cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.13145/bacdive2315.20191129.4.1
title: Brevundimonas sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive2316.20191129.4.1
title: Brevundimonas sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive2317.20191129.4.1
title: Brevundimonas sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive2318.20191129.4.1
title: Brevundimonas aveniformis Ryu et al. 2007

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive2319.20191129.4.1
title: Brevundimonas lenta Yoon et al. 2007

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive2320.20191129.4.1
title: Brevundimonas naejangsanensis Kang et al. 2009

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive2321.20191129.4.1
title: Brevundimonas basaltis Choi et al. 2010

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive2322.20191129.4.1
title: Brevundimonas halotolerans Abraham et al. 2010

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive2323.20191129.4.1
title: Brevundimonas poindexterae Abraham et al. 2010

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive2324.20191129.4.1
title: Brevundimonas staleyi Abraham et al. 2010

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive2325.20191129.4.1
title: Caulobacter fusiformis Poindexter 1964

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive2326.20191129.4.1
title: Caulobacter henricii Poindexter 1964

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive2327.20191129.4.1
title: Brevundimonas sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive2328.20191129.4.1
title: Brevundimonas sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive2329.20191129.4.1
title: Brevundimonas sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive2330.20191129.4.1
title: Brevundimonas sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive2331.20191129.4.1
title: Brevundimonas sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive2332.20191129.4.1
title: Caulobacter vibrioides Henrici and Johnson 1935

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive2333.20191129.4.1
title: Caulobacter vibrioides Henrici and Johnson 1935

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive2334.20191129.4.1
title: Caulobacter vibrioides Henrici and Johnson 1935

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive2335.20191129.4.1
title: Caulobacter vibrioides Henrici and Johnson 1935

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive2336.20191129.4.1
title: Caulobacter segnis (Urakami et al. 1990) Abraham et al. 1999

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive2337.20191129.4.1
title: Caulobacter ginsengisoli Liu et al. 2011

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive2338.20191129.4.1
title: Caulobacter mirabilis Abraham et al. 2008

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.6084/m9.figshare.12271292.v11
title: Arrival catalog of coronal mass ejections observed with STEREO Heliospheric Imagers (HELCATS ARRCAT 2.0)

description: ARRIVAL CATALOGUE 2.0 <br>In this ARRival CATalog (ARRCAT), the arrivals of solar coronal mass ejections that were tracked in the STEREO heliospheric imagers in the HELCATS project are calculated.<br>ARRCAT lists modeled arrivals of CMEs at various spacecraft and planets, between April 2007 - December 2020, based on the HIGeoCAT catalog of CMEs established at RAL Space, UK (R. A. Harrison, J. A. Davies, D. Barnes). <br>This is version 2.0, released 2020-May-13, updated 2020-Dec-11.<br>https://doi.org/10.6084/m9.figshare.12271292<br>It is based on this HIGeoCAT version: https://www.helcats-fp7.eu/catalogues/data/HCME_WP3_V06.vot using the SSEF30 model.<br>The catalog is available as python pandas dataframe (pickle), python numpy arrays (pickle, as recarray and structured array),npy (numpy, no pickle), json, csv, xlsx, txt, hdf5, html at https://helioforecast.space/arrcathttps://www.helcats-fp7.eu<br>Number of events in ARRCAT: 1608Targets: Earth_L1, STEREO-A, STEREO-B, Solar Orbiter, Parker Solar Probe (PSP), Bepi Colombo, Ulysses, Venus, Mercury, Mars.<br>For further description, see header file.<br>source code: arrcat.ipynb inhttps://github.com/cmoestl/heliocats<br>Rules: If results are produced with this catalog for peer-reviewed scientific publications,please contact christian.moestl@oeaw.ac.at for co-authorships.<br>
DOI: 10.6084/m9.figshare.13554047
title: Semantic View Selection dataset

description: This database was created in the context of our IROS 2018 paper: Semantically Meaningful View Selection. This paper introduces the problem of optimal view selection for semantic understanding. The dataset contains around 10k images representing various everyday objects under different poses, and observed under multiple views with a camera mounted on the end-effector of a UR10 robot manipulator. More details can be found in the paper.<br>If you find this dataset useful in your research, please consider citing:<br><i> @inproceedings{guerin2018semantically,<br></i><i> title = {Semantically Meaningful View Selection},<br></i><i> author = {Gu{\'e}rin, Joris and Gibaru, Olivier and Nyiri, Eric and Thieryl, Stephane and Boots, Byron},<br></i><i> booktitle = {2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},<br></i><i> pages = {1061--1066},<br></i><i> year = {2018},<br></i><i> organization = {IEEE}} </i>
DOI: 10.6084/m9.figshare.13554047.v1
title: Semantic View Selection dataset

description: This database was created in the context of our IROS 2018 paper: Semantically Meaningful View Selection. This paper introduces the problem of optimal view selection for semantic understanding. The dataset contains around 10k images representing various everyday objects under different poses, and observed under multiple views with a camera mounted on the end-effector of a UR10 robot manipulator. More details can be found in the paper.<br>If you find this dataset useful in your research, please consider citing:<br><i> @inproceedings{guerin2018semantically,<br></i><i> title = {Semantically Meaningful View Selection},<br></i><i> author = {Gu{\'e}rin, Joris and Gibaru, Olivier and Nyiri, Eric and Thieryl, Stephane and Boots, Byron},<br></i><i> booktitle = {2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},<br></i><i> pages = {1061--1066},<br></i><i> year = {2018},<br></i><i> organization = {IEEE}} </i>
DOI: 10.6084/m9.figshare.13554023
title: Screw Drivers database for 3D stereo object localization

description: A labelled dataset for 3D object localization. Each example is composed of two 300x300 images (from two fixed cameras) and the target is a vector with 6 entries representing the XYZ coordinates of two points on the screw driver handle. The two points are the uppest and lowest point of the handle on the screw driver axis.<br><br>This dataset was generated using an industrial robot to get knowledge of the exact position of the object. The XYZ coordinates are expressed in the robot frame, hence the 3D localization model needs to handle the change of frame.<br><br>A computer vision algorithm was implemented to remove the robot from some of the images. For other cases, we hide the robot physically with a t-shirt wrapped around the effector. The information relative to the situation, as well as the screw driver present on the two images can be found in the 'info.txt' file.<br>If you find this dataset useful, please consider citing:<br><i>@inproceedings{guerin2018automatic,<br></i><i> title = {Automatic Construction of Real-World Datasets for {3D} Object Localization using Two Cameras},<br></i><i> author = {Gu{\'e}rin, Joris and Gibaru, Olivier and Nyiri, Eric and Thiery, St{\'e}phane and Palos, Jorge},<br></i><i> booktitle = {IECON 2018-44th Annual Conference of the IEEE Industrial Electronics Society},<br></i><i> pages = {3655--3658},<br></i><i> year = {2018}, <br></i><i>organization = {IEEE}} </i><br>
DOI: 10.6084/m9.figshare.13554023.v2
title: Screw Drivers database for 3D stereo object localization

description: A labelled dataset for 3D object localization. Each example is composed of two 300x300 images (from two fixed cameras) and the target is a vector with 6 entries representing the XYZ coordinates of two points on the screw driver handle. The two points are the uppest and lowest point of the handle on the screw driver axis.<br><br>This dataset was generated using an industrial robot to get knowledge of the exact position of the object. The XYZ coordinates are expressed in the robot frame, hence the 3D localization model needs to handle the change of frame.<br><br>A computer vision algorithm was implemented to remove the robot from some of the images. For other cases, we hide the robot physically with a t-shirt wrapped around the effector. The information relative to the situation, as well as the screw driver present on the two images can be found in the 'info.txt' file.<br>If you find this dataset useful, please consider citing:<br><i>@inproceedings{guerin2018automatic,<br></i><i> title = {Automatic Construction of Real-World Datasets for {3D} Object Localization using Two Cameras},<br></i><i> author = {Gu{\'e}rin, Joris and Gibaru, Olivier and Nyiri, Eric and Thiery, St{\'e}phane and Palos, Jorge},<br></i><i> booktitle = {IECON 2018-44th Annual Conference of the IEEE Industrial Electronics Society},<br></i><i> pages = {3655--3658},<br></i><i> year = {2018}, <br></i><i>organization = {IEEE}} </i><br>
DOI: 10.6084/m9.figshare.13554023.v1
title: endToEnd_stereoLocalization.zip

description: A labelled dataset for 3D object localization. Each example is composed of two 300x300 images (from two fixed cameras) and the target is a vector with 6 entries representing the XYZ coordinates of two points on the screw driver handle. The two points are the uppest and lowest point of the handle on the screw driver axis.<br><br>This dataset was generated using an industrial robot to get knowledge of the exact position of the object. The XYZ coordinates are expressed in the robot frame, hence the 3D localization model needs to handle the change of frame.<br><br>A computer vision algorithm was implemented to remove the robot from some of the images. For other cases, we hide the robot physically with a t-shirt wrapped around the effector. The information relative to the situation, as well as the screw driver present on the two images can be found in the 'info.txt' file.<br>
DOI: 10.6084/m9.figshare.13560170
title: Librarians’ Use of Web Technologies to Support Students in an Online, Distance and e-Learning University in South Africa

description: The objective of this study was to examine how subject librarians’ use of web technologies to support students in an online, distance and e-learning university in South Africa. Data was collected from 68 of the 135 participants who were administered with an online questionnaire, and analysed to generate information about web technology adopter categories and examine the types of the web technologies used by the librarians.
DOI: 10.6084/m9.figshare.13560170.v1
title: Librarians’ Use of Web Technologies to Support Students in an Online, Distance and e-Learning University in South Africa

description: The objective of this study was to examine how subject librarians’ use of web technologies to support students in an online, distance and e-learning university in South Africa. Data was collected from 68 of the 135 participants who were administered with an online questionnaire, and analysed to generate information about web technology adopter categories and examine the types of the web technologies used by the librarians.
DOI: 10.25502/8vq7-tf40/d
title: Farmer's perception on agronomy performance of new plantain hybrid in the humid forest of Cameroon

description: The project aim was to promote integrated crop management technologies to improve plantain productivity of smallholder farmers in West and Central Africa. The experiments were set to evaluate in participatory manner the performance of hybrids plantain varieties and also evaluate the farmer’s appreciation of taste and their processing capability.
DOI: 10.6084/m9.figshare.12271292.v12
title: Arrival catalog of coronal mass ejections observed with STEREO Heliospheric Imagers (HELCATS ARRCAT 2.0)

description: ARRIVAL CATALOGUE 2.0 <br>In this ARRival CATalog (ARRCAT), the arrivals of solar coronal mass ejections that were tracked in the STEREO heliospheric imagers in the HELCATS project are calculated.<br>ARRCAT lists modeled arrivals of CMEs at various spacecraft and planets, between April 2007 - December 2020, based on the HIGeoCAT catalog of CMEs established at RAL Space, UK (R. A. Harrison, J. A. Davies, D. Barnes). <br>This is version 2.0, released 2020-May-13, updated 2020-Jan-12.<br>https://doi.org/10.6084/m9.figshare.12271292<br>It is based on this HIGeoCAT version: https://www.helcats-fp7.eu/catalogues/data/HCME_WP3_V06.vot using the SSEF30 model.<br>The catalog is available as python pandas dataframe (pickle), python numpy arrays (pickle, as recarray and structured array),npy (numpy, no pickle), json, csv, xlsx, txt, hdf5, html at https://helioforecast.space/arrcathttps://www.helcats-fp7.eu<br>Number of events in ARRCAT: 1616Targets: Earth_L1, STEREO-A, STEREO-B, Solar Orbiter, Parker Solar Probe (PSP), Bepi Colombo, Ulysses, Venus, Mercury, Mars.<br>For further description, see header file.<br>source code: arrcat.ipynb inhttps://github.com/cmoestl/heliocats<br>Rules: If results are produced with this catalog for peer-reviewed scientific publications,please contact christian.moestl@oeaw.ac.at for co-authorships.<br>
DOI: 10.21227/t0c9-4127
title: g

description: g
DOI: 10.6084/m9.figshare.13514308.v1
title: Dataset.rar

description: The information of users includes: UserID, gender, age and occupation and those of the items are MovieID, title and genres. All ratings are contained in the ﬁle ratings.dat. Each line of this ﬁle represents one rating of one movie by one user, and has the following format: UserID::MovieID::Rating::Timestamp. The lines within this ﬁle are ordered ﬁrst by UserID, then, within the user, by MovieID. Ratings are made on a 5 star scale. Timestamps represents seconds since midnight Coordinated Universal Time (UTC).The CMU Movie Summary Corpus contains plot summaries and metadata such as the runtime of movies.<br>
DOI: 10.25502/j4zm-1k64/d
title: Screening new plantain hybrid for yield and resistance black sigatoka diseases and pests in the humid forest of Cameroon

description: The project aim was to promote integrated crop management technologies to improve plantain productivity of smallholder farmers in West and Central Africa. The experiments were set to evaluate in participatory manner the performance of hybrids plantain varieties and also their resistance in field conditions to black sigatoka disease, nematodes and banana weevils. The  plantain genotypes evaluated on station were developed by IITA, CARBAP and FHIA.
DOI: 10.24352/ub.ovgu-2020-175
title: Behavioral and optogenetic analyses of reinforcement processing in larval Drosophila – Raw data

description: Understanding the complexity of reward processing in the brain is one of the major challenges in the contemporary neurosciences. Using Drosophila as a model organism and state-of-the-art genetic tools as well as behavioral paradigms, the study connected to the raw data files gives novel insights in reward processing and suggests timing-dependent valence reversal as an across-species principle.
DOI: 10.6084/m9.figshare.13378694
title: RNAseq data for hybrid Formica genome annotation - FASTQ 1 (Forward)

description: This RNAseq data was generated from nine wood ant individuals originating from six nests in the Långholmen population, which is of hybrid <i>Formica aquilonia</i> x <i>Formica polyctena</i> ancestry (R lineage: nest FA4, 3 individuals and W lineage: nest FA15, 1 ind.; FA17, 2 inds.; FA25, 1 ind.; FA35, 1 ind.; FAu2014a, 1 ind.). These individuals were at different larval stages and total RNA was extracted from whole bodies using an ALLPrep DNA/RNA Mini Kit (Qiagen) following manufacturer’s instructions. Individual RNA qualities were assessed using a Bioanalyzer (Agilent 2100). Libraries were constructed using NEBNext Ultra RNA Library Prep Kits and samples were sequenced on an Illumina NextSeq platform (paired-end mode, 150 bp) at the Biomedicum Functional Genomics Unit (FuGU, University of Helsinki). Raw reads were trimmed using Trimmomatic (v0.35, parameters LEADING:20 TRAILING:20 SLIDINGWINDOW:5:20 MINLEN:50 Bolger, Lohse, and Usadel 2014) and unpaired reads were discarded.Approximately 5.60 million 150bp paired reads were randomly sampled per individual and combined into two (Forward and Reverse) FASTQ files, totaling 50 million paired reads over all individuals.
DOI: 10.6084/m9.figshare.13378694.v1
title: RNAseq data for hybrid Formica genome annotation - FASTQ 1 (Forward)

description: This RNAseq data was generated from nine wood ant individuals originating from six nests in the Långholmen population, which is of hybrid <i>Formica aquilonia</i> x <i>Formica polyctena</i> ancestry (R lineage: nest FA4, 3 individuals and W lineage: nest FA15, 1 ind.; FA17, 2 inds.; FA25, 1 ind.; FA35, 1 ind.; FAu2014a, 1 ind.). These individuals were at different larval stages and total RNA was extracted from whole bodies using an ALLPrep DNA/RNA Mini Kit (Qiagen) following manufacturer’s instructions. Individual RNA qualities were assessed using a Bioanalyzer (Agilent 2100). Libraries were constructed using NEBNext Ultra RNA Library Prep Kits and samples were sequenced on an Illumina NextSeq platform (paired-end mode, 150 bp) at the Biomedicum Functional Genomics Unit (FuGU, University of Helsinki). Raw reads were trimmed using Trimmomatic (v0.35, parameters LEADING:20 TRAILING:20 SLIDINGWINDOW:5:20 MINLEN:50 Bolger, Lohse, and Usadel 2014) and unpaired reads were discarded.Approximately 5.60 million 150bp paired reads were randomly sampled per individual and combined into two (Forward and Reverse) FASTQ files, totaling 50 million paired reads over all individuals.
DOI: 10.5517/ccdc.csd.cc2306w7
title: CCDC 1936628: Experimental Crystal Structure Determination

description: Related Article: Jiwei Wang, Kemeng Yuan, Gendi Wang, Ye Liu, Jun Zhang|2020|Chem.-Eur.J.|27|212|doi:10.1002/chem.202001502
DOI: 10.5517/ccdc.csd.cc2248p6
title: CCDC 1910737: Experimental Crystal Structure Determination

description: Related Article: N. Elleuch, K. Khemekhem, H. Ammar, M. Boujelbene|2021|J.Mol.Struct.|1227|129720|doi:10.1016/j.molstruc.2020.129720
DOI: 10.5517/ccdc.csd.cc2556g1
title: CCDC 2001002: Experimental Crystal Structure Determination

description: Related Article: A.M. Petrosyan, G. Giester, G.S. Tonoyan, V.V. Ghazaryan, M. Fleck|2021|J.Mol.Struct.|1228|129717|doi:10.1016/j.molstruc.2020.129717
DOI: 10.5517/ccdc.csd.cc2556h2
title: CCDC 2001003: Experimental Crystal Structure Determination

description: Related Article: A.M. Petrosyan, G. Giester, G.S. Tonoyan, V.V. Ghazaryan, M. Fleck|2021|J.Mol.Struct.|1228|129717|doi:10.1016/j.molstruc.2020.129717
DOI: 10.5517/ccdc.csd.cc2556j3
title: CCDC 2001004: Experimental Crystal Structure Determination

description: Related Article: A.M. Petrosyan, G. Giester, G.S. Tonoyan, V.V. Ghazaryan, M. Fleck|2021|J.Mol.Struct.|1228|129717|doi:10.1016/j.molstruc.2020.129717
DOI: 10.5517/ccdc.csd.cc2556k4
title: CCDC 2001005: Experimental Crystal Structure Determination

description: Related Article: A.M. Petrosyan, G. Giester, G.S. Tonoyan, V.V. Ghazaryan, M. Fleck|2021|J.Mol.Struct.|1228|129717|doi:10.1016/j.molstruc.2020.129717
DOI: 10.15161/oar.it/73401
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.5517/ccdc.csd.cc25rvkc
title: CCDC 2018923: Experimental Crystal Structure Determination

description: Related Article: Qihao Zhu, James C. Fettinger, Petra Vasko, Philip P. Power|2020|Organometallics|39|4629|doi:10.1021/acs.organomet.0c00659
DOI: 10.5517/ccdc.csd.cc25rvmf
title: CCDC 2018925: Experimental Crystal Structure Determination

description: Related Article: Qihao Zhu, James C. Fettinger, Petra Vasko, Philip P. Power|2020|Organometallics|39|4629|doi:10.1021/acs.organomet.0c00659
DOI: 10.5517/ccdc.csd.cc26197t
title: CCDC 2027035: Experimental Crystal Structure Determination

description: Related Article: Qihao Zhu, James C. Fettinger, Petra Vasko, Philip P. Power|2020|Organometallics|39|4629|doi:10.1021/acs.organomet.0c00659
DOI: 10.5517/ccdc.csd.cc269d86
title: CCDC 2034817: Experimental Crystal Structure Determination

description: Related Article: Yihua Zhao, Shuaikang Li, Weigang Fan, Shengyu Dai|2021|J.Organomet.Chem.|932|121649|doi:10.1016/j.jorganchem.2020.121649