# README - EOSS Impact Assessment

This document describes the data associated with “Insights and impact from five cycles of Essential Open Source Software for Science” from the Open Science Program at Chan Zuckerberg Initiative. See the appendices of the report for more information about data collection methods.

## Grantee information

eoss_grantee_information.csv

Information that is publicly available on the CZI website (https://chanzuckerberg.com/eoss/proposals/) includes the following details for each of the 161 funded EOSS proposals from cycles 1 through 5:
* Cycle: EOSS1, EOSS2, EOSS3, EOSS4, EOSS5, EOSSDI
* Title: title of grant
* Summary: short description of funded work
* Project(s): open source software project(s) representing the main focus of the proposal
* PI: principal investigator of the project
These data have been collected as a csv for convenience. Key personnel, affiliations of PI and key personnel, and their GitHub handles are also present online but not included in this file. Information about the PIs and affiliations generally represent the start of the grant work and may not have been updated to reflect changes in PI during the funding period.

## Application Corpus Analysis

application_corpus_eoss_assessment.csv

This dataset contains information about all proposals received in EOSS cycles 1-5, excluding the Diversity and Inclusion supplement. This is spreadsheet-style data containing the following columns:
* Application_number: unique number assigned to each proposal for the purposes of this dataset.
* Funded: Yes or No
* Cycle: EOSS1, EOSS2, EOSS3, EOSS4, EOSS5
* Main_Category: primary category applied to each proposal by the CZI Open Science team for general summarization purposes; includes Cell Biology, Imaging, Neuroscience, Infectious Disease, Clinical Research, Structural Biology, Machine learning and data analysis, Data management and workflows, Visualization, Genomics, Bioinformatics
* Needs-label: "label" represents Documentation, Technical, Community, or Training; data are yes/no indicating whether the model for that need applied a label to a particular application. NA indicates data were not available to assess needs.
* All_auto_needs: All needs labels applied to a particular application in a single column. NA indicates data were not available to assess needs.
* Category-label: "label" represents Cell Biology, Imaging, Neuroscience, Infectious Disease, Clinical Research, Structural Biology, Machine learning and data analysis, Data management and workflows, Visualization, Genomics, Bioinformatics; data are yes/no indicating whether model applied label to a particular application. 
* All_auto_categories:  All needs labels applied to a particular application in a single column. 

## Needs Assessment Survey

needs_assessment_survey_eoss_assessment.csv

Respondents were given the following prompt: “Below are topics acknowledged by the open source community as useful for contributors and projects. Please indicate your (or your core team's) interest in each area.” This spreadsheet-style data contains a column with a unique respondent ID and then a column for each topic, scored as Not interested, Good but not essential, Urgently needed (NA if the question was not answered). Column labels include:
* Respondent: unique number assigned to each anonymous response
* Developing and enforcing a Code of Conduct
* Developing a DEI statement
* Building welcoming, inclusive OS software and communities
* UI and UX: Designing and improving user interfaces for your software
* Improving software documentation to build a user community
* Soliciting feedback from users to guide development of new features
* Project management for OSS        
* Governance for open source projects, e.g. How do we make decisions?
* Managing a of contributors: Onboarding, offboarding, and rates of turnover
* Writing contribution guidelines to encourage collaboration
* Implementing code review for your open source project
* Writing a software management plan
* Attribution, incentivization, and giving credit: Tracking metrics in OSS
* Testing and continuous integration for OSS
* Strategies and approaches for debugging
* Code modernization and major refactors: changing your mature project's basic approach
* Best practices for building robust packages: Code architecture and design
* Navigating the legal side of OSS
* Planning for sustainability in OSS

## Grantee Output Analysis

grantee_output_analysis_eoss_assessment.csv

This csv file contains labels applied to each funded EOSS proposal to describe the nature of outputs and impacts for the project as reported by grantees. Columns include:
* Grant_number: unique number assigned to each proposal for the purposes of this dataset
* Cycle: EOSS1, EOSS2, EOSS3, EOSS4, EOSS5, EOSSDI
* Main_category: primary category describing scientific domain applied to each proposal by the CZI Open Science team for general summarization purposes, includes one of the following: Cell Biology, Imaging, Neuroscience, Infectious Disease, Clinical Research, Structural Biology, Machine learning and data analysis, Data management and workflows, Visualization, Genomics, Bioinformatics
* Community: output, materials or events used to engage users or developers
* Documentation: output, any written material used as reference for users and/or developers
* Training: output, online or virtual course/workshop, mentorship
* Manuscript: output, preprint, submitted, or published manuscript
* Presentation: output, seminar or conference presentation (talk or poster)
* Proposal: output, submitted proposal and/or awarded grant
* Data: output, curated dataset (e.g., for benchmark or testing)
* CoC: impact, adoption of a CoC for a software project for the first time, implementing a new version of a CoC, or developing processes and practices for socializing and enforcing an existing CoC.
* Collaboration: impact, interactions with another software project, including (but not limited to) enabling interoperability, synchronizing roadmaps, and submitting/reviewing code.
* DEI: impact, activities related to increasing participation of individuals historically underrepresented in open source, both as developers and users of software, including (but not limited to) women, black/indigenous/people of color, individuals from LMICs.

## Grantee Impact Survey

grantee_impact_survey_eoss_assessment.csv

These data represent Likert-scale answers to questions included in a survey. This is a csv file containing the following columns:
* Respondent: unique number assigned to each anonymous response
* Project_success: How important was the EOSS funding to the success of your OSS project?
        [Not very important, Somewhat important, Very important, Extremely important]
* DEI: How important is/was CZI funding for supporting diversity, equity, and inclusion (DEI) in your funded project(s)? Examples of supporting DEI could include use of funding for workshops focused on participation of underrepresented groups, internships tailored to increase diversity and inclusion, or support for updating software to be more accessible. [Not at all important, Somewhat important, Moderately important, Very important, Extremely important]
* Biomedical: How important is/was EOSS funding to the uptake and use of your project in the biomedical research community?  Examples might include papers that used your software, advances in technology that relied on your project, or new applications of your software to biomedical research questions. [Not at all important, Somewhat important, Moderately important, Very important, Extremely important, My project is not used by the biomedical community]
* Maintainers: How important is/was EOSS funding to supporting the primary maintainers of your project(s)? [Not at all important, Somewhat important, Moderately important, Very important, Extremely important]

## CZ Software Mentions

software_mentions_eoss_assessment.csv

This csv file contains the following columns:
* oftware: name of software package as reported by PI in application
* num_papers_*: number of papers with software mention per year, where * represents year (2013, 2016-2023). Note that 2023 is included in the dataset but represents only a fraction of publications from the corpus of literature for that year.