This dataset by The COVID Tracking Project at The Atlantic captures the virus’s transmission in 65 cities and counties across the country. Many of these metropolitan areas only report the current day’s totals and remove older data from their public health dashboards so that no historical archive is available. As a result, it’s often impossible to see the impact of the virus on a particular geography over time. Our dataset captures this historical information. It is the only available metropolitan dataset that includes race and ethnicity, which allows us to improve our understanding of how COVID-19 disproportionately affects communities of color.

We have completed our data collection on this project and want to share what we’ve learned from viewing COVID-19 at the local level. Five months in, we’ve seen that local data tells a vastly different story than state-level data. Not only do trends emerge in city and county data before appearing at the state level, but state-level data also o...

This dataset tracks annual student-teacher ratio from 2010 to 2012 for Connections - High School Level vs. Illinois and Rantoul City 137 School District

This dataset contains detailed records of customer interactions handled by a customer service team through various communication channels such as inbound calls, outbound calls, and digital touchpoints. It includes over 85,000 entries with information related to the nature of the issue, product categories, agent details, and customer satisfaction scores (CSAT).

Key features include:

Issue Metadata: Timestamps for when the issue was reported and responded to.

Categorization: High-level and sub-level issue categories for better analysis.

Agent Information: Names, supervisors, managers, shift, and tenure bucket.

Customer Feedback: CSAT scores and free-text customer remarks.

Transactional Data:Order IDs, product categories, item prices, and customer city.

This dataset is ideal for exploratory data analysis (EDA), natural language processing (NLP), time-to-resolution analysis, customer satisfaction prediction, and performance benchmarking of service agents.

Feature-wise Explanation

• Unique id: A unique identifier for each customer support ticket. Used for tracking, not used in modeling.
• channel_name: The communication channel used by the customer (e.g., Email, Chat, Phone), which influences response quality and time.
• category: Broad classification of the support issue (e.g., Technical, Billing, Account), useful in understanding issue trends.
• Sub-category: More specific issue label under each category (e.g., "Login Failure" under Technical) to capture granular insights.
• Customer Remarks: Free-text input from customers about their issue; useful for sentiment analysis or NLP-based features.
• Order_id: The ID of the order associated with the issue; may not be directly useful unless joined with order metadata.
• order_date_time: Timestamp of the order; can be used to derive delays or time gaps relative to issue date.
• Issue_reported at: Time when the customer reported the issue; helps calculate response and resolution delays.
• issue_responded: Time when the support agent responded; combined with report time to calculate response duration.
• Survey_response_Date: Date when customer gave the CSAT feedback; useful to understand follow-up timing, but not always predictive.
• Customer_City: The city where the customer resides; can identify location-based trends or systemic issues.
• Product_category: The type of product involved in the support ticket; some product types may result in higher or lower CSAT.
• Item_price: Price of the item involved; higher prices might lead to higher customer expectations and affect satisfaction.
• connected_handling_time: Total time spent by the agent resolving the issue; excessive durations may signal complexity or inefficiency.
• Agent_name: Name of the support agent handling the ticket; can be encoded to understand individual performance impact.
• Supervisor: The agent’s supervisor; useful to analyze team-level trends in CSAT.
• Manager: The manager overseeing the support process; can help identify management-level influence on support quality.
• Tenure Bucket: Agent experience group (e.g., 0–6 months, 6–12 months); more experienced agents might resolve issues better.
• Agent Shift: Time shift during which the case was handled (e.g., Day, Night); night shifts might see different trends in CSAT.
• CSAT Score (Target Variable): Customer satisfaction score (1 to 5); the main variable we aim to classify using other features.

This dataset tracks annual white student percentage from 2010 to 2012 for Connections - High School Level vs. Illinois and Rantoul City 137 School District

The data addresses the dynamics of coexistence and conflict in increasingly diverse cities from a human-centred perspective. It was collected as part of the EU-funded project Coexistence and Conflict in the Age of Complexity (EmergentCommunity) in nine European cities in Finland, France, and Sweden. The dataset comprises of two parts: EmergentCommunityEthno (qualitative data) and EmergentCommunityVR (quantitative and qualitative data) that were collected during the project. In addition to these, desk research was conducted and these files have been included in the metadata description.

EmergentCommunityEthno (dataset 1):

Across the nine cities, participants consisted of people above 15-years of age, living in the studied urban neighbourhoods or using their public spaces. In Finland, data were collected in the neighbourhoods of Peltolammi and Multisilta in Tampere, in Malmi in Helsinki, and in Martti and Paavola in Hyvinkää. In Tampere, part of the data (n=31 interviews) was collected in collaboration with the EKOS research project (this part of the data is described and archived in the Finnish Social Science Data Archive, DoI: https://doi.org/10.60686/t-fsd3816). The second part of the data was collected in Sweden. The data collection sites there were the neighborhoods of Möllevången and Nydala in Malmö, Farsta and Rågsved in Stockholm, and Fröslunda and Årby in Eskilstuna. The French data were collected in the La Plaine area in Marseille; in La-Chapelle-Saint-Luc, Saint-Andre-Les-Vergers and Les Chartreux in Troyes; and in Guillotière in Lyon.

Across these sites shared methods were used in data collection, consisting of thematic interviews, walking interviews, and observations. The dataset emphasizes the diversity of experiences and the manifestations of distinctions in diverse urban environments and examines the ways in which people form bonds in relation to each other, their neighborhoods, and the broader society.

The first set of participants were located through social media groups (Facebook), from the premises of associations organizing community activities in the areas, libraries, cafes, community events, and youth centers. After this, snowball sampling was used, in addition to which targeted recruitment was applied if a population group represented in the area was completely missing from the dataset. Ethnographic observations were conducted in public spaces, community centres, cafés, stations, and shopping centres that were selected as potentially interesting places based on extant scholarship on living with difference and urban encounters. Here, attention was paid at how people used these sites, who were there and who were absent, as well as how people moved in and across the sites. Notes were made of what kinds of encounters, patterns of behaviour, cooperations, and conflicts occurred. These observations were made at various times of the day, to capture potential temporal changes. This resulted in a rich collection of fieldnotes, sketches, photographs, and movement maps.

Relevant files: 1) EmergentCommunity ethnographic matrix.pdf, 2) EmergentCommunityEthno interview questions.docx, 3) EmergentCommunity_metadata public.xlsx (contains all metadata from the project), 4) EmergentCommunityEthno_metadata.csv (contains metadata only on desk research, ethnographic interviews and fieldnotes).

EmergentCommunityVR (dataset 2):

Data collection was conducted in Helsinki, Marseille, and Malmö. The data was collected using 360-degree videos based on the aforementioned ethnographic data as stimuli to which participants were exposed. A separate video was created for each city, using specifically the data collected therein. We put together a mobile laboratory set-up that travelled to each city and collaborated with local NGOs whose premises were used as our laboratory space. The equipment and software used are explained in the document "EmergentCommunity mobile laboratory.pdf".

The inclusion criteria for participation were: being a major, healthy, not having hearing or vision impairments, being a resident in the city that the video depicted, and knowledge of the local language in which the video was executed. During the viewing of the video stimulus, participants' physiological responses were measured and their eye movements were tracked. VR eye tracking was used as it enables the precise analysis of gaze behaviour – such as fixations and saccades – within immersive, ecologically valid environments. Regarding physiological signals, the focus was on the electrical activity of the heart using electrocardiography (ECG), the electrical activity of the facial muscles using facial electromyography (fEMG), and the electrical conductivity of the skin using galvanic skin response (GSR). To complement the physiological data, a multimodal setup was established to assess the affective content of the stimulus in terms of arousal/valence, avoidance/approach, and unpredictability. After viewing, the participants were asked to evaluate the intensity of their emotional experience and to name the emotional reactions elicited by the video using a questionnaire carried out with Gorilla Experiment Builder. The questionnaire also contained background questions, from basic participant information, such as age and gender, to aspects that relate to diversity and inequality in contemporary societies: language, income, housing, education, political activity, participation, as well as political opinions and social values. After completing the measurements and the questionnaire, participants were interviewed about their experience and the thoughts it provoked, and they were asked to share information regarding their daily lives.

The purpose of the dataset was to help understand the formation of emotional experiences and the significance and functioning of emotions in the everyday life of increasingly diverse and unequal cities. The call for participation was distributed in several thematic Facebook groups (related to e.g., urban development, multiculturalism, neighborhood, local NGOs and minority communities) and via Instagram, as well as through flyers/posters in libraries, local associations, shopping centers, cafes, and on the project's Facebook page and Instagram profile. In the case of Marseille and Malmö, local assistants were used to spread the invitation within their networks and distribute participation invitation leaflets on the streets. In each city, it was possible for already registered participants to invite additional participants as well. Overall, the goal was to ensure the representativeness of the data in terms of age, gender, and minority status.

Relevant files: 1) EmergentCommunity video stimuli.pdf, 2) EmergentCommunityVR interview questions.pdf, 3) EmergentCommunityVR Gorilla questionnaires.pdf, 4) EmergentCommunity mobile laboratory.pdf, 5) EmergentCommunity_metadata public.xlsx (contains all metadata from the project), 6) EmergentCommunityVR interviews.csv (contains metadata on interviews done after watching the 360-degree video), 7) EmergentCommunityVR physio.csv (contains metadata on physiological measuring and questionnaires).

Purpose of the data

The EmergentCommunity project aimed at producing knowledge about what community means and how it is formed in increasingly diverse societies, as well as the conflicts and tensions that everyday life brings out. The project empirically examined the concrete challenges that societal changes produce for cities and coexistence. The aim was to identify how peaceful coexistence could be supported and population relations promoted in urban everyday life. The project emphasized that community relations and everyday coexistence are affective, social, and spatial phenomena, which is why a wide range of research methods from ethnography and observation to psychophysiological measurements and interviews were applied. These approaches were brought into dialogue through virtual reality by utilizing ethnography-based 360-degree videos depicting everyday life in the latter part of the project (EmergentCommunityVR). Thus, the project created new understanding of emotions formed in everyday life and produced unique knowledge in the fields of psychological and sociological emotion research. Bringing these areas together enabled a critical examination of the concept of community and the identification of the practices and ways in which communities are produced in the everyday life of diverse and unequal cities (see CORDIS database for public description, results, and reporting).

Throughout the data collection, the research focused on everyday life and the forms, practices, and interpretations of everyday coexistence in public urban spaces in the selected research neighbourhoods. Participants were also asked to share their experiences, interpretations, and views on societal change and how the change has been visible in their own neighborhoods and what thoughts and feelings it evokes in them. The data was formed through non-probability sampling (self-formed sample).

The research sites were selected by examining statistics, policy reports, and available data on demographic changes and diversity, income inequality, trends of residential and ethnic segregation in different countries and cities (desk research). We chose the countries and cities so that they would complement each other and that changes were observable in each selected context, although their forms, emphases, and manifestations might vary. After this extensive background review, we focused on the city level, complementing the available

This dataset presents the annual mean levels of fine particulate matter (PM10) in selected locations in Qatar, based on population-weighted measurements. For each station, the air quality classification is provided both in English and Arabic. Classifications include categories such as "Natural," "Clean," and "Below Natural," helping monitor air pollution trends and supporting public health and environmental policy efforts.

The Historical Generalized Land Use dataset encompasses the seven county Twin Cities (Minneapolis and St. Paul) Metropolitan Area in Minnesota. The dataset was developed by the Metropolitan Council, a regional governmental organization that deals, in part, with regional issues and long range planning for the Twin Cities area. The data were interpreted from 1984, 1990, 1997, 2000, 2005, 2010, 2016 and 2020 air photos and other source data, with additional assistance from county parcel data and assessor's information.

The Metropolitan Council has routinely developed generalized land use for the Twin Cities region since 1984 to support its statutory responsibilities and assist in long range planning for the Twin Cities area. The Council uses land use information to monitor growth and to evaluate changing trends in land consumption for various urban purposes. The Council uses the land use trend data in combination with its forecasts of households and jobs to plan for the future needs and financing of Metropolitan services (i.e. Transit, Wastewater Services, etc.). Also, in concert with individual local units of government, the land use and forecast data are used to evaluate expansions of the metropolitan urban service area (MUSA).

The Council does not specifically survey the rights-of-way of minor highways, local streets, parking lots, railroads, or other utility easements. The area occupied by these uses is included with the adjacent land uses, whose boundaries are extended to the centerline of the adjacent rights-of way or easements. The accuracy of Council land use survey data is suitable for regional planning purposes, but should not be used for detailed area planning, nor for engineering work.

Until 1997, the Metropolitan Council had manually interpreted aerial photos on mylar tracing paper into a 13-category land-use classification system to aggregate and depict changing land use data. In 1997, with technological advances in GIS and improved data, the Metropolitan Council was able to delineate land uses from digital aerial photography with counties' parcel and assessor data and captured information with straight 'heads-up' digitizing with GIS software. Also, understanding that land use data collected and maintained at the county and city level are collected at different resolutions using different classification schemes, the Metropolitan Council worked with local communities and organizations to develop a cooperative solution to integrate the Council's land use interpretation with a generally agreed upon regional classification system. By 2000, the Metropolitan Council had not only expanded their Generalized Land Use Classification system to include 22 categories, but had refined how they categorized land (removing all ownership categories) to reflect actual use. See the Entities and Atributes section of the metadata for a detailed description of each of the land use categories and available subcategories.

With the completion of the 2020 Generalized Land Use dataset, regional and local planners have the ability to map changes in urban growth and development in a geographic information system (GIS) database. By tracking land use changes, the Metropolitan Council and local planners can better visualize development trends and anticipate future growth needs.


NOTE ABOUT COMPARATIVE ANALYSIS:

It is important to understand the changes between land use inventory years and how to compare recent land use data to historical data.

In general, over the land use years, more detailed land use information has been captured. Understanding these changes can help interpret land use changes and trends in land consumption. For detailed category definitions, specific land use comparisons and how best to compare the land uses between 1984 and 2020, please refer to the Attribute Accuracy or the Data Quality section of the metadata.

It is also important to note that changes in data collection methodology also effects the ability to compare land use years:

- In 2000, the land use categories were modified to more accurately reflect the use of the land rather than ownership. Although this has minimal effect on associating categories between 1997 and 2000, is may have had an affect on some particular land use. For example, land owned by a community or county but had no apparent active use could have been classified as 'Public/ Semi-Public' prior to 2000. In 2000, land with no apparent use, regardless of who owns it, is classified as 'Undeveloped.'

- With better resolution of air photos beginning in 2000, the incorporation of property information from county assessors and the use of more accurate political boundaries (particularly on the exterior boundaries of the region), positive impacts were made on the accuracy of new land use delineations between pre-2000 land use data and data collected between 2000 and 2020. With the improved data, beginning in 2000, a greater effort to align land use designations, both new and old, to correspond with property boundaries (county parcels) where appropriate. In addition, individual properties were reviewed to assess the extent of development. In most cases, if properties under 5 acres were assessed to be at least 75% developed, then the entire property was classified as a developed land use (not 'Undeveloped'). As a result of these realignments and development assessments, changes in land use between early land use years (1984-1997) and more recent years (2000-2020) will exist in the data that do NOT necessarily represent actual land use change. These occurrences can be found throughout the region.

There are also numerous known deficiencies in the datasets. Some known deficiencies are specific to a particular year while others may span the entire time series. For more details, please refer to Attribute Accuracy of the Data Quality section of the metadata.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Virginia Coast Reserve (VCR) contains mean sea level: the arithmetic mean of hourly heights observed over the National Tidal Datum Epoch (as defined by NOAA) measurements in meter units and were aggregated to a yearly timescale.

This dataset includes periodic water level exports and well construction information for CABQ landfill monitoring wells. Groundwater monitoring activities consist of groundwater sampling collection and measuring hydrologic parameters. The monitoring program provides consistent and representative data aimed at assessing the chemical water quality of Albuquerque's underground aquifer. It determines spatial and temporal trends in water quality. Approximately 170 samples are collected from Environmental Services Division wells an an annual basis. Water table elevations are also measured to track short and long term hydrologic changes.

The information gathered through the groundwater monitoring program is used to assess the groundwater resource, project future conditions of, address contamination concerns, and provide the information necessary to protect our underground aquifer. It is available and shared with local, state and federal organizations.

More information about CABQ Groundwater Monitoring can be found here: https://www.cabq.gov/environmentalhealth/landfill-groundwater-monitoring/ground-water-monitoring

The Historical Generalized Land Use dataset encompasses the seven county Twin Cities (Minneapolis and St. Paul) Metropolitan Area in Minnesota. The dataset was developed by the Metropolitan Council, a regional governmental organization that deals, in part, with regional issues and long range planning for the Twin Cities area. The data were interpreted from 1984, 1990, 1997, 2000, 2005, 2010 and 2016 air photos and other source data, with additional assistance from county parcel data and assessor's information.

The Metropolitan Council has routinely developed generalized land use for the Twin Cities region since 1984 to support its statutory responsibilities and assist in long range planning for the Twin Cities area. The Council uses land use information to monitor growth and to evaluate changing trends in land consumption for various urban purposes. The Council uses the land use trend data in combination with its forecasts of households and jobs to plan for the future needs and financing of Metropolitan services (i.e. Transit, Wastewater Services, etc.). Also, in concert with individual local units of government, the land use and forecast data are used to evaluate expansions of the metropolitan urban service area (MUSA).

The Council does not specifically survey the rights-of-way of minor highways, local streets, parking lots, railroads, or other utility easements. The area occupied by these uses is included with the adjacent land uses, whose boundaries are extended to the centerline of the adjacent rights-of way or easements. The accuracy of Council land use survey data is suitable for regional planning purposes, but should not be used for detailed area planning, nor for engineering work.

Until 1997, the Metropolitan Council had manually interpreted aerial photos on mylar tracing paper into a 13-category land-use classification system to aggregate and depict changing land use data. In 1997, with technological advances in GIS and improved data, the Metropolitan Council was able to delineate land uses from digital aerial photography with counties' parcel and assessor data and captured information with straight 'heads-up' digitizing with GIS software. Also, understanding that land use data collected and maintained at the county and city level are collected at different resolutions using different classification schemes, the Metropolitan Council worked with local communities and organizations to develop a cooperative solution to integrate the Council's land use interpretation with a generally agreed upon regional classification system. By 2000, the Metropolitan Council had not only expanded their Generalized Land Use Classification system to include 22 categories, but had refined how they categorized land (removing all ownership categories) to reflect actual use. See the Entities and Atributes section of the metadata for a detailed description of each of the land use categories and available subcategories.

With the completion of the 2016 Generalized Land Use dataset, regional and local planners have the ability to map changes in urban growth and development in a geographic information system (GIS) database. By tracking land use changes, the Metropolitan Council and local planners can better visualize development trends and anticipate future growth needs.


NOTE ABOUT COMPARATIVE ANALYSIS:

It is important to understand the changes between land use inventory years and how to compare recent land use data to historical data.

In general, over the land use years, more detailed land use information has been captured. Understanding these changes can help interpret land use changes and trends in land consumption. For detailed category definitions, specific land use comparisons and how best to compare the land uses between 1984 and 2016, please refer to the Attribute Accuracy or the Data Quality section of the metadata.

It is also important to note that changes in data collection methodology also effects the ability to compare land use years:

- In 2000, the land use categories were modified to more accurately reflect the use of the land rather than ownership. Although this has minimal effect on associating categories between 1997 and 2000, is may have had an affect on some particular land use. For example, land owned by a community or county but had no apparent active use could have been classified as 'Public/ Semi-Public' prior to 2000. In 2000, land with no apparent use, regardless of who owns it, is classified as 'Undeveloped.'

- With better resolution of air photos beginning in 2000, the incorporation of property information from county assessors and the use of more accurate political boundaries (particularly on the exterior boundaries of the region), positive impacts were made on the accuracy of new land use delineations between pre-2000 land use data and data collected in 2000, 2005, 2010, 2016. With the improved data, beginning in 2000, a greater effort to align land use designations, both new and old, to correspond with property boundaries (county parcels) where appropriate. In addition, individual properties were reviewed to assess the extent of development. In most cases, if properties under 5 acres were assessed to be at least 75% developed, then the entire property was classified as a developed land use (not 'Undeveloped'). As a result of these realignments and development assessments, changes in land use between early land use years (1984-1997) and more recent years (2000-2016) will exist in the data that do NOT necessarily represent actual land use change. These occurrences can be found throughout the region.

There are also numerous known deficiencies in the datasets. Some known deficiencies are specific to a particular year while others may span the entire time series. For more details, please refer to Attribute Accuracy of the Data Quality section of the metadata.

https://upload.wikimedia.org/wikipedia/commons/0/09/BGMEA_Logo.jpg">

Unveiling Bangladesh's Garment Industry: Insights from BGMEA-Listed Factories

Explore the Bangladesh Garment Manufacturers and Exporters Association (BGMEA) Listed Garments in Bangladesh dataset, providing a comprehensive view of the nation's thriving garment industry. This dataset encompasses factory names, types, priorities, employee data, production capacities, establishment years, and precise geographical information. With a focus on BGMEA-affiliated facilities, this dataset allows you to analyze trends, regional concentrations, and the historical development of BGMEA-associated garment factories, offering invaluable insights into Bangladesh's textile and apparel sector. Certainly, here's a more detailed description for your "BGMEA Listed Garments in Bangladesh" dataset:

Key Data Fields:

• Factory Name: Identify each factory within the dataset.
• Factory Type: Categorize factories based on their specialization, such as textiles, apparel, and more.
• Factory Priority: Gain insights into the priority level assigned to each factory.
• Management Employees: Understand the scale of management personnel within each establishment.
• Number of Machines: Quantify the machinery used in garment production.
• Production Capacity (Yearly in Dozen): Assess the yearly production capacity, measured in dozens of garments.
• Principal Exportable Products: Explore the primary products manufactured for export markets.
• Factory Location: Observe the regional distribution of factories across Bangladesh.
• Factory Location in City: Access precise city-level location details.
• Factory Location in Town: Fine-tune your analysis with town-level location information.
• Year of Establishment: Trace the historical development of each factory through its establishment year.

Unlocking Insights:

With this dataset at your disposal, you can embark on a multitude of explorations and investigations. Dive into historical trends to uncover how the industry has evolved over time. Analyze factory types and priorities to gain a deeper understanding of specialization within the sector. Explore the distribution of factories across different regions, cities, and towns to identify key clusters. Investigate the correlation between production capacity and year of establishment to discern growth patterns.

BGMEA Affiliation:

This dataset focuses on factories listed under the Bangladesh Garment Manufacturers and Exporters Association (BGMEA). BGMEA has been a driving force in the garment industry's growth, representing and advocating for the interests of manufacturers and exporters while also playing a pivotal role in ensuring compliance with international labor standards and safety regulations.

Empower Your Research:

Whether you are a researcher studying the socio-economic impact of the garment industry, a business analyst evaluating market trends, or a policymaker shaping the industry's future, this dataset equips you with the tools to make informed decisions and drive positive change.

Start your journey into the dynamic world of Bangladesh's garment industry today, and let the data within this dataset be your guiding light.

In the fall of 2020, DC Health's Center for Policy, Planning and Evaluation (CPPE) was awarded one of ten Centers for Disease Control and Prevention (CDC), Firearm Injury Surveillance Through Emergency Rooms (FASTER) grants. This grant has allowed the District to begin regular surveillance of firearm injury visits to the city’s seven emergency departments. DC-FASTER helps address important gaps in timely data availability for firearm injuries. Timely reporting at the city level through FASTER, allows the District to detect surges in gun violence, monitor the victimization of at-risk groups, and understand trends in firearm injury. Data provided in this dashboard are updated monthly and represent the number of emergency department visits for firearm injury. All seven of the District’s Emergency rooms report to DC Health daily.

The dataset titled "Online Delivery Data" comprises 388 entries, each representing an individual's response to a survey concerning their preferences and experiences with online food delivery services in Australia. The dataset is structured into 53 columns, encompassing a wide range of information from demographic details to specific preferences and feedback on online food delivery services. Below is an in-depth description of its structure and the types of information it contains.

Dataset Overview Entries: 388 Attributes: 53 Core Attributes Description Demographic and Background Information

Age: The respondent's age. Gender: The gender of the respondent. Marital Status: Marital status of the respondent (e.g., Single, Married). Occupation: The respondent's occupation. Monthly Income: Monthly income category of the respondent. Educational Qualifications: Educational level achieved by the respondent. City: The city in Australia where the respondent resides. Family size: Number of members in the respondent's family. Service Utilization Preferences

Medium of ordering (P1 and P2): Primary and secondary preferences for ordering mediums, such as food delivery apps or direct calls. Meal preference (P1 and P2): Primary and secondary meal preferences. Preference reasons (P1 and P2): Primary and secondary reasons for their preferences. Perceptions and Attitudes

Various columns capture the respondent's attitudes towards ease and convenience, time-saving aspects, variety of choices, payment options, discounts and offers, food quality, tracking system, and several other factors related to online food delivery. Health and Hygiene Concerns

Specific concerns regarding health, delivery punctuality, hygiene, and past negative experiences with online food delivery services. Service Quality and Feedback

Attributes covering delivery time importance, packaging quality, customer service aspects (such as the number of calls to service and politeness), food freshness, temperature, taste, and quantity. Output: Likely a binary response (e.g., Yes or No) to a specific survey question, which could pertain to the respondent's overall satisfaction or willingness to recommend the service. Reviews: Open-ended feedback from respondents, providing qualitative insights into their experiences. Summary This dataset provides a comprehensive view of consumer preferences, behaviors, and satisfaction levels regarding online food delivery services in Australia. It encompasses a broad spectrum of variables from basic demographic information to detailed opinions on service quality, making it an invaluable resource for analyzing consumer trends, identifying areas for improvement in service delivery, and understanding the factors that influence customer satisfaction and loyalty in the online food delivery industry.