This data package includes the underlying data to replicate the charts presented in Lessons from China's fiscal policy during the COVID-19 pandemic, PIIE Working Paper 24-7.

If you use the data, please cite as: Huang, Tianlei. 2024. Lessons from China's fiscal policy during the COVID-19 pandemic. PIIE Working Paper 24-7. Washington: Peterson Institute for International Economics.

We compare how well participants can determine the geographical direction of an animated map transition. In our between-subject online study, each of three groups is shown map transitions in one map projection: Mercator, azimuthal equidistant projection, or two-point equidistant projection. The distances of the start and end point are varied. Map transitions zoom out and pan towards the middle point, then zoom in and continue panning, following the recommendations by Van Wijk and Nuij (IEEE InfoVis, 2003). We measure response time and accuracy in the task. We evaluate the results by the sample means per participant, using interval estimation with 95% confidence intervals. We construct the confidence intervals by using BCa bootstrapping. The study is pre-registered on OSF.io, but due to file size limitations, we were not able to submit the video stimuli there. Instead, we provide them here. This repository contains the MPEG-4 video files that were shown to the participants in the videos/ folder. These are numbered from 0 to 1199 for each of the three map projections, which are also stated in the file name, for a total of 3,600 video stimuli. An additional 3×6 example stimuli are also included. For each video stimulus, a JSON file with the same prefix file name (projection + number) is located in the metadata/ folder. These files contain the ground truth metadata for the respective stimulus. The stimuli shown for teaching the participants the task are located with the same structure under the examples/ folder. The entire source code for the study is also available in the related publication. The related repository includes: The code for generating the individual PNG frames, and JSON metadata, for each stimulus. The server and front-end code for the online study itself. The Python and R code for evaluating the study results.

Graph and download economic data for Federal Government: Current Expenditures (FGEXPND) from Q1 1947 to Q2 2025 about expenditures, federal, government, GDP, and USA.

In October 2024, the public debt of the United States was around 35.46 trillion U.S. dollars, a slight decrease from the previous month. The U.S. public debt ceiling has become one of the most prominent political issues in the States in recent years, with debate over how to handle it causing political turmoil between Democrats and Republicans. The public debt The public debt of the United States has risen quickly since 2000, and in 2022 was more than five times higher than in 2000. The public debt is the total outstanding debt that is owed by the federal government. This figure comprises debt owed to the public (for example, through bonds) and intergovernmental debt (debt owed to various governmental departments), such as Social Security. Debt in Politics The debt issue has become a highly contentious topic within the U.S. government. Measures such as stimulus packages, social programs and tax cuts add to the public debt. Additionally, spending tends to peak during large global events, such as the Great Depression, the 2008 financial crisis, or the COVID-19 pandemic - all of which had a detrimental impact on the U.S. economy. Although both major political parties in the U.S. tend to blame one another for increases in the country's debt, a recent analysis found that both parties have contributed almost equally to national expenditure. Debate on raising the debt ceiling, or the amount of debt the federal government is allowed to have at any one time, was a leading topic in the government shutdown in October 2013. Despite plans from both Democrats and Republicans on how to lower the national debt, it is only expected to increase over the next decade.

In the financial year 2026, the estimated gross fiscal deficit in India was expected to be *** percent of the GDP. This would be a decrease from the previous year's deficit in the country. What is fiscal deficit? The fiscal deficit of the government is the difference between the total expenditure incurred and the total non-debt capital receipts of the government. It indicates the total borrowing requirements of the government. Impact from the pandemic Due to concerns over gradually slowing economic growth, the government increased its fiscal spending in early 2019. With the onset of the coronavirus (COVID-19) and consequent lockdown, the unprecedented financial stimulus package led to the worsening of the gross fiscal deficit. This further stressed the tax revenue system across the country. A major impact of the pandemic was the projection of negative quarterly growth of GDP in June 2020 across India.

The graph depicts binary edges leading from row to column as well as the sum total of incoming edges for each vertex (stimulus-presentation combination).Directed Graph for Across Session Explanatory Power.

Data for paper published in: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23)

These files contain:

The data from the Graph Familiarity questionnaire used in our study (competence assessment using micro-behaviours_Demographic data and questionnaire) The interactions produced by our participants on each stimulus (competence assessment using micro-behaviours _interactions on all stimuli) All the pauses produced by our participants on each stimulus (competence assessment using micro-behaviours _Pauses)

Paper abstract Competence Assessment by Chunk Hierarchy Evaluation with Transcription-tasks (CACHET) was proposed by Cheng [14]. It analyses micro-behaviors captured during cycles of stimulus view- ing and copying in order to probe chunk structures in memory. This study extends CACHET by applying it to the domain of graphs and charts. Since drawing strategies are diverse, a new interactive stimulus presentation method is introduced: Transcription with In- cremental Presentation of the Stimulus (TIPS). TIPS aims to reduce strategy variations that mask the chunking signal by giving users manual element-by-element control over the display of the stimulus. The potential of TIPS, is shown by the analysis of six participants transcriptions of stimuli of different levels of familiarity and com- plexity that reveal clear signals of chunking. To understand how the chunk size and individual differences drive TIPS measurements, a CPM-GOMS model was constructed to formalize the cognitive process involved in stimulus comprehension and chunk creation.