Complete historical game data between California and Kentucky including scores, dates, locations, and game statistics.

About this dataset

Context

This list ranks the 55 counties in the California by British population, as estimated by the United States Census Bureau. It also highlights population changes in each county over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by British Population: This column displays the rank of county in the California by their British population, using the most recent ACS data available.
• County: The County for which the rank is shown in the previous column.
• British Population: The British population of the county is shown in this column.
• % of Total County Population: This shows what percentage of the total county population identifies as British. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total California British Population: This tells us how much of the entire California British population lives in that county. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

About this dataset

Context

This list ranks the 473 cities in the California by British population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by British Population: This column displays the rank of city in the California by their British population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• British Population: The British population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as British. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total California British Population: This tells us how much of the entire California British population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe's Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe's Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS. In adition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer

A simple Example GCD Dataset illustrating topographic change detection from a single flood event. Used in Tutorials (e.g. DoD Thresholding). Dataset is from 300m of gravel bed river near St. Helena California that underwent a New Year's Eve flood in December 2005. Two surveys (Dec 2015 and Jan 2016) 0.5m cell resolution Surveyed with hybrid of RTKGPS and Total Station

Comprehensive dataset containing 24 verified British restaurant businesses in California, United States with complete contact information, ratings, reviews, and location data.

Comprehensive dataset containing 3 verified Modern British restaurant businesses in California, United States with complete contact information, ratings, reviews, and location data.

This catalogue contains the times, locations, families, and durations of earthquakes identified near Parkfield, California, USA. Collected over the period of 2020 – 2022. The methodology used to identify the earthquakes is described by Huang, H., Hawthorne, J.C. Linking the scaling of tremor and slow slip near Parkfield, CA. Nat Commun 13, 5826 (2022). https://doi.org/10.1038/s41467-022-33158-3. The data provided here are also provided in the supplement of that paper. This catalogue contains the times (columns 1-2), locations (columns 3-5: latitude, longitude, and depth), families (column 6), and durations (columns 7).

This annotated dataset comprises locational data of grey whales in lagoons San Ignacio and Ojo de Liebre in Baja California Sur as detected from Very High Resolution (VHR) satellite imagery in January 2009, 2013 and 2015. Images were manually scanned and whales detected through the use of grids. Additional metadata includes information on image type and model, and whale distinctive characteristics (e.g., fluke or blow). This work supports the '' training'' of machine learning algorithms for automatic detection of whales from satellite imagery.

This study formed part of the Ecosystems component of the British Antarctic Survey Polar Science for Planet Earth Programme, funded by The Natural Environment Research Council. The work was supported by the UKRI Centre for Doctoral Training in Application of Artificial Intelligence to the study of Environmental Risks (reference EP/S022961/1).

This metadata record describes moored seawater temperature data collected at Santa Cruz Island, California, USA, by PISCO. Measurements were collected using StowAway Tidbit Temperature Loggers (Onset Computer Corp. TBIC32+4+27) beginning 2007-03-22. The instrument depth was 014 meters, in an overall water depth of 015 meters (both relative to Mean Sea Level, MSL). The sampling interval was 2.0 minutes.

Iris subgenus Xiphium is a small group of taxa that mostly occur in the Mediterranean Basin, a long-recognized biodiversity hotspot. Phylogenetic relationships among these Iris were reconstructed based on sequence data from 110 nuclear markers and whole plastomes using Bayesian inference and maximum likelihood methods. Best trees based on plastome and combined datasets resolved subgenus Xiphium and I. xiphium as not monophyletic while nuclear data resolved the subgenus as monophyletic but I. xiphium as not monophyletic. Topology tests indicated that the alternative hypothesis of a monophyletic subgenus cannot be rejected while a monophyletic I. xiphium can be rejected. We hypothesize that the subgenus is monophyletic based on these analyses, morphology, and biogeography and that uneven patterns of missing data is a likely reason for topological incongruence among datasets. A previously suggested informal group within the subgenus was supported. Patterns of relationships among species suggest multiple exchanges between the African and European continents but also the importance of the Strait of Gibraltar as a barrier to genetic exchange. Bayesian analyses, biodiversity hotspots, constraint trees, Iberian Peninsula, Iridaceae, maximum likelihood analyses, Mediterranean Basin, missing data, North Africa, nuclear markers, Strait of Gibraltar, targeted enrichment, whole plastomes Methods Genomic DNA was isolated from silica-dried leaf materials using protocols modified from the CTAB method of Doyle and Doyle (1987). Modifications from this procedure included RNase treatment and an ethanol precipitation with ammonium acetate following the initial isopropanol precipitation. Prior to library preparation, extracted DNA (0.4–1.2 µg) was fragmented to an average length of 500 bp with sonication (Bioruptor UDC-200, Diagenode, Denville, NJ, USA). Single index library construction and DNA enrichment followed Meyer and Kircher (2010). Targeted markers were captured and enriched with a custom myBaits-hyb capture kit designed for Iris (Daicel Arbor Biosciences, Ann Arbor, MI, USA) following the manufacturers recommendations except that kit blockers were replaced with blocking oligos from Xgen (Integrated DNA Technologies Inc., Coralville, IA, USA) and SeqCap plant capture enhancer (Roche, Burgess Hill, UK). Data for targeted markers and un-enriched plastomes was obtained using NGS 100 bp paired-end read sequencing run on an illumina 4000 (Illumina Inc., San Diego, CA, USA). DNA extraction, library preparation, and sequencing were performed at the University of California, Berkeley, California, USA. A pipeline was developed and executed on the Savio high-performance computing cluster at the University of California, Berkeley (available from communicating author). The pipeline used Trimomatic (Bolger et al. 2014) to filter and remove index sequences with parameters settings of 40 bp minimum length and a 10:20 sliding window. Using a minimum read depth of four and quality of 20%, plastomes were assembled against the I. gatesi R.C. Foster plastome (Wilson 2014) and nuclear markers were assembled against the 635 markers developed from exome data that are described above. Data for each nuclear marker was examined in Geneious 9.14 (Biomatters, Ltd., New Zealand) to select markers > 900 bp in length with < 25% missing data for each sample. Final datasets were assembled in in Geneious 9.14 (Biomatters, Ltd., New Zealand) and included plastomes, 110 selected nuclear exome markers, combined nuclear markers (nuclear), combined plastome and nuclear markers (combined), and combined nuclear and plastid coding regions (all-genes). Sites with > 50% of n’s were excluded from datasets with reading frames preserved by excluding bp in multiples of three within coding sequences. Plastomes were partitioned by coding, intron, and intergenic spacer regions and each coding region of plastome and nuclear datasets was partitioned by codon position resulting in 379 and 330 partitions, respectively. Partitions were merged and modeled for nuclear, plastome, all-gene, and combined datasets using PartitionFinder (Lanfear et al. 2012) executed in IQ-TREE v2.1.3 (Nguyen et al. 2015) resulting in 12, 12, 18, and 25 partitions, respectively. RAxML v. 8 (Stamatakis 2014) and IQ-TREE v2.1.3 (Nguyen et al. 2015) maximum likelihood (ML; Felsenstein 1981) and ML bootstrap (Felsenstein 1985) analyses on nuclear, plastome, all-gene, and combined datasets were each performed with one thousand replicates for each bootstrap. MrBayes Version 3.1.2 (Huelsenbeck and Ronquist 2001) was used to perform Bayesian Inference (BI) which was run for four million generations, with two runs and six chains that were sampled every 1,000 generations with a burn-in rate of 0.25.

The dataset includes organic carbon and nitrogen isotope data, as well as elemental abundance data from marine sediments that were obtained by push corers from hydrothermal seeps in the modern Guaymas Basin, Gulf of Mexico. Also included are pore water measurements of ammonium concentrations and nitrogen isotopes of dissolved ammonium, as well as sediment temperatures.

5km gridded Standardised Precipitation Index (SPI) data for Great Britain, which is a drought index based on the probability of precipitation for a given accumulation period as defined by McKee et al [1]. There are seven accumulation periods: 1, 3, 6, 9, 12, 18, 24 months and for each period SPI is calculated for each of the twelve calendar months. Note that values in monthly (and for longer accumulation periods also annual) time series of the data therefore are likely to be autocorrelated. The standard period which was used to fit the gamma distribution is 1961-2010. The dataset covers the period from 1862 to 2015. This version supersedes previous versions (version 2 and 3) of the same dataset due to minor errors in the data files. NOTE: the difference between this dataset with the previously published dataset "Gridded Standardized Precipitation Index (SPI) using gamma distribution with standard period 1961-2010 for Great Britain [SPIgamma61-10]" (Tanguy et al., 2015; https://doi.org/10.5285/94c9eaa3-a178-4de4-8905-dbfab03b69a0) , apart from the temporal and spatial extent, is the underlying rainfall data from which SPI was calculated. In the previously published dataset, CEH-GEAR (Tanguy et al., 2014; https://doi.org/10.5285/5dc179dc-f692-49ba-9326-a6893a503f6e) was used, whereas in this new version, Met Office 5km rainfall grids were used (see supporting information for more details). The methodology to calculate SPI is the same in the two datasets. [1] McKee, T. B., Doesken, N. J., Kleist, J. (1993). The Relationship of Drought Frequency and Duration to Time Scales. Eighth Conference on Applied Climatology, 17-22 January 1993, Anaheim, California. Full details about this dataset can be found at https://doi.org/10.5285/233090b2-1d14-4eb9-9f9c-3923ea2350ff

Historical temperature and salinity data collected from 1896-04-22 to 1961-03-26 from the World Ocean. Data were digitized from cards provided by United Kingdom hydrographic office. Dataset includes data collected during around-the-world voyage conducted by USA submarine USS Triton.