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README.bib
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@article{brandt_atmospheric_2016,
title = {Atmospheric {Chemistry} {Measurements} at {Whiteface} {Mountain}, {NY}: {Ozone} and {Reactive} {Trace} {Gases}},
volume = {16},
issn = {16808584, 20711409},
shorttitle = {Atmospheric {Chemistry} {Measurements} at {Whiteface} {Mountain}, {NY}},
url = {https://aaqr.org/articles/aaqr-15-05-simts-0376},
doi = {10.4209/aaqr.2015.05.0376},
number = {3},
urldate = {2019-08-30},
journal = {Aerosol and Air Quality Research},
author = {Brandt, Richard E. and Schwab, James J. and Casson, Paul W. and Roychowdhury, Utpal K. and Wolfe, Douglas and Demerjian, Kenneth L. and Civerolo, Kevin L. and Rattigan, Oliver V. and Felton, H. Dirk},
year = {2016},
pages = {873--884}
}
@article{schwab_atmospheric_2016,
title = {Atmospheric {Science} {Research} at {Whiteface} {Mountain}, {NY}: {Site} {Description} and {History}},
volume = {16},
issn = {16808584, 20711409},
shorttitle = {Atmospheric {Science} {Research} at {Whiteface} {Mountain}, {NY}},
url = {https://aaqr.org/articles/aaqr-15-05-simts-0343},
doi = {10.4209/aaqr.2015.05.0343},
number = {3},
urldate = {2019-08-30},
journal = {Aerosol and Air Quality Research},
author = {Schwab, James J. and Wolfe, Douglas and Casson, Paul and Brandt, Richard and Demerjian, Kenneth L. and Husain, Liquat and Dutkiewicz, Vincent A. and Civerolo, Kevin L. and Rattigan, Oliver V.},
year = {2016},
pages = {827--840}
}
@article{marwick_packaging_2018,
title = {Packaging {Data} {Analytical} {Work} {Reproducibly} {Using} {R} (and {Friends})},
volume = {72},
issn = {0003-1305},
url = {https://doi.org/10.1080/00031305.2017.1375986},
doi = {10.1080/00031305.2017.1375986},
abstract = {Computers are a central tool in the research process, enabling complex and large-scale data analysis. As computer-based research has increased in complexity, so have the challenges of ensuring that this research is reproducible. To address this challenge, we review the concept of the research compendium as a solution for providing a standard and easily recognizable way for organizing the digital materials of a research project to enable other researchers to inspect, reproduce, and extend the research. We investigate how the structure and tooling of software packages of the R programming language are being used to produce research compendia in a variety of disciplines. We also describe how software engineering tools and services are being used by researchers to streamline working with research compendia. Using real-world examples, we show how researchers can improve the reproducibility of their work using research compendia based on R packages and related tools.},
number = {1},
urldate = {2020-05-20},
journal = {The American Statistician},
author = {Marwick, Ben and Boettiger, Carl and Mullen, Lincoln},
month = jan,
year = {2018},
note = {Publisher: Taylor \& Francis
\_eprint: https://doi.org/10.1080/00031305.2017.1375986},
keywords = {Computational science, Data science, Open source software, Reproducible research},
pages = {80--88}
}
@article{bryan_excuse_2018,
title = {Excuse {Me}, {Do} {You} {Have} a {Moment} to {Talk} {About} {Version} {Control}?},
volume = {72},
issn = {0003-1305},
url = {https://doi.org/10.1080/00031305.2017.1399928},
doi = {10.1080/00031305.2017.1399928},
abstract = {Data analysis, statistical research, and teaching statistics have at least one thing in common: these activities all produce many files! There are data files, source code, figures, tables, prepared reports, and much more. Most of these files evolve over the course of a project and often need to be shared with others, for reading or edits, as a project unfolds. Without explicit and structured management, project organization can easily descend into chaos, taking time away from the primary work and reducing the quality of the final product. This unhappy result can be avoided by repurposing tools and workflows from the software development world, namely, distributed version control. This article describes the use of the version control system Git and the hosting site GitHub for statistical and data scientific workflows. Special attention is given to projects that use the statistical language R and, optionally, R Markdown documents. Supplementary materials include an annotated set of links to step-by-step tutorials, real world examples, and other useful learning resources. Supplementary materials for this article are available online.},
number = {1},
urldate = {2020-05-20},
journal = {The American Statistician},
author = {Bryan, Jennifer},
month = jan,
year = {2018},
note = {Publisher: Taylor \& Francis
\_eprint: https://doi.org/10.1080/00031305.2017.1399928},
keywords = {Data science, Git, GitHub, R language, R Markdown, Reproducibility, Workflow},
pages = {20--27},
file = {Bryan - 2018 - Excuse Me, Do You Have a Moment to Talk About Vers.pdf:/home/will/Zotero/storage/57UQ4REA/Bryan - 2018 - Excuse Me, Do You Have a Moment to Talk About Vers.pdf:application/pdf}
}
@article{white_nine_2013,
title = {Nine simple ways to make it easier to (re)use your data},
volume = {6},
copyright = {Copyright (c) 2015 Ethan P White, Elita Baldridge, Zachary T. Brym, Kenneth J. Locey, Daniel J. McGlinn, Sarah R. Supp},
issn = {1918-3178},
url = {https://ojs.library.queensu.ca/index.php/IEE/article/view/4608},
doi = {10.4033/iee.2013.6b.6.f},
language = {en},
number = {2},
urldate = {2020-05-20},
journal = {Ideas in Ecology and Evolution},
author = {White, Ethan P. and Baldridge, Elita and Brym, Zachary T. and Locey, Kenneth J. and McGlinn, Daniel J. and Supp, Sarah R.},
month = aug,
year = {2013},
note = {Number: 2},
keywords = {data, data reuse, data sharing, data structure},
file = {Full Text PDF:/home/will/Zotero/storage/JD5QN8LQ/White et al. - 2013 - Nine simple ways to make it easier to (re)use your.pdf:application/pdf}
}
@article{schwab_atmospheric_2016-1,
title = {Atmospheric {Chemistry} {Measurements} at {Whiteface} {Mountain}, {NY}: {Cloud} {Water} {Chemistry}, {Precipitation} {Chemistry}, and {Particulate} {Matter}},
volume = {16},
issn = {2071-1409},
shorttitle = {Atmospheric {Chemistry} {Measurements} at {Whiteface} {Mountain}, {NY}},
url = {https://aaqr.org/articles/aaqr-15-05-simts-0344},
doi = {10.4209/aaqr.2015.05.0344},
abstract = {ABSTRACTLong-term records of condensed-phase chemical data are presented from the Adirondack Mountain region of northern New York, USA. These data records are particularly valuable due to the combinations of aerosol, cloud, and precipitation measurements. Objectives of the research and this overview paper include the evaluation of emission reductions of regulated air pollutants and the observed effects on measured deposition, as well as the implications of changing pollutant concentration levels on human health and climate. Summer season cloud chemistry and year-round wet deposition and particulate matter data from two stations on Whiteface Mountain are presented to highlight some of the research and monitoring activities at this mountain location. Clear decreases in the anion concentrations and increases in pH over the past two decades have been observed in cloud and precipitation results. Large decreases in aerosol sulfate ({\textgreater} 80\%) and aerosol optical black carbon ({\textgreater} 60\%) have been observed for these species over the nearly 40 year summit observatory data record for these measurements, and decreases in PM2.5 mass, sulfate, nitrate, and ammonium have also been recorded over the shorter 15 year period of measurement at the Marble Mountain Lodge level. The studies cited here highlight some of the past successes of air pollution regulation under the Clean Air Act and Amendments and pave the way for future progress in reducing air pollution.},
language = {en},
number = {3},
urldate = {2020-07-02},
journal = {Aerosol and Air Quality Research},
author = {Schwab, James J. and Casson, Paul and Brandt, Richard and Husain, Liquat and Dutkewicz, Vincent and Wolfe, Douglas and Demerjian, Kenneth L. and Civerolo, Kevin L. and Rattigan, Oliver V. and Felton, H. Dirk and Dukett, James E.},
year = {2016},
note = {Publisher: Taiwan Association for Aerosol Research},
pages = {841--854}
}
@book{briney_data_2015,
title = {Data {Management} for {Researchers}: {Organize}, maintain and share your data for research success},
isbn = {978-1-78427-013-1},
shorttitle = {Data {Management} for {Researchers}},
abstract = {A comprehensive guide to everything scientists need to know about data management, this book is essential for researchers who need to learn how to organize, document and take care of their own data. Researchers in all disciplines are faced with the challenge of managing the growing amounts of digital data that are the foundation of their research. Kristin Briney offers practical advice and clearly explains policies and principles, in an accessible and in-depth text that will allow researchers to understand and achieve the goal of better research data management. Data Management for Researchers includes sections on: * The data problem – an introduction to the growing importance and challenges of using digital data in research. Covers both the inherent problems with managing digital information, as well as how the research landscape is changing to give more value to research datasets and code. * The data lifecycle – a framework for data’s place within the research process and how data’s role is changing. Greater emphasis on data sharing and data reuse will not only change the way we conduct research but also how we manage research data. * Planning for data management – covers the many aspects of data management and how to put them together in a data management plan. This section also includes sample data management plans. * Documenting your data – an often overlooked part of the data management process, but one that is critical to good management; data without documentation are frequently unusable. * Organizing your data – explains how to keep your data in order using organizational systems and file naming conventions. This section also covers using a database to organize and analyze content. * Improving data analysis – covers managing information through the analysis process. This section starts by comparing the management of raw and analyzed data and then describes ways to make analysis easier, such as spreadsheet best practices. It also examines practices for research code, including version control systems. * Managing secure and private data – many researchers are dealing with data that require extra security. This section outlines what data falls into this category and some of the policies that apply, before addressing the best practices for keeping data secure. * Short-term storage – deals with the practical matters of storage and backup and covers the many options available. This section also goes through the best practices to insure that data are not lost. * Preserving and archiving your data – digital data can have a long life if properly cared for. This section covers managing data in the long term including choosing good file formats and media, as well as determining who will manage the data after the end of the project. * Sharing/publishing your data – addresses how to make data sharing across research groups easier, as well as how and why to publicly share data. This section covers intellectual property and licenses for datasets, before ending with the altmetrics that measure the impact of publicly shared data. * Reusing data – as more data are shared, it becomes possible to use outside data in your research. This chapter discusses strategies for finding datasets and lays out how to cite data once you have found it. This book is designed for active scientific researchers but it is useful for anyone who wants to get more from their data: academics, educators, professionals or anyone who teaches data management, sharing and preservation. "An excellent practical treatise on the art and practice of data management, this book is essential to any researcher, regardless of subject or discipline." —Robert Buntrock, Chemical Information Bulletin},
language = {en},
publisher = {Pelagic Publishing Ltd},
author = {Briney, Kristin},
month = sep,
year = {2015},
note = {Google-Books-ID: gw1iCgAAQBAJ},
keywords = {Computers / Databases / Data Warehousing, Computers / Databases / General, Computers / Web / General, Language Arts \& Disciplines / Library \& Information Science / Archives \& Special Libraries, Language Arts \& Disciplines / Library \& Information Science / General, Reference / Research, Science / Research \& Methodology}
}
@misc{christensen_narsto_2000,
title = {{NARSTO} {Data} {Management} {Handbook}},
url = {https://web.archive.org/web/20030401082229/http://cdiac.esd.ornl.gov:80/programs/NARSTO/pdf/dmhb_current_version.PDF},
publisher = {NARSTO Quality Systems Science Center},
author = {Christensen, Sigurd W. and Boden, Thomas A. and Hook, Les A. and Cheng, Meng-Dawn},
month = feb,
year = {2000}
}
@article{schwab_ozone_2009,
title = {Ozone, {Trace} {Gas}, and {Particulate} {Matter} {Measurements} at a {Rural} {Site} in {Southwestern} {New} {York} {State}: 1995–2005},
volume = {59},
issn = {1096-2247},
shorttitle = {Ozone, {Trace} {Gas}, and {Particulate} {Matter} {Measurements} at a {Rural} {Site} in {Southwestern} {New} {York} {State}},
url = {https://doi.org/10.3155/1047-3289.59.3.293},
doi = {10.3155/1047-3289.59.3.293},
abstract = {A research site for atmospheric chemistry and air pollution measurements was established at Pinnacle State Park in Addison, NY, in 1995. This paper presents an overview of the site characteristics and measurement program, as well as monthly average concentrations for many of the trace gas and aerosol pollutants over the full measurement period. Monthly averaged ozone concentrations range from values as low as 15 parts per billion (ppb) during cold-season months, to values approaching 50 ppb during some spring and summer months. Sulfur dioxide (SO2), oxides of nitrogen (NOx), and reactive odd nitrogen (NOy) all show distinct seasonal variation, with summertime monthly averages as low as 1–3 ppb, and wintertime monthly averages from 6–12 ppb. The variation in carbon monoxide (CO) is much smaller, with minimums of approximately 150 ppb and maximums only rarely exceeding 250 ppb. Data for three hydrocarbon species propane, benzene, and isoprene—are presented. Propane and benzene show higher monthly averaged concentrations in the winter and lower values in the summer, with values ranging over a factor of 4–5. Isoprene, on the other hand has much higher values during the summer season, sometimes a factor of 10 or more greater than concentrations measured in the winter. Monthly averaged plots for fine particulate matter (PM2.5) beginning in 1999 show a robust summer maximum and winter minimum, and roughly a factor of two difference between the two. An empirical measure of ozone production using the correlation of hour-averaged ozone and NOy data illustrates relatively robust ozone production during some, but not all, summertime months over the time period. Also, an analysis of the frequency distribution of the hours of maximum ozone concentration shows a strong mid-afternoon peak, as expected, but also a prominent secondary maximum centered around midnight. The secondary peak is interpreted as ozone transported from ozone-producing areas to the west, including Buffalo, Cleveland, Pittsburgh, and the Ohio Valley. Finally, SO2 concentrations as a function of wind direction clearly indicate maximum impacts when the winds are out of the south (Pittsburgh and Philadelphia), with a secondary peak when the winds are from the north-northeast, consistent with the locations of major SO2 emission sources in the region.},
number = {3},
urldate = {2020-12-02},
journal = {Journal of the Air \& Waste Management Association},
author = {Schwab, James J. and Spicer, John B. and Demerjian, Kenneth L.},
month = mar,
year = {2009},
note = {Publisher: Taylor \& Francis
\_eprint: https://doi.org/10.3155/1047-3289.59.3.293},
pages = {293--309}
}