cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.15088014
title: Focal behavioural observation data for the manuscript titled "Behavioural differences between ornamented and unornamented male Red-backed Fairywrens (Malurus melanocephalus) in the nonbreeding season"

description: This is the dataset used in the main analyses in the manuscript "Behavioural differences between ornamented and unornamented male Red-backed Fairywrens (<em>Malurus melanocephalus</em>) in the nonbreeding season". Each row corresponds to a behavioural observation on a single male fairywren. <br> <strong>Columns Describing Observations and Focal Male Characteristics:<br> </strong> Observation_ID = unique identifier for each behavourial observation (or row) Date = The date the observation was conducted in YYYY-MM-DD format Year = The year the observation was conducted. Time = The time that the observation started in 24-hour format Bird_ID = A unique identifier for each focal male (equivalent to color band combinations) Observer = Identity of the research who collected the data Sex = Sex of the bird being observed (always male in this dataset) Plumage_score = Numerical scoring of a focal male's plumage colouration as per Karubian 2002 Plumage_group = Categories of plumage scores (DULL, MOULT, or BRIGHT) as per Karubian 2002. Age = Age of each focal male obtained through banding records Age_exact = Either "exact" if we were able to determine a bird's precise age or "minimum" if only a minimum estimate (e.g., after second year) was possible <br> <strong>Behavioural Variables:</strong> The variables Alarm_calling, Allopreening, Calling, Chasing, Chipping, Courtship, Flying, Foraging, Other, Preening, Scolding, Singing, Sitting, U, and Vocalizing provide the number of seconds during a focal observation in which a male was engaging in these behaviours. These behaviors are described in table S1 (an ethogram). Note that U in the dataset is equivalent to "Out of sight" in table S1. During some years of our study, certain behaviours were not considered. In these situations, these values are given as "NA" rather than an integer. In particular, only vocalizing (any vocalization other than alarm calling) was recorded in 2016 while more specific categories of vocalizations (e.g., calling, chipping, scolding, and singing) were reported in 2017 and 2018. <br> Observation_time = Duration of a focal observation in seconds The variables Alarm_calling_prop, Chasing_prop, Courtship_prop, Flying_prop, Foraging_prop, Other_Prop, Preening_prop, Sitting_prop, and Vocalizing_Prop are calculated by dividing the number of seconds spent engaged in each behaviour (the value of the corresponding column without the "prop" suffix) by the Observation_time variable minus the U (time bird was not visible) variable. For example: <br> Preening_prop = Preening/(Observation_time - U) <br>
DOI: 10.6084/m9.figshare.15088014.v1
title: Focal behavioural observation data for the manuscript titled "Behavioural differences between ornamented and unornamented male Red-backed Fairywrens (Malurus melanocephalus) in the nonbreeding season"

description: This is the dataset used in the main analyses in the manuscript "Behavioural differences between ornamented and unornamented male Red-backed Fairywrens (<em>Malurus melanocephalus</em>) in the nonbreeding season". Each row corresponds to a behavioural observation on a single male fairywren. <br> <strong>Columns Describing Observations and Focal Male Characteristics:<br> </strong> Observation_ID = unique identifier for each behavourial observation (or row) Date = The date the observation was conducted in YYYY-MM-DD format Year = The year the observation was conducted. Time = The time that the observation started in 24-hour format Bird_ID = A unique identifier for each focal male (equivalent to color band combinations) Observer = Identity of the research who collected the data Sex = Sex of the bird being observed (always male in this dataset) Plumage_score = Numerical scoring of a focal male's plumage colouration as per Karubian 2002 Plumage_group = Categories of plumage scores (DULL, MOULT, or BRIGHT) as per Karubian 2002. Age = Age of each focal male obtained through banding records Age_exact = Either "exact" if we were able to determine a bird's precise age or "minimum" if only a minimum estimate (e.g., after second year) was possible <br> <strong>Behavioural Variables:</strong> The variables Alarm_calling, Allopreening, Calling, Chasing, Chipping, Courtship, Flying, Foraging, Other, Preening, Scolding, Singing, Sitting, U, and Vocalizing provide the number of seconds during a focal observation in which a male was engaging in these behaviours. These behaviors are described in table S1 (an ethogram). Note that U in the dataset is equivalent to "Out of sight" in table S1. During some years of our study, certain behaviours were not considered. In these situations, these values are given as "NA" rather than an integer. In particular, only vocalizing (any vocalization other than alarm calling) was recorded in 2016 while more specific categories of vocalizations (e.g., calling, chipping, scolding, and singing) were reported in 2017 and 2018. <br> Observation_time = Duration of a focal observation in seconds The variables Alarm_calling_prop, Chasing_prop, Courtship_prop, Flying_prop, Foraging_prop, Other_Prop, Preening_prop, Sitting_prop, and Vocalizing_Prop are calculated by dividing the number of seconds spent engaged in each behaviour (the value of the corresponding column without the "prop" suffix) by the Observation_time variable minus the U (time bird was not visible) variable. For example: <br> Preening_prop = Preening/(Observation_time - U) <br>
DOI: 10.6073/pasta/98638c83702d4db281a6d30e614db9c2
title: MCR LTER: Coral Reef: Ocean Currents and Biogeochemistry: salinity, temperature and current at CTD and ADCP mooring FOR01 from 2004 ongoing

description: Moored and bottom mounted instrumentation (ADCP, CTD, thermistors, wave-tide meters) sampled year-round on the reef of Moorea Island, French Polynesia at site FOR01. Sampling began in 2005. All data have been interpolated onto a 20 min grid. ADCP data are organized into 1-meter bins, measured as height from the bottom, to a maximum of 20 bins. All bins may not be filled, and although post processing attempted to exclude data from bins 'above the surface', users need to exercise caution with the near surface bins. CTD parameters include Pressure, Temperature, Conductivity, Salinity and Density. The two moored CTD packages are located approximately 5 and 14 meters above the bottom and there are up to 5 additional temperature thermistors spaced vertically along the mooring line. Note that the upper CTD and temperature thermistor data was terminated in 2020, and the bottom CTD was terminated in February 2022. There are 2 bottom mounted thermistors in the vicinity of the mooring at 10 and 20 meter depth. A bottom mounted wave-tide meter at 10 meter depth provides a measure of significant wave height and dominant wave period at a 2 hour sampling interval. Tide related data may be calculated from bottom-mounted pressure sensor data. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2023). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
DOI: 10.57745/zgq5ix
title: Heart-rate.tab

description: :unav
DOI: 10.57745/h5jtof
title: Gaze-responses.tab

description: :unav
DOI: 10.5281/zenodo.7686668
title: Multi-Task Automatic Synthesizer Programming Preset Dataset

description: This data set contains different training, test, and validation data used for training the multi-task automatic synthesizer programming models in the 2023 ICASSP paper "Exploring Approaches to Multi-Task Automatic Synthesizer Programming". Data is stored in .npy (numpy pickled data) format. Files are organized with &lt;split&gt;_&lt;type&gt;.npy, where &lt;split&gt; is the data split (train, test, or valid) and &lt;type&gt; is the type of data: serum_params are parameters for Serum data (these are already one hot encoded) serum_mask are masks for Serum parameters diva_params are parameters for Diva data (these are already one hot encoded) diva_mask are masks for Diva parameters tyrell_params are parameters for TyrellN6 data (these are already one hot encoded) tyrell_mask are masks for TyrellN6 parameters hpss are labels that indicate the harmonic percentage of this preset 20 means 0-20%, 40 means 20-40%, 60 means 40-60%, 80 means 60-80%, and 100 means 80-100%, mask_single are mask for the single decoder model mels are mel spectrograms name are names of the preset params are the parameters of the preset params_single are parameters for the single decoder model synth are strings of which synthesizer is used
DOI: 10.5281/zenodo.7686667
title: Multi-Task Automatic Synthesizer Programming Preset Dataset

description: This data set contains different training, test, and validation data used for training the multi-task automatic synthesizer programming models in the 2023 ICASSP paper "Exploring Approaches to Multi-Task Automatic Synthesizer Programming". Data is stored in .npy (numpy pickled data) format. Files are organized with &lt;split&gt;_&lt;type&gt;.npy, where &lt;split&gt; is the data split (train, test, or valid) and &lt;type&gt; is the type of data: serum_params are parameters for Serum data (these are already one hot encoded) serum_mask are masks for Serum parameters diva_params are parameters for Diva data (these are already one hot encoded) diva_mask are masks for Diva parameters tyrell_params are parameters for TyrellN6 data (these are already one hot encoded) tyrell_mask are masks for TyrellN6 parameters hpss are labels that indicate the harmonic percentage of this preset 20 means 0-20%, 40 means 20-40%, 60 means 40-60%, 80 means 60-80%, and 100 means 80-100%, mask_single are mask for the single decoder model mels are mel spectrograms name are names of the preset params are the parameters of the preset params_single are parameters for the single decoder model synth are strings of which synthesizer is used
DOI: 10.5517/ccdc.csd.cc2dltxr
title: CCDC 2222635: Experimental Crystal Structure Determination

description: Related Article: Urminder Kaur, Shippy Jaiswal, Sourav Gayen, Sundargopal Ghosh|2023|J.Organomet.Chem.|989|122642|doi:10.1016/j.jorganchem.2023.122642
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dlvyt
title: CCDC 2222667: Experimental Crystal Structure Determination

description: Related Article: Urminder Kaur, Shippy Jaiswal, Sourav Gayen, Sundargopal Ghosh|2023|J.Organomet.Chem.|989|122642|doi:10.1016/j.jorganchem.2023.122642
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2drqzw
title: CCDC 2227349: Experimental Crystal Structure Determination

description: Related Article: Taiki Imagawa, Luisa Giarrana, Diego M. Andrada, Bernd Morgenstern, Masaaki Nakamoto, David Scheschkewitz|2023|J.Am.Chem.Soc.|145|4757|doi:10.1021/jacs.2c13530
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2drr0y
title: CCDC 2227350: Experimental Crystal Structure Determination

description: Related Article: Taiki Imagawa, Luisa Giarrana, Diego M. Andrada, Bernd Morgenstern, Masaaki Nakamoto, David Scheschkewitz|2023|J.Am.Chem.Soc.|145|4757|doi:10.1021/jacs.2c13530
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2f3n6f
title: CCDC 2237834: Experimental Crystal Structure Determination

description: Related Article: Daniela Dantas, Ana I. Ribeiro, Filipe Carvalho, Eva Gil-Martins, Renata Silva, Fernando Remião, Andrea Zille, Fátima Cerqueira, Eugénia Pinto, Alice M. Dias|2023|Chem.Commun.|59|2791|doi:10.1039/D3CC00372H
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.22207483
title: JRE-12-22-7305

description: Excel spreadsheet of B<strong>iomarker Panel Discriminates Diabetics with and without Periodontitis </strong> <strong>Pre- and Post-Therapy</strong>
DOI: 10.6084/m9.figshare.3761562.v2513
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.6084/m9.figshare.20311812
title: Pedestrian moral disengagement scale for sustainable urban planning

description: The transgressive behavior of pedestrians is one of the most recurrent causes of traffic accidents in Peru (Miranda & Huicho, 2010). In order to bring light to this issue, the researchers decided to develop and validate a scale that brings relevant information to those in charge of transport management to make decisions related to the design and planning of the infrastructure based on the knowledge of the pedestrian's moral disengagement. The study has two phases: developing a questionnaire with a sample of 100 pedestrians in a two-stage brainstorming validated by academic and professional experts in transportation management and psychology and applying EFA and CFA to analyze a two-stage conglomerate sample of 405 pedestrians from Metropolitan Lima. The data collected were non-parametric, and the researchers validated the correlation between scale items through Sperman's polychoric correlation. The model obtained: a p-value of less than .001, a Chi-square of 173 with 59 degrees of freedom, an RMSEA of .0692, a CFI of .937, a TLI of .917, and an SRMR of .0457. The main conclusions are that the adjustment of the model is sufficient, showing that the results obtained were within the theoretical model proposed by \cite{ref-book1}Albert Bandura (1986) and within the reliability and validation phases and that the thirteen items identified represent the eight mechanisms of moral disconnection of the pedestrian in Metropolitan Lima. Further more, it was identified that items P1.10 and item P1.13 show a relationship with an specific demographic variable (district) of statistical significance when applying Chi-squared. <br> The demografic data is the following: Mayoria de Edad (age from 18 years) values 1= yes, 0=no Genero (gender) values 1= female y 2 = male Edad (age) Distrito (Lima Metrolitana's districs): 1 = Ate 2 = Barranco 3 = Breña 4 = Callao 5 = Carabayllo 6 = Cieneguilla 7 = Comas 8 = Chorrillos 9 = El Agustino 10 = Independencia 11 = Jesús María 12 = La Molina 13 = La Victoria 14 = Lince 15 = Los Olivos 16 = Lurigancho 17 = Magdalena 18 = Miraflores 19 = Puente Piedra 20 = Pachacámac 21 = Rímac 22 = San Borja 23 = San Luis 24 = San Miguel 25 = Surco 26 = Surquillo 27 = Santa Anita 28 = San Martín de Porres 29 = San Juan de Miraflores 30 = San Juan de Lurigancho 31 = Villa María del Triunfo 32 = Villa el Salvador 33 = Pueblo Libre 34 = San Isidro 35 = Lima <br> From P1.1 to P1.16 find the answers to the questionare Dimensions of Moral Dissengagement: <br> Moral justification: P1.1, P1.13 & P1.16 Ephemistic labeling: P1.9, P1.10 & P1.14 Advantageous comparison: P1.2, P1.3 & P1.6 Displacement of responsibility: P1.4 & P1.15 Diffusion of responsibility: P1.5 & P1.7 Disregarding and distorting its consequences: P1.1 Attribution of blame: P1.12 Dehumanization: P1.8 <br> 5-point Likert scale (from 1 = agree not at all, to 5 = completely agree) response format <br> <br> <br>
DOI: 10.6084/m9.figshare.20311812.v1
title: Pedestrian moral disengagement scale for sustainable urban planning

description: The transgressive behavior of pedestrians is one of the most recurrent causes of traffic accidents in Peru (Miranda & Huicho, 2010). In order to bring light to this issue, the researchers decided to develop and validate a scale that brings relevant information to those in charge of transport management to make decisions related to the design and planning of the infrastructure based on the knowledge of the pedestrian's moral disengagement. The study has two phases: developing a questionnaire with a sample of 100 pedestrians in a two-stage brainstorming validated by academic and professional experts in transportation management and psychology and applying EFA and CFA to analyze a two-stage conglomerate sample of 405 pedestrians from Metropolitan Lima. The data collected were non-parametric, and the researchers validated the correlation between scale items through Sperman's polychoric correlation. The model obtained: a p-value of less than .001, a Chi-square of 173 with 59 degrees of freedom, an RMSEA of .0692, a CFI of .937, a TLI of .917, and an SRMR of .0457. The main conclusions are that the adjustment of the model is sufficient, showing that the results obtained were within the theoretical model proposed by \cite{ref-book1}Albert Bandura (1986) and within the reliability and validation phases and that the thirteen items identified represent the eight mechanisms of moral disconnection of the pedestrian in Metropolitan Lima. Further more, it was identified that items P1.10 and item P1.13 show a relationship with an specific demographic variable (district) of statistical significance when applying Chi-squared. <br> The demografic data is the following: Mayoria de Edad (age from 18 years) values 1= yes, 0=no Genero (gender) values 1= female y 2 = male Edad (age) Distrito (Lima Metrolitana's districs): 1 = Ate 2 = Barranco 3 = Breña 4 = Callao 5 = Carabayllo 6 = Cieneguilla 7 = Comas 8 = Chorrillos 9 = El Agustino 10 = Independencia 11 = Jesús María 12 = La Molina 13 = La Victoria 14 = Lince 15 = Los Olivos 16 = Lurigancho 17 = Magdalena 18 = Miraflores 19 = Puente Piedra 20 = Pachacámac 21 = Rímac 22 = San Borja 23 = San Luis 24 = San Miguel 25 = Surco 26 = Surquillo 27 = Santa Anita 28 = San Martín de Porres 29 = San Juan de Miraflores 30 = San Juan de Lurigancho 31 = Villa María del Triunfo 32 = Villa el Salvador 33 = Pueblo Libre 34 = San Isidro 35 = Lima <br> From P1.1 to P1.16 find the answers to the questionare Dimensions of Moral Dissengagement: <br> Moral justification: P1.1, P1.13 & P1.16 Ephemistic labeling: P1.9, P1.10 & P1.14 Advantageous comparison: P1.2, P1.3 & P1.6 Displacement of responsibility: P1.4 & P1.15 Diffusion of responsibility: P1.5 & P1.7 Disregarding and distorting its consequences: P1.1 Attribution of blame: P1.12 Dehumanization: P1.8 <br> 5-point Likert scale (from 1 = agree not at all, to 5 = completely agree) response format <br> <br> <br>
DOI: 10.6084/m9.figshare.20311812.v2
title: Pedestrian moral disengagement scale for sustainable urban planning

description: The transgressive behavior of pedestrians is one of the most recurrent causes of traffic accidents in Peru (Miranda & Huicho, 2010). In order to bring light to this issue, the researchers decided to develop and validate a scale that brings relevant information to those in charge of transport management to make decisions related to the design and planning of the infrastructure based on the knowledge of the pedestrian's moral disengagement. The study has two phases: developing a questionnaire with a sample of 100 pedestrians in a two-stage brainstorming validated by academic and professional experts in transportation management and psychology and applying EFA and CFA to analyze a two-stage conglomerate sample of 405 pedestrians from Metropolitan Lima. The data collected were non-parametric, and the researchers validated the correlation between scale items through Sperman's polychoric correlation. The model obtained: a p-value of less than .001, a Chi-square of 173 with 59 degrees of freedom, an RMSEA of .0692, a CFI of .937, a TLI of .917, and an SRMR of .0457. The main conclusions are that the adjustment of the model is sufficient, showing that the results obtained were within the theoretical model proposed by \cite{ref-book1}Albert Bandura (1986) and within the reliability and validation phases and that the thirteen items identified represent the eight mechanisms of moral disconnection of the pedestrian in Metropolitan Lima. Further more, it was identified that items P1.10 and item P1.13 show a relationship with an specific demographic variable (district) of statistical significance when applying Chi-squared. <br> The demografic data is the following: Mayoria de Edad (age from 18 years) values 1= yes, 0=no Genero (gender) values 1= female y 2 = male Edad (age) Distrito (Lima Metrolitana's districs): 1 = Ate 2 = Barranco 3 = Breña 4 = Callao 5 = Carabayllo 6 = Cieneguilla 7 = Comas 8 = Chorrillos 9 = El Agustino 10 = Independencia 11 = Jesús María 12 = La Molina 13 = La Victoria 14 = Lince 15 = Los Olivos 16 = Lurigancho 17 = Magdalena 18 = Miraflores 19 = Puente Piedra 20 = Pachacámac 21 = Rímac 22 = San Borja 23 = San Luis 24 = San Miguel 25 = Surco 26 = Surquillo 27 = Santa Anita 28 = San Martín de Porres 29 = San Juan de Miraflores 30 = San Juan de Lurigancho 31 = Villa María del Triunfo 32 = Villa el Salvador 33 = Pueblo Libre 34 = San Isidro 35 = Lima <br> From P1.1 to P1.16 find the answers to the questionare Dimensions of Moral Dissengagement: <br> Moral justification: P1.1, P1.13 & P1.16 Ephemistic labeling: P1.9, P1.10 & P1.14 Advantageous comparison: P1.2, P1.3 & P1.6 Displacement of responsibility: P1.4 & P1.15 Diffusion of responsibility: P1.5 & P1.7 Disregarding and distorting its consequences: P1.1 Attribution of blame: P1.12 Dehumanization: P1.8 <br> 5-point Likert scale (from 1 = agree not at all, to 5 = completely agree) response format <br> <br> <br>
DOI: 10.5281/zenodo.7415608
title: Montipora capitata multi-omics supplemental tables

description: Supplemental tables for <em>Montipora capitata</em> multi-omics study. <strong>Table S1.</strong> Read statistics for the microbiome 16S rRNA data from the four <em>M. capitata</em> colonies (n=3 per treatment/time point/colony). <strong>Table S2.</strong> Protein and transcript FC values for genes identified in proteomic data. The 138 stress-response related genes are also highlighted. <strong>Table S3.</strong> Major KEGG pathways used to filter proteins identified in the proteomic data. <strong>Table S4.</strong> Results from the permutational MANOVA (PERMANOVA) tests run on the transcriptomic, proteomic, and metabolomic datasets. <strong>Table S5.</strong> Results from the statistical tests run on the alpha- and beta-diversity metrics.