cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.11197504
title: GestureMidAirD3

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

Data contain 3D hand trajectories collected with Leap Motion device. There are 13 subjects, each performs 26 interface-command gestures. 

Each gesture is encoded as a sequence of 3D points, representing the position of the dominant-hand forefinger. There are 26 classes corresponding to unique gestures. See Fig. 1 of [2] for the list of gestures and their visualisation.

- Class 1: arc3Dleft- Class 2: arc3Dright- Class 3: caret- Class 4: check- Class 5: circle- Class 6: curly-bracket-left- Class 7: curly-bracket-right- Class 8: delete- Class 9: left-swipe- Class 10: pigtail- Class 11: poly3Dxyz- Class 12: poly3Dxzy- Class 13: poly3Dyxz- Class 14: poly3Dyzx- Class 15: poly3Dzxy- Class 16: poly3Dzyx- Class 17: rectangle- Class 18: right-swipe- Class 19: spiral- Class 20: square-bracket-left- Class 21: square-bracket-right- Class 22: star- Class 23: triangle- Class 24: v- Class 25: x- Class 26: zig-zag 

We make three datasets out of these data, one for each dimension. We follow the original paper (see [2]) and use data of 8 subjects for training and 5 subjects for testing. Data of a same subjects does not appear in both train and test set, so resampling may introduce bias.

Data created by Fabio M. Caputo et al. (see [1], [2]). Data edited by Hoang Anh Dau.

[1] Github repo: https://github.com/giach68/gesturesCodes[2] Caputo, Fabio M., et al. Comparing 3D trajectories for simple mid-air gesture recognition. Computers & Graphics. 73 (2018): 17-25. 

Donator: H. A. Dau
DOI: 10.5281/zenodo.11197515
title: GesturePebbleZ1

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

The data are collected with Pebble smart watch featuring 3-axis accelerometer. Data collection involves 4 subjects perform 6 gestures in two sessions several days apart. There are 6 classes corresponding to distinct gestures (see the paper for gesture depiction and annotation).  - Class 1: hh - Class 2: hu - Class 3: ud - Class 4: hud - Class 5: hh2 - Class 6: hu2  

We make two datasets out of these data.  ## GesturePebbleZ1  This uses z-axis reading only. The train set contains data of all 4 subjects collected in the first session. The test set is data of all 4 subjects collected in the second session. For processing convenience, we pad NaN to the end of each time series to the length of the longest time series.  ## GesturePebbleZ2  This uses z-axis reading only. The train set contains data of two subjects collected in both sessions. The test set is data of the other two subjects collected in both sessions. Note that in this case, a same subject does not appear in both train and test set.  

Data created by Mezari, Antigoni, and Ilias Maglogiannis (see [1]). Data edited by Hoang Anh Dau.  

[1] Mezari, Antigoni, and Ilias Maglogiannis. "Gesture recognition using symbolic aggregate approximation and dynamic time warping on motion data." Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare. ACM, 2017.

Donator: I. Maglogiannis
DOI: 10.5517/cc462ky
title: CCDC 125009: Experimental Crystal Structure Determination

description: Related Article: G.S.Hair, A.H.Cowley, R.A.Jones, B.G.McBurnett, A.Voigt|1999|J.Am.Chem.Soc.|121|4922|doi:10.1021/ja9904336
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: 10.5281/zenodo.11197514
title: GesturePebbleZ1

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

The data are collected with Pebble smart watch featuring 3-axis accelerometer. Data collection involves 4 subjects perform 6 gestures in two sessions several days apart. There are 6 classes corresponding to distinct gestures (see the paper for gesture depiction and annotation).  - Class 1: hh - Class 2: hu - Class 3: ud - Class 4: hud - Class 5: hh2 - Class 6: hu2  

We make two datasets out of these data.  ## GesturePebbleZ1  This uses z-axis reading only. The train set contains data of all 4 subjects collected in the first session. The test set is data of all 4 subjects collected in the second session. For processing convenience, we pad NaN to the end of each time series to the length of the longest time series.  ## GesturePebbleZ2  This uses z-axis reading only. The train set contains data of two subjects collected in both sessions. The test set is data of the other two subjects collected in both sessions. Note that in this case, a same subject does not appear in both train and test set.  

Data created by Mezari, Antigoni, and Ilias Maglogiannis (see [1]). Data edited by Hoang Anh Dau.  

[1] Mezari, Antigoni, and Ilias Maglogiannis. "Gesture recognition using symbolic aggregate approximation and dynamic time warping on motion data." Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare. ACM, 2017.

Donator: I. Maglogiannis
DOI: 10.5281/zenodo.11197519
title: GesturePebbleZ2

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

The data are collected with Pebble smart watch featuring 3-axis accelerometer. Data collection involves 4 subjects perform 6 gestures in two sessions several days apart. There are 6 classes corresponding to distinct gestures (see the paper for gesture depiction and annotation).  - Class 1: hh - Class 2: hu - Class 3: ud - Class 4: hud - Class 5: hh2 - Class 6: hu2  

We make two datasets out of these data.  ## GesturePebbleZ1  This uses z-axis reading only. The train set contains data of all 4 subjects collected in the first session. The test set is data of all 4 subjects collected in the second session. For processing convenience, we pad NaN to the end of each time series to the length of the longest time series.  ## GesturePebbleZ2  This uses z-axis reading only. The train set contains data of two subjects collected in both sessions. The test set is data of the other two subjects collected in both sessions. Note that in this case, a same subject does not appear in both train and test set.  

Data created by Mezari, Antigoni, and Ilias Maglogiannis (see [1]). Data edited by Hoang Anh Dau.  

[1] Mezari, Antigoni, and Ilias Maglogiannis. "Gesture recognition using symbolic aggregate approximation and dynamic time warping on motion data." Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare. ACM, 2017.

Donator: I. Maglogiannis
DOI: 10.5281/zenodo.11197520
title: GesturePebbleZ2

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

The data are collected with Pebble smart watch featuring 3-axis accelerometer. Data collection involves 4 subjects perform 6 gestures in two sessions several days apart. There are 6 classes corresponding to distinct gestures (see the paper for gesture depiction and annotation).  - Class 1: hh - Class 2: hu - Class 3: ud - Class 4: hud - Class 5: hh2 - Class 6: hu2  

We make two datasets out of these data.  ## GesturePebbleZ1  This uses z-axis reading only. The train set contains data of all 4 subjects collected in the first session. The test set is data of all 4 subjects collected in the second session. For processing convenience, we pad NaN to the end of each time series to the length of the longest time series.  ## GesturePebbleZ2  This uses z-axis reading only. The train set contains data of two subjects collected in both sessions. The test set is data of the other two subjects collected in both sessions. Note that in this case, a same subject does not appear in both train and test set.  

Data created by Mezari, Antigoni, and Ilias Maglogiannis (see [1]). Data edited by Hoang Anh Dau.  

[1] Mezari, Antigoni, and Ilias Maglogiannis. "Gesture recognition using symbolic aggregate approximation and dynamic time warping on motion data." Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare. ACM, 2017.

Donator: I. Maglogiannis
DOI: 10.5281/zenodo.11198218
title: ShakeGestureWiimoteZ
DOI: 10.5281/zenodo.11198219
title: ShakeGestureWiimoteZ
DOI: 10.5281/zenodo.11197936
title: PLAID
DOI: 10.5281/zenodo.11197897
title: PickupGestureWiimoteZ
DOI: 10.5281/zenodo.11197898
title: PickupGestureWiimoteZ
DOI: 10.5281/zenodo.11197935
title: PLAID
DOI: 10.5281/zenodo.18484093
title: augMENTOR Data: UPATRAS Pilot Data, Logs, Grades and 21st Century Competencies (Anonymized)

description: This dataset contains anonymized interaction logs, academic performance (grades), and 21st Century Competency (4Cs) assessments from the UPATRAS pilot conducted within the augMENTOR project.
DOI: 10.5281/zenodo.18484094
title: augMENTOR Data: UPATRAS Pilot Data, Logs, Grades and 21st Century Competencies (Anonymized)

description: This dataset contains anonymized interaction logs, academic performance (grades), and 21st Century Competency (4Cs) assessments from the UPATRAS pilot conducted within the augMENTOR project.
DOI: 10.5281/zenodo.11197761
title: MelbournePedestrian
DOI: 10.5281/zenodo.11197762
title: MelbournePedestrian
DOI: 10.5281/zenodo.18483133
title: SuperDARN data in netCDF format (2012-Aug)

description: 2012-Aug SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


The RST is available here: https://github.com/SuperDARN/rst



The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation.
Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications.
A brief statement on how to acknowledge use of SuperDARN data is provided below.




Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication.
A listing of radars and PIs with contact information can be found here:
(SuperDARN Radar Overview)



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6519836
title: SuperDARN data in netCDF format (2012-Aug)

description: 2012-Aug SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


The RST is available here: https://github.com/SuperDARN/rst



The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation.
Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications.
A brief statement on how to acknowledge use of SuperDARN data is provided below.




Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication.
A listing of radars and PIs with contact information can be found here:
(SuperDARN Radar Overview)



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.21950/rptihb
title: Bioethanol steam reforming for hydrogen production over Ni‑Cr/SBA 15: Influence of metal loading and Ni/Cr ratio

description: This work examines the influence of metal loading and Ni/Cr ratio of Ni-Cr/SBA-15 catalysts on bioethanol steam reforming for the first time. The characterization of the synthesized samples reveals that higher Cr amounts results in lower Ni crystallite sizes due to the promoting effect of Cr, thereby enhancing the dispersion of the active phase. The catalytic performance has been evaluated in terms of ethanol conversion and H2 TOF (min-1). Ethanol conversion exhibits an increasing trend with higher Ni content, reaching up to 90% for samples containing 15 wt.%. By increasing the Cr content (lower Ni/Cr ratio) the results evidence a similar trend. A synergistic effect between Ni and Cr was appreciated in conversion values when the Ni content was below 11 wt.% and the Cr content exceeded 2 wt.%, which coincides with smaller Ni crystallite size. Concern-ing the H2 TOF, the catalyst with the lowest Ni content (7 wt.%) exhibits higher value with a notable enhancement upon increasing Cr loading. However, considerable de-crease of H2 TOF was observed for samples with higher Ni loading. Therefore, the best catalytic performance, achieving nearly complete ethanol conversion and high hydro-gen production was reached when using catalysts with 7 wt.% Ni, the Cr loading should be increased to around 2 wt.%.
DOI: 10.60520/ieda/113368
title: EPMA Major element analysis of experimental clinopyroxene, plagioclase, Fe-spinel and glass

description: This dataset contain EPMA major element analysis of clinopyroxene, plagioclase, Fe-spinel and glass from two experimental samples.
The experiment was conducted at atmospheric pressure, with temperature oscillation between 1130°C and 1170°C using a natural sample from 2002 flank eruption of Mt. Etna (southern eruptive fissure) as starting material. The experiment was conducted in dynamic condition with a deformation gradient imposed using a Concentric Cylinder Apparatus at the University of Perugia. All the EPMA analysis details are reported in the first two sheets of the document.
DOI: 10.15468/dl.kz8csy
title: Occurrence Download

description: A dataset containing 879808 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sambucus nigra",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 879808 records from 3558 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011796-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1217163.10fu
title: SH1217163.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1217164.10fu
title: SH1217164.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.5517/cc3x0mm
title: CCDC 116300: Experimental Crystal Structure Determination

description: Related Article: M.R.Bryce, T.Finn, A.J.Moore, A.S.Batsanov, J.A.K.Howard|2000|Eur.J.Org.Chem.|2001|51|doi:10.1002/(SICI)1099-0690(200001)2000:1<51::AID-EJOC51>3.0.CO;2-K
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/cc3x0nn
title: CCDC 116301: Experimental Crystal Structure Determination

description: Related Article: M.R.Bryce, T.Finn, A.J.Moore, A.S.Batsanov, J.A.K.Howard|2000|Eur.J.Org.Chem.|2001|51|doi:10.1002/(SICI)1099-0690(200001)2000:1<51::AID-EJOC51>3.0.CO;2-K
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/cc3x0pp
title: CCDC 116302: Experimental Crystal Structure Determination

description: Related Article: M.R.Bryce, T.Finn, A.J.Moore, A.S.Batsanov, J.A.K.Howard|2000|Eur.J.Org.Chem.|2001|51|doi:10.1002/(SICI)1099-0690(200001)2000:1<51::AID-EJOC51>3.0.CO;2-K
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/cc3x0qq
title: CCDC 116303: Experimental Crystal Structure Determination

description: Related Article: M.R.Bryce, T.Finn, A.J.Moore, A.S.Batsanov, J.A.K.Howard|2000|Eur.J.Org.Chem.|2001|51|doi:10.1002/(SICI)1099-0690(200001)2000:1<51::AID-EJOC51>3.0.CO;2-K
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/cc3yf8q
title: CCDC 117653: Experimental Crystal Structure Determination

description: Related Article: R.Ponsinet, G.Chassaing, J.Vaissermann, S.Lavielle|2000|Eur.J.Org.Chem.|2001|83|doi:10.1002/(SICI)1099-0690(200001)2000:1<83::AID-EJOC83>3.3.CO;2-U
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: 10.15468/dl.rjjdgs
title: Occurrence Download

description: A dataset containing 18 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Styphnolobium parviflorum)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 18 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007695-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n56276
title: Occurrence Download

description: A dataset containing 128399 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Zehneria maysorensis (Wight & Arn.) Arn., Cucumis hirsutus Sond., Dracaena canaliculata (Carrière) Byng & Christenh., Aloe massawana Reynolds, Annona senegalensis Pers., Debregeasia longifolia (Burm.fil.) Wedd., Peltophorum dasyrhachis (Miq.) Kurz, Sieruela Raf., Perotis patens Gand., Vigna oblongifolia A.Rich., Cestrum ×cultum Francey, Digitaria perrottetii (Kunth) Stapf, Biophytum umbraculum Welw., Coelachne infirma Buse, Sesamum zanguebarium (Lour.) Byng & Christenh., Hyparrhenia variabilis Stapf, Microloma incanum Decne., Pavonia columella Cav., Pedalium murex L., Sesamum alatum Thonn., Sporobolus paniculatus (Trin.) T.Durand & Schinz, Sporobolus stapfianus Gand., Sporobolus subtilis Kunth, Paullinia pinnata L., Digitaria gazensis Rendle, Cyathula uncinulata (Schrad.) Schinz, Solanum lycopersicoides Dunal, Tripsacum andersonii J.R.Gray, Acalypha hispida Burm.f., Hibiscus platanifolius (Willd.) Sweet, Lepturus repens (G.Forst.) R.Br., Lobelia anceps L.f., Rubus praecox Bertol., Festuca juncifolia St.-Amans, Poa alpigena Lindm., Trifolium spadiceum L., Polygonum brasiliense K.Koch, Polygonum maritimum L., Leontodon hirtus L., Jacobaea erratica (Bertol.) Fourr., Rubus constrictus P.J.Müll. & Lefèvre, Microseris pygmaea D.Don, Knautia integrifolia (L.) Bertol., Erodium geoides A.St.-Hil., Clematis bonariensis Juss. ex DC., Corynephorus divaricatus (Pourr.) Breistr., Acmella glaberrima (Hassl.) R.K.Jansen, Deschampsia setacea (Huds.) Hack., Vachellia horrida (L.) Kyal. & Boatwr., Calceolaria chelidonioides Kunth)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 128399 records from 1368 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014270-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.