dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2005; Beckmann and Smith, 2004). However, to distinguish between unstructured noise (Gaussian distribution) and structured components (non-Gaussian distribution) the fMRI timeseries variance needs to be appropriately sampled. Indeed, the number of spatially independent components increases with respect to the length of the imaging session (with a linear coefficient of 2. 4 components / min, Fig. 7b ), as determined by FSL's Multivariate Exploratory Linear Optimized Decomposition into Independent Components (MELODIC) software. The majority of these sICAs, however, are time-varying imaging artefacts (i. e. motion, respiration and MR-artefacts) that need to be removed from the fMRI timeseries to obtain neurobiologically meaningful FC profiles (Fig. 7 ). (Autio et al. , 2020). (C) Representative PRIME DE-sites  (Milham et al.
, 2018). Data was distortion corrected, detrended, motion corrected and FIX-cleaned using HCP-NHP pipelines  (Autio et al. , 2020; Glasser et al. , 2013). Violin plots contain approximately 60×10 3 nodes and 1. 8×10 9 edges in YA-HCP whereas they contain approximately 18×10 3 nodes and 160×10 6 edges in macaque monkeys. Local FC (2% geodesic distance) is not shown. Scan length and number of volumes acquired were 13 min and 500 at University of California, Davis (UC-Davis), 27 min and 1,600 at University of Minnesota (UMN), 42 min and 8,192 at Mount Sinai School of Medicine (Philips) (MtS-P) and 60 min and 1,824 at Princeton University, respectively. One popular means to remove the structured time-varying artefacts from fMRI timeseries is to use model-free FMRIB's ICA-based X-noiseifier (FIX)  (Griffanti et al.
8d ), as expected. ). fMRI timeseries were preprocessed using HCP-NHP pipelines, parcellated using M132 atlas containing 91 parcels per hemisphere  (Markov et al. , 2014) , and then Spearman's Rank correlation coefficient (rho) between parcellated timeseries was calculated. Comparison of FC was limited to PRIME-DE sites that fulfilled minimum acquisition criteria (high-resolution anatomical image and a B0 field-map). (C) Test-retest (heat) scatter plot of Z-scored FC matrixes (N=1, n=2, RIKEN data). (D) Reproducibility was high (>0. 8; rho) in majority of the cortex (>78%), however, areas distant to RF receive channel coils and weaker SNR (i. e. hippocampal complex and ventral visual stream) exhibited lower reproducibility (RIKEN data was acquired using HCP-style protocols). Abbreviations: HCP the human connectome project; UC-Davis University of California, Davis; MtS Mount Sinai-Philips; IoN Institute of Neuroscience; PU Princeton University; UMN University of Minnesota, RH right hemisphere; LH left hemisphere.
e. TR ~ 3. 0 sec). Clearly, efficient isotropic high-resolution dMRI acquisition in vivo requires high-density coils with good parallel imaging capabilities, whereas ex vivo studies can alleviate this requirement by using gadolinium contrast agents to shorten T1-relaxation constants and using longer data acquisition sessions. Finally, we recommend using prescan normalization to improve quality of image registration (dMRI to structural) and to reduce the effects of head motion within a static receive field. To explore the NHP dMRI data quality, we also analyzed several PRIME-DE datasets using the HCP-NHP pipeline and FSL's 'bedpostx_gpu'  (Autio et al. , 2020; Behrens et al. , 2007; Hernandez-Fernandez et al. , 2018). Whole brain SNR of b0 images are compared in Figure  10a. Notably, the PRIME-DE data (MtS-P and UC-Davis) resulted in much lower third crossing fiber white matter volume fractions in comparison to RIKEN data despite using comparable imaging resolutions (RIKEN 0.
Figure5. Longer resting-state fMRI scan duration improves the quality of functional connectivity metrics. Functional connectivity (Z-transformed Pearson's correlation coefficient) between a seed point (single grayordinate seed) in the default mode network area and the rest of the cortical mantle. In the macaque two sessions each 51 minutes are acquired whereas in the Young Adult Human Connectome Project (YA-HCP) four sessions were acquired with each 15 min length. Both macaque and human BOLD fMRI data were acquired with repetition time ≈0. 7 sec (Autio et al. , 2020; Smith et al. , 2013)  and data was preprocessed using HCP and non-human primate (NHP)-HCP pipelines (Autio et al. , 2020; Donahue et al. , 2018; Glasser et al. , 2013) , including FreeSurfer (Fischl, 2012)  and ICA-FIX processing (Griffanti et al.
(D) Reproducibility was high (>0. 8; rho) in majority of the cortex (>78%), however, areas distant to RF receive channel coils and weaker SNR (i. e. hippocampal complex and ventral visual stream) exhibited lower reproducibility (RIKEN data was acquired using HCP-style protocols). Abbreviations: HCP the human connectome project; UC-Davis University of California, Davis; MtS Mount Sinai-Philips; IoN Institute of Neuroscience; PU Princeton University; UMN University of Minnesota, RH right hemisphere; LH left hemisphere.
Acknowledgements We express gratitude to the  Primate Neuroimaging Data-Exchange (PRIME-DE) initiative  and to all institutions that contributed to the PRIME-DE dataset. Human data were provided by the  Human Connectome Project ,  WU-Minn Consortium  (Principal Investigators:  David Van Essen  and  Kamil Ugurbil ;  1U54MH091657 ) funded by the 16  NIH Institutes  and Centers that support the  NIH Blueprint for Neuroscience Research ; and by the  McDonnell Center for Systems Neuroscience at Washington University. This research is partially supported by the program for Brain/MINDS and Brain/MINDS-beyond from  Japan Agency for Medical Research and development, AMED  ( JP20dm037006 , T. H. ), by  RIKEN   Compass to Healthy Life Research Complex Program  from  Japan Science and Technology Agency ,  JST , by  JSPS KAKENHI  Grant Number  JP20K15945  (J.
E. R) and  KU Leuven  grant  C14/17/109 ;  Fonds Wetenschappelijk Onderzoek-Vlaanderen (FWO)   G0D5817N ,  G0B8617N ,  G0C1920N ,  G0E0520N ,  VS02219N ; and the  European Union 's  Horizon 2020 Framework Programme for Research and Innovation  under Grant Agreement No  945539  ( Human Brain  Project  SGA3 ) (W. V). The authors have no conflicts of interest to declare.
(C) Test-retest (heat) scatter plot of Z-scored FC matrixes (N=1, n=2, RIKEN data). (D) Reproducibility was high (>0. 8; rho) in majority of the cortex (>78%), however, areas distant to RF receive channel coils and weaker SNR (i. e. hippocampal complex and ventral visual stream) exhibited lower reproducibility (RIKEN data was acquired using HCP-style protocols). Abbreviations: HCP the human connectome project; UC-Davis University of California, Davis; MtS Mount Sinai-Philips; IoN Institute of Neuroscience; PU Princeton University; UMN University of Minnesota, RH right hemisphere; LH left hemisphere.
2010). Empirical and simulation studies analyzing the effects of contemporary and future climate change report different effects on tree growth and mortality, depending on the elevation and topography of mountain forests in Central Europe  (Thrippleton et al. 2020 ). In the present study, we used historic drought resilience data from six managed sites along an elevational gradient (520-1020 m a. s. l. in southwestern Germany) to assess the effectiveness of overstorey removal for promoting drought resilience. Specifically, we simulated a "business as usual" (BAU) and a "do nothing" management strategy along with two adaptive strategies of high-and low-intensity overstorey removal. We applied the climatesensitive, process-based forest gap model ForClim that was modified to better capture the relationship between stand density (i.
(2018)  for further details). Here, we present the linear equation used to calculate F vegetation , dependent on LAI  (Eq. 6). F vegetation ¼ min 0:75 þ 0:03125*LAI; 1:0 ð Þ ð 6Þ In ForClim, LAI is sensitive to management interventions. Therefore, the vegetation-dependent demand factor for potential evapotranspiration in the stand is sensitive to stand density variations as needed for our research goals. Growth function To avoid zero diameter increments in the model and constrain the estimations of resistance and recovery, we derived a minimum (and maximum) change of the diameter increment from one year to the next for each species using field data from research plots and implemented those thresholds in ForClim. Our calculation was based on the pancontinental tree-ring width (TRW) database by  Cailleret et al.
2011; Xu et al. 2017). The alternative theoretical function had already been tested in ForClim by  Huber et al. (2020) , with beneficial results. Identification of drought years using ForClim v4. 0. 1. 2 We used the mean annual drought index (uDrAn) in ForClim to identify drought years. To calculate the uDrAn, we used the monthly climate data from  Dietrich et al. (2019). The index's positive values indicate an increase in yearly potential evapotranspiration and/or decreased precipitation (Figure  9 ). Strong drought effects were simulated in sites with lower soil water potential (i. e. , BS < 14 cm, steep to moderate slope; sites 221, 224, 225). Sites with higher soil water potential (i. e. , BS = 20 cm, moderate to flat slope; sites  220, 222, 223)  showed smaller drought effects. For the identification of drought years, we identified years with uDrAn > 0.
Additional data for the calculation of economic indicators References of Appendix 1    15  Differences in mean dbh of living and dead trees 3. 5. Economic assessment: sensitivity analysis of economic results Table  8  Averaged NPV, discount rate 1% Table  9  Averaged LEV, discount rate 1% RMSE ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ∑ sim-obs ð Þ 2 = n-1 ð Þ p ∑obs=n * 100 (7) PBIAS ¼ ∑ sim-obs ð Þ =n ∑obs=n * 100 (8) where sim is the result of the model for the inventory year and obs is the measurement in the inventory. We considered the total number of measurements in the inventory (the inventory includes measurements every 5 years approximately). Fig. 14  Forest recovery of remaining trees after management strategies separated by species composition.
Acknowledgements We thank  Tilo Wolff  (F. V. A. ) for providing data for model initialization and historical simulations. Nica Huber ,  Dominic Michel , and  Timothy Thrippleton  ( ETH-Zürich ) are gratefully acknowledged for their collaboration in the modifications of  ForClim. Alessandra Bottero  (W. S. L. ) is gratefully acknowledged for the data collection and analysis of dendroecological samples used to develop the diameter increment constraints. We are grateful to the two reviewers and the handling editor for their recommendations to improve the manuscript. Funding Open Access funding enabled and organized by Projekt DEAL. Funding for this research was provided by the  ForRISK  project ( ERA-NET Sumforest ). Sumforest was funded by the  European Union  under Grant Agreement No. 606803.
The  ERA-NET Sumforest project ForRISK was funded in Switzerland  through the  Federal Office for the Environment FOEN  (Grant No. 05. 0602. PZ/P382-0487 ), in France through the  French National Research Agency A. N. R. (Grant No. ANR-16-SUMF-0001-01 ), and in Germany through the  Federal Ministry for Food and Agriculture BMEL  (F. K. Z. :  2816ERA04S ).
Tools and Materials In the first module of the project, primary school students (aged 9-10) began to learn to code with Scratch 3, the free, easy-to-learn and visual programming language, and used Lego WeDo 2. 0 for educational robotics activities. They went to school one afternoon a week to find out about the main principles of coding. They used their knowledge and applied their creativity to invent a board game that is both physical and virtual. The purpose of the game is to find out about the most important monuments in their town, Tolentino. Lessons were organized as hands-on learning laboratories in which students worked in teams of two or three sharing ideas and helping each other. The second part of the project started in October 2019 and is still in progress. Students from the lower secondary school are completing the board game using Scratch, APP Inventor and QR code.
Contents of the Activities The activities included the creation of a board game simulating Tolentino during an earthquake. Players have to escape from the town, saving as many lives and cultural objects as possible. The module introduced the basic concepts of coding using Scratch. The teaching progression was: • coding: introduction to Scratch: simple animations and experiments. Basic functions of Scratch and first sprite movements; • coding: sequence of instructions and animations: drawing geometric shapes with Scratch. Starting with polygons (triangle, square …) students continued creating their own pictures; • coding: cyclical repetition of instructions; • coding and logic: synchronizing events: after the stories were created in analogical form (using paper, pen and storyboard), the children reproduced them with Scratch; • robotics: basic principles of robotics; man-machine-robots: similarities and differences; building a robot using a Lego WeDo 2.
Background: Since their domestication 10,500 years ago, goat populations with distinctive genetic backgrounds have adapted to a broad variety of environments and breeding conditions. The VarGoats project is an international 1000-genome resequencing program designed to understand the consequences of domestication and breeding on the genetic diversity of domestic goats and to elucidate how speciation and hybridization have modeled the genomes of a set of species representative of the genus Capra. Findings: A dataset comprising 652 sequenced goats and 507 public goat sequences, including 35 animals representing eight wild species, has been collected worldwide. We identified 74,274,427 single nucleotide polymorphisms (SNPs) and 13,607,850 insertion-deletions (InDels) by aligning these sequences to the latest version of the goat reference genome (ARS1).
The GoatSNP50 chip also facilitated international collaborations because it generated data through unrelated studies and different laboratories located across the globe that were directly comparable and could be easily merged. The AdaptMap project  [10] [11] [12] [13] [14] [15] [16]  was one of these collaborations that compiled goat genotypes from across the world and explored their genetic diversity. Although the AdaptMap dataset collected data from 4653 animals across 148 populations and 35 countries  [14] , it was limited to a subset of countries, and did not fully represent the variability of the Capra species worldwide. Indeed, wild goat species other than the bezoar (Capra aegagrus) were not investigated. Moreover, data generated from SNP chips are known to be distorted by ascertainment bias  [17] , a limitation that can be overcome by carefully filtering whole-genome sequencing data of sufficient depth  [18].
Selection of individual goats Animals sequenced in the VarGoats project were selected such that they represent the global genetic diversity of goats. First, based on the analysis of Colli et al. [15] , 468 animals included in the AdaptMap dataset were selected to cover each of the 19 major gene pools determined by the Admixture software, except that from North America. Whenever possible, four to five breeds were selected within each group to ensure a fair representation of within-gene pool diversity. Eleven of the individuals that were included in the AdaptMap genotyped population were not included in the final dataset (https:// datad ryad. org/ stash/ datas et/ doi: 10. 5061/ dryad. v8g21 pt), from which we extracted filtered genotyping data, thus genotypes were available for 457 individuals.
To maximize the diversity of goats represented in this population, in addition to the 468 animals selected from the AdaptMap population, 184 other animals were selected to epitomise individual breeds and lineages and to allow investigation of adaptation to unique geographic or ecological niches. These 184 animals were selected from samples submitted by consortium members. After collecting these 652 (468 + 184) individuals that were sequenced within the VarGoats project, we retrieved 217 additional sequences through the Omics Disco very Index from NextG en consortium projects (PRJEB 3134, PRJEB 3135, PRJEB 3136, PRJEB 4371, PRJEB 5166, and PRJEB 5900), and 290 sequences from public sequence data repositories (sequences extracted from NCBI 7 Feb 2019). Thus, the VarGoats dataset included 1159 goat whole sequenced genomes.
Besides our efforts to maximize the amount of diversity represented in the VarGoat dataset, part of the sequencing effort aimed at addressing specific research questions. For example, since one of our objectives was to better understand the genetic variation underpinning commercial dairy breeds, more individuals from the Alpine and Saanen breeds than from other breeds were selected for sequencing. It should also be noted that several of the sequenced individuals are closely related. Among the 1159 sequenced animals, a vast majority (1124) correspond to domestic goats (C. hircus), which represent a range of breeds from Africa (450: 39%), Europe (443, 38%), Asia (225: 20%), Oceania (25: 2%) and the Caribbean (16: 1%). The geographical distribution of the investigated populations is shown in Fig.
Code used to identify the animals A unique identification code (original ID) defined each individual. For all sequenced animals, the ID was formatted as CCSS-BBB-NNNN: where CC corresponds to the country of origin based on the ISO 3166-1 alpha-2 codes, SS correspond to the species (CH for C. hircus, CA for C. aegagrus, etc. ), BBB is the three character breed abbreviation (UNK for unknown), and NNNN is a four-digit number (with leading zeros) that is assigned sequentially within each combination of source country, species, and breed and that uniquely identifies each individual. Breed and country codes are in Additional file 1: Table  S1. For the NextGen data, we used the sample's alias as the original ID, and the run's accession number as the original ID for the remaining public data. An alternative ID, a 'working name' , was assigned for each individual to facilitate the interpretation of the results for animals from unknown breeds.
Concordance rates of the 457 AdaptMap individuals and breed ascertainment of outliers We extracted the 50 k genotypes for the 457 individuals with genotyping data from the AdaptMap project to detect potential sample mix-ups by checking the concordance rates (CR) between sequence variants and SNP array genotypes. Among the 46,654 SNPs genotyped in the AdaptMap project, 44,691 were identified in the VCF files from sequenced individuals. The distribution of CR clearly showed a disruption in this quality indicator at 70%, a value which we used as a threshold in the subsequent analyses (see Additional file 3: Figure  S1 ). As shown in Fig. 2 , low coverage sequences display lower CR values. For 14 individuals, although the sequencing depth was acceptable (between 7. 2 and 23. 6), a CR less than 70% was observed, which indicates a technical problem that leads to a lack of correspondence between the genotyped and sequenced samples (Fig.
2a. ). To check that the breed assignment indicated in the ID of these 14 animals was correct, we performed a principal component analysis (PCA) using the PLINK software version 1. 9  [19]  to compare the coordinates of these individuals with respect to all the animals belonging to those outlier breeds. The PCA was conducted on genotyping data of the autosomal SNPs from the VCF file. First of all, we removed SNPs with more than 5% of missing data. This filtering step yielded 1,890,194 SNPs which were pruned, using the indep-pairwise function ("--indep-pairwise 50 10 0. 1") in PLINK  [19] , i. e. SNPs in 50-SNP sliding windows with a step size of 10 SNPs displaying pairwise correlations between genotype allele counts greater than 0. 1, were removed. This filtering step reduced the dataset to 667,949 SNPs which were used to assess population structure.
Twelve of these 14 animals clustered with their breed counterparts (Fig. 2b ), but for two individuals (AUCH-BOE-0038 and ITCH-CCG-0014) breed assignment could not be confirmed. The 11 animals with missing genotypes, and therefore not confirmed, were marked in Additional file 2: Table  S2  with a "1" in the fourth column, whereas Fig. 1  Geographical distribution of the 1159 sampled Capra individuals included in the dataset. The geographical origin of each sequenced animal is indicated both for the country (exterior circle) and the continent (interior circle). For each location, the number of sequenced individuals and their percentage in the overall dataset is indicated the 14 animals with a concordance rate lower than 70% (Fig. 2a ) were also marked with a "1" in the fifth column. As recommended for any dataset, we advise users to perform a global analysis, such as a PCA or a population structure analysis based on the full dataset or on subsets to identify potential outliers before performing in-depth studies.
Breed information The VarGoats dataset encompasses samples from eight wild species [(C. aegagrus (BEZ), C. caucasica (CAU), C. pyrenaica (CPY), C. cylindricornis (CYL), C. falconeri (FAL), C. ibex (IBX), C. nubiana (NUB), and C. sibirica (SIB)] and from 126 C. hircus breeds (see Additional file 1: Table  S1 ). They are distributed as follows: 46 breeds from Africa (36%), 40 from Europe (32%), 34 from Asia (27%), 5 from Oceania (4%) and 1 from the Caribbean (1%). The dataset includes cosmopolitan breeds such as Alpine, Boer, and Saanen as well as local breeds that are unique to specific regions of the world. The geographical distribution of the breeds was determined based on a literature review (www. fao. org; www. races defra nce. fr; http:// eng. agrar ia. org/ goat. htm; etc. ), which allowed us to define more precisely how each breed was derived.
Sequences between the second unknown nucleotide (N) and the end of the read were also removed. Reads shorter than 30 nucleotides after trimming were discarded. Finally, the reads and their mates, that mapped to run quality control sequences (PhiX genome) were removed. These trimming steps were conducted using an in-house software based on the FastX package (http:// www. genos cope. cns. fr/ exter ne/ fastx tend/).
Read alignment and variant calling Sequence reads from each sample were processed using a pipeline based on the domain reference tools and the Genome Analysis Tool Kit-GATK (v3. 6) best practices: the Burrows-Wheeler Aligner BWA software (v 0. 7. 15) for alignment  [20] , SAMto ols (v1. 6) for handling SAM/BAM file formats and calling variants  [21] , Picar d tools (v2. 1. 1) for labelling duplicated reads, as well as GATK (v3. 6) for insertion/deletion (InDel) realignment, base recalibration and calling variants  [22] , BCFto ols (v1. 6) for handling VCF/BCF formats, Freeb ayes (v1. 1. 0) for calling variants  [23] , and snpEff (v4. 3t) for VCF annotation  [24]. Reads were mapped to the latest ARS1 genome assembly (Genbank accession GCA_001704415. 1) of C. hircus  [25]  using the BWA-MEM software with default parameters except for "-t 14 -M" and "-R" to add read groups.
The SAM output files were converted to sorted BAM files using SAMtools. S1 ). If a breed is present in multiple countries, the breed code is followed by the country code (2 letters). Each combination of color and symbol corresponds to domestic goats in a single country Pre-processing steps (marking duplicates, InDel-based realignment and base quality score recalibration-(BQSR)) were done using the Picard-MarkDuplicates and GATK tools. The "known variants" file that is necessary for the BQSR step was computed on a subset of 13 goats (AUCH-CAS-0038, BFCH-DJA-0012, CHCH-BOE-0229, ESCH-PAL-0008, ESCH-RAS-0011, ETCH-ABR-0036, FICH-LNR-0122, FRCH-ALP-0030, FRCH-CRE-0014, FRCH-SAA-0032, ITCH-GGT-0026, MZCH-PAF-0003 and ZACH-ANG-0374), which represented 13 breeds chosen from the first 248 animals that were sequenced.