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Data management plan

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also claimed to increase research efficiency since both the data collector and other researchers might be able to understand and use well-annotated data in the future. One component of a data management plan is data archiving and preservation. By deciding on an archive ahead of time, the data collector can format data during collection to make its future submission to a database easier. If data are preserved, they are more relevant since they can be re-used by other researchers. It also allows the data collector to direct requests for data to the database, rather than address requests individually. A frequent argument in favor of preservation is that data that are preserved have the potential to lead to new, unanticipated discoveries, and they prevent duplication of scientific studies that have already been conducted. Data archiving also provides insurance against loss by the data collector.
376:(ESRC) have had a research data policy in place. The current ESRC Research Data Policy states that research data created as a result of ESRC-funded research should be openly available to the scientific community to the maximum extent possible, through long-term preservation and high-quality data management. 379:
ESRC requires a data management plan for all research award applications where new data are being created. Such plans are designed to promote a structured approach to data management throughout the data lifecycle, resulting in better quality data that is ready to archive for sharing and re-use. The
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Preparing a data management plan before data are collected is claimed to ensure that data are in the correct format, organized well, and better annotated. This could arguably save time in the long term because there is no need to re-organize, re-format, or try to remember details about data. It is
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Researchers should identify an appropriate archive for the long-term preservation of their data. By identifying the archive early in the project, the data can be formatted, transformed, and documented appropriately to meet the requirements of the archive. Researchers should consult colleagues and
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practices. Most funders do not require a DMP after grants are awarded, thus robbing stakeholders of the powerful tool that an active DMP can be. Best practice would be to "require maintenance of the data management plan following award and during the active phase of a study." At present, data
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and analysis, and expanded across engineering and scientific disciplines in the 1970s and 1980s. Up until the early 2000s, DMPs were used "for projects of great technical complexity, and for limited mid-study data collection and processing purposes". In the 2000s and later,
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are the contextual details, including any information important for using data. This may include descriptions of temporal and spatial details, instruments, parameters, units, files, etc. Metadata is commonly referred to as “data about data”. Issues to be considered include:
316:(NSF) must include a Data Management Plan that is no more than two pages. This is a supplement (not part of the 15-page proposal) and should describe how the proposal will conform to the Award and Administration Guide policy (see below). It may include the following: 395:, as a place of deposit for research data, with award holders required to offer data resulting from their research grants via the UK Data Service. The Archive enables data re-use by preserving data and making them available to the research and teaching communities. 289:
Data management and preservation costs may be considerable, depending on the nature of the project. By anticipating costs ahead of time, researchers ensure that the data will be properly managed and archived. Potential expenses that should be considered are
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There are three major themes identified in the literature in terms of benefits of DMPs: professional benefits, economic benefits and institutional benefits. It has been argued that DMPs can form a catalyst for researchers to improve their
420:'s list of criteria for a DMP. Researchers shared DMP text. DMPs are often regarded as an "administrative exercise rather than an integral part" of the research process, and it has been acknowledged that DMPs do not guarantee good 276:
Early in the project, the primary researcher should identify what data will be preserved in an archive. Usually, preserving the data in its most raw form is desirable, although data derivatives and products can also be
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Include information about how data will be shared, including when the data will be accessible, how long the data will be available, how access can be gained, and any rights that the data collector reserves for using
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issues. Who owns the copyright? What are the institutional, publisher, and/or funding agency policies associated with intellectual property? Are there embargoes for political, commercial, or patent reasons?
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An individual should be identified as the primary contact person for archived data, and ensure contact information is always kept up-to-date in case there are requests for data or information about data.
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professional societies in their discipline to determine the most appropriate database, and include a backup archive in their data management plan in case their first choice goes out of existence.
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Indicate how the data should be cited by others. How will the issue of persistent citation be addressed? For example, if the data will be deposited in a public archive, will the dataset have a
80:"There is no general and definitive list of topics that should be covered in a DMP for a research project", and researchers are often left to their own devices as to how to fill out a DMP. 214:
Describe any obligations that exist for sharing data collected. These may include obligations from funding agencies, institutions, other professional organizations, and legal requirements.
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If existing data are used, what are their origins? How will the data collected be combined with existing data? What is the relationship between the data collected and existing data?
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In practice, however, DMPs often fall short of their stated goals. A 2012 review of DMP policies by research funders found that policies were missing several elements from the
384:, the ESRC's flagship data service, provides practical guidance on research data management planning suitable for social science researchers in the UK and around the world. 72:
In the 2010s, funding agencies increasingly required data management plans as part of the proposal and evaluation process, despite little or no evidence of their efficacy.
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Investigators can keep their legal rights over their intellectual property, but they still have to make their results, data, and collections available to others
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Dietrich, Dianne; Adamus, Trisha; Miner, Alison; Steinhart, Gail (2012). "De-Mystifying the Data Management Requirements of Research Funders".
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before the project begins; this may lead to data being well-managed in the present, and prepared for preservation in the future.
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Policy summarized from the NSF Award and Administration Guide, Section 4 (Dissemination and Sharing of Research Results):
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Quality assurance & quality control measures that will be taken during sample collection, analysis, and processing.
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Share data, samples, physical collections, and supporting materials with others, within a reasonable time frame
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A description of data to be produced by the project. This might include (but is not limited to) data that are:
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Parham, Susan Wells; Doty, Chris (October 2012). "NSF DMP content analysis: What are researchers saying?".
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commonly used in the respective scientific discipline? There should be justification for the format chosen.
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Hardware and/or software needed for data management, backing up, security, documentation, and preservation
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File formats that will be used, justify those formats, and describe the naming conventions used.
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Donelly, Martin (2012). "Data management plans and planning". In Pryor, Graham (ed.).
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Smale, Nicholas; Unsworth, Kathryn; Denyer, Gareth; Barr, Daniel (17 October 2018).
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is completed. The goal of a data management plan is to consider the many aspects of
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DMPs were originally used in 1966 to manage aeronautical and engineering projects'
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Miksa, Tomasz; Simms, Stephanie; Mietchen, Daniel; Jones, Sarah (28 March 2019).
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Personnel time for data preparation, management, documentation, and preservation
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After collection, how will the data be processed? Include information about
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How detailed has the metadata to be in order to make the data meaningful?
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sharing plans are more important than data management plans to funders.
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How will the data be managed in the short-term? Consider the following:
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Bulletin of the American Society for Information Science and Technology
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Williams, Mary; Bagwell, Jacqueline; Nahm Zozus, Meredith (July 2017).
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Burnette, Margaret; Williams, Sarah; Imker, Heidi (16 September 2016).
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The data management plan should include how these costs will be paid.
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How will the data be acquired? When and where will they be acquired?
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How will the metadata be created and/or captured? Examples include
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The standards to be used for data and metadata format and content
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and economic policies drove the development and uptake of DMPs.
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are to be handled both during a research project, and after the
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and data management practices, often aided by the library.
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Prepare and manage data: Guidance from the UK Data Service
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What format will be used for the metadata? What are the
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Costs associated with submitting the data to an archive
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Address any ethical or privacy issues with data sharing
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Describe the intended future uses/users for the data
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Security & protection of data and data products
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London: Facet Publishing. pp. 83–104. 387:ESRC has a longstanding arrangement with the 1059:Delivering research data management services 752:"Tools for version control of research data" 613: 535: 533: 531: 529: 527: 525: 484: 367: 340:Promptly publish with appropriate authorship 307: 920: 997: 987: 944: 652: 522: 513: 209:Policies for access, sharing, and re-use 84:Information about data & data format 1028: 24:is a formal document that outlines how 1127: 735:: CS1 maint: archived copy as title ( 171:Who will be responsible for management 1056: 267:Long-term storage and data management 875:"UK Data Archive - WHO CAN DEPOSIT?" 677:"Elements of a Data Management Plan" 374:Economic and Social Research Council 75: 13: 1051: 329:Policies and provisions for re-use 14: 1146: 1075: 632:Journal of Biomedical Informatics 502:Journal of eScience Librarianship 358:Award negotiations and conditions 352:Policies will be implemented via 312:All grant proposals submitted to 165:Backing up data and data products 773:Michener,WK and JW Brunt. 2000. 749: 1119:Plan de GestiĂłn de Datos PaGoDa 1022: 953: 914: 885: 867: 838: 827: 816: 798: 780: 767: 326:Policies for access and sharing 823:ESRC Research Data Policy 2010 743: 693: 669: 588: 570: 474:University of Virginia Library 462: 411: 1: 1103:NSF Grant Proposal Guidelines 455: 346:Share software and inventions 63: 989:10.1371/journal.pcbi.1006750 937:10.1002/bult.2012.1720390113 7: 428: 398: 314:National Science Foundation 178:Metadata content and format 10: 1151: 967:PLOS Computational Biology 777:. Blackwell Science, 180p. 645:10.1016/j.jbi.2017.05.004 368:ESRC Data Management Plan 284: 515:10.7191/jeslib.2016.1101 332:Plans for archiving data 308:NSF Data Management Plan 1098:Digital Curation Centre 1082:Data Stewardship Wizard 418:Digital Curation Centre 1057:Pryor, Graham (2014). 1031:Managing research data 879:www.data-archive.ac.uk 470:"Data Management Plan" 760:University of Antwerp 372:Since 1995, the UK's 240:persistent identifier 226:intellectual property 1061:. Facet Publishing. 141:Scientific workflows 117:Curriculum materials 108:Physical collections 18:data management plan 980:2019PLSCB..15E6750M 681:www.icpsr.umich.edu 393:University of Essex 361:Support/incentives 202:metadata standards 708:libraries.mit.edu 582:libraries.mit.edu 320:The types of data 262:) assigned to it? 42:data preservation 1142: 1072: 1045: 1044: 1026: 1020: 1019: 1001: 991: 957: 951: 950: 948: 918: 912: 911: 908:10.5062/F44M92G2 889: 883: 882: 871: 865: 864: 862: 861: 852:. 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Index

data
project
data management
metadata
data preservation
analysis
data collection
E-research
Experimental
Observational
Raw
Scientific workflows
Version control
Metadata
lab notebooks
metadata standards
intellectual property
copyright
persistent identifier
ARK
DOI
Handle
PURL
URN
National Science Foundation
Economic and Social Research Council
UK Data Service
UK Data Archive
University of Essex
data literacy

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