dataesr/scientific-paragraphs-categorization huggingface dataset:
Identification of CcTLP1 from C. cava C. cava crude protein extracts from tubers were separated using 1-D electrophoresis. Silver-stained protein bands were excised from the gel and analyzed using liquid-chromatography tandem mass spectrometry analysis (LC-ESI-MS/MS) (Fig. 1 ). Among the studied proteins a new TLP from C. cava tuber was identified (Table  3 , Band 1, Supplementary Table  S1 ). In order to obtain the full-length of the CcTLP1 coding sequence, a PCR-based approach was implemented. A series of thermal gradient PCRs was conducted. Obtained sequences were compared with databases and results confirmed high sequence similarity to thaumatin-like protein coding sequences from different plants (e. g. Populus trichocarpa, Cucumis sativus, Actinidia chinensis, Solanum tuberosum) (about 72% maximum identity, 336 total score, and E-value of 1e-88).
In order to obtain the whole gene sequence a chromosome walking technique was used. 1483 bp sequence was obtained, which presumably contained the coding region. This sequence was compared with a non-redundant protein sequence (nr) database and the position of the coding region was localized. We therefore obtained the 678 bp nucleotide sequence coding for the novel TLP (named: Corydalis cava thaumatin-like protein 1) composed of 225 aa. The coding sequence has been submitted to the GenBank database under the accession number KJ513303. To determine the phylogenetic position of CcTLP1 we compared it.
CcTLP1 domain architecture Bioinformatic analyses demonstrated that the 225 aa sequence showed high sequence similarity to the PR-5 protein (osmotin) VVTL1 from Vitis vinifera (Acc. AAB61590) (about 77. 62% maximum identity). The sequence covers a highly conserved region composed of domains GH64-TLP-SF (cd08961) and TLP (cd09215) of the thaumatin-like proteins with antifungal activities (e. g. β-glucanase). The novel protein named CcTLP1 (Corydalis cava thaumatin-like protein 1) has a predicted molecular mass of 24,234 Da and predicted pI 5. 08. Its possible secondary structure was assessed using Geneious 6. 1. 2 software. The polypeptide contains 2 alpha helices and 9 beta sheets connected with loops. Conserved domains were predicted using NCBI CDD  [66]  and GeneSilico MetaServer  [61, 67].
The values were retrieved from the protein database. h Theoretical pI of identified proteins. The values were retrieved from the protein database. i Amino acid sequence coverage for the identified proteins. sequences in Corydalis cava and data obtained from prior sequencing analysis were aligned together, which served as a template for primer design. The CcEf1RTF/CcEf1RTR primer set PCR products were sequenced and the significant similarity to known elongation factor 1 sequences was confirmed.
expr. lvl. 0. 63 and 0. 87, respectively) -the difference was not significant, probably also due to the different number of replicates. However, there was a significant (twofold) difference in CcTLP1 expression levels of young and medium-sized plants grown from both first-year and seasoned tubers as well (Table  4b , rel. expr. lvl. 0. 31 and 0. 63/0. 33 and 0. 87, respectively). The results show that the CcTLP1 gene expression level varies depending on the growth phase of the plant. It was proposed that the twofold increase of CcTLP1 gene expression level of medium-size plants contributed possibly to the development of generative organs (flowers, fruits). Detailed expression data are presented in Supplementary Table  S2.
Acknowledgements This work was supported by the  National Science Centre  Grant No. N N303 807440 , to  R. Nawrot  ( Faculty of Biology, Adam Mickiewicz University in Poznań, Poland ). Special acknowledgements for the  Mass Spectrometry Laboratory, Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Warsaw, Poland , for mass spectrometry analysis. The equipment used was sponsored in part by the  Centre for Preclinical Research and Technology (CePT) , a project co-sponsored by  European Regional Development Fund and Innovative Economy , The  National Cohesion Strategy of Poland.
35 1% 116,055,771 a Consecutive numbers of LC-MS/MS protein results. bIdentified homologous proteins and organism from which it proceeds. cDatabase accession numbers according to: NCBInr (nr); trEMBL (trm). dAssigned numbers of protein bands as indicated in Fig. 1. e Number of matched peptides obtained from LC-MS/MS data. f Mascot Search Probability Based Mowse Score. Ions score is -10*Log(P), where P is the probability that the observed match is a random event. Protein scores > 41 indicate identity or extensive homology (p < 0. 05).
The EU Copernicus Climate Change Service (C3S) has produced an operational climate service, called C3S Energy, designed to enable the energy industry and policy makers to assess the impacts of climate variability and climate change on the energy sector in Europe. The C3S Energy service covers different time horizons, for the past forty years and the future. It provides time series of electricity demand and supply from wind, solar photovoltaic and hydro power, and can be used for recent trends analysis, seasonal outlooks or the assessment of climate change impacts on energy mixes in the long-term. This paper introduces this dataset, with a focus on the design and validation of the energy conversion models, based on ENTSO-E energy data and the ERA5 climate reanalysis. Flexibility and coherence across all countries have been privileged upon models' accuracy.
These studies mainly deal with different scenarios of electricity mixes to evaluate their technical and economical feasibility, in particular to assess flexibility and reserves needs. However, climate variability and climate change impacts are either not addressed or only partially. [7]  for instance used only seven years of climate data, which is too short to capture year-to-year climate variability. Silva et al. [5]  used 31 years of wind and solar generation, reconstructed on the recent past from reanalysis data (from ERA-Interim), which to our knowledge is the most comprehensive approach at the time of this study. Climate change impacts on hydro, wind and solar power generation have been studied by several authors  [16] [17] [18] [19]  but currently, these studies do not provide open and easily accessible datasets which can be used by energy practitioners and decision makers to run energy mixes studies, via energy system modelling.
Running European power system studies requires energy datasets which are coherent, using homogenized and long-term climatic data sources, to be able to properly link energy demand and production to climate variability and impacts of future changes. Moreover, this needs to be done at a continental level in order to investigate possible im/balancing effects across a wide area (Europe in this case). In the past few years, several datasets have been developed with this goal. The EMHIRES  [20, 21]  and Renewables. Ninja  [22]  datasets in particular, provide time series of renewables capacity factor for European countries covering the last three decades, but they focus only on wind and/or solar generation, whereas hydro power has been addressed only recently by the Joint Research Centre 1 and no data is available for electricity demand in the extent described here.
In addition, these databases do not integrate the expected impact of climate change on the renewable energy sources (RES) power generation time series. Currently available datasets to run European power system studies therefore present some limitations, for instance too short datasets, or no homogeneous approaches between demand and supply from various sources, missing variables (e. g. demand or hydro), or no future projections available. This 1 See the JRC Hydro-power database (https://data. jrc. ec. europa. eu/dataset/52b00441-d3e0-44e0-8281-fda86a63546d), JRC-EFAS-Hydropower (https://doi. org/10. 5281/zenodo. 4086004) and this set of inputs/outputs for European power modelling including hydropower (https://data. jrc. ec. europa. eu/dataset/221c6cf4-98c0-4793-8e3a-78820377387f) leads to a situation where RES power generation time series are calculated independently in each prospective study.
In addition to the seemingly unnecessary duplication of effort, having disparate ad hoc approaches to producing input energy data makes it difficult to compare the outcomes of such studies. There is thus a need for a unified dataset including all relevant energy variables and time scales, to allow enough flexibility to easily integrate them in any prospective power system analysis. The Copernicus Climate Change Service (C3S -https://climate. copernicus. eu/) was launched in 2015 to lead and coordinate development of climate service infrastructure and underlying data provision mainly at the European level. The Sectoral Information System (SIS) component of C3S makes use of the C3S climate data to meet the requirements of users, with a specific focus on purveyors and policy makers. The SIS includes various sectors of which energy is a prime example.
Additionnally, national (MAR0) and regional (MAR1) maritime regions have been defined for offshore wind energy  [24] , because no such aggregation regions exist in the Eurostat reference. Overall, the C3S-E dataset is designed to fulfil the needs of both potential users, who want to assess the impact of climate on energy operations, management and planning, and energy modellers, who need a user friendly dataset ready to be utilised for their power assessment studies. The C3S-E dataset builds on the previous C3S dataset, produced by the European Climatic Energy Mixes (ECEM) project  [25]. With C3S-E both the underlying climate data and the energy models have been updated. In brief, the C3S-E dataset is based on state-of-the-art climate data covering the three streams: historical, seasonal forecasts and projections.
It is important to acknowledge that the dataset is not designed for operational activities, because the energy models are simplified compared to operational and more accurate models. Its objective is to illustrate the impacts of climate variability and climate change on the European power system, through the effects on demand and renewables generation. This paper focuses on the energy conversion models used for building the C3S-E dataset. These models have been developed and calibrated for the historical stream using the ERA5 reanalysis and then applied to all climatic data. Evaluations of the seasonal forecasts and projections streams are not discussed here. The different data sources used by the C3S-E dataset are described in section 2. The models chosen to simulate electricity demand and supply as well as the motivations for these choices are detailed in section 3.
Climate data The climate data for the C3S-E historical stream come from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis  [26]. Reanalyses are gridded datasets covering the globe, as in the case of ERA5, or a regional domain. They are reconstructions of past climate produced through the assimilation of observations in physical numerical models which have been developed explicitly for climate monitoring and research. Unlike in the case of the ECEM dataset  [25]  which was based on the previous ECMWF reanalysis, namely ERA-Interim, and for which bias adjustment was required  [27] , with ERA5 the need for bias adjustment was not considered as critical, given its generally superior quality  [26]. Therefore, to develop 2 NUTS is the geographical subdivision of the economic territory of the EU.
There are four different levels, from NUTS0 (national) to NUTS3 (small regions). For more details, see https://ec. europa. eu/eurostat/web/nuts/background the historical stream of the C3S-E energy dataset, ERA5 is used in its original form. The only data manipulation comes from interpolation onto a regular 0. 25 degree grid as well as the country averaging (NUTS0). The specific climate variables used to develop the C3S-E models are the following: • air temperature (at 2 m height) • precipitation • downward solar surface radiation (also known as Global Horizontal Irradiance) • wind speed (at both 10 m and 100 m heights) Their temporal resolution is one hour, with the distinction that for air temperature and wind speed the values are instantaneous (at the hour) whereas for precipitation and solar radiation they are accumulated (over the previous hour).
Energy data Being part of the Copernicus services, C3S-E aims at providing free and open access data, including for energy indicators. One of the major constraints in developing and assessing the C3S-E models was to identify and use freely available data, in order to fulfil this open access policy. The other constraints were: • Availability of data for all European countries; • Physical relevance both in space and time among the different variables (demand and supply from wind, solar and hydro); • Sufficient temporal data coverage to set up and train models, and then validate these on an independent period. Even if multiple energy datasets exist, only few of them fulfil the stated requirements. The choice was then made to use mainly the European Network of Transmission System Operators 3  for Electricity (ENTSO-E) databases.
The ENTSO-E is an association of 43 electricity transmission system operators (TSOs) from 36 countries across Europe, established by the EU in 2009. In this work we have used mainly the demand and generation data provided by ENTSO-E in two different forms: the Transparency Platform 4 (ENTSO-E TP hereafter) and the Power Statistics (ENTSO-E PS). From January 2015, the ENTSO-E TP made all the power grids data for all the European countries available according to the rules imposed by the EU regulation 543/2013 5. The platform was created to provide high-quality and timely available data to the energy markets' participants. It includes supply and demand data for all the European countries in a homogeneous and easy to access way. A review of the ENTSO-E TP, the methodology used to create the datasets and issues and limitation can be found in  [28]  or  [29].
ENTSO-E TP data has been used for generation. For demand (also called load), we used the ENTSO-E PS 6  "Monthly Hourly Load Values" dataset, which goes back to 2006 for some countries, and then provides a longer record length for models calibration. This dataset, which has been discontinued in 2019, also contains more robust and validated data than ENTSO-E TP, that has doubious data especially for the year 2015. The type of generation data that the TSOs calculate and provide on the ENTSO-E TP is described in the EU Regulation 543/2013 (for example in  Articles 14 and 16). This means that some differences can appear when comparing aggregated data from the ENTSO-E TP with similar data obtained from other data sources. In particular, the installed capacity data used in C3S-E is obtained from the dataset 14.
1. A (Installed Generation Capacity aggregated) and consists of the sum of installed capacity for all the units with at least 1 MW of capacity in a specific area. As described in  [28]  and  [29] , ENTSO-E data have some issues and inconsistencies. The Open Power System Data platform 7  is a very good source of information. It provides easy access to ENTSO-E and other sources of data, after some validation and cleaning, together with pieces of code, in an open and free access approach. It has been used here to run some analysis of ENTSO-E data from the TP. Figure  2  shows a visual summary of all the countries and energy variables modelled in the C3S-E dataset. In this paper and the corresponding dataset, countries are identified with their ISO 3166-1 alpha-2 code. More details about the availability of each data set are given in section 3.
Description of the energy conversion models The main goal of C3S-E is to allow users to model and assess the effects of climate variability and climate change on electricity consumption and generation. Therefore, the assumption is made that the energy system is constant in terms of installed capacity and load profile. This means that the generation capacity is fixed in time, and the long-term trend in energy demand due to, e. g. , economic growth or population change, is removed. Then, once the models are set up, one can easily re-introduce generation capacity evolution or energy consumption trends by adding these external factors to the modelled climate-dependent part of demand and supply. The models described here have been set up using the ERA5 climate reanalysis for climate variables and ENTSO-E data from 2006 onwards for demand, and from 2015 onwards for generation.
Electricity Demand models Generalized Additive Models (GAM) were chosen as the preferred approach to model daily electricity demand. GAMs are a generalization of linear models but can embed non-linear (analytical) functions to capture the relationship between the predictors and the target variables. Introduced by  [32] , they have been improved by  [33]  and many others since then. They are well known methods for load forecasting  [34] [35] [36] , and have been successfully used in the GEFCOM2012 forecasting competition  [37]. Their major advantages are that they are easy to interpret, fast to run and adaptable to different datasets. This approach requires observed load data for both the training and validation periods. The data used here are from the ENTSO-E Power Statistics (PS) database, namely the "Monthly Hourly Load Values" described in section 2.
These are Boolean flags. The climate variables can also be combined. For instance, a term can be added to consider GHI only in winter days. In addition, one or several smoothed temperatures over a few days can be considered, to account for the delayed effect of outside air temperature on electricity demand, mainly because of buildings' inertia. The choice to use combined variables and smoothed temperatures is made by iteration, in order to minimize the residuals of the model. All models have been developed with the R software. The modelling approach is the same for all the 32 countries considered, as described below. Only the start and end dates of the training and validation periods differ, based on ENTSO-E data availability and quality. The process consists of 4 steps, as described in Figure  3  for the case of France: 1.
The dataset is then divided into two parts: training period and verification period. A new GAM model is trained over the first part (2010-2014 for France, blue horizontal line on Figure  3 ), and the parameters obtained are used for the following steps. 3. The model built in step 2 is then applied on the verification period (2015-2018 for France, green horizontal line on Figure  3 ); iterations can be made to improve the model, for instance by adjusting the smoothed temperatures, or adding combined variables (like e. g. GHI for winter days); 4. Then, the complete ERA5 data from 1979 to 2019 are used with the GAM parameters obtained at step 2 to reconstruct the full 1979-2019 period, as shown by the black curve in Figure  3.
It provides a dataset of reservoirs (HRE) and run-of-river (HRO) hydro power generation, aggregated at country level and daily time resolution, for the 12 countries with the largest installed capacity: Austria (AT), Switzerland (CH), Germany (DE), Spain (ES), France (FR), Italy (IT), Norway (NO), Portugal (PT), Romania (RO), Sweden (SE) and Slovakia (SK) for HRE; for HRO, the list is the same except Sweden but with Finland (FI) (see Figure  2 ). The methodology is based on the random forests (RF) machine learning approach, and uses country aggregated predictors from ERA5, namely air temperature (TA) and total precipitation (TP) (see Table  1 ). In addition to the TA and TP on the same day as the simulated generation, the model uses time lagged TA and TP of up to 200 days.
The reference configurations have been chosen on the basis of the statistical analysis of ca. 30,000 PV installations in Germany. It was chosen to use 13 reference configurations which is a compromise between a limited number of configurations and the best possible modelling accuracy in a tractable approach. As detailed in  [38] , the weights have been derived from the above-mentioned statistical analysis and by a simple geography dependant parameterization allowing to then generalize the characteristics of the German installation to any region in Europe. The calculation method for SPV requiring numerous computations, it is hardly tractable for large datasets like multi-model multi-emission scenarios climate projections or operational multi model seasonal forecasts. This high number of calculation is needed to properly take the variation of the sun position into account, but it is not necessary for the coarser resolution of seasonal forecasts and climate projections.
Validation of the C3S-E energy conversion models This dataset as built to fulfill a Copernicus Climate Change Servicec contract. One of the key requirements was that the energy models were designed to be applied in any European region and with different kinds of climatic data (reanalysis, seasonal forecasts and projections). The most important criteria for the model selection are therefore those related to the flexibility of the models and the consistency and coherency among variables, both in space and time. Given that there are many regions in Europe with limited information on the modelled energy sources, the most accurate individual models that require very specific information could not be selected. Flexibility was therefore given more consideration than absolute accuracy in the model selection.
Reference dataset As described in section 2. 2, the ENTSO-E datasets (PS and TP) are, to our knowledge, the only source of homogenous data available for all European countries. These datasets, while providing a very good reference for C3S purposes, nonetheless present some drawbacks as explained for instance in  [28]  and  [29]. The most problematic issues for C3S-E are the following: i) Record length and quality differ among countries. There is no easily accessible documentation on the reporting and processing methodologies applied to the data, which may change over the years, creating inconsistencies. Discontinuities that can be only explained by a change in the processing method have been identified for a few countries, and some countries have a non negligible ratio of missing or obvious erroneous data.
In addition, the PS dataset was discontinued in November 2019, and the only data available for demand then comes from the TP. Some inconsistencies between PS and TP have been found too (not shown here), which for instance create offsets between the two datasets. ii) In the ENTSO-E TP, inconsistencies are found between different datasets, in particular for the first years. In many cases, the actual generation is not consistent with the installed capacity (e. g. the solar production of PV in Italy). The installed capacity from ENTSO-E TP indeed shows strong deviations from alternative data sources for several countries. Figure  5  shows for instance the installed capacity for solar power from four sources (five for France) for a few selected countries. ENTSO-E PS is available only until 2016.
IRENA take their data from Eurostat, hence the convergence between IRENA and ENTSO-E PS. The main differences are found between ENTSO-E TP and Eurostat/IRENA, the ENTSO-E TP showing obvious dubious values. One of the reasons for differences also lies in the fact that not exactly the same production units are considered in the different datasets (some consider only units with installed capacity larger than 1 MW for instance). Based on these comparisons and discussions with experts, it was decided to use the Eurostat/IRENA values of 2018 as the most representative values (the table for all energy types and countries is given in Supplementary Material Table  S1 ). Accordingly, the validation metrics and figures shown in the next sections are most likely to be relevant for this year and need to be interpreted with caution considering the installed capacity data issues for the other years.