Datasets:

derek-thomas
/

ScienceQA

@@ -205,7 +205,7 @@ When answering a question, humans utilize the information available across diffe
 ### Source Data
-SCIENCEQA is collected from elementary and high school science curricula.
 #### Initial Data Collection and Normalization
@@ -217,9 +217,9 @@ See Below
 ### Annotations
-Questions in the SCIENCEQA dataset are sourced from open resources managed by IXL Learning,
 an online learning platform curated by experts in the field of K-12 education. The dataset includes
-problems that align with California Common Core Content Standards. To construct SCIENCEQA, we
 downloaded the original science problems and then extracted individual components (e.g. questions,
 hints, images, options, answers, lectures, and solutions) from them based on heuristic rules.
 We manually removed invalid questions, such as questions that have only one choice, questions that
@@ -229,7 +229,7 @@ Also, we shuffled the answer options of each question to ensure the choices do n
 specific pattern. To make the dataset easy to use, we then used semi-automated scripts to reformat
 the lectures and solutions. Therefore, special structures in the texts, such as tables and lists, are
 easily distinguishable from simple text passages. Similar to ImageNet, ReClor, and PMR datasets,
-SCIENCEQA is available for non-commercial research purposes only and the copyright belongs to
 the original authors. To ensure data quality, we developed a data exploration tool to review examples
 in the collected dataset, and incorrect annotations were further manually revised by experts. The tool
 can be accessed at https://scienceqa.github.io/explore.html.

 ### Source Data
+ScienceQA is collected from elementary and high school science curricula.
 #### Initial Data Collection and Normalization
 ### Annotations
+Questions in the ScienceQA dataset are sourced from open resources managed by IXL Learning,
 an online learning platform curated by experts in the field of K-12 education. The dataset includes
+problems that align with California Common Core Content Standards. To construct ScienceQA, we
 downloaded the original science problems and then extracted individual components (e.g. questions,
 hints, images, options, answers, lectures, and solutions) from them based on heuristic rules.
 We manually removed invalid questions, such as questions that have only one choice, questions that
 specific pattern. To make the dataset easy to use, we then used semi-automated scripts to reformat
 the lectures and solutions. Therefore, special structures in the texts, such as tables and lists, are
 easily distinguishable from simple text passages. Similar to ImageNet, ReClor, and PMR datasets,
+ScienceQA is available for non-commercial research purposes only and the copyright belongs to
 the original authors. To ensure data quality, we developed a data exploration tool to review examples
 in the collected dataset, and incorrect annotations were further manually revised by experts. The tool
 can be accessed at https://scienceqa.github.io/explore.html.