cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48623/aperta.263322
title: Stemnesscore: An R Package To Estimate The Stemness Of Glioma Cancer Cells At Single- Cell Resolution

description: <strong>Background/aim:</strong> Glioblastoma is the most heterogeneous and the most difficult-to-treat type of brain tumor and one of the deadliest among all cancers. High plasticity of glioma cancer stem cells and the resistance they develop against multiple modalities of therapy, along with high heterogeneity, are the main challenges faced during treatment of glioblastoma. Therefore, a better understanding of the stemness characteristics of glioblastoma cells is needed. With the development of various single-cell technologies and increasing applications of machine learning, indices based on transcriptomic and/or epigenomic data have been developed to quantitatively measure cellular states and stemness. In this study, we aimed to develop a glioma-specific stemness score model using scATAC-seq data for the first time.

<strong>Materials and methods:</strong> We first applied three powerful machine-learning algorithms, i.e., random forest, gradient boosting, and extreme gradient boosting to glioblastoma scRNA-seq data to discover the most important genes associated with cellular states. We then identified promoter and enhancer regions associated with these genes. After downloading the scATAC-seq peaks and their read counts for each patient, we identified the overlapping regions between the single-cell peaks and the peaks of genes obtained through machine-learning algorithms. Then, we calculated read counts that were mapped to these overlapping regions. We finally developed a model capable of estimating the stemness score for each glioma cell using overlapping regions and the importance of genes predictive of glioblastoma cellular states. We also created an R package, accessible to all researchers, regardless of their coding proficiency.

<strong>Results:</strong> Our results showed that mesenchymal-like stem cells display the highest stemness scores compared to neural-progenitor, oligodendrocyte-progenitor, and astrocyte-like cells.

<strong>Conclusion:</strong> We conclude that scATAC-seq can be used to assess heterogeneity in glioblastoma and identify cells with high stemness characteristics. The package is publicly available at https://github.com/Necla/StemnesScoRe and includes documentation with an implementation of a real-data experiment.
DOI: 10.6084/m9.figshare.24845490
title: Data sets: Niue and Beveridge Reef foraminifera from Shallow water foraminifera from Niue and Beveridge reef (South Pacific): insights into ecological significance and ecosystem integrity

description: Table S1) Grain size analysis; Table S2) Foraminifera raw counts; Table S3) Benthic functional groups; Table S4) Key Species in reef and beach sediments
DOI: 10.6084/m9.figshare.24845490.v1
title: Data sets: Niue and Beveridge Reef foraminifera from Shallow water foraminifera from Niue and Beveridge reef (South Pacific): insights into ecological significance and ecosystem integrity

description: Table S1) Grain size analysis; Table S2) Foraminifera raw counts; Table S3) Benthic functional groups; Table S4) Key Species in reef and beach sediments
DOI: 10.6084/m9.figshare.24845463
title: Articles Dataset related to AI and Digital Marketing

description: Articles Dataset extracted from Scopus Database related to AI and Digital Marketing (Timeframe 2015-July 2023) for bibliometric analysis.
DOI: 10.6084/m9.figshare.24845463.v1
title: Articles Dataset related to AI and Digital Marketing

description: Articles Dataset extracted from Scopus Database related to AI and Digital Marketing (Timeframe 2015-July 2023) for bibliometric analysis.
DOI: 10.5281/zenodo.10395152
title: Data and codes for Landslide hazard spatiotemporal prediction based on data-driven models: Estimating where, when and how large landslide may be

description: Data and codes for Landslide hazard spatiotemporal prediction based on data-driven models: Estimating where, when and how large landslide may be
DOI: 10.5281/zenodo.10395153
title: Data and codes for Landslide hazard spatiotemporal prediction based on data-driven models: Estimating where, when and how large landslide may be

description: Data and codes for Landslide hazard spatiotemporal prediction based on data-driven models: Estimating where, when and how large landslide may be
DOI: 10.6084/m9.figshare.24829953
title: University of Reading Classical Languages Teaching Content and Expectations Survey - Summer 2023

description: This survey was a pilot study to gather opinions about AI from teaching staff in the Classics Department at the University of Reading. The results of this survey were used to develop the department's AI policy and citation guide for the 2023 Autumn Term.This project, "ChatGPT: A Conversational Language Study Tool", has been reviewed by the University of Reading University Research Ethics Committee and has been given a favourable ethical opinion for conduct.
DOI: 10.6084/m9.figshare.24829953.v1
title: University of Reading Classical Languages Teaching Content and Expectations Survey - Summer 2023

description: This survey was a pilot study to gather opinions about AI from teaching staff in the Classics Department at the University of Reading. The results of this survey were used to develop the department's AI policy and citation guide for the 2023 Autumn Term.This project has been reviewed by the University of Reading University Research Ethics Committee and has been given a favourable ethical opinion for conduct.
DOI: 10.6084/m9.figshare.24829953.v2
title: University of Reading Classical Languages Teaching Content and Expectations Survey - Summer 2023

description: This survey was a pilot study to gather opinions about AI from teaching staff in the Classics Department at the University of Reading. The results of this survey were used to develop the department's AI policy and citation guide for the 2023 Autumn Term.This project, "ChatGPT: A Conversational Language Study Tool", has been reviewed by the University of Reading University Research Ethics Committee and has been given a favourable ethical opinion for conduct.
DOI: 10.6084/m9.figshare.24835629
title: AI and Ancient Language Student Survey - Autumn 2023

description: This survey was the first part of the data-gathering portion of the "ChatGPT: A Conversational Language Study Tool" project. These survey forms were completed by students in ancient language classes in the Department of Classics at the University of Reading. The results of this survey will be compared to a second set of surveys taken in Spring 2024.This project has been reviewed by the University of Reading University Research Ethics Committee and has been given a favourable ethical opinion for conduct.
DOI: 10.6084/m9.figshare.24835629.v1
title: AI and Ancient Language Student Survey - Autumn 2023

description: This survey was the first part of the data-gathering portion of the "ChatGPT: A Conversational Language Study Tool" project. These survey forms were completed by students in ancient language classes in the Department of Classics at the University of Reading. The results of this survey will be compared to a second set of surveys taken in Spring 2024.This project has been reviewed by the University of Reading University Research Ethics Committee and has been given a favourable ethical opinion for conduct.
DOI: 10.6084/m9.figshare.24842058
title: Is AI the New Plato?: AI Benefits and Drawbacks for Study Survey - Autumn 2023

description: These surveys were collected during general AI ethics tutorials with students in the Classics and Modern Languages Departments at the University of Reading over the 2023 Autumn Term. The results of these surveys was used to support findings related to the effects of AI education on student opinion and usage of generative AI tools.This project, "ChatGPT: A Conversational Language Study Tool", has been reviewed by the University of Reading University Research Ethics Committee and has been given a favourable ethical opinion for conduct.
DOI: 10.6084/m9.figshare.24845509
title: Experimental data from Evolutionary dynamics of glucose-deprived cancer cells: insights from experimentally informed mathematical modelling

description: Experimental data, Excel file
DOI: 10.6084/m9.figshare.24845509.v1
title: Experimental data from Evolutionary dynamics of glucose-deprived cancer cells: insights from experimentally informed mathematical modelling

description: Experimental data, Excel file
DOI: 10.34740/kaggle/ds/4175424
title: Movie Identification Dataset [800 Movies]
DOI: 10.5517/ccdc.csd.cc2bc3kd
title: CCDC 2155633: Experimental Crystal Structure Determination

description: Related Article: Krishan Kumar, Mangey Ram Nagar, Anirban Karmakar, Feng-Rong Chen, Jwo-Huei Jou, Subrata Ghosh, Sunil Kumar, Subrata Banik|2023|Cryst.Growth Des.|23|8771|doi:10.1021/acs.cgd.3c00911
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.cc2bc3c6
title: CCDC 2155627: Experimental Crystal Structure Determination

description: Related Article: Krishan Kumar, Mangey Ram Nagar, Anirban Karmakar, Feng-Rong Chen, Jwo-Huei Jou, Subrata Ghosh, Sunil Kumar, Subrata Banik|2023|Cryst.Growth Des.|23|8771|doi:10.1021/acs.cgd.3c00911
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.cc2bc3g9
title: CCDC 2155630: Experimental Crystal Structure Determination

description: Related Article: Krishan Kumar, Mangey Ram Nagar, Anirban Karmakar, Feng-Rong Chen, Jwo-Huei Jou, Subrata Ghosh, Sunil Kumar, Subrata Banik|2023|Cryst.Growth Des.|23|8771|doi:10.1021/acs.cgd.3c00911
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: 10.25505/fiz.icsd.cc2d4r9m
title: CSD 2209100: Experimental Crystal Structure Determination

description: Related Article: Weiwei Guo, Masahiro Hori, Yoshihiro Ogura, Kazuki Nishimura, Kosuke Oki, Tomoyuki Ikai, Eiji Yashima, Kazuaki Ishihara|2023|J.Am.Chem.Soc.|145|27080|doi:10.1021/jacs.3c10820
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.21227/jvar-mf33
title: .py

description: Intelligence and flexibility are the two main requirements for next-generation networks that can be implemented in  network slicing  (NetS) technology.This intelligence and flexibility can have different indicators in networks, such as proactivity and resilience. In this paper, we propose a novel proactive end-to-end (E2E) resource management in a packet-based model, supporting NetS. Since guaranteeing quality of service (QoS) in NetS has many challenges, we present an intelligent method that has two characteristics: resilience and proactivity.  Guaranteeing successful slice provision  is costly, we formulate a comprehensive model of the imposed costs. To minimize the cost function, we introduce a new optimization problem with radio, processing,  and transmission  resource constraints. In addition, we introduce two new constraints that guarantee the proactivity and resilience capabilities of the network based on the probability of successful slice provisioning  (PSSP). Since the proposed optimization problem is non-convex, online and  belongs to the NP-hard category, we adopt  a deep reinforcement learning (DRL) based method to solve it. The obtained results reveal that the applied method can improve  the percentage of successful slice provisioned (PrSSP). In addition, the resiliency time is reduced comparatively.  Finally, as the main achievement, the resilient scenario improves PrSSP   compared to the non-resilient scenario.
DOI: 10.5281/zenodo.10395169
title: Data Set for Predicting the Performance of ATL Model Transformations Based on Generated Models

description: Predicting the execution time of model transformations can help to understand how a transformation reacts to a given input model without creating and transforming the respective model.

In our previous data set (https://doi.org/10.5281/zenodo.8385957), we have documented our experiments in which we predict the performance of ATL transformations using predictive models obtained from training linear regression, random forest and support vector regression. As input for the prediction, our approach uses a characterization of the input model. In these experiments, we only used data from real models.

However, a common problem is that transformation developers do not have enough models available to use such a prediction approach. Therefore, in a new variant of our experiments, we investigated whether the three considered machine learning approaches can predict the performance of transformations if we use data from generated models for training. We also investigated whether it is possible to achieve good predictions with smaller training data. The dataset provided here offers the corresponding raw data, scripts, and results.

A detailed documentation is available in documentaion.pdf.
description: This work was partially funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 358569332. The authors acknowledge the support by the state of Baden-Württemberg through bwHPC.
DOI: 10.34740/kaggle/dsv/7215978
title: COVID Vaccination in World (updated daily)
DOI: 10.5281/zenodo.10379627
title: Fluorescences des vins ( rouge, rosé et blanc) et de différentes huiles d'olive

description: Nous avons recueilli ces données dans le cadres d'expériences sur la fluorescence des liquides du quotidien.

Vous trouverez des spectres d'absorbance et d'excitation de différentes huile d'olive. Ainsi que des spectres d'absorbances, d'emission et d'excitation de différent vin.
DOI: 10.5281/zenodo.10379628
title: Fluorescences des vins ( rouge, rosé et blanc) et de différentes huiles d'olive

description: Nous avons recueilli ces données dans le cadres d'expériences sur la fluorescence des liquides du quotidien.

Vous trouverez des spectres d'absorbance et d'excitation de différentes huile d'olive. Ainsi que des spectres d'absorbances, d'emission et d'excitation de différent vin.
DOI: 10.5281/zenodo.10395170
title: Data Set for Predicting the Performance of ATL Model Transformations Based on Generated Models

description: Predicting the execution time of model transformations can help to understand how a transformation reacts to a given input model without creating and transforming the respective model.

In our previous data set (https://doi.org/10.5281/zenodo.8385957), we have documented our experiments in which we predict the performance of ATL transformations using predictive models obtained from training linear regression, random forest and support vector regression. As input for the prediction, our approach uses a characterization of the input model. In these experiments, we only used data from real models.

However, a common problem is that transformation developers do not have enough models available to use such a prediction approach. Therefore, in a new variant of our experiments, we investigated whether the three considered machine learning approaches can predict the performance of transformations if we use data from generated models for training. We also investigated whether it is possible to achieve good predictions with smaller training data. The dataset provided here offers the corresponding raw data, scripts, and results.

A detailed documentation is available in documentaion.pdf.
description: This work was partially funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 358569332. The authors acknowledge the support by the state of Baden-Württemberg through bwHPC.
DOI: 10.25345/c5862bp0x
title: MassIVE MSV000093675 - TcSERPIN_an_inhibitor_that interacts_with_cocoa_defense_proteins_and_has_biotechnological_potential_against_human_pathogens
title: The objective of this study was to characterize a serpin from Theobroma cacao, called TcSERPIN, to identify its endogenous targets and determine its function and biotechnological potential. The protease trap containing immobilized rTcSERPIN captured endogenous defense proteins from cocoa extracts that are related to metabolic pathways, stress and defense.
DOI: 10.5281/zenodo.10395328
title: The causal links between gut microbiota and diabetic nephropathy: A Mendelian randomization study. Supplementary material
DOI: 10.5281/zenodo.10395329
title: The causal links between gut microbiota and diabetic nephropathy: A Mendelian randomization study. Supplementary material
DOI: 10.3886/e195881
title: Will Economic Policy Uncertainty Affect the Carbon Intensity of Manufacturing Enterprises?

description: To explore the relationship between economic policy uncertainty, financing constraints, and carbon emissions of manufacturing enterprises, we obtained empirical research data by cleaning, filtering, and eliminating the original data from the Chinese tax survey database. The data sample covers the period from 2012 to 2016 and includes only manufacturing enterprises. The data primarily consist of enterprise carbon emissions, economic policy uncertainty in the region where the enterprises are located, and relevant control variables.
DOI: 10.3886/e195881v1
title: Will Economic Policy Uncertainty Affect the Carbon Intensity of Manufacturing Enterprises?

description: To explore the relationship between economic policy uncertainty, financing constraints, and carbon emissions of manufacturing enterprises, we obtained empirical research data by cleaning, filtering, and eliminating the original data from the Chinese tax survey database. The data sample covers the period from 2012 to 2016 and includes only manufacturing enterprises. The data primarily consist of enterprise carbon emissions, economic policy uncertainty in the region where the enterprises are located, and relevant control variables.
DOI: 10.5517/ccdc.csd.cc2fjz32
title: CCDC 2250634: Experimental Crystal Structure Determination

description: Related Article: Isha Mishra, Bhaskaran Shankar, Malaichamy Sathiyendiran|2023|Cryst.Growth Des.|23|9103|doi:10.1021/acs.cgd.3c01125
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.cc2fjz43
title: CCDC 2250635: Experimental Crystal Structure Determination

description: Related Article: Isha Mishra, Bhaskaran Shankar, Malaichamy Sathiyendiran|2023|Cryst.Growth Des.|23|9103|doi:10.1021/acs.cgd.3c01125
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.cc2fjz54
title: CCDC 2250636: Experimental Crystal Structure Determination

description: Related Article: Isha Mishra, Bhaskaran Shankar, Malaichamy Sathiyendiran|2023|Cryst.Growth Des.|23|9103|doi:10.1021/acs.cgd.3c01125
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.cc2fr61g
title: CCDC 2256615: Experimental Crystal Structure Determination

description: Related Article: Chen-Yu Lien, Jan-Niklas Boyn, Sophie W. Anferov, David A. Mazziotti, John S. Anderson|2023|Inorg.Chem.|62|19488|doi:10.1021/acs.inorgchem.3c02534
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.cc2frc0l
title: CCDC 2256769: Experimental Crystal Structure Determination

description: Related Article: Jie Wang, Run Yu, Cuicui Nian, Maoyan Liao, Zhengyu Han, Jianwei Sun, Hai Huang|2023|Org.Lett.|25|8478|doi:10.1021/acs.orglett.3c03406
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.cc2ft3r4
title: CCDC 2258466: Experimental Crystal Structure Determination

description: Related Article: Prasanna Kumar Ganta, Mallemadugula Ravi Teja, Rajiv Kamaraj, Ya-Rong Tsai, Yu-Ting Chu, Anandan Sambandam, Yi-Chun Lai, Shangwu Ding, Hsuan-Ying Chen|2023|Organometallics|42|3405|doi:10.1021/acs.organomet.3c00383
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/ds/4136821
title: [AAAI 2024] VigSet
DOI: 10.34740/kaggle/dsv/7077993
title: [IJCAI 2023] FilmSet
DOI: 10.3886/e113298v1-143903
title: Replication data for: On the Welfare Cost of Inflation and the Recent Behavior of Money Demand

description: Post-1980 US data trace out a stable long-run money demand relationship of Cagan's semi-log form between the M1-income ratio and the nominal interest rate, with an interest semielasticity below 2. Integrating under this money demand curve yields estimates of the welfare costs of modest departures from Friedman's zero nominal interest rate rule for the optimum quantity of money that are quite small. The results suggest that the Federal Reserve's current policy, which generates low but still positive rates of inflation, provides an adequate approximation in welfare terms to the alternative of moving all the way to the Friedman rule. (JEL E31, E41, E52)
DOI: 10.6084/m9.figshare.24845724
title: DNRA - Supplementary Material

description: Supplementary Tables to Thesis Aart Reinier Egas. Chapter 4.
DOI: 10.6084/m9.figshare.24845724.v1
title: DNRA - Supplementary Material

description: Supplementary Tables to Thesis Aart Reinier Egas. Chapter 4.