This dataset contains the historical Unidata Internet Data Distribution (IDD) Global Observational Data that are derived from real-time Global Telecommunications System (GTS) reports distributed via the Unidata Internet Data Distribution System (IDD). Reports include surface station (SYNOP) reports at 3-hour intervals, upper air (RAOB) reports at 3-hour intervals, surface station (METAR) reports at 1-hour intervals, and marine surface (BUOY) reports at 1-hour intervals. Select variables found in all report types include pressure, temperature, wind speed, and wind direction. Data may be available at mandatory or significant levels from 1000 millibars to 1 millibar, and at surface levels. Online archives are populated daily with reports generated two days prior to the current date.

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).

The aquatic species distribution maps have been derived from the database created in the UN Species Identification and Data Programme and was spatially limited by the project AOI. The dataset comprises 340 species distribution polygons depicting estimated range of each species inside of the project AOI

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).

In order to help the public understand the distribution of household types in our country, household registration data from the Ministry of the Interior are collected for statistical purposes to achieve the purpose of openness and transparency of the data.

Lithuania - Distribution of population by household types: Single person was 27.00% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Lithuania - Distribution of population by household types: Single person - last updated from the EUROSTAT on June of 2025. Historically, Lithuania - Distribution of population by household types: Single person reached a record high of 27.00% in December of 2024 and a record low of 12.30% in December of 2009.

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/)

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.

This dataset provides supporting information for the species distribution data used in the associated manuscript. Collections of five non-native fish species were made by a number of institutions, and several capture techniques were used. This dataset also includes number of individuals of each species captured at each locality.

Projected changes in habitat suitability for 33 marine species on the Northeast US shelf. Changes in habitat suitability are calculated based on species distribution models fit to bottom trawl survey data from the NOAA Northeast Fisheries Science Center. Positive values indicate an increase in habitat suitability by 2040-2050 relative to historical (1963-2005). The spatial resolution of projections is 0.25 x 0.25 degrees. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson acquisition_description=The following methods are excerpted from Rogers et al. (in press):
Bottom trawl data from the NOAA Northeast Fisheries Science Center (NEFSC) fall (1963-2014) surveys were used to characterize the realized thermal niches of species. At each survey station, fish of each species were counted and weighed, and surface and bottom temperature measurements were taken. Correction factors were applied to standardize catch rates for changes in vessel and gear type. A total of 33 species were selected based on their near continuous presence in the survey as well as relative importance to commercial fisheries. For 4 species, data from 1972 onwards were used because observations were irregular prior to that year.

Generalized Additive Models were used to estimate the realized thermal niches of species. We restricted k (number of knots) to 4 or 6 for each of our covariates to ensure biologically meaningful responses. Our response variable was probability of occurrence in a trawl haul, and we used a binomial response with logit transform:

p(occur\u1d67,\u2c7c) ~ logit-1 (s(ST\u1d67,\u2c7c)+s(BT\u1d67,\u2c7c)+s(meanbiomass\u1d67)+s(rugosity\u2c7c))

where ST\u1d67,\u2c7c and BT\u1d67,\u2c7c are sea surface temperature and bottom temperature measured at each haul location j in year y, and meanbiomass\u1d67 is the average annual catch across all hauls to account for interannual changes in abundance due to, e.g., fishing. Rugosity\u1d67 is a measure of benthic habitat roughness, measured as the Terrain Ruggedness Index, using the GEBCO 2014 30-arcsecond bathymetry data (downloaded 4 Feb 2015 from http://www.gebco.net/). The resulting estimated smooth functions describing the relationship between probability of occurrence and temperature can be interpreted as realized thermal niches.

For each species, the change in predicted probability of occurrence under future (2040-2050) projected climate conditions was compared to historical (1963-2005) conditions for each cell within a 0.25\u00b0x0.25\u00b0 spatial grid. Because the modeled probability of occurrence included a component of catchability, values for each species were scaled by dividing by the maximum observed or predicted probability of occurrence across the study area. Positive values for a grid square indicated a projected increase in probability of occurrence, whereas negative values indicated a projected decrease in probability of occurrence.

See related dataset for\u00a0NEFSC bottom trawl data:\u00a0"%5C%22https://www.bco-%0Admo.org/dataset/753142%5C%22">https://www.bco- dmo.org/dataset/753142\u00a0(doi:\u00a010.1575/1912/bco-dmo.753142.1) awards_0_award_nid=559955 awards_0_award_number=OCE-1426891 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1426891 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=Michael E. Sieracki awards_0_program_manager_nid=50446 cdm_data_type=Other comment=Projected changes in habitat suitability PIs: Lauren Rogers & Malin Pinsky Version date: 22-April-2019 Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.765386.1 Easternmost_Easting=-64.875 geospatial_lat_max=44.875 geospatial_lat_min=33.625 geospatial_lat_units=degrees_north geospatial_lon_max=-64.875 geospatial_lon_min=-76.875 geospatial_lon_units=degrees_east infoUrl=https://www.bco-dmo.org/dataset/765386 institution=BCO-DMO keywords_vocabulary=GCMD Science Keywords metadata_source=https://www.bco-dmo.org/api/dataset/765386 Northernmost_Northing=44.875 param_mapping={'765386': {'lat': 'master - latitude', 'lon': 'master - longitude'}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/765386/parameters people_0_affiliation=Rutgers University people_0_person_name=Malin Pinsky people_0_person_nid=554708 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=Stanford University people_1_person_name=Lauren Rogers people_1_person_nid=765425 people_1_role=Principal Investigator people_1_role_type=originator people_2_affiliation=Stanford University people_2_person_name=Robert Griffin people_2_person_nid=768380 people_2_role=Co-Principal Investigator people_2_role_type=originator people_3_affiliation=Rutgers University people_3_person_name=Kevin St. Martin people_3_person_nid=559961 people_3_role=Co-Principal Investigator people_3_role_type=originator people_4_affiliation=Princeton University people_4_person_name=Emma Fuller people_4_person_nid=748888 people_4_role=Scientist people_4_role_type=originator people_5_affiliation=Rutgers University people_5_person_name=Talia Young people_5_person_nid=752628 people_5_role=Scientist people_5_role_type=originator people_6_affiliation=National Oceanic and Atmospheric Administration - Alaska Fisheries Science Center people_6_affiliation_acronym=NOAA-AFSC people_6_person_name=Lauren Rogers people_6_person_nid=765425 people_6_role=Contact people_6_role_type=related people_7_affiliation=Woods Hole Oceanographic Institution people_7_affiliation_acronym=WHOI BCO-DMO people_7_person_name=Shannon Rauch people_7_person_nid=51498 people_7_role=BCO-DMO Data Manager people_7_role_type=related project=CC Fishery Adaptations projects_0_acronym=CC Fishery Adaptations projects_0_description=Description from NSF award abstract: Climate change presents a profound challenge to the sustainability of coastal systems. Most research has overlooked the important coupling between human responses to climate effects and the cumulative impacts of these responses on ecosystems. Fisheries are a prime example of this feedback: climate changes cause shifts in species distributions and abundances, and fisheries adapt to these shifts. However, changes in the location and intensity of fishing also have major ecosystem impacts. This project's goal is to understand how climate and fishing interact to affect the long-term sustainability of marine populations and the ecosystem services they support. In addition, the project will explore how to design fisheries management and other institutions that are robust to climate-driven shifts in species distributions. The project focuses on fisheries for summer flounder and hake on the northeast U.S. continental shelf, which target some of the most rapidly shifting species in North America. By focusing on factors affecting the adaptation of fish, fisheries, fishing communities, and management institutions to the impacts of climate change, this project will have direct application to coastal sustainability. The project involves close collaboration with the National Oceanic and Atmospheric Administration, and researchers will conduct regular presentations for and maintain frequent dialogue with the Mid-Atlantic and New England Fisheries Management Councils in charge of the summer flounder and hake fisheries. To enhance undergraduate education, project participants will design a new online laboratory investigation to explore the impacts of climate change on fisheries, complete with visualization tools that allow students to explore inquiry-driven problems and that highlight the benefits of teaching with authentic data. This project is supported as part of the National Science Foundation's Coastal Science, Engineering, and Education for Sustainability program - Coastal SEES. The project will address three questions: 1) How do the interacting impacts of fishing and climate change affect the persistence, abundance, and distribution of marine fishes? 2) How do fishers and fishing communities adapt to species range shifts and related changes in abundance? and 3) Which institutions create incentives that sustain or maximize the value of natural capital and comprehensive social wealth in the face of rapid climate change? An interdisciplinary team of scientists will use dynamic range and statistical models with four decades of geo-referenced data on fisheries catch and fish biogeography to determine how fish populations are affected by the cumulative impacts of fishing, climate, and changing species interactions. The group will then use comprehensive information on changes in fisher behavior to understand how fishers respond to changes in species distribution and abundance. Interviews will explore the social, regulatory, and economic factors that shape these strategies. Finally, a bioeconomic model for summer flounder and hake fisheries will examine how spatial distribution of regulatory authority, social feedbacks within human communities, and uncertainty affect society's ability to maintain natural and social capital. projects_0_end_date=2018-08 projects_0_geolocation=Northeast US Continental Shelf Large Marine Ecosystem projects_0_name=Adaptations of fish and fishing communities to rapid climate change projects_0_project_nid=559948 projects_0_start_date=2014-09 sourceUrl=(local files) Southernmost_Northing=33.625 standard_name_vocabulary=CF Standard Name Table v55 version=1 Westernmost_Easting=-76.875 xml_source=osprey2erddap.update_xml() v1.3

Household Budget Survey (HBS): Distribution according to type of household. Annual. National.

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.

We developed habitat suitability models for invasive plant species selected by Department of Interior land management agencies. We applied the modeling workflow developed in Young et al. 2020 to species not included in the original case studies. Our methodology balanced trade-offs between developing highly customized models for a few species versus fitting non-specific and generic models for numerous species. We developed a national library of environmental variables known to physiologically limit plant distributions (Engelstad et al. 2022 Table S1: https://doi.org/10.1371/journal.pone.0263056) and relied on human input based on natural history knowledge to further narrow the variable set for each species before developing habitat suitability models. We developed models using five algorithms with VisTrails: Software for Assisted Habitat Modeling [SAHM 2.1.2]. We accounted for uncertainty related to sampling bias by using two alternative sources of background samples, and construct ...

Preserving native species diversity is fundamental to ecosystem conservation. Selecting appropriate native species for use in restoration is a critical component of project design and may emphasize species attributes such as life history, functional type, pollinator services, and nutritional value for wildlife. Determining which species are likely to establish and persist in a particular environment is a key consideration. Species distribution models (SDMs) characterize relationships between species occurrences and the physical environment (e.g., climate, soil, topographic relief) and provide a mechanism for assessing which species may successfully propagate at a restoration site. In conjunction with information on species attributes, SDMs facilitate holistic ecosystem restoration by enabling practitioners to identify diverse, resilient assemblages of native species. This project develops SDMs for native species of fundamental ecosystem importance in order to guide restoration of Mojave Desert landscapes. The dataset contained herein provides an SDM for Atriplex hymenelytra within its Mojave Desert range based on known occurrences.

This repository contains input files to train the Deep-SDM model described in the preprint Predicting species distributions in the open oceans with convolutional neural networks.

This deposit contains:

1. Training data: CSV dataset + 38 subfolders with data for each species (named after its GBIF id)

2. Prediction data:

2.1. Global use case (solstices & equinoxes of 2021): CSV dataset + data folder

2.2. Western Indian Ocean use case: CSV dataset + data folder

1. species.csv contains the taxonomic name of each taxon, as well as its GBIF id.

2. stats.npy contains normalization factors for the data files

meds, perc1, perc99 = np.load("stats.npy") item = np.load(file)[:,:,:25] real_values = (perc99 - perc1) * item + perc1

Each of these elements can be downloaded separately by scrolling to the Files section.

Please see the README document ("README.md") and the accompanying published article: Braun, C. D., M. C. Arostegui, N. Farchadi, M. Alexander, P. Afonso, A. Allyn, S. J. Bograd, S. Brodie, D. P. Crear, E. F. Culhane, T. H. Curtis, E. L. Hazen, A. Kerney, N. Lezama-Ochoa, K. E. Mills, D. Pugh, N. Queiroz, J. D. Scott, G. B. Skomal, D. W. Sims, S. R. Thorrold, H. Welch, R. Young-Morse, R. Lewison. In press. Building use-inspired species distribution models: using multiple data types to examine and improve model performance. Ecological Applications. Accepted. DOI: < article DOI will be added when it is assigned >

This paper develops estimating parameters for Morgenstern type bivariatedistribution by using bivariate ranked set sampling procedure as an alterna-tive method to simple random sampling. This proposed procedure gives anopportunity to estimate all distribution's parameters simultaneously whichis not investigated in previous studies, yet. In the last part of this paper,simulation studies show properties of the new estimators and compare themwith some other existed estimators.

This dataset contains a collection of 13 known point locations of green sea turtles identified through direct human observation via aerial surveys between March and April of 1995. Dr. Joseph Mobley of the Marine Mammal Research Consultants (MMRC) led aerial surveys for turtles and cetaceans in Hawaiian waters from 1993-2003.

.rds and raster files can be opened in R statistical software.

Preserving native species diversity is fundamental to ecosystem conservation. Selecting appropriate native species for use in restoration is a critical component of project design and may emphasize species attributes such as life history, functional type, pollinator services, and nutritional value for wildlife. Determining which species are likely to establish and persist in a particular environment is a key consideration. Species distribution models (SDMs) characterize relationships between species occurrences and the physical environment (e.g., climate, soil, topographic relief) and provide a mechanism for assessing which species may successfully propagate at a restoration site. In conjunction with information on species attributes, SDMs facilitate holistic ecosystem restoration by enabling practitioners to identify diverse, resilient assemblages of native species. This project develops SDMs for native species of fundamental ecosystem importance in order to guide restoration of Mojave Desert landscapes.The dataset contained herein provides an SDM for Astragalus layneae within its Mojave Desert range based on known occurrences.