{"id":26662905,"url":"https://github.com/speciesfilegroup/credit_digital_science","last_synced_at":"2026-02-08T14:08:18.722Z","repository":{"id":146793928,"uuid":"86724465","full_name":"SpeciesFileGroup/credit_digital_science","owner":"SpeciesFileGroup","description":"A thought experiment. 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There must be somehwere administrators can go to find out how and why they should credit a researchers digital contributions, right?  We'll replace this with that place when we find it.\n\n A semi-currated list for administrators documenting how/why scientific researchers should get credit for their digital contributions.\n\n# Contributing\n\nBest: Fork the repository, make a pull request. Good: Submit an issue.\n\n# Related links\n\n* [Orcid IDs](https://orcid.org/)\n* [Data carpentry](http://www.datacarpentry.org/)\n* [Force11](https://www.force11.org/)\n\n\n# Digital Productivity\n\n## Products\n\nMust meet each of the following:\n\n* Citable\n* Accessible beyond the individual researcher\n* Published, public repository\n* Published, private repository [use by invitation, sign-in]\n\nAnd be in one of these categories:\n\n## Digital assets\n* Data compilations (e.g. databases, controlled vocabularies, ontologies, spreadsheets, digital image sets, shapefiles)\n* Software (e.g. code libraries, desktop or mobile applications, scripts)\n* Digital software services (e.g. application programming interfaces (APIs), software as a service layer, wrappers to computational resources)\n* Technical documents (e.g. standards like ontologies, controlled vocabularies, schemas, software manuals, video tutorials, standard operating procedures)\n* Technical illustrations (e.g. animations communicating scientific concepts, cover illustrations for journals)\n\n## Services\n* Hardware systems administration (e.g. managing a multi-user computational cluster, managing research-supporting clouds)\n* Software development administration (e.g. defining programmers tasks, merging code, reviewing code, communicating with developers support channels)\n* Technical software administration (e.g. administration of relational database, administrating research support wikis, administrating computing cluster software, administering scientific applications on the web or networks)\n* Data manipulation (e.g. data extraction, data merging, data cleanup, data validation, data packaging, instantiating databases)\n* Data archiving (e.g. properly registering datasets, annotating datasets with citable identifiers and appropriate standards, validating data in archives and archive derivatives)\n* Informatics Education (e.g. teaching researchers software carpentry principles, database management, scripted data manipulation, principles of data archiving, principles of data standards)\n\n## Metrics\n\nMetrics are dependant on the type of product being evaluated.  It is critical to note that products in these categories have a diverse range of impacts, some of which can be far beyond the original intent.  The following are variously applicable, and not intended to represent a comprehensive set.  They should not be evaluated without a deeper understanding of what they represent.  For example a research stating that they made 10,000 changes to a code base may not be as important as a researcher who made 10 changes (i.e. they simply might have a different workflow, be not as effective, etc.).  Nevertheless when taken collectively each item here is important to evaluation.\n\n* [Interesting take on managment](https://svpow.com/2017/03/17/every-attempt-to-manage-academia-makes-it-worse/)\n\n### Digital asset metrics\n* Number of times asset has been downloaded (e.g. downloading a software library)\n* Number of times a digital asset has been referenced in a publically accessible format (e.g. references can be DOIs, but also things like URIs from ontologies or controlled vocabularies)\n* Number of times asset has been accessed (e.g. page hits)\n* Number of times an asset has been independently contributed to (e.g. outside researchers voluntarily contributing code to open source code libraries, this metric is particularly important)\n* Number of dependencies (e.g. if a software library is produced, adopted by other external software developers, and required, then it is particularly impactful; determinable from sites like Github\n* Number of open source libraries pushed to repositories\n* Number of containerized applications pushed to repositories (containerized applications are applications that can be independently deployed to the cloud, their maintanence and packaging is an important technical contribution)\n* Number of researchers commits to a code base\n* Number of code bases committed to (e.g. a research committing small amounts to many applications or code bases will have a very big impact; one possible metric is the number of accepted “pull requests”, a standard mechanism for adding code to someone else’s project)\n* Number of lines of code written (note this says nothing of quality of code)\n* User time spent on digital application\n* Number of data changes by OTHER users to digital resources managed by researcher (e.g. if I make a tool that 1000 researchers use to alter data this is immensely impactful, much more so than, say, a paper that gets cited 20 times)\n* Number of users using a tool\n* Number of logins of users using a tool\n* Frequency of users returning to a tool\n* Lines of code documented, lines of documentation written\n* Hours of digital tutorials produced\n* Number of project stars (e.g. on Github a star indicates someone else has more than a passing interest in a project)\n* … many others\n\n### Digital service metrics\n* Number of external contributors “managed” (open source software development is highly impactful if external contributors are involved, this takes a lot of time to manage, but is highly rewarding)\n* Number of code reviews done\n* Lines of data extracted, manipulated, etc.\n* Number of datasets manipulated\n* Number of machines managed\n* Number of databases managed\n* Size of databases managed\n* Longevity of databases managed (seems abstract, but is increasingly important)\n* Types of software used to manage machine (e.g. address concept of technical competence)\n* Types of software managed in support of science (e.g. managing relational databases is much more demanding than desktop applications installed once and forgotten)\n* Number of researchers helped with digital problems (side by side)\n* Number of issues closed (application, database, code support)\n* Number of responses to ticketed issues (times digital support provided)\n* … many others\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fspeciesfilegroup%2Fcredit_digital_science","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fspeciesfilegroup%2Fcredit_digital_science","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fspeciesfilegroup%2Fcredit_digital_science/lists"}