The Giraffidae are represented by only two extant species, the okapi (Okapia johnstoni) and the giraffe. Both have unusual and poorly understood social systems (reviewed by Dagg & Foster 1976; du Toit 2001; Pellew 2001). Although giraffes are typically observed in aggregations, they appear to join, and to leave, them independently of others, suggesting that they do not form long-term social bonds. It may be that adaptive benefits usually ascribed to social species have exerted selective pressure on what are essentially asocial animals to aggregate in this way. These benefits might include foraging efficiency (Krebs & Davies 1997, and see Bertram 1980) and/or collective vigilance (Pulliam 1973; Elgar 1989). Alternatively, giraffes may perceive their social environment in ways that are difficult for human observers to identify (Cameron & du Toit 2005). It may be that their behaviour is modified, not by the composition of whole aggregations, but only by the identity of and distance to their immediate neighbour/s (see e.g. Treves 1998). It may also be, however, that they are able to maintain contact with one another over long distances by means of visual, olfactory and/or infrasonic signals and that they spend much more of their time in stable groups (as they perceive them) than has been appreciated hitherto. The purpose of this study is to investigate the first of these two possibilities and to contribute to the elucidation of the second. It arises from and will extend the work of Cameron and du Toit (2005).

Hypotheses Null hypothesis: (asocial) giraffes co-occur at sites of localised resources e.g. food patches. Alternative hypotheses: benefits accrue to them (as in social species) from (i) sharing vigilance effort with others and/or (ii) from cueing on public information about food resources.

Predictions 1. The frequency and/or duration of individual vigilance is expected to decrease as a function of increasing aggregation size. 2. The time individuals spend foraging is expected to increase as a function of increasing aggregation size.

Research questions 1. Does aggregation size influence the time spent vigilant by individuals? 2. Does aggregation size influence the time spent foraging by individuals? 3. What is the frequency distribution of aggregation sizes? 4. What is the frequency distribution of aggregation compositions? 5. What is the frequency distribution of nearest/close neighbours distances? 6. What is the frequency distribution of nearest/close neighbours identities?

The 2006 Second Edition TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER database. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on the latest available governmental unit boundaries. The Census TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The 2006 Second Edition TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. This shapefile represents the current State Senate Districts for New Mexico as posted on the Census Bureau website for 2006.

In a previous version of this archive, geometry data and tables of opacity calculations were given that could be used to calculate the radiative pressure and absorption on fractal dust grains under Asymptotic Giant Branch (AGB) conditions (with a peak stellar wavelength of ~ 1 micron) for aggregates containing up to 256 primary particles. Because the focus of that work was on radiative pressure from a stellar spectrum peaking at approximately 1 micron, these data only covered the wavelength range from 0.3 to 30 microns. In this updated archive the wavelength range of the data has been expanded to allow calculation of the emission of the grains at longer wavelengths. Data are calculated for three common dust materials: forsterite, (Mg2SiO4), olivine, (Mg_(2x)Fe_(2(1-x))SiO4) with x=0.5, and 'astronomical silicate' (B.T. Draine and H.M. Lee, Optical Properties of Interstellar Graphite and Silicate Grains, Astrophysical Journal, 1984). In this updated version the range of aggregate sizes (number of primary particles in the aggregate) of some of these materials has also been increased from a maximum of 256 to 1024 constituent particles.

Example fractal aggregates were generated using the Diffusion Limited Aggregation (DLA) code as described in Wozniak M., Onofri F.R.A., Barbosa S., Yon J., Mroczka J., Comparison of methods to derive morphological parameters of multi-fractal samples of particle aggregates from TEM images, Journal of Aerosol Science 47: 12–26 (2012) and Onofri F.R.A., M. Wozniak, S. Barbosa, On the Optical Characterization of Nanoparticle and their Aggregates in Plasma Systems, Contributions to Plasma Physics 51(2-3):228-236 (2011). Aggregates were generated with a constant prefactor, kf=1.3, and two fractal dimensions (Df), representing open, porous (Df=1.8) aggregates and more compact (Df=2.8) aggregates.

The geometry files were produced with the DLA software. An example run using this software is shown for aggregates with 256 primary particles and a fractal dimension of 2.8 in the file 'dla_example.png'

For the fractal dimension=1.8 data, the number of primary particles in the aggregate, N, was increased up to 1024 from the previous maximum of 256 for all three dust materials investigated. In addition, the data for MgFeSiO4 with a fractal dimension of 2.8 was increased from 256 to 1024. As in the previous archive, 12 instances of each aggregate size were generated with primary particles having a radius of 0.5. These geometry data are given in:
aggregates_kf1.3_df1.8.zip --> Geometry for a prefactor of 1.3 and fractal dimension 1.8
aggregates_kf1.3_df2.8.zip --> Geometry for a prefactor of 1.3 and fractal dimension 2.8

An example file name for an aggregate is 'N_00000032_Agg_00000008.dat' where the first number is the number of primary particles in the aggregate (N=32) and the second number is the instance number (e.g. 8 of 12). The radius of each primary particle in an aggregate is 0.5. The geometry files have 4 columns: the x, y and z coordinates of each primary particle followed by the primary particle radius. In each zip file there is also a pdf document that describes the geometry data and shows an image of each geometry file.

These geometry data were then used to calculate the opacity of the aggregates using the Multiple Sphere T-Matrix code (MSTM v 3.0) developed by Daniel Mackowski (D.W. Mackowski, M.I. Mishchenko, A multiple sphere T-matrix Fortran code for use on parallel computer clusters, Journal of Quantitative Spectroscopy and Radiative Transfer, Volume 112, Issue 13, 2011). Data were generated using the first 10 instances of each aggregate size, and the geometry data were appropriately scaled to calculate the opacity data for primary particle radii ranging from 0.001 - 1.0 microns. As noted earlier, an earlier version of this archive was focused on radiative pressure on these aggregates and only covered the spectrum of a typical AGB star (0.3 to 30 microns wavelength). In this updated version this wavelength range has been increased to the longer wavelength limits of the optical data. By default, MSTM calculations are made along the z-axis of the geometry data. Additional calculations were made along the x and y axes for each aggregate. Therefore the final data set is the average of 30 values (10 instances each in the x,y,z directions).

The opacity data files are given in:

astronomical_silicate_df1.8.zip --> astronomical silicate aggregates with fractal dimension 1.8
astronomical_silicate_df2.8.zip --> astronomical silicate aggregates with fractal dimension 2.8
forsterite_df1.8.zip --> forsterite aggregates with fractal dimension 1.8
forsterite_df2.8.zip --> forsterite aggregates with fractal dimension 2.8
olivine_df1.8.zip --> olivine aggregates with fractal dimension 1.8
olivine_df2.8.zip --> olivine aggregates with fractal dimension 2.8

In the previous version of this archive, only the table files with the averages of the 10 instances were provided. In this updated version each of the individual opacity files used to create these tables is now also provided. These opacity files are numbered similar to the geometry files. For example, the opacity calculations for N=32, instance=5, angle=3 is given by
'opacity_results_N000032_I05_A03_file.dat.' Each file begins with a short header describing the data. For example, the astronomical silicate header for this N=32, instance=5, angle=3 file is:

#############################################################################################
# Number of primary particles in aggregate: 32
# Geometry Instance Number: 5
# Geometry File Name: N_00000032_Agg_00000005.dat
# Rotation Angles: 90.000 90.000 0.000
# Number of radius values: 30
# Minimum and maximum radius values in microns: 1.00000e-003 1.00000e+000
# Number of wavelength values: 92
# Minimum and maximum wavelength values in microns: 3.00000e-001 1.00000e+004
#############################################################################################

Afterwards the columns list the line number, the primary particle radius (microns), the wavelength (microns), the extinction efficiency factor, the absorption efficiency factor, the scattering absorption efficiency factor, the asymmetry factor and the radiation pressure efficiency factor. These efficiency factors are based on the effective radius of the aggregate described later in this document.

Within each of these zipped folders is a file that contains the averages of these individual opacity files. For example 'astronomical_silicate_df1.8.dat' is the averaged data for the astronomical silicate aggregates with a fractal dimension 1.8. As in the previous archive, the first lines of these table files are a header starting with the '#' character describing the table and the source of the optical data used.

After the header, the first line of data in the table has the following nine values giving the range for the data table and number of samples in N, (aggregate size), primary particle radius (microns) and wavelength (microns). These are:
Minimum aggregate size
Maximum aggregate size
Number of Aggregate samples
Primary Particle Minimum Radius (microns)
Primary Particle Maximum Radius (microns)
Number of Primary Particle radii samples
Wavelength minimum (microns)
Wavelength maximum (microns)
Number of Wavelength samples

Subsequent lines contain 13 columns. These columns give the efficiency factors and asymmetry factor for aggregates. These efficiency factors are based on the effective radius of the aggregate given by:
a_eff = a_primary*N^(1/3)
where a_primary is the primary particle radius and N is the number of primary particles in the aggregate.

For example, the absorption opacity of an aggregate would then be = pi*a_eff^2 * Q_abs.
The values in each column are:
Column 1: Primary particle radius in microns
Column 2: Wavelength in microns
Column 3: Number of primary particles in aggregate
Column 4: Mean Q_ext, mean extinction efficiency factor
Column 5: Standard Deviation of Mean Q_ext
Column 6: Mean Q_abs, mean absorption efficiency factor
Column 7: Standard Deviation of Mean Q_abs
Column 8: Mean Q_sca, mean scattering efficiency factor
Column 9: Standard Deviation of mean Q_sca
Column 10: Mean g_cos, mean asymmetry factor
Column 11: Standard Deviation of mean asymmetry factor
Column 12: Mean Q_pr, mean radiation pressure efficiency factor
Column 13: Standard Deviation of mean

The IUC ADC became the official source of IUC statistics in April 2021, when the NHS 111 Minimum Dataset (NHS 111 MDS) was merged into a revised version of the IUC ADC. Since then, a provisional subset of the IUC ADC data is published in the month after the collection end date (eg, April data published in May), with the complete monthly IUC ADC published as Official Statistics the following month (eg, April data published in June). The IUC ADC specification is reviewed and updated annually which means not all data items will be directly comparable with the same data items collected in the previous year. The IUC ADC is used to monitor the IUC KPIs.

The 2006 Second Edition TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER database. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on the latest available governmental unit boundaries. The Census TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The 2006 Second Edition TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. This shapefile represents the current State House Districts for New Mexico as posted on the Census Bureau website for 2006.

This Excel based tool was developed to analyze means-end chain data. The tool consists of a user manual, a data input file to correctly organise your MEC data, a calculator file to analyse your data, and instructional videos. The purpose of this tool is to aggregate laddering data into hierarchical value maps showing means-end chains. The summarized results consist of (1) a summary overview, (2) a matrix, and (3) output for copy/pasting into NodeXL to generate hierarchal value maps (HVMs). To use this tool, you must have collected data via laddering interviews. Ladders are codes linked together consisting of attributes, consequences and values (ACVs).

The purpose of this exchange is to provide DoED with aggregate earnings data that will be used to measure compliance of Institutions of Higher Education with the Gainful Employment (GE) regulations effective July 1, 2012. SSA uses existing Enumeration Verification System (EVS) and Earnings Coverage (EARN) software to verify the SSN and extract earnings data from the Master Earnings File (MEF). A back-end program computes the mean and median income for each GE program and earnings report year and provide DoED with an output file containing aggregate data. DoED uses SSA data to compute a debt to income ratio.

Calculated by ESRI UK by aggregating single crime case data. The data is collated from all police forces so data availability can fluctuate by area. The data shown reflects the current data supplied. If data looks unexpectedly low for an area please check https://data.police.uk/changelog/. Some areas where there has been an issue with a data return show as unusually low. In order to return figures for larger areas the data available is aggregated - this does mean that the numbers and rates will often be lower than if full data was available.Rates are presented as rate per thousand total population in the area. Populations are derived from the ONS population estimates at LSOA level and aggregated to the different geographical levels in line with the aggregation of the crime data.

Abstract–Many well-known sufficient dimension reduction methods investigate the inverse conditional moments of the predictors given the response. The required linearity condition, the number and arrangement of slices, and the inability to detect symmetric dependence are among several long-standing issues that have negatively impacted on the use of these approaches. Motivated by two recent works dealing with the choice of number of slices, we propose a novel and effective method based on the aggregation of inverse mean estimation. The new approach can substantially improve the estimation accuracy, break down the symmetry to achieve exhaustive estimation, and is much less sensitive to the violation of the linearity condition. Both simulation studies and a real data application show the efficacy of the newly proposed approach.

Political scientists use expert surveys to assess latent features of political actors. Experts, though, are unlikely to be equally informed and assess all actors equally well. The literature acknowledges variance in measurement quality, but pays little attention to the implications of uncertainty for aggregating responses. We discuss the nature of the measurement problem in expert surveys. We then propose methods to assess the ability of experts to judge where actors stand and to aggregate expert responses. We examine the effects of aggregation for a prominent survey in the literature on party politics and EU integration. Using a Monte Carlo simulation, we demonstrate that it is better to aggregate expert responses using the median or modal response, rather than the mean.

This spatial dataset represents areas where resources may be extracted within the limits of the aggregate licence or permit for the associated site. Reporting requirements are optional, which means records will be sporadic and limited to certain areas of the province.

Additional details related to aggregates in Ontario are available in related data classes as well as online using the interactive Pits and Quarries map.

Additional Documentation

  Aggregate Extraction Area - Data Description (PDF)
  Aggregate Extraction Area - Documentation (Word)

Status

On going: data is being continually updated

Maintenance and Update Frequency

As needed: data is updated as deemed necessary

Contact

Ryan Lenethen, Integration Branch, ryan.lenethen@ontario.ca

The 2006 Second Edition TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER database. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on the latest available governmental unit boundaries. The Census TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The 2006 Second Edition TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. This shapefile represents the current State Senate Districts for New Mexico as posted on the Census Bureau website for 2006.

Meta-analysis using individual participant data (IPD) offers many benefits, including greater analytical flexibility, compared to conventional analyses based on aggregate data (AD). However, it is often hindered by restricted access to IPD. Relying solely on available IPD may introduce “data availability bias,” compromising external validity. Integrating IPD with relevant AD addresses this concern, but existing methods are restrictive, requiring precise knowledge of the IPD-to-AD parameter mapping or relying on fixed-effect models that fail to account for study-level heterogeneity. We propose a Bayesian random-effects framework to overcome these limitations. Building on existing methods, we use estimating equations to derive the conditional distributions of AD parameters, given the corresponding IPD model parameters. We then apply the multiplier bootstrap method and density ratio models to approximate these conditional distributions based on the observed data, without requiring homogeneity in the covariate distributions. Both theoretical and empirical results demonstrate that our method reduces mean squared error compared to IPD-only analysis when IPD availability is independent of the data, and reduces bias when data availability is dependent. We apply this integrated approach to complement the IPD-only analysis in the International Weight Management in Pregnancy (i-WIP) Collaborative Group study.

This data set provides two products that were derived from the recently published North American Carbon Program (NACP) Regional Synthesis 1-degree terrestrial biosphere model (TBM) and inverse model (IM) outputs (Gridded 1-deg Observation Data and Biosphere and Inverse Model Outputs, Wei et al., 2013). The first product is the aggregation of the standardized gridded 1-degree TBM and IM outputs to the Greenhouse Gas (GHG) inventory zones as defined for North America (United States, Canada, and Mexico). Depending on the data availability, the monthly/yearly Net Ecosystem Exchange (NEE), Net Primary Production (NPP), Total Vegetation Carbon (VegC), Heterotrophic Respiration (Rh), and Fire Emissions (FE) outputs from the 22 TBM and 7 IM models were aggregated from the 1-degree resolution gridded format to the inventory zones and then, further divided into Forest Lands, Crop Lands, and Other Lands sectors within each inventory zone based on the 1-km resolution GLC2000 land cover map (GLC2000, 2003).The second product is the North American national GHG inventories on the scale of inventory zones which contain estimated land-atmosphere exchange of CO2 (NEE) in forest lands, crop lands, and other lands sectors. NEE estimates were synthesized from inventory-based data on productivity, ecosystem carbon stock change, and harvested product stock change, and additional information from national-level GHG inventories of the United States, Canada, and Mexico including EPA (2011) and Environment Canada (2011).An additional summary file of annual mean NEE (2000-2006)is provided for both land sectors and reporting zones in North America and was created by combining the aggregated model output and the national GHG database and is provided. The aggregated monthly and yearly model output data and the national GHG inventories data are available in comma separated value (*.csv) format files. Also provided are detailed inventory zone spatial data as an ESRI Shapefile. Included are zone names, boundaries, and zone and land cover type area attributes. For mapping convenience, the inventory zones shapefile was merged with 1-km forest, crop, and other lands masks to create a 1-km resolution reference data file that was converted to GeoTIFF format. The GeoTIFF defines to which inventory zone and land cover type each 1-km grid cell belongs.This document provides detailed information about the content, format, and processing procedures of these two data products. Detailed descriptions of the TBMs and IMs can be found in a separate companion document: NACP Regional Synthesis - Description of Observations and Models.

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.

In accordance with Act 123/1998 Coll. on free access to environmental information, as amended, the CHMI has provided verified primary and aggregated data on outdoor air pollutants. These are the main pollutants with limit values according to current legislation: arsenic (As), benzene, benzo[a]pyrene, cadmium (Cd), carbon monoxide (CO), nickel (Ni), nitrogen dioxide (NO2), nitrogen oxides (NOX), ground-level ozone (O3), lead (Pb), particulate matter (PM10), particulate matter (PM2.5), sulphur dioxide (SO2). The data are based on measurements at stations owned by the CHMI for the period 1969-2022. The primary data are presented in measurement intervals of 30 min, 1 h or 24 h, depending on the type of measurement. Aggregated data are calculated from the above primary data. These data are: daily mean values, monthly mean values, annual mean values and the number of exceedances of the limit values for the protection of human health. Verified data for subsequent calendar years from 2020 will always be available by 1 July of the following year.

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.

This is the supplemental data for the manuscript titled Characterization of mixing in nanoparticle hetero-aggregates using convolutional neural networks submitted to Nano Select.

Motivation:

Detection of nanoparticles and classification of the material type in scanning transmission electron microscopy (STEM) images can be a tedious task, if it has to be done manually. Therefore, a convolutional neural network is trained to do this task for STEM-images of TiO2-WO3 nanoparticle hetero-aggregates. The present dataset contains the training data and some jupyter-notebooks that can be used after installation of the MMDetection toolbox (https://github.com/open-mmlab/mmdetection) to train the CNN. Details are provided in the manuscript submitted to Nano Select and in the comments of the jupyter-notebooks.

Authors and funding:

The present dataset was created by the authors. The work was funded by the Deutsche Forschungsgemeinschaft within the priority program SPP2289 under contract numbers RO2057/17-1 and MA3333/25-1.

Dataset description:

Four jupyter-notebooks are provided, which can be used for different tasks, according to their names. Details can be found within the comments and markdowns. These notebooks can be run after installation of MMDetection within the mmdetection folder.

particle_detection_training.ipynb: This notebook can be used for network training.

particle_detection_evaluation.ipynb: This notebook is for evaluation of a trained network with simulated test images.

particle_detection_evaluation_experiment.ipynb: This notebook is for evaluation of a trained network with experimental test images.

particle_detection_measurement_experiment.ipynb: This notebook is for application of a trained network to experimental data.

In addition, a script titled particle_detection_functions.py is provided which contains functions required by the notebooks. Details can be found within the comments.

The zip archive training_data.zip contains the training data. The subfolder HAADF contains the images (sorted as training, validation and test images), the subfolder json contains the annotation (sorted as training, validation and test images). Each file within the json folder provides for each image the following information:

aggregat_no: image id, the number of the corresponding image file

particle_position_x: list of particle position x-coordinates in nm

particle_position_y: list of particle position y-coordinates in nm

particle_position_z: list of particle position z-coordinates in nm

particle_radius: list of volume equivalent particle radii in nm

particle_type: list of material types, 1: TiO2, 2: WO3

particle_shape: list of particle shapes: 0: sphere, 1: box, 2: icosahedron

rotation: list of particle rotations in rad. Each particle is rotated twice by the listed angle (before and after deformation)

deformation: list of particle deformations. After the first rotation the particle x-coordinates of the particle’s surface mesh are scaled by the factor listed in deformation, y- and z-coordinates are scaled according to 1/sqrt(deformation).

cluster_index: list of cluster indices for each particle

initial_cluster_index: list of initial cluster indices for each particle, before primary clusters of the same material were merged

fractal_dimension: the intended fractal dimension of the aggregate

fractal_dimension_true: the realized geometric fractal dimension of the aggregate (neglecting particle densities)

fractal_dimension_weight_true: the realized fractal dimension of the aggregate (including particle densities)

fractal_prefactor: fractal prefactor

mixing_ratio_intended: the intended mixing ratio (fraction of WO3 particles)

mixing_ratio_true: the realised mixing ratio (fraction of WO3 particles)

mixing_ratio_volume: the realised mixing ratio (fraction of WO3 volume)

mixing_ratio_weight: the realised mixing ratio (fraction of WO3 weight)

particle_1_rho: density of TiO2 used for the calculations

particle_1_size_mean: mean TiO2 radius

particle_1_size_min: smallest TiO2 radius

particle_1_size_max: largest TiO2 radius

particle_1_size_std: standard deviation of TiO2 radii

particle_1_clustersize: average TiO2 cluster size

particle_1_clustersize_init: average TiO2 cluster size of primary clusters (before merging into larger clusters)

particle_1_clustersize_init_intended: intended TiO2 cluster size of primary clusters

particle_2_rho: density of WO3 used for the calculations

particle_2_size_mean: mean WO3 radius

particle_2_size_min: smallest WO3 radius

particle_2_size_max: largest WO3 radius

particle_2_size_std: standard deviation of WO3 radii

particle_2_clustersize: average WO3 cluster size

particle_2_clustersize_init: average WO3 cluster size of primary clusters (before merging into larger clusters)

particle_2_clustersize_init_intended: intended WO3 cluster size of primary clusters

number_of_primary_particles: number of particles within the aggregate

gyration_radius_geometric: gyration radius of the aggregate (neglecting particle densities)

gyration_radius_weighted: gyration radius of the aggregate (including particle densities)

mean_coordination: mean total coordination number (particle contacts)

mean_coordination_heterogen: mean heterogeneous coordination number (contacts with particles of the different material)

mean_coordination_homogen: mean homogeneous coordination number (contacts with particles of the same material)

radius_equiv: list of area equivalent particle radii (in projection)

k_proj: projection direction of the aggregate: 0: z-direction (axis = 2), 1: x-direction (axis = 1), 2: y-direction (axis = 0)

polygons: list of polygons that surround the particle (COCO annotation)

bboxes: list of particle bounding boxes

aggregate_size: projected area of the aggregate translated into the radius of a circle in nm

n_pix: number of pixel per image in horizontal and vertical direction (squared images)

pixel_size: pixel size in nm

image_size: image size in nm

add_poisson_noise: 1 if poisson noise was added, 0 otherwise

frame_time: simulated frame time (required for poisson noise)

dwell_time: dwell time per pixel (required for poisson noise)

beam_current: beam current (required for poisson noise)

electrons_per_pixel: number of electrons per pixel

dose: electron dose in electrons per Å2

add_scan_noise: 1 if scan noise was added, 0 otherwise

beam misposition: parameter that describes how far the beam can be misplaced in pm (required for scan noise)

scan_noise: parameter that describes how far the beam can be misplaced in pixel (required for scan noise)

add_focus_dependence: 1 if a focus effect is included, 0 otherwise

data_format: data format of the images, e.g. uint8

There are 24000 training images, 5500 validation images, 5500 test images, and their corresponding annotations. Aggregates and STEM images were obtained with the algorithm explained in the main work. The important data for CNN training is extracted from the files of individual aggregates and concluded in the subfolder COCO. For training, validation and test data there is a file annotation_COCO.json that includes all information required for the CNN training.

The zip archive experiment_test_data.zip includes manually annotated experimental images. All experimental images were filtered as explained in the main work. The subfolder HAADF includes thirteen images. The subfolder json includes an annotation file for each image in COCO format. A single file concluding all annotations is stored in json/COCO/annotation_COCO.json.

The zip archive experiment_measurement.zip includes the experimental images investigated in the manuscript. It contains four subfolders corresponding to the four investigated samples. All experimental images were filtered as explained in the manuscript.

The zip archive particle_detection.zip includes the network, that was trained, evaluated and used for the investigation in the manuscript. The network weights are stored in the file particle_detection/logs/fit/20230622-222721/iter_60000.pth. These weights can be loaded with the jupyter-notebook files. Furthermore, a configuration file, which is required by the notebooks, is stored as particle_detection/logs/fit/20230622-222721/config_file.py.

There is no confidential data in this dataset. It is neither offensive, nor insulting or threatening.

The dataset was generated to discriminate between TiO2 and WO3 nanoparticles in STEM-images. It might be possible that it can discriminate between different materials if the STEM contrast is similar to the contrast of TiO2 and WO3 but there is no guarantee.

This file contains aggregate measures from the ARDA's coding of the 2003, 2005, and 2008 U.S. State Department's "https://www.state.gov/bureaus-offices/under-secretary-for-civilian-security-democracy-and-human-rights/office-of-international-religious-freedom/" Target="_blank">International Religious Freedom Reports. This coding produced data on 199 countries and territories (see below for list of countries coded), but excluded the United States. It also includes three indexes calculated from these data: the Government Regulation of Religion Index (GRI), the Government Favoritism of Religion Index (GFI), and the Modified Social Regulation of Religion Index (MSRI) [see Grim and Finke (2006) for more information on the GRI and GFI, and see below for more information on the MSRI]. Data in this file represent mean coding responses for three of each variable from all three years of coding unless otherwise noted. Many countries have scores on variables that are expressed as decimals, and which do not correspond with a value label in the variables' descriptions. These decimal values signify that a country's scores on these variables vary over the 2003, 2005 and 2008 Reports.

This dataset contains 4 files with aggregated poker data from the flop. The original dataset consists of ~26M rows of all 5 card combinations of hands, evaluated to a rank between 1 and 7462. There are two rank distribution files, one with the data aggregated by the starting hand, the other by the flop. The weighted files contain a weighted mean of these rank distributions, roughly based on the 7 card hand type distribution.

To access these compressed files, simply use the pandas 'read_pickle()' method. To find an example, you can reference the documentation or my Poker Analysis notebook. Please note, the rank distribution files are very sparse and may need to be converted to a dense dataframe depending on what you're doing.