dataesr/scientific-paragraphs-categorization huggingface dataset:
ERA5 ERA5 is the fifth generation in the European Centre for Medium-range Weather Forecasts (ECMWF) series of reanalyses. A full technical description of the setup is given by  Hersbach et al. (2020). The dataset is derived using a 2016 version of the operational ECMWF Integrated Forecasting System (IFS) model (cycle 41r2) and a hybrid incremental 4D-Var data assimilation methodology. ERA5 is available on a regular 0. 25 • grid at 31 km horizontal grid spacing and on 137 vertical levels. Both surface-level diagnostics and data on pressure levels are available hourly. A final release of ERA5 is publicly available for 1959 onwards. ERA5 nearsurface winds are a diagnostic derived in IFS output by scaling the winds at the 40 m height level to 10 m assuming a roughness length for short grass (0.
In situ wind observations Surface (10 m) and upper air (radiosonde) wind observations from four representative permanent East Antarctic sites are used. These include Neumayer on the Ekström Ice Shelf and three stations at the foot of steep coastal slopes, Mawson, Casey and Dumont d'Urville (DDU) (Fig. 1 ). 10 m wind speed measurements from surface stations are withheld from assimilation into JRA-55, MERRA-2 and ERA5  (Kobayashi et al. , 2015; Gelaro et al. , 2017; Hersbach et al. , 2020) , but the local wind field is not entirely independent from observations due to the assimilation of upper air wind, pressure and temperature profiles. Surface meteorological observations were obtained via the SCAR READER database  (Turner et al. , 2004). Neumayer, Mawson and DDU were selected for this analysis to evenly sample the East Antarctic coastline and due to their high representativeness of the coastal lower troposphere (see  Vignon et al.
, 2019, Fig. 11). Casey, on the other hand, is less representative but is situated near complex orography which is represented differently in the three reanalyses (see Sect. 2. 3). Long-term vertical profile data from West Antarctica are currently lacking but would be highly valuable for an evaluation of reanalysis performance of this kind, for example under various configurations of the Amundsen Sea Low. High-resolution vertical wind profiles measured with Vaisala RS-92 radiosondes (Modem M2K2-DC at DDU) from the dataset collated by  Vignon et al. (2019)  are used, with technical details described in Sect. 2. 1 therein. Important post-processing steps include calculation of measurement heights using a standard hydrostatic approximation based on temperature, humidity and pressure readings  (König-Langlo et al.
Collocation of reanalyses and station measurements Comparison of gridded datasets with point observations is not straightforward, especially in regions of complex orog-raphy or where steep slopes generate an offset between the actual station elevation and that at the station location in the model  (Dong et al. , 2020). In this evaluation we compare station observations with the nearest reanalysis grid point for both the surface and upper air data as in a number of previous analyses  (Jones et al. , 2016; Gossart et al. , 2019; Tetzner et al. , 2019; Vignon et al. , 2019). Within the lowest 3000 m, sondes do not generally drift more than a few kilometres horizontally  (Vignon et al. , 2019 , Appendix C) so we do not account for sonde drift in this analysis. We recognise that the chosen grid point may not be fully representative of the local conditions at the station however.
, 2001; Adams, 2004). All reanalyses include some representation of the Law Dome but in ERA5 the peak rises to 1259 m compared to 964 m in MERRA-2 and 952 m in JRA-55. Orographic slopes from the plateau to the coast differ considerably between reanalyses (Fig. 2 , rightmost column). All three datasets have orography that is noticeably smoothed compared to the 1 km Radarsat Antarctic Mapping Project digital elevation model (DEM) version 2 (RAMP2)  (Liu et al. , 2015). ERA5 orography is derived from RAMP2 south of 60 • S and so is closest to this benchmark, but in general the slope gradient is not as steep meaning the altitude of the matched grid point is greater than the station altitude along the coastal slopes and approximately equal (within 60 m) at Neumayer on the flat Ekström Ice Shelf.
Observations and reanalysis winds are then composited by these dates. 12-hourly data are used to match the approximate frequency of sonde launches at coastal stations. SOMs are calculated using the Somoclu library for Python: https://somoclu. readthedocs. io/en/stable/ download. html (last access: 15 November 2022;  Wittek et al. , 2017). Several metrics are used to determine the appropriate number of SOM nodes. With fewer nodes, the SOM representation of synoptic states may be too generalised. Conversely, with a larger array, duplicates are introduced  (Cassano et al. , 2015; Gibson et al. , 2017)  though how undesirable this is depends on the application  (Hewitson and Crane, 2002). The similarity among SOM nodes, similarity of the composited wind fields, and correlations between data points (in this case 12-hourly MSLP fields) and their respective SOMs are quantified for various configurations in Fig.
Performance against ASCAT Here we compare the ASCAT 10 m wind dataset with collocated reanalysis 10 m neutral winds from the 2010-2017 sampling period. A breakdown of the results by month is given in Sect. S2, which shows how the availability of AS-CAT data from the near-coastal region is affected by the presence of sea ice. The fullest coverage comes from the latesummer January to March period when most of this sea ice has melted. Statistics and scatterplots shown in Fig. 3  are calculated from the region of the coastal easterlies demarcated in red in Fig. 4 , defined as where either the mean ASCAT zonal wind for the period 2010-2017 is less than zero (i. e. easterly) or the grid point is within a 12-grid box buffer zone drawn around the ERA5 land-sea mask coastline. Henceforth this is referred to as the coastal easterly domain.
54 m s -1 (i. e. becomes more negative). For reference, on average across all the 10 m surface station wind speed data from Mawson, Neumayer and Dumont d'Urville (the better exposed sites), this change in the bias induced by sampling only wind speeds above 20 m s -1 would be -4. 65, -5. 13 and -4. 53 m s -1 , respectively. Maps of the reanalysis bias with respect to ASCAT (Fig. 4 ) indicate that the largest differences between the reanalyses and observations are found in the near-coastal region. Both ERA5 and MERRA-2 exhibit a band of negatively biased wind speeds close to the coastal margins. Note that regions of missing data in JRA-55 are where not enough points could be collocated (i. e. fewer than 50) between AS-CAT and the reanalysis due to the reduced frequency (6hourly) of data compared to ERA5 and MERRA-2 (hourly).
Carvalho (2019) also finds that MERRA-2 generally outperforms JRA-55, CFSR, JRA-55 and ERA-Interim winds at high southern latitudes. We find that the more recent ERA5 now slightly outperforms MERRA-2 in the coastal region, consistent with the findings https://doi. org/10. 5194/wcd-3-1415-2022 Weather Clim. Dynam. , 3, 1415-1437, 2022  of Belmonte Rivas and Stoffelen (2019) who report an improvement in ERA5 ocean winds relative to ERA-Interim. Some of the observed differences between ASCAT and the reanalyses may be due to inconsistencies in how scatterometer and reanalysis 10 m winds are derived, although the effects of atmospheric stability are accounted for in our analysis by making use of the reanalysis neutral winds rather than stability-dependent winds. One possible remaining issue is mismatches in the designation of open ocean and sea ice in the reanalysis and scatterometer datasets; some regions of non-zero ERA5 sea ice concentrations were found within the collocated ASCAT fields.
007 m s -1 from the OSCAR Surface Currents dataset  (ESR, 2009). The analysis of Belmonte Rivas and Stoffelen (2019) includes a correction for ocean currents which reduces a positive zonal wind bias north of the coastal easterly domain in the Southern Ocean (i. e. too strong westerlies) in ERA5, with the magnitude of the effect around 0. 1 m s -1 (see Fig. 10 therein ). The effects of air density differences on the estimation of neutral winds are also expected to have a small impact on the results. Accounting for this could reduce reanalysis biases by about 0. 1 m s -1 in the near-coastal region based upon the analysis of de Kloe et al. (2017) (see Fig. 13 ). In summary, these sources of inconsistency do have a small impact but are not able to explain large wind speed biases exceeding 2 m s -1 such as those found close to complex Antarctic orography.
org/10. 5194/wcd-3-1415-2022 Weather Clim. Dynam. , 3, 1415-1437, 2022  analyses; in ERA5 the southerly layer is deep and excessively strong in the jet case (Fig. 8c ) whereas in JRA-55 the structure is realistic but the southerlies are too consistently strong (Fig. 8i ). Compared to Casey, a more complex temperature profile is observed at DDU. Although not clear from the averaged temperature profiles, near-surface inversions are again common (Fig. S10 ) but they are also accompanied by inversions at altitudes above 1000 m a. g. l. , generating a distinct reverse S shape in the temperature profile which is present in reanalyses but is smoothed and varies considerably in vertical structure between dataset. Substantial negative temperature biases occur in the lowest 1000 m in JRA-55 and MERRA-2, reaching 5 K at 100 m a.
10 ) compare relatively favourably with Neumayer upper air data. ERA5 and MERRA-2 stand out here with consistently high wind speed r values across SOM nodes while JRA-55 exhibits some degradation in fidelity in the lowest 500 m, especially for nodes (0, 2) and (1, 2) (Fig. S9o  and r ), both of which are subject to low-level flow passing over the complex coastal terrain east of Neumayer. The jet structure is well represented, including a shallow southerly flow. However, the core of the easterly jet, seen most clearly in node  (1, 1) , is consistently underestimated by all three datasets. Baroclinicity and the coastal slopes nearby are thought to introduce ageostrophic motions at Neumayer  (Kottmeier, 1986; Klöwer et al. , 2014)  though the katabatic pressure gradient force is not as large over the ice shelf as further inland (van den  Broeke and van Lipzig, 2003).
Discussion In this paper, we have evaluated the representation of Antarctic coastal winds in three current reanalyses. The analysis aims to quantify representation of the mean state and shortterm variability of coastal winds, making use of the few observational datasets available within the coastal easterly domain. There is a great deal in common between reanalyses in the representation of time-mean coastal winds and their variability. These similarities and the differences which do exist point to some critical processes which are important for representation of the coastal winds. a critical role for orography in the coastal margins; reanalysis performance with respect to ASCAT declines with increasing proximity to the coast, and biases are especially large close to complex orography such as the Transantarctic Mountains, Law Dome and around Enderby Land.
Implications for use of reanalysis datasets Reanalyses are capable of representing important features of the coastal wind field and its variability, including the existence of a jet feature peaking during strong synoptic forcing. Many of the differences against observations are consistent between datasets, including underestimated near-surface winds above 20 m s -1 , underestimated interquartile range in states with reduced pressure gradients where katabatic winds dominate, poor representation at Casey where a local orographic feature plays a major role, and improved performance at Neumayer over relatively flat surface conditions. Alongside the detailed breakdown of performance statistics across various metrics, rankings for each reanalysis by station and by metric (using only surface wind speed) are given in Appendix D.
Correlation coefficients decrease with proximity to the coast and the ASCAT fields reveal near-coastal wind features (eshttps://doi. org/10. 5194/wcd-3-1415-2022 Weather Clim. Dynam. , 3, 1415-1437, 2022 pecially close to complex orography) whose magnitude is not well captured by the reanalyses. All three reanalyses exhibit larger negative biases relative to scatterometer data at wind speeds exceeding 20 m s -1. Reanalysis performance against observations is much more sensitive to different flow regimes (characterised with SOMs) at coastal slope stations (in particular Mawson and DDU) than over the flatter terrain of Neumayer. Time series correlation coefficients (r) between both surface and upper air wind speeds and collocated reanalysis wind speeds are consistently higher in states with strong synoptic forcing and especially when low-level flow is directed onshore.
A more realistic temperature profile, better characterisation of variability and more faithful representation of orography gives greater confidence in ERA5 as a benchmark dataset for Antarctic coastal winds. It should be emphasised, however, that performance is similar between reanalyses and many key deficiencies are shared, including relatively large biases against ASCAT near the coast and at high wind speeds, poor performance close to the Law Dome and underestimation of the wind speed variability when synoptic forcing is reduced, and katabatic forcing is important. These results shed light on some of the challenges associated with evaluating model-based datasets in the Antarctic coastal region and underscore the role of orography. Furthermore, they demonstrate the usefulness of an evaluation approach designed to interrogate reanalysis or model performance under varied atmospheric states, given the high variability of the coastal sector.
MSLP composites were also found to be very visually similar to Fig. 5 , including when driven with hourly data instead of 12-hourly (not shown). Overall, A SOM configuration of 2 × 3 was deemed to be capable of capturing critical differences in reanalysis performance (associated with katabatic and synoptic states as well as variations in flow orientation) without being too large for the reader to interpret easily. available, respectively. For radiosonde data from Casey and Mawson station, we thank the Australian Bureau of Meteorology. For radiosonde data from Neumauyer, we gratefully acknowledge König-Langlo (2017) who make the data openly available at the link in the data statement. For radiosonde data from Dumont d'Urville, we thank Météo France, the DSO/DOA service for the acquisition and distribution of the data.
We are grateful to Steve Colwell for supporting and disseminating the READER dataset. C-2015 AS-CAT data are produced by remote sensing systems and sponsored by the NASA Ocean Vector Winds Science Team. Data are available at http://www. remss. com (last access: 11 August 2021); thanks to ICDC, CEN, University of Hamburg for data support. We also thank two anonymous reviewers whose insightful comments helped to improve the paper. Financial support. This research has been supported by the Natural Environment Research Council (NERC; Improved projections of winds at the crossroads between Antarctica and the Southern Ocean (grant nos. NE/V000691/1 and NE/V000969/1)). Review statement. This paper was edited by Tiina Nygård and reviewed by two anonymous referees.
Acknowledgements. Thomas Caton Harrison  and  Stavroula Biri  were funded by  NERC  grant "Improved projections of winds at the crossroads between Antarctica and the  Southern Ocean"  ( NE/V000691/1  at  BAS  and  NE/V000969/1  at  NOC ). This work forms part of the  Polar Science for Planet Earth program  of the  British Antarctic Survey. We are grateful to the  European Centre for Medium-range Weather Forecasts (ECMWF) , the  NASA Global Modeling and Assimilation Office (GMAO)  and the  Japan Meteorological Agency (JMA)  for making ERA5, MERRA-2 and JRA-55.
Data availability. ERA5 reanalysis data can be accessed from the Climate Data Store at https://cds. climate. copernicus. eu/cdsapp#!/home (last access: 10 November 2021; https://doi. org/10. 24381/cds. adbb2d47, Hersbach et al. , 2018b   and https://doi. org/10. 24381/cds. bd0915c6, Hersbach et al. ,  2018a). MERRA-2 can be accessed for surface-level data at https://doi. org/10. 5067/3Z173KIE2TPD (GMAO, 2015a) and for pressure-level data at https://doi. org/10. 5067/QBZ6MG944HW0 (GMAO, 2015b). JRA-55 can be accessed from the Research Data Archive at https://doi. org/10. 5065/D6HH6H41 (Japan Meteorological Agency, 2013). C-2015 ASCAT data are produced by Remote Sensing Systems and sponsored by the NASA Ocean Vector Winds Science Team. Data are available at https://www. remss. com/missions/ascat/ (last access: 11 August 2021;  Ricciardulli and Wentz, 2016).
Radiosonde data from Neumayer station are available online at https://doi. org/10. 1594/PANGAEA. 874564 (König-Langlo, 2017). Other radiosonde datasets should be requested directly from the institutes referenced in the Acknowledgements. SCAR READER data are available online at https://www. bas. ac. uk/project/reader/ (last access: 5 April 2021;  Colwell, 2002). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/wcd -3-1415-2022-supplement.
To examine the impact of these Aβ-mediated changes on neural coding circuits during these early stages of Aβ pathology, we focused on studying APP mice between 3-7 months of age. Single-unit recording data in the MEC were collected from 68 APP mice and littermates with negative transgene expression across four experimental groups: young APP mice (3-4. 5 months of age), adult APP mice (4. 5-7 months of age), young non-transgenic (nTG) mice (3-4. 5 months of age), adult nTG mice (4. 5-7 months of age). Thirty-one males and 37 females were used. Ten animals fell into multiple age groups. The male/female ratios were 6:5, 16:16, 9:5, and 11:10 for young APP, adult APP, young nTG, and adult nTG mice, respectively. Single-unit recording data in region CA1 of the hippocampus were collected from six adult APP mice (3:3 male/female ratio) and six adult nTG mice (2:4 male/female ratio).
Tetrodes were then advanced in increments of 25 microns to sample new putative MEC neurons, which was later confirmed by histology. For the CA1 cohort, sleep recordings were carried out prior to open field exploration to detect sharp wave and ripple activity. Once ripple amplitude was stable across days, tetrodes were no longer turned. Occasionally, tetrodes were either advanced or retracted depending on fluctuations in ripple amplitude and unit activity. In most cases for both MEC and CA1 recordings, neurons were not stable enough between recordings to reliably determine whether cells were re-sampled across days and thus we have included all cells recorded into our analysis. To record spikes and local field potentials, versadrives were connected to a multichannel amplifier tethered to a digital Neuralynx (Bozeman, MT) recording system, and data were acquired using Cheetah 5.
In cases where brain slices repeatedly came off the glass slides during Nissl-staining, slices were instead mounted using a fluorescent DAPI labeling mounting medium. Tetrode tracks were characterized to be in either the superficial or deep layers based on the location of the track tip. Only data collected from tetrodes within the MEC were included in the analysis. For hippocampal recordings, all tetrode tips that picked up single units were determined to be in region CA1 of the dorsal hippocampus. Tips from tetrodes located outside of CA1 did not pick up any single units. Spike sorting. Single units were isolated 'offline' manually using Offline Sorter 2. 8. 8 (Plexon, Inc) individually for each recording session. Neurons were separated based on the peak amplitude and principal component measures of spike waveforms.
Evaluation of the presence of biologically realistic interspike intervals, temporal autocorrelations, and cross-correlations was used to confirm single-unit isolation. The experimenter was blind to the age and genotype of the subjects and only well-separated clusters were included in the analysis. Position, direction, and velocity estimation. For all electrophysiological recordings, positional data were acquired at 30 frames per second at 720 × 480 pixel resolution (4. 9 pixels per cm) using a camera purchased from Neuralynx (Bozeman, MT). The camera was elevated at a height such that it fully captured all recording environment sizes used. The estimated position of the animal was calculated as the centroid of a group of red and green diodes positioned on the recording head stage. Head direction was calculated as the angle between the red and green diodes.
Up to five lost samples due to occlusion of tracking LEDs, or reflections in the environment were replaced by linear interpolation for both position and directional data. Running velocity was calculated using a Kalman filter. Rate maps were constructed by calculating the occupancy-normalized firing rate for 3 × 3 cm bins of position data. Data were smoothed by a two-dimensional convolution with a pseudo-Gaussian kernel involving a three pixel (9 cm) standard deviation. To visualize the periodicity of grid fields, we computed the spatial autocorrelation of the smoothed rate maps using Pearson's product-moment correlation coefficient as described in Supplementary Fig. 3. Gridness score. To quantify the spatial periodicity of MEC neurons, we calculated a 'gridness score' as described in Brandon et al.
We then calculated gridness and directionality measures. This process was repeated 50 times for each neuron, and the 99 th percentile of the resulting distribution of scores was determined as the significance criteria for both measures. This results in a gridness threshold of 0. 54 and a directionality threshold of 0. 21 which we used to define grid cells and head-direction cells in our full dataset. Any cell recorded in the MEC which did not qualify as a grid cell but had a split-half correlation ≥0. 6 was categorized as a non-grid spatially tuned cell. Putative interneurons in the MEC were selected by having a narrow waveform (<0. 3 ms) and a mean firing rate of at least 0. 5 Hz. Hippocampal neurons were classified as putative place cells if they had (1) a minimum mean firing rate of 0. 1 Hz, (2) a maximum mean firing rate of 5.
2e. To quantify speed modulation of theta power, the mean power between 7 and 12 Hz across speeds was extracted from each Theta-by-Speed spectrogram across speeds (Fig. 2e ). To quantify speed modulation of theta frequency, the frequency of the peak power for each running speed was extracted from the Theta-by-Speed spectrogram (Fig. 2e ). Analysis of covariance (ANCOVA) was performed on these extracted data. Single-cell temporal autocorrelations and intrinsic frequency. The spike times of each cell were binned at 5 ms intervals and the temporal autocorrelation for the given spike train was computed. The obtained signal was smoothed by a Gaussian kernel with 2 bin standard deviation, zero-padded to 2  13  samples and the power spectrum was calculated using the Chronux toolbox function MTSPECTRUMC from Matlab.
The intrinsic frequency of a given cell was then taken as the frequency with the max power in the 6-12 Hz range. Cross-correlations and synchrony analysis. To examine spiking synchrony, unbiased cross-correlations were computed between simultaneously recorded grid cells, head-direction cells, and putative interneurons with 5 ms temporal bins from a lag of -400 to 400 ms. The resulting cross-correlations were convolved with a 25 ms gaussian and normalized to their median absolute deviation for comparison. Path integration task. Data were collected in a ten-sided maze (diameter = 63. 8 cm) surrounded by black curtains. Steel bars were screwed into the walls of the testing room and hovered over the maze. A plastic base was positioned on these bars and acted as the ceiling for the maze. On this ceiling, an infrared camera purchased from Neuralynx was positioned and acquired positional data at 30 frames per second at 720 × 480 pixel resolution (6.