Ten Simple Rules for Digital Data Storage

Data is the central currency of science, but the nature of scientific data has changed dramatically with the rapid pace of technology. This change has led to the development of a wide variety of data formats, dataset sizes, data complexity, data use cases, and data sharing practices. Improvements in high throughput DNA sequencing, sustained institutional support for large sensor networks, and sky surveys with large-format digital cameras have created massive quantities of data. At the same time, the combination of increasingly diverse research teams and data aggregation in portals (e.g. for biodiversity data, GBIF or iDigBio) necessitates increased coordination among data collectors and institutions. As a consequence, “data” can now mean anything from petabytes of information stored in professionally-maintained databases, through spreadsheets on a single computer, to hand-written tables in lab notebooks on shelves. All remain important, but data curation practices must continue to keep pace with the changes brought about by new forms and practices of data collection and storage.

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Ten simple rules for digital data storage

10.7287/peerj.preprints.1448v1 ◽

2015 ◽

Author(s):

Edmund Hart ◽

Pauline Barmby ◽

David LeBauer ◽

François Michonneau ◽

Sarah Mount ◽

...

Keyword(s):

Data Storage ◽

Scientific Data ◽

Digital Data ◽

Digital Cameras ◽

Data Formats ◽

Simple Rules ◽

High Throughput Dna Sequencing ◽

Rapid Pace ◽

Digital Data Storage ◽

And Storage

Data is the central currency of science, but the nature of scientific data has changed dramatically with the rapid pace of technology. This change has led to the development of a wide variety of data formats, dataset sizes, data complexity, data use cases, and data sharing practices. Improvements in high throughput DNA sequencing, sustained institutional support for large sensor networks, and sky surveys with large-format digital cameras have created massive quantities of data. At the same time, the combination of increasingly diverse research teams and data aggregation in portals (e.g. for biodiversity data, GBIF or iDigBio) necessitates increased coordination among data collectors and institutions. As a consequence, “data” can now mean anything from petabytes of information stored in professionally-maintained databases, through spreadsheets on a single computer, to hand-written tables in lab notebooks on shelves. All remain important, but data curation practices must continue to keep pace with the changes brought about by new forms and practices of data collection and storage.

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Ten simple rules for digital data storage

10.7287/peerj.preprints.1448v2 ◽

2016 ◽

Cited By ~ 1

Author(s):

Edmund Hart ◽

Pauline Barmby ◽

David LeBauer ◽

François Michonneau ◽

Sarah Mount ◽

...

Keyword(s):

Data Storage ◽

Scientific Data ◽

Digital Data ◽

Digital Cameras ◽

Data Formats ◽

Simple Rules ◽

High Throughput Dna Sequencing ◽

Rapid Pace ◽

Digital Data Storage ◽

And Storage

Data is the central currency of science, but the nature of scientific data has changed dramatically with the rapid pace of technology. This change has led to the development of a wide variety of data formats, dataset sizes, data complexity, data use cases, and data sharing practices. Improvements in high throughput DNA sequencing, sustained institutional support for large sensor networks, and sky surveys with large-format digital cameras have created massive quantities of data. At the same time, the combination of increasingly diverse research teams and data aggregation in portals (e.g. for biodiversity data, GBIF or iDigBio) necessitates increased coordination among data collectors and institutions. As a consequence, “data” can now mean anything from petabytes of information stored in professionally-maintained databases, through spreadsheets on a single computer, to hand-written tables in lab notebooks on shelves. All remain important, but data curation practices must continue to keep pace with the changes brought about by new forms and practices of data collection and storage.

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New Trends in Digital Data Storage for the Internet of Things

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v6i3.359363 ◽

2018 ◽

Vol 6 (3) ◽

pp. 359-363

Author(s):

A. Saxena ◽

◽

S. Sharma ◽

S. Dangi ◽

A. Sharma ◽

...

Keyword(s):

Internet Of Things ◽

Data Storage ◽

Digital Data ◽

The Internet ◽

Digital Data Storage ◽

The Internet Of Things

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Analog and digital data storage in a phase-coded holographic memory

10.1117/12.304980 ◽

1998 ◽

Cited By ~ 3

Author(s):

Kai-Oliver Mueller ◽

Cornelia Denz ◽

Torsten Rauch ◽

Thorsten Heimann ◽

J. Trumpfheller ◽

...

Keyword(s):

Data Storage ◽

Digital Data ◽

Holographic Memory ◽

Digital Data Storage

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Instance Selection

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch161 ◽

2011 ◽

pp. 1041-1045

Author(s):

Huan Liu

Keyword(s):

Data Mining ◽

Data Storage ◽

Data Reduction ◽

Effective Means ◽

Digital Data ◽

Data Selection ◽

Instance Selection ◽

Redundant Data ◽

Performance Deterioration ◽

Digital Data Storage

The amounts of data become increasingly large in recent years as the capacity of digital data storage worldwide has significantly increased. As the size of data grows, the demand for data reduction increases for effective data mining. Instance selection is one of the effective means to data reduction. This article introduces basic concepts of instance selection, its context, necessity and functionality. It briefly reviews the state-of-the-art methods for instance selection. Selection is a necessity in the world surrounding us. It stems from the sheer fact of limited resources. No exception for data mining. Many factors give rise to data selection: data is not purely collected for data mining or for one particular application; there are missing data, redundant data, and errors during collection and storage; and data can be too overwhelming to handle. Instance selection is one effective approach to data selection. It is a process of choosing a subset of data to achieve the original purpose of a data mining application. The ideal outcome of instance selection is a model independent, minimum sample of data that can accomplish tasks with little or no performance deterioration.

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