A data management workflow of biodiversity data from the field to data users

Hackett, Rachel; Belitz, Michael W.; Gilbert, Edward; Monfils, Anna

doi:10.1002/aps3.11310

“…New technologies introduced in the last decade brought the flourishing of many new omics disciplines and multidisciplinary research approaches. This inherent heterogeneity of data sources and competencies now requires specific actions for the development and uniform adoption of standard procedures to be followed for enhanced reproducibility, sharing and reuse 5 . These actions are essential for advancing science and the utilization of research results for economic and societal benefits.…”

Section: Amendments From Versionmentioning

confidence: 99%

The need for standardisation in life science research - an approach to excellence and trust.

Hollmann

¹

,

Kremer²,

Baebler

³

et al. 2021

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Today, academic researchers benefit from the changes driven by digital technologies and the enormous growth of knowledge and data, on globalisation, enlargement of the scientific community, and the linkage between different scientific communities and the society. To fully benefit from this development, however, information needs to be shared openly and transparently. Digitalisation plays a major role here because it permeates all areas of business, science and society and is one of the key drivers for innovation and international cooperation. To address the resulting opportunities, the EU promotes the development and use of collaborative ways to produce and share knowledge and data as early as possible in the research process, but also to appropriately secure results with the European strategy for Open Science (OS). It is now widely recognised that making research results more accessible to all societal actors contributes to more effective and efficient science; it also serves as a boost for innovation in the public and private sectors. However for research data to be findable, accessible, interoperable and reusable the use of standards is essential. At the metadata level, considerable efforts in standardisation have already been made (e.g. Data Management Plan and FAIR Principle etc.), whereas in context with the raw data these fundamental efforts are still fragmented and in some cases completely missing. The CHARME consortium, funded by the European Cooperation in Science and Technology (COST) Agency, has identified needs and gaps in the field of standardisation in the life sciences and also discussed potential hurdles for implementation of standards in current practice. Here, the authors suggest four measures in response to current challenges to ensure a high quality of life science research data and their re-usability for research and innovation.

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“…Adopt EU-wide training by providing Train-the-Trainer modules and sharing of high-quality teaching materials and methods among education professionals across borders. 4. Include budget for funding programmes or projects, e.g.…”

Section: Measure 3: Roadmap For the Implementation Of Formal Quality mentioning

confidence: 99%

“…New technologies introduced in the last decade brought the flourishing of many new omics disciplines and multidisciplinary research approaches. This inherent heterogeneity of data sources and competencies now requires specific actions for the development and uniform adoption of standard procedures to be followed for enhanced reproducibility, sharing and reuse 4 . These actions are essential for advancing science and the utilization of research results for economic and societal benefits.…”

Section: Introductionmentioning

confidence: 99%

The need for standardisation in life science research - an approach to excellence and trust.

Hollmann

¹

,

Kremer²,

Baebler

³

et al. 2020

View full text Add to dashboard Cite

Today, academic researchers benefit from the changes driven by digital technologies and the enormous growth of knowledge and data, on globalisation, enlargement of the scientific community, and the linkage between different scientific communities and the society. To fully benefit from this development, however, information needs to be shared openly and transparently. Digitalisation plays a major role here because it permeates all areas of business, science and society and is one of the key drivers for innovation and international cooperation. To address the resulting opportunities, the EU promotes the development and use of collaborative ways to produce and share knowledge and data as early as possible in the research process, but also to appropriately secure results with the European strategy for Open Science (OS). It is now widely recognised that making research results more accessible to all societal actors contributes to more effective and efficient science; it also serves as a boost for innovation in the public and private sectors. However for research data to be findable, accessible, interoperable and reusable the use of standards is essential. At the metadata level, considerable efforts in standardisation have already been made (e.g. Data Management Plan and FAIR Principle etc.), whereas in context with the raw data these fundamental efforts are still fragmented and in some cases completely missing. The CHARME consortium, funded by the European Cooperation in Science and Technology (COST) Agency, has identified needs and gaps in the field of standardisation in the life sciences and also discussed potential hurdles for implementation of standards in current practice. Here, the authors suggest four measures in response to current challenges to ensure a high quality of life science research data and their re-usability for research and innovation.

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“…Advantages of born-digital environmental data include improved accuracy, reduced cost, and ready compatibility with remotely-sensed landscape data (Travaini et al 2007). As processing workflows for hand-collected data can be complicated and errorprone (Hackett 2019), reducing the steps needed to prepare data for analysis is desirable. Digital data formatted and organized at the time of collection can also be more easily ingested into other systems.…”

Section: Interoperabilitymentioning

confidence: 99%

“…It is still necessary for trained individuals to venture out into natural areas and do surveys, observe plants and animals, and record other vital information about the environment. These field workers often collect heterogenous data using a variety of tools; just one study of prairie fens in Michigan involved GPS units, digital cameras, audio recorders, and paper notebooks (Hackett 2019). Despite advances in remote sensing, data-driven biodiversity research requires more in-person environmental monitoring than ever.…”

Section: Introductionmentioning

confidence: 99%

Data Infrastructure and Local Stakeholder Engagement with Biodiversity Conservation Research

Krzton¹

2019

JeSLIB

0

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Biodiversity research that informs conservation action is increasingly data intensive. Cutting-edge projects at large institutions use massive aggregated datasets to build dynamic models and conduct novel analyses of natural systems. Most of these research institutions are geographically distant from the highest-priority conservation areas, which are found in South America, Africa, and Southeast Asia. There, data is typically collected by or with the help of local residents hired as field assistants. These field assistants have few meaningful opportunities to participate in biodiversity research and conservation beyond data logging. The literature indicates the data revolution has increased demand for impersonal and integrated large-scale systems that aggregate biodiversity data across sources with minimal friction. In this study, interviews were conducted with six active conservation workers to identify elements of these data systems that create barriers to field assistants' engagement with the projects they make possible. As both creators and consumers of data, all six relayed frustration with various aspects of their data workflows. Regarding field assistant interaction with digital data systems, they observed that their field assistants engaged only at the initial point of data entry or not at all. Some suggested mobile apps as a good solution for field data collection. Abstract ContinuedHowever, some also expressed doubt that their local assistants had the necessary knowledge background to navigate digital systems or understand scientific methodologies. These results suggest that trying to mold field assistants to fit existing data infrastructure and adapting purpose-built data systems to nontechnical users are both sub-optimal solutions. A human-mediated capacity building paradigm, which requires embedding people who are both culturally literate and data literate alongside field assistants, is explored as an alternative path to making data meaningful. Improving the accessibility of data this way can empower local communities to share ownership in biodiversity conservation.Journal of eScience Librarianship e1174 | 3 Data and Local Engagement with Biodiversity Conservation JeSLIB 2019; 8(2): e1174

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A data management workflow of biodiversity data from the field to data users

Cited by 18 publications

References 50 publications

The need for standardisation in life science research - an approach to excellence and trust.

The need for standardisation in life science research - an approach to excellence and trust.

The need for standardisation in life science research - an approach to excellence and trust.

Data Infrastructure and Local Stakeholder Engagement with Biodiversity Conservation Research

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