dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction Acetylcholinesterase (AChE; EC 3. 1. 1. 7), an α/β-hydrolase fold serine hydrolase, has evolved to catalyze termination of cholinergic nerve impulses at microsecond time-scale, allowing for efficient neurotransmission. As one of the premier biological catalysts it accelerates hydrolysis of neurotransmitter acetylcholine by an estimated 13 orders of magnitude  (Quinn, 1987). This large scale enhancement is assumed to be enabled by specifics of the 3D structure of its ~65 kDa globular catalytic subunit where the tetrahedral intermediate in ACh hydrolysis finds near-perfect accommodation at the base of a narrow, 20 Å deep active center gorge lined with predominantly aromatic amino acid side chains. Notably, no structural evidence for any significant changes in conformations of the backbone or even side-chains have been found associated with its efficient catalytic cycle, as judged by modest diversity within more than 200 X-ray structures of AChEs obtained at cryo-temperatures (~100K) and available at the Protein Data Bank (PDB; wwPDB consortium, 2019).
MMB4 binds at the peripheral anionic site (PAS) making π-π stacking interactions with Trp286 and does not protrude beyond Tyr124 choke point in the active site gorge. We compared 4K-TMA:hAChE complex with the cryotemperature (100K) structures of apo-protomer in hAChE:LG703 complex (PDB ID 6U3P,  Gorecki et al. , 2020)  and apo-hAChE (PDB ID 4EY4,  Cheung et al. , 2012) , and also with mouse 4K-TMA:mAChE complex  (Bourne et al. , 2006)  revealing structural differences in the active site cavities that can be explained by binding of the covalent reversible inhibitor or attributed to temperature effects. Conversely, binding of the MMB4 oxime to the 4K-TMA:hAChE complex does not perturb the conformation of the active site gorge. In addition, we were able to grow large 4K-TMA:hAChE crystals allowing a room-temperature neutron diffraction data collection to 3.
X-ray diffraction data collection and structure refinement Prior to the diffraction data collection crystals of 4K-TMA:hAChE or 4K-TMA:hAChE:MMB4 complexes were mounted on Molecular Dimensions elliptical Litholoops using the MiTeGen (Ithaca, NY) roomtemperature capillary setups. Room temperature X-ray crystallographic data from 4K-TMA:hAChE were collected using a Rigaku HighFlux HomeLab instrument equipped with a MicroMax-007 HF X-ray generator and Osmic VariMax optics. The diffraction images were obtained using an Eiger R 4M hybrid photon counting detector. Diffraction data were integrated, reduced and scaled using the CrysAlis Pro software suite (Rigaku Inc. , The Woodlands, TX). Room temperature diffraction data from 4K-TMA:hAChE:MMB4 were collected on the ID19 beamline at SBC-CAT using a Pilatus3 X 6M (ID19) detector at the Advanced Photon Source (APS).
These data were integrated and scaled using the HKL3000 software suite  (Minor et al. , 2006). The structures were solved by molecular replacement using Phaser  (McCoy et al. , 2007)  from the CCP4 program  (Winn et al. , 2011)  and the starting model from PDB code 6O5R  (Gerlits et al. , 2017). The refinement of the protein structures was conducted using Phenix. refine (Afonine et al. , 2012)  from the Phenix  (Adams et al. , 2010; Liebschner et al. , 2019)  suite of programs and the COOT  (Emsley et al. , 2010 ) molecular graphics program. The geometry of each final structure was then carefully checked with Molprobity  (Chen et al. , 2010) ; the data collection and refinement statics are shown in Table  1.
Neutron diffraction data collection The largest crystal of 4K-TMA:hAChE complex was mounted in a Vitrocom 2 mm-inner diameter quartz capillary containing a stabilizing solution made with 99. 8% D 2 O. H/D exchange was allowed to occur for 1 month before commencing neutron diffraction experiments. Neutron diffraction was tested at room temperature on the IMAGINE  (Meilleur et al, 2013 (Meilleur et al, , 2018 (Meilleur et al, , 2020;; Coates et al. , 2018)  instrument located at the High Flux Isotope Reactor (Oak Ridge National Laboratory) using the broad bandpass functionality with neutron wavelengths between 2. 8 and 10 Å. The quasi-Laue neutron diffraction dataset to 3. 5 Å resolution was then collected at room temperature on the LADI-III beamline at the Institut Laue-Langevin, Grenoble, France  (Blakeley et al.
, 2010)  using neutron wavelengths between 3. 45 and 4. 55 Å. A representative neutron diffraction image is shown in Fig. 2B. 22 images were collected from two different crystal orientations. At each orientation, the crystal was held stationary at different ϕ settings for each 18-h exposure. The neutron data were processed using the Daresbury Laboratory LAUE suite program LAUEGEN modified to account for the cylindrical geometry of the detector  (Campbell, 1995; Campbell et al. , 1998). The program LSCALE  (Arzt et al. , 1999)  was used to determine the wavelength-normalization curve using the intensities of symmetry-equivalent reflections measured at different wavelengths. No explicit absorption corrections were applied. These data were then merged in SCALA  (Weiss, 2001). The summary of experimental data statistics is given in Table  1.
4K-TMA is a non-hydrolyzable substrate analogue that produces a reversible hemiketal tetrahedral intermediate, when the carbonyl carbon reacts with the nucleophilic oxygen of the catalytic Ser203 side chain, in equilibrium with unreacted ketone (Fig. 1B ). Using a large crystal (~0. 6 Â 0. 3 Â 0. 3 mm ¼ 0. 05 mm 3 , Fig. 2 ) of hAChE:4K-TMA complex we collected a room-temperature X-ray diffraction dataset to 2. 8 Å resolution on the in-house X-ray source (Table  1 ). There is clear electron density for the tetrahedral adduct covalently attached to the hydroxyl oxygen of Ser203 side chain, indicating that the hemiketal intermediate is trapped in the crystalline structure (Fig. 3A ). We carefully examined the 2F O -F C and difference F O -F C electron density maps and found no indication of radiation damage to the hemiketal of the covalently bound 4K-TMA adduct in our room-temperature structure.
Comparison of 4K-TMA: hAChE complex with the 100K apo-hAChE and mouse AChE:4K-TMA structures The covalent binding of 4K-TMA ligand to the catalytic Ser203 does not dramatically alter the active site cavity of the enzyme, showing limited conformational changes (Fig. 4A ). The room-temperature structure of the 4K-TMA:hAChE complex and the 100K structure of apo-hAChE (PDB ID 4EY4,  Cheung et al. , 2012)  superimpose with a root mean square deviation (r. m. s. d. ) of 0. 7 Å for the main chain atoms. The position of the His447 side chain shifts slightly due to the presence of the covalent ligand, but imidazole maintains the hydrogen bond with Ser203. Trp86 and Trp236 that act as the walls of the choline binding site and the acyl pocket, respectively, move apart by ~0. 6 Å to accommodate the hydrophobic groups of the 4K-TMA hemiketal.
Room temperature neutron diffraction data A neutron diffraction-quality crystal of 4K-TMA:hAChE complex was grown using a temperature-gradient methodology. Large crystallization drops of 200 μL in volume were initially set up at 10 C. After the crystals were allowed to grow for one month, the crystallization temperature was gradually lowered to 6 C over a period of another month. The crystallization trays were then kept at 6 C for one more month. Crystal growth halted after about 3 months in total and the crystals did not become bigger when the temperature was reduced further to 4 C. The largest crystal grew to a volume of ~0. 1 mm 3 ; it was mounted in a quartz capillary using a stabilizing solution made with D 2 O and allowed to exchange with deuterium for one month (Fig. 2A ). Neutron diffraction data were collected using the quasi-Laue technique on the nuclear reactorbased LADI-III beamline at the Institut Laue-Langevin.
The geometry and nature of interactions of the tetrahedral intermediate mimick thus remain undisturbed by either somewhat enhanced protein dynamics in crystals at the room temperature or by low affinity ligand binding at the PAS of hAChE. We thus conclude that the large magnitude of catalytic acceleration in ACh turnover by hAChE comes from its highly evolved active center geometry and electronics where the tetrahedral intermediate of ACh fits perfectly   well, without the need for any conformational adjustments that would require an input of energy. This is even consistent with observations in active centers of catalytically closely related yet structurally slightly more diverse cholinesterases: secondary isotope effect experiments in Drosophila melanogaster AChE  (Tormos et al. , 2010)  and with X-ray structures of tetrahedrally distorted ligands stabilized by active serine in human butyrylcholinesterase  (PDB IDs 1P0I and 6XYY, Nicolet et al.
Acknowledgments The  Office of Biological and Environmental Research supported research at the Center for Structural Molecular Biology (CSMB)  at ORNL using facilities supported by the  Scientific User Facilities Division, Office of Basic Energy Sciences, U. S. Department of Energy. This research using IMAGINE beamline at the  Oak Ridge National Laboratory 's (ORNL) High Flux Isotope Reactor (HFIR) was sponsored by the  Scientific User Facilities Division, Office of Basic Energy Sciences, U. S. Department of Energy. We thank the  Institut Laue-Langevin  ( ILL, Grenoble, France ) for awarding neutron beamtime on the neutron diffraction beamline LADI-III.
Ciaian et al. (2016) ,  Figá-Talamanca and Patacca (2019, 2020) ,  Cretarola et al. (2020) ,  Ahn and Kim (2019)  and  Eom et al. (2019)  investigate whether Bitcoin returns and volatility are associated with investor attention, sentiment or by specific measures of market attractiveness. Structural breaks and/or bubbles in the dynamics have been evidenced, among others, in  Garcia et al. (2014) ,  Cheah and Fry (2015) ,  Fry and Cheah (2016) ,  Corbet et al. (2018) ,  Bouri et al. (2019) ,  Cretarola and Figà-Talamanca (2019, 2020) ,  Chaim and Laurini (2019)  and  Agosto and Cafferata (2019). Most of the above contributions focus on Bitcoin either because of data availability or as a benchmark for the whole sector. Indeed, the price dynamics of several cryptocurrencies display common movements to that of Bitcoin, e.
Preliminary analysis We consider, among the 20 cryptocurrencies with the highest market caps according to https://coinmarketcap. com/ on December 2019, those which existed and traded by January 2016: we end up with a sample for the price of four cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC) and Monero (XMR), observed from January 2016 to the end of November 2019. The first three years of data are considered for estimation purposes only, while observations from January to November, 2019 serve as test-dates in order to evaluate the profitability of the market neutral strategy to be defined thereafter. Table  1  summarizes descriptive statistics, and Figure  1  represents the prices behaviour of the four cryptocurrencies for the whole dataset. In line with the study of stylized empirical facts for general financial markets  (Cont 2001; Cont and Tankov 2004) , we perform the Augmented Dickey Fuller (ADF) and the Kwiatkowsky-Phillips-Schmidt-Shin (KPSS) tests on prices and price differences: the former tests the null hypothesis of unit root while the latter tests the null of trend stationarity against the alternative of a unit root.
A dynamic factor model for cryptocurrencies As already remarked, many cryptocurrencies are currently available for trading on online exchange platforms. It has been claimed in  Ciaian and Rajcaniova (2018)  and  Blau et al. (2020)  that the whole sector is possibly driven by the dynamics of Bitcoin (in particular in the short-run) or, alternatively, by a single common factor. Indeed, the price dynamics of several cryptocurrencies show a similar path, as evidenced in Fig. 1. We build on these findings by assuming that the prices of a basket of cryptocurrencies may be described through a dynamic factor model. If this is the case, it would be reasonable to explore the possibility of forming market neutral strategies, where gains and losses depend only on the relative behaviour of the assets. The dataset is split into two subsamples with the first three years of data (January 1, 2016-December 31, 2018) used only for model estimation and the remainder (January 1, 2019-November 30, 2019) as test dates for the performance of the market neutral strategy suggested in Sect.
We further assume, in line with  Koop and Korobilis (2010) , the independence between the factors and the error processes u i , η k , η h , for i = 1, 2,. , I and k, h = 1, 2,. K , with k = h, and between the error process u i and u j , for i, j = 1, 2,. , I , with i = j. Factors can be confidently estimated by means of principal components analysis in the limit case of an infinite market, in practice a very large market. When a limited number I of assets is given, dynamic factor models are usually estimated as state space models  (Lütkepohl and Poskitt 1991). We used the Matlab ® software for Bayesian models provided by  Koop and Korobilis (2009) , which adopts the latter methodology, since in the empirical application we have I = 4. When focusing on the first three years of our specific sample (T = December 31, 2018), the Johansen cointegration test confirms the existence of a common nonstationary factor, hence we expect one common factor, say f 1 , to be integrated I (1), see  Escribano and Peña (1994).
The univariate statistics of potassium (K) and thorium (Th) concentrations in the oceanic and continental crust of the Earth has been recently investigated from geochemical databases and airborne radiometric surveys [1]. This study demonstrates that the frequency distributions of these elements are scale-dependent. There are right-skewed for small-scale samples (typical volume of rocks analyzed for an individual sample in a dataset) but tend to be more symmetric for large-scale samples. The right-skewed behavior of K and Th is attributed to their incompatible behavior during partial melting or fractional crystallization. The scale-dependence and evolution toward normal distributions are a direct consequence of the central limit theorem applied to K and Th concentrations. The results of the results of this study may be applied to Mars, using the Mars Odyssey global.
Introduction Metadata is needed for concise knowledge communication of the energy transition to its stakeholders. The concept of metadata lends itself to the idea of maintaining a laboratory notebook: data would represent the outcome of a specific experiment, a physical notebook that contains how and what data was collected from a given project, and a database is the library where all notebooks from various projects are stored. All this data is connected to some context, which is captured by metadata here represented by a card index. Metadata enables navigation within and across databases. In other words, data is inevitably linked to its metadata, and without metadata, data loses usefulness  [1]. Hence, a credible and traceable documentation of knowledge about the energy system is not possible without metadata.
Building on a series of community workshops and discussions with dozens of researchers from June 2020 to December 2020, we elicit lessons learned and provide recommendations for advancing FAIR (meta)data standards for low carbon energy research. (We use the phrase '(meta)data' as a shorthand for 'metadata and data'. ) Thereby FAIR stands for Findability, Accessibility, Interoperability, and Reusability of data  [3]. Metadata plays a vital role in all the FAIR guiding principles. Out of the 15 criteria, 13 are explicitly referring to metadata. While metadata needs to be accessible by anybody, research data in energy science covers a wide range in openness, ranging from open data, to shared data, and closed data. While open data is understood as data 'that can be freely used, re-used and shared by anyone for any purpose'  [4] , shared data may be widely accessible, but have some conditions about its use.
Method to Enable Community-Wide Reflection on Metadata This report results from discussions within the low carbon energy research community organized through a series of online workshops between June and December 2020. The consortium of the EERAdata project has developed the method for community engagement  [5]. This project aims to create and establish a FAIR and open data ecosystem for low carbon energy research, for which the (small) project consortium is 'going where the data are'. EERAdata strives to transfer best practices in FAIRification developed in other communities to the low carbon energy research domain. To this end, a series of six workshops are conducted, following the concept of defining, implementing, and sustaining FAIR practices. The second workshop brought together experts for FAIR and open data with energy domain specialists with the aim of creating a shared understanding of FAIR and open energy (meta)data.
Existing gaps and needs cannot be looked at myopically as a researcher's problem but rather as a global problem. They will continue to negatively affect the FAIR and open data ecosystem in low-carbon energy if urgent steps are not taken to change the current narrative and practices. Secondly, constructive inputs from a wider community open up transparent discussions about metadata and tap into a diversified knowledge base. All broad community stakeholders should form resilient solutions for the current issues that hinder metadata standards. Thirdly, expert practitioners' input enabled non-specialists to understand recent research on specific topics better and generate further interest that may influence the longed-for change. All workshops were delivered in an online format. Participants could contribute to the working draft of this paper via online collaboration tools that were always available and accessible.
Writing Team 1 To address open issues in the white paper and gather feedback on the draft Writing Team 2 To discuss and draft a list of lessons learned on the metadata needs and practices in the energy domain Writing Team 3 To discuss and draft a list of recommendations on advancing metadata standards in the energy domain  As already discussed, metadata provides data to communicate the nature of the underlying data between data users, including data creators and data re-users. The extent of context needed naturally depends on the needs of the data users. Thus, a definition of the required richness and depth of metadata as well as the conditions for accessing (meta)data is inseparable from a stakeholder and needs analysis. The group of energy metadata users is diverse, ranging from different academic disciplines to the general public.
g. , missing years from time series, incomplete sampling, etc. ), lack of a user-friendly database, and (meta)data formats not being machine-actionable. The data itself should fit with the domain norms regarding semantics and packaging. In addition to this, metadata should, where possible, be provided in a Linked Data or RDF format, such as JSON-LD. The factors mentioned above decrease the ability to locate data while at the same time disrupting the pace of research, slowing adoption and use by other users  [18, 19]. Metadata and data should be registered or indexed in searchable sources that academics and other data stakeholders can easily access. Although a significant amount of data is continuously generated, only a small proportion is necessarily available in academic databases that are easy to find and access  [20].
In this sense, the lack of an appropriate format to store the data is a significant barrier since a considerable amount of information is not deposited in sustainable and widely-known repositories  [15]. Moreover, the available data is not always linked to metadata that conforms to any metadata schema. From a theoretical perspective, particularly in qualitative research, data is prone to inaccuracies, omissions, and inconsistencies  [21]. This weakens the reuseability of the data. Interdisciplinary research may be affected by a broader set of data and metadata-related challenges. For example, the interoperability criteria of the FAIR guiding principles has crucial importance and its lack means that essential information is merely absent from specific datasets, making academic research needlessly time-consuming to process multiple datasets, costly, labor-intensive, and error-prone when processing multiple datasets  [22].
The matter is even more pressing when aiming at a global adoption of circular economy concepts, in which the flows of resources, waste, and-consequently -data produced by different participants of an industrial symbioses need to be integrated. The coordination of such a deeply linked industrial infrastructure requires collaboration based on FAIR (meta)data. Finally, unresolved issues around the sharing of (meta)data and intellectual property rights need to be settled. Traditionally, the general public requires (meta)data that can be trusted to judge the accountability and transparency of governmental measures  [30] [31] [32]. Cantadora et al. [33]  emphasize the general public's data needs as a prerequisite for citizen participation in policymaking or community initiatives. The authors discuss the OECD's Open Government Data project that relies on making the governments' data available to the general public for transparency and accountability.
Gendron and Killian  [35]  refer to the concepts of a 'data citizen' and 'information democratization', whereby they argue that citizens lack knowledge on concepts like data agility and data literacy. For example, Anhalt-Depies et al. [36]  discuss knowledge gaps around privacy rights when participating in citizen-science activities. The empowerment of citizens regarding FAIR (meta)data knowledge and competences is also important for steering interest in the general public to take up new professions, such as energy data stewards and data analysts. Lhoste  [37]  investigates possibilities of engaging civil society in R&D with the help of specifically tailored research systems. Dunnett et al. [38]  is a best practice on harmonizing data for solar and wind farm locations by sourcing information from the collaborative OpenStreetMap project.
The specific gaps data scientists are confronted with are presented in regular surveys conducted by Appen, an artificial intelligence (AI) platform. Their annual Data Scientist Report presents the results of interviews with more than 100 participants on data management practices. The larger share of participants represents technology companies with sizes in a spectrum of 100 to more than 10,000 employees. The results from 2017  [39]  show that data scientists spend most of their time (51%) on data curation, i. e. , the collecting, labeling, cleaning, and organizing of data. At the same time, only 19% is devoted to data analysis and modeling. This imbalance could be rectified with the help of FAIR (meta)data. 51% of the respondents characterize the data they work on as unstructured data, pointing to the need of accommodating heterogeneous data.
However, ontologies are not limited to 1:1 relations because they also allow 'is similar to' or 'is connected to' connections. The problem also concerns the impossibility of replicating the complexity of reality-the number of links of one subdomain to others is simply too large. Even aligning a few sub-domains is beyond manual editing. However, algorithms can navigate vast amounts of relations as long as they are semantically linked. First attempts have been implemented to generate and align ontologies based on, e. g. , automated natural-language processing  [70]. Therefore, an automatically generated ontology was among those studied in the workshop, namely EuroSciVoc. This vocabulary organizes scientific topics that were extracted from the CORDIS database on EU projects. Analyzing energy-related entries, the classification fails in reproducing sufficiently the understanding of experts and links between subjects.