COVIDiSTRESS Global Survey dataset on psychological and behavioural consequences of the COVID-19 outbreak

This N = 173,426 social science dataset was collected through the collaborative COVIDiSTRESS Global Survey – an open science effort to improve understanding of the human experiences of the 2020 COVID-19 pandemic between 30th March and 30th May, 2020. The dataset allows a cross-cultural study of psychological and behavioural responses to the Coronavirus pandemic and associated government measures like cancellation of public functions and stay at home orders implemented in many countries. The dataset contains demographic background variables as well as measures of Asian Disease Problem, perceived stress (PSS-10), availability of social provisions (SPS-10), trust in various authorities, trustin governmental measures to contain the virus (OECD trust), personality traits (BFF-15), information behaviours, agreement with the level of government intervention, and compliance with preventive measures, along with a rich pool of exploratory variables and written experiences. A global consortium from 39 countries and regions worked together to build and translate a survey with variables of shared interests, and recruited participants in 47 languages and dialects. Raw plus cleaned data and dynamic visualizations are available.</div

COVIDiSTRESS Global Survey dataset on psychological and behavioural consequences of the COVID-19 outbreak

This N = 173,426 social science dataset was collected through the collaborative COVIDiSTRESS Global Survey – an open science effort to improve understanding of the human experiences of the 2020 COVID-19 pandemic between 30th March and 30th May, 2020. The dataset allows a cross-cultural study of psychological and behavioural responses to the Coronavirus pandemic and associated government measures like cancellation of public functions and stay at home orders implemented in many countries. The dataset contains demographic background variables as well as measures of Asian Disease Problem, perceived stress (PSS-10), availability of social provisions (SPS-10), trust in various authorities, trust in governmental measures to contain the virus (OECD trust), personality traits (BFF-15), information behaviours, agreement with the level of government intervention, and compliance with preventive measures, along with a rich pool of exploratory variables and written experiences. A global consortium from 39 countries and regions worked together to build and translate a survey with variables of shared interests, and recruited participants in 47 languages and dialects. Raw plus cleaned data and dynamic visualizations are available

COVIDiSTRESS Global Survey dataset on psychological and behavioural consequences of the COVID-19 outbreak

This N = 173,426 social science dataset was collected through the collaborative COVIDiSTRESS Global Survey - an open science effort to improve understanding of the human experiences of the 2020 COVID-19 pandemic between 30th March and 30th May, 2020. The dataset allows a cross-cultural study of psychological and behavioural responses to the Coronavirus pandemic and associated government measures like cancellation of public functions and stay at home orders implemented in many countries. The dataset contains demographic background variables as well as measures of Asian Disease Problem, perceived stress (PSS-10), availability of social provisions (SPS-10), trust in various authorities, trust in governmental measures to contain the virus (OECD trust), personality traits (BFF-15), information behaviours, agreement with the level of government intervention, and compliance with preventive measures, along with a rich pool of exploratory variables and written experiences. A global consortium from 39 countries and regions worked together to build and translate a survey with variables of shared interests, and recruited participants in 47 languages and dialects. Raw plus cleaned data and dynamic visualizations are available.Measurement(s) psychological measurement center dot anxiety-related behavior trait center dot Stress center dot response to center dot Isolation center dot loneliness measurement center dot Emotional Distress Technology Type(s) Survey Factor Type(s) geographic location center dot language center dot age of participant center dot responses to the Coronavirus pandemic Sample Characteristic - Organism Homo sapiens Sample Characteristic - Location global Machine-accessible metadata file describing the reported data:Peer reviewe

Stress and worry in the 2020 coronavirus pandemic : relationships to trust and compliance with preventive measures across 48 countries in the COVIDiSTRESS global survey

The COVIDiSTRESS global survey collects data on early human responses to the 2020 COVID-19 pandemic from 173 429 respondents in 48 countries. The open science study was co-designed by an international consortium of researchers to investigate how psychological responses differ across countries and cultures, and how this has impacted behaviour, coping and trust in government efforts to slow the spread of the virus. Starting in March 2020, COVIDiSTRESS leveraged the convenience of unpaid online recruitment to generate public data. The objective of the present analysis is to understand relationships between psychological responses in the early months of global coronavirus restrictions and help understand how different government measures succeed or fail in changing public behaviour. There were variations between and within countries. Although Western Europeans registered as more concerned over COVID-19, more stressed, and having slightly more trust in the governments' efforts, there was no clear geographical pattern in compliance with behavioural measures. Detailed plots illustrating between-countries differences are provided. Using both traditional and Bayesian analyses, we found that individuals who worried about getting sick worked harder to protect themselves and others. However, concern about the coronavirus itself did not account for all of the variances in experienced stress during the early months of COVID-19 restrictions. More alarmingly, such stress was associated with less compliance. Further, those most concerned over the coronavirus trusted in government measures primarily where policies were strict. While concern over a disease is a source of mental distress, other factors including strictness of protective measures, social support and personal lockdown conditions must also be taken into consideration to fully appreciate the psychological impact of COVID-19 and to understand why some people fail to follow behavioural guidelines intended to protect themselves and others from infection. The Stage 1 manuscript associated with this submission received in-principle acceptance (IPA) on 18 May 2020. Following IPA, the accepted Stage 1 version of the manuscript was preregistered on the Open Science Framework at https://osf.io/g2t3b. This preregistration was performed prior to data analysis.Peer reviewe

Stress and worry in the 2020 coronavirus pandemic: Relationships to trust and compliance with preventive measures across 48 countries in the COVIDiSTRESS global survey

The COVIDiSTRESS global survey collects data on early human responses to the 2020 COVID-19 pandemic from 173 429 respondents in 48 countries. The open science study was co-designed by an international consortium of researchers to investigate how psychological responses differ across countries and cultures, and how this has impacted behaviour, coping and trust in government efforts to slow the spread of the virus. Starting in March 2020, COVIDiSTRESS leveraged the convenience of unpaid online recruitment to generate public data. The objective of the present analysis is to understand relationships between psychological responses in the early months of global coronavirus restrictions and help understand how different government measures succeed or fail in changing public behaviour. There were variations between and within countries. Although Western Europeans registered as more concerned over COVID-19, more stressed, and having slightly more trust in the governments' efforts, there was no clear geographical pattern in compliance with behavioural measures. Detailed plots illustrating between-countries differences are provided. Using both traditional and Bayesian analyses, we found that individuals who worried about getting sick worked harder to protect themselves and others. However, concern about the coronavirus itself did not account for all of the variances in experienced stress during the early months of COVID-19 restrictions. More alarmingly, such stress was associated with less compliance. Further, those most concerned over the coronavirus trusted in government measures primarily where policies were strict. While concern over a disease is a source of mental distress, other factors including strictness of protective measures, social support and personal lockdown conditions must also be taken into consideration to fully appreciate the psychological impact of COVID-19 and to understand why some people fail to follow behavioural guidelines intended to protect themselves and others from infection. The Stage 1 manuscript associated with this submission received in-principle acceptance (IPA) on 18 May 2020. Following IPA, the accepted Stage 1 version of the manuscript was preregistered on the Open Science Framework at https://osf.io/g2t3b. This preregistration was performed prior to data analysis

Stress and worry in the 2020 coronavirus pandemic: relationships to trust and compliance with preventive measures across 48 countries in the COVIDiSTRESS global survey

The COVIDiSTRESS global survey collects data on early human responses to the 2020 COVID-19 pandemic from 173 429 respondents in 48 countries. The open science study was co-designed by an international consortium of researchers to investigate how psychological responses differ across countries and cultures, and how this has impacted behaviour, coping and trust in government efforts to slow the spread of the virus. Starting in March 2020, COVIDiSTRESS leveraged the convenience of unpaid online recruitment to generate public data. The objective of the present analysis is to understand relationships between psychological responses in the early months of global coronavirus restrictions and help understand how different government measures succeed or fail in changing public behaviour. There were variations between and within countries. Although Western Europeans registered as more concerned over COVID-19, more stressed, and having slightly more trust in the governments' efforts, there was no clear geographical pattern in compliance with behavioural measures. Detailed plots illustrating between-countries differences are provided. Using both traditional and Bayesian analyses, we found that individuals who worried about getting sick worked harder to protect themselves and others. However, concern about the coronavirus itself did not account for all of the variances in experienced stress during the early months of COVID-19 restrictions. More alarmingly, such stress was associated with less compliance. Further, those most concerned over the coronavirus trusted in government measures primarily where policies were strict. While concern over a disease is a source of mental distress, other factors including strictness of protective measures, social support and personal lockdown conditions must also be taken into consideration to fully appreciate the psychological impact of COVID-19 and to understand why some people fail to follow behavioural guidelines intended to protect themselves and others from infection. The Stage 1 manuscript associated with this submission received in-principle acceptance (IPA) on 18 May 2020. Following IPA, the accepted Stage 1 version of the manuscript was preregistered on the Open Science Framework at https://osf.io/g2t3b. This preregistration was performed prior to data analysis

Conceptualization and exploitation of a partitioned pangenome graph as a compact representation of the diversity of the genic repertoire of prokaryote species

Introduites en microbiologie en 2005, les approches pangénomiques visent à compiler l'ensemble de la diversité génomique d'une espèce. Dans ces études, on distingue généralement à l'intérieur du pangénome, le génome coeur, c'est-à-dire l'ensemble des familles de gènes où les représentants géniques sont présents dans tous les organismes; et d'autre part, le génome accessoire qui correspond aux gènes spécifiques à certains organismes seulement. Cependant, on constate que le concept de génome coeur est limitant avec un nombre important d'organismes car des gènes bien que fonctionnellement indispensables peuvent être absents de certains génomes. Pour limiter ce phénomène la quasi-totalité des études utilisent un seuil arbitraire de présence (généralement 95%) pour définir un génome coeur assoupli. De plus, cette dichotomie entre le génome coeur et accessoire ne rend pas compte des nombreuses gammes de fréquence d'apparition des gènes dans un pangénome. Ce travail de thèse a pour objectif de proposer une approche statistique basé sur un modèle mixé multivarié de Bernoulli couplé à un champ de Markov caché pour partitionner le pangénome afin d'être résilient aux absences de gènes et de mieux distinguer les différents schémas de présence/absence des gènes. En parallèle, plusieurs structures de données basées sur des graphes de pangénomes ont été développées ces dernières années. En effet, exploiter la totalité des informations disponibles dans un génome et non plus seulement la présence de gènes isolés est désormais crucial pour correctement rendre compte de l'organisation des génomes et notamment des régions de plasticité génomique dans les espèces. Cette approche se veut le chaînon manquant entre ces nouvelles approches graphiques à l'échelle de la séquence et les approches originelles en familles de gènes isolés. Pour y parvenir, ce travail de thèse s'intéresse donc à la définition, au partitionnement statistique et à l'exploitation d'un graphe d'un pangénome comme représentation compacte de la diversité du répertoire génomique des espèces procaryotes. Enfin, ce graphe est ensuite employé pour analyser la diversité pangénomique de 439 espèces procaryotes.Introduced in microbiology in 2005, pangenome approaches aim to compile the entire genomic diversity of a species. In these studies, we generally distinguish within the pangenome, the core genome, i.e. the set of gene families where gene representatives are present in all organisms; and on the other hand, the accessory genome which corresponds to genes specific to certain organisms only. However, we noticed that the concept of the core genome is limiting with a large number of organisms because genes, although functionally essentials, may be absent from some genomes. To deal with this issue, almost all studies use an arbitrary threshold of presence (generally 95%) to define a soft core genome. Moreover, this dichotomy between the core and accessory genome does not account for the many ranges of frequencies at which genes appear in a pangenome. The main goal of this thesis work is to introduce a statistical approach based on a multivariate Bernoulli mixture model coupled with a hidden Markov random field to partition the pangenome in order to be resilient to gene absences and to better distinguish the gene presence/absence patterns. In parallel, several data structures based on pangenome graphs have been developed in recent years. Indeed, exploiting all the information available in genomes and not just the presence of isolated genes is crucial to highlight genomic organization and particularly the regions of genomic plasticity in species. This approach is intended to be the missing link between these new graphic approaches at the sequence scale and the original approaches in isolated gene families. To achieve this, this thesis work therefore focuses on the definition, statistical partitioning and exploitation of a graph of a pangenome as a compact representation of the diversity of the genomic repertoire of prokaryotic species. Finally, this graph is then used to analyze the pangenomic diversity of 439 prokaryotic species

Conceptualisation et exploitation d’un graphe de pangénome partitionné comme représentation compacte de la diversité du répertoire génique des espèces procaryotes

Introduced in microbiology in 2005, pangenome approaches aim to compile the entire genomic diversity of a species. In these studies, we generally distinguish within the pangenome, the core genome, i.e. the set of gene families where gene representatives are present in all organisms; and on the other hand, the accessory genome which corresponds to genes specific to certain organisms only. However, we noticed that the concept of the core genome is limiting with a large number of organisms because genes, although functionally essentials, may be absent from some genomes. To deal with this issue, almost all studies use an arbitrary threshold of presence (generally 95%) to define a soft core genome. Moreover, this dichotomy between the core and accessory genome does not account for the many ranges of frequencies at which genes appear in a pangenome. The main goal of this thesis work is to introduce a statistical approach based on a multivariate Bernoulli mixture model coupled with a hidden Markov random field to partition the pangenome in order to be resilient to gene absences and to better distinguish the gene presence/absence patterns. In parallel, several data structures based on pangenome graphs have been developed in recent years. Indeed, exploiting all the information available in genomes and not just the presence of isolated genes is crucial to highlight genomic organization and particularly the regions of genomic plasticity in species. This approach is intended to be the missing link between these new graphic approaches at the sequence scale and the original approaches in isolated gene families. To achieve this, this thesis work therefore focuses on the definition, statistical partitioning and exploitation of a graph of a pangenome as a compact representation of the diversity of the genomic repertoire of prokaryotic species. Finally, this graph is then used to analyze the pangenomic diversity of 439 prokaryotic species.Introduites en microbiologie en 2005, les approches pangénomiques visent à compiler l'ensemble de la diversité génomique d'une espèce. Dans ces études, on distingue généralement à l'intérieur du pangénome, le génome coeur, c'est-à-dire l'ensemble des familles de gènes où les représentants géniques sont présents dans tous les organismes; et d'autre part, le génome accessoire qui correspond aux gènes spécifiques à certains organismes seulement. Cependant, on constate que le concept de génome coeur est limitant avec un nombre important d'organismes car des gènes bien que fonctionnellement indispensables peuvent être absents de certains génomes. Pour limiter ce phénomène la quasi-totalité des études utilisent un seuil arbitraire de présence (généralement 95%) pour définir un génome coeur assoupli. De plus, cette dichotomie entre le génome coeur et accessoire ne rend pas compte des nombreuses gammes de fréquence d'apparition des gènes dans un pangénome. Ce travail de thèse a pour objectif de proposer une approche statistique basé sur un modèle mixé multivarié de Bernoulli couplé à un champ de Markov caché pour partitionner le pangénome afin d'être résilient aux absences de gènes et de mieux distinguer les différents schémas de présence/absence des gènes. En parallèle, plusieurs structures de données basées sur des graphes de pangénomes ont été développées ces dernières années. En effet, exploiter la totalité des informations disponibles dans un génome et non plus seulement la présence de gènes isolés est désormais crucial pour correctement rendre compte de l'organisation des génomes et notamment des régions de plasticité génomique dans les espèces. Cette approche se veut le chaînon manquant entre ces nouvelles approches graphiques à l'échelle de la séquence et les approches originelles en familles de gènes isolés. Pour y parvenir, ce travail de thèse s'intéresse donc à la définition, au partitionnement statistique et à l'exploitation d'un graphe d'un pangénome comme représentation compacte de la diversité du répertoire génomique des espèces procaryotes. Enfin, ce graphe est ensuite employé pour analyser la diversité pangénomique de 439 espèces procaryotes

Comment optimiser l'agencement du magasin et de l'automate d'un site de répartition pharmaceutique ?

La répartition a énormément évolué et est aujourd hui un acteur de santé indispensable. Face aux nouvelles pressions économiques, elle doit développer son outil logistique pour le rendre encore plus performant. C est dans ce but que j ai entrepris la création d un tableur permettant d analyser la situation. Ce tableur a une double utilité, car il permet de donner un état des lieux tout en nous permettant de visualiser les axes de correction. Ceci afin de déplacer les produits et les positionner à un endroit permettant de faciliter la préparation de commande et le rangement des spécialités.CAEN-BU Médecine pharmacie (141182102) / SudocSudocFranceF

From Genomics to Pangenomics: the path to pangenome graphs

International audienc