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Home Page: https://reproducible-agile.github.io/
Website for Initiative "Reproducible Research" @ AGILE conference
Home Page: https://reproducible-agile.github.io/
Call: https://agile-online.org/conference-2019/call-for-workshops-2019
Deadline: 30 November 2018
Proposal document draft: https://docs.google.com/document/d/1ns7ZNfsd-tjuFGX__nuzrovdC9Q_laQdbbnjDhUtZaA/edit?usp=sharing
For ideas see #8 and https://peerj.com/preprints/27216v1/#feedback-1207
The conference organizers advise to put deadlines rather "too early", because of the high number of accepted proposals and Wageningen's hotels being potentially filled quickly. With this in mind I created a timeline draft:
@edzer @MarkusKonk I am on holidays until March 21. If you'd be available to assign the submissions to the reviewers, then we can also push that date to a couple of weeks earlier.
Alternatively, we could go way earlier and put the deadline to Feb 12 and assign reviewers on Feb 13/14.
Experience level: I use Python and R on a regular basis (but not as often as I would like to) and have experience in a couple of other languages. I have occasionally published code and/or data for my articles, but not to a level that I would call reproducible. Mac user, but can survive on Linux and Windows.
Expectations: I want to participate in the workshop because I want to make my work more reproducible and follow common conventions as closely as possible.
Experience level: I am familiar with R and Python, but don't have any (own) experiences with RR yet. My background is geoinformatics / GI technologies / remote sensing / statistics. I'm using Windows OS.
Expectations: I am looking forward to gain an insight into RR concepts and practices. As I am at the very beginning of my PhD, this will be valuable knowledge for my future work.
We can use this issue to notify the participants (@-mentioning) about preparations they should take before coming to Lund.
Experience level: My language of choice is python and I'm proficient in R and JavaScript. I'm also confident in using other languages as needed. I have published bits of code and datasets here and there for my research but it was always ad-hoc. I prefer Linux (Ubuntu and Red Hat mainly).
Expectations: I want to participate in the workshop because I want to learn more about RR. I've been wanting to make my work more reproducible for some time now but never really took the first step. I am excited to learn more about theory and best practices and eventually see how they work in real life. I'm hoping that this workshop will push me towards making my work more reproducible.
Apologies for submitting my registration after the due date. I did not know I was coming to AGILE for sure until today.
(I am a bit of an intruder in that I am a mostly a web programmer with little to no experience in computational work or scientific publications. However, I have an interest in GIS, literate programming, specifically Jupyter notebooks, and reproducibility. Hence why I would like to participate in the workshop if you have any room left.)
Experience level: I have worked mostly on python. I am also familiar with R and Java. I have not published any dataset yet. I mainly use windows OS. I have taught a bachelor's level course on GIS Development with Python using IDLE.
Expectations: Main purpose of my participation is to learn the principle and steps for making my work reproducible so that anyone going through it can have a clear picture and produce same results. It has been a challenge for me when I have been going through some research works during my Master's studies.
preferred hands-on session (R or Python),
R
a short description of experience in R and/or Python
Basic statements and R for leaflet
Computer Scientist with programming skills, I have coded in Java, Javascript, Swift, C++, etc.
http://franciscoramos.name
Data and Statistics Analysis with R and GPU Programming
@MarkusKonk @edzer Feedback welcome!
This workshop runs a public peer review. The review process described here is therefore transparent for submitters and tries to be both brief and explicit.
@o2r-project/agile-2017-org-committee
) and including one of the following statements: Review recommendation: Accept for presentation, Review recommendation: Accept for presentation after revision, Review recommendation: Reject@o2r-project/agile-2017-reviewers
) to briefly check the review arguments and cast a vote using "thumb up/down" reactions on the comment. There must be a simple majority and the review committee is the tie breaker.1 - preferred hands-on session (R or Python):
=> Python
2 - a short description of experience in R and/or Python
=> I did a bit scripting in Python with Yupiter and QGIS; not much though
3 - a summary of computational work, if available with references to published papers, data or code,
=> My main work has been around java-GIS programming mostly with/for OpenJUMP. However, I haven't touched any code now since 2 years. See:
4 - plans for future computer-based research.
=> Currently I mainly working with my team on an urban accessibility/ analysis platform see Steiniger et al. 2013 (https://www.sciencedirect.com/science/article/pii/S2214140517308691) and on urban sustainability indicators.
Experience level: I use R (incl RMarkdown) and Python (incl Jupyter notebooks) on a regular bases, but I have used other languages as well. I use git and I have experience with data analysis. I have published data and code for some of my articles, but none that could be considered fully reproducible. I have published a reproducible lecture (and practical session) on reproducible research in R, and I hope to expand that to the rest of the module I teach on programming in R for our MSc in GIScience. I use win, linux and mac on a regular basis.
Expectations: I want to participate in the workshop because I want to learn about best practices in reproducible research. I am very interested in how that is integrated in the research workflow and discuss how that can work in the reality of everyday research (rather than in the slightly idealised scenarios commonly found in books).
Do we need review guidelines? I think the reviewers might appreciate a little input, and it should help using similar standards across al reviews.
Experience level: I am proficient in Python and use it a lot now. I am also familiar with a couple of other programming languages (e.g. C#, matlab, R). I have no experience in publishing datasets. I have rich experience in spatiotemporal data (especially GPS trajectory data) analysis and mining. I mainly use Windows OS.
Expectations: I want to participate in the workshop because I would like to know more about reproducible research. I also want to make my research (i.e. data, code, publications) more reproducible, but I don't know where I can get started. I hope I can learn some experience and skills about reproducible research.
preferred hands-on session:
Python
a short description of experience in R and/or Python:
**Experience in using python as a tool for data management (mostly automating repetitive tasks) as well as some experience as part of my master's degree (using python to process some results). Example: Iterating through hundreds of data packages, finding all relevant shapefiles and access databases, converting to OGC GML and csv and renaming + repackaging relevant folders, etc. some experience in relevant packages such as numpy, use of spyder for geodata , etc **
The current suggestion is two have two workshops: one about the guidelines, and one more practical. Interested people can attend both, but they can choose better.
Contributions to the proposals are welcome:
Experience level: (e.g. programming language(s) and proficiency, published reproducible articles, published datasets, experience in data analysis, used operating system, taught courses using OER, ...)
Not so much data analysis recently. More focus on developing light-weight ontology/concept maps for education , GI workflows and domain research, such as in citizen science
Expectations: I want to participate in the workshop because ...
I would like to see how my work on conceptual modelling / workflows can be utilised in RR. And of course want to make my own work reproducible and learn how to do that better.
Experience level: (e.g. programming language(s) and proficiency, published reproducible articles, published datasets, experience in data analysis, used operating system, taught courses using OER, ...)
Python, R, Matlab
Expectations: I want to participate in the workshop because ...
Very interested in this topic.
preferred hands-on session (R or Python)
a short description of experience in R and/or Python
a summary of computational work, if available with references to published papers, data or code
plans for future computer-based research
Experience level: Python, R, played with Knitr and interested in similar for Python, published datasets, with experience in data analysis, used MacOSX, Linux, Win, taught courses in geospatial analysis with Python
Expectations: I want to participate in the workshop because I want to learn about best practices. I know about the theory of reproducible articles and literate programming ( in R primarily), but I would like to explore this in Python ,and see also how the data+code +article can be published, executed ( as per Binder), dependencies managed for longevity of the contribution. Ultimately, interactive papers would be the goal as I see it (such as exporables).
The call for workshops is out. Deadline is 30 November 2017.
Please feed free to add comments and more ideas here, I'll integrate them in this comment asap.
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