S1文件

数据集获取（Figshare）

• 源站标题：S1 File -
• DOI：10.1371/journal.pone.0287766.s002
• 发布日期：2023-10-05
• 文件数：1 个
• 文件总体积：593.49 KB
• 源站页面：https://plos.figshare.com/articles/dataset/S1_File_-/24253370

命令行下载

curl -L -o "pone.0287766.s002.xlsx" "https://ndownloader.figshare.com/files/42575108"

文件清单

• pone.0287766.s002.xlsx（593.49 KB）

文件总体积 593.49 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

Background
Online administration of surveys has a number of advantages but can also lead to increased exposure to bad actors (human and non-human bots) who can try to influence the study results or to benefit financially from the survey. We analyze data collected through an online discrete-choice experiment (DCE) survey to evaluate the likelihood that bad actors can affect the quality of the data collected.
Methods
We developed and fielded a survey instrument that included two sets of DCE questions asking respondents to select their preferred treatments for multiple myeloma therapies. The survey also included questions to assess respondents’ attention while completing the survey and their understanding of the DCE questions. We used a latent-class model to identify a class associated with perverse preferences or high model variance, and the degree to which the quality checks included in the survey were correlated with class membership. Class-membership probabilities for the problematic class were used as weights in a random-parameters logit to recover population-level estimates that minimizes exposure to potential bad actors.
Results
Results show a significant proportion of respondents provided answers with a high degree of variability consistent with responses from bad actors. We also found that a wide-ranging selection of conditions in the survey screener is more consistent with choice patterns expected from bad actors looking to qualify for the study. The relationship between the number of incorrect answers to comprehension questions and problematic choice patterns peaked around 5 out of 10 questions.
Conclusions
Our results highlight the need for a robust discussion around the appropriate way to handle bad actors in online preference surveys. While exclusion of survey respondents must be avoided under most circumstances, the impact of “bots” on preference estimates can be significant.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0287766.s002（source: https://plos.figshare.com/articles/dataset/S1_File_-/24253370）。