This README file was generated by [NAME] on [YYYY-MM-DD].

<Note: the text within [square brackets] should be replaced with suggested information; the help text within the <angle brackets> should be deleted before finalizing your document; if a field isn't relevant to your dataset, write 'Not applicable'; please consider renaming this document as [LAST NAME]_[KEYWORD]_README.txt>



GENERAL INFORMATION

Title of Dataset: <this should match the title you input in the dataset submission form>

Dataset Contact(s): [NAME], [STABLE EMAIL]

First author information
Name:
ORCID:
Institution:
Email:

Second author information
Name:
ORCID:
Institution:
Email:

<Please add additional author information as needed. Dataset authors should include all persons who contributed to data collection, analysis, etc>

- Date of data collection: <provide single date, range, or approximate date; suggested format YYYY-MM-DD>
- Geographic location of data collection: <if this is not sensitive information, please provide the latitude, longitude, or city/region, State, Country>
- Funder information: <include full name of funding source, award number, and any other relevant information, including funder ID or grant ID>



SHARING/ACCESS INFORMATION

- Licenses/restrictions placed on the data: <include the license that you selected in the dataset submission form>
- Links to publications that cite or use the data: <when possible, include permanent identifiers such as the DOI, ARK, Handle, etc. This includes works published in Deep Blue Documents>
- Links to other publicly accessible locations of the data:
- Links/relationships to ancillary datasets: <including other datasets published in Deep Blue Data or other repositories. This should include datasets required for your analyses to run>
- Was data derived from another source?
        - If yes, list source(s):
- Recommended citation for this dataset: [Last, First, Last, First,... et al. (YYYY). Title in italics [Data set], University of Michigan - Deep Blue Data. DOI.]



KEY POINTS <Provide 2-5 bullet points describing the dataset, methodology, and importance>

-
-
-



RESEARCH OVERVIEW

<an abstract that is tailored to the dataset>



METHODOLOGY

IACUC protocol number(s): <if applicable>
IRB approval/exemption number(s): <if applicable>
Other ethical board approval number(s): <if applicable>

Brief description of methods used for the collection/generation of data:
<include links or references to publications or other documentation containing experimental design or protocols used in data collection>

Methods for processing the data:
<briefly describe how the submitted data were generated or transformed from the raw or collected data>

<if the dataset contains human research participant/patient data, mention if certain data or fields were transformed or altered for de-identification. Do not provide a way for users to reverse your de-identification methods>

Instrument- or software-specific information needed to interpret the data:
<include full name and version of software, and any necessary packages or libraries needed to run scripts>

<include any additional methodological information needed to interpret and/or use the data, as appropriate>
- Standards and calibration information, if appropriate:
- Environmental/experimental conditions:
- Describe any quality-assurance procedures performed on the data:
- People involved with sample collection, processing, analysis, and/or submission:



DATA & CODE FILE OVERVIEW

File List: <list all files and folders, as appropriate for dataset organization, contained in the dataset, with a brief description of each>

- Relationship between files, if important:
- Expected input and output files for code:
- Additional related data collected that was not included in the current data package:


DATA-SPECIFIC INFORMATION FOR: [FILENAME]

<repeat this section for each dataset, folder, or file, as appropriate. For code files/scripts, include the file name and description of what the code file/script does, including any libraries, packages, or other software and their versions, if applicable. See below for more information on software and code headers>

- Number of variables:
- Number of cases/rows:
- Variable List: <list variable/header name(s), description(s), unit(s) and value labels as appropriate for each. This can also be a separate data dictionary>
- Missing data codes: <list code/symbol and definition; explain why data are missing so users can understand the missing data>
- Specialized formats or other abbreviations used:
- Link to GitHub repository with relevant code: <if applicable; please also upload any code files needed for the dataset>



SOFTWARE INFORMATION

INSTALLATION

- Step-by-step instructions: <Provide step-by-step installation instructions to install and set up your software project on a user's system>
- System requirements: <Provide system requirements, such as the operating system and any other system-level dependencies>
- Required libraries, packages, modules: <Provide a list of required dependencies (e.g., libraries, packages, modules, etc.) A package management tool can generate a list of dependencies for a project (e.g., Python's pip freeze will output a list of installed packages in a format that can be used to create a "requirements.txt" file)>
- Setup requirements: < Describe any setup requirements (e.g., environment variables, configuration files)>
- Known issues: <Provide any known issues or caveats during installation. (e.g., compatibility issues or known bugs)>


USAGE

- Step-by-step instructions: <Provide instructions on the computing environment and how to run the software or execute the code after all of the required software projects have been installed, and include a brief description of what the expected output or behaviour should be>
        <Provide usage examples>
        <Include screenshots in your deposit where appropriate>
        <Document how to run any built-in tests>
- Known limitations: <Note any known caveats or limitations>


CODE HEADERS

<if applicable, consider adding a code header to your scripts. We provide an example below, and here is an example for R files: https://blog.devgenius.io/how-to-automatically-create-headers-in-r-scripts-be69152ac23f>

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File: research_analysis.filetype
Code Creator: Your Name
Contact: Your Stable Email
Date executed: YYYY-MM-DD
Code License:

Operating System (and version as well as bit number if appropriate) executed on: e.g., Windows 10 Enterprise 64-bit, Mac, or Linux, etc.

Software Version: X.X.X

Libraries/Dependencies:
Library 1 name: Version X.X.X
Library 2 name: Version X.X.X
...

Data File Inputs:
Give the file format and general outline of data this is designed for, and the files it loads. For example, in "data.csv" or "CensusData.csv"

Outputs:
What the code saves as outputs, including file type and location, e.g., "Outputs/Graphs.pdf" or "Site1/Regression/Results/Results.csv"

To Cite Data: [Last, First, Last, First,... et al. (YYYY). Title in italics [Data set], University of Michigan - Deep Blue Data. DOI.]

Description: Summary of what this script does, e.g., This script performs data analysis for XYZ

Related Publications: List of related publications

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