cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4701251
title: Early detection of promoted campaigns on social media

description: Please cite our paper as follows, when you are using our dataset: @article{varol2017early,<br> Author = {Onur Varol and Emilio Ferrara and Filippo Menczer and Alessandro Flammini},<br> Title = {Early Detection of Promoted Campaigns on Social Media},<br> Journal = {EPJ Data Science},<br> Url = {}<br> Note = {In press},<br> Year = {2017},<br> } **** FORMAT **** Information about promoted and organics trends used in this project available in the index file: <br> `trend_information.dat`. We also provide `tweet-id` for each tweets used in our experiments and `timeseries` of features for each time window. trend_information.dat:<br> Format: {trend_name} \t {label} \t {trending_tstamp}<br> * {trend-name} is the identifier of different trends. Information about each trend can be found in the index file. <br> * label: 1 (organic), 0 (promoted)<br> * trending_tstamp provides a UNIX timestamp of the time particular hashtag is trended. tweet-ids/tweet-ids_{trend-name}.dat.gz:<br> Format: Each line contains a single tweet-id<br> * {trend-name} is the identifier of different trends. Information about each trend can be found in the index file. timeseries/timeseries<br> Format: {feature-name} \t {timeseries}<br> * {feature-name} represent different features extracted for a given trend<br> * {timeseries} contain 120 comma seperated numerical value. Trending point locates at index 60.
DOI: 10.5281/zenodo.4701250
title: Early detection of promoted campaigns on social media

description: Please cite our paper as follows, when you are using our dataset: @article{varol2017early,<br> Author = {Onur Varol and Emilio Ferrara and Filippo Menczer and Alessandro Flammini},<br> Title = {Early Detection of Promoted Campaigns on Social Media},<br> Journal = {EPJ Data Science},<br> Url = {}<br> Note = {In press},<br> Year = {2017},<br> } **** FORMAT **** Information about promoted and organics trends used in this project available in the index file: <br> `trend_information.dat`. We also provide `tweet-id` for each tweets used in our experiments and `timeseries` of features for each time window. trend_information.dat:<br> Format: {trend_name} \t {label} \t {trending_tstamp}<br> * {trend-name} is the identifier of different trends. Information about each trend can be found in the index file. <br> * label: 1 (organic), 0 (promoted)<br> * trending_tstamp provides a UNIX timestamp of the time particular hashtag is trended. tweet-ids/tweet-ids_{trend-name}.dat.gz:<br> Format: Each line contains a single tweet-id<br> * {trend-name} is the identifier of different trends. Information about each trend can be found in the index file. timeseries/timeseries<br> Format: {feature-name} \t {timeseries}<br> * {feature-name} represent different features extracted for a given trend<br> * {timeseries} contain 120 comma seperated numerical value. Trending point locates at index 60.
DOI: 10.17632/dd2j2hx9vs.1
title: Data set and supplemental materials for the article entitled "Converting a conventional flipped class into a synchronous online flipped class during COVID-19: University students’ self-regulation skills and anxiety "

description: Raw data set in SPSS, gathered to check the impact of flipped learning model on students' foreign language anxiety and L2 self-regulated learning strategy use Processed data set sample video lecture, sent to students before the actual class hour in flipped class (more video lectures can be shared if requested)
DOI: 10.17632/bstbtr4jst.1
title: Blood urea nitrogen – independent mortality marker in sepsis
DOI: 10.17632/cpyzxvv2gs.1
title: SCNS-SF34Malay dataset

description: This dataset relates to a study that aims to assess the psychometric properties of a Malay version of SCNS-SF34 among Malaysian cancer patients. This data involves 171 cancer patients. Data were collected using the structured five-factor survey via telephone call or face-to-face interviews. The internal reliability and the construct validity of SCNS-SF34M were analysed using principal component analysis with varimax rotation. The health system and information need (HSI) domain has been identified as the domain with highest mean score of 2.73 (±0.88), followed by patient care and support needs domain (2.16 (±0.90)) and physical and daily living needs domain (1.99 (±0.98)). The confirmatory factor analysis indicated a moderate model fit for RMSEA with 0.070, TLI = 0.911 and CFI = 0.924. The SCNS-SF34M was found to be a conceptually applicable and culturally appropriate scale for measuring cancer patients’ supportive care within the Malaysian context.
DOI: 10.17632/zc9h6tr55d.1
title: Wildfires - an approach based on-field inspections

description: This data consists of a sample of the potential wildfire facilitators that could explain the size of the burnt area at the Historical Site and Cultural Heritage Kalunga. For each burnt area was measured the distance to these facilitators under the premise that it could increase the human access routes and create favorable regions to ignite forest fires. Dependent variable: size of the burnt area. Independent variables: fences, houses, roads, rivers, and rural buildings.
DOI: 10.17632/cjhbng3rn3.1
title: Class Discussion and Engagement

description: This data was collected to investigate relationships between student engagement and equity of participation in class discussions for high school students. For equity of participation, this data used an application named "Equity Maps," which collects the equity of class participation for the Time Spoken, the Number of Times Spoken, and the Equity Quotient. Moreover, this data includes student responses to a modified Classroom Engagement Inventory (CEI) that consisted of nine questions: three that targeted Affective Engagement, three that targeted Behavioral Engagement, and three that targeted Cognitive Engagement. This data consists of 16 discussions across four teachers (four discussions per teacher).
DOI: 10.6084/m9.figshare.14447844
title: Data-Collection-MusicChallenge

description: Data-Collection-MusicChallenge
DOI: 10.6084/m9.figshare.14447844.v1
title: Data-Collection-MusicChallenge

description: Data-Collection-MusicChallenge
DOI: 10.17632/p8txv37r6y.1
title: Supplementary Information Dataset for "Evidence of the 1950 great Assam earthquake surface break along the Mishmi Thrust at Namche Barwa Himalayan Syntaxis"

description: Supplementary Information Dataset for "Evidence of the 1950 great Assam earthquake surface break along the Mishmi Thrust at Namche Barwa Himalayan Syntaxis"
DOI: 10.17632/h95284fmgf.1
title: Analytical modelling of food storage cooling with solar ammonia-water absorption system, powered by parabolic trough collectors and with case studies for Seville

description: Data related to the study "Analytical modelling of food storage cooling with solar ammonia-water absorption system, powered by parabolic trough collectors and with case studies for Seville"
DOI: 10.5281/zenodo.4695875
title: Assessing and Addressing Deficiencies in the HCM Weaving Segment Analyses (Project K2)

description: The data are aggregated into five-minute intervals.
DOI: 10.5281/zenodo.4695874
title: Assessing and Addressing Deficiencies in the HCM Weaving Segment Analyses (Project K2)

description: The data are aggregated into five-minute intervals.
DOI: 10.7284/141048
title: ADCP (Acoustic Doppler Current Profiler) data collected during research cruise KM2014 using a Hawaii UHDAS instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/141055
title: Anemometer data collected during research cruise KM2014 using a RM Young 85106 instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/141056
title: Anemometer data collected during research cruise KM2014 using a RM Young 85106 instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7910/dvn/okpftd
title: European Alliances Alignments (1528-1648)

description: The datasets identify a list of key actors in the European sphere from 1528 to 1648 and introduce two ways of tracking diplomatic shifts in this era. The Formal Military Alliances and Political Settlements dataset (FMAPS) lists all formal treaties and agreements reached between major actors in the European sphere that codified a form of alliance among them--and for which a start and end year could be identified. The Alignments During Armed Conflicts Dataset (ADACS) lists parties’ alignments during the period’s major armed conflicts. Both datasets were compiled through careful review of available literature about diplomatic ententes and armed conflicts throughout the period from 1528 to 1648. The ADACS and FMAPS datasets are presented in both a 'raw' format that simply lists each instance of an alliance or alignment, and a 'dyads' format that lists each instance of an alliance or alignment between two parties for each year during which it was active.
DOI: 10.7284/141052
title: Barometer data collected during research cruise KM2014 using a Vaisala PTB220 instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/141051
title: Fluorometer data collected during research cruise KM2014 using a WET Labs WETStar instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/141049
title: Gravimeter data collected during research cruise KM2014 using a Bell BGM-3 instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/141050
title: Gyrocompass data collected during research cruise KM2014 using a Sperry NAVIGAT X MK 1 instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/141057
title: INS (Inertial Navigation System) data collected during research cruise KM2014 using a Applanix POS/MV-320 V4 instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/141047
title: Meteorological Station data collected during research cruise KM2014 using a Hawaii MET-System instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/141053
title: Meteorological Station data collected during research cruise KM2014 using a Hawaii MET-System instrument system onboard the platform RV Kilo Moana

description: Processed data created by vessel technician during a Field_expedition
DOI: 10.7284/141054
title: pCO2 (Partial Pressure of Carbon Dioxide) data collected during research cruise KM2014 using a General Oceanics 8050 instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/141045
title: TSG (Thermosalinograph) data collected during research cruise KM2014 using a Sea-Bird SBE-45 instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.17632/wj6s2d8xbg.1
title: Finite Periodic Topology Optimization with Oriented Unit-cells Data Set

description: This data set includes all the relevant data for the paper "On Finite Periodic Topology Optimization with Oriented Unit-cells" by Simon Thomas, Qing Li and Grant Steven. This includes: MATLAB Code utilized with ANSYS APDL Alternative self-contained MATLAB function for replication of results MATLAB workspace files corresponding to the compliance and frequency examples in manuscript Figures within the manuscript
DOI: 10.17632/s6mwgcm4tf.1
title: T cell immune discriminants of HIV reservoir size in a pediatric cohort of vertically infected individuals

description: Flow cytometry data are divided in 3 different panel: IA panel evaluated immune activation markers in T cells subsets, ICP panel evaluates immune checkpoint molecules in T cells subsets, GAGstim panel evaluates cytokines production and Activation Induced Molecules in T cells subsets after stimulation with GAG PTE peptides pool. Virology data summarize the virological measurements made in the cohort as well as time of ART initiation and age. Molecular data reports the raw data generated by the Fluidigm approach. The flow data were used for the LASSO/Poisson regression approach to generate the signature presented in the manuscript.
DOI: 10.17632/zv8m9bcznj.1
title: Raw data-Prognostic significance of PET/CT in patients with chronic lymphocytic leukemia (CLL) treated with frontline chemoimmunotherapy

description: research data of the work - 1
DOI: 10.5281/zenodo.4699901
title: Results: Analysis of Correlation Between GDP per Capita and Average Height of Young Adults in 2019 in 164 Countries

description: These are the results obtained by conducting the experiment "Average Height of 19-year-old Males and Females and GDP per Capita in 2019 for 164 Countries". The CSV file contains the raw data produced by processing, filtering and merging the input datasets. There are two rows for each of the 164 countries. In both rows, the country name, country code and GDP per capita are given. However, one row contains the average height of 19-year-old males (indicated by the value 'Boys' in the 'Sex' column) whereas the other displays the average height of 19-year-old females (indicated by the value 'Girls'). Furthermore, there are two PNG files which display the regression plots for the average height of 19-year-old males and females, respectively. Note that the x-scale (for the GDP per capita) is logarithmic.
description: {"references": ["The World Bank, GDP per capita (current US$), Washington, DC: The World Bank, 2021. Accessed on: Apr. 13, 2021. [Online] Available: https://data.worldbank.org/indicator/NY.GDP.PCAP.CD.", "NCD Risk Factor Collaboration, Height - Evolution of adult height over time, NCD Risk Factor Collaboration, 2021. Accessed on: Apr. 18, 2021. [Online] Available: https://ncdrisc.org/data-downloads-height.html under \"Country-specific data for all countries\"."]}
DOI: 10.5281/zenodo.4699900
title: Results: Analysis of Correlation Between GDP per Capita and Average Height of Young Adults in 2019 in 164 Countries

description: These are the results obtained by conducting the experiment "Average Height of 19-year-old Males and Females and GDP per Capita in 2019 for 164 Countries". The CSV file contains the raw data produced by processing, filtering and merging the input datasets. There are two rows for each of the 164 countries. In both rows, the country name, country code and GDP per capita are given. However, one row contains the average height of 19-year-old males (indicated by the value 'Boys' in the 'Sex' column) whereas the other displays the average height of 19-year-old females (indicated by the value 'Girls'). Furthermore, there are two PNG files which display the regression plots for the average height of 19-year-old males and females, respectively. Note that the x-scale (for the GDP per capita) is logarithmic.
description: {"references": ["The World Bank, GDP per capita (current US$), Washington, DC: The World Bank, 2021. Accessed on: Apr. 13, 2021. [Online] Available: https://data.worldbank.org/indicator/NY.GDP.PCAP.CD.", "NCD Risk Factor Collaboration, Height - Evolution of adult height over time, NCD Risk Factor Collaboration, 2021. Accessed on: Apr. 18, 2021. [Online] Available: https://ncdrisc.org/data-downloads-height.html under \"Country-specific data for all countries\"."]}
DOI: 10.5285/28935552223242ca97953a8db99c2821
title: ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Version 06.1 data collection

description: Soil Moisture data (version 06.1) from the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project. This dataset collection contains three surface soil moisture datasets alongside ancillary data products. The ACTIVE and PASSIVE products have been created by fusing satellite scatterometer and radiometer soil moisture products respectively. In the case of the ACTIVE product, these have been derived from the AMI-WS and ASCAT satellite instruments and for the PASSIVE product from the satellite instruments SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, AMSR2, SMOS, GPM and SMAP. The COMBINED product is generated from the Level 2 active and passive instruments. The homogenized and merged products present a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. The products are provided as global daily images, in NetCDF-4 classic file format, the PASSIVE and COMBINED products covering the period (yyyy-mm-dd) 1978-11-01 to 2020-12-31 and the ACTIVE product covering 1991-08-05 to 2020-12-31. The soil moisture data for the PASSIVE and the COMBINED product are provided in volumetric units [m3 m-3], while the ACTIVE soil moisture data are expressed in percent of saturation [%]. In addition to the main products, an experimental break-adjusted COMBINED product is also provided for the first time at v06.1. For information regarding the theoretical and algorithmic base of the datasets, please see the Algorithm Theoretical Baseline Document (ATBD). Additional documentation and information relating to the datasets can also be found on the CCI Soil Moisture project web site or in the Product User Guide. The data set should be cited using all of the following references: 1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019 2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001 If using the COMBINED break-adjusted product, the following should also be cited in addition to the above: 3. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., "Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896.
DOI: 10.17632/y6xmsgskpd.1
title: Raw data-The NANOG transcription factor induces type 2 deiodinase expression and regulates the intracellular activation of thyroid hormone in keratinocyte carcinomas

description: research data of the work
DOI: 10.5281/zenodo.4701287
title: Supplemental Tables for "Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR" manuscript

description: Table S1, Table S2 and Table S3 for "Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR" manuscript
DOI: 10.17632/cgk5npmks2.1
title: IAB Establishment Panel (IAB BP) – Version 9317 v1

description: The IAB Establishment Panel is an annual representative survey on various topics such as the determinants of labour demand. We use Wave 2017 (DOI: 10.5164/IAB.IABBP9317.de.en.v1).The dataset was analysed via remote data access. Information for remote data access is available under https://fdz.iab.de/en/FDZ_Data_Access/FDZ_Remote_Data_Access.aspx. Three Do-Files are provided for code availability: 1. Data preperation Do-File 2. Analysis Do-File for whole sample 3. Analysis Do-File for SMEs
DOI: 10.5285/70f40e63092b4f2ab9c9860b7093db00
title: ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Version 05.3 data collection

description: Soil Moisture data (version 05.3) from the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project. This dataset collection contains three surface soil moisture datasets, alongside ancilliary data products. The ACTIVE and PASSIVE products have been created by fusing satellite scatterometer and radiometer soil moisture products respectively. In the case of the ACTIVE product, these have been derived from the AMI-WS and ASCAT satellite instruments and for the PASSIVE product from the satellite instruments SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS and SMAP. The COMBINED product is generated from the Level 2 active and passive instruments. The homogenized and merged products present a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. The products are provided as global daily images, in NetCDF-4 classic file format, the PASSIVE and COMBINED products covering the period (yyyy-mm-dd) 1978-11-01 to 2020-12-31 and the ACTIVE product covering 1991-08-05 to 2020-12-31. The soil moisture data for the PASSIVE and the COMBINED product are provided in volumetric units [m3 m-3], while the ACTIVE soil moisture data are expressed in percent of saturation [%]. For information regarding the theoretical and algorithmic base of the datasets, please see the Algorithm Theoretical Baseline Document (ATBD). Other additional documentation and information documentation relating to the datasets can also be found on the CCI Soil Moisture project web site or in the Product Specification Document. The data set should be cited using the all of the following references: 1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019 2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001 3. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070