cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc21j2rf
title: CCDC 1892294: Experimental Crystal Structure Determination

description: Related Article: Roman Gajda, Mateusz A. Domański, Maura Malinska, Anna Makal|2019|CrystEngComm|21|1701|doi:10.1039/C8CE02130A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc21j2sg
title: CCDC 1892295: Experimental Crystal Structure Determination

description: Related Article: Roman Gajda, Mateusz A. Domański, Maura Malinska, Anna Makal|2019|CrystEngComm|21|1701|doi:10.1039/C8CE02130A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc21j2th
title: CCDC 1892296: Experimental Crystal Structure Determination

description: Related Article: Roman Gajda, Mateusz A. Domański, Maura Malinska, Anna Makal|2019|CrystEngComm|21|1701|doi:10.1039/C8CE02130A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc21j2vj
title: CCDC 1892297: Experimental Crystal Structure Determination

description: Related Article: Roman Gajda, Mateusz A. Domański, Maura Malinska, Anna Makal|2019|CrystEngComm|21|1701|doi:10.1039/C8CE02130A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.21227/42f5-em40
title: power consumption data of a hotel building

description: A two-year electricity consumption data of a hotel building in Shanghai, China andand corresponding outdoor weather data.
DOI: 10.5281/zenodo.2571889
title: Supporting code and data for: "ProvBuild: Making Data Scientists More Efficient"

description: Supporting code and data for the article: "ProvBuild: Making Data Scientists More Efficient"
DOI: 10.18112/openneuro.ds001761.v1.0.1
title: Reconstructing Faces from fMRI Patterns using Deep Generative Neural Networks Dataset (fMRI)
DOI: 10.17632/7f6fx2ym5g.1
title: Data for: Combined effect of high pressure and vinegar addition on the control of Clostridium perfringens and quality in nitrite-free emulsion-type sausage

description: Combined effect of high pressure and vinegar addition on the control of Clostridium perfringens and quality in nitrite-free emulsion-type sausage
DOI: 10.5281/zenodo.2572670
title: Case Study Test System Datasheet

description: This is the datasheet for the examined 30-unit test system including 14 DG (indexed from 1 to 14), 4 IG (indexed from 15 to 18), 4 FD (indexed from 19 to 22), 4 ID (indexed from 23 to 26), and 4 ES units (indexed from 27 to 30).
DOI: 10.5281/zenodo.2572671
title: Case study test system datasheet

description: This is the datasheet for the examined  test system with a 24-hour horizon and includes 16 DG agents (indexed from 1 to 14 and 31 to 32), 4 DPV agents (indexed from 27 to 30), 6 FD agents with different scheduling periods (indexed from 15 to 18 and 33 to 34), 4 InfD agents (indexed from 23 to 26), and 4 ES agents (indexed from 19 to 22).
DOI: 10.5281/zenodo.2572620
title: MAgPIE 4.0 paper - model runs

description: Archive of runs produced for the MAgPIE 4 modeling framework paper.
DOI: 10.17632/3n9by5hcp5.1
title: Data for: Elusive Cretaceous Gondwanan theropods: the footprint evidence from central Argentina
DOI: 10.21227/prbq-s668
title: Signing in the Wild dataset

description: Our Signing in the Wild dataset consists of various videos harvested from YouTube containing people signing in various sign languages and doing so in diverse settings, environments, under complex signer and camera motion, and even group signing. This dataset is intended to be used for sign language detection.For the negative set, we created two classes of videos, labelled‘speaking’ and ‘other’. Our motivation for the ‘speaking’ class isthat speech is often accompanied by hand gestures (gesticulation),which can be easily confused with signing. Signing can be discriminatedby its linguistic nature, i.e., its distinct phonological, morphologicaland categorical (discrete) structures, while gesticulationtends to be more spontaneous, idiosyncratic and analogue in nature.For the ‘other’ class, we looked for distractors to both ‘signing’and ‘speaking’, i.e., videos containing hand movements that arequite similar to signing/gesticulation and thus might confuse a classifier.Examples include: miming, hand exercises, various manualactivities like playing instruments, painting, writing, yoga and martialarts, sports like table tennis, etc. Also included are activitiessimilar to speech, like people laughing, clapping, nodding, listening to other speakers, etc.A total of 1120 videos are included in our dataset, each videocontributing the first 6.6 minutes, resulting in 2000 frames per videowhen sampled at 5Hz. We have 1.45 million video frames in total.Our videos are untrimmed, i.e., a video can contain multiple activities,background scenes, scene cuts, and other actions done by thesame or different actors. Thus the videos are unconstrained both spatiallyand also temporally. This is in line with recent trends in videoaction recognition [8], and unlike ASLR datasets where trimmedvideos are the norm. In particular, several videos in our dataset containall 3 classes (occasionally with temporal overlap), and sometimesthe same person alternating between signing and speaking.We performed manual groundtruthing at video frame level.Since action boundaries can be inherently fuzzy, we consider a shorttemporal context (10 frames) surrounding the frame to be labelledin order to decide on its class label. We also adopt certain spatialguidelines, e.g. mouth movements must be visible for action ‘speaking’,thus eliminating distant views and when the speaker turnshis/her back to the camera. Ambiguous cases are left unlabelled.We annotate video segments that do not contain signing or speaking as ‘other’, including opening/closing credits, title screens, scenetransitions, animations, background scenes, etc.If you find this dataset useful, please cite the following paper:  Mark Borg, Kenneth P. Camilleri, "Sign Language Detection "In The Wild" With Recurrent Neural Networks", ICASSP 2019.Any comments, suggestions, feedback are welcome: mborg2005 at gmail dot com
DOI: 10.21227/w24f-yh32
title: Signing in the Wild dataset

description: Our Signing in the Wild dataset consists of various videos harvested from YouTube containing people signing in various sign languages and doing so in diverse settings, environments, under complex signer and camera motion, and even group signing. This dataset is intended to be used for sign language detection.For the negative set, we created two classes of videos, labelled‘speaking’ and ‘other’. Our motivation for the ‘speaking’ class isthat speech is often accompanied by hand gestures (gesticulation),which can be easily confused with signing. Signing can be discriminatedby its linguistic nature, i.e., its distinct phonological, morphologicaland categorical (discrete) structures, while gesticulationtends to be more spontaneous, idiosyncratic and analogue in nature.For the ‘other’ class, we looked for distractors to both ‘signing’and ‘speaking’, i.e., videos containing hand movements that arequite similar to signing/gesticulation and thus might confuse a classifier.Examples include: miming, hand exercises, various manualactivities like playing instruments, painting, writing, yoga and martialarts, sports like table tennis, etc. Also included are activitiessimilar to speech, like people laughing, clapping, nodding, listening to other speakers, etc.A total of 1120 videos are included in our dataset, each videocontributing the first 6.6 minutes, resulting in 2000 frames per videowhen sampled at 5Hz. We have 1.45 million video frames in total.Our videos are untrimmed, i.e., a video can contain multiple activities,background scenes, scene cuts, and other actions done by thesame or different actors. Thus the videos are unconstrained both spatiallyand also temporally. This is in line with recent trends in videoaction recognition [8], and unlike ASLR datasets where trimmedvideos are the norm. In particular, several videos in our dataset containall 3 classes (occasionally with temporal overlap), and sometimesthe same person alternating between signing and speaking.We performed manual groundtruthing at video frame level.Since action boundaries can be inherently fuzzy, we consider a shorttemporal context (10 frames) surrounding the frame to be labelledin order to decide on its class label. We also adopt certain spatialguidelines, e.g. mouth movements must be visible for action ‘speaking’,thus eliminating distant views and when the speaker turnshis/her back to the camera. Ambiguous cases are left unlabelled.We annotate video segments that do not contain signing or speaking as ‘other’, including opening/closing credits, title screens, scenetransitions, animations, background scenes, etc.If you find this dataset useful, please cite the following paper:  Mark Borg, Kenneth P. Camilleri, "Sign Language Detection "In The Wild" With Recurrent Neural Networks", ICASSP 2019.Any comments, suggestions, feedback are welcome: mborg2005 at gmail dot com
DOI: 10.17032/10.17032/shu-180009
title: E-assessment in undergraduate mathematics literature review data

description: A systematic review of literature on e-assessment in undergraduate mathematics was undertaken in 2012 and updated in 2017. This dataset contains metadata about articles found in that review.
DOI: 10.21427/d7sf97
title: AGAINST THE GRAIN: WHY IS TIMBER UNDERUTILISED IN THE IRISH CONSTRUCTION INDUSTRY, GIVEN ITS ENVIRONMENTAL PROPERTIES?
DOI: 10.21227/h585-mr68
title: Signing in the Wild dataset

description: Our Signing in the Wild dataset consists of various videos harvested from YouTube containing people signing in various sign languages and doing so in diverse settings, environments, under complex signer and camera motion, and even group signing. This dataset is intended to be used for sign language detection.For the negative set, we created two classes of videos, labelled‘speaking’ and ‘other’. Our motivation for the ‘speaking’ class isthat speech is often accompanied by hand gestures (gesticulation),which can be easily confused with signing. Signing can be discriminatedby its linguistic nature, i.e., its distinct phonological, morphologicaland categorical (discrete) structures, while gesticulationtends to be more spontaneous, idiosyncratic and analogue in nature.For the ‘other’ class, we looked for distractors to both ‘signing’and ‘speaking’, i.e., videos containing hand movements that arequite similar to signing/gesticulation and thus might confuse a classifier.Examples include: miming, hand exercises, various manualactivities like playing instruments, painting, writing, yoga and martialarts, sports like table tennis, etc. Also included are activitiessimilar to speech, like people laughing, clapping, nodding, listening to other speakers, etc.A total of 1120 videos are included in our dataset, each videocontributing the first 6.6 minutes, resulting in 2000 frames per videowhen sampled at 5Hz. We have 1.45 million video frames in total.Our videos are untrimmed, i.e., a video can contain multiple activities,background scenes, scene cuts, and other actions done by thesame or different actors. Thus the videos are unconstrained both spatiallyand also temporally. This is in line with recent trends in videoaction recognition [8], and unlike ASLR datasets where trimmedvideos are the norm. In particular, several videos in our dataset containall 3 classes (occasionally with temporal overlap), and sometimesthe same person alternating between signing and speaking.We performed manual groundtruthing at video frame level.Since action boundaries can be inherently fuzzy, we consider a shorttemporal context (10 frames) surrounding the frame to be labelledin order to decide on its class label. We also adopt certain spatialguidelines, e.g. mouth movements must be visible for action ‘speaking’,thus eliminating distant views and when the speaker turnshis/her back to the camera. Ambiguous cases are left unlabelled.We annotate video segments that do not contain signing or speaking as ‘other’, including opening/closing credits, title screens, scenetransitions, animations, background scenes, etc.If you find this dataset useful, please cite the following paper:  Mark Borg, Kenneth P. Camilleri, "Sign Language Detection "In The Wild" With Recurrent Neural Networks", ICASSP 2019.Any comments, suggestions, feedback are welcome: mborg2005 at gmail dot com
DOI: 10.5517/ccdc.csd.cc21j2wk
title: CCDC 1892298: Experimental Crystal Structure Determination

description: Related Article: Roman Gajda, Mateusz A. Domański, Maura Malinska, Anna Makal|2019|CrystEngComm|21|1701|doi:10.1039/C8CE02130A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc21j2xl
title: CCDC 1892299: Experimental Crystal Structure Determination

description: Related Article: Roman Gajda, Mateusz A. Domański, Maura Malinska, Anna Makal|2019|CrystEngComm|21|1701|doi:10.1039/C8CE02130A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc21j2ym
title: CCDC 1892300: Experimental Crystal Structure Determination

description: Related Article: Roman Gajda, Mateusz A. Domański, Maura Malinska, Anna Makal|2019|CrystEngComm|21|1701|doi:10.1039/C8CE02130A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc21j2zn
title: CCDC 1892301: Experimental Crystal Structure Determination

description: Related Article: Roman Gajda, Mateusz A. Domański, Maura Malinska, Anna Makal|2019|CrystEngComm|21|1701|doi:10.1039/C8CE02130A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc21j30q
title: CCDC 1892302: Experimental Crystal Structure Determination

description: Related Article: Roman Gajda, Mateusz A. Domański, Maura Malinska, Anna Makal|2019|CrystEngComm|21|1701|doi:10.1039/C8CE02130A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5523/bris.3tyiulqwo5czp2mg5dmkkcxd22
title: Data from MCDEA pump paper

description: This project supports a journal paper that presents a magnetically-coupled dielectric elastomer pump for soft robotics. All data that was used within the study for this journal paper manuscript will be made open access so that readers of our published paper can access it.
DOI: 10.5517/ccdc.csd.cc21j31r
title: CCDC 1892303: Experimental Crystal Structure Determination

description: Related Article: Roman Gajda, Mateusz A. Domański, Maura Malinska, Anna Makal|2019|CrystEngComm|21|1701|doi:10.1039/C8CE02130A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc21j32s
title: CCDC 1892304: Experimental Crystal Structure Determination

description: Related Article: Roman Gajda, Mateusz A. Domański, Maura Malinska, Anna Makal|2019|CrystEngComm|21|1701|doi:10.1039/C8CE02130A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc21j33t
title: CCDC 1892305: Experimental Crystal Structure Determination

description: Related Article: Roman Gajda, Mateusz A. Domański, Maura Malinska, Anna Makal|2019|CrystEngComm|21|1701|doi:10.1039/C8CE02130A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc21j34v
title: CCDC 1892306: Experimental Crystal Structure Determination

description: Related Article: Roman Gajda, Mateusz A. Domański, Maura Malinska, Anna Makal|2019|CrystEngComm|21|1701|doi:10.1039/C8CE02130A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc21j35w
title: CCDC 1892307: Experimental Crystal Structure Determination

description: Related Article: Roman Gajda, Mateusz A. Domański, Maura Malinska, Anna Makal|2019|CrystEngComm|21|1701|doi:10.1039/C8CE02130A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc21j36x
title: CCDC 1892308: Experimental Crystal Structure Determination

description: Related Article: Roman Gajda, Mateusz A. Domański, Maura Malinska, Anna Makal|2019|CrystEngComm|21|1701|doi:10.1039/C8CE02130A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc21j37y
title: CCDC 1892309: Experimental Crystal Structure Determination

description: Related Article: Roman Gajda, Mateusz A. Domański, Maura Malinska, Anna Makal|2019|CrystEngComm|21|1701|doi:10.1039/C8CE02130A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc21j38z
title: CCDC 1892310: Experimental Crystal Structure Determination

description: Related Article: Roman Gajda, Mateusz A. Domański, Maura Malinska, Anna Makal|2019|CrystEngComm|21|1701|doi:10.1039/C8CE02130A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc21j390
title: CCDC 1892311: Experimental Crystal Structure Determination

description: Related Article: Roman Gajda, Mateusz A. Domański, Maura Malinska, Anna Makal|2019|CrystEngComm|21|1701|doi:10.1039/C8CE02130A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc21j3b1
title: CCDC 1892312: Experimental Crystal Structure Determination

description: Related Article: Roman Gajda, Mateusz A. Domański, Maura Malinska, Anna Makal|2019|CrystEngComm|21|1701|doi:10.1039/C8CE02130A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc21j3c2
title: CCDC 1892313: Experimental Crystal Structure Determination

description: Related Article: Roman Gajda, Mateusz A. Domański, Maura Malinska, Anna Makal|2019|CrystEngComm|21|1701|doi:10.1039/C8CE02130A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.