Excel spreadsheets by species (4 letter code is abbreviation for genus and species used in study, year 2010 or 2011 is year data collected, SH indicates data for Science Hub, date is date of file preparation). The data in a file are described in a read me file which is the first worksheet in each file. Each row in a species spreadsheet is for one plot (plant). The data themselves are in the data worksheet. One file includes a read me description of the column in the date set for chemical analysis. In this file one row is an herbicide treatment and sample for chemical analysis (if taken). This dataset is associated with the following publication: Olszyk , D., T. Pfleeger, T. Shiroyama, M. Blakely-Smith, E. Lee , and M. Plocher. Plant reproduction is altered by simulated herbicide drift toconstructed plant communities. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 36(10): 2799-2813, (2017).

Dataset

This dataset was created by Pinky Verma

Contents

Download Employee Travel Excel SheetThis dataset contains information about the employee travel expenses for the year 2021. Details are provided on the employee (name, title, department), the travel (dates, location, purpose) and the cost (expenses, recoveries). Expenses are broken down in separate tabs by Quarter (Q1, Q2, Q3 and Q4). Updated quarterly when expenses are prepared. Expenses for other years are available in separate datasets.

Raw data outputs 1-18 Raw data output 1. Differentially expressed genes in AML CSCs compared with GTCs as well as in TCGA AML cancer samples compared with normal ones. This data was generated based on the results of AML microarray and TCGA data analysis. Raw data output 2. Commonly and uniquely differentially expressed genes in AML CSC/GTC microarray and TCGA bulk RNA-seq datasets. This data was generated based on the results of AML microarray and TCGA data analysis. Raw data output 3. Common differentially expressed genes between training and test set samples the microarray dataset. This data was generated based on the results of AML microarray data analysis. Raw data output 4. Detailed information on the samples of the breast cancer microarray dataset (GSE52327) used in this study. Raw data output 5. Differentially expressed genes in breast CSCs compared with GTCs as well as in TCGA BRCA cancer samples compared with normal ones. Raw data output 6. Commonly and uniquely differentially expressed genes in breast cancer CSC/GTC microarray and TCGA BRCA bulk RNA-seq datasets. This data was generated based on the results of breast cancer microarray and TCGA BRCA data analysis. CSC, and GTC are abbreviations of cancer stem cell, and general tumor cell, respectively. Raw data output 7. Differential and common co-expression and protein-protein interaction of genes between CSC and GTC samples. This data was generated based on the results of AML microarray and STRING database-based protein-protein interaction data analysis. CSC, and GTC are abbreviations of cancer stem cell, and general tumor cell, respectively. Raw data output 8. Differentially expressed genes between AML dormant and active CSCs. This data was generated based on the results of AML scRNA-seq data analysis. Raw data output 9. Uniquely expressed genes in dormant or active AML CSCs. This data was generated based on the results of AML scRNA-seq data analysis. Raw data output 10. Intersections between the targeting transcription factors of AML key CSC genes and differentially expressed genes between AML CSCs vs GTCs and between dormant and active AML CSCs or the uniquely expressed genes in either class of CSCs. Raw data output 11. Targeting desirableness score of AML key CSC genes and their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section. Raw data output 12. CSC-specific targeting desirableness score of AML key CSC genes and their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section. Raw data output 13. The protein-protein interactions between AML key CSC genes with themselves and their targeting transcription factors. This data was generated based on the results of AML microarray and STRING database-based protein-protein interaction data analysis. Raw data output 14. The previously confirmed associations of genes having the highest targeting desirableness and CSC-specific targeting desirableness scores with AML or other cancers’ (stem) cells as well as hematopoietic stem cells. These data were generated based on a PubMed database-based literature mining. Raw data output 15. Drug score of available drugs and bioactive small molecules targeting AML key CSC genes and/or their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section. Raw data output 16. CSC-specific drug score of available drugs and bioactive small molecules targeting AML key CSC genes and/or their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section. Raw data output 17. Candidate drugs for experimental validation. These drugs were selected based on their respective (CSC-specific) drug scores. CSC is the abbreviation of cancer stem cell. Raw data output 18. Detailed information on the samples of the AML microarray dataset GSE30375 used in this study.

Sample data for exercises in Further Adventures in Data Cleaning.

Abstract

The documentation covers Enterprise Survey panel datasets that were collected in Slovenia in 2009, 2013 and 2019.

The Slovenia ES 2009 was conducted between 2008 and 2009. The Slovenia ES 2013 was conducted between March 2013 and September 2013. Finally, the Slovenia ES 2019 was conducted between December 2018 and November 2019. The objective of the Enterprise Survey is to gain an understanding of what firms experience in the private sector.

As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving the business environment as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms' experiences and enterprises' perception of the environment in which they operate.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must take its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

As it is standard for the ES, the Slovenia ES was based on the following size stratification: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Slovenia ES 2009, 2013, 2019 were selected using stratified random sampling, following the methodology explained in the Sampling Manual for Slovenia 2009 ES and for Slovenia 2013 ES, and in the Sampling Note for 2019 Slovenia ES.

Three levels of stratification were used in this country: industry, establishment size, and oblast (region). The original sample designs with specific information of the industries and regions chosen are included in the attached Excel file (Sampling Report.xls.) for Slovenia 2009 ES. For Slovenia 2013 and 2019 ES, specific information of the industries and regions chosen is described in the "The Slovenia 2013 Enterprise Surveys Data Set" and "The Slovenia 2019 Enterprise Surveys Data Set" reports respectively, Appendix E.

For the Slovenia 2009 ES, industry stratification was designed in the way that follows: the universe was stratified into manufacturing industries, services industries, and one residual (core) sector as defined in the sampling manual. Each industry had a target of 90 interviews. For the manufacturing industries sample sizes were inflated by about 17% to account for potential non-response cases when requesting sensitive financial data and also because of likely attrition in future surveys that would affect the construction of a panel. For the other industries (residuals) sample sizes were inflated by about 12% to account for under sampling in firms in service industries.

For Slovenia 2013 ES, industry stratification was designed in the way that follows: the universe was stratified into one manufacturing industry, and two service industries (retail, and other services).

Finally, for Slovenia 2019 ES, three levels of stratification were used in this country: industry, establishment size, and region. The original sample design with specific information of the industries and regions chosen is described in "The Slovenia 2019 Enterprise Surveys Data Set" report, Appendix C. Industry stratification was done as follows: Manufacturing – combining all the relevant activities (ISIC Rev. 4.0 codes 10-33), Retail (ISIC 47), and Other Services (ISIC 41-43, 45, 46, 49-53, 55, 56, 58, 61, 62, 79, 95).

For Slovenia 2009 and 2013 ES, size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not a common practice, except in the sectors of construction and agriculture.

For Slovenia 2009 ES, regional stratification was defined in 2 regions. These regions are Vzhodna Slovenija and Zahodna Slovenija. The Slovenia sample contains panel data. The wave 1 panel “Investment Climate Private Enterprise Survey implemented in Slovenia” consisted of 223 establishments interviewed in 2005. A total of 57 establishments have been re-interviewed in the 2008 Business Environment and Enterprise Performance Survey.

For Slovenia 2013 ES, regional stratification was defined in 2 regions (city and the surrounding business area) throughout Slovenia.

Finally, for Slovenia 2019 ES, regional stratification was done across two regions: Eastern Slovenia (NUTS code SI03) and Western Slovenia (SI04).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions. The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions). Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module). Each variation of the questionnaire is identified by the index variable, a0.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond as (-8). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary. However, there were clear cases of low response.

For 2009 and 2013 Slovenia ES, the survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Up to 4 attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals. Further research is needed on survey non-response in the Enterprise Surveys regarding potential introduction of bias.

For 2009, the number of contacted establishments per realized interview was 6.18. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The relatively low ratio of contacted establishments per realized interview (6.18) suggests that the main source of error in estimates in the Slovenia may be selection bias and not frame inaccuracy.

For 2013, the number of realized interviews per contacted establishment was 25%. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 44%.

Finally, for 2019, the number of interviews per contacted establishments was 9.7%. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The share of rejections per contact was 75.2%.

To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

An Excel spreadsheet listing the information recorded on each of 18,686 costume designs can be viewed, downloaded, and explored. All the usual Excel sorting possibilities are available, and in addition a useful filter has been installed. For example, to find the number of designs that are Frieze Type #1, go to the top of the frieze type 2 column (column AS), click on the drop-down arrow and unselect every option box except True (i.e. True should be turned on, all other choices turned off). Then in the lower left corner, one reads “1111 of 18686 records found”.

Much more sophisticated exploration can be carried out by downloading the rich and flexible Access Database. The terms used for this database were described in detail in three sections of Deep Blue paper associated with this project. The database can be downloaded and explored.

HOW TO USE THE ACCESS DATABASE 1. Click on the Create Cohort and View Math Trait Data button, and select your cohort by clicking on the features of interest (for example: Apron and Blouse).

Note: Depending on how you exited on your previous visit to the database, there may be items to clear up before creating the cohorts.
a) (Usually unnecessary) Click on the small box near the top left corner to allow connection to Access. b) (Usually unnecessary) If an undesired window blocks part of the screen, click near the top of this window to minimize it. c) Make certain under Further Filtering that all four Exclude boxes are checked to get rid of stripes and circles, and circular buttons, and the D1 that is trivially associated with shoes.

1. Click on Filter Records to Form the Cohort button. Note the # of designs, # of pieces, and # of costumes beside Recalculate.

2. Click on Calculate Average Math Trait Frequency of Cohort button, and select the symmetry types of interest (for example: D1 and D2) .

3. To view the Stage 1 table, click on Create Stage 1 table. To edit and print this table, click on Create Excel (after table has been created). The same process works for Stages 2, 3.and 4 tables.

4. To view the matrix listing the math category impact numbers, move over to a button on the right side and click on View Matrix of Math Category Impact Numbers. To edit and print this matrix, click on Create Excel, use the Excel table as usual.

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.

Full Excel model providing life-cycle impacts of food and drink products. Contains all original inventory data and mid-point impact data, remodelling assumptions, and final standardised results. Requires Microsoft Excel 2007 or later to use.

The 1986 Census was the first mid-decade census to undertake detailed enumeration. Data on demographic, social and economic characteristics, as well as on dwellings, were collected from Canadians. The information is recorded on two data bases, the 100% data base and the 20% sample data base. The 100% data bases includes general demographic, dwelling and household data (for example: age, sex, marital status, mother tongue and structural type of dwelling) collected from the entire population. The 20% sample data base includes the general demographic data, detailed socio-economic data (for example: ethnic origin, labour force activity, schooling, income and dwellings information) collected from one-fifth of the population. The range of the 1986 Census products and services differs somewhat from the 1981 Census. The major changes are: A 40% reduction in the number of publications The replacement of the 1981 Census Summary Tapes program by the Basic Summary Cross-Tabulations Improvements in the Custom Tabulations Service The implementation of a new Semi-Custom product line Focus series is the aggregate statistics (multi-variate cross-tabulations) at census subdivision, census tract, and enumeration area levels. These 7 tables do not correspond to the print Focus series print publications. At present, EA-level tables are available on CD-ROM only.

Supplementary Data 4. Life cycle inventory selection for Food Commodities Intake Database Commodities. This table is provided as an excel file and shows which proxy group or ecoinvent life cycle inventory was used to model the impacts of each of the commodities in the Food Commodities Intake Database (FCID). For example, the commodity almond was modeled using almond production in the US from ecoinvent whereas the commodity acerola was modeled with the proxy group “tree fruit”. This table identifies which commodity impacts were modeled using proxies and the conversion factors applied if the commodity was served raw or cooked.

Finding a good data source is the first step toward creating a database. Cardiovascular illnesses (CVDs) are the major cause of death worldwide. CVDs include coronary heart disease, cerebrovascular disease, rheumatic heart disease, and other heart and blood vessel problems. According to the World Health Organization, 17.9 million people die each year. Heart attacks and strokes account for more than four out of every five CVD deaths, with one-third of these deaths occurring before the age of 70 A comprehensive database for factors that contribute to a heart attack has been constructed , The main purpose here is to collect characteristics of Heart Attack or factors that contribute to it. As a result, a form is created to accomplish this. Microsoft Excel was used to create this form. Figure 1 depicts the form which It has nine fields, where eight fields for input fields and one field for output field. Age, gender, heart rate, systolic BP, diastolic BP, blood sugar, CK-MB, and Test-Troponin are representing the input fields, while the output field pertains to the presence of heart attack, which is divided into two categories (negative and positive).negative refers to the absence of a heart attack, while positive refers to the presence of a heart attack.Table 1 show the detailed information and max and min of values attributes for 1319 cases in the whole database.To confirm the validity of this data, we looked at the patient files in the hospital archive and compared them with the data stored in the laboratories system. On the other hand, we interviewed the patients and specialized doctors. Table 2 is a sample for 1320 cases, which shows 44 cases and the factors that lead to a heart attack in the whole database,After collecting this data, we checked the data if it has null values (invalid values) or if there was an error during data collection. The value is null if it is unknown. Null values necessitate special treatment. This value is used to indicate that the target isn’t a valid data element. When trying to retrieve data that isn't present, you can come across the keyword null in Processing. If you try to do arithmetic operations on a numeric column with one or more null values, the outcome will be null. An example of a null values processing is shown in Figure 2.The data used in this investigation were scaled between 0 and 1 to guarantee that all inputs and outputs received equal attention and to eliminate their dimensionality. Prior to the use of AI models, data normalization has two major advantages. The first is to avoid overshadowing qualities in smaller numeric ranges by employing attributes in larger numeric ranges. The second goal is to avoid any numerical problems throughout the process.After completion of the normalization process, we split the data set into two parts - training and test sets. In the test, we have utilized1060 for train 259 for testing Using the input and output variables, modeling was implemented.

The Superstore Sales Data dataset, available in an Excel format as "Superstore.xlsx," is a comprehensive collection of sales and customer-related information from a retail superstore. This dataset comprises* three distinct tables*, each providing specific insights into the store's operations and customer interactions.

Purpose:This feature layer describes water quality sampling data performed at several operating coal mines in the South Fork of Cherry watershed, West Virginia.Source & Data:Data was downloaded from WV Department of Environmental Protection's ApplicationXtender online database and EPA's ECHO online database between January and April, 2023.There are five data sets here: Surface Water Monitoring Sites, which contains basic information about monitoring sites (name, lat/long, etc.) and NPDES Outlet Monitoring Sites, which contains similar information about outfall discharges surrounding the active mines. Biological Assessment Stations (BAS) contain similar information for pre-project biological sampling. NOV Summary contains locations of Notices of Violation received by South Fork Coal Company from WV Department of Environmental Protection. The Quarterly Monitoring Reports table contains the sampling data for the Surface Water Monitoring Sites, which actually goes as far back as 2018 for some mines. Parameters of concern include iron, aluminum and selenium, among others.A relationship class between Surface Water Monitoring Sites and the Quarterly Monitoring Reports allows access to individual sample results.Processing:Notices of Violation were obtained from the WV DEP AppXtender database for Mining and Reclamation Article 3 (SMCRA) Permitting, and Mining and Reclamation NPDES Permitting. Violation data were entered into Excel and loaded into ArcGIS Pro as a CSV text file with Lat/Long coordinates for each Violation. The CSV file was converted to a point feature class.Water quality data were downloaded in PDF format from the WVDEP AppXtender website. Non-searchable PDFs were converted via Optical Character Recognition, so that data could be copied. Sample results were copied and pasted manually to Notepad++, and several columns were re-ordered. Data was grouped by sample station and sorted chronologically. Sample data, contained in the associated table (SW_QM_Reports) were linked back to the monitoring station locations using the Station_ID text field in a geodatabase relationship class.Water monitoring station locations were taken from published Drainage Maps and from water quality reports. A CSV table was created with station Lat/Long locations and loaded into ArcGIS Pro. It was then converted to a point feature class.Stream Crossings and Road Construction Areas were digitized as polygon feature classes from project Drainage and Progress maps that were converted to TIFF image format from PDF and georeferenced.The ArcGIS Pro map - South Fork Cherry River Water Quality, was published as a service definition to ArcGIS Online.Symbology:NOV Summary - dark blue, solid pointLost Flats Surface Water Monitoring Sites: Data Available - medium blue point, black outlineLost Flats Surface Water Monitoring Sites: No Data Available - no-fill point, thick medium blue outlineLost Flats NPDES Outlet Monitoring Sites - orange point, black outlineBlue Knob Surface Water Monitoring Sites: Data Available - medium blue point, black outlineBlue Knob Surface Water Monitoring Sites: No Data Available - no-fill point, thick medium blue outlineBlue Knob NPDES Outlet Monitoring Sites - orange point, black outlineBlue Knob Biological Assessment Stations: Data Available - medium green point, black outlineBlue Knob Biological Assessment Stations: No Data Available - no-fill point, thick medium green outlineRocky Run Surface Water Monitoring Sites: Data Available - medium blue point, black outlineRocky Run Surface Water Monitoring Sites: No Data Available - no-fill point, thick medium blue outlineRocky Run NPDES Outlet Monitoring Sites - orange point, black outlineRocky Run Biological Assessment Stations: Data Available - medium green point, black outlineRocky Run Biological Assessment Stations: No Data Available - no-fill point, thick medium green outlineRocky Run Stream Crossings: turquoise blue polygon with red outlineRocky Run Haul Road Construction Areas: dark red (40% transparent) polygon with black outlineHaul Road No 2 Surface Water Monitoring Sites: Data Available - medium blue point, black outlineHaul Road No 2 Surface Water Monitoring Sites: No Data Available - no-fill point, thick medium blue outlineHaul Road No 2 NPDES Outlet Monitoring Sites - orange point, black outline

This formatted dataset (AnalysisDatabaseGBD) originates from raw data files from the Institute of Health Metrics and Evaluation (IHME) Global Burden of Disease Study (GBD2017) affiliated with the University of Washington. We are volunteer collaborators with IHME and not employed by IHME or the University of Washington.

The population weighted GBD2017 data are on male and female cohorts ages 15-69 years including noncommunicable diseases (NCDs), body mass index (BMI), cardiovascular disease (CVD), and other health outcomes and associated dietary, metabolic, and other risk factors. The purpose of creating this population-weighted, formatted database is to explore the univariate and multiple regression correlations of health outcomes with risk factors. Our research hypothesis is that we can successfully model NCDs, BMI, CVD, and other health outcomes with their attributable risks.

These Global Burden of disease data relate to the preprint: The EAT-Lancet Commission Planetary Health Diet compared with Institute of Health Metrics and Evaluation Global Burden of Disease Ecological Data Analysis. The data include the following: 1. Analysis database of population weighted GBD2017 data that includes over 40 health risk factors, noncommunicable disease deaths/100k/year of male and female cohorts ages 15-69 years from 195 countries (the primary outcome variable that includes over 100 types of noncommunicable diseases) and over 20 individual noncommunicable diseases (e.g., ischemic heart disease, colon cancer, etc). 2. A text file to import the analysis database into SAS 3. The SAS code to format the analysis database to be used for analytics 4. SAS code for deriving Tables 1, 2, 3 and Supplementary Tables 5 and 6 5. SAS code for deriving the multiple regression formula in Table 4. 6. SAS code for deriving the multiple regression formula in Table 5 7. SAS code for deriving the multiple regression formula in Supplementary Table 7
8. SAS code for deriving the multiple regression formula in Supplementary Table 8 9. The Excel files that accompanied the above SAS code to produce the tables

For questions, please email davidkcundiff@gmail.com. Thanks.

Adventure Works 2022 dataset

How this Dataset is created?

On the official website the dataset is available over SQL server (localhost) and CSVs to be used via Power BI Desktop running on Virtual Lab (Virtaul Machine). As per first two steps of Importing data are executed in the virtual lab and then resultant Power BI tables are copied in CSVs. Added records till year 2022 as required.

How this Dataset may help you?

this dataset will be helpful in case you want to work offline with Adventure Works data in Power BI desktop in order to carry lab instructions as per training material on official website. The dataset is useful in case you want to work on Power BI desktop Sales Analysis example from Microsoft website PL 300 learning.

How to use this Dataset?

Download the CSV file(s) and import in Power BI desktop as tables. The CSVs are named as tables created after first two steps of importing data as mentioned in the PL-300 Microsoft Power BI Data Analyst exam lab.

Images and video of habitat and organisms from ROV dives under white light; images and video of organisms taken with low light cameras/ Medusa lander/lagoratory images of bioluminescence. Excel database of information about collected samples.

This layer visualizes over 60,000 commercial flight paths. The data was obtained from openflights.org, and was last updated in June 2014. The site states, "The third-party that OpenFlights uses for route data ceased providing updates in June 2014. The current data is of historical value only. As of June 2014, the OpenFlights/Airline Route Mapper Route Database contains 67,663 routes between 3,321 airports on 548 airlines spanning the globe. Creating and maintaining this database has required and continues to require an immense amount of work. We need your support to keep this database up-to-date."To donate, visit the site and click the PayPal link.Routes were created using the XY-to-line tool in ArcGIS Pro, inspired by Kenneth Field's work, and following a modified methodology from Michael Markieta (www.spatialanalysis.ca/2011/global-connectivity-mapping-out-flight-routes).Some cleanup was required in the original data, including adding missing location data for several airports and some missing IATA codes. Before performing the point to line conversion, the key to preserving attributes in the original data is a combination of the INDEX and MATCH functions in Microsoft Excel. Example function: =INDEX(Airlines!$B$2:$B$6200,MATCH(Routes!$A2,Airlines!$D$2:Airlines!$D$6200,0))                                                

The O*NET Database contains hundreds of standardized and occupation-specific descriptors on almost 1,000 occupations covering the entire U.S. economy. The database, which is available to the public at no cost, is continually updated by a multi-method data collection program. Sources of data include: job incumbents, occupational experts, occupational analysts, employer job postings, and customer/professional association input.

Data content areas include:

• Worker Characteristics (e.g., Abilities, Interests, Work Styles)
• Worker Requirements (e.g., Education, Knowledge, Skills)
• Experience Requirements (e.g., On-the-Job Training, Work Experience)
• Occupational Requirements (e.g., Detailed Work Activities, Work Context)
• Occupation-Specific Information (e.g., Job Titles, Tasks, Technology Skills)