6 research outputs found
A Case Study of Physiotherapeutic Care for a Patient After Total Arthroplasty of the Left Hip Joint
No full textAuthor: Viola Dlouhá Supervisor: Mgr. Milan Martínek, Ph.D. Title: A Case Study of Physiotherapeutic Care for a Patient After Total Arthroplasty of the Left Hip Joint Objectives: The aim of this bachelor's thesis is to address the issues of total hip arthroplasty and ankylosing spondylitis through a case study of a specific patient, while also providing a theoretical overview of the topic. Methods: The case study was developed as part of an internship at the Thomayer University Hospital in Prague during the third year of the bachelor's program in physiotherapy at the Faculty of Physical Education and Sports, Charles University. The initial phase involved collecting a detailed patient history and conducting a kinesiological assessment. Subsequently, nine therapeutic sessions were conducted, followed by an exit examination, which was performed to the same extent as the initial assessment. The results of both examinations were compared to evaluate the effectiveness of the selected therapy. The theoretical part of the thesis complements the case study with academic insights related to the topic. All therapeutic approaches and methodologies used are based on knowledge acquired during the study and from scientific literature. Results: Based on the comparison of the initial and final examinations, it can..
Theatrical games for the training of museum educators
No full textThis project seeks to analyze the following hypothesis: theatrical improvisation has the potential to contribute to the training of Museum Educators. To analyze this hypothesis, a course was created entitled “Topics in Creative Processes A - Theatrical games for art educators and mediators” within the scope of Transversal Training at UFMG. The discipline process had as a methodology to use the theatrical games developed by the author Viola Spolin and to discuss how the games help in the practice of museum educators. During the course classes, a very enriching place for the exchange of knowledge, learning and experiences was established for students and educators. The data were collected from video recordings of classes and notes in a fieldwork notebook. Based on the analysis of the students' experience and speeches in this course, we wrote a booklet aimed at educational professionals in museums.Esta pesquisa busca analisar a seguinte hipótese: a improvisação teatral tem o potencial de contribuir para a formação de Educadores em Museus. Para analisar esta hipótese criou-se uma disciplina intitulada “Tópicos em Processos Criativos A - Jogos teatrais para arte-educadores e mediadores” no âmbito da Formação Transversal da UFMG. O processo da disciplina tinha como metodologia utilizar os jogos teatrais desenvolvidos pela autora Viola Spolin e discutir como os jogos ajudam na prática dos educadores museais. Durante as aulas da disciplina estabeleceu-se um lugar de troca de conhecimentos, aprendizagens e experiências muito enriquecedoras para os alunos e educadores. Os dados foram coletados a partir de gravação de vídeos das aulas e de anotações em caderno de trabalho de campo. A partir da análise da experiência e das falas dos/as alunos/as nesta disciplina redigimos uma cartilha direcionada a profissionais de educativos em museus
A Case Study of Physiotherapeutic Care for a Patient After Total Arthroplasty of the Left Hip Joint
No full textAuthor: Viola Dlouhá Supervisor: Mgr. Milan Martínek, Ph.D. Title: A Case Study of Physiotherapeutic Care for a Patient After Total Arthroplasty of the Left Hip Joint Objectives: The aim of this bachelor's thesis is to address the issues of total hip arthroplasty and ankylosing spondylitis through a case study of a specific patient, while also providing a theoretical overview of the topic. Methods: The case study was developed as part of an internship at the Thomayer University Hospital in Prague during the third year of the bachelor's program in physiotherapy at the Faculty of Physical Education and Sports, Charles University. The initial phase involved collecting a detailed patient history and conducting a kinesiological assessment. Subsequently, nine therapeutic sessions were conducted, followed by an exit examination, which was performed to the same extent as the initial assessment. The results of both examinations were compared to evaluate the effectiveness of the selected therapy. The theoretical part of the thesis complements the case study with academic insights related to the topic. All therapeutic approaches and methodologies used are based on knowledge acquired during the study and from scientific literature. Results: Based on the comparison of the initial and final examinations, it can...Autor: Viola Dlouhá Vedoucí práce: Mgr. Milan Martínek, Ph.D. Název: Kazuistika fyzioterapeutické péče o pacienta po TEP levého kyčelního kloubu Cíle: Cílem této bakalářské práce je zpracovat problematiku totální endoprotézy kyčelního kloubu a ankylozující spondylitidy formou kazuistiky konkrétního pacienta a zároveň tuto tématiku přiblížit v teoretické rovině. Metody: Kazuistika pacienta byla zpracována v rámci odborné praxe, která probíhala ve Fakultní Thomayerově nemocnici v Praze během třetího ročníku bakalářského studia fyzioterapie na FTVS UK. Úvodní fáze zahrnovala odebrání podrobné anamnézy a provedení vstupního kineziologického rozboru. Následně bylo realizováno celkem devět terapeutických jednotek, na jejichž konci bylo provedeno výstupní vyšetření ve stejném rozsahu jako vyšetření vstupní. Výsledky obou vyšetření byly vzájemně porovnány za účelem zhodnocení efektivity zvolené terapie. Teoretická část práce doplňuje kazuistiku o odborné poznatky vztahující se k dané problematice. Všechny použité terapeutické přístupy a metodiky vycházejí z poznatků získaných během studia a z odborné literatury. Výsledky: Na základě porovnání vstupního a výstupního vyšetření lze konstatovat, že cíle terapeutického plánu byly splněny. U pacienta došlo ke zlepšení stereotypu chůze, zvýšení rozsahu pohybu v...Katedra fyzioterapieDepartment of PhysiotherapyFakulta tělesné výchovy a sportuFaculty of Physical Education and Spor
Data from paper: Large carbon sink potential of Secondary Forests in Brazilian Amazon to mitigate climate change (public)
No full textTitle: Large carbon sink potential of Secondary Forests in the Brazilian Amazon to mitigate climate change
Contact: Viola Heinrich ([email protected])
This repository contains:
Zipped folder: Fig1_data_input.zip - all the files needed to produce Figure 1a-e of the main paper. Set the working directory to folder containing the file and use the script "Fig1a_f_plot.R" to run (see below). The folder contains the input files of the 6 driving variables used to build regrowth models seen in Figure 1 - these files are in the format "_assessment_v2.csv". The columns in the files are: A: age of secondary forest; B: 50th percentile (median) of the modal Aboveground Biomass (AGB) value for the given age (note, units are in biomass not carbon: Mg/ha/yr); C: The bias-corrected AGB value, calculated by subtracting the lowest AGB value in column B such that the AGB data starts at or near 0Mg/ha/yr at age 1. D: the number of secondary forest pixels observed to have the given age, E: "Threshold" : the threshold limits of the given driver e.g. 0 Fires in fire_assessmentv2.csv implies the corresponding secondary forest pixels experienced 0 fires throughout the analysis period. The folder also contains the output regrowth models seen in Figure 1 in the format "regrowth_model_.RData" where driver_threshold refers to the driving variable name and the associated threshold limit for the given driver.
Zipped folder: Fig2_regions_outline.zip - contains the boundaries of the 4 regions identified in Figure 2a of the main paper in a shapefile (.shp) format and the corresponding file formats needed to produce and load a shapefile.
Zipped folder: Fig1g_2b_e_variable_importance.zip - contains the output files of the random forest analysis assessing the variable importance for the whole Amazon ("whole_Amazon" subfolder) and for the different regions identified in Figure2a. Files are given as .RDS files that can be loaded in R and the corresponding figures produced using the script "Fig1g_2b_e_plot.R". Files start with the region of interest e.g. "whole_Amazon" or "NE_sector". Middle part of the filename - importance_conditionalTrue/False - this determines whether the importance was calculated using the conditional permutation (True) or not (False). The end of the file name - seed - denotes the number of the random seed that was set to extract the sample data. e.g. whole_Amazon_2500_cforest_important_conditionalTrue_seed200.RDS - shows the conditional permutation importance assessment using a sample size of 2500 when the setseed parameter was set to 200 to extract a random sample representing the whole Amazon. The remaining files are the random forest output - as .RDS file. Please note the code to produce the random forest model and the importance assessment has not been included here - this code takes multiple days to run, so only the input and outputs have been included here. Please contact the corresponding author (see end) for more information on this.
Zipped folder: Fig3_data_input.zip - all the files needed to produce Figure 3a-d of the main paper. Set the working directory to folder containing the file and use the script "Fig3_plot.R" to run (see below). The folder contains the input files of the 6 driving variables used to build regrowth models seen in Figure 3 - these files are in the format "-Group.csv". See bullet point 1 for explanations for the columns in the file. Again column E -"threshold" denotes the code used to identify the the 4 subclasses of regrowth seen in the Figure. Where 11 = No disturbance; 12 = Only burning; 21 = Only (multiple) deforestations; 22 = Both burning and multiple deforestations as disturbance. The code takes data in AGB and converts to AGC. The folder also contains the output regrowth models seen in Figure 3 in the format "regrowth_model_.RData" where region_disturbance refers to the region and the type of disturbance experienced.
Zipped folder: Fig4_5_carbon_sink_2017.zip - Contains two subfolders: a) Map_aggre_0.1deg -this folder contains .tiff files (and associated files) of the losses, gains and net change in AGC between 2016 - 2017 in secondary forests in Amazonia - this has been aggregated to 0.1 degree grid cells so each cell contains the total sum of the losses/gains experienced by secondary forests in that 0.1degree grid cell. b) secondary_forest_by_region_and_disturbance - this folder contains .tiff files (and associated files) of the secondary forest data at the original resolution (30m) for 2016 and 2017 split up according to the regions identified in Figure 2, and the type of disturbance (if any). The associated files include a .dbf file which includes additional data [read "README.txt" file in folder] - upon loading the data in a GIS software - the age of the secondary forest pixel will be displayed - open the attribute table to see more data associated with that given pixel e.g. modelled associated AGB for a given pixel. Files in this folder can be used to make Figure 4d and Figure 5 - see script "Fig4_Fig5_plot.R" in the code repository (see below).
Code: The corresponding code mentioned here can be access here: heinrichTrees/secondary-forest-regrowth-amazon-public (github.com)
Data usage: When using any code or data in this repository or another related to this study please cite Heinrich et al.2021 and the original paper as well as the DOI of this repository.
If you need anything else, please contact the corresponding author: Viola Heinrich ([email protected]
Data from paper: Large carbon sink potential of Secondary Forests in Brazilian Amazon to mitigate climate change
No full textTitle: Large carbon sink potential of Secondary Forests in the Brazilian Amazon to mitigate climate change
Contact: Viola Heinrich ([email protected])
This repository contains:
Zipped folder: Fig1_data_input.zip - all the files needed to produce Figure 1a-e of the main paper. Set the working directory to folder containing the file and use the script "Fig1_analysis_all_variables_asAGC.R" to run (see below). The folder contains the input files of the 6 driving variables used to build regrowth models seen in Figure 1 - these files are in the format "_assessment_v2.csv". The columns in the files are: A: age of secondary forest; B: 50th percentile (median) of the modal Aboveground Biomass (AGB) value for the given age (note, units are in biomass not carbon: Mg/ha/yr); C: The bias-corrected AGB value, calculated by subtracting the lowest AGB value in column B such that the AGB data starts at or near 0Mg/ha/yr at age 1. D: the number of secondary forest pixels observed to have the given age, E: "Threshold" : the threshold limits of the given driver e.g. 0 Fires in fire_assessmentv2.csv implies the corresponding secondary forest pixels experienced 0 fires throughout the analysis period.
Zipped folder: Fig1_confidence_intervals.zip - all the files need to produce the confidence intervals seen in Figure 1a-e of the main paper: units are in MgC/ha/yr as they appear in the Figure. column A: lower limit; B: upper limit
Zipped folder: Fig2_regions_outline.zip - contains the boundaries of the 4 regions identified in Figure 2a of the main paper in a shapefile (.shp) format and the corresponding file formats needed to produce and load a shapefile.
Zipped folder: Fig1g_2b_e_variable_importance.zip - contains the output files of the random forest analysis assessing the variable importance for the whole Amazon ("whole_Amazon" subfolder) and for the different regions identified in Figure2a. Files are given as .RDS files that can be loaded in R and the corresponding figures produced using the script "Fig1g_2b_e_variable_importance.R". Files start with the region of interest e.g. "whole_Amazon" or "NE_sector". Middle part of the filename - importance_conditionalTrue/False - this determines whether the importance was calculated using the conditional permutation (True) or not (False). The end of the file name - seed - denotes the number of the random seed that was set to extract the sample data. e.g. whole_Amazon_2500_cforest_important_conditionalTrue_seed200.RDS - shows the conditional permutation importance assessment using a sample size of 2500 when the setseed parameter was set to 200 to extract a random sample representing the whole Amazon. The remaining files include the sample data used to build the random forest model at each iteration - as a .csv file and the random forest output - as .RDS file. Please note the code to produce the random forest model and the importance assessment has not been included here - this code takes multiple days to run, so only the input and outputs have been included here. Please contact the corresponding author (see end) for more information on this.
Zipped folder: Fig3_data_input.zip - all the files needed to produce Figure 3a-d of the main paper. Set the working directory to folder containing the file and use the script "Fig3_analysis_byAllRegions_asAGC.R" to run (see below). The folder contains the input files of the 6 driving variables used to build regrowth models seen in Figure 3 - these files are in the format "-Group.csv". See bullet point 1 for explanations for the columns in the file. Again column E -"threshold" denotes the code used to identify the the 4 subclasses of regrowth seen in the Figure. Where 11 = No disturbance; 12 = Only burning; 21 = Only (multiple) deforestations; 22 = Both burning and multiple deforestations as disturbance. The folder also contains another set of files "_whole_class.csv" these files do not distinguish disturbance and can be used to model the regrowth for the whole region (this is not shown in any of the Figures). The code takes data in AGB and converts to AGC.
Zipped folder: Fig3_confidence_intervals.zip - all the files needed to produce the confidence intervals seen in Figure 3a-d of the main paper. These filenames are in the format _number of the region__confidence_interval_asAGC.csv. Where the number of the region: 1 - SW; 2 - SE; 3 - NW; 4 - NE. Where the disturbance combination: 1 - No disturbance; 2 - Only fire disturbance; 3 - Only deforestation disturbance; 4 - Both disturbances. so the file NE_4_1_confidence_interval_as_AGC.csv, contains the confidence intervals of the regrowth model in the NE sector of the Amazon under No disturbance. " Units are in MgC/ha/yr as they appear in the Figure. column A: lower limit; B: upper limit.
Zipped folder: Fig4_5_carbon_sink_2017.zip - Contains two subfolders: a) Map_aggre_0.1deg -this folder contains .tiff files (and associated files) of the losses, gains and net change in AGC between 2016 - 2017 in secondary forests in Amazonia - this has been aggregated to 0.1 degree grid cells so each cell contains the total sum of the losses/gains experienced by secondary forests in that 0.1degree grid cell. b) secondary_forest_by_region_and_disturbance - this folder contains .tiff files (and associated files) of the secondary forest data at the original resolution (30m) for 2016 and 2017 split up according to the regions identified in Figure 2, and the type of disturbance (if any). The associated files include a .dbf file which includes additional data [read "README.txt" file in folder] - upon loading the data in a GIS software - the age of the secondary forest pixel will be displayed - open the attribute table to see more data associated with that given pixel e.g. modelled associated AGB for a given pixel. Files in this folder can be used to make Figure 4d and Figure 5 - see script "Fig4_Fig5_analysis.R" in the code repository (see below).
Code: The corresponding code mentioned here can be access here: heinrichTrees/secondary-forest-amazonia-regrowth: This repository contains the code used to produce data shown in Heinrich et al. (github.com)
Data usage: When using any code or data in this repository or another related to this study please cite Heinrich et al.2021 and the original paper.
If you need anything else, please contact the corresponding author: Viola Heinrich ([email protected]
Data and code from paper: The carbon sink of secondary and degraded humid tropical forests
No full textThis repository contains the data and code produced for the following paper:
Title: The carbon sink of recovering secondary and degraded forests
Contact: Viola Heinrich ([email protected])
Please note:
throughout repository where files include reference to: this refers to the Central Africa region as it is termed in the main paper.
the code has not been amended for wider use and still contains set working directories for use with University of Bristol systems, you will need to change these for the scripts to run.
The data produced in this project were produced using a combination of programming languages due to differences in the author's preferences and expertise. Overall, the initial data analysis was carried out in (i) Google Earth Engine, and (ii) Arcpy (Python3.6.10). Most of the post-processing of the initial data was then carried out in R (v3.6) for which the code and output datasets are available here.
To access the code used in Google Earth Engine that was used to produce and export data from the Tropical Moist Forest dataset (e.g. Years Since Last Disturbance of secondary/degraded forest), please follow the link: https://code.earthengine.google.com/d303fc21e7b57a8fc259e0ee2b58bfb4
This repository contains the following zipped folders:
data_folder: this folder contains further folders with all the data produced for this paper.
Fig1_data_models: All data needed to produce Figure 1 of the main paper, including an .RDS version of the 6 main regrowth models produced for this paper (secondary and degraded forests in the three regions). These are the files beginning with "regrowthModel_..RDS. Additionally, the folder includes the dataframe files originally from GeoTiff files that were used to extract the Aboveground Biomass in old-growth (undisturbed forests) > e.g. the subfolder "amazon_basin_oldG_AGB" contains the .dbf files representing the AGB in old-growth forest pixels. There are 4 files as the Amazon was split up into 4 sections for computational reasons. Similarly, the Central Africa region (here referred to as congo_basin) was split up into 2 regions.
Fig2_data_models_plus_exFig3_to_5: The data needed to produce Figure 2 in the main paper as well as the Extended Data Figures 3 to 5. This includes .RDS versions of the regrowth models for secondary and degraded forests in the three regions for the different variables considered (files beginning with "regrowthModel_..RDS) e.g. "regrowtModel_borneo_deg_MaxTemo_low.rds", refers to the regrowth model shown in Figure 2c - the regrowth model for Bornean degraded forests for the variable "Maximum Temperature", where "low" refers to the lowest temperature range considered in the study. As before, files are provided giving information on the AGB in old-growth forests for each region within different conditions of each driving variable.
Fig4: All the data needed to produce Figure 4 (and Supplementary Figure 18) of the main paper. This includes the file "regrowth_in_all_basins_by_country_input_data.csv", which contains data on the total number of cells for each forest type for each Years Since Last Disturbance (YSLD) in each region.
Extended_dataFig1_input: The input for Extended Data Figure 1, including the values derived from other studies used in this comparison as well as additional notes/comments on how the data were assessed.
Extended_dataFig2_input: the input data used to determine the standardised coefficients seen in the Extended Data Figure 2.
Extended_data_table_inputs: The inputs for the Extended Data Tables 1 and 2. Inputs include the dataframe files (.dbf), of key variables that were extracted from the GeoTiff files. Only the .dbf files have been included here to limit excessively large data being uploaded.
code_folder.zip: The code in this folder was used to produce the main figures and results for the extended data tables shown in the paper.
this folder also contains a file "example_code_read_in_models.R" which provides an example of how best to read in the regrowth models for each region and forest type to extract important information such as the: (i) average growth rate in the first 20 years of analysis, (ii) all AGCs as a function of YSLD, and (iii) the estimated time it takes to reach the asymptote.
Data and Code usage: When using any code or data in this repository or another related to this study please cite Heinrich et al. and the original paper as well as the DOI of this repository.
Further source data in .xlsx format were also submitted with the main manuscript.
If you need anything else, please contact the corresponding author: Viola Heinrich ([email protected]
