Create publication-ready time series plots from uploaded research data.
Upload repeated measurements, map time and response columns, describe the figure you need, then generate a clean time series plot with trends, groups, labels, event cues, and manuscript-ready styling.
Upload data and describe the time series figure you need.
Costs 5 credits. Uses your uploaded data and prompt to generate a publication-ready PNG.
Preview
Aspect ratio: 16:9
These are sample previews. Upload your dataset to generate your own time series plot.
Use these examples as visual directions for longitudinal measurements, sensor traces, intervention timelines, cyclic signals, and monitoring data.
Prompt
Based on the uploaded longitudinal_signal.csv file, create a publication-ready time series plot showing study day on the x-axis and signal intensity on the y-axis. Use clean lines and markers, highlight any uploaded intervention window with a subtle shaded region, and keep the chart accurate to the recorded time points.
Prompt
Based on the uploaded sensor_monitoring_timeseries.tsv file, create a multi-signal time series plot comparing sensor measurements over elapsed time. Use one line per sensor, readable markers at sampled points, compact legends, and avoid connecting across missing intervals unless the uploaded data supports continuity.
Prompt
Based on the uploaded intervention_response_visits.xlsx file, create a clinical time series plot showing response before and after an intervention. Use visit week on the x-axis, clinical score on the y-axis, add an event marker only if the intervention time is included, and keep annotations restrained.
Prompt
Based on the uploaded circadian_expression.csv file, create a time series plot showing relative expression across circadian time. Preserve the cyclic trend, shade dark phases only if those intervals are present in the file, use clean markers, and keep the figure suitable for a physiology manuscript.
Prompt
Based on the uploaded multi_site_monitoring.xlsx file, create a grouped time series plot comparing measurement trends across study sites. Use separate lines and colors for each site, align all series on the shared collection-day axis, keep the legend clear, and show uncertainty only if provided.
Prompt
Based on the uploaded event_timeline_with_gaps.csv file, create a time series plot that preserves missing intervals as visible gaps. Add event markers or shaded windows only for events present in the file, use a clean continuous style for observed values, and label the gap without overcrowding the chart.
Add CSV, TSV, TXT, or Excel data with one time or order column and at least one numeric measurement column.
Tell SciFigure AI which trends, groups, markers, labels, error bars, gaps, or event windows should appear.
Create a clean time series plot, check the time order and labels, then download PNG for papers and slides.
It turns longitudinal or repeated-measurement data into a clean time series plot. Use it when the x-axis represents dates, visits, days, weeks, hours, timestamps, or ordered measurement points.
A time series plot works best with one time or order column and at least one numeric measurement column. Optional group, subject, or event columns can guide styling.
Yes. Upload a group or condition column and describe whether you want separate colored lines, markers, or confidence bands.
Yes, when the uploaded data includes replicate, standard deviation, standard error, confidence interval, or similar uncertainty columns.
Yes. Date, day, week, month, visit, and timestamp columns can be used as the x-axis when they are clearly represented in the file.
Yes, but verify the uploaded data, axis labels, time ordering, grouping, and annotations before submission.
Upload your time series data and create a publication-ready scientific plot in minutes.
