cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.11900307.v2
title: Nonlinear Luttinger Liquid Plasmons in Semiconducting Single Walled Carbon Nanotubes

description: Numerical data used to generate Figs. 4b, c, d
DOI: 10.6084/m9.figshare.11900292
title: Distance Distributions of Human Settlements for ten countries

description: City-size distributions have been studied from different perspectives in past decades. However, spatial properties of city distributions have not been widely explored. Here, we study distance distribution between pairs of cities by considering their sizes for data from ten countries. We<br>find that distance distributions present different shapes, from quasi-symmetrical to right-skewed, which exhibit changes as the lower threshold of the city size is increased. The Gini index G and the normalized Shannon entropy E_S were calculated to evaluate the inequality and heterogeneity<br>(diversity) in the distance distributions. Our results show that quasi-symmetrical distributions are characterized by a low inequality and intermediate value of entropy, while right-skewed distributions lead to higher inequality indexes with different levels of diversity. We introduce the big-club (set of<br>the largest cities) coefficient to evaluate the tendency of big settlements to be located close to (or far away from) each other, finding that most of the countries have a high big-club value. Finally, we consider a simple model based on node sizes to generate three configurations with different spatial<br>correlations (random, correlated and anti-correlated), which roughly reproduce similar values of G and E_S to those found in actual distance distributions.
DOI: 10.6084/m9.figshare.11900295.v1
title: Facultative hyperthermia during a heatwave delays injurious dehydration of an arboreal marsupial

description: Please see doi: 10.1242/jeb.219378
DOI: 10.6084/m9.figshare.11900307.v1
title: SourceData_Fig4bcd.xls

description: Numerical data used to generate Figs. 4b, c, d
DOI: 10.6084/m9.figshare.11900235
title: trustdata

description: This data is from an experiment involving use of emojis in laboratory trust games executed on mobile devices. In this paper, I explore how emojis impact trust-generated welfare and how skin tone markers -- effectively signals -- shape one's willingness to trust. Exploiting a demographically diverse subject pool of mobile device users, I execute a laboratory experiment, exogenously restricting the type of computer-mediated chat players can use in a single endowment variant of the Berg et. al, 1998 "Investment game." This game allows for the exchange of money in a manner that creates potential surplus (a growing pie), and captures intuitive measures of trust (money sent), reciprocity (a proportion returned), and social welfare (final payoffs). Student subjects may could select from a library of emojis that allow skin tone markers and can converse throughout the entirety of the trust interaction. The emoji variables are binary coded if received by an player in the game. <br><br><br>Skin tones embedded in emojis impact sharing and resulting gains.
DOI: 10.6084/m9.figshare.11900292.v1
title: Distance Distributions of Human Settlements for ten countries

description: City-size distributions have been studied from different perspectives in past decades. However,<br>spatial properties of city distributions have not been widely explored. Here, we study distance<br>distribution between pairs of cities by considering their sizes for data from ten countries. We<br>find that distance distributions present different shapes, from quasi-symmetrical to right-skewed,<br>which exhibit changes as the lower threshold of the city size is increased. The Gini index G and<br>the normalized Shannon entropy E_S were calculated to evaluate the inequality and heterogeneity<br>(diversity) in the distance distributions. Our results show that quasi-symmetrical distributions are<br>characterized by a low inequality and intermediate value of entropy, while right-skewed distributions<br>lead to higher inequality indexes with different levels of diversity. We introduce the big-club (set of<br>the largest cities) coefficient to evaluate the tendency of big settlements to be located close to (or<br>far away from) each other, finding that most of the countries have a high big-club value. Finally, we<br>consider a simple model based on node sizes to generate three configurations with different spatial<br>correlations (random, correlated and anti-correlated), which roughly reproduce similar values of G<br>and E_S to those found in actual distance distributions.
DOI: 10.6084/m9.figshare.11900229
title: PTBP Matrices

description: This is matrix data that is associated with the PTBP.
DOI: 10.6084/m9.figshare.11900229.v1
title: PTBP Matrices

description: This is matrix data that is associated with the PTBP.
DOI: 10.6084/m9.figshare.11900292.v2
title: Distance Distributions of Human Settlements for ten countries

description: City-size distributions have been studied from different perspectives in past decades. However, spatial properties of city distributions have not been widely explored. Here, we study distance distribution between pairs of cities by considering their sizes for data from ten countries. We<br>find that distance distributions present different shapes, from quasi-symmetrical to right-skewed, which exhibit changes as the lower threshold of the city size is increased. The Gini index G and the normalized Shannon entropy E_S were calculated to evaluate the inequality and heterogeneity<br>(diversity) in the distance distributions. Our results show that quasi-symmetrical distributions are characterized by a low inequality and intermediate value of entropy, while right-skewed distributions lead to higher inequality indexes with different levels of diversity. We introduce the big-club (set of<br>the largest cities) coefficient to evaluate the tendency of big settlements to be located close to (or far away from) each other, finding that most of the countries have a high big-club value. Finally, we consider a simple model based on node sizes to generate three configurations with different spatial<br>correlations (random, correlated and anti-correlated), which roughly reproduce similar values of G and E_S to those found in actual distance distributions.
DOI: 10.6084/m9.figshare.11900139
title: Data_Sesin_etal_MBI_2020.zip

description: This zip file contains three data files:<br>1 - R Script: "R_Script_Sesin_et_al_MBI_2020_Variation in glyphosate effects and accumulation in emergent macrophytes.R"2 - Data file: "EffectData.csv"3 - Data file: "AccumulationData.csv"<br><b>Descriptions of files:</b><br><b>File 1:</b> "R_Script_Sesin_et_al_MBI_2020_Variation in glyphosate effects and accumulation in emergent macrophytes.R"<br>Complete R Script of data analysis<br><b>File 2:</b> "EffectData.csv"<br>Treatment = Applied glyphosate treatment concentration in unit % (measured concentrations)Species = Plant taxon: PA (<i>Phragmites australis</i>), TL (<i>Typha latifolia</i>), TXG (<i>Typha x glauca</i>)Shoots_Dead = Number of dead shootsShoots_Total = Number of total shoots<br><b>File 3:</b> "AccumulationData.csv"<br><br>Treatment_Nom = Applied glyphosate treatment concentration in unit % (nominal concentrations)Treatment = Applied glyphosate treatment concentration in unit % (measured concentrations)Species = Plant taxon: PA (<i>Phragmites australis</i>), TL (<i>Typha latifolia</i>), TXG (<i>Typha x glauca</i>)<br>Sample_Time = Sampling time post-application of glyphosateSample_Type = Type of sample (Vegetation, Sediment or Water)ppm_fw = Concentration in unit ppm fresh weight (mg/kg for sample types Vegetation and Sediment; mg/L for sample type Water)Substance = Glyphosate or AMPAppm_dw = Concentration in unit ppm dry weight (mg/kg; applies only to sample types Vegetation and Sediment)<br>
DOI: 10.25390/caryinstitute.11900118
title: Cary Institute Aerial Infrared Deer Survey - January 2020

description: An aerial infrared imaging flight was performed at the Cary Institute of Ecological Studies property by Davis Aviation to estimate the minimum number of deer present on the property. This data is useful for management of the Cary deer herd. Flights were conducted on the nights of 30 and 31 January 2020. <br>Imaging was accomplished with a single-engine Cessna 182 airplane and using a high-resolution Mitsubishi M-600 thermal imager oriented ‘looking’ straight down through a camera hole in the belly of the airplane. The thermal imager NTSC video output was routed through a video encoder-decoder (VED) that labeled the imagery with a continuous stream of GPS-derived position, time, date, speed and altitude information. <br>After the flight, imagery was analyzed using a TV monitor and a computer monitor. As the imagery played, the VED decoded the bar-coded GPS signal that was received from the GPS during the flight. The VED recreated the original GPS signal and sent it to the computer so the mobile mapping software ‘thinks’ it is receiving a live signal. The mapping software shows the moving position of the airplane superimposed over aerial imagery of the count area on the computer screen while the recorded infrared imagery of the area below the airplane is visible on the TV monitor. The GPS updates the airplane position once per second throughout the flight and at the same rate during the post-flight analysis. <br>The entire recorded imagery has been reviewed and individual deer identified and marked on the map. <br>Contact the author for data files. Fargionem@caryinstitute.org <br><b>Description of files:</b><br>Cary deer count report 30-31 Jan 2020.pdfReport describing methods and results of January 30,31 2020 aerial infrared flight of Cary propertyPDFCary_IR_Deer_1-31-2020_Map.pdfPDF of ArcGIS map of Cary property features overlaid with deer and turkey locations PDFCary_IR_Deer_1-31-2020_Airphoto_Map.pdfPDF of ArcGIS map of Cary property features including airphotos overlaid with deer and turkey locations PDFCary deer points 30_31 Jan20.kmlFile containing deer and turkey locations from aerial infrared flight in Google Earth-readable format that was used to create ArcGIS shapefilesKeyhole Marking Language (KML)
DOI: 10.25390/caryinstitute.11900118.v1
title: Cary Institute Aerial Infrared Deer Survey - January 2020

description: An aerial infrared imaging flight was performed at the Cary Institute of Ecological Studies property by Davis Aviation to estimate the minimum number of deer present on the property. This data is useful for management of the Cary deer herd. Flights were conducted on the nights of 30 and 31 January 2020. <br>Imaging was accomplished with a single-engine Cessna 182 airplane and using a high-resolution Mitsubishi M-600 thermal imager oriented ‘looking’ straight down through a camera hole in the belly of the airplane. The thermal imager NTSC video output was routed through a video encoder-decoder (VED) that labeled the imagery with a continuous stream of GPS-derived position, time, date, speed and altitude information. <br>After the flight, imagery was analyzed using a TV monitor and a computer monitor. As the imagery played, the VED decoded the bar-coded GPS signal that was received from the GPS during the flight. The VED recreated the original GPS signal and sent it to the computer so the mobile mapping software ‘thinks’ it is receiving a live signal. The mapping software shows the moving position of the airplane superimposed over aerial imagery of the count area on the computer screen while the recorded infrared imagery of the area below the airplane is visible on the TV monitor. The GPS updates the airplane position once per second throughout the flight and at the same rate during the post-flight analysis. <br>The entire recorded imagery has been reviewed and individual deer identified and marked on the map. <br>Contact the author for data files. Fargionem@caryinstitute.org <br><b>Description of files:</b><br>Cary deer count report 30-31 Jan 2020.pdfReport describing methods and results of January 30,31 2020 aerial infrared flight of Cary propertyPDFCary_IR_Deer_1-31-2020_Map.pdfPDF of ArcGIS map of Cary property features overlaid with deer and turkey locations PDFCary_IR_Deer_1-31-2020_Airphoto_Map.pdfPDF of ArcGIS map of Cary property features including airphotos overlaid with deer and turkey locations PDFCary deer points 30_31 Jan20.kmlFile containing deer and turkey locations from aerial infrared flight in Google Earth-readable format that was used to create ArcGIS shapefilesKeyhole Marking Language (KML)
DOI: 10.6084/m9.figshare.11900055
title: Supplemental Files for Malki et al. mBio

description: Curated viral contigs produced during analysis of virome data (NCBI SRA study PRJNA540674) for Malki, Rosario, Sawaya, Szekely, Tisza, Breitbart (in review). Prokaryotic and viral community composition of freshwater springs in Florida, USA<br>
DOI: 10.6084/m9.figshare.11900055.v1
title: Supplemental Files for Malki et al. mBio

description: Curated viral contigs produced during analysis of virome data (NCBI SRA study PRJNA540674) for Malki, Rosario, Sawaya, Szekely, Tisza, Breitbart (in review). Prokaryotic and viral community composition of freshwater springs in Florida, USA<br>
DOI: 10.6084/m9.figshare.11899845
title: Mongoose Campylobacter Testing.xlsx

description: Banded mongoose <i>Campylobacter </i>spp<i> </i>prevalence and behavior data from the Chobe District of Botswana
DOI: 10.6084/m9.figshare.11899845.v1
title: Mongoose Campylobacter Testing.xlsx

description: Banded mongoose <i>Campylobacter </i>spp<i> </i>prevalence and behavior data from the Chobe District of Botswana
DOI: 10.6084/m9.figshare.11898585
title: Carstairsetal2019_bltuhsdata.csv

description: Data from Carstairs et al. 2019 Animal Conservation 22(6): 589-599 https://doi.org/10.1111/acv.12503<br>rowid = unique integerid = unique turtle identifiergroup = Headstart or Wildstatus = pre-release, release, alive, dead, removeddate = YYYY-MM-DDtime = HH:MMCL = carapace length (mm)mass = mass in gramsx = UTM x coordinate (shifted)y = UTM y coordinate (shifted)<br>Location data have been shifted (to mean x and y coordinates of 0) to protect location of endangered species.
DOI: 10.6084/m9.figshare.11898585.v2
title: Carstairsetal2019_bltuhsdata.csv

description: Data from Carstairs et al. 2019 Animal Conservation 22(6): 589-599 https://doi.org/10.1111/acv.12503<br>rowid = unique integerid = unique turtle identifiergroup = Headstart or Wildstatus = pre-release, release, alive, dead, removeddate = YYYY-MM-DDtime = HH:MMCL = carapace length (mm)mass = mass in gramsx = UTM x coordinate (shifted)y = UTM y coordinate (shifted)<br>Location data have been shifted (to mean x and y coordinates of 0) to protect location of endangered species.
DOI: 10.6084/m9.figshare.11898585.v1
title: Carstairsetal2019_bltuhsdata.csv

description: Data from Carstairs et al. 2019 Animal Conservation 22(6): 589-599 https://doi.org/10.1111/acv.12503<br>rowid = unique integerid = unique turtle identifiergroup = Headstart or Wildstatus = pre-release, release, alive, dead, removeddate = YYYY-MM-DDtime = HH:MMCL = carapace length (mm)mass = mass in gramsx = UTM x coordinate (shifted)y = UTM y coordinate (shifted)<br>Location data have been shifted (to mean x and y coordinates of 0) to protect location of endangered species.
DOI: 10.6084/m9.figshare.11898474
title: The results from an online questionnaire created to ascertain the similarities and differences of two forms of "déjà" experience: déjà vécu (event-related) and déjà visité (place-related)

description: An online questionnaire was created in 2004 to ascertain the similarities and differences of two forms of "déjà" experience: déjà vécu (event-related) and déjà visité (place-related). As of Feb. 27, 2020, the data set contains 3259 filled-out forms (from 2004 to 2019) and contains rudimentary analyses of the data within the pages of the Excel xls file. Two published papers have reported on the results of more extensive statistical analyses of earlier subsets of this data: <br><br>Funkhouer A, Schredl M (2014) Déjà Vécu and Déjà Visité Similarities and Differences: Initial Results from an Online Investigation. Journal of Consciousness Studies 21 (11-12): 7-18 and <br>Funhouser A, Purser H (2018) Déjà Vécu and Déjà Visité Similarities and Differences: Further Results From an Online Questionnaire. Explore 14 (7-8): 277-282.<br>Three books about déjà vu:<br>Neppe VM (1983) The Psychology of Déjà Vu. Johannesburg, SA: Witwatersrand University Press.<br>Brown AS (2004) the déjà vu experience. New York: Psychology Press.<br>Moulin C (2018) The Cognitive Neuropsychology of Déjà Vu. <br>New York: Rougledge.<br>Website: https://deja-experience-research.org<br>
DOI: 10.6084/m9.figshare.11898405
title: Metadata record for: Provincial and gridded population projection for China under shared socioeconomic pathways from 2010 to 2100

description: This dataset contains key characteristics about the data described in the Data Descriptor Provincial and gridded population projection for China under shared socioeconomic pathways from 2010 to 2100. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format <br> <br> Versioning Note:Version 2 was generated when the metadata format was updated from JSON to JSON-LD. This was an automatic process that changed only the format, not the contents, of the metadata.<br>
DOI: 10.6084/m9.figshare.11898474.v1
title: Survey data analysis without duplicates as of Apr 17 2019.xls

description: An online questionnaire was created in 2004 to ascertain the similarities and differences of two forms of "déjà" experience: déjà vécu (event-related) and déjà visité (place-related). As of Feb. 26, 2020, the data set contains 3297 filled-out forms (from 2004 to 2019) and contains analyses of the data within the pages of the Excel xls file. Two published papers have shown results of analysis of this data: Déjà Vécu and Déjà Visité Similarities and Differences, J<i>ournal of Consciousness Studies</i> <b>21</b> (11-12): 7-18, 2014 and (with a similar title) in <i>Explore</i> <b>14</b> (7-8): 277-282, 2018.<br>
DOI: 10.6084/m9.figshare.11898405.v1
title: Metadata record for: Provincial and gridded population projection for China under shared socioeconomic pathways from 2010 to 2100

description: This dataset contains key characteristics about the data described in the Data Descriptor Provincial and gridded population projection for China under shared socioeconomic pathways from 2010 to 2100. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format <br> <br> <br>
DOI: 10.6084/m9.figshare.11898300
title: Original data: Main experiment from Chemosensory communication of aggression: women's fine-tuned neural processing of male aggression signals

description: IBM SPSS Statistics File
DOI: 10.6084/m9.figshare.11898303
title: Original data: Sweat donation from Chemosensory communication of aggression: women's fine-tuned neural processing of male aggression signals

description: IBM SPSS Statistics File
DOI: 10.6084/m9.figshare.11898303.v2
title: Original data: Sweat donation from Chemosensory communication of aggression: women's fine-tuned neural processing of male aggression signals

description: IBM SPSS Statistics File
DOI: 10.6084/m9.figshare.11898300.v2
title: Original data: Main experiment from Chemosensory communication of aggression: women's fine-tuned neural processing of male aggression signals

description: IBM SPSS Statistics File
DOI: 10.6084/m9.figshare.11898303.v1
title: Original data: Sweat donation from Chemosensory communication of aggression: Women's fine-tuned neural processing of male aggression signals

description: IBM SPSS Statistics File
DOI: 10.6084/m9.figshare.11898291
title: Results in detail from Losing stinks! The effect of competition outcome on body odour quality

description: Tables containing detailed results of all analysis
DOI: 10.6084/m9.figshare.11898291.v1
title: Results in detail from Losing stinks! The effect of competition outcome on body odour quality

description: Tables containing detailed results of all analysis
DOI: 10.6084/m9.figshare.11898300.v1
title: Original data: Main experiment from Chemosensory communication of aggression: Women's fine-tuned neural processing of male aggression signals

description: IBM SPSS Statistics File
DOI: 10.6084/m9.figshare.11898285
title: Dataset from Losing stinks! The effect of competition outcome on body odour quality

description: Dataset supporting the article
DOI: 10.6084/m9.figshare.11898285.v1
title: Dataset from Losing stinks! The effect of competition outcome on body odour quality

description: Dataset supporting the article
DOI: 10.6084/m9.figshare.11898243
title: Forecast. SIEFORES BASIC 2 (SB2)

description: <b>SIEFORES BASIC 2 (SB2) PENSIONISSSTE </b> <br> The contribution of this work is to show the summary of the contributions of a SBE 2 beneficiary and their voluntary savings from a 1997 pension scheme, AFORES PENSIONISSSTE. We use fuzzy time series to calculate the resources the account holder will receive at the moment of retirement, based on the accumulated balance in the account statements issued by the AFORE.<br>
DOI: 10.6084/m9.figshare.11898243.v2
title: Forecast. SIEFORES BASIC 2 (SB2)

description: <b>SIEFORES BASIC 2 (SB2) PENSIONISSSTE </b> <br> The contribution of this work is to show the summary of the contributions of a SBE 2 beneficiary and their voluntary savings from a 1997 pension scheme, AFORES PENSIONISSSTE. We use fuzzy time series to calculate the resources the account holder will receive at the moment of retirement, based on the accumulated balance in the account statements issued by the AFORE.<br>
DOI: 10.6084/m9.figshare.11898243.v1
title: Forecast. SIEFORES BASIC 2 (SB2).

description: <b>SIEFORES BASIC 2 (SB2) PENSIONISSSTE </b> <br> The contribution of this work is to show the summary of the contributions of a SBE 2 beneficiary and their voluntary savings from a 1997 pension scheme, AFORES PENSIONISSSTE. We use fuzzy time series to calculate the resources the account holder will receive at the moment of retirement, based on the accumulated balance in the account statements issued by the AFORE.<br>