COVID-19大流行期间透析治疗的挑战：患者与专家观点的质性研究

数据集获取（Figshare）

• 源站标题：Challenges to Dialysis Treatment during the COVID-19 Pandemic: A Qualitative Study of Patients’ and Experts’ Perspectives.
• DOI：10.6084/m9.figshare.22231990.v1
• 发布日期：2023-03-08
• 文件数：2 个
• 文件总体积：7.55 MB
• 源站页面：https://figshare.com/articles/dataset/Challenges_to_Dialysis_Treatment_during_the_COVID-19_Pandemic_A_Qualitative_Study_of_Patients_and_Experts_Perspectives_/22231990

命令行下载

curl -L -o "LDA Natural Language Processing Technique Analysis.pdf" "https://ndownloader.figshare.com/files/39515071"
curl -L -o "Content analysis.docx" "https://ndownloader.figshare.com/files/39515080"

文件清单

• LDA Natural Language Processing Technique Analysis.pdf（7.52 MB）
• Content analysis.docx（33.10 KB）

文件总体积 7.55 MB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

Hereby I present the content analysis of interviews of this project and the natural language processing technique results. 

We used Atlas.ti version 8 for analysis and coding.  We supported our content analysis of interviews with a natural language processing technique called Latent Dirichlet Allocation (LDA) to support the thematic analysis. LDA is an unsupervised, generative, probabilistic topic modelling technique that extracts meanings of a pre-defined number of topics/concepts. The number of concepts resulted from the clearest differentiation in the representation of heat maps and in examination of words (topic coherence). For this purpose, we first created a semantic space by stemming the words, removing stop words (such as ‘‘and’’, ‘‘the’’, ‘‘a’’ and similar) and converting all text to lowercase. In addition, punctuation marks, names and personal words were removed from the text. LDA analyzes texts by considering word frequency in combination with the co-occurrence of words. LDA characterizes concepts based on word frequency and the words that best distinguish one concept from others. Separate analyses was conducted for physicians, nurses, HD patients and PD patients, assuming that these four groups had different experiences during the two COVID-19 waves. R software was used to conduct the LDA.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.22231990.v1（source: https://figshare.com/articles/dataset/Challenges_to_Dialysis_Treatment_during_the_COVID-19_Pandemic_A_Qualitative_Study_of_Patients_and_Experts_Perspectives_/22231990）。