cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13028249
title: What we bet on is not money, but good mood

description: <b> </b>A<b> </b>surprisingly large amount of lottery prizes are left unclaimed each year. This irony leads us to suspect that what we bet on is not money, but good mood. We present three studies that aim to explain why people play lottery games from an emotional perspective. In Study 1, a large-scale online survey was conducted to investigate lottery buyers’ mood changes of lottery playing. People are in their best mood before knowing whether they have won or not. In Study 2, we manipulated the way to reward (choosing lottery tickets vs. choosing cash) and compared participants’ mood changes at different stages of a rewards game in laboratory settings. First, participants in the lottery group were generally in a better mood. Second, 42% of participants from the lottery group did not show up in the laboratory to collect scratch cards. Third, participants in the lottery group took more time to return to the laboratory to check their tickets than participants in the cash group to resume their cash. In Study 3, we examined whether priming bad/good mood can improve people’s preference for lottery tickets/cash. People who were primed in a bad mood showed a high preference for lottery tickets.
DOI: 10.6084/m9.figshare.13028249.v1
title: What we bet on is not money, but good mood

description: <b> </b>A<b> </b>surprisingly large amount of lottery prizes are left unclaimed each year. This irony leads us to suspect that what we bet on is not money, but good mood. We present three studies that aim to explain why people play lottery games from an emotional perspective. In Study 1, a large-scale online survey was conducted to investigate lottery buyers’ mood changes of lottery playing. People are in their best mood before knowing whether they have won or not. In Study 2, we manipulated the way to reward (choosing lottery tickets vs. choosing cash) and compared participants’ mood changes at different stages of a rewards game in laboratory settings. First, participants in the lottery group were generally in a better mood. Second, 42% of participants from the lottery group did not show up in the laboratory to collect scratch cards. Third, participants in the lottery group took more time to return to the laboratory to check their tickets than participants in the cash group to resume their cash. In Study 3, we examined whether priming bad/good mood can improve people’s preference for lottery tickets/cash. People who were primed in a bad mood showed a high preference for lottery tickets.
DOI: 10.25345/c57b6d
title: MassIVE MSV000086215 - Phosphoproteomics after nitrate treatments reveal an important role 2 for PIN2 phosphorylation in control of root system architecture
title: Nitrate is an important signaling molecule that commands genome-wide gene expression changes that impact metabolism, physiology, plant growth and development. Although gene expression responses to nitrate at the mRNA level have been characterized in great detail, the impact of nitrate signaling at the proteome level has been much less explored. Most signaling pathways involve post-translational modifications of key protein factors and chiefly among these modifications is protein phosphorylation. In an effort to identify new components involved in nitrate responses in plants, we performed analyses of the Arabidopsis thaliana root phosphoproteome in response to nitrate treatments via liquid chromatography coupled to tandem mass spectrometry. We identified 268 phosphoproteins that show significant changes at 5 min or 20 min after nitrate treatments. The large majority of these proteins (95%) are coded by genes that are not modulated at the expression level in response to nitrate treatments in publicly available transcriptome data. Proteins identified by 5 min include potential signaling-components such as kinases or transcription factors. In contrast, by 20 min, proteins identified were associated with protein binding, transporter activity or hormone metabolism functions. Interestingly, the phosphorylation profile of NITRATE TRANSPORTER 1.1 (NRT1.1) mutant plants in response to nitrate at 5 min was significantly different (95%) as compared to wild-type plants, confirming its key role in nitrate signaling that involves phosphoproteomic changes. Our integrative bioinformatics analysis highlights auxin transport as an important mechanism modulated by nitrate signaling at the post-translational level. We experimentally validated the role of a new phosphorylation site in PIN2 function for both primary and lateral root growth responses to nitrate. Our data provide new insights into the phosphoproteome and identifies novel protein components that are regulated post-translationally, such as PIN2, in nitrate responses in Arabidopsis thaliana roots.
DOI: 10.6084/m9.figshare.7859864
title: Data from: Cryptic interactions revisited from ecological networks: mosses as a key link between trees and hummingbirds

description: README<br>Data from: Fontúrbel, F.E., F. Osorio, V. Riffo-Donoso, G.O. Carvallo & H. Rydin (2020) Cryptic interactions revisited from ecological networks: mosses as a key link between trees and hummingbirds. Functional Ecology. doi: 10.1111/1365-2435.13691<br>This entry contains a R script along with eight .csv files:<br>(1) moss-tree-quali.csv - Qualitative data on moss-tree relationships.(2) moss-tree-quanti.csv - Quantitative data on moss-tree relationships.(3) moss-tree-excAp.csv - Qualitative data on moss-tree relationships excluding the least connected species (Aextoxicon punctatum).(4) moss-tree-excWt.csv - Qualitative data on moss-tree relationships excluding the most connected species (Weinmannia trichosperma).(5) new-nest-quali.csv - Qualitative data on moss-nest relationships for new nests.(6) new-nest-quanti.csv - Quantitative data on moss-nest relationships for new nests.(7) old-nest-quali.csv - Qualitative data on moss-nest relationships for old nests.(8) old-nest-quanti.csv - Quantitative data on moss-nest relationships for old nests.(9) indbasednwrk.csv - Individual-based network data.<br><br>Data contained here may be freely used on the sole condition of cite the original paper along with this repository entry:Original data from: Fontúrbel, F.E., F. Osorio, V. Riffo-Donoso, G.O. Carvallo & H. Rydin (2020) Cryptic interaction revisited from ecological networks: mosses as a key link between trees and hummingbirds. figshare digital repository, https://doi.org/10.6084/m9.figshare.
DOI: 10.18112/openneuro.ds003207.v1.0.0
title: Think Like an Expert
DOI: 10.6084/m9.figshare.7859864.v1
title: Data from: Cryptic interaction revisited from ecological networks: mosses as a key link between trees and hummingbirds

description: README<br>Data from: Fontúrbel, F.E., F. Osorio, V. Riffo-Donoso, G.O. Carvallo & H. Rydin (2020) Cryptic interaction revisited from ecological networks: mosses as a key link between trees and hummingbirds. Functional Ecology.<br>This entry contains a R script along with eight .csv files:<br>(1) moss-tree-quali.csv - Qualitative data on moss-tree relationships.(2) moss-tree-quanti.csv - Quantitative data on moss-tree relationships.(3) moss-tree-excAp.csv - Qualitative data on moss-tree relationships excluding the least connected species (Aextoxicon punctatum).(4) moss-tree-excWt.csv - Qualitative data on moss-tree relationships excluding the most connected species (Weinmannia trichosperma).(5) new-nest-quali.csv - Qualitative data on moss-nest relationships for new nests.(6) new-nest-quanti.csv - Quantitative data on moss-nest relationships for new nests.(7) old-nest-quali.csv - Qualitative data on moss-nest relationships for old nests.(8) old-nest-quanti.csv - Quantitative data on moss-nest relationships for old nests.(9) indbasednwrk.csv - Individual-based network data.<br><br>Data contained here may be freely used on the sole condition of cite the original paper along with this repository entry:Original data from: Fontúrbel, F.E., F. Osorio, V. Riffo-Donoso, G.O. Carvallo & H. Rydin (2020) Cryptic interaction revisited from ecological networks: mosses as a key link between trees and hummingbirds. figshare digital repository, https://doi.org/10.6084/m9.figshare.
DOI: 10.6084/m9.figshare.13026854
title: R0235_20181025a_videos

description: Rat 0235, skilled reaching data collected in Leventhal laboratory by Alexandra Bova
DOI: 10.15482/usda.adc/1519580
title: Vicia villosa seed physical dormancy dataset from 2017-2019
DOI: 10.6084/m9.figshare.12442703.v1
title: Forest Resilience Threshold Experiment (FoRTE) - Analysis of aboveground wood net primary production (ANPPw)

description: These data were collected in 2018 and 2019 at the University of Michigan Biological Station as a part of the Forest Resilience and Threshold Experiment (FoRTE), and were used in the analysis of aboveground wood net primary production (ANPPw) documented in Grigri et al. (<i>unpublished</i>). <br>Diameter growth increments of a sub-sample of stems from all canopy strata were used to infer ecosystem scale ANPPw. <i>Canopy </i>refers to stems ≥8cm diameter at breast height (DBH), <i>subcanopy </i>refers to stems 1-8cm DBH, and <i>seedling/saplings </i>refers to stems &lt;1cm DBH. Dendrometer bands were used to record repeated measures of <i>canopy </i>trees. Digital calipers were used for DBH measurements in the <i>subcanopy</i>, and base diameter and height measurements of <i>seedling/saplings.</i><i><br></i><i>FoRTE_all_trees.csv </i>is 2018 census data off all trees present in experimental plots. <i>DBL_dendrobands.csv </i>is data collected from multiple dendrometer bands at different stem heights on a single tree (see Grigri et al., <i>unpublished). FoRTE_LAI.csv </i>is leaf area index (LAI) estimated from hemispherical images taken throughout the growing season of 2019 (Atkins et al., <i>in review</i>). <br>All R scripts included. Please see README.R for details.
DOI: 10.6084/m9.figshare.12442703.v2
title: Forest Resilience Threshold Experiment (FoRTE) - Analysis of aboveground wood net primary production (ANPPw)

description: These data were collected in 2018 and 2019 at the University of Michigan Biological Station as a part of the Forest Resilience and Threshold Experiment (FoRTE), and were used in the analysis of aboveground wood net primary production (ANPPw) documented in Grigri et al. (<i>unpublished</i>). <br>Diameter growth increments of a sub-sample of stems from all canopy strata were used to infer ecosystem scale ANPPw. <i>Upper canopy </i>refers to stems ≥8cm diameter at breast height (DBH), <i>subcanopy </i>refers to stems 1-8cm DBH, and <i>seedling/saplings </i>refers to stems &lt;1cm DBH. Dendrometer bands were used to record repeated measures of <i>upper canopy </i>trees. Digital calipers were used for DBH measurements in the <i>subcanopy</i>, and base diameter and height measurements of <i>seedling/saplings.</i><i><br></i><i>FoRTE_all_trees.csv </i>is 2018 census data off all trees present in experimental plots. <i>DBL_dendrobands.csv </i>is data collected from multiple dendrometer bands at different stem heights on a single tree (see Grigri et al., <i>unpublished). FoRTE_LAI.csv </i>is leaf area index (LAI) estimated from hemispherical images taken throughout the growing season of 2019 (Atkins et al., <i>in review</i>). <br>All R scripts included. Please see README.R for details.
DOI: 10.6084/m9.figshare.12442703.v3
title: Forest Resilience Threshold Experiment (FoRTE) - Scaling and analysis of aboveground wood net primary production (ANPPw)

description: These data were collected in 2018 and 2019 at the University of Michigan Biological Station as a part of the Forest Resilience and Threshold Experiment (FoRTE), and were used in the analysis of aboveground wood net primary production (ANPPw) documented in Grigri et al. (<i>unpublished</i>). <br>Diameter growth increments of a sub-sample of stems from all canopy strata were used to infer ecosystem scale ANPPw. <i>Upper canopy </i>refers to stems ≥8cm diameter at breast height (DBH), <i>subcanopy </i>refers to stems 1-8cm DBH, and <i>seedling/saplings </i>refers to stems &lt;1cm DBH. Dendrometer bands were used to record repeated measures of <i>upper canopy </i>trees. Digital calipers were used for DBH measurements in the <i>subcanopy</i>, and base diameter and height measurements of <i>seedling/saplings.</i><i><br></i><i>FoRTE_all_trees.csv </i>is 2018 census data off all trees present in experimental plots. <i>DBL_dendrobands.csv </i>is data collected from multiple dendrometer bands at different stem heights on a single tree (see Grigri et al., <i>unpublished). FoRTE_LAI.csv </i>is leaf area index (LAI) estimated from hemispherical images taken throughout the growing season of 2019 (Atkins et al., 2020). <br>Download zip for R project data, and code. Please see README.R for details.
DOI: 10.6084/m9.figshare.13009997.v1
title: VGEA: A snakemake pipeline for RNA virus genome assembly from next generation sequencing data

description: Lassa Virus (Human) and HIV-1 Test Datasets for the VGEA Pipeline
DOI: 10.5281/zenodo.4060211
title: Long-term monitoring of gaseous elementary mercury in background air at the polar station Amderma, Russian Arctic

description: The produced dataset (in MS Excel format) contains time-series of concentrations of gaseous elementary mercury (GEM, thereafter – mercury) in the background air collected at the Amderma polar station near the Amderma settlement of the Russian Arctic (69,72 deg N; 61,62 deg E; Yugor Peninsula, Russia) from 24th June 2001 until 13th February 2013. The concentrations for mercury are given in ng/m^3. Concentrations were measured in the air at every 30 minute interval using Tekran-2537A instrument. After checking quality of recorded values of the mercury concentration, the average of two consecutive measurements in two channels (average 1 hour measurements) are calculated. Data (as time-series) are grouped into three blocks covering different time periods and adjacent locations. First block covers period from 24th June 2001 to 13th February 2004 (when the analyzer was located at a distance of about 9 km from the coastline of the Kara Sea). Second block covers period from 3th April 2005 to 12th June 2010 (when the analyzer was placed at 2.5 km from the coast). Third block covers period from 15th June 2010 to 10th October 2013 (the analyzer was set up at a distance of 200 m from the coastline of the sea).
description: AMAP: Mercury in the Arctic Horizon-2020 iCUPE (Integrative and Comprehensive Understanding on Polar Environments) project
description: {"references": ["Pankratov, F., Mahura, A., Pet\u00e4j\u00e4, T., Popov, V., and Masloboev, V.: Elevated atmospheric mercury concentrations at the Russian polar station Amderma during Icelandic volcanoes' eruptions, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2018-1228, 2018."]}
DOI: 10.5281/zenodo.4060210
title: Long-term monitoring of gaseous elementary mercury in background air at the polar station Amderma, Russian Arctic

description: The produced dataset (in MS Excel format) contains time-series of concentrations of gaseous elementary mercury (GEM, thereafter – mercury) in the background air collected at the Amderma polar station near the Amderma settlement of the Russian Arctic (69,72 deg N; 61,62 deg E; Yugor Peninsula, Russia) from 24th June 2001 until 13th February 2013. The concentrations for mercury are given in ng/m^3. Concentrations were measured in the air at every 30 minute interval using Tekran-2537A instrument. After checking quality of recorded values of the mercury concentration, the average of two consecutive measurements in two channels (average 1 hour measurements) are calculated. Data (as time-series) are grouped into three blocks covering different time periods and adjacent locations. First block covers period from 24th June 2001 to 13th February 2004 (when the analyzer was located at a distance of about 9 km from the coastline of the Kara Sea). Second block covers period from 3th April 2005 to 12th June 2010 (when the analyzer was placed at 2.5 km from the coast). Third block covers period from 15th June 2010 to 10th October 2013 (the analyzer was set up at a distance of 200 m from the coastline of the sea).
description: AMAP: Mercury in the Arctic Horizon-2020 iCUPE (Integrative and Comprehensive Understanding on Polar Environments) project
description: {"references": ["Pankratov, F., Mahura, A., Pet\u00e4j\u00e4, T., Popov, V., and Masloboev, V.: Elevated atmospheric mercury concentrations at the Russian polar station Amderma during Icelandic volcanoes' eruptions, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2018-1228, 2018."]}
DOI: 10.18112/openneuro.ds002814.v1.1.0
title: TODO: name of the dataset
DOI: 10.5281/zenodo.4060221
title: NUMERICAL INVESTIGATION OF CONDENSING FLOWS IN A SUPERSONIC SEPARATOR- 0,25bar

description: As a contribution to overcoming the challenges of numerical simulation involving the phenomenon of condensation, this work aims to investigate the nucleation of simple molecules, through the classical nucleation theory (CNT) associated with the Hill’s Moments (MoM) models . The main objective of this simulations was to investigate the numerical precision of the CFD open source code - Stanford University Unstructured(SU2) - to correctly capture the occurrence of a condensation wave, and to analyze thenucleation of liquid droplets through the moments of order zero to three
DOI: 10.5281/zenodo.4060220
title: NUMERICAL INVESTIGATION OF CONDENSING FLOWS IN A SUPERSONIC SEPARATOR- 0,25bar

description: As a contribution to overcoming the challenges of numerical simulation involving the phenomenon of condensation, this work aims to investigate the nucleation of simple molecules, through the classical nucleation theory (CNT) associated with the Hill’s Moments (MoM) models . The main objective of this simulations was to investigate the numerical precision of the CFD open source code - Stanford University Unstructured(SU2) - to correctly capture the occurrence of a condensation wave, and to analyze thenucleation of liquid droplets through the moments of order zero to three
DOI: 10.6084/m9.figshare.13028150.v2
title: Wild dataset for pilot experiment on bird observation

description: Detailed information about observed animals and their activities and duration are noted here as observer was doing the observation.
DOI: 10.6084/m9.figshare.13028150
title: Wild dataset for pilot experiment on bird observation

description: Detailed information about observed animals and their activities and duration are noted here as observer was doing the observation.
DOI: 10.6084/m9.figshare.13028150.v1
title: Wild dataset for pilot experiment on bird observation

description: Detailed information about observed animals and their activities and duration are noted here as observer was doing the observation.
DOI: 10.5281/zenodo.4041477
title: SELENE_COU_test

description: Données structurées des glissements sémantiques de COU dans les langues sénégambiennes
DOI: 10.5517/ccdc.csd.cc24hvvc
title: CCDC 1981453: Experimental Crystal Structure Determination

description: Related Article: Luzia S. Germann, Mihails Arhangelskis, Martin Etter, Robert E. Dinnebier, Tomislav Friščić|2020|Chemical Science|11|10092|doi:10.1039/D0SC03629C
DOI: 10.5281/zenodo.4060232
title: Dataset GoAmazonTeste

description: Dataset de teste para a lista de exercicios 1 da disciplina PCS5787
DOI: 10.5281/zenodo.4060231
title: Dataset GoAmazonTeste

description: Dataset de teste para a lista de exercicios 1 da disciplina PCS5787
DOI: 10.5281/zenodo.4060037
title: Validation results of Non-parametric models of pH Neutralization plant Dataset

description: This repository contains a table with different validation results of a series of models tested in pH Neutralization plant and Clarke plant. In this case, the naming protocol was as follows: [plant name]_DataSet_[model variable]. The content of every CSV file is presented as follows: Column 1: { name: Model_No, type: numeric integer, limit: None, description: Code to identifiy the model (Primary Key)} Column 2: { name: KS, type: numeric float, limits: [0 1], description: Kolgomorov-Smirnov test} Column 3: { name: AD, type: numeric float, limits: [0 1], description: Anderson-Darling test} Column 4: { name: SW, type: numeric float, limits: [0 1], description: Shapiro-Wilk test} Column 5: { name: WX, type: numeric float, limits: [0 1], description: Wilcoxon test} Column 6: { name: FIT, type: numeric float, limits: [-Inf 1], description: Goodness of Fit metric} Column 7: { name: TIC, type: numeric float, limits: [0 1], description: Theil Inequality Index} Column 8: { name: Willmott, type: numeric float, limits: [0 1], description: Willmott metric} Column 9: { name: Russell_Pr, type: numeric float, limits: [0 1], description: Phase of Russell } Column 10: { name: Russell_Mr, type: numeric float, limits: [0 Inf], description: Magnitude of Russell } Column 11: { name: SG_Mr, type: numeric float, limits: [-Inf 1], description: Sprague & Geers} Column 12: { name: Anova, type: numeric float, limits: [0 1], description: Anova test} Column 13: { name: Dvure, type: numeric float, limits: [0 100], description: Dvurecenska metric} Column 14: { name: DTW, type: numeric float, limits: [0 Inf], description: DTW metric} Column 15: { name: D_DTW, type: numeric float, limits: [0 Inf], description: derivative of DTW metric}
DOI: 10.5281/zenodo.4060246
title: NUMERICAL INVESTIGATION OF CONDENSING FLOWS IN A SUPERSONIC SEPARATOR A LOW PRESSURE.

description: As a contribution to overcoming the challenges of numerical simulation involving the phenomenon of condensation, this work aims to investigate the nucleation of simple molecules, through the classical nucleation theory (CNT) associated with the Hill’s Moments (MoM) models . The main objective of this simulations was to investigate the numerical precision of the CFD open source code - Stanford University Unstructured(SU2) - to correctly capture the occurrence of a condensation wave, and to analyze thenucleation of liquid droplets through the moments of order zero to three
DOI: 10.5281/zenodo.4060036
title: Validation results of Non-parametric models of pH Neutralization plant Dataset

description: This repository contains a table with different validation results of a series of models tested in pH Neutralization plant and Clarke plant. In this case, the naming protocol was as follows: [plant name]_DataSet_[model variable]. The content of every CSV file is presented as follows: Column 1: { name: Model_No, type: numeric integer, limit: None, description: Code to identifiy the model (Primary Key)} Column 2: { name: KS, type: numeric float, limits: [0 1], description: Kolgomorov-Smirnov test} Column 3: { name: AD, type: numeric float, limits: [0 1], description: Anderson-Darling test} Column 4: { name: SW, type: numeric float, limits: [0 1], description: Shapiro-Wilk test} Column 5: { name: WX, type: numeric float, limits: [0 1], description: Wilcoxon test} Column 6: { name: FIT, type: numeric float, limits: [-Inf 1], description: Goodness of Fit metric} Column 7: { name: TIC, type: numeric float, limits: [0 1], description: Theil Inequality Index} Column 8: { name: Willmott, type: numeric float, limits: [0 1], description: Willmott metric} Column 9: { name: Russell_Pr, type: numeric float, limits: [0 1], description: Phase of Russell } Column 10: { name: Russell_Mr, type: numeric float, limits: [0 Inf], description: Magnitude of Russell } Column 11: { name: SG_Mr, type: numeric float, limits: [-Inf 1], description: Sprague & Geers} Column 12: { name: Anova, type: numeric float, limits: [0 1], description: Anova test} Column 13: { name: Dvure, type: numeric float, limits: [0 100], description: Dvurecenska metric} Column 14: { name: DTW, type: numeric float, limits: [0 Inf], description: DTW metric} Column 15: { name: D_DTW, type: numeric float, limits: [0 Inf], description: derivative of DTW metric}