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.

The interactive simulation mode is carried out using Zoom or any other video conferencing software that enables group rooms. In this mode, the game process and the associated simulation based on the Excel tool provided here are deliberately not fully automated; rather, the participants 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 breakout rooms, e.g. in combination with Moodle.

The automatic mode enables fully automatic simulation with different speed and display options, e.g. successive chart creation during simulation. Finally, both modes allow for different assumptions regarding the probabilities for the car location, the contestant’s first choice and the door opened by the host.

The simulation tool can be used in online teaching. 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 4.0 (Win)
• for Mac: Monty Hall Problem Simulation 4.0 (Mac)

Please note: Some functions are not available in the Mac version of the simulation tool provided here.

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.

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.

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 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.

Students use U.S. Geological Survey (USGS) real-time, real-world seismic data from around the planet to identify where earthquakes occur and look for trends in earthquake activity. They explore where and why earthquakes occur, learning about faults and how they influence earthquakes. Looking at the interactive maps and the data, students use Microsoft Excel to conduct detailed analysis of the most-recent 25 earthquakes; they calculate mean, median, mode of the data set, as well as identify the minimum and maximum magnitudes. Students compare their predictions with the physical data, and look for trends to and patterns in the data. A worksheet serves as a student guide for the activity.

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.

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 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.

Dataset underlying publication of the same title. Data added in Excel file is the numerical values behind Figures 2,3 and 4

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 repository is supplementary to the manuscript "A High-Performance Data Processing Workflow to Incorporate Effect-Directed Analysis in Suspect and Nontarget Screening" (DOI: To be announced) and includes an overview of all measured chemical features and annotations in a waste water treatment plant (WWTP) effluent, dust standard reference material (SRM) 2585 and fetal calf serum (FCS) sample.

Samples were measured using liquid chromatography - high resolution mass spectrometry (LC-HRMS) and fractionated into 80 micro-fractions encompassing a couple of seconds from the chromatographic run. The fractions were tested for their bioactivity in the antibiotics and the TTR-binding assay. The samples were processed separately using one, two, and three technical replicates in positive and negative ion mode. The first excel sheet includes all measured chemical features, suspect screening annotation, and corresponding bioassay responses. The second sheet includes all possible isomer annotations from the CECscreen database (DOI: 10.5281/zenodo.3956586) for the annotated features.

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 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 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 details. Processed data (after retrieval of the raw 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) Radiative temperature of maize, wheat and the bare land (in Yingke oasis maize field), vegetation and the bare land (Huazhaizi desert No. 2 plot) by the thermal cameras at a height of 1.2m above the ground. Optical photos of the scene were also taken. Raw data (read by ThermaCAM Researcher 2001) was archived in IMG format and radiative files are stored in Excel format. . (3) Photosynthesis by LI6400 in Yingke oasis maize field, carried out according to WATER specifications. Raw data were archived in the user-defined format (by notepat.exe) and processed data were in Excel format. (4) Ground object reflectance spectra in Yingke oasis maize field, Huazhaizi maize field, Huazhaizi desert No. 1 and 2 plots, by ASD FieldSpec (350～2500 nm) from Institute of Remote Sensing Applications (IRSA), CAS. Raw data were binary files direct from ASD (by ViewSpecPro), which were recorded daily in detail, and pre-processed data on reflectance were in .txt format. (5) The radiative temperature in Huazhaizi desert No. 2 plot by the handheld infrared thermometer (BNU and IRSA). Raw data, blackbody calibrated data and processed data (in Excel format) were all archived. (6) FPAR (Fraction of Photosynthetically Active Radiation) 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 Excel format. (7) The radiative temperature of the maize canopy by the automatic thermometer (FOV: 10°; emissivity: 0.95) mearsued at nadir with an time intervals of 1s in Huazhaizi desert maize field. Raw data, blackbody calibrated data and processed data were all archived as Excel files. (8) Maize albedo from two shortwave radiometer in Yingke oasis maize field. R =10H (R for FOV radius; H for the probe height). Data were archived in Excel format.

The Students in Higher Education publication is presented as folders of Microsoft Excel tables. These include breakdowns by subject of study, level of study, mode of study, age, sex, ethnicity, disability, domicile and much more. There are also tables providing a comprehensive look at the qualifications obtained by students at all levels. Data included for 1994/95 to 2015/16

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.

Source Data.xlsx includes the raw data for all figures from the main manuscript and Supplementary Information. The data is organized by each figure in separate sheets within the Excel file.

Supplementary Data.zip contains five seperate Excels:

File Name: Supplementary Data 1

Description: Comparison summary of identification between DIA-NN and DIA-BERT

File Name: Supplementary Data 2

Description: Identified peptide precursors and proteins using DIA-NN (in library-based mode) and DIA-BERT

File Name: Supplementary Data 3

Description: Parameters for simulated data by modified Synthedia

File Name: Supplementary Data 4

Description: Quantification of peptide precursors and proteins using DIA-NN (in library-based mode) and DIA-BERT in combined search

File Name: Supplementary Data 5

Description: Comparison of quantification performance on the three-species dataset using DIA-BERT with different quantification models and DIA-NN.