dataesr/scientific-paragraphs-categorization huggingface dataset:
Computational method NMA is formulated based on the harmonic approximation of the potential energy surface (Fig. 2 ). After energy minimization, the Hessian matrix is obtained and normal mode vectors and frequencies are evaluated. As a result, the internal motion of a protein is represented as a linear combination of independent normal mode vectors. This model provides useful descriptions of a complex protein molecule, and a large number of computational biophysics researchers have used this model to demonstrate not only protein dynamics but also various molecular properties such as ligand binding, volume fluctuation, heat and energy transfer, allosteric transition and so forth. Later in 1996, Tirion proposed a model called elastic network model (ENM) with simplified force field functions  [5] , and Sanejouand and coworkers developed the elNémo program based on ENM  [6, 7].
Horiuchi and Go performed molecular dynamics (MD) and Monte Carlo (MC) simulations of human lysozyme and projected these trajectories onto few normal mode axes, and demonstrated that few normal modes determine the characteristic features of protein dynamics  [9]. Kidera, A. , et al. formulated their original method for anisotropic refinement of x-ray structure factors based on NMA, and successfully separated the internal motions and external motions of human lysozyme  [10]. NMA was applied to NMR refinement  [11, 12]  as well as x-ray refinement. Recent developments in cryo-electron microscopy (cryo-EM) combined with NMA opened up new possibilities for the study of macromolecular structures  [13, 14]. Wako and coworkers provided a database of protein motions, ProMode, so that normal mode vectors are visualized on their website  [15].
Moreover, the above-described code was compiled with gcc 4. 8. 5 with the -Ofast and -march=native flags and executed on a i7-3770K processor with 8GB of main memory operated by CentOS 7. 3. No parallelism was exploited. The average modular multiplication time for the HyPoRes, pure-RNS and HPR representations can be seen in Fig. 3. Figures  2  and 3  suggest that, although a similar performance is attained for both HyPoRes and a pure-RNS approach for the prime P 383 , the HyPoRes system has a better scalability as the bit-length of the primes increases. This behaviour was predicted in Section V. Moreover, while second-order factors, such as the number of additions, limit the obtained speed-up of HyPoRes when compared with a pure-RNS approach for the smaller primes, when comparing the theoretical predictions of Fig.
ACKNOWLEDGEMENT This work was partially supported by national Portuguese funds through Fundacão para a Ciência e a Tecnologia (FCT) by the  Ph. D. grant with reference  SFRH/BD/103791/2014 ; by the  ANR  grant  ARRAND 15-CE39-0002-01 ; through the  Pessoa/Hubert Curien  programme with reference 4335 (FCT)/40832XC (CAMPUSFRANCE); and by  EU 's  Horizon 2020 research and innovation programme  under grant agreement No. 779391  (FutureTPM).
Introduction Since the sharing of research data is of great scientific, economic and social importance, the movement of Open Access is expanding more and more to research data. Indeed, the sharing of research data has several advantages in terms of efficiency, power and rigor. To take advantage of these benefits, many funding agencies put policies in place to promote the optimal use and reuse of data in which their funds were invested. They encourage good data practices, investing in data infrastructure and raising data awareness  (Swan & Brown, 2008). In addition, several formalized data management services and tools  (Figshare, Zenodo, Nakala, Dryad)  have emerged as a result of funding agency's requirement that the grant applicants explicitly document their methods of storing research data and make them available for future use.
We consider these counts informative for digital resources  (Konkiel & Scherer, 2013) , therefore we will use this alternative online metrics to measure the visibility of data. The following research questions drive this of Figshare: Is there any difference in community engagement depending on the discipline? Does institution affiliation have any impact on the visibility of research data on Figshare? To answer these questions, we have created a web application to automatically query the Figshare API; the source code is available as open source in GitHub 1.
The potential benefits are enormous but the same is true for the costs. We therefore need to lay the right foundations and the sooner we start the better  (HLEG, 2010). Data sharing can cause practical problems  (Borgman, 2012) , especially in terms of interoperability, standardization, collaboration, curation, decentralization of content integrity  (Girard, 2017). In addition, researchers who are the major producers of data can be very cautious and their current practices do not necessarily fit into this movement  (Prost & Schöpfel, 2015). The questions for the researcher are not simple either; the openness of the data implies that researchers are particularly concerned about the quality of the data they offer, as well as the clarity of the documentation attached to them. Should the researcher develop new practices and skills to enable others to replicate or reuse her/his data? Is it necessary for him to verify the integrity and interoperability of the data, to decide which data sets will be shared, considering in particular their potential for re-use and the costs generated, respecting the terms of the legal, economic, ethical and technical sharing  (Carbou, 2017) ? In addition to depositing a dataset in a repository, the researcher should also provide the appropriate information about the dataset, which is known as metadata.
Metadata describes the dataset and makes it possible for others to find, understand, and reuse the data. Besides standard information such as the creator and contributors of the dataset, the title, year of publication, and access rights, it can be necessary to add documentation such as codebooks, lab journals, informed consent forms and used software. There are various metadata standards for different disciplines, according to which the researcher must supply relevant additional information, necessary to make specific datasets comprehendible for the other users. For example, archaeological datasets require metadata about the spatial coverage area, while linguistics datasets require information about the language. Thus, as sharing is not an end in and of itself, the researcher should also develop new practices and skills to make the reusing of her/his data possible.
We also used the concept of a "group" to differentiate articles deposit by an institution account and those deposited by a researcher without a known institutional membership. Figshare makes it possible to manage several types of data: -Book -Chapter -Composition -Conference contri- bution -Data management plan -Dataset -Educational resource -Event -Figure -Funding -Journal contribution -Media -Monograph -Online resource -Peer review -Performance -Physical object -Poster -Preprint -Presentation -Report -Software -Standard -Thesis -Workflow Figshare API makes it possible to run out queries by filtering the results by the "type of item". However, we did not apply a particular filter for the type because we wanted to analyze all types of data. We encountered difficulties in determining the geographical origin of the data from research conducted in the area of human sciences present in Figshare.
The API does not provide this information. To work around this problem we had to recover the ORCID of the author, and then query the ORCID database through its API. Unfortunately, the majority of authors on Figshare do not have expose their ORCID ID. We therefore could not know if this warehouse is used by researchers in some countries more than others; thus our identification of the country of origin of the resource and the design of Figshare and its API was limited. The set of subcategories of Humanities does not necessarily correspond to disciplines, so we have selected only six, which were the most generic and which corresponded best to universally known disciplines. These were Linguistics, Law, History, Philosophy, Art and Literature. To measure community engagement, it is possible to use the overall user acceptance taking into account the quantitative (number of users) and qualitative (best practices) aspects  (Koureas et al.
Web application for data processing The next step of our study was the creation of a web application that allowed us to automatically execute various queries and to perform the necessary processing to restore the information that interests us. The creation of such application 3  was very beneficial and allowed us to reproduce the results of this research at any time without having to store them in personal databases. The application retrieved data from Figshare and performed the necessary calculations to generate tables with all expected statistics. The goal of having a reusable application was to eventually expand the scope of this study in the future, and to make any researcher able to reproduce the results at any time. Indeed, our web application allowed restoring the data for a category and a period of the researcher's choosing.
For this study, our parameters were the category Humanities and the period between January 1, 2015 and July 1, 2019, but it was possible to change the scope of the study without additional effort to collect and process the data. Note that the application could also download the data generated in CSV format for use by other tools for analysis. Figshare presents a set of categories on main navigation page 4  including the category Humanities, but when downloading an article, it allows authors to select additional sub-categories from a longer list. In our case study, we looked at 163 subcategories. The application allows restoring the information for all subcategories of Humanities and groups the results by subcategory; they are then displayed in a web page with a paging system to browse (Fig.
Altmetrics refers to data sources, tools, and metrics (other than citations) that provide potentially relevant information on the impact of scientific outputs (e. g. , the number of times a publication has been tweeted, shared on Facebook, or read in Mendeley). It also allows a broader interpretation of the concept of impact and to more diverse forms of impact analysis  (Waltman & Costas, 2014). According to Taylor and Plume altmetrics hold great promise as a source of data, indicators and insights about online attention, usage and impact of published research outputs  (Taylor & Plume, 2014). Since Figshare tracks the views and downloads statistics for the published research data, these statistics indicators were used in this study as a source for altmetrics to measure the visibility of articles.
Affiliated vs non-affiliated datasets Figshare offers a service for institutions that ensures that their data management requirements are made transparently for the researcher. This feature has attracted some universities' attention which have chosen Figshare for Institutions for management of their research data. For the six chosen fields, the majority of articles were not affiliated to any institution, and only 30% of articles had metadata records about that issue. But there was a small difference between the fields. 60%of articles in Philosophy were affiliated to an institution, followed by 53% for Art. The percentage of affiliated articles for the four other fields was lower: 35% for History, 33% for Literature, 22% for Law and only 12% for Linguistics (Fig. 4 ).
Visibility of articles Figshare provides access to online metrics, such as the number of views that we used for measuring the visibility of open data. The number of views was very uneven. We used the quartiles for each field to calculate the interquartile range, which is a measure of variability around the median. The quartile breaks down the data into quarters so that 25% of the measurements are less than the lower quartile, 50% are less than the mean, and 75% are less than the upper quartile (Tab. 2). Overall, 50% of articles has been viewed less than 52 times and 75% less than 197 times. Nevertheless, the variability around the median changes from one discipline to another. For example, 75% of Law and History articles has been viewed less than 95 times. But in the fields of Philosophy, Literature, Linguistics and Art, 50% of articles has been viewed more than 95 times.
Community engagement The current study found that there is a difference in participation in depositing research data on Figshare across the disciplines in Humanities. The number of articles in Linguistics, Law and History was twice as large as in Philosophy, Art, and Literature. We also noticed that the growth curve increased at different rates according to the disciplines. The disciplines with a higher number of affiliated articles (Philosophy, Art) had more stable evolution curve. However, while an almost identical number of articles had been uploaded on Figshare each year, the disciplines with a higher number of affiliated articles had consistently very low contribution. The contribution in disciplines with a large number of unaffiliated articles (Linguistics, Law) increased quickly from year to year and these were the disciplines with the highest number of articles published.
For History and Literature, the distribution of affiliated and unaffiliated articles was identical, but overall, the number of contributions was higher for History, and their respective growth curves changed in very different ways. The results showed that while the Humanities community engagement for Figshare varied across the disciplines, overall, it remained very weak. Indeed, the total number of datasets published in the six disciplines was very low (1361 articles for History) for almost five years. The community engagement was not related to affiliation to institution. The presence of humanities data on Figshare was not always framed by institutional membership policies. Humanities researchers were using use Figshare to for opening their research data even if the institution they work for is was not affiliated to this data warehouse.
Previous studies have shown that the management of research data is still not common practise among researchers in the humanities  (Chowdhury et al. , 2018; Neylon, 2017; Prost & Schöpfel, 2015; Stuart et al. , 2018). In humanities, individual research can rarely generate shareable data, but digitization initiatives create shareable resources such as photographs of works of art, historical documents or cultural artefacts  (Gorman, 2007). This explains why History is at the top of the list in terms of the number of articles published on Figshare. Therefore, it may be interesting to compare this results with other data warehouses like Zenodo or Dryad to study if this phenomenon is typical to Figshare or is it common in humanities practices. Technically, Figshare allows its users to download multiple data formats, so it would seem that it overcame the challenge of managing the heterogeneity of data in human sciences  (European Commission, 2015).
However, we noticed that the descriptive categories used for the data types are more or less common in the six disciplines. Indeed, the most used item type were "journal contribution", then "dataset" followed by "figures", "presentations", and "media". Nevertheless, it is interesting to note that journal contribution which is at the top of the list for all the disciplines corresponds to any type of content officially published in a scientific journal, following a peer review process. This implies that Figshare is used to satisfy the requirements of funding agencies or publishers for opening research data. The majority of data published on Figshare was described by users as journal contribution, ignoring the other types even if they are more precise about the real data format.
Visibility analysis by disciplineand by affiliation or not to an institution More than 75% of articles was viewed less than 197 times and downloaded less than 45 times. This result means that the use of Figshare is far from being enough to insure the visibility and the reuse of data. Even if some of the uploaded resources have generated much interest, the majority of research data in humanities was not viewed, and even less was downloaded. This calls into question the quality of the metadata. To be reusable, a data must first be findable. A recent study has shown that a large majority of researchers never or rarely use a metadata standards for describing research datasets  (Chowdhury et al. , 2018). The program Figshare for Institutions ensures that their data management requirements are met transparently for the researcher.
Conclusion and perspectives This study allowed us to highlight that, even if Figshare is part of logic of openness, the design of each tool determines its use, and that the exploitation of data remains closely tied to the warehouse policy and the services it makes available. We encountered limitations with our attempts to determine the country of the researcher or institution responsible for depositing the dataset in terms of the design of Figshare research features and its API. The identification of the resource's country of origin was not possible. Findings of this study support those of several abovementioned studies In the arts and humanities, it seems that sharing data globally and reusing it is not yet a common practice among the researchers  (Chowdhury et al. , 2018; Neylon, 2017; Prost & Schöpfel, 2015; Stuart et al.
, 2018). The humanities community engagement on Figshare remains low. Even when research data was published, 50% is rarely viewed and less downloaded. The results of our study provided some specific details about the affiliated articles: 70% of humanities research data on Figshare was not affiliated with any institution, but for the 30% of affiliated data, a higher average of visibility was recorded. This study has shown that affiliated articles were more viewed than unaffiliated ones published in the same period. Thus, it seems that the establishment of the warehouses, is certainly a step in the right direction, but is far from being a sufficient mean to ensure that the potential benefits will be fully exploited. We believe that the supply of these warehouses must become a systematic part of the process of producing scientific knowledge.
The increasing volume of scientific datasets requires the use of compression to reduce data storage and transmission costs, especially for the oceanographic or meteorological datasets generated by Earth observation mission ground segments. These data are mostly produced in netCDF files. Indeed, the netCDF-4/HDF5 file formats are widely used throughout the global scientific community because of the useful features they offer. HDF5 in particular offers a dynamically loaded filter plugin so that users can write compression/decompression filters, for example, and process the data before reading or writing them to disk. This study evaluates lossy and lossless compression/decompression methods through netCDF-4 and HDF5 tools on analytical and real scientific floating-point datasets. We also introduce the Digit Rounding algorithm, a new relative error-bounded data reduction method inspired by the Bit Grooming algorithm.
These methods increase the compression ratio by removing the least significant digits in the data. Indeed, at some level, these least significant digits may not be scientifically meaningful in datasets corrupted by noise. This paper studies compression and clipping methods that can be applied to scientific datasets in order to maximize the compression ratio while preserving scientific data content and numerical accuracy. It focuses on methods that can be applied to scientific datasets, i. e. vectors or matrices of floating-Published by Copernicus Publications on behalf of the European Geosciences Union. point numbers. First, lossless compression algorithms can be applied to any kind of data. The standard is the "Deflate" algorithm  (Deutsch, 1996)  native in netCDF-4/HDF5 libraries. It is widely used in compression tools such as zip, gzip, and zlib libraries, and has become a benchmark for lossless data compression.
Preprocessing methods and lossless compression can then be applied to obtain a higher compression ratio. This paper focuses on compression methods implemented for netCDF-4 or HDF5 files. These scientific file formats are widespread among the oceanographic and meteorological communities. HDF5 offers a dynamically loaded filter plugin that allows users to write compression/decompression filters (among others), and to process data before reading or writing them to disk. Consequently, many compression/decompression filters -such as Bitshuffle, Zstandard, LZ4, and Sz -have been implemented by members of the HDF5 user community and are freely available. The netCDF Operator toolkit (NCO)  (Zender, 2016b ) also offers some compression features, such as Bit Shaving, Decimal Rounding and Bit Grooming.
The rest of this paper is divided into five sections. Section 2 presents the lossless and lossy compression schemes for scientific floating-point datasets. Section 3 introduces the Digit Rounding algorithm, which is an improvement of the Bit Grooming algorithm that optimizes the number of mantissa bits preserved. Section 4 defines the performance metrics used in this paper. Section 5 describes the performance assessment of a selection of lossless and lossy compression methods on synthetic datasets. It presents the datasets and compression results before making some recommendations. Section 6 provides some compression results obtained with real CFOSAT and SWOT datasets. Finally, Sect. 7 provides our conclusions.
Compression algorithms Compression schemes for scientific floating-point datasets usually entail several steps: data reduction, preprocessing, and lossless coding. These three steps can be chained as illustrated in Fig. 1. The lossless coding step is reversible. It does not degrade the data while reducing its volume. It can be implemented by lossless compression algorithms such as Deflate, Snappy, LZ4 or Zstandard. The preprocessing step is also reversible. It rearranges the data bytes or bits to enhance lossless coding efficiency. It can be implemented by algorithms such as Shuffle or Bitshuffle. The data reduction step is not reversible because it entails data losses. The goal is to remove irrelevant data such as noise or other scientifically meaningless data. Data reduction can reduce data volume, depending on the algorithm used.
For instance, the Linear Packing and Sz algorithms reduce data volume, but Bit Shaving and Bit Grooming algorithms do not. This paper evaluates lossless compression algorithms Deflate, LZ4, and Zstandard; Deflate because it is the benchmark algorithm, LZ4 because it is a widely-used, very-highspeed compressor, and Zstandard because it provides better results than Deflate both in terms of compression ratios and of compression/decompression speeds. The Deflate algorithm uses LZ77 dictionary coding  (Ziv and Lempel, 1977)  and Huffman entropy coding  (Huffman, 1952). LZ77 and Huffman coding exploit different types of redundancies to enable Deflate to achieve high compression ratios. However, the computational cost of the Huffman coder is high and makes Deflate compression rather slow. LZ4 is a dictionary coding algorithm designed to provide high compression/decompression speeds rather than a high compression ratio.