cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc23zj8x
title: CCDC 1965749: Experimental Crystal Structure Determination

description: Related Article: Phil Liebing, Nicole Harmgarth, Yi Yang, Robert McDonald, Felix Engelhardt, Marcel Kühling, Frank T. Edelmann, Josef Takats|2021|Inorg.Chem.|60|1877|doi:10.1021/acs.inorgchem.0c03294
DOI: 10.5517/ccdc.csd.cc23zj9y
title: CCDC 1965750: Experimental Crystal Structure Determination

description: Related Article: Phil Liebing, Nicole Harmgarth, Yi Yang, Robert McDonald, Felix Engelhardt, Marcel Kühling, Frank T. Edelmann, Josef Takats|2021|Inorg.Chem.|60|1877|doi:10.1021/acs.inorgchem.0c03294
DOI: 10.5517/ccdc.csd.cc23zjbz
title: CCDC 1965751: Experimental Crystal Structure Determination

description: Related Article: Phil Liebing, Nicole Harmgarth, Yi Yang, Robert McDonald, Felix Engelhardt, Marcel Kühling, Frank T. Edelmann, Josef Takats|2021|Inorg.Chem.|60|1877|doi:10.1021/acs.inorgchem.0c03294
DOI: 10.5517/ccdc.csd.cc23zjc0
title: CCDC 1965752: Experimental Crystal Structure Determination

description: Related Article: Phil Liebing, Nicole Harmgarth, Yi Yang, Robert McDonald, Felix Engelhardt, Marcel Kühling, Frank T. Edelmann, Josef Takats|2021|Inorg.Chem.|60|1877|doi:10.1021/acs.inorgchem.0c03294
DOI: 10.5517/ccdc.csd.cc2597f5
title: CCDC 2004876: Experimental Crystal Structure Determination

description: Related Article:  Irina G. Fomina, Fedor M. Dolgushin, Pavel S. Koroteev, Yulia V. Mantrova, Vladislav M. Korshunov, Ilya V. Taydakov, Artem S. Bogomyakov, Matvey V. Fedin, Yury S. Zavorotny, Igor L. Eremenko|2021|Eur.J.Inorg.Chem.|2021|464|doi:10.1002/ejic.202000915
DOI: 10.5517/ccdc.csd.cc25f8vr
title: CCDC 2008764: Experimental Crystal Structure Determination

description: Related Article: Alexander M. Fedoseev, Mikhail S. Grigoriev, Iraida A. Charushnikova, Nina A. Budantseva, Nadezhda M. Stanetskaya, Vladimir S. Tyurin|2021|Inorg.Chem.|60|1857|doi:10.1021/acs.inorgchem.0c03315
DOI: 10.5517/ccdc.csd.cc25f8xt
title: CCDC 2008766: Experimental Crystal Structure Determination

description: Related Article: Alexander M. Fedoseev, Mikhail S. Grigoriev, Iraida A. Charushnikova, Nina A. Budantseva, Nadezhda M. Stanetskaya, Vladimir S. Tyurin|2021|Inorg.Chem.|60|1857|doi:10.1021/acs.inorgchem.0c03315
DOI: 10.5517/ccdc.csd.cc25f8yv
title: CCDC 2008767: Experimental Crystal Structure Determination

description: Related Article: Alexander M. Fedoseev, Mikhail S. Grigoriev, Iraida A. Charushnikova, Nina A. Budantseva, Nadezhda M. Stanetskaya, Vladimir S. Tyurin|2021|Inorg.Chem.|60|1857|doi:10.1021/acs.inorgchem.0c03315
DOI: 10.5517/ccdc.csd.cc25sd2g
title: CCDC 2019435: Experimental Crystal Structure Determination

description: Related Article:  Irina G. Fomina, Fedor M. Dolgushin, Pavel S. Koroteev, Yulia V. Mantrova, Vladislav M. Korshunov, Ilya V. Taydakov, Artem S. Bogomyakov, Matvey V. Fedin, Yury S. Zavorotny, Igor L. Eremenko|2021|Eur.J.Inorg.Chem.|2021|464|doi:10.1002/ejic.202000915
DOI: 10.5517/ccdc.csd.cc26h9z0
title: CCDC 2040512: Experimental Crystal Structure Determination

description: Related Article: Evgeniy A. Gusakov, Iuliia A. Topchu, Aleksandra M. Mazitova, Igor V. Dorogan, Emil R. Bulatov, Ilya G. Serebriiskii, Zinaida I. Abramova, Inna O. Tupaeva, Oleg P. Demidov, Duong Ngoc Toan, Tran Dai Lam, Duong Nghia Bang, Yanis A. Boumber, Yurii A. Sayapin, Vladimir I. Minkin|2021|RSC Advances|11|4555|doi:10.1039/D0RA10610K
DOI: 10.5517/ccdc.csd.cc26kv6v
title: CCDC 2042937: Experimental Crystal Structure Determination

description: Related Article: Alexander M. Fedoseev, Mikhail S. Grigoriev, Iraida A. Charushnikova, Nina A. Budantseva, Nadezhda M. Stanetskaya, Vladimir S. Tyurin|2021|Inorg.Chem.|60|1857|doi:10.1021/acs.inorgchem.0c03315
DOI: 10.5517/ccdc.csd.cc25f8ws
title: CCDC 2008765: Experimental Crystal Structure Determination

description: Related Article: Alexander M. Fedoseev, Mikhail S. Grigoriev, Iraida A. Charushnikova, Nina A. Budantseva, Nadezhda M. Stanetskaya, Vladimir S. Tyurin|2021|Inorg.Chem.|60|1857|doi:10.1021/acs.inorgchem.0c03315
DOI: 10.5517/ccdc.csd.cc26m3sr
title: CCDC 2044164: Experimental Crystal Structure Determination

description: Related Article: Goretti Castro, Gaoji Wang, Tanja Gambino, David Esteban-Gómez, Laura Valencia, Goran Angelovski, Carlos Platas-Iglesias, Paulo Pérez-Lourido|2021|Inorg.Chem.|60|1902|doi:10.1021/acs.inorgchem.0c03385
DOI: 10.5517/ccdc.csd.cc26m3ts
title: CCDC 2044165: Experimental Crystal Structure Determination

description: Related Article: Goretti Castro, Gaoji Wang, Tanja Gambino, David Esteban-Gómez, Laura Valencia, Goran Angelovski, Carlos Platas-Iglesias, Paulo Pérez-Lourido|2021|Inorg.Chem.|60|1902|doi:10.1021/acs.inorgchem.0c03385
DOI: 10.5517/ccdc.csd.cc26m3vt
title: CCDC 2044166: Experimental Crystal Structure Determination

description: Related Article: Goretti Castro, Gaoji Wang, Tanja Gambino, David Esteban-Gómez, Laura Valencia, Goran Angelovski, Carlos Platas-Iglesias, Paulo Pérez-Lourido|2021|Inorg.Chem.|60|1902|doi:10.1021/acs.inorgchem.0c03385
DOI: 10.5517/ccdc.csd.cc26q4fj
title: CCDC 2047067: Experimental Crystal Structure Determination

description: Related Article: Hai-Xin Tian, Miao Zha, Li-Xiao Ma, Wen-Jing Zhou, Bao-Long Li, Bing Wu|2021|Polyhedron|197|115052|doi:10.1016/j.poly.2021.115052
DOI: 10.5517/ccdc.csd.cc26q4gk
title: CCDC 2047068: Experimental Crystal Structure Determination

description: Related Article: Hai-Xin Tian, Miao Zha, Li-Xiao Ma, Wen-Jing Zhou, Bao-Long Li, Bing Wu|2021|Polyhedron|197|115052|doi:10.1016/j.poly.2021.115052
DOI: 10.5517/ccdc.csd.cc24nrh2
title: CCDC 1986154: Experimental Crystal Structure Determination

description: Related Article: Xiangyang Guo, Lifang Liu, Yu Xiao, Rashid Mehmood, Yejun Xiao, Yu Qi, Fuxiang Zhang|2021|Inorg.Chem.|60|1790|doi:10.1021/acs.inorgchem.0c03265
DOI: 10.6084/m9.figshare.12345374
title: Analyses from Watch me, watch you: ritual participation increases in-group displays and out-group monitoring in children

description: Collective rituals serve social functions for the groups that perform them, including identifying group members and signalling group commitment. A novel social group paradigm was used in an afterschool programme (<i>N</i> = 60 4–11-year-olds) to test the influence of participating in a ritual task on in-group displays and out-group monitoring over repeated exposures to the group. The results demonstrate that ritual participation increases in-group displays (i.e. time spent displaying materials to in-group members) and out-group monitoring (i.e. time spent looking at out-group members) compared with a control task across three time points. This study provides evidence for the processes by which rituals may influence children's behaviours towards in- and out-group members and discusses implications for understanding the development of ritual cognition and behaviour.
DOI: 10.6084/m9.figshare.12345371
title: Bind_Data from Watch me, watch you: ritual participation increases in-group displays and out-group monitoring in children

description: Collective rituals serve social functions for the groups that perform them, including identifying group members and signalling group commitment. A novel social group paradigm was used in an afterschool programme (<i>N</i> = 60 4–11-year-olds) to test the influence of participating in a ritual task on in-group displays and out-group monitoring over repeated exposures to the group. The results demonstrate that ritual participation increases in-group displays (i.e. time spent displaying materials to in-group members) and out-group monitoring (i.e. time spent looking at out-group members) compared with a control task across three time points. This study provides evidence for the processes by which rituals may influence children's behaviours towards in- and out-group members and discusses implications for understanding the development of ritual cognition and behaviour.
DOI: 10.6084/m9.figshare.12345371.v2
title: Bind_Data from Watch me, watch you: ritual participation increases in-group displays and out-group monitoring in children

description: Collective rituals serve social functions for the groups that perform them, including identifying group members and signalling group commitment. A novel social group paradigm was used in an afterschool programme (<i>N</i> = 60 4–11-year-olds) to test the influence of participating in a ritual task on in-group displays and out-group monitoring over repeated exposures to the group. The results demonstrate that ritual participation increases in-group displays (i.e. time spent displaying materials to in-group members) and out-group monitoring (i.e. time spent looking at out-group members) compared with a control task across three time points. This study provides evidence for the processes by which rituals may influence children's behaviours towards in- and out-group members and discusses implications for understanding the development of ritual cognition and behaviour.
DOI: 10.6084/m9.figshare.13808657.v1
title: Codebook from Watch me, watch you: ritual participation increases in-group displays and out-group monitoring in children

description: Collective rituals serve social functions for the groups that perform them, including identifying group members and signalling group commitment. A novel social group paradigm was used in an afterschool programme (<i>N</i> = 60 4–11-year-olds) to test the influence of participating in a ritual task on in-group displays and out-group monitoring over repeated exposures to the group. The results demonstrate that ritual participation increases in-group displays (i.e. time spent displaying materials to in-group members) and out-group monitoring (i.e. time spent looking at out-group members) compared with a control task across three time points. This study provides evidence for the processes by which rituals may influence children's behaviours towards in- and out-group members and discusses implications for understanding the development of ritual cognition and behaviour.
DOI: 10.6084/m9.figshare.12345374.v2
title: Analyses from Watch me, watch you: ritual participation increases in-group displays and out-group monitoring in children

description: Collective rituals serve social functions for the groups that perform them, including identifying group members and signalling group commitment. A novel social group paradigm was used in an afterschool programme (<i>N</i> = 60 4–11-year-olds) to test the influence of participating in a ritual task on in-group displays and out-group monitoring over repeated exposures to the group. The results demonstrate that ritual participation increases in-group displays (i.e. time spent displaying materials to in-group members) and out-group monitoring (i.e. time spent looking at out-group members) compared with a control task across three time points. This study provides evidence for the processes by which rituals may influence children's behaviours towards in- and out-group members and discusses implications for understanding the development of ritual cognition and behaviour.
DOI: 10.6084/m9.figshare.13808657
title: Codebook from Watch me, watch you: ritual participation increases in-group displays and out-group monitoring in children

description: Collective rituals serve social functions for the groups that perform them, including identifying group members and signalling group commitment. A novel social group paradigm was used in an afterschool programme (<i>N</i> = 60 4–11-year-olds) to test the influence of participating in a ritual task on in-group displays and out-group monitoring over repeated exposures to the group. The results demonstrate that ritual participation increases in-group displays (i.e. time spent displaying materials to in-group members) and out-group monitoring (i.e. time spent looking at out-group members) compared with a control task across three time points. This study provides evidence for the processes by which rituals may influence children's behaviours towards in- and out-group members and discusses implications for understanding the development of ritual cognition and behaviour.
DOI: 10.6084/m9.figshare.12345371.v1
title: R Code from Watch me, watch you: ritual participation increases in-group displays and out-group monitoring in children

description: R code with explanation of variables in dataset
DOI: 10.6084/m9.figshare.12345374.v1
title: Dataset from Watch me, watch you: ritual participation increases in-group displays and out-group monitoring in children

description: Dataset
DOI: 10.5287/bodleian:jxrykjazd
title: Data relating to "Regularised non-uniform segments and efficient no-slip elastohydrodynamics".
DOI: 10.48326/idoc.psup.marssi.ctx.oxia
title: Oxia
DOI: 10.6084/m9.figshare.13808516
title: Data Set - Temporal Summation

description: Data set - Temporal Summation
DOI: 10.6084/m9.figshare.13808516.v1
title: Data Set - Temporal Summation

description: Data set - Temporal Summation
DOI: 10.5281/zenodo.4525892
title: WOLOF ASR data on urban transport

description: This is document is to present a wolof speech recognition dataset collected and prepared by<br> BAAMTU Datamation (a senegalease company focused on using data to help companies to<br> leverage AI and Big Data ).<br> Motivation<br> In a country(i.e Senegal), where about 50% of the population is illiterate, using existing<br> technologies and applications that are designed to be used by people who can read is very<br> difficult for those people. Our Goal here is to use ASR technique on WOLOF to help the illiterate<br> persons to interact with apps with just with their voice in a language they can already speak (i.e<br> WOLOF).<br> We chose the urban transport use case for two reasons :<br> ● Many urban transport users can’t read nor speak french, so they can’t interact with<br> existing apps that help passengers to find a Bus for a given destination.<br> ● There is already an existing app in SENEGAL (i.e WeeGo) which help passengers to get<br> information about urban transport, so the goal here is to build an ASR model that will be<br> plugged into the App so illiterate people will be able to use it(the resulting model is<br> actually used by the WeeGo App).<br> Wolof is the most used language in SENEGAL.
DOI: 10.5281/zenodo.4525891
title: WOLOF ASR data on urban transport

description: This is document is to present a wolof speech recognition dataset collected and prepared by<br> BAAMTU Datamation (a senegalease company focused on using data to help companies to<br> leverage AI and Big Data ).<br> Motivation<br> In a country(i.e Senegal), where about 50% of the population is illiterate, using existing<br> technologies and applications that are designed to be used by people who can read is very<br> difficult for those people. Our Goal here is to use ASR technique on WOLOF to help the illiterate<br> persons to interact with apps with just with their voice in a language they can already speak (i.e<br> WOLOF).<br> We chose the urban transport use case for two reasons :<br> ● Many urban transport users can’t read nor speak french, so they can’t interact with<br> existing apps that help passengers to find a Bus for a given destination.<br> ● There is already an existing app in SENEGAL (i.e WeeGo) which help passengers to get<br> information about urban transport, so the goal here is to build an ASR model that will be<br> plugged into the App so illiterate people will be able to use it(the resulting model is<br> actually used by the WeeGo App).<br> Wolof is the most used language in SENEGAL.
DOI: 10.5281/zenodo.4525215
title: Data set from Bernardini A, Camporeale A, Pieroni M, Pieruzzi F, Figliozzi S, Lusardi P, Spada M, Mignani R, Burlina A, Carubbi F, Battaglia Y, Graziani F, Pica S, Tondi L, Chow K, Boveri S, Olivotto I, Lombardi M. Atrial Dysfunction Assessed by Cardiac Magnetic Resonance as an Early Marker of Fabry Cardiomyopathy. JACC Cardiovasc Imaging. 2020 Oct;13(10):2262-2264. doi: 10.1016/j.jcmg.2020.05.011. Epub 2020 Jun 17. PMID: 32563647.

description: Data set from the article Bernardini A, Camporeale A, Pieroni M, Pieruzzi F, Figliozzi S, Lusardi P, Spada M, Mignani R, Burlina A, Carubbi F, Battaglia Y, Graziani F, Pica S, Tondi L, Chow K, Boveri S, Olivotto I, Lombardi M. Atrial Dysfunction Assessed by Cardiac Magnetic Resonance as an Early Marker of Fabry Cardiomyopathy. JACC Cardiovasc Imaging. 2020 Oct;13(10):2262-2264. doi: 10.1016/j.jcmg.2020.05.011. Epub 2020 Jun 17. PMID: 32563647. <strong>Brief introduction</strong> Anderson-Fabry disease (AFD) cardiomyopathy is characterized by glycosphingolipid (Gb3) storage in all cellular components, with consequent left ventricular hypertrophy (LVH). Gb3 accumulation also involves atrial myocytes (1), ultimately leading to left atrial (LA) enlargement and reduced atrial compliance. Cardiac magnetic resonance (CMR) plays an important role in the assessment of the severity of Fabry cardiomyopathy. CMR feature tracking (CMR-FT) specifically allows the assessment of myocardial strain from cine images
DOI: 10.5281/zenodo.4525214
title: Data set from Bernardini A, Camporeale A, Pieroni M, Pieruzzi F, Figliozzi S, Lusardi P, Spada M, Mignani R, Burlina A, Carubbi F, Battaglia Y, Graziani F, Pica S, Tondi L, Chow K, Boveri S, Olivotto I, Lombardi M. Atrial Dysfunction Assessed by Cardiac Magnetic Resonance as an Early Marker of Fabry Cardiomyopathy. JACC Cardiovasc Imaging. 2020 Oct;13(10):2262-2264. doi: 10.1016/j.jcmg.2020.05.011. Epub 2020 Jun 17. PMID: 32563647.

description: Data set from the article Bernardini A, Camporeale A, Pieroni M, Pieruzzi F, Figliozzi S, Lusardi P, Spada M, Mignani R, Burlina A, Carubbi F, Battaglia Y, Graziani F, Pica S, Tondi L, Chow K, Boveri S, Olivotto I, Lombardi M. Atrial Dysfunction Assessed by Cardiac Magnetic Resonance as an Early Marker of Fabry Cardiomyopathy. JACC Cardiovasc Imaging. 2020 Oct;13(10):2262-2264. doi: 10.1016/j.jcmg.2020.05.011. Epub 2020 Jun 17. PMID: 32563647. <strong>Brief introduction</strong> Anderson-Fabry disease (AFD) cardiomyopathy is characterized by glycosphingolipid (Gb3) storage in all cellular components, with consequent left ventricular hypertrophy (LVH). Gb3 accumulation also involves atrial myocytes (1), ultimately leading to left atrial (LA) enlargement and reduced atrial compliance. Cardiac magnetic resonance (CMR) plays an important role in the assessment of the severity of Fabry cardiomyopathy. CMR feature tracking (CMR-FT) specifically allows the assessment of myocardial strain from cine images
DOI: 10.5281/zenodo.4519816
title: Learning Chemistry: Exploring the suitability of machine learning for the task of structure-based chemical ontology classification - Data

description: Each of the packed folders contains either data or the results from the experiments published in the respective paper titled "<em>Learning Chemistry: Exploring the suitability of machine learning for the task of structure-based chemical ontology classification". </em>Each dataset is formatted as a csv or python pickle file. chemdata_classical: Data used as input for the classical approaches (LR, Random Forest, ...) chemdata_lstm: Data used as input for the LSTM approaches. Intended to be used with cheleary classif_reports_classical: Classification metrics of all classical approaches pathlengths: Comparison between ClassyFire and classical approaches and LSTM predictions_classical: Predictions of all classical approaches predictions_lstm: Predictions and classification metrics of the LSTM
DOI: 10.5281/zenodo.4519815
title: Learning Chemistry: Exploring the suitability of machine learning for the task of structure-based chemical ontology classification - Data

description: Each of the packed folders contains either data or the results from the experiments published in the respective paper titled "<em>Learning Chemistry: Exploring the suitability of machine learning for the task of structure-based chemical ontology classification". </em>Each dataset is formatted as a csv or python pickle file. chemdata_classical: Data used as input for the classical approaches (LR, Random Forest, ...) chemdata_lstm: Data used as input for the LSTM approaches. Intended to be used with cheleary classif_reports_classical: Classification metrics of all classical approaches pathlengths: Comparison between ClassyFire and classical approaches and LSTM predictions_classical: Predictions of all classical approaches predictions_lstm: Predictions and classification metrics of the LSTM
DOI: 10.5066/p9yhb00s
title: Stream metabolism models for the Regional Stream Quality Assessments of the National Water Quality Program, 2013 to 2016

description: This dataset provides details from stream metabolism models for 20 stream sites in the United States that were sampled as part of the National Water Quality Program's Regional Stream Quality Assessments (RSQA). Metabolism was estimated at each site using the streamMetabolizer package in R. For each of the 20 sites, three files are provided; (1) the input data, which includes continuous dissolved oxygen, water temperature, light, and stream depth, (2) the output data containing the daily metabolism estimates, and (3) a site-specific html 'guide' for running the streamMetabolizer package in R. The site 'short_name' is included in each file name to distinguish the files associated with each field site. Final metabolism models were run using a constant value for K600, the mean reaeration rate coefficient scaled to a Schmidt number of 600, which was determined for each site based on initial model calibration runs. Model input data were downloaded directly from the USGS National Water Information System (NWIS) (dissolved oxygen and temperature) or directly from data loggers deployed in the field (light and depth). In addition to the stream metabolism files, a comma delimited file containing basic site characteristics is included for reference. This site file includes the USGS site ID (SITE_NO), the RSQA study name, the state where the site is located, the site 'short name', the USGS site name, and the decimal degree latitude and longitude of the sampling reach.
DOI: 10.5519/qd.1dal7zpt
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (101 records)
DOI: 10.15468/5ypb7h
title: Phytoseiid mites of Slovenia (Acari: Mesostigmata): new records and first description of the male ofAmblyseius microorientalis

description: This dataset contains the digitized treatments in Plazi based on the original journal article Kreiter, Serge, Amiri, Karima, Douin, Martial, Bohinc, Tanja, Trdan, Stanislav, Tixier, Marie-Stéphane (2020): Phytoseiid mites of Slovenia (Acari: Mesostigmata): new records and first description of the male ofAmblyseius microorientalis. Acarologia 60 (2): 203-242, DOI: 10.24349/acarologia/20204364, URL: http://dx.doi.org/10.24349/acarologia/20204364