This data about nola.gov provides a window into how people are interacting with the the City of New Orleans online. The data comes from a unified Google Analytics account for New Orleans. We do not track individuals and we anonymize the IP addresses of all visitors.

Context

This file contains 5 years of daily time series data for several measures of traffic on a statistical forecasting teaching notes website whose alias is statforecasting.com. The variables have complex seasonality that is keyed to the day of the week and to the academic calendar. The patterns you you see here are similar in principle to what you would see in other daily data with day-of-week and time-of-year effects. Some good exercises are to develop a 1-day-ahead forecasting model, a 7-day ahead forecasting model, and an entire-next-week forecasting model (i.e., next 7 days) for unique visitors.

Content

The variables are daily counts of page loads, unique visitors, first-time visitors, and returning visitors to an academic teaching notes website. There are 2167 rows of data spanning the date range from September 14, 2014, to August 19, 2020. A visit is defined as a stream of hits on one or more pages on the site on a given day by the same user, as identified by IP address. Multiple individuals with a shared IP address (e.g., in a computer lab) are considered as a single user, so real users may be undercounted to some extent. A visit is classified as "unique" if a hit from the same IP address has not come within the last 6 hours. Returning visitors are identified by cookies if those are accepted. All others are classified as first-time visitors, so the count of unique visitors is the sum of the counts of returning and first-time visitors by definition. The data was collected through a traffic monitoring service known as StatCounter.

Inspiration

This file and a number of other sample datasets can also be found on the website of RegressIt, a free Excel add-in for linear and logistic regression which I originally developed for use in the course whose website generated the traffic data given here. If you use Excel to some extent as well as Python or R, you might want to try it out on this dataset.

Context

There are a lot of unknowns when running an E-commerce store, even when you have analytics to guide your decisions.

Users are an important factor in an e-commerce business. This is especially true in a C2C-oriented store, since they are both the suppliers (by uploading their products) AND the customers (by purchasing other user's articles).

This dataset aims to serve as a benchmark for an e-commerce fashion store. Using this dataset, you may want to try and understand what you can expect of your users and determine in advance how your grows may be.

• For instance, if you see that most of your users are not very active, you may look into this dataset to compare your store's performance.

If you think this kind of dataset may be useful or if you liked it, don't forget to show your support or appreciation with an upvote/comment. You may even include how you think this dataset might be of use to you. This way, I will be more aware of specific needs and be able to adapt my datasets to suits more your needs.

This dataset is part of a preview of a much larger dataset. Please contact me for more.

Content

What is inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

The data was scraped from a successful online C2C fashion store with over 9M registered users. The store was first launched in Europe around 2009 then expanded worldwide.

Visitors vs Users: Visitors do not appear in this dataset. Only registered users are included. "Visitors" cannot purchase an article but can view the catalog.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Questions you might want to answer using this dataset:

• Are e-commerce users interested in social network feature ?
• Are my users active enough (compared to those of this dataset) ?
• How likely are people from other countries to sign up in a C2C website ?
• How many users are likely to drop off after years of using my service ?

License

CC-BY-NC-SA 4.0

For other licensing options, contact me.

How many people use social media?

              Social media usage is one of the most popular online activities. In 2024, over five billion people were using social media worldwide, a number projected to increase to over six billion in 2028.

              Who uses social media?
              Social networking is one of the most popular digital activities worldwide and it is no surprise that social networking penetration across all regions is constantly increasing. As of January 2023, the global social media usage rate stood at 59 percent. This figure is anticipated to grow as lesser developed digital markets catch up with other regions
              when it comes to infrastructure development and the availability of cheap mobile devices. In fact, most of social media’s global growth is driven by the increasing usage of mobile devices. Mobile-first market Eastern Asia topped the global ranking of mobile social networking penetration, followed by established digital powerhouses such as the Americas and Northern Europe.

              How much time do people spend on social media?
              Social media is an integral part of daily internet usage. On average, internet users spend 151 minutes per day on social media and messaging apps, an increase of 40 minutes since 2015. On average, internet users in Latin America had the highest average time spent per day on social media.

              What are the most popular social media platforms?
              Market leader Facebook was the first social network to surpass one billion registered accounts and currently boasts approximately 2.9 billion monthly active users, making it the most popular social network worldwide. In June 2023, the top social media apps in the Apple App Store included mobile messaging apps WhatsApp and Telegram Messenger, as well as the ever-popular app version of Facebook.

This map shows the access to mental health providers in every county and state in the United States according to the 2024 County Health Rankings & Roadmaps data for counties, states, and the nation. It translates the numbers to explain how many additional mental health providers are needed in each county and state. According to the data, in the United States overall there are 319 people per mental health provider in the U.S. The maps clearly illustrate that access to mental health providers varies widely across the country.The data comes from this County Health Rankings 2024 layer. An updated layer is usually published each year, which allows comparisons from year to year. This map contains layers for 2024 and also for 2022 as a comparison.County Health Rankings & Roadmaps (CHR&R), a program of the University of Wisconsin Population Health Institute with support provided by the Robert Wood Johnson Foundation, draws attention to why there are differences in health within and across communities by measuring the health of nearly all counties in the nation. This map's layers contain 2024 CHR&R data for nation, state, and county levels. The CHR&R Annual Data Release is compiled using county-level measures from a variety of national and state data sources. CHR&R provides a snapshot of the health of nearly every county in the nation. A wide range of factors influence how long and how well we live, including: opportunities for education, income, safe housing and the right to shape policies and practices that impact our lives and futures. Health Outcomes tell us how long people live on average within a community, and how people experience physical and mental health in a community. Health Factors represent the things we can improve to support longer and healthier lives. They are indicators of the future health of our communities.Some example measures are:Life ExpectancyAccess to Exercise OpportunitiesUninsuredFlu VaccinationsChildren in PovertySchool Funding AdequacySevere Housing Cost BurdenBroadband AccessTo see a full list of variables, definitions and descriptions, explore the Fields information by clicking the Data tab here in the Item Details of this layer. For full documentation, visit the Measures page on the CHR&R website. Notable changes in the 2024 CHR&R Annual Data Release:Measures of birth and death now provide more detailed race categories including a separate category for ‘Native Hawaiian or Other Pacific Islander’ and a ‘Two or more races’ category where possible. Find more information on the CHR&R website.Ranks are no longer calculated nor included in the dataset. CHR&R introduced a new graphic to the County Health Snapshots on their website that shows how a county fares relative to other counties in a state and nation. Data Processing:County Health Rankings data and metadata were prepared and formatted for Living Atlas use by the CHR&R team. 2021 U.S. boundaries are used in this dataset for a total of 3,143 counties. Analytic data files can be downloaded from the CHR&R website.

The global number of Facebook users was forecast to continuously increase between 2023 and 2027 by in total 391 million users (+14.36 percent). After the fourth consecutive increasing year, the Facebook user base is estimated to reach 3.1 billion users and therefore a new peak in 2027. Notably, the number of Facebook users was continuously increasing over the past years. User figures, shown here regarding the platform Facebook, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

How much time do people spend on social media?

              As of 2024, the average daily social media usage of internet users worldwide amounted to 143 minutes per day, down from 151 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of three hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in
              the U.S. was just two hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively.
              People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general.
              During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

Introduction

The GiGL Spaces to Visit dataset provides locations and boundaries for open space sites in Greater London that are available to the public as destinations for leisure, activities and community engagement. It includes green corridors that provide opportunities for walking and cycling.

The dataset has been created by Greenspace Information for Greater London CIC (GiGL). As London’s Environmental Records Centre, GiGL mobilises, curates and shares data that underpin our knowledge of London’s natural environment. We provide impartial evidence to support informed discussion and decision making in policy and practice.

GiGL maps under licence from the Greater London Authority.

Description

This dataset is a sub-set of the GiGL Open Space dataset, the most comprehensive dataset available of open spaces in London. Sites are selected for inclusion in Spaces to Visit based on their public accessibility and likelihood that people would be interested in visiting.

The dataset is a mapped Geographic Information System (GIS) polygon dataset where one polygon (or multi-polygon) represents one space. As well as site boundaries, the dataset includes information about a site’s name, size and type (e.g. park, playing field etc.).

GiGL developed the Spaces to Visit dataset to support anyone who is interested in London’s open spaces - including community groups, web and app developers, policy makers and researchers - with an open licence data source. More detailed and extensive data are available under GiGL data use licences for GIGL partners, researchers and students. Information services are also available for ecological consultants, biological recorders and community volunteers – please see www.gigl.org.uk for more information.

Please note that access and opening times are subject to change (particularly at the current time) so if you are planning to visit a site check on the local authority or site website that it is open.

The dataset is updated on a quarterly basis. If you have questions about this dataset please contact GiGL’s GIS and Data Officer.

Data sources

The boundaries and information in this dataset, are a combination of data collected during the London Survey Method habitat and open space survey programme (1986 – 2008) and information provided to GiGL from other sources since. These sources include London borough surveys, land use datasets, volunteer surveys, feedback from the public, park friends’ groups, and updates made as part of GiGL’s on-going data validation and verification process.

Due to data availability, some areas are more up-to-date than others. We are continually working on updating and improving this dataset. If you have any additional information or corrections for sites included in the Spaces to Visit dataset please contact GiGL’s GIS and Data Officer.

NOTE: The dataset contains OS data © Crown copyright and database rights 2025. The site boundaries are based on Ordnance Survey mapping, and the data are published under Ordnance Survey's 'presumption to publish'. When using these data please acknowledge GiGL and Ordnance Survey as the source of the information using the following citation:

‘Dataset created by Greenspace Information for Greater London CIC (GiGL), 2025 – Contains Ordnance Survey and public sector information licensed under the Open Government Licence v3.0 ’

Effect of suicide rates on life expectancy dataset

Abstract In 2015, approximately 55 million people died worldwide, of which 8 million committed suicide. In the USA, one of the main causes of death is the aforementioned suicide, therefore, this experiment is dealing with the question of how much suicide rates affects the statistics of average life expectancy. The experiment takes two datasets, one with the number of suicides and life expectancy in the second one and combine data into one dataset. Subsequently, I try to find any patterns and correlations among the variables and perform statistical test using simple regression to confirm my assumptions.

Data

The experiment uses two datasets - WHO Suicide Statistics[1] and WHO Life Expectancy[2], which were firstly appropriately preprocessed. The final merged dataset to the experiment has 13 variables, where country and year are used as index: Country, Year, Suicides number, Life expectancy, Adult Mortality, which is probability of dying between 15 and 60 years per 1000 population, Infant deaths, which is number of Infant Deaths per 1000 population, Alcohol, which is alcohol, recorded per capita (15+) consumption, Under-five deaths, which is number of under-five deaths per 1000 population, HIV/AIDS, which is deaths per 1 000 live births HIV/AIDS, GDP, which is Gross Domestic Product per capita, Population, Income composition of resources, which is Human Development Index in terms of income composition of resources, and Schooling, which is number of years of schooling.

LICENSE

THE EXPERIMENT USES TWO DATASET - WHO SUICIDE STATISTICS AND WHO LIFE EXPECTANCY, WHICH WERE COLLEECTED FROM WHO AND UNITED NATIONS WEBSITE. THEREFORE, ALL DATASETS ARE UNDER THE LICENSE ATTRIBUTION-NONCOMMERCIAL-SHAREALIKE 3.0 IGO (https://creativecommons.org/licenses/by-nc-sa/3.0/igo/).

[1] https://www.kaggle.com/szamil/who-suicide-statistics

[2] https://www.kaggle.com/kumarajarshi/life-expectancy-who

The global number of internet users in was forecast to continuously increase between 2024 and 2029 by in total 1.3 billion users (+23.66 percent). After the fifteenth consecutive increasing year, the number of users is estimated to reach 7 billion users and therefore a new peak in 2029. Notably, the number of internet users of was continuously increasing over the past years.Depicted is the estimated number of individuals in the country or region at hand, that use the internet. As the datasource clarifies, connection quality and usage frequency are distinct aspects, not taken into account here.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of internet users in countries like the Americas and Asia.

​This collection consists of total number of signatures to 1769 petitions over time (hourly resolution) along with petition metadata (title, category, time of creation), directly crawled from the website. Please see 'Related resources' section below for related data collections. This project aims to develop methodologies to study online political behaviour including use of the Internet to generate new data and experiments; to collect and analyse data on internet-mediated interactions at both individual and organisational levels; and to use this data to re-examine and where necessary develop political science knowledge and theory in light of widespread use of the Internet First, the project will re-examine the logic of collective action, assessing the impact of reduced communication and coordination costs; the changing nature of leadership; and the effects of different information environments on propensity to participate in political mobilisation. This part of the research will involve conducting laboratory and field experiments into online behaviour. Second, the research will develop the Digital-era Governance model for newer 'Web 2.0' applications and other technological developments such as cloud computing. The research will re-examine the nature of citizen-government interactions in this changing environment, examining the impact of Internet-based mediation on information exchange, organisational forms in government and citizen participation in policy-making. This part of the research will involve a comparison of government's online presence in eight countries, using webmetric techniques, and in-depth qualitative analysis of governance models, using elite interviewing and documentary analysis. Using a webscraping approach, the US White House petition website was accessed daily with an automated script to record the overall signatures to date on each active petition.

Ghana number dataset has accurate numbers attached with verified through our team. These client contact data belong to active users only. In fact, these things make it a valuable marketing resource. Whether your business is new or old, you can boost your reach and connect to a large audience with this database. Again, you will find many people who have an interest in your products and will accept from you. Moreover, the Ghana number dataset will support you make your brand more renowned. In other words, by becoming a known brand in the market, you can increase your brand value greatly. Similarly, many people will show interest in your products and services. However, the contacts on this mobile number list are active and real. Yet, you will benefit greatly if you purchase this cheap but valuable database. Ghana phone data can be a great solution for SMS and telemarketing. Anyone can use the contact lead here to reach different people in this area. Ghana phone data allows you to give product details with your messages to make them more appealing and reliable. Your product quality and content will catch the attention of the interested audience. This will create more traffic and you can reach sales from there. Likewise, the Ghana phone data is an opt-in and permission-based contact list. In addition, with an affordable yet fresh list like ours, your marketing will be more effective. People can now relate to your business more after you successfully use this tool. Thus, order the contact library now from List To Data to promote your goods and services everywhere inside the country. Ghana phone number list is a massive database. Our team promises you sincere service and active support. In general, you can contact us anytime on our website if you face any problems with our list. Our support team will solve the problem for you, thus you don’t have to worry about not obtaining the worth of your money. Further, the Ghana phone number list will aid your business in many new ways. The benefits of marketing on SMS marketing are enormous as we all know very well. Moreover, no one wants to miss out on such a huge and versatile audience in Ghana. Hence, purchasing this contact number package will be a gem for any business any day.

[1] The Progress by Population Group analysis is a component of the Healthy People 2020 (HP2020) Final Review. The analysis included subsets of the 1,111 measurable HP2020 objectives that have data available for any of six broad population characteristics: sex, race and ethnicity, educational attainment, family income, disability status, and geographic location. Progress toward meeting HP2020 targets is presented for up to 24 population groups within these characteristics, based on objective data aggregated across HP2020 topic areas. The Progress by Population Group data are also available at the individual objective level in the downloadable data set. [2] The final value was generally based on data available on the HP2020 website as of January 2020. For objectives that are continuing into HP2030, more recent data will be included on the HP2030 website as it becomes available: https://health.gov/healthypeople. [3] For more information on the HP2020 methodology for measuring progress toward target attainment and the elimination of health disparities, see: Healthy People Statistical Notes, no 27; available from: https://www.cdc.gov/nchs/data/statnt/statnt27.pdf. [4] Status for objectives included in the HP2020 Progress by Population Group analysis was determined using the baseline, final, and target value. The progress status categories used in HP2020 were: a. Target met or exceeded—One of the following applies: (i) At baseline, the target was not met or exceeded, and the most recent value was equal to or exceeded the target (the percentage of targeted change achieved was equal to or greater than 100%); (ii) The baseline and most recent values were equal to or exceeded the target (the percentage of targeted change achieved was not assessed). b. Improved—One of the following applies: (i) Movement was toward the target, standard errors were available, and the percentage of targeted change achieved was statistically significant; (ii) Movement was toward the target, standard errors were not available, and the objective had achieved 10% or more of the targeted change. c. Little or no detectable change—One of the following applies: (i) Movement was toward the target, standard errors were available, and the percentage of targeted change achieved was not statistically significant; (ii) Movement was toward the target, standard errors were not available, and the objective had achieved less than 10% of the targeted change; (iii) Movement was away from the baseline and target, standard errors were available, and the percent change relative to the baseline was not statistically significant; (iv) Movement was away from the baseline and target, standard errors were not available, and the objective had moved less than 10% relative to the baseline; (v) No change was observed between the baseline and the final data point. d. Got worse—One of the following applies: (i) Movement was away from the baseline and target, standard errors were available, and the percent change relative to the baseline was statistically significant; (ii) Movement was away from the baseline and target, standard errors were not available, and the objective had moved 10% or more relative to the baseline. NOTE: Measurable objectives had baseline data. SOURCE: National Center for Health Statistics, Healthy People 2020 Progress by Population Group database.

Dataset originally created 03/01/2019 UPDATE: Packaged on 04/18/2019 UPDATE: Edited README on 04/18/2019

I. About this Data Set This data set is a snapshot of work that is ongoing as a collaboration between Kluge Fellow in Digital Studies, Patrick Egan and an intern at the Library of Congress in the American Folklife Center. It contains a combination of metadata from various collections that contain audio recordings of Irish traditional music. The development of this dataset is iterative, and it integrates visualizations that follow the key principles of trust and approachability. The project, entitled, “Connections In Sound” invites you to use and re-use this data.

The text available in the Items dataset is generated from multiple collections of audio material that were discovered at the American Folklife Center. Each instance of a performance was listed and “sets” or medleys of tunes or songs were split into distinct instances in order to allow machines to read each title separately (whilst still noting that they were part of a group of tunes). The work of the intern was then reviewed before publication, and cross-referenced with the tune index at www.irishtune.info. The Items dataset consists of just over 1000 rows, with new data being added daily in a separate file.

The collections dataset contains at least 37 rows of collections that were located by a reference librarian at the American Folklife Center. This search was complemented by searches of the collections by the scholar both on the internet at https://catalog.loc.gov and by using card catalogs.

Updates to these datasets will be announced and published as the project progresses.

II. What’s included? This data set includes:

• The Items Dataset – a .CSV containing Media Note, OriginalFormat, On Website, Collection Ref, Missing In Duplication, Collection, Outside Link, Performer, Solo/multiple, Sub-item, type of tune, Tune, Position, Location, State, Date, Notes/Composer, Potential Linked Data, Instrument, Additional Notes, Tune Cleanup. This .CSV is the direct export of the Items Google Spreadsheet

III. How Was It Created? These data were created by a Kluge Fellow in Digital Studies and an intern on this program over the course of three months. By listening, transcribing, reviewing, and tagging audio recordings, these scholars improve access and connect sounds in the American Folklife Collections by focusing on Irish traditional music. Once transcribed and tagged, information in these datasets is reviewed before publication.

IV. Data Set Field Descriptions

IV

a) Collections dataset field descriptions

• ItemId – this is the identifier for the collection that was found at the AFC
• Viewed – if the collection has been viewed, or accessed in any way by the researchers.
• On LOC – whether or not there are audio recordings of this collection available on the Library of Congress website.
• On Other Website – if any of the recordings in this collection are available elsewhere on the internet
• Original Format – the format that was used during the creation of the recordings that were found within each collection
• Search – this indicates the type of search that was performed in order that resulted in locating recordings and collections within the AFC
• Collection – the official title for the collection as noted on the Library of Congress website
• State – The primary state where recordings from the collection were located
• Other States – The secondary states where recordings from the collection were located
• Era / Date – The decade or year associated with each collection
• Call Number – This is the official reference number that is used to locate the collections, both in the urls used on the Library website, and in the reference search for catalog cards (catalog cards can be searched at this address: https://memory.loc.gov/diglib/ihas/html/afccards/afccards-home.html)
• Finding Aid Online? – Whether or not a finding aid is available for this collection on the internet

b) Items dataset field descriptions

• id – the specific identification of the instance of a tune, song or dance within the dataset
• Media Note – Any information that is included with the original format, such as identification, name of physical item, additional metadata written on the physical item
• Original Format – The physical format that was used when recording each specific performance. Note: this field is used in order to calculate the number of physical items that were created in each collection such as 32 wax cylinders.
• On Webste? – Whether or not each instance of a performance is available on the Library of Congress website
• Collection Ref – The official reference number of the collection
• Missing In Duplication – This column marks if parts of some recordings had been made available on other websites, but not all of the recordings were included in duplication (see recordings from Philadelphia Céilí Group on Villanova University website)
• Collection – The official title of the collection given by the American Folklife Center
• Outside Link – If recordings are available on other websites externally
• Performer – The name of the contributor(s)
• Solo/multiple – This field is used to calculate the amount of solo performers vs group performers in each collection
• Sub-item – In some cases, physical recordings contained extra details, the sub-item column was used to denote these details
• Type of item – This column describes each individual item type, as noted by performers and collectors
• Item – The item title, as noted by performers and collectors. If an item was not described, it was entered as “unidentified”
• Position – The position on the recording (in some cases during playback, audio cassette player counter markers were used)
• Location – Local address of the recording
• State – The state where the recording was made
• Date – The date that the recording was made
• Notes/Composer – The stated composer or source of the item recorded
• Potential Linked Data – If items may be linked to other recordings or data, this column was used to provide examples of potential relationships between them
• Instrument – The instrument(s) that was used during the performance
• Additional Notes – Notes about the process of capturing, transcribing and tagging recordings (for researcher and intern collaboration purposes)
• Tune Cleanup – This column was used to tidy each item so that it could be read by machines, but also so that spelling mistakes from the Item column could be corrected, and as an aid to preserving iterations of the editing process

V. Rights statement The text in this data set was created by the researcher and intern and can be used in many different ways under creative commons with attribution. All contributions to Connections In Sound are released into the public domain as they are created. Anyone is free to use and re-use this data set in any way they want, provided reference is given to the creators of these datasets.

VI. Creator and Contributor Information

Creator: Connections In Sound

Contributors: Library of Congress Labs

VII. Contact Information Please direct all questions and comments to Patrick Egan via www.twitter.com/drpatrickegan or via his website at www.patrickegan.org. You can also get in touch with the Library of Congress Labs team via LC-Labs@loc.gov.

Project Summary This dataset contains all qualitative and quantitative data collected in the first phase of the Pandemic Journaling Project (PJP). PJP is a combined journaling platform and interdisciplinary, mixed-methods research study developed by two anthropologists, with support from a team of colleagues and students across the social sciences, humanities, and health fields. PJP launched in Spring 2020 as the COVID-19 pandemic was emerging in the United States. PJP was created in order to “pre-design an archive” of COVID-19 narratives and experiences open to anyone around the world. The project is rooted in a commitment to democratizing knowledge production, in the spirit of “archival activism” and using methods of “grassroots collaborative ethnography” (Willen et al. 2022; Wurtz et al. 2022; Zhang et al 2020; see also Carney 2021). The motto on the PJP website encapsulates these commitments: “Usually, history is written only by the powerful. When the history of COVID-19 is written, let’s make sure that doesn’t happen.” (A version of this Project Summary with links to the PJP website and other relevant sites is included in the public documentation of the project at QDR.) In PJP’s first phase (PJP-1), the project provided a digital space where participants could create weekly journals of their COVID-19 experiences using a smartphone or computer. The platform was designed to be accessible to as wide a range of potential participants as possible. Anyone aged 15 or older, living anywhere in the world, could create journal entries using their choice of text, images, and/or audio recordings. The interface was accessible in English and Spanish, but participants could submit text and audio in any language. PJP-1 ran on a weekly basis from May 2020 to May 2022. Data Overview This Qualitative Data Repository (QDR) project contains all journal entries and closed-ended survey responses submitted during PJP-1, along with accompanying descriptive and explanatory materials. The dataset includes individual journal entries and accompanying quantitative survey responses from more than 1,800 participants in 55 countries. Of nearly 27,000 journal entries in total, over 2,700 included images and over 300 are audio files. All data were collected via the Qualtrics survey platform. PJP-1 was approved as a research study by the Institutional Review Board (IRB) at the University of Connecticut. Participants were introduced to the project in a variety of ways, including through the PJP website as well as professional networks, PJP’s social media accounts (on Facebook, Instagram, and Twitter) , and media coverage of the project. Participants provided a single piece of contact information — an email address or mobile phone number — which was used to distribute weekly invitations to participate. This contact information has been stripped from the dataset and will not be accessible to researchers. PJP uses a mixed-methods research approach and a dynamic cohort design. After enrolling in PJP-1 via the project’s website, participants received weekly invitations to contribute to their journals via their choice of email or SMS (text message). Each weekly invitation included a link to that week’s journaling prompts and accompanying survey questions. Participants could join at any point, and they could stop participating at any point as well. They also could stop participating and later restart. Retention was encouraged with a monthly raffle of three $100 gift cards. All individuals who had contributed that month were eligible. Regardless of when they joined, all participants received the project’s narrative prompts and accompanying survey questions in the same order. In Week 1, before contributing their first journal entries, participants were presented with a baseline survey that collected demographic information, including political leanings, as well as self-reported data about COVID-19 exposure and physical and mental health status. Some of these survey questions were repeated at periodic intervals in subsequent weeks, providing quantitative measures of change over time that can be analyzed in conjunction with participants' qualitative entries. Surveys employed validated questions where possible. The core of PJP-1 involved two weekly opportunities to create journal entries in the format of their choice (text, image, and/or audio). Each week, journalers received a link with an invitation to create one entry in response to a recurring narrative prompt (“How has the COVID-19 pandemic affected your life in the past week?”) and a second journal entry in response to their choice of two more tightly focused prompts. Typically the pair of prompts included one focusing on subjective experience (e.g., the impact of the pandemic on relationships, sense of social connectedness, or mental health) and another with an external focus (e.g., key sources of scientific information, trust in government, or COVID-19’s economic impact). Each week,...

Jamaica number dataset makes your telemarketing more beneficial. Thus, this Jamaica number dataset has correct and up-to-date mobile numbers for direct marketing. As of 2024, there are about 3.27 Million mobile phone connections in Jamaica. This number is a bit higher than the total population, which is around 2.83 Million. Our List To Data website can assist in getting speedy replies from new clients for publicity. Besides, the Jamaica number dataset is effective for SMS marketing as well. As well as you have multiple chances to earn huge from other countries. So, using this contact number library is a perfect choice for reaching people in specific places. By using our library, you can enhance your marketing and find new B2C clients easily. Jamaica phone data is a great way to help your business grow. Also, this Jamaica phone data provides the most real and active phone numbers so you can easily reach people in Jamaica. Everybody can select who they want to contact based on their location, what their company does, or how big their company is. Further, the Jamaica phone data is very authentic and useful for finding new customers. At the same time, the sellers can deliver sales promotions and many offers to the consumers. Also, they can connect with the largest group of customers quickly in a selected area. List To Data includes contact leads for both businesses and individuals. Jamaica phone number list will make your business more profitable. Most importantly, a Jamaica phone number list plays a vital role in marketing and business, so take it now. Just visit our List To Data website today to get the most recent phone numbers for any business. With 95% precision, this contact book offers you contact numbers for many people who might want your services. So, the Jamaica phone number list is a great tool for reaching new customers through phone calls. In fact, you can pick from other packages on our website that fit your needs and budget. If your business is big or small, our mobile number data will help you in your entire journey. Ultimately, our team supplies this correct contact number cautiously as per your needs.

Facebook received 73,390 user data requests from federal agencies and courts in the United States during the second half of 2023. The social network produced some user data in 88.84 percent of requests from U.S. federal authorities. The United States accounts for the largest share of Facebook user data requests worldwide.

Abstract copyright UK Data Service and data collection copyright owner.Next Steps (also known as the Longitudinal Study of Young People in England (LSYPE1)) is a major longitudinal cohort study following a nationally representative group of around 16,000 who were in Year 9 attending state and independent schools in England in 2004, a cohort born in 1989-90.The first seven sweeps of the study were conducted annually (2004-2010) when the study was funded and managed by the Department for Education (DfE). The study mainly focused on the educational and early labour market experiences of young people.In 2015 Next Steps was restarted, under the management of the Centre for Longitudinal Studies (CLS) at the UCL Faculty of Education and Society (IOE) and funded by the Economic and Social Research Council. The Next Steps Age 25 survey was aimed at increasing the understanding of the lives of young adults growing up today and the transitions out of education and into early adult life.The Next Steps Age 32 Survey took place between April 2022 and September 2023 and is the ninth sweep of the study. The Age 32 Survey aimed to provide data for research and policy on the lives of this generation of adults in their early 30s. This sweep also collected information on many wider aspects of cohort members' lives including health and wellbeing, politics and social participation, identity and attitudes as well as capturing personality, resilience, working memory and financial literacy. Next Steps survey data is also linked to the National Pupil Database (NPD), the Hospital Episode Statistics (HES), the Individualised Learner Records (ILR) and the Student Loans Company (SLC). There are now two separate studies that began under the LSYPE programme. The second study, Our Future (LSYPE2) (available at the UK Data Service under GN 2000110), began in 2013 and will track a sample of over 13,000 young people annually from ages 13/14 through to age 20.Further information about Next Steps may be found on the CLS website.Secure Access datasets:Secure Access versions of Next Steps have more restrictive access conditions than Safeguarded versions available under the standard End User Licence (see 'Access' section).Secure Access versions of the Next Steps include:sensitive variables from the questionnaire data for Sweeps 1-9. These are available under Secure Access SN 8656. National Pupil Database (NPD) linked data at Key Stages 2, 3, 4 and 5, England. These are available under SN 7104.Linked Individualised Learner Records learner and learning aims datasets for academic years 2005 to 2014, England. These are available under SN 8577.detailed geographic indicators for Sweep 1 and Sweep 8 (2001 Census Boundaries) - available under SN 8189 and geographic indicators for Sweep 8 (2011 Census Boundaries) - available under SN 8190. The Sweep 1 geography file was previously held under SN 7104.Linked Health Administrative Datasets (Hospital Episode Statistics) for years 1998-2017 held under SN 8681.Linked Student Loans Company Records for years 2007-2021 held under SN 8848.When researchers are approved/accredited to access a Secure Access version of Next Steps, the Safeguarded (EUL) version of the study - Next Steps: Sweeps 1-9, 2004-2023 (SN 5545) - will be automatically provided alongside. SN 8656 - Next Steps: Sweeps 1-9, 2004-2023: Secure Access includes sensitive variables from the main Next Steps survey from Sweep 1 (age 14) to Sweep 9 (age 32). A version of these variables were previously available under SN 7104. University identifiers for Sweeps 6 and 7 have also been reinstated and are available in the young person file. International Data Access Network (IDAN)These data are now available to researchers based outside the UK. Selected UKDS SecureLab/controlled datasets from the Institute for Social and Economic Research (ISER) and the Centre for Longitudinal Studies (CLS) have been made available under the International Data Access Network (IDAN) scheme, via a Safe Room access point at one of the UKDS IDAN partners. Prospective users should read the UKDS SecureLab application guide for non-ONS data for researchers outside of the UK via Safe Room Remote Desktop Access. Further details about the IDAN scheme can be found on the UKDS International Data Access Network webpage and on the IDAN website.Latest edition informationFor the third edition (September 2024), sensitive variables for Sweep 9 (Age 32) have been added to the study. The main interview file includes unit group (4-digit) SOC2010, SOC20 and SIC2007 codes for current job, first job, job at age 25 and partner's job; university from which obtained undergraduate and post-graduate degrees; detailed long standing illness, national identity, gender and day date variables. In addition there is a separate pregnancy histories dataset which includes variables on type of fertility treatment received. Main Topics: The Safeguarded (EUL) version of Next Steps is provided alongside the Secure Access version. Additional variables which are available under Secure Access are from the following categories: date of interview (detailed)date of birth (detailed)detailed disabilitiesfull or detailed SOC/SIC codeschild care arrangementshigher Education identifierspotential school identifiers In this revised deposit, the variable denoting number of rooms in the house has been top-coded and there are truncated versions of SOC and SIC codes, longstanding illness and subject studied at university, The original version of the variables containing the detailed values is available under Secure Access. Multi-stage stratified random sample Face-to-face interview Telephone interview

Abstract copyright UK Data Service and data collection copyright owner.Next Steps (also known as the Longitudinal Study of Young People in England (LSYPE1)) is a major longitudinal cohort study following a nationally representative group of around 16,000 who were in Year 9 attending state and independent schools in England in 2004, a cohort born in 1989-90.The first seven sweeps of the study were conducted annually (2004-2010) when the study was funded and managed by the Department for Education (DfE). The study mainly focused on the educational and early labour market experiences of young people.In 2015 Next Steps was restarted, under the management of the Centre for Longitudinal Studies (CLS) at the UCL Faculty of Education and Society (IOE) and funded by the Economic and Social Research Council. The Next Steps Age 25 survey was aimed at increasing the understanding of the lives of young adults growing up today and the transitions out of education and into early adult life.The Next Steps Age 32 Survey took place between April 2022 and September 2023 and is the ninth sweep of the study. The Age 32 Survey aimed to provide data for research and policy on the lives of this generation of adults in their early 30s. This sweep also collected information on many wider aspects of cohort members' lives including health and wellbeing, politics and social participation, identity and attitudes as well as capturing personality, resilience, working memory and financial literacy.Next Steps survey data is also linked to the National Pupil Database (NPD), the Hospital Episode Statistics (HES), the Individualised Learner Records (ILR) and the Student Loans Company (SLC).There are now two separate studies that began under the LSYPE programme. The second study, Our Future (LSYPE2) (available at the UK Data Service under GN 2000110), began in 2013 and will track a sample of over 13,000 young people annually from ages 13/14 through to age 20.Further information about Next Steps may be found on the CLS website.Secure Access datasets:Secure Access versions of Next Steps have more restrictive access conditions than Safeguarded versions available under the standard End User Licence (see 'Access' section).Secure Access versions of the Next Steps include:sensitive variables from the questionnaire data for Sweeps 1-9. These are available under Secure Access SN 8656. National Pupil Database (NPD) linked data at Key Stages 2, 3, 4 and 5, England. These are available under SN 7104.Linked Individualised Learner Records learner and learning aims datasets for academic years 2005 to 2014, England. These are available under SN 8577.detailed geographic indicators for Sweep 1 and Sweep 8 (2001 Census Boundaries) - available under SN 8189 and geographic indicators for Sweep 8 (2011 Census Boundaries) - available under SN 8190. The Sweep 1 geography file was previously held under SN 7104.Linked Health Administrative Datasets (Hospital Episode Statistics) for years 1998-2017 held under SN 8681.Linked Student Loans Company Records for years 2007-2021 held under SN 8848.When researchers are approved/accredited to access a Secure Access version of Next Steps, the Safeguarded (EUL) version of the study - Next Steps: Sweeps 1-9, 2004-2023 (SN 5545) - will be automatically provided alongside. The Next Steps: Linked Health Administrative Datasets (Hospital Episode Statistics), England, 1998-2017: Secure Access study includes data files from the NHS Digital HES database for those cohort members who provided consent to health data linkage in the Age 25 sweep. The HES database contains information about all hospital admissions in England. The following linked HES data are available: 1) Accident and Emergency (A&E) The A&E dataset details each attendance to an Accident and Emergency care facility in England, between 01-04-2007 and 31-03-2017 (inclusive). It includes major A&E departments, single specialty A&E departments, minor injury units and walk in centres in England. 2) Admitted Patient Care (APC) The APC data summarises episodes of care for admitted patients, where the episode occurred between 01-04-1997 and 31-03-2017 (inclusive). 3) Critical Care (CC) The CC dataset covers records of critical care activity between 01-04-2009 and 31-03-2017 (inclusive). 4) Out Patient (OP) The OP dataset lists the outpatient appointments between 01-04-2003 and 31-03-2017 (inclusive). CLS/ NHS Digital Sub-licence agreement NHS Digital has given CLS permission for onward sharing of the Next Steps/HES dataset via the UKDS Secure Lab. In order to ensure data minimisation, NHS Digital require that researchers only access the HES variables needed for their approved research project. Therefore, the HES linked data provided by the UKDS to approved researchers will be subject to sub-setting of variables. The researcher will need to request a specific sub-set of variables from the Next Steps HES data dictionary, which will subsequently make available within their UKDS Secure Account. Once the researcher has finished their research, the UKDS will delete the tailored dataset for that specific project. Any party wishing to access the data deposited at the UK Data Service will be required to enter into a Licence agreement with CLS (UCL), in addition to the agreements signed with the UKDS, provided in the application pack.Latest edition informationFor the second edition (September 2022), 22 previously unavailable variables have been added to the A&E, APC and OP data files. The variable list has also been updated to reflect the changes.

This is a E-commerce website logs data created for helping the data analysts to practice exploratory data analysis and data visualization. The dataset has data on when the website was accessed, IP address of the source, Country, language in which website was accessed, amount of sales made by that IP address.

Included columns:

Time and duration of of accessing the website
Country, Language & Platform in which it was accessed
No. of bytes used & IP address of the person accessing website
Sales or return amount of that person