dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The management and development of the HITRAN molecular database has been supported by  NASA   AURA program  grant  NNX14AI55G  and  NASA Planetary Atmosphere  grant  NNX13AI59G , and  NASA PDART  grant  NNX16AG51G. Portions of the research described in this paper were performed at the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration, Government  sponsorship acknowledged. We gratefully acknowledge the many researchers who provided data, experimental and/or theoretical:  Abdulsamee Alkadrou ,  Lorenzo Barrett ,  Emma Barton ,  Chris Benner ,  Linda Brown ,  Jeanna Buldyreva ,  John Coxon ,  Phillip Coles ,  Ludovic Daumont ,  Jolanta Domyslawska ,  Andre Fayt ,  Photos Hadjigeorgiou ,  Shuiming Hu ,  Ekaterina Karlovets ,  Samir Kassi ,  Julien Lamouroux ,  Daniel Lisak ,  Lorenzo Lodi ,  Gang Li ,  Anwen Liu ,  Didier Mondelain ,  Tatyana Petrova ,  Laurence Régalia ,  Candice Renaud ,  Cyril Richard ,  Alexander M.
Preliminary Article Identification We used the systematic review method  (Denyer & Tranfield, 2009; Higgins & Green, 2011; Tranfield et al. , 2003)  to identify a small set of highly relevant pioneering articles. This review included 21 top management journals (e. g. , Journal of Management), management education journals (e. g. , Academy of Management Learning and Education), and specialised ERS journals (e. g. , Journal of Business Ethics) in which management competence research had been published. The Web of Science database was used as it covered all the journals identified. We identified articles centred on individual-level managerial competences for sustainability, responsibility, and ethics, by conducting a search for competen* (capturing the words competent, competence, competency) plus sustainability, responsibility, or ethics (in title, abstract, or keywords).
Preliminary Thematic Template Analysis We used an NVivo software-based thematic template analysis  (Boyatzis, 1998; Braun & Clarke, 2006; King, 2004; Vaismoradi et al. , 2013; Waring & Wainwright, 2008) , involving induction, deduction, and abduction  (Saetre & Van de Ven, 2021) , to integrate ERS competence themes into one framework. We will now briefly describe our coding through four distinct rounds (see Table  3  for greater detail). To prepare the thematic analysis, we first identified competences proposed in each article. There were articles that featured a list of competences in the main body of text. Also, there were articles focused on a particular individual competence. For these articles, we coded the competences from the article abstract and prominent descriptions in the paper.
Consolidation Article Identification To include articles with ERS management competences in non-business fields, we searched all journals covered by the multi-disciplinary Web of Sciences database. We added 'management' to the search criteria to ensure that only management practice articles were captured. This also avoided capturing organisation-level competences, which we had excluded by hand in the preliminary review. Our search for compet*, and (ethic*, or responsib*, or sustainab*), and manage* in the article title, keywords, or abstract yielded an initial list of 354 articles. The sample was narrowed down to 155 additional articles to be analysed, using the previous exclusion criteria.
Introduction The volume of data supporting chemical monitoring and human/ environmental exposure and risk assessment processes linked to various European Union (EU) regulatory, science, and policy making contexts is growing at an unprecedented pace, with a continuously expanding body of scientific literature, and numerous growing data repositories (databases and data platforms)  (European Commission, 2020a). While more data and scientific progress are clearly beneficial, it is increasingly difficult to find, compare, combine, and reuse relevant information, keep up with research developments globally, and make use of the fast growing body of associated data  (Chu and Evans, 2021). Humans are exposed to an ever-growing number of marketed chemicals in a variety of complex exposure settings  (Carney Almroth et al.
, 2017) , as well as parallel exposure data storage and management efforts. A variety of data types are covered by repositories, such as the database of registered substances information submitted under the European REACH (Registration, Evaluation, Authorisation and Restriction of Chemicals) Regulation  (European Commission, 2006) , the European Chemical Agency's (ECHA) SCIP (Substances of Concern In Products) database established under the European Waste Framework Directive  (European Commission, 2008) , the Comprehensive Food Consumption database  (EFSA, 2011)  and Chemical Hazards Database (OpenFoodTox)  (Dorne et al. , 2021)  of the European Food Safety Authority (EFSA), the Information Platform for Chemical Monitoring (IPCHEM) operated by the European Commission in collaboration with European Union Agencies (Joint Research Center, 2020;  Kephalopoulos et al.
, 2020)  and the European database on occupational exposure to hazardous chemicals (HazChem@Work)  (European Commission, 2016). However, Europe faces specific challenges and conditions for exposure-related data generation, management, sharing, and use/reuse that are related to a complex regulatory framework, including scattered databases, data that are often unpublished or not readily available, and a lack of a common glossary owing to sectorial approaches  (Bruinen de Bruin et al. , 2022; Fantke et al. , 2020b). Despite a global trend to boost data-driven and digitalization approaches in industry and regulation  (European Commission, 2021b; Fantke et al. , 2021) , reliable and high-quality exposure data are still only available for a small fraction of marketed chemicals and limited exposure settings  (Kristiansson et al.
Mapping exposure data in the literature and European exposurerelated data repositories To identify literature contributing to the exposure data landscape, the European TIM Technology Editor (https://timanalytics. eu) was used to extract article titles and keywords from publications available in the source-neutral abstract and citation database Scopus (https://scopus. com). Studies were identified with the following set of chemical exposure-related terms present in the abstract, title, or author keywords: expos* AND (chemical* OR substance* OR pesticid* OR plant protect* OR pharmaceutic* OR medic* OR cosmetic* OR biocid* OR pollut* OR contamina* OR metal* OR compound*). The analysis included all articles from the period 2010 -spring 2022 (not restricted to European authors), resulting in titles and keywords for a total of 152,896 identified articles, demonstrating the massive amount of chemical exposurerelated publications in the peer-reviewed literature.
1b ). To characterize the exposure data landscape in existing data repositories, European experts from diverse international data repository initiatives described the data extraction, analytical tools, and content of the repository hosted at or provided by their respective institutions. An overview of these repositories is given in Table  1. To characterize differences across data repositories and areas of overlap, a set of key terms was developed for three existing repositories (IPCHEM, NDS, EURO-HEALTHY) and three emerging data initiatives identified through ongoing European exposure data generation efforts (HELIX, Exposome Network, HEALS). Each term list was developed by collecting descriptors from data repository documents (e. g. , Norman Database System's glossary terms, EUROHEALTHY's Atlas terms), "search terms" from existing data repository interfaces (excluding institution names, numeric ages and timeframes, and individual chemical identifiers), and focus areas/work package titles from emerging data generation initiatives.
g. , "neurotoxicants", "mycotoxins"), and different assay types and ecotoxicity threshold values for surface and marine waters, sediments, and biota. Emerging initiatives also cover different exposure-science areas, with HELIX covering more population behaviour (e. g. , "physical activity", "drinking") and specific environment descriptors (e. g. , "heating", "sunlight exposure"), and the Exposome Network describing more general environment descriptors (e. g. , "internal environment", "external environment") and data analysis techniques ("immunomics", "metabolomics", "risk assessment"). Across repositories, the majority of key terms (91 %) were unique to each data resource or initiative, indicating each resource could meet different exposure data needs for a user. Of the terms that overlap, "environment" was the only term found in all databases, with "water", "health", "urban", and "biomonitoring" found in four of the six.
Connections between grey nodes are not shown. Node size is number of articles with each keyword ranging from 1 (smallest nodes) to 4821 (largest node). Dashed boxes correspond to highlighted challenges further described in the main text. c) Network of key terms between European data repositories and emerging data initiatives. Circle size = number of data repositories associated with that key term, range 1 (individual nodes on each database) -6 (central "environment" labelled node). Terms labelled in bold represent areas for increased harmonisation between repositories, other labelled terms are present in at least 3 repositories (representing existing areas of overlap). The full number of key terms in each data repository is reported in the table below the figure. All labelled key terms are among literature keywords.
European chemical policies requiring exposure data To assess linkages between the exposure data found in the literature and data repositories and exposure data requirements in European policies, a set of exposure-related European regulations and directives was screened and themes across these policies and directives were characterized based on associated EuroVoc terms that describe their contents. Policies and directives were identified through searches conducted in the EUR-Lex database (https://eur-lex. europa. eu), by searching in policy documents (title and body text): expos* AND (chemical* OR substance* OR pesticid* OR plant protect* OR pharmaceutic* OR medic* OR cosmetic* OR biocid* OR pollut* OR contamina* OR metal* OR compound*) (see also  Bruinen de Bruin et al. (2022)  for further details).
Strategic objectives for improving data repositories and analytics The European Commission has increasingly included requirements in the Horizon Calls to streamline and integrate data generated by EU funded projects into data repositories that are sustained long-term. Therefore, it is essential that data be published following standard approaches. Our vision is to advance exposure data generation through improved data repositories and analytics, and to enhance exposure data usability and interoperability for increased uptake into European and global chemical policies and research on health and sustainability. However, numerous challenges inhibit cross-utilization between the literature, databases, and data platforms and therefore hinder the uptake of existing exposure-related data into these policies.
Further, European policies/directives use a unique vocabulary decided upon and approved by the inter-institutional GIL EuroVoc Committee. While EuroVoc concepts were initially aligned to the National Library of Medicine Medical Subject Headings (MeSH) controlled vocabulary, this alignment has not been updated since 2017. Therefore, this vocabulary largely differs from that in the rapidly expanding literature and data repositories, which can impede efforts to identify underutilized exposure science data in regulatory contexts. With vocabularies across the literature, data repositories, and policies and regulations harmonised, additional challenges related to harvesting and harmonising data across studies can be more readily addressed. In addition to differences in terms used to describe data repositories themselves, there are numerous differences in data identifiers within resources.
There is an urgent need to establish a common European inventory of chemicals and their transformation products using unique identifiers. Different identifiers are used to describe chemicals (e. g. , different chemical names, CAS (Chemical Abstracts Service) Registry Numbers, International Chemical Identifiers (InChI)) and health outcomes (e. g. , disease ontology ID (DOID), MeSH), which can inhibit cross-utilization across resources, or incorporation of new data into existing data repositories. These differences are also present in the literature with different studies choosing to employ different vocabularies, and different scientific journals requiring different data identifiers for publication (e. g. , chemical IUPAC (International Union of Pure and Applied Chemistry) names or CAS rules).
, 2016). This is also the case for occupational data wherein authorities need data related to exposure sources, use, possible routes of workplace exposure, current and historical exposure levels, and most common risk management measures in place. The lack of such data can impede more comprehensive risk assessment strategies addressing combined exposures scenarios  (PARC, 2020). Data confidentiality can also prevent wide utilization of relevant datasets, such as human biomonitoring data (Zare  Jeddi et al. , 2021). For example, IPCHEM contains human biomonitoring data from the HBM4EU initiative  (Ganzleben et al. , 2017) , but most of these data are either not publicly available (in compliance with the General Data Protection Regulation requirements), or are only available as aggregated data without submitting for ethics board approval.
Enhanced data storage and related infrastructure In the context of the European Exposure Strategy, building a crosssector framework implementing enhanced data storage and related infrastructure to allow for exchange and innovative reuse of data-in a manner compliant with data protection legislation of widely-used data platforms (such as those in Table  1 )-is of the utmost importance. This will upscale and accelerate scientific developments and enable informed and robust exposure and risk assessments in support of EU evidencedriven policy making. To meet the needs of exposure-related data users, there needs to be a balanced combination of centralized data hubs for broadly usable exposure-related data and decentralized databases that focus on specific exposure aspects and/or meet specific application context requirements.
The legislative framework for the governance of common European Data Spaces and related opportunities specified in the European Data Strategy (European Commission, 2020a) and European Open Science Cloud (EOSC) (European Commission, 2018b) provide opportunities for operationalizing collaborative frameworks and enhanced storage and infrastructure. EOSC, in line with the principle 'as open as possible, as closed as necessary', will facilitate discovery, sharing of, access to, and use/reuse of data and services by researchers. This represents one approach to streamline exposure-related data for research and policyoriented purposes. Further, to make data available for better policy making, the European Commission has established the EU Open Data Portal, which makes the data produced and/or funded by the European Commission publicly available via a common gateway (https://data.
Increased automation in data management By building systematic, automated approaches to identify, extract, and integrate new data immediately upon publication, exposure data repositories can stay updated without requiring manual curation  (Aurisano and Fantke, 2022). This is essential to keep up with the pace of exposure data and ensure consistent data quality and constant policy relevance. To complement European goals for the Digital Decade and build a European approach to artificial intelligence (AI), the European Commission developed an AI strategy  (European Commission, 2018a). This strategy aims to streamline AI research and proposes policy options for AI regulation to enable development and uptake of AI in the EU Digital Europe Programme (European Commission, 2021b). AI can help screen available information from different sources on chemical properties, exposure, effects, production information, and use.
From set criteria for identifying the most critical chemicals (e. g. , that pose the highest risk), AI can then learn and build indicators to flag priority chemicals. AI can also potentially help with complex evaluations, such as to determine possible effects of combined exposure to chemicals and simulate exposure to chemicals for a multitude of possible exposure scenarios across different sectors and pathways (e. g. , food, consumer goods, occupational exposures), and for different populations  (Wittwehr et al. , 2020). Data structure, interoperability, and quality are essential to data value, especially in the context of AI deployment  (Emara et al. , 2022; European Commission, 2020a). For example, different databases and software applications like IPCHEM, NDS, and the International Uniform Chemical Information Database (IUCLID), include monitoring data, physicochemical properties, and (eco)toxicity data that could be relevant for decision makers and researchers.
Increased data integration In order to incorporate available exposure data from the literature and data repositories into European regulations and other decisionmaking frameworks, there needs to be enhanced data integration initiatives. Different repositories cover different data streams, including human/environmental monitoring, physicochemical properties, and chemical hazard data. By progressively linking and integrating fragmented chemical data collection efforts, one of the critical requirements for enhanced cooperation among existing regulatory frameworks will be fulfilled, as advocated by the 1S1A approach. In the long run this approach envisages increased data management efficiency, more consistent exposure and risk assessments, and improved robustness in assessments on common datasets of hazard and use and exposure assessment tools and methodologies  (CEFIC, 2021).
g. , EuroVoc descriptors include "atmospheric pollution", "prevention of pollution", and "plant health product" while NDS contains terms "atmosphere", "pollutant", and "plant protection products"), but no direct matches were formed. Similarly, IPCHEM has been acknowledged as the official reporting platform for monitoring data under Regulation (EU) 2019/1021 on persistent organic pollutants, yet there were no direct matches between generalized EuroVoc descriptors (e. g. , "pollution control measures", "waste management") and more specific IPCHEM repository terms (e. g. , IPCHEM lists specific monitoring locations and specific waste types). This is an identified area for improving interoperability of IPCHEM where one of the current objectives is to enhance data harmonisation for increased matching possibilities with various vocabularies when reporting chemical occurrences.
Innovative tools for improved data retrieval, handling, and analysis Development of systems and tools should be tailored to meet the requirements of different EU policies and regulations while facilitating broad application across the policy landscape. As part of the European strategy for exposure data, data-driven applications are promoted to increase the availability of and trust in data for policy and decision support  (European Commission, 2020a). With increasing data volume, improved data analytics and processing is necessary to leverage this potential so new techniques can be efficiently incorporated and accepted into regulations as they are developed. A discussion of specific efforts from different agencies, repositories, and emerging data-generation initiatives (e. g. , EFSA, IPCHEM, European Human Exposome Network) is given in SI Section S1.
Building interoperable data platforms requires innovative tools for data retrieval, handling, and integration, since data from different sources can be developed with varied methodologies, be of different quality, and be structured in difficult-to-use/join formats. Standardized templates for data collection and tools for data harmonisation and quality control are employed by different repositories to address these challenges and integrate diverse data sources. Additional tools can help Fig. 2. Data repository key term matches with EuroVoc descriptors and corresponding policies and directives. a) Data repository terms that are exact-word matches with EuroVoc descriptors (labelled "Database term/EuroVoc term") are matched to policies. Using only direct term matches results in limited policy/directive matches per term.
b) Example of possible expanded matches between data repositories and regulations. Multiple data repository terms (labelled "Database term") are harmonised with a "EuroVoc term", expanding possible applications for exposure-related data in 32 policies/directives. Note: all given database terms/EuroVoc terms match keywords identified in the exposure-related literature. users sift through data, such as machine learning-based tools to scour the literature (van de  Schoot et al. , 2021) , and analyse data, such as privacypreserving tools to view individual-level data (e. g. , DataSHIELD, https://www. datashield. org). There are also EU-wide initiatives to improve development and uptake of AI to advance data retrieval and handling  (European Commission, 2018a). In addition to tools for data retrieval and integration, novel methods to analyse the wealth of diverse data are necessary to increase data use and knowledge generation.
This platform will be achieved by building on existing consolidated and widely used tools and platforms (such as OHTs, IUCLID, QSAR (quantitative structure-activity relationship) Toolbox, IPCHEM, exposure models, and the Repository of Health Based Limit Values) and by streamlining chemical data flows and assessments managed by ECHA, EFSA, and EEA. This platform will aid cross-utilization and development of different tools and resources including common controlled vocabularies and chemical identifiers to support various end users (authorities, industry, academia, public) and prioritised use cases. It will also be maintained long-term through support by a sustainable EU IT infrastructure, an integral feature of the EU Green Deal Data Space (European Commission, 2022b).
3 , Action Item 1). For example, the monitoring data of the European Commission's Platform for Chemical Monitoring Data (IPCHEM) can support all steps in the regulatory cycle, including priorities for action, scientific evaluation, assessment of feasibility and impacts of a range of policy options, and subsequent evaluation of the impact and effectiveness of adopted polices/legislation. In a 2020 Workshop organized by the European Commission's Joint Research Centre, five use cases related to EU chemicals, environment, and health policy-related questions that IPCHEM data can contribute to when integrated with additional data were discussed  (Franco et al. , 2020). IPCHEM monitoring data have been used alone to support policies and the scientific community in Europe and globally  (Kephalopoulos et al.
, 2019). However, combining IPCHEM monitoring data with other data types (e. g. , toxicity data) to inform the aforementioned use cases revealed a number of challenges and limitations. These limitations differ on a case by case basis, including low availability of relevant data covering the required timeframes, exposure media, and geographic areas, lack of individual-level human biomonitoring and occupational exposure data owing to legal and ethical barriers for data sharing, discontinued monitoring efforts, and biases introduced by variability in substances monitored, sampling design, and analytical methods. Therefore, to advance the European Exposure Science Strategy, identification of data gaps in exposurerelated repositories will be considered with respect to the specific objectives and questions to be addressed by chemical, environmental, and health policies and regulations.