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 5 to 9 years years with a population of 77 (15.28%), according to the ACS 2019-2023 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 (0.40%). 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 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

Please note: for a correct view and use of this dataset it is advisable to consult it at original page on the Arezzo Portal. At the same address there are also, for the enabled datasets, additional access formats, the preview of the visualization via API call, the consultation of the fields in DCAT-AP IT format, the possibility to express an evaluation and comment on the dataset itself. All resource formats available for this dataset can be downloaded as ZIP packages: inside the package sarà available the resource in the chosen format, complete with all the information on the metadata and the license associated with it. The dataset contains Appendix 2 contained in document "E1 Implementing Technical Standards" of the Operational Plan, pages 250-254. Represents the list of implementation plans and programmes and their implementation status The dataset is a spreadsheet in Microsoft Excel *.xls format.

Context

The idea behind creating this dataset is to Use of negative and Positive word Sense for the research purpose. it has been made for research related to linguistic, like NLP, AI, Behaviour Detection and many more . it helps to: 1. Research whether language utilized in science abstracts can skew towards the employment of strikingly positive and negative words over time.
2. The yearly frequencies of positive, negative, and neutral words, plus 100 randomly selected words were normalised for the whole number of abstracts. 3. Subanalyses included pattern quantification of individual words, specificity for selected high impact journals, and comparison between author affiliations within or outside countries with English because the official majority language.

in an analysis Frequency patterns were compared with 4% of all books ever printed and digitised by use of Google Books Ngram Viewer. Main outcome measures Frequencies of positive and negative words in abstracts compared with frequencies of words with a neutral and random connotation, expressed as relative change since 1980 so it can help in these tasks too. Results absolutely the frequency of positive words increased from 2.0% (1974-80) to 17.5% (2014), a relative increase of 880% over four decades. All 25 individual positive words contributed to the rise, particularly the words “robust,” “novel,” “innovative,” and “unprecedented,” which increased in ratio up to fifteen 000%. Comparable but less pronounced results were obtained when restricting the analysis to chose journals with high impact factors. Authors affiliated to an institute during a non-English speaking country used significantly more positive words. Negative word frequencies increased from 1.3% (1974-80) to three.2% (2014), a relative increase of 257%. Over the identical period of time, no apparent increase was found in neutral or random word use, or within the frequency of positive word use in published books. so lexicographic analysis indicates that scientific abstracts are currently written with more positive and negative words, and provides an insight into the evolution of scientific writing. Apparently scientists look on the brilliant side of research results. So THis data set can play major role in research.

Content

About The Data Set: 1. Dataset is in Excel File Format. 2. Dataset Has two Column (I) Negative Word List (II) Positive Word List 3. In the Dataset Total 4699, Positive Words and Total 4722 Negative Words are theirs. 4. Dataset is collected data from different sources. 5. The dataset has some Null (nan) Values. 6. Please check the Data Once before Use.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Just to see how it can help in many NLP related Tasks.

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.

About this dataset

Context

The dataset tabulates the Excel township 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 township 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 2023, the population of Excel township was 300, a 0.99% decrease year-by-year from 2022. Previously, in 2022, Excel township population was 303, a decline of 0.98% compared to a population of 306 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Excel township increased by 17. In this period, the peak population was 308 in the year 2020. 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 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Excel township is shown in this column.
• Year on Year Change: This column displays the change in Excel township 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 township Population by Year. You can refer the same here

Shark Tank India Data set.

Shark Tank India - Season 1 to season 4 information, with 80 fields/columns and 630+ records.

All seasons/episodes of 🦈 SHARKTANK INDIA 🇮🇳 were broadcasted on SonyLiv OTT/Sony TV.

Here is the data dictionary for (Indian) Shark Tank season's dataset.

• Season Number - Season number
• Startup Name - Company name or product name
• Episode Number - Episode number within the season
• Pitch Number - Overall pitch number
• Season Start - Season first aired date
• Season End - Season last aired date
• Original Air Date - Episode original/first aired date, on OTT/TV
• Episode Title - Episode title in SonyLiv
• Anchor - Name of the episode presenter/host
• Industry - Industry name or type
• Business Description - Business Description
• Company Website - Company Website URL
• Started in - Year in which startup was started/incorporated
• Number of Presenters - Number of presenters
• Male Presenters - Number of male presenters
• Female Presenters - Number of female presenters
• Transgender Presenters - Number of transgender/LGBTQ presenters
• Couple Presenters - Are presenters wife/husband ? 1-yes, 0-no
• Pitchers Average Age - All pitchers average age, <30 young, 30-50 middle, >50 old
• Pitchers City - Presenter's town/city or place where company head office exists
• Pitchers State - Indian state pitcher hails from or state where company head office exists
• Yearly Revenue - Yearly revenue, in lakhs INR, -1 means negative revenue, 0 means pre-revenue
• Monthly Sales - Total monthly sales, in lakhs
• Gross Margin - Gross margin/profit of company, in percentages
• Net Margin - Net margin/profit of company, in percentages
• EBITDA - Earnings Before Interest, Taxes, Depreciation, and Amortization
• Cash Burn - In loss in current year; burning/paying money from their pocket (yes/no)
• SKUs - Stock Keeping Units or number of varieties, at the time of pitch
• Has Patents - Pitcher has Patents/Intellectual property (filed/granted), at the time of pitch
• Bootstrapped - Startup is bootstrapped or not (yes/no)
• Part of Match off - Competition between two similar brands, pitched at same time
• Original Ask Amount - Original Ask Amount, in lakhs INR
• Original Offered Equity - Original Offered Equity, in percentages
• Valuation Requested - Valuation Requested, in lakhs INR
• Received Offer - Received offer or not, 1-received, 0-not received
• Accepted Offer - Accepted offer or not, 1-accepted, 0-rejected
• Total Deal Amount - Total Deal Amount, in lakhs INR
• Total Deal Equity - Total Deal Equity, in percentages
• Total Deal Debt - Total Deal debt/loan amount, in lakhs INR
• Debt Interest - Debt interest rate, in percentages
• Deal Valuation - Deal Valuation, in lakhs INR
• Number of sharks in deal - Number of sharks involved in deal
• Deal has conditions - Deal has conditions or not? (yes or no)
• Royalty Percentage - Royalty percentage, if it's royalty deal
• Royalty Recouped Amount - Royalty recouped amount, if it's royalty deal, in lakhs
• Advisory Shares Equity - Deal with Advisory shares or equity, in percentages
• Namita Investment Amount - Namita Investment Amount, in lakhs INR
• Namita Investment Equity - Namita Investment Equity, in percentages
• Namita Debt Amount - Namita Debt Amount, in lakhs INR
• Vineeta Investment Amount - Vineeta Investment Amount, in lakhs INR
• Vineeta Investment Equity - Vineeta Investment Equity, in percentages
• Vineeta Debt Amount - Vineeta Debt Amount, in lakhs INR
• Anupam Investment Amount - Anupam Investment Amount, in lakhs INR
• Anupam Investment Equity - Anupam Investment Equity, in percentages
• Anupam Debt Amount - Anupam Debt Amount, in lakhs INR
• Aman Investment Amount - Aman Investment Amount, in lakhs INR
• Aman Investment Equity - Aman Investment Equity, in percentages
• Aman Debt Amount - Aman Debt Amount, in lakhs INR
• Peyush Investment Amount - Peyush Investment Amount, in lakhs INR
• Peyush Investment Equity - Peyush Investment Equity, in percentages
• Peyush Debt Amount - Peyush Debt Amount, in lakhs INR
• Ritesh Investment Amount - Ritesh Investment Amount, in lakhs INR
• Ritesh Investment Equity - Ritesh Investment Equity, in percentages
• Ritesh Debt Amount - Ritesh Debt Amount, in lakhs INR
• Amit Investment Amount - Amit Investment Amount, in lakhs INR
• Amit Investment Equity - Amit Investment Equity, in percentages
• Amit Debt Amount - Amit Debt Amount, in lakhs INR
• Guest Investment Amount - Guest Investment Amount, in lakhs INR
• Guest Investment Equity - Guest Investment Equity, in percentages
• Guest Debt Amount - Guest Debt Amount, in lakhs INR
• Invested Guest Name - Name of the guest(s) who invested in deal
• All Guest Names - Name of all guests, who are present in episode
• Namita Present - Whether Namita present in episode or not
• Vineeta Present - Whether Vineeta present in episode or not
• Anupam ...

IntroductionFollowing the identification of Local Area Energy Planning (LAEP) use cases, this dataset lists the data sources and/or information that could help facilitate this research. View our dedicated page to find out how we derived this list: Local Area Energy Plan — UK Power Networks (opendatasoft.com)

Methodological Approach Data upload: a list of datasets and ancillary details are uploaded into a static Excel file before uploaded onto the Open Data Portal.

Quality Control Statement

Quality Control Measures include: Manual review and correct of data inconsistencies Use of additional verification steps to ensure accuracy in the methodology

Assurance Statement The Open Data Team and Local Net Zero Team worked together to ensure data accuracy and consistency.

Other Download dataset information: Metadata (JSON)

Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary: https://ukpowernetworks.opendatasoft.com/pages/glossary/

Please note that "number of records" in the top left corner is higher than the number of datasets available as many datasets are indexed against multiple use cases leading to them being counted as multiple records.

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

Overview The Shinchan Universe Dataset is a comprehensive collection of data related to the beloved Japanese anime series Crayon Shinchan. This dataset includes information about movies, characters, and episodes, capturing the essence of the show with details that can be useful for various analyses, such as sentiment analysis, recommendation systems, and storytelling dynamics.

Dataset Structure Movies

Fields:

movie_id: Unique identifier for each movie.

title: Name of the movie.

release_date: Release date of the movie.

duration: Runtime of the movie (in minutes).

genre: Primary genre(s) of the movie.

synopsis: Brief description of the movie's plot.

director: Name of the movie's director(s).

main_characters: List of primary characters featured in the movie.

box_office: Gross revenue of the movie (if available).

rating: Viewer or critic ratings (e.g., IMDb, Rotten Tomatoes).

Characters

Fields:

character_id: Unique identifier for each character.

name: Character's name.

age: Character's age (where applicable).

relation: Relationship with the protagonist (e.g., friend, family).

traits: List of personality traits or quirks (e.g., mischievous, kind).

voice_actor: Name of the actor voicing the character.

appearance_count: Number of appearances across episodes/movies.

signature_phrases: Iconic lines or catchphrases used by the character.

Episodes

Fields:

episode_id: Unique identifier for each episode.

title: Title of the episode.

air_date: Original air date.

season: Season number in which the episode aired.

plot_summary: Brief summary of the episode's storyline.

main_characters: List of characters prominently featured.

location: Key locations featured in the episode.

runtime: Duration of the episode (in minutes).

key_events: Notable events or developments in the storyline.

humor_index: Subjective rating of the episode’s humor content (if applicable).

Underwear Moments (Optional, Themed Fun Data)

Fields:

scene_id: Unique identifier for the scene.

episode_id: Link to the relevant episode or movie.

context: Brief description of the situation leading to the scene.

humor_score: Rating of how funny the moment is (subjective or based on user feedback).

reactions: Summary of audience or character reactions.

Dataset Format File Formats: Available in CSV, JSON, and Excel.

Structure: Each component (Movies, Characters, Episodes, and Underwear Moments) is stored in separate tables/files for modular use.

Use Cases Recommendation Systems: Develop personalized recommendations for Shinchan fans based on character and movie data.

Sentiment Analysis: Analyze the tone of episodes or scenes using plot summaries and humor ratings.

Content Insights: Understand character popularity and episode trends.

Trivia & Fun Analytics: Use themed moments (e.g., underwear scenes) for lighthearted analysis and fan engagement.

Access 6.1M verified company records in Poland from official trade registers. Choose tailored lists, Excel, CSV or API delivery. Part of our global database of 380M verified companies. Accurate, up-to-date and ready to power your business growth.

Last 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.

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

This thesis-mpc-dataset-public-readme.txt file was generated on 2020-10-20 by Masud Petronia

GENERAL INFORMATION
1. Title of Dataset: Data underlying the thesis: Multiparty Computation: The effect of multiparty computation on firms' willingness to contribute protected data
2. Author Information A. Principal Investigator Contact Information Name: Masud Petronia Institution: TU Delft, Faculty of Technology, Policy and Management Address: Mekelweg 5, 2628 CD Delft, Netherlands Email: masud.petronia@gmail.com ORCID: https://orcid.org/0000-0003-2798-046X
3: Description of dataset: This dataset contains perceptual data of firms' willingness to contribute protected data through multi party computation (MPC). Petronia (2020, ch. 6) draws several conclusions from this dataset and provides recommendations for future research Petronia (2020, ch. 7.4).
4. Date of data collection: July-August 2020
5. Geographic location of data collection: Netherlands
6. Information about funding sources that supported the collection of the data: Horizon 2020 Research and Innovation Programme, Grant Agreement no 825225 – Safe Data Enabled Economic Development (SAFE-DEED), from the H2020-ICT-2018-2

SHARING/ACCESS INFORMATION
1. Licenses/restrictions placed on the data: CC 0
2. Links to publications that cite or use the data: Petronia, M. N. (2020). Multiparty Computation: The effect of multiparty computation on firms' willingness to contribute protected data (Master's thesis). Retrieved from http://resolver.tudelft.nl/uuid:b0de4a4b-f5a3-44b8-baa4-a6416cebe26f
3. Was data derived from another source? No
4. Citation for this dataset: Petronia, M. N. (2020). Multiparty Computation: The effect of multiparty computation on firms' willingness to contribute protected data (Master's thesis). Retrieved from https://data.4tu.nl/. doi:10.4121/13102430

DATA & FILE OVERVIEW
1. File List: thesis-mpc-dataset-public.xlsxthesis-mpc-dataset-public-readme.txt (this document)
2. Relationship between files: Dataset metadata and instructions
3. Additional related data collected that was not included in the current data package: Occupation and role of respondents (traceable to unique reference), removed for privacy reasons.
4. Are there multiple versions of the dataset? No

METHODOLOGICAL INFORMATION
1. Description of methods used for collection/generation of data: A pre- and post test experimental design. For more information; see Petronia (2020, ch. 5)
2. Methods for processing the data: Full instructions are provided by Petronia (2020, ch. 6)
3. Instrument- or software-specific information needed to interpret the data: Microsoft Excel can be used to convert the dataset to other formats.
4. Environmental/experimental conditions: This dataset comprises three datasets collected through three channels. These channels are Prolific (incentive), LinkedIn/Twitter (voluntarily), and respondents in a lab setting (voluntarily). For more information; see Petronia (2020, ch. 6.1)
5. Describe any quality-assurance procedures performed on the data: A thorough examination of consistency and reliability is performed. For more information; see Petronia (2020, ch. 6).
6. People involved with sample collection, processing, analysis and/or submission: See Petronia (2020, ch. 6)

DATA-SPECIFIC INFORMATION
1. Number of variables: see worksheet experiment_matrix of thesis-mpc-dataset-public.xlsx
2. Number of cases/rows: see worksheet experiment_matrix of thesis-mpc-dataset-public.xlsx
3. Variable List: see worksheet labels of thesis-mpc-dataset-public.xlsx
4. Missing data codes: see worksheet comments of thesis-mpc-dataset-public.xlsx
5. Specialized formats or other abbreviations used: Multiparty computation (MPC) and Trusted Third Party (TTP).

INSTRUCTIONS
1. Petronia (2020, ch. 6) describes associated tests and respective syntax.

This dataset is an additional sample list, as an Excel spreadsheet, providing details of the major sample suites collected by Delia Cangelosi during SoS RARE and not added to the master spreadsheet (https://webapps.bgs.ac.uk/services/ngdc/accessions/index.html#item165705) It includes location details and descriptions for rock samples collected in China and Namibia. Most material is still held by the institutions that did the work, as recorded in the sample list.

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

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 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

Presentation

The Prospective Fabriques are one of the service offerings of the National Agency for Territorial Cohesion. They allow territories to be accompanied, individually and collectively, in order to work on a transition (ecological, demographic, economic...) of national and territorial interest.

Data

The dataset contains: — the list of forward-looking factories

— the list of municipalities accompanied by the forward-looking factories

— the list of groups accompanied by the forward-looking factories

Useful Links

— crossing with other ANCT devices (data.gouv) — detailed presentation of the forward-looking factories (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.

Creating a robust employee dataset for data analysis and visualization involves several key fields that capture different aspects of an employee's information. Here's a list of fields you might consider including: Employee ID: A unique identifier for each employee. Name: First name and last name of the employee. Gender: Male, female, non-binary, etc. Date of Birth: Birthdate of the employee. Email Address: Contact email of the employee. Phone Number: Contact number of the employee. Address: Home or work address of the employee. Department: The department the employee belongs to (e.g., HR, Marketing, Engineering, etc.). Job Title: The specific job title of the employee. Manager ID: ID of the employee's manager. Hire Date: Date when the employee was hired. Salary: Employee's salary or compensation. Employment Status: Full-time, part-time, contractor, etc. Employee Type: Regular, temporary, contract, etc. Education Level: Highest level of education attained by the employee. Certifications: Any relevant certifications the employee holds. Skills: Specific skills or expertise possessed by the employee. Performance Ratings: Ratings or evaluations of employee performance. Work Experience: Previous work experience of the employee. Benefits Enrollment: Information on benefits chosen by the employee (e.g., healthcare plan, retirement plan, etc.). Work Location: Physical location where the employee works. Work Hours: Regular working hours or shifts of the employee. Employee Status: Active, on leave, terminated, etc. Emergency Contact: Contact information of the employee's emergency contact person. Employee Satisfaction Survey Responses: Data from employee satisfaction surveys, if applicable.

Code Url: https://github.com/intellisenseCodez/faker-data-generator

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.

Overview The OYO Hotel Rooms Dataset provides comprehensive data on hotel room listings from OYO, covering various attributes related to pricing, amenities, and customer ratings. This dataset is valuable for researchers, data scientists, and machine learning practitioners interested in hospitality analytics, price prediction, customer satisfaction analysis, and clustering-based insights.

Data Source The dataset has been collected from publicly available OYO hotel listings and includes structured information for analysis.

Features The dataset consists of multiple attributes that define each hotel room, including:

Hotel Name: The name of the hotel property. City: The location where the hotel is situated. Room Type: Category of the room (e.g., Standard, Deluxe, Suite). Price (INR): The cost per night in Indian Rupees. Discounted Price: The price after applying discounts. Rating: The customer rating for the hotel (out of 5). Reviews: The number of customer reviews. Amenities: A list of available facilities such as WiFi, AC, Breakfast, Parking, etc. Latitude & Longitude: Geolocation details for mapping and spatial analysis. Potential Use Cases Price Prediction: Using regression models to predict hotel room pricing. Customer Sentiment Analysis: Analyzing ratings and reviews to understand customer satisfaction. Market Segmentation: Clustering hotels based on price, rating, and location. Recommendation Systems: Building personalized hotel recommendations. File Format

OYO_HOTEL_ROOMS.xlsx (Excel format) – Contains structured tabular data.

Acknowledgment This dataset is intended for academic and research purposes. The data is sourced from publicly available hotel listings and does not contain any personally identifiable information.