Fix lite-256 gold for 94 dual-gold instances (use adjudicated lite-SQL-derived key) + join-key completeness patch (local003, local039, sf014); verified against prior released lite set

evaluation_set/spider2_lite_256.csv

@@ -88,7 +88,7 @@ FROM
     TractPop TP
 JOIN
     TractGroup TG
-    ON TP.""StateCountyTractID"" = TG.""StateCountyTractID"";",Dim_CensusGeography.BlockGroupID; Dim_CensusGeography.StateCountyTractID; Dim_CensusGeography.StateName; Fact_CensusValues_ACS2021.BlockGroupID; Fact_CensusValues_ACS2021.CensusValue; Fact_CensusValues_ACS2021.MetricID,6,lite_sql,sf011
+    ON TP.""StateCountyTractID"" = TG.""StateCountyTractID"";",Dim_CensusGeography.BlockGroupID; Dim_CensusGeography.BlockGroupPolygon; Dim_CensusGeography.StateAbbrev; Dim_CensusGeography.StateCountyTractID; Fact_CensusValues_ACS2021.BlockGroupID; Fact_CensusValues_ACS2021.CensusValue; Fact_CensusValues_ACS2021.MetricID,7,lite_sql,sf011
 sf012,WEATHER__ENVIRONMENT,snowflake,What were the total amounts of building and contents damage reported under the National Flood Insurance Program in the City of New York for each year from 2010 to 2019?,"SELECT 
     YEAR(claims.date_of_loss)               AS year_of_loss,
     claims.nfip_community_name,
@@ -147,7 +147,7 @@ GROUP BY
     C.""ZipCode"", SB.""StateBenchmark_Over1HrTravelTime"", SB.""TotalStatePopulation""
 ORDER BY
     ""Total_Commuters_1Hr_Travel_Time"" DESC
-LIMIT 1;",Fact_CensusValues_ACS2021_ByZip.CensusValueByZip; Fact_CensusValues_ACS2021_ByZip.MetricID; Fact_CensusValues_ACS2021_ByZip.ZipCode; Fact_StateBenchmark_ACS2021.MetricID; Fact_StateBenchmark_ACS2021.StateAbbrev; Fact_StateBenchmark_ACS2021.StateBenchmarkValue; Fact_StateBenchmark_ACS2021.TotalStatePopulation; LU_GeographyExpanded.PreferredStateAbbrev; LU_GeographyExpanded.ZipCode,9,snow_sql_near_exact,sf014
+LIMIT 1;",Dim_CensusMetrics.MetricID; Fact_CensusValues_ACS2021_ByZip.CensusValueByZip; Fact_CensusValues_ACS2021_ByZip.MetricID; Fact_CensusValues_ACS2021_ByZip.ZipCode; Fact_StateBenchmark_ACS2021.MetricID; Fact_StateBenchmark_ACS2021.StateAbbrev; Fact_StateBenchmark_ACS2021.StateBenchmarkValue; Fact_StateBenchmark_ACS2021.TotalStatePopulation; LU_GeographyExpanded.PreferredStateAbbrev; LU_GeographyExpanded.ZipCode,10,snow_sql_near_exact,sf014
 sf018,BRAZE_USER_EVENT_DEMO_DATASET,snowflake,"Examine user engagement with push notifications within a specified one-hour window on June 1, 2023.","WITH push_send AS (
     SELECT
         id,
@@ -268,7 +268,7 @@ FROM
     AND ps.user_id = poi.user_id
     AND ps.app_group_id = poi.app_group_id
 GROUP BY
-    1,2,3,4,5,6,7,8,9,10,11;",USERS_MESSAGES_PUSHNOTIFICATION_*.AD_TRACKING_ENABLED; USERS_MESSAGES_PUSHNOTIFICATION_*.APP_GROUP_ID; USERS_MESSAGES_PUSHNOTIFICATION_*.CAMPAIGN_ID; USERS_MESSAGES_PUSHNOTIFICATION_*.MESSAGE_VARIATION_API_ID; USERS_MESSAGES_PUSHNOTIFICATION_*.PLATFORM; USERS_MESSAGES_PUSHNOTIFICATION_*.TIME; USERS_MESSAGES_PUSHNOTIFICATION_*.USER_ID; USERS_MESSAGES_PUSHNOTIFICATION_INFLUENCEDOPEN_VIEW.APP_GROUP_ID; USERS_MESSAGES_PUSHNOTIFICATION_INFLUENCEDOPEN_VIEW.BROWSER; USERS_MESSAGES_PUSHNOTIFICATION_INFLUENCEDOPEN_VIEW.CAMPAIGN_ID; USERS_MESSAGES_PUSHNOTIFICATION_INFLUENCEDOPEN_VIEW.CARRIER; USERS_MESSAGES_PUSHNOTIFICATION_INFLUENCEDOPEN_VIEW.DEVICE_MODEL; USERS_MESSAGES_PUSHNOTIFICATION_INFLUENCEDOPEN_VIEW.MESSAGE_VARIATION_API_ID; USERS_MESSAGES_PUSHNOTIFICATION_INFLUENCEDOPEN_VIEW.MESSAGE_VARIATION_ID; USERS_MESSAGES_PUSHNOTIFICATION_INFLUENCEDOPEN_VIEW.PLATFORM; USERS_MESSAGES_PUSHNOTIFICATION_INFLUENCEDOPEN_VIEW.TIME; USERS_MESSAGES_PUSHNOTIFICATION_INFLUENCEDOPEN_VIEW.USER_ID; USERS_MESSAGES_PUSHNOTIFICATION_OPEN_VIEW.AD_TRACKING_ENABLED; USERS_MESSAGES_PUSHNOTIFICATION_OPEN_VIEW.APP_GROUP_ID; USERS_MESSAGES_PUSHNOTIFICATION_OPEN_VIEW.BROWSER; USERS_MESSAGES_PUSHNOTIFICATION_OPEN_VIEW.CAMPAIGN_ID; USERS_MESSAGES_PUSHNOTIFICATION_OPEN_VIEW.CARRIER; USERS_MESSAGES_PUSHNOTIFICATION_OPEN_VIEW.DEVICE_MODEL; USERS_MESSAGES_PUSHNOTIFICATION_OPEN_VIEW.MESSAGE_VARIATION_API_ID; USERS_MESSAGES_PUSHNOTIFICATION_OPEN_VIEW.MESSAGE_VARIATION_ID; USERS_MESSAGES_PUSHNOTIFICATION_OPEN_VIEW.PLATFORM; USERS_MESSAGES_PUSHNOTIFICATION_OPEN_VIEW.TIME; USERS_MESSAGES_PUSHNOTIFICATION_OPEN_VIEW.USER_ID,28,lite_sql,sf018
+    1,2,3,4,5,6,7,8,9,10,11;",USERS_MESSAGES_PUSHNOTIFICATION_BOUNCE_VIEW.AD_TRACKING_ENABLED; USERS_MESSAGES_PUSHNOTIFICATION_BOUNCE_VIEW.APP_GROUP_ID; USERS_MESSAGES_PUSHNOTIFICATION_BOUNCE_VIEW.CAMPAIGN_ID; USERS_MESSAGES_PUSHNOTIFICATION_BOUNCE_VIEW.ID; USERS_MESSAGES_PUSHNOTIFICATION_BOUNCE_VIEW.PLATFORM; USERS_MESSAGES_PUSHNOTIFICATION_BOUNCE_VIEW.TIME; USERS_MESSAGES_PUSHNOTIFICATION_BOUNCE_VIEW.USER_ID; USERS_MESSAGES_PUSHNOTIFICATION_INFLUENCEDOPEN_VIEW.APP_GROUP_ID; USERS_MESSAGES_PUSHNOTIFICATION_INFLUENCEDOPEN_VIEW.CAMPAIGN_ID; USERS_MESSAGES_PUSHNOTIFICATION_INFLUENCEDOPEN_VIEW.ID; USERS_MESSAGES_PUSHNOTIFICATION_INFLUENCEDOPEN_VIEW.PLATFORM; USERS_MESSAGES_PUSHNOTIFICATION_INFLUENCEDOPEN_VIEW.TIME; USERS_MESSAGES_PUSHNOTIFICATION_INFLUENCEDOPEN_VIEW.USER_ID; USERS_MESSAGES_PUSHNOTIFICATION_OPEN_VIEW.AD_TRACKING_ENABLED; USERS_MESSAGES_PUSHNOTIFICATION_OPEN_VIEW.APP_GROUP_ID; USERS_MESSAGES_PUSHNOTIFICATION_OPEN_VIEW.CAMPAIGN_ID; USERS_MESSAGES_PUSHNOTIFICATION_OPEN_VIEW.ID; USERS_MESSAGES_PUSHNOTIFICATION_OPEN_VIEW.PLATFORM; USERS_MESSAGES_PUSHNOTIFICATION_OPEN_VIEW.TIME; USERS_MESSAGES_PUSHNOTIFICATION_OPEN_VIEW.USER_ID; USERS_MESSAGES_PUSHNOTIFICATION_SEND_VIEW.AD_TRACKING_ENABLED; USERS_MESSAGES_PUSHNOTIFICATION_SEND_VIEW.APP_GROUP_ID; USERS_MESSAGES_PUSHNOTIFICATION_SEND_VIEW.CAMPAIGN_ID; USERS_MESSAGES_PUSHNOTIFICATION_SEND_VIEW.ID; USERS_MESSAGES_PUSHNOTIFICATION_SEND_VIEW.PLATFORM; USERS_MESSAGES_PUSHNOTIFICATION_SEND_VIEW.TIME; USERS_MESSAGES_PUSHNOTIFICATION_SEND_VIEW.USER_ID,27,lite_sql,sf018
 sf040,US_ADDRESSES__POI,snowflake,"Find the top 10 northernmost addresses in Florida's largest zip code area. What are their address numbers, street names, and types?","WITH zip_areas AS (
     SELECT
         geo.geo_id,
@@ -299,7 +299,7 @@ JOIN zip_area_ranks AS areas
     ON (addr.id_zip = areas.geo_id)
 WHERE addr.state = 'FL' AND areas.country = 'United States' AND areas.zip_area_rank = 1
 ORDER BY LATITUDE DESC
-LIMIT 10;",GEOGRAPHY_CHARACTERISTICS.GEO_ID; GEOGRAPHY_CHARACTERISTICS.RELATIONSHIP_TYPE; GEOGRAPHY_CHARACTERISTICS.VALUE; GEOGRAPHY_RELATIONSHIPS.GEO_ID; GEOGRAPHY_RELATIONSHIPS.RELATED_GEO_ID; GEOGRAPHY_RELATIONSHIPS.RELATED_LEVEL; GEOGRAPHY_RELATIONSHIPS.RELATIONSHIP_TYPE; US_ADDRESSES.ID_ZIP; US_ADDRESSES.LATITUDE; US_ADDRESSES.LONGITUDE; US_ADDRESSES.NUMBER; US_ADDRESSES.STATE; US_ADDRESSES.STREET; US_ADDRESSES.STREET_TYPE,14,lite_sql,sf040
+LIMIT 10;",GEOGRAPHY_CHARACTERISTICS.GEO_ID; GEOGRAPHY_CHARACTERISTICS.RELATIONSHIP_TYPE; GEOGRAPHY_CHARACTERISTICS.VALUE; GEOGRAPHY_INDEX.GEO_ID; GEOGRAPHY_INDEX.GEO_NAME; GEOGRAPHY_INDEX.LEVEL; GEOGRAPHY_RELATIONSHIPS.GEO_ID; GEOGRAPHY_RELATIONSHIPS.RELATED_GEO_NAME; GEOGRAPHY_RELATIONSHIPS.RELATED_LEVEL; US_ADDRESSES.ID_ZIP; US_ADDRESSES.LATITUDE; US_ADDRESSES.NUMBER; US_ADDRESSES.STATE; US_ADDRESSES.STREET; US_ADDRESSES.STREET_TYPE,15,lite_sql,sf040
 sf044,FINANCE__ECONOMICS,snowflake,"What was the percentage change in post-market close prices for the Magnificent 7 tech companies from January 1 to June 30, 2024?","WITH ytd_performance AS (
   SELECT
     ticker,
@@ -320,7 +320,7 @@ FROM
   ytd_performance
 GROUP BY
   ticker, start_of_year_date, start_of_year_price, latest_date, latest_price
-ORDER BY percentage_change_ytd DESC;",STOCK_PRICE_TIMESERIES.DATE; STOCK_PRICE_TIMESERIES.TICKER; STOCK_PRICE_TIMESERIES.VALUE; STOCK_PRICE_TIMESERIES.VARIABLE,4,lite_sql,sf044
+ORDER BY percentage_change_ytd DESC;",STOCK_PRICE_TIMESERIES.DATE; STOCK_PRICE_TIMESERIES.TICKER; STOCK_PRICE_TIMESERIES.VALUE; STOCK_PRICE_TIMESERIES.VARIABLE_NAME,4,lite_sql,sf044
 bq001,ga360,bigquery,"I wonder how many days between the first transaction and the first visit both in Feburary 2017 for each transacting visitor, along with the device used in the transaction.","DECLARE start_date STRING DEFAULT '20170201';
 DECLARE end_date STRING DEFAULT '20170228';
 
@@ -851,7 +851,7 @@ FROM
   highway_info
 ORDER BY 
   highway_length DESC
-LIMIT 5;",PLANET_FEATURES.all_tags; PLANET_FEATURES.feature_type; PLANET_FEATURES.geometry; PLANET_FEATURES.osm_id,4,lite_sql,sf_bq017
+LIMIT 5;",PLANET_FEATURES.all_tags; PLANET_FEATURES.feature_type; PLANET_FEATURES.geometry,3,lite_sql,sf_bq017
 bq018,covid19_open_data,bigquery,Which day in March and April had the highest COVID-19 confirmed case growth rate in the United States? The format is MM-DD.,"WITH us_cases_by_date AS (
   SELECT
     date,
@@ -1038,7 +1038,7 @@ ON
     AND PVP.""ProjectName"" = P.""Name""
 ORDER BY 
     P.""StarsCount"" DESC
-LIMIT 8;",PACKAGEVERSIONS.Name; PACKAGEVERSIONS.SnapshotAt; PACKAGEVERSIONS.System; PACKAGEVERSIONS.UpstreamPublishedAt; PACKAGEVERSIONS.Version; PACKAGEVERSIONS.VersionInfo; PACKAGEVERSIONTOPROJECT.Name; PACKAGEVERSIONTOPROJECT.ProjectName; PACKAGEVERSIONTOPROJECT.ProjectType; PACKAGEVERSIONTOPROJECT.RelationType; PACKAGEVERSIONTOPROJECT.System; PACKAGEVERSIONTOPROJECT.Version; PROJECTS.Name; PROJECTS.SnapshotAt; PROJECTS.StarsCount; PROJECTS.Type,16,lite_sql,sf_bq028
+LIMIT 8;",PACKAGEVERSIONS.Name; PACKAGEVERSIONS.System; PACKAGEVERSIONS.Version; PACKAGEVERSIONS.VersionInfo; PACKAGEVERSIONTOPROJECT.Name; PACKAGEVERSIONTOPROJECT.ProjectName; PACKAGEVERSIONTOPROJECT.ProjectType; PACKAGEVERSIONTOPROJECT.System; PACKAGEVERSIONTOPROJECT.Version; PROJECTS.Name; PROJECTS.StarsCount; PROJECTS.Type,12,lite_sql,sf_bq028
 bq031,noaa_data,bigquery,"Show me the daily weather data (temperature, precipitation, and wind speed) in Rochester for the first season of year 2019, converted to Celsius, centimeters, and meters per second, respectively. Also, include the moving averages (window size = 8) and the differences between the moving averages for up to 8 days prior (all values rounded to one decimal place, sorted by date in ascending order, and records starting from 2019-01-09).","WITH transrate AS (
     SELECT
         DATE(CAST(year AS INT64), CAST(mo AS INT64), CAST(da AS INT64)) AS observation_date
@@ -1264,7 +1264,7 @@ WHERE
 GROUP BY
   TO_CHAR(Date_Series_Table.day, 'YYYY-MM')
 ORDER BY
-  Patent_Date_YearMonth;",PUBLICATIONS.abstract_localized; PUBLICATIONS.country_code; PUBLICATIONS.filing_date; PUBLICATIONS.publication_number,4,lite_sql,sf_bq033
+  Patent_Date_YearMonth;",PUBLICATIONS.abstract_localized; PUBLICATIONS.application_number; PUBLICATIONS.country_code; PUBLICATIONS.filing_date,4,lite_sql,sf_bq033
 bq034,ghcn_d,bigquery,"I want to know the IDs, names of weather stations within a 50 km straight-line distance from the center of Chicago (41.8319°N, 87.6847°W)","WITH params AS (
   SELECT ST_GeogPoint(-87.6847, 41.8319) AS center,
          50 AS maxdist_km
@@ -1494,7 +1494,7 @@ WHERE
   genex.""HGNC_gene_symbol"" IN ('MDM2', 'TP53', 'CDKN1A','CCNE1')
 ORDER BY
   ""case_barcode"",
-  ""HGNC_gene_symbol"";",CLINICAL_GDC_R_*.demo__ethnicity; CLINICAL_GDC_R_*.demo__gender; CLINICAL_GDC_R_*.demo__race; CLINICAL_GDC_R_*.demo__vital_status; CLINICAL_GDC_R_*.diag__age_at_diagnosis; CLINICAL_GDC_R_*.diag__ajcc_clinical_stage; CLINICAL_GDC_R_*.diag__ajcc_pathologic_stage; CLINICAL_GDC_R_*.diag__year_of_diagnosis; CLINICAL_GDC_R_*.disease_type; CLINICAL_GDC_R_*.primary_site; CLINICAL_GDC_R_*.proj__project_id; CLINICAL_GDC_R_*.submitter_id; RNASEQ_HG19_GDC_2017_02.HGNC_gene_symbol; RNASEQ_HG19_GDC_2017_02.case_barcode; RNASEQ_HG19_GDC_2017_02.normalized_count; RNASEQ_HG19_GDC_2017_02.project_short_name; RNASEQ_HG19_GDC_2017_02.sample_barcode; SOMATIC_MUTATION_HG19_DCC_2017_02.Hugo_Symbol; SOMATIC_MUTATION_HG19_DCC_2017_02.Mutation_Status; SOMATIC_MUTATION_HG19_DCC_2017_02.case_barcode; SOMATIC_MUTATION_HG19_DCC_2017_02.project_short_name; SOMATIC_MUTATION_HG19_MC3_2017_02.Hugo_Symbol; SOMATIC_MUTATION_HG19_MC3_2017_02.case_barcode; SOMATIC_MUTATION_HG19_MC3_2017_02.project_short_name,24,lite_sql,sf_bq043
+  ""HGNC_gene_symbol"";",SOMATIC_MUTATION_HG19_DCC_2017_02.Hugo_Symbol; SOMATIC_MUTATION_HG19_DCC_2017_02.case_barcode; SOMATIC_MUTATION_HG19_DCC_2017_02.project_short_name,3,lite_sql,sf_bq043
 bq045,noaa_data,bigquery,Which weather stations in Washington State had more than 150 rainy days in 2023 but fewer rainy days than in 2022? Define a 'rainy day' as any day where the precipitation recorded is more than 0 millimeters.,"WITH WashingtonStations2023 AS 
     (
         SELECT 
@@ -1841,7 +1841,7 @@ JOIN (
         OR cpc.""group_id"" = 'A01G'
 ) AS filterData
 ON app.""patent_id"" = filterData.""patent_id""
-ORDER BY app.""date"";",APPLICATION.date; APPLICATION.patent_id; CPC_CURRENT.group_id; CPC_CURRENT.patent_id; CPC_CURRENT.subsection_id; PATENT.abstract; PATENT.country; PATENT.id; PATENT.title; USPATENTCITATION.citation_id; USPATENTCITATION.patent_id,11,lite_sql,sf_bq052
+ORDER BY app.""date"";",APPLICATION.country; APPLICATION.date; APPLICATION.patent_id; BRF_SUM_TEXT.patent_id; BRF_SUM_TEXT.text; CPC_CURRENT.group_id; CPC_CURRENT.patent_id; CPC_CURRENT.subsection_id; PATENT.id; PATENT.title; USPATENTCITATION.date; USPATENTCITATION.patent_id,12,lite_sql,sf_bq052
 bq053,new_york,bigquery,"How has the number of trees of each fall color in New York City changed from 1995 to 2015, considering only trees that were still alive in 2015 and excluding those marked as dead in 1995?","SELECT
   c.fall_color,
   SUM(d.count_growth) AS change
@@ -1983,7 +1983,7 @@ FROM totals
 INNER JOIN coinjoins
     ON totals.tx_month = coinjoins.cjs_month
 ORDER BY value_percent DESC
-LIMIT 1;",TRANSACTIONS.block_timestamp; TRANSACTIONS.input_count; TRANSACTIONS.input_value; TRANSACTIONS.is_coinbase; TRANSACTIONS.output_count; TRANSACTIONS.output_value; TRANSACTIONS.outputs,7,lite_sql,sf_bq057
+LIMIT 1;",TRANSACTIONS.block_number; TRANSACTIONS.block_timestamp_month; TRANSACTIONS.hash; TRANSACTIONS.input_count; TRANSACTIONS.input_value; TRANSACTIONS.output_count; TRANSACTIONS.output_value; TRANSACTIONS.outputs,8,lite_sql,sf_bq057
 bq059,san_francisco_plus,bigquery,"What is the highest average speed (rounded to 1 decimal, in metric m/s) for bike trips in Berkeley with trip distance greater than 1000 meters?","WITH stations AS (
   SELECT station_id
   FROM
@@ -2360,7 +2360,7 @@ WHERE
   AND (b.""tissue_type"" = 'normal' OR b.""tissue_type"" = 'tumor')
   AND (c.""item_name"" = 'Embedding medium' AND c.""item_value"" = 'Tissue freezing medium')
 ORDER BY 
-  a.""crdc_instance_uuid"";",DICOM_ALL.Columns; DICOM_ALL.LossyImageCompressionMethod; DICOM_ALL.Modality; DICOM_ALL.PatientID; DICOM_ALL.PixelSpacing; DICOM_ALL.Rows; DICOM_ALL.SOPInstanceUID; DICOM_ALL.SeriesInstanceUID; DICOM_ALL.SpecimenDescriptionSequence; DICOM_ALL.StudyInstanceUID; DICOM_ALL.VolumetricProperties; DICOM_ALL.collection_id; DICOM_ALL.collection_name; DICOM_ALL.gcs_url; DICOM_ALL.idc_webapp_collection_id; DICOM_ALL.tcia_api_collection_id,16,lite_sql,sf_bq070
+  a.""crdc_instance_uuid"";",DICOM_ALL.ImageType; DICOM_ALL.Modality; DICOM_ALL.PatientID; DICOM_ALL.SeriesInstanceUID; DICOM_ALL.SpecimenDescriptionSequence; DICOM_ALL.StudyInstanceUID; DICOM_ALL.collection_id; DICOM_ALL.gcs_url,8,lite_sql,sf_bq070
 sf_bq072,DEATH,snowflake,"Please tell me the total and Black deaths due to vehicle-related incidents and firearms separately, for each age from 12 to 18.","WITH BlackRace AS (
     SELECT CAST(""Code"" AS INT) AS CODE
     FROM DEATH.DEATH.RACE
@@ -2416,7 +2416,7 @@ JOIN (
     WHERE ""Age"" BETWEEN 12 AND 18
     GROUP BY ""Age""
 ) g
-ON g.""Age"" = v.""Age"";",DEATHRECORDS.Age; DEATHRECORDS.AgeType; DEATHRECORDS.Id; DEATHRECORDS.Race; ENTITYAXISCONDITIONS.DeathRecordId; ENTITYAXISCONDITIONS.Icd10Code; ICD10CODE.Code; ICD10CODE.Description; RACE_*.Code; RACE_*.Description,10,lite_sql,sf_bq072
+ON g.""Age"" = v.""Age"";",DEATHRECORDS.Age; DEATHRECORDS.Id; DEATHRECORDS.Race; ENTITYAXISCONDITIONS.DeathRecordId; ENTITYAXISCONDITIONS.Icd10Code; ICD10CODE.Code; ICD10CODE.Description; RACE.Code; RACE.Description,9,lite_sql,sf_bq072
 bq074,sdoh,bigquery,"Count the number of counties that experienced an increase in unemployment from 2015 to 2018, using 5-year ACS data, and a decrease in dual-eligible enrollee counts between December 1, 2015, and December 1, 2018.","WITH acs_2018 AS (
   SELECT geo_id, unemployed_pop AS unemployed_2018  
   FROM `bigquery-public-data.census_bureau_acs.county_2018_5yr` 
@@ -2829,7 +2829,7 @@ MaxYearForTopAssignee AS (
 
 SELECT filing_year
 FROM 
-    MaxYearForTopAssignee",PUBLICATIONS.application_number; PUBLICATIONS.assignee; PUBLICATIONS.assignee_harmonized; PUBLICATIONS.cpc; PUBLICATIONS.filing_date; PUBLICATIONS.ipc,6,lite_sql,sf_bq091
+    MaxYearForTopAssignee",PUBLICATIONS.application_number; PUBLICATIONS.assignee_harmonized; PUBLICATIONS.cpc; PUBLICATIONS.filing_date,4,lite_sql,sf_bq091
 sf_bq093,CRYPTO,snowflake,"Tell me the maximum and minimum net changes in balances for Ethereum Classic addresses on October 14, 2016, considering debits, credits, and gas fees, while excluding internal calls like 'delegatecall', 'callcode', and 'staticcall'.","WITH double_entry_book AS (
     -- Debits
     SELECT 
@@ -3207,7 +3207,7 @@ WHERE
     rn = 1
 ORDER BY
     ""rank""
-LIMIT 1;",INTERNATIONAL_TOP_RISING_TERMS.percent_gain; INTERNATIONAL_TOP_RISING_TERMS.rank; INTERNATIONAL_TOP_RISING_TERMS.refresh_date; INTERNATIONAL_TOP_RISING_TERMS.score; INTERNATIONAL_TOP_RISING_TERMS.term; INTERNATIONAL_TOP_RISING_TERMS.week; TOP_RISING_TERMS.percent_gain; TOP_RISING_TERMS.rank; TOP_RISING_TERMS.refresh_date; TOP_RISING_TERMS.score; TOP_RISING_TERMS.term; TOP_RISING_TERMS.week,12,lite_sql,sf_bq104
+LIMIT 1;",TOP_RISING_TERMS.dma_name; TOP_RISING_TERMS.rank; TOP_RISING_TERMS.refresh_date; TOP_RISING_TERMS.score; TOP_RISING_TERMS.term; TOP_RISING_TERMS.week,6,lite_sql,sf_bq104
 bq105,nhtsa_traffic_fatalities_plus,bigquery,"How many traffic accidents per 100,000 people, specifically due to driver distraction, were recorded in each state in the years 2015 and 2016? Identify the top five states each year with the highest rates. Exclude accidents where the distraction status of the driver was recorded as 'Not Distracted,' 'Unknown if Distracted,' or 'Not Reported.' Use state population data from the 2010 census for calculating the rate.","SELECT * FROM
 (
 SELECT
@@ -3801,7 +3801,7 @@ FROM
   LEFT JOIN cit ON fam.""family_id"" = cit.""family_id""
   LEFT JOIN gpr ON fam.""family_id"" = gpr.""family_id""
 WHERE
-  pub.""publication_date"" BETWEEN 20150101 AND 20150131;",ABS_AND_EMB.cited_by; ABS_AND_EMB.publication_number; PUBLICATIONS.citation; PUBLICATIONS.country_code; PUBLICATIONS.cpc; PUBLICATIONS.family_id; PUBLICATIONS.ipc; PUBLICATIONS.publication_date; PUBLICATIONS.publication_number,9,lite_sql,sf_bq127
+  pub.""publication_date"" BETWEEN 20150101 AND 20150131;",ABS_AND_EMB.cited_by; PUBLICATIONS.citation; PUBLICATIONS.country_code; PUBLICATIONS.cpc; PUBLICATIONS.family_id; PUBLICATIONS.ipc; PUBLICATIONS.publication_date; PUBLICATIONS.publication_number,8,lite_sql,sf_bq127
 sf_bq128,PATENTSVIEW,snowflake,"Tell me the patent title and abstract, as well as the publication date, the backward citation and forward citation count within 5 years for those published in January 2014. The detailed requirements are provided in `forward_backward_citation.md`.","SELECT
     patent.""title"",
     patent.""abstract"",
@@ -3869,7 +3869,7 @@ JOIN (
 ON app.""patent_id"" = filterData.""patent_id""
 WHERE
     TRY_CAST(app.""date"" AS DATE) < '2014-02-01' 
-    AND TRY_CAST(app.""date"" AS DATE) >= '2014-01-01';",APPLICATION.country; APPLICATION.date; APPLICATION.patent_id; CPC_CURRENT.group_id; CPC_CURRENT.patent_id; CPC_CURRENT.subsection_id; PATENT.abstract; PATENT.country; PATENT.date; PATENT.id; PATENT.title; USPATENTCITATION.citation_id; USPATENTCITATION.date; USPATENTCITATION.patent_id,14,lite_sql,sf_bq128
+    AND TRY_CAST(app.""date"" AS DATE) >= '2014-01-01';",APPLICATION.country; APPLICATION.date; APPLICATION.patent_id; CPC_CURRENT.group_id; CPC_CURRENT.patent_id; CPC_CURRENT.subsection_id; PATENT.abstract; PATENT.id; PATENT.title; USPATENTCITATION.citation_id; USPATENTCITATION.date; USPATENTCITATION.patent_id,12,lite_sql,sf_bq128
 bq130,covid19_nyt,bigquery,"Analyze daily new COVID-19 case counts from March to May 2020, identifying the top five states by daily increases. Please compile a ranking based on how often each state appears in these daily top fives. Then, examine the state that ranks fourth overall and identify its top five counties based on their frequency of appearing in the daily top five new case counts.","WITH StateCases AS (
     SELECT
         b.state_name,
@@ -4196,7 +4196,7 @@ SELECT
   ""mean_sq_between"" / ""mean_sq_within"" AS ""F""
 FROM
   numerator,
-  denominator;",RNASEQ_GENE_EXPRESSION_UNC_RSEM.HGNC_gene_symbol; RNASEQ_GENE_EXPRESSION_UNC_RSEM.normalized_count; RNASEQ_GENE_EXPRESSION_UNC_RSEM.project_short_name; RNASEQ_GENE_EXPRESSION_UNC_RSEM.sample_barcode; SOMATIC_MUTATION_MC3.Hugo_Symbol; SOMATIC_MUTATION_MC3.Variant_Classification; SOMATIC_MUTATION_MC3.project_short_name; SOMATIC_MUTATION_MC3.sample_barcode_tumor,8,lite_sql,sf_bq150
+  denominator;",RNASEQ_GENE_EXPRESSION_UNC_RSEM.HGNC_gene_symbol; RNASEQ_GENE_EXPRESSION_UNC_RSEM.normalized_count; RNASEQ_GENE_EXPRESSION_UNC_RSEM.project_short_name; RNASEQ_GENE_EXPRESSION_UNC_RSEM.sample_barcode; SOMATIC_MUTATION_MC3.SYMBOL; SOMATIC_MUTATION_MC3.Variant_Type; SOMATIC_MUTATION_MC3.sample_barcode_tumor,7,lite_sql,sf_bq150
 bq151,pancancer_atlas_2,bigquery,"Using TCGA dataset, calculate the chi-squared statistic to evaluate the association between KRAS and TP53 gene mutations in patients diagnosed with pancreatic adenocarcinoma (PAAD). Incorporate clinical follow-up data and high-quality mutation annotations to accurately determine the frequency of patients with co-occurring KRAS and TP53 mutations compared to those with each mutation occurring independently. Ensure that patient records are meticulously matched based on unique identifiers to maintain data integrity. This analysis aims to identify and quantify potential correlations between KRAS and TP53 genetic alterations within the PAAD patient population.","WITH
 barcodes AS (
    SELECT bcr_patient_barcode AS ParticipantBarcode
@@ -4746,7 +4746,7 @@ SELECT
   ""UpvotesReceived"" AS ""UpvotesReceivedByUpvotedUser"",
   ""UpvotesGiven"" AS ""UpvotesGivenByUpvotedUser""
 FROM ReciprocalUpvotes
-ORDER BY ""UpvotesReceived"" DESC, ""UpvotesGiven"" DESC;",FORUMMESSAGEVOTES.ForumMessageId; FORUMMESSAGEVOTES.FromUserId; FORUMMESSAGEVOTES.ToUserId; USERS.DisplayName; USERS.Id; USERS.UserName,6,lite_sql,sf_bq167
+ORDER BY ""UpvotesReceived"" DESC, ""UpvotesGiven"" DESC;",FORUMMESSAGEVOTES.FromUserId; FORUMMESSAGEVOTES.Id; FORUMMESSAGEVOTES.ToUserId; USERS.Id; USERS.UserName,5,lite_sql,sf_bq167
 bq172,cms_data,bigquery,"For the drug with the highest total number of prescriptions in New York State during 2014, could you list the top five states with the highest total claim counts for this drug? Please also include their total claim counts and total drug costs. ","WITH ny_top_drug AS (
   SELECT
     drug_name AS drug_name,
@@ -4921,7 +4921,7 @@ SELECT
   ""count""
 FROM count_data
 WHERE ""count"" >= 5
-  AND ""type"" = 'PullRequestEvent';",LANGUAGES.language; LANGUAGES.repo_name; _2023_*.created_at; _2023_*.repo; _2023_*.type,5,lite_sql,sf_bq182
+  AND ""type"" = 'PullRequestEvent';",LANGUAGES.language; LANGUAGES.repo_name; _20_*.created_at; _20_*.repo; _20_*.type,5,lite_sql,sf_bq182
 bq185,new_york_plus,bigquery,"What is the average valid trip duration (in minutes) for yellow taxi rides in Brooklyn with more than 3 passengers and a trip distance of at least 10 miles between February 1 and February 7, 2016?","SELECT 
     AVG(TIMESTAMP_DIFF(dropoff_datetime, pickup_datetime, SECOND) / 60.0) AS average_trip_duration_in_minutes
 FROM
@@ -5264,7 +5264,7 @@ SELECT
 FROM
     forward_citation
 WHERE
-    ""forward_citations"" = 1;",PUBLICATIONS.application_kind; PUBLICATIONS.application_number; PUBLICATIONS.citation; PUBLICATIONS.filing_date; PUBLICATIONS.grant_date; PUBLICATIONS.publication_number,6,lite_sql,sf_bq209
+    ""forward_citations"" = 1;",PUBLICATIONS.application_number; PUBLICATIONS.citation; PUBLICATIONS.filing_date; PUBLICATIONS.grant_date; PUBLICATIONS.publication_number,5,lite_sql,sf_bq209
 sf_bq210,PATENTS,snowflake,How many US B2 patents granted between 2008 and 2018 contain claims that do not include the word 'claim'?,"WITH patents_sample AS (
   SELECT 
     t1.""publication_number"" AS publication_number,
@@ -5290,7 +5290,7 @@ Publication_data AS (
 
 SELECT COUNT(nb_indep_claims)
 FROM Publication_data
-WHERE nb_indep_claims != 0",PUBLICATIONS.claims_localized; PUBLICATIONS.country_code; PUBLICATIONS.grant_date; PUBLICATIONS.kind_code; PUBLICATIONS.publication_number,5,lite_sql,sf_bq210
+WHERE nb_indep_claims != 0",PUBLICATIONS.claims_localized; PUBLICATIONS.country_code; PUBLICATIONS.grant_date; PUBLICATIONS.publication_number,4,lite_sql,sf_bq210
 sf_bq213,PATENTS,snowflake,What is the most common 4-digit IPC code among US B2 utility patents granted from June to August in 2022?,"WITH interim_table as(
 SELECT 
     t1.""publication_number"", 
@@ -5503,7 +5503,7 @@ FROM
   middle_info
 ORDER BY 
   ""category_corr_across_months""
-LIMIT 1;",SALES.category_name; SALES.date; SALES.volume_sold_liters,3,lite_sql,sf_bq219
+LIMIT 1;",SALES.category; SALES.category_name; SALES.date; SALES.volume_sold_gallons,4,lite_sql,sf_bq219
 sf_bq221,PATENTS,snowflake,"Identify the CPC technology areas with the highest exponential moving average of patent filings each year (smoothing factor 0.2), and provide the full title and the best year for each CPC group at level 5.","WITH patent_cpcs AS (
     SELECT
         cd.""parents"",
@@ -5651,7 +5651,7 @@ FROM moving_avg
 JOIN ""PATENTS"".""PATENTS"".""CPC_DEFINITION"" c ON ""cpc_group"" = c.""symbol""
 WHERE c.""level"" = 4
 GROUP BY c.""titleFull"", ""cpc_group""
-ORDER BY c.""titleFull"", ""cpc_group"" ASC;",CPC_DEFINITION.symbol; CPC_DEFINITION.titleFull; PUBLICATIONS.country_code; PUBLICATIONS.cpc; PUBLICATIONS.filing_date; PUBLICATIONS.grant_date; PUBLICATIONS.publication_number,7,lite_sql,sf_bq222
+ORDER BY c.""titleFull"", ""cpc_group"" ASC;",CPC_DEFINITION.level; CPC_DEFINITION.parents; CPC_DEFINITION.symbol; CPC_DEFINITION.titleFull; PUBLICATIONS.application_number; PUBLICATIONS.country_code; PUBLICATIONS.cpc; PUBLICATIONS.filing_date; PUBLICATIONS.grant_date,9,lite_sql,sf_bq222
 sf_bq223,PATENTS,snowflake,"Which assignees, excluding DENSO CORP itself, have cited patents assigned to DENSO CORP, and what are the titles of the primary CPC subclasses associated with these citations? Provide the name of each citing assignee, the full title of the CPC subclass, and the count of citations grouped by the assignee and the CPC subclass title. Please focus specifically on the main categories of the CPC codes,","SELECT
     REPLACE(citing_assignee, '""', '') AS citing_assignee,
     cpcdef.""titleFull"" AS cpc_title,
@@ -5686,7 +5686,7 @@ WHERE
     refs.cited_assignee = 'DENSO CORP'
     AND pubs.citing_assignee != 'DENSO CORP'
 GROUP BY
-    citing_assignee, cpcdef.""titleFull""",CPC_DEFINITION.symbol; CPC_DEFINITION.titleFull; PUBLICATIONS.assignee_harmonized; PUBLICATIONS.citation; PUBLICATIONS.cpc; PUBLICATIONS.filing_date; PUBLICATIONS.publication_number,7,lite_sql,sf_bq223
+    citing_assignee, cpcdef.""titleFull""",CPC_DEFINITION.symbol; CPC_DEFINITION.titleFull; PUBLICATIONS.assignee_harmonized; PUBLICATIONS.citation; PUBLICATIONS.cpc; PUBLICATIONS.publication_number,6,lite_sql,sf_bq223
 sf_bq224,GITHUB_REPOS_DATE,snowflake,"Which repository with an approved license in `licenses.md` had the highest combined total of forks, issues, and watches in April 2022?","WITH allowed_repos AS (
     SELECT 
         ""repo_name"",
@@ -5744,7 +5744,7 @@ INNER JOIN
     watch_counts AS wc ON ar.""repo_name"" = wc.""repo""
 ORDER BY 
     (fc.""forks"" + ic.""issue_events"" + wc.""watches"") DESC
-LIMIT 1;",LICENSES.license; LICENSES.repo_name; _202204.created_at; _202204.repo; _202204.type,5,lite_sql,sf_bq224
+LIMIT 1;",LICENSES.license; LICENSES.repo_name; _20_*.actor; _20_*.repo; _20_*.type,5,lite_sql,sf_bq224
 bq227,london,bigquery,"Could you provide the annual percentage shares, rounded to two decimal places, of the top 5 minor crime categories from 2008 in London's total crimes, with each year displayed in one row?","WITH top5_categories AS (
   SELECT minor_category
   FROM `bigquery-public-data.london_crime.crime_by_lsoa`
@@ -6140,7 +6140,7 @@ JOIN (
 ) AS filterData
 ON app.""patent_id"" = filterData.""patent_id""
 ORDER BY filterData.""bkwdCitations_3"" DESC
-LIMIT 1;",APPLICATION.date; APPLICATION.patent_id; CPC_CURRENT.category; CPC_CURRENT.patent_id; PATENT.country; PATENT.id; PATENT.number; USPATENTCITATION.citation_id; USPATENTCITATION.patent_id,9,lite_sql,sf_bq246
+LIMIT 1;",APPLICATION.country; APPLICATION.date; APPLICATION.patent_id; CPC_CURRENT.patent_id; USPATENTCITATION.date; USPATENTCITATION.patent_id,6,lite_sql,sf_bq246
 sf_bq248,GITHUB_REPOS,snowflake,"What is the proportion of files whose paths include 'readme.md' that contain the phrase 'Copyright (c)', among all repositories that do not use any programming language with 'python' in its name","WITH requests AS (
     SELECT 
         D.""id"",
@@ -6198,7 +6198,7 @@ sf_bq248,GITHUB_REPOS,snowflake,"What is the proportion of files whose paths inc
 SELECT 
     (SELECT COUNT(*) FROM requests WHERE ""content"" LIKE '%Copyright (c)%') / COUNT(*) AS ""proportion""
 FROM 
-    requests;",LANGUAGES.language; LANGUAGES.repo_name; SAMPLE_CONTENTS.content; SAMPLE_CONTENTS.sample_path; SAMPLE_CONTENTS.sample_repo_name,5,lite_sql,sf_bq248
+    requests;",LANGUAGES.language; LANGUAGES.repo_name; SAMPLE_CONTENTS.content; SAMPLE_CONTENTS.id; SAMPLE_FILES.id; SAMPLE_FILES.path; SAMPLE_FILES.repo_name,7,lite_sql,sf_bq248
 sf_bq250,GEO_OPENSTREETMAP_WORLDPOP,snowflake,"What is the total population living on the geography grid which is the farthest from any hospital in Singapore, based on the most recent population data before 2023? Note that geographic grids and distances are calculated based on geospatial data and GIS related functions. Note to use planet layer in openstreetmap.","WITH country_name AS (
   SELECT 'Singapore' AS value
 ),
@@ -6261,7 +6261,7 @@ FROM
 CROSS JOIN population p
 GROUP BY distance
 ORDER BY distance DESC
-LIMIT 1;",PLANET_LAYERS.gdal_type; PLANET_LAYERS.geometry; PLANET_LAYERS.layer_class; PLANET_LAYERS.layer_name; PLANET_LAYERS.osm_id; POPULATION_GRID_1KM.alpha_3_code; POPULATION_GRID_1KM.geo_id; POPULATION_GRID_1KM.geog; POPULATION_GRID_1KM.last_updated; POPULATION_GRID_1KM.latitude_centroid; POPULATION_GRID_1KM.longitude_centroid; POPULATION_GRID_1KM.population,12,lite_sql,sf_bq250
+LIMIT 1;",PLANET_LAYERS.geometry; PLANET_LAYERS.layer_code; POPULATION_GRID_1KM.country_name; POPULATION_GRID_1KM.geo_id; POPULATION_GRID_1KM.geog; POPULATION_GRID_1KM.last_updated; POPULATION_GRID_1KM.latitude_centroid; POPULATION_GRID_1KM.longitude_centroid; POPULATION_GRID_1KM.population,9,lite_sql,sf_bq250
 sf_bq252,GITHUB_REPOS,snowflake,"Could you please find the name of the repository that contains the most copied non-binary Swift file in the dataset, ensuring each file is uniquely identified by its ID?","WITH selected_repos AS (
   SELECT
     f.""id"",
@@ -6290,7 +6290,7 @@ WHERE
   NOT c.""binary""
   AND f.""path"" LIKE '%.swift'
 ORDER BY c.""copies"" DESC
-LIMIT 1;",SAMPLE_CONTENTS.binary; SAMPLE_CONTENTS.copies; SAMPLE_CONTENTS.id; SAMPLE_CONTENTS.sample_path; SAMPLE_FILES.id; SAMPLE_FILES.repo_name,6,lite_sql,sf_bq252
+LIMIT 1;",SAMPLE_CONTENTS.binary; SAMPLE_CONTENTS.copies; SAMPLE_CONTENTS.id; SAMPLE_FILES.id; SAMPLE_FILES.path; SAMPLE_FILES.repo_name,6,lite_sql,sf_bq252
 sf_bq254,GEO_OPENSTREETMAP,snowflake,"Can you find the names of the multipolygons with valid ids that rank in the top two in terms of the number of points within their boundaries, among those multipolygons that do not have a Wikidata tag but are located within the same geographic area as the multipolygon associated with Wikidata item Q191, analyzed through planet features?","WITH bounding_area AS (
     SELECT ""geometry"" AS geometry
     FROM GEO_OPENSTREETMAP.GEO_OPENSTREETMAP.PLANET_FEATURES,
@@ -6351,7 +6351,7 @@ WHERE wd.""osm_id"" IS NOT NULL
   AND baf.""feature_type"" = 'points'
 GROUP BY pww.name
 ORDER BY COUNT(baf.""osm_id"") DESC
-LIMIT 2",PLANET_FEATURES.all_tags; PLANET_FEATURES.feature_type; PLANET_FEATURES.geometry; PLANET_FEATURES.osm_id; PLANET_FEATURES.osm_way_id; PLANET_FEATURES_POINTS.geometry,6,lite_sql,sf_bq254
+LIMIT 2",PLANET_FEATURES.all_tags; PLANET_FEATURES.feature_type; PLANET_FEATURES.geometry; PLANET_FEATURES.osm_id,4,lite_sql,sf_bq254
 sf_bq255,GITHUB_REPOS,snowflake,"How many commit messages are there in repositories that use the 'Shell' programming language and 'apache-2.0' license, where the length of the commit message is more than 5 characters but less than 10,000 characters, and the messages do not start with the word 'merge', 'update' or 'test'?","SELECT
   COUNT(commits_table.""message"") AS ""num_messages""
 FROM (
@@ -6647,7 +6647,7 @@ FROM
   kite
 ORDER BY
   ""avg_order_value"" DESC
-LIMIT 10;",ORDERS.created_at; ORDERS.order_id; ORDERS.status; ORDERS.user_id; ORDER_ITEMS.order_id; ORDER_ITEMS.sale_price; ORDER_ITEMS.status; USERS.created_at; USERS.email; USERS.id,10,lite_sql,sf_bq265
+LIMIT 10;",ORDERS.created_at; ORDERS.num_of_item; ORDERS.order_id; ORDERS.user_id; ORDER_ITEMS.created_at; ORDER_ITEMS.order_id; ORDER_ITEMS.product_id; ORDER_ITEMS.sale_price; ORDER_ITEMS.status; ORDER_ITEMS.user_id; USERS.country; USERS.created_at; USERS.email; USERS.gender; USERS.id; USERS.traffic_source,16,lite_sql,sf_bq265
 bq268,ga360,bigquery,Identify the longest number of days between the first visit and the last recorded event (either the last visit or the first transaction) for a user where the last recorded event was associated with a mobile device.,"WITH 
 
 visit AS (
@@ -6891,7 +6891,7 @@ LEFT JOIN monthly_sales AS previous_month
   AND current_month.""delivery_month"" = DATEADD(MONTH, -1, previous_month.""delivery_month"")  -- Correctly join to previous month
 WHERE current_month.""delivery_month"" >= '2022-08-01'  -- Only show August and later data, but use July for calculation
 ORDER BY ""profit_vs_prior_month"" DESC
-LIMIT 5;",INVENTORY_ITEMS.cost; INVENTORY_ITEMS.id; ORDERS.created_at; ORDERS.delivered_at; ORDERS.order_id; ORDERS.status; ORDERS.user_id; ORDER_ITEMS.inventory_item_id; ORDER_ITEMS.order_id; ORDER_ITEMS.sale_price; USERS.id; USERS.traffic_source,12,lite_sql,sf_bq273
+LIMIT 5;",ORDERS.created_at; ORDERS.delivered_at; ORDERS.order_id; ORDERS.status; ORDERS.user_id; ORDER_ITEMS.order_id; ORDER_ITEMS.product_id; ORDER_ITEMS.sale_price; PRODUCTS.cost; PRODUCTS.id; USERS.id; USERS.traffic_source,12,lite_sql,sf_bq273
 bq275,ga360,bigquery,Can you provide a list of visitor IDs for those who made their first transaction on a mobile device on a different day than their first visit?,"WITH 
   visit AS (
     SELECT fullvisitorid, MIN(date) AS date_first_visit
@@ -7419,7 +7419,7 @@ FROM
     daily_forecasts
 ORDER BY
     ""creation_time"",
-    ""forecast_date"";",NOAA_GFS0P25.creation_time; NOAA_GFS0P25.forecast; NOAA_GFS0P25.geography; NOAA_GFS0P25.geography_polygon,4,lite_sql,sf_bq291
+    ""forecast_date"";",NOAA_GFS0P25.creation_time; NOAA_GFS0P25.forecast; NOAA_GFS0P25.geography,3,lite_sql,sf_bq291
 sf_bq294,SAN_FRANCISCO_PLUS,snowflake,"Can you provide the details of the top 5 longest bike share trips that started during the second half of 2017, including the trip ID, duration in seconds, start date, start station name, route (start station to end station), bike number, subscriber type, member's birth year, current age, age classification, gender, and the region name of the start station? Please exclude trips where the start station name, member's birth year, or member's gender is not specified.","SELECT
   ""trip_id"",
   ""duration_sec"",
@@ -8433,7 +8433,7 @@ GROUP BY
   ""segmentation_category""
 ORDER BY
   ""count_"" DESC
-LIMIT 5;",DICOM_ALL.DerivationImageSequence; DICOM_ALL.Modality; DICOM_ALL.ReferencedImageEvidenceSequence; DICOM_ALL.ReferencedImageSequence; DICOM_ALL.ReferencedSOPInstanceUID; DICOM_ALL.ReferencedSeriesSequence; DICOM_ALL.SOPClassUID; DICOM_ALL.SOPInstanceUID; DICOM_ALL.SourceImageSequence; DICOM_ALL.access; SEGMENTATIONS.SOPInstanceUID; SEGMENTATIONS.SegmentedPropertyCategory,12,lite_sql,sf_bq346
+LIMIT 5;",DICOM_ALL.Modality; DICOM_ALL.ReferencedSeriesSequence; DICOM_ALL.SOPClassUID; DICOM_ALL.SOPInstanceUID; DICOM_ALL.SeriesDescription; DICOM_ALL.SeriesInstanceUID; SEGMENTATIONS.SOPInstanceUID; SEGMENTATIONS.SegmentedPropertyCategory; SEGMENTATIONS.SegmentedPropertyType,9,lite_sql,sf_bq346
 sf_bq347,IDC,snowflake,"Which modality has the highest count of SOP instances, including MR series with SeriesInstanceUID = ""1.3.6.1.4.1.14519.5.2.1.3671.4754.105976129314091491952445656147"" and all associated segmentation data, along with the total count of instances?","WITH union_mr_seg AS (
   SELECT
     ""dicom_all_mr"".""SOPInstanceUID"",
@@ -8527,7 +8527,7 @@ SELECT
     ""osm_id""
 FROM closest_to_median
 ORDER BY diff_from_median
-LIMIT 1;",PLANET_FEATURES.all_tags; PLANET_FEATURES.feature_type; PLANET_FEATURES.geometry; PLANET_FEATURES.osm_id; PLANET_FEATURES.osm_timestamp; PLANET_NODES.all_tags; PLANET_NODES.id; PLANET_NODES.latitude; PLANET_NODES.longitude,9,lite_sql,sf_bq349
+LIMIT 1;",PLANET_FEATURES.all_tags; PLANET_FEATURES.feature_type; PLANET_FEATURES.geometry; PLANET_FEATURES.osm_id; PLANET_NODES.all_tags; PLANET_NODES.geometry; PLANET_NODES.id,7,lite_sql,sf_bq349
 bq350,open_targets_platform_1,bigquery,"For the detailed molecule data, Please display the drug id, drug type and withdrawal status for approved drugs with a black box warning and known drug type among 'Keytruda', 'Vioxx', 'Premarin', and 'Humira'","DECLARE
   my_drug_list ARRAY<STRING>;
 SET
@@ -8726,7 +8726,7 @@ WHERE
     AND DATE_TRUNC('DAY', TO_TIMESTAMP_NTZ(TO_NUMBER(""TRI"".""starttime"") / 1000000)) = DATE '2015-07-15'
 ORDER BY 
     ""WEA"".""temp"" DESC, ""ZIPSTART"".""zip_code"" ASC, ""ZIPEND"".""zip_code"" DESC
-LIMIT 1;",CITIBIKE_TRIPS.bikeid; CITIBIKE_TRIPS.end_station_latitude; CITIBIKE_TRIPS.end_station_longitude; CITIBIKE_TRIPS.start_station_latitude; CITIBIKE_TRIPS.start_station_longitude; CITIBIKE_TRIPS.starttime; GSOD_*.da; GSOD_*.mo; GSOD_*.temp; GSOD_*.wban; ZIP_CODES.city; ZIP_CODES.state_code; ZIP_CODES.zip_code; ZIP_CODES.zip_code_geom,14,lite_sql,sf_bq358
+LIMIT 1;",CITIBIKE_TRIPS.end_station_latitude; CITIBIKE_TRIPS.end_station_longitude; CITIBIKE_TRIPS.start_station_latitude; CITIBIKE_TRIPS.start_station_longitude; CITIBIKE_TRIPS.starttime; ZIP_CODES.zip_code; ZIP_CODES.zip_code_geom,7,lite_sql,sf_bq358
 sf_bq359,GITHUB_REPOS,snowflake,List the repository names and commit counts for the top two GitHub repositories with JavaScript as the primary language and the highest number of commits.,"WITH repositories AS (
     SELECT
         t2.""repo_name"",
@@ -9020,7 +9020,7 @@ WHERE
 GROUP BY
   ""package_name""
 ORDER BY
-  ""count"" DESC;",SAMPLE_CONTENTS.content; SAMPLE_CONTENTS.sample_path; SAMPLE_CONTENTS.sample_ref,3,lite_sql,sf_bq377
+  ""count"" DESC;",SAMPLE_CONTENTS.content; SAMPLE_CONTENTS.id,2,lite_sql,sf_bq377
 bq379,open_targets_platform_1,bigquery,Which target approved symbol has the overall association score closest to the mean score for psoriasis?,"WITH AvgScore AS (
   SELECT
     AVG(associations.score) AS avg_score
@@ -9220,7 +9220,7 @@ FROM
 JOIN
   ""seg_studies""
 ON
-  ""mr_studies"".""StudyInstanceUID"" = ""seg_studies"".""StudyInstanceUID"";",DICOM_ALL.SeriesDescription; DICOM_ALL.StudyInstanceUID; DICOM_ALL.collection_id; SEGMENTATIONS.SegmentedPropertyType; SEGMENTATIONS.StudyInstanceUID,5,lite_sql,sf_bq390
+  ""mr_studies"".""StudyInstanceUID"" = ""seg_studies"".""StudyInstanceUID"";",DICOM_ALL.Modality; DICOM_ALL.SOPInstanceUID; DICOM_ALL.StudyInstanceUID; SEGMENTATIONS.SOPInstanceUID,4,lite_sql,sf_bq390
 bq392,noaa_gsod,bigquery,"What are the top 3 dates in October 2009 with the highest average temperature for station number 723758, in the format YYYY-MM-DD?","WITH
   # FIRST CAST EACH YEAR, MONTH, DATE TO STRINGS
   T AS (
@@ -10043,7 +10043,7 @@ SELECT
   'Top 3 by Max Exposure' AS ""MetricGroup"",
   AVG(""seriesSizeInMB"") AS ""AverageSeriesSizeInMB""
 FROM
-  top3ByMaxExposure;",DICOM_ALL.Exposure; DICOM_ALL.ExposureInmAs; DICOM_ALL.Modality; DICOM_ALL.PatientID; DICOM_ALL.SeriesInstanceUID; DICOM_ALL.SliceThickness; DICOM_ALL.SpacingBetweenSlices; DICOM_ALL.collection_name; DICOM_ALL.instance_size,9,lite_sql,sf_bq422
+  top3ByMaxExposure;",DICOM_ALL.Exposure; DICOM_ALL.ImagePositionPatient; DICOM_ALL.PatientID; DICOM_ALL.SeriesInstanceUID; DICOM_ALL.StudyInstanceUID; DICOM_ALL.collection_id; DICOM_ALL.instance_size,7,lite_sql,sf_bq422
 bq424,world_bank,bigquery,"List the top 10 countries with respect to the total amount of long-term external debt in descending order, excluding those without a specified region.","SELECT DISTINCT
   id.country_name,
   --cs.region,
@@ -10220,7 +10220,7 @@ FROM
   base_census
 ORDER BY 
   ""avg_median_income_diff"" DESC
-LIMIT 5;",ZCTA5_2015_5YR.geo_id; ZCTA5_2015_5YR.median_income; ZCTA_*.employed_agriculture_forestry_fishing_hunting_mining; ZCTA_*.employed_arts_entertainment_recreation_accommodation_food; ZCTA_*.employed_construction; ZCTA_*.employed_information; ZCTA_*.employed_retail_trade; ZCTA_*.employed_wholesale_trade; ZCTA_*.geo_id; ZCTA_*.median_income; ZIP_CODES.state_name; ZIP_CODES.zip_code,12,lite_sql,sf_bq429
+LIMIT 5;",ZIP_CODES.state_name; ZIP_CODES.zip_code,2,lite_sql,sf_bq429
 bq430,ebi_chembl,bigquery,"Find pairs of different molecules tested in the same assay and standard type, where both have 10–15 heavy atoms, fewer than 5 activities in that assay, fewer than 2 duplicate activities, non-null standard values, and pChEMBL values over 10. For each pair, report the maximum heavy atom count, the latest publication date (calculated based on the document's rank within the same journal and year, and map it to a synthetic month and day), the highest document ID, classify the change in standard values as 'increase', 'decrease', or 'no-change' based on their values and relations, and generate UUIDs from their activity IDs and canonical SMILES.","select 
   -- *, 
   greatest(heavy_atoms_1, heavy_atoms_2) as heavy_atoms_greatest,
@@ -10382,7 +10382,7 @@ SELECT
 FROM parsed_burn_logs
 
 ORDER BY block_timestamp
-LIMIT 5;",LOGS.address; LOGS.block_number; LOGS.block_timestamp; LOGS.log_index; LOGS.topics; LOGS.transaction_hash,6,lite_sql,sf_bq444
+LIMIT 5;",LOGS.address; LOGS.block_number; LOGS.block_timestamp; LOGS.data; LOGS.log_index; LOGS.topics; LOGS.transaction_hash,7,lite_sql,sf_bq444
 bq452,_1000_genomes,bigquery,"Identify variants on chromosome 12, calculate their chi-squared scores using allele counts in cases and controls, and return the start, end, chi-squared score (after Yates's correction for continuity) of top variants where the chi-squared score is no less than 29.71679, ensuring that each group has expected counts of at least 5 for the chi-squared calculation.","SELECT *
 FROM (
   SELECT
@@ -10715,7 +10715,7 @@ FROM
   geometryChecks
 ORDER BY
   geometryChecks.""seriesSizeInMiB"" DESC
-LIMIT 5;",DICOM_ALL.Columns; DICOM_ALL.Exposure; DICOM_ALL.ExposureInmAs; DICOM_ALL.ImageOrientationPatient; DICOM_ALL.ImagePositionPatient; DICOM_ALL.ImageType; DICOM_ALL.Modality; DICOM_ALL.PatientID; DICOM_ALL.PixelSpacing; DICOM_ALL.Rows; DICOM_ALL.SeriesInstanceUID; DICOM_ALL.SeriesNumber; DICOM_ALL.TransferSyntaxUID; DICOM_ALL.XRayTubeCurrentInmA; DICOM_ALL.collection_name; DICOM_ALL.instance_size,16,lite_sql,sf_bq455
+LIMIT 5;",DICOM_ALL.Columns; DICOM_ALL.ImageOrientationPatient; DICOM_ALL.ImagePositionPatient; DICOM_ALL.ImageType; DICOM_ALL.Modality; DICOM_ALL.SOPInstanceUID; DICOM_ALL.SeriesInstanceUID; DICOM_ALL.SeriesNumber; DICOM_ALL.SliceThickness; DICOM_ALL.TransferSyntaxUID; DICOM_ALL.crdc_series_uuid; DICOM_ALL.instance_size,12,lite_sql,sf_bq455
 ga001,ga4,bigquery,I want to know the preferences of customers who purchased the Google Navy Speckled Tee in December 2020. What other product was purchased with the highest total quantity alongside this item?,"WITH
   Params AS (
     SELECT 'Google Navy Speckled Tee' AS selected_product
@@ -10752,7 +10752,7 @@ WHERE
   AND items.item_name != selected_product
 GROUP BY 1
 ORDER BY item_quantity DESC
-LIMIT 1;",events_*.ecommerce; events_*.event_name; events_*.items,3,lite_sql,sf_ga001
+LIMIT 1;",events_*.event_name; events_*.items; events_*.user_pseudo_id,3,lite_sql,sf_ga001
 ga002,ga4,bigquery,Tell me the most purchased other products and their quantities by customers who bought the Google Red Speckled Tee each month for the three months starting from November 2020.,"WITH
 Params AS (
   SELECT 'Google Red Speckled Tee' AS selected_product
@@ -11534,7 +11534,7 @@ RFM AS (
 SELECT RFM_Bucket, 
        AVG(total_spent / total_orders) AS avg_sales_per_customer
 FROM RFM
-GROUP BY RFM_Bucket",customers.customer_id; customers.customer_unique_id; order_payments.order_id; order_payments.payment_value; orders.customer_id; orders.order_id; orders.order_purchase_timestamp; orders.order_status,8,lite_sql,sf_local003
+GROUP BY RFM_Bucket",customers.customer_id; customers.customer_unique_id; order_items.order_id; order_items.price; orders.customer_id; orders.order_id; orders.order_purchase_timestamp; orders.order_status,8,lite_sql,sf_local003
 local004,E_commerce,sqlite,"Could you tell me the number of orders, average payment per order and customer lifespan in weeks of the 3 custumers with the highest average payment per order. Attention: I want the lifespan in float number if it's longer than one week, otherwise set it to be 1.0.","WITH CustomerData AS (
     SELECT
         customer_unique_id,
@@ -11713,7 +11713,7 @@ SELECT
 FROM
 Rank1
 ORDER BY match_duration 
-LIMIT 1",Belts.id; Belts.name; Matches.duration; Matches.loser_id; Matches.title_change; Matches.title_id; Matches.winner_id; Wrestlers.id; Wrestlers.name,9,lite_sql,sf_local019
+LIMIT 1",Belts.id; Belts.name; Cards.event_id; Cards.id; Cards.location_id; Cards.promotion_id; Events.id; Events.name; Locations.id; Locations.name; Matches.card_id; Matches.duration; Matches.loser_id; Matches.title_id; Matches.win_type; Matches.winner_id; Promotions.id; Promotions.name; Wrestlers.id; Wrestlers.name,20,lite_sql,sf_local019
 local022,IPL,sqlite,"Show me the names of strikers who scored no less than 100 runs in a match, but their team lost the game?","-- Step 1: Calculate players' total runs in each match
 WITH player_runs AS (
     SELECT 
@@ -11885,7 +11885,7 @@ GROUP BY
 ORDER BY
     SUM(CAST((julianday(rental.return_date) - julianday(rental.rental_date)) * 24 AS INTEGER)) DESC
 LIMIT
-    1;",address.address_id; address.city_id; category.category_id; category.name; city.city; city.city_id; customer.address_id; customer.customer_id; film_category.category_id; film_category.film_id; inventory.film_id; inventory.inventory_id; rental.customer_id; rental.inventory_id; rental.rental_date; rental.return_date,16,snow_sql_near_exact,sf_local039
+    1;",address.address_id; address.city_id; category.category_id; category.name; city.city; city.city_id; customer.address_id; customer.customer_id; film.film_id; film_category.category_id; film_category.film_id; inventory.film_id; inventory.inventory_id; rental.customer_id; rental.inventory_id; rental.rental_date; rental.return_date,17,snow_sql_near_exact,sf_local039
 local058,education_business,sqlite,"Can you provide a list of hardware product segments along with their unique product counts for 2020 in the output, ordered by the highest percentage increase in unique fact sales products from 2020 to 2021?","WITH UniqueProducts2020 AS (
     SELECT
         dp.segment,
@@ -12193,7 +12193,7 @@ ON
 JOIN
     product_abandoned AS t4 
 ON 
-    t4.page_id = t1.page_id;",shopping_cart_events.event_type; shopping_cart_events.page_id; shopping_cart_events.sequence_number; shopping_cart_events.visit_id; shopping_cart_page_hierarchy.page_id; shopping_cart_page_hierarchy.page_name; shopping_cart_page_hierarchy.product_category; shopping_cart_page_hierarchy.product_id,8,lite_sql,sf_local075
+    t4.page_id = t1.page_id;",shopping_cart_events.event_type; shopping_cart_events.page_id; shopping_cart_events.visit_id; shopping_cart_page_hierarchy.page_id; shopping_cart_page_hierarchy.page_name; shopping_cart_page_hierarchy.product_id,6,lite_sql,sf_local075
 local078,bank_sales_trading,sqlite,"Identify the top 10 and bottom 10 interest categories based on their highest composition values across all months. For each category, display the time(MM-YYYY), interest name, and the composition value","WITH get_interest_rank AS (
     SELECT
         t1.month_year,