dataesr/scientific-paragraphs-categorization huggingface dataset:
Datasets used as driver indicators of AGB carbon change Land use-based carbon fluxes (BLUE). The Bookkeeping of Land-Use Emissions model (hereafter BLUE; see ref. 73  , for documentation) is one of three bookkeeping models used in the Global Carbon Budget 1 for estimating the net CO 2 flux from land use/cover change. In BLUE, transformations of natural vegetation to agriculture (cropland, pasture) and back, including gross transitions at the sub-grid scale, are considered as well as degradation from rangeland dynamics and wood harvesting. The temporal evolution of carbon gains or losses after transformations or harvesting events is based on response curves derived from literature. These response curves describe the decay of vegetation and soil carbon, including the transfer to product pools of different lifetimes, the carbon uptake due to regrowth of vegetation, and the subsequent refilling of soil carbon pools.
Response curves in BLUE depend on literature-based carbon densities, which are implemented for 11 different plant functional types. For this study, BLUE estimates based on the Historic Land Dynamics Assessment+ (HILDA+) have been used as described by Ganzenmüller et al. 74. HILDA+ is a global dataset of annual land use/cover change between six land use categories (urban, cropland, pasture/rangeland, forest, unmanaged grass/ shrubland, no/sparse vegetation). It is publicly available  75  , covers the period from 1960 to 2019 and has a spatial resolution of 1 km  76. We used the net carbon sink from BLUE (BLUE net) as the total of all emissions from agricultural expansion (carbon source), wood harvest (carbon source), and agricultural land abandonment (carbon sink). Additionally, we used the gross carbon sink from BLUE (BLUE gross), which refers to the carbon uptake by agricultural land abandonment only.
Forest harvest (JRC). The dataset represents a forest harvest annual time-series from 2001 to 2019  50. Forest harvest is expressed as the percentage of forest area affected by management practices per year in a 0. 2°grid cell (~20 km), excluding forest losses due to fires, major windstorms, and areas with sparse forest cover. The dataset relies on the Global Forest Change (GFC)  69  product (version 1. 8), a timeseries analysis of the Landsat archive characterizing tree cover extent in the year 2000, and annual forest loss with a spatial resolution of about 30 m. Due to the spatial scale of the GFC dataset, small-scale silvicultural practices such as thinning or selective logging that may not be seen by the satellite could not be fully detected as forest loss. First, a tree cover threshold of 20% was used to define a 'forest' from the GFC tree cover product.
Then, a spatial aggregation to 0. 2°was performed and the annual forest harvest was computed as the ratio between the area of forest loss and the area of forest cover, within each grid cell. Areas with sparse forest cover-that is, where forest cover in a grid cell of 0. 2°is less than 10%-were excluded. Regions affected by forest fires, as detected by the ESA Fire Climate Change Initiative (Fire CCI version 5. 1  77  ) dataset collection, were masked out from the analysis. In the same way, in regions where the annual percentage of forest loss is greater than a given threshold, the forest loss was attributed to wind-throw and masked out. This was done under the assumption that major windstorms generally cause larger losses than those caused by forest management. The forest harvest dataset was generated using Google Earth Engine.
To account for and harmonize different forest definitions in datasets used in this study (e. g. , 20% tree cover threshold here but 30% in WRI-based AGB), we used the land use/cover map of 2010 from the HILDA+ dataset  76  , which is based on the 30% tree cover definition. We set forest harvest to 0 outside the HILDA+ forest areas. Cropland abandonment. The cropland abandonment dataset we used for this study is a synthesis map from three different data sources: 1. Abandoned arable land by Lesiv et al. 78  : The dataset covers the former Soviet Union, refers to the year ~2010, and has a spatial resolution of 10 arcseconds. Abandoned arable land is defined as land that was previously cultivated (agricultural land) but has not been utilized for more than 5 years. Fire. FireCCI51 from the ESA CCI Fire project was used to derive the change in burned area/fire between 2010 and 2019.
We extracted the monthly maps of volumetric soil moisture in m 3 m -3 , converted them to annual means, and derived the annual change between 2010 and 2019. Subsequently, we resampled the map to 0. 25°resolution using bilinear interpolation. Precipitation. We used gridded monthly precipitation from the TerraClimate data of monthly climate and climatic water balance for global terrestrial surfaces from 1958-2019 (~4-km spatial resolution)  80. From this, we derived the difference between the annual precipitation sums in 2010 and 2019 (in mm) and resampled the map to 0. 25°resolution using bilinear interpolation. Temperature. We acquired the Berkeley Earth gridded monthly surface air temperature at 1°spatial resolution  81, 82  to account for temperature as a potential driver of AGB carbon change.
The random forest regression model was applied using scikit-learn, a machine learning library in Python 3. 10. The random forest regression was run for three different subsets based on the target variable. Subset 1: AGB carbon source only, Subset 2: AGB carbon sink only, Subset 3: all AGB change values (including grid cells of the agreement but classified as "no change"). For all runs, the dataset was split into test and train subset, where the test subset was set as 25% of the dataset. The values of all predictors were standardized and scaled between -1 and 1. To derive the optimal number of decision trees used by the model, we iteratively trained the model by using 10 to 200 trees in a 10-tree interval. The number of trees in the model yielding the best performance was taken and implemented in the final model run (see Supplementary Fig.
This approximates how important the driver indicators are in the data. The Shapley value shows the average contribution of a feature value to the prediction in different coalitions. With this, we could not only rank the driver indicators by their importance but also analyze the type and direction of impact (see Fig. 5b ). Since the harmonized AGB carbon change map builds on a threshold for defining agreement and disagreement of the original datasets (L-VOD, JPL, and WRI) and it was used as a target variable in the random forest-based driver analysis, we tested the effect of different thresholds on the model performance metrics and the resulting feature importance. We used the following thresholds: 0, 0. 001, 0. 005, 0. 01, 0. 05, 0. 1, 0. 5. Supplementary Table  5  displays the different sample sizes and model performance as R² and RMSE (root mean-square error).
Farmland abandonment by Estel et al. 28 : Abandoned areas were classified based on MODIS Normalized Difference Vegetation Index (NDVI) time series in 2001-2012. The dataset has a spatial resolution of 232 m and covers Europe, including European Russia. We utilized the map of abandonment based on the following definition: At least three active cropland years during 2001-2006 were followed by five or six fallow years during 2007-2012. 3. HILDA+ cropland abandonment map: All areas where cropland has been converted into unmanaged grass/shrubland or forests during 2010-2019 have been classified as abandoned cropland based on HILDA+ annual land use/cover transitions maps 76. The dataset has global coverage and a spatial resolution of ~1 km. All three maps were reclassified to binary mask format, with 1 representing abandoned cropland and 0 representing all other areas.
The binary masks were resampled to 0. 25°resolution using bilinear interpolation and converted to floating point values. The resulting maps depict the fractions of abandoned cropland, respectively. Finally, we derived the mean fraction of abandoned cropland per pixel from all available datasets (for former Soviet Union:Lesiv et al. , Estel et al. , HILDA+; for non-former Soviet Union: Estel et al. , HILDA+). The resulting map of abandoned cropland represents the maximum fraction of a grid area affected by cropland abandonment during 2010-2019. It should not be used to measure abandonment in absolute terms.
Acknowledgements This research has been supported by the  European Commission ,  Horizon 2020 Framework Programme (VERIFY , grant no. 776810 ). We further acknowledge  Dmitry Schepaschenko  for sharing valuable information on carbon storage in relation to forest management and land use change in Eastern Europe. In addition, we would like to thank  Tobias Kümmerle  and  Christian Levers  for providing underlying data on farmland abandonment across Europe by Estel et al. 28.
( Fig. 4 Spatial patterns of AGB change in Eastern Europe during 2010-2019. a AGB change in Eastern Europe during 2010-2019 from individual datasets: L-VOD, JPL, and WRI. b Data agreement on carbon gains (source) and losses (sink). Sources are defined by ΔAGB <-0. 05 Mg C ha -1 a -1 , sinks by ΔAGB > 0. 05 Mg C ha -1 a -1. Levels of agreement represent the number of agreeing datasets. c Harmonized mean AGB change from agreeing datasets. The harmonized mean AGB change was derived for all dataset that agree on either a carbon source or sink in areas with an agreement level of at least 2. Areas of disagreement are displayed in gray.
2. 2 Farmland abandonment by Estel et al. 28 : Abandoned areas were classified based on MODIS Normalized Difference Vegetation Index (NDVI) time series in 2001-2012. The dataset has a spatial resolution of 232 m and covers Europe, including European Russia. We utilized the map of abandonment based on the following definition: At least three active cropland years during 2001-2006 were followed by five or six fallow years during 2007-2012. 3. HILDA+ cropland abandonment map: All areas where cropland has been converted into unmanaged grass/shrubland or forests during 2010-2019 have been classified as abandoned cropland based on HILDA+ annual land use/cover transitions maps 76. The dataset has global coverage and a spatial resolution of ~1 km. All three maps were reclassified to binary mask format, with 1 representing abandoned cropland and 0 representing all other areas.
EFISCEN Carbon stocks of trees from European Bottom-up (Forest Forest Information SCENario Model carbon model) (EFISCEN) based on national inventories CBM Simulated forest carbon dynamics from Bottom-up (Forest the Carbon Budget Model (CBM) carbon model) BLUE Bookkeeping model (BLUE): Net CO 2 flux Bottom-up (Land use only from land use/cover change carbon model) Datasets (incl. abbreviations and characteristics) used for comparing estimates of land-based carbon uptake over Europe. a Data available only for the EU.
Materials and Methods The full pipeline of our study is summarized in Fig. 1. 2. 1. Dataset 2. 1. 1. Data acquisition 125 healthy subjects (78 Females) were recruited at Aix Marseille University, France. Their age ranged between 20 and 75 years old. (mean = 42, sd = 16). The study was approved by the "Comité de Protection des Personnes" (agreement n°19. 09. 12. 44636). Some of the data was used in previous studies (A. Kabbara, Paban, and Hassan 2021; Aya Kabbara et al. 2020; Paban et al. 2019). Participants were asked to relax for 5 minutes with their eyes closed without falling asleep. EEG data were collected using a 64-channel Biosemi ActiveTwo system (Biosemi Instruments, Amsterdam, The Netherlands) positioned according to the standard 10-20 system montage. Two bilateral electrooculogram electrodes (EOG) recorded horizontal movements.
Data preprocessing Data were re-sampled to a common frequency of 512 Hz and segmented into 10-seconds epochs. Preprocessing was done using Automagic Matlab toolbox  (Pedroni, Bahreini, and  Langer 2019) (https://github. com/methlabUZH/automagic). Automagic preprocessing parameters are detailed in the Supplementary Materials. The number of interpolated channels did not exceed 15% of the total number of channels; otherwise, the epoch was rejected. For each subject, we chose the best 30 epochs based on Automagic quality metrics (please refer to Supplementary Materials). Subjects having less than 30 clean epochs were excluded from analysis. After preprocessing, 88 subjects were included in the analysis. Finally, the 64-channel montage wass subsampled into 32-and 19-channel montages.
Group-level consistency A split-half reliability approach was used to assess group-level consistency  (Colclough et al. 2016 ). The dataset (88 subjects) was randomly divided into two groups and. Group 𝐺1 𝐺2 connectivity matrices (C G1 and C G2 ) were obtained by averaging connectivity matrices across all subjects within a single group. Then, Pearson correlation was computed between averaged matrices C G1 and C G2. This was repeated for 100 iterations. To investigate the effect of source leakage on group-level consistency, we computed the correlation between edge length (i. e. , distance between ROIs) and its contribution to group-level consistency. The contribution of individual edges  (Finn et al. 2015; Colclough et al. 2016)  to the group-level consistency was defined as the element-wise product between edge vectors of matrices C G1 and C G2 (after z-score normalization).
Statistical Testing All statistical analyses were performed using R (R Core Team 2020). Generalized linear models were used to assess the effect of (i) number of electrodes, (ii) inverse solution, and (iii) functional connectivity metric as fixed effects on group-level repeatability and between-subjects similarity. Beta regression model was used to assess the effects of different tested factors on the absolute value of the within-subjects similarity. These models were chosen after careful inspection of the model's assumptions based on visual inspections of the model's residuals distributions. In each case, the model that best met these assumptions was chosen. Post-hoc analyses were performed using z-tests with the glht function of the multcomp package  (Hothorn, Bretz, and Westfall 2008 ) that provides corrected p-values.
2017; Wicherts et al. 2016; Bishop et al. 2015; Pernet and Poline 2015). Making the data and analysis available to the community fosters research replicability, and enables 1) running alternative analyses on the same data, and 2) validating the codes that were used  (Botvinik-Nezer et al. 2020 ). To address the issue of p-hacking, pre-registration could be adopted to prevent the misuse of analytical variability and exploitation of the researcher's degrees of freedom by setting analysis details prior to the study and prohibiting data-dependent choices. Although data and code sharing and pre-registration would not alleviate all the aspects of the analytical variability problem, it would make it transparent and quantifiable  (Botvinik-Nezer et al. 2020). Another way to address the issue of analytical variability is to run many alternative analysis pipelines (and report them eventually) on the same data set  (Botvinik-Nezer et al.
Acknowledgments The authors would like to acknowledge the  Lebanese National Council for Scientific Research (CNRS-L) , the  Agence Universitaire de la Francophonie (AUF) , and the  Lebanese University  for granting Ms. Allouch a doctoral scholarship. This work was also supported by the  Labex Cominlabs project PKSTIM  and by the  Institute of Clinical Neuroscience of Rennes  (Projects  EEGCog  and  EEGNET3 ). The authors would also like to thank  Campus France ,  Programme Hubert Curien CEDRE  ( PROJET N°42257YA ), and the  Lebanese Association for Scientific Research (LASER)  for their support.
Code availability The codes that support the results of this study are available at https://github. com/sahar-allouch/var-EEG-FC. We used Matlab (Matlab 2018), Brainstorm toolbox  (Tadel et al. 2011 ), Fieldtrip toolbox ((Oostenveld et al. 2011); http://fieldtriptoolbox. org), OpenMEEG  (Gramfort et al. 2010 ) implemented in fieldtrip, Automagic toolbox  (Pedroni, Bahreini, and Langer 2019)  for EEG preprocessing, R (R Core Team 2020) for statistical analysis, and Seaborn  (Waskom 2021)  and Plotly (Plotly Technologies Inc 2015) for visualization.
2. Group-level consistency. The dataset was divided into two halves 100 times. For each condition (number of electrodes x inverse solution algorithm x connectivity measure), the Pearson correlation was computed between averaged connectivity matrices inferred from separate halves of the dataset. eLORETA -exact low-resolution electromagnetic tomography. from different epochs. All correlation values for a single subject were averaged to obtain a single value representing the degree of within-subject similarity/consistency. Fig. 3. Between-subjects similarity. Pearson correlation was computed between all subjects for each condition (number of electrodes x inverse solution algorithm x connectivity measure). eLORETA - exact low-resolution electromagnetic tomography. LCMV -linearly constrained minimum norm eLORETA -exact low-resolution electromagnetic tomography.
Identifcation of the Strain by MALDI-TOF MS and 16S rRNA Gene Sequencing. Colonies were frst identifed using MALDI-TOF MS on a LT Microfex spectrometer (Bruker Daltonics, Bremen, Germany) as described  [6]. Te obtained spectra were imported into the MALDI Biotyper 3. 0 software (Bruker Daltonics) and matched against the references in the database (Bruker database incremented with in lab references (https://www. mediterranee-infection. com/accesressources/base-de-donnees/urms-data-base/, accessed February 2021)). Te resulting score enabled (or did not enable) the identifcation of the tested species: colonies were labeled as correctly identifed at the species level with a score ≥ 2, at the genus level with a score ≥ 1. 7. Unidentifed species (score < 1. 7) using MALDI-TOF MS were identifed using Sanger sequencing of the 16S rRNA gene.
Te 16S rRNA gene was amplifed using the universal bacterial primers pairs fD1 and rP2  [7]  (Eurogentec, Angers, France), and the resulting amplicons were sequenced using the Big Dye ® Terminator v1. 1 Cycle Sequencing Kit and 3500xL Genetic Analyzer capillary sequencer (TermoFisher, Saint-Aubin, France), as described  [8]. Te sequences were aligned using MUSCLE with default parameters, and the phylogenetic inferences were obtained using the maximum likelihood method and the MEGA X software  [9]. Bootstrap values obtained by repeating the analysis 1,000 times to generate a majority consensus tree are indicated at the nodes. Bartonella quintana was used as outgroup.
Cellular Fatty Acid Composition. Cellular fatty acid methyl ester (FAME) analysis was performed using gas chromatography/mass spectrometry (GC/MS). Several culture plates were scraped to obtain approximately 50 mg of bacterial biomass per tube. FAMEs were prepared according to Sasser's protocol  [12] , and the GC/MS analyses were then done as previously described  [13]. Te sample was prepared with approximately 15 mg of bacterial biomass per tube harvested from several culture plates. Briefy, fatty acid methyl esters were separated using an Elite 5-MS column and monitored by mass spectrometry (Clarus 500-SQ 8 S, Perkin Elmer, Courtaboeuf, France). Spectral database search was performed using MS Search 2. 0 operated with the Standard Reference Database 1A (NIST, Gaithersburg, USA) and the FAMEs mass spectral database (Wiley, Chichester, UK).
Genome Assembly and Analysis. Te total DNA genome of the strain Marseille-Q2617 was extracted and sequenced as previously described  [14]. SPAdes version 3. 10. 1 software  [15]  was used for assembling genomic reads. Mapping of reads recovered from MiSeq and MinION technologies for strain Marseille-Q2617 was carried out using CLC genomics7 (https://www. qiagenbioinformatics. com/products/). Genome strain is annotated with Prokka version 1. 13. 3, available in the Galaxy Australia online server (https://usegalaxy-au. github. io/). GCview software  [16]  was used for genome visualization. Te digital DNA-DNA hybridization (dDDH) among compared genomes was calculated by the online tool genome-to-genome distance calculator, version 3. 0  [17]. Furthermore, the average nucleotide identity was estimated using the Orthologous Average Nucleotide Identity Tool (OAT)  [18].
Results and Discussion 3. 1. Phylogenetic Analysis. Te spectra were added to the local Microfex database to allow future identifcation using MALDI-TOF MS. Te phylogenetic tree based on the 16S rRNA gene highlighted the position of the Marseille-Q2617 strain in relation to all related streptococcus species whose names have been validly published in the LPSN is presented in Figure  1. Nonidentifcation of the strain by MALDI-TOF mass spectrometry led us to carry out additional analysis. Currently, the 16S rRNA gene alone cannot discriminate strains of streptococci, and a polyphasic approach should be adopted  [19, 20]. BLASTn was carried out on the NCBI DNA database with the sequences of the 16S rRNA genes, and strain Marseille-Q2617 shows 99. 84% sequence similarity to Streptococcus mitis ATCC 49456 (NR_116207.
1). Indeed, the sequence similarity values of the 16S rRNA gene obtained are very high compared to the threshold value recommended to delimit the species barrier  [21]. In contrast, the phylogenetic tree constructed with Mega X software  [9]  based on fve concatenated genes (gyrA, ddl, gdh, rpoB, and sodA) , strain Marseille-Q2617 regarding others closely related species. Genbank accession numbers of 16S rRNA gene sequences are indicated in parenthesis. Sequences were aligned using MUSCLE with default parameters. Phylogenetic inferences were obtained using the maximum likelihood method and the MEGA X software  [9]. Bootstrap values obtained by repeating the analysis 1,000 times to generate a majority consensus tree are indicated at the nodes. Bartonella quintana was used as outgroup. Te scale bar represents a 2% nucleotide sequence divergence.
C 16:0 (56. 1%), C 18:0 (12. 7%), and C 18:1n9 (11. 5%) are the major fatty acids detected in the cell wall of Streptococcus thalassemiae sp. nov. Te draft genome of strain Marseille-Q2617 T was 2. 02 Mbp with 40. 5 mol% of G + C content. Indeed, the 16S rRNA and genome sequences of strain Marseille-Q2617 T are deposited in the GenBank database under accession numbers LR809138 and CAHJXN010000000, respectively. Te type strain of Streptococcus thalassemiae sp. nov. , is Marseille-Q2617 T (�CSUR Q2617 � CECT 30109) and was isolated from a man with major thalassemia.
Acknowledgments Te authors thank  Aurelia Caputo  for submitting the genome sequences,  Ludivine Brechard  for sequencing the genome, and  Nicholas Armstrong  for the analyses of the fatty acids. Tis study was supported by the  Institut Hospitalo-Universitaire (IHU) Mediterranean Infection, Te National Research Agency  under the  Program "Investissements d'avenir ," reference  ANR-10-IAHU-03 , the  Region Provence Alpes Cote d'Azur  and  European funding FEDER PRIMI.
Streptococcus caballi DSM 19004 TFigure 2 : 190042 Figure 2: Phylogenetic tree based on fve concatenated genes such as GyrA, ddl, gdh, rpoB, and sodA and constructed with MEGA X software. Te sequences were then aligned using MUSCLE. Bootstrap values obtained by repeating the analysis 1000 times to generate a majority consensus tree are shown at nodes. Only bootstrap values above 75% are displayed. All genomic sequences of the diferent species studied are retrieved from the NCBI database. Tey are then annotated with the Prokka software. Te targeted genes are extracted from genomes and concatenated with the Emboss union server (https://www. bioinformatics. nl/cgi-bin/emboss/union). Tis tree is in accordance with the classifcation of Patel and Gupta [4] and shows the separation of species into clades.