2025 VIS Area Curation Committee Executive Summary
Introduction
This report summarizes the findings, recommendations, and process by the VIS Area Curation Committee (ACC) regarding the areas and keywords used for paper submissions to IEEE VIS 2025. It is based on previous ACC committee reports, updated with the 2025 data. According to the Charter, the goal of this committee is to analyze and report how submissions made use of the areas and keywords to describe their contribution. It is important to understand when these descriptors no longer adequately cover the breadth of research presented at VIS.
We use submission and bidding information from VIS 2025 to analyze recent trends following the move to an area model.
The full data and source code to rebuild this project are available here.
Committee members 2025: Jean-Daniel Fekete (co-chair), Alexander Lex (co-chair), Helwig Hauser, Ana Crisan.
Last edited: 2025-10-17.
Executive Summary
2025 does not surface new major trends compared to previous reports; hence our recommendations remain the same, possibly with less overall urgency.
Overall, the area model appears to be successful.
The growth seen in the Application area in 2023 and 2024 has reverted slightly to 144 submitted papers. Given the still high number of submissions, we recommend taking action to address the load for APCs of the Application and Theoretical & Empirical areas.
Acceptance rates have returned to a level similar to before 2024 (which saw the lowest ever acceptance rate) this year. There is a slight uptick in desk rejects (4.7%).
Variability of acceptance rates between areas is substantial and has worsened relative to 2024. In particular, the area Analytics and Decision making has the lowest acceptance rate of its history. This trend may warrent further investigation.
Keywords are (with a small exception) well distributed, and the unified PC appears to provide broad and overlapping coverage.
import itertoolsimport pandas as pdimport numpy as np# Import the necessaries librariesimport plotly.offline as pioimport plotly.graph_objs as goimport plotly.express as px# [jdf] no need to specify the renderer but, for interactive use, init_notebook should be called# pio.renderers.default = "jupyterlab"# Set notebook mode to work in offline# pio.init_notebook_mode()# pio.init_notebook_mode(connected=True)width =750import sqlite3#### Data Preparation# static data – codes -> names etc.staticdata =dict( decision = { 'C': 'Confer vs. cond Accept', # relevant for the 2020 and 2021 data have a different meaning'A': 'Accept', # for the 2020 data'A2': 'Accept', # after the second round, should be 120 in 2022'R': 'Reject', # reject after the first round -- should be 322 in 2022'R2': 'Reject in round 2', # reject after the second round -- should be 2 in 2022'R-2nd': 'Reject in round 2', 'R2-S': 'Reject in round 2', # 2025'DR-S': 'Desk Reject (Scope)', # should be 7 in 2022'DR-P': 'Desk Reject (Plagiarism)', # should be 4 in 2022'AR-P': 'Admin Reject (Plagiarism)', # should be 1 in 2022'DR-F': 'Desk Reject (Format)', # should be 4 in 2022'DR': 'Desk Reject', # 2025'R-Strong': 'Reject Strong', # cannot resubmit to TVCG for a year'T': 'Reject TVCG fasttrack', # Explicitly invited to resubmit to TVCG, status in major revision }, FinalDecision = { # Just flatten to Accept, Desk-Reject, and Reject'C': 'Accept', 'A': 'Accept', # for the 2020 data'A2': 'Accept', # after the second round, should be 120 in 2022'R': 'Reject', # reject after the first round -- should be 322 in 2022'R2': 'Reject', # reject after the second round -- should be 2 in 2022'R-2nd': 'Reject', 'R2-S': 'Reject','DR-S': 'Desk-Reject', # should be 7 in 2022'DR-P': 'Desk-Reject', # should be 4 in 2022'AR-P': 'Desk-Reject', # should be 1 in 2022'DR-F': 'Desk-Reject', # should be 4 in 2022'DR': 'Desk-Reject', # in 2025'R-Strong': 'Reject','T': 'Reject', }, area = {'T&E': 'Theoretical & Empirical','App': 'Applications','S&R': 'Systems & Rendering','R&I': 'Representations & Interaction','DTr': 'Data Transformations','A&D': 'Analytics & Decisions', }, bid = { 0: 'no bid',1: 'want',2: 'willing',3: 'reluctant',4: 'conflict' }, stat = {'Prim': 'Primary', 'Seco': 'Secondary' }, keywords = pd.read_csv("../data/2021/keywords.csv", sep=';'), # 2021 is correct as there was no new keywords file in 2022 colnames = {'confsubid': 'Paper ID','rid': 'Reviewer','decision': 'Decision','area': 'Area','stat': 'Role','bid': 'Bid' })DecisionColor = {'Accept': 'green','Reject': 'orange','Desk-Reject': 'red',}DecisionOrder = {'Accept': 2,'Reject': 1,'Desk-Reject': 0, }dbcon = sqlite3.connect('../data/vis-area-chair.db') #[jdf] assume data is in ..# submissions_raw20 = pd.read_sql_query('SELECT * from submissions WHERE year = 2020', dbcon, 'sid')# submissions_raw21 = pd.read_sql_query('SELECT * from submissions WHERE year = 2021', dbcon, 'sid')# submissions_raw22 = pd.read_sql_query('SELECT * from submissions WHERE year = 2022', dbcon, 'sid')# submissions_raw23 = pd.read_sql_query('SELECT * from submissions WHERE year = 2023', dbcon, 'sid')# submissions_raw24 = pd.read_sql_query('SELECT * from submissions WHERE year = 2024', dbcon, 'sid')# submissions_raw25 = pd.read_sql_query('SELECT * from submissions WHERE year = 2024', dbcon, 'sid')submissions_raw = pd.read_sql_query('SELECT * from submissions', dbcon, 'sid')#print(submissions_raw24)submissions = (submissions_raw .join( pd.read_sql_query('SELECT * from areas', dbcon, 'aid'), on='aid' ) .assign(Keywords =lambda df: (pd .read_sql_query('SELECT * FROM submissionkeywords', dbcon, 'sid') .loc[df.index] .join( pd.read_sql_query('SELECT * FROM keywords', dbcon, 'kid'), on='kid' ) .keyword .groupby('sid') .apply(list) )) .assign(**{'# Keywords': lambda df: df.Keywords.apply(len)}) .assign(FinalDecision=lambda df: df['decision']) .replace(staticdata) .rename(columns = staticdata['colnames']) .drop(columns = ['legacy', 'aid']) .assign(DecisionColor=lambda df: df['FinalDecision'].map(DecisionColor), DecisionOrder=lambda df: df['FinalDecision'].map(DecisionOrder))# .set_index('sid')# .set_index('Paper ID')# note -- I changed the index, since 'Paper ID' was not unique for multiple years.# By not setting the index to 'Paper ID' the index remains with 'sid'.# However, 'sid' is used as a unique index in the creation of the database anyways.)# replace the old 'Paper ID' with a unique identifier, so that the code from 2021 will worksubmissions = submissions.rename(columns = {'Paper ID':'Old Paper ID'})submissions.reset_index(inplace=True)submissions['Paper ID'] = submissions['sid']submissions = submissions.set_index('Paper ID')#submissions colums: (index), sid (unique id), Paper ID (unique), Old Paper ID, Decision, year, Area, Keywords (as a list), # Keywordsall_years = submissions['year'].unique()#rates_decision computes the acceptance rates (and total number of papers) per year#rates_decision: (index), Decision, year, count, Percentagerates_decision = (submissions .value_counts(['Decision', 'year']) .reset_index()# .rename(columns = {0: 'count'}))rates_decision['Percentage'] = rates_decision.groupby(['year'])['count'].transform(lambda x: x/x.sum()*100)rates_decision = rates_decision.round({'Percentage': 1})#rates_decision computes the acceptance rates (and total number of papers) per year#rates_decision: (index), Decision, year, count, Percentagerates_decision_final = (submissions .value_counts(['FinalDecision', 'year']) .reset_index()# .rename(columns = {0: 'count'}))rates_decision_final['Percentage'] = rates_decision_final.groupby(['year'])['count'].transform(lambda x: x/x.sum()*100)rates_decision_final = rates_decision_final.round({'Percentage': 1})rates_decision_final["DecisionColor"] = rates_decision_final.FinalDecision.map(DecisionColor)#submissions#bids_raw: (index), Reviewer ID, sid (unique paper identifier over mult years), match score, bid of the reviewer, role of the reviewer, Paper IDbids_raw = (pd .read_sql_query('SELECT * from reviewerbids', dbcon) .merge(submissions_raw['confsubid'], on='sid') .replace(staticdata) .rename(columns = staticdata['colnames']))#bids_raw## Renaming Paper ID to Old Paper ID, setting Paper ID to sid, keeping all 3 for now...bids_raw = bids_raw.rename(columns = {'Paper ID':'Old Paper ID'})bids_raw['Paper ID'] = bids_raw['sid']# bids = Reviewer, sid, Bid (how the reviewer bid on this paper)# doesn't include review/sid that were not bid for [.query('Bid != "no bid"')]bids = (bids_raw .query('Bid != "no bid"')# Paper ID is not unique over multiple years!# .drop(columns = ['sid'])# [['Reviewer','Paper ID', 'Bid']] [['Reviewer','sid', 'Paper ID', 'Bid']] .reset_index(drop =True))# matchscores becomes a table to reviewer/sid with the match scores# many of these will be "NaN" since we now have multiple years together.# we need to check whether the reviewer IDs remain unique across the years!matchscores = (bids_raw# Paper ID is not unique over multiple years!# [['Reviewer','Paper ID','match']] [['Reviewer','sid','Paper ID','match']]# Paper ID is not unique over multiple years!# .set_index(['Reviewer', 'Paper ID']) .set_index(['Reviewer', 'Paper ID']) .match .unstack(level=1))# assignments = Reviewer, sid, Role (primary, secondary)# doesn't include review/sid that were not assigned [.query('Role != ""')]assignments = (bids_raw .query('Role != ""')# Paper ID is not unique over multiple years!# [['Reviewer', 'Paper ID', 'Role']] [['Reviewer', 'sid', 'Paper ID', 'Role']] .reset_index(drop =True))del dbcon#### Plot Defaultsacc_template = go.layout.Template()acc_template.layout =dict( font =dict( family='Fira Sans', color ='black', size =13 ), title_font_size =14, plot_bgcolor ='rgba(255,255,255,0)', paper_bgcolor ='rgba(255,255,255,0)', margin =dict(pad=10), xaxis =dict( title =dict( font =dict( family='Fira Sans Medium', size=13 ), standoff =10 ), gridcolor='lightgray', gridwidth=1, automargin =True, fixedrange =True, ), yaxis =dict( title =dict( font =dict( family='Fira Sans Medium', size=13 ), standoff =10, ), gridcolor='lightgray', gridwidth=1, automargin =True, fixedrange =True, ), legend=dict( title_font_family="Fira Sans Medium", ), colorway = px.colors.qualitative.T10, hovermode ='closest', hoverlabel=dict( bgcolor="white", bordercolor='lightgray', font_color ='black', font_family ='Fira Sans' ),)acc_template.data.bar = [dict( textposition ='inside', insidetextanchor='middle', textfont_size =12,)]px.defaults.template = acc_templatepx.defaults.category_orders = {'Decision': list(staticdata['decision'].values()),'FinalDecision': list(staticdata['FinalDecision'].values()),'Area': list(staticdata['area'].values()),'Short Name': staticdata['keywords']['Short Name'].tolist(),}config =dict( displayModeBar =False, scrollZoom =False, responsive =False)def aspect(ratio):return { 'width': width, 'height': int(ratio*width) }# useful data sub-products#k_all columns: (index), Paper ID, Old Paper ID, Decision, year, Area, Keywords (as a list), # Keywords, Keyword, Category, Subcategory, Short Name, Descriptionk_all = (submissions .join(submissions['Keywords'] .explode() .rename('Keyword') ) .reset_index(level =0) .merge(staticdata['keywords'], on='Keyword'))# (Old) Paper ID is not unique, however, the 'sid' is (which is the current index)#k_all.reset_index(inplace=True)#k_all.rename(columns = {'sid':'Paper ID'},inplace = True)#k_all = k_all.merge(staticdata['keywords'], on='Keyword')#k_all#k_total columns: Category, Subcategory, Short Name, Keyword, Description, #Submissions, year# counts the total number of submissions per keyword and yeark_total = staticdata['keywords'].merge( k_all.value_counts(['Short Name','year']) .rename('# Submissions') .reset_index(),# on = 'Short Name', how ='right'# how = 'outer')#k_cnt: how often was a particular keyword used among all submissions within a year????#k_cnt columns: (index), Short Name, year, c, Category, Subcategory, Keyword, Description# not clear how k_cnt and k_total differ!k_cnt = (k_all .value_counts(['Short Name','year'], sort=False) .rename('c') .to_frame() .reset_index() .merge(staticdata['keywords'], on='Short Name'))
Submissions
The number of submissions peaked in 2020 at 585 papers, which is likely due to the pandemic and the one-month extension to the deadline. The years 2021 and 2022 saw lower numbers of submissions, with 442 and 460 respectively. Submissions increased in 2023 (539) and 2024 (544), and dipped slightly in 2025 (537). Overall, this suggests a stable and healthy research field.
fig = px.bar(totals, y='year', x='count', orientation ='h', labels={'count':'Number of Submissions', 'year':'Year'}, text ='count',).update_layout( yaxis=dict(autorange="reversed", tickmode='linear'), title ='Submissions Numbers since 2020', xaxis_title ='Number of Submissions',**aspect(0.35))fig.show(config=config)
Acceptance Rates
Acceptance rates fluctuated slightly from 2020–2023 (26.8%, 24.9%, 26.1%, and 25.8%), though there was a dip (24.4%) in 2021. For 2024, we saw a rather sharp drop to 22.4%, which was partially caused by a lower first-round acceptance rate (23.2%) and amplified by three (unusual) second-round rejects. 2025 has seen a return to a level slightly lower than 2020–2023 but above the unusually low rate of 2024.