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...

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.

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%.

Abstract

The harmonized data set on health, created and published by the ERF, is a subset of Iraq Household Socio Economic Survey (IHSES) 2012. It was derived from the household, individual and health modules, collected in the context of the above mentioned survey. The sample was then used to create a harmonized health survey, comparable with the Iraq Household Socio Economic Survey (IHSES) 2007 micro data set.

----> Overview of the Iraq Household Socio Economic Survey (IHSES) 2012:

Iraq is considered a leader in household expenditure and income surveys where the first was conducted in 1946 followed by surveys in 1954 and 1961. After the establishment of Central Statistical Organization, household expenditure and income surveys were carried out every 3-5 years in (1971/ 1972, 1976, 1979, 1984/ 1985, 1988, 1993, 2002 / 2007). Implementing the cooperation between CSO and WB, Central Statistical Organization (CSO) and Kurdistan Region Statistics Office (KRSO) launched fieldwork on IHSES on 1/1/2012. The survey was carried out over a full year covering all governorates including those in Kurdistan Region.

The survey has six main objectives. These objectives are:

1. Provide data for poverty analysis and measurement and monitor, evaluate and update the implementation Poverty Reduction National Strategy issued in 2009.
2. Provide comprehensive data system to assess household social and economic conditions and prepare the indicators related to the human development.
3. Provide data that meet the needs and requirements of national accounts.
4. Provide detailed indicators on consumption expenditure that serve making decision related to production, consumption, export and import.
5. Provide detailed indicators on the sources of households and individuals income.
6. Provide data necessary for formulation of a new consumer price index number.

The raw survey data provided by the Statistical Office were then harmonized by the Economic Research Forum, to create a comparable version with the 2006/2007 Household Socio Economic Survey in Iraq. Harmonization at this stage only included unifying variables' names, labels and some definitions. See: Iraq 2007 & 2012- Variables Mapping & Availability Matrix.pdf provided in the external resources for further information on the mapping of the original variables on the harmonized ones, in addition to more indications on the variables' availability in both survey years and relevant comments.

Geographic coverage

National coverage: Covering a sample of urban, rural and metropolitan areas in all the governorates including those in Kurdistan Region.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey was carried out over a full year covering all governorates including those in Kurdistan Region.

Kind of data

Sample survey data [ssd]

Sampling procedure

----> Design:

Sample size was (25488) household for the whole Iraq, 216 households for each district of 118 districts, 2832 clusters each of which includes 9 households distributed on districts and governorates for rural and urban.

----> Sample frame:

Listing and numbering results of 2009-2010 Population and Housing Survey were adopted in all the governorates including Kurdistan Region as a frame to select households, the sample was selected in two stages: Stage 1: Primary sampling unit (blocks) within each stratum (district) for urban and rural were systematically selected with probability proportional to size to reach 2832 units (cluster). Stage two: 9 households from each primary sampling unit were selected to create a cluster, thus the sample size of total survey clusters was 25488 households distributed on the governorates, 216 households in each district.

----> Sampling Stages:

In each district, the sample was selected in two stages: Stage 1: based on 2010 listing and numbering frame 24 sample points were selected within each stratum through systematic sampling with probability proportional to size, in addition to the implicit breakdown urban and rural and geographic breakdown (sub-district, quarter, street, county, village and block). Stage 2: Using households as secondary sampling units, 9 households were selected from each sample point using systematic equal probability sampling. Sampling frames of each stages can be developed based on 2010 building listing and numbering without updating household lists. In some small districts, random selection processes of primary sampling may lead to select less than 24 units therefore a sampling unit is selected more than once , the selection may reach two cluster or more from the same enumeration unit when it is necessary.

Mode of data collection

Face-to-face [f2f]

Research instrument

----> Preparation:

The questionnaire of 2006 survey was adopted in designing the questionnaire of 2012 survey on which many revisions were made. Two rounds of pre-test were carried out. Revision were made based on the feedback of field work team, World Bank consultants and others, other revisions were made before final version was implemented in a pilot survey in September 2011. After the pilot survey implemented, other revisions were made in based on the challenges and feedbacks emerged during the implementation to implement the final version in the actual survey.

----> Questionnaire Parts:

The questionnaire consists of four parts each with several sections: Part 1: Socio – Economic Data: - Section 1: Household Roster - Section 2: Emigration - Section 3: Food Rations - Section 4: housing - Section 5: education - Section 6: health - Section 7: Physical measurements - Section 8: job seeking and previous job

Part 2: Monthly, Quarterly and Annual Expenditures: - Section 9: Expenditures on Non – Food Commodities and Services (past 30 days). - Section 10 : Expenditures on Non – Food Commodities and Services (past 90 days). - Section 11: Expenditures on Non – Food Commodities and Services (past 12 months). - Section 12: Expenditures on Non-food Frequent Food Stuff and Commodities (7 days). - Section 12, Table 1: Meals Had Within the Residential Unit. - Section 12, table 2: Number of Persons Participate in the Meals within Household Expenditure Other Than its Members.

Part 3: Income and Other Data: - Section 13: Job - Section 14: paid jobs - Section 15: Agriculture, forestry and fishing - Section 16: Household non – agricultural projects - Section 17: Income from ownership and transfers - Section 18: Durable goods - Section 19: Loans, advances and subsidies - Section 20: Shocks and strategy of dealing in the households - Section 21: Time use - Section 22: Justice - Section 23: Satisfaction in life - Section 24: Food consumption during past 7 days

Part 4: Diary of Daily Expenditures: Diary of expenditure is an essential component of this survey. It is left at the household to record all the daily purchases such as expenditures on food and frequent non-food items such as gasoline, newspapers…etc. during 7 days. Two pages were allocated for recording the expenditures of each day, thus the roster will be consists of 14 pages.

Cleaning operations

----> Raw Data:

Data Editing and Processing: To ensure accuracy and consistency, the data were edited at the following stages: 1. Interviewer: Checks all answers on the household questionnaire, confirming that they are clear and correct. 2. Local Supervisor: Checks to make sure that questions has been correctly completed. 3. Statistical analysis: After exporting data files from excel to SPSS, the Statistical Analysis Unit uses program commands to identify irregular or non-logical values in addition to auditing some variables. 4. World Bank consultants in coordination with the CSO data management team: the World Bank technical consultants use additional programs in SPSS and STAT to examine and correct remaining inconsistencies within the data files. The software detects errors by analyzing questionnaire items according to the expected parameter for each variable.

----> Harmonized Data:

• The SPSS package is used to harmonize the Iraq Household Socio Economic Survey (IHSES) 2007 with Iraq Household Socio Economic Survey (IHSES) 2012.
• The harmonization process starts with raw data files received from the Statistical Office.
• A program is generated for each dataset to create harmonized variables.
• Data is saved on the household and individual level, in SPSS and then converted to STATA, to be disseminated.

Response rate

Iraq Household Socio Economic Survey (IHSES) reached a total of 25488 households. Number of households refused to response was 305, response rate was 98.6%. The highest interview rates were in Ninevah and Muthanna (100%) while the lowest rates were in Sulaimaniya (92%).

The dataset of ground truth measurements synchronizing with the airborne WiDAS mission was obtained in the Linze station foci experimental area on May 30, 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 simultaneous ground data included: (1) soil moisture (0-5cm) measured nine times by the cutting ring method (50cm^3) along LY07 and LY08 quadrates, and once by the cutting ring method and once by ML2X Soil Moisture Tachometer in the six points of Wulidun farmland quadrates. The preprocessed soil volumetric moisture data were archived as Excel files. (2) surface radiative temperature measured by two handheld infrared thermometer (5# and 6# from Cold and Arid Regions Environmental and Engineering Research Institute which were both calibrated) in the LY07 and LY08 quadrates (98 sample points and repeated three times) and the 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 40% reference board in Wulidun farmland quadrate and the desert transit zone strips. Raw spectral data were archived as binary files, which were recorded daily in detail, and pre-processed data on reflectance were archived as Excel files. (4) maize BRDF measured by ASD Spectroradiometer (350～2 500 nm) from BNU, the 40% reference board, two observation platforms of BNU make and one of Institute of Remote Sensing Applications make in Wulidun farmland quadrate and the desert transit zone strips. Raw spectral data were archived as binary files , which were recorded daily in detail, and pre-processed data on reflectance and transmittivity (read by ViewSpecPro) were archived as text files (.txt). (5) LAI of maize, poplar and the desert scrub measured by the fisheye camera (CANON EOS40D with a lens of EF15/28), shooting straight downwards, with exceptions of higher plants, which were shot upwards in Wulidun farmland quadrate I, the desert transit zone and the poplar forest. Data included original photos (.JPG) and those processed by can_eye5.0 (in excel). (6) LAI measured by the ruler and the set square in D and H quadrates of the Wulidun farmland. Part of the samples were also measured by LI-3100 and compared with those by manual work for further correction. 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.

Matlab scripts, source C-code, mex compiled C-code, and figure data points for the paper entitled “Semi-Analytical Analytical Modelling of Linear Mode Coupling in Few -Mode Fibers”.

Folders: 0_differential_equations_solver Matlab scripts based on the Symbolic Math Toolbox for the derivation of a semi-analytical solution to the differential equations describing linear mode coupling in few-mode fibres. Scripts available for 3, 4, 5 and 6 modes.

1_C_code_for_high_precision_solution_of_polynomials C-code for the numerical evaluation of the 6-modes semi-analytical solutions obtained in 0_differential_equations_solver. Two versions: “highPrecRootFind_6M_doubleIO” uses always the same seed for the root finding section; “highPrecRootFind_6M_doubleIO_rand” uses a randomized seed for the root finding section.

2_crosstalk_vs_radial_displacement Script for plotting typical fibre coupling coefficients and plotting of the crosstalk introduced by a single fibre displacement as a function of the radial displacement and averaged in the azimuth coordinate.

3_solutions_precision Script for the evaluation of the precision of the semi-analytical solutions proposed against Runge-Kutta-Fehlberg Method (RKF45) numerical solutions.

98_poly_solvers_mex_files_compiled_for_R2014b_64bit Compiled mex C-code at 1_C_code_for_high_precision_solution_of_polynomials. Compiled for Mex Matlab R2014b 64bit.

99_fibre_parameters Typical fibre parameters used in this dataset.

100_figures_data_poins Excel files containing the data points in the figures presented in the paper.

Abstract

This dataset was generated from a set of Excel spreadsheets from an Information and Communication Technology Services (ICTS) administrative database on student applications to the University of Cape Town (UCT). This database contains information on applications to UCT between the January 2006 and December 2014. In the original form received by DataFirst the data were ill suited to research purposes. This dataset represents an attempt at cleaning and organizing these data into a more tractable format. To ensure data confidentiality direct identifiers have been removed from the data and the data is only made available to accredited researchers through DataFirst's Secure Data Service.

The dataset was separated into the following data files:

1. Application level information: the "finest" unit of analysis. Individuals may have multiple applications. Uniquely identified by an application ID variable. There are a total of 1,714,669 applications on record.
2. Individual level information: individuals may have multiple applications. Each individual is uniquely identified by an individual ID variable. Each individual is associated with information on "key subjects" from a separate data file also contained in the database. These key subjects are all separate variables in the individual level data file. There are a total of 285,005 individuals on record.
3. Secondary Education Information: individuals can also be associated with row entries for each subject. This data file does not have a unique identifier. Instead, each row entry represents a specific secondary school subject for a specific individual. These subjects are quite specific and the data allows the user to distinguish between, for example, higher grade accounting and standard grade accounting. It also allows the user to identify the educational authority issuing the qualification e.g. Cambridge Internal Examinations (CIE) versus National Senior Certificate (NSC).
4. Tertiary Education Information: the smallest of the four data files. There are multiple entries for each individual in this dataset. Each row entry contains information on the year, institution and transcript information and can be associated with individuals.

Analysis unit

Applications, individuals

Kind of data

Administrative records [adm]

Mode of data collection

Other [oth]

Cleaning operations

The data files were made available to DataFirst as a group of Excel spreadsheet documents from an SQL database managed by the University of Cape Town's Information and Communication Technology Services . The process of combining these original data files to create a research-ready dataset is summarised in a document entitled "Notes on preparing the UCT Student Application Data 2006-2014" accompanying the data.

Abstract

The World Bank in collaboration with the Kenya National Bureau of Statistics and the University of California, Berkeley are conducting the Kenya COVID-19 Rapid Response Phone Survey to track the socioeconomic impacts of the COVID-19 pandemic, the recovery from it as well as other shocks to provide timely data to inform policy. This dataset contains information from eight waves of the COVID-19 RRPS, which is part of a panel survey that targets Kenyan nationals and started in May 2020. The same households were interviewed every two months for five survey rounds, in the first year of data collection and every four months thereafter, with interviews conducted using Computer Assisted Telephone Interviewing (CATI) techniques.

The data set contains information from two samples of Kenyan households. The first sample is a randomly drawn subset of all households that were part of the 2015/16 Kenya Integrated Household Budget Survey (KIHBS) Computer-Assisted Personal Interviewing (CAPI) pilot and provided a phone number. The second was obtained through the Random Digit Dialing method, by which active phone numbers created from the 2020 Numbering Frame produced by the Kenya Communications Authority are randomly selected. The samples cover urban and rural areas and are designed to be representative of the population of Kenya using cell phones. Waves 1-7 of this survey include information on household background, service access, employment, food security, income loss, transfers, health, and COVID-19 knowledge and vaccinations. Wave 8 focused on how households were exposed to shocks, in particular adverse weather shocks and the increase in the price of food and fuel, but also included parts of the previous modules on household background, service access, employment, food security, income loss, and subjective wellbeing.

The data is uploaded in three files. The first is the hh file, which contains household level information. The ‘hhid’, uniquely identifies all household. The second is the adult level file, which contains data at the level of adult household members. Each adult in a household is uniquely identified by the ‘adult_id’. The third file is the child level file, available only for waves 3-7, which contains information for every child in the household. Each child in a household is uniquely identified by the ‘child_id’.

The duration of data collection and sample size for each completed wave was: Wave 1: May 14 to July 7, 2020; 4,061 Kenyan households Wave 2: July 16 to September 18, 2020; 4,492 Kenyan households Wave 3: September 28 to December 2, 2020; 4,979 Kenyan households Wave 4: January 15 to March 25, 2021; 4,892 Kenyan households Wave 5: March 29 to June 13, 2021; 5,854 Kenyan households Wave 6: July 14 to November 3, 2021; 5,765 Kenyan households Wave 7: November 15, 2021, to March 31, 2022; 5,633 Kenyan households Wave 8: May 31 to July 8, 2022: 4,550 Kenyan households

The same questionnaire is also administered to refugees in Kenya, with the data available in the UNHCR microdata library: https://microdata.unhcr.org/index.php/catalog/296/

Geographic coverage

National coverage covering rural and urban areas

Analysis unit

Household, Individual

Sampling procedure

The COVID-19 RRPS with Kenyan households has two samples. The first sample consists of households that were part of the 2015/16 KIHBS CAPI pilot and provided a phone number. The 2015/16 KIHBS CAPI pilot is representative at the national level stratified by county and place of residence (urban and rural areas). At least one valid phone number was obtained for 9,007 households and all of them were included in the COVID-19 RRPS sample. The target respondent was the primary male or female household member from the 2015/16 KIHBS CAPI pilot. The second sample consists of households selected using the Random Digit Dialing method. A list of random mobile phone numbers was created using a random number generator from the 2020 Numbering Frame produced by the Kenya Communications Authority. The initial sampling frame therefore consisted of 92,999,970 randomly ordered phone numbers assigned to three networks: Safaricom, Airtel and Telkom. An introductory text message was sent to 5,000 randomly selected numbers to determine if numbers were in operation. Out of these, 4,075 were found to be active and formed the final sampling frame. There was no stratification and individuals that were called were asked about the households they live in. Until wave 7 sampled households that were not reached in earlier waves were also contacted along with households that were interviewed before. In wave 8 only households that had previously participated in the survey were contacted for interview. The “wave” variable represents in which wave the households were interviewed in.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was administered in English and is provided as a resource in pdf format. Additionally, questionnaires for each wave are also provided in Excel format coded for SCTO. The same questionnaire is also administered to refugees in Kenya, with the data available in the UNHCR microdata library: https://microdata.unhcr.org/index.php/catalog/296/

Abstract

A Knowledge, Attitudes, and Practices (KAP) survey was conducted in Ajuong Thok and Pamir Refugee Camps in November 2018 to determine the current Water, Sanitation, and Hygiene (WASH) conditions as well as hygiene attitudes and practices within the households (HHs) surveyed. The assessment utilized a systematic random sampling method, and a total of 1,040 HHs (520 HHs in each location) were surveyed using mobile data collection (MDC) within a period of 10 days. Data was cleaned and analyzed in Excel. The summary of the results is presented in this report.

The findings showed that the overall average number of liters of water per person per day was 21, in both Ajuong Thok and Pamir Camps, which was slightly higher than the recommended Office of the United Nations High Commissioner for Refugees (UNHCR) minimum standard of at least 20 liters of water available per person per day. This is a slight improvement from the 19.5 liters reported the previous year. The average HH size was six people. Women comprised 83.2% of the surveyed respondents and males 16.8%. Almost all the respondents were refugees, constituting 99.6%. The refugees were aware of the key health and hygiene practices, possibly as a result of routine health and hygiene messages delivered to them by Samaritan´s Purse (SP), Africa Humanitarian Action (AHA) and International Rescue Committee (IRC). Most refugees had knowledge about keeping water containers clean, washing hands during critical times, safe excreta disposal and disease prevention.

Geographic coverage

Ajuong Thok and Pamir Refugee Camps

Analysis unit

Households

Universe

All households in Ajuong Thok and Pamir Refugee Camps

Kind of data

Sample survey data [ssd]

Sampling procedure

Households were selected using systematic random sampling. Enumerators systematically walked through each row in each block of the camps, in such a way as to give each HH a chance to be selected. For each block, the enumerators began at one corner and went row by row, systematically using the sampling interval (SI) to select HHs. The first HH sampled in each block was determined by selecting a random number between 1 and the SI, (6 in Ajuong Thok and 7 in Pamir). After selecting the first HH, the SI was used to identify the next respondent HH. The female head of the household was the preferred respondent. If she was not available, another adult (over 15 years of age) with knowledge of the HH´s WASH practices was surveyed. If no one qualified to answer the survey, the HH was replaced systematically using the SI.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire used to collect the data consists of the following sections: - Demographics - Water - Sanitation - Hygiene - NFI Distribution

Cleaning operations

The data collected was uploaded to a server at the end of each day. IFormBuilder generated a Microsoft (MS) Excel spreadsheet dataset which was then cleaned and analyzed using MS Excel.

Given that SP is currently implementing a WASH program in Ajuong Thok and Pamir, the assessment data collected in these camps will not only serve as the endline for UNHCR 2018 programming but also as the baseline for 2019 programming.

Data was anonymized through decoding and local suppression.

Abstract

The main objectives of the survey were: - To obtain weights for the revision of the Consumer Price Index (CPI) for Funafuti; - To provide information on the nature and distribution of household income, expenditure and food consumption patterns; - To provide data on the household sector's contribution to the National Accounts - To provide information on economic activity of men and women to study gender issues - To undertake some poverty analysis

Geographic coverage

National, including Funafuti and Outer islands

Analysis unit

• Household
• individual

Universe

All the private household are included in the sampling frame. In each household selected, the current resident are surveyed, and people who are usual resident but are currently away (work, health, holydays reasons, or border student for example. If the household had been residing in Tuvalu for less than one year: - but intend to reside more than 12 months => The household is included - do not intend to reside more than 12 months => out of scope

Kind of data

Sample survey data [ssd]

Sampling procedure

It was decided that 33% (one third) sample was sufficient to achieve suitable levels of accuracy for key estimates in the survey. So the sample selection was spread proportionally across all the island except Niulakita as it was considered too small. For selection purposes, each island was treated as a separate stratum and independent samples were selected from each. The strategy used was to list each dwelling on the island by their geographical position and run a systematic skip through the list to achieve the 33% sample. This approach assured that the sample would be spread out across each island as much as possible and thus more representative.

For details please refer to Table 1.1 of the Report.

Sampling deviation

Only the island of Niulakita was not included in the sampling frame, considered too small.

Mode of data collection

Face-to-face [f2f]

Research instrument

There were three main survey forms used to collect data for the survey. Each question are writen in English and translated in Tuvaluan on the same version of the questionnaire. The questionnaires were designed based on the 2004 survey questionnaire.

HOUSEHOLD FORM - composition of the household and demographic profile of each members - dwelling information - dwelling expenditure - transport expenditure - education expenditure - health expenditure - land and property expenditure - household furnishing - home appliances - cultural and social payments - holydays/travel costs - Loans and saving - clothing - other major expenditure items

INDIVIDUAL FORM - health and education - labor force (individu aged 15 and above) - employment activity and income (individu aged 15 and above): wages and salaries, working own business, agriculture and livestock, fishing, income from handicraft, income from gambling, small scale activies, jobs in the last 12 months, other income, childreen income, tobacco and alcohol use, other activities, and seafarer

DIARY (one diary per week, on a 2 weeks period, 2 diaries per household were required) - All kind of expenses - Home production - food and drink (eaten by the household, given away, sold) - Goods taken from own business (consumed, given away) - Monetary gift (given away, received, winning from gambling) - Non monetary gift (given away, received, winning from gambling)

Questionnaire Design Flaws Questionnaire design flaws address any problems with the way questions were worded which will result in an incorrect answer provided by the respondent. Despite every effort to minimize this problem during the design of the respective survey questionnaires and the diaries, problems were still identified during the analysis of the data. Some examples are provided below:

Gifts, Remittances & Donations Collecting information on the following: - the receipt and provision of gifts - the receipt and provision of remittances - the provision of donations to the church, other communities and family occasions is a very difficult task in a HIES. The extent of these activities in Tuvalu is very high, so every effort should be made to address these activities as best as possible. A key problem lies in identifying the best form (questionnaire or diary) for covering such activities. A general rule of thumb for a HIES is that if the activity occurs on a regular basis, and involves the exchange of small monetary amounts or in-kind gifts, the diary is more appropriate. On the other hand, if the activity is less infrequent, and involves larger sums of money, the questionnaire with a recall approach is preferred. It is not always easy to distinguish between the two for the different activities, and as such, both the diary and questionnaire were used to collect this information. Unfortunately it probably wasn?t made clear enough as to what types of transactions were being collected from the different sources, and as such some transactions might have been missed, and others counted twice. The effects of these problems are hopefully minimal overall.

Defining Remittances Because people have different interpretations of what constitutes remittances, the questionnaire needs to be very clear as to how this concept is defined in the survey. Unfortunately this wasn?t explained clearly enough so it was difficult to distinguish between a remittance, which should be of a more regular nature, and a one-off monetary gift which was transferred between two households.

Business Expenses Still Recorded The aim of the survey is to measure "household" expenditure, and as such, any expenditure made by a household for an item or service which was primarily used for a business activity should be excluded. It was not always clear in the questionnaire that this was the case, and as such some business expenses were included. Efforts were made during data cleaning to remove any such business expenses which would impact significantly on survey results.

Purchased goods given away as a gift When a household makes a gift donation of an item it has purchased, this is recorded in section 5 of the diary. Unfortunately it was difficult to know how to treat these items as it was not clear as to whether this item had been recorded already in section 1 of the diary which covers purchases. The decision was made to exclude all information of gifts given which were considered to be purchases, as these items were assumed to have already been recorded already in section 1. Ideally these items should be treated as a purchased gift given away, which in turn is not household consumption expenditure, but this was not possible.

Some key items missed in the Questionnaire Although not a big issue, some key expenditure items were omitted from the questionnaire when it would have been best to collect them via this schedule. A key example being electric fans which many households in Tuvalu own.

Cleaning operations

Consistency of the data: - each questionnaire was checked by the supervisor during and after the collection - before data entry, all the questionnaire were coded - the CSPRo data entry system included inconsistency checks which allow the NSO staff to point some errors and to correct them with imputation estimation from their own knowledge (no time for double entry), 4 data entry operators. - after data entry, outliers were identified in order to check their consistency.

All data entry, including editing, edit checks and queries, was done using CSPro (Census Survey Processing System) with additional data editing and cleaning taking place in Excel.

The staff from the CSD was responsible for undertaking the coding and data entry, with assistance from an additional four temporary staff to help produce results in a more timely manner.

Although enumeration didn't get completed until mid June, the coding and data entry commenced as soon as forms where available from Funafuti, which was towards the end of March. The coding and data entry was then completed around the middle of July.

A visit from an SPC consultant then took place to undertake initial cleaning of the data, primarily addressing missing data items and missing schedules. Once the initial data cleaning was undertaken in CSPro, data was transferred to Excel where it was closely scrutinized to check that all responses were sensible. In the cases where unusual values were identified, original forms were consulted for these households and modifications made to the data if required.

Despite the best efforts being made to clean the data file in preparation for the analysis, no doubt errors will still exist in the data, due to its size and complexity. Having said this, they are not expected to have significant impacts on the survey results.

Under-Reporting and Incorrect Reporting as a result of Poor Field Work Procedures The most crucial stage of any survey activity, whether it be a population census or a survey such as a HIES is the fieldwork. It is crucial for intense checking to take place in the field before survey forms are returned to the office for data processing. Unfortunately, it became evident during the cleaning of the data that fieldwork wasn?t checked as thoroughly as required, and as such some unexpected values appeared in the questionnaires, as well as unusual results appearing in the diaries. Efforts were made to indentify the main issues which would have the greatest impact on final results, and this information was modified using local knowledge, to a more reasonable answer, when required.

Data Entry Errors Data entry errors are always expected, but can be kept to a minimum with

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.

Abstract

The General Household Survey-Panel (GHS-Panel) is implemented in collaboration with the World Bank Living Standards Measurement Study (LSMS) team as part of the Integrated Surveys on Agriculture (ISA) program. The objectives of the GHS-Panel include the development of an innovative model for collecting agricultural data, interinstitutional collaboration, and comprehensive analysis of welfare indicators and socio-economic characteristics. The GHS-Panel is a nationally representative survey of approximately 5,000 households, which are also representative of the six geopolitical zones. The 2023/24 GHS-Panel is the fifth round of the survey with prior rounds conducted in 2010/11, 2012/13, 2015/16 and 2018/19. The GHS-Panel households were visited twice: during post-planting period (July - September 2023) and during post-harvest period (January - March 2024).

Geographic coverage

National

Analysis unit

• Households • Individuals • Agricultural plots • Communities

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The original GHS‑Panel sample was fully integrated with the 2010 GHS sample. The GHS sample consisted of 60 Primary Sampling Units (PSUs) or Enumeration Areas (EAs), chosen from each of the 37 states in Nigeria. This resulted in a total of 2,220 EAs nationally. Each EA contributed 10 households to the GHS sample, resulting in a sample size of 22,200 households. Out of these 22,200 households, 5,000 households from 500 EAs were selected for the panel component, and 4,916 households completed their interviews in the first wave.

After nearly a decade of visiting the same households, a partial refresh of the GHS‑Panel sample was implemented in Wave 4 and maintained for Wave 5. The refresh was conducted to maintain the integrity and representativeness of the sample. The refresh EAs were selected from the same sampling frame as the original GHS‑Panel sample in 2010. A listing of households was conducted in the 360 EAs, and 10 households were randomly selected in each EA, resulting in a total refresh sample of approximately 3,600 households.

In addition to these 3,600 refresh households, a subsample of the original 5,000 GHS‑Panel households from 2010 were selected to be included in the new sample. This “long panel” sample of 1,590 households was designed to be nationally representative to enable continued longitudinal analysis for the sample going back to 2010. The long panel sample consisted of 159 EAs systematically selected across Nigeria’s six geopolitical zones.

The combined sample of refresh and long panel EAs in Wave 5 that were eligible for inclusion consisted of 518 EAs based on the EAs selected in Wave 4. The combined sample generally maintains both the national and zonal representativeness of the original GHS‑Panel sample.

Sampling deviation

Although 518 EAs were identified for the post-planting visit, conflict events prevented interviewers from visiting eight EAs in the North West zone of the country. The EAs were located in the states of Zamfara, Katsina, Kebbi and Sokoto. Therefore, the final number of EAs visited both post-planting and post-harvest comprised 157 long panel EAs and 354 refresh EAs. The combined sample is also roughly equally distributed across the six geopolitical zones.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The GHS-Panel Wave 5 consisted of three questionnaires for each of the two visits. The Household Questionnaire was administered to all households in the sample. The Agriculture Questionnaire was administered to all households engaged in agricultural activities such as crop farming, livestock rearing, and other agricultural and related activities. The Community Questionnaire was administered to the community to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

GHS-Panel Household Questionnaire: The Household Questionnaire provided information on demographics; education; health; labour; childcare; early child development; food and non-food expenditure; household nonfarm enterprises; food security and shocks; safety nets; housing conditions; assets; information and communication technology; economic shocks; and other sources of household income. Household location was geo-referenced in order to be able to later link the GHS-Panel data to other available geographic data sets (forthcoming).

GHS-Panel Agriculture Questionnaire: The Agriculture Questionnaire solicited information on land ownership and use; farm labour; inputs use; GPS land area measurement and coordinates of household plots; agricultural capital; irrigation; crop harvest and utilization; animal holdings and costs; household fishing activities; and digital farming information. Some information is collected at the crop level to allow for detailed analysis for individual crops.

GHS-Panel Community Questionnaire: The Community Questionnaire solicited information on access to infrastructure and transportation; community organizations; resource management; changes in the community; key events; community needs, actions, and achievements; social norms; and local retail price information.

The Household Questionnaire was slightly different for the two visits. Some information was collected only in the post-planting visit, some only in the post-harvest visit, and some in both visits.

The Agriculture Questionnaire collected different information during each visit, but for the same plots and crops.

The Community Questionnaire collected prices during both visits, and different community level information during the two visits.

Cleaning operations

CAPI: Wave five exercise was conducted using Computer Assisted Person Interview (CAPI) techniques. All the questionnaires (household, agriculture, and community questionnaires) were implemented in both the post-planting and post-harvest visits of Wave 5 using the CAPI software, Survey Solutions. The Survey Solutions software was developed and maintained by the Living Standards Measurement Unit within the Development Economics Data Group (DECDG) at the World Bank. Each enumerator was given a tablet which they used to conduct the interviews. Overall, implementation of survey using Survey Solutions CAPI was highly successful, as it allowed for timely availability of the data from completed interviews.

DATA COMMUNICATION SYSTEM: The data communication system used in Wave 5 was highly automated. Each field team was given a mobile modem which allowed for internet connectivity and daily synchronization of their tablets. This ensured that head office in Abuja had access to the data in real-time. Once the interview was completed and uploaded to the server, the data was first reviewed by the Data Editors. The data was also downloaded from the server, and Stata dofile was run on the downloaded data to check for additional errors that were not captured by the Survey Solutions application. An excel error file was generated following the running of the Stata dofile on the raw dataset. Information contained in the excel error files were then communicated back to respective field interviewers for their action. This monitoring activity was done on a daily basis throughout the duration of the survey, both in the post-planting and post-harvest.

DATA CLEANING: The data cleaning process was done in three main stages. The first stage was to ensure proper quality control during the fieldwork. This was achieved in part by incorporating validation and consistency checks into the Survey Solutions application used for the data collection and designed to highlight many of the errors that occurred during the fieldwork.

The second stage cleaning involved the use of Data Editors and Data Assistants (Headquarters in Survey Solutions). As indicated above, once the interview is completed and uploaded to the server, the Data Editors review completed interview for inconsistencies and extreme values. Depending on the outcome, they can either approve or reject the case. If rejected, the case goes back to the respective interviewer’s tablet upon synchronization. Special care was taken to see that the households included in the data matched with the selected sample and where there were differences, these were properly assessed and documented. The agriculture data were also checked to ensure that the plots identified in the main sections merged with the plot information identified in the other sections. Additional errors observed were compiled into error reports that were regularly sent to the teams. These errors were then corrected based on re-visits to the household on the instruction of the supervisor. The data that had gone through this first stage of cleaning was then approved by the Data Editor. After the Data Editor’s approval of the interview on Survey Solutions server, the Headquarters also reviews and depending on the outcome, can either reject or approve.

The third stage of cleaning involved a comprehensive review of the final raw data following the first and second stage cleaning. Every variable was examined individually for (1) consistency with other sections and variables, (2) out of range responses, and (3) outliers. However, special care was taken to avoid making strong assumptions when resolving potential errors. Some minor errors remain in the data where the diagnosis and/or solution were unclear to the data cleaning team.

Response

The dataset of ground truth measurements synchronizing with the airborne WiDAS mission was obtained in the Linze station foci experimental area on Jun. 29, 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 simultaneous ground data included: (1) soil moisture (0-5cm) nine times by the cutting ring (50cm^3) along LY06 and LY07 strips, and once by the cutting ring method and once by ML2X Soil Moisture Tachometer in the six points of Wulidun farmland quadrates. The preprocessed soil volumetric moisture data were archived as Excel files. (2) surface radiative temperature measured three times 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 (98 sample points and repeated three times) and the Wulidun farmland quadrates (various points and repeated three times). Data were archived as Excel files. (3) maize canopy component temperature measured by the 5# handheld infrared thermometer (from Cold and Arid Regions Environmental and Engineering Research Institute) in Wulidun farmland quadrates. Six directions were measured, canopy backlighting and frontlighting, half height backlighting and frontlighting, the light and the shaded bareland, with each direction 20 measurements. (4) 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 quadrates. 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 (5) mltiangle maize spectrum 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). (6) LAI of maize measured 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). (7) LAI of maize measured by LAI2000 in 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. 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.

The dataset of ground truth measurement synchronizing with the airborne WiDAS mission was obtained in the Yingke oasis and Huazhaizi desert steppe foci experimental areas on Jun. 29, 2008. WiDAS, composed of four CCD cameras, one mid-infrared thermal imager (AGEMA 550), and one infrared thermal imager (S60), can acquire VNIR, MIR and TIR band data. The simultaneous ground data included: (1) Atmospheric parameters in Huazhaizi desert No. 2 plot from CE318 (produced by CIMEL in France). The total optical depth, aerosol optical depth, Rayleigh scattering coefficient, column water vapor in 936 nm, particle size spectrum and phase function were then retrieved from these observations. The optical depth in 1020nm, 936nm, 870nm, 670nm and 440nm were all acquired by CE318. Those data include the raw data in .k7 format and can be opened by ASTPWin. ReadMe.txt is attached for detail. Processed data in Excel format are on optical depth, rayleigh scattering, aerosol optical depth, the horizontal visibility, the near surface air temperature, the solar azimuth, zenith, solar distance correlation factors, and air column mass number. (2) Emissivity of maize and wheat in the Yingke oasis by portable 102F (2.0~25.0um) from BNU. Warm blackbody, cold blackbody, the target and the au-plating board of known emissivity. Raw data of those four measurements were archived in *.WBX, *.CBX, *.SAX and *.CBX Besides, the spectral radiance and emissivity calculated by 102F were archived in *.RAX and .EMX, respectively. Meanwhile, the final spectral emissivity of targets were also calculated by TES (ISSTES). (3) LAI of mazie and wheat in Yingke oasis maize field. The maximum leaf length and width of leaves were measured. Data were archived as Excel files of Jul. 2. (4) FPAR (Fraction of Photosynthetically Active Radiation) of maize and wheat by SUNSACN and the digital camera in Yingke oasis maize field. FPAR= (canopyPAR－surface transmissionPAR－canopy reflection PAR+surface reflectionPAR) /canopy PAR; APAR=FPAR canopy PAR. Data were archived in MS Office Word format. (5) the radiative temperature by the automatic thermometer (FOV: 10°; emissivity: 0.95), measured at nadir with time intervals of one second in Yingke oasis maize field (one from BNU and the other from Institute of Remote Sensing Applications), Huazhaizi desert maize field (only one from BNU for continuous radiative temperature of the maize canopy) and Huazhaizi desert No. 2 plot (two for reaumuria soongorica canopy and the background bare soil). Raw data, blackbody calibrated data and processed data were all archived as Excel files. (6) the component temperature in Yingke oasis maize field (by the handheld radiometer and the thermal image from BNU), Yingke oasis wheat field and Huazhaizi desert maize field. For maize, the component temperature included the vertical canopy temperature, the bare land temperature and the plastic film temperature; for the wheat, it included the vertical canopy temperature, the half height temperature, the lower part temperature and the bare land temperature. The data included raw data (in MS Office Word format), recorded data and the blackbody calibrated data (in Excel format). (7) Maize albedo by the shortwave radiometer in Yingke oasis maize field. R =10H (R for FOV radius; H for the observation height). Data were archived in MS Office Excel format. (8) the radiative temperature by the handheld radiometer in Yingke oasis maize field and Huazhaizi desert maize field (the vertical canopy observation and the transect observation for both fields), and Huazhaizi desert No. 2 plot (the NE-SW diagonal observation). The data included raw data (in .doc format), recorded data and the blackbody calibrated data (in Excel format). (9) ground object reflectance spectra in Yingke oasis maize field by ASD FieldSpec (350～2 500 nm) from BNU. The vertical canopy observation and the line-transect observation were used. The data included raw data (from ASD, read by ViewSpecPro), recorded data and processed data on reflectance (in Excel format).

This dataset includes counts by taxon for megafauna in images collected during surveys with deep-submergence vehicles at inactive sulfide mounds near the 9 50’ N hydrothermal vent field on the East Pacific Rise. Images were collected with a down-looking digital still camera. We provide image areas for estimating megafauna density (counts per area of seafloor). Here we provide data from three surveys: one during HOV Alvin Dive 5044 at Lucky’s Mound on 25 December 2019 on cruise AT42-21 and two during ROV Jason II Dives 1309 and 1311, on the oceanic rise (between Lucky’s Mound and Sentry Spire) on 7 April 2021 and at Sentry Spire on 9 April 2021, respectively, on cruise RR2102. Megafauna were manually annotated to morphotype using ImageJ software. Morphotypes were identified to the lowest taxonomic level and assigned to a feeding mode. This dataset is provided in two formats: long-format comma-separated variable (csv) file and wide-format Excel (xlsx) file. This dataset is analyzed in a manuscript by Meneses et al. (2024).

Amazon Sales Dataset Description This dataset contains 250 records of Amazon sales transactions, including details about the products sold, customers, payment methods, and order statuses.

Columns Description: Order ID - Unique identifier for each order (e.g., ORD0001).

Date - Date of the order.

Product - Name of the product purchased.

Category - Product category (Electronics, Clothing, Home Appliances, etc.).

Price - Price of a single unit of the product.

Quantity - Number of units purchased in the order.

Total Sales - Total revenue from the order (Price × Quantity).

Customer Name - Name of the customer.

Customer Location - City where the customer is based.

Payment Method - Mode of payment (Credit Card, Debit Card, PayPal, etc.).

Status - Order status (Completed, Pending, or Cancelled).

This dataset can be used for sales analysis, customer behavior insights, and revenue trends visualization. 🚀