The USDA Agricultural Research Service (ARS) recently established SCINet , which consists of a shared high performance computing resource, Ceres, and the dedicated high-speed Internet2 network used to access Ceres. Current and potential SCINet users are using and generating very large datasets so SCINet needs to be provisioned with adequate data storage for their active computing. It is not designed to hold data beyond active research phases. At the same time, the National Agricultural Library has been developing the Ag Data Commons, a research data catalog and repository designed for public data release and professional data curation. Ag Data Commons needs to anticipate the size and nature of data it will be tasked with handling. The ARS Web-enabled Databases Working Group, organized under the SCINet initiative, conducted a study to establish baseline data storage needs and practices, and to make projections that could inform future infrastructure design, purchases, and policies. The SCINet Web-enabled Databases Working Group helped develop the survey which is the basis for an internal report. While the report was for internal use, the survey and resulting data may be generally useful and are being released publicly. From October 24 to November 8, 2016 we administered a 17-question survey (Appendix A) by emailing a Survey Monkey link to all ARS Research Leaders, intending to cover data storage needs of all 1,675 SY (Category 1 and Category 4) scientists. We designed the survey to accommodate either individual researcher responses or group responses. Research Leaders could decide, based on their unit's practices or their management preferences, whether to delegate response to a data management expert in their unit, to all members of their unit, or to themselves collate responses from their unit before reporting in the survey. Larger storage ranges cover vastly different amounts of data so the implications here could be significant depending on whether the true amount is at the lower or higher end of the range. Therefore, we requested more detail from "Big Data users," those 47 respondents who indicated they had more than 10 to 100 TB or over 100 TB total current data (Q5). All other respondents are called "Small Data users." Because not all of these follow-up requests were successful, we used actual follow-up responses to estimate likely responses for those who did not respond. We defined active data as data that would be used within the next six months. All other data would be considered inactive, or archival. To calculate per person storage needs we used the high end of the reported range divided by 1 for an individual response, or by G, the number of individuals in a group response. For Big Data users we used the actual reported values or estimated likely values. Resources in this dataset:Resource Title: Appendix A: ARS data storage survey questions. File Name: Appendix A.pdfResource Description: The full list of questions asked with the possible responses. The survey was not administered using this PDF but the PDF was generated directly from the administered survey using the Print option under Design Survey. Asterisked questions were required. A list of Research Units and their associated codes was provided in a drop down not shown here. Resource Software Recommended: Adobe Acrobat,url: https://get.adobe.com/reader/ Resource Title: CSV of Responses from ARS Researcher Data Storage Survey. File Name: Machine-readable survey response data.csvResource Description: CSV file includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed. This information is that same data as in the Excel spreadsheet (also provided).Resource Title: Responses from ARS Researcher Data Storage Survey. File Name: Data Storage Survey Data for public release.xlsxResource Description: MS Excel worksheet that Includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel

PROJECT OBJECTIVE

We are a part of XYZ Co Pvt Ltd company who is in the business of organizing the sports events at international level. Countries nominate sportsmen from different departments and our team has been given the responsibility to systematize the membership roster and generate different reports as per business requirements.

Questions (KPIs)

TASK 1: STANDARDIZING THE DATASET

• Populate the FULLNAME consisting of the following fields ONLY, in the prescribed format: PREFIX FIRSTNAME LASTNAME.{Note: All UPPERCASE)
• Get the COUNTRY NAME to which these sportsmen belong to. Make use of LOCATION sheet to get the required data
• Populate the LANGUAGE_!poken by the sportsmen. Make use of LOCTION sheet to get the required data
• Generate the EMAIL ADDRESS for those members, who speak English, in the prescribed format :lastname.firstnamel@xyz .org {Note: All lowercase) and for all other members, format should be lastname.firstname@xyz.com (Note: All lowercase)
• Populate the SPORT LOCATION of the sport played by each player. Make use of SPORT sheet to get the required data

TASK 2: DATA FORMATING

• Display MEMBER IDas always 3 digit number {Note: 001,002 ...,D2D,..etc)
• Format the BIRTHDATE as dd mmm'yyyy (Prescribed format example: 09 May' 1986)
• Display the units for the WEIGHT column (Prescribed format example: 80 kg)
• Format the SALARY to show the data In thousands. If SALARY is less than 100,000 then display data with 2 decimal places else display data with one decimal place. In both cases units should be thousands (k) e.g. 87670 -> 87.67 k and 12 250 -> 123.2 k

TASK 3: SUMMARIZE DATA - PIVOT TABLE (Use SPORTSMEN worksheet after attempting TASK 1) • Create a PIVOT table in the worksheet ANALYSIS, starting at cell B3,with the following details:

• In COLUMNS; Group : GENDER.
• In ROWS; Group : COUNTRY (Note: use COUNTRY NAMES).
• In VALUES; calculate the count of candidates from each COUNTRY and GENDER type, Remove GRAND TOTALs.

TASK 4: SUMMARIZE DATA - EXCEL FUNCTIONS (Use SPORTSMEN worksheet after attempting TASK 1)

• Create a SUMMARY table in the worksheet ANALYSIS,starting at cell G4, with the following details:

• Starting from range RANGE H4; get the distinct GENDER. Use remove duplicates option and transpose the data.
• Starting from range RANGE GS; get the distinct COUNTRY (Note: use COUNTRY NAMES).
• In the cross table,get the count of candidates from each COUNTRY and GENDER type.

TASK 5: GENERATE REPORT - PIVOT TABLE (Use SPORTSMEN worksheet after attempting TASK 1)

• Create a PIVOT table report in the worksheet REPORT, starting at cell A3, with the following information:

• Change the report layout to TABULAR form.
• Remove expand and collapse buttons.
• Remove GRAND TOTALs.
• Allow user to filter the data by SPORT LOCATION.

Process

• Verify data for any missing values and anomalies, and sort out the same.
• Made sure data is consistent and clean with respect to data type, data format and values used.
• Created pivot tables according to the questions asked.

• About Dataset Safa S. Abdul-Jabbar, Alaa k. Farhan

• Context This is the first Dataset for various ordinary patients in Iraq. The Dataset provides the patients’ Cell Blood Count test information that can be used to create a Hematology diagnosis/prediction system. Also, this Data was collected in 2022 from Al-Zahraa Al-Ahly Hospital. These data can be cleaned & analyzed using any programming language because it is provided in an excel file that can be accessed and manipulated easily. The user just needs to understand how rows and columns are arranged because the data was collected as images(CBC images) from the laboratories and then stored the extracted data in an excel file.  Content This Dataset contains 500 rows. For each row (patient information), there are 21 columns containing CBC test features that can be described as follows:

• ID: Patients Identifier

• WBC: White Blood Cell, Normal Ranges: 4.0 to 10.0, Unit: 10^9/L.

• LYMp: Lymphocytes percentage, which is a type of white blood cell, Normal Ranges: 20.0 to 40.0, Unit: %

• MIDp: Indicates the percentage combined value of the other types of white blood cells not classified as lymphocytes or granulocytes, Normal Ranges: 1.0 to 15.0, Unit: %

• NEUTp: Neutrophils are a type of white blood cell (leukocytes); neutrophils percentage, Normal Ranges: 50.0 to 70.0, Unit: %

• LYMn: Lymphocytes number are a type of white blood cell, Normal Ranges: 0.6 to 4.1, Unit: 10^9/L.

• MIDn: Indicates the combined number of other white blood cells not classified as lymphocytes or granulocytes, Normal Ranges: 0.1 to 1.8, Unit: 10^9/L.

• NEUTn: Neutrophils Number, Normal Ranges: 2.0 to 7.8, Unit: 10^9/L.

• RBC: Red Blood Cell, Normal Ranges: 3.50 to 5.50, Unit: 10^12/L

• HGB: Hemoglobin, Normal Ranges: 11.0 to 16.0, Unit: g/dL

• HCT: Hematocrit is the proportion, by volume, of the Blood that consists of red blood cells, Normal Ranges: 36.0 to 48.0, Unit: %

• MCV: Mean Corpuscular Volume, Normal Ranges: 80.0 to 99.0, Unit: fL

• MCH: Mean Corpuscular Hemoglobin is the average amount of haemoglobin in the average red cell, Normal Ranges: 26.0 to 32.0, Unit: pg

• MCHC: Mean Corpuscular Hemoglobin Concentration, Normal Ranges: 32.0 to 36.0, Unit: g/dL

• RDWSD: Red Blood Cell Distribution Width, Normal Ranges: 37.0 to 54.0, Unit: fL

• RDWCV: Red blood cell distribution width, Normal Ranges: 11.5 to 14.5, Unit: %

• PLT: Platelet Count, Normal Ranges: 100 to 400, Unit: 10^9/L

• MPV: Mean Platelet Volume, Normal Ranges: 7.4 to 10.4, Unit: fL

• PDW: Red Cell Distribution Width, Normal Ranges: 10.0 to 17.0, Unit: %

• PCT: The level of Procalcitonin in the Blood, Normal Ranges: 0.10 to 0.28, Unit: %

• PLCR: Platelet Large Cell Ratio, Normal Ranges: 13.0 to 43.0, Unit: %

• Acknowledgements We thank the entire Al-Zahraa Al-Ahly Hospital Hospital team, especially the hospital manager, for cooperating with us in collecting this data while maintaining patients' confidentiality.

File name definitions:

'...v_50_175_250_300...' - dataset for velocity ranges [50, 175] + [250, 300] m/s

'...v_175_250...' - dataset for velocity range [175, 250] m/s

'ANNdevelop...' - used to perform 9 parametric sub-analyses where, in each one, many ANNs are developed (trained, validated and tested) and the one yielding the best results is selected

'ANNtest...' - used to test the best ANN from each aforementioned parametric sub-analysis, aiming to find the best ANN model; this dataset includes the 'ANNdevelop...' counterpart

Where to find the input (independent) and target (dependent) variable values for each dataset/excel ?

input values in 'IN' sheet

target values in 'TARGET' sheet

Where to find the results from the best ANN model (for each target/output variable and each velocity range)?

open the corresponding excel file and the expected (target) vs ANN (output) results are written in 'TARGET vs OUTPUT' sheet

Check reference below (to be added when the paper is published)

https://www.researchgate.net/publication/328849817_11_Neural_Networks_-_Max_Disp_-_Railway_Beams

The open repository consists of two folders; Dataset and Picture. The dataset folder consists file “AWS Dataset Pangandaraan.xlsx”. There are 10 columns with three first columns as time attributes and the other six as atmosphere datasets. Each parameter has 8085 data, and Each parameter has a parameter index at the bottom of the column we added, including mMinimum, mMaximum, and Average values.

For further use, the user can choose one or more parameters for calculating or analyzing. For example, wind data (speed and direction) can be utilized to calculate Waves using the Hindcast method. Furthermore, the user can filter data by using the feature in Excel to extract the exact time range for analyzing various phenomena considered correlated to atmosphere data around Pangandaran, Indonesia.

The second folder, named “Picture,” contains three figures, including the monthly distribution of datasets, temporal data, and wind rose. Furthermore, the user can filter data by using the feature in Excel sheet to extract the exact time range for analyzing various phenomena considered correlated to atmosphere data around Pangandaran, Indonesia

Dataset

This dataset was created by denggui feng613

Contents

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Excel. The dataset can be utilized to gain insights into gender-based income distribution within the Excel population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Excel, among individuals aged 15 years and older with income, there were 153 men and 160 women in the workforce. Among them, 129 men were engaged in full-time, year-round employment, while 74 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 1.55% fell within the income range of under $24,999, while 24.32% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 10.85% of men in full-time roles earned incomes exceeding $100,000, while none of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

https://i.neilsberg.com/ch/excel-al-income-distribution-by-gender-and-employment-type.jpeg" alt="Excel, AL gender and employment-based income distribution analysis (Ages 15+)">

Content

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

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

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 median household income by gender. You can refer the same here

The Alaska Geochemical Database Version 2.0 (AGDB2) contains new geochemical data compilations in which each geologic material sample has one "best value" determination for each analyzed species, greatly improving speed and efficiency of use. Like the Alaska Geochemical Database (AGDB) before it, the AGDB2 was created and designed to compile and integrate geochemical data from Alaska in order to facilitate geologic mapping, petrologic studies, mineral resource assessments, definition of geochemical baseline values and statistics, environmental impact assessments, and studies in medical geology. This relational database, created from the Alaska Geochemical Database (AGDB) that was released in 2011, serves as a data archive in support of present and future Alaskan geologic and geochemical projects, and contains data tables in several different formats describing historical and new quantitative and qualitative geochemical analyses. The analytical results were determined by 85 laboratory and field analytical methods on 264,095 rock, sediment, soil, mineral and heavy-mineral concentrate samples. Most samples were collected by U.S. Geological Survey (USGS) personnel and analyzed in USGS laboratories or, under contracts, in commercial analytical laboratories. These data represent analyses of samples collected as part of various USGS programs and projects from 1962 through 2009. In addition, mineralogical data from 18,138 nonmagnetic heavy mineral concentrate samples are included in this database. The AGDB2 includes historical geochemical data originally archived in the USGS Rock Analysis Storage System (RASS) database, used from the mid-1960s through the late 1980s and the USGS PLUTO database used from the mid-1970s through the mid-1990s. All of these data are currently maintained in the National Geochemical Database (NGDB). Retrievals from the NGDB were used to generate most of the AGDB data set. These data were checked for accuracy regarding sample location, sample media type, and analytical methods used. This arduous process of reviewing, verifying and, where necessary, editing all USGS geochemical data resulted in a significantly improved Alaska geochemical dataset. USGS data that were not previously in the NGDB because the data predate the earliest USGS geochemical databases, or were once excluded for programmatic reasons, are included here in the AGDB2 and will be added to the NGDB. The AGDB2 data provided here are the most accurate and complete to date, and should be useful for a wide variety of geochemical studies. The AGDB2 data provided in the linked database may be updated or changed periodically.

No description is available. Visit https://dataone.org/datasets/6ffb72520e80a412991cd50d38f324d6 for complete metadata about this dataset.

BackgroundThe Qualitative aspect of health care delivery is one of the major factors in reducing morbidity and mortality in a health care setup. The expanding suburban secondary health care delivery facilities of the Municipal Corporation of Greater Mumbai are an important part of the healthcare backbone of Mumbai and therefore the quality of care delivered here needed standardization.Material and MethodsThe project was completed over a period of one year from Jan to Dec, 2013 and implemented in three phases. The framework with components and sub-components were developed and formats for data collection were standardized. The benchmarks were based on past performance in the same hospital and probability was used for development of normal range. An Excel spreadsheet was developed to facilitate data analysis.ResultsThe indicators comprise of 3 components - Statutory Requirements, Patient care & Cure and Administrative efficiency. The measurements made, pointed to the broad areas needing attention.ConclusionThe Indicators for patient care and monitoring standards can be used as a self assessment tool for health care setups for standardization and improvement of delivery of health care services.

This data set contains R code for a hypothetical exposure model described in the manuscript "A quantitative source-to-outcome case study to demonstrate the integration of human health and ecological endpoints using the Aggregate Exposure Pathway and Adverse Outcome Pathway frameworks". Additionally, this data set contains an Excel file that provides the range of parameters used in Monte Carlo simulations to generate iterations of the exposure network. This dataset is associated with the following publication: Hines, D., R. Conolly, and A. Jarabek. A quantitative source-to-outcome case study to demonstrate the integration of human health and ecological endpoints using the Aggregate Exposure Pathway and Adverse Outcome Pathway frameworks. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 53(8): 11002-11012, (2019).

The annual Retail store data CD-ROM is an easy-to-use tool for quickly discovering retail trade patterns and trends. The current product presents results from the 1999 and 2000 Annual Retail Store and Annual Retail Chain surveys. This product contains numerous cross-classified data tables using the North American Industry Classification System (NAICS). The data tables provide access to a wide range of financial variables, such as revenues, expenses, inventory, sales per square footage (chain stores only) and the number of stores. Most data tables contain detailed information on industry (as low as 5-digit NAICS codes), geography (Canada, provinces and territories) and store type (chains, independents, franchises). The electronic product also contains survey metadata, questionnaires, information on industry codes and definitions, and the list of retail chain store respondents.

Excel Age-Range creator for Office for National Statistics (ONS) Mid year population estimates (MYE) covering each year between 1999 and 2016 These files take into account the revised estimates for 2002-2010 released in April 2013 down to Local Authority level and the post 2011 estimates based on the Census results. Scotland and Northern Ireland data has not been revised, so Great Britain and United Kingdom totals comprise the original data for these plus revised England and Wales figures. This Excel based tool enables users to query the single year of age raw data so that any age range can easily be calculated without having to carry out often complex, and time consuming formulas that could also be open to human error. Simply select the lower and upper age range for both males and females and the spreadsheet will return the total population for the range. Please adhere to the terms and conditions of supply contained within the file. Tip: You can copy and paste the rows you are interested in to another worksheet by using the filters at the top of the columns and then select all by pressing Ctrl+A. Then simply copy and paste the cells to a new location. ONS Mid year population estimates Open Excel tool (London Boroughs, Regions and National, 1999-2016) Also available is a custom-age tool for all geographies in the UK. This full MYE dataset by single year of age (SYA) age and gender is available as a Datastore package here. Ward Level Population estimates Single year of age population tool for 2002 to 2015 for all wards in London. New 2014 Ward boundary estimates Ward boundary changes in May 2014 only affected three London boroughs - Hackney, Kensington and Chelsea, and Tower Hamlets. The estimates between 2001-2013 have been calculated by the GLA by taking the proportion of a the old ward that falls within the new ward based on the proportion of population living in each area at the 2011 Census. Therefore, these estimates are purely indicative and are not official statistics and not endorsed by ONS. From 2014 onwards, ONS began publishing official estimates for the new ward boundaries. Download here.

Data on the distribution of invasive cane toads in New South Wales was collated from all available sources, to quantify rates of expansion and to identify correlates of that rate of spread. We also conducted pilot studies to comapre alternative emthods of detecting invasion-front populations of toads in the field.

Data set for: Yamashita T, Vavladeli A, Pala A, Galan K, Crochet S, Petersen SSA, Petersen CCH (2018) Diverse long-range axonal projections of excitatory layer 2/3 neurons in mouse barrel cortex. Front Neuroanat 12: 33. https://doi.org/10.3389/fnana.2018.00033

There are 25 files in this data upload:

1. '2018_Yamashita_FrontNeuroanat.pdf' - this a pdf version of the online publication.

2. 'Yamashita_Figure2_Quantification.xlsx' - this is a Microsoft Excel file giving the locations of high density axonal projections from layer 2/3 pyramidal neurons in the mouse C2 barrel column in the coordinate frame of Paxinos & Franklin (2001) The mouse brain in stereotaxic coordinates. Academic Press. The data are plotted in Figure 2 of Yamashita et al., 2018.

3. 'Yamashita_Figure7_Quantification.xlsx' - this is a Microsoft Excel file giving the dendritic length, number of dendrites, number of dendritic nodes and total axonal length, as well as the axonal length in the different projection zones for each reconstructed neuron. The data are plotted in Figure 7 of Yamashita et al., 2018.

4. 'Yamashita_SupMov1_S2P_AP049.mov' - this is a QuickTime video file, showing the 3D structure of neuron AP049 featured in Figure 3 of Yamashita et al., 2018.

5. 'Yamashita_SupMov2_M1P_TY308.mov' - this is a QuickTime video file, showing the 3D structure of neuron TY308 featured in Figure 5 of Yamashita et al., 2018.

6. 'AV198.zip' - this zipped folder contains data relating to mouse AV198: a) 'AV198_stack.tif' the z-stack of whole-brain fluorescence images from expression of tdTomato in layer 2/3 neurons of the C2 barrel column of mouse AV198. b) 'AV198_ROI_Box.zip' can be loaded into FIJI (https://fiji.sc) and indicates projection regions by a box. c) 'AV198_ROI_Point.zip' can be loaded into FIJI (https://fiji.sc) and indicates projection regions by a point. d) 'AV198_Paxinos' is a folder showing the coronal fluorescent brain sections in pdf format overlaid on the equivalent drawing from Paxinos & Franklin (2001) The mouse brain in stereotaxic coordinates. Academic Press.

7. 'AV199.zip' - same as 'AV198.zip' but for mouse AV199.

8. 'AV201.zip' - same as 'AV198.zip' but for mouse AV201.

9. 'AV202.zip' - same as 'AV198.zip' but for mouse AV202.

10. 'AV203.zip' - same as 'AV198.zip' but for mouse AV203.

11. 'AP042.ASC' - Neurolucida (http://www.mbfbioscience.com/neurolucida) data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP042. Brain contours are also traced.

12. 'AP044.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP044. Brain contours are also traced.

13. 'AP046.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP046. Brain contours are also traced.

14. 'AP047.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP047. Brain contours are also traced.

15. 'AP049.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP049. Brain contours are also traced.

16. 'TY220.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY220. Brain contours are also traced.

17. 'TY288.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY288. Brain contours are also traced.

18. 'TY300.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY300. Brain contours are also traced.

19. 'TY302.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY302. Brain contours are also traced.

20. 'TY308.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY308. Brain contours are also traced.

21. 'TY310.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY310. Brain contours are also traced.

22. 'TY337.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY337. Brain contours are also traced.

23. 'TY345.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY345. Brain contours are also traced.

24. 'TY367.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY367. Brain contours are also traced.

25. 'TY369.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY369. Brain contours are also traced.

A range of quarterly Excel spreadsheets and SuperTABLE datacubes. The spreadsheets contain broad level data covering all the major items of the Labour Force Survey in time series format, including seasonally adjusted and trend estimates. The datacubes contain more detailed and cross classified original data than the spreadsheets.

The dataset contains information from a cohort of 799 patients admitted in the hospital for COVID-19, characterized with sociodemographic and clinical data. Retrospectively, from November 2020 to January 2021, data was collected from the medical records of all hospital admissions that occurred from March 1st, 2020, to December 31st, 2020. The analysis of these data can contribute to the definition of the clinical and sociodemographic profile of patients with COVID-19. Understanding these data can contribute to elucidating the sociodemographic profile, clinical variables and health conditions of patients hospitalized by COVID-19. To this end, this database contains a wide range of variables, such as: Month of hospitalization Gender Age group Ethnicity Marital status Paid work Admission to clinical ward Hospitalization in the Intensive Care Unit (ICU)COVID-19 diagnosisNumber of times hospitalized by COVID-19Hospitalization time in daysRisk Classification ProtocolData is presented as a single Excel XLSX file: dataset.xlsx of clinical and sociodemographic characteristics of hospital admissions by COVID-19: retrospective cohort of patients in two hospitals in the Southern of Brazil. Researchers interested in studying the data related to patients affected by COVID-19 can extensively explore the variables described here. Approved by the Research Ethics Committee (No. 4.323.917/2020) of the Federal University of Santa Catarina.

The Adventure Works dataset is a comprehensive and widely used sample database provided by Microsoft for educational and testing purposes. It's designed to represent a fictional company, Adventure Works Cycles, which is a global manufacturer of bicycles and related products. The dataset is often used for learning and practicing various data management, analysis, and reporting skills.

Key Features of the Adventure Works Dataset:

1. Company Overview: - Industry: Bicycle manufacturing - Operations: Global presence with various departments such as sales, production, and human resources.

2. Data Structure: - Tables: The dataset includes a variety of tables, typically organized into categories such as: - Sales: Information about sales orders, products, and customer details. - Production: Data on manufacturing processes, inventory, and product specifications. - Human Resources: Employee details, departments, and job roles. - Purchasing: Vendor information and purchase orders.

3. Sample Tables: - Sales.SalesOrderHeader: Contains information about sales orders, including order dates, customer IDs, and total amounts. - Sales.SalesOrderDetail: Details of individual items within each sales order, such as product ID, quantity, and unit price. - Production.Product: Information about the products being manufactured, including product names, categories, and prices. - Production.ProductCategory: Data on product categories, such as bicycles and accessories. - Person.Person: Contains personal information about employees and contacts, including names and addresses. - Purchasing.Vendor: Information on vendors that supply the company with materials.

4. Usage: - Training and Education: It's widely used for teaching SQL, data analysis, and database management. - Testing and Demonstrations: Useful for testing software features and demonstrating data-related functionalities.

5. Tools: - The dataset is often used with Microsoft SQL Server, but it's also compatible with other relational database systems.

The Adventure Works dataset provides a rich and realistic environment for practicing a range of data-related tasks, from querying and reporting to data modeling and analysis.

Excel Age-Range creator for Office for National Statistics (ONS) Mid year population estimates (MYE) covering each year between 1999 and 2014

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These files take into account the revised estimates for 2002-2010 released in April 2013 down to Local Authority level and the post 2011 estimates based on the Census results. Scotland and Northern Ireland data has not been revised, so Great Britain and United Kingdom totals comprise the original data for these plus revised England and Wales figures.

This Excel based tool enables users to query the single year of age raw data so that any age range can easily be calculated without having to carry out often complex, and time consuming formulas that could also be open to human error. Simply select the lower and upper age range for both males and females and the spreadsheet will return the total population for the range. Please adhere to the terms and conditions of supply contained within the file.

Tip: You can copy and paste the rows you are interested in to another worksheet by using the filters at the top of the columns and then select all by pressing Ctrl+A. Then simply copy and paste the cells to a new location.

ONS Mid year population estimates

Open Excel tool (London Boroughs, Regions and National, 1999-2014)

Also available is a custom-age tool for all geographies in the UK. Open the tool for all UK geographies (local authority and above) for: 2010, 2011, 2012, 2013, and 2014.

This full MYE dataset by single year of age (SYA) age and gender is available as a Datastore package here.

Ward Level Population estimates

Excel single year of age population tool for 2002 to 2013 for all wards in London.

New 2014 Ward boundary estimates

This data is only for wards in the three London boroughs that changed their ward boundaries in May 2014. The estimates in this spreadsheet have been calculated by the GLA by taking the proportion of a the old ward that falls within the new ward based on the proportion of population living in each area at the 2011 Census. Therefore, these estimates are purely indicative and are not official statistics and not endorsed by ONS.

This dataset includes an excel file containing all the data needed to carry out the analyses detailed in the manuscript and shown in full in Appendix S1 of the manuscript. The data is the raw input into the analyses conducted in the analysis. The first sheet in the excel file is a key and the other sheets contain data and are labeled according to the tables they relate to in Appendix S1 of the manuscript.