About this dataset

Context

The dataset tabulates the Excel 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 Excel. The dataset can be utilized to understand the population distribution of Excel by age. For example, using this dataset, we can identify the largest age group in Excel.

Key observations

The largest age group in Excel, AL was for the group of age 45 to 49 years years with a population of 74 (15.64%), according to the ACS 2018-2022 5-Year Estimates. At the same time, the smallest age group in Excel, AL was the 85 years and over years with a population of 2 (0.42%). 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

Variables / Data Columns

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

The EU-U.S. and Swiss-U.S. Privacy Shield Frameworks are mechanisms that companies can use to comply with data protection requirements when transferring personal data from the European Union and Switzerland to the United States. ITA's Privacy Shield Team maintains two lists that are made available to the public: 1) the Privacy Shield Active List, and 2) the Privacy Shield Inactive List. The Active List is an authoritative list of U.S. organizations that have self-certified to the Department of Commerce and declared their commitment to adhere to the Privacy Shield Principles. The Inactive List is an authoritative list of U.S. organizations that are no longer self-certified under Privacy Shield and are therefore no longer assured of the benefits of using Privacy Shield to receive personal data from the European Union and/or Switzerland. Upon request, the Privacy Shield Team may provide a copy of the list in the form of an Excel spreadsheet.

About this dataset

Context

The dataset tabulates the Excel 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 Excel 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 Excel was 539, a 1.46% decrease year-by-year from 2021. Previously, in 2021, Excel population was 547, a decline of 1.08% compared to a population of 553 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Excel decreased by 36. In this period, the peak population was 713 in the year 2010. The numbers suggest that the population has already reached its peak and is showing a trend of decline. 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 Excel is shown in this column.
• Year on Year Change: This column displays the change in Excel 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 Excel Population by Year. You can refer the same here

ISO 3166-1-alpha-2 English country names and code elements. This list states the country names (official short names in English) in alphabetical order as given in ISO 3166-1 and the corresponding ISO 3166-1-alpha-2 code elements.

These are the most recent Data Dictionary (pop-ups) and Panarctic Species List (PASL) zip files for all the vegetation plot data entered into Turboveg for the Alaska AVA. These files are necessary to correctly use the Turboveg data with regards to coded data. The Data Dictionary file will be updated when new datasets are entered into Turboveg which result in additions to coded data such as references, author code, habitat type, surficial geology, etc. Updates to the PASL will occur less frequently. Check the dates in the file names to be certain that you are using the most current files. Our data model is a set of tables that comprise our relational database. The Excel spreadsheet included in the resources below provides information about each field in our database, such as data type, description, if it is a required field, whether the information within the field is selected from a pop-up list, and whether the field is a standard within Turboveg or is specific to the AVA. Using Turboveg: 1) Download the installation file available through the link at Alaska Arctic Geoecological Atlas portal from the official Turboveg webpage (general installation file for worldwide users, however, some adjustments will be needed when using data from AAVA after installation of this program). 2) Open the Turboveg program and restore the most recent Data Dictionary and PASL zipped files into the Turboveg program by using the function 'Database-Backup/Restore-Restore.' All the previous versions of data dictionary files and PASL that are already in program will be overwritten. 3) Use the Alaska-AVA following the manual for Turboveg for Windows which is available at http://www.synbiosys.alterra.nl/turboveg/tvwin.pdf

Version: 5

Authors: Carlota Balsa-Sánchez, Vanesa Loureiro

Date of data collection: 2023/09/05

General description: The publication of datasets according to the FAIR principles, could be reached publishing a data paper (or software paper) in data journals or in academic standard journals. The excel and CSV file contains a list of academic journals that publish data papers and software papers.
File list:

- data_articles_journal_list_v5.xlsx: full list of 140 academic journals in which data papers or/and software papers could be published
- data_articles_journal_list_v5.csv: full list of 140 academic journals in which data papers or/and software papers could be published

Relationship between files: both files have the same information. Two different formats are offered to improve reuse

Type of version of the dataset: final processed version

Versions of the files: 5th version
- Information updated: number of journals, URL, document types associated to a specific journal.

Version: 4

Authors: Carlota Balsa-Sánchez, Vanesa Loureiro

Date of data collection: 2022/12/15

General description: The publication of datasets according to the FAIR principles, could be reached publishing a data paper (or software paper) in data journals or in academic standard journals. The excel and CSV file contains a list of academic journals that publish data papers and software papers.
File list:

- data_articles_journal_list_v4.xlsx: full list of 140 academic journals in which data papers or/and software papers could be published
- data_articles_journal_list_v4.csv: full list of 140 academic journals in which data papers or/and software papers could be published

Relationship between files: both files have the same information. Two different formats are offered to improve reuse

Type of version of the dataset: final processed version

Versions of the files: 4th version
- Information updated: number of journals, URL, document types associated to a specific journal, publishers normalization and simplification of document types
- Information added : listed in the Directory of Open Access Journals (DOAJ), indexed in Web of Science (WOS) and quartile in Journal Citation Reports (JCR) and/or Scimago Journal and Country Rank (SJR), Scopus and Web of Science (WOS), Journal Master List.

Version: 3

Authors: Carlota Balsa-Sánchez, Vanesa Loureiro

Date of data collection: 2022/10/28

General description: The publication of datasets according to the FAIR principles, could be reached publishing a data paper (or software paper) in data journals or in academic standard journals. The excel and CSV file contains a list of academic journals that publish data papers and software papers.
File list:

- data_articles_journal_list_v3.xlsx: full list of 124 academic journals in which data papers or/and software papers could be published
- data_articles_journal_list_3.csv: full list of 124 academic journals in which data papers or/and software papers could be published

Relationship between files: both files have the same information. Two different formats are offered to improve reuse

Type of version of the dataset: final processed version

Versions of the files: 3rd version
- Information updated: number of journals, URL, document types associated to a specific journal, publishers normalization and simplification of document types
- Information added : listed in the Directory of Open Access Journals (DOAJ), indexed in Web of Science (WOS) and quartile in Journal Citation Reports (JCR) and/or Scimago Journal and Country Rank (SJR).

Erratum - Data articles in journals Version 3:

Botanical Studies -- ISSN 1999-3110 -- JCR (JIF) Q2
Data -- ISSN 2306-5729 -- JCR (JIF) n/a
Data in Brief -- ISSN 2352-3409 -- JCR (JIF) n/a

Version: 2

Author: Francisco Rubio, Universitat Politècnia de València.

Date of data collection: 2020/06/23

General description: The publication of datasets according to the FAIR principles, could be reached publishing a data paper (or software paper) in data journals or in academic standard journals. The excel and CSV file contains a list of academic journals that publish data papers and software papers.
File list:

- data_articles_journal_list_v2.xlsx: full list of 56 academic journals in which data papers or/and software papers could be published
- data_articles_journal_list_v2.csv: full list of 56 academic journals in which data papers or/and software papers could be published

Relationship between files: both files have the same information. Two different formats are offered to improve reuse

Type of version of the dataset: final processed version

Versions of the files: 2nd version
- Information updated: number of journals, URL, document types associated to a specific journal, publishers normalization and simplification of document types
- Information added : listed in the Directory of Open Access Journals (DOAJ), indexed in Web of Science (WOS) and quartile in Scimago Journal and Country Rank (SJR)

Total size: 32 KB

Version 1: Description

This dataset contains a list of journals that publish data articles, code, software articles and database articles.

The search strategy in DOAJ and Ulrichsweb was the search for the word data in the title of the journals.
Acknowledgements:
Xaquín Lores Torres for his invaluable help in preparing this dataset.

Abstract

The dataset was derived by the Bioregional Assessment Programme from HUN_GW_Model_v01l. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

The dataset includes text and excel version of two datafiles pertaining to the groundwater monitoring bores and the surface water gauging stations where the model predicts water levels and baseflow estimates respectively. Also included is an excel file which lists the extraction rates used in the modellling for production bores.

probe_points_plus_extras.xyz GW model output points

no_repeats_with_elevation.txt - points where the groundwater model provides baseflow estimates that are then fed into the river model.

Purpose

Used to generate shapefiles for the two datasets

Dataset History

The dataset was created by exporting text files from the groundwater model after calibration and simulation were complete. Text files were converted to excel spreadsheets.

Dataset Citation

Bioregional Assessment Programme (2016) HUN GW model output points v01. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/63573849-6e91-45b4-a97c-ad59e48eeb9f.

Dataset Ancestors

• Derived From HUN GW Model Mines raw data v01

• Derived From HUN GW Model v01

• Derived From HUN GW Model code v01

The South Florida Water Management District (SFWMD) and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 174 National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in central and south Florida. The change factors were computed as the ratio of projected future to historical extreme precipitation depths fitted to extreme precipitation data from various downscaled climate datasets using a constrained maximum likelihood (CML) approach. The change factors correspond to the period 2050-2089 (centered in the year 2070) as compared to the 1966-2005 historical period. A Microsoft Excel workbook is provided that tabulates best models for each downscaled climate dataset and for all downscaled climate datasets considered together. Best models were identified based on how well the models capture the climatology and interannual variability of four climate extreme indices using the Model Climatology Index (MCI) and the Model Variability Index (MVI) of Srivastava and others (2020). The four indices consist of annual maxima consecutive precipitation for durations of 1, 3, 5, and 7 days compared against the same indices computed based on the PRISM and SFWMD gridded precipitation datasets for two climate regions: climate region 4 in South Central Florida, and climate region 5 in South Florida. The PRISM dataset is based on the Parameter-elevation Relationships on Independent Slopes Model interpolation method of Daly and others (2008). The South Florida Water Management District’s (SFWMD) precipitation super-grid is a gridded precipitation dataset developed by modelers at the agency for use in hydrologic modeling (SFWMD, 2005). This dataset is considered by the SFWMD as the best available gridded rainfall dataset for south Florida. Best models were selected based on MCI and MVI evaluated within each individual downscaled dataset. In addition, best models were selected by comparison across datasets and referred to as "ALL DATASETS" hereafter. Due to the small sample size, all models in the using the Weather Research and Forecasting Model (JupiterWRF) dataset were considered as best models.

The provided files operate with Excel. File 1 Data file with full list of records: File containing a full list of records of standardised values of resprouting (R), seeding (S) and flammability (F) obtained for species from different regions, through an unstructured literature review. Ordinal categorical values 1, 2 and 3 indicate low, intermediate and high values of the corresponding variable. Cells with "N/A" indicate data not available. Total number of records = 4,411; total number of species = 4,032. References for the "Source" column are provided as a separate file.

File 2 List of references used to compile the database: File containing the list of references with full citations for the "Source" column of the data file.

About this dataset

Context

The dataset tabulates the population of Excel township by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Excel township. The dataset can be utilized to understand the population distribution of Excel township by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Excel township. 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 Excel township.

Key observations

Largest age group (population): Male # 45-49 years (15) | Female # 20-24 years (21). 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 Excel township population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Excel township is shown in the following column.
• Population (Female): The female population in the Excel township 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 Excel township 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 Excel township Population by Gender. You can refer the same here

This dataset contains three files listing the experimental items and recorded data associated with the experiment on Corrective Questions run on Gorilla in July-September 2023. The content of each file is described below. For an in-depth description of the experiment items, procedure, and results, see the NLLT paper On Corrective Questions and the Position of Focus.Written Experimental Items - This Excel file contains the 30 experimental items in written form. For each item, the listed context, the names of speaker 1 and speaker 2, and the question Q were shown on the screen and the participants would read them. The Reply listed in the final clumn, however, was provided as audio. The written replies visible here are only provided for your convenience.Audio Stimuli - This zipped file contains the 30 audio recordings used as stimuli. These constituted the actual Reply to the Context and Question listed in the previous file.Experimental Data – UCL RDR - This Excel file lists the recorded assessments of the 30 participants that completed every experimental task. The participants are listed as numbers from 1 to 30 in the first column, with their assessments for all 30 experimental items following on the same row. Some minor statistical calculations are also provided.

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple datasets. The source dataset is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. The dataset consists of an excel spreadsheet and shapefile representing the locations of simulation nodes used in the AWRA-R model. Some of the nodes correspond to gauging station locations or dam …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme from multiple datasets. The source dataset is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. The dataset consists of an excel spreadsheet and shapefile representing the locations of simulation nodes used in the AWRA-R model. Some of the nodes correspond to gauging station locations or dam locations whereas other locations represent river confluences or catchment outlets which have no gauging. These are marked as "Dummy". Purpose Locations are used as pour points in oder to define reach areas for river system modelling. Dataset History Subset of data for the Hunter that was extracted from the Bureau of Meteorology's hydstra system and includes all gauges where data has been received from the lead water agency of each jurisdiction. Simulation nodes were added in locations in which the model will provide simulated streamflow. There are 3 files that have been extracted from the Hydstra database to aid in identifying sites in each bioregion and the type of data collected from each on. These data were used to determine the simulation node locations where model outputs were generated. The 3 files contained within the source dataset used for this determination are: Site - lists all sites available in Hydstra from data providers. The data provider is listed in the #Station as _xxx. For example, sites in NSW are _77, QLD are _66. Some sites do not have locational information and will not be able to be plotted. Period - the period table lists all the variables that are recorded at each site and the period of record. Variable - the variable table shows variable codes and names which can be linked to the period table. Relevant location information and other data were extracted to construct the spreadsheet and shapefile within this dataset. Dataset Citation Bioregional Assessment Programme (XXXX) HUN AWRA-R simulation nodes v01. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/fda20928-d486-49d2-b362-e860c1918b06. Dataset Ancestors Derived From National Surface Water sites Hydstra

Data from the draft Finance Law (PLF) for 2021 and its annexes, namely: Nomenclatures Appropriations according to destination axis (mission/programme/action) and nature axis (title/category) for the general budget (GB, special allocation accounts (CAS), financial assistance accounts (CCF) and for ancillary budgets (BA) breakdown of full-time equivalent (FTE) posts by ministry and destination (mission/programme) for the general budget (BG) and the related budgets (BA) list of taxes affected list of tax expenditures These data are presented in 2 formats: XLSX and CSV

Presentation Vehicle registration, RSA, tax, driver’s license, access to online services... Do you need help with your administrative procedures? No matter where you live, in the city or in the countryside, France services is a one-stop shop that gives access to the main public service bodies in a single place: Ministry of the Interior, Ministry of Justice, Public Finance, Employment Department, Pension Insurance, Sickness Insurance, CAF, MSA and Post Office. Data The data are jointly edited by ANCT and the Bank of Territories. They correspond to the information entered by the staff members present in the structure. The dataset contains: — list of structures (fixed and mobile) labelled France services and attached antennas, accompanied by postal address, contact details (telephone, e-mail address); opening hours; — the list of municipalities with services in France; For the description of the fields, refer to the pdf file of metadata presented in documentation below. Useful links — presentation of the programme France services (Ministry of Cohesion of the Territories and Relations with Local and Regional Authorities); — the map directory (ANCT) — Opening the data file If you are using the Microsoft Excel spreadsheet, a particular operation is required to open the data file: 1. Create a new Excel workbook 2. Click on the Data tab located in the ribbon and then click from the text_ 3. Choose the location of the csv file and click Importer 4. In the window that opens, choose the option Delimited and in File Origin, choose 65001: Unicode UTF8. Click on Next 5. Select only the Separator Virgule. Click on Next _6. Choose the right column data format by referring to the dataset documentation. Click Finish**.

This dataset contains the results of a survey about the use of open government data applied to public agents working in public institutions in Brazil. It has two sets, one with questionnaire responses and metadata and the second with a coding table with interview extracts: 1) In the first dataset, each row holds a response to a questionnaire about the public agent's perceptions of the use and reuse of open government data in Brazilian public institutions. Columns store the questionnaire questions. Data were collected between 8 June and 13 July 2021, and this sample is composed of responses from 40 federal, state, and municipal public administrators. Thus, this dataset contains 40 rows and 158 columns. Data were collected on the LimeSurvey platform, where it was screened for missing values and incomplete responses. After cleaning, data were exported to Excel in tabular format. Questionnaire responses are provided in two files ResultsSurvey_OGDUseBRPubInstitutions_DataSet_PT and ResultsSurvey_OGDUseBRPubInstitutions_DataSet_EN. They contain the same information in Portuguese and English. 2) The second dataset records the code table of the interviews about the benefits, barriers, enablers, and drivers of open government data (OGD) use in Brazilian public institutions. A questionnaire applied to public agents working in Brazilian public institutions was followed up by interviews to broaden an understanding of the use of OGD. Nine interviews were conducted between May 17-31, 2022. This dataset represents the perspective of these public agents. The dataset contains 97 lines and six columns. Each row of the dataset lists the factor code used in the questionnaire, the factor descriptions in Portuguese and English, the interviewee code, the transcription extract of an interviewee narration collected in Portuguese, and the English translation. After collection in Portuguese, interviews were automatically transcribed using the NVivo Transcription software. Then, they were anonymized, and a human reviewed the transcriptions. Interviews were coded using NVivo and used the questionnaire factors to guide coding. Coded extracts were translated to English using Google and Microsoft translators. Then, translated extracts were revised by a human and were used for reporting. The coding table was exported to Excel. Interviews extracts are provided in one file, InterviewsExtracts_OGDUseBR_PublicInstitutions_Dataset.

About this dataset

Context

The dataset tabulates the Excel township 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 Excel township. The dataset can be utilized to understand the population distribution of Excel township by age. For example, using this dataset, we can identify the largest age group in Excel township.

Key observations

The largest age group in Excel Township, Minnesota was for the group of age 50 to 54 years years with a population of 31 (10.73%), according to the ACS 2018-2022 5-Year Estimates. At the same time, the smallest age group in Excel Township, Minnesota was the 25 to 29 years years with a population of 6 (2.08%). 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

Variables / Data Columns

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

Following a request from the European Commission, in 2018 EFSA released a renovated database of host plant species of Xylella spp. (including both species X. fastidiosa and X. taiwanensis) together with a scientific report (EFSA, 2018). EFSA was tasked to maintain and update this database periodically. In May 2021, EFSA released the fourth update of the Xylella spp. host plant database (VERSION 4) with information retrieved from literature search up to December 2020, Europhyt outbreak notifications up to 18 March 2021, and communications of research groups and national authorities (EFSA, 2021). The protocol applied for the extensive literature review, data collection and reporting, as well as results and lists of host plants are described in detail in the related scientific report (EFSA, 2021). The overall number of Xylella spp. host plants determined with at least two different detection methods or positive with one method (between: sequencing, pure culture isolation) reaches now 385 plant species, 179 genera and 67 families (category A – see section 2.4.2 of EFSA (2021)). Such numbers rise to 638 plant species, 289 genera and 87 families if considered regardless of the detection method applied (category E, see section 2.4.2 of EFSA (2021). The Excel files here attached represent the VERSION 4 of the Xylella spp. host plants database. For a detailed description of the information included in the database, please consult the related scientific report (EFSA, 2021). The Excel file “Xylella spp. host plants database – VERSION 4” contains several sheets: the LEGENDA (with extensive description of each table), the full detailed raw data of the Xylella spp. host plant database (sheet “observation”) and several examples of data extraction. Additional Excel files contain the lists of host plant species of X. fastidiosa (subsp. unknown (i.e. not reported), fastidiosa, multiplex, pauca, morus, sandyi, tashke, fastidiosa/sandyi) and X. taiwanensis infected naturally, artificially and in not specified conditions, and according to different categories (A,B,C,D,E – see section 2.4.2 of EFSA (2021)). The Excel file “new_host_plant_species_v4” contain the list of new host plant species added to the database in this fourth update. Question number: EFSA-Q-2017-00221 Correspondence: alpha@efsa.europa.eu Bibliography: EFSA (European Food Safety Authority), 2018. Scientific report on the update of the Xylella spp. host plant database. EFSA Journal 2018;16(9):5408, 87 pp. https://doi.org/10.2903/j.efsa.2018.5408 EFSA (European Food Safety Authority), Delbianco A, Gibin D, Pasinato L and Morelli M, 2021. Scientific report on the update of the Xylella spp. host plant database – systematic literature search up to 31 December 2020. EFSA Journal 2021;19(6):6674, 70 pp. https://doi.org/10.2903/j.efsa.2021.6674

Excel file with multiple worksheets and graphs summarising the status and threats to IUNC red-listed flora and fauna in the Solomon Islands. Accessed from IUCN red list October 2018.

Multiple-page Excel spreadsheet, containing a complete data set on Prehistoric pottery

About this dataset

Context

The dataset tabulates the population of Excel by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Excel. The dataset can be utilized to understand the population distribution of Excel by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Excel. 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 Excel.

Key observations

Largest age group (population): Male # 45-49 years (72) | Female # 15-19 years (50). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 Excel population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Excel is shown in the following column.
• Population (Female): The female population in the Excel 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 Excel 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 Excel Population by Gender. You can refer the same here