cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3266124
title: Meta-analysis summary-level results of histology GWAS -- females, visceral

description: MarkerName -- name of the SNP
	Allele1 -- first allele
	Allele2 -- second allele
	Freq1 -- frequency of Allele1
	FreqSE -- standard error of the freq estimate
	MinFreq -- lower confidence bound of the freq estimate
	MaxFreq -- upper confidence bound of the freq estimate
	Weight -- sample size weight
	Zscore -- z-score of the SNP
	P-value -- p-value of the SNP
	Direction -- directions of the betas in the cohorts, in the order the cohorts are listed in the corresponding *.metalParams.txt file
	HetISq -- I-squared estimate for heterogeneity test
	HetChiSq -- Chi-Square estimate for heterogeneity test
	HetDf -- degrees of freedom for heterogeneity test
	HetPVal -- p-value for heterogeneity test
DOI: 10.5281/zenodo.3266147
title: Source ELISA data for the manuscript "Restrained expansion of the recall germinal center response as biomarker of protection for influenza vaccination in mice"

description: This repository contains the source ELISA data for the manuscript "Restrained expansion of the recall germinal center response as biomarker of protection for influenza vaccination in mice" currently under review by PLOS ONE. It supports the following figures: Fig 4A: rHA ELISA data miniHA study.xlsx<br> Fig 4B: Competition ELISA data miniHA study.xlsx<br> S7 Fig: Competition ELISA data POC study.xlsx Files include Raw OD's per plate and reported values analysis.
DOI: 10.17632/5wzxxh8vzy.1
title: Data for: Fabrication and mechanical properties of WC-10Co cemented carbides with plate-like WC grains
DOI: 10.17632/93v26fj77w.1
title: Data for: An approach to fabricate high performance cooler with nearly ideal emissive spectrum for above ambient radiative cooling

description: These are the research data files of "An approach to fabricate high performance cooler with nearly ideal emissive spectrum for above ambient radiative cooling".
DOI: 10.5281/zenodo.3266226
title: MA14KD [ORIGINAL] Dataset: Visual Attraction of Movie Trailers

description: <strong>MA14KD</strong> (Movie Attract 14K Dataset) provides a set of <strong>10 VISUAL features </strong>extracted from <strong>14074 movie</strong> <strong>and tv series trailers</strong>. The movie IDs are in agreement with the movie IDs provided by another rating dataset that also contains movie genres and tags (see the description within the file). More details can be found in the following publication:

<em>Farshad B. Moghaddam, Mehdi Elahi, Reza Hosseini, Christoph Trattner, Marko Tkalcic, <strong>Predicting Movie Popularity and Ratings with Visual Features</strong>, IEEE SMAP’19, 9-10 June 2019, Larnaca, Cyprus</em>
description: {"references": ["Farshad B. Moghaddam, Mehdi Elahi, Reza Hosseini, Christoph Trattner, Marko Tkalcic, Predicting Movie Popularity and Ratings with Visual Features, IEEE SMAP'19, 9-10 June 2019, Larnaca, Cyprus", "Elahi, Mehdi and Deldjoo, Yashar and Bakhshandegan Moghaddam, Farshad and Cella, Leonardo and Cereda, Stefano and Cremonesi, Paolo, Exploring the semantic gap for movie recommendations, Proceedings of the Eleventh ACM Conference on Recommender Systems, 326\u2013330, 2017 ACM", "San Pedro, Jose, and Stefan Siersdorfer. \"Ranking and classifying attractive"]}
DOI: 10.5281/zenodo.3266227
title: MA14KD [ORIGINAL] Dataset: Visual Attraction of Movie Trailers

description: <strong>MA14KD</strong> (Movie Attract 14K Dataset) provides a set of <strong>10 VISUAL features </strong>extracted from <strong>14074 movie</strong> <strong>and tv series trailers</strong>. The movie IDs are in agreement with the movie IDs provided by another rating dataset that also contains movie genres and tags (see the description within the file). More details can be found in the following publication:

<em>Farshad B. Moghaddam, Mehdi Elahi, Reza Hosseini, Christoph Trattner, Marko Tkalcic, <strong>Predicting Movie Popularity and Ratings with Visual Features</strong>, IEEE SMAP’19, 9-10 June 2019, Larnaca, Cyprus</em>
description: {"references": ["Farshad B. Moghaddam, Mehdi Elahi, Reza Hosseini, Christoph Trattner, Marko Tkalcic, Predicting Movie Popularity and Ratings with Visual Features, IEEE SMAP'19, 9-10 June 2019, Larnaca, Cyprus", "Elahi, Mehdi and Deldjoo, Yashar and Bakhshandegan Moghaddam, Farshad and Cella, Leonardo and Cereda, Stefano and Cremonesi, Paolo, Exploring the semantic gap for movie recommendations, Proceedings of the Eleventh ACM Conference on Recommender Systems, 326\u2013330, 2017 ACM", "San Pedro, Jose, and Stefan Siersdorfer. \"Ranking and classifying attractive"]}
DOI: 10.5281/zenodo.3265871
title: BioTIME

description: The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. The database consists of 11 tables; one raw data table plus ten related meta data tables. For further information please see our associated data paper.

This data consists of several elements:


	BioTIMESQL_02_04_2018.sql - an SQL file for the full public version of BioTIME which can be imported into any mySQL database. 
	BioTIMEQuery_02_04_2018.csv - data file, although too large to view in Excel, this can be read into several software applications such as R or various database packages. 
	BioTIMEMetadata_02_04_2018.csv  - file containing the meta data for all studies. 
	BioTIMECitations_02_04_2018.csv - file containing the citation list for all studies. 
	BioTIMECitations_02_04_2018.xlsx - file containing the citation list for all studies (some special characters are not supported in the csv format). 
	BioTIMEInteractions_02_04_2018.Rmd - an r markdown page providing a brief overview of how to interact with the database and associated .csv files (this will not work until field paths and database connections have been added/updated).


Please note: any users of any of this material should cite the associated data paper in addition to the DOI listed here.

To cite the data paper use the following:

Dornelas M, Antão LH, Moyes F, Bates, AE, Magurran, AE, et al. BioTIME: A database of biodiversity time series for the Anthropocene. <em>Global Ecol Biogeogr.</em> 2018; 27:760 - 786. https://doi.org/10.1111/geb.12729
DOI: 10.5281/zenodo.3254605
title: Workflow Trace Archive alibaba2018 trace

description: Trace description unavailable.
DOI: 10.5281/zenodo.3254606
title: Workflow Trace Archive alibaba2018 trace

description: Trace description unavailable.
DOI: 10.5281/zenodo.3266235
title: MA14KD [AGGREGATED] Dataset: Visual Attraction of Movie Trailers

description: <strong>MA14KD</strong> (Movie Attract 14K Dataset) provides a set of <strong>181 aggregated VISUAL features </strong>extracted from <strong>14074 movie</strong> <strong>and tv series trailers</strong>. The movie IDs are in agreement with the movie IDs provided by another rating dataset that also contains movie genres and tags (see the description within the file). More details can be found in the following publication: <em>Farshad B. Moghaddam, Mehdi Elahi, Reza Hosseini, Christoph Trattner, Marko Tkalcic, <strong>Predicting Movie Popularity and Ratings with Visual Features</strong>, IEEE SMAP’19, 9-10 June 2019, Larnaca, Cyprus</em>
description: {"references": ["San Pedro, Jose, and Stefan Siersdorfer. \"Ranking and classifying attractive", "Farshad B. Moghaddam, Mehdi Elahi, Reza Hosseini, Christoph Trattner, Marko Tkalcic, Predicting Movie Popularity and Ratings with Visual Features, IEEE SMAP'19, 9-10 June 2019, Larnaca, Cyprus", "Elahi, Mehdi and Deldjoo, Yashar and Bakhshandegan Moghaddam, Farshad and Cella, Leonardo and Cereda, Stefano and Cremonesi, Paolo, Exploring the semantic gap for movie recommendations, Proceedings of the Eleventh ACM Conference on Recommender Systems, 326\u2013330, 2017 ACM"]}
DOI: 10.5281/zenodo.3266236
title: MA14KD [AGGREGATED] Dataset: Visual Attraction of Movie Trailers

description: <strong>MA14KD</strong> (Movie Attract 14K Dataset) provides a set of <strong>181 aggregated VISUAL features </strong>extracted from <strong>14074 movie</strong> <strong>and tv series trailers</strong>. The movie IDs are in agreement with the movie IDs provided by another rating dataset that also contains movie genres and tags (see the description within the file). More details can be found in the following publication: <em>Farshad B. Moghaddam, Mehdi Elahi, Reza Hosseini, Christoph Trattner, Marko Tkalcic, <strong>Predicting Movie Popularity and Ratings with Visual Features</strong>, IEEE SMAP’19, 9-10 June 2019, Larnaca, Cyprus</em>
description: {"references": ["San Pedro, Jose, and Stefan Siersdorfer. \"Ranking and classifying attractive", "Farshad B. Moghaddam, Mehdi Elahi, Reza Hosseini, Christoph Trattner, Marko Tkalcic, Predicting Movie Popularity and Ratings with Visual Features, IEEE SMAP'19, 9-10 June 2019, Larnaca, Cyprus", "Elahi, Mehdi and Deldjoo, Yashar and Bakhshandegan Moghaddam, Farshad and Cella, Leonardo and Cereda, Stefano and Cremonesi, Paolo, Exploring the semantic gap for movie recommendations, Proceedings of the Eleventh ACM Conference on Recommender Systems, 326\u2013330, 2017 ACM"]}
DOI: 10.6073/pasta/adb4dcaf35ac77cf71ec5dd90320d894
title: Snow grain data for Niwot Ridge and Green Lakes Valley, 1995 - ongoing.

description: Snow pits were excavated at various locations on Niwot Ridge and within the Green Lakes Valley. Temperature and snow density were measured at various depths throughout the snow cover profiles to characterize the temperature and snow water equivalent (SWE) of the snowpack throughout the year. Snow density was measured at 10-cm intervals using a 1000-ml cutter. Data on snow grain qualities were collected beginning in the 1994-95 snow season.
DOI: 10.6073/pasta/d08107c14f0ae672534e43bd79ce564a
title: Snow water equivalent data for Niwot Ridge and Green Lakes Valley, 1993 - ongoing.

description: Snow pits were excavated at various locations on Niwot Ridge and within the Green Lakes Valley. Temperature and snow density were measured at various depths throughout the snow cover profiles to characterize the temperature and snow water equivalent (SWE) of the snowpack throughout the year. Snow density was measured at 10-cm intervals using a 1000-ml cutter. Data on snow grain qualities were collected beginning in the 1994-95 snow season.
DOI: 10.6073/pasta/db491e295691979a2c41df725ff41438
title: Snow cover profile data for Niwot Ridge and Green Lakes Valley, 1993 - ongoing.

description: ABSTRACT.Snow pits were excavated at various locations on Niwot Ridge and within the Green Lakes Valley. Temperature and snow density were measured at various depths throughout the snow cover profiles to characterize the temperature and snow water equivalent (SWE) of the snowpack throughout the year. Snow density was measured at 10-cm intervals using a 1000-ml cutter. Data on snow grain qualities were collected beginning in the 1994-95 snow season.
DOI: 10.17632/sydxyfsdnt.1
title: Data for: SEISMIC IMAGING OF THE OLDUVAI BASIN

description: These data are seismic data collected over the Olduvai Basin. They consist of five profiles, with each length of 1.2 km. In each profile, there are 240 chanels with the geophones spacing 5 m. The active shots with a spacing of 10 m for a profile are covered in all in-line area and the cross-line area with adjacent profiles. The data are in matlab format. Each file is the data for one profile. More description in details can be find in the adress below: https://csim.kaust.edu.sa/files/FieldData/Olduvai/OlduvaiGeorgeProject.htm
DOI: 10.5281/zenodo.3241732
title: FT39 Scherer (5) 4-key fagottino: measurements, photos, endoscopic video

description: Dataset of FT39 Scherer (5) 4-key fagottino containing detailed external and internal measurements, photos, and an endoscopic video.
DOI: 10.5281/zenodo.3241733
title: FT39 Scherer (5) 4-key fagottino:  measurements, photos, endoscopic video

description: Dataset of FT39 Scherer (5) 4-key fagottino containing detailed external and internal measurements, photos, and an endoscopic video.
DOI: 10.25345/c5mw83
title: MassIVE MSV000084051 - GNPS - IIN - Pseudonocardia Extracts Gouping - Illumina
title: This MassIVE contains files for testing and evaluation of ion identity networking workflow. Samples were extracted with EtOAc from monocultures of Pseudonocardia symbionts of Attine ants (Genera Tchachymyrmex). Extracts were analyzed by LC-MS/MS performed in an UFLC Shimadzu system using a column C18 Phenomenex (250 mm x 4,6mm)  and  Q-TOF micro mass spectrometer (Waters) equipped with ESI source.
DOI: 10.6073/pasta/8b30a6b459b693f26a5367a3443994ee
title: Desert Fertilization Experiment: investigation of Sonoran desert ecosystem response to atmospheric deposition and experimental nutrient addition, ongoing since 2006

description: Launched in 2006 with support from the National Science Foundation
  (NSF) and leveraged by the CAP LTER, the Carbon and Nitrogen
  deposition (CNdep) project sought to answer the fundamental question
  of whether elemental cycles in urban ecosystems are qualitatively
  different from those in non-urban ecosystems. Ecosystem scientists,
  atmospheric chemists, and biogeochemists tested the hypothesis that
  distinct biogeochemical pathways result from elevated inorganic
  nitrogen and organic carbon deposition from the atmosphere to the
  land. To test the hypothesis, scientists examined the responsiveness
  of Sonoran desert ecosystems to nutrient enrichment by capitalizing on
  a gradient of atmospheric deposition in and around the greater Phoenix
  metropolitan area. Fifteen desert study sites were established, with
  five locations each west and east of the urban core, and in the urban
  core in desert preserves. In addition to the gradient of atmospheric
  deposition in and around the urban core, select study plots at each of
  the fifteen desert locations receive amendments of nitrogen,
  phosphorus, or nitrogen + phosphorus fertilizer. Measured variables
  include soil properties, perennial and annual plant growth, and
  atmospheric deposition of nitrogen. At the close of the initial grant
  period, the CAP LTER assumed responsibility for the project, renamed
  the Desert Fertilization Experiment, which provides a remarkable
  platform to study the long-term effects of nutrient enrichment on
  ecosystem properties.
DOI: 10.5281/zenodo.3266625
title: Full Resolution Snapshot for NG-Aqua

description: The velocity, pressure, temperature, and humidity fields for a single time step (day 180.5) of the NG-Aqua simulation.
DOI: 10.5281/zenodo.3266626
title: Full Resolution Snapshot for NG-Aqua

description: The velocity, pressure, temperature, and humidity fields for a single time step (day 180.5) of the NG-Aqua simulation.
DOI: 10.5281/zenodo.3266257
title: Context-Aware Dataset: STS - South Tyrol Suggests IoT Mobile App Data

description: <strong>STS dataset </strong>was collected by a context-aware recommender system mobile app named as<strong> "South Tyrol Suggests"</strong>. The app provides <strong>context-aware recommendations</strong> for attractions, events, public services, restaurants, and much more based on the rating preferences and personality factors of users.

<strong>Contextual</strong> <strong>variables</strong> includes 


	<strong>distance:</strong> far away, near by
	<strong>time available:</strong> half day, one day, more than one day
	<strong>temperature:</strong> burning, hot, warm, cool, cold, freezing
	<strong>crowdedness:</strong> crowded, not crowded, empty
	<strong>knowledge of surroundings:</strong> new to area, returning visitor, citizen of the area
	<strong>season:</strong> spring, summer, autumn, winter
	<strong>budget:</strong> budget traveler, price for quality, high spender
	<strong>daytime:</strong> morning, noon, afternoon, evening, night
	<strong>weather:</strong> clear sky, sunny, cloudy, rainy, thunderstorm, snowing
	<strong>companion:</strong> alone, with friends/colleagues, with family, with girlfriend/boyfriend, with children
	<strong>mood:</strong> happy, sad, active, lazy weekday: weekday, weekend
	<strong>travel goal:</strong> visiting friends, business, religion, health care, social event, education, scenic/landscape, hedonistic/fun, activity/sport
	<strong>means of transport:</strong> no transportation means, a bicycle, a car, public transport


More details can be found here:

<em>Braunhofer, Matthias, Mehdi Elahi, and Francesco Ricci. "<strong>Techniques for cold-starting context-aware mobile recommender systems for tourism</strong>." Intelligenza Artificiale 8, no. 2 (2014): 129-143.</em>
description: {"references": ["Braunhofer, Matthias, Mehdi Elahi, and Francesco Ricci. \"Techniques for cold-starting context-aware mobile recommender systems for tourism.\" Intelligenza Artificiale 8, no. 2 (2014): 129-143.", "Elahi, Mehdi, Matthias Braunhofer, Francesco Ricci, and Marko Tkalcic. \"Personality-based active learning for collaborative filtering recommender systems.\" In Congress of the Italian Association for Artificial Intelligence, pp. 360-371. Springer, Cham, 2013.", "Elahi, Mehdi. \"Empirical Evaluation of Active Learning Strategies in Collaborative Filtering.\"", "Braunhofer, Matthias, Mehdi Elahi, and Francesco Ricci. \"STS: A Context-Aware Mobile Recommender System for Places of Interest.\" In UMAP Workshops. 2014.", "Braunhofer, Matthias, Mehdi Elahi, Mouzhi Ge, Francesco Ricci, and Thomas Schievenin. \"STS: Design of Weather-Aware Mobile Recommender Systems in Tourism.\" In AI* HCI@ AI* IA. 2013."]}
DOI: 10.5281/zenodo.3266258
title: Context-Aware Dataset: STS - South Tyrol Suggests IoT Mobile App Data

description: <strong>STS dataset </strong>was collected by a context-aware recommender system mobile app named as<strong> "South Tyrol Suggests"</strong>. The app provides <strong>context-aware recommendations</strong> for attractions, events, public services, restaurants, and much more based on the rating preferences and personality factors of users.

<strong>Contextual</strong> <strong>variables</strong> includes 


	<strong>distance:</strong> far away, near by
	<strong>time available:</strong> half day, one day, more than one day
	<strong>temperature:</strong> burning, hot, warm, cool, cold, freezing
	<strong>crowdedness:</strong> crowded, not crowded, empty
	<strong>knowledge of surroundings:</strong> new to area, returning visitor, citizen of the area
	<strong>season:</strong> spring, summer, autumn, winter
	<strong>budget:</strong> budget traveler, price for quality, high spender
	<strong>daytime:</strong> morning, noon, afternoon, evening, night
	<strong>weather:</strong> clear sky, sunny, cloudy, rainy, thunderstorm, snowing
	<strong>companion:</strong> alone, with friends/colleagues, with family, with girlfriend/boyfriend, with children
	<strong>mood:</strong> happy, sad, active, lazy weekday: weekday, weekend
	<strong>travel goal:</strong> visiting friends, business, religion, health care, social event, education, scenic/landscape, hedonistic/fun, activity/sport
	<strong>means of transport:</strong> no transportation means, a bicycle, a car, public transport


More details can be found here:

<em>Braunhofer, Matthias, Mehdi Elahi, and Francesco Ricci. "<strong>Techniques for cold-starting context-aware mobile recommender systems for tourism</strong>." Intelligenza Artificiale 8, no. 2 (2014): 129-143.</em>
description: {"references": ["Braunhofer, Matthias, Mehdi Elahi, and Francesco Ricci. \"Techniques for cold-starting context-aware mobile recommender systems for tourism.\" Intelligenza Artificiale 8, no. 2 (2014): 129-143.", "Elahi, Mehdi, Matthias Braunhofer, Francesco Ricci, and Marko Tkalcic. \"Personality-based active learning for collaborative filtering recommender systems.\" In Congress of the Italian Association for Artificial Intelligence, pp. 360-371. Springer, Cham, 2013.", "Elahi, Mehdi. \"Empirical Evaluation of Active Learning Strategies in Collaborative Filtering.\"", "Braunhofer, Matthias, Mehdi Elahi, and Francesco Ricci. \"STS: A Context-Aware Mobile Recommender System for Places of Interest.\" In UMAP Workshops. 2014.", "Braunhofer, Matthias, Mehdi Elahi, Mouzhi Ge, Francesco Ricci, and Thomas Schievenin. \"STS: Design of Weather-Aware Mobile Recommender Systems in Tourism.\" In AI* HCI@ AI* IA. 2013."]}
DOI: 10.5281/zenodo.3266159
title: Absorption-Based Query Answering for Expressive Description Logics : Evaluation Data

description: Sources ( <code>Sources.zip</code> ), evaluation data ( <code>Evaluation.zip</code> ) with all ontologies and queries, as well as the evaluation results ( <code>Results.zip</code> ) of the publication "<em>Absorption-Based Query Answering for Expressive Description Logics</em>" from the 18th International Semantic Web Conference (ISWC), October 26-30, 2019, Auckland, New Zealand. For easily reproducing the evaluation, you can also use the Docker image <em>koncludeeval/abqa</em> (available at https://hub.docker.com/r/koncludeeval/abqa). See also readme files in archives for more details.

You may only use/share/etc. the included reasoners/ontologies as restricted by their licences (see <code>Sources.zip</code>). In particular, Konclude and OWL BGP are available under the LGPLv3 (https://www.gnu.org/licenses/lgpl-3.0.en.html), Pellet under the AGPLv3 (https://www.gnu.org/licenses/agpl-3.0.en.html), and PAGOdA under an Academic Licence (http://www.cs.ox.ac.uk/isg/tools/PAGOdA/PAGOdA_Academic_Licence.txt).
DOI: 10.5281/zenodo.3266160
title: Absorption-Based Query Answering for Expressive Description Logics : Evaluation Data

description: Sources ( <code>Sources.zip</code> ), evaluation data ( <code>Evaluation.zip</code> ) with all ontologies and queries, as well as the evaluation results ( <code>Results.zip</code> ) of the publication "<em>Absorption-Based Query Answering for Expressive Description Logics</em>" from the 18th International Semantic Web Conference (ISWC), October 26-30, 2019, Auckland, New Zealand. For easily reproducing the evaluation, you can also use the Docker image <em>koncludeeval/abqa</em> (available at https://hub.docker.com/r/koncludeeval/abqa). See also readme files in archives for more details.

You may only use/share/etc. the included reasoners/ontologies as restricted by their licences (see <code>Sources.zip</code>). In particular, Konclude and OWL BGP are available under the LGPLv3 (https://www.gnu.org/licenses/lgpl-3.0.en.html), Pellet under the AGPLv3 (https://www.gnu.org/licenses/agpl-3.0.en.html), and PAGOdA under an Academic Licence (http://www.cs.ox.ac.uk/isg/tools/PAGOdA/PAGOdA_Academic_Licence.txt).
DOI: 10.21227/db82-hx16
title: Neural Network Enhanced Robot Tool Identification and Calibration for Bilateral Teleoperation

description: This data is for training and validating the neural networks.
DOI: 10.21227/x7bc-7n98
title: Neural Network Enhanced Robot Tool Identification and Calibration for Bilateral Teleoperation

description: This data is for training and validating the neural networks.
DOI: 10.25345/c5h64r
title: MassIVE MSV000084052 - GNPS Workflow Fagonia Crude hyrolyzed 2july2019
title: Fagonia GNPS hyrolyzed 2 july 2019 stevens group wenbin