cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.2825573
title: US Middle School Student Survey of Values, Interests and Bonding to School

description: This SPSS file contains raw de-identified survey responses to questions asked of US middle school students in grades 6 and 7 that assess their interest in 11 non-academic activities, the importance of 15 terminal values, and their level of bonding to school.
DOI: 10.5281/zenodo.2827024
title: Youtube cookery channels viewers comments in Hinglish

description: The data was collected from the famous cookery Youtube channels in India. The major focus was to collect the viewers' comments in Hinglish languages. The datasets are taken from top 2 Indian cooking channel named Nisha Madhulika channel and Kabita’s Kitchen channel. Both the datasets comments are divided into seven categories:- Label 1- Gratitude Label 2- About the recipe Label 3- About the video Label 4- Praising Label 5- Hybrid Label 6- Undefined Label 7- Suggestions and queries All the labelling has been done manually. <strong>Nisha Madhulika dataset:</strong> <strong>Dataset characteristics: Multivariate</strong> <strong>Number of instances: 4900</strong> <strong>Area: Cooking </strong> <strong>Attribute characteristics: Real</strong> <strong>Number of attributes: 3</strong> <strong>Date donated: March, 2019</strong> <strong>Associate tasks: Classification</strong> <strong>Missing values: Null</strong> <strong>Kabita Kitchen dataset:</strong> <strong>Dataset characteristics: Multivariate</strong> <strong>Number of instances: 4900</strong> <strong>Area: Cooking </strong> <strong>Attribute characteristics: Real</strong> <strong>Number of attributes: 3</strong> <strong>Date donated: March, 2019</strong> <strong>Associate tasks: Classification</strong> <strong>Missing values: Null</strong> There are two separate datasets file of each channel named as preprocessing and main file . The files with preprocessing names are generated after doing the preprocessing and exploratory data analysis on both the datasets. This file includes: Id Comment text Labels Count of stop-words Uppercase words Hashtags Word count Char count Average words Numeric The main file includes: Id comment text Labels Please cite the paper https://www.mdpi.com/2504-2289/3/3/37 <strong>MDPI and ACS Style</strong> Kaur, G.; Kaushik, A.; Sharma, S. Cooking Is Creating Emotion: A Study on Hinglish Sentiments of Youtube Cookery Channels Using Semi-Supervised Approach. <em>Big Data Cogn. Comput.</em> <strong>2019</strong>, <em>3</em>, 37.
description: {"references": ["Kaur, G.; Kaushik, A.; Sharma, S. Cooking Is Creating Emotion: A Study on Hinglish Sentiments of Youtube Cookery Channels Using Semi-Supervised Approach. Big Data Cogn. Comput. 2019, 3, 37."]}
DOI: 10.5281/zenodo.2827025
title: Youtube cookery channels viewers comments in Hinglish

description: The data was collected from the famous cookery Youtube channels in India. The major focus was to collect the viewers' comments in Hinglish languages. The datasets are taken from top 2 Indian cooking channel named Nisha Madhulika channel and Kabita’s  Kitchen channel.

Both the datasets comments are divided into seven categories:-

Label 1- Gratitude

Label 2- About the recipe

Label 3- About the video

Label 4- Praising

Label 5- Hybrid

Label 6- Undefined

Label 7- Suggestions and queries

All the labelling has been done manually.

 

<strong>Nisha Madhulika dataset:</strong>

<strong>Dataset characteristics: Multivariate</strong>

<strong>Number of instances: 4900</strong>

<strong>Area: Cooking </strong>

<strong>Attribute characteristics: Real</strong>

<strong>Number of attributes: 4</strong>

<strong>Date donated: March, 2019</strong>

<strong>Associate tasks: Classification</strong>

<strong>Missing values: Null</strong>

 

<strong>Kabita Kitchen dataset:</strong>

<strong>Dataset characteristics: Multivariate</strong>

<strong>Number of instances: 4900</strong>

<strong>Area: Cooking </strong>

<strong>Attribute characteristics: Real</strong>

<strong>Number of attributes: 4</strong>

<strong>Date donated: March, 2019</strong>

<strong>Associate tasks: Classification</strong>

<strong>Missing values: Null</strong>

 

There are two separate datasets file of each channel named as preprocessing and main file .

The files with preprocessing names are generated after doing the preprocessing and exploratory data analysis on both the datasets. This file includes:


	 Id
	User
	Comment text
	Labels



	Count of stop-words
	Uppercase words
	Hashtags
	Word count
	Char count
	Average words
	Numeric


 

The main file includes:


	Id
	user
	comment text
	Labels
DOI: 10.7910/dvn/5f1rri/3p1els
title: invpat2008_oc.tab

description: Subsettable inventor-patent lower bound results dataset for 2008-2010, inclusive
DOI: 10.7910/dvn/5f1rri/y0oxin
title: invpat1978_oc.tab

description: Subsettable inventor-patent lower bound results dataset for 1978-1980, inclusive.
DOI: 10.7910/dvn/5f1rri/fno73j
title: invpat1984_oc.tab

description: Subsettable inventor-patent lower bound results dataset for 1984-1986, inclusive.
DOI: 10.7910/dvn/5f1rri/vlza1u
title: invpat1990_oc.tab

description: Subsettable inventor-patent lower bound results dataset for 1990-1992, inclusive.
DOI: 10.7910/dvn/5f1rri/4yiwjx
title: invpat1999_oc.tab

description: Subsettable inventor-patent lower bound results dataset for 1999-2001, inclusive
DOI: 10.7910/dvn/5f1rri/q2gaek
title: invpat2002_oc.tab

description: Subsettable inventor-patent lower bound results dataset for 2002-2004, inclusive
DOI: 10.7910/dvn/5f1rri/uwrecb
title: invpat1993_oc.tab

description: Subsettable inventor-patent lower bound results dataset for 1993-1995, inclusive.
DOI: 10.7910/dvn/5f1rri/4l0dyq
title: invpat1975_uc.tab

description: Subsettable inventor-patent upper bound results dataset for 1975-1977, inclusive.
DOI: 10.7910/dvn/wqalyl/6ocmhv
title: CCAFS VBS step-by-step guidelines final_French.docx

description: C'est le guide de mise en œuvre de l'étude au niveau du village.
DOI: 10.7910/dvn/wqalyl/ghip7t
title: CCAFS VBS Manual Final 2_French_formatted.docx

description: Manuel d'opérations pour l'étude de base Village
DOI: 10.7910/dvn/wqalyl/ujvzsl
title: CCAFS VBS Site Analysis Report french2.doc

description: C'est le modèle pour le rapport de site pour l'étude de base au niveau du village.
DOI: 10.7910/dvn/wqalyl/21vhht
title: CCAFS VBS step-by-step guidelines final.docx

description: This is the implementation guide for the village level study.
DOI: 10.7910/dvn/wqalyl/bmeb6q
title: CCAFS VBS Debriefing Template french.doc

description: Ce fichier contient le modèle pour l'étude au niveau du village. Les équipes d'enquête aura complété ce modèle pour chaque village.
DOI: 10.7910/dvn/wqalyl/yvrfxq
title: ToR Village baseline survey teams 20110412.docx

description: Terms of Reference for the Village Baseline Survey teams
DOI: 10.17632/b9yxkx4wkt.1
title: Data for: Role of Fairness, Accountability, and Transparency in Algorithms: Proposing Algorithmic Affordance

description: This is the data for the study.
DOI: 10.7910/dvn/fgx2f4/nqppig
title: Household2007-Data.7z

description: Zipped Data Files in Original STATA format
DOI: 10.7910/dvn/ljagxf/iqon8b
title: DRSRS Final Report.doc

description: Report, describing the methods and variables
DOI: 10.7910/dvn/ljagxf/zu6kom
title: Kenya_landuse.tab

description: data set
DOI: 10.7910/dvn/jsguaf/fnamyv
title: Cyprus_Port_List

description: List and coordinates of Medieval Cyprus harbors from Book of Curiosities
DOI: 10.17632/f2x4bs2srf.1
title: Data for: Meteorological parameters and gaseous pollutant concentrations as predictors of daily continuous PM2.5 concentrations using deep neural network in Beijing-Tianjin-Hebei, China

description: PM2.5,AOD,gaseous pollutant and meteorological data used for PM2.5 concentrations prediction
DOI: 10.17632/hc2dp7dz4r.1
title: Data for: Meteorological parameters and gaseous pollutant concentrations as predictors of daily continuous PM2.5 concentrations using deep neural network in Beijing-Tianjin-Hebei, China

description: The data include AOD, meteorological data and ground monitoring data
DOI: 10.7910/dvn/mxbeor/yahcio
title: Supplemental Paper.pdf

description: model solving and results
DOI: 10.7910/dvn/5f1rri/mt9cng
title: invpat1999_uc_3.tab

description: Subsettable inventor-patent upper bound results dataset for 1999-2001, inclusive
DOI: 10.7910/dvn/5f1rri/qfmt1q
title: invpat2005_uc.tab

description: Subsettable inventor-patent upper bound results dataset for 2005-2007, inclusive
DOI: 10.7910/dvn/5f1rri/uqggkp
title: invpat2008_uc.tab

description: Subsettable inventor-patent upper bound results dataset for 2008-2010, inclusive
DOI: 10.7910/dvn/5f1rri/ctfoqz
title: invpat2002_uc.tab

description: Subsettable inventor-patent upper bound results dataset for 2002-2004, inclusive
DOI: 10.7910/dvn/5f1rri/utu6ep
title: invpat1993_uc.tab

description: Subsettable inventor-patent upper bound results dataset for 1993-1995, inclusive.
DOI: 10.7910/dvn/5f1rri/6psqo7
title: invpat1990_uc.tab

description: Subsettable inventor-patent upper bound results dataset for 1990-1992, inclusive.
DOI: 10.7910/dvn/5f1rri/8sbani
title: invpat1984_uc.tab

description: Subsettable inventor-patent upper bound results dataset for 1984-1986, inclusive.
DOI: 10.7910/dvn/5f1rri/layz9q
title: invpat1996_uc.tab

description: Subsettable inventor-patent upper bound results dataset for 1996-1998, inclusive.
DOI: 10.7910/dvn/5f1rri/blsssu
title: invpat1987_uc.tab

description: Subsettable inventor-patent upper bound results dataset for 1987-1989, inclusive.
DOI: 10.7910/dvn/5f1rri/d2qflt
title: invpat1981_uc.tab

description: Subsettable inventor-patent upper bound results dataset for 1981-1983, inclusive.
DOI: 10.7910/dvn/5f1rri/0iuovj
title: invpat1978_uc.tab

description: Subsettable inventor-patent upper bound results dataset for 1978-1980, inclusive.
DOI: 10.7910/dvn/kue8zx/chsvai
title: Licht2011_CIM_replication_1.do

description: This file will replicate all models and graphs discussed in the paper.  On a fast computer (quad-core running Stata 11-MC) it takes about ten minutes.  On a slower/older computer, it could take much longer than that (hours rather than minutes).  To run the file you will need to download all of the replication datasets available here.  Be sure to save them in the same directory, and then to change the  line at the top of the do-file.  If you miss this step, Stata will look in the wrong place for the data files and will immediately stumble.  Running the do-file will result in a series of data files (containing the substantive interpretation variables and simulation output), as well as 9 graphs to be saved to your computer.  In all, these files will take up about 460MB of space.  If you wish to utilize any of the data in the replication files for your own research, please contact the authors directly for permission and for detailed information on the variables involved.
DOI: 10.7910/dvn/kue8zx/48v8iq
title: Meinke2005rep.tab

description: This is the replication dataset for: Meinke, Scott R. 2005. "Long-Term Change and Stability in House Voting Decisions: the Case of the Minimum Wage." Legislative Studies Quarterly, 30(1):103.
DOI: 10.7910/dvn/kue8zx/h0gmyj
title: BLE&J2008rep.tab

description: This is the replication dataset for:   Balch-Lindsay, Dylan, Andrew Enterline and Kyle Joyce. 2008. "Third-Party Intervention and the Civil War Process." Journal of Peace Research, 45(3):345.
DOI: 10.7910/dvn/kue8zx/nrwzyp
title: GolubEUPdata.tab

description: This is the replication dataset for: Golub, Jonathan. 2007. "Survival Analysis and EU Decision Making". European Union Politics, 53(4):733.
DOI: 10.5281/zenodo.2828710
title: Youtube cookery channels viewers comments in Hinglish

description: The data was collected from the famous cookery Youtube channels in India. The major focus was to collect the viewers' comments in Hinglish languages. The datasets are taken from top 2 Indian cooking channel named Nisha Madhulika channel and Kabita’s  Kitchen channel.

Both the datasets comments are divided into seven categories:-

Label 1- Gratitude

Label 2- About the recipe

Label 3- About the video

Label 4- Praising

Label 5- Hybrid

Label 6- Undefined

Label 7- Suggestions and queries

All the labelling has been done manually.

 

<strong>Nisha Madhulika dataset:</strong>

<strong>Dataset characteristics: Multivariate</strong>

<strong>Number of instances: 4900</strong>

<strong>Area: Cooking </strong>

<strong>Attribute characteristics: Real</strong>

<strong>Number of attributes: 3</strong>

<strong>Date donated: March, 2019</strong>

<strong>Associate tasks: Classification</strong>

<strong>Missing values: Null</strong>

 

<strong>Kabita Kitchen dataset:</strong>

<strong>Dataset characteristics: Multivariate</strong>

<strong>Number of instances: 4900</strong>

<strong>Area: Cooking </strong>

<strong>Attribute characteristics: Real</strong>

<strong>Number of attributes: 3</strong>

<strong>Date donated: March, 2019</strong>

<strong>Associate tasks: Classification</strong>

<strong>Missing values: Null</strong>

 

There are two separate datasets file of each channel named as preprocessing and main file .

The files with preprocessing names are generated after doing the preprocessing and exploratory data analysis on both the datasets. This file includes:


	 Id
	Comment text
	Labels



	Count of stop-words
	Uppercase words
	Hashtags
	Word count
	Char count
	Average words
	Numeric


 

The main file includes:


	Id
	comment text
	Labels
DOI: 10.7910/dvn/dblesx/27jx8j
title: Linzer-lctab.R
DOI: 10.17632/2kw472ksgf.1
title: Data for: Research on Ion-transport Loss of UV-FAIMS

description: All research data can be obtained in the manuscript and view with software Origion-64.
DOI: 10.17632/t3fbjy5s66.1
title: Data for: Research on Ion-transport Loss of UV-FAIMS

description: All research data can be obtained in the manuscript and view with software Origion-64.
DOI: 10.7910/dvn/urum5b/1ndcj9
title: NationalMeritDataSupp.doc
DOI: 10.7910/dvn/b5xfd1/m7hrxe
title: page for ajps.pdf

description: Page for AJPS
DOI: 10.17632/t8t2ysp9nt.1
title: Data for: Insights from Rock-Eval analysis on the influence of sample weight on hydrocarbon generation from Lower Permian organic-matter rich rocks, West Bokaro basin, India

description: All the raw data are available here.
DOI: 10.7910/dvn/pmkk9s/saxz7l
title: 00_Complete Bibliography.pdf
DOI: 10.5281/zenodo.2827032
title: Evaluation of Materials for Low Temperature Asphalt Pavement Performance, BBR Field study

description: Asphalt mixtures were collected from seven construction projects, both of the plant and during laydown. One SGC cylindrical specimen or ‘puck’ of 80 mm thickness was prepared for each type of mixture. By doing so, 14 different SGC pucks were obtained. The pucks were labeled as ‘UT-01-P’ to ‘UT-07-P’ for plant mixtures and ‘UT-01-F’ to ‘UT-07-F’ for mixtures that were obtained during laydown in the field. Because of compacting to a specific height, the air voids of the compacted pucks varied from 3% to 6%.

In Lab A, the specimens were tested in the Bending Beam Rheometer (BBR) by following the AASHTO TP 125-16 testing procedure at three different temperatures: -12°C, -18°C, and -24°C. On the other hand, in Lab C, the specimens were tested at only one temperature  (PG + 10°C). In average, 12 specimens were tested for each mixture at both of the laboratories.
DOI: 10.5281/zenodo.2827033
title: Evaluation of Materials for Low Temperature Asphalt Pavement Performance, BBR Field study

description: Asphalt mixtures were collected from seven construction projects, both of the plant and during laydown. One SGC cylindrical specimen or ‘puck’ of 80 mm thickness was prepared for each type of mixture. By doing so, 14 different SGC pucks were obtained. The pucks were labeled as ‘UT-01-P’ to ‘UT-07-P’ for plant mixtures and ‘UT-01-F’ to ‘UT-07-F’ for mixtures that were obtained during laydown in the field. Because of compacting to a specific height, the air voids of the compacted pucks varied from 3% to 6%.

In Lab A, the specimens were tested in the Bending Beam Rheometer (BBR) by following the AASHTO TP 125-16 testing procedure at three different temperatures: -12°C, -18°C, and -24°C. On the other hand, in Lab C, the specimens were tested at only one temperature  (PG + 10°C). In average, 12 specimens were tested for each mixture at both of the laboratories.
DOI: 10.25810/6qjv-1296.13
title: Indian Newspaper Coverage of Climate Change or Global Warming, 2004-2019 - April 2019

description: The Media and Climate Change Observatory Data monitors 74 sources (across newspapers, radio and TV) in 38 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://sciencepolicy.colorado.edu/icecaps/research/media_coverage/index.html. These data include Excel (.xlsx), CSV (.csv), and plain-text README files (.txt).
DOI: 10.25810/fj1y-8c74.4
title: International Wire Services Coverage of Climate Change or Global Warming, 2004-2019 - March 2019

description: The Media and Climate Change Observatory Data monitors 79 sources (across newspapers, radio and TV) in 38 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://sciencepolicy.colorado.edu/icecaps/research/media_coverage/index.html. These data include Excel (.xlsx), CSV (.csv), and plain-text README files (.txt).
DOI: 10.25810/558f-c477.13
title: Australia Newspaper Coverage of Climate Change or Global Warming, 2004-2019 - April 2019

description: The Media and Climate Change Observatory Data monitors 74 sources (across newspapers, radio and TV) in 38 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://sciencepolicy.colorado.edu/icecaps/research/media_coverage/index.html. These data include Excel (.xlsx), CSV (.csv), and plain-text README files (.txt).
DOI: 10.17632/rynhc7p97y.1
title: Data for: Surely you don't eat parsnip skins? Categorising the edibility of food waste

description: The dataset is connect to a study that presents a novel methodology to categorise food waste into food (edible parts) and its associated inedible parts, accounting for cultural differences. The methodology section describes how a survey was used to obtain information on whether people eat certain parts of food and, irrespective of if they eat those parts, whether they consider them edible. The method allows existing food-waste definitions that require a split between edible parts (wasted food) and associated inedible parts to be put into practice in a transparent and reproducible way. The questionnaire was distributed by an on-line polling company (Populus) using an on-line poll between 20th and 22nd September 2017. The sample of 1,092 adults was a nationally representative sample of adults in the UK. Quotas on age, gender and region were set. Targets for quotas and weights were taken from the 2012 National Readership Survey, a random probability face-to-face survey conducted annually with 34,000 adults. The data were weighted by Populus to the known profile of the UK using age, gender, and government office region, social grade, taken a foreign holiday in the last 3 years, tenure, number of cars in the household and working status.
DOI: 10.5519/qd.r9zabp8x
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (10 records)
DOI: 10.25810/es52-xr23.13
title: Canadian Newspaper Coverage of Climate Change or Global Warming, 2004-2019 - April 2019

description: The Media and Climate Change Observatory Data monitors 74 sources (across newspapers, radio and TV) in 38 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://sciencepolicy.colorado.edu/icecaps/research/media_coverage/index.html. These data include Excel (.xlsx), CSV (.csv), and plain-text README files (.txt).
DOI: 10.25919/5cdb6b6852ee3
title: Parkes observations for project P982 semester 2018OCTS_05

description: We propose observations with the ultra-wide-bandwidth, low frequency receiver installed at the Parkes telescope to carry out the first coherently de-dispersed search for new pulsars in Southern globular clusters. We have chosen the three most likely clusters for new discoveries.  We propose to observe each cluster from rise-to-set and will process the resulting data to study the known pulsars (allowing us, for the first time, to study the wide-band properties of their signal), whilst also looking for currently unknown pulsars. We note that the first black-hole--pulsar binary system is likely to be found in a globular cluster environment.
DOI: 10.25828/gc5a-gq30
title: Continuous Multi-Purpose Household Survey 2003
DOI: 10.7910/dvn/9oh8dz/3jkgkj
title: Export_Shotgun.tab

description: Total shotgun exports, by federally licensed firearms manufacturer (RDS key), 2017.
DOI: 10.7910/dvn/9oh8dz/6kjhvj
title: Export_Revolvers.tab

description: Total revolver exports, by federally licensed firearms manufacturer (RDS key), 2017.
DOI: 10.7910/dvn/9oh8dz/aapyeg
title: Rifles.tab

description: Total rifles manufactured, by federally licensed firearms manufacturer (RDS key), 2017.
DOI: 10.7910/dvn/9oh8dz/gv7s68
title: Shotguns.tab

description: Total shotguns manufactured, by federally licensed firearms manufacturer (RDS key), 2017.
DOI: 10.7910/dvn/9oh8dz/ijzsvr
title: Export_Misc.tab

description: Total miscellaneous firearms exports, by federally licensed firearms manufacturer (RDS key), 2017.
DOI: 10.7910/dvn/9oh8dz/nnj81u
title: Pistols.csv

description: Total pistols manufactured, by type, by federally licensed firearms manufacturer (RDS key), 2017.
DOI: 10.7910/dvn/9oh8dz/o8wz44
title: ARMER2017.tab

description: Total exports and total firearms manufactured, by type, for federally licensed firearms manufacturers in the US, in 2017.
DOI: 10.7910/dvn/9oh8dz/shx5d4
title: afmer_2017_final.pdf

description: Original ATF (2019) Report.
DOI: 10.7910/dvn/9oh8dz/wfc8zf
title: desktop.ini
DOI: 10.7910/dvn/9oh8dz/iorcky
title: Export_Pistols.tab

description: Total pistol exports, by federally licensed firearms manufacturer (RDS key), 2017.
DOI: 10.7910/dvn/9oh8dz/mrcmsf
title: Misc.tab

description: Total miscellaneous firearms manufactured, by federally licensed firearms manufacturer (RDS key), 2017.
DOI: 10.7910/dvn/9oh8dz/sjrxl7
title: Export_Rifles.tab

description: Total rifle exports, by federally licensed firearms manufacturer (RDS key), 2017.
DOI: 10.7910/dvn/9oh8dz/wn0qxg
title: Revolvers.tab

description: Total revolvers manufactured, by type, by federally licensed firearms manufacturer (RDS key), 2017.
DOI: 10.7910/dvn/9oh8dz/zkwlbs
title: ARMER2017_readme.txt

description: metadata