cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.60593/ur.d.26462215.v1
title: Titanic

description: <b>Titanic Dataset (for Machine Learning)</b>The Titanic dataset is a classic and widely used dataset for machine learning and data analysis. It contains information about the passengers of the RMS Titanic, which tragically sank on its maiden voyage on April 15, 1912. The dataset provides details about each passenger, including their demographics, ticket information, and survival status. This dataset is often used to demonstrate and practice various machine learning techniques, particularly classification.This dataset is divided into two: training set & testing set.<b>Dataset Variables:</b>PassengerId: count for each passengerSurvived: 0 = No; 1 = YesName: name of passengerSex: passenger's sexAge: passenger's ageSibSp: number of siblings/spouses abroad the TitanicParch: number of parents/children abroad the TitanicTicket: ticket numberFare: passenger fareCabin: cabin numberEmbarked: port where passenger embarked (C = Cherbourg; Q = Queenstown; S = Southampton)
DOI: 10.60593/ur.d.26462404.v1
title: Restaurant Menu (Data Cleaning)

description: <b>Restaurant Menu Dataset</b>With approximately 45,000 menus dating from the 1840s to the present, The New York Public Library’s restaurant menu collection is one of the largest in the world. The menu data has been transcribed, dish by dish, into this dataset. For more information, please see http://menus.nypl.org/about.This dataset is not clean and contains many missing values, making it perfect to practice data cleaning tools and techniques.<b>Dataset Variables:</b>id: identifier for menuname: sponsor: who sponsored the meal (organizations, people, name of restaurant)event: categoryvenue: type of place (commercial, social, professional)place: where the meal took place (often a geographic location)physical_description: dimension and material description of the menuoccasion: occasion of the meal (holidays, anniversaries, daily)notes: notes by librarians about the original materialcall_number: call number of the menukeywords: language: date: date of the menulocation: organization or business who produced the menulocation_typecurrency: system of money the menu uses (dollars, etc)currency_symbol: symbol for the currency ($, etc)status: completeness of the menu transcription (transcribed, under review, etc)page_count: how many pages the menu hasdish_count: how many dishes the menu has
DOI: 10.17876/musewide/dr.1/18158
title: Object GUO_ID 18158 in candels-cdfs-37

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.60593/ur.d.26462404
title: Restaurant Menu (Data Cleaning)

description: <b>Restaurant Menu Dataset</b>With approximately 45,000 menus dating from the 1840s to the present, The New York Public Library’s restaurant menu collection is one of the largest in the world. The menu data has been transcribed, dish by dish, into this dataset. For more information, please see http://menus.nypl.org/about.This dataset is not clean and contains many missing values, making it perfect to practice data cleaning tools and techniques.<b>Dataset Variables:</b>id: identifier for menuname: sponsor: who sponsored the meal (organizations, people, name of restaurant)event: categoryvenue: type of place (commercial, social, professional)place: where the meal took place (often a geographic location)physical_description: dimension and material description of the menuoccasion: occasion of the meal (holidays, anniversaries, daily)notes: notes by librarians about the original materialcall_number: call number of the menukeywords: language: date: date of the menulocation: organization or business who produced the menulocation_typecurrency: system of money the menu uses (dollars, etc)currency_symbol: symbol for the currency ($, etc)status: completeness of the menu transcription (transcribed, under review, etc)page_count: how many pages the menu hasdish_count: how many dishes the menu has
DOI: 10.17876/musewide/dr.1/18162
title: Object GUO_ID 18162 in candels-cdfs-40

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.60593/ur.d.26462428
title: Zoonotic Disease

description: <b>Zoonotic Disease Dataset</b>Zoonotic diseases are infections that spread between people and animals. This dataset contains information to investigate the correlation between climate variables (temperature, precipitation) and zoonotic disease in different countries across different years. The data is clean and does not have missing values.<b>Dataset Variables:</b>Country: region from where data was collectedYear: year when data was collectedTemperature: collected in degrees Celsius Precipitation: collected in millimeters (mm)Zoonotic Cases: number of zoonotic infections Population Density: number of people per kilometer square of countryUrbanization Rate: percentage of country's population living in urban areas<br>
DOI: 10.60593/ur.d.26462428.v1
title: Zoonotic Disease

description: <b>Zoonotic Disease Dataset</b>Zoonotic diseases are infections that spread between people and animals. This dataset contains information to investigate the correlation between climate variables (temperature, precipitation) and zoonotic disease in different countries across different years. The data is clean and does not have missing values.<b>Dataset Variables:</b>Country: region from where data was collectedYear: year when data was collectedTemperature: collected in degrees Celsius Precipitation: collected in millimeters (mm)Zoonotic Cases: number of zoonotic infections Population Density: number of people per kilometer square of countryUrbanization Rate: percentage of country's population living in urban areas<br>
DOI: 10.17876/musewide/dr.1/18163
title: Object GUO_ID 18163 in candels-cdfs-40

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.60593/ur.d.26462452
title: Meteorite Landings

description: <b>Meteorite Landings Dataset</b>The Meteoritical Society collects data on meteorites landings. This dataset, taken from public available NASA datasets, includes the location, mass, composition, and fall year for over 45,000 meteorites that have struck our planet.<b>Dataset Variables:</b>name: name given to meteoriteid: unique identifier given to meteoritenametype: Valid (typical meteorite) or Relict (meteorite that has been highly degraded by weather on Earth)recclass: classification given to meteoritemass (g): mass of meteorite, in gramsfall: Fall (meteorite's fall was observed) or Found (meteorite's fall was not observed)year: the year the meteorite fell or year it was foundreclat: latitude of meteorite's landingreclong: longitude of the meteorite's landingGeoLocation: a parentheses-enclose, comma-separated tuple that combines reclat and reclong
DOI: 10.60593/ur.d.26462452.v1
title: Meteorite Landings

description: <b>Meteorite Landings Dataset</b>The Meteoritical Society collects data on meteorites landings. This dataset, taken from public available NASA datasets, includes the location, mass, composition, and fall year for over 45,000 meteorites that have struck our planet.<b>Dataset Variables:</b>name: name given to meteoriteid: unique identifier given to meteoritenametype: Valid (typical meteorite) or Relict (meteorite that has been highly degraded by weather on Earth)recclass: classification given to meteoritemass (g): mass of meteorite, in gramsfall: Fall (meteorite's fall was observed) or Found (meteorite's fall was not observed)year: the year the meteorite fell or year it was foundreclat: latitude of meteorite's landingreclong: longitude of the meteorite's landingGeoLocation: a parentheses-enclose, comma-separated tuple that combines reclat and reclong
DOI: 10.17876/musewide/dr.1/18164
title: Object GUO_ID 18164 in candels-cdfs-40

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.60593/ur.d.26462479
title: Business Revenue Growth

description: <b>Business Revenue Growth Dataset</b>The Inc. 5000 is Inc. Magazine’s annual list of the 5000 fastest growing private companies in the United States. This dataset was compiled by measuring each company's percentage revenue growth over a four-year period.<br>
DOI: 10.60593/ur.d.26462479.v1
title: Business Revenue Growth

description: <b>Business Revenue Growth Dataset</b>The Inc. 5000 is Inc. Magazine’s annual list of the 5000 fastest growing private companies in the United States. This dataset was compiled by measuring each company's percentage revenue growth over a four-year period.<br>
DOI: 10.60593/ur.d.26462485
title: Modeling User Knowledge

description: <b>Modelling User Knowledge Dataset</b>This dataset contains students' knowledge status about the subject of Electrical DC Machines, where the users' knowledge class were classified using intuitive knowledge classifier.The dataset does not contain any missing values.<b>Dataset Variables:</b>STG: the degree of study time for goal object materialsSCG: the degree of repetition number of user for goal object materials STR: the degree of study time of user for related objects with goal objectLPR: the exam performance of user for related objects with goal objectPEG: the exam performance of user for goal objectsUNS: the knowledge level of userClass Labels: Very Low: 50 Low: 129 Middle: 122 High: 130<br>
DOI: 10.60593/ur.d.26462485.v1
title: Modeling User Knowledge

description: <b>Modelling User Knowledge Dataset</b>This dataset contains students' knowledge status about the subject of Electrical DC Machines, where the users' knowledge class were classified using intuitive knowledge classifier.The dataset does not contain any missing values.<b>Dataset Variables:</b>STG: the degree of study time for goal object materialsSCG: the degree of repetition number of user for goal object materials STR: the degree of study time of user for related objects with goal objectLPR: the exam performance of user for related objects with goal objectPEG: the exam performance of user for goal objectsUNS: the knowledge level of userClass Labels: Very Low: 50 Low: 129 Middle: 122 High: 130<br>
DOI: 10.17876/musewide/dr.1/18166
title: Object GUO_ID 18166 in candels-cdfs-37

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/18170
title: Object GUO_ID 18170 in candels-cdfs-37

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.60593/ur.d.26462560
title: Cancer

description: <b>Cancer Dataset</b>The data set contains part of the data for a study of oral condition of cancer patients conducted at the Mid-Michigan Medical Center in 1999. The oral conditions of the patients were measured and recorded at the initial stage, at the end of the second week, at the end of the fourth week, and at the end of the sixth week. Patients were divided into two groups at random: One group received a placebo and the other group received aloe juice treatment.The dataset is small with 25 rows and 9 features.<b>Dataset Variables:</b>ID: patient identification number TRT: treatment group (0 = placebo; 1 = aloe juice)AGE: patient's age in yearsWEIGHTIN: patient's weight at initial stageSTAGE- initial cancer stage, coded 1 through 4TOTALCIN: oral condition at the initial stageTOTALCW2: oral condition at the end of week 2TOTALCW4: oral condition at the end of week 4TOTALCW6: oral condition at the end of week 6<br><br>
DOI: 10.60593/ur.d.26462560.v1
title: Cancer

description: <b>Cancer Dataset</b>The data set contains part of the data for a study of oral condition of cancer patients conducted at the Mid-Michigan Medical Center in 1999. The oral conditions of the patients were measured and recorded at the initial stage, at the end of the second week, at the end of the fourth week, and at the end of the sixth week. Patients were divided into two groups at random: One group received a placebo and the other group received aloe juice treatment.The dataset is small with 25 rows and 9 features.<b>Dataset Variables:</b>ID: patient identification number TRT: treatment group (0 = placebo; 1 = aloe juice)AGE: patient's age in yearsWEIGHTIN: patient's weight at initial stageSTAGE- initial cancer stage, coded 1 through 4TOTALCIN: oral condition at the initial stageTOTALCW2: oral condition at the end of week 2TOTALCW4: oral condition at the end of week 4TOTALCW6: oral condition at the end of week 6<br><br>
DOI: 10.17876/musewide/dr.1/18181
title: Object GUO_ID 18181 in candels-cdfs-37

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.60593/ur.d.26462569
title: COVID-19 Behaviors

description: <b>COVID-19 Behaviors Dataset</b>Behavioral analysis of how populations in different countries responded to the pandemic. The data is based on a monthly survey where respondents note counts of various activities along with a 0-100 score related to how much they agree with a given statement. The data was collected by the Imperial College London YouGov Covid 19 Behaviour Tracker Data Hub. Additional information can be found: https://github.com/YouGov-Data/covid-19-tracker.
DOI: 10.60593/ur.d.26462569.v1
title: COVID-19 Behaviors

description: <b>COVID-19 Behaviors Dataset</b>Behavioral analysis of how populations in different countries responded to the pandemic. The data is based on a monthly survey where respondents note counts of various activities along with a 0-100 score related to how much they agree with a given statement. The data was collected by the Imperial College London YouGov Covid 19 Behaviour Tracker Data Hub. Additional information can be found: https://github.com/YouGov-Data/covid-19-tracker.
DOI: 10.17876/musewide/dr.1/18186
title: Object GUO_ID 18186 in candels-cdfs-37

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.60593/ur.d.26462593
title: Postgraduation Employment Over Time (2003 - 2021)

description: <b>College Graduates Over Time (2003-2021) Dataset</b>This dataset was taken from the National Center for Science and Engineering Statistics (NCSES). The table details the number of employed college graduates by sex, level of highest degree, and major occupation from the years 2003–2021.<b>Dataset Variables:</b><i>Grouped Data:</i> Sex, Level of Highest Degree, Occupation<i>Range of Years: </i>2003, 2010, 2013, 2015, 2017, 2019, 2021
DOI: 10.60593/ur.d.26462593.v1
title: Postgraduation Employment Over Time (2003 - 2021)

description: <b>College Graduates Over Time (2003-2021) Dataset</b>This dataset was taken from the National Center for Science and Engineering Statistics (NCSES). The table details the number of employed college graduates by sex, level of highest degree, and major occupation from the years 2003–2021.<b>Dataset Variables:</b><i>Grouped Data:</i> Sex, Level of Highest Degree, Occupation<i>Range of Years: </i>2003, 2010, 2013, 2015, 2017, 2019, 2021
DOI: 10.17876/musewide/dr.1/18188
title: Object GUO_ID 18188 in candels-cdfs-37

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.60593/ur.d.26462635
title: Income Level of Workers (1960-2022)

description: <b>Income Level of Workers (1960-2022) Dataset</b><br>Data extracted from the United States Census Bureau detailing the number and median earnings of total workers and full-time, year-round workers by sex and female-to-male earnings ratio from the years 1960 to 2022.<br>
DOI: 10.60593/ur.d.26462635.v1
title: Income Level of Workers (1960-2022)

description: <b>Income Level of Workers (1960-2022) Dataset</b><br>Data extracted from the United States Census Bureau detailing the number and median earnings of total workers and full-time, year-round workers by sex and female-to-male earnings ratio from the years 1960 to 2022.<br>
DOI: 10.17876/musewide/dr.1/18202
title: Object GUO_ID 18202 in candels-cdfs-37

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.60593/ur.d.26801089
title: University of Rochester room impulse response dataset

description: The dataset includes impulse responses recorded from 14 different rooms. Each room has unique acoustic properties, providing a wide range of RT60, clarity, and EDT values. The recordings are in 48kHz, 32bit, mono WAV files. The dataset is organized by room, with each subfolder containing the impulse responses specific to that room, as well as a general layout of each room and plots of acoustic data.This dataset supports Estimating direction of arrival in reverberant environments for wake-word detection using a single structural vibration sensor, published in the <i>Journal of the Acoustical Society of America, </i>Vol. 156, Iss. 4, October, 2024.If you plan to download this dataset, we would appreciate it very much if you could fill out the Google form at https://forms.gle/jnuP2dYRK3CPmXQG6. This will help us understand the usage and impacts of this dataset. Your feedback will also help us improve any future extensions of this work.
DOI: 10.60593/ur.d.26801089.v3
title: University of Rochester room impulse response dataset

description: The dataset includes impulse responses recorded from 14 different rooms. Each room has unique acoustic properties, providing a wide range of RT60, clarity, and EDT values. The recordings are in 48kHz, 32bit, mono WAV files. The dataset is organized by room, with each subfolder containing the impulse responses specific to that room, as well as a general layout of each room and plots of acoustic data.This dataset supports Estimating direction of arrival in reverberant environments for wake-word detection using a single structural vibration sensor, published in the <i>Journal of the Acoustical Society of America, </i>Vol. 156, Iss. 4, October, 2024.If you plan to download this dataset, we would appreciate it very much if you could fill out the Google form at https://forms.gle/jnuP2dYRK3CPmXQG6. This will help us understand the usage and impacts of this dataset. Your feedback will also help us improve any future extensions of this work.
DOI: 10.17876/musewide/dr.1/18205
title: Object GUO_ID 18205 in candels-cdfs-37

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.60593/ur.d.26801089.v2
title: University of Rochester room impulse response dataset

description: The dataset includes impulse responses recorded from 14 different rooms. Each room has unique acoustic properties, providing a wide range of RT60, clarity, and EDT values. The recordings are in 48kHz, 32bit, mono WAV files. The dataset is organized by room, with each subfolder containing the impulse responses specific to that room, as well as a general layout of each room and plots of acoustic data.This dataset supports Estimating direction of arrival in reverberant environments for wake-word detection using a single structural vibration sensor, published in the <i>Journal of the Acoustical Society of America, </i>Vol. 156, Iss. 4, October, 2024.If you plan to download this dataset, we would appreciate it very much if you could fill out the Google form at https://forms.gle/jnuP2dYRK3CPmXQG6. This will help us understand the usage and impacts of this dataset. Your feedback will also help us improve any future extensions of this work.
DOI: 10.60593/ur.d.27172986
title: Using Virtual Reality to Study Spatial Mapping and Threat Learning

description: This data is from a virtual reality threat conditioning study comparing those that learned the contingencies of the paradigm (learners) and those who did not. Participants collected flowers in a virtual environment one at a time, and after collecting a flower, they rated on a scale (0-9) how likely they were to get a shock (9 being more likely). Unknown to participants, half of the environment predicted shock (at reinforcement rates 35%, 50%, and 60%), while the other half was "safe". Participants also collected and returned objects to their original location 4 times each. 2 objects were located in the "safe" half, two were located in the dangerous half. The distance between where the participant placed the object and it's original location was measured. State and trait anxiety scores were measured before (state and trait) and after (state only) the conditioning task. Participants also completed the Raven's Progressive Matrices before the task. During the task, skin conductance level was collected as participants approached a flower (SCL) and after they collected the flower and rated the likelihood of shock but before receiving a shock (SCR). At the end of the task, participants were asked to name the objects they collected and their locations. <br><b>Learner:</b> 1 - learner; 0 -non-learner<b>Reinforcement rate</b>: Shock reinforcement rate: 35%, 50%, 60%<b>Age:</b> Participant's age<b>Gender: </b>Participant's gender<b>STAI_State_Pre:</b> State Trait Anxiety Inventory score; State, before task<b>STAI_State_Post</b>: State Trait Anxiety Inventory score; State, after task<b>STAI_Trait</b>: State Trait Anxiety Inventory score; Trait<b>Raven</b>: Raven visual IQ score<b>Number_mem_obj</b>: Number of objects remembered<b>Number_mem_loc:</b> Number of object locations remembered<b>SCR_Safe_n:</b><b><strong> </strong></b>SCR (jitter period) for n'th safe trial; normalized and range-corrected<b>SCR_Dang_n: </b>SCR (jitter period) for n'th dangerous trial; normalized and range-corrected<b>SCL_Safe_n:</b> SCL (approaching flower) for n'th safe trial; normalized and range-corrected<b>SCL_Dang_n:</b> SCL (approaching flower) for n'th dangerous trial; normalized and range-corrected<b>Exp_Safe_n:</b> Shock expectation rating for n'th safe trial<b>Exp_Dang_n:</b> Shock expectation rating for n'th dangerous trial<b>Dist_n_k:</b> Distance of object n, iteration k<b>Condition</b> A - Dangerous objects: 1, 2; Safe objects: 3, 4; B - Dangerous objects: 3, 4; Safe objects: 1, 2;<br>