cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21954/ou.rd.27854583
title: Supplementary materials for PhD thesis “Perceptions of transactional distance: a comparative study of distance learning in the UK and Russia”

description: <i>This dataset </i><i>comprises</i><i> the files contained on a CD-ROM which was attached to the thesis when it was </i><i>submitted</i><i> in 2007</i><i>. It was uploaded to ORDO in 2024 for preservation purposes.</i><i> </i><i>For more information, please refer to the </i><i>thesis </i><i> “</i><i>Perceptions of transactional distance: a comparative study of distance learning in the UK and Russia</i><i>” via ORO</i><i>.</i><b>Abstract</b>This study addressed a need for more empirical comparative research on distance and e-leaming through exploring learner perceptions of transactional distance theory, one of the most influential theoretical frameworks in the field.<br><br>It investigated the role of different models of integrating the Web into distance teaching and learning, cultural milieu and institutional contexts in shaping learner experiences of distance study on undergraduate and postgraduate courses in Management and Technology at of the Open University in the United Kingdom and the International Institute of Management LINK in the Russian Federation.<br><br>The results of quantitative data analysis suggested that the participants enrolled on different types of online courses and courses of similar content taught in different countries differed significantly on virtually all of the dimensions of transactional distance. The analysis of qualitative data supported the salience of cultural factors in shaping distance learner experiences. In their accounts of the challenges of learning at a distance UK students stressed the importance of independence and the need for flexibility in delivering course content, while their Russian colleagues suggested that formal nature of online discourse and insufficient number of face-to-face sessions created additional difficulties to their successful progress on the programmes under study.<br><br>Based on the findings of this project, a new conceptual framework of research into transactional distance was proposed which highlighted the importance of immediate course contexts, wider social factors and mediating effects of individual learner characteristics.<br><br>Drawing on the results of empirical work a new definition of the concept of transactional distance was provided, which conceptualised transactional distance as primarily a context-specific phenomenon.
DOI: 10.21954/ou.rd.27854583.v1
title: Supplementary materials for PhD thesis “Perceptions of transactional distance: a comparative study of distance learning in the UK and Russia”

description: <i>This dataset </i><i>comprises</i><i> the files contained on a CD-ROM which was attached to the thesis when it was </i><i>submitted</i><i> in 2007</i><i>. It was uploaded to ORDO in 2024 for preservation purposes.</i><i> </i><i>For more information, please refer to the </i><i>thesis </i><i> “</i><i>Perceptions of transactional distance: a comparative study of distance learning in the UK and Russia</i><i>” via ORO</i><i>.</i><b>Abstract</b>This study addressed a need for more empirical comparative research on distance and e-leaming through exploring learner perceptions of transactional distance theory, one of the most influential theoretical frameworks in the field.<br><br>It investigated the role of different models of integrating the Web into distance teaching and learning, cultural milieu and institutional contexts in shaping learner experiences of distance study on undergraduate and postgraduate courses in Management and Technology at of the Open University in the United Kingdom and the International Institute of Management LINK in the Russian Federation.<br><br>The results of quantitative data analysis suggested that the participants enrolled on different types of online courses and courses of similar content taught in different countries differed significantly on virtually all of the dimensions of transactional distance. The analysis of qualitative data supported the salience of cultural factors in shaping distance learner experiences. In their accounts of the challenges of learning at a distance UK students stressed the importance of independence and the need for flexibility in delivering course content, while their Russian colleagues suggested that formal nature of online discourse and insufficient number of face-to-face sessions created additional difficulties to their successful progress on the programmes under study.<br><br>Based on the findings of this project, a new conceptual framework of research into transactional distance was proposed which highlighted the importance of immediate course contexts, wider social factors and mediating effects of individual learner characteristics.<br><br>Drawing on the results of empirical work a new definition of the concept of transactional distance was provided, which conceptualised transactional distance as primarily a context-specific phenomenon.
DOI: 10.15468/dl.ez4vtk
title: Occurrence Download

description: A dataset containing 41 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Camellia parvimuricata Hung T.Chang"
 ]
}

 The dataset includes 41 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025861-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sb9emr
title: Occurrence Download

description: A dataset containing 57986 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "Geometry POLYGON((113.27105 46.80607,120.25635 38.6288,123.90539 37.7273,129.95236 38.22041,134.43546 43.27152,135.79082 46.80607,134.53972 49.3139,131.93327 50.85242,127.24165 53.34925,122.96707 54.69655,118.58822 54.51539,115.1477 52.27807,112.95828 48.76715,113.27105 46.80607))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Year 2019-2024"
 ]
}

 The dataset includes 57986 records from 47 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025860-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6096/93ea38
title: Suivi photographique de la Falaise de Socoa - Campagne du 2011-04-04

description: Série de photographies réalisée sur le site de Socoa du 04-04-2011 pour un suivi photogrammétrique. 2 types de photos : (A) de la plateforme depuis la falaise; (B) de la falaise depuis la plateforme; (C) zoom sur une zone effondrée depuis le 18/02.
DOI: 10.6096/i3xtnt
title: Suivi photographique de la Falaise de Socoa - Campagne du 2011-02-18

description: Série de photographies réalisée sur le site de Socoa du 18-02-2011 pour un suivi photogrammétrique. 2 types de photos : (A) de la plateforme depuis la falaise; (B) de la falaise depuis la plateforme.
DOI: 10.6084/m9.figshare.27878955
title: sub1012_multilayer_net.mat

description: The multilayer network involving all participants, as described in the manuscript titled "Topographic, cognitive, and neurobiological profiling of the interdependent structural and functional connectome in the human brain".
DOI: 10.6084/m9.figshare.27878955.v1
title: sub1012_multilayer_net.mat

description: The multilayer network involving all participants, as described in the manuscript titled "Topographic, cognitive, and neurobiological profiling of the interdependent structural and functional connectome in the human brain".
DOI: 10.6096/icqje2
title: Suivi photographique de la Falaise de Socoa - Campagne du 2011-01-19

description: Série de photographies réalisée sur le site de Socoa du 19-01-2011 pour un suivi photogrammétrique.2 types de photos : (A) de la falaise depuis la plateforme; (B) de la plateforme depuis la falaise.
DOI: 10.6084/m9.figshare.27144824
title: Journalism Versus Churnalism: How News Factors in Press Releases Affect Journalistic Processing of Ocean Plastic Research in Newspapers Globally

description: This study evaluates the impact of news factors in press releases sent from scientific organizations on the representation of ocean plastic research in newspapers. We tracked 84 press releases discussing peer-reviewed ocean plastic studies, published between 2017 and 2021 on EurekAlert! and identified their presence in 495 English-language newspapers worldwide. Text comparison showed that press releases contribute highly to the construction of newspaper articles, with copied text segments in over half of the articles. News factors highlighting the prestige of the scientific study, like research published in high-impact journals or conducted by elite researchers were indicators of high newspaper coverage, as were the news factors bad news, magnitude, and social/economic relevance. Scientific press releases usually provide essential details about research, such as sample characteristics, research sites, and methods. However, funding information and research limitations were only marginally present. This study showed that providing research details does not reduce newsworthiness, instead sometimes even increasing it. Providing research details in press releases can influence journalists’ understanding and evaluation of the scientific study and thereby their decision to report. Hence, to assess the newsworthiness of scientific research, we propose that news factor analysis should include research details.
DOI: 10.6084/m9.figshare.27144824.v1
title: Journalism Versus Churnalism: How News Factors in Press Releases Affect Journalistic Processing of Ocean Plastic Research in Newspapers Globally

description: This study evaluates the impact of news factors in press releases sent from scientific organizations on the representation of ocean plastic research in newspapers. We tracked 84 press releases discussing peer-reviewed ocean plastic studies, published between 2017 and 2021 on EurekAlert! and identified their presence in 495 English-language newspapers worldwide. Text comparison showed that press releases contribute highly to the construction of newspaper articles, with copied text segments in over half of the articles. News factors highlighting the prestige of the scientific study, like research published in high-impact journals or conducted by elite researchers were indicators of high newspaper coverage, as were the news factors bad news, magnitude, and social/economic relevance. Scientific press releases usually provide essential details about research, such as sample characteristics, research sites, and methods. However, funding information and research limitations were only marginally present. This study showed that providing research details does not reduce newsworthiness, instead sometimes even increasing it. Providing research details in press releases can influence journalists’ understanding and evaluation of the scientific study and thereby their decision to report. Hence, to assess the newsworthiness of scientific research, we propose that news factor analysis should include research details.
DOI: 10.6096/2hrmsp
title: Suivi photographique de la Falaise de Socoa - Campagne du 2010-11-05

description: Série de photographies réalisée sur le site de Socoa du 05-11-2010 pour un suivi photogrammétrique.2 types de photos : (A) de la plateforme depuis la falaise; (B) de la falaise depuis la plateforme (1ere vrai campagne).
DOI: 10.6084/m9.figshare.24310793
title: DeepADRA2A: predicting adrenergic α2a inhibitors using deep learning

description: Adrenergic α2a (ADRA2A) receptors play a crucial role in modulating various physiological actions, thereby influencing the proper functioning of different systems in the body. ADRA2A regulation is associated with a wide range of effects, including alterations in blood pressure, hypertension, heightened heart rate, etc. Inhibition of these receptors results in the release of noradrenaline, leading to heightened physiological activity, improved alertness, reduced blood pressure, and alleviation of hypertension. Conventional approaches for identifying ADRA2A inhibitors are burdened with high costs, labor-intensive procedures, and time-consuming processes. In light of these challenges, leveraging the power of artificial intelligence offers a promising solution for drug discovery and development. This study endeavors to harness the potential of artificial intelligence to develop robust models capable of accurately predicting ADRA2A inhibitors and non-inhibitors. By doing so, we aim to streamline and expedite the identification of potential drug candidates in this domain. In this study, we employed four different machine learning (ML) and deep learning (DL) algorithms to develop prediction models based on various molecular descriptors (1D, 2D, and molecular fingerprints). Among these models, the DL-based prediction model demonstrated superior performance, achieving accuracies of 98.25% and 97.23% on the training and test datasets, respectively. These results underscore the efficacy of DL-based model, as a highly effective tool for predicting ADRA2A inhibitors. The model is made available at https://github.com/PGlab-NIPER/DeepADRA2A.git. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.24310793.v1
title: DeepADRA2A: predicting adrenergic α2a inhibitors using deep learning

description: Adrenergic α2a (ADRA2A) receptors play a crucial role in modulating various physiological actions, thereby influencing the proper functioning of different systems in the body. ADRA2A regulation is associated with a wide range of effects, including alterations in blood pressure, hypertension, heightened heart rate, etc. Inhibition of these receptors results in the release of noradrenaline, leading to heightened physiological activity, improved alertness, reduced blood pressure, and alleviation of hypertension. Conventional approaches for identifying ADRA2A inhibitors are burdened with high costs, labor-intensive procedures, and time-consuming processes. In light of these challenges, leveraging the power of artificial intelligence offers a promising solution for drug discovery and development. This study endeavors to harness the potential of artificial intelligence to develop robust models capable of accurately predicting ADRA2A inhibitors and non-inhibitors. By doing so, we aim to streamline and expedite the identification of potential drug candidates in this domain. In this study, we employed four different machine learning (ML) and deep learning (DL) algorithms to develop prediction models based on various molecular descriptors (1D, 2D, and molecular fingerprints). Among these models, the DL-based prediction model demonstrated superior performance, achieving accuracies of 98.25% and 97.23% on the training and test datasets, respectively. These results underscore the efficacy of DL-based model, as a highly effective tool for predicting ADRA2A inhibitors. The model is made available at https://github.com/PGlab-NIPER/DeepADRA2A.git. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.24310791
title: Exploring natural compound, Panicutine as leucine-rich repeat kinase 2 inhibitor against Parkinson’s disease: a structure-guided approach

description: Leucine-rich repeat kinase 2 (LRRK2) is a promising drug target for the therapeutic management of Parkinson’s disease (PD) and other neurodegenerative disorders. LRRK2 inhibitors have the potential to modulate neuroinflammation, reduce alpha-synuclein aggregation and improve motor symptoms in PD patients. Although LRRK2 inhibitors are still in the early stages of clinical development, the identification of potent and selective inhibitors through structure-guided approaches provides a promising avenue for the development of effective therapies for PD and other neurodegenerative disorders. In this study, natural compounds from the IMPPAT database were screened using a state-of-the-art computational virtual screening approach to identify potential inhibitors of LRRK2. We carried out a docking screening on a library of natural compounds and identified a few compounds with strong binding affinity, docking score and specificity towards LRRK2 as the top hits. These hits were then subjected to further analysis based on multiple parameters for the Pan-assay interference compounds and their physicochemical and pharmacokinetics evaluation followed by a detailed interaction analysis. After careful evaluation, one natural compound, Panicutine, was identified as a promising candidate for LRRK2 due to its significant affinity and specificity towards the LRRK2 binding pocket. Additionally, it exhibited drug-like properties with blood–brain barrier permeability as determined by ADMET properties. To gain a deeper understanding of the stability and conformational changes of the LRRK2–ligand complex, MD simulations were conducted for 100 nanoseconds under explicit solvent conditions followed by principal component analysis and free energy dynamics. The simulation results demonstrated that the LRRK2–Panicutine complex remained stable throughout the simulation trajectories. Based on these findings, it is concluded that Panicutine has the potential to act as a LRRK2 inhibitor against PD and other neurodegenerative disorders. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.24310794.v1
title: Exploring bixin from <i>Bixa orellana</i> L. seeds: quantification and <i>in silico</i> insights into its anti-cancer potential

description: Bixin, the key pigment of <i>Bixa orellana</i> L., is an apo-carotenoid found in the seed arils. The present study aimed to quantitatively determine the bixin content of seeds and explore its anti-cancer activity through <i>in silico</i> studies. The bixin content from the seeds of the local genotype, TNMTP8, quantified by RP-HPLC was 4.58 mg per gram. The prediction of pharmacological activity suggested that bixin may serve as a BRAF, MMP9, TNF expression inhibitors, and TP53 expression enhancer. According to molecular docking analysis, bixin interacted with eight different skin cancer targets and had the lowest binding energy compared to the standard drug, 5-fluorouracil. The binding score between bixin and the targets ranged from −4.7 to −8.7 kcal/mol. The targets BRAF and SIRT3 interacted well with bixin, with binding energies as low as −8.3 and −8.7 kcal/mol, respectively. Hence, the dynamic behavior of these two docked complexes throughout a 500 ns trajectory run was investigated further. The Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF) values, and total contacts as a function of time recorded during scrutiny suggest that both complexes were stable. This was validated by post-molecular dynamics analysis using Molecular Mechanics Generalized Born Surface Area (MM-GBSA). Principal component analysis (PCA) was used to analyze the significant differences in motion exhibited by BRAF-Bixin and SIRT3-Bixin. The results showed that bixin is a promising source for potential treatment interventions in skin cancer therapies. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.24367748.v1
title: Computational designing of the ligands of Protein L affinity chromatography based on molecular docking and molecular dynamics simulations

description: Protein L is a multidomain protein from <i>Peptostreptococcus magnus</i> with binding affinity to kappa light chain of human immunoglobulin (Ig) which is used for the purification of antibody fragments by affinity chromatography. The advances in protein engineering and computational biology approaches lead to the development of engineered affinity ligands with improved properties including binding affinity. In this study, molecular dynamics simulations (MDs) and Osprey software were used to design single B domains of the Protein L with higher affinity to antibody fragments. The modified B domains were then polymerized to ligand with six B domains by homology modeling methods. The results showed that single B domain mutants of MB1 (Thr865Trp) and MB2 (Thr847Met-Thr865Trp) had higher binding affinity to Fab compared to the wild single B domain. Also, MDs and molecular docking results showed that the polymerized Proteins L including the wild and mutated six B domains (6B0, 6B1, and 6B2) were stable during MDs and the two mutants of 6B1 and 6B2 showed higher binding affinity to Fab relative to the wild type. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.24310791.v1
title: Exploring natural compound, Panicutine as leucine-rich repeat kinase 2 inhibitor against Parkinson’s disease: a structure-guided approach

description: Leucine-rich repeat kinase 2 (LRRK2) is a promising drug target for the therapeutic management of Parkinson’s disease (PD) and other neurodegenerative disorders. LRRK2 inhibitors have the potential to modulate neuroinflammation, reduce alpha-synuclein aggregation and improve motor symptoms in PD patients. Although LRRK2 inhibitors are still in the early stages of clinical development, the identification of potent and selective inhibitors through structure-guided approaches provides a promising avenue for the development of effective therapies for PD and other neurodegenerative disorders. In this study, natural compounds from the IMPPAT database were screened using a state-of-the-art computational virtual screening approach to identify potential inhibitors of LRRK2. We carried out a docking screening on a library of natural compounds and identified a few compounds with strong binding affinity, docking score and specificity towards LRRK2 as the top hits. These hits were then subjected to further analysis based on multiple parameters for the Pan-assay interference compounds and their physicochemical and pharmacokinetics evaluation followed by a detailed interaction analysis. After careful evaluation, one natural compound, Panicutine, was identified as a promising candidate for LRRK2 due to its significant affinity and specificity towards the LRRK2 binding pocket. Additionally, it exhibited drug-like properties with blood–brain barrier permeability as determined by ADMET properties. To gain a deeper understanding of the stability and conformational changes of the LRRK2–ligand complex, MD simulations were conducted for 100 nanoseconds under explicit solvent conditions followed by principal component analysis and free energy dynamics. The simulation results demonstrated that the LRRK2–Panicutine complex remained stable throughout the simulation trajectories. Based on these findings, it is concluded that Panicutine has the potential to act as a LRRK2 inhibitor against PD and other neurodegenerative disorders. Communicated by Ramaswamy H. Sarma