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

The DOI matches its Crossref record; 78 of the 79 references it cites were found in a registry or an index.

Ecological data sharing

Open access
Michener, William K.·2015·Ecological Informatics
DOI 10.1016/j.ecoinf.2015.06.010
Open this paper in CoChat — free accountOpen PDF

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.

Overview

Integrity signals

Registry

Crossref · 10.1016/j.ecoinf.2015.06.010

Cites

78 of 78 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Show all 79 references

Research works printed but not linked: 1

  1. Juday, 1946, List of publications dealing with Wisconsin limnology 1871–1945, Trans. Wis. Acad. Sci. Arts Lett., 36, 469

Cited by

Sorted by citations

Items 201–239 of 239

Related

See all

Metadata

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
Licence
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

About these checks

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.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review