Oxygen Exposure For Benthic Megafauna Near San by Kaggle | Environmental and Climate Sciences

About this Dataset

Oxygen Exposure For Benthic Megafauna Near San

Oxygen Exposure for Benthic Megafauna near San Diego

Spatially Varying Environmental Risk

About this dataset

This dataset captures an in-depth look into the environmental conditions of the underwater world off Southern California's coast. It provides invaluable information related to spatial risk variation, such as oxygen exposure levels, depths and habitat criteria of 53 species of benthic and epibenthic megafauna recorded during the three-year study. This data will provide insight into aquatic life dynamics and potentially generate improved management strategies for protecting these vital species. Moreover, due to the importance that waters play within our planet's fragile ecosystem, a proper understanding of their affairs could lead to greater marine sustainability in the long-term. Ultimately, this dataset may help answer our questions about how exactly ocean life is responding to intense human activity and its effects on today's seaside communities

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How to use the dataset

Download and install the dataset: The dataset contains two .csv files, each containing data from the three-year study on oxygen exposure for benthic and epibenthic megafauna off the coast of San Diego in Southern California. Download these two files to your computer and save them for further analysis.

Familiarize yourself with the datasets: Each file includes very detailed information about a particular variable related to the study (for example, SpeciesMetadata contains species-level information on 53 species of benthic and epibenthic megafauna). Read through each data sheet carefully in order to gain a better understanding of what's included in each column.

Clean up any outliers or missing values: Once you understand which columns are important for your analysis, you can begin cleaning up any outliers or missing values that may be present in your dataset. This is an important step as it will help ensure that further analysis is performed accurately.

Choose an appropriate visualization method: Depending on what type of results you want to show from your analysis, choose an appropriate visualization method (e.g., bar plot, scatterplot). Also consider if adding labeling such as color with respect to categories would improve legibility of figures you produce from this dataset during exploratory data analyses stages.

Choose a statistical test suitable for this type of project: Once allyour visuals have been produced its time to interpret results using statistics tests depending on how many categorical variables are presentin the data set (i.e t-test or ANOVA). As well understand key outputs like p_values so experiment could effectively conclude if thereare significant differences between treatmentswhen comparing distributions among samples/populations being studied here.. Be sureto adjust mean size/sample size when performing statistic testsuitably accordingto determining adequate power when selecting applicable tests etc.

Research Ideas

Comparing the effects of different environmental factors (depth, temperature, salinity etc.) on depth-specific distributions of oxygen and benthic megafauna.

Identifying and mapping vulnerable areas for benthic species based on environmental factors and oxygen exposure patterns.

Developing models to predict underlying spatial risk variables for endangered species to inform conservation efforts in the study area

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: ROVObservationData.csv

File: SpeciesMetadata.csv

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

Rovobservationdata

@kaggle.thedevastator_oxygen_exposure_for_benthic_megafauna_near_san_d.rovobservationdata

185.94 KB
2404 rows
120 columns


CREATE TABLE rovobservationdata (
  "datetime" TIMESTAMP,
  "easting" DOUBLE,
  "northing" DOUBLE,
  "depth" DOUBLE,
  "temp" DOUBLE,
  "sal" DOUBLE,
  "oxy" DOUBLE,
  "grain" VARCHAR,
  "bedform" VARCHAR,
  "ac" BIGINT,
  "ae" BIGINT,
  "an" BIGINT,
  "ap" BIGINT,
  "as" BIGINT,
  "ba" BIGINT,
  "bas" BIGINT,
  "bc" BIGINT,
  "bgo" BIGINT,
  "bgu" BIGINT,
  "bi" BIGINT,
  "bo" BIGINT,
  "br" BIGINT,
  "bs" BIGINT,
  "ca" BIGINT,
  "cc" BIGINT,
  "cd" BIGINT,
  "cg" BIGINT,
  "ch" BIGINT,
  "com" BIGINT,
  "cp" BIGINT,
  "cr" BIGINT,
  "cs" BIGINT,
  "cu" BIGINT,
  "de" BIGINT,
  "dp" BIGINT,
  "eo" BIGINT,
  "ep" BIGINT,
  "eu" BIGINT,
  "fej" BIGINT,
  "fl" BIGINT,
  "fo" BIGINT,
  "fr" BIGINT,
  "go" BIGINT,
  "ha" BIGINT,
  "hal" BIGINT,
  "hb" BIGINT,
  "hl" BIGINT,
  "ii" BIGINT,
  "io" BIGINT,
  "ir" BIGINT,
  "jde" BIGINT,
  "jm" BIGINT,
  "kb" BIGINT,
  "kw" DOUBLE,
  "lc" BIGINT,
  "lcom" BIGINT,
  "li" BIGINT,
  "ln" BIGINT,
  "lo" BIGINT,
  "ls" BIGINT,
  "lss" BIGINT,
  "lu" BIGINT,
  "ly" BIGINT,
  "lz" BIGINT,
  "mc" BIGINT,
  "mr" DOUBLE,
  "ms" BIGINT,
  "mse" BIGINT,
  "ol" BIGINT,
  "ow" BIGINT,
  "pa" BIGINT,
  "pc" BIGINT,
  "pem" BIGINT,
  "pg" BIGINT,
  "ph" BIGINT,
  "pi" BIGINT,
  "pl" BIGINT,
  "po" BIGINT,
  "pp" BIGINT,
  "ppl" BIGINT,
  "ps" BIGINT,
  "pt" BIGINT,
  "pyr" BIGINT,
  "ra" BIGINT,
  "rb" BIGINT,
  "rh" BIGINT,
  "ro" BIGINT,
  "ru" BIGINT,
  "rw" BIGINT,
  "sa" BIGINT,
  "sb" BIGINT,
  "sc" BIGINT,
  "scom" BIGINT,
  "sd" BIGINT,
  "se" BIGINT,
  "sec" BIGINT,
  "sh" BIGINT,
  "shc" BIGINT,
  "sl" BIGINT,
  "sm" BIGINT
);

Speciesmetadata

@kaggle.thedevastator_oxygen_exposure_for_benthic_megafauna_near_san_d.speciesmetadata

10.02 KB
111 rows
7 columns


CREATE TABLE speciesmetadata (
  "speciescode" VARCHAR,
  "mobility" VARCHAR,
  "speciesname" VARCHAR,
  "commonname" VARCHAR,
  "majortaxa" VARCHAR,
  "group" VARCHAR,
  "quantscale" VARCHAR
);