dataesr/scientific-paragraphs-categorization huggingface dataset:
In contrast, the features of learning-based methods are domain or dataset-specific, typically requiring heavy human intervention for their definition. Note also that JedAI's learning-free methods are inherently crafted for highly noisy and heterogeneous data  [24] : to address possible errors in attribute values, their domain-agnostic functionality considers all values in each entity profile rather than relying on a particular (set of) attribute(s). JedAI has been presented as a demo to two different communities, namely Semantic Web  [27]  and databases  [28]. In this work, we present its structure and characteristics in more detail, introduce the new features of version 2. 1, and provide experimental evidence of its performance over real data. The rest of the paper is structured as follows: Section 2 delves into JedAI's architecture, Section 3 elaborates on the new features in version 2.
ARCHITECTURE JedAI's architecture appears in Figure  2. It is modular so that it can be easily extended by expert users in the future. Every component implements a simple (Java) interface such that every new class (algorithm) implementing it can be seamlessly added. JedAI consists of two parts: (i) JedAI-core 7 , the back-end that essentially constitutes a library of state-of-the-art methods, and (ii) JedAI-gui 8 , the front-end that facilitates the use of the library. Below, we describe each part in detail. 1) Data Reading loads from the disk into main memory the data collection(s) to be processed along with the respective golden standard. The following data formats are supported: CSV, XML, OWL and RDF files as well as relational databases and SPARQL endpoints. Any mixture of these formats is possible in case of Clean-Clean ER.
To this end, it reports a series of measures for e↵ectiveness and time e ciency. This step also includes Data Writing, which stores intermediate or end results into any of the supported data formats. In case a structured format is selected (CSV or relational database), the output retains the original entity ids. When selecting a semi-structured format (XML, RDF or SPARQL endpoint), the user has to specify the URI prefix, in case it is not available, i. e. , when the original data were structured. To store the output to databases or SPARQL endpoints, the user should also provide the necessary credentials, if applicable, along with the table and the dataset namespace, respectively. Regarding the use of optional steps, Schema Clustering is indispensable for heterogeneous data sources that involve a large number of noisy attributes.
Additionally, we altered the way Block Building is handled by both the command-line interface and JedAI-gui. Instead of selecting a single method, they are now able to combine the results of multiple approaches. In this way, we satisfy a user requirement, which was articulated by businesses that applied JedAI to incomplete or noisy data. As an example, consider a customer database that abounds in profiles with missing or erroneous information. Applying a single block building technique, such as bigram blocking, yields a set of blocks with low levels of redundancy. This means that the block cooccurrence patterns for matching entities are scarce, downgrading the performance of Block and Comparison Cleaning methods, which are crucial for deriving high quality candidate matches  [26]. To leverage their performance, we can apply them to the union of blocks formed by two or more block building methods (e.
EXPERIMENTS We now evaluate the performance of JedAI with respect to the state-of-the-art in the literature. To this end, we use the four real-world, structured, Clean-Clean ER datasets that were introduced in  [17  while D 3 and D 4 involve bibliographic data. We applied the following workflow to all of them: Token Blocking for Block Building, Block Purging with size constraints along with Block Filtering for Block Cleaning, Cardinality Node Pruning (CNP) for Comparison Cleaning, Profile Matcher for Entity Matching and Unique Mapping Clustering (UMC) for Entity Clustering. This workflow involves five parameters: (i) the maximum block size (Block Purging), (ii) the ratio of retained blocks (Block Filtering), (iii) the weighting scheme (CNP), (iv) the representation model in combination with the similarity metric (Profile Matcher), and (v) the minimum similarity for a pair of matches (UMC).
To explore the potential of this workflow, we finetuned these parameters in three ways: (i) stepby-step random configuration, where we used the methodology of  [26]  for independently optimizing each method until CNP 9 and the F-Measure for optimizing the last two methods, (ii) holistic random configuration, whose goal is to maximize the overall F-Measure, and (iii) step-by-step grid configuration, where we used the same criteria as the first case. We compare these configurations against three state-ofthe-art domain-specific, learning-based systems: (i) COSY, which is a commercial system 10 that achieves the top performance in  [17] , (ii) DeepMatcher  [19] , and (iii) Magellan  [16]. For the last two, we consider the top performance that is reported in  [19]  among all configurations and dataset versions.
e. , word embeddings). JedAI is very close to COSY and outperforms Magellan to a significant extent over D 2 , and vice versa over D 4. It is worth associating these measurements with the corresponding time e ciency of each system. To measure JedAI's running time, we used a laptop with an Intel i7-4710MQ @ 2. 50GHz, running Ubuntu 18. 04. 3 LTS and Java 8. We applied each configuration to every dataset 11 , allocating 1 GB of RAM and performing 10 iterations with a clear Java cache. The average running times appear in Figure  5 (b). We do not report the time performance of the baseline systems, as they all rely on manually-defined blocks of high performance, which are not reproducible, due to lack of details. We observe the high e ciency of JedAI's configurations, as they process every dataset within few seconds -less than 2 seconds for D 1 and D 3 , from 3 to 8 seconds for D 2 and from 8 to 14 seconds for D 4.
D 2 and D 4 are the most time consuming datasets, because they involve higher levels of noise and, thus, a larger number of candidate matches is processed. D 4 also involves the largest number of entities by far. Similar running times are reported in  [17]  for COSY, while Magellan and DeepMatcher require few seconds and few hours, respectively, for training their matching models, after having performed blocking with the help of an expert and labelling a considerable number of comparisons  [19] , operations that are very expensive in time. Therefore, we conclude that JedAI is much faster than the last two systems. Another advantage of JedAI over Magellan is its ability to process Dirty ER datasets that Magellan cannot handle. This is illustrated in Table  2 , which reports JedAI's performance over two established dirty datasets  [3, 26] : Cora (1,295 entities, 11 The corresponding code is available here: https://github.
CONCLUSIONS We presented JedAI, a user-friendly ER toolkit that fulfills the two main challenges arising in data integration  [12] : the development of extensible, opensource tools, and the provision of solutions that apply not only to structured, but also to semi-or even unstructured data. We described JedAI's unique characteristics and elaborated on its main components, highlighting the new features in version 2. 1. We demonstrated the high performance of its workflows and verified that it is ideal for the development phase of ER solutions, as it facilitates the identification of the best end-to-end workflows for a particular dataset and use case. Yet, the resulting workflow should be optimized for production systems. In the future, JedAI will support pre-trained embeddings and will also allow for combining multiple algorithms and/or representations models for the pairwise comparisons during Entity Matching.
Special care will be taken to progressively evaluate the performance of end-to-end workflows, a feature that is particularly useful when processing very large datasets. In later versions, we intend to enrich JedAI with support for supervised learning techniques. To comply with its domain-agnostic foundations, we will begin with approaches that leverage generic, schema-agnostic features, like those in  [2]. Upon successful completion of this extension, we will also consider schema-based features and constraints, which call for fundamental changes for their incorporation. Special care will be taken to add active learning techniques from top crowd-sourced ER approaches  [7, 9] , thus addressing the sensitivity to cluster size for some of JedAI's workflows.
SUMMARY A volume calibration method implemented using the LCM-LNE-Cnam constant-volume flowmeter has been characterised. Successive measurements of pressure rise rates, due to a steady leak rate, are performed successively in a standard volume and in an unknown volume. From the value of the standard volume and the ratio of pressure rise rates, the unknown volume can be determined. Special care taken in measurement and data analysis has led to a standard relative uncertainty between 0. 03 % and 2 % for volumes ranging from 1 to 3000 cm3, roughly an order of magnitude higher than capabilities of the gravimetric volume calibration.
Data collection based on the type of shadowing Masters, huntsmen, and sometimes hunting association presidents were systematically interviewed before the observation sequences. On the day of a hunt, we came to the forest when they had invited us and headed to the meeting point. We were briefly introduced to the hunters and followers at the first meeting, and we were introduced by crew members to the followers, who agreed to take us along in their cars. In this operation, the members of the crew introduced us to followers who served as 'volunteers' and, thus, were actively working in the association, or 'regulars,' who attended very regularly and had already followed this crew around on hunts for a long time. It happened that we had to ask several people before they accepted; it also happened that we had to change vehicles, and, thus, the person being shadowed, during the hunt.
The use of artificial intelligence (AI) in a variety of research fields is speeding up multiple digital revolutions, from shifting paradigms in healthcare, precision medicine and wearable sensing, to public services and education offered to the masses around the world, to future cities made optimally efficient by autonomous driving. When a revolution happens, the consequences are not obvious straight away, and to date, there is no uniformly adapted framework to guide AI research to ensure a sustainable societal transition. To answer this need, here we analyze three key challenges to interdisciplinary AI research, and deliver three broad conclusions: 1) future development of AI should not only impact other scientific domains but should also take inspiration and benefit from other fields of science, 2) AI research must be accompanied by decision explainability, dataset bias transparency as well as development of evaluation methodologies and creation of regulatory agencies to ensure responsibility, and 3) AI education should receive more attention, efforts and innovation from the educational and scientific communities.
Additionally, in the future development of AI, the reverse direction should also be considered, namely investigating ways in which AI can take inspiration and can benefit from other fields of science. (2) How could regulatory agencies help correct existing data biases and discriminations induced by AI? To ensure this, AI research must be accompanied by decision explainability and dataset and algorithm bias analysis as well as creation of regulatory agencies and development of evaluation methodologies and tools. In all cases, AI research should guarantee privacy as well as economical and ecological sustainability of the data and algorithms based on it. (3) How can we manage the impact of this AI revolution once AI tools are deployed in the real world, particularly how to ensure trust of the scientific peers and the general public? This includes establishing public trust in AI through education, explainable solutions, and regulation.
The developers have to explicitly detail how they handle extreme cases and show that security of the user is ensured. It should be mandatory for the owner and user of the data to clearly and transparently state the known biases expressed the dataset (similar to the way the secondary effects of medicines are clearly stated on the medication guide). While some of these are already addressed by the GDPR in the EU, similar regulation and standards are needed globally. An alternative, complementary approach would be to rely on the classical scientific method practices developed over the centuries. Relying on observation, hypothesis formulation, experimentation  (Rawal et al. , 2020)  and evaluation allows us to understand causal relationships and promotes rigorous practices. AI would certainly benefit from explicitly integrating these practices into its research ecosystem  (Forde and Paganini, 2019).
Biases and Ethical Standards in Artificial Intelligence To control the functioning of AI algorithms and their potential inherent biases, clear, transparent and interpretable methodologies and best practices are required. Trustworthiness of AI-driven projects can be ensured by, for example, using open protocols of the algorithms functionality, introducing traceability (logs, model versioning, data used and transformations done on data) or the pre-definition of insurance datasets. In transversal domains such as software development, tools have been devised to prevent mistakes and model deterioration over time (such as automated unit tests). Establishing similar standards for AI would force data scientists to design ways to detect and eliminate biases, ultimately making sure that the algorithm is behaving as intended.
These tree-based methods can also quickly become difficult to use because, as the size of the system grows, so does the size and number of the trees. Other formalisms can be used to counter this problem, for example graph-based representations like the one described by Sommestad et al. in  [28]  which has been put in practice in the Cysemol language  [27]. This approach derives risks from a description of the system architecture which also helps the systematic identification of scenarios. Cysemol also functions as an expert system to reduce the need of a dedicated cybersecurity expert for assessing threats, however because of the constant evolution of the field, this system still has to be regularly maintained. The STPA-Sec ontology introduced by Pereira et al. [21]  is another recent development which aims at facilitating the construction of threat scenarios, however it does not provide a way to manage the expert's knowledge for assessing those scenarios.
Framework for safety-security design and analysis The design and analysis framework covers all the phases of the method introduced in Section 3 and is built on top of Papyrus [3], an Eclipse module for Model Driven Engineering supporting UML2. 0 and which is available under the EPL license. The main modeling and analysis features are summarized in the following paragraphs. To support modeling, the user can first define the parameters for the analysis as described in Subsection 3. 1. Some default templates are available and can be imported so as to provide guidance in this task. Dedicated diagrams are implemented to separately model vulnerabilities and security countermeasures, as well as to visualize the imported knowledge bases (see Figure  2 ). These model elements are to be reused during the definition of the target system which is modeled in two steps.
This paper presents an overview of the current status of the VAMDC e-infrastructure including the current status of the VAMDC connected (or to be connected) databases, updates on the latest technological development within the infrastructure and a presentation of some application tools that make use of the VAMDC e-infrastructure. We analyse the past 10 years of VAMDC development and operation, and assess their impact both on the field of atomic and molecular (A&M) physics itself and on heterogeneous data management in international cooperation. The highly sophisticated VAMDC infrastructure and the related databases developed over this long term make them a perfect resource of sustainable data for future applications in many fields of research. However, we also discuss the current limitations that prevent VAMDC from becoming the main publishing platform and the main source of A&M data for user communities, and present possible solutions under investigation by the consortium.
Introduction The Virtual Atomic and Molecular Data Centre (VAMDC) has been developed to interconnect atomic and molecular databases, thus providing a single location where users can access atomic and molecular (A&M) data. The VAMDC portal currently provides access to 38 databases containing a wide range of data from atomic spectroscopy (VALD, section 2. 1. 2) to PAH theoretical data (PAH, section 2. 2. 2). The paper presents the current status of the VAMDC project and its underlying databases, and gives some examples of the exploitation of VAMDC by some exemplar user communities; we describe open issues in the project and our vision for future developments. The VAMDC project 1 originated as a European Union framework 7 (FP7) research infrastructure project  [1]  that was funded between 2009 and 2012, and subsequently extended through the SUP@VAMDC project 2   [2]  between 2012 and 2014.
Current Status of VAMDC Connected Databases The VAMDC nodes are distributed such that they are located at the members' and partners' sites. At present most of the databases included in the VAMDC e-infrastructure are databases used primarily in astrophysics. The common output format is the XML Schema for Atoms, Molecules and Solids (XSAMS) (see section 3. 1. 1) that uses the tree-structured form of the data model. All of the current VAMDC databases are listed on the VAMDC portal 5 which also provides a short introduction to the database, a link to its public graphical interface and contact details of a member of that database team to whom inquiries may be directed. In addition, it is possible to list the atoms and the molecules that can be queried from those databases. Table  1  provides a summary of the 38 databases currently accessible via VAMDC.
It includes eight databases that are in the process of joining the consortium. It should be noted that a database might be off-line for a period of time due to maintenance issues. Among the databases already present in the VAMDC e-infrastructure in 2016 and cited in our last publication  [3] , some have evolved with respect to their data content, functionalities and internal structure, and improved their interoperability with the VAMDC ecosystem. Some of them have not evolved, and two have been disconnected from the VAMDC ecosystem. Ten databases have been added. Among these new databases is a new node at NIFS in Japan.
NIFS Databases The National Institute for Fusion Science (NIFS) has compiled and developed an extensive atomic and molecular numerical database on collision processes which has been open for public access 6     since 1997. A data compilation on collisional cross sections for hydrogen, its isotopes and helium  [41, 42]  was initiated by a collaborative working group of atomic and plasma researchers from Japanese universities, organised at the Institute of Plasma Physics, Nagoya University, in 1973. Subsequent compilations were published in IPPJ-AM reports in 1977-1989 and as NIFS-DATA reports since 1989 Table  1. Table of databases connected to VAMDC (C) and to be connected (TC). User fields are labelled as follows for applications in astrophysics and planetary science: stellar physics (STEL), solar physics (SOL), interstellar medium (ISM), earth (E), planets (PL), exoplanets (EXO), brown dwarfs (BDW), comets (C).
Partnerships are indicated as Full Members (FM) according to the VAMDC Consortium MoU and partners following the VAMDC technical quality chart only (P). covering electron-impact ionisation and excitation cross sections, heavy-particle collision cross sections and plasma-wall interaction properties. The first retrieval and display database system, the Atomic and Molecular Data Interactive System (AMDIS), was constructed in a mainframe computer for electron-impact ionisation and excitation cross sections of atoms and atomic ions in 1981  [43] , with the database system being subsequently extended to include other collision processes. Currently the database system consists of eight sub-databases accessible via internet, full details are described by Murakami et al. [44]. Numerical data of collision cross sections and rate coefficients of various collision processes are compiled mainly from the published literature as well as bibliographic data and additional information such as experimental or theoretical methods used to derive the data are attached.
Initially, data were collected for elements that were important mainly for fusion science; later the database was extended to include many more atoms and molecules applicable to various plasma applications. Usually, several data sets obtained from different publications are stored for the same collision processes. Users can compare those data sets to check their reliability. Data can be queried by elements, charge states or various other query fields such as initial and final states, author, published year, and retrieved data are displayed as a numerical table or as a graph. One of the sub-databases, AMDIS-IONIZATION has been available from the VAMDC portal with search functionality by element since 2017  [4]. AMDIS-IONIZATION has electron-impact ionisation cross sections and rate coefficients for atoms and atomic ions.
VALD The Vienna Atomic Line Database (VALD) compilation is a critically assessed collection of radiative transition data aimed primarily at the stellar astrophysics community. VALD contains data on energy levels, wavelengths, oscillator strengths and line broadening parameters for nearly all stable elements in the periodic table and for a few simple molecules. For atoms, VALD includes 6 ionisation stages. The VALD interface allows a search of the whole data collection or on a restricted subset that is based on high-precision laboratory measurements. The Moscow VAMDC node serves high-precision data only, while the Uppsala VAMDC node provides the most complete VALD data set. VALD also includes atomic data for individual rare-earth elements/ions, also provided by the DREAM database  7   [45]  (see  Section 2.
NIST ASD The Atomic Spectra Database (ASD)  [7]  at the National Institute of Standards and Technology (USA) contains critically evaluated atomic data including energy levels, radiative transition probabilities and oscillator strengths, ionisation potentials, and observed and accurately calculated wavelengths of spectral lines between the hard X-ray and infrared regions of spectra. As of July 2020, ASD provides data on more than 112,000 energy levels, 280,000 spectral lines and 118,000 radiative transition probabilities for elements from H (Z = 1) to Ds (Z = 110). The majority of the data on energies and spectral lines were collected and evaluated from experimental papers, and in most cases realistic uncertainties are provided as well. ASD is directly linked to the set of the NIST Atomic Spectroscopy Bibliographic Databases  [53] , which allows an immediate access to the original sources of data.
In addition to a tabular output of data in various formats, ASD also offers rich graphical services, e. g. online-generated Grotrian diagrams of levels and transitions. ASD serves as the basis for the NIST LIBS (Laser-Induced Breakdown Spectroscopy) Database  [54] , which allows on-the-fly generation of realistic spectra for Saha-Boltzmann plasmas that are used for analysis of elemental abundances and diagnostics of terrestrial and astrophysical plasmas.