cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc28bqgt
title: CCDC 2095676: Experimental Crystal Structure Determination

description: Related Article: Angshuman Roy Choudhuary mayank|2023|CSD Communication|||
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.cc28nwqk
title: CCDC 2105449: Experimental Crystal Structure Determination

description: Related Article: Adva Shpatz Dayan, lioz etgar|2023|CSD Communication|||
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.cc29812q
title: CCDC 2122882: Experimental Crystal Structure Determination

description: Related Article: Yuyang Ma, Qijun Pan, Caiyun Ou, Yinxia Cai, Xiaoyu Ma, Chao Liu|2023|Org.Biomol.Chem.|21|7597|doi:10.1039/D3OB01200J
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.cc29fg6f
title: CCDC 2128094: Experimental Crystal Structure Determination

description: Related Article: Fengwei Ma|2023|CSD Communication|||
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.cc21mn9n
title: CCDC 1895721: Experimental Crystal Structure Determination

description: Related Article: Poornima Singh, Ajai Singh|2023|CSD Communication|||
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.cc29gv2q
title: CCDC 2129423: Experimental Crystal Structure Determination

description: Related Article: Bolesław Picur, Iwona Bryndal, Tadeusz Lis|2023|CSD Communication|||
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.cc29gv5t
title: CCDC 2129426: Experimental Crystal Structure Determination

description: Related Article: Bolesław Picur, Iwona Bryndal, Tadeusz Lis|2023|CSD Communication|||
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.cc29y9f0
title: CCDC 2143322: Experimental Crystal Structure Determination

description: Related Article: Saleh Alsaee|2023|CSD Communication|||
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.cc2bd5nl
title: CCDC 2156659: Experimental Crystal Structure Determination

description: Related Article: Tehila Wallach, Tal binyamin, Shir Yudco, Adva Dayan, Lioz Etgar|2023|CSD Communication|||
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.cc2bf464
title: CCDC 2157575: Experimental Crystal Structure Determination

description: Related Article: Hamidou Keita|2023|CSD Communication|||
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.cc2bj7jn
title: CCDC 2160561: Experimental Crystal Structure Determination

description: Related Article: Dongwei Zhang, Liguo Zhou|2023|CSD Communication|||
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.cc2blppb
title: CCDC 2162922: Experimental Crystal Structure Determination

description: Related Article: Saleh Alsaee|2023|CSD Communication|||
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: 10.15482/usda.adc/1529771
title: Data From: Influence of cold temperature and exposure time on egg overwintering survival in the white-whiskered grasshopper (Orthoptera: Acrididae)

description: Data are from a laboratory experiment conducted to examine the effects of cold temperatures on egg survival of Ageneotettix deorum, the white-whiskered grasshopper which is a common rangeland pest species in the Northern Great Plains region.
Abstract from published manuscript:
The effect of cold temperatures and exposure time on egg survival and hatching success were examined in the white-whiskered grasshopper, Ageneotettix deorum. Temperature treatments ranged from 4°C to -35°C, with treatment times ranging from 48 to 240 hours. Both decreasing temperatures and exposure time negatively affected egg survival, with a temperature below -25°C being lethal. Similar lethal temperatures are known for several North American grasshopper species. The relatively shallow location of A. deorum egg pods would result in increased vulnerability of eggs to cold temperatures in the absence of snow.
Resources in this dataset:


Resource title: Column descriptions for Winter Egg Survival and Daily Hatch
File name: Column Heading Winter Egg Survival and Hatch Count.xlsx


Resource title: Ageneotettix deorum hatch survival
File name: Winter Egg Survival.xlsx
Resource description: The data are total numbers of hatched and unhatched eggs at 3 significant temperatures (4, -20, and -25 degrees Celsius).


Resource title: Daily Hatch
File name: Hatch Count.xlsx
Resource description: The data are the daily hatch count for egg pods held at the various time and temperature parameters.
DOI: 10.5281/zenodo.8394414
title: Eddy Covariance and Agronomical Meta Data Set from a One Year Agroecosystem Site (RNG6)

description: Dataset of soil, plant, forage, agronomical, and eddy flux data from one year of commercial bioenergy feedstock production in California funded under the DOE ARPA-E SMARTFARM program in Phase I. Data set is provided to restricted users only. Data sets include 1 year of 30-minute interval flux data. Full data sets will be published through Ameriflux at https://ameriflux.lbl.gov/ starting August 12, 2022, with annual updates thereafter. Some data, such as methane and nitrous oxide are embargoed. This work was funded through the U.S. Department of Energy, ARPA-E, under Cooperative Agreement DE-AR0001228 led by ARVA Intelligence Corp. (https://www.arvaintelligence.com) in collaboration with Lawrence Berkeley National Laboratory (LBNL).
DOI: 10.5281/zenodo.7081207
title: Product Reviews for Ordinal Quantification

description: This data set comprises a labeled training set, validation samples, and testing samples for ordinal quantification. The goal of quantification is not to predict the class label of each individual instance, but the distribution of labels in unlabeled sets of data. The data is extracted from the McAuley data set of product reviews in Amazon, where the goal is to predict the 5-star rating of each textual review. We have sampled this data according to three protocols that are designed for the evaluation of quantification methods. The first protocol is the artificial prevalence protocol (APP), where all possible distributions of labels are drawn with an equal probability. The second protocol, APP-OQ(50%), is a variant thereof, where only the smoothest 50% of all APP samples are considered. This variant is targeted at ordinal quantification, where classes are ordered and a similarity of neighboring classes can be assumed. 5-star ratings of product reviews lie on an ordinal scale and, hence, pose such an ordinal quantification task. The third protocol considers "real" distributions of labels. These distributions stem from actual products in the original data set. The data is represented by a RoBERTa embedding. In our experience, logistic regression classifiers work well with this representation. You can extract our data sets yourself, for instance, if you require a raw textual representation. The original McAuley data set is public already and we provide all of our extraction scripts. Extraction scripts and experiments: https://github.com/mirkobunse/regularized-oq Original data by McAuley: https://jmcauley.ucsd.edu/data/amazon/
description: {"references": ["M. Bunse, A. Moreo, F. Sebastiani, M. Senz (2022). Ordinal Quantification through Regularization.", "J. McAuley, C. Targett, Q. Shi, A. van den Hengel (2015). Image-based recommendations on styles and substitutes."]}
DOI: 10.5281/zenodo.7081208
title: Product Reviews for Ordinal Quantification

description: This data set comprises a labeled training set, validation samples, and testing samples for ordinal quantification. It appears in our research paper "<em>Ordinal Quantification Through Regularization</em>", which we have published at ECML-PKDD 2022. The data is extracted from the McAuley data set of product reviews in Amazon, where the goal is to predict the 5-star rating of each textual review. We have sampled this data according to two protocols that are suited for quantification research. The goal of quantification is not to predict the star rating of each individual instance, but the distribution of ratings in sets of textual reviews. More generally speaking, quantification aims at estimating the distribution of labels in unlabeled samples of data. The first protocol is the artificial prevalence protocol (APP), where all possible distributions of labels are drawn with an equal probability. The second protocol, APP-OQ, is a variant thereof, where only the smoothest 20% of all APP samples are considered. This variant is targeted at ordinal quantification, where classes are ordered and a similarity of neighboring classes can be assumed. 5-star ratings of product reviews lie on an ordinal scale and, hence, pose such an ordinal quantification task. This data set comprises two representations of the McAuley data. The first representation consists of TF-IDF features. The second representation is a RoBERTa embedding. This second representation is dense, while the first is sparse. In our experience, logistic regression classifiers work well with both representations. RoBERTa embeddings yield more accurate predictors than the TF-IDF features. You can extract our data sets yourself, for instance, if you require a raw textual representation. The original McAuley data set is public already and we provide all of our extraction scripts. Extraction scripts and experiments: https://github.com/mirkobunse/ecml22 Original data by McAuley: https://jmcauley.ucsd.edu/data/amazon/
description: {"references": ["M. Bunse, A. Moreo, F. Sebastiani, M. Senz (2022). Ordinal Quantification through Regularization.", "J. McAuley, C. Targett, Q. Shi, A. van den Hengel (2015). Image-based recommendations on styles and substitutes."]}
DOI: 10.5281/zenodo.8176791
title: Product Reviews for Ordinal Quantification

description: This data set comprises a labeled training set, validation samples, and testing samples for ordinal quantification. The goal of quantification is not to predict the class label of each individual instance, but the distribution of labels in unlabeled sets of data. The data is extracted from the McAuley data set of product reviews in Amazon, where the goal is to predict the 5-star rating of each textual review. We have sampled this data according to three protocols that are suited for quantification research. The first protocol is the artificial prevalence protocol (APP), where all possible distributions of labels are drawn with an equal probability. The second protocol, APP-OQ(50%), is a variant thereof, where only the smoothest 50% of all APP samples are considered. This variant is targeted at ordinal quantification, where classes are ordered and a similarity of neighboring classes can be assumed. 5-star ratings of product reviews lie on an ordinal scale and, hence, pose such an ordinal quantification task. The third protocol considers "real" distributions of labels. These distributions stem from actual products in the original data set. The data is represented by a RoBERTa embedding. In our experience, logistic regression classifiers work well with this representation. You can extract our data sets yourself, for instance, if you require a raw textual representation. The original McAuley data set is public already and we provide all of our extraction scripts. Extraction scripts and experiments: https://github.com/mirkobunse/regularized-oq Original data by McAuley: https://jmcauley.ucsd.edu/data/amazon/
description: {"references": ["M. Bunse, A. Moreo, F. Sebastiani, M. Senz (2022). Ordinal Quantification through Regularization.", "J. McAuley, C. Targett, Q. Shi, A. van den Hengel (2015). Image-based recommendations on styles and substitutes."]}
DOI: 10.5281/zenodo.8405476
title: Product Reviews for Ordinal Quantification

description: This data set comprises a labeled training set, validation samples, and testing samples for ordinal quantification. The goal of quantification is not to predict the class label of each individual instance, but the distribution of labels in unlabeled sets of data. The data is extracted from the McAuley data set of product reviews in Amazon, where the goal is to predict the 5-star rating of each textual review. We have sampled this data according to three protocols that are designed for the evaluation of quantification methods. The first protocol is the artificial prevalence protocol (APP), where all possible distributions of labels are drawn with an equal probability. The second protocol, APP-OQ(50%), is a variant thereof, where only the smoothest 50% of all APP samples are considered. This variant is targeted at ordinal quantification, where classes are ordered and a similarity of neighboring classes can be assumed. 5-star ratings of product reviews lie on an ordinal scale and, hence, pose such an ordinal quantification task. The third protocol considers "real" distributions of labels. These distributions stem from actual products in the original data set. The data is represented by a RoBERTa embedding. In our experience, logistic regression classifiers work well with this representation. You can extract our data sets yourself, for instance, if you require a raw textual representation. The original McAuley data set is public already and we provide all of our extraction scripts. Extraction scripts and experiments: https://github.com/mirkobunse/regularized-oq Original data by McAuley: https://jmcauley.ucsd.edu/data/amazon/
description: {"references": ["M. Bunse, A. Moreo, F. Sebastiani, M. Senz (2022). Ordinal Quantification through Regularization.", "J. McAuley, C. Targett, Q. Shi, A. van den Hengel (2015). Image-based recommendations on styles and substitutes."]}
DOI: 10.6084/m9.figshare.24243181.v1
title: Data and code needed to recreate "Impact of Adjustment for Differential Testing by Age and Sex on Apparent Epidemiology of SARS-CoV-2 Infection in Ontario, Canada".

description: Data and Stata code for recreation of "Impact of Adjustment for Differential Testing by Age and Sex on Apparent Epidemiology of SARS-CoV-2 Infection in Ontario, Canada". For questions about analysis please contact me directly at david.fisman@gmail.com or david.fisman@utoronto.ca.Paper abstract: Surveillance of communicable diseases typically relies on case counts for estimates of risk, and counts can be strongly influenced by testing rates. In the Canadian province of Ontario, testing rates varied markedly by age, sex, geography and time over the course of the SARS-CoV-2 pandemic. We applied a standardization-based approach to test-adjustment to better understand pandemic dynamics from 2020 to 2022, and to better understand when test-adjustment is necessary for accurate estimation of risk. SARS-CoV-2 case counts by age, sex, public health unit and week were obtained from Ontario’s Case and Contact Management system (CCM), which includes all SARS-CoV-2 cases from March 2020 to August 2022. Complete data on testing volumes was obtained from the Ontario Laboratory Information System (OLIS). Case counts were adjusted for under-testing using a previously published standardization-based approach that estimates case numbers that would have been expected if the entire population was tested at the same rate as most-tested age and sex groups. Logistic regression was used to identify threshold testing rates beyond which test-adjustment was unnecessary. Testing rates varied markedly by age, sex, public health unit and pandemic wave. After adjustment for under-testing, overall case counts increased threefold. Adjusted epidemic curves suggested, in contrast to reported case counts, that the first two pandemic waves were equivalent in size, and that there were three distinct pandemic waves in 2022, due to the emergence of Omicron variants. Under-reporting was greatest in children and young males, and varied significantly across public health units, with variation explained partly by testing rates and prevalence of multigenerational households. Test adjustment resulted in little change in the epidemic curve during pandemic waves when testing rates were highest; we found that test-adjustment did not increase case counts once weekly per capita testing rates exceeded 6.3%. We conclude that standardization-based adjustment for differential testing by age and sex, and for dynamic changes in testing over time, results in a different picture of infection risk during the SARS-CoV-2 pandemic in Ontario; test-adjusted epidemic curves are concordant with observed patterns of mortality during the pandemic and have face validity. This methodology offers an alternative to sero-epidemiology for identification of true burden of infection when reinfection, sero-reversion, and non-specificity of serological assays make sero-epidemiology challenging.
DOI: 10.6084/m9.figshare.24243649
title: Supplementary Material for: Efficacy of a Self-Directed Video-based Caregiver-Implemented Language Programme

description: Introduction. Caregiver-implemented language programmes are effective for alleviating early language difficulties. This study examined the efficacy of a self-directed video-based caregiver-implement language programme in Chinese families. Method. This study consisted of two stages. In Stage 1, 31 caregiver-child dyads (typically developing children) completed the training programme (Group 1) in the form of six video-based training modules. In Stage 2, 28 caregiver-child dyads (children with language difficulties) receiving active speech therapy were randomly assigned to the training (Group 2) and control arms (Group 3). Group 2 received the same training as Group 1 in addition to their regular therapy while Group 3 was kept as status quo. Caregivers completed a quiz on their knowledge of language facilitation techniques (LFTs) and submitted caregiver-child interaction videos at the start and end of the training. Outcome measures included programme completion rate, quiz scores, and use of LFTs and children’s communication skills in the videos. A pre-post design and a between-group designs were adopted in the Stage 1 and 2 studies respectively. Results. A completion rate of about 60% in both stages was noted. Significantly higher post-training knowledge scores were found in Groups 1 and 2. General but non-significant growth in use of parallel talk and gesture, and significant gains in children’s vocalization in the training arm were observed. Conclusion. The self-directed video-based training programme would be useful in imparting information to caregivers. However, the modest improvements in the use of LFTs suggested direct coaching appeared to still play a significant role in enhancing the actual implementation of LFTs. Further investigation on a larger scale is required to evaluate the effectiveness of the training program for promoting the wider use of this mode as a preventive measure.