86 Global import shipment records of Combine Harvester with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

240 Global import shipment records of Combine Parts with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

About this dataset

Context

The dataset tabulates the data for the Combine, TX population pyramid, which represents the Combine population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Combine, TX, is 29.7.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Combine, TX, is 23.9.
• Total dependency ratio for Combine, TX is 53.7.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Combine, TX is 4.2.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Combine population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Combine for the selected age group is shown in the following column.
• Population (Female): The female population in the Combine for the selected age group is shown in the following column.
• Total Population: The total population of the Combine for the selected age group is shown in the following column.

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 Combine Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the Combine population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Combine. The dataset can be utilized to understand the population distribution of Combine by age. For example, using this dataset, we can identify the largest age group in Combine.

Key observations

The largest age group in Combine, TX was for the group of age 5 to 9 years years with a population of 311 (11.19%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Combine, TX was the 85 years and over years with a population of 6 (0.22%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Combine is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Combine total population. 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 Combine Population by Age. You can refer the same here

Aim: To evaluate current and future dynamics of 25 tree species spanning USA and Canada. Location: USA and Canada Methods: We combine, for the first time, the species compositions from relative importance derived from the USA's Forest Inventory Analysis (FIA) with gridded estimates based on Canada's National Forest Inventory (NFI-kNN) ) based photo plot data to evaluate future habitats and colonization potentials for 25 tree species. Using 21 climatic variables under RCP 4.5 and RCP 8.5, we model climatic habitat suitability (HQ) within a consensus based multi-model ensemble regression approach. A migration model is used to assess colonization likelihoods (CL) for ~100 years and combined with HQ to evaluate the various combinations of HQ+CL outcomes for the 25 species. Results: At a continental scale, many species in the conterminous USA lose suitable climatic habitat (especially under RCP 8.5) while Canada and USA's Alaska gain climate habitat. For most species, even under optimistic migration rates, only a small portion of overall future suitable habitat is projected to be naturally colonized in ~ 100 years, although considerable variation exists among species. Main conclusions: For the species examined here, habitat losses were primarily experienced along southern range limits, while habitat gains were associated with northern range limits (especially under RCP 8.5). However, for many species, southern range limits are projected to remain relatively intact, albeit with reduced habitat quality. Our models predict that only a small portion of the climatic habitat generated by climate change will be colonized naturally by the end of the current century - even with optimistic tree migration rates. However, considerable variation among species points to the need for significant management efforts, including assisted migration, for economic or ecological reasons. Our work highlights the need to employ range-wide data, evaluate colonization potentials, and enhance cross-border collaborations.

Chile Underutilization Rate: Foreign: Combine: Unemployment and Potential Labor Force data was reported at 12.073 % in Dec 2024. This records an increase from the previous number of 11.591 % for Nov 2024. Chile Underutilization Rate: Foreign: Combine: Unemployment and Potential Labor Force data is updated monthly, averaging 11.489 % from Apr 2013 (Median) to Dec 2024, with 141 observations. The data reached an all-time high of 29.335 % in Jul 2020 and a record low of 6.068 % in Dec 2013. Chile Underutilization Rate: Foreign: Combine: Unemployment and Potential Labor Force data remains active status in CEIC and is reported by National Institute of Statistics. The data is categorized under Global Database’s Chile – Table CL.G038: Underutilization Rate.

Groundwater is an important source of drinking and irrigation water throughout Idaho, and groundwater quality is monitored by various Federal, State, and local agencies. The historical, multi-agency records of groundwater quality include a valuable dataset that has yet to be compiled or analyzed on a statewide level. The purpose of this study is to combine groundwater-quality data from multiple sources into a single database, to summarize this dataset, and to perform bulk analyses to reveal spatial and temporal patterns of water quality throughout Idaho. Data were retrieved from the Water Quality Portal (www.waterqualitydata.us), the Idaho Department of Environmental Quality, and the Idaho Department of Water Resources. Analyses included counting the number of times a sample location had concentrations above Maximum Contaminant Levels (MCL), performing trends tests, and calculating correlations between water-quality analytes.

Chile Underutilization Rate: Foreign: Combine: Unemployment and Involuntary Part-Time: Male data was reported at 7.006 % in Jan 2025. This records an increase from the previous number of 6.656 % for Dec 2024. Chile Underutilization Rate: Foreign: Combine: Unemployment and Involuntary Part-Time: Male data is updated monthly, averaging 9.674 % from Apr 2013 (Median) to Jan 2025, with 142 observations. The data reached an all-time high of 21.883 % in Jul 2020 and a record low of 4.977 % in Feb 2015. Chile Underutilization Rate: Foreign: Combine: Unemployment and Involuntary Part-Time: Male data remains active status in CEIC and is reported by National Institute of Statistics. The data is categorized under Global Database’s Chile – Table CL.G038: Underutilization Rate.

Chile Underutilization Rate: Combine: Unemployment and Potential Labor Force data was reported at 16.418 % in Jan 2025. This records an increase from the previous number of 16.388 % for Dec 2024. Chile Underutilization Rate: Combine: Unemployment and Potential Labor Force data is updated monthly, averaging 15.316 % from Mar 2010 (Median) to Jan 2025, with 179 observations. The data reached an all-time high of 30.189 % in Jul 2020 and a record low of 13.559 % in Dec 2018. Chile Underutilization Rate: Combine: Unemployment and Potential Labor Force data remains active status in CEIC and is reported by National Institute of Statistics. The data is categorized under Global Database’s Chile – Table CL.G038: Underutilization Rate.

This dataset provides information about the number of properties, residents, and average property values for Combine Drive cross streets in Saint Pauls, NC.

(Includes MeSH 2023 and 2024 changes) The MeSH 2025 Update - Combine Report lists new Entry Combinations. These are cases where a new, precoordinated Descriptor has been created to replace an existing Descriptor / Qualifier combination. This report includes MeSH changes from previous years, starting from 2023.

About this dataset

Context

The dataset tabulates the population of Combine by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Combine. The dataset can be utilized to understand the population distribution of Combine by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Combine. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Combine.

Key observations

Largest age group (population): Male # 5-9 years (181) | Female # 15-19 years (166). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Combine population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Combine is shown in the following column.
• Population (Female): The female population in the Combine is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Combine for each age group.

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 Combine Population by Gender. You can refer the same here

Turkey Agricultural Equipment and Machinery: Combine Harvester data was reported at 17.199 Unit th in 2017. This records an increase from the previous number of 16.247 Unit th for 2016. Turkey Agricultural Equipment and Machinery: Combine Harvester data is updated yearly, averaging 11.685 Unit th from Dec 1952 (Median) to 2017, with 66 observations. The data reached an all-time high of 19.875 Unit th in 1977 and a record low of 3.222 Unit th in 1952. Turkey Agricultural Equipment and Machinery: Combine Harvester data remains active status in CEIC and is reported by Turkish Statistical Institute. The data is categorized under Global Database’s Turkey – Table TR.B024: Agricultural Equipment and Machinery.

According to Cognitive Market Research, the global database automation market size is USD 1,714.0 million in 2024 and will expand at a compound annual growth rate (CAGR) of 28.3% from 2024 to 2031. Market Dynamics of Database Automation Market Key Drivers for Database Automation Market Technological advancement- Rising advances in AI are providing a wide range of applications across multiple platforms. The growing amount of complicated data is also driving development in the automation space for the incorporation of Intelligence. Additionally, fraud protection, branding oversight, customer support, and real-time decision-making capabilities are some of the advantages and uses of artificial Intelligence in database automation. Because of this, a number of businesses combine database automation with cutting-edge technology like deep learning, ML, computer vision, and more to increase automation efficiency. This is likely to encourage the development of the market for database automation. The market's expansion is also being aided by the fast-increasing quantity of information across industries. Key Restraints for Database Automation Market Database automation is experiencing market constraints because of concerns about the safety and protection of information held in data. The absence of professional skills is also hampering the market growth. Introduction of the Database Automation Market Database automation is a procedure that expedites the provisioning, configuring, patching, securing, and management of a business's databases by automating database management functions. Extremely reliable database systems cause complexity and the recurrence of things with minimal variation. With the decreasing complications and redundant systems, database automation makes supplies, updating, changing, disaster recovery, expanding, and other database activities easier. The necessity of automating time-consuming database operations in order to provide immediate forecasting information has increased as a result of the development of analysis, propelling the database automation market's expansion.

This statistic displays the number of combine harvesters in the Italian region of Sicily in 2017 and 2018. According to data, the number of registered combine harvesters in the region of Sicily reached 24 units in 2018, while in 2017 the number of vehicles was 17.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Distribution models should take into account the different limiting factors that simultaneously influence species ranges. Species distribution models built with different explanatory variables can be combined into more comprehensive ones, but the resulting models should maximize complementarity and avoid redundancy. Our aim was to compare the different methods available for combining species distribution models. We modelled 19 threatened vertebrate species in mainland Spain, producing models according to three individual explanatory factors: spatial constraints, topography and climate, and human influence. We used five approaches for model combination: Bayesian inference, Akaike weight averaging, stepwise variable selection, updating, and fuzzy logic. We compared the performance of these approaches by assessing different aspects of their classification and discrimination capacity. We demonstrated that different approaches to model combination give rise to disparities in the model outputs. Bayesian integration was systematically affected by an error in the equations that are habitually used in distribution modelling. Akaike weights produced models that were driven by the best single factor and therefore failed at combining the models effectively. The updating and the stepwise approaches shared recalibration as the basic concept for model combination, were very similar in their performance, and showed the highest sensitivity and discrimination capacity. The fuzzy-logic approach yielded models with the highest classification capacity according to Cohen's kappa. In conclusion: i) Bayesian integration, employing the currently used equation, and the Akaike weight procedure should be avoided; ii) the updating and stepwise approaches can be considered minor variants of the same recalibrating approach; and iii) there is a trade-off between this recalibrating approach, which has the highest sensitivity, and fuzzy logic, which has the highest overall classification capacity. Recalibration is better if unfavourable conditions in one environmental factor may be counterbalanced with favourable conditions in a different factor, otherwise fuzzy logic is better.

This is a COMBINE archive of Brännmark et al, 2013 (BIOMD0000000448), consisting the original SBML model as well as SED-ML model which recreates some of the results of the original paper.

Data from this project focuses on the evaluation of breeding lines. Significant progress was made in advancing breeding populations directed towards release of improved varieties in Tanzania. Thirty promising F4:7, 1st generation 2014 PIC (Phaseolus Improvement Cooperative) and ~100 F4:6, 2nd generation 2015 PIC breeding lines were selected. In addition, ~300 F4:5, 3rd generation 2016 PIC single plant selections were completed in Arusha and Mbeya. These breeding lines, derived from 109 PIC populations specifically developed to combine abiotic and biotic stress tolerance, showed superior agronomic potential compared with checks and local landraces. The diversity, scale, and potential of the material in the PIC breeding pipeline is invaluable and requires continued support to ensure the release of varieties that promise to increase the productivity of common bean in the E. African region. Data available includes databases, spreadsheets, and images related to the project. Resources in this dataset:Resource Title: Data Dictionary. File Name: ADP-1_DD.pdfResource Title: ADP-1 Database. File Name: ADP1-DB.zipResource Description: This file is a link to a draft version of the development and characterization of the common bean diversity panel (ADP) database in Microsoft Access. Preliminary information is provided in this database, while the full version is being prepared. In order to use the database you’ll need to download the complete file, extract it and open the MS access file. You must allow active content when opening the database for it to work properly. Downloaded on November 17, 2017.Resource Title: Anthracnose Screening of Andean Diversity Panel (ADP) . File Name: Anthracnose-screening-of-ADP.pdfResource Description: Approximately 230 ADP lines of the ADP were screened with 8 races of anthracnose under controlled conditions at Michigan State University. Dr. James Kelly has provided this valuable dataset for sharing in light of the Open Data policy of the US government. This dataset represents the first comprehensive screening of the ADP with a broad set of races of a specific pathogen.Resource Title: ARS - Feed the Future Shared Data . File Name: ARS-FtF-Data-Sharing.zipResource Description: The data provided herein is an early draft version of the data that has been generated by the ARS Feed-the-Future Grain Legumes Project that is focused on common bean research. Resource Title: PIC (Phaseolus Improvement Cooperative) Populations . File Name: PIC-breeding-populations.xlsxResource Description: The complete list of PIC breeding populations (Excel Format) PIC (Phaseolus Improvement Cooperative) populations are bulked populations for improvement of common bean in Feed the Future Countries, with a principal focus on sub-Saharan Africa. These populations are for distribution to collaborators, are segregating for key biotic and abiotic stress constraints, and can be used for selection and release of improved cultivars/germplasm. Many of these populations are derived from crosses between ADP landrances and cultivars from sub-Saharan Africa and other improved genotypes with key biotic or abiotic stress tolerance. Phenotypic and genotypic information related to the parents of the crosses can be found in the ADP Database.

This dataset provides information about the number of properties, residents, and average property values for Combine Circle cross streets in Apex, NC.