dataesr/scientific-paragraphs-categorization huggingface dataset:
Mid term (~6 years) • Improved standardisation of generation of complex data incl time-series, functional and imaging data for integration in computational models • Providing a link between existing models and datasets for easy access to relevant data • Good strategies (including training) to improve reproducibility, credibility, and validation of models and to assess the efficiency of tools, leading to the development of quality marks, thereby increasing the quality of workflow outcomes Long term (~10 years) • Improved interoperability of data and models to enable FAIR model connection and integration (at different scales) so as to facilitate the development of multi-scale modelling frameworks; EU-STANDS4PM and those developing infrastructures for human digital twins (including corresponding standards for data and models) that are currently forming and will be funded in the near future, will help to jointly create a pan-European cloud-infrastructure for data integration and modelling in health research and personalised medicine.
One way to engage with the industrial sector is through joint workshops and collaboration with other ELIXIR Communities interested in working with industry. Other objectives to achieve industrial embedding are listed in Table  7. An important aspect of these objectives is the use of Key Performance Indicators (KPIs) and "gap analysis surveys" to measure overall long-term performance and to identify priorities for improvement. The potential is enormous, given the capabilities of the deployment of data and models to describe biological systems and to enable actionable knowledge for a vast range of translational applications. plans of the ELIXIR consortium. The manuscript is overall logical and well-written. However, there are some points that the authors should consider and that would improve the manuscript overall: Overall, the thrust of the manuscript and ELIXIR as a whole seems to center on the concept that Systems Biology is predicated on the generation of mechanistic mathematical models, with datasets envisioned as being used for calibration/verification/validation of these mechanistic models.
While this is certainly a major goal for many in this field, there seems to be little mention of machine learning (i. e. data-driven modeling) as an alternative (or, ideally, an adjunct approach that could synergize with or integrate machine learning with mechanistic modeling), though there is mention of a focus group on machine learning and a short term goal stated as "Understanding how big/smart data meets models meaningfully. " It would seem that there is a need to establish workflows and pipelines wherein data are generated, data-driven modeling/machine learning is performed to identify important features in the datasets, and those features are, in some way, included in the resultant mechanistic models. In this way, the current gap between data-driven and mechanistic modeling could be bridged.
(Gillespie et al. , 2022)es and Deposition Databases are already highly used by the systems biology community. For example, data from BRENDA(Chang et al. , 2021),STRING (Szklarczyk et al. , 2021), and Reactome(Gillespie et al. , 2022)are essential both for construction of molecular pathways and parameterization of molecular reactions. There is potential for the Systems Biology Community to add new data resources to the ELIXIR list of services.
visualisation, combination, and execution. COMBINE standards form the basis of model resources like the ELIXIR deposition database BioModels, as well as JWS online(Peters  et al. , 2017). The ELIXIR Core Data Resource BRENDA, and the ELIXIR resource SABIO-RK(Wittig et al. , 2018)both offer export of mainly literature-based enzymology/reaction kinetics data in SBML format. ELIXIR has strong links to COMBINE, enabling and embodying information exchange between the organisations. When analysing data one often stumbles upon the problem that different data models still yield different descriptions, even though both descriptions are FAIR; there is more than one way for a model to be FAIR. FAIR identifiers from different databases, such as those provided by BridgeDb and MetaNetX), ontol-.
development and embedding of systems medicine (Gillespie et al. , 2022)es and Deposition Databases are already highly used by the systems biology community. For example, data from BRENDA(Chang et al. , 2021),STRING (Szklarczyk et al. , 2021), and Reactome(Gillespie et al. , 2022)are essential both for construction of molecular pathways and parameterization of molecular reactions. There is potential for the Systems Biology Community to add new data resources to the ELIXIR list of services. 7. Need to support industrial and societal embedding (addressing, among others, pharmacology; toxicology; diagnostics; synthetic biology; agronomy; including microbial biotechnology and the bio-economy) Many areas of relevance to systems biology are already embed- ded into activities of ELIXIR's Platforms, Communities and Focus Groups (see the Systems Biology within ELIXIR section below) and have the potential for further developments.
In particular we identify the following: Data platform: (identification and modelling of network struc- tures that are prognostic and predictive; integration with related systems such as microbes or expososomes; personalising models using patient data; clinically validating models; addressing ELSA (Ethical, Legal and Social Aspects) for clinical, sensitive data; interfacing with epidemiology) 5. Provisioning of modelling as a service (provision- ing of sophisticated data resources, tools and rich standards that are useful both for data mining and experimental design; availability of a pool of experts; availability of blueprint models to help with building new models) training expertise) 6. Capacity building and training (different trainee backgrounds; need for a systems biology learning path and broad promotion of the integrated systems biology framework; availability and use of standardised datasets in training materials; need for a broad Engagement of the Platform with curation efforts like BioModels (Malik-Sheriff et al.
interoperability where there are different descriptions of the same or related data. visualisation, combination, and execution. COMBINE standards form the basis of model resources like the ELIXIR deposition database BioModels, as well as JWS online(Peters  et al. , 2017). The ELIXIR Core Data Resource BRENDA, and the ELIXIR resource SABIO-RK(Wittig et al. , 2018)both offer export of mainly literature-based enzymology/reaction kinetics data in SBML format. ELIXIR has strong links to COMBINE, enabling and embodying information exchange between the organisations. When analysing data one often stumbles upon the problem that different data models still yield different descriptions, even though both descriptions are FAIR; there is more than one way for a model to be FAIR. FAIR identifiers from different databases, such as those provided by BridgeDb and MetaNetX), ontol- Challenged Using descriptive models and linking them to predictive models.
, 2001)9)g with gene regulation databases (e. g. transcription factor and microRNA target linking databases) and with databases of chemical interactions with molecular biological targets (e. g. drug-target or molecular toxicology databases). Moreover, such networks can be mapped to biological ontologies (e. g. from Gene Ontology (The Gene Ontology Consortium, 2019)), disease-related genes (e. g. from OMIM(Amberger et al. , 2019)), and variants (known from e. g. dbSNP(Sherry et al. , 2001)or observed experimentally). Of course, the possibility of linking multiple resources creates new standardisation and interoperability challenges (cf section on Strengthening standardisation & interoperability). A new development is the link from descriptive models to predictive models in SBML. Since pathway models already support standard descriptions of reactions (e.
Acknowledgments These activities were partially funded by the  Italian Space Agency  contracts 2016-1 3 Journal of Low Temperature Physics (2022) 209: 631-639 10. A. Paiella  et al. , J. Low Temp. Phys. 184,  97-102 (2016 ). https:// doi. org/ 10. 1007/ s10909-015-1470-z.
Genome-Wide Data Analysis We genotyped the 1,240k SNP positions using pileupCaller (https://github. com/stschiff/sequenceTools/tree/master/ src-pileupCaller) obtaining pseudohaploid calls for each variant position on the capture. We merged the 1,240k SNPs with the modern-day Human Origins data set, as well as relevant ancient samples (https://reich. hms. harvard. edu/allen-ancient-dna-resource-aadr-downloadablegenotypes-present-day-and-ancient-dna-data, version 44. 3), to perform a principal component analysis (PCA). Owing to the incomplete nature of the ancient data, ancient individuals were projected onto the genetic variation of 1363 modern West Eurasians (fig. 1A ). Based on the position in PC space, we found that the DER individuals fall together with published LBK individuals, as well as contemporaneous early Neolithic individuals from southeastern Europe and western Anatolia (fig.
LBK Inter-site Comparisons We further tested the observations on LBK population structure inferred from biological relatedness results using inbreeding coefficients and effective population size comparisons of LBK sites in Germany with other already published EEFs. We used READ (Monroy  Kuhn et al. 2018)  to test for relatedness between individuals from DER and other LBK sites in Germany, mainly already published data from Halberstadt-Sonntagsfeld (Germany_LBK_HBS, N = 29) and Stuttgart-Mühlhausen (Germany_LBK_SMH, N = 29)  (Lipson et al. 2017; Rivollat et al. 2020 ). To do this, we first estimated the median relatedness in each German LBK group separately and found them to be comparable with each other (LBK_HBS = 0. 24; LBK_SMH = 0. 25; LBK_DER = 0. 24). We then performed a joint biological relatedness analysis of all German LBK sites using READ and found no evidence of firstor second-degree relatedness between individuals from different sites.
We chose to compare the LBK to the Anatolian farmers because these populations are known to be closely related based on our PCA and Admixture analyses, as well as previous studies  (Mathieson et al. 2015; Lazaridis et al. 2016). SNPs with the largest pair-wise Fst values between Anatolian farmers and the LBK, as well as between LBK and HGs were prioritized, since these SNPs could indicate selection for the new environmental or lifestyle conditions associated with the expansion into and across Europe, and ongoing Neolithization, respectively. Based on the LSBL analysis, we prioritized N = 9549 genetic loci under the empirical quantile ,0. 01 (top 1%) that was calculated based on the distribution of the LSBL statistic following previous studies  (Bigham et al. 2010). Second, to test for selection since the deeper split of LBK and Anatolia_N from WHG, we merged the ancient farmer populations (LBK and Anatolia) into a single EF group, and compared it with WHG, and modern Esan (ESN) individuals from the 1KG data set (The 1000 Genomes Project Consortium 2015) as a non-European farming group comparison.
In a third analogous LSBL analysis, we also compared EF to WHG and an African HG cohort represented by Biaka and Mbuti from the SGDP and HGDP data sets, respectively  (Mallick et al. 2016; Bergström et al. 2020). During this deeper time frame, we expect the Neolithization in Western Eurasia to have had a substantial effect due to the change in subsistence and increased population densities and disease susceptibility. We also performed an ancestry-aware selection scan using our newly developed method AIMLESS. For each position on the genome for which we had allele frequencies for all three groups (LBK, Anatolia_N, and WHG), we performed a maximum-likelihood estimation (MLE) of allele frequencies in the LBK based on the observed data (unconstrained model), which we compared with the MLE based on the most likely admixture of Anatolia_N and WHG ancestries (constrained model).
Genotyping Pseudohaploid genotyping was performed using SAMtools mpileup  (Li and Durbin 2009)  and PileupCaller (https://github. com/stschiff/sequenceTools/blob/master/ src/SequenceTools/PileupCaller. hs) for the 1,240k sites. Pseudohaploid genotypes were merged with the Human Origin Affymetrix dataset of modern individuals around the globe  (Patterson et al. 2012) , as well as published ancient West Eurasian individuals (https://reich. hms. harvard. edu/allen-ancient-dna-resource-aadr-downloadablegenotypes-present-day-and-ancient-dna-data, version 44. 3). SmartPCA from EIGENSOFT v7. 2. 1 (http://www. hsph. harvard. edu/alkes-price/software/) was used to perform a PCA projection of ancient samples on top of the variation of the modern data. Unsupervised admixture was performed on k components from 1 to 17 with random seeding after LD pruning in plink with the following settings (-indep-pair-wise 200 25 0.
F-statistics R package admixr was used to perform f-statistics and qpAdm  (Petr et al. 2019). We performed f4 tests to determine if there were differences in HG ancestry in the individuals from DER compared with other EEF groups, as well as to test whether LBK individuals had additional HG ancestry Early Neolithic European Farmers • https://doi. org/10. 1093/molbev/msac108 MBE compared to individuals from Anatolia Neolithic (Bar8 and Bar31  (Hofmanová et al. 2016) ). Based on the admixture analysis, we formally the admixture components using qpAdm as implemented in the admixr package  (Petr et al. 2019 ) using a 12 sample outgroup (Mbuti, Papuan, Onge, Han, Karitiana, Mota, Ust-Ishim, Russia_Mal'ta1, Vestonice, CHG, Israel_Natufian, Villabruna) + additional European HG sources  (Villabruna, Russia_EHG, GoyetQ2).
HLA Typing A development version of OptiType 2  (Szolek et al. 2014)  was used to determine HLA Class I and II alleles from ≥30 bp single-end sequencing data retrieved from the immunocapture libraries (https://github. com/FRED-2/ OptiType, tag DER). Fifteen of 432 allele calls were overruled in favor of runner-up alleles based on anomalous coverage patterns induced by short reads cross-mapping with alleles of different loci.
Selection Scans LSBL was used on the 1,240k genotype data to determine sites in the genome that have undergone recent selective sweeps. This method relies on pair-wise Fst values for three populations to identify loci with extreme Fst difference between population of interest and two other groups used for comparison. Resulting LSBL values were ranked and empirical P values were determined. Resulting SNPs were annotated using the R package BioMart  (Durinck et al. 2009). We also performed a pathway enrichment analysis using gowinda  (Kofler and Schlotterer 2012)  comparing the top 1% LSBL hits to all loci analyzed (1,240k SNPs after QC). This method performs gene-set enrichment base on GO classifications and designed specifically for GWA studies. Gowinda was designed to take into account the assumptions that are often violated in genome-wide associations studies, such as longer genes having more SNPs, and overlapping genes being sampled in clusters  (Kofler and Schlotterer 2012).
Haplotype-Based Scan Genome-wide 1,240k capture data from Derenburg, LBK and Iron Gates HG individuals were imputed using GLIMPSE following the standard procedures  (Rubinacci et al. 2021). Genotype likelihoods were computed using bcftools mpileup  (Li 2011). The GLIMPSE_phase function was used to run imputation in chunks with window size of 2,000,000 bps and buffer size of 200,000 bps. The chunks were then ligated using GLIMPSE_ligate and haplotypes were sampled using GLIMPSE_sample. The 1000G reference panel was used for imputation (The 1000 Genomes Project Consortium 2015). Samples with endogenous DNA coverage on the 1,240k capture of above 0. 5× were included in the analysis. REHH package was used to run haplotype-based selection scans (Gautier and Vitalis 2012).
Acknowledgments We thank the  of the Archaeogenetics Department of the Max Planck Institute for the Science of Human History , especially  Ellen Späth  for sampling, the  Population Genetics group  for helpful feedback, and  Guido Brandt ,  Antje Wissgott  and  Stephen Clayton  for sequencing and handling of the raw data. We are indebted to  State Office for Heritage Management and Archaeology Saxony-Anhalt-State Museum of Prehistory, Germany , and all archaeologists and students involved in the excavations at Derenburg-Meerenstieg II. We would also like to thank  Iain Mathieson ,  Gaspard Kerner , and  Vanessa Villalba-Mouco  for helpful discussions during the preparation of the manuscript. The study was funded by the  Max Planck Society , and the  French (ANR)  and  German (DFG) Research Foundations ,  INTERACT  project,  ANR-17-FRAL-0010 ,  DFG-HA-5407/4-1 ,  2018-2021  (to M.
Data Availability Genomic data (BAM and fastq format) are available at the European Nucleotide Archive under accession number PRJEB52488, genotypes in eigenstrat format can be found at https://edmond. mpdl. mpg. de/dataset. xhtml?persistentId= doi:10. 17617/3. HOKI5I The code for AIMLESS is available at https://edmond. mpdl. mpg. de/dataset. xhtml?persistentId=doi: 10. 17617/3. HOKI5I.
Here, we describe the protocol for the global-and localscale intercomparison of lake model simulations completed for the second phase of ISIMIP (ISIMIP2), as well as the extensions to this protocol that have been implemented for the new phase three simulations (ISIMIP3). The evolution of the modelling protocol from ISIMIP2 to ISIMIP3, and the rationale for these advancements, will be described in individual sections related to the experimental set-up of the Lake Sector, such as changes to lake model forcing datasets and background information on lake mapping. First, we provide an overview of the climate data and climate change scenarios available through ISIMIP that were used as forcing data for lake impact models. Next, we explain the rationale behind the ISIMIP Lake Sector, give an overview of the impact simulations and briefly describe the lake models used at local and global spatial domains.
Case-study lakes in the local domain Lakes in the local domain had sufficient information to allow the lake models to be parameterized using individual lake bathymetry and to be calibrated against measured water temperature profiles. Consequently, the local lake dataset was a unique resource for testing and evaluating lake model performance. For ISIMIP2, bathymetric data and historical data on water temperature from 52 lakes and 10 reservoirs (Fig. 1a , Table  S1 ) were shared among participating modelling teams. Since reservoirs were treated like regular lakes in the simulations, all waterbodies are hereafter called "lakes" (see  Sect. 4. 3). The geographical distribution of the lakes encompassed a gradient of five major climatic groups in the Köppen-Geiger climate classification, including tropical, arid, temperate, boreal, and polar.
Representative lakes in the global domain Lake simulations in the global domain considered a single, generic lake in each grid cell that contains lakes in the 0. 5 • × 0. 5 • ISIMIP global grid. For a given grid cell, such a lake is termed "representative" because it is assumed to represent real lakes bound by its coordinates by sampling their bathymetric information to perform uncalibrated lake model simulations  (Subin et al. , 2012). The background data and sampling methods for generating representative lakes has evolved from ISIMIP2 to ISIMIP3. In the global domain of ISIMIP2, generic lakes in each grid cell used average lake depth and surface area information from a rasterized version of the Global Lake and Wetland Database (see  Sect. 3. 5. 1; Lehner and Döll, 2004). Using the mean lake characteristics in the grid cell could not account for the spatial distribution, variability, and non-uniform distribution in the depth and area of lakes in a grid cell.
How-ever, similar representations of lakes were used in Earth system and numerical weather prediction models  (Subin et al. , 2012; Balsamo et al. , 2012; Thiery et al. , 2015 Thiery et al. , , 2016 Thiery et al. , , 2017;; Vanderkelen et al. , 2021) , and the grid cell lake representation was a necessary trade-off between computational feasibility and global representativeness. This global-scale lake coverage of 17 500 generic lakes made it possible to represent lakes in all major climatic classes and their subclasses, which was not possible in the local lake domain. In the global simulations of ISIMIP3, this process was improved with a better method of characterizing generic lakes for each grid cell to represent true lakes with morphological characteristics from newer databases: HydroLAKES  (Lehner and Messager, 2016)  and Globathy  (Khazaei et al.
In this study, we based the initial number of layers on the initial https://doi. org/10. 5194/gmd-15-4597-2022 Geosci. Model Dev. , 15, 4597-4623, 2022 water depth. In addition, GLM includes modules for surface heat exchange and ice-snow dynamics, vertical mixing, and water balance dynamics. GLM can be coupled to the Aquatic Ecodynamics Modelling Library (AED) to simulate water quality dynamics and ecosystem interactions. The General Ocean Turbulence Model (GOTM v5. 3) is a one-dimensional model that simulates the most important hydrodynamic and thermodynamic processes related to vertical mixing  (Umlauf and Lemmin, 2005). GOTM was developed by  Burchard et al. (1999)  for modelling turbulence in the oceans, but it has been recently adapted for use in lakes  (Sachse et al. , 2014). Typically, GOTM is used as a standalone model for investigating the dynamics of boundary layers in natural waters, but it can also be coupled to a biogeochemical model using the Framework for Aquatic Biogeochemical Models (FABM;  Bruggeman and Bolding, 2014).
MyLake (v1. 12) is a one-dimensional process-based model used to simulate physical, chemical, and biological dynamics in lakes  (Saloranta and Andersen, 2007). The model simulates thermal stratification, lake ice and snow cover, and phosphorus-phytoplankton dynamics. It also contains a simple sediment box model. MyLake runs at a daily time step using regularly spaced water layers whose vertical resolution is defined by the user. Different versions of the open-source code have been applied to simulate algal blooms  (Moe et al. , 2016) , CO 2(g) and CH 4(g)  (Kiuru et al. , 2019) , internal phosphorus loads  (Markelov et al. , 2019) , and light attenuation dynamics  (Pilla and Couture, 2021). Simstrat (v2. 1. 2) is a one-dimensional hydrodynamic model, which specifically includes vertical mixing induced by internal seiches that is not included in most other models  (Goudsmit et al.
ISIMIP2a The Lake Sector simulation protocol for ISIMIP2a was completed in early 2020. This phase focused on the calibration of eight models in the local domain toward projecting these models with meteorological forcings from the gridded ISIMIP2a observations. The same set of gridded meteorological observations was then used for global lake simulations. However, calibrating these lake models globally was unfeasible because of a lack of measured lake water temperatures at this scale. Meteorological data from 1979 to 2016 at the ISIMIP grid scale (EWEMBI, "EartH2Observe, WFDEI, and ERA-Interim data Merged and Bias-corrected for ISIMIP", Lange, 2019a) were used as inputs for calibrating the local lake models. From EWEMBI, the grid cell from each local lake's geographical location (Fig. 1  and Table  S1 ) was used for the model calibration.