question
stringlengths
23
161
answer
stringlengths
1
193
โŒ€
type
stringclasses
5 values
columns_used
stringlengths
2
161
โŒ€
column_types
stringclasses
132 values
sample_answer
stringlengths
1
228
โŒ€
dataset
stringclasses
65 values
Is the person with the highest net worth self-made?
True
boolean
['finalWorth', 'selfMade']
['number[uint32]', 'boolean']
False
001_Forbes
Does the youngest billionaire identify as male?
True
boolean
['age', 'gender']
['number[UInt8]', 'category']
True
001_Forbes
Is the city with the most billionaires in the United States?
True
boolean
['city', 'country']
['category', 'category']
True
001_Forbes
Is there a non-self-made billionaire in the top 5 ranks?
True
boolean
['rank', 'selfMade']
['number[uint16]', 'boolean']
False
001_Forbes
Does the oldest billionaire have a philanthropy score of 5?
False
boolean
['age', 'philanthropyScore']
['number[UInt8]', 'number[UInt8]']
False
001_Forbes
What is the age of the youngest billionaire?
19.0
number
['age']
['number[UInt8]']
32.0
001_Forbes
How many billionaires are there from the 'Technology' category?
343
number
['category']
['category']
0
001_Forbes
What's the total worth of billionaires in the 'Automotive' category?
583600
number
['category', 'finalWorth']
['category', 'number[uint32]']
0
001_Forbes
How many billionaires have a philanthropy score above 3?
25
number
['philanthropyScore']
['number[UInt8]']
0
001_Forbes
What's the rank of the wealthiest non-self-made billionaire?
3
number
['selfMade', 'rank']
['boolean', 'number[uint16]']
288
001_Forbes
Which category does the richest billionaire belong to?
Automotive
category
['finalWorth', 'category']
['number[uint32]', 'category']
Food & Beverage
001_Forbes
What's the country of origin of the oldest billionaire?
United States
category
['age', 'country']
['number[UInt8]', 'category']
United Kingdom
001_Forbes
What's the gender of the billionaire with the highest philanthropy score?
M
category
['philanthropyScore', 'gender']
['number[UInt8]', 'category']
M
001_Forbes
What's the source of wealth for the youngest billionaire?
drugstores
category
['age', 'source']
['number[UInt8]', 'category']
fintech
001_Forbes
What is the title of the billionaire with the lowest rank?
null
category
['rank', 'title']
['number[uint16]', 'category']
null
001_Forbes
List the top 3 countries with the most billionaires.
['United States', 'China', 'India']
list[category]
['country']
['category']
['United States', 'China', 'Brazil']
001_Forbes
List the top 5 sources of wealth for billionaires.
['real estate', 'investments', 'pharmaceuticals', 'diversified', 'software']
list[category]
['source']
['category']
['diversified', 'media, automotive', 'Semiconductor materials', 'WeWork', 'beverages']
001_Forbes
List the top 4 cities where the youngest billionaires live.
['', 'Los Angeles', 'Jiaozuo', 'Oslo']
list[category]
['age', 'city']
['number[UInt8]', 'category']
['San Francisco', 'New York', 'Wuhan', 'Bangalore']
001_Forbes
List the bottom 3 categories with the fewest billionaires.
['Logistics', 'Sports', 'Gambling & Casinos']
list[category]
['category']
['category']
['Service', 'Fashion & Retail', 'Manufacturing']
001_Forbes
List the bottom 2 countries with the least number of billionaires.
['Colombia', 'Andorra']
list[category]
['country']
['category']
['Canada', 'Egypt']
001_Forbes
List the top 5 ranks of billionaires who are not self-made.
[3, 10, 14, 16, 18]
list[number]
['selfMade', 'rank']
['boolean', 'number[uint16]']
[288, 296, 509, 523, 601]
001_Forbes
List the bottom 3 ages of billionaires who have a philanthropy score of 5.
[48.0, 83.0, 83.0]
list[number]
['philanthropyScore', 'age']
['number[UInt8]', 'number[UInt8]']
[]
001_Forbes
List the top 6 final worth values of billionaires in the 'Technology' category.
[171000, 129000, 111000, 107000, 106000, 91400]
list[number]
['category', 'finalWorth']
['category', 'number[uint32]']
[]
001_Forbes
List the top 4 ranks of female billionaires.
[14, 18, 21, 30]
list[number]
['gender', 'rank']
['category', 'number[uint16]']
[]
001_Forbes
List the top 2 final worth values of billionaires in the 'Automotive' category.
[219000, 44800]
list[number]
['category', 'finalWorth']
['category', 'number[uint32]']
[]
001_Forbes
Did any children below the age of 18 survive?
True
boolean
[Age, Survived]
['number[UInt8]', 'boolean']
True
002_Titanic
Were there any passengers who paid a fare of more than $500?
True
boolean
[Fare]
['number[double]']
False
002_Titanic
Is every passenger's name unique?
True
boolean
[Name]
['text']
True
002_Titanic
Were there any female passengers in the 3rd class who survived?
True
boolean
[Sex, Pclass, Survived]
['category', 'number[uint8]', 'boolean']
True
002_Titanic
How many unique passenger classes are present in the dataset?
3
number
[Pclass]
['number[uint8]']
3
002_Titanic
What's the maximum age of the passengers?
80.0
number
[Age]
['number[UInt8]']
69.0
002_Titanic
How many passengers boarded without any siblings or spouses?
604
number
[Siblings_Spouses Aboard]
['number[uint8]']
12
002_Titanic
On average, how much fare did the passengers pay?
32.31
number
[Fare]
['number[double]']
23.096459999999997
002_Titanic
Which passenger class has the highest number of survivors?
1
category
[Pclass, Survived]
['number[uint8]', 'boolean']
3
002_Titanic
What's the most common gender among the survivors?
female
category
[Sex, Survived]
['category', 'boolean']
female
002_Titanic
Among those who survived, which fare range was the most common: (0-50, 50-100, 100-150, 150+)?
0-50
category
[Fare, Survived]
['number[double]', 'boolean']
0-50
002_Titanic
What's the most common age range among passengers: (0-18, 18-30, 30-50, 50+)?
18-30
category
[Age]
['number[UInt8]']
18-30
002_Titanic
Name the top 3 passenger classes by survival rate.
[1, 2, 3]
list[category]
[Pclass, Survived]
['number[uint8]', 'boolean']
[1, 3, 2]
002_Titanic
Could you list the lower 3 fare ranges by number of survivors: (0-50, 50-100, 100-150, 150+)?
['50-100', '150+', '100-150']
list[category]
[Fare, Survived]
['number[double]', 'boolean']
[50-100, 150+, 100-150]
002_Titanic
What are the top 4 age ranges('30-50', '18-30', '0-18', '50+') with the highest number of survivors?
['30-50', '18-30', '0-18', '50+']
list[category]
[Age, Survived]
['number[UInt8]', 'boolean']
[30-50, 18-30, 0-18, 50+]
002_Titanic
What are the top 2 genders by average fare paid?
['female', 'male']
list[category]
[Sex, Fare]
['category', 'number[double]']
[female, male]
002_Titanic
What are the oldest 3 ages among the survivors?
[80, 63, 63]
list[number]
[Age, Survived]
['number[UInt8]', 'boolean']
[56.0, 47.0, 42.0]
002_Titanic
Which are the top 4 fares paid by survivors?
[512.239, 512.239, 512.239, 263]
list[number]
[Fare, Survived]
['number[double]', 'boolean']
[133.65, 39.0, 35.5, 30.5]
002_Titanic
Could you list the youngest 3 ages among the survivors?
[1, 1, 1]
list[number]
[Age, Survived]
['number[UInt8]', 'boolean']
[14.0, 24.0, 28.0]
002_Titanic
Which are the lowest 4 fares among those who didn't survive?
[0, 0, 0, 0]
list[number]
[Fare, Survived]
['number[double]', 'boolean']
[0.0, 7.22, 7.75, 8.05]
002_Titanic
Is the average age of the respondents above 30?
True
boolean
['What is your age? ๐Ÿ‘ถ๐Ÿป๐Ÿ‘ต๐Ÿป']
['number[uint8]']
True
003_Love
Are there more single individuals than married ones in the dataset?
True
boolean
['What is your civil status? ๐Ÿ’']
['category']
False
003_Love
Do the majority of respondents have a height greater than 170 cm?
True
boolean
[What's your height? in cm ๐Ÿ“]
['number[uint8]']
True
003_Love
Is the most frequent hair color black?
False
boolean
['What is your hair color? ๐Ÿ‘ฉ๐Ÿฆฐ๐Ÿ‘ฑ๐Ÿฝ']
['category']
False
003_Love
How many unique nationalities are present in the dataset?
13
number
[What's your nationality?"]"
['category']
1
003_Love
What is the average gross annual salary?
56332.81720430108
number
['Gross annual salary (in euros) ๐Ÿ’ธ']
['number[UInt32]']
62710.0
003_Love
How many respondents wear glasses all the time?
98
number
['How often do you wear glasses? ๐Ÿ‘“']
['category']
5
003_Love
What's the median age of the respondents?
33.0
number
['What is your age? ๐Ÿ‘ถ๐Ÿป๐Ÿ‘ต๐Ÿป']
['number[uint8]']
32.5
003_Love
What is the most common level of studies achieved?
Master
category
['What is the maximum level of studies you have achieved? ๐ŸŽ“']
['category']
Master
003_Love
Which body complexity has the least number of respondents?
Very thin
category
['What is your body complexity? ๐Ÿ‹๏ธ']
['category']
Obese
003_Love
What's the most frequent eye color?
Brown
category
['What is your eye color? ๐Ÿ‘๏ธ']
['category']
Brown
003_Love
Which sexual orientation has the highest representation?
Heterosexual
category
['What's your sexual orientation?']
['category']
Heterosexual
003_Love
List the top 3 most common areas of knowledge.
['[Computer Science]', '[Business]', '[Enginering & Architecture]']
list[category]
['What area of knowledge is closer to you?']
['list[category]']
['[Computer Science]', '[Business]', '[Enginering & Architecture]']
003_Love
List the bottom 3 hair lengths in terms of frequency.
['Medium', 'Long', 'Bald']
list[category]
['How long is your hair? ๐Ÿ’‡๐Ÿปโ™€๏ธ๐Ÿ’‡๐Ÿฝโ™‚๏ธ']
['category']
['Short', 'Medium', 'Long']
003_Love
Name the top 5 civil statuses represented in the dataset.
['Single', 'Married', 'In a Relationship', 'In a Relationship Cohabiting', 'Divorced']
list[category]
['What is your civil status? ๐Ÿ’']
['category']
['Married', 'In a Relationship', 'In a Relationship Cohabiting', 'Single', 'Divorced']
003_Love
What are the 4 least common hair colors?
['Red', 'Other', 'White', 'Blue']
list[category]
['What is your hair color? ๐Ÿ‘ฉ๐Ÿฆฐ๐Ÿ‘ฑ๐Ÿฝ']
['category']
['Brown', 'Black']
003_Love
What are the top 4 maximum gross annual salaries?
[500000.0, 360000.0, 300000.0, 300000.0]
list[number]
['Gross annual salary (in euros) ๐Ÿ’ธ']
['number[UInt32]']
[150000.0, 130000.0, 125000.0, 120000.0]
003_Love
Name the bottom 3 values for the happiness scale.
[2, 2, 2]
list[number]
['Happiness scale']
['number[uint8]']
[7, 10, 6]
003_Love
What are the 5 highest ages present in the dataset?
[65, 62, 60, 60, 59]
list[number]
['What is your age? ๐Ÿ‘ถ๐Ÿป๐Ÿ‘ต๐Ÿป']
['number[uint8]']
[65, 60, 51, 50, 50]
003_Love
List the bottom 6 skin tone values based on frequency.
[2, 1, 6, 0, 7, 8]
list[number]
['What is your skin tone?']
['number[uint8]']
[3, 1, 6, 2, 7, 0]
003_Love
Are there any trips with a total distance greater than 30 miles?
False
boolean
['trip_distance']
['number[double]']
False
004_Taxi
Were there any trips that cost more than $100 in total?
False
boolean
['total_amount']
['number[double]']
False
004_Taxi
Is there any trip with more than 6 passengers?
False
boolean
['passenger_count']
['number[uint8]']
False
004_Taxi
Did all the trips use a payment type of either 1 or 2?
False
boolean
['payment_type']
['number[uint8]']
True
004_Taxi
What is the maximum fare amount charged for a trip?
75.25
number
['fare_amount']
['number[double]']
40.8
004_Taxi
How many unique pickup locations are in the dataset?
96
number
['PULocationID']
['number[uint16]']
18
004_Taxi
What is the average tip amount given by passengers?
2.74
number
['tip_amount']
['number[double]']
1.5
004_Taxi
How many trips took place in the airport area?
99807
number
['Airport_fee']
['number[UInt8]']
0
004_Taxi
Which payment type is the most common in the dataset?
1
category
['payment_type']
['number[uint8]']
1
004_Taxi
Which vendor has the most trips recorded?
2
category
['VendorID']
['number[uint8]']
2
004_Taxi
What is the most common drop-off location?
236
category
['DOLocationID']
['number[uint16]']
90
004_Taxi
On which date did the first recorded trip occur?
2023-01-31
category
['tpep_pickup_datetime']
['date[ns, UTC]']
2023-02-01
004_Taxi
Which are the top 3 most frequent pickup locations?
[161, 237, 236]
list[category]
['PULocationID']
['number[uint16]']
[237, 236, 161]
004_Taxi
Name the 4 most common rate codes used.
[1, 2, 5, 4]
list[category]
['RatecodeID']
['number[uint8]']
[1, 2, 5, 3]
004_Taxi
list the 2 most frequent store and forward flags.
['N', 'Y']
list[category]
['store_and_fwd_flag']
['category']
['N', 'Y']
004_Taxi
Identify the top 4 payment types used by frequency
[1, 2, 4, 3]
list[category]
['payment_type']
['number[uint8]']
[1, 2, 3]
004_Taxi
Report the 4 highest toll amounts paid.
[0, 0, 0, 0]
list[number]
['tolls_amount']
['number[uint8]']
[0, 0, 0, 0]
004_Taxi
list the top 3 longest trip distances
[19.83, 19.74, 19.68]
list[number]
['trip_distance']
['number[double]']
[8.32, 5.93, 2.8]
004_Taxi
Identify the 5 largest total amounts paid for trips.
[80.0, 80.0, 80.0, 80.0, 80.0]
list[number]
['total_amount']
['number[double]']
[45.8, 39.9, 33.2, 25.2, 24.87]
004_Taxi
Report the 6 highest fare amounts charged.
[75.25, 74.4, 73.0, 73.0, 73.0, 73.0]
list[number]
['fare_amount']
['number[double]']
[40.8, 28.9, 21.2, 17.0, 14.9, 13.5]
004_Taxi
Are there any complaints made in Brooklyn?
True
boolean
['borough']
['category']
True
005_NYC
Do any complaints have 'Dog' as a descriptor?
True
boolean
['descriptor']
['category']
False
005_NYC
Were there any complaints raised in April?
True
boolean
['month_name']
['category']
True
005_NYC
Is the Mayor's office of special enforcement one of the agencies handling complaints?
True
boolean
['agency']
['category']
False
005_NYC
How many complaints have been made in Queens?
23110
number
['borough']
['category']
0
005_NYC
What's the total number of unique agencies handling complaints?
22
number
['agency']
['category']
7
005_NYC
How many complaints were raised at midnight?
14811
number
['hour']
['number[uint8]']
2
005_NYC
How many unique descriptors are present in the dataset?
1131
number
['descriptor']
['category']
16
005_NYC
Which borough has the most complaints?
BROOKLYN
category
['borough']
['category']
QUEENS
005_NYC
Which month sees the highest number of complaints?
July
category
['month_name']
['category']
January
005_NYC
Which weekday has the least complaints?
Sunday
category
['weekday_name']
['category']
Thursday
005_NYC
Which agency is least frequently handling complaints?
ACS
category
['agency']
['category']
DOHMH
005_NYC
List the top 5 most frequent complaint types.
['Noise - Residential', 'HEAT/HOT WATER', 'Illegal Parking', 'Blocked Driveway', 'Street Condition']
list[category]
['complaint_type']
['category']
[HEAT/HOT WATER, Building/Use, Noise - Residential, General Construction/Plumbing, Air Quality]
005_NYC
Which 4 agencies handle the most complaints?
['NYPD', 'HPD', 'DOT', 'DSNY']
list[category]
['agency']
['category']
[NYPD, HPD, DOB, DSNY]
005_NYC
Name the 2 least frequent descriptors for complaints by inverse alphabetical order.
['Booting Company', 'Ready NY - Businesses', 'Animal']
list[category]
['descriptor']
['category']
['Structure - Outdoors, Air: Odor/Fumes', 'Restaurant (AD2)', '12 Dead Animals']
005_NYC

๐Ÿ’พ๐Ÿ‹๏ธ๐Ÿ’พ DataBench ๐Ÿ’พ๐Ÿ‹๏ธ๐Ÿ’พ

This repository contains the original 65 datasets used for the paper Question Answering over Tabular Data with DataBench: A Large-Scale Empirical Evaluation of LLMs which appeared in LREC-COLING 2024.

Large Language Models (LLMs) are showing emerging abilities, and one of the latest recognized ones is tabular reasoning in question answering on tabular data. Although there are some available datasets to assess question answering systems on tabular data, they are not large and diverse enough to evaluate this new ability of LLMs. To this end, we provide a corpus of 65 real world datasets, with 3,269,975 and 1615 columns in total, and 1300 questions to evaluate your models for the task of QA over Tabular Data.

Usage

from datasets import load_dataset

# Load all QA pairs
all_qa = load_dataset("cardiffnlp/databench", name="qa", split="train")

# Load SemEval 2025 task 8 Question-Answer splits
semeval_train_qa = load_dataset("cardiffnlp/databench", name="semeval", split="train")
semeval_dev_qa = load_dataset("cardiffnlp/databench", name="semeval", split="dev")

You can use any of the individual integrated libraries to load the actual data where the answer is to be retrieved.

For example, using pandas in Python:

import pandas as pd

# "001_Forbes", the id of the dataset
ds_id = all_qa['dataset'][0] 

# full dataset
df = pd.read_parquet(f"hf://datasets/cardiffnlp/databench/data/{ds_id}/all.parquet")

# sample dataset
df = pd.read_parquet(f"hf://datasets/cardiffnlp/databench/data/{ds_id}/sample.parquet")

๐Ÿ“š Datasets

By clicking on each name in the table below, you will be able to explore each dataset.

Name Rows Cols Domain Source (Reference)
1 Forbes 2668 17 Business Forbes
2 Titanic 887 8 Travel and Locations Kaggle
3 Love 373 35 Social Networks and Surveys Graphext
4 Taxi 100000 20 Travel and Locations Kaggle
5 NYC Calls 100000 46 Business City of New York
6 London Airbnbs 75241 74 Travel and Locations Kaggle
7 Fifa 14620 59 Sports and Entertainment Kaggle
8 Tornados 67558 14 Health Kaggle
9 Central Park 56245 6 Travel and Locations Kaggle
10 ECommerce Reviews 23486 10 Business Kaggle
11 SF Police 713107 35 Social Networks and Surveys US Gov
12 Heart Failure 918 12 Health Kaggle
13 Roller Coasters 1087 56 Sports and Entertainment Kaggle
14 Madrid Airbnbs 20776 75 Travel and Locations Inside Airbnb
15 Food Names 906 4 Business Data World
16 Holiday Package Sales 4888 20 Travel and Locations Kaggle
17 Hacker News 9429 20 Social Networks and Surveys Kaggle
18 Staff Satisfaction 14999 11 Business Kaggle
19 Aircraft Accidents 23519 23 Health Kaggle
20 Real Estate Madrid 26026 59 Business Idealista
21 Telco Customer Churn 7043 21 Business Kaggle
22 Airbnbs Listings NY 37012 33 Travel and Locations Kaggle
23 Climate in Madrid 36858 26 Travel and Locations AEMET
24 Salary Survey Spain 2018 216726 29 Business INE
25 Data Driven SEO 62 5 Business Graphext
26 Predicting Wine Quality 1599 12 Business Kaggle
27 Supermarket Sales 1000 17 Business Kaggle
28 Predict Diabetes 768 9 Health Kaggle
29 NYTimes World In 2021 52588 5 Travel and Locations New York Times
30 Professionals Kaggle Survey 19169 64 Business Kaggle
31 Trustpilot Reviews 8020 6 Business TrustPilot
32 Delicatessen Customers 2240 29 Business Kaggle
33 Employee Attrition 14999 11 Business Kaggle(modified)
34 World Happiness Report 2020 153 20 Social Networks and Surveys World Happiness
35 Billboard Lyrics 5100 6 Sports and Entertainment Brown University
36 US Migrations 2012-2016 288300 9 Social Networks and Surveys US Census
37 Ted Talks 4005 19 Social Networks and Surveys Kaggle
38 Stroke Likelihood 5110 12 Health Kaggle
39 Happy Moments 100535 11 Social Networks and Surveys Kaggle
40 Speed Dating 8378 123 Social Networks and Surveys Kaggle
41 Airline Mentions X (former Twitter) 14640 15 Social Networks and Surveys X (former Twitter)
42 Predict Student Performance 395 33 Business Kaggle
43 Loan Defaults 83656 20 Business SBA
44 IMDb Movies 85855 22 Sports and Entertainment Kaggle
45 Spotify Song Popularity 21000 19 Sports and Entertainment Spotify
46 120 Years Olympics 271116 15 Sports and Entertainment Kaggle
47 Bank Customer Churn 7088 15 Business Kaggle
48 Data Science Salary Data 742 28 Business Kaggle
49 Boris Johnson UK PM Tweets 3220 34 Social Networks and Surveys X (former Twitter)
50 ING 2019 X Mentions 7244 22 Social Networks and Surveys X (former Twitter)
51 Pokemon Features 1072 13 Business Kaggle
52 Professional Map 1227 12 Business Kern et al, PNAS'20
53 Google Patents 9999 20 Business BigQuery
54 Joe Biden Tweets 491 34 Social Networks and Surveys X (former Twitter)
55 German Loans 1000 18 Business Kaggle
56 Emoji Diet 58 35 Health Kaggle
57 Spain Survey 2015 20000 45 Social Networks and Surveys CIS
58 US Polls 2020 3523 52 Social Networks and Surveys Brandwatch
59 Second Hand Cars 50000 21 Business DataMarket
60 Bakery Purchases 20507 5 Business Kaggle
61 Disneyland Customer Reviews 42656 6 Travel and Locations Kaggle
62 Trump Tweets 15039 20 Social Networks and Surveys X (former Twitter)
63 Influencers 1039 14 Social Networks and Surveys X (former Twitter)
64 Clustering Zoo Animals 101 18 Health Kaggle
65 RFM Analysis 541909 8 Business UCI ML

๐Ÿ—๏ธ Folder structure

Each folder represents one dataset. You will find the following files within:

  • all.parquet: the processed data, with each column tagged with our typing system, in parquet.
  • qa.parquet: contains the human-made set of questions, tagged by type and columns used, for the dataset (sample_answer indicates the answers for DataBench lite)
  • sample.parquet: sample containing 20 rows of the original dataset (DataBench lite)
  • info.yml: additional information about the dataset

๐Ÿ—‚๏ธ Column typing system

In an effort to map the stage for later analysis, we have categorized the columns by type. This information allows us to segment different kinds of data so that we can subsequently analyze the model's behavior on each column type separately. All parquet files have been casted to their smallest viable data type using the open source Lector reader.

What this means is that in the data types we have more granular information that allows us to know if the column contains NaNs or not (following pandaโ€™s convention of Int vs int), as well as whether small numerical values contain negatives (Uint vs int) and their range. We also have dates with potential timezone information (although for now theyโ€™re all UTC), as well as information about categoriesโ€™ cardinality coming from the arrow types.

In the table below you can see all the data types assigned to each column, as well as the number of columns for each type. The most common data types are numbers and categories with 1336 columns of the total of 1615 included in DataBench. These are followed by some other more rare types as urls, booleans, dates or lists of elements.

Type Columns Example
number 788 55
category 548 apple
date 50 1970-01-01
text 46 A red fox ran...
url 31 google.com
boolean 18 True
list[number] 14 [1,2,3]
list[category] 112 [apple, orange, banana]
list[url] 8 [google.com, apple.com]

๐Ÿ”— Reference

You can download the paper here.

If you use this resource, please use the following reference:

@inproceedings{oses-etal-2024-databench,
    title = "Question Answering over Tabular Data with DataBench: A Large-Scale Empirical Evaluation of LLMs",
    author = "Jorge Osรฉs Grijalba and Luis Alfonso Ureรฑa-Lรณpez and
    Eugenio Martรญnez Cรกmara and Jose Camacho-Collados",
    booktitle = "Proceedings of LREC-COLING 2024",
    year = "2024",
    address = "Turin, Italy"
}
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