cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.16944029
title: Self-regulation as rehabilitation outcome: what is important according to former patients?

description: Self-regulation refers to self-management and self-control, with or without disability. Outcomes of rehabilitation with respect to self-regulation are unclear. This study aims to identify elements of self-regulation that former patients consider important in the context of medical rehabilitation. Qualitative exploration based on focus group discussions (FGDs). Transcripts were analysed using thematic analysis as well as open coding. Forty individuals participated in seven diagnosis-related FGDs. Six subthemes were raised in the FGDs which could be merged into three main themes. Two main themes are conditional for regaining self-regulation: 1) having insight into one’s condition and abilities (i.e., insight into impairments. consequences of impairments. abilities); 2) to know how to cope with the consequences of the condition (be able to communicate limitations; have to trust in body and functioning). The subject of the last theme 3) is how to apply self-regulation in one’s own life (to make use of abilities and optimize functioning). Three main themes of self-regulation in the context of medical rehabilitation were identified by former patients, partly relating to the ability to self-regulate and partly to the execution of self-regulation. This knowledge can be used to define specific rehabilitation goals and further develop rehabilitation outcome measurement.IMPLICATIONS FOR REHABILITATIONAwareness of the fundamental subthemes of self-regulation in rehabilitation will positively contribute to theory building and improve clinical practice (e.g., goal setting).Paying explicit attention to the six subthemes as standard elements of rehabilitation will help to provide a comprehensive view concerning self-regulation.The conceptual model of self-regulation, based on patient perspectives, can contribute to the measurement of rehabilitation outcomes. Awareness of the fundamental subthemes of self-regulation in rehabilitation will positively contribute to theory building and improve clinical practice (e.g., goal setting). Paying explicit attention to the six subthemes as standard elements of rehabilitation will help to provide a comprehensive view concerning self-regulation. The conceptual model of self-regulation, based on patient perspectives, can contribute to the measurement of rehabilitation outcomes.
DOI: 10.6073/pasta/5bf72766aea02082cadd1cac475fec66
title: Phenologies of the Tabonuco Forest trees and shrubs

description: These data are being used to, among other things, (1) determine the seasonality of flowering and fruiting in Tabonuco forest and test hypotheses concerning the causation of seasonality (or lack thereof), (2) test the effect of annual variation in rainfall and other climatic variables on seed and fruit production of individual species, and (3) compare the relative dispersal of species on the Luquillo Forest Dynamics Plot by applying information on the spatial distribution of canopy trees to the data on seed and fruit fall. These data also serve as background information on the flowering and fruiting of individual species. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
DOI: 10.6084/m9.figshare.21676646
title: Data used in Binomic tests

description: Data used in Binomic tests in article
DOI: 10.6084/m9.figshare.21676646.v1
title: Data used in Binomic tests

description: Data used in Binomic tests in article
DOI: 10.5281/zenodo.7401839
title: tinyIFD dataset

description: The datasets deposited here are related to the tinyIFD publication submitted to JCIM. It contains the clean PDB files of template receptors, both apo and holo forms. Also included are the target ligand (to be docked to a template receptor). The files are organized in such way that it can be directly placed at the root of the tinyIFD workflow. The main dataset is similar to Schrodinger's IFD-MD dataset and contains 369 cross-docking cases, while the Mpro dataset contains 35 cross-docking cases.
DOI: 10.5281/zenodo.7401838
title: tinyIFD dataset

description: The datasets deposited here are related to the tinyIFD publication submitted to JCIM. It contains the clean PDB files of template receptors, both apo and holo forms. Also included are the target ligand (to be docked to a template receptor). The files are organized in such way that it can be directly placed at the root of the tinyIFD workflow. The main dataset is similar to Schrodinger's IFD-MD dataset and contains 369 cross-docking cases, while the Mpro dataset contains 35 cross-docking cases.
DOI: 10.6084/m9.figshare.17919114
title: Neurocognitive profile of adolescents with early-onset schizophrenia and their unaffected siblings

description: We investigated the neurocognitive profiles of Early-Onset Schizophrenia (EOS; onset before age 18) and paired unaffected siblings and the little-studied effect of age-of-onset and duration of illness on cognitive performance. 31 EOS probands, and 31 of their siblings, had four cognitive domains assessed: (a) Memory: California Verbal Learning Test, and the Wechsler Memory Scale-Revised; (b) Working memory: Digit Span; (c) Attention: Degraded-Stimulus Continuous Performance Test, Span of Apprehension (SPAN), and Trail Making Test (TMT) part A; (d) Executive function: Wisconsin card sorting task, and TMT part B. Diagnosis was confirmed using the structured clinical interview for DSM-IV. While EOS showed a generalised neurocognitive deficit (0.25–0.50 effect size) compared with siblings, across all cognitive domains, significantly greater patient deficits were observed with, working memory, attention, and executive function and minimal differences for digit span forward, block design and false alarms on the SPAN-12 confirmed by repeated measures MANOVA. Patients with earlier onset (12–15) showed greater deficits on false alarm and digits backward scores. Siblings showed individual cognitive task profiles similar to patients, confirming familial effects. EOS showed much more variable scores than siblings with more individual tasks showing 2 SD deficits than siblings. Long duration patients had greater z-score variability across tasks. Duration of illness was a more important characteristic in patients with onset 16 and over than in younger onset patients with comparable durations. Both the similarity of sibling pair profiles and greater patient variability across task provide further support for neurobiological heterogeneity in schizophrenia.
DOI: 10.6084/m9.figshare.20771246
title: Marginal Structural Models to Estimate Causal Effects of Right-to-Carry Laws on Crime

description: Right-to-carry (RTC) laws allow the legal carrying of concealed firearms for defense, in certain states in the United States. I used modern causal inference methodology from epidemiology to examine the effect of RTC laws on crime over a period from 1959 up to 2016. I fitted marginal structural models (MSMs), using inverse probability weighting (IPW) to correct for criminological, economic, political and demographic confounders. Results indicate that RTC laws significantly increase violent crime by 7.5% and property crime by 6.1%. RTC laws significantly increase murder and manslaughter, robbery, aggravated assault, burglary, larceny theft and motor vehicle theft rates. Applying this method to this topic for the first time addresses methodological shortcomings in previous studies such as conditioning away the effect, overfit and the inappropriate use of county level measurements. Data and analysis code for this article are available online.
DOI: 10.6084/m9.figshare.19330666
title: Policy Implications of Statistical Estimates: A General Bayesian Decision-Theoretic Model for Binary Outcomes

description: How should we evaluate the effect of a policy on the likelihood of an undesirable event, such as conflict? The significance test has three limitations. First, relying on statistical significance misses the fact that uncertainty is a continuous scale. Second, focusing on a standard point estimate overlooks the variation in plausible effect sizes. Third, the criterion of substantive significance is rarely explained or justified. A new Bayesian decision-theoretic model, “causal binary loss function model,” overcomes these issues. It compares the expected loss under a policy intervention with the one under no intervention. These losses are computed based on a particular range of the effect sizes of a policy, the probability mass of this effect size range, the cost of the policy, and the cost of the undesirable event the policy intends to address. The model is more applicable than common statistical decision-theoretic models using the standard loss functions or capturing costs in terms of false positives and false negatives. I exemplify the model’s use through three applications and provide an R package. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.19330666.v2
title: Policy Implications of Statistical Estimates: A General Bayesian Decision-Theoretic Model for Binary Outcomes

description: How should we evaluate the effect of a policy on the likelihood of an undesirable event, such as conflict? The significance test has three limitations. First, relying on statistical significance misses the fact that uncertainty is a continuous scale. Second, focusing on a standard point estimate overlooks the variation in plausible effect sizes. Third, the criterion of substantive significance is rarely explained or justified. A new Bayesian decision-theoretic model, “causal binary loss function model,” overcomes these issues. It compares the expected loss under a policy intervention with the one under no intervention. These losses are computed based on a particular range of the effect sizes of a policy, the probability mass of this effect size range, the cost of the policy, and the cost of the undesirable event the policy intends to address. The model is more applicable than common statistical decision-theoretic models using the standard loss functions or capturing costs in terms of false positives and false negatives. I exemplify the model’s use through three applications and provide an R package. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.19750119
title: Changes in Crime Rates during the COVID-19 Pandemic

description: We estimate changes in the rates of five FBI Part 1 crimes during the 2020 spring COVID-19 pandemic lockdown period and the period after the killing of George Floyd through December 2020. We use weekly crime rate data from 28 of the 70 largest cities in the United States from January 2018 to December 2020. Homicide rates were higher throughout 2020, including during early 2020 prior to March lockdowns. Auto thefts increased significantly during the summer and remainder of 2020. In contrast, robbery and larceny significantly declined during all three post-pandemic periods. Point estimates of burglary rates pointed to a decline for all four periods of 2020, but only the pre-pandemic period was statistically significant. We construct a city-level openness index to examine whether the degree of openness just prior to and during the lockdowns was associated with changing crime rates. Larceny and robbery rates both had a positive and significant association with the openness index implying lockdown restrictions reduced offense rates whereas the other three crime types had no detectable association. While opportunity theory is a tempting post hoc explanation of some of these findings, no single crime theory provides a plausible explanation of all the results. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.20771246.v1
title: Marginal Structural Models to Estimate Causal Effects of Right-to-Carry Laws on Crime

description: Right-to-carry (RTC) laws allow the legal carrying of concealed firearms for defense, in certain states in the United States. I used modern causal inference methodology from epidemiology to examine the effect of RTC laws on crime over a period from 1959 up to 2016. I fitted marginal structural models (MSMs), using inverse probability weighting (IPW) to correct for criminological, economic, political and demographic confounders. Results indicate that RTC laws significantly increase violent crime by 7.5% and property crime by 6.1%. RTC laws significantly increase murder and manslaughter, robbery, aggravated assault, burglary, larceny theft and motor vehicle theft rates. Applying this method to this topic for the first time addresses methodological shortcomings in previous studies such as conditioning away the effect, overfit and the inappropriate use of county level measurements. Data and analysis code for this article are available online.
DOI: 10.6084/m9.figshare.19330666.v1
title: Policy Implications of Statistical Estimates: A General Bayesian Decision-Theoretic Model for Binary Outcomes

description: How should we evaluate the effect of a policy on the likelihood of an undesirable event, such as conflict? The significance test has three limitations. First, relying on statistical significance misses the fact that uncertainty is a continuous scale. Second, focusing on a standard point estimate overlooks the variation in plausible effect sizes. Third, the criterion of substantive significance is rarely explained or justified. A new Bayesian decision-theoretic model, “causal binary loss function model,” overcomes these issues. It compares the expected loss under a policy intervention with the one under no intervention. These losses are computed based on a particular range of the effect sizes of a policy, the probability mass of this effect size range, the cost of the policy, and the cost of the undesirable event the policy intends to address. The model is more applicable than common statistical decision-theoretic models using the standard loss functions or capturing costs in terms of false positives and false negatives. I exemplify the model’s use through three applications and provide an R package.
DOI: 10.6084/m9.figshare.19750119.v2
title: Changes in Crime Rates during the COVID-19 Pandemic

description: We estimate changes in the rates of five FBI Part 1 crimes during the 2020 spring COVID-19 pandemic lockdown period and the period after the killing of George Floyd through December 2020. We use weekly crime rate data from 28 of the 70 largest cities in the United States from January 2018 to December 2020. Homicide rates were higher throughout 2020, including during early 2020 prior to March lockdowns. Auto thefts increased significantly during the summer and remainder of 2020. In contrast, robbery and larceny significantly declined during all three post-pandemic periods. Point estimates of burglary rates pointed to a decline for all four periods of 2020, but only the pre-pandemic period was statistically significant. We construct a city-level openness index to examine whether the degree of openness just prior to and during the lockdowns was associated with changing crime rates. Larceny and robbery rates both had a positive and significant association with the openness index implying lockdown restrictions reduced offense rates whereas the other three crime types had no detectable association. While opportunity theory is a tempting post hoc explanation of some of these findings, no single crime theory provides a plausible explanation of all the results. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.21630002
title: Hala Data dictioonary.docx

description: data was collected using online google form questionnaire from medical labrotarits ,nurses and medwives which carried out at khartoum state.
DOI: 10.5281/zenodo.7401882
title: Normalized Difference Vegetation Index (NDVI) data

description: The raster data 'viCH_Landsat_NAfill.tif' represents Normalized Difference Vegetation Index (NDVI) data of Switzerland. Raster pixels have 250x250 m resolution and contain median values of the years 2013 to 2017. Data was collected by the Landsat satellite and was downloaded via Google Earth Engine. Missing values were closed with a local neighborhood average. The raster data 'viNE_Modis2018_NAfill.tif' represents NDVI data of North Eurasia, at 1x1 km resolution, and averaged over the growing season 2018. Data was collected by the Modis sensor and was downloaded via Google Earth Engine. Missing values were closed with a local neighborhood average.
DOI: 10.5281/zenodo.7401881
title: Normalized Difference Vegetation Index (NDVI) data

description: The raster data 'viCH_Landsat_NAfill.tif' represents Normalized Difference Vegetation Index (NDVI) data of Switzerland. Raster pixels have 250x250 m resolution and contain median values of the years 2013 to 2017. Data was collected by the Landsat satellite and was downloaded via Google Earth Engine. Missing values were closed with a local neighborhood average. The raster data 'viNE_Modis2018_NAfill.tif' represents NDVI data of North Eurasia, at 1x1 km resolution, and averaged over the growing season 2018. Data was collected by the Modis sensor and was downloaded via Google Earth Engine. Missing values were closed with a local neighborhood average.
DOI: 10.18112/openneuro.ds004350.v1.0.0
title: Executive Functioning Data
DOI: 10.7910/dvn/6jlt17
title: How Nonmarket Externalities Affect the U.S. Business Dynamism: An Economic Approach to Drug Epidemic

description: Research Time-Series Data on the Influence of Drug Epidemic on the U.S. Business Dynamism
DOI: 10.5281/zenodo.7401155
title: Code and data for "Coupling cell shape and velocity leads to oscillation and circling in keratocyte galvanotaxis"

description: Code and data to reproduce "Coupling cell shape and velocity leads to oscillation and circling in keratocyte galvanotaxis" Biophys. J. doi: https://doi.org/10.1016/j.bpj.2022.11.021
DOI: 10.5281/zenodo.7401154
title: Code and data for "Coupling cell shape and velocity leads to oscillation and circling in keratocyte galvanotaxis"

description: Code and data to reproduce "Coupling cell shape and velocity leads to oscillation and circling in keratocyte galvanotaxis" Biophys. J. doi: https://doi.org/10.1016/j.bpj.2022.11.021
DOI: 10.5281/zenodo.7401939
title: Force-tuned Avidity of Spike Variant-ACE2 Interactions viewed on the Single-Molecule Level - MD simulations Dataset

description: Models of SARS-CoV-2 virus spike protein bound to 1-3 of ACE2 receptors embedded in lipid nanodisks. Systems include all files in GROMACS format needed to reproduce simulations performed in the "Force-tuned Avidity of Spike Variant-ACE2 Interactions viewed on the Single-Molecule Level" article. Details system box size (x-y-z) [nm] Number of atoms Spike +<br> 1x ACE2, full length 33.44834 28.96711 57.95573 5,665,217 Spike +<br> 1x ACE2, truncated 28.05757 24.29856 46.74417 3,203,907 Spike +<br> 2x ACE2, truncated 28.30864 21.23141 48.40873 2,936,398 Spike +<br> 3x ACE2, truncated 28.32733 21.24544 48.30436 2,936,588
DOI: 10.5281/zenodo.7401938
title: Force-tuned Avidity of Spike Variant-ACE2 Interactions viewed on the Single-Molecule Level - MD simulations Dataset

description: Models of SARS-CoV-2 virus spike protein bound to 1-3 of ACE2 receptors embedded in lipid nanodisks. Systems include all files in GROMACS format needed to reproduce simulations performed in the "Force-tuned Avidity of Spike Variant-ACE2 Interactions viewed on the Single-Molecule Level" article. Details system box size (x-y-z) [nm] Number of atoms Spike +<br> 1x ACE2, full length 33.44834 28.96711 57.95573 5,665,217 Spike +<br> 1x ACE2, truncated 28.05757 24.29856 46.74417 3,203,907 Spike +<br> 2x ACE2, truncated 28.30864 21.23141 48.40873 2,936,398 Spike +<br> 3x ACE2, truncated 28.32733 21.24544 48.30436 2,936,588
DOI: 10.5517/ccdc.csd.cc2bjxkc
title: CCDC 2161213: Experimental Crystal Structure Determination

description: Related Article: Ekaterina K. Pylova, Radmir M. Khisamov, Denis A. Bashirov, Taisiya S. Sukhikh, Sergey N. Konchenko|2022|CrystEngComm|24|8256|doi:10.1039/D2CE01215D
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bn146
title: CCDC 2164207: Experimental Crystal Structure Determination

description: Related Article: Jun Geng, Hongyan Lin, Xiaohui Li, Junjun Lu, XiuLi Wang|2022|CrystEngComm|24|8286|doi:10.1039/D2CE01397E
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bp7ny
title: CCDC 2165370: Experimental Crystal Structure Determination

description: Related Article: L. R. Hunter, J. D. Sellars, M. R. Probert|2022|CrystEngComm|24|8208|doi:10.1039/D2CE00499B
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bp7mx
title: CCDC 2165369: Experimental Crystal Structure Determination

description: Related Article: L. R. Hunter, J. D. Sellars, M. R. Probert|2022|CrystEngComm|24|8208|doi:10.1039/D2CE00499B
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bp7r1
title: CCDC 2165373: Experimental Crystal Structure Determination

description: Related Article: L. R. Hunter, J. D. Sellars, M. R. Probert|2022|CrystEngComm|24|8208|doi:10.1039/D2CE00499B
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bp7w5
title: CCDC 2165377: Experimental Crystal Structure Determination

description: Related Article: L. R. Hunter, J. D. Sellars, M. R. Probert|2022|CrystEngComm|24|8208|doi:10.1039/D2CE00499B
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.