Metadata for IRW tables

We supplement the data in the IRW tables with various kinds of metadata about the tables. For clarity, we separately describe the qualitative and quantitative metadata.

Quantitative Metadata

A variety of features of the tables (e.g., the number of rows) are pre-computed (using this code) to help users select datasets with desirable features. We provide information on:

• n_responses The number of rows in the table.
• n_categories The number of unique values of the resp column in the table. [Note: This needs to be treated with some care given that NA values are considered a level.]
• n_participants The number of objects being measured.
• n_items The number of probes being used to measure.
• responses_per_participant The average number of responses for an object of measurement.
• responses_per_item The average number of responses for a probe.
• density The average number of responses for an individual measurement object to an individual probe.
• variables The names of all the columns in the table.

Qualitative Metadata

The IRW tables have been annotated with additional information about the sample of objects being measured and the nature of the probes being used to measure. For each table, we describe:

• age range The age range of the measurement objects (when they are humans).
• child age (for child-focused studies) Additional information on the age range of child-focused studies.
• sample A description of the sample of objects (e.g., is it a convenience sample?).
• construct type A classification of the construct type (e.g., is this a cognitive measure?).
• measurement tool The type of measure (e.g., are these ratings? collected responses?).
• item format The format of the item.
• primary language(s) The language used in the assessment.
• construct name A formal description of the construct (from the original source).

Note that this information was produced by human raters; some degree of subjectivity should be expected.

Querying IRW tables

Given the volume of tables in the IRW and their heterogeneity, being able to effectively query IRW tables is essential. To do so, we recommend using the irw::irw_filter() function. Information on that function is here. Below we provide a variety of simple use cases; once a user has identified the appropriate tables, irw::irw_fetch() can be used to easily download them.

R

irw::irw_filter(n_participants=c(100000,Inf)) #Those tables with more than 100000 participants (using default density filter)

 [1] "aslec_insomnia_wang2025" "criticalperiod_syntax"  
 [3] "enem_2013_1mil_ch"       "enem_2013_1mil_cn"      
 [5] "enem_2013_1mil_lc"       "enem_2013_1mil_mt"      
 [7] "enem_2014_1mil_ch"       "enem_2014_1mil_cn"      
 [9] "enem_2014_1mil_lc"       "enem_2015_1mil_ch"      
[11] "enem_2016_1mil_ch"       "enem_2016_1mil_lc"      
[13] "enem_2018_1mil_cn"       "enem_2018_1mil_lc"      
[15] "enem_2018_1mil_mt"       "enem_2019_1mil_ch"      
[17] "enem_2019_1mil_cn"       "enem_2019_1mil_lc"      
[19] "enem_2019_1mil_mt"       "enem_2020_1mil_lc"      
[21] "enem_2021_1mil_ch"       "enem_2022_1mil_ch"      
[23] "ffm_AGR"                 "ffm_CSN"                
[25] "ffm_EST"                 "ffm_EXT"                
[27] "ffm_OPN"                 "gender_roles"           
[29] "isi_insomnia_wang2025"   "mbft_anunciacao_2024"   
[31] "nonverbal_immediacy"     "phq_insomnia_wang2025"  
[33] "pisa2003_science"        "pisa2006_read"          
[35] "riasec"                 

irw::irw_filter(n_participants=c(100000,Inf),density=NULL) #All tables with more than 100000 participants

 [1] "aslec_insomnia_wang2025"  "criticalperiod_syntax"   
 [3] "enem_2013_1mil_ch"        "enem_2013_1mil_cn"       
 [5] "enem_2013_1mil_lc"        "enem_2013_1mil_mt"       
 [7] "enem_2014_1mil_ch"        "enem_2014_1mil_cn"       
 [9] "enem_2014_1mil_lc"        "enem_2014_1mil_mt"       
[11] "enem_2015_1mil_ch"        "enem_2015_1mil_cn"       
[13] "enem_2015_1mil_lc"        "enem_2015_1mil_mt"       
[15] "enem_2016_1mil_ch"        "enem_2016_1mil_cn"       
[17] "enem_2016_1mil_lc"        "enem_2016_1mil_mt"       
[19] "enem_2017_1mil_ch"        "enem_2017_1mil_cn"       
[21] "enem_2017_1mil_lc"        "enem_2017_1mil_mt"       
[23] "enem_2018_1mil_ch"        "enem_2018_1mil_cn"       
[25] "enem_2018_1mil_lc"        "enem_2018_1mil_mt"       
[27] "enem_2019_1mil_ch"        "enem_2019_1mil_cn"       
[29] "enem_2019_1mil_lc"        "enem_2019_1mil_mt"       
[31] "enem_2020_1mil_ch"        "enem_2020_1mil_cn"       
[33] "enem_2020_1mil_lc"        "enem_2020_1mil_mt"       
[35] "enem_2021_1mil_ch"        "enem_2021_1mil_cn"       
[37] "enem_2021_1mil_lc"        "enem_2021_1mil_mt"       
[39] "enem_2022_1mil_ch"        "enem_2022_1mil_cn"       
[41] "enem_2022_1mil_lc"        "enem_2022_1mil_mt"       
[43] "ffm_AGR"                  "ffm_CSN"                 
[45] "ffm_EST"                  "ffm_EXT"                 
[47] "ffm_OPN"                  "ftna_kasper_2022"        
[49] "gender_roles"             "gilbert_meta_52"         
[51] "isi_insomnia_wang2025"    "mbft_anunciacao_2024"    
[53] "neurips_2020"             "nonverbal_immediacy"     
[55] "phq_insomnia_wang2025"    "pisa2000_math"           
[57] "pisa2000_read"            "pisa2000_science"        
[59] "pisa2003_math"            "pisa2003_problem_solving"
[61] "pisa2003_read"            "pisa2003_science"        
[63] "pisa2006_math"            "pisa2006_read"           
[65] "pisa2006_science"         "pisa2009_math"           
[67] "pisa2009_read"            "pisa2009_science"        
[69] "pisa2012_math"            "pisa2012_read"           
[71] "pisa2012_science"         "pisa2015_math"           
[73] "pisa2015_read"            "pisa2015_science"        
[75] "pisa2018_math"            "pisa2018_read"           
[77] "pisa2018_science"         "pisa2022_math"           
[79] "pisa2022_read"            "pisa2022_science"        
[81] "riasec"                   "twod_rotation_mather2023"

irw::irw_filter(n_categories=c(10,Inf),density=NULL) #Tables with responses in 10 or more categories

 [1] "cub_relgoods"                           
 [2] "deception_game"                         
 [3] "deception_professors"                   
 [4] "DEMOS"                                  
 [5] "det_naismith_2023"                      
 [6] "DMCT_Addis_2020_PSIQ"                   
 [7] "ecps_sahm_2024_ia"                      
 [8] "ecps_sahm_2024_trust"                   
 [9] "envirisk_lalot_2025"                    
[10] "estcrm_epia"                            
[11] "estcrm_selfeff"                         
[12] "evpromisi_stone_2021_global"            
[13] "Fh_Okcsr_Roos_2022_study2_Feeling_Heard"
[14] "figure_skating"                         
[15] "florida_twins_par"                      
[16] "fullscaleiq_memory"                     
[17] "fullscaleiq_mentalrotation"             
[18] "gilbert_meta_95"                        
[19] "HEARD_Roch_2022_SWLPWI"                 
[20] "ieswriting_molloy_2022"                 
[21] "immer12_immer"                          
[22] "mgkt"                                   
[23] "ml_harper_2015"                         
[24] "much_tte_2025_currentmotivation"        
[25] "nas_rogoza_2024_study5_nas"             
[26] "nas_rogoza_2024_study5_ngs"             
[27] "nas_rogoza_2024_study5_nvs"             
[28] "psiq_woelk2022"                         
[29] "realpic_souza2021"                      
[30] "rightwing_authoritariansim"             
[31] "SBD_Smith_2020"                         
[32] "sned_bendall_2024"                      
[33] "tears"                                  
[34] "timss_tam"                              
[35] "western_reserve_project"                
[36] "wine_luckett2021"                       

irw::irw_filter(var='rt') #Tables with response time data

 [1] "artistic_preferences"                     
 [2] "brain_hemisphere"                         
 [3] "broadband_inventories"                    
 [4] "chess_lnirt"                              
 [5] "credentialform_lnirt"                     
 [6] "dd_rotation"                              
 [7] "depression_anxiety_stress"                
 [8] "DMCT_Addis_2020_MCT"                      
 [9] "face_memory_test"                         
[10] "ffm_AGR"                                  
[11] "ffm_CSN"                                  
[12] "ffm_EST"                                  
[13] "ffm_EXT"                                  
[14] "ffm_OPN"                                  
[15] "fisher_temperment"                        
[16] "fullscaleiq_memory"                       
[17] "fullscaleiq_mentalrotation"               
[18] "fullscaleiq_vocab"                        
[19] "gcbs_brotherton_2013"                     
[20] "introversion_extroversion"                
[21] "machivallianism_test_main"                
[22] "mgkt"                                     
[23] "much_tte_2025_matrixreasoning"            
[24] "nature_relatedness"                       
[25] "nomt_hooper_2024_study2"                  
[26] "nonverbal_immediacy"                      
[27] "orev_bohn2024"                            
[28] "protestant_workethic"                     
[29] "roar_lexical"                             
[30] "sned_bendall_2024"                        
[31] "vocab_assessment_3_to_8_year_old_children"

irw::irw_filter(age_range="Child (<18y)") #Tables with child-focused data

 [1] "ALSECYPIAMH_WU_2022_CPS"                 
 [2] "ALSECYPIAMH_WU_2022_Empathy"             
 [3] "ALSECYPIAMH_WU_2022_MIL"                 
 [4] "ALSECYPIAMH_WU_2022_NEI"                 
 [5] "ALSECYPIAMH_WU_2022_PEI"                 
 [6] "ALSECYPIAMH_WU_2022_PHQ"                 
 [7] "ALSECYPIAMH_WU_2022_PIL"                 
 [8] "ALSECYPIAMH_WU_2022_SDQ"                 
 [9] "ALSECYPIAMH_WU_2022_SWEMWBS"             
[10] "ALSECYPIAMH_WU_2022_SWLS"                
[11] "aslec_insomnia_wang2025"                 
[12] "BAFACALO_Golino_2013_CIS"                
[13] "content_literacy_intervention_g1"        
[14] "difnlr_msatb"                            
[15] "EEN_Lacey_2024_Children"                 
[16] "florida_twins_auth"                      
[17] "florida_twins_leq"                       
[18] "gilbert_meta_11"                         
[19] "gilbert_meta_2"                          
[20] "gilbert_meta_55"                         
[21] "gilbert_meta_64"                         
[22] "gilbert_meta_7"                          
[23] "gilbert_meta_8"                          
[24] "isi_insomnia_wang2025"                   
[25] "mpsycho_ceaq"                            
[26] "mpsycho_Rogers"                          
[27] "mpsycho_youthdep"                        
[28] "mpsycho_zareki"                          
[29] "oxfordcovid_xue_2024_pswq_c"             
[30] "oxfordcovid_xue_2024_school"             
[31] "phq_insomnia_wang2025"                   
[32] "pisa2003_science"                        
[33] "pisa2006_read"                           
[34] "PPPASBPNSSedaisw_Pedro_2022_EBSV"        
[35] "PPPASBPNSSedaisw_Pedro_2022_NPBE"        
[36] "PPPASBPNSSedaisw_Pedro_2022_PANAS"       
[37] "PROMISPME_Forrest_2021_GHGlobal_Children"
[38] "PROMISPME_Forrest_2021_Physical_Children"
[39] "PROMISPME_Forrest_2021_Physical_Proxy"   
[40] "PROMISPME_Forrest_2021_PosAff_Children"  
[41] "PROMISPME_Forrest_2021_Psych_Children"   
[42] "psychtools_blot"                         
[43] "PTAM_Kretzschmar_2017_Raven"             
[44] "PTAM_Kretzschmar_2017_Reasoning"         
[45] "scoliosis_dueber"                        
[46] "test_taking_patterns_over_time"          
[47] "transreas_mokken"