Systems Conferences Analysis

These files represent all the raw and processed data, as well as the code to process it, to analyze some of the top systems conferences during 2017. Each subdirectory holds its own README.md file to describe the files in it.

Prerequisites for running the R analyses

To install all software prerequisites from scratch, you can find detailed instruction in INSTALL.md. They have been tested on Ubuntu 20.04.3-LTS and MacOS 10.15.7. If you have access to a Linux machine with Docker, a much easier path is to just run of the prebuilt images at docker hub.

To reproduce a single analysis, find the subdirectory of pubs/ that contains it (the README.md in pubs/ lists them all). Ensure that you've met the software prerequisites, then type make in that directory.

Directory description

The following describes the subdirectories in this repository.

• data/: All files with raw data, downloaded or scraped from the internet, or manually collected. Each file and subdirectory has a data description file in the corresponding .md file.
• docs/: HTML documentation of the project, output of pubs/web.
• features/: Data files in tidy CSV format processed from the raw data. Each file and subdirectory has a data description file in the corresponding .md There's a Makefile to recompute the feature files from data/ if necessary. file.
• src/: The source code files to help collect raw data and convert it into tidy featues.
• pubs/: Parent directory for all publications on this data. The statistical analyses and resulting output documents can be reproduced with the Makefile.

Data addition workflow

The following describes the process of adding new data for a new, single conference, using the scripts in the src/ subdirectory.

1. Download the call-for-papers (in text format) to the data/cfp/ directory.
2. Create a new file for this conference, named after its short-name and year (e.g., /data/conf/ISCA_17.json), and using any of the other conferences as a template.
3. Edit this conference file and add as many fields as you can gather yourself (follow the example from existing files and description in data/conf/README.md). Several fields can be obtained from the CFP and conference web page. Some statistics can be obtained after the conference from the proceedings (e.g., number of submissions) or from the PC chairs. The past papers and citations statistics can sometimes be obtained from IEEE or ACM (try to get them as close to the postdate as possible). Google Scholar metrics sometimes has the statistics for h5_index and h5_median. Paper data should be copied from the conference web page, with topics added judiciously. The script src/reformat_conf.py can be used to check the syntax of your file, tidy up its formatting, and automatically add fields for paper key and topics.
4. As close to the postdate as possible, obtain author/PC statistics from Google Scholar (GS) and fill in a new file in the data/authors/ directory. This process often requires significant labor to disambiguate between different GS profiles, especially for common names. It is better to conservatively omit GS data for an author (set author to "NA") than to add wrong information.
5. Use src/aggregate_authors.py with the new file(s) to update the author stats files. The script tries to identify duplicate names with inconsistent naming and some mistakes in the authors file.
6. Optionally, the script src/merge_all_authors.py calls aggregate_authors.py on all author files and recreates the authors stats from scratch. It also calls compare_interests.py, which checks that all discovered authors interests exist in the interests mapping file, and no extraneous mappings exist either.
7. Periodically, gather paper statistics about the conference and add them to a file with the same name under the data/papers/ directory. Once this file is created, the script download_fulltext.py can be used to locate any PDF version of those papers for which GS found an eprint URL and download them into the ./fulltext/ directory. This directory is not supplied in the repository due to its size and to preserve the papers' copyright.
8. Use the supplied Makefile here or under features/ to generate updated features from the existing (and possibly new) data files. If necessary, add your new data files to the Makefile. The computation of the csv files in features/ may require, in addition to the general installation instructions, that you run: pip3 install python-dateutil editdistance pytesseract.