Following these principles ensures that our data are organized, permanent, and reproducible. Please follow these data management principles for all data collected in the lab, as well as for analysis of that data.
We maintain a Lab server, blamhub, at http://10.17.101.32:5000/, for storing all data generated by the lab, as well as backing up analysis code. You can access blamhub from anywhere within the hopkins network, and should be able to access it from outside using VPN (has anyone done this?).
All raw data should be copied to blamhub immediately after it is collected. If you do not have access to blamhub, please talk to Alex about creating an account. In addition to keeping backups of data, also include a backup of the source code for the experiment software used to collect the data.
You are encouraged to also keep a backup of analysis code and intermediate steps in your data analysis (e.g. processed data containing reach direction, reaction time, etc.), either on blamhub, or github or both. If you are unfamiliar with github, or need a refresher, see Alex’s guide here.
For all published/submitted work, data and accompanying analysis code should be archived on blamhub in a dedicated folder within the ‘Publications’ folder on blamhub. Feel free to organize the data in whichever way feel is suitable, however, please ensure that the folder includes:
- A copy of the paper
- An obvious first port of call (‘readme.txt’ or ‘runme.m’) that explains what code does what.
- Analysis code includes the full pipeline from raw data to all plots, tables and statistics appearing in the manuscript. Ideally, in separate m-files (‘makeFig1.m’, ‘Expt1_stats.m’).
- A document detailing (de-identified) participant information (participant ID, dates run, age, gender, handedness, etc.). For patients, this should also include an clinical evaluations conducted as part of the experiment (e.g. ICARS, MOCA).
Thank you for your co-operation to help keep our science reproducible and permanent!