This dataset includes the community survey forms in English and Arabic, the collected data, and the responses of the open-end answers in Arabic and their translation in English.A structured questionnaire, consisted of 43 questions, was used for a community survey in Ghaddar in 2021. Ghaddar is an administrative town of the Old Dongola Unit, Goulid County, Northern State of the Republic of Sudan, with the population around 6000. Ghaddar is located in the immediate north of the archaeological site, Old Dongola.The aim of the survey was to understand the community’s life, experiences and perspective towards archaeology and heritage, ideas about tourism and related development, and the evaluation of the engagement programmes conducted at Old Dongola from 2019 to 2020.The 43 single or multiple-choice questions were divided into seven themes; 1) life in Ghaddar, 2) archaeological works in the area, 3) benefits of archaeological work in Old Dongola, 4) benefits from tourism development, 5) heritage and archaeology, 6) community engagement programmes, 7) demographic questions.The questions in Themes 1) to 4) and 7) are the same as the first community survey and was developed by Katarzyna Radziwiłko and Tomomi Fushiya (Polish Centre of Mediterranean Archaeology, University of Warsaw) in 2019. The first survey questionnaire was developed in English and was translated into Arabic by Mohamed Hassan Siedahmed. The survey questionnaire that was used in the survey 2019 was modified in 2021, by Tomomi Fushiya, to combine with an evaluation of community engagement programmes; two questions were added under Theme 5), and five questions under the new theme, Theme 6). The Tohamy Abulghasim translated the additional questions.A random sampling method was applied to collect the data in Ghaddar. The collection of the data was carried out by three local recent graduates (Umm Salma Abu AlZine Mohamed, Manal Mohamed, Wafa Ahmed), the head of tourism office (Abeer Babiker), and Tohamy Abulghasim, under the supervision of Tomomi Fushiya, in five different areas of Ghaddar from 6 to 15 February 2021. 195 respondents answered the questionnaire and six were considered defective due to incomplete responses and were omitted from the analysis. The analysed responses were in total 189 (Women: 95; Men 89; No answer 5). The collected data was entered to SPSS by Tomomi Fushiya for frequency and tabulation analyses.The data collection was carried out as a part of the Dialogue community engagement project (2019-2022) within the framework of a multidisciplinary project, ArchaeoCDN. Archaeological Centre of Scientific Excellence, led by Dr. hab. Artur Obłuski (PCMA, UW), funded by the Ministry of Science and Higher Education of the Republic of Poland.Tomomi Fushiya conducted the fieldwork at Old Dongola as a member of the PCMA, UW archaeological project, headed by Dr. hab. Artur Obłuski (the director of PCMA, UW). The PCMA, UW Old Dongola project has obtained research permission to work in Old Dongola from the National Corporation for Antiquities and Museums, Sudan.

Abstract

The Cambodia Socio-Economic Survey (CSES) asks questions to a country wide sample of households and household members about housing conditions, education, economic activities, household production and income, household level and structure of consumption, health, victimization, etc. There are also questions related to people in the labour force, e.g. labour force participation.

Poverty reduction is a major commitment by the Royal Government of Cambodia. Accurate statistical information about the living standards of the population and the extent of poverty is an essential instrument to assist the Government in diagnosing the problems, in designing effective policies for reducing poverty and in monitoring and evaluating the progress of poverty reduction. The Millennium Development Goals (MDG) has been adopted by the Royal Government of Cambodia and a National Strategic Development Plan (NSDP) has been developed. The MDGs are also incorporated into the “Rectangular Strategy of Cambodia”.

Cambodia is still a predominantly rural and agricultural society. The vast majority of the population get their subsistence in households as self-employed in agriculture. The level of living is determined by the household's command over labour and resources for own-production in terms of land and livestock for agricultural activities, equipments and tools for fishing, forestry and construction activities and income-earning activities in the informal and formal sector. The CSES aims to estimate household income and consumption/expenditure as well as a number of other household and individual characteristics.

The main objective of the survey is to collect statistical information about living conditions of the Cambodian population and the extent of poverty. The survey can be used for identifying problems and making decisions based on statistical data.

The main user is the Royal Government of Cambodia (RGC) as the survey supports monitoring the National Strategic Development Plan (NSDP) by different socio-economic indicators. Other users are university researchers, analysts, international organizations e.g. the World Bank and NGO’s. The World Bank has published a report on poverty profile and social indicators using CSES 2007 data . In this regard, the CSES continues to serve all stakeholders involved as essential instruments in order to assist in diagnosing the problems and designing their most effective policies. The CSES micro data at NIS is available for research and analysis by external researchers after approval by Senior Minister of Planning. The interesting research questions that could be put to the data are many; NIS welcomes new research based on CSES data.

General Objectives: CSES 2013 will continue the work started through CSES 2004 and the annual CSES 2007 to 2014 and would primarily aim at producing information needed for planning and policy making for reduction of poverty in Cambodia. Reduction of poverty has been given high priority in Cambodia's National Strategic Development Plan (NSDP 2009-2013). In addition to this, the survey data help in various other ways in developmental planning and policy making in the country. They would also prove useful for the production of National Accounts in Cambodia.

A long-term objective of the entire project is to build national capability in NIS for conducting socio-economic surveys and for utilizing survey data for planning for national development and social welfare.

Specific Objectives Among specific objectives, the following deserve special mention: 1) Obtain data on infrastructural facilities in villages, especially facilities for schooling and health care and associated problems. 2) Obtain data on retail prices of selected food, non-food and medicine items prevailing in the villages. 3) Collect data on utilization of education, housing and land ownership 4) Collect data on household assets and outstanding loans. 5) Collect data on household's construction activities. 6) Collect information on maternal health, child health/care. 7) Collect information on health care seeking and expenditure of the household members related to illness, injury and disability. 8) Collect information on economic activities including the economic activities for children aged between 5 and 17 years. 9) Collect information on victimization by the household 10) Collect information on the presence of the household members.

Geographic coverage

National Phnom Penh / Other Urban / Other Rural

Analysis unit

• Households
• Individuals

Universe

All resident households in Cambodia

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling design in the CSES 2013 survey is a three-stage design. In stage one a sample of villages is selected, in stage two an Enumeration Area (EA) is selected from each village selected in stage one, and in stage three a sample of households is selected from each EA selected in stage two.

Stage 1: A random sample of PSUs was selected from each stratum. The sampling method was systematic PPS (PPS=sampling with probability proportional to size). The size measure used was the number of households in the PSU according to the sampling frame.

Stage 2: One EA was selected by Simple Random Sampling (SRS), in each village selected in stage 1.

Stage 3: In each selected EA a sample of 10 households was selected. The selection of households was done in the field by the supervisors/interviewers. All households in selected EAs were listed by the enumerator. The sample of households was then selected from the list by systematic sampling with a random start (the start value controlled by NIS).

Mode of data collection

Face-to-face [f2f]

Research instrument

Three different questionnaires or forms were used in the survey:

Form 1: Household listing sheets to be used in the sampling procedure in the enumeration areas.

Form 2: Village questionnaire answered by the village leader about economy and infrastructure, crop production, health, education, retail prices and sales prices of agriculture, employment and wages, and recruitment of children for work outside the village.

Form 3: Household questionnaire with questions for each household member, including modules on migration, education and literacy, housing conditions, crop production, household liabilities, durable goods, construction activities, nutrition, fertility and child care, child feeding and vaccination, health of children, mortality, current economic activity, health and illness, smoking, HIV/AIDS awareness, and victimization.

The interviewer is responsible for filling up Form 1 and Form 3 to respondents. . For Form 2, the supervisors will be asked to canvass this form. In case that the supervisors are absent for any reason, the interviewers may be also asked to help fill up this form (Form 2).

Cleaning operations

The NIS team commenced their work of checking and coding and coding in begining of February after the first month of fieldwork was completed. Supervisors from the field delivered questionaires to NIS. Sida project expert and NIS Survey Manager helped in solving relevant matters that become apparent when reviewing questionires on delivery.

Response rate

The CSES 2013 enjoyed almost a 100 percent response rate. The high response rate together with close and systematic fieldwork supervision by the core group members were a major contribution for achieving high quality survey results.

Sampling error estimates

In order to provide a basis for assessing the reliability or precision of CSES estimates, the estimation of the magnitude of sampling error in the survey data were computed. Since most of the estimates from the survey are in the form of weighted ratios, thus variances for ratio estimates are computed.

The Coefficients of Variation (CV) on national level estimates are generally below 4 percent. The exception is the CV for total value of assets where there are rather high CVs especially in the urban areas, which should be expected.

The CVs are somewhat higher in the urban and rural domains but still generally below 7 percent. For the five zones, the average CVs are in the range 5 to 13 percent with a few exceptions where the CVs are above 20 percent. For provinces the CVs for food consumption are 9 percent on average.

The sample take within Primary Sampling Units (PSU) was set to 10 households per PSU in the CSES 1999. When data on variances became available, it was possible to make crude calculations of the optimal sample take within PSU. Calculations on some of the central estimates in the CSES 1999 show that the design effects in most cases are in the range 1 to 5.

Intra-cluster correlation coefficients have been calculated based on the design effects. These correlation coefficients are somewhat high. The reason is that the characteristics that are measured tend to be concentrated (clustered) within the PSUs. The optimal sample size within PSUs under different assumptions on cost ratios and intra-cluster correlation coefficients was then calculated. The cost ratio is the average cost for adding a village to the sample divided by the average cost of including an extra household in the sample. In the CSES, it was chosen to adopt a fairly low cost ratio due to the fact that the interview time per household is long. Under this assumption the optimal sample size is probably around 10 households per village for many of the CSES indicators.

Max Foundation

Max Foundation is a Netherlands-based NGO that works towards a healthy start for every child in the most effective and long-lasting way. Over the past 15 years, our teams in Bangladesh and Ethiopia have reached almost 3 million people, supporting communities in reducing stunting and undernutrition by gaining better access to clean water, sanitation and hygiene, as well as healthy diets and care for mother and child.

Maximising our impact and cost efficiency are at the core of our work, which makes quantifying and analysing our programmes crucial. We therefore collect a lot of information on the communities we work with; to understand them better and see where and how we can improve as an organisation.
This data set is one of many we are making publicly available because we believe that data in the development sector should be open: not as a goal in itself, but as a way to help the sector be more effective and create more impact.

Content

These data were collected between Q1 and Q3 in 2018 (with a few observations earlier and later) in the areas in Bangladesh where Max Foundation is active. The original data set is a census, and covers basically all households in the area (some could not be found or declined consent). In order to protect privacy, villages with fewer than 50 responses are excluded and only 15% of the data is present in this dataset, stratified by village. The data were collected because in 2018 Max Foundation scaled up its operations, both geographically and thematically (from a focus on WASH (water, sanitation and hygiene) to including nutrition and food security). The data give a detailed picture of the access to WASH in the area.

Privacy and links to our other data

All of Max Foundation's data are collected and processed according to GDPR standards and explicit informed consent is given by all respondents. They are also clearly informed that choosing not to participate in data collection will in no way affect their eligibility for, or receiving of, products or services from Max Foundation.

Furthermore, we enforce strong privacy protections on our open data to minimise the risk of these data being used to cause harm or re-identify individuals. Concretely this means: - Administrative units up to the Union can be directly identified with the BD_ loc_xx data. The ward and village are masked by random numbers. However, to ensure it is still possible to compare our data sets, these random numbers are consistent across all data sets. This means that village '1' in this data is the same as village '1' in all of our other Bangladesh datasets, unless stated otherwise; - Household numbers are randomised and these are NOT kept the same between datasets; - Sensitive variables are omitted, censored or bucketed.

The column descriptions specify any transformations done to the data.

Acknowledgements

These data could have not been collected without the generous support from the Embassy of the Kingdom of the Netherlands in Dhaka and numerous other donors who have supported us over the years. Special thanks to our Bangladesh team for their excellent work in guiding the data collection process.

What you can do for our communities

We invite you to share any interesting insights you have derived from the data with us. From visualising our impact, to uncovering which parts of our programmes are most strongly related with reducing stunting, to making new connections we may have not even considered; we are eager to hear how we can be more effective in what we do and how we do it.

More detailed data insights are available from our internal data, such as the linking of households between datasets. Please note that we would be happy to share more detailed data with researchers, students and many others once proper agreements are in place.

As we value impact above all else, we are happy to work with anyone who can help us to improve our impact. We are constantly adapting our approach based on internal and external findings, and invite you to join us on this journey. Together we can ensure that every child has a healthy start.

Max Foundation

Max Foundation is a Netherlands-based NGO that works towards a healthy start for every child in the most effective and long-lasting way. Over the past 15 years, our teams in Bangladesh and Ethiopia have reached almost 3 million people, supporting communities in reducing stunting and undernutrition by gaining better access to clean water, sanitation and hygiene, as well as healthy diets and care for mother and child.

Maximising our impact and cost efficiency are at the core of our work, which makes quantifying and analysing our programmes crucial. We therefore collect a lot of information on the communities we work with; to understand them better and see where and how we can improve as an organisation.
This data set is one of many we are making publicly available because we believe that data in the development sector should be open: not as a goal in itself, but as a way to help the sector be more effective and create more impact.

Content

Max Foundation's programmes are business-driven, locally owned and geared to creating sustainable solutions. We therefore work with, train and support entrepreneurs to help their communities access water, sanitation and hygiene, and nutrition products and services more easily. We collect revenue and cost data on these entrepreneurs annually. We work with different types of entrepreneurs who provide a range of products and services. - Health Promotion Agents (HPAs) sell small items such as sanitary napkins, soaps, family planning services, vegetable seeds and child growth measurements; - Local Sanitation Entrepreneurs (LEs) build latrines and other hardware; (NB we hope to add this data soon) - Sweepers empty latrines periodically and make small repairs or changes to latrines. Data is collected annually and the figures provided cover the entire year, unless stated otherwise. Note that the first dataset is most comprehensive, and covers basic demographic information as well. In later years we only collected financial information. The IDs of entrepreneurs are consistent between years, however, it must be noted that there is significant churn and many entrepreneurs either quit or shift their business focus to products not directly related to Max Foundation activities. Costs, revenues and profits are given in local currency (Bangladesh Taka).

Privacy and links to our other data

All of Max Foundation's data are collected and processed according to GDPR standards and explicit informed consent is given by all respondents. They are also clearly informed that choosing not to participate in data collection will in no way affect their eligibility for, or receiving of, products or services from Max Foundation.

Furthermore, we enforce strong privacy protections on our open data to minimise the risk of these data being used to cause harm or re-identify individuals. Concretely this means: - Administrative units up to the Union can be directly identified with the BD_ loc_xx data (which can be found in our Max Foundation Bangladesh 2018 WASH Census dataset). The ward and village are masked by random numbers. However, to ensure it is still possible to compare our data sets, these random numbers are consistent across all datasets. This means that village '1' in this data is the same as village '1' in all of our other Bangladesh datasets, unless stated otherwise; - Sensitive variables are omitted.

Acknowledgements

These data could have not been collected without the generous support from the Embassy of the Kingdom of the Netherlands in Dhaka and numerous other donors who have supported us over the years. Special thanks to our Bangladesh team for their excellent work in guiding the data collection process.

What you can do for our communities

We invite you to share any interesting insights you have derived from the data with us. From visualising our impact, to uncovering which parts of our programmes are most strongly related with reducing stunting, to making new connections we may have not even considered; we are eager to hear how we can be more effective in what we do and how we do it.

More detailed data insights are available from our internal data, such as the linking of households between datasets. Please note that we would be happy to share more detailed data with researchers, students and many others once proper agreements are in place.

As we value impact above all else, we are happy to work with anyone who can help us to improve our impact. We are constantly adapting our approach based on internal and external findings, and invite you to join us on this journey. Together we can ensure that every child has a healthy start.

Goalkeeper and Midfielder Statistics

Leveraging Statistical Data Of Goalkeepers and Midfielders

By [source]

About this dataset

Welcome to Kaggle's dataset, where we provide rich and detailed insights into professional football players. Analyze player performance and team data with over 125 different metrics covering everything from goal involvement to tackles won, errors made and clean sheets kept. With the high levels of granularity included in our analysis, you can identify which players are underperforming or stand out from their peers for areas such as defense, shot stopping and key passes. Discover current trends in the game or uncover players' hidden value with this comprehensive dataset - a must-have resource for any aspiring football analyst!

More Datasets

For more datasets, click here.

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How to use the dataset

• Define Performance: The first step of using this dataset is defining what type of performance you are measuring. Are you looking at total goals scored? Assists made? Shots on target? This will allow you to choose which metrics from the dataset best fit your criteria.

• Descriptive Analysis: Once you have chosen your metric(s), it's time for descriptive analysis. This means analyzing the patterns within the data that contribute towards that metric(s). Does one team have more potential assist makers than another? What about shot accuracy or tackles won %? With descriptive analysis, we'll look for general trends across teams or specific players that influence performance in a meaningful way.

• Predictive Analysis: Finally, we can move onto predictive analysis. This type of analysis seeks to answer two questions: what are factors that predict player performance? And which factors are most important when predicting performance? Utilizing various predictive models—ex – Logistic regression or Random forest -we can determine which variables in our dataset best explain a certain metric’s outcome—for example –expected goals per match -and build models that accurately predict future outcomes based on given input values associated with those factors.

By following these steps outlined here, you'll be able to get started in finding relationships between different metrics from this dataset and leveraging these insights into predictions about player performance!

Research Ideas

• Creating an advanced predictive analytics model: By using the data in this dataset, it would be possible to create an advanced predictive analytics model that can analyze player performance and provide more accurate insights on which players are likely to have the most impact during a given season.
• Using Machine Learning algorithms to identify potential transfer targets: By using a variety of metrics included in this dataset, such as shots, shots on target and goals scored, it would be possible to use Machine Learning algorithms to identify potential transfer targets for a team.
• Analyzing positional differences between players: This dataset contains information about each player's position as well as their performance metrics across various aspects of the game (e.g., crosses attempted, defensive clearances). Thus it could be used for analyzing how certain positional groupings perform differently from one another in certain aspects of their play over different stretches of time or within one season or matchday in particular.

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: DEF PerApp 2GWs.csv | Column name | Description | |:----------------------------|:------------------------------------------------------------| | Name | Name of the player. (String) | | App. | Number of appearances. (Integer) | | Minutes | Number of minutes played. (Integer) | | Shots | Number of shots taken. (Integer) | | Shots on Target | Number of shots on target. (Integer) ...

Abstract

HIES collects a wealth of information on HH income and expenditure, such as source of income by industry, HH expenditure on goods and services, and income and expenditure associated with subsistence production and consumption. In addition to this, HIES collects information on sectoral and thematic areas, such as education, health, labour force, primary activities, transport, information and communication, transfers and remittances, food expenditure (acquisition) and gender. The Pacific Islands regionally standardized HIES instruments and procedures were adopted by NSO for the 2015/2016 HIES. These standards, were designed to feed high-quality data to HIES data end users for: • deriving expenditure weights and other useful data for the revision of the CPI; • supplementing the data available for use in compiling official estimates of various components in the System of NA; • supplementing the data available for production of the balance of payments; and • gathering information on poverty lines and the incidence of poverty in Niue.

The data allow for the production of useful indicators and information on the industries covered in the survey, including providing data to inform indicators under the United Nations Sustainable Development Goals (SDGs). This report, the above listed outputs, and additional thematic analyses, collectively provide information to assist with multisector planning and policy formation. The 2015/2016 HIES was conducted to update the 2002 HIES data and aimed to estimate the total amount HH spent and earnt over the past 12 months at the national level (total expenditure and income).

Geographic coverage

National coverage.

Analysis unit

Household (private) Individual

Universe

HIES covered all persons who were considered to be usual residents of private dwellings (must have been living in Niue for a period of 12-months, or have intention to live in Niue for a period of 12-months in order to be included in the survey).

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample frame used for the selection was the latest HH listing available in 2015. In total 513 resident HHs were listed, and 224 HHs were randomly selected out of this updated list. This sample of 224 HHs is divided in 2 different lists, a first list (list A) of 160 HHs to interview in priority, and an extra list (list B) of 64 HHs to use in case of replacement required (unavailability of the HHs to respond to the interview, or refusal).

The sample distribution for the 2015/16 Household Income and Expenditure Survey (HIES). Small island surveys are inevitably subject to sampling error. In the case of Niue, financial and human resource constraints prevent increasing the sample size, however it is important to note that the application of the results will be limited due to sampling error. Despite this, small-island sampling is a common phenomenon in the Pacific and the aggregated results of HIES will be sufficient to serve the primary objectives of HIES. Given the sample size, the 2015/16 HIES will only report aggregated results at the national level (i.e., no disaggregation by urban/rural, for example). Compared with the 2002 HIES, the sampling strategy for the 2015/16 HIES has an increased overall sample size (by 59 households). As a result, the projected RSEs will be lower than in the previous HIES, which is indicative that the 2015/16 sampling strategy will improve the statistical validity of the results.

HIES schedule The HIES is scheduled to begin on 2 November 2011. Prior to this, the Statistics Niue Office will collaborate with SPC to deliver a two week training of enumerators, data entry operators and supervisors. The training is scheduled for 20 to 30 October 2015. The field operations will occur over eight rounds consisting of three-weeks per round. During each round, 20 households will be interviewed and all of the data will be entered into the database. Table 3 presents the HIES schedule with the corresponding staff requirements.

HIES method Each team will interview and enter data of a randomly selected set of households (10 per team, 20 in total per round) over a period of 3-weeks. Each 3-week block is called a round and there will be 8 rounds in total for Niue 2015/16 HIES. The round schedule for an enumerator, data entry operator and supervisor is presented below. In summary, each enumerator will visit each of the 5 households 7 times per round. They will visit households 1 to 3 on every odd day and households 4 and 5 on every even day over 14 days. The data entry of all Modules starts from the beginning of each round as soon as the first visits to the households are completed. Their activities during each visit is summarised below. · Module questionnaires have to be filled in by the enumerator during week 1 (visits 1, 2, 3 and 4) and entered by the data entry operator in the same week. During the second week module data are edited and checked during visits 5, 6 and 7. · Household diary 1 is delivered to the household during the first visit and picked during the fourth visit. Diary 2 is dropped during the fourth visit and picked during the seventh visit. During each visit the diary has to be properly checked by the enumerator. · Day 17 to 19 in week 3 are used to catch up with any delay during the round, to prepare the team for the next round and for the data entry operator and supervisor to complete data entry of the diaries.

Mode of data collection

Face-to-face [f2f]

Research instrument

Four modules are completed by paper-based personal interview, including: 1. Demographic information - characteristics of Household (HH) members, including activity and education profile; 2. HH characteristics and expenditure; 3. Individual expenditure; and 4. Individual and HH income.

Depending on the information being collected, a recall period (ranging from the last 7 days to the last 12 months) is applied to various sections of the questionnaire. The forms were completed by face-to-face interview, usually with the HH head providing most of the information, with other HH members being interviewed when necessary. The interviews took place over a 2-week period such that the HH diary, which is completed by the HH on a daily basis for 2 weeks, can be monitored while the module interviews take place. The HH diary collects information on the HH's daily expenditure on goods and services; and the harvest, capture, collection or slaughter of primary produce (fruit, vegetables and animals) by intended purpose (home consumption, sale or to give away). The income and expenditure data from the modules and the diary are concatenated (ensuring that double counting does not occur), annualised, and extrapolated to form the income and expenditure aggregates presented herein.

The questionnaire being in English, which could be a second language for both the interviewers and respondents, and the need to complete a written diary (noting that: three-quarters of diaries were in Niuean; HHs were given the opportunity to complete a Niuen written diary; and enumerators could mostly converse in Niuean when required).

Cleaning operations

Software used was CSPro.

Response rate

On the overall 156 HHs were successfully interviewed (98 per cent of the sample), and 118 (74 percent) come from the list A (original list of HHs to interview). 38 HHs come from the replacement list (replacements occurred in 24 percent of the cases). Finally, it is interesting to note that 4 HHs are missing (interview incomplete or poor data quality) and were dropped from the final dataset.

Sampling error estimates

Amount SE RSE Total amount 95% Interval
Total expenditure 19,282,670 957,770 4.97% 17,405,440 21,159,890
Total consumption expendiutre 16,827,260 708,250 4.21% 15,439,090 18,215,440
Total non consumption expnditure (inc investment) 2,455,410 442,050 18.00% 1,589,000 3,321,820
Total cash expenditure 16,246,310 861,460 5.30% 14,557,850 17,934,770
Total subsistence expenditure 1,395,160 155,870 11.17% 1,089,650 1,700,660
Total food expenditure 5,118,690 277,330 5.42% 4,575,120 5,662,260

Data appraisal

Non-sampling errors cannot be readily measured, however it is worth noting the issues associated with non-sampling errors, including: • both respondents and interviewers may not entirely understand the information required from the survey, which can result in misinterpretation of the question being asked and the incorrect response; • enumerator and respondent fatigue, resulting in underreporting, especially in completion of the HH diary; • unwillingness to fully disclose information - especially in a small-island context - such as income and expenditure on some items (e.g., alcohol, tobacco and cash donations); • the questionnaire being in English, which could be a second language for both the interviewers and respondents, and the need to complete a written diary (noting that: three-quarters of diaries were in Niuean; HHs were given the opportunity to complete a Niuen written diary; and enumerators could mostly converse in Niuean when required); and • the

Abstract

The Community Survey (CS) is a nationally representative, large-scale household survey which was conducted from February to March 2007. The Community Survey is designed to provide information on the trends and levels of demographic and socio-economic data, such as population size and distribution; the extent of poor households; access to facilities and services, and the levels of employment/unemployment at national, provincial and municipality level. The data can be used to assist government and the private sector in the planning, evaluation and monitoring of programmes and policies. The information collected can also be used to assess the impact of socio-economic policies and provide an indication as to how far the country has gone in its strides to eradicate poverty.

Censuses 1996 and 2001 are the only all-inclusive censuses that Statistics South Africa has thus far conducted under the new democratic dispensation. Demographic and socio-economic data were collected and the results have enabled government and all other users of this information to make informed decisions. When cabinet took a decision that Stats SA should not conduct a census in 2006, it created a gap in information or data between Census 2001 and the next Census scheduled to be carried out in 2011. A decision was therefore taken to carry out the Community Survey in 2007.

The main objectives of the survey were: · To provide estimates at lower geographical levels than existing household surveys; · To build human, management and logistical capacities for Census 2011; and · To provide inputs into the preparation of the mid-year population projections.

The wider project strategic theme is to provide relevant statistical information that meets user needs and aspirations. Some of the main topics that are covered by the survey include demography, migration, disability and social grants, educational levels, employment and economic activities.

Geographic coverage

The survey covered the whole of South Africa, including all nine provinces as well as the four settlement types - urban-formal, urban-informal, rural-formal (commercial farms) and rural-informal (tribal areas).

Analysis unit

Households

Universe

The Community Survey covered all de jure household members (usual residents) in South Africa. The survey excluded collective living quarters (institutions) and some households in EAs classified as recreational areas or institutions. However, an approximation of the out-of-scope population was made from the 2001 Census and added to the final estimates of the CS 2007 results.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design

The sampling procedure that was adopted for the CS was a two-stage stratified random sampling process. Stage one involved the selection of enumeration areas, and stage tow was the selection of dwelling units.

Since the data are required for each local municipality, each municipality was considered as an explicit stratum. The stratification is done for those municipalities classified as category B municipalities (local municipalities) and category A municipalities (metropolitan areas) as proclaimed at the time of Census 2001. However, the newly proclaimed boundaries as well as any other higher level of geography such as province or district municipality, were considered as any other domain variable based on their link to the smallest geographic unit - the enumeration area.

The Frame

The Census 2001 enumeration areas were used because they give a full geographic coverage of the country without any overlap. Although changes in settlement type, growth or movement of people have occurred, the enumeration areas assisted in getting a spatial comparison over time. Out of 80 787 enumeration areas countrywide, 79 466 were considered in the frame. A total of 1 321 enumeration areas were excluded (919 covering institutions and 402 recreational areas).

On the second level, the listing exercise yielded the dwelling frame which facilitated the selection of dwellings to be visited. The dwelling unit is a structure or part of a structure or group of structures occupied or meant to be occupied by one or more households. Some of these structures may be vacant and/or under construction, but can be lived in at the time of the survey. A dwelling unit may also be within collective living quarters where applicable (examples of each are a house, a group of huts, a flat, hostels, etc.).

The Community Survey universe at the second-level frame is dependent on whether the different structures are classified as dwelling units (DUs) or not. Structures where people stay/live were listed and classified as dwelling units. However, there are special cases of collective living quarters that were also included in the CS frame. These are religious institutions such as convents or monasteries, and guesthouses where people stay for an extended period (more than a month). Student residences - based on how long people have stayed (more than a month) - and old-age homes not similar to hospitals (where people are living in a communal set-up) were treated the same as hostels, thereby listing either the bed or room. In addition, any other family staying in separate quarters within the premises of an institution (like wardens' quarters, military family quarters, teachers' quarters and medical staff quarters) were considered as part of the CS frame. The inclusion of such group quarters in the frame is based on the living circumstances within these structures. Members are independent of each other with the exception that they sleep under one roof.

The remaining group quarters were excluded from the CS frame because they are difficult to access and have no stable composition. Excluded dwelling types were prisons, hotels, hospitals, military barracks, etc. This is in addition to the exclusion on first level of the enumeration areas (EAs) classified as institutions (military bases) or recreational areas (national parks).

The Selection of Enumeration Areas (EAs)

The EAs within each municipality were ordered by geographic type and EA type. The selection was done by using systematic random sampling. The criteria used were as follows: In municipalities with fewer than 30 EAs, all EAs were automatically selected. In municipalities with 30 or more EAs, the sample selection used a fixed proportion of 19% of all sampled EAs. However, if the selected EAs in a municipality were less than 30 EAs, the sample in the municipality was increased to 30 EAs.

The Selection of Dwelling Units

The second level of the frame required a full re-listing of dwelling units. The listing exercise was undertaken before the selection of DUs. The adopted listing methodology ensured that the listing route was determined by the lister. Thisapproach facilitated the serpentine selection of dwelling units. The listing exercise provided a complete list of dwelling units in the selected EAs. Only those structures that were classified as dwelling units were considered for selection, whether vacant or occupied. This exercise yielded a total of 2 511 314 dwelling units.

The selection of the dwelling units was also based on a fixed proportion of 10% of the total listed dwellings in an EA. A constraint was imposed on small-size EAs where, if the listed dwelling units were less than 10 dwellings, the selection was increased to 10 dwelling units. All households within the selected dwelling units were covered. There was no replacement of refusals, vacant dwellings or non-contacts owing to their impact on the probability of selection.

Mode of data collection

Face-to-face [f2f]

Research instrument

Consultation on Questionnaire Design Ten stakeholder workshops were held across the country during August and September 2004. Approximately 367 stakeholders, predominantly from national, provincial and local government departments, as well as from research and educational institutions, attended. The workshops aimed to achieve two objectives, namely to better understand the type of information stakeholders need to meet their objectives, and to consider the proposed data items to be included in future household surveys. The output from this process was a set of data items relating to a specific, defined focus area and outcomes that culminated with the data collection instrument (see Annexure B for all the data items).

Questionnaire Design The design of the CS questionnaire was household-based and intended to collect information on 10 people. It was developed in line with the household-based survey questionnaires conducted by Stats SA. The questions were based on the data items generated out of the consultation process described above. Both the design and questionnaire layout were pre-tested in October 2005 and adjustments were made for the pilot in February 2006. Further adjustments were done after the pilot results had been finalised.

Cleaning operations

Editing The automated cleaning was implemented based on an editing rules specification defined with reference to the approved questionnaire. Most of the editing rules were categorised into structural edits looking into the relationship between different record type, the minimum processability rules that removed false positive readings or noise, the logical editing that determine the inconsistency between fields of the same statistical unit, and the inferential editing that search similarities across the domain. The edit specifications document for the structural, population, mortality and housing edits was developed by a team of Stats SA subject-matter specialists, demographers, and programmers. The process was successfully

This dataset compares manager feedback surveys with 360-degree reviews, highlighting differences in scope, anonymity, and feedback depth.

Max Foundation

Max Foundation is a Netherlands-based NGO that works towards a healthy start for every child in the most effective and long-lasting way. Over the past 15 years, our teams in Bangladesh and Ethiopia have reached almost 3 million people, supporting communities in reducing stunting and undernutrition by gaining better access to clean water, sanitation and hygiene, as well as healthy diets and care for mother and child.

Maximising our impact and cost efficiency are at the core of our work, which makes quantifying and analysing our programmes crucial. We therefore collect a lot of information on the communities we work with; to understand them better and see where and how we can improve as an organisation.

This dataset is one of many we are making publicly available because we believe that data in the development sector should be open: not as a goal in itself, but as a way to help the sector be more effective and create more impact.

Content

These data are collected quarterly at the village-level, in aggregate. In Max Foundation's Healthy Village Approach, our team has created several indicators to track how villages are progressing on WASH (water, sanitation and hygiene), nutrition, and SRHR (sexual and reproductive health and rights) & Baby WASH.

Privacy and links to our other data

All of Max Foundation's data are collected and processed according to GDPR standards and explicit informed consent is given by all respondents. They are also clearly informed that choosing not to participate in data collection will in no way affect their eligibility for, or receiving of, products or services from Max Foundation.

Furthermore, we enforce strong privacy protections on our open data to minimise the risk of these data being used to cause harm or re-identify individuals.

Concretely this means: - Village are masked by random numbers. However, to ensure it is still possible to compare our data sets, these random numbers are consistent across all datasets. This means that village '1' in this data is the same as village '1' in all of our other Bangladesh datasets, unless stated otherwise. Higher level administrative units can be deduced from matching the village numbers to the bd_ loc_XX datasets in the Max Foundation Bangladesh 2018 WASH Census dataset. - Population counts have been bucketed. The values represent the mid-point of a given bucket, for the number of households in the village, which is bucketed by 20 households, the value 50 represents 40-60 households. The values have also been censored at the upper end, and some at the lower end as well. The column descriptions specify any transformations done to the data.

A final note to anyone trying to link Max Foundation's various datasets; as data is self-reported, sometimes by individuals other times by whole communities, there may be differences in for instance the number of households or the number of stunted children in a given village in this dataset versus in another. Some differences can be explained by differences in definitions (a household is a concept that is often hard to define and its interpretation may vary from person to person), and others by a lack of information on the part of a respondent. We therefore encourage you to look at these differences and see which value makes the most sense for the specific analysis you are conducting.

Acknowledgements

These data could have not been collected without the generous support from the Embassy of the Kingdom of the Netherlands in Dhaka and numerous other donors who have supported us over the years. Special thanks to our Bangladesh team for their excellent work in guiding the data collection process.

What you can do for our communities

We invite you to share any interesting insights you have derived from the data with us. From visualising our impact, to uncovering which parts of our programmes are most strongly related with reducing stunting, to making new connections we may have not even considered; we are eager to hear how we can be more effective in what we do and how we do it.

More detailed data insights are available from our internal data, such as the linking of households between datasets. Please note that we would be happy to share more detailed data with researchers, students and many others once proper agreements are in place.

As we value impact above all else, we are happy to work with anyone who can help us to improve this. We are constantly adapting our approach based on internal and external findings, and invite you to join us on this journey. Together we can ensure that every child has a healthy start.

U.S. Pandemic Mental Health Care

Impact on Households in Previous 4 Weeks

By US Open Data Portal, data.gov [source]

About this dataset

This U.S. Household Pandemic Impacts dataset assesses the mental health care that households in America have been receiving over the past four weeks during the Covid-19 pandemic. Produced by a collaboration between the U.S. Census Bureau, and five other federal agencies, this survey was designed to measure both social and economic impacts of Covid-19 on American households, such as employment status, consumer spending trends, food security levels and housing disruptions among other important factors. The data collected was based on an internet questionnaire which was conducted through emails and text messages sent to randomly selected housing units from across America linked with email addresses or cell phone numbers from the Census Bureau Master Address File Data; all estimates comply with NCHS Data Presentation Standards for Proportions. Be sure to check out more about how U.S Government Works for further details!

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How to use the dataset

This dataset can be useful to examine the impact of the Covid-19 pandemic on access to and utilization of mental health care by U.S. households in the last 4 weeks.

By studying this dataset, you can gain insight into how people’s mental health has been affected by the pandemic and identify trends based on population subgroups, states, phases of the survey and more.

Instructions for Use: - To get started, open up ‘csv-1’ found in this dataset. This file contains information on access to and utilization of mental health care by U.S households in the last 4 weeks, broken down into 14 different columns (e.g., Indicator, Group, State).
- Familiarize yourself with each column label (e.g., Time Period Start Date), data type (e

Research Ideas

• Analyzing the impact of pandemic-induced stress on different demographic groups, such as age and race/ethnicity.
• Comparing the mental health care services received in different states over time.
• Investigating the correlation between socio-economic status and access to mental health care services during Covid-19 pandemic

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: csv-1.csv | Column name | Description | |:---------------------------|:-------------------------------------------------------------------| | Indicator | The type of indicator being measured. (String) | | Group | The group (by age, gender or race) being measured. (String) | | State | The state where the data was collected. (String) | | Subgroup | A narrower level categorization within Group. (String) | | Phase | Phase number reflective of survey iteration. (Integer) | | Time Period | A label indicating duration captured by survey period. (String) | | Time Period Label | A label indicating duration captured by survey period. (String) | | Time Period Start Date | Beginning date for surveyed period. (DateFormat ‘YYYY-MM-DD’) | | Time Period End Date | End date for surveyed period. (DateFormat ‘YYYY-MM-DD’) | | Value | The value of the indicator being measured. (Float) | | LowCI | The lower confidence interval of the value. (Float) | | HighCI | The higher confidence interval of the value. (Float) | | Quartile Range | The quartile range of the value. (String) | | Suppression Flag | A f...

A comprehensive dataset of tools, methods, best practices, pros and cons, and insights around anonymous employee feedback.

Abstract

A household budget survey or Household Income and Expenditure survey (HES) as it is commonly called, is one of the most important economic surveys carried out by the Management and Information Systems Division (MISD). The survey is household-based and serves to provide up-to-date and comprehensive information on the components of the average household budget.

Household expenditure surveys are normally carried out every five to seven years so that updated information can be obtained on spending patterns and most importantly, on the composition of the 'basket of goods'.

In a HES, information on both income and expenditure is collected. Background variables such as household composition, age and sex structure and economic activity are also included to help classify the households in various demographic and socio-economic groups and to provide updated estimates on previous household surveys.

The primary purpose of the HES was to collect up-to-date detailed information on the expenditure of households to provide new weights for the calculation of the Cost of Living Index estimated here by the Retail Price Index (RPI).

A second important use of this survey is to provide data on aggregate consumers' expenditure and income to be used in the compilation of the Gross Domestic Product (GDP) and National Income accounts. The 'expenditure approach' of the GDP calculation usually estimates the consumer expenditure component. Results from this survey will thus provide data to crosscheck those estimates.

Another key purpose of the HES survey is that it makes available information on the level and distribution of household incomes. Such information is useful in the assessment of the social and economic planning systems. The distribution of household income provides an approximate measure of poverty in society.

In general, the survey provides the public with useful and interesting information on current spending patterns of the households in Seychelles. These patterns are expected to have changed considerably over the last decade.

Geographic coverage

The survey covered households on Mahe, Praslin and La Digue (the three mainly inhabited islands), and for practical consideration, excluded those on the outer islands.

Analysis unit

• Households
• Individuals
• Consumption expenditure commodities / items

Universe

Persons living in hospitals, military barracks, prisons etc. were excluded. Households headed by expatriates were also excluded, because the income and spending patterns of such households are expected to be different from those of the average Seychellois household.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Design The most appropriate sampling frame available was the list of households obtained from the 1997 Population and Housing Census. Although not updated over the two years prior to the survey, the database provided the ideal frame for direct sampling given that the sampling units would be the households themselves.

The frame listed 17,878 households enumerated during the 1997 census covering all the islands. In consideration of logistic and administrative problems, the geographical coverage was restricted to the three main islands (Mahe, Praslin and La Digue), which account for 99% of all households.

The sampling was done in two stages. An overall sample of 10% (around 1788 households) was desired. In the first stage the households were stratified by district. The sample size was distributed among the districts representative of their size (number of households), to determine the number of households to be drawn from each district (i.e. proportional allocation). From each district, the allocated number of households was then drawn using systematic sampling method whereby households are selected at equal intervals starting from a chosen random number. With each household having the same probability of being selected, the sample becomes self-weighting.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

The data were captured on personal computers using a programme written in DELPHI. The software for data capturing made provisions to enter all details collected. For the account book (Form HES3) items purchased or acquired (although it would not be possible to analyse all the descriptive details because of the variety of specifications, units, packaging etc, description and units of items) were captured to help identify commonly purchased items for future pricing.

The data files were then merged into one database and processed in SPSS and MS EXCEL for tabulation .

Response rate

The original sample drawn included 1696 households representing around 9.5 percent of households on Mahe, Praslin and La Digue. The enumeration covered 1219 households but after post-enumeration checks, data from just over 800 or 67% of these households were used in the final analysis.

The Super Bowl is one of the highlights of the sporting calendar, but many viewers tune in for more than just the game itself. During a January 2025 survey in the United States, almost 35 percent of respondents stated that the famous Super Bowl commercials were one of the parts of the event they were looking forward to.

This dataset presents the raw data obtained from 1415 online and representative Australian that were asked to aesthetically rate 180 photos of typical coral reef landscapes. Mean aesthetic ratings of 180 photos were collected from the survey, as well as from an expert research team, contributing mean ratings of coral reef health, coral cover, coral pattern, coral topography, fish abundance, and visibility.

Please note that CSIRO have published a version of this dataset on 29 May 2019, which should be considered the primary source of data information (i.e. citation for data files found on the CSIRO Portal). The published eAtlas version includes files supplied by the project to the eAtlas for publication. The eAtlas version differs in format (RatingsAesthetics.csv - includes the photo mean score) and includes a second spreadsheet containing information not available in the CSIRO version (Ratings-All.csv) which captures each photo's ratings against five factors (coral health, coral cover, coral topography, fish abundance and visibility), as outlined in point two below. The CSIRO version contains the SPSS data extract and codebook (xlsx file), as well as the photo ratings summary (PhotoRatingsInd.xlsx) without the calculated mean.

Methods:
1. A survey was constructed to collect simple demographic information about each participant, the self-rated level of interest in coral reefs, and aesthetic ratings for each photo on a scale of 1-10 (where 1=extremely unattractive, and 10=extremely attractive). Once an individual agreed to partake in the survey, they were sent a survey with 50 photographs randomly chosen from the pool of 181 photographs. It was noted that the quality of responses could be affected if more than 50 photos were viewed (where 50 photos represented a ten-minute survey). The style of the survey was not dissimilar from very popular online games in which individuals are asked to rank aesthetic preferences of fashion or interior design items. A full list of the images used in the survey is available in Appendix 1 (1-90)

A total of 1,417 individuals participated in the study, where each photo was rated at least 380 times on the ten-point scale. Twenty-nine percent of the sample came from Queensland, and 71% were distributed across Australia. Some 62.3% of people came from Metropolitan Australia, whilst 37.7 came from rural/regional Australia. Some 51.4% were female. Participants represented a range of experiences with the Great Barrier Reef, where 7.2% had never visited, and 7.9% did not find coral reefs that interesting. Most participants (99.6%) were not part of a GBR based club or community groups, such as a spear-fishing club. The average age for the sample population was 46.96 (standard error=0.471), and ranged from 16 to 89.

2. We identified 180 underwater coral reef photographs from those that were publicly available (www.gbrmpa.gov.au) or existed in the combined image libraries of the study authors. They represented typical underwater images from the GBR, with a common oblique perspective taken from approximately 5-10 m above a coral substrate. This perspective characterised the image that a person would see as soon as they placed their head beneath the water, and it was similar to the visual perspective used in monitoring surveys conducted by manta-towing at the Australian Institute of Marine Science. Some photos were duplicated and placed randomly, and some were modified using photo editing software to manipulate one feature independent of others, for the purposes of ‘checking’ the consistency and subtleties associated with making aesthetic judgements.

Each photo was rated for each of the five factors (on a scale of low, medium, high) by members of the research team with experience in coral reefs; coral health, coral cover, coral topography, fish abundance, and visibility. Given that there were insufficient photos representing abundant fish and poor visibility, a total of 20 photos were manipulated to enhance or de-emphasise certain factors. These photos ensured that we could attribute differences in aesthetic appeal of each photo to at least one of the five factors. The final set of photos represented realistic coral reef images across all five factors, with a greater representation of images containing moderately high coral cover to capture the nuances across the scale of potential ratings and also to aide engagement during online rating sessions



Format:
This dataset consists of two CSV files and two PDF files. The two CSV files contain the data on aesthetic ratings from an online survey, and ratings on reef health and abundance. eAtlas Note: The original files were provided as Excel spreadsheet tables and were converted to CSV files. Photographs and analysis were originally supplied as word document files and have been converted to PDF files.



References:

Marshall, N.A., Marshall, P.A., and Smith, A.K. (2017) Managing for Aesthetic Values in the Great Barrier Reef: Identifying indicators and linking Reef Aesthetics with Reef Health. Report to the National Environmental Science Programme. Reef and Rainforest Research Centre Limited, Cairns (102 pp.).

Data Location:

This dataset is filed in the eAtlas enduring data repository at: eAtlas/nesp3/3.2.4_Defining-assessing-GBR-aesthetics

This dataset comprises 10,000 artificially generated student essays using GPT4, accompanied by holistic scores ranging from 1 to 6. These essays were generated based on the data from the Automated Essay Scoring 2.0 competition.

The Method

My aim was to produce essays that closely resembled those in the original AES dataset, essentially creating paraphrases while ensuring they remained distinct compositions. Equally important was maintaining scores consistent with the original holistic scoring system used in the competition. To accomplish this, I followed the process outlined below:

Prompt Template

The basic prompt template looks like this:

prompt_template = ''''
You are a {AGE} year old German student writing an English test, but you're stuck! Luckily, your neighbour is doing well and so you take a glimpse at his sheet and you could catch the following text:

=========
"{TEXT}"
=========

But you cannot simply copy it, you need to change it a bit so the teacher doesn't notice that you copied it, 
hence you copy it with the following rules:
- Paraphrase the text just a bit
- Adhere to the style and level of the original text
- Sprinkle some errors into the text, akin to the original
- Remember your age and incroporate that into the essay so it's feasible for a {AGE} year old student who writes not in his native language!

Output only the essay
'''

The produced essay woud be scored the same score as the original essay passed into the {TEXT} variable.

This prompt tries to implement a couple of ideas:

• Through the incentive of being a student who copies another student in a test situation, but doesn't want to be noticed by the teacher, the model should produce a mixture of copied, paraphrased and own text chunks. This would also allow the essay to be of similar quality (hopefully meaning similar score).
• Through the {AGE} variable, I tried to enforce the score of the original essay by prompting essays with a lower score, a lower age (minimum 11, highest 14) and thus also lowering the quality of the produced essay. The formular for the age is defined as: \(age = 15 - (4 - (originalEssayScore // 2))\)
• I tried to replicate the spelling and grammar mistakes of the original essay into the newly produced essay, but language models are really hard to get them to add random mistakes into their outputs, hence: I counted the spelling mistakes in the original essay through python's spellchecker and added as much random mistakes into the newly generated essays to again, replicate the score as best as I can.
• Since we have a class imbalance (way more 3s and 4s than 1s and 6s), I purposely made the essay pool, from which a new essay was prompted from, imbalanced in favour of those lower-quantity classes. The dataset should therefore contain more of these.

Examples

Here are some examples:

Initially, the author addresses the perception that Mars is tooy hazardous to explore. Many people are deterred by Mars' reputation as a dangerous and inhospitable planet. The author acknowledges these challenges but demonstrates how safe travel can still be achieved. By detailing Jthe plan proposed by the National Aeronautics and Space Administration (NASA) for astronauts to float above the dangerous conditions, the writer assures the audience of the safety measures in place. Specific aspects of the plan, such as Earth-like air pressure and abundant solar power, are highlighted to emphasize the feasibility of human survival. Drawing a comparison to a blimp-like vehicle, the author simplifies the concept for better understanding. By dispelling the notion of Mars being too perilous, the writer strengthens the argument for explorRing the planet.

Furthermore, the writer emphasizes the educational potential that studying Mars offers. Beyond simple facts about Mars' proximity in size and density to Earth, the author delves into the possibility of Mars once resembling Earth. Describing Mars' current environment as Earth-like with rocky surfaces, valleys, mountains, and craters, the author suggests that Mars may have supported life in the past, similar to Earth. This parallel betwveen the two planets Hcaptivates the audienc...

A league table of the 120 cryptocurrencies with the highest market cap reveals how diverse each crypto is and potentially how much risk is involved when investing in one. Bitcoin (BTC), for instance, had a so-called "high cap" - a market cap worth more than 10 billion U.S. dollars - indicating this crypto project has a certain track record or, at the very least, is considered a major player in the cryptocurrency space. This is different in Decentralize Finance (DeFi), where Bitcoin is only a relatively new player. A concentrated market The number of existing cryptocurrencies is several thousands, even if most have a limited significance. Indeed, Bitcoin and Ethereum account for nearly 75 percent of the entire crypto market capitalization. As crypto is relatively easy to create, the range of projects varies significantly - from improving payments to solving real-world issues, but also meme coins and more speculative investments. Crypto is not considered a payment method While often talked about as an investment vehicle, cryptocurrencies have not yet established a clear use case in day-to-day life. Central bankers found that usefulness of crypto in domestic payments or remittances to be negligible. A forecast for the world's main online payment methods took a similar stance: It predicts that cryptocurrency would only take up 0.2 percent of total transaction value by 2027.

Hospital Care Quality Measures

Timely & Effective Care Across the U.S

By Health [source]

About this dataset

This dataset includes provider-level data revealing the quality of timely and effective care from hospitals across the United States. It allows us to analyze heart attack, heart failure, pneumonia, surgical, emergency department, preventive care for children's asthma and stroke prevention and treatment data for pregnancy and delivery care courtesy of the Centers for Medicare & Medicaid Services. With this dataset you can analyze hospital's performance on all these areas using Hospital Name, Addresss , City , State , ZIP Code , County Name , Phone Number as well as scores creditable to Measure Name , Sample size from which it was derived a Footnote explanation based on location. Dig deep into each provider's level of care with this dataset to understand their performance on providing timely effective care

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How to use the dataset

To get the most out of this dataset, it is important to understand each column in the dataset: Hospital Name identifies the health care facility; Address provides the address of the hospital; City identifies the city where it is located; State specifies which state it belongs to; ZIP Code denotes its specific zip code; County Name mentions what county it belongs to; Phone Number connects you with an immediate contact at the facility if needed; Condition categorizes types of tests/treatments being monitored in that case study; Measure Name outlines all related measures under said condition umbrella or metric(s) studied as part of that investigative research project/condition category (i.e., infection prevention); Score grades out how well that measure was doing compared against expectations or goals for quality & safe patient protections (higher scores are indicative of better performance on those surveyed & tracked items); Sample details how many patients were involved in this particular study topic component and involved participant sample size selection & unit evaluation criteria definition considerations during research recruitment and retention efforts associated with a particular area of specialty treatment/testing cluster system activity factors reviewed directionally by researchers via cohort based review activities over time [note: matching non-patients or control subject population reference points also sometimes may be used depending on written scope descriptions outlined by investigators]; Footnotes can amplify additional evaluations/CAVEATS sometimes noted regarding high-lighted findings(-such as improvement yet still not meeting standards), etc.; Measure Start Date defines when all test students were allowed entry into their respective study groups associated with one another for convergence analysis purposes within a defined subject patient group prospectively selected category designation feature component selection batch cases (new patients added mid-project have crossed design frontiers at random intervals sometimes necessary). Lastly, Measure End Date reflects terminal endpoint lead review periods cut off times when no new data entries can be accepted post-data collection stopped official time period specifications if designated by protocol order via institutional clinical trial board IRB approved advanced notification statements issued throughout any official project undertaking design process stages at its multiplex points).

Understanding each column's features will assist you in selecting relevant variables from this dataset according to your research needs. Additionally, using Location can help narrow down search results geographically. With this information researchers can gain valuable insight into overall trends regarding timely and effective care in different hospitals across different states

Research Ideas

• Create an interactive heatmap to visualize provider-level data across different states. This can allow researchers, consumers and policy makers to identify areas of excellence as well as opportunities for improvement in timely and effective care measures.
• Develop a web app that allows users to locate hospitals in their area based on any given health condition, measure name, score or timeframe data provided by this dataset. This could give patients access to quality care options and help them make informed decisions while seeking medical attention.
• Utilizing the geographic coordinates data included in the Location column, create a virtual tour function that lets people virtually explore the interior of hospital facilities associated with this dataset...

Owl Behavioral Analysis

Assessing Nocturnal Activity in Owls

By Shad Reynolds [source]

About this dataset

This dataset offers a detailed glimpse into the behavior of a Barn Owl, Sparkles, over the course of 3 months. Analyzing this data provides important insights into how Barn Owls interact with their environment and how they exhibit their behaviors in controlled settings. The data consists of 59 observations collected over 87 days between August 2020 to October 2020 and covers various aspects including vocalizations, mobility, food intake, preening activities etc. Each observation contains information on different aspects related to Sparkle's behavior such as head tilts per minute (HPTPM), location changes per minute (LCPM), secondary feather movement (SFM) etc., giving us an idea about her overall activity levels and interactions with her environment. With these insights we can observe the trends in Sparkle’s behaviour that can help us out when it comes to helping other birds in similar situations or understand what other animals might be going through as well

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How to use the dataset

This dataset contains detailed observations of the behavior of Sparkle, an adult barn owl (Tyto alba) housed at the facilities of Washington State University’s Center for Neurotechnology. The data was collected as part of a research study to examine how owls may use their long-term memory to plan ahead when making hunting decisions, and ultimately to reveal clues about how adaptive decision making works.

The dataset has 20 columns with different readings from the environment taken over 3 days: date, time (in 24 hour format), activity level (based on Sparkle's movement), air temperature, luminance or brightness in Lux, moisture levels in percentage (%) points from 0% relative humidity to 100%, and 14 ethograms consisting of wing flapping/flipping behaviors expressed by Sparkle.

How to use this dataset

The data contained inside this dataset offers insights into not only owl behavior but potentially sheds light on decision making across vertebrates as a whole. To begin using this data follow these steps:

• First familiarize yourself with the columns contained within the dataset by studying each individual column name and measurement type associated with it i.e Date(yy mm dd)/Time(military)/Temperature etc..
• Visualize basic relationships between columns using simple charts e.g Line charts depicting time against Temperature 3 Activity Level vs ethogram comparisons can be visually displayed using scatterplots allowing correlations between both sets of measurements types to be studied more carefully . These chart illustrations can even get broken down further into 1 day intervals for example or other significant events like when food is given for easier comprehension .
  4 Look at relationships between date/time intervals vs environmental factors like air temperature/moisture levels or even different activity variables amongst one another e.g Dating and exploring any cause-effect relationships which may exist between specific environmental parameters that could effect owls behavior such as can increased temperatures effect feeding behavior?
  5 Utilizing Hypothesis testing -With hypothesis testing one can come up with potential ideas that might explain why certain variables are showing unusual patterns or behaviors , compile datasets that have couple unique characteristics present inside them , and apply various statistical tests measures i n order to prove which situations occur more often than others thus identifying actual events controlling a situation rather than random occurrence based ones

Following these steps will enable you to understand woodland creature behaviour better while proving endearing functions such as prediction capability related directly back towards ecosystem well being

Research Ideas

• Analyzing changes in owl behavior over time by comparing the data points of multiple owls in the same area.
• Using the data to study environmental factors that may influence owl behavior, such as climate, vegetation, and any other landscaping elements present near the nests.
• Creating automated detection systems for observing owl activity by using machine learning algorithms to interpret complex behavioral patterns from this dataset

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

**Unknown License - Please check the dataset d...

Apple Iphones sold in India

Price, Rating, and Reviews

By Tony Paul [source]

About this dataset

This dataset contains detailed information about Apple iPhones that have been sold in India. Each entry includes the product name, brand, sale price, maximum retail price (MRP), universal product code (UPC), number of reviews and ratings obtained from customers, discount percentage offered on various products, as well as the random access memory (RAM) size associated with each product. Dive into this comprehensive collection of Apple products for a better understanding of selling iPhone models in India and accurately capture insights about customer preferences and market trends!

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How to use the dataset

Here is how to use this dataset effectively: - Start by exploring the headers of each column to understand the data features available in the dataset; you should be able to identify which columns contain what kind of data. - To get an overview of your data, calculate summary statistics such as means and standard deviations for numerical columns (e.g., Sale Price, Mrp etc.). - Visualize your data using a variety of techniques like histograms, scatter plots and correlation matrices - this will help you look for possible relationships between different variables. You may also consider creating pair plots that allow you to compare and visualize pairs of variables against each other at a glance. - Finally, start building models or perform exploratory analysis such as hypothesis testing with the help of various statistical methods or machine learning algorithms for further insights into the Apple iPhone sales in India!

Research Ideas

• Developing an AI-based Product Recommender System using the attributes of Apple Iphones (e.g. price, discount percentage, ratings, reviews & RAM) for customers who are looking to purchase new Apple phone in India
• Creating a brand intelligence system that analyses the popularity of different Apple product models and rank them according to their performance over time
• Using Machine Learning to build a predictive model for forecasting sales patterns and predicting demand for future sales of Apple Iphones in India

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: apple_products.csv | Column name | Description | |:------------------------|:--------------------------------------------------------------------------| | Product Name | The name of the Apple iPhone product. (String) | | Product URL | The URL of the product page. (String) | | Brand | The brand of the Apple iPhone product. (String) | | Sale Price | The price of the Apple iPhone product at the time of sale. (Numeric) | | Mrp | The maximum retail price of the Apple iPhone product. (Numeric) | | Discount Percentage | The percentage of discount offered on the Apple iPhone product. (Numeric) | | Number Of Ratings | The number of ratings given to the Apple iPhone product. (Numeric) | | Number Of Reviews | The number of reviews given to the Apple iPhone product. (Numeric) | | Upc | The universal product code of the Apple iPhone product. (String) | | Star Rating | The star rating of the Apple iPhone product. (Numeric) | | Ram | The Random Access Memory size of the Apple iPhone product. (Numeric) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Tony Paul.

We utilized a dataset of Machine Design materials, which includes information on their mechanical properties. The dataset was obtained from the Autodesk Material Library and comprises 15 columns, also referred to as features/attributes. This dataset is a real-world dataset, and it does not contain any random values. However, due to missing values, we only utilized seven of these columns for our ML model. You can access the related GitHub Repository here: https://github.com/purushottamnawale/material-selection-using-machine-learning

To develop a ML model, we employed several Python libraries, including NumPy, pandas, scikit-learn, and graphviz, in addition to other technologies such as Weka, MS Excel, VS Code, Kaggle, Jupyter Notebook, and GitHub. We employed Weka software to swiftly visualize the data and comprehend the relationships between the features, without requiring any programming expertise.

My Problem statement is Material Selection for EV Chassis. So, if you have any specific ideas, be sure to implement them and add the codes on Kaggle.

A Detailed Research Paper is available on https://iopscience.iop.org/article/10.1088/1742-6596/2601/1/012014