dataesr/scientific-paragraphs-categorization huggingface dataset:
Usage Notes The data can be downloaded from the FIGSHARE data repository under the terms of Creative Commons Zero (CC0) waiver. We also provide FunAndes database in the TRY Plant Trait Database (https://www. try-db. org). Users of FunAndes data are invited to cite this publication: Báez et al. xx. FunAndes -A functional trait database of Andean plants. Scientific Data. 00:00-00, and the accompanying FIGSHARE dataset  22. Fig. 3  Phylogenetic distribution of trait data in FunAndes showing the total number of observations per taxa for the five most represented functional traits: Plant growth form, leaf compoundness, specific leaf area (SLA), wood density, and leaf thickness. The phylogenetic tree shows information for 150 families and 2,690 species. The tree is based on a recent plant phylogeny  28  , nomenclature of The Plant List (http://www.
Louis; the Taylor and Davidson families; Dirección General de Biodiversidad, the Bolivian Park Service (SERNAP). EV thanks the Corkery Family Fund and the Center for Sustainable Forestry at Pack Forest, both from University of Washington, USA for funding support during his PhD program, and the financial support from the 'Bosques Andinos' Initiative (http://www. bosquesandinos. org/) that was important for the collection of trait data at San Eusebio. Data collection in Peru was supported by NSF LTREB DEB 1754647, the Gordon and Betty Moore Foundation's Andes to Amazon initiative, and RAINFOR. We thank the Open Access Publication Funds of University of Göttingen.
Acknowledgements This work was possible thanks to funding from the  Living Earth Collaborative (LEC)  at  Washington University in St. Louis , for the working group ' A synthesis of patterns and mechanisms of diversity and forest change in the Andes: A global biodiversity hotspot' (organized by  JST , JAM, and SB), and by the  German Centre for Integrative Biodiversity Research (iDiv) , for the working group 'sAndes: Tree diversity, composition and carbon storage in Andean tropical montane forests' (organized by LC and MJM). The authors are grateful to  Jens Kattge  for making available the TRY data presented in this paper. AA-Q thanks the  Herbario Nacional de Bolivia , the  German Academic Exchange Service (DAAD)  and  DFG  for funding ( HE3041/20-1 ); to  Ricardo Sonco  and  Marcelo Reguerin ,  Arely Palabral ,  Heike Heklau , and the  Chulumani community.
SB thanks the financial support of the  Alexander von Humboldt Foundation  through a  Georg Foster Fellowship ; SB, MB and HV acknowledge  VLIR-UOS  grants  COFOREC (EC2018SIN223A103 ) and  COFOREC II  ( EC2020SIN279A103 ). GBD was funded through a PhD grant by the  Spanish Ministry of Education  (MINEDU;  FPU14/05303 ) and is indebted to  Alex Nina ,  Jorge Armijos ,  Gonzalo Bañares ,  José Sánchez ,  Ángel Delso ,  Anselmo Vergaray ,  "Rosho" Tamayo ,  Reynerio Ishuiza , and national parks rangers for assistance in the field. CB thanks  CONICET, Argentina , for a Doctoral Scholarship. LC and MJM were partly funded through projects  CGL2013-45634-P ,  CGL2015-72431-EXP ,  CGL2016-75414-P ,  PID2019-105064GB-I00 , and  S2018/EMT-4 338. JGA and CBS acknowledge  Luis Torres ,  Mara Paneghel ,  Iñigo Gómez ,  Maaike Pyck ,  Manuel Marca ,  Daniel Irygoin ,  Piher Maceda  and  Pontificia Universidad Católica de Perú (PUCP).
In this white paper, we describe the founding of a new ELIXIR Community -the Systems Biology Community -and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology -databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure -are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR's future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities.
Executive summary This white paper presents the future strategy of the new ELIXIR Systems Biology Community. This emerging ELIXIR Community was established upon the recommendation of ELIXIR's Systems Biology Focus Group to develop and coordinate ELIXIR's interactions with the broader systems biology community. The infrastructure aspects of systems biology -databases, tools and standards development, as well as training and access to cloud infrastructure -are not only appropriate components of the ELIXIR infrastructure, but will prove effective drivers of ELIXIR's future support of advanced biological applications and personalised medicine. Systems biology is defined here as: modelling and understanding living systems in terms of their thousands of molecular interaction properties using a wide range of approaches, which can be further classified as bottom-up (starting from the molecular components) or top-down (starting from system behaviour).
A key feature of systems biology is that, because of the complexity of the systems that are studied and the variety of data that is collected, it often requires collaboration between different laboratories and between computational biologists and experimentalists, often asynchronous in both space and time (e. g. through the literature and databases). It is essential that all the components that go towards building a systems model, from datasets and data collection methods to the models themselves, are FAIR (Findable, Accessible, Interoperable and Reusable)  (Wilkinson et al. , 2016). Historically, in Europe and worldwide, there have been significant investments in the development and applications of systems biology (see Table  1  & Table 2 ). In Europe, this culminated in the establishment of the ESFRI ISBE.
MetaNetX/ MNXref MetaNetX is a unified namespace of metabolites and biochemical reactions developed to bring together models and resources published by other groups. It is distributed under open-source licence as a database (MNXref). In addition, a standalone software suite (MNXtools) will soon be released to critically assess and suggest improvement for Genome-Scale Metabolic Models, w. r. t to biochemistry. Table  2. Summary of international initiatives and resources in Systems Biology.
Mid-term: • Improved standardisation of meta-data to describe time-series, functional and imaging data such as to facilitate their integration in computational models; Providing a link between existing models, datasets, and analysis tools 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; • Developing the basis for theoretical and practical multi-scale modelling frameworks; • Providing the basis for developing Digital Twins (microbes, bioreactors, organs, organisms, ecosystems); • Extend the use of synthetic and standardised datasets in most systems biology training events; • Support current and future trainers via Train the Trainer ELIXIR events; • Continue and review the process of gap analysis survey and KPIs to include SME and industry; • Involve small and medium-size enterprises in the capacity building process; • Identify new areas, themes and challenges in systems biology.
Better interoperability would enable the building of efficient systems biology modelling pipelines. Here, the ELIXIR Systems Biology Community can learn from the recent advances in the standardisation of sharing of biological data and data analysis pipeline development, where the wider ELIXIR community has played an important role. Finally, we need to overcome the conceptual barrier to mathematical modelling that is still present in biomedical sciences. While biology is a mature science, in the sense that we have a decent understanding of the major processes that make it work, we mostly do not understand the details to make it truly applicable. To get at those details, and to take advantage of all the big and small datasets collected in the last decades, we need to build more and better systems biology models for better data interpretation, such that systems biology consistently demonstrates utility and becomes a part of all new biomedical studies.
This puts interoperability and flexibility at the centre of building tools and resources. As a consequence, systems biology standards and resources must aim for improved interoperability. The international COMBINE initiative, with its standards SBML, CellML, SBGN, SED-ML and others  (Hucka et al. , 2015; Waltemath et al. , 2020) , has developed a range of interoperability standards for systems biology models, their   et al. , 2021). Some early attempts to harmonise the content of these built on the BioPAX standard  (Demir et al. , 2010)  or simply on gene lists (e. g. Pathway Commons  (Rodchenkov et al. , 2020)  and the Molecular Signatures Database, MSigDb, at the Broad Institute  (Subramanian et al. , 2005) ). More recently, dedicated converters allow translation between Reactome, WikiPathways, and the Disease Map resource MINERVA  (Gawron et al.
Furthermore, standards and solutions will need to be developed to link dispersed health information to integrating models such as disease maps and digital twins. At the same time, it will be important to improve interoperability between maps, models and digital twins and ELIXIR's Core Data Resources and Deposition Databases. At the core of systems medicine lies the involvement of the user communities: the clinicians and the patients. The patient-oriented approach with the integration of personalised data, both clinical and omics, into network-based analyses and models will enable tailored stratification, therapies, disease management strategies and monitoring. A key element that systems medicine can bring to ELIXIR is its engagement with clinical researchers collecting disease-relevant data and the application of its approaches close to the clinic.
, 2020) , integrated with interaction databases and text mining platforms. We can consider this effort as a blueprint for building qualitative systems medicine models, which will feed downstream, detailed modelling workflows  (Fröhlich et al. , 2018). A future aim for systems medicine within the Systems Biology Community will be to facilitate the construction of disease maps for currently unrepresented disease areas. Incorporating individual differences into systems biology models can be used to create personalised models akin to a "digital twin" of a patient with a disease  (Fey et al. , 2015). Modern omics technologies, such as genome sequencing, allow molecular profiling of individual patients, with unprecedented resolution down to single cells. A prominent example is The Cancer Genome Atlas, an enormous repository of multi-omics data from over 11,000 cases across 33 cancer types  (Hutter & Zenklusen, 2018).
For example, constructing models using a combination of datasets describing an individual might increase their identifiability and aspects such as data ownership and the ability to withdraw consent from datasets could affect the persistence of models. The Community will therefore engage with ELSI efforts in the Human Data Communities, within ELIXIR and beyond, to investigate, for example, the impact of ELSI issues on building, constructing and sharing models based on human data. Close collaboration is also envisioned with the Genome of Europe initiative, which will start by collecting ELSI compliant human datasets from the general European population. Another ELSI aspect is engagement with industry, to ensure ethically acceptable uptake of the techniques and infrastructures it develops from lab to the bedside.
Capacity building and training The stark rise of high-throughput technologies and related datasets describing the complexity of biological systems in health and disease, has been a driver of the emergence of systems biology, which comes with inherent training challenges. The ELIXIR Systems Biology Community has identified several challenges that need to be addressed for developing and delivering successful systems biology training that could be achieved within different time frames. These are detailed below. Diversity in trainee backgrounds. Owing to the diversity of systems biology approaches, heterogeneity of trainee backgrounds is common. Trainees often differ greatly in their knowledge of biology and mathematical modelling, as illustrated in Figure  3 , and their levels of experience with software tools or systems biology methods also varies from 'novice', to 'competent practitioner' and 'expert' user.
Finding the optimal balance between providing sufficient information on systems biology databases and software and not overloading trainees with too many new concepts, represents one of the main training challenges. To accommodate different trainee backgrounds, an option might be to pre-screen trainees well-ahead of the training event to understand their expectations of what they will achieve at the event. This could be in the form of a questionnaire, hosted by TeSS  (Beard et al. , 2020)  or sent out on a mailing list, with the objective to (1) assess the trainee's abilities, (2) find out their training needs, and (3) make recommendations on existing courses based on (1) and (  2 ). This may prove a good opportunity to synergize training with other biomedical communities (e. g. fluxomics, microbiomics) and offer general bioinformatics courses (e.
Availability and use of standardised datasets in training materials. With the ever-changing systems biology landscape, new tools and massive amounts of data become available and so, trainers need to continuously adapt to these developments. One of the training challenges relates to the lack of standardised and FAIR datasets underpinning specific topics and the lack of suitable documentation for some of the tools or databases. This makes it difficult for trainers to keep their materials up-to-date. In addition, training materials are often focused on specific software or databases, rather than providing an overview of a given topic. Sharing real or synthetic datasets could prove especially useful, for example, as a part of practical hands-on exercises or use cases. The ELIXIR Systems Biology Community will aim to (1) promote the use of appropriate data sets for testing and/or validation in different omics areas, underpinned given topic, and models that can be run at a click.
g. Sharma et al. , 2020]. PBPK modelling is also starting to be explored as a key element of a safe-by-design approach for material development (for instance in the DIAGONAL EU project for evaluation of nanomaterials There is a strong parallel between systems biology approaches described above for pharmacology and those used in toxicology. The same kinetics ("does it get there?") and dynamics ("what does it do?") modelling approaches apply. Mechanistic models used in toxicology like adverse outcome models and quantitative approaches for read-across, predicting endpoint information for one substance by using data from the same endpoint from (an)other substance(s), also have their counterparts in pharmacology. Bioinformatics approaches towards mining literature and databases for comprehensive metabolic data have led to the development of maps based on genome-scale metabolic models (GEMs) for a multitude of organisms, including humans  (Robinson et al.
Systems Biology within ELIXIR The infrastructure aspects of systems biology and systems medicine -databases, tools and standards development as well as training and access to cloud infrastructure -are not only appropriate components of the ELIXIR infrastructure, but will be essential for ELIXIR's future support of advanced biological applications and personalised medicine (see Figure  4 ). Because in systems biology experimental data relate to emergence of function through computational models, and because computational models need to be based on realistic experimental data, integrated data-and-model repositories such as JWS Online, BioModels and Metabolic Atlas are essential. Models and data need to be accessible to external users, for curation, validation and critical evaluation: the model-data repositories need to be 'live' in that they readily simulate the spatial-temporal behaviour of living systems.
They should enable 'what-if' computational experiments in silico, in order to serve bioengineering, medicine, pharmacology and basic biology. Further, these repositories should allow for constant refinement and update of the models that they host. We can build on the landscape of data resources that ELIXIR already has in the form of Core Data Resources for knowledge used to build the models, and Deposition Databases for parameter tuning, validation, etc. Standardised model annotation of the described components such as genes, reactions, or metabolites is required to ensure interoperability between models and FAIR repositories hosting large omics datasets. While extensive guidelines and test suites for such annotation have been developed in the COMBINE context and MEMOTE  (Lieven et al. , 2020) , respectively, their evolution and consistent application would benefit from a strong infrastructure context.
There are clear synergies between the objectives and activities of the Systems Biology Community and the ELIXIR Platforms, as described in the following sections. The data platform. The ELIXIR Core Data Resources and Deposition Databases are already highly used by the systems biology community. For example, data from BRENDA, STRING, and Reactome are essential both for construction of molecular pathways and parameterization of molecular reactions. Furthermore, an essential systems biology database, BioModels, is already part of the ELIXIR Data Platform. With JWS-Online, it facilitates the discovery, deposition, and re-use of systems biology models. SABIO-RK is an ELIXIR Node service which provides curated data manually extracted from literature to modellers. This demonstrates the compatibility of models for data with data for data.
Systems biology could contribute new databases to the platform, such as JWS-online, Make Me My Model and many others, which could serve as an example for data service-based resources and databases. Finally, through ELIXIR, the Systems Biology Community, which already features among the largest users of the ELIXIR data resources, should be able to provide support and advice for further development of these resources, so that they become more compatible with predictive, modelling-based knowledge development, making the data more actionable. The engagement of the Data Platform in curation efforts also supports the maintenance of systems biology resources and improves the data quality. The tools platform. Systems biologists have in the past developed a range of tools, which could find their home within the Tools Platform.
g. semantic ontologies) and thereby contribute to a knowledge-based infrastructure for biotechnology. • Plant sciences: An interdisciplinary group of researchers very active on the border between experimental and computational approaches that aims at building a tools service bundle that will support plant scientists in the integration and linking of diverse datasets with an extension towards systems biology. Many of the current activities of the Community are in the field of experimental data management tools. Expertise from the systems biology community would in the short term enable the extension of these activities to the management of plant systems biology models which are becoming increasingly prominent in the field. • Food and nutrition community: Aims to support the research towards the effects food choice and nutrition have on health and well-being.
This typically is a system-wide effect where many small changes work in combination. • Toxicology: Aims to support risk assessment of chemicals, drugs, cosmetics ingredients and nanomaterials to lead to safer products. This includes the combination of toxicokinetic (exposure, uptake, distribution, metabolism) and the toxicodynamic (molecular interactions and complex connected events in adverse outcome pathways). Both aspects and their combination strongly lean on models and modelling. • Marine metagenomics (Microbiome): Aims to develop a sustainable metagenomics infrastructure to enhance research and industrial innovation within the marine domain. It develops standards and best practices for the marine domain, provides databases specific to marine metagenomics and develops tools and pipelines to enhance metagenomics analyses.
There is an opportunity to link these objectives to a systems medicine perspective on rare diseases. ELIXIR also runs a number of focus groups (FGs) that are relevant to the Systems Biology Community: • Machine learning (ML): This focus group was initiated in October 2019 to address needs related to the application of ML in mining large omics datasets to uncover new insights in the field of medicine. These complement activities in systems medicine. Goals of the ML focus group relate to the development of controlled terminology/ontology and services for ML model description. There is an opportunity to align these developments with efforts on standardisation of models in systems biology. • EOSC: Connects ELIXIR's EOSC-related activities, which run along the axes: a) Consolidation of e-Infrastructure services and positioning these services as an embedded "supply-chain" for data-intensive scientific collaborations; b) Open Science in practice; c) Integrating the user-focussed services from research infrastructures with their user communities to enable interdisciplinary research aligned with the major societal challenges.
• Biocuration: This group was established in 2021 and builds a network of database developers and curators in ELIXIR. The Systems Biology Community benefits from the interaction with the International Society for Biocuration and the engagement for a better visibility and recognition of the work of biocurations, e. g. community curation efforts in model resources. • FAIR training: The FAIR Training FG was formed in 2018 with the aim of improving the production and diffusion of FAIR training materials across Nodes. These activities closely complement those of the Systems Biology Community whose applications often rely on multi-omic and holistic datasets and respective descriptors and identifiers for effective delivery of materials and tools of systems biology. • Biodiversity: This group is focused on understanding and cataloguing the capabilities, interests and ongoing projects we have in this area across the ELIXIR Nodes.
This is of particular importance for complex, multistep operations that are required for many processes, such as those pertaining to biobased production of chemicals, pharmaceuticals or materials. The ESFRI programme IBISBA works on the interoperability and deployment of such concepts and standards and workflows, but many challenges remain. This is relevant for ELIXIR since these designs in the end lead to data covered in the infrastructure. Standards for linking models with sensitive data, including electronic health records, as well as other person-related data and commercial data, with ELIXIR Core Data Resources and Deposition Databases are an identified need. ELIXIR and members of national ELIXIR Nodes are already active in defining such standards and connecting them to existing research projects in the domain as partners in the European standardisation initiative EU-STANDS4PM (European standardisation framework for data integration and data-driven in silico models for personalised medicine) that very recently has published guidelines and recommendations for data integration and model validation for computational models in the domain of clinical applications in personalised medicine  (Collin et al.