Graphs for all figures are provided along with codes that implement the results described in the paper. We simulate how a spin chain subject to timed local pulses develops long-range entanglement and how timed pulses can also drive a Hubbard chain to a maximally-correlated $\eta$-pairing state. All simulations are performed using exact diagonalization in Mathematica. In Figure 2 we obtain how the central-spin magnetization and the bipartite entanglement in an XY spin-1/2 chain evolves in time. We also obtain the distribution among symmetry sectors with different levels of entanglement and concurrence matrices that show the build-up of long-range Bell pairs. In Figure 3 we show how the result generalizes to larger systems and how the entanglement and preparation time scale with the system size. We also show how the protocol is not sensitive to random timing error of the pulses. In Figure 4 we calculate how the fidelity is affected by several types of imperfections, showing it is relatively robust. In Figure 7 we compute experimentally measurable spin-spin correlations at different stages of the protocol. In Figure 8 we calculate level statistics in the presence of integrability breaking and show that the scaling of entanglement and preparation time are largely unaffected. In Figure 5 we illustrate the protocol for $\eta$-pairing by simulating the evolution of a strongly-interacting, finite Hubbard chain. In Figure 6 we compute signatures of $eta$ pairing, including the average number of $\eta$ pairs, their momentum distribution, and the overlap with the maximally-correlated state as a function of system size.

This dataset consists of mathematical question and answer pairs, from a range of question types at roughly school-level difficulty. This is designed to test the mathematical learning and algebraic reasoning skills of learning models.

## Example questions

 Question: Solve -42*r + 27*c = -1167 and 130*r + 4*c = 372 for r.
 Answer: 4
 
 Question: Calculate -841880142.544 + 411127.
 Answer: -841469015.544
 
 Question: Let x(g) = 9*g + 1. Let q(c) = 2*c + 1. Let f(i) = 3*i - 39. Let w(j) = q(x(j)). Calculate f(w(a)).
 Answer: 54*a - 30

It contains 2 million (question, answer) pairs per module, with questions limited to 160 characters in length, and answers to 30 characters in length. Note the training data for each question type is split into "train-easy", "train-medium", and "train-hard". This allows training models via a curriculum. The data can also be mixed together uniformly from these training datasets to obtain the results reported in the paper. Categories:

• algebra (linear equations, polynomial roots, sequences)
• arithmetic (pairwise operations and mixed expressions, surds)
• calculus (differentiation)
• comparison (closest numbers, pairwise comparisons, sorting)
• measurement (conversion, working with time)
• numbers (base conversion, remainders, common divisors and multiples, primality, place value, rounding numbers)
• polynomials (addition, simplification, composition, evaluating, expansion)
• probability (sampling without replacement)

The U.S. Geological Survey has been characterizing the regional variation in shear stress on the sea floor and sediment mobility through statistical descriptors. The purpose of this project is to identify patterns in stress in order to inform habitat delineation or decisions for anthropogenic use of the continental shelf. The statistical characterization spans the continental shelf from the coast to approximately 120 m water depth, at approximately 5 km resolution. Time-series of wave and circulation are created using numerical models, and near-bottom output of steady and oscillatory velocities and an estimate of bottom roughness are used to calculate a time-series of bottom shear stress at 1-hour intervals. Statistical descriptions such as the median and 95th percentile, which are the output included with this database, are then calculated to create a two-dimensional picture of the regional patterns in shear stress. In addition, time-series of stress are compared to critical stress values at select points calculated from observed surface sediment texture data to determine estimates of sea floor mobility.

New physicochemical data for perfluoro(7-methylbicyclo[4.3.0]nonane) and perfluoro(butylcyclohexane) with a purity ≥ 0.991 mass fraction are presented: the temperature and heat of the solid–liquid phase transition, the dependence of saturated vapor pressure, viscosity, density (liquid molar volume), and refractive index on temperature; the coefficients of the Antoine equation; 19F and 13C NMR and FTIR spectra; and gas chromatography–mass spectrometry data. For the perfluoro(7-methylbicyclo[4.3.0]nonane)–perfluoro(butylcyclohexane) binary system, the dependences are given: refractive index (at 15 °C) and density (at 20 °C) on the composition and boiling point on the composition in the pressure range from 20 kPa to atmospheric pressure. The average integral value of the relative volatility coefficient for various concentration ranges at atmospheric pressure is determined.

The USDA Agricultural Research Service (ARS) recently established SCINet , which consists of a shared high performance computing resource, Ceres, and the dedicated high-speed Internet2 network used to access Ceres. Current and potential SCINet users are using and generating very large datasets so SCINet needs to be provisioned with adequate data storage for their active computing. It is not designed to hold data beyond active research phases. At the same time, the National Agricultural Library has been developing the Ag Data Commons, a research data catalog and repository designed for public data release and professional data curation. Ag Data Commons needs to anticipate the size and nature of data it will be tasked with handling. The ARS Web-enabled Databases Working Group, organized under the SCINet initiative, conducted a study to establish baseline data storage needs and practices, and to make projections that could inform future infrastructure design, purchases, and policies. The SCINet Web-enabled Databases Working Group helped develop the survey which is the basis for an internal report. While the report was for internal use, the survey and resulting data may be generally useful and are being released publicly. From October 24 to November 8, 2016 we administered a 17-question survey (Appendix A) by emailing a Survey Monkey link to all ARS Research Leaders, intending to cover data storage needs of all 1,675 SY (Category 1 and Category 4) scientists. We designed the survey to accommodate either individual researcher responses or group responses. Research Leaders could decide, based on their unit's practices or their management preferences, whether to delegate response to a data management expert in their unit, to all members of their unit, or to themselves collate responses from their unit before reporting in the survey. Larger storage ranges cover vastly different amounts of data so the implications here could be significant depending on whether the true amount is at the lower or higher end of the range. Therefore, we requested more detail from "Big Data users," those 47 respondents who indicated they had more than 10 to 100 TB or over 100 TB total current data (Q5). All other respondents are called "Small Data users." Because not all of these follow-up requests were successful, we used actual follow-up responses to estimate likely responses for those who did not respond. We defined active data as data that would be used within the next six months. All other data would be considered inactive, or archival. To calculate per person storage needs we used the high end of the reported range divided by 1 for an individual response, or by G, the number of individuals in a group response. For Big Data users we used the actual reported values or estimated likely values. Resources in this dataset:Resource Title: Appendix A: ARS data storage survey questions. File Name: Appendix A.pdfResource Description: The full list of questions asked with the possible responses. The survey was not administered using this PDF but the PDF was generated directly from the administered survey using the Print option under Design Survey. Asterisked questions were required. A list of Research Units and their associated codes was provided in a drop down not shown here. Resource Software Recommended: Adobe Acrobat,url: https://get.adobe.com/reader/ Resource Title: CSV of Responses from ARS Researcher Data Storage Survey. File Name: Machine-readable survey response data.csvResource Description: CSV file includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed. This information is that same data as in the Excel spreadsheet (also provided).Resource Title: Responses from ARS Researcher Data Storage Survey. File Name: Data Storage Survey Data for public release.xlsxResource Description: MS Excel worksheet that Includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel

Studies utilizing Global Positioning System (GPS) telemetry rarely result in 100% fix success rates (FSR). Many assessments of wildlife resource use do not account for missing data, either assuming data loss is random or because a lack of practical treatment for systematic data loss. Several studies have explored how the environment, technological features, and animal behavior influence rates of missing data in GPS telemetry, but previous spatially explicit models developed to correct for sampling bias have been specified to small study areas, on a small range of data loss, or to be species-specific, limiting their general utility. Here we explore environmental effects on GPS fix acquisition rates across a wide range of environmental conditions and detection rates for bias correction of terrestrial GPS-derived, large mammal habitat use. We also evaluate patterns in missing data that relate to potential animal activities that change the orientation of the antennae and characterize home-range probability of GPS detection for 4 focal species; cougars (Puma concolor), desert bighorn sheep (Ovis canadensis nelsoni), Rocky Mountain elk (Cervus elaphus ssp. nelsoni) and mule deer (Odocoileus hemionus). Part 1, Positive Openness Raster (raster dataset): Openness is an angular measure of the relationship between surface relief and horizontal distance. For angles less than 90 degrees it is equivalent to the internal angle of a cone with its apex at a DEM location, and is constrained by neighboring elevations within a specified radial distance. 480 meter search radius was used for this calculation of positive openness. Openness incorporates the terrain line-of-sight or viewshed concept and is calculated from multiple zenith and nadir angles-here along eight azimuths. Positive openness measures openness above the surface, with high values for convex forms and low values for concave forms (Yokoyama et al. 2002). We calculated positive openness using a custom python script, following the methods of Yokoyama et. al (2002) using a USGS National Elevation Dataset as input. Part 2, Northern Arizona GPS Test Collar (csv): Bias correction in GPS telemetry data-sets requires a strong understanding of the mechanisms that result in missing data. We tested wildlife GPS collars in a variety of environmental conditions to derive a predictive model of fix acquisition. We found terrain exposure and tall over-story vegetation are the primary environmental features that affect GPS performance. Model evaluation showed a strong correlation (0.924) between observed and predicted fix success rates (FSR) and showed little bias in predictions. The model's predictive ability was evaluated using two independent data-sets from stationary test collars of different make/model, fix interval programming, and placed at different study sites. No statistically significant differences (95% CI) between predicted and observed FSRs, suggest changes in technological factors have minor influence on the models ability to predict FSR in new study areas in the southwestern US. The model training data are provided here for fix attempts by hour. This table can be linked with the site location shapefile using the site field. Part 3, Probability Raster (raster dataset): Bias correction in GPS telemetry datasets requires a strong understanding of the mechanisms that result in missing data. We tested wildlife GPS collars in a variety of environmental conditions to derive a predictive model of fix aquistion. We found terrain exposure and tall overstory vegetation are the primary environmental features that affect GPS performance. Model evaluation showed a strong correlation (0.924) between observed and predicted fix success rates (FSR) and showed little bias in predictions. The models predictive ability was evaluated using two independent datasets from stationary test collars of different make/model, fix interval programing, and placed at different study sites. No statistically significant differences (95% CI) between predicted and observed FSRs, suggest changes in technological factors have minor influence on the models ability to predict FSR in new study areas in the southwestern US. We evaluated GPS telemetry datasets by comparing the mean probability of a successful GPS fix across study animals home-ranges, to the actual observed FSR of GPS downloaded deployed collars on cougars (Puma concolor), desert bighorn sheep (Ovis canadensis nelsoni), Rocky Mountain elk (Cervus elaphus ssp. nelsoni) and mule deer (Odocoileus hemionus). Comparing the mean probability of acquisition within study animals home-ranges and observed FSRs of GPS downloaded collars resulted in a approximatly 1:1 linear relationship with an r-sq= 0.68. Part 4, GPS Test Collar Sites (shapefile): Bias correction in GPS telemetry data-sets requires a strong understanding of the mechanisms that result in missing data. We tested wildlife GPS collars in a variety of environmental conditions to derive a predictive model of fix acquisition. We found terrain exposure and tall over-story vegetation are the primary environmental features that affect GPS performance. Model evaluation showed a strong correlation (0.924) between observed and predicted fix success rates (FSR) and showed little bias in predictions. The model's predictive ability was evaluated using two independent data-sets from stationary test collars of different make/model, fix interval programming, and placed at different study sites. No statistically significant differences (95% CI) between predicted and observed FSRs, suggest changes in technological factors have minor influence on the models ability to predict FSR in new study areas in the southwestern US. Part 5, Cougar Home Ranges (shapefile): Cougar home-ranges were calculated to compare the mean probability of a GPS fix acquisition across the home-range to the actual fix success rate (FSR) of the collar as a means for evaluating if characteristics of an animal’s home-range have an effect on observed FSR. We estimated home-ranges using the Local Convex Hull (LoCoH) method using the 90th isopleth. Data obtained from GPS download of retrieved units were only used. Satellite delivered data was omitted from the analysis for animals where the collar was lost or damaged because satellite delivery tends to lose as additional 10% of data. Comparisons with home-range mean probability of fix were also used as a reference for assessing if the frequency animals use areas of low GPS acquisition rates may play a role in observed FSRs. Part 6, Cougar Fix Success Rate by Hour (csv): Cougar GPS collar fix success varied by hour-of-day suggesting circadian rhythms with bouts of rest during daylight hours may change the orientation of the GPS receiver affecting the ability to acquire fixes. Raw data of overall fix success rates (FSR) and FSR by hour were used to predict relative reductions in FSR. Data only includes direct GPS download datasets. Satellite delivered data was omitted from the analysis for animals where the collar was lost or damaged because satellite delivery tends to lose approximately an additional 10% of data. Part 7, Openness Python Script version 2.0: This python script was used to calculate positive openness using a 30 meter digital elevation model for a large geographic area in Arizona, California, Nevada and Utah. A scientific research project used the script to explore environmental effects on GPS fix acquisition rates across a wide range of environmental conditions and detection rates for bias correction of terrestrial GPS-derived, large mammal habitat use.

GLAH05 Level-1B waveform parameterization data include output parameters from the waveform characterization procedure and other parameters required to calculate surface slope and relief characteristics. GLAH05 contains parameterizations of both the transmitted and received pulses and other characteristics from which elevation and footprint-scale roughness and slope are calculated. The received pulse characterization uses two implementations of the retracking algorithms: one tuned for ice sheets, called the standard parameterization, used to calculate surface elevation for ice sheets, oceans, and sea ice; and another for land (the alternative parameterization). Each data granule has an associated browse product.

The databases ESTAR, PSTAR, and ASTAR calculate stopping-power and range tables for electrons, protons, or helium ions. Stopping-power and range tables can be calculated for electrons in any user-specified material and for protons and helium ions in 74 materials.

We developed a MATLAB routine which can perform different regressions including residual analysis of data from a wide range of chamber experiment set-ups.

AbstractDespite a great deal of theoretical attention, we have limited empirical data about how ploidy influences the rate of adaptation. We evolved isogenic haploid and diploid populations of Saccharomyces cerevisiae for 200 generations in seven different environments. We measured the competitive fitness of all ancestral and evolved lines against a common competitor and find that in all seven environments haploid lines adapted faster than diploids, significantly so in three environments. We apply theory that relates the rates of adaptation and measured effective population sizes to the properties of beneficial mutations. We obtained rough estimates of the average selection coefficients in haploids between 2-10% for these first selected mutations. Results were consistent with semi-dominant to dominant mutations in four environments and recessive to additive mutations in two other environments. These results are consistent with theory that predicts haploids should evolve faster than diploids at large population sizes. Usage notesRaw data to calculate rate of adaptationRaw dataset for rate of adaptation calculations (Figure 1) and related statistics.dataall.csvR code to analyze raw data for rate of adaptationCompetition Analysis.RRaw data to calculate effective population sizesdatacount.csvR code to analayze effective population sizesR code used to analyze effective population sizes; Figure 2Cell Count Ne.RR code to determine our best estimate of the dominance coefficient in each environmentR code to produce figures 3, S4, S5 -- what is the best estimate of dominance? Note, competition and effective population size R code must be run first in the same session.what is h.R

Overview The Human Vital Signs Dataset is a comprehensive collection of key physiological parameters recorded from patients. This dataset is designed to support research in medical diagnostics, patient monitoring, and predictive analytics. It includes both original attributes and derived features to provide a holistic view of patient health.

Attributes Patient ID

Description: A unique identifier assigned to each patient. Type: Integer Example: 1, 2, 3, ... Heart Rate

Description: The number of heartbeats per minute. Type: Integer Range: 60-100 bpm (for this dataset) Example: 72, 85, 90 Respiratory Rate

Description: The number of breaths taken per minute. Type: Integer Range: 12-20 breaths per minute (for this dataset) Example: 16, 18, 15 Timestamp

Description: The exact time at which the vital signs were recorded. Type: Datetime Format: YYYY-MM-DD HH:MM Example: 2023-07-19 10:15:30 Body Temperature

Description: The body temperature measured in degrees Celsius. Type: Float Range: 36.0-37.5°C (for this dataset) Example: 36.7, 37.0, 36.5 Oxygen Saturation

Description: The percentage of oxygen-bound hemoglobin in the blood. Type: Float Range: 95-100% (for this dataset) Example: 98.5, 97.2, 99.1 Systolic Blood Pressure

Description: The pressure in the arteries when the heart beats (systolic pressure). Type: Integer Range: 110-140 mmHg (for this dataset) Example: 120, 130, 115 Diastolic Blood Pressure

Description: The pressure in the arteries when the heart rests between beats (diastolic pressure). Type: Integer Range: 70-90 mmHg (for this dataset) Example: 80, 75, 85 Age

Description: The age of the patient. Type: Integer Range: 18-90 years (for this dataset) Example: 25, 45, 60 Gender

Description: The gender of the patient. Type: Categorical Categories: Male, Female Example: Male, Female Weight (kg)

Description: The weight of the patient in kilograms. Type: Float Range: 50-100 kg (for this dataset) Example: 70.5, 80.3, 65.2 Height (m)

Description: The height of the patient in meters. Type: Float Range: 1.5-2.0 m (for this dataset) Example: 1.75, 1.68, 1.82 Derived Features Derived_HRV (Heart Rate Variability)

Description: A measure of the variation in time between heartbeats. Type: Float Formula: 𝐻 𝑅

𝑉

Standard Deviation of Heart Rate over a Period Mean Heart Rate over the Same Period HRV= Mean Heart Rate over the Same Period Standard Deviation of Heart Rate over a Period ​

Example: 0.10, 0.12, 0.08 Derived_Pulse_Pressure (Pulse Pressure)

Description: The difference between systolic and diastolic blood pressure. Type: Integer Formula: 𝑃

𝑃

Systolic Blood Pressure − Diastolic Blood Pressure PP=Systolic Blood Pressure−Diastolic Blood Pressure Example: 40, 45, 30 Derived_BMI (Body Mass Index)

Description: A measure of body fat based on weight and height. Type: Float Formula: 𝐵 𝑀

𝐼

Weight (kg) ( Height (m) ) 2 BMI= (Height (m)) 2

Weight (kg) ​

Example: 22.8, 25.4, 20.3 Derived_MAP (Mean Arterial Pressure)

Description: An average blood pressure in an individual during a single cardiac cycle. Type: Float Formula: 𝑀 𝐴

𝑃

Diastolic Blood Pressure + 1 3 ( Systolic Blood Pressure − Diastolic Blood Pressure ) MAP=Diastolic Blood Pressure+ 3 1 ​ (Systolic Blood Pressure−Diastolic Blood Pressure) Example: 93.3, 100.0, 88.7 Target Feature Risk Category Description: Classification of patients into "High Risk" or "Low Risk" based on their vital signs. Type: Categorical Categories: High Risk, Low Risk Criteria: High Risk: Any of the following conditions Heart Rate: > 90 bpm or < 60 bpm Respiratory Rate: > 20 breaths per minute or < 12 breaths per minute Body Temperature: > 37.5°C or < 36.0°C Oxygen Saturation: < 95% Systolic Blood Pressure: > 140 mmHg or < 110 mmHg Diastolic Blood Pressure: > 90 mmHg or < 70 mmHg BMI: > 30 or < 18.5 Low Risk: None of the above conditions Example: High Risk, Low Risk This dataset, with a total of 200,000 samples, provides a robust foundation for various machine learning and statistical analysis tasks aimed at understanding and predicting patient health outcomes based on vital signs. The inclusion of both original attributes and derived features enhances the richness and utility of the dataset.

Aim: Despite the wide distribution of many parasites around the globe, the range of individual species varies significantly even among phylogenetically related taxa. Since parasites need suitable hosts to complete their development, parasite geographical and environmental ranges should be limited to communities where their hosts are found. Parasites may also suffer from a trade-off between being locally abundant or widely dispersed. We hypothesize that the geographical and environmental ranges of parasites are negatively associated to their host specificity and their local abundance. Location: Worldwide Time period: 2009 to 2021 Major taxa studied: Avian haemosporidian parasites Methods: We tested these hypotheses using a global database which comprises data on avian haemosporidian parasites from across the world. For each parasite lineage, we computed five metrics: phylogenetic host-range, environmental range, geographical range, and their mean local and total number of observations in the database. Phylogenetic generalized least squares models were ran to evaluate the influence of phylogenetic host-range and total and local abundances on geographical and environmental range. In addition, we analysed separately the two regions with the largest amount of available data: Europe and South America. Results: We evaluated 401 lineages from 757 localities and observed that generalism (i.e. phylogenetic host range) associates positively to both the parasites’ geographical and environmental ranges at global and Europe scales. For South America, generalism only associates with geographical range. Finally, mean local abundance (mean local number of parasite occurrences) was negatively related to geographical and environmental range. This pattern was detected worldwide and in South America, but not in Europe. Main Conclusions: We demonstrate that parasite specificity is linked to both their geographical and environmental ranges. The fact that locally abundant parasites present restricted ranges, indicates a trade-off between these two traits. This trade-off, however, only becomes evident when sufficient heterogeneous host communities are considered. Methods We compiled data on haemosporidian lineages from the MalAvi database (http://130.235.244.92/Malavi/ , Bensch et al. 2009) including all the data available from the “Grand Lineage Summary” representing Plasmodium and Haemoproteus genera from wild birds and that contained information regarding location. After checking for duplicated sequences, this dataset comprised a total of ~6200 sequenced parasites representing 1602 distinct lineages (775 Plasmodium and 827 Haemoproteus) collected from 1139 different host species and 757 localities from all continents except Antarctica (Supplementary figure 1, Supplementary Table 1). The parasite lineages deposited in MalAvi are based on a cyt b fragment of 478 bp. This dataset was used to calculate the parasites’ geographical, environmental and phylogenetic ranges. Geographical range All analyses in this study were performed using R version 4.02. In order to estimate the geographical range of each parasite lineage, we applied the R package “GeoRange” (Boyle, 2017) and chose the variable minimum spanning tree distance (i.e., shortest total distance of all lines connecting each locality where a particular lineage has been found). Using the function “create.matrix” from the “fossil” package, we created a matrix of lineages and coordinates and employed the function “GeoRange_MultiTaxa” to calculate the minimum spanning tree distance for each parasite lineage distance (i.e. shortest total distance in kilometers of all lines connecting each locality). Therefore, as at least two distinct sites are necessary to calculate this distance, parasites observed in a single locality could not have their geographical range estimated. For this reason, only parasites observed in two or more localities were considered in our phylogenetically controlled least squares (PGLS) models. Host and Environmental diversity Traditionally, ecologists use Shannon entropy to measure diversity in ecological assemblages (Pielou, 1966). The Shannon entropy of a set of elements is related to the degree of uncertainty someone would have about the identity of a random selected element of that set (Jost, 2006). Thus, Shannon entropy matches our intuitive notion of biodiversity, as the more diverse an assemblage is, the more uncertainty regarding to which species a randomly selected individual belongs. Shannon diversity increases with both the assemblage richness (e.g., the number of species) and evenness (e.g., uniformity in abundance among species). To compare the diversity of assemblages that vary in richness and evenness in a more intuitive manner, we can normalize diversities by Hill numbers (Chao et al., 2014b). The Hill number of an assemblage represents the effective number of species in the assemblage, i.e., the number of equally abundant species that are needed to give the same value of the diversity metric in that assemblage. Hill numbers can be extended to incorporate phylogenetic information. In such case, instead of species, we are measuring the effective number of phylogenetic entities in the assemblage. Here, we computed phylogenetic host-range as the phylogenetic Hill number associated with the assemblage of hosts found infected by a given parasite. Analyses were performed using the function “hill_phylo” from the “hillr” package (Chao et al., 2014a). Hill numbers are parameterized by a parameter “q” that determines the sensitivity of the metric to relative species abundance. Different “q” values produce Hill numbers associated with different diversity metrics. We set q = 1 to compute the Hill number associated with Shannon diversity. Here, low Hill numbers indicate specialization on a narrow phylogenetic range of hosts, whereas a higher Hill number indicates generalism across a broader phylogenetic spectrum of hosts. We also used Hill numbers to compute the environmental range of sites occupied by each parasite lineage. Firstly, we collected the 19 bioclimatic variables from WorldClim version 2 (http://www.worldclim.com/version2) for all sites used in this study (N = 713). Then, we standardized the 19 variables by centering and scaling them by their respective mean and standard deviation. Thereafter, we computed the pairwise Euclidian environmental distance among all sites and used this distance to compute a dissimilarity cluster. Finally, as for the phylogenetic Hill number, we used this dissimilarity cluster to compute the environmental Hill number of the assemblage of sites occupied by each parasite lineage. The environmental Hill number for each parasite can be interpreted as the effective number of environmental conditions in which a parasite lineage occurs. Thus, the higher the environmental Hill number, the more generalist the parasite is regarding the environmental conditions in which it can occur. Parasite phylogenetic tree A Bayesian phylogenetic reconstruction was performed. We built a tree for all parasite sequences for which we were able to estimate the parasite’s geographical, environmental and phylogenetic ranges (see above); this represented 401 distinct parasite lineages. This inference was produced using MrBayes 3.2.2 (Ronquist & Huelsenbeck, 2003) with the GTR + I + G model of nucleotide evolution, as recommended by ModelTest (Posada & Crandall, 1998), which selects the best-fit nucleotide substitution model for a set of genetic sequences. We ran four Markov chains simultaneously for a total of 7.5 million generations that were sampled every 1000 generations. The first 1250 million trees (25%) were discarded as a burn-in step and the remaining trees were used to calculate the posterior probabilities of each estimated node in the final consensus tree. Our final tree obtained a cumulative posterior probability of 0.999. Leucocytozoon caulleryi was used as the outgroup to root the phylogenetic tree as Leucocytozoon spp. represents a basal group within avian haemosporidians (Pacheco et al., 2020).

Title of program: MARS-1-FOR-EFR-DWBA Catalogue Id: ABPB_v1_0

Nature of problem The package SATURN-MARS-1 consists of two programs SATURN and MARS for calculating cross sections of reactions transferring nucleon(s) primarily between two heavy ions. The calculations are made within the framework of the finite-range distorted wave Born approximation(DWBA). The first part, SATURN, prepares the form factor(s) either for exact finite (EFR) or for no-recoil (NR) approach. The prepared form factor is then used by the second part MARS to calculate either EFR-DWBA or NR-DWBA cross-s ...

Versions of this program held in the CPC repository in Mendeley Data abpb_v1_0; MARS-1-FOR-EFR-DWBA; 10.1016/0010-4655(74)90012-5

This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)

New flux data are presented for nine non-variable stars and 14 evolved variable stars with spectral types M and C. The data are from measurements of 21 passbands in the wavelength range from 7440{AA} to 10834{AA}, and they are comparable to measurements made by Wing some 40 years ago. Because the extinction algorithm applied to the new data is based partly on up-to-date calculations of telluric water-vapor effects, those calculations are tested for accuracy. In addition, methods used to calibrate standard stars both outside and inside the Paschen confluence are explained. After reddening corrections are applied to the flux data for the variable stars, those data are used to calculate color temperatures. In turn, those temperatures are used to derive blanketing corrections to color temperatures measured in the Wing filter system. Indices of absorption strength are calculated by comparing the flux data to blackbody colors derived from the color temperatures. It is found that the standard errors of those temperatures range from 3% to less than 1%. For the variable stars, the standard errors for the flux data range from 6.8mmag to 11.6mmag. For the non-variable stars, the corresponding standard error is about 6.0mmag. Cone search capability for table J/PASP/120/1183/stdmags (Flux data for standard stars) Cone search capability for table J/PASP/120/1183/stars (Cool variable stars positions (from Simbad))

It is argued that univariate long memory estimates based on ex post data tend to underestimate the persistence of ex ante variables (and, hence, that of the ex post variables themselves) because of the presence of unanticipated shocks whose short-run volatility masks the degree of long-range dependence in the data. Empirical estimates of long-range dependence in the Fisher equation are shown to manifest this problem and lead to an apparent imbalance in the memory characteristics of the variables in the Fisher equation. Evidence in support of this typical underestimation is provided by results obtained with inflation forecast survey data and by direct calculation of the finite sample biases. To address the problem of bias, the paper introduces a bivariate exact Whittle (BEW) estimator that explicitly allows for the presence of short memory noise in the data. The new procedure enhances the empirical capacity to separate low-frequency behaviour from high-frequency fluctuations, and it produces estimates of long-range dependence that are much less biased when there is noise contaminated data. Empirical estimates from the BEW method suggest that the three Fisher variables are integrated of the same order, with memory parameter in the range (0.75, 1). Since the integration orders are balanced, the ex ante real rate has the same degree of persistence as expected inflation, thereby furnishing evidence against the existence of a (fractional) cointegrating relation among the Fisher variables and, correspondingly, showing little support for a long-run form of Fisher hypothesis.

This dataset provides the equation of state data for lead in the temperature and pressure range from room temperature to 10 MK, and from atmospheric pressure to 107GPa. The thermodynamic properties of the shock Hugoniot line, 300 K isotherm, melting line, and temperature dense transition zone were calculated.

This dataset contains Python numerical computation code for studying the phenomena of acoustic superluminescence and Hawking radiation in specific rotating acoustic black hole models. The code is based on the radial wave equation of scalar field (acoustic disturbance) under the effective acoustic metric background derived from analysis. Dataset generation process and processing methods: The core code is written in Python language, using standard scientific computing libraries NumPy and SciPy. The main steps include: (1) defining model parameters (such as A, B, m) and calculation range (frequency $\ omega $from 0.01 to 2.0, turtle coordinates $r ^ * $from -20 to 20); (2) Implement the mutual conversion function between the radial coordinate $r $and the turtle coordinate $r ^ * $, where the inversion of $r ^ * (r) $is numerically solved using SciPy's' optimize.root_scalar 'function (such as Brent's method), and special attention is paid to calculations near the horizon $r_H=| A |/c $to ensure stability; (3) Calculate the effective potential $V_0 (r ^ *, \ omega) $that depends on $r (r ^ *) $; (4) Convert the second-order radial wave equation into a system of quaternion first-order real valued ordinary differential equations; (5) The ODE system was solved using SciPy's' integrate. solve_ivp 'function (using an adaptive step size RK45 method with relative and absolute error margins set to $10 ^ {-8} $), applying pure inward boundary conditions (normalized unit transmission) at the field of view and asymptotic behavior at infinity; (6) Extract the reflection coefficient $\ mathcal {R} $and transmission coefficient $\ mathcal {T} $from the numerical solution; (7) Calculate the Hawking radiation power spectrum $P_ \ omega $based on the derived Hawking temperature $TH $, event horizon angular velocity $\ Omega-H $, Bose Einstein statistics, and combined with the gray body factor $| \ mathcal {T} | ^ 2 $. The calculation process adopts the natural unit system ($\ hbar=k_B=c=1 $) and sets the feature length $r_0=1 $. Dataset content: This dataset mainly includes a Python script file (code for numerical research on superluminescence and Hawking radiation of rotating acoustic black holes. py) and a README documentation file (README. md). The Python script implements the complete calculation process mentioned above. The README file provides a detailed explanation of the code's functionality, the required dependency libraries (Python 3, NumPy, SciPy) for running, the running methods, and the meaning of parameters. This dataset does not contain any raw experimental data and is only theoretical calculation code. Data accuracy and validation: The reliability of the code has been validated through two key indicators: (1) Flow conservation relationship$|\ mathcal{R}|^2 + [(\omega-m\Omega_H)/\omega]|\mathcal{T}|^2 = 1$ The numerical approximation holds within the calculated frequency range (with a deviation typically on the order of $10 ^ {-8} $or less); (2) Under the condition of superluminescence $0<\ omega1 $, which is consistent with theoretical expectations. File format and software: The code is in standard Python 3 (. py) format and can run in any standard Python 3 environment with NumPy and SciPy libraries installed. The README file is in Markdown (. md) format and can be opened with any text editor or Markdown viewer. No special or niche software is required.

Data analyzed in paper: Czechowski Z. Asymmetric Finite-Range Persistence in Time Series Generated by the Modified Discrete Langevin Model, Symmetry 2025, 17, 287.

Case: M3 (Eq. (14)), asymmetric persistence given by Eqs. (20), (21)

GLAH05 Level-1B waveform parameterization data include output parameters from the waveform characterization procedure and other parameters required to calculate surface slope and relief characteristics. GLAH05 contains parameterizations of both the transmitted and received pulses and other characteristics from which elevation and footprint-scale roughness and slope are calculated. The received pulse characterization uses two implementations of the retracking algorithms: one tuned for ice sheets, called the standard parameterization, used to calculate surface elevation for ice sheets, oceans, and sea ice; and another for land (the alternative parameterization). Each data granule has an associated browse product.

Dataset and codes for "Observation of Acceleration and Deceleration Periods at Pine Island Ice Shelf from 1997–2023 "

• Description of the data and file structure

The MATLAB codes and related datasets are used for generating the figures for the paper "Observation of Acceleration and Deceleration Periods at Pine Island Ice Shelf from 1997–2023".

Files and variables

File 1: Data_and_Code.zip

Directory: Main_function

**Description:****Include MATLAB scripts and functions. Each script include discriptions that guide the user how to used it and how to find the dataset that used for processing.

MATLAB Main Scripts: Include the whole steps to process the data, output figures, and output videos.

Script_1_Ice_velocity_process_flow.m

Script_2_strain_rate_process_flow.m

Script_3_DROT_grounding_line_extraction.m

Script_4_Read_ICESat2_h5_files.m

Script_5_Extraction_results.m

MATLAB functions: Five Files that includes MATLAB functions that support the main script:

1_Ice_velocity_code: Include MATLAB functions related to ice velocity post-processing, includes remove outliers, filter, correct for atmospheric and tidal effect, inverse weited averaged, and error estimate.

2_strain_rate: Include MATLAB functions related to strain rate calculation.

3_DROT_extract_grounding_line_code: Include MATLAB functions related to convert range offset results output from GAMMA to differential vertical displacement and used the result extract grounding line.

4_Extract_data_from_2D_result: Include MATLAB functions that used for extract profiles from 2D data.

5_NeRD_Damage_detection: Modified code fom Izeboud et al. 2023. When apply this code please also cite Izeboud et al. 2023 (https://www.sciencedirect.com/science/article/pii/S0034425722004655).

6_Figure_plotting_code:Include MATLAB functions related to Figures in the paper and support information.

Director: data_and_result

Description:**Include directories that store the results output from MATLAB. user only neeed to modify the path in MATLAB script to their own path.

1_origin : Sample data ("PS-20180323-20180329", “PS-20180329-20180404”, “PS-20180404-20180410”) output from GAMMA software in Geotiff format that can be used to calculate DROT and velocity. Includes displacment, theta, phi, and ccp.

2_maskccpN: Remove outliers by ccp < 0.05 and change displacement to velocity (m/day).

3_rockpoint: Extract velocities at non-moving region

4_constant_detrend: removed orbit error

5_Tidal_correction: remove atmospheric and tidal induced error

6_rockpoint: Extract non-aggregated velocities at non-moving region

6_vx_vy_v: trasform velocities from va/vr to vx/vy

7_rockpoint: Extract aggregated velocities at non-moving region

7_vx_vy_v_aggregate_and_error_estimate: inverse weighted average of three ice velocity maps and calculate the error maps

8_strain_rate: calculated strain rate from aggregate ice velocity

9_compare: store the results before and after tidal correction and aggregation.

10_Block_result: times series results that extrac from 2D data.

11_MALAB_output_png_result: Store .png files and time serties result

12_DROT: Differential Range Offset Tracking results

13_ICESat_2: ICESat_2 .h5 files and .mat files can put here (in this file only include the samples from tracks 0965 and 1094)

14_MODIS_images: you can store MODIS images here

shp: grounding line, rock region, ice front, and other shape files.

File 2 : PIG_front_1947_2023.zip

Includes Ice front positions shape files from 1947 to 2023, which used for plotting figure.1 in the paper.

File 3 : PIG_DROT_GL_2016_2021.zip

Includes grounding line positions shape files from 1947 to 2023, which used for plotting figure.1 in the paper.

Data was derived from the following sources:
Those links can be found in MATLAB scripts or in the paper "**Open Research" **section.