dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability. The complete hourly dataset is available as an Rdata file (COV3ER_2016_dataset. Rdata) containing the whole dataset, together with a data description file (COV3ER_2016_dataset-description. xlsx) and a script for reading and plotting the data (COV3ER_2016_dataset_make_graphs. R). The data and code for visualization are available at https://doi. org/10. 15454/IRZ9XX (Loubet, 2021). Supplement. The Supplement includes a PDF document with supplementary figures and tables and an Excel file containing the averaged fluxes, mixing ratios and LOD f (Table  S3 ). Also available are three CSV files containing correlation tables between VOCs (Tables  S4a, S4b, S4c ). The supplement related to this article is available online at: https://doi. org/10. 5194/acp-22-2817-2022supplement.
Acknowledgements This work was granted access to the  HPC resources of IDRID, TGCC  and  CINES  under the allocation  A0072B06153  and  A0092B06153  made by  GENCI  ( Grand Equipement National de Calcul Intensif ) and also for the computing time at  CRIANN  ( Centre Régional Informatique et d'Applications Numériques de Normandie ) under the scientific project No. 2006011. This work has been also founded by the CHAIRLIFT European project  831881 -CHAiRLIFT -H2020-CS2-CFP08-2018-01.
| Fuel cell Saadabadi et al. 56  conducted a comprehensive study on the application of hybrid anaerobic digestion (AD) unit and solid oxide fuel cell (SOFC) (Figure  7 ). Furthermore, an outline for the benefits and difficulties of the hybrid integration of AD and SOFC systems was examined to measure the performance of this system for electricity generation and usage of heat produced by SOFC. ] [58] [59]  A new configuration of the hybrid combination of SOFC and MGT fueled by biogas was proposed by Wongchanapai et al. 60  The key concept of this research was the application of the internal reformation unit in SOFC for the internal transformation of methane into hydrogen. A comprehensive set of data was obtained and reported in this study. 60 he achievements of hybrid integration of a molten carbonate fuel cell (MCFC) and MGT fueled by natural gas and biogas for power production (Figure  8 ) were analyzed by Huang et al.
5%) than its counterparts CHP and SOFCs (30. 4% and 32. 9%, respectively). In another study, Prodromidis and Coutelieris in 2017  85  designed a software tool, named THERMAS, incorporating energy and exergy theory assessments of a biogas-SOFC plant. Experimental and theoretical study of different scenarios fed by biogas various compositions showed that the SOFC system used pure methane to generate electrical energy displayed an energy efficiency of up to 80-90%, while a value of 96% was obtained through simulation. Likewise, Chouhan et al. 86  developed a survey on electricity generation using the SOFC system. A thermodynamic analysis using the Peng-Robinson (PR) formula of state with various compositions of CH 4 (45-80%) was realized at different temperatures (573-1273 K) and steam to CH 4 molar ratios (S/C = 0-2) at 1 atm pressure.
104 The SOFC was integrated with the internal reforming unit. Three gasifiers, namely, dual, circulating, and bubbling fluidized beds, have been used to produce syngas products  104  for the use in the SOFC, which was combined with the ORC and an absorption chiller. This integration system resulted in 50% exergy efficiency. The performance of SOFC using biogas as fuel has been optimized using exergy analysis. 85 For this purpose, a simulation code THERMAS has been used to analyze the effects of temperature and fuel composition on the system performance. The system in question consisted of the preheater, SOFC, reformer, and burner. The results of the simulation indicated that low methane fuel was the ideal option from an exergy efficiency standpoint. The most important result obtained in the analysis  85  was that the temperatures of the fuel cell and the reformer should be the same and above 1000 K in optimal conditions.
Mycobacterium tuberculosis complex (MTC) CRISPR locus diversity has long been studied solely investigating the presence/absence of a known set of spacers. Unveiling the genetic mechanisms of its evolution requires a more exhaustive reconstruction in a large amount of representative strains. In this article, we point out and resolve, with a new pipeline, the problem of CRISPR reconstruction based directly on short read sequences in M. tuberculosis. We first show that the process we set up, that we coin as "CRISPRbuilder-TB" (https://github. com/cguyeux/ CRISPRbuilder-TB), allows an efficient reconstruction of simulated or real CRISPRs, even when including complex evolutionary steps like the insertions of mobile elements. Compared to more generalist tools, the whole process is much more precise and robust, and requires only minimal manual investigation.
Second, we show that more than 1/3 of the currently complete genomes available for this complex in the public databases contain largely erroneous CRISPR loci. Third, we highlight how both the classical experimental in vitro approach and the basic in silico spoligotyping provided by existing analytic tools miss a whole diversity of this locus in MTC, by not capturing duplications, spacer and direct repeats variants, and IS6110 insertion locations. This description is extended in a second article that describes MTC-CRISPR diversity and suggests general rules for its evolution. This work opens perspectives for an in-depth exploration of M. tuberculosis CRISPR loci diversity and of mechanisms involved in its evolution and its functionality, as well as its adaptation to other CRISPR locus-harboring bacterial species.
The genomic diversity of the CRISPR locus has been investigated in detail as early as 2000 in a study by J. van Emdben et al. showing that spacer duplication, spacer variation, and IS6110 insertion sites could be found in the various phylogenetical lineages of MTC  [14]. However, it concerned a very small sample (n = 34) and did not include any investigation on the cas genes  [15, 16]. Understanding evolutionary dynamics of this locus now requires the exploration of CRISPR-Cas region on an extended dataset. The classic in vitro approach to spoligotyping lists the presence or absence of a well-known list of spacers in a sample. This robust method has been largely applied in vitro  [3]. This approach however did not allow the exploration of many characteristics such as if the order of the spacers is different in one strain or the other.
CRISPRbuilder-TB is inspired by previous developments  [24, 25]  and particularizes the De Bruijn approach to the specific case of reconstructing CRISPR loci based on SRA, with the condition that its standard DR sequence is known. Applied on TB SRA without any genome assembly step, CRISPRbuilder-TB proved reliable and robust with reads of more than 75bp. The remarkable elements that were found in this locus by MTC lineages is the subject of a separate publication  [1]. We emphasize its usefulness by showing that close to 50% of MTC complete genomes available in public NCBI database exhibit unreliable CRISPRs, and by detailing the different kind of evolutionary events that can affect CRISPRs, at least in MTC genomes.
Description of CRISPRbuilder-TB A known limitation to CRISPRs reconstructions is the difficulty to handle large sets of reads. As our aim is to reconstruct a CRISPR whose common DR is known, the first step of CRISPRbuilder-TB is to filter the SRA. We kept not only reads similar to DR sequences but also reads similar to other sequences that may be linked to the CRISPR (Fig  1 step 1 ). We call this catalogue of sequences linked to CRISPR, the "catalogue of remarkable sequences". This catalogue is thereafter also aimed to express the composition of a definite CRISPR in a self-explanatory way, referring to known DR and spacers. We thus aimed at making this catalogue as exhaustive as possible. To do so, we explored a large and representative set of SRA from MTC strains (Dataset #3, Fig 2, n = 434, L1 to L6, animal strains and M.
CRISPR region in other MTC complete genomes To explore the reliability of CRISPR regions of complete genomes available in Public databases, we extracted the CRISPR-locus from all the additional MTC complete genomes we identified at the time of the study (n = 187, sample #2, Fig 2). According to the SNPs that they carried, these genomes belonged to L1 (n = 6), L2 (n = 44), L3 (n = 2), L4 (n = 112), L6 (n = 2), L_bovis (n = 15), unknown(mic+can) (n = 6). None belonged to L5. At the IS6110 level, all complete genomes had the common insertion downstream of spacer 34. Only another IS6110 copy was identified, in front of spacer 46 of strains of sublineage L2. 2. 1. Most sequences seemed of high quality according to the presence of known CRISPR remarkable sequences and to the presence of compatible Coll et al.
Discussion We set up a fast semi-automatic pipeline called CRISPRbuilder-TB, available at https://github. com/cguyeux/CRISPRbuilder-TB to reconstruct CRISPR-Cas locus from MTC short reads sequencing runs. This tool is in the line of tools avoiding genome assembly to reduce computational time and potential biases such as PhyResSE, Snippy, etc. [27]. CRISPR loci are renown for the difficulty to reconstruct them. Existing tools have until now focused on their detection, an approximate reconstruction to enable comparisons in complex samples such as metagenomics data (S1 Table ). Little attention has been brought to the exact reconstruction of CRISPRs. This exact reconstruction is necessary to infer evolutionary events affecting the CRISPR loci. As CRISPR locus has been used for more than two decades in Mycobacterium tuberculosis complex as a first-line molecular epidemiology tool, we focused on this species.
In that case, CRISPRCasFinder with its user-friendly interface may be more suitable  [29]. However, it should be noted that in the MTC alone, we can have up to a hundred spacers, counting duplicates, and up to 3-4 IS in the CRISPR, thus up to 15000 nucleotides. Reads have to be really long to uncover complete loci. We also want to highlight here that, even if other tools might prove more suited to deal with long-reads technologies and if these technologies are probably the future of genomics, it would be regrettable to ignore the past. For MTC alone, there are more than 70,000 SRR available, most acquired using short-reads sequencing methods. It is likely that, within the next 5 to 10 years, the richness of these databases will continue to be exploited. CRISPRbuilder-TB may all the same be applied on other species/samples for which short reads data continue to accumulate.
On the use of CRISPRs from complete genomes in public databases The systematic study of the MTC CRISPR loci of the complete genomes highlights some errors in CRISPR public data that may be true for other species. We will now remind of the main pitfalls that we identified in this species and provide some advices on how to deal with other species. By exploring MTC complete genomes deposited in public databases, we first showed that reference samples had high quality CRISPR reconstructions, likely due to partial or full Sanger sequencing. Conversely, another relatively large proportion of the ~200 complete genomes (more than 1/3) showed a clearly problematic locus, not trustworthy at all. This does not mean that there is no benefit in sharing such data, which can be informative for the rest of the genome.
This collection was completed by SRA queries on the NCBI search engine, with taxid values of 33894 and 78331, corresponding respectively to M. tuberculosis variant africanum and M. canettii organisms. The names of SRA run accessions (SRR) were compiled, then the actual WGS sequencing data were automatically downloaded via the fastq-dump command of the sra-tools package. This led to a database of about 3,500 runs in the form of reads. This database is meant to be a good representative of MTC diversity, both at the lineage level and regarding geographical origins. A first selection on these runs was carried out, first of all concerning the sequencing technology, which should have been paired-end Illumina to avoid having to manage different formats in our scripts. We also recovered the size of the reads and the average coverage, and discarded all runs, either corresponding to weak covers (<50x) or with too short reads (minimum size of reads: 75 bp).
For each run and each lineage-defining SNP, this 41 nt sequence was then blasted on the read sequences (blastn, maximum e-value 1e-5, from a local blast database calculated for each genome). At each blast output, we then counted the number of matches that contain the 41 nt version of H37Rv, and the number of matches that contain this pattern whose central nucleotide has been replaced by the SNP tabulated in S2 Table  (SNP sheets). If the number of mutated units was significantly higher than that of reference H37Rv, the line associated with this SNP was then added to the genome considered. As a second annotation, we provided the in silico-derived old and new formats of spoligotypes based on the presence/absence of known spacers. It follows the same principles as Spolpred  [17]. We blasted each spacer on each of the read sequences (blastn, e-value < 1e-6), and we calculated the number of matches for each spacer (without looking at whether the sequences matched exactly, as spacers could have been mutated): if this number of matches exceeded 5% of the mean genome coverage, then we considered that the spacer could be added to the spoligotypes.
Listing of CRISPR-Cas remarkable sequences. Direct repeats and spacers As each spacer lies between two DR sequences, and each DR lies between two spacers, we planned to detect new spacers using known DRs, and new DR variants using known spacers to build an exhaustive "catalogue of remarkable sequences". We started it with published sequences. DR0 is the name given for reference DR  [14] , reference spacers k are referred to as esp k (S2 Table ). We then looked for spacer variants, using regular expressions in randomly picked up runs from the sample #3 database. More specifically we searched in all the reads for patterns made up of: the last 12 nucleotides of the DR0, followed by a variable sequence with a size between 10 to 70 nucleotides, followed by the first 12 nucleotides of the DR0 (findall method of the python re module, patterns: DR0[-12:]([ATCG]{10,70})[DR0:12] and its reverse complement).
Other sequences of interest To this collection of subpatterns to be discovered in the CRISPR loci, we added: 1. the beginning and end sequences of IS6110 and its reverse complement (40 bp each time). 2. CRISPR approximate borders: sequences corresponding to Rv2816c (cas2 gene of the cas locus) and Rv2813c, reputed to border the CRISPR locus  [10]. 3. CRISPR exact-flanking sequences: the reads including the end of the cas2 gene have been extracted from a small collection of genomes from the database presenting spacer 1 in its "new spoligotype" to retrieve likely ancestral closest border to CRISPR locus. The consensus sequence located downstream has been reconstructed, then the reads including the latter were recovered. These included a DR0 followed by the spacer "new" number 1. After verification (blast), this CRISPR-flanking pattern was indeed found in a large set of genomes in our collection, so it was added as such to the catalogue of patterns of interest.
The result of this post-processing of the previously obtained contigs is a reasonably sized character string, including patterns of the form � spX(Y) � for the variant Y of the spacer X, � DRX � for variant number X of the DR, as well as the words � begin_IS6110 � , � end_IS6110 � , � begin_IS6110c � , � end_IS6110c � , � starting_pattern � , � ending_pattern � , � Rv2816c � , and � Rv2813c �. This translation makes it easier to understand the contigs obtained, and makes it easy to detect a break in the order in which the spacers appear. It also allows to detect new variants that had not been detected until now, and to add them after naming to the database of remarkable sequences. In the vast majority of the cases studied manually (but read exceptions in Duplication paragraph below), one to three contigs depending on the number of IS6110 insertions in the locus (those with the highest scores) were sufficient to reconstruct the entire locus.
And this doubling of the number of matches is a form of cross-validation of the duplication. At this stage, we are therefore able to reconstruct the entire CRISPR locus from Illumina paired-end reads, provided that the coverage and size of the reads are reasonable, and this by being able to detect duplications, spacer and DR variants, and IS insertions. This process is 95% automated (https://github. com/cguyeux/CRISPRbuilder-TB), but it requires human intervention to finalize the assembly of the contigs (S1 Fig). Once this locus has been reconstructed, the resulting spoligotypes (old and new) can be compared to spoligotypes based on presence/absence of spacer sequences.
Runs' additional selection A final point remains to be clarified at this stage, namely how the WGS runs used here for CRISPR reconstruction were selected from our database of ~3,500 items. Indeed, although much of the reconstruction has been automated, the remaining 5% takes a little time to be properly carried out. Not wanting to waste time rebuilding loci where nothing has happened, in terms of insertion and duplication, we have taken part of the pipeline detailed above to make a selection of the runs of interest. These correspond to samples carrying duplications as well as samples carrying IS6110 insertions. For a given run, we focus on reads returning matches during a blast on sequences of interest (DR and spacers). This again is performed using k-mers derived from the reads as described above.
CRISPRs using our pipeline (SRA, dataset #3, see Fig 2). Legend IS6110 sheet. Column A: lineage assignation according to Coll et al. , column B: Lineage and Sublineage according to Palittapongarnpim et al. for L1, Shitikov et al. for L2, Stu ¨cki et al. for L4; column C: spoligotyping-based alias name, column D: SIT nomenclature, column E: selection set, column F: Accession number, column G: common Strain name, column H: location, column I: spoligotype (old), column J to end: the first line designates the name of the gene or the spacer ID; the second title line designates the spacer ID according to the classical spoligotyping nomenclature (also visible by yellow shade).
A Guide to Applied Researchers To facilitate practical applications, we have implemented in Matlab all the tests proposed in this paper using real data sets. First, download the zip folder, package. zip, from the author's website: http://cred. u-paris2. fr/PIROTTE/package. zip. This folder contains all the matlab files, data files, and an instruction file readme. txt. The readme. txt file gives clear instructions on (i) which test to follow, (ii) which file to run, and (iii) how to modify the input part of the code to change the data set, the spatial weight matrices, and to redefine the variables. Based on our theoretical arguments and empirical findings, we recommend the following: • for SLR models, the AQS test T r⇤ SLR , given in (2. 19), should be used; • for SPD models, the two fully robust AQS tests T rr SPD and T rr⇤ SPD , given respectively in (3.
Acknowledgements We would like to thank  Gianfranco Piras ,  James LeSage , and the participants of the  XII World Conference of the Spatial Econometrics Association, WVPU Vienna ,  June 2018 , for their valuable suggestions and helpful comments. We are grateful to Editor  Serena Ng  for her encouragements in revising the paper, and for her helpful and constructive comments that have led to a much enhanced version of the paper. Helpful comments from three anonymous referees and two associate editors are also gratefully acknowledged. Zhenlin Yang  gratefully acknowledges the research support from  Singapore Management University  under Grant  C244/MSS16E003.
The modelling of electricity systems with substantial shares of renewable resources, such as solar power, wind power and hydropower, requires datasets on renewable resource profiles with high spatiotemporal resolution to be made available to the energy modelling community. Whereas such resources exist for solar power and wind power profiles on diurnal and seasonal scales across all continents, this is not yet the case for hydropower. Here, we present a newly developed open-access African hydropower atlas, containing seasonal hydropower generation profiles for nearly all existing and several hundred future hydropower plants on the African continent. The atlas builds on continental-scale hydrological modelling in combination with detailed technical databases of hydropower plant characteristics and can facilitate modelling of power systems across Africa.
Amendments from Version 2 The article has been improved based on suggested clarifications by referee #3. Several phrasings have been clarified and various additional disclaimers and/or explanations have been provided through additional text and references. In particular, the discussion section has been updated with additional disclaimers related mainly to the bias-correction process of the data, clarifying some of the shortcomings of this approach related to a lack of a harmonised observational dataset that would have allowed for a spatiotemporally fully streamlined bias-correction. We have also clarified several other concepts, e. g. the definition of "capacity factor" and "storable component", in the manuscript text.
To inform long-term planning and modelling of renewable power capacity expansion, it is crucial that reliable resource profiles of VRE and hydropower are available to the modelling community  13. The inclusion of such resource profiles at high spatiotemporal resolution, from hourly to seasonal and interannual timescales and across geospatial zones of different resource strengths, is crucial to accurately represent modern renewable technologies in energy system models. For this reason, dedicated spatiotemporal databases on solar and wind resource strength and availability have been developed, such as the Global Solar Atlas 14 and the Global Wind Atlas 15 or the reanalysis-based web interface "renewables. ninja"  16. Such resources typically allow the user to select locations on the world map and extract representative resource profiles for VRE from hourly to seasonal and interannual timescales, which can then be used in energy modelling exercises.
The picture is different for hydropower. Comprehensive and integrated databases of hydropower resources are currently unavailable to the modelling community at the required level of detail  17. This is a consequence of the challenge of accurately modelling river flows across a wide range of river basins with different hydrometeorological conditions within a single model framework  18  , as well as the wide disparity in individual hydropower plants' technical characteristics  19. A consequence of this comparative disparity vis-à-vis solar and wind power, and the resulting lack of comprehensive hydropower databases, is that hydropower plants -which are more and more considered to be an important lever to support VRE uptake thanks to their flexibility of dispatch (for reservoir plants) and potential seasonal synergy with VRE (for run-of-river plants) -are often represented coarsely and without the warranted spatiotemporal detail in energy models 9.