dataesr/scientific-paragraphs-categorization huggingface dataset:
Protocols for battery assembly previously defined for lab-scale cell formats  [109]  must be transposed to experimental LSF cells as those described in Figure  7. Common sample holders/changers and/or use of fiducial markers are crucial for data collection 1) across different types of probes / set-ups on the same battery and/or 2) across several batteries (Gear wheels in Figure  6 ). This fundamental attribute of the LSF workflow is common to other lab-scale multi-dataset methodologies, for instance multiscale correlation microscopy, where the routine collection and manipulation of data on samples across multiple instruments necessitates controlled sample transfers and spatial references to enable the transfer of system coordinates. [92].
On-The-Fly Analysis and On-Line Control of Data Acquisition Identifying and designing tasks that can be carried out without the need of an external operator must be at the forefront of the development of automated workflows. The implementation of automated data acquisition, when coupled with automated analysis, is a unique opportunity to develop better machine learning models, enabling the comparison of results from experimental and theoretical studies, as well as being crucial for future accelerated research. Integrating appropriate feedback loops, including extensive material databases and advanced theoretical predictions, promises to profoundly change our use of synchrotron and neutron sources for battery research. By the real-time interpretation of results acquired during operando measurements, data acquisition conditions such as cycling protocols, energy/angle ranges, time/length resolutions, regions of interest, and so on, may be modified on-the-fly by a centralized AI to provide direct and fast access to required information, and adapt acquisition strategies/trajectories (Blue box in Figure  6 ).
Innovative Data Management Perhaps the main challenge that battery researchers face today for multimodal testing is the acquisition, registration, and handling of often bulky and pluriform (multi-layered multidimensional) datasets -all of which share a common reference   [36]  Copyright 2013, Royal Society of Chemistry. Neutron diffraction cell from ILL/ICMCB/LRSC. Reproduced with permission. [94] opyright 2013, Electrochemical Society. Imaging cell from HZB. Reproduced with permission. [95] Copyright 2019, Wiley and Sons. Neutron diffraction cell cylindrical neutron diffraction cell from PSI. Reproduced according to the terms of the CC-BY license. [96] Copyright 2018, The Authors. Neutron imaging cell from PSI/LEPMI. Small angle cell from TUM/MLZ, Reproduced according to the terms of the CC-BY license.
Imaging cell from ESRF/Mateis, reproduced with permission. [103] Copyright 2021, American Chemical Society. Diffraction cell from ESRF, Reproduced with permission. [104] Copyright 2020, Royal Society of Chemistry. b) Progresses targeted within BIGMAP project to develop multi-techniques-compatible (NI/SANS) and/or multi-modal (NI/XCT) cells. c) Future developments at horizon 2030 for highly versatile cells compatible with an extended portfolio of X-rays and neutron tools. system. Appropriate software/methodology must be developed capable of handling such diverse and complex datasets. Dedicated data management methodologies, if possible generalized across the community,  [110]  are needed in order to treat these large datasets generated from advanced workflows. In this respect, a promising candidate is the CHADA method  [111]  using the MODA system developed by the European Material Characterisation Council (EMMC) and adaptative to three particular correlative workflows, that is, sequential, interactive, and coupled systems, respectively.
Novel AI-Strategies High-throughput instruments of course require brilliant beams coupled with advanced detectors in order to generate sufficiently large data sets for high-fidelity data, whilst minimizing acquisition times to remain economically viable. In the context of BIG-MAP, the possibility of generating and analyzing large amounts of high-fidelity data at acceptable costs will be fundamental during the initial AI/ML/DL training process. Physicsor machine-learning-based models can be used to interrogate whole datasets and/or select sub-sets to be analyzed preferentially in order to reconstruct global information, therefore saving time and enabling correlations not foreseen during the experiments. For instance, the 3D reconstruction and segmentation of tomographic data can be accelerated by reducing the number of slices and filling the gaps to obtain the full highresolution mappings, without the need to exploit all acquired slices, or indeed automatically modifying the acquisition protocol itself to select the appropriate amount of data needed.
Combinations of coarse-scale specimen-mapping followed by high resolution mappings in selected regions save time and optimize scientific output. The development of "AI-based packages" capable to manipulate, visualize, and interrogate the multi-faceted, multi-scale datasets is required. For instance, the DRFAM3D software  [113]  employs a three-level classification, that is, "elements", "features", and "ensemble of features" that represent three different length scales. Each voxel contains, respectively both attributes from the input data along with output data obtained from analysis and modelling performed on the input data. [114] omplementary methods include advanced coupling to modeling, for example, for analyzing spectroscopic or diffraction data using DFT calculations. [115] Also, the advent of statistical methodologies such as principal components analysis (PCA) for analysing operando XAS and transmission X-ray microscopy represents a unique opportunity to extend automated analysis to other LSF techniques relevant to batteries.
New Access Modes and Infrastructures The multi-modal automated and correlative experimental workflow proposed in Figure  6  implies a profound methodological change for both battery and LSF scientists, to provide the framework for integrating and accelerating all aspects of battery characterization and development, and to achieve the move toward efficient reduction of big datasets into meaningful output parameters. Accordingly, new modes of cooperation must be organized to stimulate fast exchange and interconnections between the various communities in battery R&D (academic researchers, materials developers, industrials, endusers), LSF expert users, LSF scientists, computer scientists, big data analysts; bringing together advances in computer science, informatics, robotics, manufacturing, sensors, materials science (including combinatorial synthesis), and characterization science (including lab scale and LSF).
Acknowledgements The authors acknowledge  BATTERY 2030+  funded by the  European Union 's  Horizon 2020 research and innovation program  under Grant Agreement No. 957213. The authors acknowledge  BIG-MAP  funded by the  European Union 's  Horizon 2020 research and innovation program  under Grant Agreement No. 957189. Lukas Helfen  from  ILL  is acknowledged for his inputs in neutron sections. H. R. and E. C. acknowledge STREAMLINE, funded by the  European Union 's  Horizon 2020 research and innovation program  under Grant Agreement No. 870313.
An additional interpretation, for which the analysis was not explicitly optimized, is provided for vector leptoquarks that decay into third-generation SM particles. The full Run-2 dataset of proton-proton (pp) collisions recorded with the ATLAS detector at the Large Hadron Collider (LHC) is analyzed. This dataset corresponds to an integrated luminosity of 139 fb -1 , taken from 2015 through 2018, at a center-of-mass energy of √  = 13 TeV. The investigated SUSY signal model is motivated by gauge-mediated SUSY breaking (GMSB)  [41] [42] [43]  and natural gauge mediation  [44]. In this -parity-conserving scenario, only three SUSY particles are assumed to be sufficiently light to be relevant: the lighter scalar partner of the top quark t (top squark or stop), the lighter scalar partner of the tau lepton τ (tau slepton or stau), and the spin-3/2 partner of the graviton, the gravitino G.
43 TeV assuming (LQ u/d 3 → ℓ) = 1 and up to 1. 22 TeV assuming (LQ u/d 3 → ℓ) = 0. 5. The CMS Collaboration has published a search of the full Run-2 dataset for single or pair production of scalar or vector leptoquarks coupling to third-generation fermions, excluding pair-produced scalar leptoquarks with masses up to about 1. 0 TeV  [60]. CMS has also reported several searches for third-generation leptoquarks based on 35. 9 fb -1 of Run-2 data  [61] [62] [63] [64] [65]. An additional interpretation of the search results is provided for pair production of vector leptoquarks LQ v 3. Again, it is assumed that the vector leptoquarks can only decay into third-generation SM particles. The electric charge of the vector leptoquarks and their decay modes are the same as those of the up-type scalar leptoquarks in the middle diagram of Figure  1.
Data and simulated event samples The dataset used in this analysis was collected with the ATLAS detector in proton-proton collisions provided by the LHC during its second run from 2015 to 2018. The data was taken at a center-of-mass energy of √  = 13 TeV with a minimum separation of 25 ns between consecutive crossings of proton bunches from the two beams. Events are selected with triggers on missing transverse momentum  [72] , and data-quality requirements are applied to ensure that all elements of the detectors were operational during data-taking  [73]. The total integrated luminosity amounts to 139 fb -1 with an uncertainty of 1. 7%  [74] , obtained using the LUCID-2 detector  [75]  for the primary luminosity measurements. Monte Carlo (MC) simulation was used to generate samples of collision events, which model the expected kinematics of the investigated signal and SM background processes.
The observed exclusion reach slightly exceeds the expected exclusion reach, as the sensitivity to the stop-stau signal model for tau-slepton masses larger than 200 GeV is dominated by the di-tau SR with an observed deficit. Top-squark masses of up to 1. 4 TeV and tau-slepton masses of up to 1. 2 TeV are excluded at the 95% confidence level in this specific model. These are the strongest mass limits for these two supersymmetric particles in a simplified model from Run 2 of the LHC to date. They extend significantly beyond the limits from the previous ATLAS analysis, which are shown in the plot for comparison. The gain in sensitivity is partly due to the larger dataset used in the analysis, but also due to improved reconstruction and identification algorithms for tau leptons and -jets, and an improved signal-region strategy with reoptimized selection requirements and the added single-tau signal region targeting low tau-slepton masses.
Conclusion In this paper, a search for new phenomena in final states with hadronically decaying tau leptons, -jets and large missing transverse momentum is presented. This signature provides sensitivity to models in which the new particles preferentially decay into third-generation Standard Model particles. The analysis exploits the full dataset recorded with the ATLAS detector in Run 2 of the LHC, corresponding to 139 fb -1 of proton-proton collisions at √  = 13 TeV. No significant excess of events is observed over the Standard Model expectation. The results are thus interpreted in terms of exclusion limits at 95% confidence level for two simplified models with pair production of supersymmetric top squarks or leptoquarks which are assumed to only decay into third-generation fermions. In the case of the supersymmetric model, masses up to 1.
4 TeV are excluded for top squarks decaying via tau sleptons into nearly massless gravitinos across a wide range of tau-slepton masses. For both up-type and down-type scalar leptoquarks, masses up to about 1. 25 TeV can be excluded. For vector leptoquarks with minimal couplings, masses up to about 1. 5 TeV can be excluded, and up to about 1. 8 TeV for vector leptoquarks with additional couplings to gauge bosons. The larger dataset, updated reconstruction and identifications algorithms for tau leptons and -jets, and optimized analysis strategy yield significantly better sensitivity than in earlier LHC studies. Based on the considered benchmark models, the search yields the strongest mass limits to date on pair-produced top squarks and on pair-produced third-generation scalar and vector leptoquarks at intermediate values of the branching fraction into a quark and a charged lepton.
Study population Individual-level data related to children born VPT or EPT, as well as the sociodemographic characteristics of their parents, were collected as part of the Effective Perinatal Intensive care in Europe (EPICE) and Screening to improve Health In very Preterm infantS (SHIPS) studies. A detailed description of the design, outcome measures and data collection processes for the EPICE and SHIPS studies can be found in Zeitlin et al. 22  Briefly, the EPICE and SHIPS studies constituted and followed up an area-based cohort of children born between 22 +0 and 31 +6 weeks of gestation in 2011/2012 in all maternity units in 19 regions across 11 European countries: Belgium (Flanders); Denmark (Eastern Region); Estonia (entire country); France (Burgundy, Ile-de-France and the Northern region); Germany (Hesse and Saarland); Italy (Emilia-Romagna, Lazio and Marche); the Netherlands (Central and Eastern region); Poland (Wielkopolska); Portugal (Lisbon and Northern region); Sweden (greater Stockholm) and the United Kingdom (East Midlands, Northern, and Yorkshire and the Humber regions).
Measurement of resource use When participating children reached their fifth year of age, parents were invited to complete a self-reported questionnaire designed to collect information on their children's health, development and use of health care and other services during the previous year, the parents' circumstances, health, wellbeing and restrictions imposed by their preterm child's health. The survey questionnaire attempted to capture how much of the additional restrictions were directly attributable to their child's health. The questionnaire was piloted to ascertain its acceptability, comprehension and reliability, and reminder letters were sent to parents to increase the response and completion rates. Data from the SHIPS questionnaire at 5 years of age provided detailed information about children's service use and expenses over the preceding year, including: (i) use of hospital outpatient services such as contacts with paediatricians, accident and emergency departments, ear, nose and throat specialists, eye specialists, speech/language therapists, psychologists, psychiatrists, physiotherapists or motor developmental therapists, respiratory or asthma specialists and routine follow-up services for preterm born children; (ii) hospital inpatient stays; (iii) use of community health and social services such as contacts with general practitioners, dieticians, health visitors, school nurses, occupational therapists and early childhood intervention services; and (iv) additional expenses incurred by parents resulting from caring for their child, including equipment (e.
Valuation of resource use Lack of central resources or publications listing unit costs for different episodes or aspects of care in many of the participating countries made it necessary to employ an iterative approach to valuing resource inputs. The sources are described in Appendix 1. Unit costs were taken from national sources or publications where available. When such sources were not available, information was extracted from the relevant peer-reviewed literature, typically costing exercises within published economic evaluations or cost analyses or through the data collection systems of SHIPS collaborating centres. For example, for the UK, unit costs were mainly sourced from the Personal Social Services Research Unit's (PSSRU) Unit Costs of Health and Social Care compendium  25  and additional publications were searched for unit costs not listed in the PSSRU reports.
The collected cost items were validated with local health economists and project investigators in each region. Unit costs were converted to the reference year of 2016, the latest year for which indices were available at the beginning of this study, using a cost conversion tool. 26 Country-specific currencies were converted to EUR (€) using purchasing power parity (PPP) values published by the Organisation for Economic Co-operation and Development (OECD). Work absences as a result of the health of the child were valued using nationally specific earnings data published by the OECD. 27.
The study population was drawn from 11 countries, offering a large sample of approximately 3700 EPT/VPT born children. In addition, we constructed a database of unit costs and economic costs for ten categories of hospital outpatient services, seven categories of community health and social services, equipment costs and indirect costs across 11 European countries. Another strength of this study is the representativeness of the cohort as the regions were selected to represent the diversity of organisational systems in European countries and in accordance with their availability to carry out the study. The preterm birth rates in this study resemble those in the EURO-Peristat report. 40 The EPICE cohort  22  shows preterm birth rates ranging from 0. 9 to 1. 5% for the regions in participating countries while the EURO-Peristat report  40  shows preterm birth rates ranging from 0.
Although we endeavoured to collect appropriate unit cost data for each country, central databases listing relevant unit costs are not available in every country. To mitigate this, we consulted with local health economists and researchers, and we referred to relevant literature. Moreover, we attempted to minimise differences in living standards between the countries by applying PPPs. As costing analysis for multi-country comparisons is intrinsically open to the issue of heterogeneity in prices for services, it is important to minimise the differences across countries. This is to adjust for differences in purchasing power and allow for comparisons of living standards and economic productivity between countries. Fourth, even though the analysis was conducted from a stated societal perspective, we recognise that some potentially important resource/cost categories may have been excluded.
A more extended analysis of this model was presented in  [1] , paying particular attention to the reach of different proposed experiments involving far detectors placed around the LHC main facilities. The results of this analysis are shown in Fig. 3. We can see that BBN (as well as other astrophysical and cosmological data) tightly constrain small values of the sh mixing angle for small (sub-GeV) exotic scalar masses, whereas existing LHC data probe, instead, larger values of m s. The remaining, intermediate parameter space can be probed by proposed experiments such as MATHUSLA or SHIP, hence providing a more extended coverage of the allowed parameter space. Put simply, given the constraints stemming from BBN and the conventional LHC experiments, it is meaningful to choose the design of far detectors in order to probe regions of parameter space that cannot be covered by these two sources of experimental information.
Over 80 causative genes of CMT have been identified with different frequencies  [2] , and many more remain unknown. The natural history of these varffus forms of CMT remains poorly understood, at least in part, because these are rare disorders and individual centers do not follow enough patients to perform natural history studies. Furthermore, validated clinical instruments for measuring disease severity have become available only recently and have not yet been employed in many of the rare CMT subtypes. The Inherited Neuropathies Consortium (INC) is a member of the Rare Diseases Clinical Research Network (RDCRN) and was created in part to perform natural history studies in CMT. Quantifiable clinical data add to the literature in providing the clinical severity of a variety of CMT subtypes and also act as a baseline for a longitudinal natural history study of CMT subtypes, a prerequisite for clinical trials.
Daily administration of progesterone elevated the steady-state levels of Pmp22 and Mpz mRNA in the sciatic nerve, resulting in enhanced Schwann cell pathology and a more progressive clinical neuropathy. In contrast, administration of the selective progesterone receptor antagonist reduced overexpression of Pmp22 and improved the CMT phenotype, without obvious side effects, in wild type or transgenic rats  [29]. Taken together, these data provide proof of principle that the progesterone receptor of myelin-forming Schwann cells is a promising pharmacological target for therapy of CMT1A. Unfortunately, a clinical trial evaluating Ulipristal acetate, a selective progesterone receptor modulator, in CMT1A patients has been prematurely discontinued (Clinical trial N • NCT02600286) on the request of European and French regulatory agency due to serious liver injury in patients treated with ulipristal acetate in another unrelated clinical trial.
The small molecule IFB-088 (also named Sephin1), a PP1c/PPP1R15A phosphatase complex inhibitor developed by InFlectis BioScience (France), has been shown to ameliorate myelination and to largely prevent the motor, morphological, and molecular defects of S63del mice  [55]. A phase 1 study with IFB-088 has been completed in healthy volunteers (EudraCT number: 2018-000443-29; ClinicalTrials. gov Identifier: NCT03610334), and the results of the phase 1 repeated dose-escalation study support the safety and good tolerability profile of IFB-088 in human. Thus, InFlectis BioScience is preparing a phase 2 clinical trial in CMT1. A pharmacological treatment boosting the UPR, like IFB-088/sephin1 could have direct implications for different CMT subtypes where protein misfolding, protein mistrafficking, and ER retention have been observed.
A high-resolution climate projections dataset is obtained by statistically downscaling climate projections from the CMIP5 experiment using the ERA5 reanalysis from the Copernicus Climate Change Service. This global dataset has a spatial resolution of 0. 25 °x 0. 25 °, comprises 21 climate models and includes 5 surface daily variables at monthly resolution: air temperature (mean, minimum, and maximum), precipitation, and mean near-surface wind speed. Two greenhouse gas emissions scenarios are available: one with mitigation policy (RCP4. 5) and one without mitigation (RCP8. 5). The downscaling method is a Quantile Mapping method (QM) called the Cumulative Distribution Function transform (CDF-t) method that was first used for wind values and is now referenced in dozens of peer-reviewed publications.
Value of the Data • The high resolution, number of models and variables available offer a great opportunity for both researchers and climate change adaptation practitioners to study climate change features and feed this data into impact models for any region around the world. • The dataset is obtained by statistically downscaling climate projections from the CMIP5 experiment using the ERA5 reanalysis from the Copernicus Climate Change service, a data product extensively used around the world for historical climate analysis. A great advantage of this dataset is thus to provide an extension of the ERA5 reanalysis into the future. • The dataset is global, has a spatial resolution of 0. 25 °x 0. 25 °, comprises 21 climate models allowing to address model uncertainty and includes 5 surface daily variables at monthly resolution: air temperature (mean, minimum, and maximum), precipitation, and mean nearsurface wind speed.