The values of the statistical parameters correspond to the average over 50 independent runs for each value μ1 and were calculated as described in Methods. The standard deviation for each determination is shown after the sign ±.

Feature layer generated from running the Summarize center and dispersion on suitableSitesOverlay_Revised.

This data release provides models detailing previous efforts to understand the distribution of domestic poultry throughout China. These data support a previous USGS publication.

The values of the statistical parameters were determined from the same 50 populations of Table 1, recalculating the distance values with respect to S2.

1. Knowledge of the spatial distribution of populations is fundamental to management plans for any species. When tracking data are used to describe distributions, it is sometimes assumed that the reported locations of individuals delineate the spatial extent of areas used by the target population.

2. Here, we examine existing approaches to validate this assumption, highlight caveats, and propose a new method for a more informative assessment of the number of tracked animals (i.e. sample size) necessary to identify distribution patterns. We show how this assessment can be achieved by considering the heterogeneous use of habitats by a target species using the probabilistic property of a utilisation distribution. Our methods are compiled in the R package SDLfilter.

3. We illustrate and compare the protocols underlying existing and new methods using conceptual models and demonstrate an application of our approach using a large satellite tracking data-set of flatback turtles, Natator d...

This bar chart displays total students (people) by description using the aggregation sum in Stanford. The data is about universities.

Yearly citation counts for the publication titled "Describing the distribution of engagement in an Internet support group by post frequency: A comparison of the 90-9-1 Principle and Zipf's Law".

Feature layer generated from running the Summarize center and dispersion on Urban Farms 2018.

Attendance absences have a substantial impact on student’s future physical and mental health as well as academic progress. Numerous personal, familial, and social issues are among the causes of student absences. Any kind of absence from school should be minimized. Extremely high rates of student absences may indicate the abrupt commencement of a serious school health crisis or public health crisis, such as the spread of tuberculosis or COVID-19, which provides school health professionals with an early warning. We take the extreme values in absence data as the object and attempt to apply the extreme value theory (EVT) to describe the distribution of extreme values. This study aims to predict extreme instances of student absences. School health professionals can take preventative measures to reduce future excessive absences, according to the predicted results. Five statistical distributions were applied to individually characterize the extreme values. Our findings suggest that EVT is a useful tool for predicting extreme student absences, thereby aiding preventative measures in public health.

This dataset consists of two files. File 1: A quadrat photo dataset of Krascheninnikovia compacta community, which is divided into 14 subfiles according to the survey plots. Each subfile includes quadrat photos, environment photos and a sample description of plot. It contains 97 quadrat photos and 14 quadrat descriptions in total. All subfiles are named after plot number and photos are named after plot-quadrat-replicate. File 2: A dataset describing the community characteristics and geographic distribution of Krascheninnikovia compacta, which contains 3 worksheets: 1. Geographic and climatic information of the community plots; 2. Survey data of plots; 3. Species diversity information of the community.

A tracer breakthrough curve (BTC) for each sampling station is the ultimate goal of every quantitative hydrologic tracing study, and dataset size can critically affect the BTC. Groundwater-tracing data obtained using in situ automatic sampling or detection devices may result in very high-density data sets. Data-dense tracer BTCs obtained using in situ devices and stored in dataloggers can result in visually cluttered overlapping data points. The relatively large amounts of data detected by high-frequency settings available on in situ devices and stored in dataloggers ensure that important tracer BTC features, such as data peaks, are not missed. Alternatively, such dense datasets can also be difficult to interpret. Even more difficult, is the application of such dense data sets in solute-transport models that may not be able to adequately reproduce tracer BTC shapes due to the overwhelming mass of data. One solution to the difficulties associated with analyzing, interpreting, and modeling dense data sets is the selective removal of blocks of the data from the total dataset. Although it is possible to arrange to skip blocks of tracer BTC data in a periodic sense (data decimation) so as to lessen the size and density of the dataset, skipping or deleting blocks of data also may result in missing the important features that the high-frequency detection setting efforts were intended to detect. Rather than removing, reducing, or reformulating data overlap, signal filtering and smoothing may be utilized but smoothing errors (e.g., averaging errors, outliers, and potential time shifts) need to be considered. Appropriate probability distributions to tracer BTCs may be used to describe typical tracer BTC shapes, which usually include long tails. Recognizing appropriate probability distributions applicable to tracer BTCs can help in understanding some aspects of the tracer migration. This dataset is associated with the following publications: Field, M. Tracer-Test Results for the Central Chemical Superfund Site, Hagerstown, Md. May 2014 -- December 2015. U.S. Environmental Protection Agency, Washington, DC, USA, 2017. Field, M. On Tracer Breakthrough Curve Dataset Size, Shape, and Statistical Distribution. ADVANCES IN WATER RESOURCES. Elsevier Science Ltd, New York, NY, USA, 141: 1-19, (2020).

A detailed analysis of the texture, matrix, and elements of the microfacies from the carbonate sequence recovered in ODP Hole 639D resulted in a typological classification of 10 major microfacies types and their variants. The variations in distribution and succession of type microfacies allowed us to divide the carbonate sequence into 12 facies-defined subunits. Based on the analyzed characteristics and their relations, we also propose a paleoenvironmental interpretation involving a mixed carbonate/terrigenous ramp model instead of the previous, classical zoned carbonate platform.

"How-to" content is among the most well-known video genres on YouTube, spanning from tutorials to practical demonstrations. As of March 2021, it appeared that when it came to content triggered by question keywords on YouTube.com, videos providing context in the description ranked higher among the platform's search results. Roughly ** percent of high ranking videos presented descriptions between *** and *** words. Content providing no description performed poorly on the YouTube.com search ranking, with only two percent of the videos with zero words in the description figuring among the highest ranking content.

This data set provides information on species type, abundance, and distribution, and sediment description for the Phase IIIA survey area of the Long Island Sound Cable Fund (LISCF) Seafloor Habitat Mapping Initiative. This data set contains the results of image analyses of frame captures of video collected by the Ponar Imaging and Sampling System for Assessing Habitat (PISSAH) developed by the Long Island Sound Mapping and Research Collaborative (LISMaRC) to obtain both physical sediment grab samples and ultra-high definition (4K) video using the latest version of GoPro cameras. A four-day survey using the PISSAH deployed from the Research Vessel Weicker was conducted from June 12-16, 2023 including mobilization and demobilization. The PISSAH was used to acquire both physical sediment grab samples as well as the GoPro video from 60 sites in the Phase III area of the Long Island Sound Cable Fund (LISCF) Seafloor Habitat Mapping Initiative. These sites were identified in the Phase IIIA area based upon an analysis of existing acoustic backscatter data obtained from multiple surveys by NOAA that exhibited what appeared to be inconsistent gray scale settings. Multiple GoPro cameras with lights captured both forward-looking and down-looking points of view. The down-looking video files were reviewed and two to five still images (frame grabs) were captured in the .tiff format for image analysis. The images were color corrected using the IrfanView software. Each image was then analyzed using the ImageJ software for point count and percent cover of observed taxa, biogenic features and sediment type. The results of this analysis and attendant maps were provided to the team led by Roger Flood from the Stony Brook University to assist with the interpretation of new and existing acoustic backscatter data in the area. The data file is in ESRI Shapefile format. Funding was provided by the Long Island Sound Cable Fund Seafloor Habitat Mapping Initiative administered cooperatively by the EPA Long Island Sound Study and the Connecticut Department of Energy and Environmental Protection (DEEP).

This survey assesses the impact of drug use and drug trafficking on communities across the EU. It focuses particularly on how serious the use of illicit drugs and drug trafficking are perceived to be at the local level, the prevalence of related issues, and potential regulatory measures. Approximately 40% of respondents view the use of illicit drugs as a serious problem, while 41% consider drug trafficking to be a major concern.

Processed data

Processed data files for the Eurobarometer surveys are published in .xlsx format.

• Volume A "Countries/EU" The file contains frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of (weighted) replies for each country or territory and for (weighted) EU results.
• Volume AP "Previous survey trends" The file compares to the previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each country or territory foreseen in Volume A and for (weighted) results.
• Volume AA "Groups of countries" The file contains (labelled) frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of (weighted) replies for groups of countries specified by the managing unit on the part of the EC.
• Volume AAP "Trends of groups of countries" The file contains shifts compared to the previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each groups of countries foreseen in Volume AA and for (weighted) results.
• Volume B "EU/socio-demographics" The file contains (labelled) frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies for the EU as a whole (weighted) and cross-tabulated by some 20 sociodemographic, socio-political or other variables, depending on the request from the managing unit on the part of the EC or the managing department of the other contracting authorities.
• Volume BP "Trends of EU/socio-demographics" The file contains shifts compared to the previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each country or territory foreseen in Volume B above)and for (weighted) results.
• Volume C "Country/socio-demographics" The file contains (labelled) weighted frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies for each country or territory surveyed separately and cross-tabulated by some 20 socio-demographic, socio-political or other variables (including a regional breakdown).
• Volume D "Trends"" The file compares to previous polls in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each country or territory foreseen in Volume A and for (weighted) results. _

For SPSS files and questionnaires, please contact GESIS - Leibniz Institute for the Social Sciences: https://www.gesis.org/eurobarometer

Little is known about how mutualistic interactions affect the distribution of species richness on broad geographic scales. Because mutualism positively affects the fitness of all species involved in the interaction, one hypothesis is that the richness of species involved should be positively correlated across their range, especially for obligate relationships. Alternatively, if mutualisms are facilitative (e.g., involving multiple mutualistic partners), the distribution of mutualists should not necessarily be related, and patterns in species distributions might be more strongly correlated with environmental factors. In this study, we compared the distributions of plants and vertebrate animals involved in seed-dispersal mutualisms across the United States and Canada. We compiled geographic distributions of plants dispersed by frugivores and scatter-hoarding animals, and compared their distribution of richness to the distribution in disperser richness. We found that the distribution of animal dispersers shows a negative relationship to the distribution of the plants that they disperse, and this is true whether the plants dispersed by frugivores or scatter-hoarders are considered separately or combined. In fact, the mismatch in species richness between plants and the animals that disperse their seeds is dramatic, with plants species richness greatest in the in the eastern United States and the animal species richness greatest in the southwest United States. Environmental factors were corelated with the difference in the distribution of plants and their animal mutualists and likely are more important in the distribution of both plants and animals. This study is the first to describe the broad-scale distribution of seed-dispersing vertebrates and compare the distributions to the plants they disperse. With these data, we can now identify locations that warrant further study either to understand better seed-dispersal mutualisms or the factors that influence the distribution of the plants and animals involved in these mutualisms.

This dataset contains the digitized treatments in Plazi based on the original journal article Campos-Filho, Ivanklin Soares, Sfenthourakis, Spyros, Gallo, Jéssica Scaglione, Gallão, Jonas Eduardo, Torres, Dayana Ferreira, Chagas-Jr, Amazonas, Horta, Lília, Carpio-Díaz, Yesenia Margarita, López-Orozco, Carlos Mario, Borja-Arrieta, Ricardo, Araujo, Paula Beatriz, Taiti, Stefano, Bichuette, Maria Elina (2023): Shedding light into Brazilian subterranean isopods (Isopoda, Oniscidea): expanding distribution data and describing new taxa. Zoosystema 45 (19): 531-599, DOI: 10.5252/zoosystema2023v45a19, URL: https://sciencepress.mnhn.fr/sites/default/files/articles/pdf/zoosystema2023v45a19.pdf

This bar chart displays graduate students (people) by description using the aggregation sum in New York. The data is about universities.

This is a dataset download, not a document. The Open button will start the download.This data layer is an element of the Oregon GIS Framework. Oregon Fish Habitat Distribution. These data describe areas of suitable habitat believed to be used currently or historically by native or non-native fish populations. The term "currently" is defined as within the past five reproductive cycles. Historical habitat includes suitable habitat that fish no longer access and will not access in the foreseeable future without human intervention. This information is based on sampling, the best professional opinion of Oregon Dept. of Fish and Wildlife or other natural resources agency staff biologists or modeling (see the fhdBasis field). Due to natural variations in run size, water conditions, or other environmental factors, some habitats identified may not be used annually. These data now comply with the Oregon Fish Habitat Distribution Data Standard that was adopted by the Oregon Geographic Information Council in April 2020. The Standard document can be found at: https://www.oregon.gov/geo/standards/OregonFishHabitatDistributionDataStandard_v4.pdf. Historical habitat distribution data are within the scope of the standard and are identified via the habitat use (fhdUseType) attribute. Historical habitats are only identified outside of currently accessible habitat and are not comprehensive. Data representing current habitat for anadromous and resident salmonid species are generally more comprehensive than data for non-game and non-native fish species. All datasets are subject to update as new information becomes available. Key features of the Oregon Fish Habitat Distribution Data include: species, run, life history, habitat use, origin, production, the basis for each record, originator name, originator entity and reference. Habitat distribution data are mapped at a 1:24,000 scale statewide and are based on the National Hydrography dataset. The data are made available as GIS files in both shapefile and ESRI geodatabase format. The data were developed over an extensive time period ranging from 1996 to 2022. The data are now managed on the National Hydrography Dataset and have been synchronized to December 2021 NHD geometry.Procedures_Used: These data were originally created through a process where 1:100,000 scale fish habitat distribution data (current as of 2001) were plotted on 1:24,000 scale USGS quadrangle maps and then provided to ODFW and other natural resources agency field staff. Based on survey data, supporting documentation, and the best professional judgment of the field biologists, different types (spawning, rearing, migration, etc.) of species specific habitat distribution (see the fhdUseTy field) were marked on the maps with colored pens. Additional attributes such as source contributors, agencies and the basis of the data were also collected. These hardcopy data were then digitized by ODFW GIS staff and stored as event tables based on the PNW River Reach files at 1:100,000 scale. Habitat locations identified outside of the 1:100,000 scale stream network were captured as upstream points associated with 1:24,000 scale streams. Beginning in 2007 and ending in 2008, the data were migrated to events associated with the Pacific Northwest Framework Hydrography 1:24,000 scale stream network. All habitat distribution records are now in a single, consistent linear event format. Revisions: The first 1:100,000 scale version of the data was completed in 1996. Significant revisions at that scale were also made in 1999. The 1:24K Mapping Project occurred between 2001 - 2003 and data were published in 2004. Data were migrated to events mapped on the 1:24,000 PNW Framework hydrography in 2007 and 2008. Significant revisions since 2004 include additions of anadromous habitat in the upper Deschutes and upper North Santiam basins and coho habitat in the coastal basins. Numerous, less significant revisions have occurred throughout the state, in particular where the location and passage status of blocking barriers was recently verified. In 2014 the data were migrated to the National Hydrography Dataset (NHD) as Hydrography Event Mgt. Tool (HEM) compliant event feature classes. NHD data are current as of April 2014. The fish habitat distribution data schema was modified in July 2015 to comply with version 3.0 of the data standard. Optional attributes for describing additional record Basis details (date, name, entity, project, method) were added at version 3.0. Reviews_Applied_to_Data: All distribution data have been reviewed by ODFW or other natural resource agency staff (typically district biologists). The date of last review exists in the attribute table for each distribution record. Related_Spatial_and_Tabular_Data_Sets: Oregon Fish Passage Barrier Data Standard Dataset (see Passage Status attribute) barriers to anadromous salmonid migration https://nrimp.dfw.state.or.us/nrimp/default.aspx?pn=fishbarrierdata National Hydrography Dataset (https://nhd.usgs.gov/data.html) Other_References_Cited: Oregon Fish Habitat Distribution Data Standard: https://www.oregon.gov/DAS/CIO/GEO/fit/bioscience/docs/OregonFishHabitatDistributionDataStandardv3.pdf The attribute element fhdRefID can be used to locate more detailed reference information (please contact the data steward to obtain the detailed reference information), however the recently added optional Basis detail fields will eventually be populated with this source information.

While there is a general consensus in this survey that cybersecurity is a high priority among companies (71%), taking action remains the main challenge: 74% of the companies have not provided any training or raised awareness among their employees. Additionally, 68% of companies stated that no training or awareness raising about cybersecurity is needed. 16% are unaware of relevant training opportunities and 8% mention budget constraints as a reason.

Processed data

Processed data files for the Eurobarometer surveys are published in .xlsx format.

• Volume A "Countries/EU" The file contains frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of (weighted) replies for each country or territory and for (weighted) EU results.
• Volume AP "Previous survey trends" The file compares to the previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each country or territory foreseen in Volume A and for (weighted) results.
• Volume AA "Groups of countries" The file contains (labelled) frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of (weighted) replies for groups of countries specified by the managing unit on the part of the EC.
• Volume AAP "Trends of groups of countries" The file contains shifts compared to the previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each groups of countries foreseen in Volume AA and for (weighted) results.
• Volume B "EU/socio-demographics" The file contains (labelled) frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies for the EU as a whole (weighted) and cross-tabulated by some 20 sociodemographic, socio-political or other variables, depending on the request from the managing unit on the part of the EC or the managing department of the other contracting authorities.
• Volume BP "Trends of EU/socio-demographics" The file contains shifts compared to the previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each country or territory foreseen in Volume B above)and for (weighted) results.
• Volume C "Country/socio-demographics" The file contains (labelled) weighted frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies for each country or territory surveyed separately and cross-tabulated by some 20 socio-demographic, socio-political or other variables (including a regional breakdown).
• Volume D "Trends"" The file compares to previous polls in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each country or territory foreseen in Volume A and for (weighted) results. _

For SPSS files and questionnaires, please contact GESIS - Leibniz Institute for the Social Sciences: https://www.gesis.org/eurobarometer