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Spake L, Hassan A, Schaffnit SB, Alam N, Amoah AS, Badjie J, Cerami C, Crampin A, Dube A, Kaye MP, Kotch R, Liew F, McLean E, Munthali-Mkandawire S, Mwalwanda L, Petersen AC, Prentice AM, Zohora FT, Watts J, Sear R, Shenk MK, Sosis R, Shaver JH. A practical guide to cross-cultural and multi-sited data collection in the biological and behavioural sciences. Proc Biol Sci 2024; 291:20231422. [PMID: 38654647 PMCID: PMC11040250 DOI: 10.1098/rspb.2023.1422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 03/21/2024] [Indexed: 04/26/2024] Open
Abstract
Researchers in the biological and behavioural sciences are increasingly conducting collaborative, multi-sited projects to address how phenomena vary across ecologies. These types of projects, however, pose additional workflow challenges beyond those typically encountered in single-sited projects. Through specific attention to cross-cultural research projects, we highlight four key aspects of multi-sited projects that must be considered during the design phase to ensure success: (1) project and team management; (2) protocol and instrument development; (3) data management and documentation; and (4) equitable and collaborative practices. Our recommendations are supported by examples from our experiences collaborating on the Evolutionary Demography of Religion project, a mixed-methods project collecting data across five countries in collaboration with research partners in each host country. To existing discourse, we contribute new recommendations around team and project management, introduce practical recommendations for exploring the validity of instruments through qualitative techniques during piloting, highlight the importance of good documentation at all steps of the project, and demonstrate how data management workflows can be strengthened through open science practices. While this project was rooted in cross-cultural human behavioural ecology and evolutionary anthropology, lessons learned from this project are applicable to multi-sited research across the biological and behavioural sciences.
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Affiliation(s)
- Laure Spake
- Binghamton University (SUNY), Binghamton, NY, USA
| | - Anushé Hassan
- London School of Hygiene and Tropical Medicine, London, UK
| | | | - Nurul Alam
- International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh
| | - Abena S. Amoah
- London School of Hygiene and Tropical Medicine, London, UK
- Malawi Epidemiology and Intervention Research Unit, Lilongwe, Malawi
- Leiden University Medical Center, Leiden, The Netherlands
| | - Jainaba Badjie
- Medical Research Council Unit The Gambia at the London School of Hygiene and Tropical Medicine (MRCG@LSHTM), Fajara, The Gambia
| | - Carla Cerami
- London School of Hygiene and Tropical Medicine, London, UK
- Medical Research Council Unit The Gambia at the London School of Hygiene and Tropical Medicine (MRCG@LSHTM), Fajara, The Gambia
| | - Amelia Crampin
- International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh
- University of Glasgow, Glasgow, UK
| | - Albert Dube
- Malawi Epidemiology and Intervention Research Unit, Lilongwe, Malawi
| | - Miranda P. Kaye
- Pennsylvania State University, University Park, PA, USA
- University of Chicago, Chicago, IL, USA
| | - Renee Kotch
- Pennsylvania State University, University Park, PA, USA
| | - Frankie Liew
- London School of Hygiene and Tropical Medicine, London, UK
| | - Estelle McLean
- London School of Hygiene and Tropical Medicine, London, UK
- Malawi Epidemiology and Intervention Research Unit, Lilongwe, Malawi
| | | | - Lusako Mwalwanda
- Malawi Epidemiology and Intervention Research Unit, Lilongwe, Malawi
| | | | - Andrew M. Prentice
- London School of Hygiene and Tropical Medicine, London, UK
- Medical Research Council Unit The Gambia at the London School of Hygiene and Tropical Medicine (MRCG@LSHTM), Fajara, The Gambia
| | - Fatema tuz Zohora
- International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh
| | - Joseph Watts
- University of Chicago, Chicago, IL, USA
- University of Canterbury, Christchurch, New Zealand
| | - Rebecca Sear
- London School of Hygiene and Tropical Medicine, London, UK
| | - Mary K. Shenk
- Pennsylvania State University, University Park, PA, USA
| | | | - John H. Shaver
- University of Otago, Dunedin, New Zealand
- Baylor University, Waco, TX, USA
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Korcsok B, Korondi P. How do you do the things that you do? Ethological approach to the description of robot behaviour. Biol Futur 2023; 74:253-279. [PMID: 37812380 DOI: 10.1007/s42977-023-00178-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Accepted: 09/08/2023] [Indexed: 10/10/2023]
Abstract
The detailed description of behaviour of the interacting parties is becoming more and more important in human-robot interaction (HRI), especially in social robotics (SR). With the rise in the number of publications, there is a substantial need for the objective and comprehensive description of implemented robot behaviours to ensure comparability and reproducibility of the studies. Ethograms and the meticulous analysis of behaviour was introduced long ago in animal behaviour research (cf. ethology). The adoption of this method in SR and HRI can ensure the desired clarity over robot behaviours, while also providing added benefits during robot development, behaviour modelling and analysis of HRI experiments. We provide an overview of the possible uses and advantages of ethograms in HRI, and propose a general framework for describing behaviour which can be adapted to the requirements of specific studies.
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Affiliation(s)
- Beáta Korcsok
- ELKH-ELTE Comparative Ethology Research Group, Budapest, Hungary.
- Department of Mechatronics, Optics and Mechanical Engineering Informatics, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Budapest, Hungary.
| | - Péter Korondi
- Department of Mechatronics, Faculty of Engineering, University of Debrecen, Debrecen, Hungary
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Cervantes F, Altwegg R, Strobbe F, Skowno A, Visser V, Brooks M, Stojanov Y, Harebottle DM, Job N. BIRDIE: A data pipeline to inform wetland and waterbird conservation at multiple scales. Front Ecol Evol 2023. [DOI: 10.3389/fevo.2023.1131120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023] Open
Abstract
IntroductionEfforts to collect ecological data have intensified over the last decade. This is especially true for freshwater habitats, which are among the most impacted by human activity and yet lagging behind in terms of data availability. Now, to support conservation programmes and management decisions, these data need to be analyzed and interpreted; a process that can be complex and time consuming. The South African Biodiversity Data Pipeline for Wetlands and Waterbirds (BIRDIE) aims to help fast and efficient information uptake, bridging the gap between raw ecological datasets and the information final users need.MethodsBIRDIE is a full data pipeline that takes up raw data, and estimates indicators related to waterbird populations, while keeping track of their associated uncertainty. At present, we focus on the assessment of species abundance and distribution in South Africa using two citizen-science bird monitoring datasets, namely: the African Bird Atlas Project and the Coordinated Waterbird Counts. These data are analyzed with occupancy and state-space models, respectively. In addition, a suite of environmental layers help contextualize waterbird population indicators, and link these to the ecological condition of the supporting wetlands. Both data and estimated indicators are accessible to end users through an online portal and web services.Results and discussionWe have designed a modular system that includes tasks, such as: data cleaning, statistical analysis, diagnostics, and computation of indicators. Envisioned users of BIRDIE include government officials, conservation managers, researchers and the general public, all of whom have been engaged throughout the project. Acknowledging that conservation programmes run at multiple spatial and temporal scales, we have developed a granular framework in which indicators are estimated at small scales, and then these are aggregated to compute similar indicators at broader scales. Thus, the online portal is designed to provide spatial and temporal visualization of the indicators using maps, time series and pre-compiled reports for species, sites and conservation programmes. In the future, we aim to expand the geographical coverage of the pipeline to other African countries, and develop more indicators specific to the ecological structure and function of wetlands.
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Labuce A, Gorokhova E. A script-based workflow to calculate zooplankton community indicator for environmental status assessment in the Baltic Sea. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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5
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Kim AY, Herrmann V, Bareto R, Calkins B, Gonzalez‐Akre E, Johnson DJ, Jordan JA, Magee L, McGregor IR, Montero N, Novak K, Rogers T, Shue J, Anderson‐Teixeira KJ. Implementing GitHub Actions continuous integration to reduce error rates in ecological data collection. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Albert Y. Kim
- Program in Statistical and Data Sciences; Smith College Northampton Massachusetts USA
- Conservation Ecology Center Smithsonian National Zoo & Conservation Biology Institute Front Royal Virginia USA
| | - Valentine Herrmann
- Conservation Ecology Center Smithsonian National Zoo & Conservation Biology Institute Front Royal Virginia USA
| | - Ross Bareto
- School of Forest, Fisheries, & Geomatics Sciences University of Florida Gainesville Florida USA
| | - Brianna Calkins
- Conservation Ecology Center Smithsonian National Zoo & Conservation Biology Institute Front Royal Virginia USA
| | - Erika Gonzalez‐Akre
- Conservation Ecology Center Smithsonian National Zoo & Conservation Biology Institute Front Royal Virginia USA
| | - Daniel J. Johnson
- School of Forest, Fisheries, & Geomatics Sciences University of Florida Gainesville Florida USA
| | - Jennifer A. Jordan
- Conservation Ecology Center Smithsonian National Zoo & Conservation Biology Institute Front Royal Virginia USA
| | - Lukas Magee
- School of Forest, Fisheries, & Geomatics Sciences University of Florida Gainesville Florida USA
| | - Ian R. McGregor
- Center for Geospatial Analytics North Carolina State University Raleigh North Carolina USA
| | - Nicolle Montero
- School of Forest, Fisheries, & Geomatics Sciences University of Florida Gainesville Florida USA
| | - Karl Novak
- School of Forest, Fisheries, & Geomatics Sciences University of Florida Gainesville Florida USA
| | - Teagan Rogers
- Conservation Ecology Center Smithsonian National Zoo & Conservation Biology Institute Front Royal Virginia USA
| | - Jessica Shue
- Smithsonian Environmental Research Center Edgewater Maryland USA
| | - Kristina J. Anderson‐Teixeira
- Conservation Ecology Center Smithsonian National Zoo & Conservation Biology Institute Front Royal Virginia USA
- Forest Global Earth Observatory Smithsonian Tropical Research Institute Panama Republic of Panama
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Bledsoe EK, Burant JB, Higino GT, Roche DG, Binning SA, Finlay K, Pither J, Pollock LS, Sunday JM, Srivastava DS. Data rescue: saving environmental data from extinction. Proc Biol Sci 2022; 289:20220938. [PMID: 35855607 PMCID: PMC9297007 DOI: 10.1098/rspb.2022.0938] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Historical and long-term environmental datasets are imperative to understanding how natural systems respond to our changing world. Although immensely valuable, these data are at risk of being lost unless actively curated and archived in data repositories. The practice of data rescue, which we define as identifying, preserving, and sharing valuable data and associated metadata at risk of loss, is an important means of ensuring the long-term viability and accessibility of such datasets. Improvements in policies and best practices around data management will hopefully limit future need for data rescue; these changes, however, do not apply retroactively. While rescuing data is not new, the term lacks formal definition, is often conflated with other terms (i.e. data reuse), and lacks general recommendations. Here, we outline seven key guidelines for effective rescue of historically collected and unmanaged datasets. We discuss prioritization of datasets to rescue, forming effective data rescue teams, preparing the data and associated metadata, and archiving and sharing the rescued materials. In an era of rapid environmental change, the best policy solutions will require evidence from both contemporary and historical sources. It is, therefore, imperative that we identify and preserve valuable, at-risk environmental data before they are lost to science.
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Affiliation(s)
- Ellen K. Bledsoe
- The Living Data Project, Canadian Institute of Ecology and Evolution, Vancouver, British Columbia, Canada,School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, USA,Department of Biology, University of Regina, Regina, Saskatchewan, Canada
| | - Joseph B. Burant
- The Living Data Project, Canadian Institute of Ecology and Evolution, Vancouver, British Columbia, Canada,Department of Biology, McGill University, Montreal, Quebec, Canada,Département de sciences biologiques, Université de Montréal, Montréal, Québec, Canada
| | - Gracielle T. Higino
- The Living Data Project, Canadian Institute of Ecology and Evolution, Vancouver, British Columbia, Canada,Department of Zoology and Biodiversity Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Dominique G. Roche
- The Living Data Project, Canadian Institute of Ecology and Evolution, Vancouver, British Columbia, Canada,Department of Biology and Institute for Environment & Interdisciplinary Science, Carleton University, Ottawa, Ontario, Canada
| | - Sandra A. Binning
- The Living Data Project, Canadian Institute of Ecology and Evolution, Vancouver, British Columbia, Canada,Département de sciences biologiques, Université de Montréal, Montréal, Québec, Canada
| | - Kerri Finlay
- The Living Data Project, Canadian Institute of Ecology and Evolution, Vancouver, British Columbia, Canada,Department of Biology, University of Regina, Regina, Saskatchewan, Canada
| | - Jason Pither
- The Living Data Project, Canadian Institute of Ecology and Evolution, Vancouver, British Columbia, Canada,Department of Biology and Okanagan Institute for Biodiversity, Resilience, and Ecosystem Services, University of British Columbia, Kelowna, British Columbia, Canada
| | - Laura S. Pollock
- The Living Data Project, Canadian Institute of Ecology and Evolution, Vancouver, British Columbia, Canada,Department of Biology, McGill University, Montreal, Quebec, Canada
| | - Jennifer M. Sunday
- The Living Data Project, Canadian Institute of Ecology and Evolution, Vancouver, British Columbia, Canada,Department of Biology, McGill University, Montreal, Quebec, Canada
| | - Diane S. Srivastava
- The Living Data Project, Canadian Institute of Ecology and Evolution, Vancouver, British Columbia, Canada,Department of Zoology and Biodiversity Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
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CAN-SAR: A database of Canadian species at risk information. Sci Data 2022; 9:289. [PMID: 35680916 PMCID: PMC9184579 DOI: 10.1038/s41597-022-01381-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 05/11/2022] [Indexed: 11/21/2022] Open
Abstract
Threatened species lists describe the conservation status of species and are key tools used to inform decisions for biodiversity conservation. These lists are rich in information obtained during status assessment and recovery planning processes, ranging from biological attributes to actions that support recovery. Data compiled from species lists allow for analyses, including assessing trends in threats, prioritizing actions, and identifying barriers to achieving recovery objectives. For legally protected species at risk of extinction in Canada, such analyses are challenging owing to a lack of comprehensive and accessible data reflecting information compiled from listing and recovery documents. To encourage ongoing synthesis and minimise duplication of efforts, we initiated CAN-SAR: a database of Canadian Species at Risk information. This transparent, open-access, and searchable database contains information transcribed from listing documents, including listing date, and derived variables. Derived variables required interpretation for which we developed standardised criteria to record information, including classification of recovery actions. The CAN-SAR database is updateable, and will contribute towards improved recovery planning to safeguard species of conservation concern. Measurement(s) | threatened species • threat classes • recovery actions | Technology Type(s) | document review | Sample Characteristic - Organism | multiple | Sample Characteristic - Location | Canada |
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Hurley AG, Peters RL, Pappas C, Steger DN, Heinrich I. Addressing the need for interactive, efficient, and reproducible data processing in ecology with the datacleanr R package. PLoS One 2022; 17:e0268426. [PMID: 35551557 PMCID: PMC9098071 DOI: 10.1371/journal.pone.0268426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 04/29/2022] [Indexed: 11/18/2022] Open
Abstract
Ecological research, just as all Earth System Sciences, is becoming increasingly data-rich. Tools for processing of “big data” are continuously developed to meet corresponding technical and logistical challenges. However, even at smaller scales, data sets may be challenging when best practices in data exploration, quality control and reproducibility are to be met. This can occur when conventional methods, such as generating and assessing diagnostic visualizations or tables, become unfeasible due to time and practicality constraints. Interactive processing can alleviate this issue, and is increasingly utilized to ensure that large data sets are diligently handled. However, recent interactive tools rarely enable data manipulation, may not generate reproducible outputs, or are typically data/domain-specific. We developed datacleanr, an interactive tool that facilitates best practices in data exploration, quality control (e.g., outlier assessment) and flexible processing for multiple tabular data types, including time series and georeferenced data. The package is open-source, and based on the R programming language. A key functionality of datacleanr is the “reproducible recipe”—a translation of all interactive actions into R code, which can be integrated into existing analyses pipelines. This enables researchers experienced with script-based workflows to utilize the strengths of interactive processing without sacrificing their usual work style or functionalities from other (R) packages. We demonstrate the package’s utility by addressing two common issues during data analyses, namely 1) identifying problematic structures and artefacts in hierarchically nested data, and 2) preventing excessive loss of data from ‘coarse,’ code-based filtering of time series. Ultimately, with datacleanr we aim to improve researchers’ workflows and increase confidence in and reproducibility of their results.
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Affiliation(s)
- Alexander G. Hurley
- Climate Dynamics and Landscape Evolution, GFZ German Research Centre for Geosciences, Potsdam, Germany
- * E-mail:
| | - Richard L. Peters
- Laboratory of Plant Ecology, Department of Plants and Crops, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium
- Department of Environmental Sciences, University of Basel, Basel, Switzerland
| | - Christoforos Pappas
- Centre d’étude de la forêt, Université du Québec à Montréal, Montréal, Canada
- Département Science et Technologie, Téluq, Université du Québec, Montréal, Canada
- Department of Civil Engineering, University of Patras, Rio Patras, Greece
| | - David N. Steger
- Climate Dynamics and Landscape Evolution, GFZ German Research Centre for Geosciences, Potsdam, Germany
- Humboldt-Universität zu Berlin, Berlin, Germany
- Natural Sciences Unit, German Archaeological Institute DAI, Berlin, Germany
| | - Ingo Heinrich
- Climate Dynamics and Landscape Evolution, GFZ German Research Centre for Geosciences, Potsdam, Germany
- Humboldt-Universität zu Berlin, Berlin, Germany
- Natural Sciences Unit, German Archaeological Institute DAI, Berlin, Germany
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9
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Ensuring Prevention Science Research is Synthesis-Ready for Immediate and Lasting Scientific Impact. PREVENTION SCIENCE : THE OFFICIAL JOURNAL OF THE SOCIETY FOR PREVENTION RESEARCH 2021; 23:809-820. [PMID: 34291384 DOI: 10.1007/s11121-021-01279-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/23/2021] [Indexed: 12/24/2022]
Abstract
When seeking to inform and improve prevention efforts and policy, it is important to be able to robustly synthesize all available evidence. But evidence sources are often large and heterogeneous, so understanding what works, for whom, and in what contexts can only be achieved through a systematic and comprehensive synthesis of evidence. Many barriers impede comprehensive evidence synthesis, which leads to uncertainty about the generalizability of intervention effectiveness, including inaccurate titles/abstracts/keywords terminology (hampering literature search efforts), ambiguous reporting of study methods (resulting in inaccurate assessments of study rigor), and poorly reported participant characteristics, outcomes, and key variables (obstructing the calculation of an overall effect or the examination of effect modifiers). To address these issues and improve the reach of primary studies through their inclusion in evidence syntheses, we provide a set of practical guidelines to help prevention scientists prepare synthesis-ready research. We use a recent mindfulness trial as an empirical example to ground the discussion and demonstrate ways to ensure the following: (1) primary studies are discoverable; (2) the types of data needed for synthesis are present; and (3) these data are readily synthesizable. We highlight several tools and practices that can aid authors in these efforts, such as using a data-driven approach for crafting titles, abstracts, and keywords or by creating a repository for each project to host all study-related data files. We also provide step-by-step guidance and software suggestions for standardizing data design and public archiving to facilitate synthesis-ready research.
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10
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McCord SE, Webb NP, Van Zee JW, Burnett SH, Christensen EM, Courtright EM, Laney CM, Lunch C, Maxwell C, Karl JW, Slaughter A, Stauffer NG, Tweedie C. Provoking a Cultural Shift in Data Quality. Bioscience 2021; 71:647-657. [PMID: 34084097 PMCID: PMC8169311 DOI: 10.1093/biosci/biab020] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Ecological studies require quality data to describe the nature of ecological processes and to advance understanding of ecosystem change. Increasing access to big data has magnified both the burden and the complexity of ensuring quality data. The costs of errors in ecology include low use of data, increased time spent cleaning data, and poor reproducibility that can result in a misunderstanding of ecosystem processes and dynamics, all of which can erode the efficacy of and trust in ecological research. Although conceptual and technological advances have improved ecological data access and management, a cultural shift is needed to embed data quality as a cultural practice. We present a comprehensive data quality framework to evoke this cultural shift. The data quality framework flexibly supports different collaboration models, supports all types of ecological data, and can be used to describe data quality within both short- and long-term ecological studies.
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Affiliation(s)
- Sarah E McCord
- US Department of Agriculture ARS Jornada Experimental Range, Las Cruces, New Mexico, United States
| | - Nicholas P Webb
- US Department of Agriculture ARS Jornada Experimental Range, Las Cruces, New Mexico, United States
| | - Justin W Van Zee
- US Department of Agriculture ARS Jornada Experimental Range, Las Cruces, New Mexico, United States
| | - Sarah H Burnett
- Bureau of Land Management, National Operations Center, Denver, Colorado, United States
| | - Erica M Christensen
- US Department of Agriculture ARS Jornada Experimental Range, Las Cruces, New Mexico, United States
| | - Ericha M Courtright
- US Department of Agriculture ARS Jornada Experimental Range, Las Cruces, New Mexico, United States
| | - Christine M Laney
- Battelle-National Ecological Observatory Network, Boulder, Colorado, United States
| | - Claire Lunch
- US Department of Agriculture ARS Jornada Experimental Range, Las Cruces, New Mexico, United States
| | - Connie Maxwell
- New Mexico State University, in Las Cruces, New Mexico,United States
| | - Jason W Karl
- Department of Forest, Rangeland, and Fire Sciences, University of Idaho, Moscow, Idaho, United States
| | - Amalia Slaughter
- US Department of Agriculture ARS Jornada Experimental Range, Las Cruces, New Mexico, United States
| | - Nelson G Stauffer
- US Department of Agriculture ARS Jornada Experimental Range, Las Cruces, New Mexico, United States
| | - Craig Tweedie
- University of Texas-El Paso, El Paso, Texas, United States
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Beck MW, O’Hara C, Stewart Lowndes JS, D. Mazor R, Theroux S, J. Gillett D, Lane B, Gearheart G. The importance of open science for biological assessment of aquatic environments. PeerJ 2020; 8:e9539. [PMID: 32742805 PMCID: PMC7377246 DOI: 10.7717/peerj.9539] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 06/23/2020] [Indexed: 11/22/2022] Open
Abstract
Open science principles that seek to improve science can effectively bridge the gap between researchers and environmental managers. However, widespread adoption has yet to gain traction for the development and application of bioassessment products. At the core of this philosophy is the concept that research should be reproducible and transparent, in addition to having long-term value through effective data preservation and sharing. In this article, we review core open science concepts that have recently been adopted in the ecological sciences and emphasize how adoption can benefit the field of bioassessment for both prescriptive condition assessments and proactive applications that inform environmental management. An example from the state of California demonstrates effective adoption of open science principles through data stewardship, reproducible research, and engagement of stakeholders with multimedia applications. We also discuss technical, sociocultural, and institutional challenges for adopting open science, including practical approaches for overcoming these hurdles in bioassessment applications.
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Affiliation(s)
- Marcus W. Beck
- Southern California Coastal Water Research Project, Costa Mesa, CA, USA
- Tampa Bay Estuary Program, Saint Petersburg, FL, USA
| | - Casey O’Hara
- Bren School of Environmental Sciences & Management, University of California, Santa Barbara, CA, USA
| | | | - Raphael D. Mazor
- Southern California Coastal Water Research Project, Costa Mesa, CA, USA
| | - Susanna Theroux
- Southern California Coastal Water Research Project, Costa Mesa, CA, USA
| | - David J. Gillett
- Southern California Coastal Water Research Project, Costa Mesa, CA, USA
| | - Belize Lane
- Department of Civil and Environmental Engineering, Utah Water Research Laboratory, Utah State University, Logan, UT, USA
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Taylor SD, White EP. Automated data-intensive forecasting of plant phenology throughout the United States. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2020; 30:e02025. [PMID: 31630468 PMCID: PMC9285964 DOI: 10.1002/eap.2025] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 08/16/2019] [Accepted: 09/04/2019] [Indexed: 05/29/2023]
Abstract
Phenology, the timing of cyclical and seasonal natural phenomena such as flowering and leaf out, is an integral part of ecological systems with impacts on human activities like environmental management, tourism, and agriculture. As a result, there are numerous potential applications for actionable predictions of when phenological events will occur. However, despite the availability of phenological data with large spatial, temporal, and taxonomic extents, and numerous phenology models, there have been no automated species-level forecasts of plant phenology. This is due in part to the challenges of building a system that integrates large volumes of climate observations and forecasts, uses that data to fit models and make predictions for large numbers of species, and consistently disseminates the results of these forecasts in interpretable ways. Here, we describe a new near-term phenology-forecasting system that makes predictions for the timing of budburst, flowers, ripe fruit, and fall colors for 78 species across the United States up to 6 months in advance and is updated every four days. We use the lessons learned in developing this system to provide guidance developing large-scale near-term ecological forecast systems more generally, to help advance the use of automated forecasting in ecology.
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Affiliation(s)
- Shawn D. Taylor
- School of Natural Resources and Environment, 103 Black HallUniversity of FloridaGainesvilleFlorida32611USA
| | - Ethan P. White
- Department of Wildlife Ecology and Conservation, 110 Newins‐Ziegler HallUniversity of FloridaGainesvilleFlorida32611USA
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Falster DS, FitzJohn RG, Pennell MW, Cornwell WK. Datastorr: a workflow and package for delivering successive versions of 'evolving data' directly into R. Gigascience 2019; 8:5482388. [PMID: 31042286 PMCID: PMC6506717 DOI: 10.1093/gigascience/giz035] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2018] [Revised: 10/12/2018] [Accepted: 03/18/2019] [Indexed: 11/13/2022] Open
Abstract
The sharing and re-use of data has become a cornerstone of modern science. Multiple platforms now allow easy publication of datasets. So far, however, platforms for data sharing offer limited functions for distributing and interacting with evolving datasets- those that continue to grow with time as more records are added, errors fixed, and new data structures are created. In this article, we describe a workflow for maintaining and distributing successive versions of an evolving dataset, allowing users to retrieve and load different versions directly into the R platform. Our workflow utilizes tools and platforms used for development and distribution of successive versions of an open source software program, including version control, GitHub, and semantic versioning, and applies these to the analogous process of developing successive versions of an open source dataset. Moreover, we argue that this model allows for individual research groups to achieve a dynamic and versioned model of data delivery at no cost.
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Affiliation(s)
- Daniel S Falster
- Evolution & Ecology Research Centre, and School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney NSW 2052, Australia
| | - Richard G FitzJohn
- Department of Infectious Disease Epidemiology, Imperial College London, Faculty of Medicine, Norfolk Place, London W2 1PG, UK
| | - Matthew W Pennell
- Department of Zoology and Biodiversity Research Centre, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - William K Cornwell
- Evolution & Ecology Research Centre, and School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney NSW 2052, Australia
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