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.

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.

This dataset contains home range size, habitat availability and selection ratio data, calculated from GPS data fixes collected from individual European nightjars, in four concurrent years (2015-2018). Home ranges are 95% areas of use, presented in hectares. Habitat availability data are presented as the percentage (%) of each habitat category (n = 6, pooled from 14 original habitat types) available to each individual within their 95% home range. Selection ratios are Manly Selection Ratios for 14 habitat types and express the extent to which each habitat type is used by each individual bird, compared to how much of it is available. Selection Ratios >1 express positive selection – i.e. used more than expected, given availability. Selection Ratios <1 express avoidance – i.e. used less than expected, given availability.

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.

Understanding species abundances and distributions, especially at local to landscape scales, is critical for land managers and conservationists to prioritize management decisions and informs the effort and expense that may be required. The metrics of range size and local abundance reflect aspects of the biology and ecology of a given species, and together with its per capita (or per unit area) effects on other members of the community comprise a well-accepted theoretical paradigm describing invasive species. Although these metrics are readily calculated from vegetation monitoring data, they have not generally (and effect in particular) been applied to native species. We describe how metrics defining invasions may be more broadly applied to both native and invasive species in vegetation management, supporting their relevance to local scales of species conservation and management. We then use a sample monitoring dataset to compare range size, local abundance and effect as well as summary calculations of landscape penetration (range size × local abundance) and impact (landscape penetration × effect) for native and invasive species in the mixed-grass plant community of western North Dakota, USA. This paper uses these summary statistics to quantify the impact for 13 of 56 commonly encountered species, with statistical support for effects of 6 of the 13 species. Our results agree with knowledge of invasion severity and natural history of native species in the region. We contend that when managers are using invasion metrics in monitoring, extending them to common native species is biologically and ecologically informative, with little additional investment. Resources in this dataset:Resource Title: Supporting Data (xlsx). File Name: Espeland-Sylvain-BiodivConserv-2019-raw-data.xlsxResource Description: Occurrence data per quadrangle, site, and transect. Species Codes and habitat identifiers are defined in a separate sheet.Resource Title: Data Dictionary. File Name: Espeland-Sylvain-BiodivConserv-2019-data-dictionary.csvResource Description: Details Species and Habitat codes for abundance data collected.Resource Title: Supporting Data (csv). File Name: Espeland-Sylvain-BiodivConserv-2019-raw-data.csvResource Description: Occurrence data per quadrangle, site, and transect.Resource Title: Supplementary Table S1.1. File Name: 10531_2019_1701_MOESM1_ESM.docxResource Description: Scientific name, common name, life history group, family, status (N= native, I= introduced), percent of plots present, and average cover when present of 56 vascular plant species recorded in 1196 undisturbed plots in federally-managed grasslands of western North Dakota. Life history groups: C3 = cool season perennial grass, C4 = warm season perennial grass, SE = sedge, SH = shrub, PF= perennial forb, BF = biennial forb, APF = annual, biennial, or perennial forb.

Analysis of ‘Data from: Range size, local abundance and effect inform species descriptions at scales relevant for local conservation practice’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/8da85082-87e9-40ae-8639-4f1d1b06a007 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

Understanding species abundances and distributions, especially at local to landscape scales, is critical for land managers and conservationists to prioritize management decisions and informs the effort and expense that may be required. The metrics of range size and local abundance reflect aspects of the biology and ecology of a given species, and together with its per capita (or per unit area) effects on other members of the community comprise a well-accepted theoretical paradigm describing invasive species. Although these metrics are readily calculated from vegetation monitoring data, they have not generally (and effect in particular) been applied to native species. We describe how metrics defining invasions may be more broadly applied to both native and invasive species in vegetation management, supporting their relevance to local scales of species conservation and management. We then use a sample monitoring dataset to compare range size, local abundance and effect as well as summary calculations of landscape penetration (range size × local abundance) and impact (landscape penetration × effect) for native and invasive species in the mixed-grass plant community of western North Dakota, USA. This paper uses these summary statistics to quantify the impact for 13 of 56 commonly encountered species, with statistical support for effects of 6 of the 13 species. Our results agree with knowledge of invasion severity and natural history of native species in the region. We contend that when managers are using invasion metrics in monitoring, extending them to common native species is biologically and ecologically informative, with little additional investment.

--- Original source retains full ownership of the source dataset ---

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.

Summary and methods used to calculate the physical characteristics used to compare the home range estimators.

Contrasting effects of host or local specialization: widespread haemosporidians are host generalist whereas local specialists are locally abundant

https://doi.org/10.5061/dryad.j3tx95xfb

Description of the data and file structure

This is 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.

Sharing/Access information

The data that support the findings of this study are openly available in MalAvi at http://130.235.244.92/Malavi/ (Bensch et al. 2009).

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.

This Location Data & Foot traffic dataset available for all countries include enriched raw mobility data and visitation at POIs to answer questions such as:

-How often do people visit a location? (daily, monthly, absolute, and averages). -What type of places do they visit ? (parks, schools, hospitals, etc) -Which social characteristics do people have in a certain POI? - Breakdown by type: residents, workers, visitors. -What's their mobility like enduring night hours & day hours?
-What's the frequency of the visits partition by day of the week and hour of the day?

Extra insights -Visitors´ relative income Level. -Visitors´ preferences as derived by their visits to shopping, parks, sports facilities, churches, among others.

Overview & Key Concepts Each record corresponds to a ping from a mobile device, at a particular moment in time and at a particular latitude and longitude. We procure this data from reliable technology partners, which obtain it through partnerships with location-aware apps. All the process is compliant with applicable privacy laws.

We clean and process these massive datasets with a number of complex, computer-intensive calculations to make them easier to use in different data science and machine learning applications, especially those related to understanding customer behavior.

Featured attributes of the data Device speed: based on the distance between each observation and the previous one, we estimate the speed at which the device is moving. This is particularly useful to differentiate between vehicles, pedestrians, and stationery observations.

Night base of the device: we calculate the approximated location of where the device spends the night, which is usually their home neighborhood.

Day base of the device: we calculate the most common daylight location during weekdays, which is usually their work location.

Income level: we use the night neighborhood of the device, and intersect it with available socioeconomic data, to infer the device’s income level. Depending on the country, and the availability of good census data, this figure ranges from a relative wealth index to a currency-calculated income.

POI visited: we intersect each observation with a number of POI databases, to estimate check-ins to different locations. POI databases can vary significantly, in scope and depth, between countries.

Category of visited POI: for each observation that can be attributable to a POI, we also include a standardized location category (park, hospital, among others). Coverage: Worldwide.

Delivery schemas We can deliver the data in three different formats:

Full dataset: one record per mobile ping. These datasets are very large, and should only be consumed by experienced teams with large computing budgets.

Visitation stream: one record per attributable visit. This dataset is considerably smaller than the full one but retains most of the more valuable elements in the dataset. This helps understand who visited a specific POI, characterize and understand the consumer's behavior.

Audience profiles: one record per mobile device in a given period of time (usually monthly). All the visitation stream is aggregated by category. This is the most condensed version of the dataset and is very useful to quickly understand the types of consumers in a particular area and to create cohorts of users.

A vegetation phenology dataset for the Northern Hemisphere( the latitude ranging from 30°N to 90°N, the longitude ranging from -180°E to 180°E), including start of season (SOS_merge), SOS uncertainty range (SOS_merge_r), end of season (EOS_merge), and EOS uncertainty range (EOS_merge_r). This dataset is generated by merging four vegetation phenology datasets including MODIS MCD12Q2(https://lpdaac.usgs.gov/products/mcd12q2v061/), MEaSUREs VIPPHEN(https://lpdaac.usgs.gov/products/vipphen_ndviv004/), GIMMS NDVI3g(http://data.globalecology.unh.edu/data/GIMMS_NDVI3g_Phenology/), GIMMS NDVI4g(https://doi.org/10.5281/zenodo.7649107) using the reliability ensemble averaging method. The uncertainty range is calculated based on the weight of each dataset and the deviation between REA result and data sources, the upper and lower uncertainty limits are measured by REA result and the uncertainty range.

The spatial resolution of the new dataset is 0.05° and its temporal scale spans 1982–2022. The new dataset was validated using data from the ground-based PhenoCam dataset from 280 sites over the period 2000–2018, which provided 1410 site–year combinations.

The dataset is stored in TIFF format, the unit of “SOS_merge” and “EOS_merge” is day of year (DOY), and the unit of “SOS_merge_r” and “EOS_merge_r” is day.

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.

In this study, we introduce the count-based Morgan fingerprint (C-MF) to represent chemical structures of contaminants and develop machine learning (ML)-based predictive models for their activities and properties. Compared with the binary Morgan fingerprint (B-MF), C-MF not only qualifies the presence or absence of an atom group but also quantifies its counts in a molecule. We employ six different ML algorithms (ridge regression, SVM, KNN, RF, XGBoost, and CatBoost) to develop models on 10 contaminant-related data sets based on C-MF and B-MF to compare them in terms of the model’s predictive performance, interpretation, and applicability domain (AD). Our results show that C-MF outperforms B-MF in nine of 10 data sets in terms of model predictive performance. The advantage of C-MF over B-MF is dependent on the ML algorithm, and the performance enhancements are proportional to the difference in the chemical diversity of data sets calculated by B-MF and C-MF. Model interpretation results show that the C-MF-based model can elucidate the effect of atom group counts on the target and have a wider range of SHAP values. AD analysis shows that C-MF-based models have an AD similar to that of B-MF-based ones. Finally, we developed a “ContaminaNET” platform to deploy these C-MF-based models for free use.

TreeGOER (Tree Globally Observed Environmental Ranges) is a database that documents the environmental ranges (minimum, maximum, median, mean and 5%, 25%, 75% and 95% quantiles) for 48,129 tree species and for 51 environmental variables, including 38 bioclimatic variables, 8 soil variables and 3 topographic variables. These ranges were calculated after cleaning occurrence records and standardizing species names with the WorldFlora R package to World Flora Online or the World Checklist of Vascular Plants for a global GBIF occurrence download of 44,267,164 occurrences (GBIF.org 2021 GBIF Occurrence Download https://doi.org/10.15468/dl.77gcvq). The 5% and 95% quantiles were calculated separately for two methods of outlier detection and for the full data set. The process of compilation of TreeGOER with 30 arc-seconds global grid layers, two examples of BIOCLIM applications that investigated the effects of climate change on global tree diversity patterns and R scripts to repeat these analyses have been described by Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology, 00, 1–16. https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914.

TreeGOER can be used in combination with the CitiesGOER database (https://doi.org/10.5281/zenodo.8175429) that documents the conditions for the same environmental variables (except elevation) for 52,602 cities with a human population ≥ 5000. TreeGOER could also be used with the TreeGOER Global Zones atlas that can be obtained from https://doi.org/10.5281/zenodo.8252756. This high resolution atlas includes sheets with global zones for the Climatic Moisture Index (CMI) and the number of months with average temperature > 10 degrees C (Tmo10); these are zones for which presence of the 48,129 species was documented by TreeGOER.

Changes between different versions of the databases are documented in a specific sheet in the metadata file.

The development of TreeGOER was supported by the Darwin Initiative to project DAREX001 of Developing a Global Biodiversity Standard certification for tree-planting and restoration, by Norway’s International Climate and Forest Initiative through the Royal Norwegian Embassy in Ethiopia to the Provision of Adequate Tree Seed Portfolio project in Ethiopia, and by the Green Climate Fund through the IUCN-led Transforming the Eastern Province of Rwanda through Adaptation project. When using TreeGOER in your work, cite the publication (Kindt 2023) as well as this repository using the DOI (https://doi.org/10.5281/zenodo.7922927).

We employ a nationwide phone call dataset from Jan. 2015 to Dec. 2016. The log interaction duration and log interaction frequency in each phase (intermediate results) are both provided. Currently, we upload the Results folder to Google Drive. (https://drive.google.com/drive/folders/1h4rHZvzzQO7niYMelbzToJZernOij1dv?usp=sharing)

Please download the files from google drive for replication purposes.

In each file, we list tie ranges and interactions in all phases. For example, in 'Results/Graph_season_TR_Duration.txt', the former eight columns are tie range and the latter eight columns are log interaction duration. Tie range is calculated by the length of the second shortest path of two nodes. '-1' means that one node of this connection has no interaction with others in this phase. '100' means that there is no second path between two nodes, indicating that the tie range is infinite. '101' means that the degree of one node is 1, indicating that the tie range is infinite.

Differential privacy is applied to protect the privacy of users. Concretely, we add a Gaussian noise with μ=0, σ=5 to log interactions. When reproducing the results, please remove all numpy.log in the codes, and minus a σ for the calculation of error bars.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

Raw 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

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.

Dataset for the paper Metallic Plate Permeability Estimation using Single Frequency Eddy Current Testing in the Presence of Probe Lift-off.

The training dataset consists of 90k simulation samples calculated by the Dodd and Deeds analytical model. The inductance spectrum in the range of f∈[1,510] kHz with 100 frequency points calculated from the single frequency inductance at 105.64kHz and 3 features including plate permeability μer, probe lift-off l, characteristic spatial α0l. the relative permeability is evenly distributed in the range of μr∈[50,1000] and probe lift-off l∈[1,50] mm.

Examples of the real and imaginary parts of data are shown below. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5741725%2F5c4b07fd90cc1200f3540b9eced6ef10%2Fre_ups.jpg?generation=1680035687641246&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5741725%2Fd71f533c77fafba41ef822ad41b647cb%2Fim_ups.jpg?generation=1680035700011515&alt=media" alt="">

The test dataset consists of 6 measurements of the real metallic plates.