Detailed information about the ASN for the IP AS20764.

URL: https://geoscience.data.qld.gov.au/dataset/bh020764

1000 Genomes gVCF mapped to hs37d5 for NA20764. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307

Find plants that grow well in 20764's climate.

[The product of this gene is a member of the saposin-like protein (SAPLIP) family and is located in the cytotoxic granules of T cells, which are releas ]

Concept: Average interest rate from new credit operations started in the reference period, which are under regulation by the National Monetary Council (CMN) or linked to budget funds. The rate is weighted by the value of operations. Refers to special financing operations which require proof of proper use of funds, linked to medium and long term production and investments projects. Funds origins are shares of checking accounts and savings accounts and funds from governmental programs. Source: Central Bank of Brazil – Statistics Department 20764-average-interest-rate-of-earmarked-new-credit-operations---non-financial-corporations---bndes 20764-average-interest-rate-of-earmarked-new-credit-operations---non-financial-corporations---bndes

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Salamanca Town, New York, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 20,764, while the mean income for the highest quintile (20% of households with the highest income) is 146,040. This indicates that the top earners earn 7 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 196,950, which is 134.86% higher compared to the highest quintile, and 948.52% higher compared to the lowest quintile.

https://i.neilsberg.com/ch/salamanca-town-ny-mean-household-income-by-quintiles.jpeg" alt="Mean household income by quintiles in Salamanca Town, New York (in 2022 inflation-adjusted dollars))">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2022 inflation-adjusted dollars for the specific income level.

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/.

Recommended for further research

This dataset is a part of the main dataset for Salamanca town median household income. You can refer the same here

About this dataset

Context

The dataset tabulates the Culpeper population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Culpeper across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Culpeper was 20,764, a 1.10% increase year-by-year from 2021. Previously, in 2021, Culpeper population was 20,539, an increase of 1.95% compared to a population of 20,147 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Culpeper increased by 11,057. In this period, the peak population was 20,764 in the year 2022. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Culpeper is shown in this column.
• Year on Year Change: This column displays the change in Culpeper population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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/.

Recommended for further research

This dataset is a part of the main dataset for Culpeper Population by Year. You can refer the same here

[Gene description is missing or is less than 50 characters]

Bletilla striata is an endangered orchid that has been used for millennia as a medicinal herb, in cosmetics and as a horticultural plant. To construct the first nucleotide database for this species and to develop abundant EST-SSR markers for facilitating further studies, various tissues and organs of plants in the main developmental stages were harvested for mRNA isolation and subsequent RNA sequencing. A total of 106,054,784 clean reads were generated by using Illumina paired-end sequencing technology. The reads were assembled into 127,261 unigenes by the Trinity package; the unigenes had an average length of 612 bp and an N50 of 957 bp. Of these unigenes, 67,494 (51.86%) were annotated in a series of databases. Of these annotated unigenes, 41,818 and 24,615 were assigned to gene ontology categories and clusters of orthologous groups, respectively. Additionally, 20,764 (15.96%) unigenes were mapped onto 275 pathways using the KEGG database. In addition, 25,935 high-quality EST-SSR primer pairs were developed from the 15,433 unigenes by MISA mining. To validate the accuracy of the newly designed markers, 87 of 100 randomly selected primers were effectively amplified; 63 of those yielded PCR products of the expected size, and 25 yielded products with significant amounts of polymorphism among the 4 landraces. Furthermore, the transferability test of the 25 polymorphic markers was performed in 6 individuals of two closely related genus Phalaenopsis and dendrobium. Which results showed a total of 5 markers can successfully amplified among these populations. This research provides a comprehensive nucleotide database and lays a solid foundation for functional gene mining and genomic research in B. striata. The developed EST-SSR primers could facilitate phylogenetic studies and breeding.

Latest Global Trade Data of 85444290 with updated records of 2022. Highly authentic Global Trade Data of 85444290on Eximpedia.

Market will be USD 20,764 million in 2025 and will reach USD 34,516 million in 2035 with a compound annual growth rate (CAGR) of 5.2% for the forecast period.

Metric	Value
Industry Size (2025E)	USD 20,764 Million
Industry Value (2035F)	USD 34,516 Million
CAGR (2025 to 2035)	5.2%

Country Wise Outlook

Country	CAGR (2025 to 2035)
USA	5.1%

Country	CAGR (2025 to 2035)
UK	5.0%

Region	CAGR (2025 to 2035)
European Union	5.2%

Country	CAGR (2025 to 2035)
Japan	5.1%

Country	CAGR (2025 to 2035)
South Korea	5.0%

Competitive Outlook

Company Name	Estimated Market Share (%)
Ashley Furniture Industries	10-15%
Brown Jordan	8-12%
Lloyd Flanders	6-10%
Trex Company, Inc.	5-9%
Kettal	4-7%
Other Companies (combined)	50-60%

Each zip file contains the text description file: description.txt. It also contains the data of one or two measurement, A and possibly B. The files are named as follows (#### is the sample ID, @ the letter of the measurement):

• ####-@.ply and ####-@.png: 3D reconstruction in Stanford PLY format, and associated texture file.
• @-im-000.jpg, @-im-010.jpg, ..., @-im-360.jpg: JPEG images of the sample, the angle is in degrees. 0 degree correspond to the position used during scanning.
• @.mhdr.h5: HDF5 file containing masked HDR raw scan data. Masked values are represented by nan.

The zip files correspond to the sample IDs, according to the following table:

Mineral                  Sample IDs Datapoints
----------------------------------------------------------------
Actinolite              0020, 0021, 0064   56840 
Albite                      0107   22521 
Almandine           0025, 0026, 0073, 0074   17425 
Andalusite                    0014   18862 
Anhydrite                    0004   30832 
Apatite               0089(2), 0090(2)   69062 
Aragonite                    0061   40111 
Arsenopyrite                 0087(2)   66333 
Augite                      0038    8503 
Barite                      0006   37130 
Beryl                     0075(2)   37743 
Biotite                 0049(2), 0050   45400 
Blende                    0086(2)   15596 
Bronzite                     0112   50452 
Bytownite                    0103   14666 
Calcite         0010, 0011, 0052, 0078, 0079   112501 
Cassiterite                0119, 0120   38535 
Celestite              0000, 0001, 0002   56075 
Chalcedony              0108(2), 0109(2)   96431 
Chalcopyrite                   0106   19375 
Chlorite                     0013   75802 
Clinochlore                   0126   45301 
Coal                    0081, 0082   44664 
Copper                      0101   14556 
Diopside                     0069   58959 
Dolomite               0091(2), 0092(2)   76810 
Enstatite                    0047   31072 
Epidote                  0023, 0024   47306 
Fluorite               0003(2), 0012(2)   98638 
Galena                   0053, 0054   17931 
Garnet                      0115   27331 
Glaucophane          0016, 0017, 0076, 0077   200830 
Goethite                     0114   10717 
Graphite                  0083, 0084   20047 
Grossular                 0030, 0031   46592 
Gypsum              0005(2), 0007, 0063   159829 
Halite                   0008, 0056   66201 
Halloysite                 0121, 0122   102028 
Hematite         0039(2), 0085(2), 0095, 0096   165228 
Hornblende                    0046    9175 
Hypersthene                   0111   62932 
Ilmenite                     0116   16256 
Kaolinite                    0113   20764 
Kyanite                     0029   12320 
Labradorite             0088(2), 0104(2)   62762 
Limonite                  0128, 0129   64550 
Magnetite                 0055, 0072    7103 
Microcline                    0071   60699 
Montmorillonite           0123, 0124, 0125   87986 
Muscovite                    0034   62481 
Nepheline                   0097(2)   51930 
Olivine                     0065    7965 
Omphacite                 0019, 0067   108032 
Opal                       0102   34404 
Orthoclase                    0057   49824 
Phlogopite                 0045, 0070   105119 
Pyrite                   0042, 0048   31598 
Pyrolusite                 0117, 0118   45534 
Pyrrhotite                    0051   21572 
Quartz                 0009(2), 0035   126760 
Rutile                      0093    4959 
Sanidine                   0099(2)   49260 
Serpentine                 0018, 0068   78937 
Siderite                     0080   11651 
Silicified wood                0127(2)   92935 
Sillimanite                0032, 0033   70546 
Sodalite                  0043, 0060   61852 
Sphalerite                    0105   27485 
Staurolite              0027, 0028, 0066   30628 
Sulfur                   0036, 0037   34751 
Talc           0022, 0040, 0041, 0058, 0059   138317 
Titanite                     0094    4121 
Tourmaline                 0044, 0062   68419 
Tremolite                0098(2), 0100   70913 
Zircon                    0110(2)    5110 

Each securities firm monthly balance sheet/income and expenditure summary data (Stock Exchange).

Data obtained from computational DFT calculations on Monoclinic VP3O9 is provided. Available data include crystal structure, bandgap energy, stability, density of states, and calculation input/output files. This structure was obtained from ICSD (Collection code = 20764)

Lumasaba sometimes known as Lugisu is a Bantu language spoken in the Eastern part of Uganda. This dataset contains a total of 39,999 sentences. The sentences are split into two separate files. One file contains 20,764 sentences from the Northern dialect and another one contains 19,235 sentences from the Southern dialect. This dataset was compiled by a team of Linguists and researchers from the Makerere AI and Data Science Research Lab and Marconi Research and Innovation Lab at Makerere University. This dataset was created with support from Lacuna Fund.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Taylor township, Blair County, Pennsylvania, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 20,764, while the mean income for the highest quintile (20% of households with the highest income) is 216,049. This indicates that the top earners earn 10 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 429,772, which is 198.92% higher compared to the highest quintile, and 2069.79% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

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/.

Recommended for further research

This dataset is a part of the main dataset for Taylor township median household income. You can refer the same here

20764 Global export shipment records of Tweezer with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

The global ice maker water filter market is experiencing robust growth, driven by increasing consumer awareness of water purity and the rising prevalence of ice makers in residential and commercial settings. The market, estimated at $500 million in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033, reaching approximately $900 million by 2033. This growth is fueled by several key trends: the increasing adoption of advanced filtration technologies like carbon block and granular activated carbon (GAC) filters offering superior contaminant removal; a shift towards convenient, readily available filter replacements; and the rising demand for premium ice makers incorporating high-efficiency filtration systems. The residential segment currently holds the largest market share, driven by increasing household disposable incomes and a preference for purified ice in beverages and food preparation. However, the commercial segment, encompassing hotels, restaurants, and office buildings, is poised for significant growth due to rising health and hygiene standards in these sectors. Major players like Whirlpool, Electrolux, GE, Kenmore, 3M, and WHEELTON are actively engaged in product innovation and strategic partnerships to enhance their market presence and cater to the diverse needs of consumers. Geographic variations in water quality and consumer preferences influence regional market dynamics; North America and Europe currently dominate the market, but Asia-Pacific is projected to witness significant growth in the coming years due to rapid urbanization and rising middle-class incomes. Challenges such as fluctuating raw material prices and stringent regulatory requirements present potential restraints to market expansion. Despite the positive outlook, certain restraints exist. The relatively short lifespan of filters requires frequent replacements, potentially impacting consumer spending. Furthermore, the cost of premium filters with advanced filtration capabilities can deter price-sensitive consumers. To overcome these challenges, manufacturers are focusing on developing longer-lasting, high-performance filters at competitive price points. Additionally, increased marketing and consumer education regarding the health benefits of filtered water are expected to drive market expansion further. The competitive landscape is characterized by both established appliance manufacturers and specialized filter companies, leading to product innovation and price competition.

Simulated dataset WplusH_HToZZTo4L_M130_13TeV_powheg2-minlo-HWJ_JHUgenV6_pythia8 in MINIAODSIM format for 2015 collision data.

See the description of the simulated dataset names in: About CMS simulated dataset names.

These simulated datasets correspond to the collision data collected by the CMS experiment in 2015.