cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12366734
title: DECM Machine Learning Training Corpus

description: The DECM Corpus is a digital corpus of the texts of Relaciones Geográficas de Nueva España (the Geographic Reports of New Spain) with different versions, including a machine ready version, a gold standard annotated dataset, and an automatically annotated version ready for text mining and machine learning experiments.This version contains a sample of the RGs manually annotated by multiple researchers with the software of our industry partner, Tagtog. This corpus has been used to carry out the NLP and ML experiments and the files are available in JSON and TSV format. These files are composed by texts and annotations. This is also accompanied by the DECM ontology which provides an explanation of the entities and labels produced. This corpus can be used for further experimentation with Artificial Intelligence methods.<br>
DOI: 10.6084/m9.figshare.12366734.v2
title: DECM Gold Standard Corpus

description: The DECM Corpus is a digital corpus of the texts of Relaciones Geográficas de Nueva España (the Geographic Reports of New Spain) with different versions, including a machine ready version, a gold standard annotated dataset, and an automatically annotated version ready for text mining and machine learning experiments.This version contains a sample of the RGs manually annotated by multiple researchers with the software of our industry partner, Tagtog. This corpus has been used to carry out the NLP and ML experiments and the files are available in JSON and TSV format. These files are composed by texts and annotations. This is also accompanied by the DECM ontology which provides an explanation of the entities and labels produced. This corpus can be used for further experimentation with Artificial Intelligence methods.<br>
DOI: 10.5281/zenodo.7340928
title: MNI-registered stroke lesion masks

description: This dataset contains the MNI-registered lesion masks of patients with strokes at different locations.<br> The dataset is described in Dietze A, Sörös P, Bröer M, Methner A, Pöntynen H, Sundermann B, Witt K and Dietz M (2022) Effects of acute ischemic stroke on binaural perception. Front. Neurosci. 16:1022354. doi: 10.3389/fnins.2022.1022354
DOI: 10.5281/zenodo.7340927
title: Dataset for: Effects of acute ischemic stroke on binaural perception, Dietze et al., Frontiers in Neurosciences, 2022

description: This dataset contains the MNI-registered lesion masks of patients with strokes at different locations and the psychoacoustic results (tone in noise detection and lateralization) of stroke and control groups.<br> The dataset is described in Dietze A, Sörös P, Bröer M, Methner A, Pöntynen H, Sundermann B, Witt K and Dietz M (2022) Effects of acute ischemic stroke on binaural perception. Front. Neurosci. 16:1022354. doi: 10.3389/fnins.2022.1022354
DOI: 10.5281/zenodo.7415436
title: Dataset for: Effects of acute ischemic stroke on binaural perception, Dietze et al., Frontiers in Neurosciences, 2022

description: This dataset contains the MNI-registered lesion masks of patients with strokes at different locations and the psychoacoustic results (tone in noise detection and lateralization) of stroke and control groups.<br> The dataset is described in Dietze A, Sörös P, Bröer M, Methner A, Pöntynen H, Sundermann B, Witt K and Dietz M (2022) Effects of acute ischemic stroke on binaural perception. Front. Neurosci. 16:1022354. doi: 10.3389/fnins.2022.1022354
DOI: 10.6084/m9.figshare.12366956
title: DECM Annotated Corpus

description: The DECM Corpus is a digital corpus of the texts of Relaciones Geográficas de Nueva España (the Geographic Reports of New Spain) with different versions, including a machine ready version, a gold standard annotated dataset, and an automatically annotated version ready for text mining and machine learning experiments.This is the version of the entire RG corpus automatically annotated using the ML models trained with the DECM Gold Standard Corpus. The files are available in JSON and TSV format, and it also contains the file for the DECM Ontology. This corpus can be further used for quantitative and qualitative research, as well as advanced analyses using text mining techniques, corpus linguistics and other methods such as Geographical Text Analysis. <br>
DOI: 10.6084/m9.figshare.12366956.v2
title: DECM Machine Annotated Corpus

description: The DECM Corpus is a digital corpus of the texts of Relaciones Geográficas de Nueva España (the Geographic Reports of New Spain) with different versions, including a machine ready version, a gold standard annotated dataset, and an automatically annotated version ready for text mining and machine learning experiments.This is the version of the entire RG corpus automatically annotated using the ML models trained with the DECM Gold Standard Corpus. The files are available in JSON and TSV format, and it also contains the file for the DECM Ontology. This corpus can be further used for quantitative and qualitative research, as well as advanced analyses using text mining techniques, corpus linguistics and other methods such as Geographical Text Analysis. <br>
DOI: 10.5522/04/21689093
title: Beta Skewed Traffic Test Dataset - gamma=0.2

description: This set of data houses a test set of 1000 graphs with locally skewed traffic at a rate of gamma=0.2. The throughput labels are calculated with the same methodology as the other beta sets just subjected to different traffic conditions.
DOI: 10.5522/04/21689093.v1
title: Beta Skewed Traffic Test Dataset - gamma=0.2

description: This set of data houses a test set of 1000 graphs with locally skewed traffic at a rate of gamma=0.2. The throughput labels are calculated with the same methodology as the other beta sets just subjected to different traffic conditions.
DOI: 10.5522/04/21689087
title: ER Test Dataset

description: This dataset consists of 25-45 node graphs generated via ER [1] with uniformly randomly scattered nodes over a grid the size of north america with a minimim of 100km between nodes. This is a test dataset to test how the model reacts to different structures of graphs. <br> [1] P. Erdos and A. Renyi, ‘On the Evolution of Random Graphs’, in Publication of the Mathematical Institute of the Hungarian Academy of Sciences, 1960, pp. 17–61. <br> <br>
DOI: 10.5522/04/21689087.v1
title: ER Test Dataset

description: This dataset consists of 25-45 node graphs generated via ER [1] with uniformly randomly scattered nodes over a grid the size of north america with a minimim of 100km between nodes. This is a test dataset to test how the model reacts to different structures of graphs. <br> [1] P. Erdos and A. Renyi, ‘On the Evolution of Random Graphs’, in Publication of the Mathematical Institute of the Hungarian Academy of Sciences, 1960, pp. 17–61. <br> <br>
DOI: 10.5522/04/21689099
title: Beta Skewed Traffic Test Dataset - gamma=1.0

description: This set of data houses a test set of 1000 graphs with locally skewed traffic at a rate of gamma=1.0. The throughput labels are calculated with the same methodology as the other beta sets just subjected to different traffic conditions.
DOI: 10.5522/04/21689099.v1
title: Beta Skewed Traffic Test Dataset - gamma=1.0

description: This set of data houses a test set of 1000 graphs with locally skewed traffic at a rate of gamma=1.0. The throughput labels are calculated with the same methodology as the other beta sets just subjected to different traffic conditions.
DOI: 10.5522/04/21689075
title: BA Test Datset

description: This dataset consists of 25-45 node graphs generated via BA [1] with uniformly randomly scattered nodes over a grid the size of north america with a minimim of 100km between nodes. This is a test dataset to test how the model reacts to different structures of graphs. <br> [1] A.-L. Barabasi and R. Albert, ‘Emergence of scaling in random networks’, <em>Science</em>, vol. 286, no. 5439, pp. 509–512, Oct. 1999, doi: 10.1126/science.286.5439.509.<br> <br>
DOI: 10.5522/04/21689075.v1
title: BA Test Datset

description: This dataset consists of 25-45 node graphs generated via BA [1] with uniformly randomly scattered nodes over a grid the size of north america with a minimim of 100km between nodes. This is a test dataset to test how the model reacts to different structures of graphs. <br> [1] A.-L. Barabasi and R. Albert, ‘Emergence of scaling in random networks’, <em>Science</em>, vol. 286, no. 5439, pp. 509–512, Oct. 1999, doi: 10.1126/science.286.5439.509.<br> <br>
DOI: 10.5522/04/21689090
title: Beta Test Dataset

description: This set of data houses the 5000 graphs used for testing the beta model that has been trained on the beta dataset. It houses topologies generated via SNR-BA [1] with nodes scattered uniformly randomly with a minimum radius of 100km between them over a grid the size of north america. The throughput labels are calculated via maximising the routing and wavelength assignment with zero blocking using first-fit k-shortest-paths and implementing the physical layer impairments using the gaussian noise model. <br> [1] R. Matzner, D. Semrau, R. Luo, G. Zervas, and P. Bayvel, ‘Making intelligent topology design choices: understanding structural and physical property performance implications in optical networks [Invited]’, J. Opt. Commun. Netw., JOCN, vol. 13, no. 8, pp. D53–D67, Aug. 2021, doi: 10.1364/JOCN.423490. <br>
DOI: 10.5522/04/21689090.v1
title: Beta Test Dataset

description: This set of data houses the 5000 graphs used for testing the beta model that has been trained on the beta dataset. It houses topologies generated via SNR-BA [1] with nodes scattered uniformly randomly with a minimum radius of 100km between them over a grid the size of north america. The throughput labels are calculated via maximising the routing and wavelength assignment with zero blocking using first-fit k-shortest-paths and implementing the physical layer impairments using the gaussian noise model. <br> [1] R. Matzner, D. Semrau, R. Luo, G. Zervas, and P. Bayvel, ‘Making intelligent topology design choices: understanding structural and physical property performance implications in optical networks [Invited]’, J. Opt. Commun. Netw., JOCN, vol. 13, no. 8, pp. D53–D67, Aug. 2021, doi: 10.1364/JOCN.423490. <br>
DOI: 10.5522/04/21695687
title: Beta Training Dataset

description: This training dataset included optical network topologies that are generated via SNR-BA method [1] with nodes scattered uniformly randomly over a grid the size of the north american continent. Here there is a minimum radius that is adhered to (100km) between the nodes. The nodes are between scales of 25-45 nodes. <br> The routings of the network are computed under uniform bandwidth conditions with the first-fit k-shortest-path (FF-kSP) algorithm and sequential loading (SL) until the maximum state of the network is found at zero blocking. The Gaussian noise (GN) model is used to calculate the signal-to-noise ratio of paths and the total throughput of the network. This throughput is given as a training label. <br> [1]<br> R. Matzner, D. Semrau, R. Luo, G. Zervas, and P. Bayvel, ‘Making intelligent topology design choices: understanding structural and physical property performance implications in optical networks [Invited]’, <em>J. Opt. Commun. Netw., JOCN</em>, vol. 13, no. 8, pp. D53–D67, Aug. 2021, doi: 10.1364/JOCN.423490.<br> <br> <br>
DOI: 10.5522/04/21695687.v1
title: Beta Training Dataset

description: This training dataset included optical network topologies that are generated via SNR-BA method [1] with nodes scattered uniformly randomly over a grid the size of the north american continent. Here there is a minimum radius that is adhered to (100km) between the nodes. The nodes are between scales of 25-45 nodes. <br> The routings of the network are computed under uniform bandwidth conditions with the first-fit k-shortest-path (FF-kSP) algorithm and sequential loading (SL) until the maximum state of the network is found at zero blocking. The Gaussian noise (GN) model is used to calculate the signal-to-noise ratio of paths and the total throughput of the network. This throughput is given as a training label. <br> [1]<br> R. Matzner, D. Semrau, R. Luo, G. Zervas, and P. Bayvel, ‘Making intelligent topology design choices: understanding structural and physical property performance implications in optical networks [Invited]’, <em>J. Opt. Commun. Netw., JOCN</em>, vol. 13, no. 8, pp. D53–D67, Aug. 2021, doi: 10.1364/JOCN.423490.<br> <br> <br>
DOI: 10.5522/04/21695996
title: Gamma Test Dataset

description: This set of data houses the 5000 graphs used for testing the gamma model that has been trained on the gamma dataset. It houses topologies generated via SNR-BA [1] with nodes scattered uniformly randomly with a minimum radius of 100km between them over a grid the size of north america. The throughput labels are calculated via maximising the routing and wavelength assignment with zero blocking using first-fit k-shortest-paths and implementing the physical layer impairments using the gaussian noise model. [1] R. Matzner, D. Semrau, R. Luo, G. Zervas, and P. Bayvel, ‘Making intelligent topology design choices: understanding structural and physical property performance implications in optical networks [Invited]’, J. Opt. Commun. Netw., JOCN, vol. 13, no. 8, pp. D53–D67, Aug. 2021, doi: 10.1364/JOCN.423490.
DOI: 10.5522/04/21695996.v1
title: Gamma Test Dataset

description: This set of data houses the 5000 graphs used for testing the gamma model that has been trained on the gamma dataset. It houses topologies generated via SNR-BA [1] with nodes scattered uniformly randomly with a minimum radius of 100km between them over a grid the size of north america. The throughput labels are calculated via maximising the routing and wavelength assignment with zero blocking using first-fit k-shortest-paths and implementing the physical layer impairments using the gaussian noise model. [1] R. Matzner, D. Semrau, R. Luo, G. Zervas, and P. Bayvel, ‘Making intelligent topology design choices: understanding structural and physical property performance implications in optical networks [Invited]’, J. Opt. Commun. Netw., JOCN, vol. 13, no. 8, pp. D53–D67, Aug. 2021, doi: 10.1364/JOCN.423490.
DOI: 10.5517/ccdc.csd.cc2bwzw3
title: CCDC 2171856: Experimental Crystal Structure Determination

description: Related Article: Jun-Jun Li, Zhen-Jie Feng, Xue-Song Wu, Ying Li, Di Wu, Jing Sun, Xin-Long Wang, Zhong-Min Su|2022|New J.Chem.|46|22739|doi:10.1039/D2NJ03746G
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.cc2cnr0t
title: CCDC 2194676: Experimental Crystal Structure Determination

description: Related Article: Zhi-Yuan Xue, Jia-Lin Yu, Qing-Qing Xia, Yu-Qi Zhu, Ming-Xue Wu, Xiaomin Liu, Xing-Huo Wang|2022|ACS Applied Materials and Interfaces|14|53359|doi:10.1021/acsami.2c17600
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.cc2cpjsd
title: CCDC 2195444: Experimental Crystal Structure Determination

description: Related Article: Hui Liu, Dan-Dan Ren, Peng-Fu Gao, Kun Zhang, Ya-Pan Wu, Hong-Ru Fu, Lu-Fang Ma|2022|Chemical Science|13|13922|doi:10.1039/D2SC05353E
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.cc2cpr50
title: CCDC 2195642: Experimental Crystal Structure Determination

description: Related Article: Zhuanling Bai, Benjamin Scheibe, Joseph M. Sperling, Thomas E. Albrecht-Schönzart|2022|Inorg.Chem.|61|19193|doi:10.1021/acs.inorgchem.2c02840
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.cc2cpr61
title: CCDC 2195643: Experimental Crystal Structure Determination

description: Related Article: Zhuanling Bai, Benjamin Scheibe, Joseph M. Sperling, Thomas E. Albrecht-Schönzart|2022|Inorg.Chem.|61|19193|doi:10.1021/acs.inorgchem.2c02840
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.cc2cpr72
title: CCDC 2195644: Experimental Crystal Structure Determination

description: Related Article: Zhuanling Bai, Benjamin Scheibe, Joseph M. Sperling, Thomas E. Albrecht-Schönzart|2022|Inorg.Chem.|61|19193|doi:10.1021/acs.inorgchem.2c02840
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.cc2cpr83
title: CCDC 2195645: Experimental Crystal Structure Determination

description: Related Article: Zhuanling Bai, Benjamin Scheibe, Joseph M. Sperling, Thomas E. Albrecht-Schönzart|2022|Inorg.Chem.|61|19193|doi:10.1021/acs.inorgchem.2c02840
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.cc2cpr94
title: CCDC 2195646: Experimental Crystal Structure Determination

description: Related Article: Zhuanling Bai, Benjamin Scheibe, Joseph M. Sperling, Thomas E. Albrecht-Schönzart|2022|Inorg.Chem.|61|19193|doi:10.1021/acs.inorgchem.2c02840
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.cc2cprb5
title: CCDC 2195647: Experimental Crystal Structure Determination

description: Related Article: Zhuanling Bai, Benjamin Scheibe, Joseph M. Sperling, Thomas E. Albrecht-Schönzart|2022|Inorg.Chem.|61|19193|doi:10.1021/acs.inorgchem.2c02840
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.cc2cprc6
title: CCDC 2195648: Experimental Crystal Structure Determination

description: Related Article: Zhuanling Bai, Benjamin Scheibe, Joseph M. Sperling, Thomas E. Albrecht-Schönzart|2022|Inorg.Chem.|61|19193|doi:10.1021/acs.inorgchem.2c02840
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.cc2cprd7
title: CCDC 2195649: Experimental Crystal Structure Determination

description: Related Article: Zhuanling Bai, Benjamin Scheibe, Joseph M. Sperling, Thomas E. Albrecht-Schönzart|2022|Inorg.Chem.|61|19193|doi:10.1021/acs.inorgchem.2c02840
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.cc2cprf8
title: CCDC 2195650: Experimental Crystal Structure Determination

description: Related Article: Zhuanling Bai, Benjamin Scheibe, Joseph M. Sperling, Thomas E. Albrecht-Schönzart|2022|Inorg.Chem.|61|19193|doi:10.1021/acs.inorgchem.2c02840
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.cc2cprg9
title: CCDC 2195651: Experimental Crystal Structure Determination

description: Related Article: Zhuanling Bai, Benjamin Scheibe, Joseph M. Sperling, Thomas E. Albrecht-Schönzart|2022|Inorg.Chem.|61|19193|doi:10.1021/acs.inorgchem.2c02840
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.cc2cprhb
title: CCDC 2195652: Experimental Crystal Structure Determination

description: Related Article: Zhuanling Bai, Benjamin Scheibe, Joseph M. Sperling, Thomas E. Albrecht-Schönzart|2022|Inorg.Chem.|61|19193|doi:10.1021/acs.inorgchem.2c02840
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.cc2cprjc
title: CCDC 2195653: Experimental Crystal Structure Determination

description: Related Article: Zhuanling Bai, Benjamin Scheibe, Joseph M. Sperling, Thomas E. Albrecht-Schönzart|2022|Inorg.Chem.|61|19193|doi:10.1021/acs.inorgchem.2c02840
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.