Presentation Launched in 2014, the new National Urban Renewal Programme (NPNRU) covers 480 priority neighbourhoods: 216 of national interest and 264 of regional interest, where about three million inhabitants live. This programme covers neighborhoods of large metropolises, medium-sized cities, large social housing complexes or buildings in old degraded centres, located in France and overseas. The National Agency for Urban Renewal (Anru), responsible for the implementation of the NPNRU, now has EUR 10 billion for the financial support of projects presented by local authorities. In total, 40 billion euros will be invested in these 480 neighbourhoods for demolition and reconstruction of social housing, the development of public spaces or the treatment of degraded condominiums. The list of priority districts benefiting from this programme is annexed to the Order of 29 April 2015 on the list of priority districts of the city policy with the most important urban dysfunctions and targeted as a priority by the new National Urban Renewal Programme and to the Order of 15 January 2019 correcting the list of priority districts of the city policy with the most important urban dysfunctions and referred to as complementary by the new National Urban Renewal Programme. ### Data The dataset contains: — the list of municipalities covered by the programme — the list of priority districts registered according to the decrees #### Useful links — Detailed presentation of the programme (Anru) — Open 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 in the ribbon and then on From text 3. Choose the location of the csv file and click on Import 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 Virgul. Click on Next 6. Choose the right column data format by referring to the documentation of the dataset. Click on Finish.

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

All Comparisons of Differentially Expressed Genes - excel sheet containing the annotations and fold change values of the all the differentially expressed genes between the different clone comparisonsFinal List of Common Genes - excel sheet containing the list of genes that were commonly differentially expressed between all the aphid clone comparisons. Also contains table and bar chart presenting the number of times each candidate gene selected from previous literature was found in each aphid clone comparison.Non-direct and Direct Competition - excel sheet containing number of nymphs produced by all 6 clones on the 3 host plants in the non-direct competition, and the number of nymphs produced by the two clones NS and Viola in the direct competition experiment.sterror - excel sheet containing the means and standard error values of the 6 grouped resistant and susceptible clones in the non-direct competition experiment, used to make the bar plot for the non-direct competition experiment.sterror2 - excel sheet containing the means and standard error values of the resistant clone Viola and susceptible clone NS in the direct competition experiment, used to make the bar plot for the direct competition experiment.cabbagettest - excel sheet containing the number of nymphs produce by the 6 grouped resistant and susceptible clones on the 3 host plants, used to conduct the unpaired t tests to compare the reproductive performance of resistant and susceptible clones on the 3 different host plants when in not in competitiondirectcompetition - excel sheet containing the number of nymphs produce by the resistant clone Viola and susceptible clone NS on the 3 host plants, used to conduct the unpaired t tests comparing the reproductive performance of resistant and susceptible clones on the 3 different host plants when in direct competitionAPHID HOST SHIFT DISS Rscript - R script containing all my statistical tests: unpaired t tests of resistant and susceptible clones on the 3 host plants when in direct and non direct competition, and kruskal Wallis tests and post hoc Dunns test to identify significant differences between individual and resistant and susceptible clones on the different host plants. Also contains all my code for my bar charts for the non-direct and direct competition experiments and the code for my box plots showing the significant differences between individual clones and resistant and susceptible clones on the different host plants.Up and Down-regulated Genes Graph - excel sheet containing the number of and and down regulated genes in each aphid clone comparison and the bar graph generated from this data.

About this dataset

Context

The dataset tabulates the data for the Excel, AL population pyramid, which represents the Excel population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Excel, AL, is 61.2.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Excel, AL, is 26.9.
• Total dependency ratio for Excel, AL is 88.1.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Excel, AL is 3.7.

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 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 for the selected age group is shown in the following column.
• Population (Female): The female population in the Excel for the selected age group is shown in the following column.
• Total Population: The total population of the Excel for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

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

About this dataset

Context

The dataset tabulates the data for the Excel Township, Minnesota population pyramid, which represents the Excel township population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Excel Township, Minnesota, is 25.7.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Excel Township, Minnesota, is 37.7.
• Total dependency ratio for Excel Township, Minnesota is 63.5.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Excel Township, Minnesota is 2.7.

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 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 for the selected age group is shown in the following column.
• Population (Female): The female population in the Excel township for the selected age group is shown in the following column.
• Total Population: The total population of the Excel township for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Excel township Population by Age. You can refer the same here

Our goals with this dataset were to 1) isolate, culture, and identify two fungal life stages of Aspergillus flavus, 2) characterize the volatile emissions from grain inoculated by each fungal morphotype, and 3) understand how microbially-produced volatile organic compounds (MVOCs) from each fungal morphotype affect foraging, attraction, and preference by S. oryzae. This dataset includes that derived from headspace collection coupled with GC-MS, where we found the sexual life stage of A. flavus had the most unique emissions of MVOCs compared to the other semiochemical treatments. This translated to a higher arrestment with kernels containing grain with the A. flavus sexual life stage, as well as a higher cumulative time spent in those zones by S. oryzae in a video-tracking assay in comparison to the asexual life stage. While fungal cues were important for foraging at close-range, the release-recapture assay indicated that grain volatiles were more important for attraction at longer distances. There was no significant preference between grain and MVOCs in a four-way olfactometer, but methodological limitations in this assay prevent broad interpretation. Overall, this study enhances our understanding of how fungal cues affect the foraging ecology of a primary stored product insect. In the assays described herein, we analyzed the behavioral response of Sitophilus oryzae to five different blends of semiochemicals found and introduced in wheat (Table 1). Briefly, these included no stimuli (negative control), UV-sanitized grain, clean grain from storage (unmanipulated, positive control), as well as grain from storage inoculated with fungal morphotype 1 (M1, identified as the asexual life stage of Aspergillus flavus) and fungal morphotype 2 (M2, identified as the sexual life stage of A. flavus). Fresh samples of semiochemicals were used for each day of testing for each assay. In order to prevent cross-contamination, 300 g of grain (tempered to 15% grain moisture) was initially sanitized using UV for 20 min. This procedure was done before inoculating grain with either morphotype 1 or 2. The 300 g of grain was kept in a sanitized mason jar (8.5 D × 17 cm H). To inoculate grain with the two different morphologies, we scraped an entire isolation from a petri dish into the 300 g of grain. Each isolation was ~1 week old and completely colonized by the given morphotype. After inoculation, each treatment was placed in an environmental chamber (136VL, Percival Instruments, Perry, IA, USA) set at constant conditions (30°C, 65% RH, and 14:10 L:D). This procedure was the same for both morphologies and was done every 2 weeks to ensure fresh treatments for each experimental assay. See file list for descriptions of each data file. Resources in this dataset:Resource Title: Ethovision Movement Assay. File Name: ponce_lizarraga_ethovision_assay_microbial_volatiles_2020.csvResource Software Recommended: Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Olfactometer Round 1 Assay - With Fused Air Permeable Glass. File Name: ponce_lizarraga_first_round_olfactometer_fungal_study_2020.csvResource Software Recommended: Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Olfactometer Round 2 Assay - With Fused Air Permeable Glass Containing Holes. File Name: ponce_lizarraga_second_round_olfactometer_fungal_study_2021.csvResource Software Recommended: Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Small Release-Recapture Assay. File Name: ponce_lizarraga_small_release_recapture_assay.csvResource Software Recommended: Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Large Release-Recapture Assay. File Name: ponce_lizarraga_large_release_recapture_assay.csvResource Software Recommended: Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Headspace Volatile Collection Assay. File Name: sandra_headspace_volatiles_2020.csvResource Software Recommended: Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: README file list. File Name: file_list_stored_grain_Aspergillus_Sitophilus_oryzae.txt

Two experiments investigated Estes and Maddox’ theory (2002) that word frequency mirror effect in episodic recognition memory is due to word likeness rather than frequency of experience with a word. In Experiment 1, sixteen first year psychology students at the University of Newcastle studied lists of high and low frequency words crossed with high-neighbourhood-density and low-neighbourhood-density words and were given an episodic recognition test and asked to rate words as new or old and provide ratings of confidence according to a three point scale with six possible responses: sure old, probably old, possibly old, possibly new, probably new and sure new. Experiment 2 included twenty-three first year psychology students at the University of Newcastle who were tested using lexical decision task lists of words and nonwords. Testing was undertaken on a computer that presented the stimuli and recorded the participants’ responses using a program written in Turbo Pascal 6.0 with millisecond accurate timing. The dataset contains one Microsoft Excel file in .xls format containing data for Experiments 1 and 2.

AbstractThe dataset provided here contains the efforts of independent data aggregation, quality control, and visualization of the University of Arizona (UofA) COVID-19 testing programs for the 2019 novel Coronavirus pandemic. The dataset is provided in the form of machine-readable tables in comma-separated value (.csv) and Microsoft Excel (.xlsx) formats.Additional InformationAs part of the UofA response to the 2019-20 Coronavirus pandemic, testing was conducted on students, staff, and faculty prior to start of the academic year and throughout the school year. These testings were done at the UofA Campus Health Center and through their instance program called "Test All Test Smart" (TATS). These tests identify active cases of SARS-nCoV-2 infections using the reverse transcription polymerase chain reaction (RT-PCR) test and the Antigen test. Because the Antigen test provided more rapid diagnosis, it was greatly used three weeks prior to the start of the Fall semester and throughout the academic year.As these tests were occurring, results were provided on the COVID-19 websites. First, beginning in early March, the Campus Health Alerts website reported the total number of positive cases. Later, numbers were provided for the total number of tests (March 12 and thereafter). According to the website, these numbers were updated daily for positive cases and weekly for total tests. These numbers were reported until early September where they were then included in the reporting for the TATS program.For the TATS program, numbers were provided through the UofA COVID-19 Update website. Initially on August 21, the numbers provided were the total number (July 31 and thereafter) of tests and positive cases. Later (August 25), additional information was provided where both PCR and Antigen testings were available. Here, the daily numbers were also included. On September 3, this website then provided both the Campus Health and TATS data. Here, PCR and Antigen were combined and referred to as "Total", and daily and cumulative numbers were provided.At this time, no official data dashboard was available until September 16, and aside from the information provided on these websites, the full dataset was not made publicly available. As such, the authors of this dataset independently aggregated data from multiple sources. These data were made publicly available through a Google Sheet with graphical illustration provided through the spreadsheet and on social media. The goal of providing the data and illustrations publicly was to provide factual information and to understand the infection rate of SARS-nCoV-2 in the UofA community.Because of differences in reported data between Campus Health and the TATS program, the dataset provides Campus Health numbers on September 3 and thereafter. TATS numbers are provided beginning on August 14, 2020.Description of Dataset ContentThe following terms are used in describing the dataset.1. "Report Date" is the date and time in which the website was updated to reflect the new numbers2. "Test Date" is to the date of testing/sample collection3. "Total" is the combination of Campus Health and TATS numbers4. "Daily" is to the new data associated with the Test Date5. "To Date (07/31--)" provides the cumulative numbers from 07/31 and thereafter6. "Sources" provides the source of information. The number prior to the colon refers to the number of sources. Here, "UACU" refers to the UA COVID-19 Update page, and "UARB" refers to the UA Weekly Re-Entry Briefing. "SS" and "WBM" refers to screenshot (manually acquired) and "Wayback Machine" (see Reference section for links) with initials provided to indicate which author recorded the values. These screenshots are available in the records.zip file.The dataset is distinguished where available by the testing program and the methods of testing. Where data are not available, calculations are made to fill in missing data (e.g., extrapolating backwards on the total number of tests based on daily numbers that are deemed reliable). Where errors are found (by comparing to previous numbers), those are reported on the above Google Sheet with specifics noted.For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu

The series consists of a list of files registered on the computer-based Records and Correspondence Management System (RCMS), under Registry 01 Corporate Management Division. It was created by exporting file data from the RCMS system into a Microsoft Excel spreadsheet. It is an artificial series, created by the Department of Justice at the request of PROV, to provide access to VPRS 12607 General Correspondence Files, Registry 01 Corporate Management Division.

The list captured the file number, key-term classification, file title, and certain additional information for each file.

Organisation of the Data:

The data is organised into 13 columns, or fields, presumably corresponding to discrete fields within the RCMS database.

The columns, from left to right, are as follows:

1. FILE.YEAR - The year the file was raised.

2. REGISTRY - The number of the registry in which the file has been registered on the RCMS system. The files referred to by this series were registered under Registry 01 Corporate Management Division.

3. FILE SEQUENCE - The sequential number allocated to each file as it is raised. Numbers start again from one each year.

4. FILE PART - The part number of the file.

The FILE.YEAR, REGISTRY, FILE SEQUENCE, and FILE PART fields, taken together, provide the file number.

5. KEY TERM - In theory, this is term used to describe the principle subject area of the file.

6. DESCRIPTOR.1, DESCRIPTOR.2 and DESCRIPTOR.3 (Columns 6 to 8) - In theory, these are narrower terms used to break the general subject area into smaller, more specific areas.

7. KWOC.1, KWOC.2, KWOC.3, and KWOC.4 (Key Word Out of Context) (Columns 9 to 12) - Provide for free text description of the file.

The KEY-TERM, DESCRIPTOR, and KWOC fields, taken together, provide the file title.

In practice, many different terms have been used in the key-term and descriptor fields. There appears to have been little control over the creation of new terms and the way in which the terms are used.

8. ADD.FILE.INFO (Additional File Information) - This field contains useful information about previous and subsequent files, related files, file closure, and so forth.

Identifying Top-numbered Files:

This series also records the original file numbers for files that have been top-numbered into VPRS 12607 from other correspondence registries that operated in the Law Department in the 1980's. The details are as follows:

Files top-numbered from the Central Correspondence Registry (VPRS 266 Inward Registered Correspondence 1857-1986) - the original file number is recorded in the field "ADD.FILE.INFO".

Files top-numbered from the Courts Management Division Registry (VPRS 12705 General Correspondence Files, Courts Management Division) - the original file number is recorded in the fields "KWOC 3" and "KWOC 4".

Files top-numbered from the Buildings and Property Registry - the original file number is recorded in the field "KWOC 4".

Files top-numbered from the Human Resource Management Registry - the original file number is recorded in the field "KWOC 4".

Files top-numbered from RCMS Registry 02 Courts and Tribunals Division - the original file number is recorded in the fields "KWOC 3" and "KWOC 4".

Researchers should not discount the possibility that file numbers may be recorded in fields other than those specified above.

The number of COVID vaccinations carried out and payments made for these vaccinations to individual pharmacies, listed by their ODS code and with full postal address details. Could you provide the data for the month of January 2024 in EXCEL format please. The data should be: Column1--Administration Month Column2--ODS Code Column3--Pharmacy Name Column4--Pharmacy Trading Name Column5--Pharmacy Address Column6--Pharmacy Post Code Column7--Number of Vaccinations Claimed Column8--Number of Vaccinations Paid Column9--Payment Amount GB Response A copy of the information is attached. The NHSBSA calculates payments for Covid-19 vaccinations to Pharmacies and Primary Care Network (PCN) providers in England. Covid-19 vaccination data is keyed in via Point of Care (POC) Systems and they are transferred to the NHSBSA Manage Your Service (MYS) application. Each month, vaccine providers submit claims to request payment based on the data that has been transferred into MYS. To be paid in a timely fashion, such claims must be submitted during a specified declaration submission period. Should claims be submitted outside of the submission period, they will be processed in the following period. This means that in some cases, there is a difference between the number of vaccines that have been 'claimed' and the number that have been 'paid'. Both the number of 'claimed' and 'paid' vaccinations have been reported in this request. When considering the nature of the vaccine data, there are several ways it can be reported over time: Administration Month - This is the month in which the vaccine was administered to the patient. Payment Month - This is the month in which the payment was made to the vaccine dispenser. Note that all payments for Pharmacies are paid one month later than those for PCN providers. Keying Month - This is the month in which the vaccine record first appeared on the MYS system. Submission/Claim Month - This is the month in which the claim for payment for a vaccination occurred. For example, suppose that a PCN patient is given a Covid-19 vaccination dose 1 in January (Administration Month) and then the paper record of this is misplaced for a while. The record is found and keyed into a POC system during February (Keying Month). The Provider is allowed to claim for keying during February in the first five days of March, but they're slightly late and authorise the claim on 7 March (Submission Month). As the claim is outside the submission window, it is not paid in March, it will instead be processed during April (Payment Month). Another example could be a Pharmacy patient is given a Covid-19 vaccination dose 1 in January (Administration Month), keyed in January (Keying Month), then submitted in February (Submission Month) and then payments are calculated in February, however as this is for a pharmacy, the payments are held back and not paid until March (Payment Month). For the purposes of this request, we have chosen to report by Administration Month. Data included in this request is limited to vaccinations carried out by Pharmacies only. Data included in this request is also limited to vaccinations administered in January 2024. The latest data used is a snapshot of the MYS system data that was taken on 6 February 2024. This is the snapshot of data taken after the January 2024 submission period that was used to calculate payments. Pharmacy name and address are as held at this date. This payment data does not include any adjustments made by NHSBSA Provider Assurance as part of post-payment verification exercises. These adjustments are made at account level and may relate to several months of activity. Payment data includes payments made and those scheduled for payment in the future. Payments comprise an Item of Service (IoS) fee and potentially a supplementary fee. Payments do not relate to the value of the drugs dispensed. The total used for the payment calculation may not match the totals shown in 'live' POC systems or MYS that continue to receive updates after the snapshot used to calculate payments was taken. Vaccination records are limited to those which have been associated with a declaration submission. This may include late submission declarations received after the deadline for declarations such records are not processed until the next month. Please note that some vaccinations attract a supplementary fee, so it is not possible to determine the number of vaccinations by dividing the total paid by the basic IoS fee. It is possible for new records from old administration months to be entered in the future, thus the totals here for each administration month could change when more data is processed. Please note that this request and our response is published on our Freedom of Information disclosure log at: https://opendata.nhsbsa.net/dataset/foi-01727

The PFRP Atomic Planning Units (APU) is an ESRI shapefile containing a wide range of derived data relevant to the PFRP. The APUs store data from a range of Primary data sources (3 vegetation, old growth forest, biophysical naturalness, priority flora and fauna, land tenure time series, PFRP properties, geology, riverine zones) to produce a wide range of Derived data fields for use in many facets of the work of the PFRP. All Primary data inputs are fully intersected such that every polgyon differs in at least one data attribute from each of its neighbours. The APUs are designed to accumulate data without losing data from previous versions, subject to minimum size thresholds for polygons. APU versions are numbered sequentially and by date (e.g. APU520_28Oct04.shp) and are backwards compatible with previous versions. The APU data is a developmental data set designed for multi-criteria assessment work from within a single data set, and as such has limitations on accuracy and reliability which may be less than the input data sets. A Microsoft Excel Workbook provides extended detail on major changes between versions, field definitions and type, keys to attributes within fields and parameters for application of logical consistency rules. The current version (at 23 November 2004) is APU520_28Oct04.shp, which comprises approximately 1.1 million polygons and database records.

Go to http://on.ny.gov/1J8tPSN on the New York Lottery website for past Mega Millions results and payouts.

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 ...

This repository contains the raw data used for a systematic review of the impact of background music on cognitive task performance (Cheah et al., 2022). Our intention is to facilitate future updates to this work. Contents description This repository contains eight Microsoft Excel files, each containing the synthesised data pertaining to each of the six cognitive domains analysed in the review, as well as task difficulty, and population characteristics:

raw-data-attention raw-data-inhibition raw-data-language raw-data-memory raw-data-thinking raw-data-processing-speed raw-data-task-difficulty raw-data--population Files description Tabs organisation The files pertaining to each cognitive domain include individual tabs for each cognitive task analysed (c.f. Figure 2 in the original paper for the list of cognitive tasks). The file with the population characteristics data also contains separate tabs for each characteristic (extraversion, music training, gender, and working memory capacity). Tabs contents In all files and tabs, each row corresponds to the data of a test. The same article can have more than one row if it reports multiple tests. For instance, the study by Cassidy and MacDonald (2007; cf. Memory.xlsx, tab: Memory-all) contains two experiments (immediate and delayed free recall) each with multiple test (immediate free recall: tests 25 – 32; delayed free recall: tests 58 – 61). Each test (one per row), in this experiment, pertains to comparisons between conditions where the background music has different levels of arousal, between groups of participants with different extraversion levels, between different tasks material (words or paragraphs) and different combinations of the previous (e.g., high arousing music vs silence test among extraverts whilst completing an immediate free recall task involving paragraphs; cf. test 30). The columns are organised as follows:

"TESTS": the index of the test in a particular tab (for easy reference); "ID": abbreviation of the cognitive tasks involved in a specific experiment (see glossary for meaning); "REFERENCE": the article where the data was taken from (see main publications for list of articles); "CONDITIONS": an abbreviated description of the music condition of a given test; "MEANS (music)": the average performance across all participants in a given experiment with background music; "MEANS (silence)": the average performance across all participants in a given experiment without background music. Then, in horizontal arrangement, we also include groups of two columns that breakdown specific comparisons related to each test (i.e., all tests comparing the same two types of condition, e.g., L-BgM vs I-BgM, will appear under the same set of columns). For each one, we indicate mean difference between the respective conditions ("MD" column) and the direction of effect ("Standard Metric" column). Each file also contains a "Glossary" tab that explains all the abbreviations used in each document. Bibliography Cheah, Y., Wong, H. K., Spitzer, M., & Coutinho, E. (2022). Background music and cognitive task performance: A systematic review of task, music and population impact. Music & Science, 5(1), 1-38. https://doi.org/10.1177/20592043221134392

This spreadsheet is a compilation of data compiled by the Montana Bureau of Mines and Geology, published as a Web feature service, a Web map service, an ESRI service, and as a downloadable Excel spreadsheet for the National Geothermal Data System. The document contains 9 worksheets, including information about the template, notes related to revisions of the template, Resource provider information, the data, a field list (data mapping view) and a worksheet with vocabularies for use in populating the spreadsheet (data valid terms). Data from 72 wells are included.

The Statistical Abstract is the Nation's best known and most popular single source of statistics on the social, political, and economic organization of the country. The print version of this reference source has been published since 1878 while the compact disc version first appeared in 1993. This disc is designed to serve as a convenient, easy-to-use statistical reference source and guide to statistical publications and sources. The disc contains over 1,400 tables from over 250 different gove rnmental, private, and international organizations. The 1998 Statistical Abstract on CD-ROM, like the book, is a statistical reference and guide to over 250 statistical publications and sources from government and private organizations. This compact disc (CD) has 1,500 tables and charts from over 250 sources. Text and tables can be viewed or searched with the software. Tables and charts cover these subjects in 31 sections and 2 appendices: Population, Vital Statistics, Health and Nutrition, Education, Law Enforcement, Courts and Prisons, Geography and Environment, Parks, Recreation and Travel, Elections, State and Local Government, Finances and Employment, Federal Government, Finances and Employment, National Defense and Veterans Affairs, Social Insurance and Human Services, Labor Force, Employment and Earnings, Income, Expenditure and Wealth, Prices, Banking, Finance and Insurance, Business Enterprise, Communications, Energy, Science, Transportation -- Land, Transportation -- Air and Water, Agriculture, Forests and Fisheries, Mining and Mineral Products, Construction and Housing, Manufactures, Domestic Trade and Services, Foreign Commerce and Aid, Outlying Areas, Comparative International Statistics, State Rankings, Population of MSAs, Congressional District Profiles. There are changes this year in both the content of the information on the disc and software used for accessing and installing the information. As usual, updates have been made to most of the more than 1,500 tables and charts that were on the previous disc with new or more recent data. The spreadsheet files which are available in both the Excel or Lotus formats for these ta bles will usually have more information than displayed in the book or Adobe Acrobat files. There are also 93 new tables on such subjects as family planning, women's health, persons with disabilities, health insurance coverage, ambulatory surgery, school violence, household use of public libraries, public library of the Internet, toxic chemical releases, leisure activity, NCAA sports and high school athletic programs, voter registration, licensed child care centers, foster care, home-based businesses, employee benefits, home equity debt, use of debit credit cards, alcohol-related fatal accidents, computer shipments, and foreign stock market indices. See Appendix V on the disc for a complete list of the new tables presented. In the software area, a new opening screen using the DemoShield software has been added. This provide better access to the electronic version of the booklet which is available from the opening screen, the new tutorial step the user through the principal ways to search for information on this disc and other related helpful information. It will also facilitate the installation process for the Adobe Acrobat Reader, the new Microsoft Excel Viewer, and QuickTime for viewing movies. The Adobe Acrobat Reader and Search engine, version 3.01, is on the disc. The Acrobat Reader allows users to view, navigate, search, and print on demand any of the pages from the book. Note to Users: This CD is part of a collection located in the Data Archive of the Odum Institute for Research in Social Science, at the University of North Carolina at Chapel Hill. The collection is located in Room 10, Manning Hall. Users may check the CDs out subscribing to the honor system. Items can be checked out for a period of two weeks. Loan forms are located adjacent to the collection.