<commit_before><% @title_text = "Data Management General Guidance" %>
<% @display_menu = 'help' %>
<div class="static-page">
	<h1 class"title">Data Management General Guidance</h1>
	<hr/>
	
	<h2>Table of Contents</h2>
	<ul>
		<li><a title="Introduction" href="#introduction">Introduction</a></li>
		<li><a title="Types of Data" href="#types">Types of Data</a></li>
		<li><a title="File Formats" href="#formats">File Formats</a></li>
		<li><a title="Organizing Files" href="#organize">Organizing Files</a></li>
		<li><a title="Metadata: Data Documentation" href="#metadata">Metadata: Data Documention</a></li>
		<li><a title="Persistent Identifiers" href="#identifiers">Persistent Identifiers</a></li>
		<li><a title="Security and Storage" href="#security">Security and Storage</a></li>
		<li><a title="Sharing and Archiving" href="#share">Sharing and Archiving</a></li>
		<li><a title="Citing Data" href="#cite">Citing Data</a></li>
		<li><a title="Copyright and Privacy" href="#copyright">Copyright and Privacy</a></li>
	</ul>
	
	<h2 id="introduction">Introduction</h2>
	
	<h3>What is a data management plan?</h3>
	<p>A data management plan is a formal document that outlines what you will do with your data during and after a research project. Most researchers collect data with some form of plan in mind, but it's often inadequately documented and incompletely thought out. Many data management issues can be handled easily or avioded entirely by planning ahead. With the right process and framework it doesn't take too long and can pay-off enormously in the long run.</p>
	
	<h3>Who requires a plan?</h3>
	<p>In February of 2013, the White House <a title="Office of Science and Technology Police (OSTP)" href="http://www.whitehouse.gov/administration/eop/ostp" target="_blank">Office of Science and Technology Policy (OSTP)</a> issued a <a title="memorandum" href="http://www.whitehouse.gov/sites/default/files/microsites/ostp/ostp_public_access_memo_2013.pdf" target="_blank">memorandum</a> directing Federal agencies that provide significant research funding to develop a plan to expand public access to research. Among other requriments, the plans must</p>
	<blockquote>Ensure that all extramural researchers receiving Federal grants and contracts for scientific research and intramural researchers develop data management plans, as appropriate, describing how they will provide for long-term preservation of, and access to, scientific data in digital formats resulting from federally funded research, or explaining why long-term preservation and access cannot be justified</blockquote>
	<p>The National Science Foundation (NSF) already requires a 2-page plan as part of the funding proposal process. Soon most or all US Federally funded grants will require some form of data management plan.</p>
	
	<h3>We can help</h3>
	<p>We have been working with internal and external partners to make data management plan development less complicated. By getting to know your research and data, we can match your specific needs with data management best practices in your field to develop a data management plan that works for you. If you do this work at the beginning of your research process, you will have a far easier time following-through and complying with funding agency and publisher requirements.</p>
	<p>We recommend that those applying for funding from US Federal agencies, such as the NSF, use the DMPTool. The DMPTool provides guidance for many of the NSF Directorate and Division requirements, along with links to UC resources, services, and help. <a title="Contact UC3" href="http://www.cdlib.org/services/uc3/contact.html" target="_blank">Contact UC3</a> if you have questions and would like feedback on your plan.</p>
	
	<h2 id="types">Types of Data</h2>
	<p>Research projects generate and collect countelss varieties of data. To forumulate a data management plan, it's useful to categorize  your data in four ways: by source, format, stability, and volume.</p>
	
	<h3>What's the source of the data?</h3>
	<p>Although data comes from many different sources, but they can be grouped into four main categories. The category(ies) your data comes from will affect the choices that you make throughout your data management plan.</p>
	
	<h4>Observational</h4>
	<ul>
		<li>Captured in real-time, typically outside the lab</li>
		<li>Usually irreplaceable and therefore the most important to safeguard</li>
		<li>Examples: Sensor readings, telemetry, survey results, images</li>
	</ul>
	
	<h4>Experimental</h4>
	<ul>
		<li>Typically generated in the lab or under controlled conditions</li>
		<li>Often reproducible, but can be expensive or time-consuming</li>
		<li>Examples: gene sequences, chromatograms, magnetic field readings</li>
	</ul>
	<commit_msg>Fix typo on DMP General Guidance page<commit_after><% @title_text = "Data Management General Guidance" %>
<% @display_menu = 'help' %>
<div class="static-page">
	<h1 class"title">Data Management General Guidance</h1>
	<hr/>
	
	<h2>Table of Contents</h2>
	<ul>
		<li><a title="Introduction" href="#introduction">Introduction</a></li>
		<li><a title="Types of Data" href="#types">Types of Data</a></li>
		<li><a title="File Formats" href="#formats">File Formats</a></li>
		<li><a title="Organizing Files" href="#organize">Organizing Files</a></li>
		<li><a title="Metadata: Data Documentation" href="#metadata">Metadata: Data Documention</a></li>
		<li><a title="Persistent Identifiers" href="#identifiers">Persistent Identifiers</a></li>
		<li><a title="Security and Storage" href="#security">Security and Storage</a></li>
		<li><a title="Sharing and Archiving" href="#share">Sharing and Archiving</a></li>
		<li><a title="Citing Data" href="#cite">Citing Data</a></li>
		<li><a title="Copyright and Privacy" href="#copyright">Copyright and Privacy</a></li>
	</ul>
	
	<h2 id="introduction">Introduction</h2>
	
	<h3>What is a data management plan?</h3>
	<p>A data management plan is a formal document that outlines what you will do with your data during and after a research project. Most researchers collect data with some form of plan in mind, but it's often inadequately documented and incompletely thought out. Many data management issues can be handled easily or avoided entirely by planning ahead. With the right process and framework it doesn't take too long and can pay-off enormously in the long run.</p>
	
	<h3>Who requires a plan?</h3>
	<p>In February of 2013, the White House <a title="Office of Science and Technology Police (OSTP)" href="http://www.whitehouse.gov/administration/eop/ostp" target="_blank">Office of Science and Technology Policy (OSTP)</a> issued a <a title="memorandum" href="http://www.whitehouse.gov/sites/default/files/microsites/ostp/ostp_public_access_memo_2013.pdf" target="_blank">memorandum</a> directing Federal agencies that provide significant research funding to develop a plan to expand public access to research. Among other requriments, the plans must</p>
	<blockquote>Ensure that all extramural researchers receiving Federal grants and contracts for scientific research and intramural researchers develop data management plans, as appropriate, describing how they will provide for long-term preservation of, and access to, scientific data in digital formats resulting from federally funded research, or explaining why long-term preservation and access cannot be justified</blockquote>
	<p>The National Science Foundation (NSF) already requires a 2-page plan as part of the funding proposal process. Soon most or all US Federally funded grants will require some form of data management plan.</p>
	
	<h3>We can help</h3>
	<p>We have been working with internal and external partners to make data management plan development less complicated. By getting to know your research and data, we can match your specific needs with data management best practices in your field to develop a data management plan that works for you. If you do this work at the beginning of your research process, you will have a far easier time following-through and complying with funding agency and publisher requirements.</p>
	<p>We recommend that those applying for funding from US Federal agencies, such as the NSF, use the DMPTool. The DMPTool provides guidance for many of the NSF Directorate and Division requirements, along with links to UC resources, services, and help. <a title="Contact UC3" href="http://www.cdlib.org/services/uc3/contact.html" target="_blank">Contact UC3</a> if you have questions and would like feedback on your plan.</p>
	
	<h2 id="types">Types of Data</h2>
	<p>Research projects generate and collect countelss varieties of data. To forumulate a data management plan, it's useful to categorize  your data in four ways: by source, format, stability, and volume.</p>
	
	<h3>What's the source of the data?</h3>
	<p>Although data comes from many different sources, but they can be grouped into four main categories. The category(ies) your data comes from will affect the choices that you make throughout your data management plan.</p>
	
	<h4>Observational</h4>
	<ul>
		<li>Captured in real-time, typically outside the lab</li>
		<li>Usually irreplaceable and therefore the most important to safeguard</li>
		<li>Examples: Sensor readings, telemetry, survey results, images</li>
	</ul>
	
	<h4>Experimental</h4>
	<ul>
		<li>Typically generated in the lab or under controlled conditions</li>
		<li>Often reproducible, but can be expensive or time-consuming</li>
		<li>Examples: gene sequences, chromatograms, magnetic field readings</li>
	</ul>
	
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