用于开发学习对象的临床内容的构建与验证

数据集获取（Figshare）

• 源站标题：Construction and validation of clinical contents for development of learning objects
• DOI：10.6084/m9.figshare.5861427.v1
• 发布日期：2018-02-07
• 文件数：5 个
• 文件总体积：32.26 KB
• 源站页面：https://scielo.figshare.com/articles/dataset/Construction_and_validation_of_clinical_contents_for_development_of_learning_objects/5861427

命令行下载

curl -L -o "Table_1.xls" "https://ndownloader.figshare.com/files/10407204"
curl -L -o "Table_2.xls" "https://ndownloader.figshare.com/files/10407207"
curl -L -o "Table_3.xls" "https://ndownloader.figshare.com/files/10407213"
curl -L -o "Table_4.xls" "https://ndownloader.figshare.com/files/10407225"
curl -L -o "Table_5.xls" "https://ndownloader.figshare.com/files/10407231"

文件清单

• Table_1.xls（5.63 KB）
• Table_2.xls（5.63 KB）
• Table_3.xls（9.73 KB）
• Table_4.xls（5.63 KB）
• Table_5.xls（5.63 KB）

文件总体积 32.26 KB（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

ABSTRACT Objective: to describe the process of construction and validation of clinical contents for health learning objects, aimed at patients in the treatment of head and neck cancer. Method: descriptive, methodological study. The development of the script and the storyboard were based on scientific evidence and submitted to the appreciation of specialists for validation of content. The agreement index was checked quantitatively and the suggestions were qualitatively evaluated. Results: The items described in the roadmap were approved by 99% of expert experts. The suggestions for adjustments were inserted in their entirety in the final version. The free-marginal kappa statistical test, for multiple evaluators, presented value equal to 0.68%, granting a substantial agreement. Conclusion: The steps taken in the construction and validation of the content for the production of educational material for patients with head and neck cancer were adequate, relevant and suitable for use in other subjects.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.5861427.v1（source: https://scielo.figshare.com/articles/dataset/Construction_and_validation_of_clinical_contents_for_development_of_learning_objects/5861427）。