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It is a common platform to deposit, store and share the research data in the area of social and behavioral sciences. openICPSR is undergoing development commiting international archiving standard and is currently free for all users to share their data up to a 2GB limit. It has a distribution network of over 760 institutions, governed by the Attribution 4.0 Creative Commons License and its' data catalog indexed by major search engines. OpenICPSR is a research data-sharing service that allows depositors to rapidly self-publish research data, enabling the public to access the data without charge. Otherwise via standard ICPSR deposits, one can publish and preserve reseach data with restricted-use having nominal charge. ICPSR is part of the Institute for Social Research at the University of Michigan.
The Qualitative Data Repository (QDR) is a dedicated archive for storing and sharing digital data (and accompanying documentation) generated or collected through qualitative and multi-method research in the social sciences. QDR provides search tools to facilitate the discovery of data, and also serves as a portal to material beyond its own holdings, with links to U.S. and international archives. The repository’s initial emphasis is on political science.
WorldData.AI comes with a built-in workspace – the next-generation hyper-computing platform powered by a library of 3.3 billion curated external trends. WorldData.AI allows you to save your models in its “My Models Trained” section. You can make your models public and share them on social media with interesting images, model features, summary statistics, and feature comparisons. Empower others to leverage your models. For example, if you have discovered a previously unknown impact of interest rates on new-housing demand, you may want to share it through “My Models Trained.” Upload your data and combine it with external trends to build, train, and deploy predictive models with one click! WorldData.AI inspects your raw data, applies feature processors, chooses the best set of algorithms, trains and tunes multiple models, and then ranks model performance.