cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.19368527.v1
title: Supplementary Material for: Bullying Perpetration and Victimization as Social Mechanisms in the Development of Borderline Personality Traits during Adolescence: A Longitudinal Study

description: <b><i>Introduction:</i></b> Borderline personality features are often associated with toxic social relationships and problematic peer functioning. Less is known, however, about the extent to which bullying experiences may shape the development and maintenance of borderline-related traits during crucial periods of rapid normative developments in impulse and emotion regulation skills. Given the core interpersonal character of borderline personality pathology (BPP), such research focus may be relevant to better understand possible causal social mechanisms in the development of personality difficulties within the borderline trait spectrum. <b><i>Method:</i></b> The current longitudinal study examined whether experiences of both bullying perpetration and victimization in pre-adolescence mediated the developmental course of BPP traits between childhood and adolescence. To examine these associations, a sample of children (<i>N</i> = 242; 57% girls; <i>M</i><sub>age</sub> = 10.87 years) was recruited and followed up 1 and 4 years later. <b><i>Results:</i></b> Mediation analysis indicated that the prospective link between child and adolescent BPP trait vulnerability was shaped by pre-adolescent experiences of bullying victimization, but not by bullying perpetration. <b><i>Discussion:</i></b> These results indicate that the continuity between child and adolescent borderline trait features are partly explained by exposure to bullying victimization, indicating that the impact of environmental invalidation on BPP development also extends to the peer context.
DOI: 10.18112/openneuro.ds004068.v1.0.1
title: AutomatedZShimSpinalCord
DOI: 10.34740/kaggle/dsv/3308869
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.17037/data.00002817
title: Clinical codelist - CPRD Aurum - chronic neurological disease

description: SNOMED codes for a clinical diagnosis of, or clinical review specific to, a neurological disease such as Parkinson’s disease, motor neurone disease, multiple sclerosis (MS) or cerebral palsy.
DOI: 10.5517/ccdc.csd.ccvmf95
title: CCDC 793237: Experimental Crystal Structure Determination

description: Related Article: Ramachandran Rajamanickam, Rani Mannangatty, Jayanthi Sampathkumar, Kabilan Senthamaraikannan, Barathi Diravidamani|2022|J.Mol.Struct.|1259|132747|doi:10.1016/j.molstruc.2022.132747
DOI: 10.5517/ccdc.csd.ccvqy4m
title: CCDC 796611: Experimental Crystal Structure Determination

description: Related Article: Ramachandran Rajamanickam, Rani Mannangatty, Jayanthi Sampathkumar, Kabilan Senthamaraikannan, Barathi Diravidamani|2022|J.Mol.Struct.|1259|132747|doi:10.1016/j.molstruc.2022.132747
DOI: 10.5517/ccdc.csd.cc25vwc9
title: CCDC 2021831: Experimental Crystal Structure Determination

description: Related Article: Jing He, Mohan Yu, Maofu Pang, Yanqing Fan, Zhe Lian, Ying Wang, Wenguang Wang, Yajun Liu, Hua Jiang|2022|Chem.-Eur.J.|28|e202103832|doi:10.1002/chem.202103832
DOI: 10.5517/ccdc.csd.cc25vwdb
title: CCDC 2021832: Experimental Crystal Structure Determination

description: Related Article: Jing He, Mohan Yu, Maofu Pang, Yanqing Fan, Zhe Lian, Ying Wang, Wenguang Wang, Yajun Liu, Hua Jiang|2022|Chem.-Eur.J.|28|e202103832|doi:10.1002/chem.202103832
DOI: 10.5517/ccdc.csd.cc27nl2l
title: CCDC 2075359: Experimental Crystal Structure Determination

description: Related Article: Jing He, Mohan Yu, Maofu Pang, Yanqing Fan, Zhe Lian, Ying Wang, Wenguang Wang, Yajun Liu, Hua Jiang|2022|Chem.-Eur.J.|28|e202103832|doi:10.1002/chem.202103832
DOI: 10.5517/ccdc.csd.cc27nl5p
title: CCDC 2075362: Experimental Crystal Structure Determination

description: Related Article: Jing He, Mohan Yu, Maofu Pang, Yanqing Fan, Zhe Lian, Ying Wang, Wenguang Wang, Yajun Liu, Hua Jiang|2022|Chem.-Eur.J.|28|e202103832|doi:10.1002/chem.202103832
DOI: 10.5517/ccdc.csd.cc27wm2v
title: CCDC 2082117: Experimental Crystal Structure Determination

description: Related Article: Jing He, Mohan Yu, Maofu Pang, Yanqing Fan, Zhe Lian, Ying Wang, Wenguang Wang, Yajun Liu, Hua Jiang|2022|Chem.-Eur.J.|28|e202103832|doi:10.1002/chem.202103832
DOI: 10.5517/ccdc.csd.cc28xsmm
title: CCDC 2113041: Experimental Crystal Structure Determination

description: Related Article: Jaya Mehara, Brandon T. Watson, Anurag Noonikara-Poyil, Adway O. Zacharias, Jana Roithová, H. V. Rasika Dias|2022|Chem.-Eur.J.|28|e202103984|doi:10.1002/chem.202103984
DOI: 10.5517/ccdc.csd.cc28xsnn
title: CCDC 2113042: Experimental Crystal Structure Determination

description: Related Article: Jaya Mehara, Brandon T. Watson, Anurag Noonikara-Poyil, Adway O. Zacharias, Jana Roithová, H. V. Rasika Dias|2022|Chem.-Eur.J.|28|e202103984|doi:10.1002/chem.202103984
DOI: 10.5517/ccdc.csd.cc28xspp
title: CCDC 2113043: Experimental Crystal Structure Determination

description: Related Article: Jaya Mehara, Brandon T. Watson, Anurag Noonikara-Poyil, Adway O. Zacharias, Jana Roithová, H. V. Rasika Dias|2022|Chem.-Eur.J.|28|e202103984|doi:10.1002/chem.202103984
DOI: 10.5517/ccdc.csd.cc28xsqq
title: CCDC 2113044: Experimental Crystal Structure Determination

description: Related Article: Jaya Mehara, Brandon T. Watson, Anurag Noonikara-Poyil, Adway O. Zacharias, Jana Roithová, H. V. Rasika Dias|2022|Chem.-Eur.J.|28|e202103984|doi:10.1002/chem.202103984
DOI: 10.5517/ccdc.csd.cc28xsrr
title: CCDC 2113045: Experimental Crystal Structure Determination

description: Related Article: Jaya Mehara, Brandon T. Watson, Anurag Noonikara-Poyil, Adway O. Zacharias, Jana Roithová, H. V. Rasika Dias|2022|Chem.-Eur.J.|28|e202103984|doi:10.1002/chem.202103984
DOI: 10.5517/ccdc.csd.cc29210g
title: CCDC 2117114: Experimental Crystal Structure Determination

description: Related Article: Lucas Kistner, Dario Kowatsch, Andreas Marz, Elisabeth Kaifer, Hans-Jörg Himmel|2022|Chem.-Eur.J.|28|e202104016|doi:10.1002/chem.202104016
DOI: 10.5517/ccdc.csd.cc29211h
title: CCDC 2117115: Experimental Crystal Structure Determination

description: Related Article: Lucas Kistner, Dario Kowatsch, Andreas Marz, Elisabeth Kaifer, Hans-Jörg Himmel|2022|Chem.-Eur.J.|28|e202104016|doi:10.1002/chem.202104016
DOI: 10.5517/ccdc.csd.cc29212j
title: CCDC 2117116: Experimental Crystal Structure Determination

description: Related Article: Lucas Kistner, Dario Kowatsch, Andreas Marz, Elisabeth Kaifer, Hans-Jörg Himmel|2022|Chem.-Eur.J.|28|e202104016|doi:10.1002/chem.202104016
DOI: 10.5517/ccdc.csd.cc29213k
title: CCDC 2117117: Experimental Crystal Structure Determination

description: Related Article: Lucas Kistner, Dario Kowatsch, Andreas Marz, Elisabeth Kaifer, Hans-Jörg Himmel|2022|Chem.-Eur.J.|28|e202104016|doi:10.1002/chem.202104016
DOI: 10.5517/ccdc.csd.cc26v5r0
title: CCDC 2050952: Experimental Crystal Structure Determination

description: Related Article: Elahe Keshavarzian, Zahra Asadi, Monika Kucerakova, Michal Dusek, Banafsheh Rastegari|2022|Polyhedron|218|115758|doi:10.1016/j.poly.2022.115758
DOI: 10.5517/ccdc.csd.cc2bbqzd
title: CCDC 2155274: Experimental Crystal Structure Determination

description: Related Article: M. Chodkiewicz, S. Pawlędzio, M. Woińska, K. Woźniak|2022|IUCrJ|9|298|doi:10.1107/S2052252522000690
DOI: 10.5517/ccdc.csd.cc2bbr0g
title: CCDC 2155275: Experimental Crystal Structure Determination

description: Related Article: M. Chodkiewicz, S. Pawlędzio, M. Woińska, K. Woźniak|2022|IUCrJ|9|298|doi:10.1107/S2052252522000690
DOI: 10.5517/ccdc.csd.cc2bbr1h
title: CCDC 2155276: Experimental Crystal Structure Determination

description: Related Article: M. Chodkiewicz, S. Pawlędzio, M. Woińska, K. Woźniak|2022|IUCrJ|9|298|doi:10.1107/S2052252522000690
DOI: 10.5517/ccdc.csd.cc2bbtfy
title: CCDC 2155350: Experimental Crystal Structure Determination

description: Related Article: M. Chodkiewicz, S. Pawlędzio, M. Woińska, K. Woźniak|2022|IUCrJ|9|298|doi:10.1107/S2052252522000690
DOI: 10.34740/kaggle/dsv/3308901
title: 2021 World Population (updated daily)
DOI: 10.17037/data.00002818
title: Clinical codelist - CPRD Aurum - Migraine

description: SNOMED codes for a clinical diagnosis of migraine.
DOI: 10.17037/data.00002819
title: Clinical codelist - CPRD Aurum - non-haematological cancers

description: SNOMED codes for a clinical diagnosis of haematological cancer.
DOI: 10.5281/zenodo.6362062
title: SingleCellExperiment Object MIS-C data (Hoste et al., 2022)

description: Data from https://rupress.org/jem/article/219/2/e20211381/212918/TIM3-TRBV11-2-T-cells-and-IFN-signature-in
description: {"references": ["Levi Hoste, Lisa Roels, Leslie Naesens, Victor Bosteels, Stijn Vanhee, Sam Dupont, Cedric Bosteels, Robin Browaeys, Niels Vandamme, Kevin Verstaen, Jana Roels, Karel F.A. Van Damme, Bastiaan Maes, Elisabeth De Leeuw, Jozefien Declercq, Helena Aegerter, Leen Seys, Ursula Smole, Sofie De Prijck, Manon Vanheerswynghels, Karlien Claes, Veronique Debacker, Gert Van Isterdael, Lynn Backers, Kathleen B.M. Claes, Paul Bastard, Emmanuelle Jouanguy, Shen-Ying Zhang, Gilles Mets, Joke Dehoorne, Kristof Vandekerckhove, Petra Schelstraete, Jef Willems, MIS-C Clinicians, Patrick Stordeur, Sophie Janssens, Rudi Beyaert, Yvan Saeys, Jean-Laurent Casanova, Bart N. Lambrecht, Filomeen Haerynck, Simon J. Tavernier; TIM3+ TRBV11-2 T cells and IFN\u03b3 signature in patrolling monocytes and CD16+ NK cells delineate MIS-C. J Exp Med 7 February 2022; 219 (2): e20211381. doi: https://doi.org/10.1084/jem.20211381"]}
DOI: 10.6084/m9.figshare.19368557.v1
title: Matrix of pairwise correlation coefficients

description: Matrix of pairwise correlation coefficients
DOI: 10.6084/m9.figshare.19368557
title: Matrix of pairwise correlation coefficients

description: Matrix of pairwise correlation coefficients
DOI: 10.5281/zenodo.6362263
title: GERONTE H2020 Project - GERDAT002 - Composition of the health care professional consortium

description: <strong>The present document is a dataset generated as part of Deliverable D1.1. of the GERONTE project, which has received funding from the European Union’s Horizon 2020 Programme under Grant Agreement N°945218. It aims to provide the geriatric oncology professional community</strong> <strong>with a dataset of core health care professionals that should be involvoed in the evaluation and treatment trajectories of older patients with cancer and multimorbidity.</strong> GERONTE is a 5-year research and innovation project (April 2021 to Mars 2026) funded by the European Union within the framework of the H2020 Research and Innovation programme, in response to the health societal challenge topic SC1-BHC-24-2020 “Healthcare interventions for the management of the elderly multimorbid patient”. The overall aim of GERONTE is to improve quality of life - defined as well-being on three levels: global health status, physical functioning and social functioning- for older multimorbid patients, while reducing overall costs of care. To this end, GERONTE will co-design, test, and prepare for deployment an innovative cost-effective patient-centred holistic health management system, hereafter referred to as the GERONTE intervention. GERONTE intervention will rely on an ICT based application for real-time collection and integration of standardised clinical and home patient-reported data. GERONTE intervention will be demonstrated in the context of care of multimorbid patients having cancer as a dominant morbidity, and be adaptable to any other combination of morbidities. An important component of the GerOnTe care pathway was to determine which health care professionals should be included in the health care professional consortium (HPC) providing care for the patient. Beforehand, we had considered the option of four core members and at least eight other participants depending on the patient’s specificities or profile. Based on clinical experience, we developed a list of 15 potential participants, including general practitioner, one or more oncology specialists (such as surgeons, medical oncologists, radiotherapists), geriatrician, oncology nurse, social worker, clinical pharmacist, physiotherapist, anaesthesiologist, home care nurse, dietician, occupational therapist, spiritual helpers/clerics, psychologist/psychiatrist, palliative care specialist, organ-specific physician(s) such as cardiologist, pulmonologist, nephrologist, rheumatologist etc. This list was presented to the expert panel, and they were asked to determine whether or not these participants should be involved in decision-making and/or the subsequent oncologic care trajectory; experts could specify if these participants should be involved for all patients, only in specific situations/profiles, or did not need to be involved. The results of this expert panel survey and the subsequent composition of the health care professional consortium forms the basis of this dataset.
DOI: 10.6084/m9.figshare.19368317
title: File S1.xlsx

description: Gene lists for Sureselect probe set 1 and set 2
DOI: 10.6084/m9.figshare.19368317.v1
title: File S1.xlsx

description: Gene lists for Sureselect probe set 1 and set 2
DOI: 10.5281/zenodo.6362248
title: GERONTE H2020 project - GERDAT001 - Core multimorbidity dataset

description: <strong>The present document is a dataset generated as part of Deliverable D1.1. of the GERONTE project, which has received funding from the European Union’s Horizon 2020 Programme under Grant Agreement N°945218. It aims to provide the geriatric oncology professional community with a dataset of core comorbidity data to be included and assessed in the evaluation of older patients with cancer and multimorbidity. </strong> GERONTE is a 5-year research and innovation project (April 2021 to Mars 2026) funded by the European Union within the framework of the H2020 Research and Innovation programme, in response to the health societal challenge topic SC1-BHC-24-2020 “Healthcare interventions for the management of the elderly multimorbid patient”. The overall aim of GERONTE is to improve quality of life - defined as well-being on three levels: global health status, physical functioning and social functioning- for older multimorbid patients, while reducing overall costs of care. To this end, GERONTE will co-design, test, and prepare for deployment an innovative cost-effective patient-centred holistic health management system, hereafter referred to as the GERONTE intervention. GERONTE intervention will rely on an ICT based application for real-time collection and integration of standardised clinical and home patient-reported data. GERONTE intervention will be demonstrated in the context of care of multimorbid patients having cancer as a dominant morbidity, and be adaptable to any other combination of morbidities. An important component of the GerOnTe care pathway was to determine which comorbidity information is need for optimizing treatment decision making and the subsequent care trajectory. We showed a list of comorbidities from literature and subsequently asked an expert panel to determine which of these were relevant for oncologic decision making and care. As the experts also stated that additional information on the severity of a comorbid condition is needed to know if it impacts an oncologic decision or a treatment trajectory. Therefore, we asked the experts to state for the sixteen somatic/psychiatric comorbidities considered important in the previous round, whether or not the presence itself is sufficient information or if they needed extra information quantifying the severity; if so, we also asked which information. This led to the composition of the dataset presented here.
DOI: 10.5281/zenodo.6362401
title: GERONTE H2020 project - GERDAT004 - Dataset of self-management recommendations

description: <strong>The present document is a dataset generated as part of Deliverable D1.1. of the GERONTE project, which has received funding from the European Union’s Horizon 2020 Programme under Grant Agreement N°945218. It aims to provide the geriatric oncology professional community with a dataset of self-management recommendations that can be used in the care for older patients with cancer and multimorbidity.</strong> GERONTE is a 5-year research and innovation project (April 2021 to Mars 2026) funded by the European Union within the framework of the H2020 Research and Innovation programme, in response to the health societal challenge topic SC1-BHC-24-2020 “Healthcare interventions for the management of the elderly multimorbid patient”. The overall aim of GERONTE is to improve quality of life - defined as well-being on three levels: global health status, physical functioning and social functioning- for older multimorbid patients, while reducing overall costs of care. To this end, GERONTE will co-design, test, and prepare for deployment an innovative cost-effective patient-centred holistic health management system, hereafter referred to as the GERONTE intervention. GERONTE intervention will rely on an ICT based application for real-time collection and integration of standardised clinical and home patient-reported data. GERONTE intervention will be demonstrated in the context of care of multimorbid patients having cancer as a dominant morbidity, and be adaptable to any other combination of morbidities. Patient empowerment by supporting self-management is an important component of the Geronte care pathway. As patients will be monitoring themselves at home, to register side-effects of treatment, decompensation of comorbidities and signs of functional decline, they will also be faced with questions about how to deal with the issues that they are having. While one important component of the care pathway is early signalling of complications to allow for early intervention by health care professionals, there is also a lot that patients can do for themselves at home to enhance their life-style, decrease burden of signs and symptoms or to improve outcomes. This led to the composition of a dataset to be included in the GERONTE care pathway, which is presented here.
DOI: 10.6084/m9.figshare.19368314
title: File S3.xlsx

description: RNA samples list
DOI: 10.6084/m9.figshare.19368314.v1
title: File S3.xlsx

description: RNA samples list
DOI: 10.17037/data.00002820
title: Clinical codelist - HES - non-haematological cancers

description: ICD-10 code for a diagnosis of nonhaematological cancer
DOI: 10.6084/m9.figshare.19368311
title: File S4.xlsx

description: Lists of regulated <i>C. glabrata</i> genes (.gmt file) sampled from the literature for use in the GSEA software<br>
DOI: 10.6084/m9.figshare.19368311.v1
title: File S4.xlsx

description: Lists of regulated <i>C. glabrata</i> genes (.gmt file) sampled from the literature for use in the GSEA software<br>
DOI: 10.6084/m9.figshare.19368308
title: File S2.xlsx

description: RNAseq results
DOI: 10.6084/m9.figshare.19368308.v1
title: File S2.xlsx

description: RNAseq results
DOI: 10.6084/m9.figshare.17427671
title: Supplementary Material for: Clinical profiles of Asians with NAFLD: A systematic review and meta-analysis

description: Introduction: NAFLD is increasingly prevalent in Asia, where people suffer more metabolic comorbidities at a lower body mass index (BMI), suggesting potential differences in their clinical profile. Therefore, we attempted to characterize the clinical profile of Asians with NAFLD via a meta-analytic approach. Methods: We searched Pubmed, EMBASE, and Cochrane databases from January 1, 2000 to January 17, 2019. Two authors independently reviewed and selected 104 articles (2,247,754 persons) that identified NAFLD in Asians and reported relevant data, especially BMI and ALT, and excluded individuals with other liver disease and excessive alcohol consumption. Individual patient-level data were obtained from seven cohorts in Asia to complement meta-analyzed data. Results: Overall, the mean age was 52.07 (95%CI:51.28-52.85) years with those from Southeast Asia (42.66, 95%CI: 32.23-53.11) being significantly younger. The mean BMI was 26.2 kg/m2, higher in moderate-severe vs. mild hepatic steatosis (28.3 vs. 25.7) patients and NFS ≥-1.455 vs.
DOI: 10.6084/m9.figshare.17427671.v1
title: Supplementary Material for: Clinical profiles of Asians with NAFLD: A systematic review and meta-analysis

description: Introduction: NAFLD is increasingly prevalent in Asia, where people suffer more metabolic comorbidities at a lower body mass index (BMI), suggesting potential differences in their clinical profile. Therefore, we attempted to characterize the clinical profile of Asians with NAFLD via a meta-analytic approach. Methods: We searched Pubmed, EMBASE, and Cochrane databases from January 1, 2000 to January 17, 2019. Two authors independently reviewed and selected 104 articles (2,247,754 persons) that identified NAFLD in Asians and reported relevant data, especially BMI and ALT, and excluded individuals with other liver disease and excessive alcohol consumption. Individual patient-level data were obtained from seven cohorts in Asia to complement meta-analyzed data. Results: Overall, the mean age was 52.07 (95%CI:51.28-52.85) years with those from Southeast Asia (42.66, 95%CI: 32.23-53.11) being significantly younger. The mean BMI was 26.2 kg/m2, higher in moderate-severe vs. mild hepatic steatosis (28.3 vs. 25.7) patients and NFS ≥-1.455 vs.
DOI: 10.5281/zenodo.6362414
title: GERONTE H2020 project - GERDAT004 - Dataset of self-management recommendations

description: <strong>The present document is a dataset generated as part of Deliverable D1.1. of the GERONTE project, which has received funding from the European Union’s Horizon 2020 Programme under Grant Agreement N°945218. It aims to provide the geriatric oncology professional community with a dataset of self-management recommendations that can be used in the care for older patients with cancer and multimorbidity.</strong> GERONTE is a 5-year research and innovation project (April 2021 to Mars 2026) funded by the European Union within the framework of the H2020 Research and Innovation programme, in response to the health societal challenge topic SC1-BHC-24-2020 “Healthcare interventions for the management of the elderly multimorbid patient”. The overall aim of GERONTE is to improve quality of life - defined as well-being on three levels: global health status, physical functioning and social functioning- for older multimorbid patients, while reducing overall costs of care. To this end, GERONTE will co-design, test, and prepare for deployment an innovative cost-effective patient-centred holistic health management system, hereafter referred to as the GERONTE intervention. GERONTE intervention will rely on an ICT based application for real-time collection and integration of standardised clinical and home patient-reported data. GERONTE intervention will be demonstrated in the context of care of multimorbid patients having cancer as a dominant morbidity, and be adaptable to any other combination of morbidities. Patient empowerment by supporting self-management is an important component of the Geronte care pathway. As patients will be monitoring themselves at home, to register side-effects of treatment, decompensation of comorbidities and signs of functional decline, they will also be faced with questions about how to deal with the issues that they are having. While one important component of the care pathway is early signalling of complications to allow for early intervention by health care professionals, there is also a lot that patients can do for themselves at home to enhance their life-style, decrease burden of signs and symptoms or to improve outcomes. This led to the composition of a dataset to be included in the GERONTE care pathway, which is presented here.
DOI: 10.6084/m9.figshare.19368302
title: File S5.xlsx

description: List of <i>C. albicans</i> and <i>C. glabrata</i> ortholog genes regulated in the host.
DOI: 10.17633/rd.brunel.19362089
title: Experimental and numerical study of rate-dependent mode-I failure of a structural adhesive dataset

description: Data for the experimental and numerical results reported in the article: L. Škec, G. Alfano, "Experimental and numerical study of rate-dependent mode-I failure of a structural adhesive"