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references.bib
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@article{wilkinson_fair_2016,
title = {The {FAIR} {Guiding} {Principles} for scientific data management and stewardship},
volume = {3},
copyright = {2016 The Author(s)},
issn = {2052-4463},
url = {https://www.nature.com/articles/sdata201618},
doi = {10.1038/sdata.2016.18},
abstract = {There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.},
language = {en},
number = {1},
urldate = {2024-11-12},
journal = {Scientific Data},
author = {Wilkinson, Mark D. and Dumontier, Michel and Aalbersberg, IJsbrand Jan and Appleton, Gabrielle and Axton, Myles and Baak, Arie and Blomberg, Niklas and Boiten, Jan-Willem and da Silva Santos, Luiz Bonino and Bourne, Philip E. and Bouwman, Jildau and Brookes, Anthony J. and Clark, Tim and Crosas, Mercè and Dillo, Ingrid and Dumon, Olivier and Edmunds, Scott and Evelo, Chris T. and Finkers, Richard and Gonzalez-Beltran, Alejandra and Gray, Alasdair J. G. and Groth, Paul and Goble, Carole and Grethe, Jeffrey S. and Heringa, Jaap and ’t Hoen, Peter A. C. and Hooft, Rob and Kuhn, Tobias and Kok, Ruben and Kok, Joost and Lusher, Scott J. and Martone, Maryann E. and Mons, Albert and Packer, Abel L. and Persson, Bengt and Rocca-Serra, Philippe and Roos, Marco and van Schaik, Rene and Sansone, Susanna-Assunta and Schultes, Erik and Sengstag, Thierry and Slater, Ted and Strawn, George and Swertz, Morris A. and Thompson, Mark and van der Lei, Johan and van Mulligen, Erik and Velterop, Jan and Waagmeester, Andra and Wittenburg, Peter and Wolstencroft, Katherine and Zhao, Jun and Mons, Barend},
month = mar,
year = {2016},
note = {Publisher: Nature Publishing Group},
keywords = {Publication characteristics, Research data},
pages = {160018},
}
@article{bahim_fair_2020,
title = {The {FAIR} {Data} {Maturity} {Model}: {An} {Approach} to {Harmonise} {FAIR} {Assessments}},
volume = {19},
issn = {1683-1470},
shorttitle = {The {FAIR} {Data} {Maturity} {Model}},
url = {https://datascience.codata.org/articles/10.5334/dsj-2020-041},
doi = {10.5334/dsj-2020-041},
abstract = {The CODATA Data Science Journal is a peer-reviewed, open access, electronic journal, publishing papers on the management, dissemination, use and reuse of research data and databases across all research domains, including science, technology, the humanities and the arts. The scope of the journal includes descriptions of data systems, their implementations and their publication, applications, infrastructures, software, legal, reproducibility and transparency issues, the availability and usability of complex datasets, and with a particular focus on the principles, policies and practices for open data. All data is in scope, whether born digital or converted from other sources.},
language = {en-US},
number = {1},
urldate = {2024-11-12},
journal = {Data Science Journal},
author = {Bahim, Christophe and Casorrán-Amilburu, Carlos and Dekkers, Makx and Herczog, Edit and Loozen, Nicolas and Repanas, Konstantinos and Russell, Keith and Stall, Shelley},
month = oct,
year = {2020},
}
@misc{esip_checklist_2022,
type = {online resource},
title = {Checklist to {Examine} {AI}-readiness for {Open} {Environmental} {Datasets}},
url = {https://esip.figshare.com/articles/online_resource/Checklist_to_Examine_AI-readiness_for_Open_Environmental_Datasets/19983722/1},
abstract = {This checklist is developed to help data users, producers, and managers to assess the readiness for level for AI applications and target data improvement in the future.
Note that some of these factors came from the draft readiness matrix developed by the OSTP Subcommittee on Open Science and some have been added based on further research. Definitions for some concepts are listed at the end of this document. This checklist is developed through a collaboration of ESIP Data Readiness Cluster members including representatives from NOAA, NASA, USGS, and other organizations. The checklist will be updated periodically to reflect community feedback.
The current version for the checklist is v.0.2.},
language = {en},
urldate = {2024-11-12},
journal = {figshare},
author = {ESIP, Data Readiness Cluster},
month = jun,
year = {2022},
doi = {10.6084/m9.figshare.19983722.v1},
note = {Publisher: ESIP},
}
@article{jones_quantifying_2019,
title = {Quantifying {FAIR}: metadata improvement and guidance in the {DataONE} repository network},
shorttitle = {Quantifying {FAIR}},
url = {https://knb.ecoinformatics.org/view/doi:10.5063/F14T6GP0},
doi = {10.5063/F14T6GP0},
abstract = {DataONE has consistently focused on interoperability among data repositories to enable seamless access to well-described data on the Earth and the environment. Our existing services promote data discovery and access through harmonization of the diverse metadata specifications used across communities, and through our integrated data search portal and services. In terms of the FAIR principles, we have done a good job at Findable and Accessible, while as a community we have placed less emphasis on Interoperable and Reusable. We present new DataONE services for quantitatively providing guidance on metadata completeness and effectiveness relative to the FAIR principles. The services produce guidance for FAIRness at both the level of an individual data set and trends through time for repository, user, and funder data collections. These analytical results regarding conformance to FAIR principles are preliminary and based on proposed quantitative assessment metrics for FAIR which will be changed with input from the community. The current statistics are based on version 0.2.0 of the DataONE FAIR suite. Thus, these results should not be viewed as conclusive about the data sets presented, but rather illustrate the types of quantitative comparisons that will be able to be made when the FAIR metrics at DataONE have been finalized.},
language = {en},
urldate = {2024-11-12},
author = {Jones, Matthew and Slaughter, Peter and Habermann, Ted},
year = {2019},
note = {Publisher: urn:node:KNB},
}
@misc{jones_ecological_2019,
title = {Ecological {Metadata} {Language} version 2.2.0},
url = {https://eml.ecoinformatics.org},
abstract = {The Ecological Metadata Language (EML) defines a comprehensive vocabulary and a readable XML markup syntax for documenting research data. It is in widespread use in the earth and environmental sciences, and increasingly in other research disciplines as well. EML is a community-maintained specification, and evolves to meet the data documentation needs of researchers who want to openly document, preserve, and share data and outputs. EML includes modules for identifying and citing data packages, for describing the spatial, temporal, taxonomic, and thematic extent of data, for describing research methods and protocols, for describing the structure and content of data within sometimes complex packages of data, and for precisely annotating data with semantic vocabularies. EML includes metadata fields to fully detail data papers that are published in journals specializing in scientific data sharing and preservation.},
urldate = {2024-11-12},
publisher = {KNB Data Repository},
author = {Jones, Matthew and O'Brien, Margaret and Mecum, Bryce and Boettiger, Carl and Schildhauer, Mark and Maier, Mitchell and Whiteaker, Timothy and Earl, Stevan and Chong, Steven},
year = {2019},
doi = {10.5063/F11834T2},
keywords = {metadata},
}
@article{wyngaard_emergent_2019,
title = {Emergent {Challenges} for {Science} {sUAS} {Data} {Management}: {Fairness} through {Community} {Engagement} and {Best} {Practices} {Development}},
volume = {11},
copyright = {http://creativecommons.org/licenses/by/3.0/},
issn = {2072-4292},
shorttitle = {Emergent {Challenges} for {Science} {sUAS} {Data} {Management}},
url = {https://www.mdpi.com/2072-4292/11/15/1797},
doi = {10.3390/rs11151797},
abstract = {The use of small Unmanned Aircraft Systems (sUAS) as platforms for data capture has rapidly increased in recent years. However, while there has been significant investment in improving the aircraft, sensors, operations, and legislation infrastructure for such, little attention has been paid to supporting the management of the complex data capture pipeline sUAS involve. This paper reports on a four-year, community-based investigation into the tools, data practices, and challenges that currently exist for particularly researchers using sUAS as data capture platforms. The key results of this effort are: (1) sUAS captured data—as a set that is rapidly growing to include data in a wide range of Physical and Environmental Sciences, Engineering Disciplines, and many civil and commercial use cases—is characterized as both sharing many traits with traditional remote sensing data and also as exhibiting—as common across the spectrum of disciplines and use cases—novel characteristics that require novel data support infrastructure; and (2), given this characterization of sUAS data and its potential value in the identified wide variety of use case, we outline eight challenges that need to be addressed in order for the full value of sUAS captured data to be realized. We conclude that there would be significant value gained and costs saved across both commercial and academic sectors if the global sUAS user and data management communities were to address these challenges in the immediate to near future, so as to extract the maximal value of sUAS captured data for the lowest long-term effort and monetary cost.},
language = {en},
number = {15},
urldate = {2024-11-15},
journal = {Remote Sensing},
author = {Wyngaard, Jane and Barbieri, Lindsay and Thomer, Andrea and Adams, Josip and Sullivan, Don and Crosby, Christopher and Parr, Cynthia and Klump, Jens and Raj Shrestha, Sudhir and Bell, Tom},
month = jan,
year = {2019},
note = {Number: 15
Publisher: Multidisciplinary Digital Publishing Institute},
keywords = {community, data, drone, FAIR, management, practices, RPAS, standards, sUAS, UAV},
pages = {1797},
}
@misc{thomer_minimum_2020,
title = {A minimum information framework the {FAIR} collection of earth and environmental science data with drones},
url = {https://zenodo.org/records/4124167},
abstract = {This repository contains a minimum information framework (MIF) for data collected by small unmanned aerial systems (AKA sUAS AKA RPAs AKA drones). A MIF is essentially a framework for the development for further data standards; it enumerates the metadata needed for the collection of FAIR (Findable Accessible Interoperable and Reusable) scientific data with drones/sUAS/RPAs.
The MIF was drafted through examination of 3 case studies of data collection with drones, and then refined through iterative rounds of community feedback and reflection on the authors' own work with drone-based data collection. We are currently writing a short paper further describing the development of the standard.
This project was funded as an ESIP Lab and we thank ESIP for their support.
Please cite as: Thomer, Andrea K., Swanz, Sarah, Barbieri, Lindsay, Wyngaard, Jane. (2020). A minimum information framework the FAIR collection of earth and environmental science data with drones. DOI: 10.5281/zenodo.4017647},
urldate = {2024-11-15},
publisher = {Zenodo},
author = {Thomer, Andrea and Swanz, Sarah and Barbieri, Lindsay and Wyngaard, Jane},
month = oct,
year = {2020},
doi = {10.5281/zenodo.4124167},
}
@misc{marco_a_janssen_towards_2008,
type = {Text.{Article}},
title = {Towards a {Community} {Framework} for {Agent}-{Based} {Modelling}},
copyright = {JASSS@soc.surrey.ac.uk},
url = {https://www.jasss.org/11/2/6.html},
abstract = {Agent-based modelling has become an increasingly important tool for scholars studying social and social-ecological systems, but there are no community standards on describing, implementing, testing and teaching these tools. This paper reports on the establishment of the Open Agent-Based Modelling Consortium, www.openabm.org, a community effort to foster the agent-based modelling development, communication, and dissemination for research, practice and education.},
language = {en},
urldate = {2024-11-15},
author = {Marco A. Janssen, Lilian Na'ia Alessa},
month = mar,
year = {2008},
note = {Publisher: JASSS},
}
@article{simmonds_addressing_2020,
title = {Addressing {Model} {Data} {Archiving} {Needs} for the {Department} of {Energy}’s {Environmental} {Systems} {Science} {Community}},
copyright = {CC BY Attribution 4.0 International},
url = {https://eartharxiv.org/repository/view/260/},
abstract = {Researchers in the Department of Energy’s ESS program use a variety of models to advance robust, scale-aware predictions of terrestrial and subsurface ecosystems. ESS projects typically conduct field observations and experiments coupled with modeling exercises using a model-experimental (ModEx) approach that enables iterative co-development of experiments and models, and ensures that experimental data needed to parameterize and test models are collected. Thus preserving “model data” comprising the outputs from simulations, as well as driving, parameterization and validation data with associated codes is becoming increasingly important. The ESS-DIVE repository stores data associated with the ESS programs and conducted a months long survey of the ESS community to identify needs for archiving, sharing, and utilizing model data. Here, we present the results of the community survey, and the proposed ESS-DIVE approach over the short-term (next 3 years) and long-term (4-10 years) to support the needs of the ESS modeling community. In the short-term ESS-DIVE proposes to work on functionality that supports archiving of model data associated with publications, with an emphasis on developing community guidelines and standards that make the data more discoverable, accessible and usable. The long-term vision is to broadly enable data-model integration, and knowledge generation from model and observational data. This vision will be achieved through close partnerships with the ESS community.},
language = {en},
urldate = {2024-11-15},
author = {Simmonds, Maegen and Riley, William J. and Cholia, Shreyas and Varadharajan, Charuleka},
month = may,
year = {2020},
note = {Publisher: EarthArXiv},
}