to .csv files for ingestion into the EIDC.

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.

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)

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.

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.

Six bones from La Draga (Spain, Holocene, samples LD_01 to LD_06) and Bayisha Karst Cave (China, Pleistocene, samples BKC_07 to BKC_12) were sampled for this study. Initial sampling was divided into three sub-samples for the three digestion durations tested here (site code_sample number_3h, site code_sample number_6h, and site code_sample number_18h). Samples were then processed according to the ZooMS protocol: they were demineralised in 0.6 M hydrochloric acid (HCl) for 24 hours. The HCl supernatant was then removed and samples were rinsed thrice in 100 µL ammonium bicarbonate (50 mM, NH4HCO3, hereafter AmBic) for subsequent gelatinisation in a final volume of 100 µL AmBic for one hour at 65°C. Following gelatinisation, the 100 µL of the AmBic solution was transferred to a new microtube, to which 0.8 µg trypsin (Promega) was added for incubation at 37°C, with mild agitation at 300 rpm (VWR, Thermal Shake lite). Digestion occurred for either 3, 6, or 18 hours. To stop trypsin digestion, 2 µL of 5% trifluoroacetic acid (TFA) was added to each sample. The digested extracts were then split into two parts for separate analyses via matrix-assisted laser desorption/ionisation-time of flight mass spectrometry (MALDI-ToF MS) and liquid-chromatography tandem mass spectrometry (LC-MS/MS). To assess any potential contamination by non-endogenous peptides, we performed the extraction of laboratory blanks alongside the samples for each enzymatic digestion condition. Mass spectrometry analyses MALDI-ToF MS and ZooMS data analysis For ZooMS data analysis, before MALDI-ToF MS analysis, peptides were cleaned and desalted using C18 ZipTips (Thermo Fisher) and subsequently spotted in triplicate, consisting of 0.5 µL eluted peptides and 0.5 µL alpha-cyano-4-hydroxycinnamic acid (CHCA) matrix solution, on a 384-well Opti-ToF MALDI plate insert (AB Sciex, Framingham, MA, 01701, USA) and allowed to air-dry at room temperature. MALDI spectra were automatically acquired with an AB SCIEX 5800 MALDI-ToF spectrometer (Framingham, MA, 01701, USA) in positive reflector mode for MS acquisition. Before sample acquisition, an external plate model calibration was achieved on 13 adjacent MS standard spots with a standard peptide mix (Proteomix Peptide calibration mix4, LaserBioLabs, Sophia Antipolis, France) containing bradykinin fragment 1-5 (573.315 Da), human angiotensin II (1046.542 Da), neurotensin (1672.917 Da), ACTH fragment 18-39 (2464.199) and oxidised insulin B chain (3494.651 Da). The concentration in the prepared mixture was between 27 to 167 fmol/µL. The calibration was validated according to the laboratory specifications (resolution above 10000 for 573 Da, 12000 for 1046 Da, and 15 to 25000 for other masses, error tolerance <50ppm). For the spectra where peptides resulting from trypsin autolysis were detected, an internal recalibration was applied to decrease the error tolerance below 10 ppm (trypsin peptides: 842.509 Da, 1045.56 Da, and 2211.104 Da). Laser intensity was set at 50% after optimization of the signal-to-noise ratio on several spots, then operated at up to 3,000 shots accumulated per spot, covering a mass-to-charge range of 1000 to 3500 Da for sample analysis. The triplicate data files were merged in R and converted into .msd files. ZooMS taxonomic identifications were assessed using mMass through manual peptide marker mass identification in comparison to a database of peptide marker series for medium- to large-sized mammals. Glutamine deamidation values were calculated using the Betacalc3 package. Shotgun proteomics For SPIN data analysis, peptide extracts were first separated using an Evosep One (Evosep, Odense, Denmark) with the 100 samples-per-day method (cycle of 14.4 min). Loading of samples was conducted at a flow rate of 2 uL/min using mobile phases of A: 5% acetonitrile and 0.1% formic acid in H2O and B: 0.1% formic acid in H2O with a gradient of 11.5 min at 1.5 uL/min. A polymicro flexible fused silica capillary tubing of 150 um inner diameter and 16 cm long home-pulled was packed with C18 bounded silica particles of 1.9 um diameter (ReproSil-Pur, C18-AQ, Dr. Maisch, Germany). The column was mounted on an electrospray source with a column oven set at 60°C with a source voltage of +2000 V, along with an ion transfer tube set at 275°C. An Exploris 480 (Thermo Fisher Scientific) was operating in data-dependent mode consisting of a first MS1 scan at a resolution of 60 000 between m/z of 350 and 1400. The twelve most intense monoisotopic precursors were selected if above 2e5 intensity with a charge state between 2 and 6 and were then dynamically excluded after one appearance with their isotopes (20 ppm) for 20 seconds. The selected peptides were acquired on MS2 at Orbitrap resolving power of 15000, normalised collision energy (HCD) set at 30%, quadrupole isolation width of 1.3 m/z, and first m/z of 120. Quality control was assessed on HeLa cells using QC displayed of 1289 protein groups for 5561 peptides at a repeating sequencing of 2.90% on MaxQuant v.2.2.3.0. The following parameters were used for the search: the raw data were searched against the human full proteome, with carbamidomethyl (C) as fixed modification and oxidation (M) and acetyl (protein N term) as variable; digestion was set as tryptic and all other parameters were kept as default. MaxQuant search All .raw files were analysed using MaxQuant (v.2.3.1) in two different searches. The first search was performed as described in Ruther et al., 2022 against the protein sequences database provided there. Variable modifications included oxidation (M), deamidation (NQ), Gln (Q) -> pyro-Glu, Glu (E) -> pyro-Glu, and proline (P) hydroxylation. The internal MaxQuant contaminant list was replaced with an in-house database provided by Ruther et al., 2022 (Supplementary File PR200512_HumanCons.fasta). Since all specimens except for one were identified as belonging to either Bos sp. or Bison sp., a second search was performed against the whole Bos taurus reference proteome (downloaded from Uniprot on 2022-01-20) to explore the presence of other, additional non-collagenous proteins (NCPs). Variable modifications for this search included oxidation (M), deamidation (NQ), and proline (P) hydroxylation. The internal MaxQuant contaminant list was used. Both searches were run in semi-specific Trypsin/P digestion mode. Up to five variable modifications were allowed per peptide and all other settings were left as default for both searches. Measurement of electricity consumption A power monitor (Cowell, model no.: PMB01) was placed in between the heating block (VWR, Thermal Shake lite) and the utilised power outlet to measure electricity consumption using either 96-well plates or Eppendorf tubes for 18 hours at 37°C. The measurements for both tubes (1.5 mL Eppendorf Protein LoBind, Eppendorf) and plates (PCR Plate, 96-well, low profile, non-skirted, 0.3 mL, Thermo Fisher Scientific) were separately conducted over the time frame of 18 hours, and replicated thrice in total. Measurements started when the heating block had reached a stable temperature of 37°C. The maximum number of tubes, 40 units, were placed in the heating block with 100µL AmBic in each tube to imitate experiment conditions. Likewise, each well in the 96-well plate was filled with 100 µL AmBic. The emission intensity (gCO2eq; grams of carbon dioxide equivalent) was then calculated by alcesusing the kWh measured and gCO2eq/kWh values available through Electricity Maps for the dates on which our experiments were conducted. The gCO2eq/kWh values were obtained from various countries (Australia, Brazil, Germany, Denmark, France, Japan, the USA, and South Africa). With this selection, we hope to cover a range of countries where high-throughput palaeoproteomics facilities exist. Furthermore, countries differ significantly in the amount of carbon released for each unit of electricity consumed, the so-called carbon intensity, for example, due to the use of nuclear energy or largely completed transitions to wind and solar energy sources. The absolute impact of electricity consumption is therefore very different depending on the country, and our selection of countries aims to also cover this range of carbon intensities. Lastly, emission intensities were calculated for each tube and PCR plate well across the three digestion durations (18h, 6h, and 3h), and for each country included in the study. # Increasing sustainability in palaeoproteomics by optimizing digestion times for large-scale archaeological bone analyses [https://doi.org/10.5061/dryad.cz8w9gj8j](https://doi.org/10.5061/dryad.cz8w9gj8j) ## Description of the data and file structure Data deposited on Dryad are structured as follows: 1. Digestion_time_Datasheet.csv containing all information concerning sample names, experimental information (sampling amount), and the palaeoproteomics methods data tested in this study (ZooMS and SPIN). 2. Electicitymeasurement.csv concerning all data gathered during the measurement of electricity consumption of the three digestion times tested in the paper. 3. Three folders: Full proteome MQ (txt files generated after the MaxQuant search against Bos taurus full proteome); msd_files_3replicates (.msd files of all LC-MS/MS raw data) and a SPIN MQ (txt files generated after the MaxQuant search against the SPIN database). 4. Four R code markdowns with statistical analyses of the paper, figure generation, etc. (Full Proteome.Rmd; Main text figures.Rmd; SPIN.Rmd and ZooMS.Rmd). Empty cells in the .csv files indicate that no data were recorded or that the corresponding column does not apply. ## Sharing/Access information Data linked to this paper can be found here (for MALDI-MS raw data and associated spectra merging code): https://doi.org/10.5281/zenodo.8290650 and using identifier PXD045027 on the ProteomeXchange data repository (LC-MS/MS raw data and associated MaxQuant searches output files) ## Code/Software After spectral identification, proteomic data analysis was conducted largely through R v.4.1.2 using tidyverse v.1.3.1, seqinr v.4.2-8, ggpubr v.0.4.0, ggdist v.3.3.0, data.table v.1.14.2, ggsci v.2.9, progressr v.0.10.0, gmp v.0.6-6, reshape2 v.1.4.4, stringi v.1.7.6, MALDIquant v.1.2, MALDIquantForeign v.0.13, janitor v.2.2.0, and wesanderson v.0.3.6. The R scripts used for the shotgun proteomics analysis are available under Rüther et al., 2022. Deamidation was quantified based on spectral intensities. Depending on data types, statistics were calculated using two-way ANOVA (Type II and Type III), linear modelling from lmerTest v.3.1-3, lme4 v.1.1-34, MASS v.7.3-60, and Kruskal Wallis tests from carData v.3.0-5, car v.3.1-0, and rstatix v.0.7.2. As prerequisites for ANOVA tests, normal distribution of residuals was checked using the Shapiro-Wilk normality test and homogeneity of the variances was assessed by Levene’s test. Palaeoproteomic analysis of skeletal proteomes is used to provide taxonomic identifications for an increasing number of archaeological specimens. The success rate depends on a range of taphonomic factors and differences in the extraction protocols employed. By analyzing 12 archaeological bone specimens from two archaeological sites, we demonstrate that reducing digestion duration from 18 to 3 hours has no measurable impact on the obtained taxonomic identifications. Peptide marker recovery, COL1 sequence coverage, or proteome complexity are also not significantly impacted. Although we observe minor differences in sequence coverage and glutamine deamidation, these are not consistent across our dataset. A 6-fold reduction in digestion time reduces electricity consumption, and therefore CO2 emission intensities. We furthermore demonstrate that working in 96-well plates further reduces electricity consumption by 60%, in comparison to individual microtubes. Reducing digestion time therefore has no impact on the taxonomic identifications, while reducing the environmental impact of palaeoproteomic projects.

Evaporation experiments are frequently used to identify the hydraulic properties of soils by inverse simulations with the Richards equation or simplified calculation methods. Evaporation experiments with an extended instrumentation were conducted in a temperature-controlled lab. Evaporation rates were measured gravimetrically, soil water pressure head was measured using mini-tensiometers and relative humidity sensors, and soil temperature was measured using thermocouples. The measurements were evaluated by inverse modeling with the Richards equation and the soil hydraulic properties, i.e. the water retention and hydraulic conductivity curves, were identified. The dataset contains all experimental data and the results of the inverse simulation, i.e. fitted time series of pressure head and the identified soil hydraulic properties of two soils (sand and silt loam).