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Dietary Restriction Transgenerational Fitness

Three-Generational Study in *Caenorhabditis elegans*

@kaggle.thedevastator_dietary_restriction_transgenerational_fitness

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About this Dataset

Dietary Restriction Transgenerational Fitness

Dietary Restriction Transgenerational Fitness

Three-Generational Study in Caenorhabditis elegans

By [source]

About this dataset

This dataset provides an opportunity to explore the potential long-term consequences of dietary restriction on transgenerational fitness in Caenorhabditis elegans. Dietary restriction has been demonstrated to increase lifespan in a broad variety of organisms and improve health in humans, but it may also lead to unintended consequences for future generations. In this three-generation study, we followed the P0 parental generation, which was subjected to temporary fasting (TF) and tracked the mortality risk and age-specific reproduction outcomes of their descendants across F1-F3. Additionally, we also tracked length/pixel and length/mm measurements for each worm in this study. Analyzing this data could help us understand the implications of dietary restriction beyond just improved human health--revealing how such interventions could impact future generations as well

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How to use the dataset

This dataset contains data from a three-generation study on dietary restriction and its effects on transgenerational fitness in Caenorhabditis elegans. This guide will help you understand how to use the data for your own research.

First, familiarize yourself with the columns that are present in the dataset: Worm, Treatment, Parent.No, F0.Treat, Lineage, Block, Set Up Death.Date Cause Observed Total.Rep Na.Start 1 2 3 4 5 6 7 F1.Treat G.Parent No Worm Order NA Start Day2 UnAdded SetUp F1D2 Day4 Unadded SetUpF1D4 8

Next decide what kind of information you want to extract from this set– understanding mortality risks or age-specific reproduction? Specific lineage or block assignment? Demographics (for example number of offspring produced in a day)?
Once you have decided on what type of information is relevant for your research objectives compare each worm’s characteristics by looking at values for corresponding column headings like Treatment and F0 treat; Parent and Grandparent no; Offspring production numbers 1-8; Lineage; Series Setup Dates etc... You could also compare specific demographics such as mean number of offspring per day using descriptive statistics or check effects using regression analysis
If you want an impression across generations try plotting the trends pertaining to each worm over three generations with parameters like total reproduction over three generations plotted against mortality risk etc… You can also plot death date across these generations and check if mortality has predictable patterns crossed generations -and if so what kind – age related or other factors involved ?

Ultimately draw your eventual conclusions based off of methods like these i mentioned above – understand individual parameters if need be , formulate hypotheses around it , test them out on this dataset . Voila ! You now have reliable conclusions that help explain Dietary Restriction Transgenerational Effects !

Research Ideas

  • Examine how the composition of successive generations is affected by dietary restriction and study whether this has an effect on transgenerational fitness.
  • Identify patterns in mortality rates between generations to better understand the underlying causes of death in worms subjected to dietary restriction and its effects on lifespan.
  • Use WormLength data to compare body size changes between worms subjected to different treatments, analyze behavioral traits related to feeding/locomotion, and study whether there is a correlation between body size and fertility in different generations of worms exposed to dietary restriction or other treatments

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: F1.csv

Column name Description
Worm Unique identifier for each worm. (String)
Treatment Treatment group assigned to the worm. (String)
Parent.No Number of the parent worm. (Integer)
F0.Treat Treatment group assigned to the parent worm. (String)
Lineage Lineage of the worm. (String)
Block Block of the experiment. (Integer)
Set Up Date the worm was set up. (Date)
Death.Date Date the worm died. (Date)
Cause Cause of death for the worm. (String)
Observed Number of days the worm was observed. (Integer)
Total.Rep Total number of offspring produced by the worm. (Integer)
Na.Start Number of offspring produced by the worm on day one. (Integer)
1 Number of offspring produced by the worm on day one. (Integer)
2 Number of offspring produced by the worm on day two. (Integer)
3 Number of offspring produced by the worm on day three. (Integer)
4 Number of offspring produced by the worm on day four. (Integer)
5 Number of offspring produced by the worm on day five. (Integer)
6 Number of offspring produced by the worm on day six. (Integer)
7 Number of offspring produced by the worm on day seven. (Integer)

File: F2.csv

Column name Description
Worm Unique identifier for each worm. (String)
Treatment Treatment group assigned to the worm. (String)
Parent.No Number of the parent worm. (Integer)
F0.Treat Treatment group assigned to the parent worm. (String)
Lineage Lineage of the worm. (String)
Block Block of the experiment. (Integer)
Set Up Date the worm was set up. (Date)
Death.Date Date the worm died. (Date)
Cause Cause of death for the worm. (String)
Observed Number of days the worm was observed. (Integer)
Total.Rep Total number of offspring produced by the worm. (Integer)
1 Number of offspring produced by the worm on day one. (Integer)
2 Number of offspring produced by the worm on day two. (Integer)
3 Number of offspring produced by the worm on day three. (Integer)
4 Number of offspring produced by the worm on day four. (Integer)
5 Number of offspring produced by the worm on day five. (Integer)
6 Number of offspring produced by the worm on day six. (Integer)
7 Number of offspring produced by the worm on day seven. (Integer)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .

Tables

F1

@kaggle.thedevastator_dietary_restriction_transgenerational_fitness.f1
  • 31.66 kB
  • 822 rows
  • 22 columns
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CREATE TABLE f1 (
  "worm" BIGINT,
  "treatment" VARCHAR,
  "parent_no" BIGINT,
  "f0_treat" VARCHAR,
  "lineage" VARCHAR,
  "block" BIGINT,
  "set_up" VARCHAR,
  "death_date" VARCHAR,
  "cause" VARCHAR,
  "death" BIGINT,
  "observed" BIGINT,
  "matricide" BIGINT,
  "total_rep" BIGINT,
  "na_start" DOUBLE,
  "n_1" DOUBLE  -- 1,
  "n_2" DOUBLE  -- 2,
  "n_3" DOUBLE  -- 3,
  "n_4" DOUBLE  -- 4,
  "n_5" DOUBLE  -- 5,
  "n_6" DOUBLE  -- 6,
  "n_7" DOUBLE  -- 7,
  "n_8" DOUBLE  -- 8
);

F2

@kaggle.thedevastator_dietary_restriction_transgenerational_fitness.f2
  • 36.04 kB
  • 658 rows
  • 29 columns
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CREATE TABLE f2 (
  "worm" VARCHAR,
  "treatment" VARCHAR,
  "parent_no" BIGINT,
  "f1_treat" VARCHAR,
  "g_parent_no" VARCHAR,
  "f0_treat" VARCHAR,
  "worm_order" BIGINT,
  "lineage" VARCHAR,
  "block" BIGINT,
  "set_up" VARCHAR,
  "death_date" VARCHAR,
  "cause" VARCHAR,
  "death" BIGINT,
  "observed" BIGINT,
  "matricide" BIGINT,
  "total_rep" BIGINT,
  "na_start" DOUBLE,
  "day2_unadded" DOUBLE,
  "setup_f1d2" DOUBLE,
  "day4_unadded" DOUBLE,
  "setup_f1d4" DOUBLE,
  "n_1" DOUBLE  -- 1,
  "n_2" DOUBLE  -- 2,
  "n_3" DOUBLE  -- 3,
  "n_4" DOUBLE  -- 4,
  "n_5" DOUBLE  -- 5,
  "n_6" DOUBLE  -- 6,
  "n_7" DOUBLE  -- 7,
  "n_8" DOUBLE  -- 8
);

F3

@kaggle.thedevastator_dietary_restriction_transgenerational_fitness.f3
  • 31.4 kB
  • 636 rows
  • 27 columns
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CREATE TABLE f3 (
  "worm" VARCHAR,
  "treatment" VARCHAR,
  "parent_no" VARCHAR,
  "f2_treat" VARCHAR,
  "g_parent_no" VARCHAR,
  "f1_treat" VARCHAR,
  "g_g_parent_no" VARCHAR,
  "f0_treat" VARCHAR,
  "worm_order" BIGINT,
  "lineage" VARCHAR,
  "block" BIGINT,
  "set_up" VARCHAR,
  "death_date" VARCHAR,
  "cause" VARCHAR,
  "death" BIGINT,
  "observed" BIGINT,
  "matricide" BIGINT,
  "na_start" DOUBLE,
  "total_rep" DOUBLE,
  "n_1" DOUBLE  -- 1,
  "n_2" DOUBLE  -- 2,
  "n_3" DOUBLE  -- 3,
  "n_4" DOUBLE  -- 4,
  "n_5" DOUBLE  -- 5,
  "n_6" DOUBLE  -- 6,
  "n_7" DOUBLE  -- 7,
  "n_8" DOUBLE  -- 8
);

P0

@kaggle.thedevastator_dietary_restriction_transgenerational_fitness.p0
  • 23.4 kB
  • 670 rows
  • 20 columns
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CREATE TABLE p0 (
  "worm" BIGINT,
  "treatment" VARCHAR,
  "founder" BIGINT,
  "exp_block" BIGINT,
  "setup_date" VARCHAR,
  "death_date" VARCHAR,
  "cause" VARCHAR,
  "death" BIGINT,
  "observed" BIGINT,
  "matricide" BIGINT,
  "total_rep" BIGINT,
  "na_start" DOUBLE,
  "n_1" DOUBLE  -- 1,
  "n_2" DOUBLE  -- 2,
  "n_3" DOUBLE  -- 3,
  "n_4" DOUBLE  -- 4,
  "n_5" DOUBLE  -- 5,
  "n_6" DOUBLE  -- 6,
  "n_7" DOUBLE  -- 7,
  "n_8" DOUBLE  -- 8
);

Wormlength

@kaggle.thedevastator_dietary_restriction_transgenerational_fitness.wormlength
  • 51.23 kB
  • 1,966 rows
  • 9 columns
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CREATE TABLE wormlength (
  "worm" VARCHAR,
  "plate" BIGINT,
  "measure" BIGINT,
  "treatment" VARCHAR,
  "length_pixel" DOUBLE,
  "length_mm" DOUBLE,
  "period" VARCHAR,
  "stage" VARCHAR,
  "generation" VARCHAR
);

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