cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2fg5gk
title: CCDC 2247948: Experimental Crystal Structure Determination

description: Related Article: Stephan Reichl, Felix Riedlberger, Martin Piesch, Gábor Balázs, Michael Seidl, Manfred Scheer|2023|Chemical Science|14|7285|doi:10.1039/D3SC01488F
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.8104175
title: Systematic comparison of Mendelian randomization studies and randomized controlled trials using electronic databases

description: <strong>Supplementary Dataset 1. </strong>Select ClinicalTrials.Gov data fields for the RCT studies identified as the<br> <em>main </em>dataset.<br> <strong>Supplementary Dataset 2.</strong> Select ClinicalTrials.Gov data fields for the RCT studies identified as the<br> <em>literature </em>dataset.<br> <strong>Supplementary Dataset 3.</strong> Comparison of frequency of RCT general features between the <em>Main</em><br> dataset and background of all RCTs available in ClinicalTrials.Gov. <strong>Supplementary Dataset 4. </strong>PubMed and SemMedDB data for published MR and RCT studies.<br> <strong>Supplementary Dataset 5. </strong>Case series of MR and RCT studies with matching exposures<br> (interventions) and outcomes (conditions).
DOI: 10.5517/ccdc.csd.cc2fghjy
title: CCDC 2248260: Experimental Crystal Structure Determination

description: Related Article: Xiao-Ju Si, Xiaofang Zhao, Jianli Wang, Xinhai Wang, Yuanshuo Zhang, Dandan Yang, Mao-Ping Song, Jun-Long Niu|2023|Chemical Science|14|7291|doi:10.1039/D3SC01787G
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.cc2fghr5
title: CCDC 2248267: Experimental Crystal Structure Determination

description: Related Article: Ayan Dhara, Anton Dmitrienko, Rahaf N. Hussein, Ariel Sotomayor, Benjamin H. Wilson, Stephen J. Loeb|2023|Chemical Science|14|7215|doi:10.1039/D3SC01346D
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.cc2fghs6
title: CCDC 2248268: Experimental Crystal Structure Determination

description: Related Article: Ayan Dhara, Anton Dmitrienko, Rahaf N. Hussein, Ariel Sotomayor, Benjamin H. Wilson, Stephen J. Loeb|2023|Chemical Science|14|7215|doi:10.1039/D3SC01346D
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.cc2fj4ch
title: CCDC 2249836: Experimental Crystal Structure Determination

description: Related Article: Kai-Hsin Chang, Yu-Chiang Peng, Kuan-Hsuan Su, Yi Xian Lin, Jiun-Chi Liu, Ying-Hsuan Liu, Chao-Hsien Hsu, Hsiao-Ching Yang, and Pi-Tai Choua|2023|CSD Communication|||
description: Related Article: Kai-Hsin Chang, Yu-Chiang Peng, Kuan-Hsuan Su, Yi-Hsien Lin, Jiun-Chi Liu, Ying-Hsuan Liu, Chao-Hsien Hsu, Hsiao-Ching Yang, Pi-Tai Chou|2023|Chemical Science|14|7237|doi:10.1039/D3SC01441J
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.cc2fll5t
title: CCDC 2252186: Experimental Crystal Structure Determination

description: Related Article: Dezhi Yang, Meng Zhu, Taimin Wang, Yixuan He, Lang Xie, Jiayong Zhang, Bin Cheng|2023|Org.Biomol.Chem.|21|5413|doi:10.1039/D3OB00511A
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.cc2fll6v
title: CCDC 2252187: Experimental Crystal Structure Determination

description: Related Article: Dezhi Yang, Meng Zhu, Taimin Wang, Yixuan He, Lang Xie, Jiayong Zhang, Bin Cheng|2023|Org.Biomol.Chem.|21|5413|doi:10.1039/D3OB00511A
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.cc2fm9q3
title: CCDC 2252885: Experimental Crystal Structure Determination

description: Related Article: Manyam Subbi Reddy, Jagadeesh Babu Nanubolu, Surisetti Suresh|2023|Org.Biomol.Chem.|21|5387|doi:10.1039/D3OB00861D
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.cc2fnqvp
title: CCDC 2254253: Experimental Crystal Structure Determination

description: Related Article: Koji Yamamoto, Shun Matsui, Shin-ichiro Kato, Yosuke Nakamura|2023|Org.Biomol.Chem.|21|5398|doi:10.1039/D3OB00795B
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.cc2fnqwq
title: CCDC 2254254: Experimental Crystal Structure Determination

description: Related Article: Koji Yamamoto, Shun Matsui, Shin-ichiro Kato, Yosuke Nakamura|2023|Org.Biomol.Chem.|21|5398|doi:10.1039/D3OB00795B
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.cc2fnqxr
title: CCDC 2254255: Experimental Crystal Structure Determination

description: Related Article: Koji Yamamoto, Shun Matsui, Shin-ichiro Kato, Yosuke Nakamura|2023|Org.Biomol.Chem.|21|5398|doi:10.1039/D3OB00795B
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.cc2fnr40
title: CCDC 2254262: Experimental Crystal Structure Determination

description: Related Article: Koji Yamamoto, Shun Matsui, Shin-ichiro Kato, Yosuke Nakamura|2023|Org.Biomol.Chem.|21|5398|doi:10.1039/D3OB00795B
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.cc2fnrnj
title: CCDC 2254278: Experimental Crystal Structure Determination

description: Related Article: Koji Yamamoto, Shun Matsui, Shin-ichiro Kato, Yosuke Nakamura|2023|Org.Biomol.Chem.|21|5398|doi:10.1039/D3OB00795B
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.cc2fsr77
title: CCDC 2258109: Experimental Crystal Structure Determination

description: Related Article: Rana Chatterjee, Swadhapriya Bhukta, Kishore Kumar Angajala, Rambabu Dandela|2023|Org.Biomol.Chem.|21|5419|doi:10.1039/D3OB00625E
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.7636788
title: Validation of E-region Model Electron Density Profiles using High-Resolution Cross Sections

description: Zenodo Dataset: Arecibo ISR observation on February 9, 2012 COSMIC-1 GPS radio occultation on February 9, 2012 Millstone Hills ISR observation on February 16, 2012 IRI-2016 output AURIC model outputs Geomagnetic and Solar indices on February 2012 Associated python scripts to read and plot data
DOI: 10.5281/zenodo.7636789
title: Validation of E-region Model Electron Density Profiles using High-Resolution Cross Sections

description: Zenodo Dataset: Arecibo ISR observation on February 9, 2012 COSMIC-1 GPS radio occultation on February 9, 2012 Millstone Hills ISR observation on February 16, 2012 IRI-2016 output AURIC model outputs Geomagnetic and Solar indices on February 2012 Associated python scripts to read and plot data
DOI: 10.17903/fk2/tvh7sg
title: Μουσείο Πόλης του Βόλου

description: Στη συλλογή αυτή περιλαμβάνονται συνεντεύξεις που πραγματοποιήθηκαν στο πλαίσιο του ερευνητικού προγράμματος «Σχεδιάζοντας το Μουσείο της Πόλης του Βόλου». Αφορούν διάφορες πτυχές της ιστορίας του Βόλου που θα ενσωματωθούν στις μελλοντικές εκθέσεις του Μουσείου. Οι κύριες θεματικές που καλύφθηκαν στις συνεντεύξεις αυτές αφορούν τη βιομηχανική εργασία, την πολυπολιτισμικότητα, τις θρησκευτικές ομάδες, τη δεκαετία του 1940, τη μετανάστευση και την καθημερινή ζωή. Τα δεδομένα των συνεντεύξεων (περιλήψεις, αποσπάσματα, ηχητικά αρχεία) έχουν ενσωματωθεί σε βάση δεδομένων αναζητήσιμη με λέξεις κλειδιά.
DOI: 10.6084/m9.figshare.23629962
title: Roetgen et al. PAC Data to Share.xlsx

description: Data are provided from Roettgen et al. (in preparation). The purpose of this study was to explore word characteristics that may predict vocabulary learning following explicit vocabulary instruction. Data from two recent efficacy studies of an explicit vocabulary instruction program delivered to preschool children (<em>Story Friends; </em>Kelley et al., 2020; Madsen et al., 2022) were included in this secondary analysis. Vocabulary words were categorized by word type and coded using the Perceptual Accessibility Continuum (Hadley et al., 2021). Multi-level modeling revealed that both perceptual accessibility and word type predicted vocabulary learning. Of the perceptual accessibility components, shape and concreteness contributed significantly to the model, but imageability did not. Results indicate that word characteristics (word type, perceptual accessibility) predict vocabulary knowledge of preschool children following explicit instruction. Results have potential implications for identifying and ordering words to teach based on relative contributions of various word characteristics.
DOI: 10.5281/zenodo.8104370
title: SpatialSense

description: Datase for the paper: [SpatialSense: An Adversarially Crowdsourced Benchmark for Spatial Relation Recognition](https://arxiv.org/abs/1908.02660) <br> [Kaiyu Yang](https://www.cs.princeton.edu/~kaiyuy/), [Olga Russakovsky](https://www.cs.princeton.edu/~olgarus/), and [Jia Deng](https://www.cs.princeton.edu/~jiadeng/) <br> International Conference on Computer Vision (ICCV) 2019 ```bibtex<br> @inproceedings{yang2019spatialsense,<br> title={SpatialSense: An Adversarially Crowdsourced Benchmark for Spatial Relation Recognition},<br> author={Yang, Kaiyu and Russakovsky, Olga and Deng, Jia},<br> booktitle={International Conference on Computer Vision (ICCV)},<br> year={2019}<br> }<br> ``` Please visit https://github.com/princeton-vl/SpatialSense for details.
DOI: 10.5281/zenodo.8104369
title: SpatialSense

description: Datase for the paper: [SpatialSense: An Adversarially Crowdsourced Benchmark for Spatial Relation Recognition](https://arxiv.org/abs/1908.02660) <br> [Kaiyu Yang](https://www.cs.princeton.edu/~kaiyuy/), [Olga Russakovsky](https://www.cs.princeton.edu/~olgarus/), and [Jia Deng](https://www.cs.princeton.edu/~jiadeng/) <br> International Conference on Computer Vision (ICCV) 2019 ```bibtex<br> @inproceedings{yang2019spatialsense,<br> title={SpatialSense: An Adversarially Crowdsourced Benchmark for Spatial Relation Recognition},<br> author={Yang, Kaiyu and Russakovsky, Olga and Deng, Jia},<br> booktitle={International Conference on Computer Vision (ICCV)},<br> year={2019}<br> }<br> ``` Please visit https://github.com/princeton-vl/SpatialSense for details.
DOI: 10.5281/zenodo.8117522
title: Modelling potential growth of forest restoration options in Indonesia

description: Potential growth of secondary forest and tree species typology were assessed using biophysical productivity model developed by IIASA's Agriculture, Forestry, and Ecosystems Services Group. The methodology involved the integration of random forest algorithm, ground data, remote sensing products, soil properties, and literature on yield tables (more detailed methodology publication in preparation). To calibrate the model, the MODIS NPP dataset was adjusted using forest biomass and land cover maps. The model utilized ERA5-Land monthly averaged meteorological data from 2006 to 2015, with a resolution of 0.1° x 0.1°, in addition to soil properties, land cover, and elevation. This comprehensive approach allowed for the determination of spatially explicit site index values for plantations, secondary forests, and primary forests. The resulting productivity information are reflected in growth curves for both fast and slow-growing commercial species as well as native tree species. The parametrization of Chapman-Richards growth curves was conducted using data from representative tree species available in the literature. In the case of peatland areas, potential growth are also modelled using water regime scenarios provided by IIASA's EPIC model, offering insights into different implications of the potentially varying water table conditions in peatland.
description: Funding acknowledgment: This work was supported by the RESTORE+ project (www.restoreplus.org), which is part of the International Climate Initiative (IKI), supported by the Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection (BMUV) based on a decision adopted by the German Bundestag.
DOI: 10.5281/zenodo.8117521
title: Modelling potential growth of forest restoration options in Indonesia

description: Potential growth of secondary forest and tree species typology were assessed using biophysical productivity model developed by IIASA's Agriculture, Forestry, and Ecosystems Services Group. The methodology involved the integration of random forest algorithm, ground data, remote sensing products, soil properties, and literature on yield tables (more detailed methodology publication in preparation). To calibrate the model, the MODIS NPP dataset was adjusted using forest biomass and land cover maps. The model utilized ERA5-Land monthly averaged meteorological data from 2006 to 2015, with a resolution of 0.1° x 0.1°, in addition to soil properties, land cover, and elevation. This comprehensive approach allowed for the determination of spatially explicit site index values for plantations, secondary forests, and primary forests. The resulting productivity information are reflected in growth curves for both fast and slow-growing commercial species as well as native tree species. The parametrization of Chapman-Richards growth curves was conducted using data from representative tree species available in the literature. In the case of peatland areas, potential growth are also modelled using water regime scenarios provided by IIASA's EPIC model, offering insights into different implications of the potentially varying water table conditions in peatland.
description: Funding acknowledgment: This work was supported by the RESTORE+ project (www.restoreplus.org), which is part of the International Climate Initiative (IKI), supported by the Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection (BMUV) based on a decision adopted by the German Bundestag.
DOI: 10.6084/m9.figshare.23629881
title: Processed X-ray Avoidance Data

description: Trials.mat is matlab data structure containing an entry for each experimental trial. Each trial has a numeric strain code and dose code indicating the strain of worm and dose rate of X-ray stimulation, respectively. Keys.mat is contains cell arrays indicating the strains/dose rates associated with numeric codes in Trials data structure. The WLactivity variable within the Trials data structure contains the Worm Lab activity metric calculated at each time point during the trial. The time variable withing the Trials data structure contains information regarding the timing of the X-ray stimulation for the trial.
DOI: 10.6084/m9.figshare.23629881.v1
title: Processed X-ray Avoidance Data

description: Trials.mat is matlab data structure containing an entry for each experimental trial. Each trial has a numeric strain code and dose code indicating the strain of worm and dose rate of X-ray stimulation, respectively. Keys.mat is contains cell arrays indicating the strains/dose rates associated with numeric codes in Trials data structure. The WLactivity variable within the Trials data structure contains the Worm Lab activity metric calculated at each time point during the trial. The time variable withing the Trials data structure contains information regarding the timing of the X-ray stimulation for the trial.
DOI: 10.15132/10000224
title: Follow-COVID

description: Follow-COVID is a study of over 100 patients across multiple regions within Scotland over the course of 6 years. The study seeks to identify the long term consequences and future care needs of COVID-19 survivors.
description: As COVID-19 is a new disease, there is a need to identify the long term consequences and future care needs of COVID19 survivors. Follow-COVID has recruited over 100 patients and has used blood testing, urine, respiratory samples, and tests of blood vessel function to measure the long term consequences of this disease.

The study consists of a preliminary patient visit, followed by a second in-person visit several months later. There are then phone call consultations 1 year, 2 years and 5 years after the first visit, giving a longitudinal view of the progression of symptoms and long covid.
DOI: 10.17903/fk2/sfpcv2
title: Ζιάκας, Μικρή Μόσχα Γρεβενών

description: Εθνογραφία της κοινότητας Ζιάκα Γρεβενών για τη δεκαετία του 1940. Διερευνάται το νόημα που έδωσαν οι κάτοικοι του χωριού στο μήνυμα του ΕΑΜ στην εμφύλια διαμάχη που ακολούθησε την απελευθέρωση. Κλειδί για την ερμηνεία αποτέλεσαν τα γεγονότα του Μεσοπολέμου και η πολιτικοποίηση των κατοίκων μέσα από τις γραμμές του Αγροτικού Κόμματος και του ΚΚΕ. Μετά το τέλος του Εμφυλίου σχεδόν όλοι οι κάτοικοι εγκαταστάθηκαν ως πολιτικοί πρόσφυγες στην Ανατολική Ευρώπη. Κατά τη διάρκεια της επιτόπιας έρευνας οι περισσότεροι είχαν επιστρέψει στην Ελλάδα.
DOI: 10.5281/zenodo.8111528
title: Paper under Revision

description: Data used for the publication. Abstract will be added after paper is formally accepted.
DOI: 10.5281/zenodo.8111527
title: Paper under Revision

description: Data used for the publication. Abstract will be added after paper is formally accepted.
DOI: 10.5281/zenodo.8114989
title: FD Simulator test parameters and results from NYC case study

description: This repository gives the dataset for Fleet-Demand platform (FD platform). The full FD platform code description can be found here. The files are under two categories: 1. MATSim input file: "config-with-mode-vehicles-drt-tele_2_income.xml": configuration file defining input file names and parameters for MATSim in the initial cycle of FD platform cycle (cycle 0). "config-with-mode-vehicles-drt-tele_2_income_updated.xml": configuration file defining input file names and parameters for MATSim in FD platform cycles after the initial cycle. "population_large_two_drt_added.xml": initial trip plan containing 35000 agents with initially assigned ride-pooling services ("drt_1" and "drt_2"). The trip plan was extracted from the MATSim-NYC population file, which can be found here. "new_subpopulation_income_onefifth.xml": subpopulation file assigning agent to either "Low_Income" or "Not_Low_Income". The value of time (VOT) of the two groups are defined in the configuration file "separated-network_shrunk_clean.xml": Network file containing road and transit network of Manhatta, Brooklyn, and Queens. This network file is extracted from the MATSim-NYC network file. 2. DVRPT input file "nyc_sub_nodes_update.csv": File defining the nodes that constructs the ride-pooling service area. "nyc_sub_network_update_new_1_27_max.csv": File defining the shortest path time cost for all node pairs. "nyc_sub_stops.csv": File defining the stops of the ride-pooling service. "nyc_sub_links.csv": File defining the links of the ride-pooling service area.