dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Description The high-resolution climate projections dataset covers the globe at a 0. 25 °x0. 25 °spatial resolution and at monthly temporal resolution for five surface variables. It comprises 21 models (see Table  1  ) from the CMIP5 experiment  [1]  with simulations for the historical period  and the 21st century (2006 to 2100) under two emissions scenarios: one with mitigation policy (Representative Concentration Pathway 4. 5 or RCP4. 5) and one with no mitigation (Representative concentration Pathway 8. 5 or RCP8. 5). The following five surface land and ocean variables are downscaled: mean daily temperature, daily minimum and maximum temperature (at 2 m), total precipitation, and surface wind speed (at 10 m). The combination of models and scenarios represents 36 climate projections including all variables except for a few models that didn't provide some (see Table  1  ).
Other variables, models and emissions scenarios could be added in the near future. The data was produced with a statistical downscaling method using the ERA5 reanalysis  [2]  for calibration and training (see next section for details). The interest of the downscaled data is the removal of model biases at a resolution more compatible with the requirements of assessments and further modeling of the impacts of climate change. In other terms it corrects the climatology (distribution) of model values to make them comparable with a reference observational dataset  [3]  , which in this case is the ERA5 reanalysis. In the following subsections, we present the file naming conventions then proceed an illustration of the bias removal over the historical period and the climate change signal differences at the end of century.
Experimental Design, Materials and Methods Four datasets are used in this work: • The reanalysis data that is used as reference for calibrating the statistical algorithm over a training period. The reanalysis grid sets the final resolution of the downscaled projections. • The original model climate projections that come in a variety of spatial resolutions (typically between 2. 0 °x2. 0 °and 0. 75 °x0. 75 °) and referred to as "raw". • The raw data interpolated on the reanalysis grid and referred to as "interpolated". • The downscaled data obtained from the interpolated data and the reanalysis data used for statistical calibration (both on the same grid) and referred as "downscaled". The raw and reanalysis data are input data that need to be sourced. The interpolated data is just an intermediary dataset needed by the methodology while the downscaled data is the final dataset.
Data sourcing The reanalysis data is the ERA5 reanalysis  [2]. ERA5 is the latest climate reanalysis being produced by ECMWF as part of implementing the EU-funded Copernicus Climate Change Service (C3S), providing hourly data on atmospheric, land-surface and sea-state parameters together with estimates of uncertainty from 1979 to present day. ERA5 data are available on the C3S Climate Data Store on regular latitude-longitude grids at 0. 25 °x 0. 25 °resolution. We compute the daily data from the ERA5 hourly data for all necessary variables. The climate simulations hail from The Coupled Model Intercomparison Project Phase 5 (CMIP5) experiment  [1]. They support the Fifth Assessment Report (AR5) of the Intergovern- mental Panel on Climate Change (IPCC). We use projections from 2 emissions scenarios: RCP4.
5 (moderate mitigation policy scenario) and RCP8. 5 (a no mitigation policy scenario). Daily data of necessary variables are extracted from the Copernicus Climate Change Service that hosts a subset of the CMIP5 archive. The data covers the period from January 1st 1950 to December 31st 2100 (except for some models). All models have different spatial resolutions ranging between 0. 75 °to 3 °The list of models is shown on Table  1.
Remapping Remapping is a preliminary task required by the downscaling methodology. It consists in spatially interpolating the raw simulations (between 0. 75 °and 3 °resolution) onto the ERA5 grid (0. 25 °x 0. 25 °). We use the Climate Data Operators (CDO, 2016) software from the Max Planck Institute that gathers various algorithms for interpolation used by the scientific community. Daily temperature (mean, minimum, maximum) and daily wind speed are interpolated with a bicubic method. Daily precipitation is interpolated sequentially (to 1. 5 °then to 0. 75 °, and then 0. 25 °) with a conservative method.
Technical quality control We use the Quality Assurance tool (QA-DKRZ, https://readthedocs. org/projects/qa-dkrz ) developed by the Deutsches Klimarechenzentrum (DKRZ) to check conformance of meta-data of climate simulations given in NetCDF format with conventions and rules of projects. During the Quality Assurance process of the DKRZ, the following criteria are checked: 1 Number of datasets is correct and > 0 2 Size of every dataset is > 0 3 The datasets and corresponding metadata are accessible 4 The data sizes are controlled and correct 5 The spatial-temporal coverage description "metadata" is consistent with the data, 6 Time steps are correct and the time coordinate is continuous 7 The format is correct 8 Variable description and data are consistent.
Vautard personal communication): • temperature between [ -2; 2] in K, • precipitation between [ -1; 1] in mm. day-1, • surface wind speed between [ -1; 1] in m. s-1. In this case, values are higher than previously to account for higher discrepancies but small enough to avoid unrealistic changes. If values are outside the range, the quality control raises an error and the simulation is rejected and thus not included in the dataset.
Acknowledgments The activities described in this paper were funded by the  Copernicus Climate Change Service. ECMWF implements the  Copernicus Climate Change Service  and the Copernicus Atmosphere Monitoring Service on behalf of the  European Commission. To process the data, this study benefited from the IPSL mesocenter ESPRI facility that is supported by  CNRS  ,  SU , and  Ecole Polytechnique  partly funded by IS- ENES3  project. We also thank the  Institut Pierre Simon Laplace  for assistance with the Synda software and the  Deutsches Klimarechenzentrum  for the Quality Assurance tool.
Crewe (1988)  posed the question 'has the electorate become Thatcherite?', to which he answered 'no', pointing out that by some analyses the population was taking a 'hard line' on some issues before 1979 and that by 1987 was actually showing quite 'anti'-Thatcherite sentiments. Crewe subsequently (1989) used data from a 1988 MORI poll and focused on the topic of self-reliance. This again suggested that not only was there little enthusiasm for this, if anything respondents were moving away from support for this idea  (1989:247). Crewe and Searing (1988)  further argued that there was little evidence that her ideology had gained much by way of popular support, and, in fact, the electorate had become less  Thatcherite (1988:376). McAllister and Mughan (1987) , using data from the British Election Studies for 1974-1983, found that an analysis of the  October 1974 October , 1979 October and 1983  general elections suggested there had been "little fundamental change in the electorate's overall attitudinal structure  " (1987:47).
Studlar and McAllister (1992)  extended these analyses to include the 1987 general election and came to very similar conclusions. From this point onwards the data analyses started to become more sophisticated, with analysts tackling regional shifts, the notion of 'political generations' and examining longitudinal patterns from the British Election Studies dating back to 1963. Johnston and Pattie (1990) , for example, demonstrated regional variations in public Thatcherite attitudes, but nevertheless concluded that "the Thatcherite project has failed, in that the majority of the electorate. did not embrace its core values to any significant extent  " (1990:492). Summarising this body of work, it appears that UK political scientists believed that Thatcher had had little impact on attitudes. Their collective endeavours had suggested that the electorate had remained resolutely unimpressed by much of her approach to the challenges which the country faced, and in many respects had started to lean away from her policies.
One of the benefits of the BSAS series is its ability to track change over decades. Rowlingson et al (2010:10)  show that the percentage agreeing that "governments ought to redistribute income" stood at 45% in 1987, rose slightly to 48% in 1991 and then fell to 36% in 2009. The percentage of people agreeing that the "government ought to spend more on benefits" fell from 55% in 1987 to 27% in 2009. In this light, it appears that Thatcherite attitudes -which New Labour had ceased to contest -have asserted themselves in the decades since 1990. More recent studies have become more sophisticated still. The data sets used are more varied, the time frames longer and the idea of there being 'political generations' has become more thoroughly embedded in this approach. Tilley (2002) , for example, has shown that younger generations tended to disassociate with the Conservative Party, but that this trend stopped and went into reverse in the 1980s and 1990s (p129-30).
4: Regression Modelling The data relating to Thatcherite desires (home ownership etc. ) were used as dependent variables in their raw form (a 5 point Likert scale ranging from 1 = Strongly Disagree to 5 = Strong Agree). In order to retain as many cases as possible for analyses, we recoded those people who already owned their own businesses, wanted to educate their children privately or to own their own homes as '6' (with the exception of those without children aged over 19, who were dropped from the analyses for the education question). Our logic being that ownership of a home was the realisation of the desire to own a home, and as such scored higher (  6 ) than the highest level of desire to own (5). These models are reported in Table  One ). This exercise was repeated for the Neo-Liberal and Neo-Conservative values (outlined in Figures  One  and Two ).
Organic fractions and standards were diluted (1:200 for organic fraction and 1:2,000 for standard) in methanol. Ten microliter volumes were then sprayed as 8 mm bands on a TLC plate (Alugram R Xtra SIL G UV254, Macherey-Nagel) using an automatic sampler (ATS4, Camag, Moirans, France) connected to visionCATS Camag TLC software V2. 4. The TLC plates were developed in an automatic developing chamber (ADC 2, Camag) with a mobile phase containing cyclohexane:ethyl acetate (7:2) over a 70 mm migration distance. Spots were observed using UV-light at 254 nm (CV-415. LS, Uvitech, England).
Measurement of Bacterial Surface Charge Three independent mid-phase cultures of strain PA14 (ca. 10 7 CFU/mL) were centrifuged (5,000 g, 10 min). The pellets were washed three times in distilled water and resuspended to yield 10 9 CFU/mL prior to adding 8 mg/mL citral and antibiotics (tobramycin or colistin). Zeta potential was measured in a folded capillary cell (DTS 1070; Malvern Instruments, Worchestershire, United Kingdom) at 25 • C using a Zetasizer Nano ZS (Malvern Instruments) equipped with a 633 nm He-Ne laser, and piloted with Zetasizer software v7. 02. Bacteria were allowed to equilibrate for 120 s at 25 • C. Zeta potential measurements were performed three times for each replicate sample. The Smoluchowski equation was used to calculate the potential from nine electrophoretic mobility determinations.
The acquisition of mass spectra was conducted in ESI (Electro-Spray Ionization) positive ionization mode using a capillary voltage of 4,000 V and the following conditions: nebulizer nitrogen gas pressure, 30 psig; drying gas flow rate, 8 L/min; and drying temperature, 340 • C. The scan range was m/z 100-2,000 at 1 s/scan. Data acquisition was performed using MassHunter Qualitative Analysis software (B. 07. 00, Agilent).
FUNDING This work was supported by the grants from the  Region "Bourgogne Franche-Comté " and from the  French cystic fibrosis associations "Vaincre la Mucoviscidose" and "Grégory Lemarchal. "GM acknowledges the  Interdisciplinary Thematic Institute (ITI) InnoVec (Innovative Vectorization of Biomolecules, IdEx ,  ANR-10-IDEX-0002 ) and  SFRI  ( ANR-20-SFRI-0012 ).
This special issue presents a compilation of 10 papers, described below, that highlight recent advances and future directions within the EM research community. Investigations of crustal and lithospheric architecture continue to be a mainstay of MT studies. Comeau et al. (2020)  present a study of the tectonic terranes of Mongolia based upon a 350-km-long MT profile. Their resistivity model images zones of elevated upper-crustal conductivity coincident with surface faulting and seismicity; they interpret these zones to mark suture zones within the Central Asian Orogenic Belt. The authors further image a distinct zone of lower-crustal conductivity in part of the model and suggest this conductor may be related to an exotic terrane speculated from geochemical data to exist in the same region. Finally, a step in lithospheric thickness is interpreted beneath Southern Mongolia from the model.
The authors present their improved shift-invariant sparse coding (ISISC) method and apply it to synthetic data contaminated with either pseudo-random square wave or impulsive noise sources. They demonstrate the performance and effectiveness of the ISISC algorithm, applying their approach to real MT data sets contaminated with controlled-source EM noise and unknown non-MT noise. This data-driven approach offers an attractive possibility for removing complex periodic noise signals (e. g. , pipeline signals) from MT data and pushes the applicability of MT closer to urban areas. Other sources of MT noise are unique to the seafloor environment. Motion-induced EM signals due to seafloor currents are a common source of noise plaguing seafloor measurements. Chen et al. (2020)  describe an adaptive-correlation filter which incorporates seafloor current-meter data as a reference signal in order to reduce motion-induced noise in marine MT data.
Participants Data used in this study were obtained from ADNI (adni. loni. usc. edu). ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial MRI, PET, other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of MCI and early AD. For up-to-date information, see www. adni-info. org. Participants were recruited at sites throughout the United States and Canada within ADNI during the ADNI-2 and ADNI-GO phases of the study. Baseline ages ranged from 55 to 90 years old. Participants were English-or Spanish-speaking, non-depressed, in good general health, and had baseline diagnoses of cognitively normal (CN), MCI, or AD dementia.
, 2017). Cortical thickness comparison maps were generated to demonstrate areas of significant difference between each MCI group and A-CN and between mismatch MCI and the other two MCI groups. Overlap maps were generated to depict mismatch MCI's overlap of control-referenced cortical thinning with prodromal AD and neurodegeneration-only MCI, respectively. The longitudinal imaging analysis of MTL subregion and WMH volumes included scans that occurred up to 4. 5 years from baseline for each participant whose first and last MRI scans were at least 1. 2 years apart. The volumes of each region in follow-up scans were estimated using Automatic Longitudinal Hippocampal Atrophy (ALOHA) software, which is an unbiased registration-based longitudinal pipeline  (Das et al. , 2012). Exploratory analyses using baseline amyloid PET and the first tau PET scan (performed, on average, more than 5 years from baseline, based on the timing of incorporation of tau PET scans into the ADNI protocol) were also pursued using publicly available processed PET data from ADNI that included a composite measure of florbetapir PET and flortaucipir uptake in regions akin to Braak staging  (Braak and Braak, 1991; Landau et al.
Statistical analysis Statistical analyses were conducted using SPSS version 26. Mean age at baseline MRI and years of education of mismatch MCI were compared to each of the other three groups using independent samples t-tests. All statistical tests were two-sided, and statistical significance was set at a level of < 0. 05. Sex and ApoE4 carrier status (zero ApoE4 alleles versus one or two ApoE4 alleles) were compared between mismatch MCI and each of the three other groups using Pearson chi-square analyses. Median MMSE, GDS, and length of follow-up were compared between mismatch MCI and the other three groups using the Mann-Whitney U-Test for non-parametric analysis. Z-scores, referenced to baseline performance of 161 A-CN controls, were calculated for each participant's baseline performance on the RAVLT trial 1, RAVLT 5-minute delayed recall, and the animal naming test as measures of immediate memory, episodic memory, and category fluency, respectively.
Mean baseline Aß42, p-tau, and t-tau levels in CSF were compared between mismatch MCI and the three other groups using independent sample t-tests. The mean volumes of the hippocampus, anterior hippocampus, and posterior hippocampus were adjusted for age and intracranial volume using unstandardized beta coefficients from linear regression analysis of 161 A-CN. Mean cortical thickness values for extrahippocampal MTL regions (ERC, BA35, BA36, and PHC) were, similarly, adjusted for age. WMH volume was log-transformed to provide a more normal distribution and adjusted for age and intracranial volume using unstandardized beta coefficients from a linear regression analysis of the 140 A-CN for whom these data were available. The mean values for each of these structural measures and log WMH volume for mismatch MCI were compared to each of the other groups using independent sample t-tests.
Amyloid and tau PET scans Tau burden over time was further assessed using publicly available processed tau PET data with flortaucipir from ADNI (see Table  4 ). Due to the later incorporation of tau PET in the ADNI protocol these scans were acquired a number of years after the baseline of the present study (~5 years). Mean SUVR values in Braak I/II, III/IV and V/VI were examined. Note that, based on prior work, an SUVR cut-off of 1. 26 in the Braak III/ IV region has been used to define tau positivity  (Harrison et al. , 2020) , and an SUVR cut-off of 1. 11 in a composite amyloid region has been used to define amyloid positivity  (Schreiber et al. , 2015). The mean SUVR in Braak III/IV for mismatch MCI was below this cutoff (1. 18 ± 0. 21) for T+ despite the tau PET scan occurring approximately five years after the initial T-designation, based on CSF p-tau.
While the β-amyloid in mismatch MCI is thought to be in a preclinical stage (given normal CSF p-tau), its presence may have some bearing on the relative likelihood or distributions of other non-AD pathologies, such as amyloid angiopathy and alphasynuclein  (Swirski et al. , 2014; Charidimou et al. , 2018; Bassil et al. , 2020). Additionally, the neurodegeneration-only group may be relatively enriched in individuals without neurodegenerative pathology, as some individuals with the A-T-(N+) biomarker status may have physiologically small hippocampi and sufficiently impaired cognitive test performance to qualify for an MCI diagnosis in ADNI without actually having a progressive syndrome. The longitudinal cognitive data provided a clearer contrast between mismatch MCI and prodromal AD. Across all four cognitive measures, the prodromal AD group displayed greater rates of decline compared to mismatch MCI.
The lack of pathologic validation with autopsy data limits our ability to draw definitive conclusions regarding the pathologic underpinnings of the biomarker-defined MCI groups. The ADNI MCI cohort, which is restricted to amnestic-predominant cases with low Modified Hachinski Ischemic scores and has limited racial, socioeconomic, and educational diversity, does not represent the full breadth of MCI. The A/T/(N) model allows researchers to select from several different methods for defining each of the three binary biomarkers, and, therefore, our choices regarding biomarker selection and cut-offs influenced the composition of the groups and the results. It is possible that selection of a neurodegeneration biomarker that was less specific for AD pathology than hippocampal volume might have led to a more inclusive and larger mismatch MCI group with stronger indicators of non-AD pathologies.
Given the critical role of tau pathology in the cognitive and neurodegenerative phenotype of AD, we propose that this mismatch group reflects individuals with non-AD causes of their MCI status. The current findings have relevance for enrollment in clinical trials of such "mismatch" patients in light of the potential differences in underlying pathologies and prognosis from prodromal AD. These results also have relevance for clinical care in light of the prognostic implications. Future directions for this research include the use of amyloid and tau PET scans, rather than CSF Aß42 and p-tau, to define amyloid and tau status. This less-invasive approach provides information about the anatomic distribution of amyloid and tau accumulation and is now feasible using ADNI data as an ever-growing number of participants have undergone these scans.
, 2016). Focusing on such basic operations allows investigation of how patterns of coordinated neural firing may translate into primitive low-level information processing  (Clawson et al. , 2019) , akin to the algorithmic level. Here, we hypothesize that the key differences between control and epileptic networks are not only present at the structural level, but also at a more general and core algorithmic level of quantifiable primitive operations. To test this hypothesis, a multilevel experimental approach is required  (Scott et al. , 2018). Multichannel electrode recordings of neural populations provide such a dataset which spans two levels of analysis: the action potential at the neuronal level and oscillations at the population level. As neural computation is brain state dependent  (Quilichini & Bernard, 2012) , we consider the global brain states of theta (THE) and slow oscillations (SO), which can be recorded during anesthesia.
Substates are more contrasted in epilepsy The vertical color bars in Figure  2B  qualitatively show that individual neurons can take different firing, storage or sharing values across substates. In order to quantify these differences, we measure how "contrasted" are different substates. If we consider the firing feature of a given neuron, we first calculate its global mean firing rate (over the whole duration of the recording), and its mean firing rate within each substate. The relative contrast is defined as the difference between the substate mean firing rate and the global mean firing rate, normalized by the global mean firing rate. Evaluating contrast allows better tracking of the differing compositions of substates at the single neuron level. Figure  2B  shows the relative contrast plots for the 44 recorded neurons and the various substates in the same dataset and substate decomposition we use as an example in Figure  2A.
Data information. We use in this work a portion of the data (5 out of 7 original experiments) initially published by  Clawson et al. 2019  as control data, which includes local field potentials (LFPs) and single-unit recordings obtained from the dorsomedial entorhinal cortex (mEC) and the dorsal hippocampus (HPC) of anesthetized rats. Six recordings are original data, which includes LFPs and single-units recorded in the mEC and HPC recorded simultaneously under anesthesia in epileptic condition. See Figures S1 for details on recordings, number of cells, and layers recorded.