Grids of monthly mean temperature, derived from CLIMAT bulletins on a 0.1x0.1 degree grid, provided by WMO RA VI Regional Climate Centre (RCC) on Climate Monitoring

Mean environmental conditions during data collection with the Flint GNSS receiver in the deciduous stand.

This paper focuses on optimizing the management of delayed trains in operational scenarios by scientifically categorizing train delay levels. It employs static and dynamic models grounded in real-world train delay data from high-speed railways. This classification aids dispatchers in swiftly identifying and predicting delay extents, thus enhancing mitigation strategies’ efficiency. Key indicators, encompassing initial delay duration, station impacts, average station delay, delayed trains’ cascading effects, and average delay per affected train, inform the classification. Applying the K-means clustering algorithm to standardized delay indicators yields an optimized categorization of delayed trains into four levels, reflecting varying risk levels. This static classification offers a comprehensive overview of delay dynamics. Furthermore, utilizing Markov chains, the study delves into sequential dynamic analyses, accounting for China’s railway context and specifically addressing fluctuations during the Spring Festival travel rush. This research, combining static and dynamic approaches, provides valuable insights for bolstering railway operational efficiency and resilience amidst diverse delay scenarios.

a Root mean squared errorb Circular error probable 50c Positional dilution of precisiond Carrier-to-noise densityMean static horizontal position accuracy, PDOP, and signal-to-noise values for the Flint GNSS receiver in the second study, under leaf-off conditions (n = 30).

The Central England Temperature (HadCET) daily mean series is anchored to Gordon Manley’s original temperature record prior to 1973. Between 1848 and 1878, adjustments are applied to account for periods when only a single station was in use.

These historical temperature series are representative of the Midlands region in England, UK (a roughly triangular area of the United Kingdom enclosed by Bristol, Lancashire and London).

From 1973 onwards, multiple adjustments ensure continuity with Manley’s series, homogenise the current station selection with Manley’s original dataset, and correct for the effects of increasing urbanisation.

These static adjustments are calculated on a monthly basis and are applied uniformly to all daily values within each month from 1973 to the present.

Urbanisation adjustments remain static from November 2004 onward, while adjustments between 1974 and October 2004 are graded to reflect a progressive increase in urbanisation effects over time.

This dataset contains the post-Manley extended adjustments, station homogenisation adjustments, and static urban corrections.

Stations used in the construction of the CET daily series between 1772 and 1852 include: Kennington, Crane Court, Lyndon Hall, Syon House, Somerset House, Greenwich Observatory, Chiswick

Stations used in the construction of the CET daily series from 1853 onwards include: Radcliffe (Oxford), Cambridge (legacy), Ross-on-Wye, Rothamsted, Malvern, Stonyhurst, Ringway, Squires Gate, Pershore College

The current station selection used is Rothamsted, Stonyhurst and Pershore College.

The dataset is compiled by the Met Office Hadley Centre.

Latest provisional release data are available via the Hadley Centre Observations website (https://www.metoffice.gov.uk/hadobs/hadcet/data/download.html).

ABSTRACT The global navigation satellite system (GNSS) is the basis for localized crop management by allowing the georeferencing of collected data and the generation of maps by different systems that compose precision agriculture. There is a demand for low-cost navigation systems to enable their use in agriculture. Therefore, the objective of this study is to integrate a low-cost GNSS module to a single-board computer using Kalman filtering to obtain navigation data. The system was evaluated by performing one static and two kinematic experiments, with three repetitions each. In the static experiment, the mean error was 3.25 m with a root mean square error (RMSE) of 3.73 m. In the first kinematic experiment, data variability was lower at a velocity of 1.39 m s−1. In the second kinematic experiment, the mean error was 1.26 and 1.13 m, and the RMSE was 1.45 and 1.27 m for data obtained before and after filtering, respectively. In conclusion, the system reduces the lateral errors in linear sections but is not indicated for sections that change direction. Moreover, this system can be used in agricultural applications such as soil sampling and crop yield monitoring.

This database contains a subset of another online database compiled by the authors (1). The purpose of this database is to provide traceability over the source data and methodology used to estimate elasticities for the relationship between indicators of the built environment and transit use.The 505 elasticity estimates contained in this workbook are sourced directly or derived using information available in 76 prior studies.Main contentOverview - Complete index of database content, include calculation stepsMetadata - Index of column headers describing attributes and corresponding levels in 'Database'Database - Database information for 505 data points from 76 studies. Study attributes and quantitative information relevant to screening and calculation steps is included. Calculation steps10_ Mean elasticities - Calculation of mean elasticities based on average of the weighted elasticities for data points of each indicator11_results_summary - Summary of mean elasticity and significance level for each indicatorSample_only Static table containing data for the 226 data points in the final sampleNotes1 - Aston, Laura; Currie, Graham; Delbosc, Alexa; Kamruzzaman, MD; O'Hare, Tyler; Teller, David (2019): Built environment and transit use empirical research database. figshare. Dataset. Available on figshare: https://doi.org/10.26180/5c3fe01b7fd7e

Grid with estimated reference evapotranspiration per month with a spatial resolution of 10 arc minutes. The dataset contains mean monthly values for global land areas, excluding Antarctica, for the period 1961-1990. The dataset has been prepared according to the FAO Penman - Monteith method with limited climatic data as described in FAO Irrigation and Drainage Paper 56. The dataset consists of 12 ASCII-grids with mean monthly data in mm/day * 10, and one ASCII-grid with yearly data in mm/year.

Overview: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Air temperature (2 m): Temperature of air at 2m above the surface of land, sea or in-land waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. The original ERA5-Land dataset (period: 2000 - 2020) has been reprocessed to: - aggregate ERA5-Land hourly data to daily data (minimum, mean, maximum) - while increasing the spatial resolution from the native ERA5-Land resolution of 0.1 degree (~ 9 km) to 30 arc seconds (~ 1 km) by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: 1. spatially aggregate CHELSA to the resolution of ERA5-Land 2. calculate difference of ERA5-Land - aggregated CHELSA 3. interpolate differences with a Gaussian filter to 30 arc seconds 4. add the interpolated differences to CHELSA Data available is the daily average, minimum and maximum of air temperature (2 m). Spatial resolution: 30 arc seconds (approx. 1000 m) Temporal resolution: Daily Pixel values: °C * 10 (scaled to Integer; example: value 238 = 23.8 %) Software used: GDAL 3.2.2 and GRASS GIS 8.0.0 (r.resamp.stats -w; r.relief) Original ERA5-Land dataset license: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf CHELSA climatologies (V1.2): Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. http://dx.doi.org/doi:10.5061/dryad.kd1d4 Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. https://doi.org/10.1038/sdata.2017.122

This file contains the map layers used for map 22 ‘overview map_rivers’ in the National Water Programme 2022-2027. The maps in the National Water Programme are formatted as a printed map at A4 level and are only checked and established at this level. This means that this data, which was used to create the printed cards, has no legal status. The risk of using this data lies with the user. In addition, it should be borne in mind that some map elements are indicative; the locations are not exact. This applies, for example, to the display of project locations. Finally, it is important that the datasets in this publication were used to create printed maps for the National Water Programme in 2022. It is static data, which will not be kept up-to-date at this place after its publication in 2022. This file contains the map layers used for map 22 ‘overview map_rivers’ in the National Water Programme 2022-2027. The maps in the National Water Programme are formatted as a printed map at A4 level and are only checked and established at this level. This means that this data, which was used to create the printed cards, has no legal status. The risk of using this data lies with the user. In addition, it should be borne in mind that some map elements are indicative; the locations are not exact. This applies, for example, to the display of project locations. Finally, it is important that the datasets in this publication were used to create printed maps for the National Water Programme in 2022. It is static data, which will not be kept up-to-date at this place after its publication in 2022.

The dataset includes intensity bin composites of column-integrated mosit static energy (MSE) spatial variance budget feedback terms for GCMs, reanalyses, and CloudSat for:

Starr, J. C., A. A. Wing, S. J. Camargo, D. Kim, T. Y. Lee, and J. Moon: Using the moist static energy variance budget to evaluate tropical cyclones in climate models against reanalyses and satellite observations. Journal of Climate, In Review.

Description of Files for GCMs and Reanalyses

For each of the GCMs and reanalyses used in this study, there are 4 netcdf files that are saved, 2 for intensity bin composites with maximum wind speed (Vmax) as the binning metric and 2 for minimum mean sea level pressure (MSLP). Considering each of the GCMs and reanalyses have the same file format, the AM4 model will be used as an example for what each file contains and how they are organized. Each reanalysis and GCM will have its own .tar containing the four netcdf files mentioned.

self-collected traffic data by means of TEUs in urban areas.

Maps of monthly mean snow depth derived from SYNOP observations on a 0.1x0.1 degree grid, provided by WMO RA VI Regional Climate Centre (RCC) on Climate Monitoring WMO-RA6-RCC-CM

Transcranial direct current stimulation (tDCS) is used to modulate brain function, and can modulate motor and postural control. While the acute effect of tDCS is well documented on patients, little is still known whether tDCS can alter the motor control of healthy trained participants. This study aimed to assess the acute effect of tDCS on postural control of parkour practitioners, known for their good balance abilities and their neuromuscular specificities that make them good candidates for tDCS intervention. Eighteen parkour practitioners were tested on three occasions in the laboratory for each stimulation condition (2 mA; 20 minutes)–primary motor cortex (M1), dorsolateral prefrontal cortex (dlPFC) and sham (placebo). Postural control was evaluated PRE and POST each stimulation by measuring Center of Pressure (CoP) displacements on a force platform during static conditions (bipedal and unipedal stance). Following M1 stimulation, significant decreases were observed in CoP area in unipedal (from 607.1 ± 297.9 mm2 to 451.1 ± 173.9 mm2, P = 0.003) and bipedal (from 157.5 ± 74.1 mm2 to 117.6 ± 59.8 mm2 P

Kawaii, which is a Japanese word that means cute, lovely, and adorable, is an essential factor in promoting positive emotions in people. The characteristics of a target’s appearance that induce such feelings of kawaii have been thoroughly investigated around the notion of Konrad Lorenz’s famous baby schema. Such knowledge has been exploited to design the appearance of commercial products to increase their social acceptance and commercial appeal. However, the effects of the number of targets and showing their mutual relationships (like friendship) have not been investigated in the context of kawaii. Therefore, in this study, we conducted three web-based experiments and focused on how such factors contribute to feelings of kawaii toward social robots. In Experiment 1, the feelings of kawaii toward static images of targets were compared when they appeared alone or with another target: persons (twin boys/girls), non-human objects (cherries), and social robots. The results showed that the feeling of kawaii was stronger for two targets that displayed a mutual relationship (e.g., looking at each other and/or making physical contact) than for one target alone and for two-independent targets. In Experiment 2, these findings were replicated using video clips of robots. Two-related targets were rated as more kawaii than two-independent targets or a single target. These two experiments consistently show the advantage of multiple robots that display their mutual relationship for enhancing the viewer’s feeling of kawaii. Experiment 3 examined the effect of the number of robots (from one to ten) and found that two robots induced the strongest feeling of kawaii. These results indicate that not only the physical characteristics of a target itself but also the number of targets and their perceived relationships affect feelings of kawaii.