Name: Insects Flight Dynamics
Creator: Kaggle
License: https://creativecommons.org/publicdomain/zero/1.0/

About this Dataset

Insects Flight Dynamics

Flight Performance Characteristics of Three Insect Species

Drosophila melanogaster, Isoleucinella rotunda, and Calopteron reticulatum

By [source]

About this dataset

This dataset consists of flight performance data from three species of insects: Drosophila melanogaster, Ichthyophis guttulatus, and Callorhinus ursinus. It offers a unique opportunity to explore the intricate mechanics of flying creatures. Discover how body mass and flight position can affect the speed, distance, and agility of each species. How does the interplay between these two dimensions influence overall performance? Analyze the differences between each species with this extensive collection of experimental data. Separate out individual participants with their corresponding Trial numbers and Fly numbers. Uncover relationships between related individuals or events using movie number as an additional parameter for detailed study. Explore underlying biophysical principles present in nature by interpreting results through a combination of static parameters like type, status, lambda length, max_numnn as well as dynamic values like Difference_x and Difference_y to determine any correlation factors influencing an insect's navigational ability in different conditions or environments!

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How to use the dataset

This dataset contains flight performance data collected from three species of insect: Drosophila melanogaster, Ichthyophis guttulatus, and Callorhinus ursinus. This dataset can be used to gain insights into the mechanics of flying creatures by understanding how the body mass of different species affect their abilities to fly.

This dataset offers a comprehensive collection of data relating to these insects’ flight characteristics. It contains 24 columns with information such as trial number, fly number, other number, type, status, lambda (body mass), Difference_x and Difference_y (coordinates) as well as Distance between two species and Movie_number for recording purposes.

To utilize this data set for research purposes you may consider using descriptive analysis focuses exploring relationships in each variable separately or using univariate/multivariate tests such as t-test and ANOVA to analyze whether significant differences exist between various animals based on this data set. Additionally features selection or dimensional reduction methods such as PCA could be used if needed for research projects involving larger datasets over multiple recordings across time frames.

You could also use this data set to create predictive models that attempts at identifying patterns in insect behavior through machine learning classification algorithms like naïve-bayes or logistic regression which can detect categories within the enumerated types column of this dataset dependent on the values given with each feature type keys ((e.,g., 1 = drosophila melanogaster). Rule based models like decision trees may also be influential when working with impermanent element variables such Content Type / Uncertainty Potential / Activity Score within large multivariate datasets across multiple recordings over time frames enabling upmost accuracy in results being achieved when seeking out definable ‘rules’ applied most often under given specific conditions for these insects characteristic traits benchmarks being monitored throughout the nature habitat environment eco-systems encompassing those monitored subject matters situations contained entity relationship detail found deep within or attempting replicate real life intrinsic events unknown yet known from infinity realm towards eternity scope by expectation form intricate outer bounds extensions must implement decision making practice solutions their sustaining impact most likely too excellently handled out mechanisms big date allow processing fundamental active query machine activations intangibility finding minds feats feat not easily actualized towards ubiquitous functional scheme ability formation causing events mental thought matrices challenge some way submodular distinct partitions parts building blocks structures resulting comparison alternations advancement external relations sequences code stories implementing meaningful details space area contemplating access scan raw crystallize thought intelligence kinds neural networks brute force numeric clustering

Research Ideas

Comparing the flight performance between different species of insect to understand how their body mass, anatomy, and wingspan can affect their flying ability.

Analyzing the neural networks of the species to measure their cognitive abilities and decipher how they process information in order to fly.

Examining the differences in x-coordinates and y-coordinates between two species of insect as well as any changes in distance over time, which can provide insights into their behavior patterns when flying near each other or away from each other

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: Accumulative_distribution_random.csv

Column name	Description
Type	The type of insect being studied. (String)
Trial_number	The number of the trial being conducted. (Integer)
Fly_number	The number of the fly being studied. (Integer)
Other_number	An additional number associated with the fly. (Integer)
Difference_x	The difference in x-coordinates between two species for a given trial number & movie number combination. (Float)
Difference_y	The difference in y-coordinates between two species for a given trial number & movie number combination. (Float)
Distance	The total distance traversed by each fly number, trial number combination associated along with movie number value given in this dataset. (Float)

File: Nearest_neighbor_population_lambda.csv

Column name	Description
Type	The type of insect being studied. (String)
Definition	A description of the data being collected. (String)
Movie_number	The number of the movie being used for the study. (Integer)
Status	The status of the insect during the study. (String)
Lambda	The body mass of the insect being studied. (Float)
Max_numNN	The maximum number of neural networks assigned to the insect. (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

Dmel F L

@kaggle.thedevastator_flight_performance_characteristics_of_three_inse.dmel_f_l

38.35 MB
39,597 rows
132 columns

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Group Identification

@kaggle.thedevastator_flight_performance_characteristics_of_three_inse.group_identification

1.38 MB
228,384 rows
7 columns

CREATE TABLE group_identification (
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  "movie_number" BIGINT,
  "fly_number" BIGINT,
  "time" BIGINT,
  "state" BIGINT,
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Group Inout

@kaggle.thedevastator_flight_performance_characteristics_of_three_inse.group_inout

10.91 kB
541 rows
8 columns

CREATE TABLE group_inout (
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);

Group Inout Size

@kaggle.thedevastator_flight_performance_characteristics_of_three_inse.group_inout_size

18.18 kB
1,154 rows
10 columns

CREATE TABLE group_inout_size (
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);

Group Judgment

@kaggle.thedevastator_flight_performance_characteristics_of_three_inse.group_judgment

4.09 MB
768,640 rows
7 columns

CREATE TABLE group_judgment (
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);

Group Judgment Correction

@kaggle.thedevastator_flight_performance_characteristics_of_three_inse.group_judgment_correction

3.65 MB
768,000 rows
8 columns

CREATE TABLE group_judgment_correction (
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  "time" BIGINT,
  "proximate_population" BIGINT,
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  "state" BIGINT
);

Inter Individual Distance

@kaggle.thedevastator_flight_performance_characteristics_of_three_inse.inter_individual_distance

98.59 kB
6,080 rows
6 columns

CREATE TABLE inter_individual_distance (
  "id" BIGINT,
  "type" VARCHAR,
  "movie_number" BIGINT,
  "fly_number" BIGINT,
  "other_number" BIGINT,
  "distance" DOUBLE
);

Inter Individual Distance Random

@kaggle.thedevastator_flight_performance_characteristics_of_three_inse.inter_individual_distance_random

98.59 kB
6,080 rows
6 columns

CREATE TABLE inter_individual_distance_random (
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  "other_number" BIGINT,
  "distance" DOUBLE
);

Ir F A

@kaggle.thedevastator_flight_performance_characteristics_of_three_inse.ir_f_a

37.27 MB
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138 columns

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39,254 rows
132 columns

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  "n_249_629852" DOUBLE  -- 249.629852,
  "n_408_401308" DOUBLE  -- 408.401308,
  "n_2_086008" DOUBLE  -- 2.086008
);

Ir F C

@kaggle.thedevastator_flight_performance_characteristics_of_three_inse.ir_f_c

37.7 MB
39,254 rows
120 columns

CREATE TABLE ir_f_c (
  "n_0" BIGINT  -- 0,
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  "n_98_105295" DOUBLE  -- 98.105295,
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  "n_393_847849" DOUBLE  -- 393.847849,
  "n_2_25738" DOUBLE  -- 2.25738
);

Ir F D

@kaggle.thedevastator_flight_performance_characteristics_of_three_inse.ir_f_d

38.19 MB
39,361 rows
120 columns

CREATE TABLE ir_f_d (
  "n_0" BIGINT  -- 0,
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  "n_119_817326" DOUBLE  -- 119.817326,
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  "n_1_680418" DOUBLE  -- 1.680418
);

Ir F E

@kaggle.thedevastator_flight_performance_characteristics_of_three_inse.ir_f_e

38.17 MB
39,369 rows
120 columns

CREATE TABLE ir_f_e (
  "n_0" BIGINT  -- 0,
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  "n_104_874591" DOUBLE  -- 104.874591,
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);

Ir F F

@kaggle.thedevastator_flight_performance_characteristics_of_three_inse.ir_f_f

38.08 MB
39,253 rows
120 columns

CREATE TABLE ir_f_f (
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Ir F G

@kaggle.thedevastator_flight_performance_characteristics_of_three_inse.ir_f_g

38.21 MB
39,368 rows
120 columns

CREATE TABLE ir_f_g (
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Ir F H

@kaggle.thedevastator_flight_performance_characteristics_of_three_inse.ir_f_h

38.31 MB
39,362 rows
120 columns

CREATE TABLE ir_f_h (
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Ir F I

@kaggle.thedevastator_flight_performance_characteristics_of_three_inse.ir_f_i

38.23 MB
39,254 rows
120 columns

CREATE TABLE ir_f_i (
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Ir F J

@kaggle.thedevastator_flight_performance_characteristics_of_three_inse.ir_f_j

38.34 MB
39,254 rows
120 columns

CREATE TABLE ir_f_j (
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  "n__0_495913" DOUBLE  -- -0.495913,
  "n_12" BIGINT  -- 12,
  "n_431_554301" DOUBLE  -- 431.554301,
  "n_242_485891" DOUBLE  -- 242.485891,
  "n_1_96499" DOUBLE  -- 1.96499,
  "n_0_707428" DOUBLE  -- 0.707428,
  "n_1_412505" DOUBLE  -- 1.412505,
  "n_13" BIGINT  -- 13,
  "n_88_371346" DOUBLE  -- 88.371346,
  "n_250_946054" DOUBLE  -- 250.946054,
  "n_2_086307" DOUBLE  -- 2.086307,
  "n_0_698859" DOUBLE  -- 0.698859,
  "n__1_314202" DOUBLE  -- -1.314202,
  "n_14" BIGINT  -- 14,
  "n_206_844043" DOUBLE  -- 206.844043,
  "n_259_614882" DOUBLE  -- 259.614882,
  "n_1_845549" DOUBLE  -- 1.845549,
  "n_0_624951" DOUBLE  -- 0.624951,
  "n__1_079165" DOUBLE  -- -1.079165,
  "n_15" BIGINT  -- 15,
  "n_197_129007" DOUBLE  -- 197.129007,
  "n_306_34062" DOUBLE  -- 306.34062,
  "n_1_710005" DOUBLE  -- 1.710005,
  "n_0_654691" DOUBLE  -- 0.654691,
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  "n_16" BIGINT  -- 16,
  "n_152_392649" DOUBLE  -- 152.392649,
  "n_369_566483" DOUBLE  -- 369.566483,
  "n_1_914746" DOUBLE  -- 1.914746
);

Ir F K

@kaggle.thedevastator_flight_performance_characteristics_of_three_inse.ir_f_k

38.39 MB
39,254 rows
120 columns

CREATE TABLE ir_f_k (
  "n_0" BIGINT  -- 0,
  "n_342_059807" DOUBLE  -- 342.059807,
  "n_99_836835" DOUBLE  -- 99.836835,
  "n_1_888794" DOUBLE  -- 1.888794,
  "n_0_706207" DOUBLE  -- 0.706207,
  "n_1_459534" DOUBLE  -- 1.459534,
  "n_1" BIGINT  -- 1,
  "n_132_445779" DOUBLE  -- 132.445779,
  "n_124_234397" DOUBLE  -- 124.234397,
  "n_1_927562" DOUBLE  -- 1.927562,
  "n_0_790933" DOUBLE  -- 0.790933,
  "n__0_073787" DOUBLE  -- -0.073787,
  "n_2" BIGINT  -- 2,
  "n_394_20514" DOUBLE  -- 394.20514,
  "n_138_459758" DOUBLE  -- 138.459758,
  "n_2_061107" DOUBLE  -- 2.061107,
  "n_0_715168" DOUBLE  -- 0.715168,
  "n_1_270443" DOUBLE  -- 1.270443,
  "n_3" BIGINT  -- 3,
  "n_389_046291" DOUBLE  -- 389.046291,
  "n_183_407624" DOUBLE  -- 183.407624,
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  "n_0_681357" DOUBLE  -- 0.681357,
  "n_0_834221" DOUBLE  -- 0.834221,
  "n_4" BIGINT  -- 4,
  "n_286_801796" DOUBLE  -- 286.801796,
  "n_195_18497" DOUBLE  -- 195.18497,
  "n_1_290996" DOUBLE  -- 1.290996,
  "n_0_69206" DOUBLE  -- 0.69206,
  "n__0_530624" DOUBLE  -- -0.530624,
  "n_5" BIGINT  -- 5,
  "n_199_272482" DOUBLE  -- 199.272482,
  "n_200_195082" DOUBLE  -- 200.195082,
  "n_1_910891" DOUBLE  -- 1.910891,
  "n_0_644038" DOUBLE  -- 0.644038,
  "n_1_54907" DOUBLE  -- 1.54907,
  "n_6" BIGINT  -- 6,
  "n_394_073365" DOUBLE  -- 394.073365,
  "n_230_362214" DOUBLE  -- 230.362214,
  "n_1_550165" DOUBLE  -- 1.550165,
  "n_0_653729" DOUBLE  -- 0.653729,
  "n__0_671432" DOUBLE  -- -0.671432,
  "n_7" BIGINT  -- 7,
  "n_462_386977" DOUBLE  -- 462.386977,
  "n_231_403878" DOUBLE  -- 231.403878,
  "n_2_015286" DOUBLE  -- 2.015286,
  "n_0_671936" DOUBLE  -- 0.671936,
  "n_0_790425" DOUBLE  -- 0.790425,
  "n_8" BIGINT  -- 8,
  "n_137_841805" DOUBLE  -- 137.841805,
  "n_233_546044" DOUBLE  -- 233.546044,
  "n_1_69277" DOUBLE  -- 1.69277,
  "n_0_762836" DOUBLE  -- 0.762836,
  "n__0_143964" DOUBLE  -- -0.143964,
  "n_9" BIGINT  -- 9,
  "n_122_210817" DOUBLE  -- 122.210817,
  "n_242_442667" DOUBLE  -- 242.442667,
  "n_1_901477" DOUBLE  -- 1.901477,
  "n_0_781809" DOUBLE  -- 0.781809,
  "n_0_741109" DOUBLE  -- 0.741109,
  "n_10" BIGINT  -- 10,
  "n_302_072576" DOUBLE  -- 302.072576,
  "n_274_583351" DOUBLE  -- 274.583351,
  "n_1_606989" DOUBLE  -- 1.606989,
  "n_0_586919" DOUBLE  -- 0.586919,
  "n__0_619566" DOUBLE  -- -0.619566,
  "n_11" BIGINT  -- 11,
  "n_103_682475" DOUBLE  -- 103.682475,
  "n_277_134637" DOUBLE  -- 277.134637,
  "n_2_08756" DOUBLE  -- 2.08756,
  "n_0_734346" DOUBLE  -- 0.734346,
  "n_1_080992" DOUBLE  -- 1.080992,
  "n_12" BIGINT  -- 12,
  "n_413_76233" DOUBLE  -- 413.76233,
  "n_300_064278" DOUBLE  -- 300.064278,
  "n_1_753531" DOUBLE  -- 1.753531,
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  "n_13" BIGINT  -- 13,
  "n_403_097874" DOUBLE  -- 403.097874,
  "n_299_305042" DOUBLE  -- 299.305042,
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  "n_0_759016" DOUBLE  -- 0.759016,
  "n__0_276919" DOUBLE  -- -0.276919,
  "n_14" BIGINT  -- 14,
  "n_338_292104" DOUBLE  -- 338.292104,
  "n_315_977281" DOUBLE  -- 315.977281,
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  "n_0_702859" DOUBLE  -- 0.702859,
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  "n_15" BIGINT  -- 15,
  "n_165_087994" DOUBLE  -- 165.087994,
  "n_328_218358" DOUBLE  -- 328.218358,
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  "n_0_814903" DOUBLE  -- 0.814903,
  "n__0_294413" DOUBLE  -- -0.294413,
  "n_16" BIGINT  -- 16,
  "n_388_465096" DOUBLE  -- 388.465096,
  "n_330_562528" DOUBLE  -- 330.562528,
  "n_1_922006" DOUBLE  -- 1.922006
);

Length Of Stay

@kaggle.thedevastator_flight_performance_characteristics_of_three_inse.length_of_stay

13.97 kB
465 rows
7 columns

CREATE TABLE length_of_stay (
  "id" BIGINT,
  "type" VARCHAR,
  "movie_number" BIGINT,
  "fly_number" BIGINT,
  "total_duration" DOUBLE,
  "mean_duration" DOUBLE,
  "number_of_stay_events" BIGINT
);

Nearest Neighbor Population

@kaggle.thedevastator_flight_performance_characteristics_of_three_inse.nearest_neighbor_population

2.58 MB
539,520 rows
6 columns

CREATE TABLE nearest_neighbor_population (
  "id" BIGINT,
  "type" VARCHAR,
  "definition" BIGINT,
  "movie_number" BIGINT,
  "status" VARCHAR,
  "numnn" BIGINT
);

Nearest Neighbor Population Lambda

@kaggle.thedevastator_flight_performance_characteristics_of_three_inse.nearest_neighbor_population_lambda

8.33 kB
192 rows
7 columns

CREATE TABLE nearest_neighbor_population_lambda (
  "id" BIGINT,
  "type" VARCHAR,
  "definition" BIGINT,
  "movie_number" BIGINT,
  "status" VARCHAR,
  "lambda" DOUBLE,
  "max_numnn" BIGINT
);

Size Individual

@kaggle.thedevastator_flight_performance_characteristics_of_three_inse.size_individual

3.14 kB
60 rows
2 columns

CREATE TABLE size_individual (
  "calocasiae" DOUBLE,
  "dmelanogaster" DOUBLE
);

Size Mean Sd

@kaggle.thedevastator_flight_performance_characteristics_of_three_inse.size_mean_sd

4.16 kB
1 row
5 columns

CREATE TABLE size_mean_sd (
  "id" BIGINT,
  "c_alo_mean" DOUBLE,
  "c_alo_sd" DOUBLE,
  "d_mel_mean" DOUBLE,
  "d_mel_sd" DOUBLE
);

Speed

@kaggle.thedevastator_flight_performance_characteristics_of_three_inse.speed

9.96 MB
768,000 rows
7 columns

CREATE TABLE speed (
  "id" BIGINT,
  "type" VARCHAR,
  "movie_number" BIGINT,
  "fly_number" BIGINT,
  "time" BIGINT,
  "speed" DOUBLE,
  "state" BIGINT
);