cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12048846.v1
title: Rolling

description: 3 views videos (training and test) of rolling dough action
DOI: 10.6084/m9.figshare.12049551
title: Lemon

description: 3 views videos (training and test) of squeezing lemon action
DOI: 10.6084/m9.figshare.12048993.v1
title: Reaching

description: 3 views videos (training and test) of reaching action
DOI: 10.6084/m9.figshare.12049158.v1
title: Code

description: Auxiliary functions (in Matlab) to load the data
DOI: 10.6084/m9.figshare.12047523.v1
title: Transport

description: 3 views videos (training and test) of object transporting action
DOI: 10.6084/m9.figshare.12048849.v1
title: Salad

description: 3 views videos (training and test) of mixing salad action
DOI: 10.6084/m9.figshare.12049593
title: Mezzaluna

description: 3 views videos (training and test) of mincing with mezzaluna action
DOI: 10.6084/m9.figshare.12049656
title: Mixing

description: 3 views videos (training and test) of mixing action
DOI: 10.6084/m9.figshare.12049470
title: Eating

description: 3 views videos (training and test) of eating action
DOI: 10.6084/m9.figshare.12049890
title: Openbottle

description: 3 views videos (training and test) of open bottle action
DOI: 10.6084/m9.figshare.12049959
title: Pestling

description: 3 views videos (training and test) of pestling action
DOI: 10.6084/m9.figshare.12049443
title: Dish

description: 3 views videos (training and test) of cleaning dish action
DOI: 10.6084/m9.figshare.12049995
title: PouringSingle

description: 3 views videos (training and test) of pouring in a single container action
DOI: 10.6084/m9.figshare.12049161.v1
title: ReadMe

description: ReadMe file with indications on how to use the dataset
DOI: 10.6084/m9.figshare.12051771.v1
title: Scenes

description: 3 views Scenes videos (test)
DOI: 10.6084/m9.figshare.12049095.v1
title: PouringMult

description: 3 views videos (training and test) of pouring in multiple containers action
DOI: 10.6084/m9.figshare.12049443.v1
title: Dish

description: 3 views videos (training and test) of cleaning dish action
DOI: 10.6084/m9.figshare.12049224.v1
title: Cut

description: 3 views videos (training and test) of cutting bread action
DOI: 10.6084/m9.figshare.12049551.v1
title: Lemon

description: 3 views videos (training and test) of squeezing lemon action
DOI: 10.6084/m9.figshare.12052401.v1
title: MOCAP

description: Motion Capture data (training and test)
DOI: 10.6084/m9.figshare.12049470.v1
title: Eating

description: 3 views videos (training and test) of eating action
DOI: 10.6084/m9.figshare.12049890.v1
title: Openbottle

description: 3 views videos (training and test) of open bottle action
DOI: 10.6084/m9.figshare.12049593.v1
title: Mezzaluna

description: 3 views videos (training and test) of mincing with mezzaluna action
DOI: 10.6084/m9.figshare.12051759.v1
title: Spreading

description: 3 views videos (training and test) of spreading action
DOI: 10.6084/m9.figshare.12049656.v1
title: Mixing

description: 3 views videos (training and test) of mixing action
DOI: 10.6084/m9.figshare.12049995.v1
title: PouringSingle

description: 3 views videos (training and test) of pouring in a single container action
DOI: 10.6084/m9.figshare.12049512.v1
title: Eggs

description: 3 views videos (training and test) of beating eggs action
DOI: 10.6084/m9.figshare.12049938.v1
title: Pan

description: 3 views videos (training and test) of pan action
DOI: 10.21258/1469279
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 891763
DOI: 10.21258/1447185
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 902188
DOI: 10.21258/1464681
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 902202
DOI: 10.21258/1446093
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 892930
DOI: 10.21258/1449175
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 897450
DOI: 10.21258/1476311
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 902149
DOI: 10.21258/1456853
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 899121
DOI: 10.21258/1433551
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 897064
DOI: 10.21258/1467840
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 898190
DOI: 10.6084/m9.figshare.12049164.v1
title: Carrot

description: 3 views videos (training and test) of grating carrot action
DOI: 10.6084/m9.figshare.12049959.v1
title: Pestling

description: 3 views videos (training and test) of pestling action
DOI: 10.21258/1474845
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 897934
DOI: 10.21258/1436742
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 897148
DOI: 10.21258/1453482
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 897740
DOI: 10.21258/1436475
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 897411
DOI: 10.21258/1459094
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 892839
DOI: 10.21258/1455771
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 902287
DOI: 10.21258/1469945
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 891981
DOI: 10.5281/zenodo.4310112
title: Lausanne tree canopy

description: Tree canopy map of Lausanne at the 1m resolution obtained with DetecTree [1] from SWISSIMAGE 2016. <strong>Citation</strong> If you use this dataset, the source, i.e., SWISSIMAGE 2016 <em>must</em> be acknowledged. Additionally, a citation to DetecTree would certainly be appreciated. Note that DetecTree is based on the methods of Yang et al. [2], therefore it seems fair to reference their work too. An example citation in an academic paper might read as follows: The tree canopy dataset for the agglomeration of Lausanne has been obtained from the SWISSIMAGE 2016 aerial imagery dataset with the Python library DetecTree (Bosch, 2020), which is based on the approach of Yang et al. (2009). <strong>Technical specifications</strong> <strong>Source</strong>: SWISSIMAGE 2016 <strong>CRS</strong>: CH1903+/LV95 – Swiss CH1903+/LV95 (EPSG:2056) <strong>Resolution</strong>: 1m <strong>Extent</strong>: From file agglom-extent.shp. Obtained with the Urban footprinter. See the lausanne-agglom-extent repository for more details. <strong>Method</strong>: supervised learning (AdaBoost) with 4 classifiers on manually-generated ground truth masks for 7 training tiles (out of a total 499 tiles) of 512x512 pixels. See Yang et al. [2] for more details. <strong>Accuracy</strong>: 91.75%, estimated from a manually-generated ground truth mask for 1 tile of 512x512 pixels. <strong>Acknowledgements</strong> With the support of the École Polytechnique Fédérale de Lausanne (EPFL) <strong>References</strong> Bosch, M. (2020). Detectree: Tree detection from aerial imagery in Python. Journal of Open Source Software (under review). Yang, L., Wu, X., Praun, E., & Ma, X. (2009). Tree detection from aerial imagery. In Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (pp. 131-137). ACM.
DOI: 10.5281/zenodo.4310111
title: Lausanne tree canopy

description: Tree canopy map of Lausanne at the 1m resolution obtained with DetecTree [1] from SWISSIMAGE 2016. <strong>Citation</strong> If you use this dataset, the source, i.e., SWISSIMAGE 2016 <em>must</em> be acknowledged. Additionally, a citation to DetecTree would certainly be appreciated. Note that DetecTree is based on the methods of Yang et al. [2], therefore it seems fair to reference their work too. An example citation in an academic paper might read as follows: The tree canopy dataset for the agglomeration of Lausanne has been obtained from the SWISSIMAGE 2016 aerial imagery dataset with the Python library DetecTree (Bosch, 2020), which is based on the approach of Yang et al. (2009). <strong>Technical specifications</strong> <strong>Source</strong>: SWISSIMAGE 2016 <strong>CRS</strong>: CH1903+/LV95 – Swiss CH1903+/LV95 (EPSG:2056) <strong>Resolution</strong>: 1m <strong>Extent</strong>: From file agglom-extent.shp. Obtained with the Urban footprinter. See the lausanne-agglom-extent repository for more details. <strong>Method</strong>: supervised learning (AdaBoost) with 4 classifiers on manually-generated ground truth masks for 7 training tiles (out of a total 499 tiles) of 512x512 pixels. See Yang et al. [2] for more details. <strong>Accuracy</strong>: 91.75%, estimated from a manually-generated ground truth mask for 1 tile of 512x512 pixels. <strong>Acknowledgements</strong> With the support of the École Polytechnique Fédérale de Lausanne (EPFL) <strong>References</strong> Bosch, M. (2020). Detectree: Tree detection from aerial imagery in Python. Journal of Open Source Software (under review). Yang, L., Wu, X., Praun, E., & Ma, X. (2009). Tree detection from aerial imagery. In Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (pp. 131-137). ACM.
DOI: 10.21258/1454193
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 897162
DOI: 10.21258/1464226
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 898871
DOI: 10.21258/1438535
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 898105
DOI: 10.21258/1433608
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 896993
DOI: 10.21258/1454119
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 902066
DOI: 10.21258/1461092
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 897915
DOI: 10.21258/1438800
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 891911
DOI: 10.21258/1457316
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 897509
DOI: 10.21258/1437563
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 896986
DOI: 10.21258/1471133
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 891741
DOI: 10.6084/m9.figshare.12725624
title: Test data of the simulation system.xls

description: The is the related simulation data for the paper named "Flexibility aggregation of multi-services offered by massive distributed energy resources".
DOI: 10.6084/m9.figshare.12725624.v1
title: Test data of the simulation system.xls

description: The is the related simulation data for the paper named "Flexibility aggregation of multi-services offered by massive distributed energy resources".
DOI: 10.21258/1435049
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 897191
DOI: 10.18112/openneuro.ds003422.v1.0.0
title: NI-edu-data-introduction
DOI: 10.21258/1467836
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 897708
DOI: 10.21258/1435459
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 892041
DOI: 10.21258/1451436
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 897027
DOI: 10.21258/1448270
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 891720
DOI: 10.21258/1466180
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 902250
DOI: 10.6084/m9.figshare.13213832
title: Metadata record for: Public perceptions of multiple risks during the COVID-19 pandemic in Italy and Sweden

description: This dataset contains key characteristics about the data described in the Data Descriptor Public perceptions of multiple risks during the COVID-19 pandemic in Italy and Sweden. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
DOI: 10.6084/m9.figshare.13213832.v1
title: Metadata record for: Public perceptions of multiple risks during the COVID-19 pandemic in Italy and Sweden

description: This dataset contains key characteristics about the data described in the Data Descriptor Public perceptions of multiple risks during the COVID-19 pandemic in Italy and Sweden. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
DOI: 10.6084/m9.figshare.13348064
title: Meta-markers for neuronal cell types (BICCN Mouse MOp)

description: We extracted replicable markers for neuron cell types from a compendium of 7 scRNAseq datasets generated by the BICCN in the mouse primary motor cortex (https://doi.org/10.1101/2020.02.29.970558). The markers were extracted using the MetaMarkers package (https://github.com/gillislab/MetaMarkers) using default parameters and keeping the top 1000 markers for each cell type.<br>We present markers for cell types at each level of the hierarchy defined by the BICCN. - biccn_class_markers.csv: highest level with only 3 cell types (excitatory neurons, inhibitory neurons, non-neurons). - biccn_subclass_markers.csv: intermediate level containing 13 cell types (e.g. PV+ interneurons, L6b excitatory neurons). - biccn_cluster_markers.csv: high-resolution level containing 86 cell types (e.g. Chandelier cells).<br>The number of informative markers varies by cell type. To find the best number of markers, we looked for optimal annotation performance for each cell type. The results are summarized in "optimal_number_markers.csv". For each cell type, performance is reported in column "f1" (0.75 indicates good performance, 1 is perfect performance). To read this table, pick a cell type of interest, find optimal performance, then the range of genes that lead to optimal performance. For example, Chandelier cells (Pvalb Vipr2_2) are perfectly characterized by 50 to 200 markers (F1&gt;0.99).
DOI: 10.6084/m9.figshare.13348064.v2
title: Meta-markers for neuronal cell types (BICCN Mouse MOp)

description: We extracted replicable markers for neuron cell types from a compendium of 7 scRNAseq datasets generated by the BICCN in the mouse primary motor cortex (https://doi.org/10.1101/2020.02.29.970558). The markers were extracted using the MetaMarkers package (https://github.com/gillislab/MetaMarkers) using default parameters and keeping the top 1000 markers for each cell type.<br>We present markers for cell types at each level of the hierarchy defined by the BICCN. - biccn_class_markers.csv: highest level with only 3 cell types (excitatory neurons, inhibitory neurons, non-neurons). - biccn_subclass_markers.csv: intermediate level containing 13 cell types (e.g. PV+ interneurons, L6b excitatory neurons). - biccn_cluster_markers.csv: high-resolution level containing 86 cell types (e.g. Chandelier cells).<br>The number of informative markers varies by cell type. To find the best number of markers, we looked for optimal annotation performance for each cell type. The results are summarized in "optimal_number_markers.csv". For each cell type, performance is reported in column "f1" (0.75 indicates good performance, 1 is perfect performance). To read this table, pick a cell type of interest, find optimal performance, then the range of genes that lead to optimal performance. For example, Chandelier cells (Pvalb Vipr2_2) are perfectly characterized by 50 to 200 markers (F1&gt;0.99).
DOI: 10.21258/1442113
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 902263
DOI: 10.5517/ccdc.csd.cc21l7pl
title: CCDC 1894369: Experimental Crystal Structure Determination

description: Related Article: Mark E Light|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc21z28d
title: CCDC 1905733: Experimental Crystal Structure Determination

description: Related Article: Mark E Light|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.ccpxfyy
title: CCDC 652951: Experimental Crystal Structure Determination

description: Related Article: Małgorzata Widelicka, Paweł Ławniczak, Adam Pietraszko, Katarzyna Pogorzelec-Glaser, Andrzej Łapiński|2020|CrystEngComm|22|2031|doi:10.1039/C9CE01397K
DOI: 10.21258/1473753
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 898224
DOI: 10.5517/ccdc.csd.cc239vcn
title: CCDC 1945881: Experimental Crystal Structure Determination

description: Related Article: Pierre Thesmar, Olivier Baudoin|2019|J.Am.Chem.Soc.|141|15779|doi:10.1021/jacs.9b09359
description: Related Article: Pierre Thesmar, Seemon Coomar, Alessandro Prescimone, Daniel Häussinger, Dennis Gillingham, Olivier Baudoin|2020|Chem.-Eur.J.|26|15298|doi:10.1002/chem.202003683
DOI: 10.5517/ccdc.csd.cc239yzc
title: CCDC 1945993: Experimental Crystal Structure Determination

description: Related Article: Pierre Thesmar, Olivier Baudoin|2019|J.Am.Chem.Soc.|141|15779|doi:10.1021/jacs.9b09359
description: Related Article: Pierre Thesmar, Seemon Coomar, Alessandro Prescimone, Daniel Häussinger, Dennis Gillingham, Olivier Baudoin|2020|Chem.-Eur.J.|26|15298|doi:10.1002/chem.202003683
DOI: 10.5517/ccdc.csd.cc23b0wc
title: CCDC 1946052: Experimental Crystal Structure Determination

description: Related Article: Pierre Thesmar, Olivier Baudoin|2019|J.Am.Chem.Soc.|141|15779|doi:10.1021/jacs.9b09359
description: Related Article: Pierre Thesmar, Seemon Coomar, Alessandro Prescimone, Daniel Häussinger, Dennis Gillingham, Olivier Baudoin|2020|Chem.-Eur.J.|26|15298|doi:10.1002/chem.202003683
DOI: 10.5517/ccdc.csd.ccp14wq
title: CCDC 626723: Experimental Crystal Structure Determination

description: Related Article: Mark E Light and Pedro Cintas|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc23d84y
title: CCDC 1948199: Experimental Crystal Structure Determination

description: Related Article: Mark E Light and Pedro Cintas|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc23dktx
title: CCDC 1948499: Experimental Crystal Structure Determination

description: Related Article: Mark E Light and Pedro Cintas|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc244pbb
title: CCDC 1970711: Experimental Crystal Structure Determination

description: Related Article: Chuyang Jing, Liang Wang, Guangqian Zhu, Hongbin Hou, Li Zhou, Qinggang Wang|2020|Organometallics|39|4019|doi:10.1021/acs.organomet.0c00591
DOI: 10.5517/ccdc.csd.cc244pff
title: CCDC 1970714: Experimental Crystal Structure Determination

description: Related Article: Chuyang Jing, Liang Wang, Guangqian Zhu, Hongbin Hou, Li Zhou, Qinggang Wang|2020|Organometallics|39|4019|doi:10.1021/acs.organomet.0c00591
DOI: 10.5517/ccdc.csd.cc244phh
title: CCDC 1970716: Experimental Crystal Structure Determination

description: Related Article: Chuyang Jing, Liang Wang, Guangqian Zhu, Hongbin Hou, Li Zhou, Qinggang Wang|2020|Organometallics|39|4019|doi:10.1021/acs.organomet.0c00591
DOI: 10.5517/ccdc.csd.cc25fw8s
title: CCDC 2009335: Experimental Crystal Structure Determination

description: Related Article: Pierre Thesmar, Seemon Coomar, Alessandro Prescimone, Daniel Häussinger, Dennis Gillingham, Olivier Baudoin|2020|Chem.-Eur.J.|26|15298|doi:10.1002/chem.202003683
DOI: 10.5517/ccdc.csd.cc25fw9t
title: CCDC 2009336: Experimental Crystal Structure Determination

description: Related Article: Pierre Thesmar, Seemon Coomar, Alessandro Prescimone, Daniel Häussinger, Dennis Gillingham, Olivier Baudoin|2020|Chem.-Eur.J.|26|15298|doi:10.1002/chem.202003683
DOI: 10.5517/ccdc.csd.cc25fxtc
title: CCDC 2009383: Experimental Crystal Structure Determination

description: Related Article: Pierre Thesmar, Seemon Coomar, Alessandro Prescimone, Daniel Häussinger, Dennis Gillingham, Olivier Baudoin|2020|Chem.-Eur.J.|26|15298|doi:10.1002/chem.202003683
DOI: 10.5517/ccdc.csd.cc25gpt5
title: CCDC 2010127: Experimental Crystal Structure Determination

description: Related Article: Pierre Thesmar, Seemon Coomar, Alessandro Prescimone, Daniel Häussinger, Dennis Gillingham, Olivier Baudoin|2020|Chem.-Eur.J.|26|15298|doi:10.1002/chem.202003683
DOI: 10.5517/ccdc.csd.cc25gpv6
title: CCDC 2010128: Experimental Crystal Structure Determination

description: Related Article: Pierre Thesmar, Seemon Coomar, Alessandro Prescimone, Daniel Häussinger, Dennis Gillingham, Olivier Baudoin|2020|Chem.-Eur.J.|26|15298|doi:10.1002/chem.202003683
DOI: 10.5517/ccdc.csd.cc2681m5
title: CCDC 2033526: Experimental Crystal Structure Determination

description: Related Article: Tai-Ming Shao, Zu-Zhuang Wei, Xiao-Ling Luo, Qi-Pin Qin, Ming-Xiong Tan, Jia-Jing Zeng, Chun-Jie Liang, Hong Liang|2020|New J.Chem.|44|19885|doi:10.1039/D0NJ04753H
DOI: 10.21258/1433835
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 892078
DOI: 10.5517/ccdc.csd.cc2681n6
title: CCDC 2033527: Experimental Crystal Structure Determination

description: Related Article: Tai-Ming Shao, Zu-Zhuang Wei, Xiao-Ling Luo, Qi-Pin Qin, Ming-Xiong Tan, Jia-Jing Zeng, Chun-Jie Liang, Hong Liang|2020|New J.Chem.|44|19885|doi:10.1039/D0NJ04753H
DOI: 10.5517/ccdc.csd.cc26pg2g
title: CCDC 2046405: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, R.W. Harrington|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc26pg3h
title: CCDC 2046406: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, R.W. Harrington|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc26pg9p
title: CCDC 2046412: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, R.W. Harrington|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc26pgcr
title: CCDC 2046414: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, R.W. Harrington|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc26pghw
title: CCDC 2046418: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, R.W. Harrington|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc26ph0f
title: CCDC 2046434: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, R.W. Harrington|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc26phhx
title: CCDC 2046449: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, R.W. Harrington|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc26phkz
title: CCDC 2046451: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, R.W. Harrington|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc26phq4
title: CCDC 2046456: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, R.W. Harrington|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc26phr5
title: CCDC 2046457: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, R.W. Harrington|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc26rmk5
title: CCDC 2048497: Experimental Crystal Structure Determination

description: Related Article: Brian W. Skelton, Gellert Sipos, Reto Dorta|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc26rml6
title: CCDC 2048498: Experimental Crystal Structure Determination

description: Related Article: Brian W. Skelton, Gellert Sipos, Reto Dorta|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc26rmm7
title: CCDC 2048499: Experimental Crystal Structure Determination

description: Related Article: Brian W. Skelton, Gellert Sipos, Reto Dorta|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc26rmqb
title: CCDC 2048502: Experimental Crystal Structure Determination

description: Related Article: Brian W. Skelton, Gellert Sipos, Reto Dorta|2020|CSD Communication|||
DOI: 10.21258/1465963
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 898917
DOI: 10.18154/rwth-2020-09886
title: Sample Dataset for Search Engine Evaluation for Research Data in an RDF-based Knowledge Graph

description: Data records and files created and used in the master thesis "An Efficient Semantic Search Engine for Research Data in an RDF-based Knowledge Graph" (DOI: 10.18154/RWTH-2020-09883).
DOI: 10.6084/m9.figshare.13348193
title: Test data in the simulation

description: This is the test data in the simulation of the submitted article.
DOI: 10.6084/m9.figshare.13348193.v1
title: Test data in the simulation

description: This is the test data in the simulation of the submitted article.
DOI: 10.21258/1446949
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 902131
DOI: 10.21258/1465260
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 899551
DOI: 10.21258/1474867
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 892995
DOI: 10.21258/1434235
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 899157
DOI: 10.21258/1451385
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 891942
DOI: 10.21258/1476568
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 898177