cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7982426
title: ClinicalRisk

description: Clinical trials aim to study new tests and evaluate their effects on human health outcomes, which has a huge market size. However, carrying out clinical trials is expensive and time-consuming and often ends in no results. <br> It will revolutionize clinical practice if we can develop an effective model to automatically estimate the status of a clinical trial and find out possible failure reasons. <br> However, it is challenging to develop such a model because of the lack of a benchmark dataset. <br> To address these challenges, in this paper, we first build a new dataset by extracting the publicly available clinical trial reports from ClinicalTrials.gov. The associated status of each report is treated as the status label. To analyze the failure reasons, domain experts help us manually annotate each failed report based on the description associated with it. More importantly, we examine several state-of-the-art text classification baselines on this task and find out that the unique format of the clinical trial protocols plays an essential role in affecting prediction accuracy, demonstrating the need for specially designed clinical trial classification models.
DOI: 10.5281/zenodo.7982425
title: ClinicalRisk

description: Clinical trials aim to study new tests and evaluate their effects on human health outcomes, which has a huge market size. However, carrying out clinical trials is expensive and time-consuming and often ends in no results. <br> It will revolutionize clinical practice if we can develop an effective model to automatically estimate the status of a clinical trial and find out possible failure reasons. <br> However, it is challenging to develop such a model because of the lack of a benchmark dataset. <br> To address these challenges, in this paper, we first build a new dataset by extracting the publicly available clinical trial reports from ClinicalTrials.gov. The associated status of each report is treated as the status label. To analyze the failure reasons, domain experts help us manually annotate each failed report based on the description associated with it. More importantly, we examine several state-of-the-art text classification baselines on this task and find out that the unique format of the clinical trial protocols plays an essential role in affecting prediction accuracy, demonstrating the need for specially designed clinical trial classification models.
DOI: 10.5517/ccdc.csd.cc294sr0
title: CCDC 2119772: Experimental Crystal Structure Determination

description: Related Article: Guanghua Liu, Lei Gao, Yi Han, Yao Xiao, Bochao Du, Jianye Gong, Jinlian Hu, Fujin Zhang, He Meng, Xiang Li, Xueliang Shi, Zhe Sun, Jianguo Wang, Gaole Dai, Chunyan Chi, Qing Wang|2023|Angew.Chem.,Int.Ed.|62|e202301348|doi:10.1002/anie.202301348
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.cc29k7z3
title: CCDC 2131745: Experimental Crystal Structure Determination

description: Related Article: Huixin Qiu, Jiayi Ren, Long Zhang, Ran Song, Wen Si, Daoshan Yang, Lirong Wen, Jian Lv|2023|Green Chemistry|25|3948|doi:10.1039/D2GC03441G
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.cc29k8tz
title: CCDC 2131771: Experimental Crystal Structure Determination

description: Related Article: Mirai Nakata, Tsunehisa Hirashita, Yoshikazu Konishi, Shuki Araki|2023|Org.Biomol.Chem.|21|4282|doi:10.1039/D3OB00362K
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.cc2bj7fk
title: CCDC 2160558: Experimental Crystal Structure Determination

description: Related Article: Xiaoqiang Liang, Sen Wang, Jingyu Feng, Zhen Xu, Zhenyu Guo, Hui Luo, Feng Zhang, Chen Wen, Lei Feng, Chengan Wan, Maria-Magdalena Titirici|2023|Inorg.Chem.Front.|10|2961|doi:10.1039/D2QI02436E
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.23258006
title: Exploring the ethical implications of ChatGPT and other AI Chatbots and the regulation of disinformation propagation.csv.zip

description: Chatbots, driven by artificial intelligence (AI) such as ChatGPT, are playing an increasingly pivotal role in the digital age and are disrupting numerous industries. As these technologies rapidly advance and their influence expands, they present a range of ethical and regulatory challenges. One critical concern is the potential use of these AI chatbots to disseminate disinformation. Given the capacity of these systems to generate text that closely mimics human conversation based on training data, there is a significant risk of them being manipulated to widely disseminate inaccurate or deceptive information. Such misuse could result in various societal problems, including the intensification of political divisiveness or the spreading of damaging misinformation. This paper embarks on a critical exploration of these ethical issues, with a specific focus on the potential misuse of AI chatbots in the propagation of disinformation. This research further investigates potential regulatory interventions that could alleviate these issues. In the rapidly evolving world of AI technology, creating a robust regulatory framework that balances the benefits of AI chatbots with the prevention of their misuse is crucial. Therefore, this paper aims to contribute to the ongoing dialogue about the ethical use of AI and the development of effective regulatory strategies.
DOI: 10.5517/ccdc.csd.cc2bj7hm
title: CCDC 2160560: Experimental Crystal Structure Determination

description: Related Article: Xiaoqiang Liang, Sen Wang, Jingyu Feng, Zhen Xu, Zhenyu Guo, Hui Luo, Feng Zhang, Chen Wen, Lei Feng, Chengan Wan, Maria-Magdalena Titirici|2023|Inorg.Chem.Front.|10|2961|doi:10.1039/D2QI02436E
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.cc2bm437
title: CCDC 2163338: Experimental Crystal Structure Determination

description: Related Article: Shuang Peng, Yan Li, Rui Ge, Wen-Xin Chen, Tian-Yu Wang, Xin-Xiong Li, Shou-Tian Zheng|2022|Chem.Commun.|58|6789|doi:10.1039/D2CC01833K
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.cc2c6rn0
title: CCDC 2181242: Experimental Crystal Structure Determination

description: Related Article: Kyriaki Koupepidou, Varvara I. Nikolayenko, Debobroto Sensharma, Andrey A. Bezrukov, Matthias Vandichel, Sousa Javan Nikkhah, Dominic C. Castell, Kolade A. Oyekan, Naveen Kumar, Aizhamal Subanbekova, William G. Vandenberghe, Kui Tan, Leonard J. Barbour, Michael J. Zaworotko|2023|J.Am.Chem.Soc.|145|10197|doi:10.1021/jacs.3c01113
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.cc2c6rq2
title: CCDC 2181244: Experimental Crystal Structure Determination

description: Related Article: Kyriaki Koupepidou, Varvara I. Nikolayenko, Debobroto Sensharma, Andrey A. Bezrukov, Matthias Vandichel, Sousa Javan Nikkhah, Dominic C. Castell, Kolade A. Oyekan, Naveen Kumar, Aizhamal Subanbekova, William G. Vandenberghe, Kui Tan, Leonard J. Barbour, Michael J. Zaworotko|2023|J.Am.Chem.Soc.|145|10197|doi:10.1021/jacs.3c01113
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: 10.34740/kaggle/dsv/5807580
title: Balochi Language to English Translation Dataset
DOI: 10.5281/zenodo.7968629
title: Utilización de datos satelitales para la evaluación y mejora de los pronósticos numéricos en alta resolución a muy corto plazo

description: Datos generados postprocesando experimentos de asimilación de datos, como parte de la tesis presentada para optar al título de Doctora de la Universidad de Buenos Aires en el Área de Ciencias de la Atmósfera y los Océanos Directora: Vito Galligani Co-Director: Juan Ruiz Consejera de estudios: Celeste Saulo
DOI: 10.5281/zenodo.7968628
title: Utilización de datos satelitales para la evaluación y mejora de los pronósticos numéricos en alta resolución a muy corto plazo

description: Datos generados postprocesando experimentos de asimilación de datos, como parte de la tesis presentada para optar al título de Doctora de la Universidad de Buenos Aires en el Área de Ciencias de la Atmósfera y los Océanos Directora: Vito Galligani Co-Director: Juan Ruiz Consejera de estudios: Celeste Saulo
DOI: 10.25919/jj6q-sa64
title: Parkes observations for project P455 semester 2023APRS_01

description: This project is to continue timing and profile studies of the first double-pulsar system, a unique laboratory for gravitational physics. Important results published in our 53-page Phys. Rev. X paper (Kramer et al., December 2021) include the first measurements of higher-order light-propagation effects and of the relativistic deformation of the orbit and highlight the importance of a long term observational campaign, including VLBI observations, for this remarkable system. The main aims of this proposal are to provide the strongest tests to date for general relativity and to measure for the first time the moment-of-inertia of a neutron star. Additionally, we will determine the system geometry and map the pulsar beams via geodetic precession. We will exploit the high sensitivity and broad bandwidth of the UWL receiver.
DOI: 10.25919/jz57-5c14
title: Parkes observations for project PX600 semester 2020APRS_86

description: The Galactic Center (GC) is a key target for the radio component of Breakthrough Listen (BL) program. Offering the largest amount of stars in any given direction in the sky, the GC is widely cited as a location believed to harbour advanced civilizations, and it is also the most energetic region in the Milky Way. We aim to conduct BL-GC Survey with Parkes radio telescope for around 350 hours. We will cover the frequency range from 700 MHz to 4 GHz, utilizing the newly installed ultra-wideband receiver. Our team has already leveraged both standard and bespoke tools to construct a flexible software stack to search data for signals of interest. We will look for signals from extraterrestrial intelligence (ETI) by searching for both simple narrow-band signals and complex modulated signals. Along with that, as an ancillary science, we will also search for accelerated pulsars — likely orbiting a supermassive black hole at the centre of the Milky Way Galaxy. Data products produced during this program will also be publically available to engage larger pulsar community
DOI: 10.5281/zenodo.7983019
title: Alkane dataset for reactive chemistry neural network potentials at DFT and CASPT2 level

description: datasets used for training and evaluating neural networks in the following work: Neural Network Potentials for Reactive Chemistry: CASPT2 Quality Potential Energy Surfaces for Bond Breaking https://doi.org/10.26434/chemrxiv-2023-13cv6
DOI: 10.5281/zenodo.7983018
title: Alkane dataset for reactive chemistry neural network potentials at DFT and CASPT2 level

description: datasets used for training and evaluating neural networks in the following work: Neural Network Potentials for Reactive Chemistry: CASPT2 Quality Potential Energy Surfaces for Bond Breaking https://doi.org/10.26434/chemrxiv-2023-13cv6
DOI: 10.5281/zenodo.7983008
title: Identification of four novel loci associated with psychotropic drug-induced weight gain in a Swiss psychiatric longitudinal study: A GWAS analysis

description: Summary statistics for the GWAS with different outcomes presented in the publication. PSYMETAB_GWAS_FREQ_CEU.BMI_change_1mo_all.glm - BMI change between 1 month vs baseline PSYMETAB_GWAS_FREQ_CEU.BMI_change_3mo_all.glm - BMI change between 3 months vs baseline PSYMETAB_GWAS_FREQ_CEU.BMI_change_6mo_all.glm - BMI change between 6 months vs baseline PSYMETAB_GWAS_FREQ_CEU.BMI_change_all.glm - BMI change between last follow-up time vs baseline PSYMETAB_GWAS_FREQ_CEU.BMI_slope_6mo_all.glm - BMI slope fitted for the first 6 months of followup data PSYMETAB_GWAS_FREQ_CEU.BMI_slope_all.glm - BMI slope fitted for all available follow-up data
DOI: 10.5281/zenodo.7983009
title: Identification of four novel loci associated with psychotropic drug-induced weight gain in a Swiss psychiatric longitudinal study: A GWAS analysis

description: Summary statistics for the GWAS with different outcomes presented in the publication. PSYMETAB_GWAS_FREQ_CEU.BMI_change_1mo_all.glm - BMI change between 1 month vs baseline PSYMETAB_GWAS_FREQ_CEU.BMI_change_3mo_all.glm - BMI change between 3 months vs baseline PSYMETAB_GWAS_FREQ_CEU.BMI_change_6mo_all.glm - BMI change between 6 months vs baseline PSYMETAB_GWAS_FREQ_CEU.BMI_change_all.glm - BMI change between last follow-up time vs baseline PSYMETAB_GWAS_FREQ_CEU.BMI_slope_6mo_all.glm - BMI slope fitted for the first 6 months of followup data PSYMETAB_GWAS_FREQ_CEU.BMI_slope_all.glm - BMI slope fitted for all available follow-up data
DOI: 10.6084/m9.figshare.23258070
title: Supplementary table Excel file from Cellular RNA levels define heterotrophic substrate-uptake rate sub-guilds in activated sludge microbial communities

description: A heterotrophic-specialist model was proposed previously to divide wastewater treatment plant (WWTP) heterotrophs into sub-guilds of consumers of readily or slowly degradable substrates (RDS or SDS, respectively). The substrate degradation rate model coupled to metabolic considerations predicted that RNA and polyhydroxyalkanoate (PHA) levels would be positively correlated in the activated sludge communities with high RNA and PHA occurring in RDS-consumers, and low RNA with no PHA accumulation occurring in SDS-consumers because their external substrates are always present. This prediction was verified in previous studies and in the current one. Thus, RNA and PHA levels were used as biomarkers of the RDS- and SDS-consumer sub-guilds for cell sorting using flow cytometry of samples from three WWTPs. Subsequently, 16S rRNA gene amplicon sequencing revealed that the sorted groups were highly similar over time and among WWTPs, and demonstrated a clear segregation by RNA levels. Predicted ecophysiological traits based on 16S rRNA phylogeny suggested that the high-RNA population showed RDS-consumer traits such as higher <i>rrn</i> copy numbers per genome. Using a mass-flow immigration model, it appeared that the high-RNA populations exhibited high immigration rates more frequently than low-RNA populations, but the differences in frequencies were less with increasing solids residence times.
DOI: 10.6084/m9.figshare.23258070.v1
title: Supplementary table Excel file from Cellular RNA levels define heterotrophic substrate-uptake rate sub-guilds in activated sludge microbial communities

description: A heterotrophic-specialist model was proposed previously to divide wastewater treatment plant (WWTP) heterotrophs into sub-guilds of consumers of readily or slowly degradable substrates (RDS or SDS, respectively). The substrate degradation rate model coupled to metabolic considerations predicted that RNA and polyhydroxyalkanoate (PHA) levels would be positively correlated in the activated sludge communities with high RNA and PHA occurring in RDS-consumers, and low RNA with no PHA accumulation occurring in SDS-consumers because their external substrates are always present. This prediction was verified in previous studies and in the current one. Thus, RNA and PHA levels were used as biomarkers of the RDS- and SDS-consumer sub-guilds for cell sorting using flow cytometry of samples from three WWTPs. Subsequently, 16S rRNA gene amplicon sequencing revealed that the sorted groups were highly similar over time and among WWTPs, and demonstrated a clear segregation by RNA levels. Predicted ecophysiological traits based on 16S rRNA phylogeny suggested that the high-RNA population showed RDS-consumer traits such as higher <i>rrn</i> copy numbers per genome. Using a mass-flow immigration model, it appeared that the high-RNA populations exhibited high immigration rates more frequently than low-RNA populations, but the differences in frequencies were less with increasing solids residence times.
DOI: 10.5517/ccdc.csd.cc2dbnw9
title: CCDC 2214791: Experimental Crystal Structure Determination

description: Related Article: Masatoshi Yamada, Koichi Murai, Kotaro Yamaguchi, Mitsuhisa Yamano|2023|Org.Process Res.Dev.|27|972|doi:10.1021/acs.oprd.3c00088
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.cc2ddpq7
title: CCDC 2216739: Experimental Crystal Structure Determination

description: Related Article: W. Nakanishi, Y. Matsushita, M. Takeuchi, K. Sagisaka|2023|Phys.Chem.Chem.Phys.(PCCP)|25|13702|doi:10.1039/D2CP05982G
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.cc2df9w1
title: CCDC 2217333: Experimental Crystal Structure Determination

description: Related Article: Lee T. Birchall, Amber T. Raja, Lewis Jackson, Helena J. Shepherd|2023|Cryst.Growth Des.|23|1768|doi:10.1021/acs.cgd.2c01282
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.cc2dfb17
title: CCDC 2217338: Experimental Crystal Structure Determination

description: Related Article: Lee T. Birchall, Amber T. Raja, Lewis Jackson, Helena J. Shepherd|2023|Cryst.Growth Des.|23|1768|doi:10.1021/acs.cgd.2c01282
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.21619329
title: A retrospective view of the relationship of soluble Fas with anemia and outcomes in chronic kidney disease

description: Our data contains: Gender; Age_baseline (years); Group_NDD_CKD; BMI; Smoker at that moment; Proteinuria; Creat baseline; eGFR_baseline; CKDstage; eGFR &lt;60; eGFR&gt;60; sUrea_basel; DM_cause; Hypetension_cause; Cause of CKD; Hgb_baseline; Hgb_lower10_baseline; Hgb (mg/dl)_baseline; Hgb_baseline (g/l); Hct_b; Hct_basel; VCMb_; HCM_b; Anemia_baseline; ESA; months_to_ESA; rHuEPOU/week; rHuEPO/Kg/week; platelets_baseline; Iron_replacement; Fe_baseline; Trasferrin_sat_basel; Ferritin_baseline; Albumin_baseline; PCR; logPCR; PCR_baseline_LxH; serum_sFas_baseline; sFas/eGFR_at baseline; sFas1000; logsFas; IL-6.b; log_IL_6.b; IFNg.b; logIFNg.b; serum_EPO.basel; logserumEPO_b; EPO/Hb. mg/dl_b_; log_EPO/Hb. mg/dl_b_; Epo/Hgb_baseline; logEpo/Hgb_b; EPO/Hct basel; log EPO/Hct basel; PTH_baseline; logpth; Leuko_b; RAS_blocker; CaXP; LVH; t_CHOLT; Hgb_after 1 year; Hgb_2; Hgb_4; Hgb_6; Hgb_8; Hgb_10; Hgb_12; Hct; Hct1; Hct2; Hct 4; Hct 6; Hct 8; Hct 12; Hgb_last; VCM_last; HCM_last; Hct last; Anemia_end followup; Leuko 2; Leuko 4; Leuko 6; Leuko 8; Leuko 12; Alb_after 2; Alb 4; Alb 6; alb12; Fe 2; Fe 4; Fe 6; Fe 8; Fe 10; Fe 12; Ferrit i; Ferrit 2; Ferrit4; Ferrit 6; Ferrit 8; Ferrit 12; Transf sat_after 2 y ; Transf sat_4; Transf sat_ 6; Transf sat_ 8; Transf sat_10; Transf sat_12; PCR_after 2 y; PCR 4; PCR6; PCR10; PCR12; Weight_after 2 years; Weight_6; Peso12; eGFR_after 1 y; eGFR_2; eGFR_4; eGFR_6; eGFR_8; eGFR_12; eGFR_last; Ant. C.Ca; b-bloq; Diuretic; Vit B; Folate; CVDisease; Time to CVD (years); Hypertens; DM ; NDD_CKD_to_Dialysis; Time to dialysis (months); T to dialysis (years); to_Anemia (years); RBC_Transfus; RBCs units; Time_ RBC_transfusion (months); followup (years); Mortality; Time to mortality (months); Cause_mortality; Year of mortality;