cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.1301655.v1
title: PsbS is required for systemic acquired acclimation and post-excess-light-stress optimization of chlorophyll fluorescence decay times in <i>Arabidopsis</i>

description: Systemic acquired acclimation (SAA) is an important light acclimatory mechanism that depends on the global adjustments of non-photochemical quenching and chloroplast retrograde signaling. As the exact regulation of these processes is not known, we measured time-resolved fluorescence of chlorophyll <i>a</i> in <i>Arabidopsis thaliana</i> leaves exposed to excess light, in leaves undergoing SAA, and in leaves after excess light episode. We compare the behavior induced in wild-type plants with null mutant of non-photochemical quenching (<i>npq4–1</i>). The wild type rosettes exhibit a small reduction of fluorescence decay times in leaves directly exposed to excess light and in leaves undergoing SAA in ambient low light. However in <i>npq4–1</i> exposition to excess light results in much faster fluorescence decay, which is insensitive to excitation power. At the same time <i>npq4–1</i> leaves undergoing SAA displayed intermediate fluorescence decay. The <i>npq4–1</i> plants also lost the ability to optimize florescence decay, and thus chlorophyll <i>a</i> dynamics up to 2 h after excess light episode. The fluorescence decay dynamics in both WT and <i>npq4–1</i> can be described by a set of 3 maximum decay times. Based on the results, we concluded that functional PsbS is required for optimization of absorbed photon fate and optimal light acclimatory responses such as SAA or after excess light stress.
DOI: 10.6084/m9.figshare.1301624
title: Methyl Orange adsorption by reuse of a waste adsorbent poly(AAc/AM/SH)-MB superabsorbent hydrogel: matrix effects, adsorption thermodynamic and kinetics studies

description: The traditional method for the treatment of used adsorbents is usually recovery for recycling or direct discarding them. In the present study, a more potential and economical method is described to reutilize a waste adsorbent. Poly(AAc/AM/SH) SAHs have proved to be a good adsorbent for cationic MB dye, and after adsorption, the SAHs were recovered for recycling. In this work, the waste MB dye loaded poly(AAc/AM/SH) SAHs were not recovered but directly applied to adsorb an anionic MO dye from another waste solution. The poly(AAc/AM/SH) SAHs after the MB dye adsorption were stable and suitable for MO dye adsorption for altered surface structures within a wide pH range. The various factors affecting the MO dye adsorption, including pH, contact time, ionic strength, initial concentration of the MO dye, and temperature, were systematically investigated. The equilibrium adsorption data fitted very well to the Langmuir adsorption isotherm and the maximum MO dye adsorption capacity reached to a high of 134 mg/g at 30°C. The thermodynamic parameters such as Δ<i>H</i><sup>0</sup>, Δ<i>G</i><sup>0</sup>, and Δ<i>S</i><sup>0</sup> for the MO dye adsorption processes onto the SAHs were also evaluated, and the obtained negative Δ<i>G</i><sup>0</sup> and Δ<i>H</i><sup>0</sup> values confirmed that the MO adsorption process was spontaneous as well as exothermic. The kinetic studies indicate that the MO dye adsorption process was well consistent with the pseudo-second-order kinetic model. The desorption studies showed that the regeneration of the poly(AAc/AM/SH)-MB SAHs adsorbent can be easily achieved.
DOI: 10.6084/m9.figshare.1301624.v1
title: Methyl Orange adsorption by reuse of a waste adsorbent poly(AAc/AM/SH)-MB superabsorbent hydrogel: matrix effects, adsorption thermodynamic and kinetics studies

description: The traditional method for the treatment of used adsorbents is usually recovery for recycling or direct discarding them. In the present study, a more potential and economical method is described to reutilize a waste adsorbent. Poly(AAc/AM/SH) SAHs have proved to be a good adsorbent for cationic MB dye, and after adsorption, the SAHs were recovered for recycling. In this work, the waste MB dye loaded poly(AAc/AM/SH) SAHs were not recovered but directly applied to adsorb an anionic MO dye from another waste solution. The poly(AAc/AM/SH) SAHs after the MB dye adsorption were stable and suitable for MO dye adsorption for altered surface structures within a wide pH range. The various factors affecting the MO dye adsorption, including pH, contact time, ionic strength, initial concentration of the MO dye, and temperature, were systematically investigated. The equilibrium adsorption data fitted very well to the Langmuir adsorption isotherm and the maximum MO dye adsorption capacity reached to a high of 134 mg/g at 30°C. The thermodynamic parameters such as Δ<i>H</i><sup>0</sup>, Δ<i>G</i><sup>0</sup>, and Δ<i>S</i><sup>0</sup> for the MO dye adsorption processes onto the SAHs were also evaluated, and the obtained negative Δ<i>G</i><sup>0</sup> and Δ<i>H</i><sup>0</sup> values confirmed that the MO adsorption process was spontaneous as well as exothermic. The kinetic studies indicate that the MO dye adsorption process was well consistent with the pseudo-second-order kinetic model. The desorption studies showed that the regeneration of the poly(AAc/AM/SH)-MB SAHs adsorbent can be easily achieved.
DOI: 10.6084/m9.figshare.1301620
title: PKC53e RNAi preliminary experiments

description: Description of the experiment and the figures <em>Drosophila </em>self-learning. The flies are tethered to a torque meter which measures the angular momentum around the fly's vertical body axis (yaw torque), caused by attempted turning maneuvers. Attempted left (or right) turning maneuvers are punished during training phases and no predictive stimuli are present. Thus, the only predictor of punishment is the behavior of the fly. If the fly continue to avoid the punished behavior when laser is absent during test phases, we talk about self-learning. Scoring and learning protocol. A score corresponding to the proportion of time the fly is doing the safe turning attempt is calculated for each 2 minutes period. Before training the score is around 0 (no preference). During training phases, the score rises since the fly avoid doing the laser punishment. After two rounds of 4 minutes training separated by a 2min test, a final 2 minutes test round is performed. The performance during these two minutes is used for statistical analysis. After the experiment, the torque force of the fly is tested again using its optomotor response and the accuracy of the laser position (the fly is sacrificed by letting the laser on for about 15s). If any of these two tests is not satisfactory, the data is not included in the analysis. <br><br> <strong>Representation legend .</strong> The score during the final test is depicted in boxplots (bar is the median, boxes are quartiles, bars are 100% of the data not considering the outliers presented by points). Detailed information. Genetics: parental lines UAS-PKC53e.RNAi lines (V: from VDRC, T: Trip line) males were crossed to elavGal4; tubgal80ts females. Only females were tested, Flies were grown at 25 degrees and put for two days at 32 degrees after eclosion before being tested.. NB: data where the pretest score was higher than 0.6 were excluded from the analysis. Full genotype of the flies tested is indicated below: elavG4;tG80 x CS : UAS_PKC53eRNAi_vienna/+ . CS x 53eRi_V : elavG4/+;tubGal80ts/+;UAS_PKC53eRNAi_vienna/+ . CS x 53eRi_T : elavG4/+;tubGal80ts/+ . elavG4;tG80 x 53eRi_T : elavG4/+;tubGal80ts/UAS_PKC53eRNAi_Trip . elavG4;tG80 x 53eRi_V : UAS_PKC53eRNAi_Trip/+ . ------ flybase numbers: tubGal80<sup>ts</sup>= P{tubP-GAL80ts} (FBtp0017264)[on the 2nd chromosome], elavGal4= FBti0002575 UAS-PKC53e.RNAi_V= FBti0159711 UAS-PKC53e.RNAi_T= FBti0128750
DOI: 10.6084/m9.figshare.1301608
title: Muskellunge Spawning Site Selection in Northern Wisconsin Lakes and a GIS-Based Predictive Habitat Model

description: Spawning habitat degradation has been linked to declines in naturally reproducing Muskellunge <i>Esox masquinongy</i> populations, and managers require efficient methods to identify and protect these habitats. We collected spawning habitat data from 28 lakes in northern Wisconsin to determine Muskellunge spawning habitat selection and to create a GIS-based model for predicting the locations of spawning sites. Spawning site selection by Muskellunge may be more complex than previously thought. Muskellunge showed selection for spawning in habitats with a sheltered effective fetch and east-facing shorelines. The strongest selection was for habitats with a combination of moderate slope, small flats, and concave bathymetric curvature. Muskellunge selected against steeply sloping shorelines; very large areas of shallow flats; developed shorelines; herbaceous wetlands; and complex-leafed submersed aquatic vegetation. Lake trophic status appears to interact with other habit variables to determine spawning site selection; sites without submersed aquatic vegetation were more strongly selected in eutrophic lakes than in other lake types. A GIS model of spawning site selection was created using the machine learning program MaxEnt (Maximum Entropy Modeling). The model predicted that Muskellunge would spawn in areas with moderately sheltered effective fetches, moderate to small areas of shallow flats, away from outflowing streams, and (to a lesser extent) along shorelines facing east or west. The model was tested on novel lakes using area-under-the-curve (AUC) analysis, in which values ranged from 0.5 (predictions no better than random) to 1.0 (perfect assignment). The mean AUC<i><sub>test</sub></i> value (i.e., the expectation of model performance for a novel lake) was 0.637 (SD = 0.12). When the model was used to designate the best 20% of available spawning habitat area for Muskellunge in each lake (based on the relative probability of spawning), that area contained 32% of the spawning sites. The model provides an efficient method for management agencies and conservation groups to use in designating spawning habitat for conservation and in communicating with the public through spawning habitat maps.Received January 26, 2014; accepted October 7, 2014
DOI: 10.6084/m9.figshare.1301620.v1
title: PKC53e RNAi preliminary experiments

description: Description of the experiment and the figures <em>Drosophila </em>self-learning. The flies are tethered to a torque meter which measures the angular momentum around the fly's vertical body axis (yaw torque), caused by attempted turning maneuvers. Attempted left (or right) turning maneuvers are punished during training phases and no predictive stimuli are present. Thus, the only predictor of punishment is the behavior of the fly. If the fly continue to avoid the punished behavior when laser is absent during test phases, we talk about self-learning. Scoring and learning protocol. A score corresponding to the proportion of time the fly is doing the safe turning attempt is calculated for each 2 minutes period. Before training the score is around 0 (no preference). During training phases, the score rises since the fly avoid doing the laser punishment. After two rounds of 4 minutes training separated by a 2min test, a final 2 minutes test round is performed. The performance during these two minutes is used for statistical analysis. After the experiment, the torque force of the fly is tested again using its optomotor response and the accuracy of the laser position (the fly is sacrificed by letting the laser on for about 15s). If any of these two tests is not satisfactory, the data is not included in the analysis. <br><br> <strong>Representation legend .</strong> The score during the final test is depicted in boxplots (bar is the median, boxes are quartiles, bars are 100% of the data not considering the outliers presented by points). Detailed information. Genetics: parental lines UAS-PKC53e.RNAi lines (V: from VDRC, T: Trip line) males were crossed to elavGal4; tubgal80ts females. Only females were tested, Flies were grown at 25 degrees and put for two days at 32 degrees after eclosion before being tested.. NB: data where the pretest score was higher than 0.6 were excluded from the analysis. Full genotype of the flies tested is indicated below: elavG4;tG80 x CS : UAS_PKC53eRNAi_vienna/+ . CS x 53eRi_V : elavG4/+;tubGal80ts/+;UAS_PKC53eRNAi_vienna/+ . CS x 53eRi_T : elavG4/+;tubGal80ts/+ . elavG4;tG80 x 53eRi_T : elavG4/+;tubGal80ts/UAS_PKC53eRNAi_Trip . elavG4;tG80 x 53eRi_V : UAS_PKC53eRNAi_Trip/+ . ------ flybase numbers: tubGal80<sup>ts</sup>= P{tubP-GAL80ts} (FBtp0017264)[on the 2nd chromosome], elavGal4= FBti0002575 UAS-PKC53e.RNAi_V= FBti0159711 UAS-PKC53e.RNAi_T= FBti0128750
DOI: 10.6084/m9.figshare.1301608.v2
title: Muskellunge Spawning Site Selection in Northern Wisconsin Lakes and a GIS-Based Predictive Habitat Model

description: Spawning habitat degradation has been linked to declines in naturally reproducing Muskellunge <i>Esox masquinongy</i> populations, and managers require efficient methods to identify and protect these habitats. We collected spawning habitat data from 28 lakes in northern Wisconsin to determine Muskellunge spawning habitat selection and to create a GIS-based model for predicting the locations of spawning sites. Spawning site selection by Muskellunge may be more complex than previously thought. Muskellunge showed selection for spawning in habitats with a sheltered effective fetch and east-facing shorelines. The strongest selection was for habitats with a combination of moderate slope, small flats, and concave bathymetric curvature. Muskellunge selected against steeply sloping shorelines; very large areas of shallow flats; developed shorelines; herbaceous wetlands; and complex-leafed submersed aquatic vegetation. Lake trophic status appears to interact with other habit variables to determine spawning site selection; sites without submersed aquatic vegetation were more strongly selected in eutrophic lakes than in other lake types. A GIS model of spawning site selection was created using the machine learning program MaxEnt (Maximum Entropy Modeling). The model predicted that Muskellunge would spawn in areas with moderately sheltered effective fetches, moderate to small areas of shallow flats, away from outflowing streams, and (to a lesser extent) along shorelines facing east or west. The model was tested on novel lakes using area-under-the-curve (AUC) analysis, in which values ranged from 0.5 (predictions no better than random) to 1.0 (perfect assignment). The mean AUC<i><sub>test</sub></i> value (i.e., the expectation of model performance for a novel lake) was 0.637 (SD = 0.12). When the model was used to designate the best 20% of available spawning habitat area for Muskellunge in each lake (based on the relative probability of spawning), that area contained 32% of the spawning sites. The model provides an efficient method for management agencies and conservation groups to use in designating spawning habitat for conservation and in communicating with the public through spawning habitat maps.Received January 26, 2014; accepted October 7, 2014
DOI: 10.6084/m9.figshare.1301608.v1
title: Muskellunge Spawning Site Selection in Northern Wisconsin Lakes and a GIS-Based Predictive Habitat Model

description: Spawning habitat degradation has been linked to declines in naturally reproducing Muskellunge <i>Esox masquinongy</i> populations, and managers require efficient methods to identify and protect these habitats. We collected spawning habitat data from 28 lakes in northern Wisconsin to determine Muskellunge spawning habitat selection and to create a GIS-based model for predicting the locations of spawning sites. Spawning site selection by Muskellunge may be more complex than previously thought. Muskellunge showed selection for spawning in habitats with a sheltered effective fetch and east-facing shorelines. The strongest selection was for habitats with a combination of moderate slope, small flats, and concave bathymetric curvature. Muskellunge selected against steeply sloping shorelines; very large areas of shallow flats; developed shorelines; herbaceous wetlands; and complex-leafed submersed aquatic vegetation. Lake trophic status appears to interact with other habit variables to determine spawning site selection; sites without submersed aquatic vegetation were more strongly selected in eutrophic lakes than in other lake types. A GIS model of spawning site selection was created using the machine learning program MaxEnt (Maximum Entropy Modeling). The model predicted that Muskellunge would spawn in areas with moderately sheltered effective fetches, moderate to small areas of shallow flats, away from outflowing streams, and (to a lesser extent) along shorelines facing east or west. The model was tested on novel lakes using area-under-the-curve (AUC) analysis, in which values ranged from 0.5 (predictions no better than random) to 1.0 (perfect assignment). The mean AUC<i><sub>test</sub></i> value (i.e., the expectation of model performance for a novel lake) was 0.637 (SD = 0.12). When the model was used to designate the best 20% of available spawning habitat area for Muskellunge in each lake (based on the relative probability of spawning), that area contained 32% of the spawning sites. The model provides an efficient method for management agencies and conservation groups to use in designating spawning habitat for conservation and in communicating with the public through spawning habitat maps.Received January 26, 2014; accepted October 7, 2014
DOI: 10.6084/m9.figshare.1301545
title: Method of preparation of active element for sensors for oxidizing and reducing agent, acids based on stable organic radicals, applied on inert supports with internal standart

description: A.V. Yakovenko, S.V.Kolotilov, O.V.Shvets, N.V. Kasian, V.G. Ilyin, V.V. Pavlishchuk., “Method of preparation of active element for sensors for oxidizing and reducing agent, acids based on stable organic radicals, applied on inert supports with internal standart”, Ukraine Patent 23727 U, № u200612910, 11.06.2007
DOI: 10.6084/m9.figshare.1301545.v1
title: Method of preparation of active element for sensors for oxidizing and reducing agent, acids based on stable organic radicals, applied on inert supports with internal standart

description: A.V. Yakovenko, S.V.Kolotilov, O.V.Shvets, N.V. Kasian, V.G. Ilyin, V.V. Pavlishchuk., “Method of preparation of active element for sensors for oxidizing and reducing agent, acids based on stable organic radicals, applied on inert supports with internal standart”, Ukraine Patent 23727 U, № u200612910, 11.06.2007
DOI: 10.6084/m9.figshare.1301521
title: OMWS performance test files

description: This set of files was created to run a standard performance test against an OMWS web service. OMWS (openModeller Web Service) is a SOAP protocol for ecological niche modelling. Currently there are two versions of the protocol: OMWS1 and OMWS2. OMWS1 contains only individual operations, such as createModel, projectModel, etc. The main motivation for creating OMWS2 was to allow clients to specify complex ENM experiments in a single request (runExperiment) containing multiple jobs with or without dependencies between them. Two client programs written in Python were created to run a functionally equivalent test on OMWS1 and OMWS2 service endpoints to measure performance and compare their efficiency on different back-end configurations. All files that are necessary for these tests are included in the zip file.
DOI: 10.6084/m9.figshare.1301521.v1
title: OMWS performance test files

description: This set of files was created to run a standard performance test against an OMWS web service. OMWS (openModeller Web Service) is a SOAP protocol for ecological niche modelling. Currently there are two versions of the protocol: OMWS1 and OMWS2. OMWS1 contains only individual operations, such as createModel, projectModel, etc. The main motivation for creating OMWS2 was to allow clients to specify complex ENM experiments in a single request (runExperiment) containing multiple jobs with or without dependencies between them. Two client programs written in Python were created to run a functionally equivalent test on OMWS1 and OMWS2 service endpoints to measure performance and compare their efficiency on different back-end configurations. All files that are necessary for these tests are included in the zip file.
DOI: 10.6084/m9.figshare.1301501
title: Dataset from the Research on Handedness, Digit Ratio and Child Overconfidence

description: Dataset from the research on child overconfidence, handedness and digit ratio.
DOI: 10.6084/m9.figshare.1301463
title: Mendeley reader counts for F1000Prime papers

description: The raw data are provided as a comma separated values (CSV) file.<br>Columns 1, 2, and 4 contain identifiers. Column 3 indicates if this is<br>the first occurence of this paper (1) or if the paper occurs again (2,<br>3, 4, ...) with a new recommendation. Columns 5 and 6 contain the<br>PubmedID and DOI. Column 7 contains the total number of Mendeley readers<br>of this paper. Columns 8 to 32 contain the number of Mendeley readers in<br>the disciplines, while column 33 contains the sum of readers over all<br>disciplines. Note that the value in column 33 and 7 is not equal for all<br>papers, because the discipline is a voluntary piece of information
DOI: 10.6084/m9.figshare.1301501.v2
title: Dataset from the Research on Handedness, Digit Ratio and Child Overconfidence

description: Dataset from the research on child overconfidence, handedness and digit ratio.
DOI: 10.6084/m9.figshare.1301463.v1
title: Mendeley reader counts for F1000Prime papers

description: The raw data are provided as a comma separated values (CSV) file.<br>Columns 1, 2, and 4 contain identifiers. Column 3 indicates if this is<br>the first occurence of this paper (1) or if the paper occurs again (2,<br>3, 4, ...) with a new recommendation. Columns 5 and 6 contain the<br>PubmedID and DOI. Column 7 contains the total number of Mendeley readers<br>of this paper. Columns 8 to 32 contain the number of Mendeley readers in<br>the disciplines, while column 33 contains the sum of readers over all<br>disciplines. Note that the value in column 33 and 7 is not equal for all<br>papers, because the discipline is a voluntary piece of information
DOI: 10.6084/m9.figshare.1301501.v1
title: Dataset from the Research on Handedness, Digit Ratio and Infant Overconfidence

description: Dataset from the research on infant overconfidence, handedness and digit ratio.
DOI: 10.6084/m9.figshare.1301448
title: Sinkholes data

description: Shapefiles and vegetation data for part of Dinaric Mountains (Snežnik region) of Slovenia.
DOI: 10.6084/m9.figshare.1301450
title: Dataset used in the analysis presented in "Economic Behavior under the Influence of Alcohol: An Experiment on Time Preferences, Risk-taking, and Altruism"

description: This dataset contains all the data used in the analysis presented in the paper "Economic Behavior under the Influence of Alcohol: An Experiment on Time Preferences, Risk-taking, and Altruism" that is forthcoming on PlosOne. The description of the variables can be found in the manuscript.
DOI: 10.6084/m9.figshare.1301450.v1
title: Dataset used in the analysis presented in "Economic Behavior under the Influence of Alcohol: An Experiment on Time Preferences, Risk-taking, and Altruism"

description: This dataset contains all the data used in the analysis presented in the paper "Economic Behavior under the Influence of Alcohol: An Experiment on Time Preferences, Risk-taking, and Altruism" that is forthcoming on PlosOne. The description of the variables can be found in the manuscript.
DOI: 10.6084/m9.figshare.1301416
title: FASTQ file for adrenal tissue (1)

description: This is for an exercise based on the Illumina BodyMap 2.0 data set.
DOI: 10.6084/m9.figshare.1301425
title: Reference genome sequence for chr19 from UCSC (hg19)

description: Reference genome sequence for chr19 from UCSC (hg19)
DOI: 10.6084/m9.figshare.1301415
title: FASTQ file for adrenal tissue (2)

description: This is for an exercise based on the Illumina BodyMap 2.0 data set.