dataesr/scientific-paragraphs-categorization huggingface dataset:
They concluded that Web of Science is of little value to mathematics education, despite its widespread use to measure scholarly output in other disciplines. A further limitation of all three major journal ranking systems -Web of Science (Impact Factor, IF), Scopus (Scopus Journal Ranking, SJR), and Google Scholar (h5-index) -is that they only trace citations within their own databases, thus excluding the vast majority of mathematics education journals. Nivens and Otten (2017) warn of a further problem: when journal citation metrics are improperly used to draw conclusions about the impact of articles published in particular journals. They show that there is little correlation between a journal's citation-based measures of impact (such as IF) and the number of citations received by articles published in that journal.
A heuristic analysis of possible research data references In this section, we report on the current status of our preliminary investigations. A more in-depth analysis is in preparation. The zbMATH database [2] currently contains more than 30M references. Of those, currently 53. 7% link back to other publications that are indexed in zbMATH. Other references are out of the scope: overall, 36. 7% have a DOI and 10. 9% have a DOI, but not one connected to a publication within zbMATH. One can estimate from this that more than 75% of references are connected to the published literature. Moreover, much of the rest consists of literature available at the arXiv, other repositories, or personal homepages. We used the following heuristic to detect links to nonliterature online resources. There are about 795,000 references containing a ('http', 'www.
', 'ftp') link to a website. Excluding the most common patterns to literature repositories leaves us with about 161,000 links. Of these, 20,518 are links to mathematical software as identified in the swMATH database [3]. For the remaining 141,000 references, we identified 3 common link patterns: references to mathematical online compendia such as the Online Encyclopedia of Integer Sequences [4], references to normative data like standards or benchmarks, and references to community-maintained websites such as Wikipedia or MathOverflow. There is a large variety of different links included, and it becomes clear that there is an extremely long tail of specific data used in relatively few publications. Although we did not yet identify a suitable method to classify the links automatically into rea-Mathematical Research Data -An Analysis Through zbMATH References Klaus Hulek (Gottfried Wilhelm Leibniz Universität Hannover), Fabian Müller (FIZ Karlsruhe, Berlin, Germany), Moritz Schubotz (FIZ Karlsruhe, Berlin, Germany), Olaf Teschke (FIZ Karlsruhe, Berlin, Germany) Are there mathematical research data? In fitting with our data-driven age, research data have become an increasingly important aspect of scientific work.
4. Reusability: research data should be available in a form that facilitates building upon these results in a manner that is as efficient as possible. This also prevents the unnecessary repetition of work and uses human resources and available publication space more efficiently. 5. Long-term preservation: storage of research data in a dedicated infrastructure framework ensures that its longevity is independent of individuals or institutions. our data from swMATH confirm a linear growth of the citation rates of the DLMF project. Despite the prominent link to citation instructions (http://purl. org/zb/4) only fewer than 1% of all citations that DLMF received in the zbMATH database use a deep link to a chapter, formula or section. This example illustrates that although the heuristic above may be helpful in identifying interesting datasets, the distinction "literature" vs.
OEIS The On-Line Encyclopedia of Integer Sequences (OEIS [4]) is a browsable and searchable online resource launched in 1996 that grew out of N. J. A. Sloane's 1973 book A Handbook of Integer Sequences [13]. Starting in 1994, there are 2,752 references to it in zbMATH (http://purl. org/zb/5). Of these, more than 70% cite OEIS as a whole, while the remaining refer to one or, in about 5% of the cases, several actual entries of the database (with a single reference citing as many as 14 sequences in one case). However, in contrast to the previous example, the references to the online service have quickly substituted those to the printed handbook (compare to http://purl. org/zb/6). The easy usability of the OEIS and its powerful search features (which benefit from the rather simple data shape of integer sequences) appear to be a crucial factor here, making it a model for highly findable, accessible, and reusable mathematical data.
Examples Singular While it is still debated whether software code should be considered as research data, its output certainly is. Here we will take the example of the computer algebra system SINGULAR [6], which is widely used and has been frequently cited in mathematical papers throughout the last two decades (http://purl. org/zb/1). Here, as for other mathematical software, we can employ the swMATH database to track its usage in mathematical papers, although it is frequently referenced in a rather diverse form, ranging from the direct weblink or the manual to the related book [7] (see [8] for the current status of standardisation for software citations). An analysis of these publications reveals that the involved computational results almost never exist in a fully FAIR form, although the initial additional effort would likely pay off greatly in the long term.
This appears to be a general issue for computational results: The recent article [9] demands (emphasizing the reproducibility aspect) that results should be reproducible in identical, and comparable to runs in varied, settings. For long-running computations, this involves in particular the explicit saving of intermediate states (checkpoints). This involves among other things an exact specification of the computing environment used (software, libraries, versions, etc. ) and the possibility for the full publication of all relevant entities (i. e. code/algorithms together with input datasets and results). Overall, while mathematics already enjoys an appropriate service to interlink information on the used software via swMATH, the task of adequately documenting the computational output still needs to be addressed.
This is also confirmed by a recent study by the NIST library [12]  based on citation data from the Web of science dataset, which obtained a similar pattern (cf. , Fig. 1 ). According to the NIST data analysis and the assumption of a linear growth model, the DLMF will be cited more often than the printed book as early as 2028. As depicted in Figure  1 , likely become an important issue in the future. The requirement of an utmost level of confirmability for mathematical results in connection with the growing importance of computer aided computations and proofs will almost certainly be a driving force in establishing standards which should eventually lead to an interconnected, powerful infrastructure. However, the amount of work required to reach this goal is substantial: mathematical research data exists in very different forms, from small databases through to diverse software and its output to huge amounts of data, some of them created in collaboration with other sciences.
Currently, they are not even always referenced in a transparent manner, but are often intrinsically connected to the literature. Building a framework that cross-links the various types of mathematical research data will require substantial metadata and semantic enrichment, enabling them to serve as "deep data" in an infrastructure facilitating new research dimensions, not just within mathematics but also its applications. To achieve this goal, we at zbMATH are investigating diverse approaches: For one, we analyze citation data and mathematical formulae to identify similar (or even plagiarized) content [25]. Moreover, we connect our datasets to external datasets such as Wikidata or MathOverflow [23, 24]. Additionally, after having switched to a LaTeX the input format for zbMATH reviews [22], we are considering to allow for semantically enriched LaTeX dialects as used in the DLMF and DRMF [21] projects, or optional semantic annotations for mathematical formulae via graphical tools [20].
Further resources, big data vs. deep data, interdisciplinary issues The reader is free to explore further examples by analysing our dataset of non-literature references generated by the procedure described above) available at github (http://purl. org/zb/8), e. g. , by checking for entries collected in the catalogue of mathematical datasets [15]. As indicated by the discussed examples, mathematical research data are typically no "big data" of many terabytes (although there exists, e. g. , the rather large collection of finite lattices [16]) but come along with highly diverse and sophisticated descriptional metadata, necessary to facilitate their FAIR usage. In this sense, mathematical metadata are rather "deep data" [19], which would require extensive semantic enrichment before they could be properly cross-linked with each other and the literature, finally leading to a framework from which a mathematician could benefit in everyday work.
The vision of a Global Digital Mathematics Library [17] can be understood as such an infrastructure. Another important aspect is, of course, interdisciplinarity. Mathematics, as the language of exact sciences, is naturally connected to other disciplines, which have their own collections of research data. These are often of a different nature, and are preserved according to the standards of the discipline. Large genome or medical datasets may also be of interest for mathematical work, but are associated with quite different legal and computational aspects. One may even ask whether a precise definition of mathematical research data is possible; certainly, the distinction is not always as clear as between Calabi-Yau data (mathematical) and LHC measurements (physical) in high-energy physics. Mathematical modelling and simulation are now omnipresent in many sciences, and the related computations open up a whole new dimension of interdisciplinary research data [18].
Acknowledgements I thank  Jean Dhombres  who permitted me to exploit his book with  Jean-Bernard Robert  [D-R], and gave me the honour of reading a preliminary version of this article, allowing me to correct some of my mistakes. The two referees of the SMF contributed to significant improvements of the manuscript, making the text clearer and more precise while emphasising important points. Special thanks also to the  Institut de Mathématiques de Jussieu-Paris Rive gauche and Sorbonne Université  for providing me with all the resources to do this work. The EMS Newsletter thanks  La Gazette des Mathématiciens  for authorisation to republish this text, which is an english translation of the paper [ La Gazette des Mathématiciens ,  N°1 58 , pp. 7-24, Octobre 2018].
1. What is being evaluated and for what purpose? -Individuals or institutions? Research output or other forms of scholarly work? -For decisions about hiring, promotion and tenure? -For decisions about institutional resource allocation and continuation or cessation of funding for research centres or institutes? 2. What problems arise in evaluating scholarly work in mathematics education? -Mathematics education research journals are not ad- equately represented in citation databases. -Journal citation metrics are improperly used as an indi- cator of article quality. -Predatory publishers exploit inexperienced researchers.
Our analysis will be illustrated with an e-assessment system called TeSLA (Adaptative Trust-based e-assessment System for Learning), designed through a H2020 project funded by the European Commission. This system relies on several anti-cheating countermeasures which consist in the real-time gathering of several biometric samples to ensure the authentication of the learner during the whole remote assessment session. This process allows the university instructors to be convinced of the learner's identity when grading the assessment. However, biometric samples are regarded as highly sensitive data, from both ethical and legal points of view. Therefore, on top of normalizing the use of biometrics in an academic context, the choices made in the conception of this framework lead to an extremely large collection by the TeSLA system of various biometric samples from all enrolled learners, which should necessarily come with strong guarantees regarding the justification and the processing of such sensitive data.
This legal principle of minimization can receive two technical implementations. Firstly, the data controller should design his or her database expurged of any personal data not strictly necessary in order to accomplish the goal pursued. Secondly, it is also possible to select -initially -the data strictly necessary for the accomplishment of the purpose announced and -in a second step -to add personal data in order to ensure and maintain the integrity of the developed system. This second approach tries to legitimize the collection and processing of additional personal data for ensuring the security and correctness of a system. If both approaches are potentially valid, they must nevertheless be applied in accordance with all the principles of the GDPR. The second approach consists of a balance between two legal principles: data minimization and security.
The purpose of securing the data exchanges with the TLS protocol is on one hand to ensure the confidentiality and integrity of the data, and on the other hand, to make sure that no one is impersonating any of the components in the architecture. The underlying Public Key Infrastructure for the TLS management in the TeSLA architecture is explained in  [17]. A classic scenario of a learner taking an e-assignment can be summed up as follows. First, the learner has to log in on the VLE where the client-side plugin has been integrated. Then, the learner requests the e-assignment through the learner tool available on the VLE. Before sending the request to the TeSLA domain through the TEP, the real name of the learner is replaced by the TeSLA ID obtained from the TIP. Then, the TEP fetches the e-assignment in its database and sends it back to the VLE.
C. PRIVACY MANAGEMENT IN TeSLA Ensuring privacy consists in minimizing the personal information retrieved by the TeSLA system during its interactions with the learners, while anonymizing the data exchanged and stored in the databases whenever practical  [6]. The TeSLA architecture provides the learner with pseudonymized identifiers, which hide the learner's genuine identity when taking e-assessment activities. Yet, in the context of e-assessment, full anonymity cannot be provided to learners. Indeed, the very nature of the notion of assessment requires to grade identified students, and makes it mandatory to store the association between the TeSLA ID and the real name of the learner. Therefore, in such context, anonymity can only be partial. In other words, only pseudonymity can be provided to learners when taking an e-assessment.
Indeed, since the association between the learner's true identity and the TeSLA ID is stored in the TIP database, any claim of anonymization is impossible, even though the learner's identity is never transmitted to the TeSLA domain. The TeSLA ID is a random number computed by the TIP following version 4 of the UUID standard  [19]. The TIP database shall be shared with all the VLEs. Since any interaction between the university domain and the TeSLA domain involves the client-side plugin and the TEP, ensuring pseudonimity only requires that all requests sent to the TEP are first redirected to the TIP in order to obtain the learner's TeSLA ID. Regarding sensitive personal data, the most crucial aspect of this platform has to do with the management of the biometric samples. Indeed, as highlighted in Section IV-B, several types of biometric data are gathered in real time from the learner during the e-assessment process.
, hence significantly improving the learners' privacy. Another way to integrate anonymous certification into the TeSLA architecture consists in anonymizing the post processing of completed e-assignments. Indeed, as described in Section IV-B, after a learner takes an e-assessment, the assignment is sent to various external instruments, that perform anti-cheating post processings such as checking whether the assignment contains plagiarism. However, if the assignment is sent along with the TeSLA ID of the learner, it becomes technically possible for the instruments to keep track of a learner's data over time, as well as to correlate the data, for example in order to perform a more in-depth analysis for learners who happened to raise suspicions of cheating according to the results of other instruments.
In the case where anonymous certification is used instead, relying on the aforementioned attributes, it becomes impossible for instruments to perform such tracking and correlation, hence enhancing the learners' privacy. In terms of malleable signatures, we can envision the adaptation of the TeSLA architecture towards the combination of anonymization and sanitization. Similar approaches exist in the literature, in order to enforce lifelogging protection schemes  [22] , i. e. , environments expected to record information about everyday lives of users via smart devices. Similarly to TeSLA, lifelogging environments involve the collection of large datasets, including sensitive information about users interacting with smart devices such as personal assistants, mobile cellphones, and Internet-connected watches  [30].
B. TRUST AND TRANSPARENCY In terms of trust, enhanced features beyond learners' privacy can also be added to future releases of the architecture. A system like TeSLA, where learners have to take e-assessments under strict anti-cheating countermeasures, requires a high degree of trust from learners in order to be widely deployed and accepted as a legitimate assessment tool. TeSLA should provide public guarantees that its claims regarding privacy and security are met, meaning that TeSLA is as transparent as possible with respect to personal data management processes. Though it is not directly related to security and privacy, TeSLA should also ensure transparency regarding the anticheating decision processes, and let learners know how these decisions are made while informing them of possible resorts at their disposal in case of false positive detection.
However, the choice of biometric-based authentication for learners who are taking e-assessments entails other issues. Firstly, the biometric samples are collected from the learner's computer, which by definition has no guarantee whatsoever regarding security. Even if the samples are not meant to be stored on the learner's computer, the risk of personal data theft at this point is independent from the TeSLA architecture, but is induced by the choice to rely on biometry. As such, it should be taken into account for further improvement of the TeSLA system. Secondly, even though the biometric samples are anonymized before they are sent to the TeSLA instruments, it may be better not to send such sensitive data to TeSLA at all, and decentralize Trusted Third Parties (TTPs) as much as possible. The role of TeSLA is to offer a specific service, namely the possibility to take e-assessments.
VII. CONCLUSION We have addressed the influence of conception paradigms on data protection in virtual learning environments such as Moodle. We have argued that new e-learning environments may alter the relationship between students and academic knowledge, if they are not properly conceived. E-learning environments can potentially be used to track the students, profile their habits, and retrieve personal data. We have used as use case the recent advances of a EU project called TeSLA, which promotes the use of an e-assessment framework to remotely assess students, i. e. , by reducing physical studentteacher. We have illustrated our analysis by examine how the design choices impact the efficiency and legal compliance of personal data protection means. We have also proposed some alternative designs that could lead to significant improvements in these matters.
In this work we introduce and analyze a new and comprehensive multilayer dataset covering a wide spectrum of international relationships between coutries. We select two cross sections of the dataset corresponding to years 2003 and 2010 with 19 layers and 112 nodes to study the structure and evolution of the network. Country centrality is measured by the multiplex PageRank (MultiRank) and the multiplex hub and authority scores (MultiHub and MultiAuth). We find that the MultiHub measure has the highest correlation to GDP per capita, with respect to the other multilayer measures and to their single layer analogues. Finally we analyze the differences in the ranking between GDP per capita and the multilayer centrality measures to evaluate them as measures of development.
Data As acknowledged in the previous section, this work takes advantage of previous applied network studies mapping international relations among countries. We have collected and integrated multiple data sources in a unique dataset spanning several years and we have selected among the different layers the ones we thought were the most significant to describe the role of a country in the global system. Figure  12  in the Appendix provides a full overview of all the data we collected for years 1990-2015, also with examples of layers which have not been included in our multiplex. In this section instead we first explain the criteria we have followed to select layers, then we describe their main structural properties and finally we describe the method we have used to normalize them. All the details about the sources we have consulted for the selected layers are reported in Table  1  with references to the literature and URL links to online sources when retrievable.
A selection of international networks Our main objective in building the international multiplex has been to identify two cross sections with a sufficient number of nodes and layers to compare the structure of the multiplex before and after the Great Recession. From the larger dataset showed in Fig. 12  in the Appendix we have first selected a set of layers with non overlapping characteristics, avoiding duplicates and partial sources. The selection of the two years in the dataset to be compared is based on two criteria: first network measures often evolve slowly, thus a sufficiently long period is needed to capture some changes; second depending on the choice of the years some layers might not be present in the final selection, due to lack of data. To partially mitigate this second issue we have relaxed the constraint on some significant layers including them in the final cross sections even when they are missing in a given year, but data are available for an interval around it.
By choosing years 2003 and 2010 as reference points we obtain the results shown in the last two columns of Table  1 , t 0 and t 1 , where we can see that the two cross sections are scattered: migration stock, FDI and aid layers are not available for the 2003 reference year 5. In the rest of the paper we will occasionally refer to the cross section in 2003 as first cross section and to the crosssection in 2010 as second cross section. Our main goal in the construction of the international multiplex was to include as many countries as possible. By looking at the column nodes of Table  1  one can see that the number of reported nodes for some layers differs across time. To obtain a stable and large set of countries we proceeded in two steps. First, we have identified non missing observations: if the node is the target or the source of at least one non-zero edge in any of the years available for a given layer it belongs to the dataset.