dataesr/scientific-paragraphs-categorization huggingface dataset:
Metadata Databases Questionnaire to Build Community-Driven Consensus on Metadata for Nanosafety The transformation of nanosafety research into a data intensive field and the increased regulatory requirements for nanosafety, has led to a community-wide consensus on the need for data interoperability. Despite the strong community interest and the creation of a number of databases to host nanosafety-related data and dedicated nanoinformatics and e-infrastructure projects to create data platforms, nanosafety data remains fragmented and disparate and is lacking the necessary degree of FAIRness, especially interoperability. As a result, the return on investment (RoI) on publicly funded research is suboptimal, restricting full data exploitation and indeed data driven innovation  [70, 71]. Many argue that in terms of metadata schemas and/or standards, nanosafety research lags behind other scientific fields such as life sciences and medical research.
• Project databases: NanoCommons Knowledge base, ACEnano, IOM (representing a number of FP7 and H2020 projects including MARINA and PATROLS); • Institutional or "Centre" databases: Nanomaterial-Biological Interactions (NBI) Knowledge base, Safe and Sustainable Nanotechnology (S 2 NANO), RIVM-ECOTOX and the Center for the Environmental Interaction of Nanomaterials NanoInformatics Knowledge Commons (CEINT-NIKC). Most of the databases are project-or institute-specific, meaning that the data submission system is only for internal users, with the exception of the NBI, NanoCommons and ACEnano databases. Additionally, for most databases the uploaded data is only made public after a long embargo period decreasing data FAIRness. In the results section, responses to the questionnaire are grouped by topics and specific good-practise examples are presented.
Case Studies to Demonstrate the Value and Relevance of Metadata in Nanosafety Data Harmonisation The (meta)data gathered by data creators are mainly defined by specific scientific questions. While the captured data can be applicable to various nano-related fields, the metadata and their level of detail may vary depending on the specific research question. The introduction of nanoinformatics requiring large, high-quality datasets makes it obvious that being able to answer the original question, e. g. , evaluating the NM activity for a specific toxicology endpoint, is by far not the only possible usage. It is not necessary to look at new and unforeseeable applications because existing predictive models for these endpoints require interoperability of data from multiple resources. To achieve maximum exploitation, it is essential the produced data are usable by diverse disciplines in different contexts.
Together this information represents the absolute minimum (meta)data, which need to be provided with the dataset describing a biological assay. However, NPs undergo significant and fast transformations in vitro due to agglomeration promoted by molecular entities within the cell culture media. Thus, more information on the fate of the NP needs to be provided as (meta)data to identify the factors influencing the results of the assay. This will allow the data customer to evaluate the interoperability of the offered data with results from other sources, to more accurately estimate changes in size, density and other physicochemical characteristics caused by the environment. Using a specific case study based on an in silico dose modelling approach, we demonstrate how agglomeration of NPs can significantly change the DD and therefore the outcomes of in vitro experiments  [73, 75, 76].
NMs Dissolution: Achieving Consensus on Terminology and Metadata Usage Achieving greater uniformity in metadata usage, whether pursued within just the multidisciplinary nanosafety community or more broadly to all disciplines through the FAIR initiative will alter everyday practice and the tools, e. g. , LIMS, ELNs, etc. , used. In order to gauge the implications, we surveyed colleagues whose work had involved the measurement or interpretation of dissolution data. Dissolution was chosen as the focal point as it had been proposed as a pilot project in the EU-US Nanoinformatics Roadmap 2030  [40]  and has gained increased regulatory attention. Materials that are highly soluble or dissolve quickly are not biopersistent and the toxicological testing of the dissolution products, not the NM, may suffice.
Data Coverage Most of the nanosafety databases include both experimental and literature curated data. Of the 7, ACEnano and the IOM contain experimental data only. ACEnano has the additional feature that it only reports physicochemical characterisation. It is specifically optimised for new, emerging methodologies without standard operating procedures (SOPs), as well as interlaboratory comparison experiments (round-robins). Both ACEnano and IOM demand detailed coverage of the protocols used to allow comparison of the produced results (see below). The NBI and S 2 Nano databases focus on the biological activity and toxic potential of NM, while RIVM-ECOTOX and CEINT-NIKC cover ecotoxicology data. IOM contains data spanning a wide spectrum of nanosafety research, as it is populated with data from various EU funded projects, like MARINA  [81] , SUN  [82] , NanoSolutions  [83] , PATROLS  [84] , Gracious  [85] , and more.
Similarly, the NanoCommons Knowledge Base is built to accommodate a wide range of nano-related data and is currently hosting data from NanoMILE  [86]  (bio-nano interactions, physicochemical characterisation, toxicity and ecotoxicity), NanoFASE  [87]  (environmental fate, exposure and hazard), NanoFARM  [88] , SmartNanoTox  [89] , and other projects, as well as computational and literature data. IOM is also on course to start accommodating simulation data, through its partnership with the NanoInformaTIX project, while the NBI, NanoCommons and S 2 Nano, which already accommodate such data. Computational and simulation data are becoming more and more prominent, due to the continuous evolution and increase of nanoinformatics, as expressed through the EU-US NanoInformatics Roadmap 2030  [40]. This results in an increased demand for data management and sharing from such projects.
Metadata and Data Templates The data contained within the reviewed databases is curated under specific guidelines in 4 of the cases, although some forms of templates are used in most cases (mainly ISA-TAB-Nano based templates  [31] , see below). However, the different approaches taken show that there is still a large uncertainty regarding the best way to present metadata with respect to its content/completeness. For example, data templates created by one project are adopted by other projects only to a very limited extent and their use almost always requires adaptations and customisations. Often such templates are focusing on technical details such as encoding language (HTML or XML) or the file format (CSV vs. JSON vs. MS Excel), as well as its associated schema (defined below), which might easily become outdated.
The assumption is that data are transferred from the creator to the data manager and finally to the data customer using the same format. Instead, the current field-specific nature of metadata combined with the need for data to be interoperable should lead to the establishment of metadata requirements aiming to facilitate cross-field dataset understanding for both humans and machines  [90]. In terms of reporting, metadata requirements are considered essential in the majority of the databases (ACEnano, NanoCommons, IOM, CEINT-NIKC). These databases include and/or request all types of relevant metadata (bibliographical, descriptive, technical, as per Figure  2 ). The rest consider metadata desirable and include either bibliographical and technical (RIVM-ECOTOX) or descriptive (NBI) metadata. To further guide the implementation of metadata, a short summary of template development in general followed by nano-specific definitions is provided.
For reporting scientific research, schema focusing on publishing data in journals and databases often lack the ability to accurately define the meaning of a specific metadata or data element  [54]. The field specificity of metadata and the need for interoperability, has led to the establishment of metadata requirements to be addressed during data collection to facilitate a common understanding of the meaning and representation of the data and make them both human and machine readable  [48]. Metadata requirements can be either generic (e. g. , Dublin Core  [91] , DOI [92]) or field specific (e. g. ,  ISO 19, 115  for geographic data and Darwin Core for biodiversity informatics  [93] ). The latter requires community consensus and the development of specific guidelines on the types of metadata to be captured and how to store it, along with clearly defined terms and is called a metadata schema.
New Challenges Arising from Nanoinformatics The issue of the data format required for computational workflows, and its differentiation from currently used formats, has been stressed in the EU-US NanoInformatics Roadmap 2030  [40]  and is now beginning to be addressed in conjunction with the development of computational specific templates. Examples include nano-specific computational templates based on the Minimum Information About a Simulation Experiment (MIASE)  [104]  guidelines, the Modelling Data (MODA) templates  [105]  and nanoQSAR reporting formats, being expanded by NanoCommons, SmartNanoTox and NanoSolveIT. Furthermore, the NanoSolveIT project is using the Enalos Cloud Platform to develop a dedicated database for computational ready-to-use data. These attempts take into account the increasing number of computational descriptors at various levels and the specific needs for the reporting of in silico workflows.
Several studies have used Simplified Molecular-Input Line-Entry System (SMILES) and quasi-SMILES approaches for model development  [110] [111] [112] [113] 115, 117, 118] , or enriched the dataset with atomistic computation and molecular descriptors to help decode the cytotoxicity mechanism of metal oxide NMs  [116]. To accommodate this model versatility and assist with reproducibility, the OECD recommends that QSAR models are accompanied by a QMRF report (QSAR Model Reporting Format)  [109] , a harmonised template for reporting the key features of QSAR models, including validation information, structured according to the OECD principles for the validation of QSAR models  [25]. The OECD principles, embedded in the QMRF report, require the modellers to provide the necessary information so that the modelling workflow is described using in a reliable and Another important modelling approach is the highly versatile nanoQSAR modelling  [109].
Such models have wide applicability, and have been used, among others, to predict the cytotoxicity  [110] [111] [112] [113] [114] [115] [116]  of NMs in human and animal cell lines and the thermal conductivity of nanofluids  [117, 118]. Besides QSAR  [110, 111, 113, 115, 116] , QFAR (quantitative feature activity relationship)  [112]  and QSTR (Quantitative structure toxicity relationship)  [114]  modelling have also been employed. Several studies have used Simplified Molecular-Input Line-Entry System (SMILES) and quasi-SMILES approaches for model development  [110] [111] [112] [113] 115, 117, 118] , or enriched the dataset with atomistic computation and molecular descriptors to help decode the cytotoxicity mechanism of metal oxide NMs  [116]. To accommodate this model versatility and assist with reproducibility, the OECD recommends that QSAR models are accompanied by a QMRF report (QSAR Model Reporting Format)  [109] , a harmonised template for reporting the key features of QSAR models, including validation information, structured according to the OECD principles for the validation of QSAR models  [25].
From the responses submitted to our metadata questionnaire, the NanoCommons, NBI, CEINT-NIKC and IOM databases consider images to be a type of metadata. Based on the IOM response, besides information on where and when they were captured, images can contain information about the image itself in image-file-inherent metadata (if preserved by the technology/workflow). Such information can assist with image handling during their processing and analysis for the development of deep learning models. Additionally, the file format selected should be open in order to promote accessibility and communication of results amongst users of different instruments or software; a tool that helps advance this is Bio-Formats  [123] , a standardised library that enables reading from many life sciences proprietary file formats and outputs to open ones, while transferring the image acquisition metadata into a common open data model.
g. , assay type) to study their significance and correlation with observed toxicity. A meta-analysis by  Wilhelm et al. (2016)  on the efficiency of delivery of organic and inorganic NM to tumour tissues, used bibliographical (e. g. , document type, keywords, language) and descriptive metadata (e. g. , experimental replicates) to limit the studies used (from over 40,000) to 117 of the "highest quality"  [126]. No further metadata usage is reported for the analysis, but the favourable results led to the development of the Cancer Nanomedicine Repository (CNR), a database for the submission of relevant data  [131]. CNR is an open database where experts can submit their datasets, but no specific metadata are requested with the exception of the DOI linked to the published research paper and the method used to quantify the concentration of the NM in the tumour.
Gernand and Casman (2014) performed a meta-analysis on a collection of 136 types of CNTs from 17 publications and used regression trees to correlate the pulmonary toxicity of CNTs based on 41 controlling factors (including intrinsic and extrinsic independent variables) including impurities, CNT geometry/dimensions and exposure characteristics  [125]. They acknowledged that the lack of standardised characterisation protocols and reporting was a limiting factor in the model success and accuracy. To overcome these limitations, the dataset was expanded to include both NMs properties and experimental conditions, which were treated as independent variables to assist with data interoperability and analysis. The exposure mode was found, for the specific experimental conditions, to have minimal effect (0.
001-0. 5%) on the overall dataset variance reduction. On the other hand, a number of studies  [116, 124, 127, 128, 130]  found a significant correlation between the cytotoxicity assay type and cell viability, while one  [129]  found a significant correlation between the NM dispersion protocols and cytotoxicity. All stressed the significance of protocol standardisation to reduce inconsistencies between studies, with Labouta et al. (  2019 ) limiting the studies used for analysis to those involving commonly used cell viability/cytotoxicity assays in order to overcome the challenge of integrating heterogeneous cytotoxicity data  [130]. Another issue that arises when combining datasets from different studies is data gaps and how to fill them. Bilal et al. (2019)  commented on the complexity of data originating from different experimental platforms/settings and the need for advanced data mining tools and extraction techniques to acquire as much information as possible from integrative analysis of the body of evidence  [124]  as demonstrated graphically in Figure  7.
Combining data without paying attention to the production source may lead to increased uncertainty, as data may have been produced using various experimental or computational workflows. Similarly, Ha et al. (  2018 ) demonstrated the benefits of data gap filling using techniques like supplier specifications, references using the same NM or computational estimation from other physicochemical properties in meta-analysis and provided a grading system that evaluated the dataset quality based on each parameter's metadata (i. e. , origin of measurement/calculation)  [128]. Nanomaterials 2020, 10, x 26 of 50 analysis to those involving commonly used cell viability/cytotoxicity assays in order to overcome the challenge of integrating heterogeneous cytotoxicity data  [130]. Another issue that arises when combining datasets from different studies is data gaps and how to fill them.
Bilal et al. (2019)  commented on the complexity of data originating from different experimental platforms/settings and the need for advanced data mining tools and extraction techniques to acquire as much information as possible from integrative analysis of the body of evidence  [124]  as demonstrated graphically in Figure  7. Combining data without paying attention to the production source may lead to increased uncertainty, as data may have been produced using various experimental or computational workflows. Similarly, Ha et al. (  2018 ) demonstrated the benefits of data gap filling using techniques like supplier specifications, references using the same NM or computational estimation from other physicochemical properties in meta-analysis and provided a grading system that evaluated the dataset quality based on each parameter's metadata (i.
Data Management, FAIRification and Reusability One of the key aspects of databases is, or rather should be, the establishment of data management processes that allow further exploitation of the hosted data. Any exploitation outside the original data scope and ownership should add value to the data itself, and for the data creator/owner through proper attribution/citation of the reused work and the database hosting the data. Implementation of a clear ownership and licensing system, in combination with the assignment of unique identifiers (e. g. , database specific, DOI) is central to achieving this, and is best achieved through use of data management plans (DMP). Of the database responses to our metadata questionnaire, three of the EU-based databases, namely the ACEnano, NanoCommons, and IOM DMPs, in the case of IOM are project specific.
In most cases, these are confidential and available only to consortia members, with the exception of NanoCommons which has published and is periodically updating its DMP  [132]  as part of its efforts to enhance data management and re-usability across the nanosafety community. All databases that responded, provide fully or partly open access data. The latter refers to embargoed data that is available only to data owners and/or relevant partners or following individual agreements for a fixed period. More data are becoming publicly available as the embargo period ends or as relevant datasets are being published. In terms of referencing/attribution of the existing data, the NanoCommons, ACEnano and IOM databases offer either an internal unique identifier and/or DOI assignment. These databases, including CEINT-NIKC and S 2 Nano, offer a licensing system for all data they host, especially if publicly available, and ownership remains with the data creators/providers.
In this way, the appropriate attribution is facilitated, which is expected or requested in all databases. As commented by the IOM submitters, there is the belief that specific processes to monitor data reuse are not sufficiently, if at all, implemented in current databases, either procedurally or technically. This is further enhanced by the question of long-term sustainability of these databases and who will ensure and enforce proper reuse, taking into account the current Open Data requirements of the European Commission. In terms of FAIR metrics and FAIRification, we quickly present the current status from the reviewed databases, specifically on the technical FAIR principles according to the classification above. The NanoCommons, IOM and NBI databases claim to provide FAIR data, although this is mainly true for newly curated data in the case of the IOM database.
Based on the questionnaire responses, the IOM, ACEnano, NanoCommons, and NIKC link to specific protocols used to produce the data to the dataset. However, ACEnano is going one step beyond the other databases by making the protocols a central step of the data upload process and integrating information extracted from the protocols directly as metadata. The starting point of this unique approach is the ACEnano protocol module, using highly structured questionnaires to document all experimental steps and specific settings in an annotated and computer-readable way. Three different types of protocols are supported: sample preparation, measurement and data analysis. For example, the user provides detailed information on the storage, dilution, heating or sonication processes during the sample preparation and details on the equipment starting from type and producer of complex machinery down to pipets in the measurement protocols to document even small variations performed for specific samples.
All this information is annotated with ontology terms supported by an ontology lookup service  [134]. For the data upload, defined workflows are provided in the data module, in which information on the data provider and the specific NM sample under investigation is collected, then one of each kind of protocols is linked and finally the datafiles are uploaded. The data annotated with the metadata from the protocols and the additional information provided within the data upload workflow can then be searched, browsed and accessed via the ACEnano warehouse user interface or via application programming interfaces (APIs) for their direct integration into downstream analysis and modelling workflows. A similar approach has been established by the CEINT-NIKC database and the respective data templates.
NIKC has linked all data submitted to the database to both the methods and instruments used to produce the data. This approach was initially employed to allow interoperability of complex ecotoxicological experimental data (mesocosm experiments) through the collaboration between CEINT and the NanoFASE and Serenade projects. NanoCommons project adopted this approach and included analytical metadata on the methods and instruments used to all templates for data curation and uploading. Furthermore, NanoCommons in collaboration with NanoFASE expanded the NIKC templates to accommodate automatic semantic annotation of the datasets using ontological terms, which are mapped to those used from the NIKC's nano-dictionary and to the eNM and associated ontologies.
Quality Assurance and Quality Control Quality assurance and control (QA/QC) of data are key for increasing data reusability and providing confidence to data customers and needs to be addressed at the community level. Currently, the nanosafety field is lagging behind life and medical sciences in reporting the metadata needed for quality evaluation, although substantial attempts are currently underway to improve this. Even published datasets, in several cases, do not disclose the full protocols (or algorithms, source codes for simulation data) used, or reference other publications that are difficult/time consuming to access or in some cases irrelevant to the current publication. The issue with the quality of both the data and metadata has been discussed in the previous paper of this series by Marchese et al.
[28]. Currently, most databases do not follow an established quality management system, with only a few using the ISO-9001 for data quality and/or ISO-27001 for data security. This either moves the workload for manual QC of the datasets to the data curators, as is the case with CEINT-NIKC, or if this is not possible it is left to the submitter/uploader to QC the dataset individually without clear guidelines and requirements, which is especially problematic when it comes to metadata that are more difficult to retrieve post-experimental. This is further enhanced by lack of knowledge or incorrect (old-fashioned) mindsets regarding the need for detailed DMPs at the very beginning of projects spanning their entire lifecycle, and the allocation of substantial QA/QC processes for metadata testing and gap filling and the current lack of available tools to facilitate this.