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We use the Enron email dataset to build a network of email addresses. It contains 614586 emails sent over the period from 6 January 1998 until 4 February 2004. During the pre-processing, we remove the periods of low activity and keep the emails from 1 January 1999 until 31 July 2002 which is 1448 days of email records in total. Also, we remove email addresses that sent less than three emails over that period. In total, the Enron email network contains 6 600 nodes and 50 897 edges.

To build a graph G = (V, E), we use email addresses as nodes V. Every node v_i has an attribute which is a time-varying signal that corresponds to the number of emails sent from this address during a day. We draw an edge e_ij between two nodes i and j if there is at least one email exchange between the corresponding addresses.

Column 'Count' in 'edges.csv' file is the number of 'From'->'To' email exchanges between the two addresses. This column can be used as an edge weight.

The file 'nodes.csv' contains a dictionary that is a compressed representation of time-series. The format of the dictionary is Day->The Number Of Emails Sent By the Address During That Day. The total number of days is 1448.

'id-email.csv' is a file containing the actual email addresses.

Dataset Card for CNN Dailymail Dataset

  Dataset Summary

The CNN / DailyMail Dataset is an English-language dataset containing just over 300k unique news articles as written by journalists at CNN and the Daily Mail. The current version supports both extractive and abstractive summarization, though the original version was created for machine reading and comprehension and abstractive question answering.

  Supported Tasks and Leaderboards

'summarization': Versions… See the full description on the dataset page: https://huggingface.co/datasets/abisee/cnn_dailymail.

The total number of user mailboxes in Umeå kommun and how many are active each day of the reporting period. A mailbox is considered active if the user sent or read any email.

CNN/Daily Mail is a dataset for text summarization. Human generated abstractive summary bullets were generated from news stories in CNN and Daily Mail websites as questions (with one of the entities hidden), and stories as the corresponding passages from which the system is expected to answer the fill-in the-blank question. The authors released the scripts that crawl, extract and generate pairs of passages and questions from these websites.

In all, the corpus has 286,817 training pairs, 13,368 validation pairs and 11,487 test pairs, as defined by their scripts. The source documents in the training set have 766 words spanning 29.74 sentences on an average while the summaries consist of 53 words and 3.72 sentences.

Overview This Extended Golf Play Dataset is a rich and detailed collection designed to extend the classic golf dataset. It includes a variety of features to cover many aspects of data science. This dataset is especially useful for teaching because it offers many small datasets within it, each one created for a different learning purpose.

Core Features: Outlook: Type of weather (sunny, cloudy, rainy, snowy). Temperature: How hot or cold it is, in Celsius. Humidity: How much moisture is in the air, as a percent. Windy: If it is windy or not (True or False). Play: If golf was played or not (Yes or No). Extra Features: ID: Each player's unique number. Date: The day the data was recorded. Weekday: What day of the week it is. Holiday: If the day is a special holiday (Yes or No). Season: Time of the year (spring, summer, autumn, winter). Crowded-ness: How crowded the golf course is. PlayTime-Hour: How long people played golf, in hours. Text Features: Review: What players said about their day at golf. EmailCampaign: Emails the golf place sent every day. MaintenanceTasks: Work done to take care of the golf course. Mini Datasets Collection This dataset includes a special set of mini datasets:

Each mini dataset focuses on a specific teaching point, like how to clean data or how to combine datasets. They're perfect for beginners to practice with real examples. Along with these datasets, you'll find notebooks with step-by-step guides that show you how to use the data. Learning With This Dataset Students can use this dataset to learn many skills:

Seeing Data: Learn how to make graphs and see patterns. Sorting Data: Find out which data helps to predict if golf will be played. Finding Odd Data: Spot data that doesn't look right. Understanding Data Over Time: Look at how things change day by day or month by month. Grouping Data: Learn how to put similar days together. Learning From Text: Use players' reviews to get more insights. Making Recommendations: Suggest the best time to play golf based on past data. Who Can Use This Dataset This dataset is for everyone:

New Learners: It's easy to understand and has guides to help you learn. Teachers: Great for classes on how to see and understand data. Researchers: Good for testing new ways to analyze data.

Original Data Source: ⛳️ Golf Play Dataset Extended

We have developed an application and solution approach (using this dataset) for automatically generating and suggesting short email responses to support queries in a university environment. Our proposed solution can be used as one tap or one click solution for responding to various types of queries raised by faculty members and students in a university. Office of Academic Affairs (OAA), Office of Student Life (OSL) and Information Technology Helpdesk (ITD) are support functions within a university which receives hundreds of email messages on the daily basis. Email communication is still the most frequently used mode of communication by these departments. A large percentage of emails received by these departments are frequent and commonly used queries or request for information. Responding to every query by manually typing is a tedious and time consuming task. Furthermore a large percentage of emails and their responses are consists of short messages. For example, an IT support department in our university receives several emails on Wi-Fi not working or someone needing help with a projector or requires an HDMI cable or remote slide changer. Another example is emails from students requesting the office of academic affairs to add and drop courses which they cannot do it directly. The dataset consists of emails messages which are generally received by ITD, OAA and OSL in Ashoka University. The dataset also contains intermediate results while conducting machine learning experiments.

Aeslc (Email Subject Generation Task)

A collection of email messages of employees in the Enron Corporation.

By Huggingface Hub [source]

About this dataset

The AESLC (Automatic Extraction of Semantically-Linked Corporate Communications) dataset provides a unique and captivating glimpse into the lives of Enron employees - from the perspective of communications sent via emails during a period between 1999 to 2004. These anonymous emails not only provide fascinating insight into the daily professional activities, interactions, and relationships within Enron employees, but also offer an educational opportunity for those interested in further exploring corporate communication. Containing such features as email body and subject lines, researchers can tap into this invaluable resource to research topics surrounding linguistics, sentiment analysis, and data mining. Unlock their secrets by discovering what messages were shared amongst these before the breach of scandal that caused their company’s downfall!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This comprehensive dataset includes anonymized emails sent by then Enron employees in the period of 1999 and 2004. By delving into this unique dataset, you can gain a deeper insight into the lives of former Enron employees as well as their professional activities and relationships.

In this guide, we'll provide a walkthrough on how to use this dataset and make meaningful discoveries from it. Let's get started!

Research Ideas

• Analyzing the connections between Enron employees by tracking their email communications over time to uncover trends and correlations.
• Examining the emails for keywords or topics as a way to classify each email in order to gain better understanding of what Enron employees were discussing and what activities they were engaging in.
• Using sentiment analysis techniques on the emails in order to gain insight into the emotional state of Enron employees at different points in time or during particular events or incidents such as when allegations against Enron emerged

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: validation.csv | Column name | Description | |:-----------------|:--------------------------------------------------------------| | email_body | The body of the email sent by Enron employees. (Text) | | subject_line | The subject line of the email sent by Enron employees. (Text) |

File: train.csv | Column name | Description | |:-----------------|:--------------------------------------------------------------| | email_body | The body of the email sent by Enron employees. (Text) | | subject_line | The subject line of the email sent by Enron employees. (Text) |

File: test.csv | Column name | Description | |:-----------------|:--------------------------------------------------------------| | email_body | The body of the email sent by Enron employees. (Text) | | subject_line | The subject line of the email sent by Enron employees. (Text) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Huggingface Hub.

DomainIQ is a comprehensive global Domain Name dataset for organizations that want to build cyber security, data cleaning and email marketing applications. The dataset consists of the DNS records for over 267 million domains, updated daily, representing more than 90% of all public domains in the world.

The data is enriched by over thirty unique data points, including identifying the mailbox provider for each domain and using AI based predictive analytics to identify elevated risk domains from both a cyber security and email sending reputation perspective.

DomainIQ from Datazag offers layered intelligence through a highly flexible API and as a dataset, available for both cloud and on-premises applications. Standard formats include CSV, JSON, Parquet, and DuckDB.

Custom options are available for any other file or database format. With daily updates and constant research from Datazag, organizations can develop their own market leading cyber security, data cleaning and email validation applications supported by comprehensive and accurate data from Datazag. Data updates available on a daily, weekly and monthly basis. API data is updated on a daily basis.

Dataset of Elon musk tweets updated and recorded automatically every day starting from September 2, 2021 (due to a limit of Twitter API)

Content tweets only (if the tweet is not a reply of any other tweet)

License

CC0

Original Data Source: Elon Musk Tweets (Updated Daily Automatically)

Analysis of ‘Medallion Drivers - Active’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/1c75e7ca-9626-4f3b-b18e-39c1efbc7f11 on 13 February 2022.

--- Dataset description provided by original source is as follows ---

PLEASE NOTE: This dataset, which includes all TLC Licensed Drivers who are in good standing and able to drive, is updated every day in the evening between 4-7pm. Please check the 'Last Update Date' field to make sure the list has updated successfully. 'Last Update Date' should show either today or yesterday's date, depending on the time of day. If the list is outdated, please download the most recent list from the link below. http://www1.nyc.gov/assets/tlc/downloads/datasets/tlc_medallion_drivers_active.csv

This is a list of drivers with a current TLC Driver License, which authorizes drivers to operate NYC TLC licensed yellow and green taxicabs and for-hire vehicles (FHVs). This list is accurate as of the date and time shown in the Last Date Updated and Last Time Updated fields. Questions about the contents of this dataset can be sent by email to: licensinginquiries@tlc.nyc.gov.

--- Original source retains full ownership of the source dataset ---

PLEASE NOTE: This dataset, which includes all TLC Licensed Drivers who are in good standing and able to drive, is updated every day in the evening between 4-7pm. Please check the 'Last Update Date' field to make sure the list has updated successfully. 'Last Update Date' should show either today or yesterday's date, depending on the time of day. If the list is outdated, please download the most recent list from the link below. http://www1.nyc.gov/assets/tlc/downloads/datasets/tlc_medallion_drivers_active.csv

This is a list of drivers with a current TLC Driver License, which authorizes drivers to operate NYC TLC licensed yellow and green taxicabs and for-hire vehicles (FHVs). This list is accurate as of the date and time shown in the Last Date Updated and Last Time Updated fields. Questions about the contents of this dataset can be sent by email to: licensinginquiries@tlc.nyc.gov.

The sonic data within the building array is composed of 26 days of 30-minute average data from 30 sonic anemometers. The unobstructed tower sonic data is also the same, but of the 5 heights of the tower. The data files have 48 columns associated with date and time identifiers as well as meteorological turbulence measurements. This dataset is not publicly accessible because: The data were not collected by EPA and are hosted external to the agency. It can be accessed through the following means: The detailed sonic dataset is freely available to others wishing to perform additional analysis however, it is large and not readily posted. The complete dataset is included in the comprehensive JR II data archive set up by the DHS Science and Technology (S&T) Directorate, Chemical Security Analysis Center (CSAC). To obtain the data, an email request can be sent to JackRabbit@st.dhs.gov. The user can then access the archive on the Homeland Security Information Network (HSIN). Format: The sonic data within the Jack Rabbit II (JRII) mock-urban building array are in 30-minute averaged daily excel files separated by each sonic anemometer with numerous variables. The unobstructed, raw 10Hz tower data are in .dat files and processed into 30-minute average daily csv files by sonic height. This dataset is associated with the following publication: Pirhalla, M., D. Heist, S. Perry, S. Hanna, T. Mazzola, S.P. Arya, and V. Aneja. Urban Wind Field Analysis from the Jack Rabbit II Special Sonic Anemometer Study. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, USA, 243: 14, (2020).

The U.S. Census Bureau, in collaboration with five federal agencies, launched the Household Pulse Survey to produce data on the social and economic impacts of Covid-19 on American households. The Household Pulse Survey was designed to gauge the impact of the pandemic on employment status, consumer spending, food security, housing, education disruptions, and dimensions of physical and mental wellness. The survey was designed to meet the goal of accurate and timely weekly estimates. It was conducted by an internet questionnaire, with invitations to participate sent by email and text message. The sample frame is the Census Bureau Master Address File Data. Housing units linked to one or more email addresses or cell phone numbers were randomly selected to participate, and one respondent from each housing unit was selected to respond for him or herself. Estimates are weighted to adjust for nonresponse and to match Census Bureau estimates of the population by age, sex, race and ethnicity, and educational attainment. All estimates shown meet the NCHS Data Presentation Standards for Proportions,

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General description

SAPFLUXNET contains a global database of sap flow and environmental data, together with metadata at different levels. SAPFLUXNET is a harmonised database, compiled from contributions from researchers worldwide.

The SAPFLUXNET version 0.1.5 database harbours 202 globally distributed datasets, from 121 geographical locations. SAPFLUXNET contains sap flow data for 2714 individual plants (1584 angiosperms and 1130 gymnosperms), belonging to 174 species (141 angiosperms and 33 gymnosperms), 95 different genera and 45 different families. More information on the database coverage can be found here: http://sapfluxnet.creaf.cat/shiny/sfn_progress_dashboard/.

The SAPFLUXNET project has been developed by researchers at CREAF and other institutions (http://sapfluxnet.creaf.cat/#team), coordinated by Rafael Poyatos (CREAF, http://www.creaf.cat/staff/rafael-poyatos-lopez), and funded by two Spanish Young Researcher's Grants (SAPFLUXNET, CGL2014-55883-JIN; DATAFORUSE, RTI2018-095297-J-I00 ) and an Alexander von Humboldt Research Fellowship for Experienced Researchers).

Changelog

Compared to version 0.1.4, this version includes some changes in the metadata, but all time series data (sap flow, environmental) remain the same.

For all datasets, climate metadata (temperature and precipitation, ‘si_mat’ and ‘si_map’) have been extracted from CHELSA (https://chelsa-climate.org/), replacing the previous climate data obtained with Wordclim. This change has modified the biome classification of the datasets in ‘si_biome’.

In ‘species’ metadata, the percentage of basal area with sap flow measurements for each species (‘sp_basal_area_perc’) is now assigned a value of 0 if species are in the understorey. This affects two datasets: AUS_MAR_UBD and AUS_MAR_UBW, where, previously, the sum of species basal area percentages could add up to more than 100%.

In ‘species’ metadata, the percentage of basal area with sap flow measurements for each species (‘sp_basal_area_perc’) has been corrected for datasets USA_SIL_OAK_POS, USA_SIL_OAK_1PR, USA_SIL_OAK_2PR.

In ‘site’ metadata, the vegetation type (‘si_igbp’) has been changed to SAV for datasets CHN_ARG_GWD and CHN_ARG_GWS.

Variables and units

SAPFLUXNET contains whole-plant sap flow and environmental variables at sub-daily temporal resolution. Both sap flow and environmental time series have accompanying flags in a data frame, one for sap flow and another for environmental variables. These flags store quality issues detected during the quality control process and can be used to add further quality flags.

Metadata contain relevant variables informing about site conditions, stand characteristics, tree and species attributes, sap flow methodology and details on environmental measurements. The description and units of all data and metadata variables can be found here: Metadata and data units.

To learn more about variables, units and data flags please use the functionalities implemented in the sapfluxnetr package (https://github.com/sapfluxnet/sapfluxnetr). In particular, have a look at the package vignettes using R:

remotes::install_github(

'sapfluxnet/sapfluxnetr',

build_opts = c("--no-resave-data", "--no-manual", "--build-vignettes")

)

library(sapfluxnetr)

to list all vignettes

vignette(package='sapfluxnetr')

variables and units

vignette('metadata-and-data-units', package='sapfluxnetr')

data flags

vignette('data-flags', package='sapfluxnetr')

Data formats

SAPFLUXNET data can be found in two formats: 1) RData files belonging to the custom-built 'sfn_data' class and 2) Text files in .csv format. We recommend using the sfn_data objects together with the sapfluxnetr package, although we also provide the text files for convenience. For each dataset, text files are structured in the same way as the slots of sfn_data objects; if working with text files, we recommend that you check the data structure of 'sfn_data' objects in the corresponding vignette.

Working with sfn_data files

To work with SAPFLUXNET data, first they have to be downloaded from Zenodo, maintaining the folder structure. A first level in the folder hierarchy corresponds to file format, either RData files or csv's. A second level corresponds to how sap flow is expressed: per plant, per sapwood area or per leaf area. Please note that interconversions among the magnitudes have been performed whenever possible. Below this level, data have been organised per dataset. In the case of RData files, each dataset is contained in a sfn_data object, which stores all data and metadata in different slots (see the vignette 'sfn-data-classes'). In the case of csv files, each dataset has 9 individual files, corresponding to metadata (5), sap flow and environmental data (2) and their corresponding data flags (2).

After downloading the entire database, the sapfluxnetr package can be used to: - Work with data from a single site: data access, plotting and time aggregation. - Select the subset datasets to work with. - Work with data from multiple sites: data access, plotting and time aggregation.

Please check the following package vignettes to learn more about how to work with sfn_data files:

Quick guide

Metadata and data units

sfn_data classes

Custom aggregation

Memory and parallelization

Working with text files

We recommend to work with sfn_data objects using R and the sapfluxnetr package and we do not currently provide code to work with text files.

Data issues and reporting

Please report any issue you may find in the database by sending us an email: sapfluxnet@creaf.uab.cat.

Temporary data fixes, detected but not yet included in released versions will be published in SAPFLUXNET main web page ('Known data errors').

Data access, use and citation

This version of the SAPFLUXNET database is open access and corresponds to the data paper submitted to Earth System Science Data in August 2020.

When using SAPFLUXNET data in an academic work, please cite the data paper, when available, or alternatively, the Zenodo dataset (see the ‘Cite as’ section on the right panels of this web page).

The dataset is a collection of aggregated clinical parameters for the participants (such as clinical scores), parameters extracted from the utilized devices (such as average heart rate per day, average gait speed etc.), and coupled events about them (such as falls, loss of orientation etc.). It contains information which was collected during the clinical evaluation of the older people from medical experts.This information represents the clinical status of the older person across different domains, e.g. physical, psychological, cognitive etc.

The dataset contains several medical features which are used by clinicians to assess the overall state of the older people.

The purpose of the Virtual Patient Model is to assess the overall state of the older people based on their medical parameters, and to find associations between these parameters and frailty status.

A list of the recorded clinical parameters and their description is shown below:

- part_id: The user ID, which should be a 4-digit number

- q_date: The recording timestamp, which follows the “YYYY-MM-DDTHH:mm:ss.fffZ” format (eg. 14 September 2017 12:23:34.567, is formatted as 2019-09-14T12:23:34.567Z)

- clinical_visit: As several clinical evaluations were performed to each older adult, this number shows for which clinical evaluation these measurements refer to

- fried: Ordinal categorization of frailty level according to Fried operational definition of frailty

- hospitalization_one_year: Number of nonscheduled hospitalizations in the last year

- hospitalization_three_years: Number of nonscheduled hospitalizations in the last three years

- ortho_hypotension: Presence of orthostatic hypotension

- vision: Visual difficulty (qualitative ordinal evaluation)

- audition: Hearing difficulty (qualitative ordinal evaluation)

- weight_loss: Unintentional weight loss >4.5 kg in the past year (categorical answer)

- exhaustion_score: Self-reported exhaustion (categorical answer)

- raise_chair_time: Time in seconds to perform a lower limb strength clinical test

- balance_single: Single foot station (Balance) (categorical answer)

- gait_get_up: Time in seconds to perform the 3meters’ Timed Get Up And Go Test

- gait_speed_4m: Speed for 4 meters’ straight walk

- gait_optional_binary: Gait optional evaluation (qualitative evaluation by the investigator)

- gait_speed_slower: Slowed walking speed (categorical answer)

- grip_strength_abnormal: Grip strength outside the norms (categorical answer)

- low_physical_activity: Low physical activity (categorical answer)

- falls_one_year: Number of falls in the last year

- fractures_three_years: Number of fractures during the last 3 years

- fried_clinician: Fried’s categorization according to clinician’s estimation (when missing data for answering the Fried’s operational frailty definition questionnaire)

- bmi_score: Body Mass Index (in Kg/m²)

- bmi_body_fat: Body Fat (%)

- waist: Waist circumference (in cm)

- lean_body_mass: Lean Body Mass (%)

- screening_score: Mini Nutritional Assessment (MNA) screening score

- cognitive_total_score: Montreal Cognitive Assessment (MoCA) test score

- memory_complain: Memory complain (categorical answer)

- mmse_total_score: Folstein Mini-Mental State Exam score

- sleep: Reported sleeping problems (qualitative ordinal evaluation)

- depression_total_score: 15-item Geriatric Depression Scale (GDS-15)

- anxiety_perception: Anxiety auto-evaluation (visual analogue scale 0-10)

- living_alone: Living Conditions (categorical answer)

- leisure_out: Leisure activities (number of leisure activities per week)

- leisure_club: Membership of a club (categorical answer)

- social_visits: Number of visits and social interactions per week

- social_calls: Number of telephone calls exchanged per week

- social_phone: Approximate time spent on phone per week

- social_skype: Approximate time spent on videoconference per week

- social_text: Number of written messages (SMS and emails) sent by the participant per week

- house_suitable_participant: Subjective suitability of the housing environment according to participant’s evaluation (categorical answer)

- house_suitable_professional: Subjective suitability of the housing environment according to investigator’s evaluation (categorical answer)

- stairs_number: Number of steps to access house (without possibility to use elevator)

- life_quality: Quality of life self-rating (visual analogue scale 0-10)

- health_rate: Self-rated health status (qualitative ordinal evaluation)

- health_rate_comparison: Self-assessed change since last year (qualitative ordinal evaluation)

- pain_perception: Self-rated pain (visual analogue scale 0-10)

- activity_regular: Regular physical activity (ordinal answer)

- smoking: Smoking (categorical answer)

- alcohol_units: Alcohol Use (average alcohol units consumption per week)

- katz_index: Katz Index of ADL score

- iadl_grade: Instrumental Activities of Daily Living score

- comorbidities_count: Number of comorbidities

- comorbidities_significant_count: Number of comorbidities which affect significantly the person’s functional status

- medication_count: Number of active substances taken on a regular basis

The Reminder extension for CKAN enhances data management by providing automated email notifications based on dataset expiry dates and update subscriptions. Designed to work with CKAN versions 2.2 and up, but tested on 2.5.2, this extension offers a straightforward mechanism for keeping users informed about dataset updates and expirations, promoting better data governance and engagement. The extension leverages a daily cron job to check expiry dates and trigger emails. Key Features: Data Expiry Notifications: Sends email notifications when datasets reach their specified expiry date. A daily cronjob process determines when to send these emails. Note that failure of the cronjob will prevent email delivery for that day. Dataset Update Subscriptions: Allows users to subscribe to specific datasets to receive notifications upon updates via a subscription form snippet that can be included in dataset templates. Unsubscribe Functionality: Includes an unsubscribe link in each notification email, enabling users to easily manage their subscriptions. Configuration Settings: Supports at least one recipient for reminder emails via configuration settings in the CKAN config file. Bootstrap Styling: Intended for use with Bootstrap 3+ for styling, but may still work with Bootstrap 2 with potential style inconsistencies. Technical Integration: The Reminder extension integrates into CKAN via plugins, necessitating the addition of reminder to the ckan.plugins setting in the CKAN configuration file. The extension requires database initialization using paster commands to support the subscription functionality. Setting up a daily cronjob is necessary for the automated sending of reminder and notification emails. Benefits & Impact: By implementing the Reminder extension, CKAN installations can improve data management and user engagement. Automated notifications ensure that stakeholders are aware of dataset expirations and updates, leading to better data governance, and more active user involvement in data ecosystems. This extension provides an easy-to-implement solution for managing data lifecycles and keeping users informed.

For Login DuckDuckGo Please Visit: 👉 DuckDuckGo Login Account

In today’s digital age, privacy has become one of the most valued aspects of online activity. With increasing concerns over data tracking, surveillance, and targeted advertising, users are turning to privacy-first alternatives for everyday browsing. One of the most recognized names in private search is DuckDuckGo. Unlike mainstream search engines, DuckDuckGo emphasizes anonymity and transparency. However, many people wonder: Is there such a thing as a "https://duckduckgo-account.blogspot.com/ ">DuckDuckGo login account ?

In this comprehensive guide, we’ll explore everything you need to know about the DuckDuckGo login account, what it offers (or doesn’t), and how to get the most out of DuckDuckGo’s privacy features.

Does DuckDuckGo Offer a Login Account? To clarify up front: DuckDuckGo does not require or offer a traditional login account like Google or Yahoo. The concept of a DuckDuckGo login account is somewhat misleading if interpreted through the lens of typical internet services.

DuckDuckGo's entire business model is built around privacy. The company does not track users, store personal information, or create user profiles. As a result, there’s no need—or intention—to implement a system that asks users to log in. This stands in stark contrast to other search engines that rely on login-based ecosystems to collect and use personal data for targeted ads.

That said, some users still search for the term DuckDuckGo login account, usually because they’re trying to save settings, sync devices, or use features that may suggest a form of account system. Let’s break down what’s possible and what alternatives exist within DuckDuckGo’s platform.

Saving Settings Without a DuckDuckGo Login Account Even without a traditional DuckDuckGo login account, users can still save their preferences. DuckDuckGo provides two primary ways to retain search settings:

Local Storage (Cookies) When you customize your settings on the DuckDuckGo account homepage, such as theme, region, or safe search options, those preferences are stored in your browser’s local storage. As long as you don’t clear cookies or use incognito mode, these settings will persist.

Cloud Save Feature To cater to users who want to retain settings across multiple devices without a DuckDuckGo login account, DuckDuckGo offers a feature called "Cloud Save." Instead of creating an account with a username or password, you generate a passphrase or unique key. This key can be used to retrieve your saved settings on another device or browser.

While it’s not a conventional login system, it’s the closest DuckDuckGo comes to offering account-like functionality—without compromising privacy.

Why DuckDuckGo Avoids Login Accounts Understanding why there is no DuckDuckGo login account comes down to the company’s core mission: to offer a private, non-tracking search experience. Introducing login accounts would:

Require collecting some user data (e.g., email, password)

Introduce potential tracking mechanisms

Undermine their commitment to full anonymity

By avoiding a login system, DuckDuckGo keeps user trust intact and continues to deliver on its promise of complete privacy. For users who value anonymity, the absence of a DuckDuckGo login account is actually a feature, not a flaw.

DuckDuckGo and Device Syncing One of the most commonly searched reasons behind the term DuckDuckGo login account is the desire to sync settings or preferences across multiple devices. Although DuckDuckGo doesn’t use accounts, the Cloud Save feature mentioned earlier serves this purpose without compromising security or anonymity.

You simply export your settings using a unique passphrase on one device, then import them using the same phrase on another. This offers similar benefits to a synced account—without the need for usernames, passwords, or emails.

DuckDuckGo Privacy Tools Without a Login DuckDuckGo is more than just a search engine. It also offers a range of privacy tools—all without needing a DuckDuckGo login account:

DuckDuckGo Privacy Browser (Mobile): Available for iOS and Android, this browser includes tracking protection, forced HTTPS, and built-in private search.

DuckDuckGo Privacy Essentials (Desktop Extension): For Chrome, Firefox, and Edge, this extension blocks trackers, grades websites on privacy, and enhances encryption.

Email Protection: DuckDuckGo recently launched a service that allows users to create "@duck.com" email addresses that forward to their real email—removing trackers in the process. Users sign up for this using a token or limited identifier, but it still doesn’t constitute a full DuckDuckGo login account.

Is a DuckDuckGo Login Account Needed? For most users, the absence of a DuckDuckGo login account is not only acceptable—it’s ideal. You can:

Use the search engine privately

Customize and save settings

Sync preferences across devices

Block trackers and protect email

—all without an account.

While some people may find the lack of a traditional login unfamiliar at first, it quickly becomes a refreshing break from constant credential requests, data tracking, and login fatigue.

The Future of DuckDuckGo Accounts As of now, DuckDuckGo maintains its position against traditional account systems. However, it’s clear the company is exploring privacy-preserving ways to offer more user features—like Email Protection and Cloud Save. These features may continue to evolve, but the core commitment remains: no tracking, no personal data storage, and no typical DuckDuckGo login account.

Final Thoughts While the term DuckDuckGo login account is frequently searched, it represents a misunderstanding of how the platform operates . Unlike other tech companies that monetize personal data, DuckDuckGo has stayed true to its promise of privacy .