The United States Geological Survey Science Data Lifecycle Model

Faundeen, John L.; Burley, Thomas E.; Carlino, Jennifer; Govoni, David L.; Henkel, Heather S.; Holl, Sally; Hutchison, Vivian B.; Martín, Elizabeth; Montgomery, Ellyn T.; Ladino, Cassandra; Tessler, Steven; Zolly, Lisa

doi:10.3133/ofr20131265

Cited by 45 publications

(44 citation statements)

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“…The DCPVocab was developed to provide specific terms for representing relationships among research practices, types of data, and curation roles and activities (Chao, Cragin, & Palmer, ). Additionally, the many data lifecycle models developed by curation communities may be thought of as an approach to noncomputational process representation (for example, CCSDS, ; Faundeen et al, ; Higgins, ). These models can be helpful when planning or describing data curation work in broad terms.…”

Section: Background: Provenance and Reproducibility Through Workflow mentioning

confidence: 99%

Documenting provenance in noncomputational workflows: Research process models based on geobiology fieldwork in Yellowstone National Park

Thomer

Wickett

Baker

et al. 2018

Asso for Info Science & Tech

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A comprehensive record of research data provenance is essential for the successful curation, management, and reuse of data over time. However, creating such detailed metadata can be onerous, and there are few structured methods for doing so. In this case study of data curation in support of geobiology research conducted at Yellowstone National Park, we describe a method of "Research Process Modeling" for documenting noncomputational data provenance in a structured yet flexible way. The method combines systems analysis techniques to model research activities, the World Wide Web Consortium Provenance (PROV) ontology to illustrate relationships between data products, and simple inventory methods to account for research processes and data products. It also supports collaborative data curation between information professionals and researchers, and is therefore a significant step toward producing more useable and interpretable research data. We demonstrate how this method describes data provenance more robustly than "flat" metadata alone and fills a critical gap in the documentation of provenance for field-based and noncomputational workflows. We discuss potential applications of this approach to other research domains.

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Section: Background: Provenance and Reproducibility Through Workflow mentioning

confidence: 99%

Documenting provenance in noncomputational workflows: Research process models based on geobiology fieldwork in Yellowstone National Park

Thomer

Wickett

Baker

et al. 2018

Asso for Info Science & Tech

View full text Add to dashboard Cite

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“…According to [4], USGS 4 cannot justify or allow the acquisition of useless data. Data must be acquired and maintained to meet a scientific need.…”

Section: ) Usgs Lifecyclementioning

confidence: 99%

“…However, lifecycle management of data or DLM 1 was defined in [3] as the set of processes implemented to manage the data of the company for their definition until withdrawn. USGS 2 in [4] describe that the data must be handled and managed once decision making to collect or use data until they become obsolete or no longer needed. The phases crossed by a data from creation to deletion, or external storage, is initially related to customer needs, context and type of data.…”

Section: Introductionmentioning

confidence: 99%

Data lifecycles analysis: Towards intelligent cycle

Arass

Tikito

Souissi

2017

2017 Intelligent Systems and Computer Vision (ISCV)

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Abstract-As companies generate and handle increasingly large amounts of data along with the Big Data era, several model of data lifecycle have been proposed to deal with this situation. The analysis, the management and the use of data becomes more complicated or almost impossible in some cases for the companies. To transform these data to a knowledge, the choice of the adequate lifecycle that matches with the company expectations becomes essential.For this goal, this paper aims to be a guide to assist companies to choose a lifecycle that fits their data management vision. For this, we identify the relevant criteria of selection cycles and defined a rating system to each of these criteria. In this paper, we study the available lifecycles of data in the literature that we consider relevant. As a result of this study, we classify these cycles following two types: first analysis oriented phases and the second based on relevant criteria.

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“…Whereas a traditional view of data focuses on collecting, processing, analysing data and publishing results only, a life cycle view reveals the additional importance of finding, storing and sharing data [14]. Throughout this article we present a researcher-focused data life cycle framework that has commonalities with other published frameworks [14,[17][18][19][20] but is aimed at life science researchers specifically ( Fig. 1).…”

Section: Introductionmentioning

confidence: 99%

Best Practice Data Life Cycle Approaches for the Life Sciences

Griffin

Khadake

LeMay³

et al. 2017

Preprint

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Throughout history, the life sciences have been revolutionised by technological advances; in our era this is manifested by advances in instrumentation for data generation, and consequently researchers now routinely handle large amounts of heterogeneous data in digital formats. The simultaneous transitions towards biology as a data science and towards a 'life cycle' view of research data pose new challenges. Researchers face a bewildering landscape of data management requirements, recommendations and regulations, without necessarily being able to access data management training or possessing a clear understanding of practical approaches that can assist in data management in their particular research domain.Here we provide an overview of best practice data life cycle approaches for researchers in the life sciences/bioinformatics space with a particular focus on 'omics' datasets and computer-based data processing and analysis. We discuss the different stages of the data life cycle and provide practical suggestions for useful tools and resources to improve data management practices.

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The United States Geological Survey Science Data Lifecycle Model

Cited by 45 publications

References 0 publications

Documenting provenance in noncomputational workflows: Research process models based on geobiology fieldwork in Yellowstone National Park

Documenting provenance in noncomputational workflows: Research process models based on geobiology fieldwork in Yellowstone National Park

Data lifecycles analysis: Towards intelligent cycle

Best Practice Data Life Cycle Approaches for the Life Sciences

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