cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57745/imuse1
title: Map_Orleans.html

description: Zoomable map showing the geographic position and characteristics (species, date of observation and number of tents) of each tree included in the survey conducted in Orléans agglomeration. Information appear when the operator points to the tree with the mouse. Target trees are represented in blue, neighbors within the cluster are in green.
DOI: 10.57745/d1qupu
title: raw_data.tab

description: Raw nest counts and characteristics of the observed trees in the 3 agglomerations. Column 1, agglomeration (name of the agglomeration (urban district) where the tree was observed); Column 2, tree id (a unique tree identifier); Column 3, cluster id (a unique identifier for the tree clusters); Column 4, longitude (EPSG:4326); Column 5, latitude (EPSG:4326); Column 6, longitude (epsg:2154); Column 7, latitude (epsg:2154); Column 8, species (tree species); Column 9, size (size of the tree in meter); Column 10, number of tents (number of pine processionary moth tents observed on the individual tree by the field operator); Column 11, date (date of the observation, expressed as yyyy/mm/dd); Column 12, operator (name of the field operator who characterized the tree and the level of infestation; AB - Alexis Bernard, TFM - Thierry Fanjas-Mercere, LG - Louis Gros, BN - Benoit Nusillard, PP - Patrick Pineau, JP - Julien Pradel, AT - Alain Talbi); Column 13, target (description of the variable: Designs the tree as the original target of the cluster or not); Column 14, region (administrative region of France where the tree was observed); Column 15, department (administrative department of France where the tree was observed); Column 16, municipality (town where the tree was observed).
DOI: 10.57745/u0n5yi
title: Barplot_AllSpecies.png

description: Prevalence (percentage of infested trees / surveyed trees) (left) and mean number of tents per infested tree (right) for all tree species surveyed in the 3 agglomerations. Species for which fewer than 10 individuals were characterized are represented in grey, species for which between 10 and 100 individuals were surveyed are represented in yellow, species for which at least 101 individuals were surveyed are represented in red. Numbers shown above each bar correspond to the total number of surveyed trees (left panel), and to the number of infested trees / number of surveyed trees (right panel).
DOI: 10.57745/rww6dh
title: Barplot_AllSpecies_FR.png

description: Barplot labelled in French showing prevalence (percentage of infested trees / surveyed trees) (left) and mean number of tents per infested tree (right) for all tree species surveyed in the 3 agglomerations. Species for which fewer than 10 individuals were characterized are represented in grey, species for which between 10 and 100 individuals were surveyed are represented in yellow, species for which at least 101 individuals were surveyed are represented in red. Numbers shown above each bar correspond to the total number of surveyed trees (left panel), and to the number of infested trees / number of surveyed trees (right panel).
DOI: 10.57745/ddp3fe
title: Barplot_Species_10_Indiv_Min_FR.png

description: Barplot labelled in French showing prevalence (percentage of infested trees / surveyed trees) (left) and mean number of tents per infested tree (right) for the tree species for which at least 10 individuals were surveyed in the 3 agglomerations. Species for which between 10 and 100 individuals were surveyed are represented in yellow, species for which at least 101 individuals were surveyed are represented in red. Numbers shown above each bar correspond to the total number of surveyed trees (left panel), and to the number of infested trees / number of surveyed trees (right panel).
DOI: 10.57745/eelvu3
title: Map_PDF_La_Baule.pdf

description: Map in pdf format showing the geographic position of each tree included in the survey conducted in La Baule agglomeration. Target trees are represented in blue, neighbors within the cluster are in green.
DOI: 10.57745/ahszas
title: Map_PDF_Orleans.pdf

description: Map in pdf format showing the geographic position of each tree included in the survey conducted in Orléans agglomeration. Target trees are represented in blue, neighbors within the cluster are in green.
DOI: 10.57745/ipr4wj
title: Map_PDF_Montpellier.pdf

description: Map in pdf format showing the geographic position of each tree included in the survey conducted in Montpellier agglomeration. Target trees are represented in blue, neighbors within the cluster are in green.
DOI: 10.57745/o7htt0
title: ReadMe_Counts_Pine_Processionary_Moth_Tents.txt

description: Read me file containing the description of the files included in this dataset
DOI: 10.5281/zenodo.8399301
title: Subset of 'MLSUM: The Multilingual Summarization Corpus' for constraints annotation experiment

description: [EN] Subset of 'MLSUM: The Multilingual Summarization Corpus' for constraints annotation experiment.



	
Description: MLSUM is a dataset of newspappers articles aimed at training summaring model. We use it for a constraints annotation experiment on newspapper titles according to their topic classification.
	
Content: For constraints annotation experiment based on data similarity, this dataset have been subsetted (randomly pick 75 articles in the following 14 most used topics: 'economie', 'politique', 'sport', 'planete' (renamed in 'ecologie'), 'sciences', 'police-justice', 'disparitions', 'emploi', 'sante', 'musiques', 'arts', 'educations', 'climat' (renamed in 'meteo'), 'immobilier') and filtered (keep articles that have an obvious topics regarding their titles, without their bodies). Two reviewers have working on this task in order to limit the subjectivity of the filtering. This subsetted dataset is used (1) to estimate needed time to annotate titles similarity with constraints (MUST-LINK, CANNOT-LINK) and (2) to test interactive clustering methodology (constraints annotation and constrained clustering).
	
Origin: The dataset is bassed on the original 'MLSUM: The Multilingual Summarization Corpus' dataset (https://doi.org/10.48550/arXiv.2004.14900).




[FR] Echantillon de 'MLSUM: The Multilingual Summarization Corpus' pour une expérience d'annotation de contraintes.



	
Description : MLSUM est un ensemble de données d'articles de journaux destinés à l'entraînement d'un modèle de résumé automatique. Nous l'utilisons pour une expérience d'annotation de contraintes sur des titres de journaux en fonction de leur classification thématique.
	
Contenu : Pour une expérience d'annotation de contraintes basée sur la similarité des données, cet ensemble de données a été échantillonné (sélectionner au hasard de 75 articles dans les 14 sujets les plus utilisés : 'économie', 'politique', 'sport', 'planète' (renommé en « écologie »). ), 'sciences', 'police-justice', 'disparitions', 'emploi', 'sante', 'musiques', 'arts', 'éducations', 'climat' (renommé en 'meteo'), 'immobilier' ) et filtré (conserver les articles qui ont un sujet évident par rapport à leur titre, sans leur corps). Deux relecteurs ont travaillé sur cette tâche afin de limiter la subjectivité du filtrage. Ce sous-ensemble de données est utilisé (1) pour estimer le temps nécessaire pour annoter la similarité des titres avec des contraintes (MUST-LINK, CANNOT-LINK) et (2) pour tester la méthodologie de clustering interactif (annotation de contraintes et clustering contraint).
	
Origine : L'ensemble de données est basé sur l'ensemble de données original 'MLSUM : The Multilingual Summarization Corpus' (https://doi.org/10.48550/arXiv.2004.1490).
DOI: 10.5281/zenodo.8399302
title: Subset of 'MLSUM: The Multilingual Summarization Corpus' for constraints annotation experiment

description: [EN] Subset of 'MLSUM: The Multilingual Summarization Corpus' for constraints annotation experiment.



	
Description: MLSUM is a dataset of newspappers articles aimed at training summaring model. We use it for a constraints annotation experiment on newspapper titles according to their topic classification.
	
Content: For constraints annotation experiment based on data similarity, this dataset have been subsetted (randomly pick 75 articles in the following 14 most used topics: 'economie', 'politique', 'sport', 'planete' (renamed in 'ecologie'), 'sciences', 'police-justice', 'disparitions', 'emploi', 'sante', 'musiques', 'arts', 'educations', 'climat' (renamed in 'meteo'), 'immobilier') and filtered (keep articles that have an obvious topics regarding their titles, without their bodies). Two reviewers have working on this task in order to limit the subjectivity of the filtering. This subsetted dataset is used (1) to estimate needed time to annotate titles similarity with constraints (MUST-LINK, CANNOT-LINK) and (2) to test interactive clustering methodology (constraints annotation and constrained clustering).
	
Origin: The dataset is bassed on the original 'MLSUM: The Multilingual Summarization Corpus' dataset (https://doi.org/10.48550/arXiv.2004.14900).




[FR] Echantillon de 'MLSUM: The Multilingual Summarization Corpus' pour une expérience d'annotation de contraintes.



	
Description : MLSUM est un ensemble de données d'articles de journaux destinés à l'entraînement d'un modèle de résumé automatique. Nous l'utilisons pour une expérience d'annotation de contraintes sur des titres de journaux en fonction de leur classification thématique.
	
Contenu : Pour une expérience d'annotation de contraintes basée sur la similarité des données, cet ensemble de données a été échantillonné (sélectionner au hasard de 75 articles dans les 14 sujets les plus utilisés : 'économie', 'politique', 'sport', 'planète' (renommé en « écologie »). ), 'sciences', 'police-justice', 'disparitions', 'emploi', 'sante', 'musiques', 'arts', 'éducations', 'climat' (renommé en 'meteo'), 'immobilier' ) et filtré (conserver les articles qui ont un sujet évident par rapport à leur titre, sans leur corps). Deux relecteurs ont travaillé sur cette tâche afin de limiter la subjectivité du filtrage. Ce sous-ensemble de données est utilisé (1) pour estimer le temps nécessaire pour annoter la similarité des titres avec des contraintes (MUST-LINK, CANNOT-LINK) et (2) pour tester la méthodologie de clustering interactif (annotation de contraintes et clustering contraint).
	
Origine : L'ensemble de données est basé sur l'ensemble de données original 'MLSUM : The Multilingual Summarization Corpus' (https://doi.org/10.48550/arXiv.2004.1490).
DOI: 10.17028/rd.lboro.24552223
title: Supplemental information files for The Net Promoter Score interrogated through a services marketing lens: review and recommendations for service organizations

description: Supplementary files for article The Net Promoter Score interrogated through a services marketing lens: review and recommendations for service organizationsThe Net Promoter Score (NPS) is used by many service organizations as a key customer loyalty metric and driver of growth. Yet, despite its widespread adoption and consequent benchmarking benefits, the methodological soundness and usefulness of NPS has been questioned. Compounding these problems, few guidelines on NPS enhancement strategies exist, since research on antecedents to NPS remains elusive. The combination of (a) questionable research underpinning the original proposal for NPS, and (b) an under-researched nomological net, creates an unfortunate paradox, whereby service organizations strive to raise a score that may or may not lead to organizational success, with little evidence-based knowledge on how to raise this score strategically. To address this, we utilize a guiding framework to interrogate NPS though a services marketing lens. We use this framework to underpin a critical review of NPS as it applies to service organizations, and through the review, identify and discuss ten NPS interrogations. We then propose recommendations that address each of these to enhance further knowledge development and improve ongoing practice of NPS by service organizations.
DOI: 10.5281/zenodo.7307432
title: French trainset for chatbots dealing with usual requests on bank cards

description: [EN] French training dataset for chatbots dealing with usual requests on bank cards.



	
Description: This dataset represents examples of common customer requests relating to bank cards management. It can be used as a training set for a small chatbot intended to process these usual requests.
	
Content: The questions are asked in French. The dataset is divided into 10 intents of 100 questions each, for a total of 1 000 questions.
	
Intents scope: Intents are constructed in such a way that all questions arising from the same intention have the same response or action. The scope covered concerns: loss or theft of cards; the swallowed card; the card order; consultation of the bank balance; insurance provided by a card; card unlocking; virtual card management; management of bank overdraft; management of payment limits; management of contactless mode.
	
Origin: Intents scope is inspired by a chatbot currently in production, and the wording of the questions are inspired by the usual customers requests.




[FR] Jeu d'entraînement en français d'assistants conversationnels traitant des demandes courantes sur les cartes bancaires.



	
Description : Cet ensemble de données représente des exemples de demandes usuelles des clients concernant la gestion des cartes bancaires. Il peut être utilisé comme jeu d'entraînement pour un assistant conversationnel destiné à traiter ces demandes courantes.
	
Contenu : Les questions sont formulées en français. L'ensemble de données est divisé en 10 intentions de 100 questions chacune, pour un total de 1 000 questions.
	
Périmètre des intentions : Les intentions sont construites de telle manière que toutes les questions issues d'une même intention ont la même réponse ou action. Le périmètre couvert concerne : la perte ou le vol de cartes ; la carte avalée ; la commande des cartes ; la consultation du solde bancaire ; l'assurance fournie par une carte ; le déverrouillage de la carte ; la gestion de cartes virtuelles ; la gestion du découvert bancaire ; la gestion des plafonds de paiement ; la gestion du mode sans contact.
	
Origine : Le périmètre des intentions est inspiré par un chatbot actuellement en production, et la formulation des questions est inspirée de demandes courantes de clients.
DOI: 10.5281/zenodo.4769949
title: French trainset for chatbots dealing with usual requests on bank cards

description: [EN] French training dataset for chatbots dealing with usual requests on bank cards.



	
Description: This dataset represents examples of common customer requests relating to bank cards management. It can be used as a training set for a small chatbot intended to process these usual requests.
	
Content: The questions are asked in French. The dataset is divided into 10 intents of 100 questions each, for a total of 1 000 questions.
	
Intents scope: Intents are constructed in such a way that all questions arising from the same intention have the same response or action. The scope covered concerns: loss or theft of cards; the swallowed card; the card order; consultation of the bank balance; insurance provided by a card; card unlocking; virtual card management; management of bank overdraft; management of payment limits; management of contactless mode.
	
Origin: Intents scope is inspired by a chatbot currently in production, and the wording of the questions are inspired by the usual customers requests.




[FR] Jeu d'entraînement en français d'assistants conversationnels traitant des demandes courantes sur les cartes bancaires.



	
Description : Cet ensemble de données représente des exemples de demandes usuelles des clients concernant la gestion des cartes bancaires. Il peut être utilisé comme jeu d'entraînement pour un assistant conversationnel destiné à traiter ces demandes courantes.
	
Contenu : Les questions sont formulées en français. L'ensemble de données est divisé en 10 intentions de 100 questions chacune, pour un total de 1 000 questions.
	
Périmètre des intentions : Les intentions sont construites de telle manière que toutes les questions issues d'une même intention ont la même réponse ou action. Le périmètre couvert concerne : la perte ou le vol de cartes ; la carte avalée ; la commande des cartes ; la consultation du solde bancaire ; l'assurance fournie par une carte ; le déverrouillage de la carte ; la gestion de cartes virtuelles ; la gestion du découvert bancaire ; la gestion des plafonds de paiement ; la gestion du mode sans contact.
	
Origine : Le périmètre des intentions est inspiré par un chatbot actuellement en production, et la formulation des questions est inspirée de demandes courantes de clients.
DOI: 10.5281/zenodo.4769950
title: French trainset for chatbots dealing with usual requests on bank cards

description: [EN] French training dataset for chatbots dealing with usual requests on bank cards.



	
Description: This dataset represents examples of common customer requests relating to bank cards management. It can be used as a training set for a small chatbot intended to process these usual requests.
	
Content: The questions are asked in French. The dataset is divided into 10 intents of 50 questions each, for a total of 500 questions.
	
Intents scope: Intents are constructed in such a way that all questions arising from the same intention have the same response or action. The scope covered concerns: loss or theft of cards; the swallowed card; the card order; consultation of the bank balance; insurance provided by a card; card unlocking; virtual card management; management of bank overdraft; management of payment limits; management of contactless mode.
	
Origin: Intents scope is inspired by a chatbot currently in production, and the wording of the questions are inspired by the usual customers requests.




[FR] Jeu d'entraînement en français d'assistants conversationnels traitant des demandes courantes sur les cartes bancaires.



	
Description : Cet ensemble de données représente des exemples de demandes usuelles des clients concernant la gestion des cartes bancaires. Il peut être utilisé comme jeu d'entraînement pour un assistant conversationnel destiné à traiter ces demandes courantes.
	
Contenu : Les questions sont formulées en français. L'ensemble de données est divisé en 10 intentions de 50 questions chacune, pour un total de 500 questions.
	
Périmètre des intentions : Les intentions sont construites de telle manière que toutes les questions issues d'une même intention ont la même réponse ou action. Le périmètre couvert concerne : la perte ou le vol de cartes ; la carte avalée ; la commande des cartes ; la consultation du solde bancaire ; l'assurance fournie par une carte ; le déverrouillage de la carte ; la gestion de cartes virtuelles ; la gestion du découvert bancaire ; la gestion des plafonds de paiement ; la gestion du mode sans contact.
	
Origine : Le périmètre des intentions est inspiré par un chatbot actuellement en production, et la formulation des questions est inspirée de demandes courantes de clients.
DOI: 10.6084/m9.figshare.24559852.v1
title: Czech Butterfly Barcoding

description: Dataset for Czech Butterfly BarcodingThis dataset consists of separate nexus files for each butterfly species covered in the Czech Republic Butterfly barcoding scheme.Nexus files include newly generated sequences from the Czech Republic, and database data from BOLD (Ratnasingham & Hebert 2007) (downloaded on 12 October 2022), which were georeferenced and sequenced from the 5' end. We manually aligned the samples. We prepared the nexus files which serve as inputs for both POPART programme (Leigh & Bryant 2015; available from https://popart.maths.otago.ac.nz/) and rhierbaps R package (Tonkin-Hill et al. 2018). Both results of POPART and rhierbaps were checked visually and the dataset was cleaned to omit database samples which were either shorter than 500 bp, or potentially misidentified (dissimilarity of sequences represented by long branches in haplotype networks was evaluated using the BLAST algorithm, Altschul et al. 1990, in the NCBI database). After such cleaning, the final dataset (samplelist_metadata.xlxs) and final nexus files, presented here, were produced.<br>Altschul, S.F., Gish, W., Miller, W., Myers, E.W. & Lipman, D.J. (1990). Basic local alignment search tool. J. Mol. Biol., 215, 403–410.Leigh, J.W. & Bryant, D. (2015). popart: full-feature software for haplotype network construction. Methods Ecol. Evol., 6, 1110–1116.Ratnasingham, S. & Hebert, P.D.N. (2007). bold: The Barcode of Life Data System (http://www.barcodinglife.org). Mol Ecol. Notes, 7, 355–364.Tonkin-Hill, G., Lees, J.A., Bentley, S.D., Frost, S.D.W. & Corander, J. (2018). RhierBAPS: An R implementation of the population clustering algorithm hierBAPS. Wellcome Open Res., 3, 93.
DOI: 10.25828/yx8q-gd28
title: Quarterly Labour Force Survey 2023, Quarter 3
DOI: 10.25403/upresearchdata.24310696
title: Three round modified online Delphi study to reach consensus on person-centred handover practices in emergency departments

description: Between the period of February and May 2023 a three round modified online Delphi study was conducted to reach consensus on the attributes and concept definition of person-centred handover practices between emergency care practitioners and healthcare professionals in the emergency department. Participants were requested to complete a questionnaire of predefined attributes and concept definition. A rating of 1 to 4 on a Likert scale was used and participants had to explain their rating of each. After each round three members of the research team assessed the level of consensus based on percentage of agreement and comments were analysed used content analysis. Adjustments were then made to the attributes and concept definition and returned to participants to complete the online questionnaire again with the updated information. Anonymous feedback on the previous round was provided to participants. Consensus was identified as a % above 70 for each attribute and the concept definition. The dataset contains data gathered during each round as well as the refinements made after analysis.
DOI: 10.25403/upresearchdata.24310696.v1
title: Three round modified online Delphi study to reach consensus on person-centred handover practices in emergency departments

description: Between the period of February and May 2023 a three round modified online Delphi study was conducted to reach consensus on the attributes and concept definition of person-centred handover practices between emergency care practitioners and healthcare professionals in the emergency department. Participants were requested to complete a questionnaire of predefined attributes and concept definition. A rating of 1 to 4 on a Likert scale was used and participants had to explain their rating of each. After each round three members of the research team assessed the level of consensus based on percentage of agreement and comments were analysed used content analysis. Adjustments were then made to the attributes and concept definition and returned to participants to complete the online questionnaire again with the updated information. Anonymous feedback on the previous round was provided to participants. Consensus was identified as a % above 70 for each attribute and the concept definition. The dataset contains data gathered during each round as well as the refinements made after analysis.