ds4owd-002.bib

@misc{benaleya2022researcha,
  title = {Research {{Beyond}} the {{Lab}}, {{Spring Term}} 2022, {{Global Health Engineering}}, {{ETH Zurich}}. {{Raw}} Data and Analysis-Ready Derived Data on Waste Management in Public Spaces in {{Zurich}}, {{Switzerland}}.},
  author = {Ben Aleya, Ali and Biek, Daniel and Boynton, Lin and Jaeggi, Julia and Loos, Sebastian Camilo and {Meyer-Piening}, Chiara and Ogwang, Jonathan Olal and Overhoff, Milena and Sch{\"o}bitz, Lars and Sigrist, Samuel and Tilley, Elizabeth and Triebold, Nicolas Y.C. and Oda, Vigen and Vijay, Saloni},
  year = {2022},
  month = nov,
  publisher = {{Zenodo}},
  doi = {10.5281/ZENODO.7331120},
  urldate = {2023-12-04},
  abstract = {This repository contains all raw and derived data produced as part of the ETH Zurich course "Research Beyond the Lab: Open Science and Research Methods for a Global Engineer" (151-8102-00L) offered in spring term 2022. Students were assigned teams of four to conduct a collaborative research project broadly addressing the theme of ``Trash in the Public Spaces of Zurich'' in collaboration with Entsorgung \& Recycling Z{\"u}rich (ERZ), the waste management department at Stadt Z{\"u}rich. Research methods and design are taught in the first half of the course. Surveys and a waste characterisation study are then designed based on the research questions students have developed in their respective teams. The collected raw data is used in the course to teach principles of research data management, tidy data structures, reproducible research with R \& RStudio, and collaboration and version control with Git \& GitHub.},
  copyright = {Creative Commons Attribution 4.0 International, Open Access},
  keywords = {\#swm}
}

@article{broman2018data,
  title = {Data {{Organization}} in {{Spreadsheets}}},
  author = {Broman, Karl W. and Woo, Kara H.},
  year = {2018},
  month = jan,
  journal = {The American Statistician},
  volume = {72},
  number = {1},
  pages = {2--10},
  publisher = {{Taylor \& Francis}},
  issn = {0003-1305},
  doi = {10.1080/00031305.2017.1375989},
  urldate = {2022-03-09},
  abstract = {Spreadsheets are widely used software tools for data entry, storage, analysis, and visualization. Focusing on the data entry and storage aspects, this article offers practical recommendations for organizing spreadsheet data to reduce errors and ease later analyses. The basic principles are: be consistent, write dates like YYYY-MM-DD, do not leave any cells empty, put just one thing in a cell, organize the data as a single rectangle (with subjects as rows and variables as columns, and with a single header row), create a data dictionary, do not include calculations in the raw data files, do not use font color or highlighting as data, choose good names for things, make backups, use data validation to avoid data entry errors, and save the data in plain text files.},
  keywords = {Data management,Data organization,Microsoft Excel,Spreadsheets},
  file = {/Users/lschoebitz/Zotero/storage/8MZ65GX8/Broman and Woo - 2018 - Data Organization in Spreadsheets.pdf;/Users/lschoebitz/Zotero/storage/NT8B67DR/00031305.2017.html}
}

@manual{dickinson2021jmpwashdata,
  type = {Manual},
  title = {Jmpwashdata: {{WHO}}/{{UNICEF}} Joint Monitoring Programme Water and Sanitation Data},
  author = {Dickinson, Nicolas},
  year = {2021}
}

@article{wilson2017good,
  title = {Good Enough Practices in Scientific Computing},
  author = {Wilson, Greg and Bryan, Jennifer and Cranston, Karen and Kitzes, Justin and Nederbragt, Lex and Teal, Tracy K.},
  year = {2017},
  month = jun,
  journal = {PLOS Computational Biology},
  volume = {13},
  number = {6},
  pages = {e1005510},
  publisher = {{Public Library of Science}},
  issn = {1553-7358},
  doi = {10.1371/journal.pcbi.1005510},
  urldate = {2022-03-11},
  abstract = {Author summary Computers are now essential in all branches of science, but most researchers are never taught the equivalent of basic lab skills for research computing. As a result, data can get lost, analyses can take much longer than necessary, and researchers are limited in how effectively they can work with software and data. Computing workflows need to follow the same practices as lab projects and notebooks, with organized data, documented steps, and the project structured for reproducibility, but researchers new to computing often don't know where to start. This paper presents a set of good computing practices that every researcher can adopt, regardless of their current level of computational skill. These practices, which encompass data management, programming, collaborating with colleagues, organizing projects, tracking work, and writing manuscripts, are drawn from a wide variety of published sources from our daily lives and from our work with volunteer organizations that have delivered workshops to over 11,000 people since 2010.},
  langid = {english},
  keywords = {Computer software,Control systems,Data management,Metadata,Programming languages,Reproducibility,Software tools,Source code},
  file = {/Users/lschoebitz/Zotero/storage/CNY5NPKL/Wilson et al. - 2017 - Good enough practices in scientific computing.pdf;/Users/lschoebitz/Zotero/storage/2DMY23LA/article.html}
}