cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/6bhr52dc5v.1
title: Test Anxiety, Wellbeing, Buoyancy, Adaptability, and Mental Health Risk

description: Data were collected from 1198 participants aged 16-19 years from 3 sixth form colleges (academic track upper secondary education), located in England, in a two-wave design. Each wave of measurement was separated by seven months.

Multidimensional Test Anxiety Scale (Putwain et al., 2020) labelled MTAS in the dataset.
School-Related Wellbeing Scale (Loderer et al., 2016) labelled W in the dataset.
Social, Academic, and Emotional Behavior Risk Screener (von der Embse et al., 2016) labelled S in the dataset.
Academic Buoyancy Scale (Martin & Marsh, 2008) labeled B in the dataset.
Adaptability scale (Martin et al., 2012) labeled A in the dataset.

In addition socio-demographic data was collected for gender (male or female), age, and ethnic heritage (Asian, Black, White, Other/ mixed heritage). Missing data were coded as 999.

Data were saved as an SPSS .sav file (that includes all items for the above scales, response ranges and anchors, and the categorical coding for the aforementioned socio-demographic data.

The principal research question was as follows: Is test anxiety reciprocally related to wellbeing, buoyancy, adaptability, and mental health risk?
We hypothesised that test anxiety would be positively related to mental health risk, and negatively related to wellbeing, buoyancy and adaptability; all relations were expected to be directional.

References

Chorpita, B. F., Moffitt, C., & Gray, J. (2005). Psychometric properties of the Revised Child Anxiety and Depression Scale in a clinical sample. Behaviour Research and Therapy, 43, 309-322. https://doi.org/10.1016/j.brat.2004.02.004
Loderer, K., Vogl, E., & Pekrun, R. (2016, August 24-27). Students’ well-being at school revisited: Development and initial validation of a unidimensional self-report scale [Conference session]. International Conference on Motivation (ICM), Thessaloniki, Greece. http://www.psy.auth.gr/el/conference/programme.
Martin, A.J. & Marsh, H.W. (2008) Academic buoyancy: towards an understanding of students’ everyday academic resilience, Journal of School Psychology, 46, 53–83. https://doi.org/10.1016/j.jsp.2007.01.002
Martin, A. J., Nejad, H., Colmar, S., & Liem, G. A. D. (2012). Adaptability: Conceptual and empirical perspectives on responses to change, novelty and uncertainty. Australian Journal of Guidance and Counselling,22, 58–81. https://doi.org/10.1017/jgc.2012.8
Putwain, D.W., von der Embse, N.P., Rainbird, E.C., & West, G. (20209). The development and validation of a new Multidimensional Test Anxiety Scale (MTAS). European Journal of Psychological Assessment. Advance online pulication, https://doi.org/10.1027/1015-5759/a000604
von der Embse, N. P., Pendergast, L., Kilgus, S. P., & Eklund, K. R. (2016). Evaluating the applied use of a mental health screener: Structural validity of the Social, Academic, and Emotional Behavior Risk Screener. Psychological Assessment, 28, 1265–1275.doi:10.1037/pas0000253
DOI: 10.17632/cxgfzg3kcs.1
title: Tcommodus_reproductivestrategies

description: Pre-maturation social environment experienced by females may affect their post-maturation reproductive strategies, including mating preferences and investment in offspring. Whether the pre-maturation social environment affects other aspects of females’ reproductive strategies, including the degree of polyandry and the post-copulatory decisions, is still an open question. To address this question, we performed laboratory experiments using the highly polyandrous cricket Teleogryllus commodus. Previous studies showed that juvenile females reared in an acoustic environment with male calls of different rates (MIX) are more responsive to highly-attractive calls than females reared in an environment with male calls of only high calling rates (HIGH). We exposed juvenile females to the same two acoustic environments (MIX and HIGH) and estimated their degree of polyandry, time of spermatophore retention, and offspring quality. We found that the acoustic environment did not change a female’s mating rate, indicating that the higher responsiveness shown in a previous study does not translate into higher degree of polyandry. An increased number of mates reduced offspring quality, suggesting that there is an optimum number of mates for females. Finally, when compared with females of the HIGH group, females of the MIX group retained spermatophores for shorter periods and produced higher quality offspring if mated with highly-attractive males. This finding suggests that the pre-maturation acoustic environment interacts with the quality of the males to determine post-copulatory female decisions and eventually offspring quality. Taken together, our results indicate that both the pre- and post-mating strategies of females are subject to socially induced plasticity.
DOI: 10.17632/9yx8hfnhxy.1
title: Social Anhedonia, Loneliness and Faces

description: Data: The data contains the Revised Social Anhedonia Scale, UCLA Loneliness Scale and the upright faces with different emotions % accuracy correct from the Palmero database of expressions. Higher scores on all variables mean greater endorsement of the variable under consideration.
The aim of the study was to examine whether those with social anhedonia had reduced sensitivity to detecting emotional expressions and whether this was modified by loneliness. We also examined associations between social anhedonia and social functioning as well as considering whether loneliness mediated the relationship between social anhedonia and social functioning. 
Loneliness and social anhedonia were not related to accuracy for expression labelling. The relationship between social anhedonia and recognition of surprise was moderated by loneliness, such that the association was in the opposite direction and only significant at the extreme levels of loneliness. Social anhedonia was related to both loneliness and social functioning but loneliness did not mediate the relationship between social anhedonia and social functioning.
DOI: 10.17632/x97ycnv8kn.1
title: Data for: Sustainable Arsenite Removal from Silicate-rich Water by Using Redox Polymer-based Fe(III) Oxides Nanocomposite

description: Raw data
DOI: 10.17632/tyr4g866fs.1
title: AM of Inc625-YSZ

description: Microstructural evolution and mechanical properties of Inconel+YSZ alloy produced by laser powder bed fusion
DOI: 10.5066/p9g9br1d
title: Data from a turtle trapping effort at a release site of head-started alligator snapping turtles, Macrochelys temminckii, in southwest Louisiana in 2018

description: This dataset contains initial data from head-started alligator snapping turtles released by Louisiana Department of Wildlife and Fisheries (LDWF) from November 2015 to October 2016. In addition, it contains data from a five-day trapping effort at each of seven release sites by the United States Geological Survey (USGS) from late June to early October 2018. Trapping was completed using hoop nets of three sizes. We recaptured eight head-started alligator snapping turtles as well as four individuals native to the creek.
DOI: 10.5281/zenodo.5486680
title: CoVaxxy Tweet IDs data set

description: A collection of Tweet IDs related to Covid-19 Vaccines, gathered from Twitter since Jan 4, 2021. Please see https://arxiv.org/abs/2101.07694 for more information.
DOI: 10.6084/m9.figshare.16529640.v9
title: Ensemble of Nucleic Acid Absolute Quantitation Modules for Accurate Copy Number Variation Detection and Targeted RNA Profiling

description: NGS data of the publication: Ensemble of Nucleic Acid Absolute Quantitation Modules for Accurate Copy Number Variation Detection and Targeted RNA Profiling
DOI: 10.6084/m9.figshare.14386716.v1
title: Mitochondrial DNA A3243G variant-associated retinopathy: a meta-analysis of the clinical course of visual acuity and correlation with systemic manifestations

description: The mitochondrial DNA A3243G (m.3243A&gt;G) variant causes a wide spectrum of phenotypes, with pigmentary retinopathy as the most common ocular finding. We undertook this meta-analysis to investigate the clinical course of visual acuity (VA) in patients with m.3243A&gt;G variant and provide key clinical correlations with systemic manifestations. A PubMed literature search was performed and studies were selected after satisfying pre-set inclusion criteria. Demographic and clinical data, including retinal findings and systemic manifestations were recorded. Cross-sectional and linear regression analyses were used to investigate the relationship between VA and age, as well as between the age at diagnosis of retinopathy and the mean ages at diagnosis of sensorineural hearing loss or diabetes. The age and prevalence of systemic manifestations among patients with and without retinopathy were studied using <i>t-</i>tests and Mann–Whitney U-tests (performed on binarized data). Likelihood ratios were computed. The mean VA (average of both eyes) of 90 patients (72.2% female; 65/90) were collected from 18 studies published between 1990 and 2018. The baseline mean age was 45.2 years (range 17 to 92). The mean logMAR VA was 0.10 (- 0.12 to 1.39). There was a statistically significant linear correlation between the logMAR VA and age (<i>p = </i>.008). The VA of patients less than or equal to 50 years of age was significantly better than that of patients older than 50 years (0.06 vs.0.18 logMAR, <i>p</i> = .002). 67 patients (74.4%) showed a characteristic pigmentary retinopathy with a mean age at diagnosis of 47.9 years (17 to 92) and VA of 0.14 logMAR (- 0.12 to 1.24). Age at diagnosis of retinopathy was linearly correlated with age at diagnosis of hearing loss or diabetes (<i>p .001). Patients with retinopathy were more likely to have hearing loss (83.6% vs. 56.5%, <i>p = </i>.03) or diabetes (56.7% vs. 17.4%, <i>p = </i>.001) than those without retinopathy. Those with both hearing loss and diabetes had an earlier onset of retinopathy than those without (46.4 vs. 60.4 years, <i>p</i> = .01). Patients without both hearing loss and diabetes were 5.3–fold less likely to develop a retinopathy.</i> Patients with m.3243A&gt;G variant pigmentary retinopathy maintain highly functional VA until around the fifth decade of life, after which significant visual decline ensues. Patients without hearing loss and diabetes have a lower likelihood of exhibiting a retinopathy, which tends to appear about one decade after hearing loss and diabetes are diagnosed.
DOI: 10.6084/m9.figshare.14386716
title: Mitochondrial DNA A3243G variant-associated retinopathy: a meta-analysis of the clinical course of visual acuity and correlation with systemic manifestations

description: The mitochondrial DNA A3243G (m.3243A&gt;G) variant causes a wide spectrum of phenotypes, with pigmentary retinopathy as the most common ocular finding. We undertook this meta-analysis to investigate the clinical course of visual acuity (VA) in patients with m.3243A&gt;G variant and provide key clinical correlations with systemic manifestations. A PubMed literature search was performed and studies were selected after satisfying pre-set inclusion criteria. Demographic and clinical data, including retinal findings and systemic manifestations were recorded. Cross-sectional and linear regression analyses were used to investigate the relationship between VA and age, as well as between the age at diagnosis of retinopathy and the mean ages at diagnosis of sensorineural hearing loss or diabetes. The age and prevalence of systemic manifestations among patients with and without retinopathy were studied using <i>t-</i>tests and Mann–Whitney U-tests (performed on binarized data). Likelihood ratios were computed. The mean VA (average of both eyes) of 90 patients (72.2% female; 65/90) were collected from 18 studies published between 1990 and 2018. The baseline mean age was 45.2 years (range 17 to 92). The mean logMAR VA was 0.10 (- 0.12 to 1.39). There was a statistically significant linear correlation between the logMAR VA and age (<i>p = </i>.008). The VA of patients less than or equal to 50 years of age was significantly better than that of patients older than 50 years (0.06 vs.0.18 logMAR, <i>p</i> = .002). 67 patients (74.4%) showed a characteristic pigmentary retinopathy with a mean age at diagnosis of 47.9 years (17 to 92) and VA of 0.14 logMAR (- 0.12 to 1.24). Age at diagnosis of retinopathy was linearly correlated with age at diagnosis of hearing loss or diabetes (<i>p .001). Patients with retinopathy were more likely to have hearing loss (83.6% vs. 56.5%, <i>p = </i>.03) or diabetes (56.7% vs. 17.4%, <i>p = </i>.001) than those without retinopathy. Those with both hearing loss and diabetes had an earlier onset of retinopathy than those without (46.4 vs. 60.4 years, <i>p</i> = .01). Patients without both hearing loss and diabetes were 5.3–fold less likely to develop a retinopathy.</i> Patients with m.3243A&gt;G variant pigmentary retinopathy maintain highly functional VA until around the fifth decade of life, after which significant visual decline ensues. Patients without hearing loss and diabetes have a lower likelihood of exhibiting a retinopathy, which tends to appear about one decade after hearing loss and diabetes are diagnosed.
DOI: 10.6084/m9.figshare.16584119
title: McKee full submission.pdf

description: Submission to the Australian eSafety Commissioner call for evidence on mandatory age verification for pornography
DOI: 10.5281/zenodo.4277989
title: SIRAH-CoV2 initiative: RBD triple glycosylated at Asn331, 343, and 481 from PDB structure 6XEY

description: This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of a Spike's RBD from SARS-CoV2 glycosylated at Asn331, 343, and 481 with Man9 glycosylation trees. The initial coordinates correspond to amino acids 327 to 532 taken from the PDB structure 6XEY. Missing loops and glycosylation trees were added with CHARMM-GUI (http://www.charmm-gui.org). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in Machado et al. JCTC 2019, adding 150 mM NaCl according to Machado & Pantano JCTC 2020. Glycan parameters correspond to those reported by Garay et al. The files 6XEY-RBD-3Man9_SIRAHcg_0-4us.tar, 6XEY-RBD-3Man9_SIRAHcg_4-8us.tar, and 6XEY-RBD-3Man9_SIRAHcg_8-10us.tar, contain all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using SirahTools can be found at www.sirahff.com. Additionally, the file 6XEY-RBD-3Man9_SIRAHcg_glycoprot_10us.tar contains only the protein coordinates, while 6XEY-RBD-3Man9_SIRAHcg_glycoprot_skip10ns.tar contains one frame every 10ns. To take a quick look at the trajectory: 1- Untar the file 6XEY-RBD-3Man9_SIRAHcg_glycoprot_skip10ns.tar 2- Open the trajectory on VMD using the command line: vmd 6XEY-RBD-3Man9_SIRAHcg_glycoprot.prmtop 6XEY-RBD-3Man9_SIRAHcg_glycoprot.ncrst 6XEY-RBD-3Man9_SIRAHcg_10us_skip10ns.nc -e sirah_vmdtk.tcl Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. This dataset is part of the SIRAH-CoV2 initiative. For further details, please contact Pablo Garay (pgaray@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).
description: {"references": ["Machado & Pantano Bioinformatics 2016 (DOI:10.1093/bioinformatics/btw020)", "Machado & Pantano JCTC 2020 (DOI:10.1021/acs.jctc.9b00953)", "Machado et al. JCTC 2019 (DOI: 10.1021/acs.jctc.9b00006)", "Garay et al. 2020 (DOI:10.1101/2020.12.18.423446v1)"]}
DOI: 10.5281/zenodo.4250123
title: SIRAH-CoV2 initiative: SARS-CoV-2 helicase (PDB id:6ZSL)

description: This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 Helicase protein (PDB id:6ZSL). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in Machado et al. JCTC 2019, adding 150 mM NaCl according to Machado & Pantano JCTC 2020. Parameters for Zinc ions are those reported in Klein et al. 2020. The files 6ZSL_SIRAHcg_rawdata_0-4us.tar, 6ZSL_SIRAHcg_rawdata_4-8us.tar, and 6ZSL_SIRAHcg_rawdata_8-10us.tar contain all the raw information required to visualize (using VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using SirahTools can be found at www.sirahff.com. Additionally, the file 6ZSL_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 6ZSL_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns. To take a quick look at the trajectory: 1- Untar the file 6ZSL_SIRAHcg_10us_prot_skip10ns.tar 2- Open the trajectory on VMD using the command line: vmd 6ZSL_SIRAHcg_prot.prmtop 6ZSL_SIRAHcg_prot.ncrst 6ZSL_SIRAHcg_prot_10us_skip10ns.nc -e sirah_vmdtk.tcl Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. This dataset is part of the SIRAH-CoV2 initiative. For further details, please contact Pablo Garay (pgaray@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).
description: {"references": ["Machado et al. JCTC 2019 (DOI: 10.1021/acs.jctc.9b00006)", "Machado & Pantano JCTC 2020 (DOI:10.1021/acs.jctc.9b00953)", "Machado & Pantano Bioinformatics 2016 (DOI:10.1093/bioinformatics/btw020)", "Klein et al. JCIM 2020 (DOI:10.1021/acs.jcim.0c00160)"]}
DOI: 10.5281/zenodo.4277988
title: SIRAH-CoV2 initiative: RBD triple glycosylated at Asn331, 343, and 481 from PDB structure 6XEY

description: This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of a Spike's RBD from SARS-CoV2 glycosylated at Asn331, 343, and 481 with Man9 glycosylation trees. The initial coordinates correspond to amino acids 327 to 532 taken from the PDB structure 6XEY. Missing loops and glycosylation trees were added with CHARMM-GUI (http://www.charmm-gui.org). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in Machado et al. JCTC 2019, adding 150 mM NaCl according to Machado & Pantano JCTC 2020. Glycan parameters correspond to those reported by Garay et al. The files 6XEY-RBD-3Man9_SIRAHcg_0-4us.tar, 6XEY-RBD-3Man9_SIRAHcg_4-8us.tar, and 6XEY-RBD-3Man9_SIRAHcg_8-10us.tar, contain all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using SirahTools can be found at www.sirahff.com. Additionally, the file 6XEY-RBD-3Man9_SIRAHcg_glycoprot_10us.tar contains only the protein coordinates, while 6XEY-RBD-3Man9_SIRAHcg_glycoprot_skip10ns.tar contains one frame every 10ns. To take a quick look at the trajectory: 1- Untar the file 6XEY-RBD-3Man9_SIRAHcg_glycoprot_skip10ns.tar 2- Open the trajectory on VMD using the command line: vmd 6XEY-RBD-3Man9_SIRAHcg_glycoprot.prmtop 6XEY-RBD-3Man9_SIRAHcg_glycoprot.ncrst 6XEY-RBD-3Man9_SIRAHcg_10us_skip10ns.nc -e sirah_vmdtk.tcl Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. This dataset is part of the SIRAH-CoV2 initiative. For further details, please contact Pablo Garay (pgaray@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).
description: {"references": ["Machado & Pantano Bioinformatics 2016 (DOI:10.1093/bioinformatics/btw020)", "Machado & Pantano JCTC 2020 (DOI:10.1021/acs.jctc.9b00953)", "Machado et al. JCTC 2019 (DOI: 10.1021/acs.jctc.9b00006)", "Garay et al. 2020 (DOI:10.1101/2020.12.18.423446v1)"]}
DOI: 10.5281/zenodo.4250122
title: SIRAH-CoV2 initiative: SARS-CoV-2 helicase (PDB id:6ZSL)

description: This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 Helicase protein (PDB id:6ZSL). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in Machado et al. JCTC 2019, adding 150 mM NaCl according to Machado & Pantano JCTC 2020. Parameters for Zinc ions are those reported in Klein et al. 2020. The files 6ZSL_SIRAHcg_rawdata_0-4us.tar, 6ZSL_SIRAHcg_rawdata_4-8us.tar, and 6ZSL_SIRAHcg_rawdata_8-10us.tar contain all the raw information required to visualize (using VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using SirahTools can be found at www.sirahff.com. Additionally, the file 6ZSL_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 6ZSL_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns. To take a quick look at the trajectory: 1- Untar the file 6ZSL_SIRAHcg_10us_prot_skip10ns.tar 2- Open the trajectory on VMD using the command line: vmd 6ZSL_SIRAHcg_prot.prmtop 6ZSL_SIRAHcg_prot.ncrst 6ZSL_SIRAHcg_prot_10us_skip10ns.nc -e sirah_vmdtk.tcl Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. This dataset is part of the SIRAH-CoV2 initiative. For further details, please contact Pablo Garay (pgaray@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).
description: {"references": ["Machado et al. JCTC 2019 (DOI: 10.1021/acs.jctc.9b00006)", "Machado & Pantano JCTC 2020 (DOI:10.1021/acs.jctc.9b00953)", "Machado & Pantano Bioinformatics 2016 (DOI:10.1093/bioinformatics/btw020)", "Klein et al. JCIM 2020 (DOI:10.1021/acs.jcim.0c00160)"]}
DOI: 10.6084/m9.figshare.16558503
title: Efficient Depicting of Intertemporal Flexibility from Multi-Energy Systems: An Elimination Perspective

description: The detailed parameters of cases studied in “Efficient Modeling of Intertemporal Flexibility from Multi-Energy Systems: An Elimination Perspective ”.
DOI: 10.6084/m9.figshare.16558503.v1
title: Efficient Depicting of Intertemporal Flexibility from Multi-Energy Systems: An Elimination Perspective

description: The detailed parameters of cases studied in “Efficient Modeling of Intertemporal Flexibility from Multi-Energy Systems: An Elimination Perspective ”.
DOI: 10.6084/m9.figshare.16529640.v10
title: Ensemble of Nucleic Acid Absolute Quantitation Modules for Accurate Copy Number Variation Detection and Targeted RNA Profiling

description: NGS data of the publication: Ensemble of Nucleic Acid Absolute Quantitation Modules for Accurate Copy Number Variation Detection and Targeted RNA Profiling