These data are species distribution information assembled for assessing the impacts of land-use barriers, facilitative interactions with other species, and loss of long-distance animal dispersal on predicted species range patterns for four common species in pinyon-juniper woodlands in the western United States. The layers in the data release are initial distribution records of two kinds: point occurrence records and a raster layer for the general vegetation types where the species is a co-dominant, compiled from other sources. Both types of data are the baseline information in species distribution models for the associated publication(see Larger Work Citation).

The Mechanical MNIST – Distribution Shift dataset contains the results of finite element simulation of heterogeneous material subject to large deformation due to equibiaxial extension at a fixed boundary displacement of d = 7.0. The result provided in this dataset is the change in strain energy after this equibiaxial extension. The Mechanical MNIST dataset is generated by converting the MNIST bitmap images (28x28 pixels) with range 0 - 255 to 2D heterogeneous blocks of material (28x28 unit square) with varying modulus in range 1- s. The original bitmap images are sourced from the MNIST Digits dataset, (http://www.pymvpa.org/datadb/mnist.html) which corresponds to Mechanical MNIST – MNIST, and the EMNIST Letters dataset (https://www.nist.gov/itl/products-and-services/emnist-dataset) which correspond to Mechanical MNIST – EMNIST Letters. The Mechanical MNIST – Distribution Shift dataset is specifically designed to demonstrate three types of data distribution shift: (1) covariate shift, (2) mechanism shift, and (3) sampling bias, for all of which the training and testing environments are drawn from different distributions. For each type of data distribution shift, we have one dataset generated from the Mechanical MNIST bitmaps and one from the Mechanical MNIST – EMNIST Letters bitmaps. For the covariate shift dataset, the training dataset is collected from two environments (2500 samples from s = 100, and 2500 samples from s = 90), and the test data is collected from two additional environments (2000 samples from s = 75, and 2000 samples from s = 50). For the mechanism shift dataset, the training data is identical to the training data in the covariate shift dataset (i.e., 2500 samples from s = 100, and 2500 samples from s = 90), and the test datasets are from two additional environments (2000 samples from s = 25, and 2000 samples from s = 10). For the sampling bias dataset, datasets are collected such that each datapoint is selected from the broader MNIST and EMNIST inputs bitmap selection by a probability which is controlled by a parameter r. The training data is collected from two environments (9800 from r = 15, and 200 from r = -2), and the test data is collected from three different environments (2000 from r = -5, 2000 from r = -10, and 2000 from r = 1). Thus, in the end we have 6 benchmark datasets with multiple training and testing environments in each. The enclosed document “folder_description.pdf'” shows the organization of each zipped folder provided on this page. The code to reproduce these simulations is available on GitHub (https://github.com/elejeune11/Mechanical-MNIST/blob/master/generate_dataset/Equibiaxial_Extension_FEA_test_FEniCS.py).

Transparency in data visualization is an essential ingredient for scientific communication. The traditional approach of visualizing continuous quantitative data solely in the form of summary statistics (i.e., measures of central tendency and dispersion) has repeatedly been criticized for not revealing the underlying raw data distribution. Remarkably, however, systematic and easy-to-use solutions for raw data visualization using the most commonly reported statistical software package for data analysis, IBM SPSS Statistics, are missing. Here, a comprehensive collection of more than 100 SPSS syntax files and an SPSS dataset template is presented and made freely available that allow the creation of transparent graphs for one-sample designs, for one- and two-factorial between-subject designs, for selected one- and two-factorial within-subject designs as well as for selected two-factorial mixed designs and, with some creativity, even beyond (e.g., three-factorial mixed-designs). Depending on graph type (e.g., pure dot plot, box plot, and line plot), raw data can be displayed along with standard measures of central tendency (arithmetic mean and median) and dispersion (95% CI and SD). The free-to-use syntax can also be modified to match with individual needs. A variety of example applications of syntax are illustrated in a tutorial-like fashion along with fictitious datasets accompanying this contribution. The syntax collection is hoped to provide researchers, students, teachers, and others working with SPSS a valuable tool to move towards more transparency in data visualization.

This dataset provides maps of the distribution of ecosystem functional types (EFTs) and the interannual variability of EFTs at 0.05 degree resolution across the conterminous United States (CONUS) for 2001 to 2014. EFTs are groupings of ecosystems based on their similar ecosystem functioning that are used to represent the spatial patterns and temporal variability of key ecosystem functional traits without prior knowledge of vegetation type or canopy architecture. Sixty-four EFTs were derived from the metrics of a 2001-2014 time-series of satellite images of the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) product MOD13C2. EFT diversity was calculated as the modal (most repeated) EFT and interannual variability was calculated as the number of unique EFTs for each pixel.

The indicator gives for each type of household the percentage of the total population.

Traditional differential expression genes (DEGs) identification models have limitations in small sample size datasets because they require meeting distribution assumptions, otherwise resulting high false positive/negative rates due to sample variation. In contrast, tabular data model based on deep learning (DL) frameworks do not need to consider the data distribution types and sample variation. However, applying DL to RNA-Seq data is still a challenge due to the lack of proper labeling and the small sample size compared to the number of genes. Data augmentation (DA) extracts data features using different methods and procedures, which can significantly increase complementary pseudo-values from limited data without significant additional cost. Based on this, we combine DA and DL framework-based tabular data model, propose a model TabDEG, to predict DEGs and their up-regulation/down-regulation directions from gene expression data obtained from the Cancer Genome Atlas database. Compared to five counterpart methods, TabDEG has high sensitivity and low misclassification rates. Experiment shows that TabDEG is robust and effective in enhancing data features to facilitate classification of high-dimensional small sample size datasets and validates that TabDEG-predicted DEGs are mapped to important gene ontology terms and pathways associated with cancer.

This dataset provides a map of the distribution of ecosystem functional types (EFTs) at 0.05 degree resolution across Mexico for 2001 to 2014. EFTs are groupings of ecosystems based on their similar ecosystem functioning that are used to represent the spatial patterns and temporal variability of key ecosystem functional traits without prior knowledge of vegetation type or canopy architecture. Sixty-four EFTs were derived from the metrics of a 2001-2014 time-series of satellite images of the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) product MOD13C2. EFT diversity was calculated as the modal (most repeated) EFT for each pixel.

Finland - Distribution of population by household types: Single person was 25.80% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Finland - Distribution of population by household types: Single person - last updated from the EUROSTAT on July of 2025. Historically, Finland - Distribution of population by household types: Single person reached a record high of 25.80% in December of 2024 and a record low of 19.00% in December of 2010.

This dataset was intially aimed for publication on GBIF (see details below), but we have now restricted it to a 'metadata' entry, and the corresponding ecosystem dataset is published on Zenodo: https://doi.org/10.5281/zenodo.7812549. It compiles data gathered on ecosystem-types and their distribution based on a series of field studies led by the author, in Seychelles and West and Central Africa (Senterre 2014, Senterre & Wagner 2014, Senterre 2016, Senterre et al. 2017, 2019, 2020, 2021a, 2022). The aims of this dataset are:

1. To share in an explicit and transparent way data on proposed taxonomies of ecosystems, i.e. conceptualizations of ecosystem-types, including explicit ecosystem names and management of synonymies.

2. To develop ecosystem red listing based on transparent and falsifiable distribution raw data, combining distribution modeling (maps) and in situ observation of individual stand occurrences.

3. To illustrate in detail how to deal with ecosystem data following the approach described in Senterre et al. (2021b) (i.e. "ecosystemology" approach).

Although GBIF is currently not able to cater appropriately for ecosystem data and is designed in a species-centric view, GBIF is the largest repository of biodiversity data in the world and therefore it is relevant to at least explore the possibility of addressing that gap. In addition, as we will show here, we suggest that only a few additions and adjustments to the current GBIF structure would be required to integrate the treatment of ecosystem data in a standardized way, following the "ecosystemology" approach (ecosystem taxonomy) proposed by Senterre et al. 2021b (http://dx.doi.org/10.1016/j.ecocom.2021.100945).

In the ‘sampling method’ section of these metadata, we present in detail the suggested needs for adjustments and additions in the GBIF structure, and we explain our short term strategy to publish an existing ecosystemology dataset using the current GBIF structure, by squeezing information within available and suitable fields of GBIF (mostly free text fields that are related to the ecosystem or habitat). Several fields are thus stored within a GBIF field by using the pipe separator (|).

We then developed a series of R scripts that take the ecosystem data squeezed into the GBIF fields and that restore the tables needed to do an ecosystem taxonomy treatment (by splitting columns at the pipe separators). Finally, we compile ecosystem checklists, taxonomies and occurrence data into an R shiny application. In addition, we integrate the use of Google Earth Engine (EE) and we develop the method to integrate these with the GBIF dataset toward the production of complete distribution maps and their use in Red Listing of Ecosystems (RLE).

The R scripts developed are available here: https://github.com/bsenterre/ecosystemology

The corresponding shiny app is available here: https://shiny.bio.gov.sc/bioeco/ (earlier version : https://bsenterre.shinyapps.io/ecosystemology/)

Estonia - Distribution of population by household types: Single person was 22.30% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Estonia - Distribution of population by household types: Single person - last updated from the EUROSTAT on July of 2025. Historically, Estonia - Distribution of population by household types: Single person reached a record high of 22.30% in December of 2024 and a record low of 15.00% in December of 2009.

The Remote Sensing Coastal Change Simple Data Service provides timely and long-term access to emergency, provisional, and approved photogrammetric imagery, derivatives, and ancillary data through a web service via HyperText Transfer Protocol to a folder/file structure organized by data collection platform and survey (collection effort) with metadata sufficient to facilitate both human and machine access. Data are acquired, processed, and published using standardized workflows. Each data type added to the service has a peer-reviewed metadata and data review of sample data generated with standardized methods to ensure compliance with U.S. Geological Survey (USGS) Fundamental Science Practices (FSP).

Distribution of population by tenure status, type of household and income group

Germany - Distribution of population by tenure status, type of household and income group - EU-SILC survey was 47.20% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Germany - Distribution of population by tenure status, type of household and income group - EU-SILC survey - last updated from the EUROSTAT on June of 2025. Historically, Germany - Distribution of population by tenure status, type of household and income group - EU-SILC survey reached a record high of 53.40% in December of 2011 and a record low of 46.50% in December of 2022.

In the first half of 2024, healthcare providers reported *** data breaches in the U.S. healthcare sector, becoming the entity with the highest number of reported breach incidents. As of the time of the reporting, business associates ranked second with the number of reported data breaches.

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) measurement data and machine learning were used in this work to classify six different types of plastics. In order to take into account the characteristics of the measurement data, the local maxima of the measurement data were first examined in a preprocessing step. Several machine learning methods were then implemented to create a model that could successfully classify the plastics. To visualize the data distribution, we applied a dimensionality reduction method, namely, principal component analysis. Finally, to distinguish between the six types of plastics, we conducted an ensemble analysis using four tree-based algorithms: decision tree, random forest, gradient boosting, and LIGHTGBM. This approach can identify the feature importance of plastic samples and allow the inference of the chemical properties of each plastic type. In this way, ToF-SIMS data could be utilized to successfully classify plastics and enhance explainability.

This table contains 10 series, with data for years 2014 - 2016 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada);  North American Industry Classification System (NAICS) (1 item: Hotels, motor hotels and motels);  Distribution of sales, type of service provided (10 items: Total sales; Room or unit accommodation for travellers; Meals and non-alcoholic beverages, prepared and served or dispensed for immediate consumption; Alcoholic beverages, prepared and served or dispensed for immediate consumption; ...).

This release accompanies the paper "Increased accuracy of starch granule type quantification using mixture distributions".

FIA Modeled Abundance:�This dataset portrays the live tree mean basal area (square feet per acre) of the species across the contiguous United States. The underlying data publication contains raster maps of live tree basal area for each tree species along with corresponding assessment data. An efficient approach for mapping multiple individual tree species over large spatial domains was used to develop these raster datasets. The method integrates vegetation phenology derived from MODIS imagery and raster data describing relevant environmental parameters with extensive field plot data of tree species basal area to create maps of tree species abundance and distribution at a 250-meter (m) pixel size for the contiguous United States. The approach uses the modeling techniques of k-nearest neighbors and canonical correspondence analysis, where model predictions are calculated using a weighting of nearest neighbors based on proximity in a feature space derived from the model. The approach also utilizes a stratification derived from the 2001 National Land-Cover Database tree canopy cover layer.�This data depicts current species abundance and distribution across the contiguous United States, modeled by using FIA field plot data. Although the absolute values associated with the maps differ from species to species, the highest values within each map are always associated with darker colors. The Little's Range Boundaries show the historical tree species ranges across North America. This is a digital representation of maps by Elbert L. Little, Jr., published between 1971 and 1977. These maps were based on botanical lists, forest surveys, field notes and herbarium specimens.Forest-type Groups:This dataset portrays the forest type group. Each group is a subset of the National Forest Type dataset which portrays 28 forest type groups across the contiguous United States. These data were derived from MODIS composite images from the 2002 and 2003 growing seasons in combination with nearly 100 other geospatial data layers, including elevation, slope, aspect, ecoregions, and PRISM climate data.Harvest Growth:This data shows the percentage of timber that is harvested when compared to the total live volume, at a county-by-county level. Timber volume in forests is constantly in flux, and harvest plays an important role in shaping forests. While most counties have some timber harvest, harvest volumes represent low percentages of standing timber volume.Carbon Harvest:The Carbon Harvest raster dataset represents Mg of annual pulpwood harvested (carbon) by county, derived from the Forest Inventory Analysis in 2016.

The Report Includes Global Data Center Rack PDU Manufacturers and the Market is Segmented by Rack PDU Type (Basic, Metered, Monitored, and Switched), by Data Center Type (Colocation, Hosting, and Other Data Center Types), by Geography (North America, Europe, Asia Pacific, Rest of the World). The Market Sizes and Forecasts are Provided in Terms of Value USD for all the Above Segments.