dataesr/scientific-paragraphs-categorization huggingface dataset:
Mitogenomic dataset construction In order to assemble a mitogenomic data set for assessing mitochondrial diversity among P. cristatus and O. megalotis subspecies, we generated seven new Carnivora mitogenomes using Illumina shotgun sequencing (Supplementary File 8). Briefly, we extracted total genomic DNA total using the DNeasy Blood and Tissue Kit (Qiagen) for P. c. cristatus (NMB 12641), Additional read mappings were done with Geneious  (Kearse et al. , 2012)  to close gaps when the mitochondrial genome was fragmented. Finally, we downloaded all RefSeq carnivoran mitogenomes available in Genbank (135 species as of July 1 st , 2019) and the mitogenome of the Malayan pangolin (Manis javanica) to use as an outgroup.
Mitogenomic phylogenetics and dating Mitochondrial protein-coding genes were individually aligned using MACSE v2  (Ranwez et al. , 2018)  with default parameters, and ribosomal RNA genes using MAFFT  (Katoh and Standley, 2013)  algorithm FFT-NS-2 with option --adjustdirection. A nucleotide supermatrix was created by concatenating protein-coding and ribosomal RNA genes for the 142 taxa (140 species and two subspecies). Phylogenetic inferences were performed with Maximum likelihood (ML) as implemented in IQ-TREE 1. 6. 8 (Nguyen et al. , 2015)  with the GTR+G4+F model. Using the resulting topology, divergence time estimation was performed using Phylobayes v4. 1c  (Lartillot et al. , 2013)  with strict clock (CL), autocorrelated (LN or TK02), and uncorrelated (UGAM or UCLM) models combined with 18 fossil calibrations (Supplementary File 9).
Mitochondrial diversity and barcoding gap analyses To check if a threshold between intraspecific variation and interspecific divergence could be determined across the Carnivora  (Meyer and Paulay, 2005) , two mitochondrial barcoding datasets were assembled from all COX1 and CYTB sequences available for Carnivora plus the corresponding sequences for each of the two subspecies of O. megalotis and P. cristatus, respectively. After aligning each barcoding dataset with MACSE v2, ML phylogenetic inferences were performed with IQ-TREE 1. 6. 6 using the optimal substitution model as determined by ModelFinder  (Kalyaanamoorthy et al. , 2017). Then, pairwise patristic distances between all individuals were calculated from the resulting ML phylogram. Finally, based on the actual taxonomic assignment, patristic distances were considered as intraspecific variation between two individuals belonging to the same species and as interspecific divergence between individuals of different species.
Hybrid assembly of short and long reads Short reads were cleaned using Trimmomatic 0. 33  (Bolger et al. , 2014)  by removing low quality bases from their beginning (LEADING:3) and end (TRAILING:3), and by removing reads shorter than 50 bp (MINLEN:50). Quality was measured for sliding windows of four base pairs and had to be greater than 15 on average (SLIDINGWINDOW:4:15). For MinION sequencing, basecalling of fast5 files was performed using Guppy v3. 1. 5 (developed by ONT) with the high accuracy option, which takes longer but is more accurate than the standard fast model (Appendix 1 -Figure  1 ). Long-read adapters were removed using Porechop v0. 2. 3 (https://github. com/rrwick/Porechop). To take advantage of both the high accuracy of Illumina short reads sequencing and the size of MinION long reads, assemblies were performed using the MaSuRCA hybrid genome assembler  (Zimin et al.
Finally, the two GDI were compared to check if the Southern populations were more structured than the entire populations. To contextualize these results, the same GDI measures were estimated for welldefined species of Carnivora. The species pairs used to make the comparison and thus help gauge the taxonomic status of the bat-eared fox and aardwolf subspecies were selected according to the following criteria: (1) the two species had to be as closely related as possible, (2) they had both reference genomes and short reads available, (3) their estimated coverage for the two species had to be greater than 15x, and (4) short-read sequencing data had to be available for two individuals for one species of the pair. Given that, four species   Kim et al. , 2016). Raw reads for the three individuals of each species pair were downloaded, cleaned and mapped as described above.
Then, the same GDI estimation protocol was applied to each species pair by estimating the GDI within species, using two individuals of the same species, and the GDI between species, using one individual of each species of the pair. To check the robustness of the genetic differentiation index estimation, two additional analyses were conducted. First, given that the estimation could be biased by the depth-ofcoverage used for the genotype calling, the reads used for all individuals were randomly subsampled to obtain a homogenised depth-of-coverage of about 15x. Based on these new datasets, genetic differentiation indices were re-estimated for each group. Second, to show the consistency of the results, when few individuals are used for the estimates, a permuted subsampling approach, drawing from a larger dataset, was performed.
Using the species pairs Ursus maritimus/Ursus arctos, for which sequencing data were available for 10 individuals of each species, genetic differentiation indices were estimated using all possible combinations, using either two individuals for Ursus arctos or one individual for each species (i. e. 45 Ursus arctos/Ursus arctos and 100 Ursus arctos/Ursus maritimus). Given the number of possible combinations, estimates were performed on only five replicates (instead of 10) of 100 regions of 100,000bp for each combination (Figure  3.
Demographic analyses Historical demographic variations in effective population size were estimated using the Pairwise Sequentially Markovian Coalescent (PSMC) model implemented in the software PSMC (https://github. com/lh3/psmc) (Li and Durbin, 2011). As described above, cleaned short reads were mapped against the corresponding reference genome using BWA-MEM  (Li, 2013)  and genotyping was performed using Freebayes v1. 3. 1-16 (git commit id: g85d7bfc)  (Garrison and Marth, 2012)  for the three individuals of each species. VCF files were converted to fasta format using a custom python script, excluding positions with quality below 20 and a depth-of-coverage below 10x or higher than 200x. Diploid sequences in fasta format were converted into PSMC fasta format using a C++ program written using the BIO++ library  (Guéguen et al.
, 2013)  with a block length of 100bp and excluding blocks containing more than 20% missing data as implemented in "fq2psmcfa" (https://github. com/lh3/psmc). PSMC analyses were run for all other populations, testing several -t and -p parameters including -p "4+30*2+4+6+10"  (Nadachowska-Brzyska et al. , 2013)  and -p "4+25*2+4+6"  (Kim et al. , 2016)  but also -p "4+10*3+4", -p "4+20*2+4" and -p "4+20*3+4". Overall, the tendencies were similar, but some parameters led to unrealistic differences between the two individuals from the South African population of Otocyon megalotis. We chose to present the results obtained using the parameters -t15 -r4 -p "4+10*3+4". For this parameter setting the variance in ancestral effective population size was estimated by bootstrapping the scaffolds 100 times.
To scale PSMC results, based on several previous studies on large mammals, a mutation rate of 10 -8 mutation/site/generation  (Ekblom et al. , 2018; Gopalakrishnan et al. , 2017)  and a generation time of two years  (Clark, 2005; Koehler and Richardson, 1990; van Jaarsveld, 1993)  were selected. Results were plotted in Rv3. 63 (R core Team, 2020) using the function "psmc. results" (https://doi. org/10. 5061/dryad. 0618v/4) (Liu and Hansen, 2017)  modifed using ggplot2  (Wickham, 2016)  and cowplot  (Wilke, 2016).
Phylogenomic inferences To infer the Carnivora phylogenetic relationships, all carnivoran genomes available on Genbank, the DNAZoo website (https://www. dnazoo. org), and the OrthoMaM database  (Scornavacca et al. , 2019)  as of February 11th, 2020 were downloaded (Supplementary File 12). In cases where more than one genome was available per species, the assembly with the best BUSCO scores was selected. Then, we annotated our two reference genome assemblies and the other unannotated assemblies using MAKER2  (Holt and Yandell, 2011)  following the recommendations of the DNA Zoo protocol (https://www. dnazoo. org/post/the-firstmillion-genes-are-the-hardest-to-make-r). In the absence of available transcriptomic data, this method leveraged the power of homology combined with the thorough knowledge accumulated on the gene content of mammalian genomes.
As advised, a mammal-specific subset of UniProtKB/Swiss-Prot, a manually annotated, non-redundant protein sequence database, was used as a reference for this annotation step  (Boutet et al. , 2016). Finally, the annotated coding sequences (CDSs) recovered for the Southern aardwolf (P. c. cristatus) were used to assemble those of the Eastern aardwolf (P. c. septentrionalis) by mapping the resequenced Illumina reads using BWA-MEM  (Li, 2013). Orthologous genes were extracted following the orthology delineation process of the OrthoMaM database (OMM)  (Scornavacca et al. , 2019). First, for each orthologous-gene alignment of OMM, a HMM profile was created via hmmbuild, using default parameters of the HMMER toolkit  (Eddy, 2011) , and all HMM profiles were concatenated and summarised using hmmpress to construct a HMM database.
Then, for each CDS newly annotated by MAKER, hmmscan was used on the HMM database to retrieve the best hits among the orthologous gene alignments. For each orthologous gene alignment, the most similar sequences for each species were detected via hmmsearch. Outputs from hmmsearch and hmmscan were discarded, if the first-hit score was not substantially better than the second (hit 2 < 0. 9 hit 1 ). This ensures our orthology predictions for the newly annotated CDSs to be robust. Then, the cleaning procedure of the OrthoMaM database was applied to the set of orthologous genes obtained. This process, implemented in a singularity image  (Kurtzer et al. , 2017)  named OMM_MACSE. sif (Ranwez et al. , 2021) , is composed of several steps including nucleotide sequence alignment at the amino acid level with MAFFT  (Katoh and Standley, 2013) , refining alignments to handle frameshifts with MACSE v2  (Ranwez et al.
, 2018) , cleaning of non-homologous sequences, and masking of erroneous/dubious parts of gene sequences with HMMcleaner (Di  Franco et al. , 2019). Finally, the last step of the cleaning process was to remove sequences that generated abnormally long branches during gene tree inferences. This was done by reconstructing gene trees using IQ-TREEv1. 6. 8  (Nguyen et al. , 2015)  with the MFP option to select the best-fitting model for each gene. Then, the sequences generating abnormally long branches were identified and removed by PhylteR (https://github. com/damiendevienne/phylter). This software allows detection and removal of outliers in phylogenomic datasets by iteratively removing taxa in genes and optimising a concordance score between individual distance matrices. Phylogenomic analyses were performed using maximum likelihood (ML) using IQ-TREE 1.
, to test whether the two pairs of allopatric subspecies diverged synchronously or in two different time periods, Bayesian molecular dating inferences were performed on the 142-taxon ML mitogenomic tree. The resulting divergence times were slightly different depending on the clock model used (strict clock [CL], autocorrelated [LN or TK02] and uncorrelated [UGAM or UCLM]) despite the convergence of the MCMC chains for all models. Cross-validation analyses resulted in the selection of the LN and UGAM models as the models with the best fit based on a higher cross-likelihood score than that of CL (LN and UGAM versus CL mean scores = 35 8). Unfortunately, these two statistically indistinguishable models provided different divergence times for the two pairs of subspecies, with LN favouring a synchronous divergence (approximately 1 Mya [95% credibility interval (CI) : 6.
Acknowledgements We are indebted to the  Broad Institute  (www. broadinstitute. org), the  DNA Zoo  (www. dnazoo. org),and numerous other sequencing centres and institutions for making their mammalian genomic data publically available. We would like to thank  Rachid Koual  and  Amandine Magdeleine  for technical help with DNA extractions and library preparations,  Aude Caizergues  and  Nathalie Delsuc  for fieldwork assistance,  Christian Fontaine ,  Jean-Christophe Vié  ( Faune Sauvage ,  French Guiana) ,  Corine Esser  ( Fauverie du Mont Faron, Toulon, France ),  François Catzeflis  ( ISEM Mammalian Tissue Collection ),  Adam Ferguson  and  Bruce Patterson  ( Field Museum of Natural History, Chicago, USA ), and  Lily Crowley  and  Andrew Swales  ( Hamerton Zoo Park, UK ) for access to tissue samples.
Funding This work was supported by grants from the  European Research Council  ( ERC-2015-CoG-683257   ConvergeAnt  project),  Investissements d'Avenir of the Agence Nationale de la Recherche  ( CEMEB :  ANR-10-LABX-0004 ;  ReNaBi-IFB :  ANR-11-INBS-0013 ;  MGX :  ANR-10-INBS-09 ), and the  National Research Foundation of South Africa  (Grant specific unique reference number  86321 ).
Data access Genome assemblies, associated Illumina and Nanopore sequence reads, and mitogenomes have been submitted to the National Center for Biotechnology Information (NCBI) and will be available after publication under BioProject number PRJNA681015. The full analytical pipeline, phylogenetic datasets (mitogenomic and genomic), corresponding trees, and other supplementary materials are available from zenodo. org (DOI: 10. 5281/zenodo. 4479226).
Finally , to test whether the two pairs of allopatric subspecies diverged synchronously or in two different time periods, Bayesian molecular dating inferences were performed on the 142-taxon ML mitogenomic tree. The resulting divergence times were slightly different depending on the clock model used (strict clock [CL], autocorrelated [LN or TK02] and uncorrelated [UGAM or UCLM]) despite the convergence of the MCMC chains for all models. Cross-validation analyses resulted in the selection of the LN and UGAM models as the models with the best fit based on a higher cross-likelihood score than that of CL (LN and UGAM versus CL mean scores = 35 8). Unfortunately, these two statistically indistinguishable models provided different divergence times for the two pairs of subspecies, with LN favouring a synchronous divergence (approximately 1 Mya [95% credibility interval (CI) : 6.
What this study adds This consortium profile paper introduces (1) LongITools' scientific concepts that are primarily based on longitudinal modeling; (2) the metadata for the project; (3) the expected impact of the project; and finally (4) the strengths and challenges of this endeavor. The overarching aim of LongITools is to understand the environmental, biological, and psychosocial dimensions of CM-NCDs, taking life course factors and a longitudinal approach into consideration. LongITools will improve our understanding on how the exposome, i. e. , the combined exposures throughout the life course of an individual, contributes to the risk of CM-NCDs.
4 This consortium profile describes the studies involved in LongITools and the FAIR metadata that the project will build and promote. LongITools is coordinated by the University of Oulu in Finland and includes 15 academic and 3 small-and medium-sized enterprise (SME) partners across Europe (eTable 1; http://links. lww. com/EE/A168). The participants all complement one another, bringing together the full range of technical and specialist expertise in epidemiology, (epi-) genetics, metabolomics, lifestyle, mathematics, economics, policy making, and sensor technology that are required to create a critical mass of expertise for the project.
Who is in the study? LongITools builds upon and leverages prospective birth cohorts, longitudinal studies in adults, register-based follow-ups, randomized controlled trials (RCTs), patient databases, as well as maternity and hospital biobanks. Currently, these add up to 25 different studies including 11 million individuals across Europe (Table  1 ). Birth cohorts within the project will provide substantial longitudinal data from pregnancy to adolescence and early adulthood, complemented by prospective adult cohorts, with multiple follow-ups during adulthood and in older age. The RCTs involved are focused on the role of nutrition and physical activity in general health and metabolism. These will not only provide comprehensive biological and exposome profiles of study participants but will also allow an in-depth analysis in more controlled settings.
Federated data analysis approach. LongITools will use DataSHIELD, when technically, scientifically, and ethically relevant, which was developed as part of the EU-FP7 Biobank Standardisation and Harmonisation for Research Excellence in the European Union Project. 8, 9 ataSHIELD enables researchers to analyze data from partner institutions swiftly and securely, respecting the current national and European data protection regulations. To briefly summarize its use, data holders store individual-level data on their own local data warehouse servers and link to the DataSHIELD client portal using the MOLGENIS Armadillo server (https://github. com/molgenis/molgenis-service-armadillo). The connection between the data warehouse and the client portal is restricted so that only analysis commands can pass through from the client portal to the data server and only nondisclosive summary statistics are sent from the data server to the client portal.
Economic and policy impact. LongITools will build a comprehensive data set of policy interventions targeting the exposome and health care, which were implemented in the time span and locations covered by the birth cohorts (Figure  2 ). The aim is to investigate if and how such policy interventions have affected the insurgency of CM-NCDs, in terms of both health status and economic implications. [22]  Knowledge exploitation. The theoretical framework will be carried out on existing data from the LongITools consortium to train artificial intelligence (AI) algorithms, such as random forests, support vector machines and deep neural networks, which will enable translation of data and knowledge into simple and available predictive tools for scientists, citizens, policy makers, or other end users.
Strengths and Challenges LongITools comprises a vast amount of prospective data collected in Europe, harnessed to enhance exposome research, as well as longitudinal and econometric modeling. When combined, these data offer immense potential to inform future European health policy. Furthermore, the data are organized to enable direct replication under the FAIR principles. While sample size allowing statistical power is deemed essential for robust evidence-based strategies, it is also important to combine study designs to validate findings under different statistical assumptions. Another strength of LongITools is the inclusion of data from RCTs for in-depth sensitivity analyses and to identify novel pathways that could be generalized in the cohort setting. Finally, LongITools includes longitudinal birth cohorts and aging cohorts from the same geographical location, which enables us to study the changing environment and its association with cardiovascular and metabolic health.
The key challenge faced by LongITools, and more broadly by all epidemiological study, is to translate the findings into meaningful change for global health. To tackle this, LongITools operates in close collaboration with policy makers throughout the project to convert the results into evidence-based policy options. A critical mass of data and expertise brought together in LongITools offers a substantial resource, which also leads to another challenge faced by the consortium: how to best combine the characteristics of the cohorts involved. The cohorts were established for their own individual purposes before being brought together under this project, and the methods of data collection have thus not been standardized a priori across the consortium. Therefore, consideration is required for the transferability of the statistical models and harmonization of the data.
However, this also gives us the opportunity to examine if similar processes operate in different environments and thus to draw conclusions on generalizability. In addition, owing to the internationality of the project, differences in technology, questionnaire data and biospecimen collection methods, terminology and diagnosis definitions, country-specific measurement techniques, and ethical requirements among the studies exist. This heterogeneity can introduce differences in the results between the studies, which can be analyzed when necessary; we can generalize where possible and be specific when needed. In addition, the environmental exposures are harmonized by using the same model, which can also mitigate possible inconsistencies between the studies. The consortium has made significant progress in overcoming these challenges by developing and updating harmonization manual for the key variables and the overarching advantage of LongITools is that all studies provide rich data on similar key exposures and the outcome measures of interest.
Conclusion LongITools provides a collection of studies across different time periods and encompassing different life stages, which will enable us to use a life course approach to study the exposome and its role in the trajectories of cardiometabolic health. Valuing the idea of open science, through its innovative data infrastructure, LongITools will spread new knowledge rapidly and efficiently to the other European Human Exposome Network projects and beyond. The generated and combined knowledge can then be used to develop innovative products and services with the potential to create new markets. In this way, LongITools aims to improve EU citizens' cardiovascular and metabolic health and thereby reduce individual and societal burdens and health care costs of CM-NCDs. Through the cooperation between research teams and SMEs and by using our extensive data, we expect to make several breakthrough discoveries.