Ecological data sharing

Michener, William K.

doi:10.1016/j.ecoinf.2015.06.010

Cited by 239Research Data Management PracticesScientific Computing and Data ManagementData Analysis and Archiving

Ecological data sharing

Open access

Michener, William K.·2015·Ecological Informatics

DOI 10.1016/j.ecoinf.2015.06.010

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Abstract

Data sharing is the practice of making data available for use by others. Ecologists are increasingly generating and sharing an immense volume of data. Such data may serve to augment existing data collections and can be used for synthesis efforts such as meta-analysis, for parameterizing models, and for verifying research results (i.e., study reproducibility). Large volumes of ecological data may be readily available through institutions or data repositories that are the most comprehensive available and can serve as the core of ecological analysis. Ecological data are also employed outside the research context and are used for decision-making, natural resource management, education, and other purposes. Data sharing has a long history in many domains such as oceanography and the biodiversity sciences (e.g., taxonomic data and museum specimens), but has emerged relatively recently in the ecological sciences. A review of several of the large international and national ecological research programs that have emerged since the mid-1900s highlights the initial failures and more recent successes as well as the underlying causes—from a near absence of effective policies to the emergence of community and data sharing policies coupled with the development and adoption of data and metadata standards and enabling tools. Sociocultural change and the move towards more open science have evolved more rapidly over the past two decades in response to new requirements set forth by governmental organizations, publishers and professional societies. As the scientific culture has changed so has the cyberinfrastructure landscape. The introduction of community-based data repositories, data and metadata standards, software tools, persistent identifiers, and federated search and discovery have all helped promulgate data sharing. Nevertheless, there are many challenges and opportunities especially as we move towards more open science. Cyberinfrastructure challenges include a paucity of easy-to-use metadata management systems, significant difficulties in assessing data quality and provenance, and an absence of analytical and visualization approaches that facilitate data integration and harmonization. Challenges and opportunities abound in the sociocultural arena where funders, researchers, and publishers all have a stake in clarifying policies, roles and responsibilities, as well as in incentivizing data sharing. A set of best practices and examples of software tools are presented that can enable research transparency, reproducibility and new knowledge by facilitating idea generation, research planning, data management and the dissemination of data and results.

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Individual and systematic barriers hinder ecological information sharing.
“Individual and systematic barriers hinder ecological information sharing (Michener, 2015)”
From Policy-driven monitoring and evaluation: Does it support adaptive management of socio-ecological systems? (2019) · cites this paper as Michener, 2015
Calls for transparency are not always easily achieved.
“Firstly, the calls for transparency are not always easily achieved.”
From Policy-driven monitoring and evaluation: Does it support adaptive management of socio-ecological systems? (2019) · cites this paper as Michener, 2015

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Authors: Michener, W. K.
Affiliations: University of New Mexico (US)
Corresponding author: Michener, William K.
ORCID: Michener, William K.
Venue: Ecological Informatics
Publisher: Elsevier BV
Published: 2015-09
Volume: 29
Pages: 33-44
ISSN: 1574-9541 (ISSN-L)
DOI: 10.1016/j.ecoinf.2015.06.010
OpenAlex: W855927983
Cited by: 239
References: 79
Citations by year: 2026: 14 · 2025: 27 · 2024: 24 · 2023: 13 · 2022: 26 · 2021: 30 · 2020: 18 · 2019: 31 · 2018: 26 · 2017: 18 · 2016: 11
FWCI: 50.34
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Ecological Informatics (publishedVersion, cc-by)
Funders: NSF (IIA-1301346, IIA-1329470, ACI-1430508)
Topics: Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
Social Sciences › Sociology and Political Science › Data Analysis and Archiving
Keywords: data repositories, open science, metadata standards, data quality, data provenance, cyberinfrastructure, research reproducibility, data sharing policy, persistent identifiers, research transparency, data management, natural resource management

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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Ecological data sharing

Abstract

Integrity signals

Claims the paper makes

Claims other papers make about this one

Cites

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Record

About these checks