Study information

The sample included in this dataset represents five children who participated in a number line intervention study. Originally six children were included in the study, but one of them fulfilled the criterion for exclusion after missing several consecutive sessions. Thus, their data is not included in the dataset.

All participants were currently attending Year 1 of primary school at an independent school in New South Wales, Australia. For children to be able to eligible to participate they had to present with low mathematics achievement by performing at or below the 25th percentile in the Maths Problem Solving and/or Numerical Operations subtests from the Wechsler Individual Achievement Test III (WIAT III A & NZ, Wechsler, 2016). Participants were excluded from participating if, as reported by their parents, they have any other diagnosed disorders such as attention deficit hyperactivity disorder, autism spectrum disorder, intellectual disability, developmental language disorder, cerebral palsy or uncorrected sensory disorders.

The study followed a multiple baseline case series design, with a baseline phase, a treatment phase, and a post-treatment phase. The baseline phase varied between two and three measurement points, the treatment phase varied between four and seven measurement points, and all participants had 1 post-treatment measurement point.

The number of measurement points were distributed across participants as follows:

Participant 1 – 3 baseline, 6 treatment, 1 post-treatment

Participant 3 – 2 baseline, 7 treatment, 1 post-treatment

Participant 5 – 2 baseline, 5 treatment, 1 post-treatment

Participant 6 – 3 baseline, 4 treatment, 1 post-treatment

Participant 7 – 2 baseline, 5 treatment, 1 post-treatment

In each session across all three phases children were assessed in their performance on a number line estimation task, a single-digit computation task, a multi-digit computation task, a dot comparison task and a number comparison task. Furthermore, during the treatment phase, all children completed the intervention task after these assessments. The order of the assessment tasks varied randomly between sessions.

Measures

Number Line Estimation. Children completed a computerised bounded number line task (0-100). The number line is presented in the middle of the screen, and the target number is presented above the start point of the number line to avoid signalling the midpoint (Dackermann et al., 2018). Target numbers included two non-overlapping sets (trained and untrained) of 30 items each. Untrained items were assessed on all phases of the study. Trained items were assessed independent of the intervention during baseline and post-treatment phases, and performance on the intervention is used to index performance on the trained set during the treatment phase. Within each set, numbers were equally distributed throughout the number range, with three items within each ten (0-10, 11-20, 21-30, etc.). Target numbers were presented in random order. Participants did not receive performance-based feedback. Accuracy is indexed by percent absolute error (PAE) [(number estimated - target number)/ scale of number line] x100.

Single-Digit Computation. The task included ten additions with single-digit addends (1-9) and single-digit results (2-9). The order was counterbalanced so that half of the additions present the lowest addend first (e.g., 3 + 5) and half of the additions present the highest addend first (e.g., 6 + 3). This task also included ten subtractions with single-digit minuends (3-9), subtrahends (1-6) and differences (1-6). The items were presented horizontally on the screen accompanied by a sound and participants were required to give a verbal response. Participants did not receive performance-based feedback. Performance on this task was indexed by item-based accuracy.

Multi-digit computational estimation. The task included eight additions and eight subtractions presented with double-digit numbers and three response options. None of the response options represent the correct result. Participants were asked to select the option that was closest to the correct result. In half of the items the calculation involved two double-digit numbers, and in the other half one double and one single digit number. The distance between the correct response option and the exact result of the calculation was two for half of the trials and three for the other half. The calculation was presented vertically on the screen with the three options shown below. The calculations remained on the screen until participants responded by clicking on one of the options on the screen. Participants did not receive performance-based feedback. Performance on this task is measured by item-based accuracy.

Dot Comparison and Number Comparison. Both tasks included the same 20 items, which were presented twice, counterbalancing left and right presentation. Magnitudes to be compared were between 5 and 99, with four items for each of the following ratios: .91, .83, .77, .71, .67. Both quantities were presented horizontally side by side, and participants were instructed to press one of two keys (F or J), as quickly as possible, to indicate the largest one. Items were presented in random order and participants did not receive performance-based feedback. In the non-symbolic comparison task (dot comparison) the two sets of dots remained on the screen for a maximum of two seconds (to prevent counting). Overall area and convex hull for both sets of dots is kept constant following Guillaume et al. (2020). In the symbolic comparison task (Arabic numbers), the numbers remained on the screen until a response was given. Performance on both tasks was indexed by accuracy.

The Number Line Intervention

During the intervention sessions, participants estimated the position of 30 Arabic numbers in a 0-100 bounded number line. As a form of feedback, within each item, the participants’ estimate remained visible, and the correct position of the target number appeared on the number line. When the estimate’s PAE was lower than 2.5, a message appeared on the screen that read “Excellent job”, when PAE was between 2.5 and 5 the message read “Well done, so close! and when PAE was higher than 5 the message read “Good try!” Numbers were presented in random order.

Variables in the dataset

Age = age in ‘years, months’ at the start of the study

Sex = female/male/non-binary or third gender/prefer not to say (as reported by parents)

Math_Problem_Solving_raw = Raw score on the Math Problem Solving subtest from the WIAT III (WIAT III A & NZ, Wechsler, 2016).

Math_Problem_Solving_Percentile = Percentile equivalent on the Math Problem Solving subtest from the WIAT III (WIAT III A & NZ, Wechsler, 2016).

Num_Ops_Raw = Raw score on the Numerical Operations subtest from the WIAT III (WIAT III A & NZ, Wechsler, 2016).

Math_Problem_Solving_Percentile = Percentile equivalent on the Numerical Operations subtest from the WIAT III (WIAT III A & NZ, Wechsler, 2016).

The remaining variables refer to participants’ performance on the study tasks. Each variable name is composed by three sections. The first one refers to the phase and session. For example, Base1 refers to the first measurement point of the baseline phase, Treat1 to the first measurement point on the treatment phase, and post1 to the first measurement point on the post-treatment phase.

The second part of the variable name refers to the task, as follows:

DC = dot comparison

SDC = single-digit computation

NLE_UT = number line estimation (untrained set)

NLE_T= number line estimation (trained set)

CE = multidigit computational estimation

NC = number comparison

The final part of the variable name refers to the type of measure being used (i.e., acc = total correct responses and pae = percent absolute error).

Thus, variable Base2_NC_acc corresponds to accuracy on the number comparison task during the second measurement point of the baseline phase and Treat3_NLE_UT_pae refers to the percent absolute error on the untrained set of the number line task during the third session of the Treatment phase.

The Magnetic Field Properties Calculator will computes the estimated values of Earth's magnetic field(declination, inclination, vertical component, northerly component, easterly component, horizontal intensity, or total intensity), for a specific location, elevation and date or range of dates based on the current International Geomagnetic Reference Field (IGRF). The calculated result is a grid that contains the calculated component and the annual change of the component over the geographical area specified. WDeclination is calculated using the current World Magnetic Model (WMM) or International Geomagnetic Reference Field (IGRF) model. While results are typically accurate to 30 minutes of arc, users should be aware that several environmental factors can cause disturbances in the magnetic field.

A dataset of car tax calculations for company cars by operating cycle, manufacturer, model, and derivative.

This calculator is a handy tool for interested parties to estimate two key life cycle metrics, fossil energy consumption (Etot) and greenhouse gas emission (ghgtot) ratios, for geothermal electric power production. It is based solely on data developed by Argonne National Laboratory for DOE's Geothermal Technologies office. The calculator permits the user to explore the impact of a range of key geothermal power production parameters, including plant capacity, lifetime, capacity factor, geothermal technology, well numbers and depths, field exploration, and others on the two metrics just mentioned. Estimates of variations in the results are also available to the user.

This is the Baltic and North Sea Climatology (BNSC) for the Baltic Sea and the North Sea in the range 47 ° N to 66 ° N and 15 ° W to 30 ° E. It is the follow-up project to the KNSC climatology. The climatology was first made available to the public in March 2018 by ICDC and is published here in a slightly revised version 2. It contains the monthly averages of mean air pressure at sea level, and air temperature, and dew point temperature at 2 meter height. It is available on a 1 ° x 1 ° grid for the period from 1950 to 2015. For the calculation of the mean values, all available quality-controlled data of the DWD (German Meteorological Service) of ship observations and buoy measurements were taken into account during this period. Additional dew point values were calculated from relative humidity and air temperature if available. Climatologies were calculated for the WMO standard periods 1951-1980, 1961-1990, 1971-2000 and 1981-2010 (monthly mean values). As a prerequisite for the calculation of the 30-year-climatology, at least 25 out of 30 (five-sixths) valid monthly means had to be present in the respective grid box. For the long-term climatology from 1950 to 2015, at least four-fifths valid monthly means had to be available. Two methods were used (in combination) to calculate the monthly averages, to account for the small number of measurements per grid box and their uneven spatial and temporal distribution: 1. For parameters with a detectable annual cycle in the data (air temperature, dew point temperature), a 2nd order polynomial was fitted to the data to reduce the variation within a month and reduce the uncertainty of the calculated averages. In addition, for the mean value of air temperature, the daily temperature cycle was removed from the data. In the case of air pressure, which has no annual cycle, in version 2 per month and grid box no data gaps longer than 14 days were allowed for the calculation of a monthly mean and standard deviation. This method differs from KNSC and BNSC version 1, where mean and standard deviation were calculated from 6-day windows means. 2. If the number of observations fell below a certain threshold, which was 20 observations per grid box and month for the air temperature as well as for the dew point temperature, and 500 per box and month for the air pressure, data from the adjacent boxes was used for the calculation. The neighbouring boxes were used in two steps (the nearest 8 boxes, and if the number was still below the threshold, the next sourrounding 16 boxes) to calculate the mean value of the center box. Thus, the spatial resolution of the parameters is reduced at certain points and, instead of 1 ° x 1 °, if neighboring values are taken into account, data from an area of 5 ° x 5 ° can also be considered, which are then averaged into a grid box value. This was especially used for air pressure, where the 24 values of the neighboring boxes were included in the averaging for most grid boxes. The mean value, the number of measurements, the standard deviation and the number of grid boxes used to calculate the mean values are available as parameters in the products. The calculated monthly and annual means were allocated to the centers of the grid boxes: Latitudes: 47.5, 48.5, ... Longitudes: -14.5, -13.5, … In order to remove any existing values over land, a land-sea mask was used, which is also provided in 1 ° x 1 ° resolution. In this version 2 of the BNSC, a slightly different database was used, than for the KNSC, which resulted in small changes (less than 1 K) in the means and standard deviations of the 2-meter air temperature and dew point temperature. The changes in mean sea level pressure values and the associated standard deviations are in the range of a few hPa, compared to the KNSC. The parameter names and units have been adjusted to meet the CF 1.6 standard.

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. This catalogue entry provides post-processed ERA5 hourly single-level data aggregated to daily time steps. In addition to the data selection options found on the hourly page, the following options can be selected for the daily statistic calculation:

The daily aggregation statistic (daily mean, daily max, daily min, daily sum*) The sub-daily frequency sampling of the original data (1 hour, 3 hours, 6 hours) The option to shift to any local time zone in UTC (no shift means the statistic is computed from UTC+00:00)

*The daily sum is only available for the accumulated variables (see ERA5 documentation for more details). Users should be aware that the daily aggregation is calculated during the retrieval process and is not part of a permanently archived dataset. For more details on how the daily statistics are calculated, including demonstrative code, please see the documentation. For more details on the hourly data used to calculate the daily statistics, please refer to the ERA5 hourly single-level data catalogue entry and the documentation found therein.

Motivated by the recent experimental discovery of strongly surface-plane-dependent superconductivity at surfaces of KTaO3 single crystals, we calculate the electron-phonon coupling strength, λ, of doped KTaO3 along the reciprocal-space high-symmetry directions. Using the Wannier-function approach implemented in the EPW package, we calculate λ across the experimentally covered doping range and compare its mode-resolved distribution along the [001], [110] and [111] reciprocal-space directions. We find that the electron-phonon coupling is strongest in the optical modes around the Γ point, with some distribution to higher k values in the [001] direction. The electron-phonon coupling strength as a function of doping has a dome-like shape in all three directions and its integrated total is largest in the [001] direction and smallest in the [111] direction, in contrast to the experimentally measured trends in critical temperatures. This disagreement points to a non-BCS character of the superconductivity. Instead, the strong localization of λ in the soft optical modes around Γ suggests an importance of ferroelectric soft-mode fluctuations, which is supported by our findings that the mode-resolved λ values are strongly enhanced in polar structures. The inclusion of spin-orbit coupling has negligible influence on our calculated mode-resolved λ values.

This dataset contains additional "small" habitat cores that had a minimum size of 1 female marten home range (300ha), but were too small to meet the minimum size threshold of 5 female home ranges (1500ha) used to define cores in the Primary Model. This dataset also contains the habitat cores from the Primary Model (i.e. cores ≥1500ha). The description following this paragraph is adapted from the the metadata description for developing cores in the Primary Model. These methods are identical to those used in developing cores in the Primary Model, with one exception: The minimum habitat core size parameter used in the Core Mapper tool was set to 300ha instead of 1500ha. It should be noted that a single core in this dataset actually slightly exceeded the 1500ha threshold for its final area calculation but was not present in the Primary Model set of habitat cores. We determined that this was because the "1500ha cutoff" in the tool was actually applied before the core was expanded by 977m to fill in interior holes and then subsequently trimmed back (In the Core Mapper tool, this is controlled by the "Expand cores by this CWD value" and "Trim back expanded cores" parameters). We derived the habitat cores using a tool within Gnarly Landscape Utilities called Core Mapper (Shirk and McRae 2015). To develop a Habitat Surface for input into Core Mapper, we started by assigning each 30m pixel on the modeled landscape a habitat value equal to its GNN OGSI (range = 0-100). In areas with serpentine soils that support habitat potentially suitable for coastal marten (see report for details), we assigned a minimum habitat value of 31, which is equivalent to the 33rd percentile of OGSI 80 pixels in the marten’s historical range. Pixels with higher OGSI retained their normal habitat value. Our intention was to allow the modified serpentine pixels to be more easily incorporated into habitat cores if there were higher value OGSI pixels in the vicinity, but not to have them form the entire basis of a core. We also excluded pixels with a habitat value <1.0 from inclusion in habitat cores. We then used a moving window to calculate the average habitat value within a 977m radius around each pixel (derived from the estimated average size of a female marten’s home range of 300 ha). Pixels with an average habitat value ≥36.0 were then incorporated into habitat cores. After conducting a sensitivity analysis by running a set of Core Mapper trials using a broad range of habitat values, we chose ≥36.0 as the average habitat value because it is the median OGSI of pixels within the marten’s historical range classified by the GNN as “OGSI 80” (Davis et al. 2015). It generated a set of habitat cores that were not overly generous (depicting most of the landscape as habitat core) or strict (only mapping cores in a few locations with very high OGSI such as Redwood State and National Parks) (see Appendix 3 of the referenced report for more details, including example maps from our sensitivity analysis). We then set Core Mapper to expand the habitat cores by 977 cost-weighted meters, a step intended to consolidate smaller cores that were probably relatively close together from a marten’s perspective. This was followed by a “trimming” step that removed pixels from the expansion that did not meet the moving window average so the net result was rather small changes in the size of the habitat cores, but filling in many individual isolated pixels with a habitat value of 0. This is an abbreviated and incomplete description of the dataset. Please refer to the spatial metadata for a more thorough description of the methods used to produce this dataset, and a discussion of any assumptions or caveats that should be taken into consideration.

[ Derived from parent entry - See data hierarchy tab ]

This is the Baltic and North Sea Climatology (BNSC) for the Baltic Sea and the North Sea in the range 47 ° N to 66 ° N and 15 ° W to 30 ° E. It is the follow-up project to the KNSC climatology. The climatology was first made available to the public in March 2018 by ICDC and is published here in a slightly revised version 2. It contains the monthly averages of mean air pressure at sea level, and air temperature, and dew point temperature at 2 meter height. It is available on a 1 ° x 1 ° grid for the period from 1950 to 2015. For the calculation of the mean values, all available quality-controlled data of the DWD (German Meteorological Service) of ship observations and buoy measurements were taken into account during this period. Additional dew point values were calculated from relative humidity and air temperature if available. Climatologies were calculated for the WMO standard periods 1951-1980, 1961-1990, 1971-2000 and 1981-2010 (monthly mean values). As a prerequisite for the calculation of the 30-year-climatology, at least 25 out of 30 (five-sixths) valid monthly means had to be present in the respective grid box. For the long-term climatology from 1950 to 2015, at least four-fifths valid monthly means had to be available. Two methods were used (in combination) to calculate the monthly averages, to account for the small number of measurements per grid box and their uneven spatial and temporal distribution: 1. For parameters with a detectable annual cycle in the data (air temperature, dew point temperature), a 2nd order polynomial was fitted to the data to reduce the variation within a month and reduce the uncertainty of the calculated averages. In addition, for the mean value of air temperature, the daily temperature cycle was removed from the data. In the case of air pressure, which has no annual cycle, in version 2 per month and grid box no data gaps longer than 14 days were allowed for the calculation of a monthly mean and standard deviation. This method differs from KNSC and BNSC version 1, where mean and standard deviation were calculated from 6-day windows means. 2. If the number of observations fell below a certain threshold, which was 20 observations per grid box and month for the air temperature as well as for the dew point temperature, and 500 per box and month for the air pressure, data from the adjacent boxes was used for the calculation. The neighbouring boxes were used in two steps (the nearest 8 boxes, and if the number was still below the threshold, the next sourrounding 16 boxes) to calculate the mean value of the center box. Thus, the spatial resolution of the parameters is reduced at certain points and, instead of 1 ° x 1 °, if neighboring values are taken into account, data from an area of 5 ° x 5 ° can also be considered, which are then averaged into a grid box value. This was especially used for air pressure, where the 24 values of the neighboring boxes were included in the averaging for most grid boxes. The mean value, the number of measurements, the standard deviation and the number of grid boxes used to calculate the mean values are available as parameters in the products. The calculated monthly and annual means were allocated to the centers of the grid boxes: Latitudes: 47.5, 48.5, ... Longitudes: -14.5, -13.5, … In order to remove any existing values over land, a land-sea mask was used, which is also provided in 1 ° x 1 ° resolution. In this version 2 of the BNSC, a slightly different database was used, than for the KNSC, which resulted in small changes (less than 1 K) in the means and standard deviations of the 2-meter air temperature and dew point temperature. The changes in mean sea level pressure values and the associated standard deviations are in the range of a few hPa, compared to the KNSC. The parameter names and units have been adjusted to meet the CF 1.6 standard.

Policies requiring biodiversity no net loss or net gain as an outcome of environmental planning have become more prominent worldwide, catalysing interest in biodiversity offsetting as a mechanism to compensate for development impacts on nature. Offsets rely on credible and evidence-based methods to quantify biodiversity losses and gains. Following the introduction of the United Kingdom’s Environment Act in November 2021, all new developments requiring planning permission in England are expected to demonstrate a 10% biodiversity net gain from 2024, calculated using the statutory biodiversity metric framework (Defra, 2023). The metric is used to calculate both baseline and proposed post-development biodiversity units, and is set to play an increasingly prominent role in nature conservation nationwide. The metric has so far received limited scientific scrutiny. This dataset comprises a database of statutory biodiversity metric unit values for terrestrial habitat samples across England. For each habitat sample, we present biodiversity units alongside five long-established single-attribute proxies for biodiversity (species richness, individual abundance, number of threatened species, mean species range or population, mean species range or population change). Data were compiled for species from three taxa (vascular plants, butterflies, birds), from sites across England. The dataset includes 24 sites within grassland, wetland, woodland and forest, sparsely vegetated land, cropland, heathland and shrub, i.e. all terrestrial broad habitats except urban and individual trees. Species data were reused from long-term ecological change monitoring datasets (mostly in the public domain), whilst biodiversity units were calculated following field visits. Fieldwork was carried out in April-October 2022 to calculate biodiversity units for the samples. Sites were initially assessed using metric version 3.1, which was current at the time of survey, and were subsequently updated to the statutory metric for analysis using field notes and species data. Species data were derived from 24 long-term ecological change monitoring sites across the Environmental Change Network (ECN), Long Term Monitoring Network (LTMN) and Ecological Continuity Trust (ECT), collected between 2010 and 2020. Methods Study sites We studied 24 sites across the Environmental Change Network (ECN), Long Term Monitoring Network (LTMN) and Ecological Continuity Trust (ECT). Biodiversity units were calculated following field visits by the authors, whilst species data (response variables) were derived from long-term ecological change monitoring datasets collected by the sites and mostly held in the public domain (Table S1). We used all seven ECN sites in England. We selected a complementary 13 LTMN sites to give good geographic and habitat representation across England. We included four datasets from sites supported by the ECT where 2 x 2m vascular plant quadrat data were available for reuse. The 24 sites included samples from all terrestrial broad habitats (sensu Defra 2023) in England, except urban and individual trees: grassland (8), wetland (6), woodland and forest (5), sparsely vegetated land (2), cropland (2), heathland and shrub (1). Non-terrestrial broad habitats (rivers and lakes, marine inlets and transitional waters) were excluded. Our samples ranged in biodiversity unit scores from 2 to 24, the full range of the metric. Not all 24 sites had long-term datasets from all taxa: 23 had vascular plant data, 8 had bird data, and 13 had butterfly data. We chose these three taxa as they are the most comprehensively surveyed taxa in England’s long-term biological datasets. Together they represent a taxonomically broad, although by no means representative, sample of English nature. Biodiversity unit calculation Baseline biodiversity units were attributed to each vegetation quadrat using the statutory biodiversity metric (Defra, 2023) (Equation 1). Sites were visited by the authors between April and October 2022, i.e. within the optimal survey period indicated in the metric guidance. Sites were assessed initially using metric version 3.1 (Panks et al., 2022), which was current at the time of survey, and were subsequently updated to the statutory metric for analysis using field notes and species data.. Following the biodiversity metric guidance, we calculated biodiversity units at the habitat parcel scale, such that polygons with consistent habitat type and condition are the unit of assessment. We assigned habitat type and condition score to all quadrats falling within the parcel. Where the current site conditions (2022) and quadrat data (2010 to 2020) differed from each other in habitat or condition, e.g. the % bracken cover, we deferred to the quadrat data in order to match our response and explanatory variables more fairly. Across all samples, area was set to 1 ha arbitrarily, and strategic significance set to 1 (no strategic significance), to allow comparison between sites. To assign biodiversity units to the bird and butterfly transects, we averaged the biodiversity units of plant quadrats within the transect routes plus a buffer of 500 m (birds) or 100 m (butterflies). Quadrats were positioned to represent the habitats present at each site proportionally, and transect routes were also positioned to represent the habitats present across each site. Although units have been calculated as precisely as possible for all taxa, we recognize that biodiversity units are calculated more precisely for the plant dataset than the bird and butterfly dataset: the size of transect buffer is subjective, and some transects run adjacent to offsite habitat that could not be accessed. Further detail about biodiversity unit calculation can be found in the Supporting Information. Equation 1. Biodiversity unit calculation following the statutory biodiversity metric (Defra, 2023) Size of habitat parcel × Distinctiveness × Condition × Strategic Significance = Biodiversity Units Species response variable calculation We reused species datasets for plants, birds and butterflies recorded by the sites to calculate our response variables (Table S1). Plant species presence data were recorded using 2 x 2m quadrats of all vascular plant species at approximately 50 sample locations per site (mean 48.1, sd 3.7), stratified to represent all habitat types on site. If the quadrat fell within woodland or scrub, trees and shrubs rooted within a 10 x 10 m plot centred on the quadrat were also counted and added to the quadrat species records, with any duplicate species records removed. We treated each quadrat as a sample point, and the most recent census year was analysed (ranging between 2011-2021). Bird data were collected annually using the Breeding Birds Survey method of the British Trust for Ornithology: two approximately parallel 1 km long transects were routed through representative habitat on each site. The five most recent census years were analysed (all fell between 2006-2019), treating each year as a sample point (Bateman et al., 2013). Butterfly data were collected annually using the Pollard Walk method of the UK Butterfly Monitoring Scheme: a fixed transect route taking 30 to 90 minutes to walk (c. 1-2 km) was established through representative habitat on each site. The five most recent census years were analysed (all fell between 2006-2019), treating each year as a sample point. Full detail of how these datasets were originally collected in the field can be found in Supporting Information. For species richness estimates we omitted any records with vague taxon names not resolved to species level. Subspecies records were put back to the species level, as infraspecific taxa were recorded inconsistently across sites. Species synonyms were standardised across all sites prior to analysis. For bird abundance we used the maximum count of individuals recorded per site per year for each species as per the standard approach (Bateman et al., 2013). For butterfly abundance we used sum abundance over 26 weekly visits each year for each species at each site, using a GAM to interpolate missing weekly values (Dennis et al., 2013). Designated taxa were identified using the Great Britain Red List data held by JNCC (2022); species with any Red List designation other than Data Deficient or Least Concern were summed. Plant species range and range change index data followed PLANTATT (Hill et al., 2004). Range was measured as the number of 10x10 km cells across Great Britain that a species is found in. The change index measures the relative magnitude of range size change in standardised residuals, comparing 1930-1960 with 1987-1999. For birds, species mean population size across Great Britain followed Musgrove et al., 2013. We used the breeding season population size estimates to match field surveys. Bird long-term population percentage change (generally 1970-2014) followed Defra (2017). For butterflies, range and change data followed Fox et al., 2015. Range data was occupancy of UK 10 km squares 2010-2014. Change was percent abundance change 1976-2014. For all taxa, mean range and mean change were averaged from all the species present in the sample, not weighted by the species’ abundance in the sample. · Bateman, I. J., Harwood, A. R., Mace, G. M., Watson, R. T., Abson, D. J., Andrews, B., et al. (2013). Bringing ecosystem services into economic decision-making: Land use in the United Kingdom. Science (80-. ). 341, 45–50. doi: 10.1126/science.1234379. · British Trust for Ornithology (BTO), 2022. Breeding Bird methodology and survey design. Available online at https://www.bto.org/our-science/projects/breeding-bird-survey/research-conservation/methodology-and-survey-design · Defra, 2023. Statutory biodiversity metric tools and guides. https://www.gov.uk/government/publications/statutory-biodiversity-metric-tools-and-guides. · Dennis, E. B., Freeman, S. N., Brereton, T., and

S is a probability of cultivation based on a series of environmental conditions on a global scale. Here, S is created to compare settlement locations throughout Utah to explain initial Euro-American settlement of the region. S is one of two proxies created specifically for Utah for comparison of environmental productivity throughout the state. The data are presented as a raster file where any one pixel represents the probability of cultivation from zero to one, normalized on a global scale (Ramankutty et al., 2002). Because S is normalized on a global scale, the range of values of S for Utah U.S.A does not cover the global spectrum of S, thus the highest S value in the data is 0.51. S was originally created by Ramankutty et al. (2002) on a global scale to understand probability of cultivation based on a series of environmental factors. The Ramankutty et al. (2002) methods were used to build a regional proxy of agricultural suitability for the state of Utah. Adapting the methods in Ramankutty et al. (2002), we created a higher resolution dataset of S specific to the state of Utah. S is composed of actual and potential evapotranspiration rates from 2000-2013, growing degree days, soil carbon density, and soil pH. The Moisture Index is calculated as: MI = ETact /PET Where ETact is the actual evapotranspiration and PET is the potential evapotranspiration. This calculation results in a zero to one index representing global variation in moisture. MI was calculated for the study area (Utah) using a raster of annual actual ETact and PET evapotranspiration data from 2000 to 2013 derived from the MODIS instrumentation (Mu, Zhao, & Running, 2011; Mu, Zhao, & Running, 2013; Numerical Terradynamic Simulation Group, 2013). Using ArcMap 10.3.1 Raster Calculator (Spatial Analyst), a raster dataset is created at a resolution of 2.6 kilometers.containing values representative of the average Moisture Index for Utah over a period of fourteen years (ESRI, 2015). The data were collected remotely by satellite (MODIS) and represents reflective surfaces (urban areas, lakes, and the Utah Salt Flats) as null values in the dataset. Areas of null values that were not bodies of water were interpolated using Inverse Distance Weighting (3d Analyst) in ArcMap 10.3.1 (ESRI, 2015). The probability of cultivation (S) is calculated as a normalized product of growing degree days (GDD), available moisture (MI), soil carbon density (Csoil), and soil pH (pHsoil). The equation is divided into two general components: S = Sclim * Ssoil where Sclim = f1(GDD) f2(MI) and Ssoil = g1(Csoil) g2(pHsoil) Climate suitability (Sclim) is calculated as a normalized probability density function of cropland area to Growing Degree-days (f1[GDD]) and probability density function of cropland area to Moisture Index (f2[MI]) (Ramankutty et al. 2002). Soil suitability (Ssoil) is calculated using a sigmoidal function of the soil carbon density and soil acidity/alkalinity. The optimum soil carbon range is from 4 to 8 kg of C/m2 and the optimum range of soil pH is from 6 to 7 (Ramankutty et al. 2002). The resulting S value varies from zero to one indicating the probability of agricultural on a global scale. To implement the equation for S, growing degree-days (GDD) are calculated using usmapmaker.pl Growing Degree-days calculator and PRISM climate maps with a minimum temperature threshold of 50 degrees Fahrenheit (Coop, 2010; Daly, Gibson, Taylor, Johnson, & Pasteris, 2002; Willmott & Robeson, 1995; “US Degree-Day Map Maker,” n.d.). Moisture Index data is calculated as described above. To calculate the overall climate suitability (Sclim), the resulting raster datasets of Growing Degree-days and Moisture Index are combined in ArcMap 10.3.1 using the Raster Calculator (Spatial Analyst) to create climate suitability (Sclim) raster dataset with a resolution of 2.6 kilometers sq. To calculate soil suitability, the functions provided by Ramankutty et al. (2002) are applied to soil data derived from the SSURGO soil dataset compiled using NRCS Soil Data Viewer 6.1 to create thematic maps of average soil pH within the top 30 centimeters and average carbon density within the top 30 centimeters ( Soil Survey Staff, 2015; NRCS Soils, n.d.). However, there are missing values in the SSURGO soil dataset for the state of Utah, resulting in datasets using soil pH to have null values in portions of the state (Soil Survey Staff, 2015). The resulting raster datasets of soil pH and carbon density are combined in ArcMap 10.3.1 using the Raster Calculator (Spatial Analyst) to create a soil suitability (Ssoil) raster dataset with a resolution of 9.2 kilometers sq (ESRI, 2015). The climate suitability raster dataset and soil suitability raster dataset are combined in ArcMap 10.3.1 using the Raster Calculator (Spatial Analyst) generating a S raster dataset with a resolution of 9.2 kilometers (ESRI, 2015). Projection: GCS_WGS_1984 Citations Coop, L. B. (2010). U. S. degree-day mapping calculator and publisher – Documentation Version 4.0. Oregon State University Integrated Plant Protection Center Web Site Publication E.10-04-2:http://uspest.org/wea/mapmkrdoc.html Daly, C., Gibson, W. P., Taylor, G. H., Johnson, G. L., & Pasteris, P. (2002). A knowledge-based approach to the statistical mapping of climate. Climate Research, 22(2), 99–113. ESRI. (2015). ArcGIS Desktop: Release (Version 10.3.1). Redlands, CA: Environmental Systems Research Institute. Mu, Q., Zhao, M., & Running, S. W. (2013). MODIS Global Terrestrial Evapotranspiration (ET) Product (NASA MOD16A2/A3). Algorithm Theoretical Basis Document, Collection, 5. Retrieved from http://www.ntsg.umt.edu/sites/ntsg.umt.edu/files/MOD16_ATBD.pdf Mu, Q., Zhao, M., & Running, S. W. (2011). Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sensing of Environment, 115(8), 1781–1800. NRCS Soils. (2015). Soil Data Viewer (Version 6.1). Natural Resources Conservation Service. Retrieved from http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcs142p2_053619 Numerical Terradynamic Simulation Group. (2014, July 29). MODIS Global Evapotranspiration Project (MOD16). University of Montana. Ramankutty, N., Foley, J. A., Norman, J., & Mcsweeney, K. (2002). The global distribution of cultivable lands: current patterns and sensitivity to possible climate change. Global Ecology and Biogeography, 11(5), 377–392. http://doi.org/10.1046/j.1466-822x.2002.00294.x Soil Survey Staff. (n.d.). Web Soil Survey. United States Department of Agriculture. Retrieved from http://websoilsurvey.nrcs.usda.gov/ U.S. Degree-Day Map Maker. (n.d.). Retrieved August 12, 2014, from http://pnwpest.org/cgi-bin/usmapmaker.pl\ Willmott, C. J., & Robeson, S. M. (1995). Climatologically aided interpolation (CAI) of terrestrial air temperature. International Journal of Climatology, 15(2), 221–229. http://doi.org/10.1002/joc.3370150207

The data includes measured data from Ecoregions 69 and 70 in West Virginia. Paired biological and chemical grab samples are included. These data were used to estimate SC extirpation concentration (XC95) for benthic invertebrate genera. Also included are cumulative frequency distribution plots, scatter plots fitted with generalized additive models, and biogeographical maps of observations of each genus. The metadata and full data set is available in Supplemental Appendices S4 and S5, respectively. The output of 176 XC95 values from Ecoregions 69 and 70 are provided in Supplemental Appendix S6. Supplemental Appendix S7 depicts the probability of observing a genus for discrete ranges of SC. Supplemental Appendix S8 depicts the proportion of occurrence of a genus for discrete ranges of SC. Supplemental Appendix S9 shows the biogeographic distributions of the genera included in the data set. We also discuss limitations of this method to help avoid misinterpretations and inferential errors. A data dictionary is provided in Cond_DataFileColumnMetada-20161221. This dataset is associated with the following publication: Cormier, S., L. Zheng, E. Leppo, and A. Hamilton. Step-by-Step Calculation and Spreadsheet Tools for Predicting Stressor Levels that Extirpate Genera and Species. Integrated Environmental Assessment and Management. Allen Press, Inc., Lawrence, KS, USA, 14(2): 174-180, (2018).

These data were gathered during the Books, Minds, and Bodies research project in 2015-16. The project was designed to investigate the therapeutic potential of shared reading, and involved running 2 reading groups over 2 consecutive terms and recording participants' discussions of the texts being read aloud together. These recordings were subsequently transcribed and used for analysis of emotional variance and linguistic similarity.

Consistent with the ethical approval granted for the study, word order in the transcripts has been randomized so as to preclude any personal data being disclosed. This was done by tokenizing the text of each transcript into grammatical and lexical units (i.e. punctuation signs and words). These were shuffled using the "Random" module in the Python programming language, which provides a range of mathematical operations for collections of discrete objects. Nevertheless, grouping variables were preserved at the level of group (MT and HT terms) and session ID. As the calculation of values for emotional variance (on the dimensions of valence, arousal, and dominance) does not require syntax to be preserved, randomizing the data in this way should not affect the future calculation of word norm values.

The dataset also includes text/discussion similarity calculations, qualitative coding results, and participants' post-participation feedback data.

NB: this dataset replaces 'Books, Minds, and Bodies: raw transcript text plus VAD values' at https://ora.ox.ac.uk/objects/uuid:c370b75b-d37e-41be-89bb-cbb67a0c8614

Thermophysical constants of pure fluids, including critical temperature, Tc, critical pressure, pc, critical density, ρc, and acentric factor, ω, are fundamental for calculating thermophysical properties over wide pressure and temperature ranges. However, complete sets of (Tc, pc, ρc, and ω) values, along with traceable determination methods and careful evaluations, are available only for a limited number of fluids in commonly used databases. To address this gap, we compiled, examined, and fitted complete (Tc, pc, ρc, ω) values of 1422 pure fluids, mainly from three sources: NIST’s REFPROP 10.0 database, NIST’s ThermoData Engine 10 and Dortmund Data Bank 2023. The fitting was carried out using the experimental temperature, pressure, and density (T, p, ρ) data and saturated temperature and pressure (Tsat, psat) data, primarily with the Yang-Frotscher-Richter EoS. The results should be considered optimized values that best match experimental data, rather than absolute physical constants. The reliability of each value was assessed to guide future experimental research. This study is the first in a series aimed at determining effective thermophysical constants of thousands of fluids for the accurate calculation of all important thermophysical properties. The results will be implemented in the software OilMixProp and Ebsilon for practical applications.

The Surface Ocean CO₂ Atlas (SOCAT) version 2019 dataset (Bakker et al., 2016) is a quality-controlled dataset containing 25.7 million surface ocean gaseous CO₂ measurements collated from thousands of individual submissions. These gaseous CO₂ measurements are typically collected at many different depths (of the order of several metres below the surface) using many different systems, and the sampling depth varies dependent upon the sampling platform and/or setup. Different platforms (e.g. ships of opportunity, research vessels) and systems will collect water samples at different depths, and the sampling depth can even vary dependent upon sea state. Therefore, the collated SOCAT dataset contains high quality data, but these data are all valid for different and inconsistent depths. Therefore the SOCAT provided individual gaseous CO₂ measurements and gridded data are sub-optimal for calculating global or regional atmosphere-ocean gas exchange (and the resultant net CO₂ sinks) and sub-optimal for verifying gas fluxes from (or assimilation into) numerical models. Accurate calculations of CO₂ flux between the atmosphere and oceans require CO₂ concentrations at the top and bottom of the mass boundary layer, the ~100 μm deep layer that forms the interface between the ocean and the atmosphere (Woolf et al., 2016). Ignoring vertical temperature gradients across this very small layer can result in significant biases in the concentration differences and the resulting gas fluxes (e.g. ~5 to 29% underestimate in global net CO₂ sink values, Woolf et al., 2016). It is currently impossible to measure the CO₂ concentrations either side of this very thin layer, but it is possible to calculate the concentrations either side of this layer using the SOCAT data, satellite observations and knowledge of the carbonate system. Therefore to enable the SOCAT data to be optimal for an accurate atmosphere-ocean gas flux calculation, a reanalysis methodology was developed to enable the calculation of the fugacity of CO₂ (fCO₂) for the bottom of the mass boundary layer (termed sub-skin value). The theoretical basis and justification for this is described in detail within Woolf et al., (2016) and the re-analysis methodology is described in detail in (Goddijn-Murphy et al., 2015). The re-analysis calculation exploits paired in situ temperature and fCO₂ measurements in the SOCAT dataset, and uses an Earth observation dataset to provide a depth-consistent (sub-skin) temperature field to which all fugacity data are reanalysed. The outputs provide paired fCO₂ (and partial pressure of CO₂) and temperature data that correspond to a consistent sub-skin layer temperature. These can then be used to accurately calculate concentration differences and atmosphere-ocean CO₂ gas fluxes. This data submission contains a reanalysis of the fugacity of CO₂ (fCO₂) from the SOCAT version 2019 dataset to a consistent sub-skin temperature field. The reanalysis was performed using a tool that is distributed within the FluxEngine open source software toolkit (https://github.com/oceanflux-ghg/FluxEngine) (Shutler et al., 2016; Holding et al., in-review). All data processing and driver scripts are available from the FluxEngine ancillary tools repository https://github.com/oceanflux-ghg/FluxEngineAncillaryTools. The NOAA Optimum Interpolation Sea Surface Temperature (OISST) dataset (Reynolds et al., 2007) was used to provide the climate quality and depth consistent temperature data. The original OISST data were first resampled to provide monthly mean values on a 1º by 1º degree grid. These data were then used as the temperature input for the reanalysis. The resulting reanalysed data are provided as a tab-separated value file (individual data points) and as netCDF-5 file (gridded monthly means). These are the same file formats as provided by SOCAT and analogous to the SOCAT single data point and gridded data. Each row in the tab-separated value file corresponds to a row in the original SOCAT version 2019 dataset. The original SOCAT version 2019 data are included in full, with four additional columns containing the reanalysed data: * T_reynolds - The temperature (in degrees C) taken from the consistent OISST temperature field for the corresponding time and location. * fCO2_reanalysed - The fugacity of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_reynolds. * pCO2_SST - The partial pressure of CO₂ (in μatm) corresponding to the in situ (measured) temperature. * pCO2_reanalysed - The partial pressure of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_reynolds. The netCDF gridded version of the reanalysed dataset contains monthly mean data, binned into a 1º by 1º grid and uses the same units, missing value indicators and time and space resolution as the original SOCAT gridded product to maximise compatibility. The gridding is performed using the SOCAT gridding methodology (Sabine et al. 2013). The implementation of the gridding has been verified by performing the gridding on the original (non-reanalysed) SOCAT data and all results were identical to 8 decimal places. The result of gridding the original SOCAT data are included within these netCDF data, along with additional variables containing the equivalent results for the reanalysed SOCAT data. Statistical sample mean, minimum, maximum, standard deviation and count data for each grid cell are included, with unweighted and cruise-weighted versions (following the convention used by SOCAT). Full meta data are included within the file. Notes: 1. Due to the temporal range of the OISST dataset the reanalysed values are only available from 1981 onwards. Pre-1981 rows contain "NaN" (not-a-number) in the reanalysis columns. 2. The download for this submission is provided as a single .zip file (1.1 GB, uncompressed: 10.7GB) containing two files: SOCATv2019_reanalysed_subskin.tsv (containing every data point, ungridded) and SOCATv2019_reanalysed_subskin.nc (the gridded monthly mean data). How to cite these data: Please cite this PANGAEA submission, the theory (Woolf et al., 2016), the reanalysis methodology (Goddijn-Murphy et al., 2015), the FluxEngine toolbox which was used to perform the reanalysis (Shutler et al., 2016, Holding et al. in review) and the original SOCAT dataset (Bakker et al., 2016) and/or gridded equivalent (Sabine et al., 2013). Acknowledgements: The Surface Ocean CO₂ Atlas (SOCAT) is an international effort, endorsed by the International Ocean Carbon Coordination Project (IOCCP), the Surface Ocean Lower Atmosphere Study (SOLAS) and the Integrated Marine Biosphere Research (IMBeR) program, to deliver a uniformly quality-controlled surface ocean CO₂ database. The many researchers and funding agencies responsible for the collection of data and quality control are thanked for their contributions to SOCAT. These data were provided by two Integrated Carbon Observing System (ICOS) European Union (EU) readiness projects, Ringo (grant no. 730944) and BONUS Integral (grant no. 03FO773A).

This dataset contains all kinds of neutron focusing supermirror design algorithms used in the design of neutron focusing super-mirror, and the methods for comparing the design results of various algorithms, as well as the data generated in the design and comparison process. Various neutron supermirror design algorithms refer to the previous literature, and the corresponding design process is realized by writing Python code. The method for comparing the design results of various algorithms is original in my paper, and also is realized by writing Python code by myself. All data are calculated and generated using Python code written by ourselves. This dataset includes the code and the data that support the findings of Selection diagram of design algorithms for neutron-focusing supermirrors. Refer to our github webpage for details: https://github.com/MoWenbo19/Neutron-Supermirror.The SMlib.py file is a common function package for neutron supermirror algorithm, which contains the implementation process of eight neutron super-mirror design algorithms, the calculation of neutron super-mirror reflectivity (including the consideration of film roughness, film thickness non-uniformity, material absorption and other factors), and the calculation of coating thickness distribution on curved surfaces.The calculation process folder contains the calculation process files for the results, pictures, etc. in the paper:(1) First use ABC parameter selection. py, GRB parameter selection_ Parallel. py and Mas parameter selection_ Parallel. py calculates the initial design parameters of the three design algorithms for reflection performance comparison, and the results are arranged in the supermirror algorithm design parameter.xlsx table. Each row represents the design parameters of the three algorithms when the supermirror with m value shown in the second column is to be designed. The average reflectivity of the three algorithms under the initial design parameters is shown in Figure 2. The corresponding calculation and drawing code is the initial condition average reflectivity. py file, and the results are saved as Rmean0.npy file.(2) Then use ThicknessDistribution_ The New-ROC.py file calculates the film thickness distribution under different ROC, and saves the results in the ROC folder. The results of ROC=50, 80 and 160mm are shown in Figure 4, and the corresponding calculation and drawing code is the thickness range. py file.(3) Next, select the normalized point position of the film thickness, as shown in Figure 5. The corresponding calculation and drawing code thickness selects. py file.(4) The next step is to calculate the neutron intensity Q distribution, as shown in Figure 6. The corresponding calculation and drawing code is IQ customization_ Points. py file.(5) Next, calculate the influence of film thickness nonuniformity on the average reflectivity of the three algorithms. The calculation process code is ROC-m_ 1d_ double_ 3. py file, in which the results without considering the neutron intensity Q distribution are saved as R_ mean_ 3. npy file, the result of considering neutron intensity Q distribution is saved as R_ mean_ IQ_ 3. npy file. The comparison and selection of the average reflectivity of the three algorithms are shown in Figures 7 and 10. The corresponding calculation and drawing codes are selected for different uniformity algorithms_ Encrypt the grid. py file.(6) Then analyze the influence of thickness nonuniformity on the reflectivity curves of the three algorithms, as shown in Figures 8 and 9. The corresponding calculation and drawing codes are thickness change comparison. py file and subsection comparison. py file. Figure 11. The corresponding calculation and drawing code is thickness change comparison. py file .(7) Next, compare the influence of film thickness nonuniformity and neutron intensity Q distribution on the reflectivity of the algorithm, as shown in Figure 12. The corresponding calculation and drawing codes are selected for different uniformity algorithms_ Encrypt the grid. py file.(8) Next, the thickness of each layer in the supermirror multilayer film structure designed by three algorithms is compared, as shown in Figure 13. The corresponding calculation and drawing code is a segmented comparison. py file.(9) Finally, the film number allocation strategies of the three algorithms are compared, as shown in Figure 14. The corresponding calculation and drawing code is a segmented comparison. py file.

X-ray diffraction data were obtained from 10 thermolysin crystals that were not soaked in asparagine (first column) and from 10 thermolysin crystals that were soaked in 100 mM asparagine overnight (second column). We disregard crystal size because of the long soak times (all crystals were approximately 100 µm). For each group of ten crystals, the average and standard deviation for the refined occupancy are shown separately for each of the methods used for the refinement (two conventional PHENIX refinements, three electron counting methods described in §2.2, and the average of these three). Since the crystals were soaked overnight (t→∞ so that Omax = Orefine) the intra-crystalline dissociation constant Kdcryst is readily obtained from Omax using Eq. 2 (shown in the third column) [20]. There is a significant discrepancy between the Kdcryst value obtained from the curve fitted Omax (7.5 mM) and the value from the overnight soak Omax (17 mM). The occupancy refinement protocols all have higher precision (as seen by the low standard deviation) than accuracy. This high precision is sufficient to demonstrate that smaller crystals reach high occupancy faster. We report Kdcryst to confirm that the binding affinity is within the expected range for a small molecule product, but with significant uncertainty. We did not perform a similar analysis for lysozyme binding to N-acetyl glucosamine because the value obtained from the curve fitted Omax (5.4 mM) was very close to reported values (4–6 mM) [22].

NDVI Data Set (1. NDVI.nc)

• Global Vegetation Greenness (NDVI) from AVHRR GIMMS-3G+, 1981-2022
• Variable: Normalized Difference Vegetation Index (NDVI)
• Area: Global (60°S ~ 70°N, -180°W ~ 180°E)
• Period: 1982-01-01 ~ 2022-12-31
• Horizontal resolution: 0.25° × 0.25° (Regridded from original 0.0833° × 0.0833°)
• Temporal resolution: Bi-monthly (1st–15th and 16th–end of each month)
• Source: https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=2187

Meteorological Data Set (2.Temperature.nc, ... , 6.Cloud_cover.nc)

• Agrometeorological indicators from 1979 to present derived from reanalysis, Copernicus Climate Change Service
• Variable: Normalized Difference Vegetation Index (NDVI)
• Area: Global (60°S ~ 70°N, -180°W ~ 180°E)
• Period: 1982-01-01 ~ 2022-12-31
• Horizontal resolution: 0.25° × 0.25° (Regridded from original 0.1° × 0.1°)
• Temporal resolution: Bi-monthly (1st–15th and 16th–end of each month)
• The meteorological data were converted from daily values to bi-monthly average values.
• Variables: 2m temperature (K), 2m relative humidity (%), 10m wind speed (m s⁻¹), Precipitation flux (mm day⁻¹), Solar radiation flux (J m⁻² day⁻¹), Cloud cover (dimensionless)
• Source: https://cds.climate.copernicus.eu/datasets/sis-agrometeorological-indicators?tab=overview

Pre-processing code (Set_data_1~3)

• Set_data_1 : Combining raw data for NDVI and checking for missing values in the original data
• Set_data_2 : Combining annual data, calculating semi-monthly averages, and setting the latitude and longitude ranges for meteorological data.
• Set_data_3 : Synchronization of latitude, longitude, and resolution between NDVI and meteorological data.

Analysis code (code1~5)

• code_1 : This script processes climate data for variables by calculating their seasonal anomalies and time-averaged values. It performs the following steps:
  
  • Monthly Mean Calculation: The script first calculates the monthly mean for each variable over a period of 41 years.
  • Semi-Monthly Mean Calculation: It then computes the semi-monthly mean by combining the monthly mean data.
  • Anomaly Calculation: The script calculates the anomaly by subtracting the semi-monthly mean from the original data.
  • Time Mean Calculation: Finally, the time-mean for the entire time period is calculated for each variable.

• code_2 : This script calculates the linear regression slope, intercept, correlation coefficient (r-value), p-value, and standard error for a given climate variable (in this case, temperature anomaly) over time at each latitude and longitude point. The steps involved are:
  • Load Data: The script loads the input NetCDF file and extracts the time and temperature anomaly (or other climate data) values.
  • Linear Regression: For each spatial point (latitude, longitude), the script performs a linear regression between time and the corresponding climate data to determine the slope, intercept, r-value, p-value, and standard error.
  • Save Results: The regression results are saved into a new NetCDF file with variables for slope, intercept, r-value, p-value, and standard error for each latitude and longitude point.

• code_3 : This script processes NDVI (Normalized Difference Vegetation Index) data by performing the following steps:
  
  • Prepare Heatmap Data: It reshapes the NDVI data into a 4D array of the shape (latitude, longitude, years, 24 months), where each year contains 24 months of data.
  • Compute NDVI Histograms: It computes histograms of the NDVI data for each latitude, longitude, and year, adjusting the NDVI values into 20 bins for analysis.
  • Save Histogram Data: The histogram data is then saved to a .npy file, which stores the data for further analysis.

• code_4 : This script performs k-means clustering on NDVI data, based on histograms of NDVI values:
  1. Load Data: It loads NDVI data and histogram data (NDVI values in bins) from files.
  2. Filter Data: It filters out regions with zero values to focus on areas of interest.
  3. Reshape Data: The data is reshaped into a 2D format to prepare for clustering.
  4. K-Means Clustering: The script applies k-means clustering to the reshaped histogram data.
  5. Mean NDVI Calculation: It calculates the mean NDVI value for each cluster by extracting values from the NDVI data.
  6. Reordering Clusters: The clusters are reordered based on their mean NDVI values.
  7. Save Results: Finally, the script saves the cluster labels and non-zero indices into separate files.

• code_5 : This script processes NDVI (Normalized Difference Vegetation Index) data by clustering and saving the data for each cluster.
  
  • Load Data
    
    • Loads NDVI slope data (slope) from a NetCDF file.
    • Loads precomputed cluster labels (cluster_labels_8.npy) and valid data locations (non_zero_indices_8.npy).
  • Save NDVI Data by Cluster
    
    • Categorizes NDVI data based on clusters.
    • Creates a 2D array for each cluster and assigns NDVI data to the corresponding cluster coordinates.
    • Saves the clustered NDVI data as .npy files for further analysis.
  • Create Directory and Execute
    
    • Creates the output directory (if it does not exist).
    • Runs the main function to save the clustered NDVI data.

Acknowledgments

This work was also supported by Global - Learning & Academic research institution for Master’s·PhD students, and Postdocs (LAMP) Program of the National Research Foundation of Korea (NRF) grant funded by the Ministry of Education (No. RS-2023-00301914).

During R/V Maria S. Merian cruise MSM97/2 a sensor box (AddOn Box) was attached to a PocketFerryBox (4H Jena, Germany) in-line obtaining waters from the ships two autonomous measurement systems (RSWS). For the measurement of underway absorption spectra to calculate optical nitrate, the AddOn Box was equipped with two UV-process spectrophotometer (ProPS and OPUS, TriOS Mess- und Datentechnik GmbH, Germany). Data were recorded continuously in a ten-minute interval along the cruise track with an optical path length of 10 mm. The integration time was 256 ms. The ProPS photometer is equipped with a Deuterium lamp as light source whereas the OPUS photometer is equipped with a Xenon flash lamp. Baseline calibrations were done for both UV-photometer using ultrapure water. For the ProPS, the calibration was done in the laboratory before the cruise whereas the calibration for the OPUS was done by the manufacturer. The water inlet of the RSWS is allocated at approx. 6.5 m below the sea surface. While one box is measuring, the other box is being cleaned. The boxes switch after a user-defined measuring and cleaning interval. On MSM97/2, the boxes were alternating every 12 hours, including one short cleaning procedure during measurements after 4 hours. Absorption spectra obtained by the AddOn Box during the alternation process of the two RSWS, as well as during the internal cleaning procedure are not included. Spectra from station work are included. Within this dataset, absorption spectra were cut to a relevant wavelength range (from 189-360 nm (ProPS) and 199-360 nm (OPUS) to 210-260 nm). No further correction was done for the absorbance spectra in this processing step. After the cruise, gathered absorption spectra were used to improve current processing algorithms of optical nitrate detection for coastal and open ocean waters. Processing was performed according to Zielinski et al. (2011) and Frank et al. (2014) using MATLAB (R2018a). Detailed processing steps for the calculation of optical nitrate can be found on Zenodo (doi 10.5281/zenodo.4091420 ). Temperature and salinity data are necessary to compute nitrate values from derived UV-spectra and were taken from the attached PocketFerryBox using a SBE45 probe (Sea Bird Scientific, USA). A linear CDOM-offset correction was carried out. Nitrate reference spectra and salinity reference spectra were measured in the laboratory before the cruise. Corresponding nitrate reference samples using wet chemical analysis will be published separately.

The Case Mix Index (CMI) is the average relative DRG weight of a hospital’s inpatient discharges, calculated by summing the Medicare Severity-Diagnosis Related Group (MS-DRG) weight for each discharge and dividing the total by the number of discharges. The CMI reflects the diversity, clinical complexity, and resource needs of all the patients in the hospital. A higher CMI indicates a more complex and resource-intensive case load. Although the MS-DRG weights, provided by the Centers for Medicare & Medicaid Services (CMS), were designed for the Medicare population, they are applied here to all discharges regardless of payer. Note: It is not meaningful to add the CMI values together.