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.

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 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.

Identification of study species

We identified 40 marine species with documented shifts in range limits along the coastline (<15 km from shore) of North America, including plants, invertebrates, fish, a protist, and a bird. Of these, 26 species were compiled by Sorte et al. (2010), and we added 14 species from an updated literature review. We searched Google Scholar (on 08/20/2019) using this search string: marine "range expansion" species "range shift". We reviewed titles and, when appropriate, abstracts and text of the first 600 results, identifying 12 additional species from eight papers. We added two species (Brachidontes adamsianus and Mexacanthina lugubris) from our literature files and personal observations. We excluded migratory or pelagic species with large biogeographic ranges, for which it was difficult to confirm historical native ranges.

Review of published impacts

Evidence of species’ impacts was compiled from online database searches an...

While inspecting the brain magnetic resonance imaging (MRI) scans from a sample of Multiple Sclerosis (MS) patients, blind to any clinical, cognitive and demographic information, it caught our attention the presence of ovoidal or circular, partially stellate, regions of signal intensities similar to that of the normal brain parenchyma in Fluid Attenuated Inversion Recovery (FLAIR) surrounded by hyperintensities in the periventricular region in a reasonable number of scans, seemingly corresponding in all cases to hypointense regions (i.e. with the same signal level of the cerebrospinal fluid) in T1-weighted. The ovoidal shape of these features, clearly distinctive due to their homogeneously lower signal with respect to their surroundings in the FLAIR sequence prompted us to refer them as FLAIR 'pseudocavities'. The idea that they could be differentially distinctive and indicative of an underlying process of different aetiology from their surroundings is not implausible. Inversion recovery imaging can potentially discriminate among tissues based on subtle differences in T1 characteristics. Specifically, the FLAIR sequence exploits the fact that many types of pathology have elevated T1 and T2 values resulting from increased free water content compared to background tissue. Higher specific absorption rate due to additional 180 degrees, together with the increased dynamic range, and the additive T1 and T2 contrast, make FLAIR highly susceptible to differentially reflect subtle pathological processes (Bydder & Young, 1985). We, hence, systematically reviewed the literature in the last 10 years (i.e. from March 1999 up to March 2019) to investigate the definitions of MS lesions used up to date and their characterisation, to establish if what we called 'FLAIR 'pseudocavities'' have been described previously. This dataset is conformed by an excel file (Microsoft excel 97-2003 (.xls)) with multiple worksheets which contain all the references found in the two databases explored (i.e. Medline and EMBASE), as well as the data extracted and the results of the analyses. Briefly, from just over a hundred studies that defined MRI lesions in MS, more than half characterised lesions based on the criteria that they were hyperintense on T2-weighted, FLAIR and PD-weighted series, and more than a quarter of the studies characterised lesions based on the criteria that they were hyperintense on T2-weighted, FLAIR and PD-weighted and that they were hypointense on T1-weighted series. The literature review confirmed that what we refer to as FLAIR 'pseudocavities' have not yet been acknowledged in the MS literature. Note: The dataset contains a master excel spreadsheet with multiple worksheets. The data from each worksheet in the excel file is also provided as a .csv file

Dataset on the locations within a country where internal armed conflicts initially break out, 1946-2008

Background and AimsInfectious diseases (IDs) are major causes of morbidity and mortality and their surveillance is critical. In 2002, we implemented a simple and versatile homemade tool, named EPIMIC, for the real-time systematic automated surveillance of IDs at Marseille university hospitals, based on the data from our clinical microbiology laboratory, including clinical samples, tests and diagnoses.MethodsThis tool was specifically designed to detect abnormal events as IDs are rarely predicted and modeled. EPIMIC operates using Microsoft Excel software and requires no particular computer skills or resources. An abnormal event corresponds to an increase above, or a decrease below threshold values calculated based on the mean of historical data plus or minus 2 standard deviations, respectively.ResultsBetween November 2002 and October 2013 (11 years), 293 items were surveyed weekly, including 38 clinical samples, 86 pathogens, 79 diagnosis tests, and 39 antibacterial resistance patterns. The mean duration of surveillance was 7.6 years (range, 1 month-10.9 years). A total of 108,427 Microsoft Excel file cells were filled with counts of clinical samples, and 110,017 cells were filled with counts of diagnoses. A total of 1,390,689 samples were analyzed. Among them, 172,180 were found to be positive for a pathogen. EPIMIC generated a mean number of 0.5 alert/week on abnormal events.ConclusionsEPIMIC proved to be efficient for real-time automated laboratory-based surveillance and alerting at our university hospital clinical microbiology laboratory-scale. It is freely downloadable from the following URL: http://www.mediterranee-infection.com/article.php?larub=157&titre=bulletin-epidemiologique (last accessed: 20/11/2015).

Context: The University Attendance Sheet Dataset is a comprehensive collection of attendance records from various university courses. This dataset is valuable for analyzing student attendance patterns, studying the impact of attendance on academic performance, and exploring factors influencing student engagement. It provides a rich resource for researchers, educators, and students interested in understanding attendance dynamics within a university setting.

Content: The dataset includes the following information:

Student ID: A unique identifier for each student. Course ID: A unique identifier for each course. Date: The date of the attendance record. Attendance Status: Indicates whether the student was present, absent, or had an excused absence on a particular date. The dataset contains records from multiple academic semesters, covering a wide range of courses across different disciplines. By examining this dataset, researchers can investigate attendance trends across different courses, identify patterns related to student performance, and explore correlations between attendance and other academic variables.

Acknowledgements: We would like to express our gratitude to the university administration, faculty members, and students who contributed to the collection and organization of this dataset. Their cooperation and support have made this dataset possible, enabling valuable insights into student attendance dynamics.

Inspiration: The inspiration behind creating this dataset stems from the recognition of the significant role attendance plays in a student's academic journey. By making this dataset available on Kaggle, we hope to facilitate research and analysis on attendance patterns, identify interventions to improve student engagement, and provide educators with valuable insights to enhance their teaching strategies. We also encourage collaboration and exploration of the dataset to uncover new findings and generate knowledge that can benefit the education community as a whole.

By leveraging the University Attendance Sheet Dataset, we aspire to contribute to the ongoing efforts to improve student success and foster an environment that promotes active participation and learning within higher education institutions.

In the case study titled "Blinkit: Grocery Product Analysis," a dataset called 'Grocery Sales' contains 12 columns with information on sales of grocery items across different outlets. Using Tableau, you as a data analyst can uncover customer behavior insights, track sales trends, and gather feedback. These insights will drive operational improvements, enhance customer satisfaction, and optimize product offerings and store layout. Tableau enables data-driven decision-making for positive outcomes at Blinkit.

The table Grocery Sales is a .CSV file and has the following columns, details of which are as follows:

• Item_Identifier: A unique ID for each product in the dataset. • Item_Weight: The weight of the product. • Item_Fat_Content: Indicates whether the product is low fat or not. • Item_Visibility: The percentage of the total display area in the store that is allocated to the specific product. • Item_Type: The category or type of product. • Item_MRP: The maximum retail price (list price) of the product. • Outlet_Identifier: A unique ID for each store in the dataset. • Outlet_Establishment_Year: The year in which the store was established. • Outlet_Size: The size of the store in terms of ground area covered. • Outlet_Location_Type: The type of city or region in which the store is located. • Outlet_Type: Indicates whether the store is a grocery store or a supermarket. • Item_Outlet_Sales: The sales of the product in the particular store. This is the outcome variable that we want to predict.