Video content for Research and Evidence and Practice

This interactive sales dashboard is designed in Excel for B2C type of Businesses like Dmart, Walmart, Amazon, Shops & Supermarkets, etc. using Slicers, Pivot Tables & Pivot Chart.

Dashboard Overview

1. Sales dashboard ==> basically, it is designed for the B2C type of business. like Dmart, Walmart, Amazon, Shops & supermarkets, etc.
2. Slices ==> slices are used to drill down the data, on the basis of yearly, monthly, by sales type, and by mode of payment.
3. Total Sales/Total Profits ==> here is, the total sales, total profit, and profit percentage these all are combined into a monthly format and we can hide or unhide it to view it as individually or comparative.
4. Product Visual ==> the visual indicates product-wise sales for the selected period. Only 10 products are visualized at a glance, and you can scroll up & down to view other products in the list.
5. Daily Sales ==> It shows day-wise sales. (Area Chart)
6. Sales Type/Payment Mode ==> It shows sales percentage contribution based on the type of selling and mode of payment.
7. Top Product & Category ==> this is for the top-selling product and product category.
8. Category ==> the final one is the category-wise sales contribution.

Datasheets Overview

1. The dataset has the master data sheet or you can call it a catalog. It is added in the table form.
2. The first column is the product ID the list of items in this column is unique.
3. Then we have the product column instead of these two columns, we can manage with only one also but I kept it separate because sometimes product names can be the same, but some parameters will be different, like price, supplier, etc.
4. The next column is the category column, which is the product category. like cosmetics, foods, drinks, electronics, etc.
5. Then we have 4th column which is the unit of measure (UOM) you can update it also, based on the products you have.
6. And the last two columns are buying price and selling price, which means unit purchasing price and unit selling price.

Input Sheet

The first column is the date of Selling. The second column is the product ID. The third column is quantity. The fourth column is sales types, like direct selling, are purchased by a wholesaler or ordered online. The fifth column is a mode of payment, which is online or in cash. You can update these two as per requirements. The last one is a discount percentage. if you want to offer any discount, you can add it here.

Analysis Sheet: where all backend calculations are performed.

So, basically these are the four sheets mentioned above with different tasks.

However, a sales dashboard enables organizations to visualize their real-time sales data and boost productivity.

A dashboard is a very useful tool that brings together all the data in the forms of charts, graphs, statistics and many more visualizations which lead to data-driven and decision making.

Questions & Answers

1. What percentage of profit ratio of sales are displayed in the year 2021 and year 2022? ==> Total profit ratio of sales in the year 2021 is 19% with large sales of PRODUCT42, whereas profit ratio of sales for 2022 is 22% with large sales of PRODUCT30.
2. Which is the top product that have large number of sales in year 2021-2022? ==> The top product in the year 2021 is PRODUCT42 with the total sales of $12,798 whereas in the year 2022 the top product is PRODUCT30 with the total sales of $13,888.
3. In Area Chart which product is highly sold on 28th April 2022? ==> The large number of sales on 28th April 2022 is for PRODUCT14 with a 24% of profit ratio.
4. What is the sales type and payment mode present? ==> The sale type and payment modes show the sales percentage contribution based on the type of selling and mode of payment. Here, the sale types are Direct Sales with 52%, Online Sales with 33% and Wholesaler with 15%. Also, the payment modes are Online mode and Cash equally distributed with 50%.
5. In which month the direct sales are highest in the year 2022? ==> The highest direct sales can be easily identified which is designed by monthly format and it’s the November month where direct sales are highest with 28% as compared with other months.
6. Which payment mode is highly received in the year 2021 and year 2022? ==> The payments received in the year 2021 are the cash payments with 52% as compared with online transactions which are 48%. Also, the cash payment highly received is in the month of March, July and October with direct sales of 42%, Online with 45% and wholesaler with 13% with large sales of PRODUCT24. ==> The payments received in the year 2022 are the Online payments with 52% as compared with cash payments which are 48%. Also, the online payment highly received is in the month of Jan, Sept and December with direct sales of 45%, Online with 37% and whole...

Abstract

The Survey on Interest Rate Controls 2020 was conducted as a World Bank Group study on interest rate controls (IRCs) in lending and deposit markets around the world. The study aims to identify the different types of formal (or de jure) controls, the countries that apply then, how they implement them, and the reasons for doing so. The objective of the study is to advance knowledge on this topic by providing an evidence base for investigating the impact of IRCs on economic outcomes.

The survey investigates present IRCs in each surveyed country, the reasons why they have been applied, the framework and resources associated with their application and the details as to their level and functioning. The focus is on legal forms of control (i.e. codified into law) as opposed to de facto controls. The new database on interest rate controls, a popular form of financial repression is based on a survey of 108 countries, representing 88 percent of global gross domestic product. The interest rate controls presented in this dataset were in effect in 2019.

Geographic coverage

Global Survey, covering 108 countries, representing 88 percent of global GDP.

Analysis unit

Regulation at the national level.

Universe

Banking supervisors and Local Banking Associations.

Kind of data

Sample survey data [ssd]

Mode of data collection

Mail Questionnaire [mail]

Research instrument

Bank supervisors and banking associations were provided with a standard excel file with five parts. The survey was structured in five parts, each placed in a different excel sheet. Part A: Introduction. Countries with no IRCs in place were asked to only answer this sheet and leave the rest blank. Part B: Presented the definitions of controls, institutions, products and additional aspects that will be covered in the survey. Part C: Introduced a set of qualitative questions to describe the IRCs in place. Part D: Displayed a set of tables to quantitatively describe the IRCs in place. Part E: Laid out the final set of questions, covering sanctions and control mechanisms that support the IRCs' enforcement. The questionnaire is provided in the Documentation section in pdf and excel.

In the beginning, the case was just data for a company that did not indicate any useful information that would help decision-makers. In this case, I had to ask questions that could help extract and explore information that would help decision-makers improve and evaluate performance. But before that, I did some operations in the data to help me to analyze it accurately: 1- Understand the data. 2- Clean the data “By power query”. 3- insert some calculation and columns like “COGS” cost of goods sold by power query. 4- Modeling the data and adding some measures and other columns to help me in analysis. Then I asked these questions: To Enhance Customer Loyalty What is the most used ship mode by our customer? Who are our top 5 customers in terms of sales and order frequency? To monitor our strength and weak points Which segment of clients generates the most sales? Which city has the most sales value? Which state generates the most sales value? Performance measurement What are the top performing product categories in terms of sales and profit? What is the most profitable product that we sell? What is the lowest profitable product that we sell? Customer Experience On Average how long does it take the orders to reach our clients? Based on each Shipping Mode

Then started extracting her summaries and answers from the pivot tables and designing the data graphics in a dashboard for easy communication and reading of the information as well. And after completing these operations, I made some calculations related to the KPI to calculate the extent to which sales officials achieved and the extent to which they achieved the target.

The Influencing Travel Behaviour Team (ITB) provide road safety education, training and publicity to schools, communities, businesses and Leeds residents. We promote sustainable travel throughout Leeds along with helping schools and businesses to develop and implement their travel plans (which promote safe, sustainable and less car dependent patterns of travel). Each year we request mode of travel data from schools in Leeds via a SIMS report or excel spreadsheet. The 10 modes of travel specified in the data collection are: Bus (type not known), Car Share (children travelling together from different households), Car/Van, Cycle, Dedicated School Bus, Other, Public Bus Service, Taxi, Train, Walk (including scooting) This collection forms part of the Statutory duty local authorities have to monitor the success of promoting sustainable travel, and in some cases is linked to a school’s planning obligated travel plan. It is an important part of improving road safety and promoting healthy lifestyles among children in Leeds but since the council declared a climate emergency in March of this year the data is even more valuable. The data helps us understand the environmental context in Leeds and work to effectively limit carbon emissions wherever possible. We strongly encourage all schools to provide the data but not all of them respond to the request and we do not always receive a response for every pupil/student so some school response rates may be low.

The data used to generate figures 3 - 7. Including: zip file of spectral csv files used to generate the heat map in figure 3; Excel file containing normalised spectra examples from three experimental cavities for figure 4; Excel file containing auto correlation traces from three experimental cavities including a longer time scale trace for the figure inset for figure 5; Excel file containing the measured mode locking and multi-pulsing thresholds of six experimental cavities with theoretical plot for figure 6; and an Excel file containing the multi-pulsing thresholds expressed in nonlinear phase shift normalised to pi shown in figure 7.

MDRC conducted the Expanding Children’s Early Learning (ExCEL) P3 study in partnership with the Boston Public Schools, the University of Michigan, and the Harvard Graduate School of Education. ExCEL P3 explored several leading approaches for sustaining children’s early preschool gains. These approaches include addressing the full range of relevant skills, improving instructional alignment, boosting the magnitude of early impacts, and promoting quality in the years after preschool. ExCEL P-3 takes advantage of a special opportunity in the Boston Public Schools (BPS) to explore factors affecting children’s outcomes. BPS phased in a system-wide integrated curriculum for preschool through 2nd grade that emphasizes the need for instruction in each grade to build on the lessons and skills that children learned in the previous grade. This curriculum is called Focus and is an innovative approach that scaffolds both the content and mode of instruction as children progress from K1 (BPS prekindergarten), to K2 (BPS kindergarten), to first and second grade. The study began in the Fall of 2016 by recruiting 21 schools and 10 community-based preschool providers. Within those schools and centers, the team enrolled prekindergarten teachers and students into the study. The research team collected several data sources from participants, including child assessments, classroom observations, teacher reports on children, teacher surveys, teacher reports on students, and parent surveys. Data were collected in fall and spring of prekindergarten, fall and spring of kindergarten, spring of first grade, and winter of fifth grade. Parent surveys only were collected in spring of second and third grade. See User Guide for more details.

Spreadsheet Software Market Size And Forecast

Spreadsheet Software Market size was valued at USD 10.05 Billion in 2023 and is expected to reach USD 14.55 Billion by 2031, with a CAGR of 7.8% from 2024-2031.

Global Spreadsheet Software Market Drivers

The market drivers for the Spreadsheet Software Market can be influenced by various factors. These may include:

Increasing Data Volume: As organizations generate and collect more data, the need for efficient data analysis and management tools, such as spreadsheet software, grows. Rising Demand for Data Visualization: Users increasingly seek sophisticated tools to visualize data for better insights. Spreadsheet software can provide charts and graphs, making data interpretation easier.

Global Spreadsheet Software Market Restraints

Several factors can act as restraints or challenges for the Spreadsheet Software Market, These may include:

Market Saturation: Many organizations already use established spreadsheet software such as Microsoft Excel or Google Sheets. The reliance on these platforms can make it difficult for new entrants or alternative solutions to capture market share. High Competition: The market is highly competitive, with numerous players offering similar features and functionalities. This can lead to price wars and reduced profit margins for software providers.

The Monty Hall Problem (Three-Door Problem) is a well-known example for a counterintuitive problem in probability theory. This site provides a VBA-based spreadsheet implementation in Excel for an interactive and automatic simulation of the Monty Hall Problem.

In the interactive simulation mode, participants (students) are organized into pairs. Within each pair, one student assumes the role of the host, while the other takes on the role of the contestant. In this simulation mode, the game process and the associated simulation based on the Excel tool provided here are deliberately not fully automated; rather, the students in the role of hosts and contestants should carry out essential steps themselves, interact with each other, and thus become an active part of the simulation. The settings allow for different assumptions regarding, among other things, the random or conscious nature of decisions. This allows a range of different game situations to be mapped - from a purely random game (based solely on Excel’s random number generator) on the one hand to a purely conscious game (based on possibly tactical decisions and expectations of the participants) on the other. The results template can be used to aggregate the results of the interactive simulation of the groups, e.g. in combination with Moodle.

The fully automatic simulation comes in two modes and enables different speed and display options, e.g. successive chart creation during simulation.

Both the interactive and automatic simulation modes allow for different assumptions regarding the probabilities for the car location, the contestant’s first choice and the door opened by the host.

Both the interactive and automatic simulation modes can be carried out in online and face-to-face teaching. The online variant can be conducted using Zoom or any other video conferencing software that enables group rooms.

Carrying out the interactive and automatic simulation provides data in the form of absolute and relative frequencies for wins and losses depending on whether the contestant switches doors or not. The results can then be discussed.

Versions of the simulation tool: - for Windows: Monty Hall Problem Simulation 5.0 (Win) - for Mac: Monty Hall Problem Simulation 5.0 (Mac)

Please note: The simulation tool is optimized for use with Windows. Some options are not available in the Mac version of the simulation tool provided here.

State estimates for these years are no longer available due to methodological concerns with combining 2019 and 2020 data. We apologize for any inconvenience or confusion this may causeBecause of the COVID-19 pandemic, most respondents answered the survey via the web in Quarter 4 of 2020, even though all responses in Quarter 1 were from in-person interviews. It is known that people may respond to the survey differently while taking it online, thus introducing what is called a mode effect.When the state estimates were released, it was assumed that the mode effect was similar for different groups of people. However, later analyses have shown that this assumption should not be made. Because of these analyses, along with concerns about the rapid societal changes in 2020, it was determined that averages across the two years could be misleading.For more detail on this decision, see the 2019-2020state data page.

The evolution of internal fertilization has occurred repeatedly and independently across the tree of life. As it has evolved, internal fertilization has reshaped sexual selection and the covariances among sexual traits such as testes size and gamete traits. But it is unclear whether fertilization mode also shows evolutionary associations with traits other than primary sex traits. Theory predicts that fertilization mode and body size should covary, but formal tests with phylogenetic control are lacking. We used a phylogenetically-controlled approach to test the covariance between fertilization mode and adult body size (while accounting for latitude, offspring size, and offspring developmental mode) among 1,232 species of marine invertebrates from 3 phyla. Within all phyla, external fertilizers are consistently larger than internal fertilizers: the consequences of fertilization mode extend to traits that are only indirectly related to reproduction. We suspect that other traits may a...

The provided dataset is a synthetic dataset which represents sales information for a company, containing 5000 entries with 24 columns. The data encompasses various aspects of sales transactions, including order details, customer information, product details, pricing, and shipping information. Below is a detailed breakdown of each column:

Column Descriptions: 1. Order No: Unique identifier for each order. 2. Order Date: Date when the order was placed. 3. Customer Name: Name of the customer placing the order. 4. Address: Customer's address (one entry appears to be missing). 5. City: City where the customer is located. 6. State: State where the customer is located. 7. Customer Type: Type of customer (e.g., retail, wholesale). 8. Account Manager: Name of the account manager handling the order. 9. Order Priority: Priority level of the order. 10. Product Name: Name of the product being sold. 11. Product Category: Category to which the product belongs. 12. Product Container: Container type for the product. 13. Ship Mode: Mode of shipping for the order. 14. Ship Date: Date when the order was shipped. 15. Cost Price: Cost price of the product. 16. Retail Price: Retail price at which the product is sold. 17. Profit Margin: Margin between retail and cost prices. 18. Order Quantity: Quantity of products ordered. 19. Sub Total: Subtotal cost of the order. 20. Discount %: Percentage of discount applied to the order. 21. Discount $: Dollar amount of the discount. 22. Order Total: Total cost of the order after applying discounts. 23. Shipping Cost: Cost associated with shipping the order. 24. Total: Overall total cost, including product cost, discounts, and shipping.

Dataset Characteristics: The dataset is diverse, containing both categorical and numerical data. It includes temporal information with "Order Date" and "Ship Date" in datetime format. Some columns like "Cost Price," "Retail Price," and others related to monetary values are currently stored as objects, which may need conversion for accurate numerical analysis. The dataset provides a comprehensive snapshot of the sales process, making it suitable for various analytical and exploratory tasks.

The dataset is divided into 4 worksheets. The first contains DESeq2 output from the AcoR cell line analysis; the second contains HTSeq-count output for each sample used in this study. The final two worksheets contain the comparisons of the DESeq2 output from this study to previously published comparisons of slender BSF vs. stumpy form [13], and slender BSF vs. PCFs [48]. These worksheets also contain columns with calculated distance from an “X = Y” line for each gene, in both comparisons. Hypothetically, if log2 fold change for a gene in the AcoR/WT comparison was equal to that from the other comparisons, the gene would fall on an X = Y line when plotted on a scatter plot. These columns are the calculated deviation from this line for each gene. Positive values indicate a higher log2 fold change in the AcoR/WT dataset, and conversely, negative values indicate a lower log2 fold change in the AcoR/WT dataset, when compared to the aforementioned data. (XLSX)

The national values are presented in the Excel file “HAD_2011_Valeurs_Nationales”: — by homogeneous support group (GHPC); — by association Main Loading Mode x Associated Loading Mode; — by Main Loading Mode. The 2011 PMSI data is also available by GHPC (number of days) to assess the survey rate by GHPC. In addition, in order to enlighten you and guide you in the reading of the information, the document “HAD 2011 National Values Reading Guide” completes the Excel file. It presents: — the composition of the 2011 sample; — the methods for calculating national cost values based on the ENCC data relating to the 2011 activity of institutions: taking into account the geographical coefficient, deletion of certain sequences, setting operations, etc.; — instructions on the content of the different tabs of the Excel file.

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%.

This study investigates pricing and coordination strategies for a dual-channel supply chain (DCSC), considering technological innovations in emergencies. We have established the DCSC model consisting of a manufacturer, a retailer, and an E-commerce platform (ECP). Whether manufacturers choose to invest in technological innovation during emergencies can be divided into traditional production mode and technological innovation mode. Using the reverse induction method to solve the Stackelberg game problem, explore the pricing and channel selection strategies of each member in a DCSC under different modes. In addition, a revenue-sharing contract for a DCSC under emergencies was designed and improved. Research has shown that under emergencies, consumers’ technological innovation preference can increase the profits of each member in the DCSC and manufacturers’ technological innovation level. Manufacturers are more willing to choose technological innovation mode rather than traditional production mode. However, an increase in the commission rate of ECP can hinder the level of technological innovation of manufacturers and affect the issue of choosing between offline channel and ECP channel. Specifically, when the commission rate exceeds a certain threshold, the offline channel should be chosen. Finally, traditional revenue-sharing contracts fail to effectively coordinate DCSC that incorporate technological innovation during emergencies. To address this limitation, an improved revenue-sharing contract is proposed, which enhances the level of technological innovation while achieving Pareto improvements within the DCSC.

The data set contains the data presented in the journal articles. The data set is in the form of an Excel spreadsheet. Each table in the journal article is a unique worksheet in the Excel spreadsheet. The other worksheet in the spreadsheet contains the raw data used to calculate the data presented in the journal article. This dataset is associated with the following publication: Gallardo, V., B. Morris, and E. Rhodes. The Use of Hollow Fiber Dialysis Filters Operated in Axial Flow Mode for Recovery of Microorganisms in Large Volume Water Samples with High Loadings of Particulate Matter. The Journal of Microbiology. Springer, New York, NY, USA, ., (2019).

The dataset of ground truth measurement synchronizing with the airborne WiDAS mission and Envisat ASAR was obtained in the Linze station foci experimental area on Jul. 11, 2008. WiDAS, composed of four CCD cameras, one mid-infrared thermal imager (AGEMA 550), and one infrared thermal imager (S60), can acquire CCD, MIR and TIR band data. The data were in AP mode and VV/VH polarization combinations, and the overpass time was approximately at 11:26 BJT. The simultaneous ground data included the following items: (1) soil moisture (0-5cm) measured once by the cutting ring method at the corner points of the 40 subplots of the west-east desert transit zone strip , once by the cutting ring method in the nine subplots of the north-south desert transit zone, nine times in the LY06 and LY07 strips quadrates,and once by the cutting ring and once by ML2X Soil Moisture Tachometer in the Wulidun farmland. The preprocessed soil volumetric moisture data were archived as Excel files. (2) the surface radiative temperature measured by three handheld infrared thermometer (5# and 6# from Cold and Arid Regions Environmental and Engineering Research Institute, and one from Institute of Geographic Sciences and Natural Resources, which were all calibrated) in LY06 and LY07 strips (49 points and repeated three times), and Wulidun farmland quadrates (various points and repeated three times). Data were archived as Excel files. (3) spectrum of maize, soil and soil with known moisture measured by ASD Spectroradiometer (350～2 500 nm) from BNU and the reference board (40% before Jun. 15 and 20% hereafter) in Wulidun farmland. Raw spectral data were binary files , which were recorded daily in detail, and pre-processed data on reflectance (by ViewSpecPro) were archived as Excel files. (4) maize BRDF measured by ASD Spectroradiometer (350～2 500 nm) from BNU, the reference board (40% before Jun. 15 and 20% hereafter), two observation platforms of BNU make and one of Institute of Remote Sensing Applications make in Wulidun farmland. Raw spectral data were archived as binary files, which were recorded daily in detail, and pre-processed data on reflectance and transmittivity were archived as text files (.txt). (5) LAI measured in the maize quadrate, poplar quadrate and desert scrub quadrate in Wulidun farmland, the desert transit zone strips and the poplar forest quadrate by the fisheye camera (CANON EOS40D with a lens of EF15/28), shooting straight downwards, with exceptions of higher plants, which were shot upwards. Data included original photos (.JPG) and those processed by can_eye5.0 (in excel). (6) LAI of maize measured by LAI2000 in Linze station quadrates and Wulidun farmland quadrates. Data educed from LAI2000 periodically were archived as text files (.txt) and marked with one ID. Raw data (table of word and txt) and processed data (Excel) were included. Besides, observation time, the observation method and the repetition were all archived. (7) LAI measured by the ruler and the set square in B2 and B3 of Linze station quadrates. Data were archived as Excel files. See the metadata record “WATER: Dataset of setting of the sampling plots and stripes in the Linze station foci experimental area” for more information of the quadrate locations.

This is supplementary data for the paper 'Optimisation of mobility hub locations for a sustainable mobility system'. The Excel file 'InputParameters' contains the parameters used as input for the bilevel optimization model. Note that it contains two sheets: one for the calibrated parameters in the utility function, and one for the mode-specific input parameters. The external cost data are based on the study by Bieler, C. & Sutter, D. (2019), whereas the cost parameters were derived from the websites of the local service providers.

The result folder contains the result files of all the experiments discussed in the paper. Each subfolder corresponds to one test instance. The subfolders contain the information on the built mobility hubs (build_mobilityhubs.csv), the modal split information (wegcount.rating.csv for both absolute and proportional data), the number of transfers for each mode at each station (transfercount.csv), and also the full list of modes that each user group used in their travels (user_paths.csv). Note that the stations are given by ID, and the ID is taken from the GTFS data for Aachen.

The additional experiments from Section 5.5 on the modal split for a higher number of bike- and car-sharing stations are contained in the "Further Maximization of Sharing Modes Test.zip." Each subfolder contains specific data for the test instances, while the Excel sheet modal_split_Percent.xlsx summarizes and visualizes the modal split data.

Further result data can be provided upon request.

Bieler, C., Sutter, D., 2019. Externe Kosten des Verkehrs in Deutschland: Straßen-, Schienen-, Luft- und Binnenschiffverkehr 2017.

About this dataset

Context

The dataset presents median household incomes for various household sizes in Excel, AL, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Excel did not include 2, 5, 6, or 7-person households. Across the different household sizes in Excel the mean income is $71,010, and the standard deviation is $39,365. The coefficient of variation (CV) is 55.44%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $25,559. It then further increased to $93,229 for 4-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/excel-al-median-household-income-by-household-size.jpeg" alt="Excel, AL median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

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

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Excel median household income. You can refer the same here