cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.5446333.v1
title: Fidelity to a motivational interviewing intervention for those with post-stroke aphasia: a small-scale feasibility study

description: <b>Objective:</b> Depression after stroke is common, and talk-based psychological therapies can be a useful intervention. While a third of stroke survivors will experience communication difficulties impeding participation in talk-based therapies, little guidance exists to guide delivery for those with aphasia. We need to understand how to adapt talk-based therapies in the presence of aphasia. This study aimed to explore the feasibility of motivational interviewing (MI) in people with post-stroke aphasia. <b>Methods:</b> In a small-scale feasibility study, consecutive patients admitted to an acute stroke ward were screened for eligibility. People with moderate to severe aphasia were eligible. Those consenting received an intervention consisting of up to eight MI sessions delivered twice per week over four weeks. Sessions were modified using aids and adaptations for aphasia. Session quality was measured using the Motivational Interviewing Skills Code (MISC) to assess MI fidelity. <b>Results:</b> Three consenting patients identified early post-stroke took part; one male and two females ages ranging between 40s and 80s. Participants attended between five and eight MI sessions over four weeks. Aids and adaptations included visual cues, rating scales, and modified reflections incorporating verbal and non-verbal behaviors. Sessions were tailored to individual participant need. Threshold MISC ratings could be achieved for all participants however, ratings were reduced when aids and adaptations were not used. <b>Discussion:</b> This small-scale feasibility study suggests that it is feasible to adapt MI for people with moderate to severe post-stroke aphasia. These findings merit further exploration of adapted MI as an intervention for this patient group.
DOI: 10.6084/m9.figshare.5446150
title: EXCEL PHD.xlsx

description: it was to determine wether or not there was repositioing error or not in patients with mechanical low back pain
DOI: 10.6084/m9.figshare.5446150.v1
title: EXCEL PHD.xlsx

description: it was to determine wether or not there was repositioing error or not in patients with mechanical low back pain
DOI: 10.25442/hku.12832529
title: Study of FPMINT derivative acting on Human Equilibrative Nucleoside Transporters

description: This is the study of effects of FPMINT derivative on cell viability, Western Blot on ENT1 and ENT2 protein expression, uridine uptake of concentration dependence, kinetics and reversibility.
DOI: 10.25442/hku.12832529.v1
title: Study of FPMINT derivative acting on Human Equilibrative Nucleoside Transporters

description: This is the study of effects of FPMINT derivative on cell viability, Western Blot on ENT1 and ENT2 protein expression, uridine uptake of concentration dependence, kinetics and reversibility.
DOI: 10.6084/m9.figshare.5445658
title: Orbital_Wall_Fx_op.xlsx

description: Raw data for "Pediatric Orbital Wall Fractures: Prognostic Factors of Diplopia and Ocular Motility Limitation"<br><b></b>
DOI: 10.6084/m9.figshare.5445658.v1
title: Orbital_Wall_Fx_op.xlsx

description: Raw data for "Pediatric Orbital Wall Fractures: Prognostic Factors of Diplopia and Ocular Motility Limitation"<br><b></b>
DOI: 10.6084/m9.figshare.5445610
title: Annotation of de novo assembly from Arctic charr in Lake Thingvallavatn, Iceland

description: Tab-delimited text file with detailed annotation of all the filtered transcripts in the Trinity assembly.<b>gene_id:</b> Trinity gene identification code<b>transcript_id:</b> Trinity transcript identification code<b>sprot_Top_BLASTX_hit:</b> Top blastx hit from the SwissProt database<b>TrEMBL_Top_BLASTX_hit: </b>Top blastx hit from the TrEMBL database<b>RNAMMER: </b>ribosomal RNA prediction<b>prot_id:</b> Id for protein prediction<b>prot_coords:</b> Coordinates for predicted proteins<b>sprot_Top_BLASTP_hit:</b> Top blastp hit for predicted proteins from the SwissProt database<b>TrEMBL_Top_BLASTP_hit: </b>Top blastp hit for predicted proteins from the TrEMBL database<b>Pfam: </b>Protein domain prediction from Pfam<b>SignalP:</b> Prediction of signal peptides<b>TmHMM:</b> Prediction for transmembrane domains<b>eggnog:</b> Annotation to the eggNOG database<b>gene_ontology_blast: </b>Gene ontology categories based on blast results from SwissProt and TrEMBL<b>gene_ontology_pfam:</b> Gene ontology based on Pfam protein domains<b>SSncbi_Top_BLASTN_trans: </b>Id for top blastn transcripts in NCBI Salmo salar Annotation<b>SSncbi_Top_BLASTX_trans:</b> Id for top blastx transcripts in NCBI Salmo salar Annotation<b>SSncbi_Top_BLASTN_gene_name:</b> Gene name based on top blastn hit in the NCBI Salmo salar Annotation<b>SSncbi_Top_BLASTX_gene_name:</b> Gene name based on top blastx hit in the NCBI Salmo salar Annotation<b>OM_Top_BLASTN_trans: </b>Id for top blastn transcripts in Oncorhynchus mykiss genome annotation<b>OM_Top_BLASTX_trans:</b> Id for top blastx transcripts in Oncorhynchus mykiss genome annotation<b>SalmoBase_Top_BLASTN_trans:</b> Id for top blastn transcripts in SalmoBase Salmo salar Annotation<b>SalmoBase_Top_BLASTX_trans:</b> Id for top blastx transcripts in SalmoBase Salmo salar Annotation<b>SalmoBase_Top_BLASTN_gene_name: </b>Gene name based on top blastn hit in the SalmoBase Salmo salar Annotation<b>SalmoBase_Top_BLASTX_gene_name:</b> Gene name based on top blastx hit in the SalmoBase Salmo salar Annotation
DOI: 10.17608/k6.auckland.5445601
title: Protocol for systematic review: Associations of children’s active school travel with perceptions of the physical environment and characteristics of the social environment: a systematic review

description: Systematic review protocol using PROSPERO criteria for review titled: Associations of children’s active school travel with perceptions of the physical environment and characteristics of the social environment: a systematic review.<br>
DOI: 10.6084/m9.figshare.5445589
title: mammals.concatenated.align.tar.gz

description: Compresed nexus file containing concatenated alignment from 3787 UCE loci of 100 mammal species. Character sets are specified in the sets block of the nexus file.<br>
DOI: 10.6084/m9.figshare.5445610.v1
title: Annotation of de novo assembly from Arctic charr in Lake Thingvallavatn, Iceland

description: Tab-delimited text file with detailed annotation of all the filtered transcripts in the Trinity assembly.<b>gene_id:</b> Trinity gene identification code<b>transcript_id:</b> Trinity transcript identification code<b>sprot_Top_BLASTX_hit:</b> Top blastx hit from the SwissProt database<b>TrEMBL_Top_BLASTX_hit: </b>Top blastx hit from the TrEMBL database<b>RNAMMER: </b>ribosomal RNA prediction<b>prot_id:</b> Id for protein prediction<b>prot_coords:</b> Coordinates for predicted proteins<b>sprot_Top_BLASTP_hit:</b> Top blastp hit for predicted proteins from the SwissProt database<b>TrEMBL_Top_BLASTP_hit: </b>Top blastp hit for predicted proteins from the TrEMBL database<b>Pfam: </b>Protein domain prediction from Pfam<b>SignalP:</b> Prediction of signal peptides<b>TmHMM:</b> Prediction for transmembrane domains<b>eggnog:</b> Annotation to the eggNOG database<b>gene_ontology_blast: </b>Gene ontology categories based on blast results from SwissProt and TrEMBL<b>gene_ontology_pfam:</b> Gene ontology based on Pfam protein domains<b>SSncbi_Top_BLASTN_trans: </b>Id for top blastn transcripts in NCBI Salmo salar Annotation<b>SSncbi_Top_BLASTX_trans:</b> Id for top blastx transcripts in NCBI Salmo salar Annotation<b>SSncbi_Top_BLASTN_gene_name:</b> Gene name based on top blastn hit in the NCBI Salmo salar Annotation<b>SSncbi_Top_BLASTX_gene_name:</b> Gene name based on top blastx hit in the NCBI Salmo salar Annotation<b>OM_Top_BLASTN_trans: </b>Id for top blastn transcripts in Oncorhynchus mykiss genome annotation<b>OM_Top_BLASTX_trans:</b> Id for top blastx transcripts in Oncorhynchus mykiss genome annotation<b>SalmoBase_Top_BLASTN_trans:</b> Id for top blastn transcripts in SalmoBase Salmo salar Annotation<b>SalmoBase_Top_BLASTX_trans:</b> Id for top blastx transcripts in SalmoBase Salmo salar Annotation<b>SalmoBase_Top_BLASTN_gene_name: </b>Gene name based on top blastn hit in the SalmoBase Salmo salar Anno-tation<b>SalmoBase_Top_BLASTX_gene_name:</b> Gene name based on top blastx hit in the SalmoBase Salmo salar Anno-tation
DOI: 10.6084/m9.figshare.5445589.v1
title: mammals.concatenated.align.tar.gz

description: Compresed nexus file containing concatenated alignment from 3787 UCE loci of 100 mammal species. Character sets are specified in the sets block of the nexus file.<br>
DOI: 10.6084/m9.figshare.5445508
title: Ensemble Graphics

description: One graphic can convey a lot of information and several graphics linked together in a coherent ensemble can convey even more. This article describes the principles underlying ensemble graphics, their key dimensions, and where they are of most use. Ensemble graphics must have strong common themes and carefully constructed layouts. Appropriate interactive tools are important. Ensembles can be used flexibly for EDA or in a more structured fashion for supporting modeling and for presentations.
DOI: 10.17608/k6.auckland.5445601.v4
title: Protocol for systematic review: Associations of children’s active school travel with perceptions of the physical environment and characteristics of the social environment: a systematic review

description: Systematic review protocol using PROSPERO criteria for review titled: Associations of children’s active school travel with perceptions of the physical environment and characteristics of the social environment: a systematic review.<br>
DOI: 10.6084/m9.figshare.5445508.v2
title: Ensemble Graphics

description: One graphic can convey a lot of information and several graphics linked together in a coherent ensemble can convey even more. This article describes the principles underlying ensemble graphics, their key dimensions, and where they are of most use. Ensemble graphics must have strong common themes and carefully constructed layouts. Appropriate interactive tools are important. Ensembles can be used flexibly for EDA or in a more structured fashion for supporting modeling and for presentations.
DOI: 10.6084/m9.figshare.5445508.v1
title: Ensemble Graphics

description: One graphic can convey a lot of information and several graphics linked together in a coherent ensemble can convey even more. This paper describes the principles underlying ensemble graphics, their key dimensions, and where they are of most use. Ensemble graphics must have strong common themes and carefully constructed layouts. Appropriate interactive tools are important. Ensembles can be used flexibly for EDA or in a more structured fashion for supporting modelling and for presentations.
DOI: 10.6084/m9.figshare.5445199
title: RetrogradeMotorNeuronLabeling

description: These protocols can be used for retrograde labeling of motor neurons from peripheral tissues of rodents.
DOI: 10.6084/m9.figshare.5445241
title: Louisiana Cultural Heritage Institution Citations on Wikipedia

description: Dataset of Louisiana cultural heritage institution links on Wikipedia as of July 25, 2017.
DOI: 10.6084/m9.figshare.5445199.v1
title: RetrogradeMotorNeuronLabeling

description: These protocols can be used for retrograde labeling of motor neurons from peripheral tissues of rodents.
DOI: 10.6084/m9.figshare.5443627
title: Code for Fast and Scalable Implementation of the Bayesian SVM

description: This dataset contains the Julia code package for the Bayesian SVM algorithm described in the ECML PKDD 2017 paper; Wenzel et al.: Bayesian Nonlinear Support Vector Machines for Big Data.<br>Files are provided in .jl format; containing Julia language code: a high-performance dynamic programming language for numerical computing. These files can be accessed by openly available text edit software. To run the code please see the description below or the more detailed wiki <b><br></b><b>BSVM.jl</b> - contains the module to run the Bayesian SVM algorithm.<br><b>AFKMC2.jl</b> - File for the Assumption Free K MC2 algorithm (KMeans)<br><b><br></b><b>KernelFunctions.jl</b> - Module for the kernel type<br><b>DataAccess.jl</b> - Module for either generating data or exporting from an existing dataset<br><br><b>run_test.jl </b>and<b> paper_experiments.jl</b><b> </b>- Modules to run on a file and compute accuracy on a nFold cross validation, also to compute the brier score and the logscore<br><b>test_functions.jl</b> and <b>paper_experiment_functions.jl</b> - Sets of datatype and functions for efficient testing.<br><b>ECM.jl</b> - Module for expectation conditional maximization (ECM) for nonlinear Bayesian SVM<br>For datasets used in the related experiments please see https://doi.org/10.6084/m9.figshare.5443621<b><br></b><b>Requirements</b><br>The BayesianSVM only works for version of Julia &gt; 0.5. Other necessary packages will automatically be added in the installation. It is also possible to run the package from Python, to do so please check Pyjulia. If you prefer to use R you have the possibility to use RJulia. All these are a bit technical due to the fact that Julia is still a young package.<br><b>Installation</b><br>To install the last version of the package in Julia run<br> Pkg.clone("git://github.com/theogf/BayesianSVM.jl.git")<b><br></b><b>Running the Algorithm</b><br>Here are the basic steps for using the algorithm :<br> using BayesianSVM Model = BSVM(X_training,y_training) Model.Train() y_predic = sign(Model.Predict(X_test)) y_uncertaintypredic = Model.PredictProb(X_test) Where X_training should be a matrix of size NSamples x NFeatures, and y_training should be a vector of 1 and -1<br>You can find a more complete description in the Wiki<br><b>Background</b><br>We propose a fast inference method for Bayesian nonlinear support vector machines that leverages stochastic variational inference and inducing points. Our experiments show that the proposed method is faster than competing Bayesian approaches and scales easily to millions of data points. It provides additional features over frequentist competitors such as accurate predictive uncertainty estimates and automatic hyperparameter search.<br><br><b>Please also check out our github repository:</b>github.com/theogf/BayesianSVM.jl<br>
DOI: 10.6084/m9.figshare.5443627.v1
title: Code for Fast and Scalable Implementation of the Bayesian SVM

description: This dataset contains the Julia code package for the Bayesian SVM algorithm described in the ECML PKDD 2017 paper; Wenzel et al.: Bayesian Nonlinear Support Vector Machines for Big Data.<br>Files are provided in .jl format; containing Julia language code: a high-performance dynamic programming language for numerical computing. These files can be accessed by openly available text edit software. To run the code please see the description below or the more detailed wiki <b><br></b><b>BSVM.jl</b> - contains the module to run the Bayesian SVM algorithm.<br><b>AFKMC2.jl</b> - File for the Assumption Free K MC2 algorithm (KMeans)<br><b><br></b><b>KernelFunctions.jl</b> - Module for the kernel type<br><b>DataAccess.jl</b> - Module for either generating data or exporting from an existing dataset<br><br><b>run_test.jl </b>and<b> paper_experiments.jl</b><b> </b>- Modules to run on a file and compute accuracy on a nFold cross validation, also to compute the brier score and the logscore<br><b>test_functions.jl</b> and <b>paper_experiment_functions.jl</b> - Sets of datatype and functions for efficient testing.<br><b>ECM.jl</b> - Module for expectation conditional maximization (ECM) for nonlinear Bayesian SVM<br>For datasets used in the related experiments please see https://doi.org/10.6084/m9.figshare.5443621<b><br></b><b>Requirements</b><br>The BayesianSVM only works for version of Julia &gt; 0.5. Other necessary packages will automatically be added in the installation. It is also possible to run the package from Python, to do so please check Pyjulia. If you prefer to use R you have the possibility to use RJulia. All these are a bit technical due to the fact that Julia is still a young package.<br><b>Installation</b><br>To install the last version of the package in Julia run<br> Pkg.clone("git://github.com/theogf/BayesianSVM.jl.git")<b><br></b><b>Running the Algorithm</b><br>Here are the basic steps for using the algorithm :<br> using BayesianSVM Model = BSVM(X_training,y_training) Model.Train() y_predic = sign(Model.Predict(X_test)) y_uncertaintypredic = Model.PredictProb(X_test) Where X_training should be a matrix of size NSamples x NFeatures, and y_training should be a vector of 1 and -1<br>You can find a more complete description in the Wiki<br><b>Background</b><br>We propose a fast inference method for Bayesian nonlinear support vector machines that leverages stochastic variational inference and inducing points. Our experiments show that the proposed method is faster than competing Bayesian approaches and scales easily to millions of data points. It provides additional features over frequentist competitors such as accurate predictive uncertainty estimates and automatic hyperparameter search.<br><br><b>Please also check out our github repository:</b>github.com/theogf/BayesianSVM.jl<br>
DOI: 10.6084/m9.figshare.5443621
title: Experimental test datasets for Fast and Scalable Implementation of the Bayesian SVM

description: This record contains seven real-world test datasets used in experiments with the Bayesian SVM algorithm in the ECML PKDD 2017 paper; Wenzel et al.: Bayesian Nonlinear Support Vector Machines for Big Data.<br>For code used in the related experiments please see https://doi.org/10.6084/m9.figshare.5443627<b><br></b>The datasets are used in the related experiments to compare the prediction performance, the quality of the uncertainty estimates and run time of the various methods. Collectively these contain containing millions of samples. The datasets are all from the Rätsch benchmark datasets commonly used to test the accuracy of binary nonlinear classifiers.<br>Data files are in .data format used by Analysis Studio, a statistical analysis and data mining program. It contains mined data in a plain text, tab-delimited format, including an Analysis Studio file header. The raw data is can be openly accessed via text edit software.<br><br>The data are from a range of disciplines that correspond to applications considered in the related publication:<br><b>Processed_BreastCancer.data</b><b>Processed_Diabetis.data</b><b>Processed_Flare.data</b><b>Processed_German.data</b><b>Processed_Heart.data</b><b>Processed_Splice.data</b><b>Processed_Waveform.data</b><br><b>Background</b><br>We propose a fast inference method for Bayesian nonlinear support vector machines that leverages stochastic variational inference and inducing points. Our experiments show that the proposed method is faster than competing Bayesian approaches and scales easily to millions of data points. It provides additional features over frequentist competitors such as accurate predictive uncertainty estimates and automatic hyperparameter search.<br><br><b>Please also check out our github repository:</b>https://github.com/theogf/BayesianSVM.jl<br>
DOI: 10.6084/m9.figshare.5443615.v1
title: REM Sleep Imposes a Vascular Load in COPD Patients Independent of Sleep Apnea

description: Arterial stiffness, a marker for cardiovascular risk, is increased in patients with Chronic Obstructive Pulmonary Disease (COPD) and Obstructive Sleep Apnea (OSA). The specific influence of both on arterial stiffness during sleep is unknown. Nocturnal arterial stiffness (Pulse Propagation Time (PPT) of the finger pulse wave) was calculated in 142 individuals evaluated for sleep apnea: 27 COPD patients (64.7 ± 11y, 31.2 ± 8 kg/m<sup>2</sup>), 72 patients with cardiovascular disease (CVD group, 58.7 ± 13y, 33.6 ± 6 kg/m<sup>2</sup>) and 43 healthy controls (HC group 49.3 ± 12y, 27.6 ± 3 kg/m<sup>2</sup>). Sleep stage related PPT changes were assessed in a subsample of COPD patients and matched controls (n = 12/12). Arterial stiffness during sleep was increased in COPD patients (i.e. shortened PPT) compared to healthy controls (158.2 ± 31 vs. 173.2 ± 38 ms, p = 0.075) and to patients with CVD (161.4 ± 41 ms). Arterial stiffening was particular strong during REM sleep (145.9 ± 28 vs. 172.4 ± 43 ms, COPD vs. HC, p = 0.003). In COPD, time SaO<sub>2</sub> &lt; 90% was associated with reduced arterial stiffness (Beta +1.7 ms (1.1–2.3)/10 min, p &lt; 0.001). Sleep apnea did not affect PPT. In COPD, but not in matched controls, arterial stiffness increased from wakefulness to REM-sleep (ΔPPT-8.9 ± 10% in COPD and 3.7 ± 12% in matched controls, p = 0.021). Moreover, REM-sleep related arterial stiffening was correlated with elevated daytime blood pressure (r = −0.92, p &lt; 0.001) and increased myocardial oxygen consumption (r = −0.88, p &lt; 0.01). Hypoxia and REM sleep modulate arterial stiffness. In contrast to healthy controls, REM sleep imposes a vascular load in COPD patients independent of sleep apnea indices, intermittent and sustained hypoxia. The link between REM-sleep, vascular stiffness and daytime cardiovascular function suggests that REM-sleep plays a role for increased cardiovascular morbidity of COPD patients.
DOI: 10.6084/m9.figshare.5443576
title: Baboon gut microbiome dataset

description: pyrosequence data associated with "Ren T, Grieneisen LE, Alberts SC, Archie EA, Wu M. Development, diet and dynamism: longitudinal and cross-sectional predictors of gut microbial communities in wild baboons. Environ Microbiol. 2015 Mar 26. doi:10.1111/1462-2920.12852."
DOI: 10.6084/m9.figshare.5443567
title: Drone-based Structure-from-Motion survey of Fort Fisher State Recreation Area beach, North Carolina, USA, 09/06/17

description: Digital Surface Model (DSM), Ground Control Point (GCP) coordinates (recorded using an RTK GNSS system), Structure-from-Motion report, and raw aerial images from a drone-based survey of the beach at Fort Fisher State Recreation Are, NC on September 6th, 2017. GCP coordinates are reported in WGS 84 (EPSG::4326), but the file FF4_GCP.xlsx contains both WGS 84 and NAD 83, Geoid 9. The former was chosen for this model because this was the coordinate system of the image GPS metadata. There was insufficient overlap between the images due to a mistake in programming the flight path, but hopefully there is something useful. <br>
DOI: 10.6084/m9.figshare.5443576.v1
title: Baboon gut microbiome dataset

description: pyrosequence data associated with "Ren T, Grieneisen LE, Alberts SC, Archie EA, Wu M. Development, diet and dynamism: longitudinal and cross-sectional predictors of gut microbial communities in wild baboons. Environ Microbiol. 2015 Mar 26. doi:10.1111/1462-2920.12852."
DOI: 10.6084/m9.figshare.12901370.v1
title: Supplemental File.zip

description: <b>Response of the ileum transcriptome to Fructo-oligosaccharides in Taiping chicken</b>