cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2b07wh
title: CCDC 2145196: Experimental Crystal Structure Determination

description: Related Article: Andrey A. Fokin, Olga K. Reshetylova, Vladyslav V. Bakhonsky, Alexander E. Pashenko, Alena Kivernik, Tatyana S. Zhuk, Jonathan Becker, Jeremy E. P. Dahl, Robert M. K. Carlson, Peter R. Schreiner|2022|Org.Lett.|24|4845|doi:10.1021/acs.orglett.2c00982
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.cc2b0fjb
title: CCDC 2145371: Experimental Crystal Structure Determination

description: Related Article: Mamina Bhol, Bhaskaran Shankar, Malaichamy Sathiyendiran|2022|Inorg.Chem.|61|11497|doi:10.1021/acs.inorgchem.2c02061
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.cc2b0fkc
title: CCDC 2145372: Experimental Crystal Structure Determination

description: Related Article: Mamina Bhol, Bhaskaran Shankar, Malaichamy Sathiyendiran|2022|Inorg.Chem.|61|11497|doi:10.1021/acs.inorgchem.2c02061
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.cc2b0fld
title: CCDC 2145373: Experimental Crystal Structure Determination

description: Related Article: Mamina Bhol, Bhaskaran Shankar, Malaichamy Sathiyendiran|2022|Inorg.Chem.|61|11497|doi:10.1021/acs.inorgchem.2c02061
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.cc2b0fmf
title: CCDC 2145374: Experimental Crystal Structure Determination

description: Related Article: Mamina Bhol, Bhaskaran Shankar, Malaichamy Sathiyendiran|2022|Inorg.Chem.|61|11497|doi:10.1021/acs.inorgchem.2c02061
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.cc2b0fng
title: CCDC 2145375: Experimental Crystal Structure Determination

description: Related Article: Mamina Bhol, Bhaskaran Shankar, Malaichamy Sathiyendiran|2022|Inorg.Chem.|61|11497|doi:10.1021/acs.inorgchem.2c02061
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.cc2br56h
title: CCDC 2167216: Experimental Crystal Structure Determination

description: Related Article: Jackson S. McCarthy, Mary Jo McCormick, John H. Zimmerman, H. Rhodes Hambrick, Wilson M. Thomas, Colin D. McMillen, Paul S. Wagenknecht|2022|Inorg.Chem.|61|11366|doi:10.1021/acs.inorgchem.2c01564
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: 10.6084/m9.figshare.20391459
title: The biomechanical signature of loss of consciousness: computational modelling of elite athlete head injuries

description: Spreadsheet describing the processed data supporting the results from the publication. Processed data in NIFTI format is available upon request.
DOI: 10.6084/m9.figshare.20391459.v1
title: The biomechanical signature of loss of consciousness: computational modelling of elite athlete head injuries

description: Spreadsheet describing the processed data supporting the results from the publication. Processed data in NIFTI format is available upon request.
DOI: 10.6084/m9.figshare.20391495
title: Data and Image Set

description: Data Set prepared for L/D manuscript
DOI: 10.6084/m9.figshare.20186133.v2
title: Transportation in urban land change models: a systematic review and future directions

description: Urban land change and transportation infrastructure development often interact and collectively lead to significant socioeconomic and biophysical impacts. Here, we performed a systematic review to identify how urban land change modeling studies account for transportation infrastructure. We found that less than one-fifth of urban land modeling studies explicitly incorporated a transportation component. Of these, most incorporated transportation based on relatively simple distance-based variables. Despite the recognized influence of transportation on urban growth, only a few studies attempted to capture the dynamic interaction between the two. We present a conceptual modeling framework and argue for a renewed focus on capturing the dynamic interaction between urban land change and transportation development in modeling studies. Such focus is essential to develop a well-informed understanding of the implications for urban forms and landscapes of the wide-ranging changes in transportation systems that accompany rapid urbanization around the world.
DOI: 10.6084/m9.figshare.19834324
title: Database (single file)

description: The database presented here provides recordings of everyday walk scenarios in a natural urban environment, including synchronized IMU-, FSR-, and gaze data. Twenty healthy participants (five females, fifteen males, between 18 and 69 years old, 178.5 $\pm$ 7.64 cm, 72.9 $\pm$ 8.7 kg) wore a full-body Lycra suit with 17 IMU sensors, insoles with eight pressure sensing cells per foot, and a mobile eye tracker. They completed three different walk courses, where each trial consisted of several minutes of walking, including a variety of common elements such as ramps, stairs, and pavements. The data is annotated in detail to enable machine-learning-based analysis and prediction.
DOI: 10.6084/m9.figshare.20224005.v1
title: Database (splitted 8/9)

description: The database presented here provides recordings of everyday walk scenarios in a natural urban environment, including synchronized IMU-, FSR-, and gaze data. Twenty healthy participants (five females, fifteen males, between 18 and 69 years old, 178.5 $\pm$ 7.64 cm, 72.9 $\pm$ 8.7 kg) wore a full-body Lycra suit with 17 IMU sensors, insoles with eight pressure sensing cells per foot, and a mobile eye tracker. They completed three different walk courses, where each trial consisted of several minutes of walking, including a variety of common elements such as ramps, stairs, and pavements. The data is annotated in detail to enable machine-learning-based analysis and prediction.
DOI: 10.6084/m9.figshare.20224014
title: Database (splitted 2/9)

description: The database presented here provides recordings of everyday walk scenarios in a natural urban environment, including synchronized IMU-, FSR-, and gaze data. Twenty healthy participants (five females, fifteen males, between 18 and 69 years old, 178.5 $\pm$ 7.64 cm, 72.9 $\pm$ 8.7 kg) wore a full-body Lycra suit with 17 IMU sensors, insoles with eight pressure sensing cells per foot, and a mobile eye tracker. They completed three different walk courses, where each trial consisted of several minutes of walking, including a variety of common elements such as ramps, stairs, and pavements. The data is annotated in detail to enable machine-learning-based analysis and prediction.
DOI: 10.6084/m9.figshare.19834324.v1
title: Database (single file)

description: The database presented here provides recordings of everyday walk scenarios in a natural urban environment, including synchronized IMU-, FSR-, and gaze data. Twenty healthy participants (five females, fifteen males, between 18 and 69 years old, 178.5 $\pm$ 7.64 cm, 72.9 $\pm$ 8.7 kg) wore a full-body Lycra suit with 17 IMU sensors, insoles with eight pressure sensing cells per foot, and a mobile eye tracker. They completed three different walk courses, where each trial consisted of several minutes of walking, including a variety of common elements such as ramps, stairs, and pavements. The data is annotated in detail to enable machine-learning-based analysis and prediction.
DOI: 10.6084/m9.figshare.20224005
title: Database (splitted 8/9)

description: The database presented here provides recordings of everyday walk scenarios in a natural urban environment, including synchronized IMU-, FSR-, and gaze data. Twenty healthy participants (five females, fifteen males, between 18 and 69 years old, 178.5 $\pm$ 7.64 cm, 72.9 $\pm$ 8.7 kg) wore a full-body Lycra suit with 17 IMU sensors, insoles with eight pressure sensing cells per foot, and a mobile eye tracker. They completed three different walk courses, where each trial consisted of several minutes of walking, including a variety of common elements such as ramps, stairs, and pavements. The data is annotated in detail to enable machine-learning-based analysis and prediction.
DOI: 10.6084/m9.figshare.20224008
title: Database (splitted 5/9)

description: The database presented here provides recordings of everyday walk scenarios in a natural urban environment, including synchronized IMU-, FSR-, and gaze data. Twenty healthy participants (five females, fifteen males, between 18 and 69 years old, 178.5 $\pm$ 7.64 cm, 72.9 $\pm$ 8.7 kg) wore a full-body Lycra suit with 17 IMU sensors, insoles with eight pressure sensing cells per foot, and a mobile eye tracker. They completed three different walk courses, where each trial consisted of several minutes of walking, including a variety of common elements such as ramps, stairs, and pavements. The data is annotated in detail to enable machine-learning-based analysis and prediction.
DOI: 10.6084/m9.figshare.20224008.v1
title: Database (splitted 5/9)

description: The database presented here provides recordings of everyday walk scenarios in a natural urban environment, including synchronized IMU-, FSR-, and gaze data. Twenty healthy participants (five females, fifteen males, between 18 and 69 years old, 178.5 $\pm$ 7.64 cm, 72.9 $\pm$ 8.7 kg) wore a full-body Lycra suit with 17 IMU sensors, insoles with eight pressure sensing cells per foot, and a mobile eye tracker. They completed three different walk courses, where each trial consisted of several minutes of walking, including a variety of common elements such as ramps, stairs, and pavements. The data is annotated in detail to enable machine-learning-based analysis and prediction.
DOI: 10.6084/m9.figshare.20224011.v1
title: Database (splitted 4/9)

description: The database presented here provides recordings of everyday walk scenarios in a natural urban environment, including synchronized IMU-, FSR-, and gaze data. Twenty healthy participants (five females, fifteen males, between 18 and 69 years old, 178.5 $\pm$ 7.64 cm, 72.9 $\pm$ 8.7 kg) wore a full-body Lycra suit with 17 IMU sensors, insoles with eight pressure sensing cells per foot, and a mobile eye tracker. They completed three different walk courses, where each trial consisted of several minutes of walking, including a variety of common elements such as ramps, stairs, and pavements. The data is annotated in detail to enable machine-learning-based analysis and prediction.
DOI: 10.6084/m9.figshare.20224011
title: Database (splitted 4/9)

description: The database presented here provides recordings of everyday walk scenarios in a natural urban environment, including synchronized IMU-, FSR-, and gaze data. Twenty healthy participants (five females, fifteen males, between 18 and 69 years old, 178.5 $\pm$ 7.64 cm, 72.9 $\pm$ 8.7 kg) wore a full-body Lycra suit with 17 IMU sensors, insoles with eight pressure sensing cells per foot, and a mobile eye tracker. They completed three different walk courses, where each trial consisted of several minutes of walking, including a variety of common elements such as ramps, stairs, and pavements. The data is annotated in detail to enable machine-learning-based analysis and prediction.
DOI: 10.6084/m9.figshare.20224014.v1
title: Database (splitted 2/9)

description: The database presented here provides recordings of everyday walk scenarios in a natural urban environment, including synchronized IMU-, FSR-, and gaze data. Twenty healthy participants (five females, fifteen males, between 18 and 69 years old, 178.5 $\pm$ 7.64 cm, 72.9 $\pm$ 8.7 kg) wore a full-body Lycra suit with 17 IMU sensors, insoles with eight pressure sensing cells per foot, and a mobile eye tracker. They completed three different walk courses, where each trial consisted of several minutes of walking, including a variety of common elements such as ramps, stairs, and pavements. The data is annotated in detail to enable machine-learning-based analysis and prediction.
DOI: 10.6084/m9.figshare.20224017.v1
title: Database (splitted 3/9)

description: The database presented here provides recordings of everyday walk scenarios in a natural urban environment, including synchronized IMU-, FSR-, and gaze data. Twenty healthy participants (five females, fifteen males, between 18 and 69 years old, 178.5 $\pm$ 7.64 cm, 72.9 $\pm$ 8.7 kg) wore a full-body Lycra suit with 17 IMU sensors, insoles with eight pressure sensing cells per foot, and a mobile eye tracker. They completed three different walk courses, where each trial consisted of several minutes of walking, including a variety of common elements such as ramps, stairs, and pavements. The data is annotated in detail to enable machine-learning-based analysis and prediction.
DOI: 10.6084/m9.figshare.20224017
title: Database (splitted 3/9)

description: The database presented here provides recordings of everyday walk scenarios in a natural urban environment, including synchronized IMU-, FSR-, and gaze data. Twenty healthy participants (five females, fifteen males, between 18 and 69 years old, 178.5 $\pm$ 7.64 cm, 72.9 $\pm$ 8.7 kg) wore a full-body Lycra suit with 17 IMU sensors, insoles with eight pressure sensing cells per foot, and a mobile eye tracker. They completed three different walk courses, where each trial consisted of several minutes of walking, including a variety of common elements such as ramps, stairs, and pavements. The data is annotated in detail to enable machine-learning-based analysis and prediction.
DOI: 10.6084/m9.figshare.20224023
title: Database (splitted 7/9)

description: The database presented here provides recordings of everyday walk scenarios in a natural urban environment, including synchronized IMU-, FSR-, and gaze data. Twenty healthy participants (five females, fifteen males, between 18 and 69 years old, 178.5 $\pm$ 7.64 cm, 72.9 $\pm$ 8.7 kg) wore a full-body Lycra suit with 17 IMU sensors, insoles with eight pressure sensing cells per foot, and a mobile eye tracker. They completed three different walk courses, where each trial consisted of several minutes of walking, including a variety of common elements such as ramps, stairs, and pavements. The data is annotated in detail to enable machine-learning-based analysis and prediction.
DOI: 10.6084/m9.figshare.20224023.v1
title: Database (splitted 7/9)

description: The database presented here provides recordings of everyday walk scenarios in a natural urban environment, including synchronized IMU-, FSR-, and gaze data. Twenty healthy participants (five females, fifteen males, between 18 and 69 years old, 178.5 $\pm$ 7.64 cm, 72.9 $\pm$ 8.7 kg) wore a full-body Lycra suit with 17 IMU sensors, insoles with eight pressure sensing cells per foot, and a mobile eye tracker. They completed three different walk courses, where each trial consisted of several minutes of walking, including a variety of common elements such as ramps, stairs, and pavements. The data is annotated in detail to enable machine-learning-based analysis and prediction.
DOI: 10.6084/m9.figshare.20224026
title: Database (splitted 1/9)

description: The database presented here provides recordings of everyday walk scenarios in a natural urban environment, including synchronized IMU-, FSR-, and gaze data. Twenty healthy participants (five females, fifteen males, between 18 and 69 years old, 178.5 $\pm$ 7.64 cm, 72.9 $\pm$ 8.7 kg) wore a full-body Lycra suit with 17 IMU sensors, insoles with eight pressure sensing cells per foot, and a mobile eye tracker. They completed three different walk courses, where each trial consisted of several minutes of walking, including a variety of common elements such as ramps, stairs, and pavements. The data is annotated in detail to enable machine-learning-based analysis and prediction.
DOI: 10.6084/m9.figshare.20224026.v1
title: Database (splitted 1/9)

description: The database presented here provides recordings of everyday walk scenarios in a natural urban environment, including synchronized IMU-, FSR-, and gaze data. Twenty healthy participants (five females, fifteen males, between 18 and 69 years old, 178.5 $\pm$ 7.64 cm, 72.9 $\pm$ 8.7 kg) wore a full-body Lycra suit with 17 IMU sensors, insoles with eight pressure sensing cells per foot, and a mobile eye tracker. They completed three different walk courses, where each trial consisted of several minutes of walking, including a variety of common elements such as ramps, stairs, and pavements. The data is annotated in detail to enable machine-learning-based analysis and prediction.
DOI: 10.6084/m9.figshare.20224029
title: Database (splitted 6/9)

description: The database presented here provides recordings of everyday walk scenarios in a natural urban environment, including synchronized IMU-, FSR-, and gaze data. Twenty healthy participants (five females, fifteen males, between 18 and 69 years old, 178.5 $\pm$ 7.64 cm, 72.9 $\pm$ 8.7 kg) wore a full-body Lycra suit with 17 IMU sensors, insoles with eight pressure sensing cells per foot, and a mobile eye tracker. They completed three different walk courses, where each trial consisted of several minutes of walking, including a variety of common elements such as ramps, stairs, and pavements. The data is annotated in detail to enable machine-learning-based analysis and prediction.
DOI: 10.6084/m9.figshare.20226783.v1
title: Database (splitted 9/9)

description: The database presented here provides recordings of everyday walk scenarios in a natural urban environment, including synchronized IMU-, FSR-, and gaze data. Twenty healthy participants (five females, fifteen males, between 18 and 69 years old, 178.5 $\pm$ 7.64 cm, 72.9 $\pm$ 8.7 kg) wore a full-body Lycra suit with 17 IMU sensors, insoles with eight pressure sensing cells per foot, and a mobile eye tracker. They completed three different walk courses, where each trial consisted of several minutes of walking, including a variety of common elements such as ramps, stairs, and pavements. The data is annotated in detail to enable machine-learning-based analysis and prediction.
DOI: 10.6084/m9.figshare.20226783
title: Database (splitted 9/9)

description: The database presented here provides recordings of everyday walk scenarios in a natural urban environment, including synchronized IMU-, FSR-, and gaze data. Twenty healthy participants (five females, fifteen males, between 18 and 69 years old, 178.5 $\pm$ 7.64 cm, 72.9 $\pm$ 8.7 kg) wore a full-body Lycra suit with 17 IMU sensors, insoles with eight pressure sensing cells per foot, and a mobile eye tracker. They completed three different walk courses, where each trial consisted of several minutes of walking, including a variety of common elements such as ramps, stairs, and pavements. The data is annotated in detail to enable machine-learning-based analysis and prediction.
DOI: 10.5517/ccdc.csd.cc1zvyzr
title: CCDC 1843166: Experimental Crystal Structure Determination

description: Related Article: Chen Jianfeng, Ma Li, Huang Xiaoting, Chen Hongda|2018|U.S.Patents|||
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.cc1zvz1v
title: CCDC 1843168: Experimental Crystal Structure Determination

description: Related Article: Chen Jianfeng, Ma Li, Huang Xiaoting, Chen Hongda|2018|U.S.Patents|||
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: 10.5519/qd.fkicfhnq
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (152954 records)
DOI: 10.6084/m9.figshare.20391552
title: Dataset from A Diversified Portfolio of Tokenised Revenue Streams Can Provide Hedging Opportunities for Renewable Electricity Generators

description: Raw data from <em>A Diversified Portfolio of Tokenised Revenue Streams Can Provide Hedging Opportunities for Renewable Electricity Generators</em>
DOI: 10.6084/m9.figshare.20391552.v1
title: Dataset from A Diversified Portfolio of Tokenised Revenue Streams Can Provide Hedging Opportunities for Renewable Electricity Generators

description: Raw data from <em>A Diversified Portfolio of Tokenised Revenue Streams Can Provide Hedging Opportunities for Renewable Electricity Generators</em>
DOI: 10.6084/m9.figshare.19354334
title: Near-Surface Seismic Measurements in Gravel Pit, Over Highway Tunnel and Underground Tubes

description: This data repository contains measurement data in the SEG-2 data format from seismic refraction campaigns conducted in Bavaria, Germany. The measurements have been recorded at three different sites: a gravel pit with near-surface ground water layer, a surface above a highway tunnel and a surface above underground tubes. The data can be used for seismic imaging methods such as travel time tomography or full waveform inversion.<br>
DOI: 10.21227/m0de-0h56
title: Machine Learning Feature Dimensionality Reduction Dataset

description: Contains 12 datasets for feature selection dimensionality reduction and machine learning. The dimension of the selected datasets ranges from 10 to 1000, and the number of instances ranges from 200 to 3000.
DOI: 10.5519/qd.t0swqax7
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (5180033 records)