dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, the complexity of a DSL can be prohibitive, as in the case of Modelica. In all these scenarios, the net result is a balkanization of digital scientific knowledge because for each new piece of software, designing a new DSL is often the choice of least effort. These considerations lead to two important criteria for good digital scientific notations that existing ones do not satisfy at the same time: • Generality. While it is unrealistic to expect that a single formal language could be adequate for computer-aided research in all scientific disciplines, it should be usable across narrowly defined domains of research, and be extendable to treat newly discovered scenarios. • Simplicity. The implementation of user interfaces based on a digital scientific notation should not require a disproportionate effort compared to the implementation of the scientific functionality of a piece of software.
Background LAMBDA  [9]  is a language used to describe security attacks. It is based on the general concept that an attack can succeed if a set of conditions (called preconditions) is satisfied, and will have effects (called postconditions) on the system if it succeeds. It is also described by a scenario, being the different actions needed to be combined to perform the attack. Attacks described in LAMBDA can be of any granularity going from elementary steps that an attacker needs to carry out to progress towards his objective, to the description of a complete scenario. CRIM  [10]  is a correlation engine that draws connection between attacks described in LAMBDA: if a postcondition of an attack A 1 matches with a precondition of an attack A 2 , then the realisation of A 1 favours the realisation of A 2.
Acknowledgements The  Cyber CNI Chair of Institute Mines TELECOM  is held by  IMT Atlantique  and supported by  Airbus Defence and Space, Amossys, BNP Paribas ,  EDF, Orange ,  La Poste ,  Nokia , the  Regional Council of Brittany  and  Société Générale. It  has been acknowledged by the  Center of excellence in Cybersecurity.
Data collection and sample The data were collected through questionnaire survey from a large university in India. To ensure the eligibility of respondents, initially, they were asked whether they have an account on any social media site (Facebook, Twitter, etc. ) and whether they have ever subscribed to, liked, or joined any online community's brand page using any SNS. The questionnaire was given only to the eligible respondents. This was done with the objective of observing the behaviour of respondents irrespective of their membership in SNSs' brand communities. The present paper considered students as the target population as nowadays students are 'tech-savvy'  (Nadeem et al. , 2015)  have greater access to the Internet  (Bolton et al. , 2013) ; and in this way they contribute as a key demographic of SNSs  (Burbary, 2011) , particularly in participating in online brand communities on social media  (Kamboj and Rahman, 2016).
The situation of data sharing in astronomy is positioned in the current general context of a political push towards, and rapid development of, scientific data sharing. Data is already one of the major infrastructures of astronomy, thanks to the data and service providers and to the International Virtual Observatory Alliance (IVOA). Other disciplines are moving on in the same direction. International organisations, in particular the Research Data Alliance (RDA), are developing building blocks and bridges to enable scientific data sharing across borders. The liaisons between RDA and astronomy, and RDA activities relevant to the librarian community, are discussed.
Introduction Astronomy has been a pioneer of scientific data sharing. Historical services such as the database of the International Ultraviolet Explorer, a UV satellite which was operational between  1978 and 1996 [? ] , or SIMBAD  [? ] , operated by the CDS since 1983 succeeding the Bibliographic Star Index (BSI) and Catalogue of Stellar Identifications (CSI), were already accessible remotely before the advent of the web. For many years astronomers have been using remote data services in their daily research work: networked on-line services have been developed since the beginnings of the internet, and further federated in the Virtual Observatory (VO). Data sharing thus became a routine for astronomers, and an established part of the research landscape for the discipline. But astronomy is not alone: science data sharing is currently a hot topic at the political level and for many funding agencies.
Many disciplines are working at setting up data services and a disciplinary interoperability framework. Organisations such as the Research Data Alliance (RDA) work on building blocks and bridges to facilitate data sharing. This paper describes several aspects of this fast evolving landscape, the political push towards Open Data (Section 2), the path towards and scientific impact of open data, taking astronomy as example (Section 3), examples of other disciplines for which data already is or is becoming a research infrastructure (Section 4), and the role of the Research Data Alliance (Section 5). The conclusion (Section 6) summarizes the fast evolution of the context and how astronomy should fit in, and the evolving role of librarians in data curation. ⋆ e-mail: francoise. genova@astro. unistra.
Data sharing is a powerful way to optimize the scientific return of these large infrastructures, as demonstrated by the well-known example of the bibliometric impact of the HST archive: more than half of the papers published each year from HST observations are using archival data 4. In addition, data sharing is indeed at the core of astronomy scientific needs, allowing multi-wavelength/multimessenger/multi-technique astronomy, the study of time variability and of phenomena at different scales, the comparison of theoretical models with observations, etc. The fact that the community succeeded in setting up world-wide sharing of FAIR data, makes the distributed astronomical data one of the essential infrastructures of the discipline, as recognized for instance in the European Roadmap of astronomical infrastructures developed by ASTRONET 5 [? ].
Finally, the fact that the development of observatory archives and data services is driven by community needs has been essential to ensure widespread adoption of remote data usage as a routine of astronomers' scientific work. The astronomical data infrastructure has very diverse components, among which of course is the observations: the archives from the ground and space based telescopes and the large sky surveys, which contain homogeneous observations of up to billions of objects. It also includes data from publications, value-added databases which gather homogenized information in particular from publications or observations, such as SIMBAD, VizieR [? ] and the Aladin image database [? ] at CDS, NED [? ], and the ADS [? ] for bibliographic data, and more and more modelling results. SIMBAD, for instance, contains information gathered from catalogues and publications, including all the names of a given object.
Networking and Interoperability -The Astronomical Virtual Observatory This short description of SIMBAD already demonstrates that astronomical on-line services are networked. This networking begun as soon as the on-line resources began to be developed, in the early stages of the internet in 1993-1994. It was implemented through collaboration between the providers of added-value services, journals and observation archives, taking advantage of the capacity of the web to link web pages. It is still available and used, and is well illustrated for instance by the multiple links to distant resources available in the ADS. Around 2000, the idea to go beyond this already powerful networking of resources by providing seamless access to on-line data emerged as the Virtual Observatory concept. The Virtual Observatory is a framework of standards and tools which allows users to discover, access and use data.
This is thanks to the work of the data and service providers. Another one is seamless access to data through interoperable tools such as Aladin or Topcat, which are well suited to science needs thanks to the work of the VO developers. The VO was adopted by the data and service providers because it gives more visibility to their data by making them discoverable, and also because it provides access tools and building blocks for their systems: there is no need to reinvent the wheel, the VO developers already worked and pooled resources and expertise to propose relevant and handy solutions. The current priorities of the IVOA are linked to the needs of the future large astronomical projects: multi-dimensional data, with a first milestone reached in May 2017 with a complete initial set of relevant standards; and time domain.
The current European VO project is closely aligned with these priorities. The European Commission has been funding a series of projects since 2001 to support the coordination of VO activities in Europe  [? ]. The current one is a Work Package of the Astronomy ESFRI 7  and Research Infrastructure Cluster ASTERICS 8  , called Data Access, Discovery and Interoperability (DADI), which is foreseen for 4. 5 million euros over 4 years starting in May 2015 (the whole budget of ASTERICS is 15 million euros). DADI aims at "making the ESFRI and pathfinder project data available for discovery and usage by the whole astronomical community, interoperable in a homogeneous international framework (the Virtual Observatory), and accessible by a set of common tools. " DADI and ASTERICS involve both astronomy and astroparticle physics telescopes, which is particularly important with the rapid development of multi-messenger astronomy.
Big and smaller data in astronomy Another trendy word in the data world is "Big Data. " Astronomy can be taken as an example of "Big Data" science, because the ground and space-based telescopes and the large surveys produce large data volumes. But the discipline also takes care of its "smaller data", the so-called Long Tail of data [? ], in particular the research results which are linked to publications. This started in 1993, with the agreement between the CDS and Astronomy & Astrophysics that the "long" tables in the papers would be published on line in an ftp server by the CDS [? ]. The agreement was later extended to other journals. These data come with a standard description, which has been the basis to ingest them in the VizieR system when it was implemented. VizieR can store any kind of astronomical quantity which can be found in a publication.
One can note here that variety is also one of the characteristics of Big Data. The standard description and the UCD controlled vocabulary for astronomical quantities [? ] provide a homogeneous frame for the very heterogeneous database content. In August 2017 VizieR contains more than 16,000 "catalogues", which are actually data sets which can include tabular as well as non-tabular data such as images, spectra, time series, etc. attached to a publication, or object lists and other data from large surveys  [? ]. VizieR approach to the Long Tail of Data is thus to preserve data validated by a publication, and to make it fully discoverable and interoperable in the Virtual Observatory, usable and citable through ADS (and soon through a DOI). "VizieR Photometry Viewer" [? ] is an example of value-added service made possible by the homogeneous data description and the rich information stored about the data [? ]: it extracts all the spectral points available in VizieR in a given direction on the sky, and displays them on a single plot taking into account the photometric system used in the original observation.
Data as an infrastructure: examples from different disciplines The notion and management of Research Infrastructures can be different from country to country. This is a domain where European coordination made a difference and enabled the development of a regional strategy, which includes global infrastructures such as SKA. The European landscape of Research Infrastructures is structured by the ESFRI Roadmap, which covers all scientific disciplines and is regularly updated. The ESFRI Research Infrastructures are infrastructures of pan-European interest defined as "facilities, resources or services of a unique nature identified by European research communities to conduct top-level research activities in all fields". The first Roadmap was published in 2006, the last update [? ] in 2016, and the next version is foreseen for 2018.
More and more European countries also develop a National Roadmap, taking into account the ESFRI Roadmap as well as their national priorities. All research infrastructures have aspects linked to data, and ESFRI Roadmap candidates have to answer questions about their data management and policy, and their computing and network needs. This is also the case for instance for the French National Roadmap candidates. Some of the infrastructures on the Roadmaps are fully "virtual", for instance the CDS is in the French Roadmap. The way different disciplines develop their data infrastructure is strongly dependent from the disciplinary culture and organisation. It is interesting to observe that the Humanities have been present in the ESFRI Roadmap from the beginning, with two projects, DARIAH and CLARIN, which are distributed data infrastructures: DARIAH 9 supports digital research in the arts and humanities in gen-eral, CLARIN 10  is focused on digital language resources.
In France, the national Humanities research infrastructure Huma-Num 11  provides added-value services in support of the data life cycle and in addition manages disciplinary consortia set up for 4 years (with possible renewal) to organise communities around data sharing topics relevant to their needs. This is very useful for disciplines which are more loosely organised than astronomy. It also coordinates French participation in DARIAH and CLARIN. The national organisation in Earth Sciences is different: there are 4 disciplinary "Poles": Solid Earth, Ocean, Atmosphere, Continental Surfaces and Interfaces, in which all the relevant research organisations, including the French Space Agency CNES, participate. "Inter-Poles" Technical discussions and collaboration on topics of common interest are organised.
An overarching structure is being built, which is a candidate for the national Roadmap. Each Pole has strong links with the ESFRI and international projects in its domains. The way different disciplines organise themselves to tackle the development of their disciplinary interoperability framework was discussed during a session [? ] of the SciDataCon 2016 Conference 12  , which was held in Denver (USA) on 2016, September 11 th -13 th. The panel included representatives from humanities, linguistics, astronomy, earth sciences, material sciences and crystallography. Many commonalities were found: • the development of the data infrastructure must be science driven; • defining the disciplinary part of the interoperability standards is mandatory but difficult; • it is important not only to share data, but also tools, user interfaces, etc; • sociological aspects are more challenging than the technical ones.
Once again, incentives to data sharing appeared to be a key issue for all. Governance is more diverse, linked, as explained earlier, to the discipline organisation and history. It was also found that many sociological and technological aspects can be shared, and it was suggested that the RDA is a good vehicle for that, with roles also for CODATA  13  and WDS 14  of the International Council for Science ICSU. As explained in [? ], international organizations such as GEO, CODATA and the RDA can be used to help disciplines which are not strongly organised at the international level to discuss their data framework. International discussion forums can be established as CODATA Task Groups and/or RDA Interest and Working Groups (Section 5). In addition, the WDS "community of excellence" is a host for individual data providers as regular members, but also for disciplinary networks once they are constituted, and it organizes interaction between its members.
The Research Data Alliance The Research Data Alliance 15 is a community-driven international organisation created in 2013 by the Australian government, the European Commission and the USA NSF, with the mission to build the social and technical bridges to enable open sharing of data. In August 2017, the RDA has more than 5900 members from 129 countries, 66% from academia and research, 14% from public administration, 11% from enterprise and industries. It hosts focused Working Groups and Interest Groups tackling many different aspects of data interoperability challenges, in themes such as domain science, community needs, data referencing and sharing, data stewardship and services, or base infrastructure. Any member can propose a Group, and in August 2017 there are 88 active Groups, 30 Working Groups which prepare implementable deliverables in 18 months, and 58 Interest Groups which organise discussion and exchange of good practices on a specific topic and can also produce outputs.
The activities of the Working Groups and Interest Groups, including their email exchanges and the documents they produce, are documented on the RDA web site, as well as the recommendations and outputs, and adoption cases. RDA members have a large variety of profiles, which makes it a remarkable international, neutral forum gathering very diverse expertise. They include data providers, researchers, project and program managers, and publishers. Librarians make more than 10% of membership. There is not Astronomy Group in the RDA, because it would duplicate the IVOA, but astronomy is present, and the experience gained in building the IVOA and the astronomical data infrastructure is used in the RDA context. For instance, the author of this paper is currently co-chair of the RDA Technical Advisory Board.
It is also interesting to note that the RDA Group working on an International Materials Resource Registry will reuse the concepts of the IVOA Registry of Resources. The IVOA Registry of Resources is already customized to their needs by planetary sciences and the Virtual Atomic and Molecular Data Centre VAMDC. The RDA allows wider dissemination. Data experts from the astronomical community participate in several other RDA Groups, for instance those dealing with Data Provenance. Astronomy is also involved in the Disciplinary Collaboration Framework Interest Group recently proposed as an inter-disciplinary forum for discussion, which aims at becoming the voice of scientific disciplines in the RDA. Many RDA activities are of interest for the LISA community. In particular, one of the RDA recommendations is a catalogue of requirements [? ] and a set of procedures [? ] for core audit and certification of data repositories.
For instance, a Working Group made widely adopted recommendations for dynamic data citation (when data evolves in time keep track of the queries in a "query store"). Several Working Groups involving publishers and data providers, managed in collaboration between the RDA and the WDS, have also been tackling different aspects of data publishing. This includes for instance Publishing Data Workflows and Bibliometrics, and currently the Scholarly Link Exchange Working Group (Scholix) which aims to enable a comprehensive global view of the links between scholarly literature and data. The exchange of good practices and lessons learnt in the Interest Group on the Long Tail of Research Data and the one on Domain Repositories can also be of interest for the LISA community. Finally, the Libraries for Research Data Interest Group produced a very successful output, 23 Things: Libraries for Research Data [? ], which provides an overview of practical, free, online resources and tools that one can begin using today to incorporate research data management into one's practice of librarianship.
Conclusions These are exciting times for scientific data sharing! Astronomy has been at the forefront and, thanks to the efforts of many organisations and individuals, data is now one of the disciplinary research infrastructures. This includes many different distributed on-line resources, the IVOA being a rare example of an operational, global interoperability framework. Even if many astronomers do not realize it, the Virtual Observatory enables scientists to routinely Find, Access and Interoperate data and tools. The context of scientific data sharing is evolving very fast, and many disciplines are moving on. It is important for the astronomy community to be involved in initiatives such as the RDA, to ensure that our requirements are taken into account in the emerging data sharing building blocks.
Sc. students, a large fraction of our annual student group, having already earned their B. S. in computer science or, more rarely, in mathematics. All students were familiar with basic integer arithmetic concepts in mathematics and computer science. The puzzle is given as a group project assignment in an OR and CP introductory course. We use one 90 minute kickstart session in which students visit the CSPLib page, explore available resources (references and models), and implement a first model (usually the division or product model). Then, we give homework to provide a solution checker using exact integer arithmetic (for instance in Python) and to verify their results. At the beginning of the next session, a debriefing points out that the models return invalid solutions. The top students have already discovered this problem, but have not identified its causes, and do not know how to solve it.
Currently, this technique is being developed as a new activity standardization method at PTB. Validation measurements were carried out with a revived 4πβ-γ coincidence counting system with a PC in the beta channel. Additional measurements were carried out using liquid scintillation counters with two and three photomultiplier tubes. The counting efficiencies for these systems were determined using CIEMAT/NIST efficiency tracing and the TDCR method  (Broda et al. , 2007). Both methods require computed beta spectra and other nuclear decay data as input data. The results of both 4πβ-γ coincidence counting techniques are in very good agreement and their results do not depend on the beta spectrum calculation. In contrast to coincidence counting, the TDCR method and CIEMAT/NIST efficiency tracing yield results which depend to a certain extent on the above-mentioned input data.
4πβ(LS)-γ coincidence measurements The 4πβ(LS)-γ coincidence system comprises a TDCR counter with an automated sample changer and a cylindrical NaI scintillation detector (102 mm × 102 mm) below the optical chamber of the LS system. The amplified and discriminated anode signals from the three Hamamatsu R331-05 PMTs are fed into an FPGA-based coincidence unit referred to as the 4KAM module  (Nähle et al. , 2014). More details about the 4πβ(LS)-γ coincidence system, its electronics and the signal treatment are described by  Marganiec-Gałązka et al. (2017). In this work, the common extendable dead time of the 4KAM module was adjusted to 30 μs. The coincidence resolving times were defined as 77. 5 ns and 76. 5 ns for the beta channel (3 PMTs of the LS system) and for the β-γ coincidences, respectively.
TDCR measurements The LS samples were measured in two custom-built TDCR systems of PTB. The first system makes use of the MAC3 coincidence module  (Bouchard and Cassette, 2000)  with 40 ns coincidence resolving time and was described by  Nähle et al. (2010). The second system is a new LS-γ counter with an automated sample changer as described in Section 2. 2. For the (pure) TDCR measurements, the coincidence resolving time in this system was about 76. 5 ns. Further details about the counters and general adjustments are described by  Nähle et al. (2010)  and  Marganiec-Gałązka et al. (2017).