cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2bqwlk
title: CCDC 2166949: Experimental Crystal Structure Determination

description: Related Article: Debabrata Dhara, Felipe Fantuzzi, Marcel Härterich, Rian D. Dewhurst, Ivo Krummenacher, Merle Arrowsmith, Conor Pranckevicius, Holger Braunschweig|2022|Chemical Science|13|9693|doi:10.1039/D2SC02783F
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.cc2bqwnm
title: CCDC 2166951: Experimental Crystal Structure Determination

description: Related Article: Debabrata Dhara, Felipe Fantuzzi, Marcel Härterich, Rian D. Dewhurst, Ivo Krummenacher, Merle Arrowsmith, Conor Pranckevicius, Holger Braunschweig|2022|Chemical Science|13|9693|doi:10.1039/D2SC02783F
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.cc2bqwpn
title: CCDC 2166952: Experimental Crystal Structure Determination

description: Related Article: Debabrata Dhara, Felipe Fantuzzi, Marcel Härterich, Rian D. Dewhurst, Ivo Krummenacher, Merle Arrowsmith, Conor Pranckevicius, Holger Braunschweig|2022|Chemical Science|13|9693|doi:10.1039/D2SC02783F
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.cc2bqwqp
title: CCDC 2166953: Experimental Crystal Structure Determination

description: Related Article: Debabrata Dhara, Felipe Fantuzzi, Marcel Härterich, Rian D. Dewhurst, Ivo Krummenacher, Merle Arrowsmith, Conor Pranckevicius, Holger Braunschweig|2022|Chemical Science|13|9693|doi:10.1039/D2SC02783F
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.cc2bqwrq
title: CCDC 2166954: Experimental Crystal Structure Determination

description: Related Article: Debabrata Dhara, Felipe Fantuzzi, Marcel Härterich, Rian D. Dewhurst, Ivo Krummenacher, Merle Arrowsmith, Conor Pranckevicius, Holger Braunschweig|2022|Chemical Science|13|9693|doi:10.1039/D2SC02783F
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.cc2bsdq8
title: CCDC 2168410: Experimental Crystal Structure Determination

description: Related Article: Jinxiao Lyu, Dongcheng Liu, Chunfei Wang, Zhao Zhang, Xuanjun Zhang|2022|J.Mater.Chem.B|10|6307|doi:10.1039/D2TB01078J
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.cc2bsdr9
title: CCDC 2168411: Experimental Crystal Structure Determination

description: Related Article: Jinxiao Lyu, Dongcheng Liu, Chunfei Wang, Zhao Zhang, Xuanjun Zhang|2022|J.Mater.Chem.B|10|6307|doi:10.1039/D2TB01078J
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.cc2c3m48
title: CCDC 2178219: Experimental Crystal Structure Determination

description: Related Article: Stefanie Plöger, Christian Mück-Lichtenfeld, Constantin G. Daniliuc, Armido Studer|2022|Chemical Science|13|9749|doi:10.1039/D2SC03860A
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.cc2c6pkv
title: CCDC 2181177: Experimental Crystal Structure Determination

description: Related Article: Sven van Vliet, Georgios Alachouzos, Folkert de Vries, Lukas Pfeifer, Ben L. Feringa|2022|Chemical Science|13|9713|doi:10.1039/D2SC03518A
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.cc2c6plw
title: CCDC 2181178: Experimental Crystal Structure Determination

description: Related Article: Sven van Vliet, Georgios Alachouzos, Folkert de Vries, Lukas Pfeifer, Ben L. Feringa|2022|Chemical Science|13|9713|doi:10.1039/D2SC03518A
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.7019177
title: Jemma Properties Methods - NUCC

description: Jemma Properties Methods - NUCC &gt; file: Jemma_Properties_Methods_NUCC.csv<br> &gt; size: 333.6 MB<br> &gt; num_rows: 8,543,752<br> &gt; columns: method_id, num_unique_callees
DOI: 10.5281/zenodo.7019176
title: Jemma Properties Methods - NUCC

description: Jemma Properties Methods - NUCC &gt; file: Jemma_Properties_Methods_NUCC.csv<br> &gt; size: 333.6 MB<br> &gt; num_rows: 8,543,752<br> &gt; columns: method_id, num_unique_callees
DOI: 10.5281/zenodo.6540136
title: Data to accompany the publication "Combined biophysical and genetic modelling approaches reveal complementary information about population connectivity of New Zealand green-lipped mussels"

description: Data to accompany the publication "Combined biophysical and genetic modelling approaches reveal complementary information about population connectivity of New Zealand green-lipped mussels". migrationmatrix14.txt contains the particle tracking matrix, with the total number of particles that migrated from row i to column j (out of a total of 2217864 particles released per population). mussel_microsat_Genepop.txt contains the microsatellite data for each population in Genepop format.
DOI: 10.5281/zenodo.6540137
title: Data to accompany the publication "Combined biophysical and genetic modelling approaches reveal complementary information about population connectivity of New Zealand green-lipped mussels"

description: Data to accompany the publication "Combined biophysical and genetic modelling approaches reveal complementary information about population connectivity of New Zealand green-lipped mussels". migrationmatrix14.txt contains the particle tracking matrix, with the total number of particles that migrated from row i to column j (out of a total of 2217864 particles released per population). mussel_microsat_Genepop.txt contains the microsatellite data for each population in Genepop format.
DOI: 10.5281/zenodo.7017665
title: Data to accompany the publication "Combined biophysical and genetic modelling approaches reveal complementary information about population connectivity of New Zealand green-lipped mussels"

description: Data to accompany the publication "Combined biophysical and genetic modelling approaches reveal complementary information about population connectivity of New Zealand green-lipped mussels". migrationmatrix14.txt contains the particle tracking matrix, with the total number of particles that migrated from row i to column j (out of a total of 2217864 particles released per population). mussel_microsat_Genepop.txt contains the microsatellite data for each population in Genepop format.
DOI: 10.25574/06769
title: Chandra X-ray Observatory ObsId 6769

description: AO7 Calibration Observation of PSR B1821-24 to Improve the Absolute Timing Calibration.
DOI: 10.3886/e178381
title: Civil War Peace Agreements and Gender Inclusion

description: Replication data for the following publication:<br>Olson Lounsbery, Marie, Nicole Gerring, and Kaitlyn Rose. 2022, Accepted. "Civil War Peace Agreements and Gender Inclusion." Defence and Peace Economics.
DOI: 10.3886/e178381v1
title: Civil War Peace Agreements and Gender Inclusion

description: Replication data for the following publication:<br>Olson Lounsbery, Marie, Nicole Gerring, and Kaitlyn Rose. 2022, Accepted. "Civil War Peace Agreements and Gender Inclusion." Defence and Peace Economics.
DOI: 10.25574/06867
title: Chandra X-ray Observatory ObsId 6867

description: Chandra ToO Observation of the Blazar 3C 279 in High State as Part of a Multifrequency Campaign
DOI: 10.25574/06892
title: Chandra X-ray Observatory ObsId 6892

description: Testing Broad-Band Jet-Powered Emission in the LLAGN M81*
DOI: 10.25574/06893
title: Chandra X-ray Observatory ObsId 6893

description: Testing Broad-Band Jet-Powered Emission in the LLAGN M81*
DOI: 10.25574/06894
title: Chandra X-ray Observatory ObsId 6894

description: Testing Broad-Band Jet-Powered Emission in the LLAGN M81*
DOI: 10.25574/06895
title: Chandra X-ray Observatory ObsId 6895

description: Testing Broad-Band Jet-Powered Emission in the LLAGN M81*
DOI: 10.25574/06896
title: Chandra X-ray Observatory ObsId 6896

description: Testing Broad-Band Jet-Powered Emission in the LLAGN M81*
DOI: 10.25574/06897
title: Chandra X-ray Observatory ObsId 6897

description: Testing Broad-Band Jet-Powered Emission in the LLAGN M81*
DOI: 10.25574/06898
title: Chandra X-ray Observatory ObsId 6898

description: Testing Broad-Band Jet-Powered Emission in the LLAGN M81*
DOI: 10.25574/06899
title: Chandra X-ray Observatory ObsId 6899

description: Testing Broad-Band Jet-Powered Emission in the LLAGN M81*
DOI: 10.25574/06900
title: Chandra X-ray Observatory ObsId 6900

description: Testing Broad-Band Jet-Powered Emission in the LLAGN M81*
DOI: 10.25574/06901
title: Chandra X-ray Observatory ObsId 6901

description: Testing Broad-Band Jet-Powered Emission in the LLAGN M81*
DOI: 10.25574/06908
title: Chandra X-ray Observatory ObsId 6908

description: High-resolution spectroscopy of 'binary' AGN in the colliding galaxies of NGC 6240
DOI: 10.25574/06909
title: Chandra X-ray Observatory ObsId 6909

description: High-resolution spectroscopy of 'binary' AGN in the colliding galaxies of NGC 6240
DOI: 10.6084/m9.figshare.20597373
title: File S1. Centroid protein sequences.

description: Amino acid sequences of Centroid proteins used in Figures S5 and S6.
DOI: 10.6084/m9.figshare.20597373.v1
title: File S1. Centroid protein sequences.

description: Amino acid sequences of Centroid proteins used in Figures S5 and S6.
DOI: 10.6084/m9.figshare.20597469
title: Supplementary_Material.docx

description: Supplementary Materials for subgroup analysis based on patients' age groups
DOI: 10.6084/m9.figshare.20597469.v1
title: Supplementary_Material.docx

description: Supplementary Materials for subgroup analysis based on patients' age groups
DOI: 10.6084/m9.figshare.20583558.v1
title: UrbanLake_finalData.zip

description: A high-resolution circa-2020 map of urban lakes (&gt;0.001 km<sup>2</sup>) in China. The 10-m-resoultion Sentinel-2 imagery as well as a simple but robust water extraction method were used to generate waterbodies in China on Google Earth Engine. After initially filtering out urban water bodies based on the spatial relationships with urban area boundary, we combined high-resolution historical imagery to manually remove non-lake waters such as rivers and paddy fields, and to edit and supplement the missing urban lakes separately. The accuracy of our dataset was evaluated in terms of both area and count of lakes by comparing with manually vectorizing results in randomly sampled urban units. The results showed that our dataset is highly accurate and trustworthy, with the averaged accuracy of 81.85% in area and 93.35% in count.
DOI: 10.21954/ou.rd.20587893
title: 2021-22 UKRI Compliance Report

description: The OU's 2021/22 open access compliance and financial report for UKRI Block Grant fund spending.
DOI: 10.21954/ou.rd.20587893.v1
title: 2021-22 UKRI Compliance Report

description: The OU's 2021/22 open access compliance and financial report for UKRI Block Grant fund spending.
DOI: 10.5281/zenodo.7018979
title: PyKEEN Benchmarking Experiment Model Files

description: Model Weights This repository provides weights of the models from the benchmarking study conducted in "Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework" which have been upgraded to compatible with PyKEEN 1.9. The weights are organized as zipfiles, which are named by the dataset-interaction function configuration. For each of these combinations, we chose the best according to validation Hits@10 to include into this repository. For each model, we have three files: <code>configuration.json</code> contains the (pipeline) configuration used to train the model. It can loaded as <pre class="sourceCode python"><code class="sourceCode python">import pathlib import json configuration = json.loads(pathlib.Path("configuration.json").read_text())</code></pre> Since the configuration is intended for the <em>pipeline</em>, we need some custom code to re-create the model without re-training it. <pre class="sourceCode python"><code class="sourceCode python">from pykeen.datasets import get_dataset from pykeen.models import ERModel, model_resolver configuration = configuration["pipeline"] # load the triples factory dataset = get_dataset( dataset=configuration["dataset"], dataset_kwargs=configuration.get("dataset_kwargs", None) ) model: ERModel = model_resolver.make( configuration["model"], configuration["model_kwargs"], triples_factory=dataset.training )</code></pre> Note, that this only creates the model instance, but does <em>not</em> load the weights, yet. <code>state_dict.pt</code> contains the weights, stored via <code>torch.save</code>. They can be loaded via <pre class="sourceCode python"><code class="sourceCode python">import torch state_dict = torch.load("state_dict.pt")</code></pre> We can load these weights into the model by using <code>Module.load_state_dict</code> <pre class="sourceCode python"><code class="sourceCode python">model.load_state_dict(state_dict, strict=False)</code></pre> Note that we set <code>strict=False</code>, since the exported weights do <em>not</em> contain regularizers' state, while the re-instantiated models may have regularizers. <code>results.json</code> contains the results obtained by the original runs. It can be read by <pre class="sourceCode python"><code class="sourceCode python">import pathlib import json configuration = json.loads(pathlib.Path("results.json").read_text())</code></pre> Note that some of the recently added metrics are not available in those results.
DOI: 10.5281/zenodo.7018978
title: PyKEEN Benchmarking Experiment Model Files

description: Model Weights This repository provides weights of the models from the benchmarking study conducted in "Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework" which have been upgraded to compatible with PyKEEN 1.9. The weights are organized as zipfiles, which are named by the dataset-interaction function configuration. For each of these combinations, we chose the best according to validation Hits@10 to include into this repository. For each model, we have three files: <code>configuration.json</code> contains the (pipeline) configuration used to train the model. It can loaded as <pre class="sourceCode python"><code class="sourceCode python">import pathlib import json configuration = json.loads(pathlib.Path("configuration.json").read_text())</code></pre> Since the configuration is intended for the <em>pipeline</em>, we need some custom code to re-create the model without re-training it. <pre class="sourceCode python"><code class="sourceCode python">from pykeen.datasets import get_dataset from pykeen.models import ERModel, model_resolver configuration = configuration["pipeline"] # load the triples factory dataset = get_dataset( dataset=configuration["dataset"], dataset_kwargs=configuration.get("dataset_kwargs", None) ) model: ERModel = model_resolver.make( configuration["model"], configuration["model_kwargs"], triples_factory=dataset.training )</code></pre> Note, that this only creates the model instance, but does <em>not</em> load the weights, yet. <code>state_dict.pt</code> contains the weights, stored via <code>torch.save</code>. They can be loaded via <pre class="sourceCode python"><code class="sourceCode python">import torch state_dict = torch.load("state_dict.pt")</code></pre> We can load these weights into the model by using <code>Module.load_state_dict</code> <pre class="sourceCode python"><code class="sourceCode python">model.load_state_dict(state_dict, strict=False)</code></pre> Note that we set <code>strict=False</code>, since the exported weights do <em>not</em> contain regularizers' state, while the re-instantiated models may have regularizers. <code>results.json</code> contains the results obtained by the original runs. It can be read by <pre class="sourceCode python"><code class="sourceCode python">import pathlib import json configuration = json.loads(pathlib.Path("results.json").read_text())</code></pre> Note that some of the recently added metrics are not available in those results.
DOI: 10.5281/zenodo.7019181
title: PyKEEN Benchmarking Experiment Model Files

description: Model Weights This repository provides weights of the models from the benchmarking study conducted in "Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework" which have been upgraded to compatible with PyKEEN 1.9. The weights are organized as zipfiles, which are named by the dataset-interaction function configuration. For each of these combinations, we chose the best according to validation Hits@10 to include into this repository. For each model, we have three files: <code>configuration.json</code> contains the (pipeline) configuration used to train the model. It can loaded as <pre class="sourceCode python"><code class="sourceCode python">import pathlib import json configuration = json.loads(pathlib.Path("configuration.json").read_text())</code></pre> Since the configuration is intended for the <em>pipeline</em>, we need some custom code to re-create the model without re-training it. <pre class="sourceCode python"><code class="sourceCode python">from pykeen.datasets import get_dataset from pykeen.models import ERModel, model_resolver configuration = configuration["pipeline"] # load the triples factory dataset = get_dataset( dataset=configuration["dataset"], dataset_kwargs=configuration.get("dataset_kwargs", None) ) model: ERModel = model_resolver.make( configuration["model"], configuration["model_kwargs"], triples_factory=dataset.training )</code></pre> Note, that this only creates the model instance, but does <em>not</em> load the weights, yet. <code>state_dict.pt</code> contains the weights, stored via <code>torch.save</code>. They can be loaded via <pre class="sourceCode python"><code class="sourceCode python">import torch state_dict = torch.load("state_dict.pt")</code></pre> We can load these weights into the model by using <code>Module.load_state_dict</code> <pre class="sourceCode python"><code class="sourceCode python">model.load_state_dict(state_dict, strict=False)</code></pre> Note that we set <code>strict=False</code>, since the exported weights do <em>not</em> contain regularizers' state, while the re-instantiated models may have regularizers. <code>results.json</code> contains the results obtained by the original runs. It can be read by <pre class="sourceCode python"><code class="sourceCode python">import pathlib import json configuration = json.loads(pathlib.Path("results.json").read_text())</code></pre> Note that some of the recently added metrics are not available in those results.
DOI: 10.5281/zenodo.7019253
title: Data related to NAHAYO et al (2022), to appear in AGU Space Weather Journal (2022SW003092)

description: Geomagnetic data related to the publication by Nahayo, et al. (2022). The file ending in "event1.csv" is for the first event (October 2003) and the second file, filename ending "event2.csv" for the second event discussed in the paper (March 2015).
DOI: 10.5281/zenodo.7019252
title: Data related to NAHAYO et al (2022), to appear in AGU Space Weather Journal (2022SW003092)

description: Geomagnetic data related to the publication by Nahayo, et al. (2022). The file ending in "event1.csv" is for the first event (October 2003) and the second file, filename ending "event2.csv" for the second event discussed in the paper (March 2015).
DOI: 10.5066/p92kgw4s
title: Video Data for Cannon Discharge Events - May 3, 2016 - Taupo, New Zealand

description: The data include video record segments for a period of ~3.5 hours beginning 2016/05/03 at approximately the beginning of the universal time day. Each video record documents 10 active source explosions from three video cameras (30 total video segments). The data are part of an assessment of directed blast dynamics on a surrounding acoustic sensor array. The acoustic data is part of an independent data set which is archived in the IRIS FDSN (International Federation of Digital Seismograph Networks). Together the data support an exploratory assessment of energy dynamics similar to those observed for small scale wet eruptions from phreatic and phreato-magmatic systems (Jolly et al, submitted).
DOI: 10.5523/bris.2scid2vyz8jh82aj85guyzqrz6
title: Promoting sustainable diets using eco-labelling and social nudges: a randomised online experiment

description: This study used a between-subjects, parallel-group, experiment al design to investigate how eco-labelling and social nudging influence sustainable food choice. Participants (n = 1,399) were randomly allocated, to one of three study conditions which varied in the labelling of a burrito: an eco-label, a social nudge label or no label control. They were then asked to choose a hypothetical food item from a menu containing three choices: a beef burrito, a chicken burrito or a vegetarian burrito.
DOI: 10.7284/149475
title: GNSS (Global Navigation Satellite System) data collected during research cruise LMG0307 using a Ashtech ADU2 instrument system onboard the platform RV Laurence M. Gould

description: Original (unprocessed) data collected during a Field_expedition