dataesr/scientific-paragraphs-categorization huggingface dataset:
The previous papers addressed the various aspects leading to high quality datasets as described in detail by Hendren et al. [27]  and focussed on nanoscience curation workflows  [26]  and evaluation of the quality and completeness of curated data  [28]. In fact, survey results in  Karcher et al. (2018)  around database integration further outlined the importance of metadata in that integration  [29]. In all cases, the need for (meta)data completeness was stressed as a necessity in order to be able to reconstruct the exact experimental setup and methods for improved reproducibility, for evaluating data quality and its combinatorial and interoperable alignment and relevance with data from different sources. Shortly after the paper by Marchese et al. [28]  was published, the FAIR principles  [33]  were introduced including a set of guidelines for improving the existing infrastructure that supports the reusability of scholarly data.
There are two facets to the FAIR principles, one being those issues associated with the technical requirements that data(base) managers must attend to if users are to be able to recall the datasets, and the other being actions that the scientists, and the subject matter experts, must address if their work is to be considered credible by other users. The first facet overlaps with library science, or more specifically, library and information science, and similar issues are encountered when finding a book in a library, i. e. , call numbers, subject accession numbers, and subject headings. In library science, the curator's role includes acquisition, curation (selection of books and deciding when to discard them), archiving, retrieval and circulation for the purpose of dispersing knowledge. Libraries are experienced in annotating (e.
g. , PubMed's searchable Medical Subject Headings or MeSH) and there is even the concept of precision and recall as a metric for library effectiveness. Precision and recall measure the retrieval of information in terms of recovering all of the books on a subject without returning too many that are not of interest (see Arora et al. [34]  for a nanotechnology database example). These elements certainly contribute to FAIR, but they are more likely to be considered procedural, administrative, and technical relative to the substantive, subject-matter and scientific considerations found in a dataset. We have elected to use the term 'technical' for these purposes, but refer the reader to a series of articles by Dr. Birger Hjørland  [35] [36] [37]  for a fuller overview of Library and Information Sciences as it moves from 'collections to connections.
'  The technical nature of the FAIR principles is meant to guide data(base) managers, in data digitisation and storage processes, down a path that would be resolvable for machines and, through them to database users. Topics like persistent identifiers, indexing in searchable resources, defined vocabulary, communications protocol and licenses are clearly covered in the FAIR principles, while metadata are more loosely defined as they must meet domain-relevant community standards. In the following sections, we will refer to principles describing features, which need to be implemented by the database or search/browsing services, and those associated with library science and which are subject domain-independent, as technical, while features such as subject domain concepts and standards, especially metadata completeness, that need to be defined by the specific community, as scientific.
The only non-technical guidance on the (meta)data completeness and (re)usability is that the metadata need to be sufficiently well-described and rich that they can be automatically (or with minimal human effort) linked or integrated, like-with-like, with other data sources  [33]. Defining what completeness means in a specific community is only the first step since, science is continuously progressing and data becomes more interlinked over the different disciplines and communities, but does not provide guidance on how scientific information should be translated into metadata. Such guidance needs to originate from people with sufficient knowledge of the experimental practices but also with an understanding of data or library science, who essentially liaise between the two disciplines, and shepherd the data creators (experimentalists, computational scientists) through the processes, by raising awareness of their responsibilities as data creators and the need for active participation in the definition and validation of metadata coverage, as discussed later in this paper.
Although the original "technical" FAIR principles are mainly focussed on how data should be shared  [33] , and not on what needs to be reported to make the data reusable with high confidence, the authors were very successful in raising concerns about the amount and variability of shared data. As a result, FAIRness, FAIRification (the process of making data FAIR), and FAIR measures have been introduced into requirements for data management plans of all major funding agencies. On the other hand, this highly technical focus could be used as an argument to push the responsibility to achieve data FAIRness to data curators and database managers, which have to develop the access protocols and search engines, and may been seen to absolve data producers of responsibility. Thus, a gap still exists when it comes to the scientific FAIR principles needed to ensure that high-quality data generation and collection, and metadata processing have sufficient "completeness" to facilitate the FAIRification process.
It would be desirable to create a "dictionary" of terms regularly used and thus to use persistent ontological IDs for the metadata and data produced. SA2: Make sure that the metadata can be accessed from the same resource as the data. If the data warehouse is not flexible enough to provide the scientific metadata through its metadata access functionalities, provide it in a standardised way (either standard file formats like ISA or supplemented by a clear access protocol-see recommendations for further details). SA3: Provide your protocols in FAIR resources, in addition to the materials and methods section of the corresponding paper. Remember that your data might be used in another context and exact descriptions are needed. Linking protocols to data via electronic laboratory notebooks is one approach to achieve this.
SR2: Establish a feedback loop between data creators, analysts and customers to continuously improve the metadata completeness and quality. Keep in mind that scientific progress can lead to new use cases and go beyond "standards" defined at a specific point of time. After defining the necessary scientific FAIR principles, the next step is to define and implement the process of how to undertake the (meta)data collection, curation and transferability between stakeholders, and the storage and sharing of data and metadata to maximise quality and FAIRness. To achieve this, we firstly need to identify the mosaic that constitutes the data ecosystem, establish the different roles comprising it and optimise the potential interoperabilities. The concept of data completeness as outlined by Marchese et al.
[28]  is quite abstract and is strongly linked with specific data usage, i. e. , use of data for regulatory decision making would have much stricter requirements for data completeness than, for example, utilisation of data for modelling where gaps can be input, or end-points with missing data removed from the input dataset. To progress the FAIRification of nanosafety data, there is a need to define metadata reporting standards (in the sense of guidelines for what information needs be reported rather than as standard templates with defined, fixed metadata fields such as the aforementioned ISA-TAB-Nano templates), and to assign clear roles and responsibilities for metadata creation and reporting in order to transform this concept into a practical action plan. These standards are not meant to be set in stone, and indeed activities to do so failed outside the very restrictive and standardised regulatory field.
In other cases, they may be collapsed into a small circle of closely situated actors, limiting the possibility for cross-field communication. Nanosafety research ranges from NM synthesis and characterisation, to exposure, fate, hazard, systems biology etc. , and data sharing and metadata coverage has to work effectively in such a multidisciplinary environment. The lack of clear processes, uncertainties in responsibilities, and missing coordination often leads to a negligence mentality. In such cases, data collection is postponed until the last moment when some of the data producers have already moved on to other positions and a clear succession plan does not exist to assist with continuous and uninterrupted processes and workflow. Additionally, there is often the expectation that database developers and managers need to take care of data storage and sharing in a form directly and easily usable by the researchers, even if it is not clear what this exactly means.
Often, this translates to internal spreadsheet templates that only people within the lab can make sense of, making it really hard to sustain a proper database. Similarly, database managers are expected to provide standard data and metadata templates to guarantee the quality of the data even without the necessary domain-specific scientific knowledge. To overcome this, the establishment of specific and detailed workflows, which include complete data and metadata capture, storage, handling, and analysis linked to the data-related roles 1-5 are necessary. Furthermore, implementation should begin at the experimental design stage, be aligned to existing ontologies and nanosafety schema and linked to distinct tasks and timelines for all of these five data roles (see Figure  2 ). In practice, many co-dependencies between these data roles and responsibilities can be identified (Table  1 ).
As conveyed in Table  1 , the elements in the data lifecycle are consistent with the definition of nanoinformatics as: (1) the science and practice of setting mission objectives and determining which information is relevant to meeting the safety, health, wellbeing, and productivity objectives of the nanotechnology community; (2) developing and implementing effective mechanisms for collecting, validating, storing, sharing, analysing, modelling, and applying the information; (3) confirming that appropriate decisions were made and that desired mission outcomes were achieved as a result of that information; and, finally, (4) conveying experience to the broader community, contributing to generalised knowledge, and updating standards and training  [27, 39, 40]. In contrast to the scenarios described above, where the database system is responsible for defining the metadata and standard templates for reporting them, the heaviest workload and thus the main responsibilities in the data lifecycle pictured here are divided between data creators and analysts.
Data curators and managers subsequently act as the link between them and the data customers. Data curators ensure that the data produced is of sufficient quality and discoverable (annotation) and can effectively answer the objectives set as per the requirements of the data customers. Data managers, on the other hand, ensure data discoverability, accessibility, and where applicable, combination with other datasets, from a technical (database functionality) standpoint. One of the drawbacks in these interdependent relationships is the difficulty in communication and understanding between the different parties. For the relationship and the entire workflow to be successful, correct understanding of the needs and requirements of each role is essential. This is not always straightforward due to differences in terminology definition and mindset in general.
• Technical metadata: information on the data file types, the size of data, dates of creation and/or modification, types of compression and more, mostly related with databases and interoperability. In short, metadata offer the context and all necessary explanatory information to make a dataset understandable, reproducible and interoperable. However, they come with the caveat that metadata can be defined in different ways, even within the same field of study. For example, data consumers may request that legal issues be addressed more specifically, such as trade names, use patterns, and quality statements involving chain-of-custody equipment calibration frequency. During experiments, metadata capturing can be divided into three interrelated operational workflow categories (Figure  4 ). The first includes the metadata created by the data creators (experimentalists, modellers) to design and perform an experimental/modelling workflow.
The instruments' category feeds directly to the data creators' metadata, as it can lead to the refinement of workflows and explain deviations from standardised protocols (e. g. , ISO, OECD). Both feed into the third category of potential metadata, the bibliographic metadata (shown in navy in Figures  1  and 2 ), added by the data curators. Data curators are responsible for checking the existing metadata in terms of quality and completeness, requesting further information when needed for completeness and documenting the quality assessment as additional metadata. Curators are also responsible for enriching the produced datasets with bibliographical metadata (e. g. , experimentalists contact details, affiliations), assist with acquisition of digital object identifiers (DOI), registration and assignment of unique identifiers to NMs used during the experimental process and semantic annotation using defined ontologies to increase interoperability  [58].
Quality and completeness for both the data and metadata will be evaluated either against current standards or most likely against standards/workflows agreed within the project/database they are part of, while, at the same time, extending current standards. Given the continuous evolution of best practices and respective standards, each dataset could include a description of the metadata standards used in accordance with current best practices. This will ensure that the datasets will, in the future, not be flagged of lower quality or lacking necessary metadata. As noted earlier, experimentalists may themselves act as curators. This creates certain issues when it comes to data completeness, reliability and acceptability relative to scientific disciplines. Those in the physical sciences will likely be less aware of specialised requirements of some data consumers, such as regulatory agencies and the state-of-the-art in some fields (e.
g. , thermodynamics) may not be as expansive in terms of metadata as the biological sciences. Even within the biological sciences, data users/consumers will differ in terms of their data completeness requirements with some looking to peer review for that purpose, while others will be alert to the additional regulatory metadata requirements  [59] , such as the Klimisch score  [60]  when following OECD Test Guidelines for the Testing of Chemicals  [61]  and good laboratory practice (GLP)  [62]. This results in certain databases (e. g. , DaNa 2. 0 ) not incorporating data submissions that do not meet these requirements or in constraining the range of testing considered pertinent to decision making relative to recent advances in the field or acceptable instrumentation or experimental techniques.
On the other hand, under the FAIR principles  [33]  metadata completeness translates into the experiment being reproducible. The metadata in this case can be used to make comparisons, uncover patterns, and develop hypotheses even if they are not regulatory compliant or are mechanistically focussed and thus have broader scope than the purely toxicological studies captured in DaNa 2. 0. Some consensus also exists, within the nanosafety community, that all possible metadata need to be reported for a dataset to be complete, so that data consumers can then decide which metadata, and thus data, are important for them to rely on  [63]. This approach of capturing the widest set of data and metadata, offers higher freedom on how to re-use the data and is more supportive of FAIR and data-driven innovation, but puts a large burden on data providers.
Curators are also responsible for enriching the produced datasets with bibliographical metadata (e. g. , experimentalists contact details, affiliations), assist with acquisition of digital object identifiers (DOI), registration and assignment of unique identifiers to NMs used during the experimental process and semantic annotation using defined ontologies to increase interoperability  [58]. Quality and completeness for both the data and metadata will be evaluated either against current standards or most likely against standards/workflows agreed within the project/database they are part of, while, at the same time, extending current standards. Given the continuous evolution of best practices and respective standards, each dataset could include a description of the metadata standards used in accordance with current best practices.
This will ensure that the datasets will, in the future, not be flagged of lower quality or lacking necessary metadata. As noted earlier, experimentalists may themselves act as curators. This creates certain issues when it comes to data completeness, reliability and acceptability relative to scientific disciplines. Those in the physical sciences will likely be less aware of specialised requirements of some data consumers, such as regulatory agencies and the state-of-the-art in some fields (e. g. , thermodynamics) may not be as expansive in terms of metadata as the biological sciences. Even within the biological sciences, data users/consumers will differ in terms of their data completeness requirements with some looking to peer review for that purpose, while others will be alert to the additional regulatory metadata requirements  [59] , such as the Klimisch score  [60]  when following OECD Test Guidelines Another barrier to developing a generic metadata schema, which could for example be applied to the entirety of materials science, is terminology use.
g. , academia), and small research institutes and/or groups may struggle to take part in developing or implementing such practices  [64, 65]. Some of these barriers could be overcome by simplifying and incorporating such practices early in the experimental design phase supported by software collecting metadata automatically and preparing them in a FAIR way. Automatically created log files from experimental equipment or from modelling and simulation software are one source of automatic metadata creation. Another solution is through the modification of laboratory information management systems (LIMS) and electronic lab notebooks (ELNs) to provide functionality for metadata collection and preparation for upload to data warehouses based on the information the user has provided as described in further detail in Section 1.
3. To overcome these barriers, achieve the desired interoperability, and increase FAIRness, mapping, and communication between the different metadata, schema are needed. This can be achieved using taxonomies or ontologies, which are hierarchically structured domain specific dictionaries  [66]  that may (ontologies) or may not (taxonomies) contain relationships between the terms at various levels, to supplement the metadata. The terms included in taxonomies/ontologies have specific definitions and are assigned with unique IDs. In this way, it is possible to semantically annotate the metadata with specific terms, which become machine findable and interoperable with similar datasets, and supports indexing of data. To achieve this integration, it is necessary for the different datasets to be annotated using the same or linked (mapped) ontologies to ensure that the various terms are defined in the same way.
This requires a complex ontological system, encompassing terms that can describe all parts of a dataset and its accompanying metadata, including the data itself, the units, methods, protocols, statistics, instruments, software used, etc. The most used nano-specific ontologies are the NanoParticle Ontology (NPO)  [67]  and the eNanoMapper (eNM)  [68]  ontology. The latter covers various aspects of the biological effects of NMs and is based on full use of terms and branches from other ontologies (e. g. , NPO, Ontology of Adverse Events (OAE), Experimental Factor Ontology (EFO), Ontology for Biomedical Investigations (OBI), Chemical Information Ontology (CHEMINF), BioAssay Ontology (BAO)), although losing their inherent hierarchical arrangements, and limiting addition of new terms to those not available in other ontologies.
Thus, the issue of metadata harmonisation partly shifted to harmonisation of ontologies. An increasing number of nanosafety projects (e. g. , NanoFASE, SmartNanoTox, ASINA) are including an ontology task (supported by NanoCommons Transnational Access) to allow their partners to annotate datasets and in this way increase their interoperability. One successful attempt is the environmentally focussed eNanoGrammar developed by CEINT for the NIKC database. This nano-dictionary was initially loosely based on the eNM ontology, and was created to fit the nature of the data it is trying to accommodate. It includes more terms, specific definitions, and a different relational structure to eNM (which as noted above loses hierarchical structure when importing terms from other ontologies) suitable for capturing the environmental fate, exposure, and effects of NM.
ACEnano is continuously collecting terminology used as metadata for physicochemical characterisation data, checks for their availability in existing ontologies or prepares them (including clear, unambiguous definitions) for integration in the most relevant ontology. Such focused activities are good ways to increase coverage and test the ontology structure since they directly target the needs of specific users and might result in separate ontologies targeting specific domains of nanosafety research, e. g. , instrument ontologies, methods ontologies etc. However, it is absolutely essential that the developments are synchronised and aligned to avoid incompatibilities and duplication of work. Generalising, one specific ontology to cover all involved aspects might be a way to guarantee this and would take into account that linked databases should be agnostic, with the ontology and its structure being independent from the database-internal data model.
Using the examples above, the eNanoGrammar is a dictionary for a specific domain, which could be extended to an ontology inheriting terms from eNM as an upstream, more general ontology. In contrast, ACEnano decided to fit the additional terms into the eNM structure and to request addition of the terms either via NanoCommons extensions of eNM or in one of its downstream ontologies if the terms are more general and useful in other areas. Whatever approach is finally adopted, it will require commitment from the full nanosafety community, along with intensive training to demonstrate the benefits with respect to data FAIRness. Having introduced the key role of metadata to support interoperability and reuse of nanosafety data, the rest of this paper explores the current state of the art in the implementation of metadata in currently operational nanosafety databases and develops two case studies to demonstrate the value of metadata in facilitating data reuse and sharing across communities.