dataesr/scientific-paragraphs-categorization huggingface dataset:
Users can compare experimental and theoretical data for one process with a graphic output of the database. Open ADAS [693] is also this type of database for datasets relevant to spectroscopic diagnostics of fusion and solar plasmas. Various theoretical datasets are stored for fundamental data, such as a set of energy levels and electron impact excitation effective collision strengths. Derived data calculated with the ADAS software package are also available, such as photo emissivity coefficients for emission line intensities of an atomic ion. Databases that provide one set of calculated data for one process are also categorized as this second type, such as opacity databases  [694] ,  [695]. There are some attempts to access various AM databases from one website. LXCat, the Plasma Data Exchange Project  [696] , is the project to collect AM data from various databases for low-temperature plasmas and provide them to users from one website.
Databases on electron scattering cross sections, differential scattering cross sections, swarm transport data, and online BE solvers are available. VAMDC  [697]  is also the project to access various databases from one website and provide data with the same XML format. Currently, 46 databases on spectral lines, opacities, and collision cross sections of atoms and molecules are connected to VAMDC, including NIST ASD, CHIANTI, and NIFS databases. The XML schema, XSAMS, was developed under the collaboration coordinated by the IAEA Atomic and Molecular Datat Unit (see Fig. 43 ). Current AM numerical databases have been developed and maintained to be available for communities with big efforts by researchers on atomic physics and various plasma physics for many years. Databases on such fundamental data are useful for various applications.
C. Materials Database In most industrial plasmas, as well as in fusion plasmas, the plasma is "contained," and therefore, plasma surface interactions are important in determining the operation and characterization of the plasma. Many plasmas are specifically designed to interact with surfaces; for example, atmospheric plasmas are being used to sterilize surfaces in medicine  [698] ,  [699] , which requires understanding both of the "sterilizing agents" in the plasma (ions, UV photons, and radicals) and the properties of the surfaces. Indeed, medical applications are a good example of the myriad of materials with which plasma may interact-metals, plastics, ceramics, and glass. Plasma treatment is recognized as a valuable method for treating surfaces and may be scaled up for large-scale manufacturing, for example, introducing hydrophobic properties in materials  [700].
Plasmas may "activate" processes on surfaces or even activate drugs  [701] ,  [702]. Plasma waste remediation and waste treatment  [703]  require detailed knowledge of plasma surface interactions including with (and in) liquids and may be used even for radioactive waste  [704]. However, to date, there are no databases that focus on plasma interactions with such materials, and there have been few studies to explore in detail the physicochemical changes induced by plasmas across such a range of materials. Rather, publications are scattered and often present a limited dataset for one plasma and one material, making cross-comparison difficult. In contrast, the fusion community has developed a detailed materials database since the materials used in plasma confinement chambers and the plasma-wall interactions are pivotal to the operation and sustaining of a fusion plasma.
Accordingly, the fusion plasma community has developed and maintained databases that detail and analyze the properties of relevant materials and their critical parameters for fusion environments. In Europe, this work has been performed under the EUROfusion program with the data recorded in the EUROfusion database and handbook  [705]. The database has established protocols to obtain the raw data and introduce screening procedures and data storage to ensure quality and thence acceptance (and adoption) by the international community. Similarly, the IAEA has compiled data and published reviews for many years often resulting from IAEA CRPs, for example, the recent CRP on plasma-wall interaction for irradiated tungsten and tungsten alloys in fusion devices  [706]. Such reviews are commonly published in the IAEA's journal series Atomic and Plasma-Material Interaction Data for Fusion (APID) with 18 volumes from 1991 to 2019  [707].
Unfortunately, not all these data are yet available online, but IAEA has a large repository of databases: https://amdis. iaea. org/databases/. Newer resources for nuclear fusion energy research hosted by the IAEA focus on atomistic modeling of candidate materials for fusion reactors: MD simulations of collision cascades  (CascadesDB [708] ) and DFT simulations of radiation-induced defect structures (DefectDB [709]). These have been developed and are maintained with the active support of the fusion materials modeling community and, in the case of CascadesDB, provide powerful visualization and data exploration tools [710] and allow downloads in multiple data formats (XML, JSON, and plain text). A further database, under development, HCDB [711], hosts a heterogeneous collection of data in a hierarchical format, combining the structure of a relational database while providing some of the schemaless flexibility of NoSQL database technologies.
For example, experimental results from a round-robin comparison exercise on deuterium retention in standardized steel samples may be stored in the same database as literature values for hydrogen diffusion coefficients in different materials without the need to construct new databases for each of these applications. The IAEA's Atomic and Molecular Bibliographic Data System, AMBDAS, [712] includes data on surface processes including chemical reactions, desorption, reflection, secondary electron emission, sputtering, trapping (and detrapping), and AM processes on the surface, such as neutralization, ionization, and dissociation. The ALADDIN database [713] has both AM and particle-surface data, and together, these two online databases provide the most detailed and accessible materials data albeit with a focus on the fusion community and the materials used in fusion reactors.
D. Plasma Chemistry Databases Plasmas are strong sources of chemistry both in their treatment of surfaces and the (often complex) chemistry within the plasma leading to the creation of reactive species that, in turn, provide the main resource for the action of the plasma. It is, therefore, important that the chemistry of the plasma is understood if the plasma properties are to be characterized and, through this, natural plasma phenomena, such as aurorae unraveled. In the development of industrial plasmas, such chemistry should be both derived and modeled if the plasma's functionality is to be tuned and optimized for plasma usage. Thus, the assembly of plasma chemical databases is an important part of future plasma development. Tennyson et al. [714] ,  [715]  defined three criteria for developing a chemistry-inclusive plasma model: 1) the chemistry should be complete, that is, it contains all the important reactions for the given plasma; 2) it should be consistent, that is, the reactions should not be unbalanced, thus resulting in the plasma composition being driven away from the true composition; and 3) the plasma chemistry should be correct.
With these 15 species, more than 100 "reactions" may be necessary to define the inherent plasma chemistry, most of which have never been measured. For even the simplest plasmas, as those used in etching, the number of reactions taking place may be more than a thousand making it unfeasible to make a "complete" model. It is, therefore, necessary to determine the "critical" or most important reactions to characterize and describe the physical and chemical properties of the plasma. However, since several important reactions remain completely uncharacterized (e. g. , those involving molecular radicals), it is possible that models will neglect key processes due to the unavailability of such data. This lack of data is, therefore, a challenge in meeting the second criterion that the dataset should be "consistent" since some reaction pathways may be indeterminate or even unknown such that production and destruction routes for important reactants may not be complete.
While spectroscopy may be used to identify species, deriving number densities by spectroscopic measurements is difficult since excited species are populated both by direct excitation and by "cascade" as higher excited atomic/molecular states decay into the lower state, and such cascade cross sections are largely unknown. Such cascade processes are responsible for more than 80% of the formation of metastable species in many plasmas. Despite these challenges and limitations, plasma chemistry databases have been assembled for different research fields. One of the most complete is the KIDA database  [717] , a database for astrochemical (interstellar medium and planetary atmospheres) studies that contain over 700 species and up to 10 000 reactions tuned to the low-temperature environment of space.
The data have been assembled into several "networks" for specific conditions (e. g. , distinctive planetary atmospheres): https://kida. astrochem-tools. org/networks. html. This database provides references to all included reactions while commenting on their validity (making corrections where necessary) and where there are several alternative values that may make recommendations as to the values to use. The QDB  [714]  is a commercial database that contains chemistry data for industrial plasma modeling from preassembled and validated chemistry sets, allowing users to assemble their own unique database for their specific plasma. It has about 50 preassembled datasets used in common plasma etching processes incorporating electron, heavy particle, photon collision cross sections, and AM species reaction rates.
It also hosts some data for surface processes split into two categories: data for plasma simulations, such as sticking coefficients for atomic oxygen, atomic fluorine, fluorocarbons, and silane radicals; and data for surface mechanisms, such as specific etches, where it provides a set of individual reactions with their associated probabilities. Such chemical databases are expected to increase in coming years as the chemistry induced by plasmas is utilized in more applications, including medical processes  [643] ,  [723]  and waste treatment  [724] ,  [725].
E. Low-Temperature Plasma Database In the field of low-temperature plasma science, central databases providing fundamental data for the analysis, and interpretation of measurement results, theoretical modeling and simulations have been used and maintained for many years. These include, for example, the NIST atomic spectra database  [726] ; LXCat  [727]  for electron and ion scattering cross sections, swarm parameters, reaction rates, energy distribution functions, and so on; and Quantemol-DB  [714]  for plasma species, reactions, and chemistries. However, the results of application-oriented research in the area of low-temperature plasmas are mainly published in traditional journal publications and poorly structured, and often not accessible in digital form for direct reuse. This not only suspends the continuous life cycle of research data but also inhibits technology transfer since comprehensive datasets for comparison and validation studies are often lacking.
In particular, the application of AI/ML methods to data-driven science and technology requires large datasets in well-defined formats. Data must be shared with machine-readable metadata containing information on how the data can be accessed, how they can interoperate with applications or work flows for analysis, storage, and processing, and in which context they can be reused. Initiatives in many research fields are underway to develop or advance systems and standards for documentation and sharing of research data to meet these requirements and to make it easier to find such data and make it interoperable and reusable in accordance with the FAIR data principles  [674] ,  [728] ,  [729] ,  [730]. Furthermore, funding agencies and publishers are starting to issue policies requiring researchers to preserve and share the research data collected during the course of a research grant or presented in a paper.
Both the practical needs and formal requirements have motivated work on providing a central database for research data in low-temperature plasma science. In general, three options are available for publishing research data in digital form: first, institutional repositories, which operated by universities or individual research institutions and accommodate data from all disciplines; second, subject-specific repositories for collecting research data from a specific research area; and third, generic repositories that are open to all types of data from any source, such as Figshare or Zenodo. Each option has its own advantages and disadvantages. Institutional solutions, for example, can be linked easily local data management and quality assurance processes. Generic repositories generally impose no restrictions or quality criteria on the data, making them particularly easy for individual researchers to use.
Subject-specific databases have the advantage over the former that the data can be documented and stored according to appropriate metadata standards and data models. This aspect is particularly important in the context of data-driven research where data should be findable and reusable by automated processes. Many research communities with large-scale experiments, and mostly, homogeneous data already have established solutions, e. g. , high-energy physics and astrophysics  [375] ,  [731]. Research in low-temperature plasma science, however, is often characterized by smallscale table-top experiments involving diverse methods and devices. Furthermore, application-oriented research in the field of plasma science often involves researchers from other disciplines, such as electrical engineering, biology, and medicine.
As a result, research data are extremely heterogeneous, and convenient infrastructures are needed to manage and link these data in the sense of making them available for datadriven research. The data platform INPTDAT and plasma MDS (PlasmaMDS), an MDS for the uniform description of data in the field of applied plasma science, have recently been developed to address this challenge  [732]. As illustrated in Fig. 44 , the concept underlying these developments is that data obtained in the course of research in a specific subject area by means of a specific experiment and involving specific devices are assigned by the data producers to the respective topic, to a concrete application if applicable, as well as to the experiment, devices, and substrates used. In this way, a graph of linked data and further information, e.
g. , from patents and device descriptions, is created, and research data available for specific applications, devices, and/or substrates can be found and reused immediately. This is particularly beneficial if similar experiments or devices are used in different subject areas and for various applications. An example from the field of plasma technology is a plasma source being used both in plasma surface technology for the functionalization of materials and in plasma medicine for biomedical applications. Up until now, the data and knowledge gained in the respective fields (plasma surface technology and plasma medicine) have only rarely been brought together and reused in an interdisciplinary manner. The concept implemented by INPTDAT and PlasmaMDS supports cross-domain reuse of research data by making the data directly accessible for machines and scientists from different fields via linking with topics, applications, methods, and devices.
F. Selections of Recommended Data The compilation of data in itself is valuable but when confronted by multiple datasets for the same cross section or reaction how is the user to select one set over another? This is a major challenge for user community members who often do not have a detailed knowledge of the methods by which such data are collected and, thus, cannot easily distinguish between the myriad of data presented to them. When should they use experimental data, and when should they use theoretical data? Is the data collected or calculated by one methodology more reliable than that of another? Is newer data necessarily more reliable than older data? These questions are often asked by the user and modeling communities, and some data providers (such as QDB) offer a service to provide recommended datasets having the expertise to analyze the data and determine recommended and self-consistent datasets.
However, more broadly, how are recommended datasets derived and is it necessary? To answer the question of whether there is a need for recommended datasets, it is only necessary to consider the use of spectroscopy to determine the number density of excited species in a plasma. The cross sections used for a specific spectral emission may be used to determine the number density of the emitting species; if different cross-sectional data are used to calibrate different instruments viewing the plasma, then the same observational data will be "translated" into different number densities. Accordingly, for projects such as JET plasma and in future ITER, it is recognized that agreed cross sections for key diagnostics should be agreed  [736]. Similarly, many of the discrepancies between different models may be due to the use of different cross sections and reaction rates rather than different physical and chemical processes included in each model.
Unraveling the data used in different models and the influence of the choice of that data has attracted the attention of both data compilers and users in recent years with discussions of the methodology to provide "recommended data" being held in several meetings, for example, those chaired by the IAEA and data centers, such as VAMDC (https://vamdc. org) and VESPA (http://www. europlanet-vespa. eu). Some broad guidelines for recommending datasets have emerged from such meetings. 1) All recommended data should have been previously published and, therefore, have been subject to peer review. 2) Estimates of uncertainties in the data should be provided. This is standard for experimental data but has been less common in theoretical data. However, recently, publishers have required a discussion of uncertainties in theoretical/computational data  [737].
3) It is preferable for recommended data to be in datasets rather than individual processes. For example, consider electron scattering cross section data: individual cross sections may be recommended from different sources, but the summation of these individual cross sections should be consistent with the recommended total cross section. Similarly, integrated differential cross sections should be consistent with the recommended integral cross section, summed partial ionization cross sections should be consistent with the recommended total ionization cross section, and momentum transfer cross sections should be consistent with recommended elastic and inelastic cross sections. These guidelines demonstrate that providing recommended datasets is a challenging exercise and requires a wide knowledge of the methods by which such data is generated and often the researchers involved.
Experimental data are often prone to systematic effects that are known to the community; for example, the community may know the energy and angular ranges over which data have been demonstrated to be reliable and ranges in which systematic effects may lead to larger uncertainties. Extrapolation of data over angular ranges to obtain an integral cross section may be known to be problematic in some systems (e. g. , electron scattering from targets with dipole moments may show strong forward scattering in regions where experimental errors are large). Some theoretical methods may also be known to be more accurate over some particular energy range. These limitations are not always clear to the general user looking at published data but are known by the community. Therefore, it is the community with its expertise that is best suited to provide recommended datasets.
However, with a few exceptions (e. g. , Section I and the atmospheric community with its HITRAN database), there are few institutional structures to compile and recommend datasets in part due to a lack of funding for such activities. International organizations, such as the IAEA, are able to provide a stable and long-term platform for database resources serving particular communities. As computing infrastructure, including cloud computing facilities, become cheaper and more available, this has enabled such institutions to collect and serve a wider variety of data. For example, the well-established ALADDIN database of evaluated plasma collisional data at the IAEA is now supplemented by a larger database of unevaluated data, CollisionDB  [738] , which accepts (with provenance) data from all published sources and provides a searchable interface enabling such data to be compared, aggregated, and assessed.
Recent European Union activities, leading to the creation of VAMDC and VESPA, are encouraging, but ensuring the sustainability of such efforts remains a challenge. Hence, most data compilations are due to the efforts and enthusiasm of individuals, such as the KIDA and LXCat astrochemistry, and plasma databases with data for individual targets are published by small academic consortia and often result from a specific need that they have identified for other research; Table  III  gives a summary of active databases in this area. Several initiatives have tried to provide a longer term approach to multiple targets, for example, initiatives to develop recommended datasets for electron scattering from molecular targets used in the semiconductor industry by  Christophorou and Olthoff [739]  at NIST and, more recently, teams led by Song et al.
The collection, compilation, and preparation of recommended datasets for plasma studies, therefore, remain one of the most important, yet also the most challenging aspects of modern plasma research. The increasing development of "virtual factories" and the concept of a "digital twin"  [741] ,  [742]  in which a plasma processing plant and the procedure are modeled prior to construction places increasing emphasis on the quality and quantity of the input data used in such models. However, despite the recognition of the need to collect and compile such data, the community is small, and in many cases, such as AM data, this community is steadily declining in numbers as other areas of science and technology attract more funding. This is a dangerous trend since the production of such data underpins all aspects of plasma technology, from the provision of diagnostics for characterizing the plasma to the design of plasma itself for specific applications.
The amount of data required is already far in excess of that practical to assemble by experiment, with many targets being unsuitable for experimental research (short-lived radicals, highly reactive species, and species that are obtained only from highly toxic precursors); hence, the majority of the data must be evaluated by theoretical calculations with the limited experimental data being used to benchmark such calculations. While semiempirical methods may be attractive to users, and commercial packages, such as Quantemol, are available, they should be used with caution, and the user is advised to cooperate with a more experienced user and take advantage of expert advice where offered (as in the case of Quantemol  [743] ). Recently, there have been some attempts to use ML  [368] ,  [744]  to derive datasets with an ML-based method being to construct a model for predicting total ionization cross sections Q ion of large molecules without the high cost of ab initio calculations.