AboutScientific posters are one of the most common forms of scholarly communication, with millions presented at conferences each year. They contain early-stage insights that, if shared beyond the conference, could accelerate scientific discovery. This dataset contains the data files related to our analysis of poster sharing and reuse. See this inventory for all related resources, including the paper.Standards followedThis dataset is structured according to the SPARC Data Structure v3.0.2 (c.f. related manuscript and documentation) and curated using the SPARC data curation software SODA v17.0.0.Using the datasetSimply download this dataset. The main data files are in the primary folder. The derivative folder contains data files derived from the primary files. There is a manifest.xlsx file at the root level that describes all the files. See this inventory for a link to the Jupyter notebook we developed to analyze and visualize the data.LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License (see LICENSE file).How to citeIf you use this dataset, please follow the citation instruction presented on Zenodo.

AboutScientific posters are one of the most common forms of scholarly communication, with millions presented at conferences each year. They contain early-stage insights that, if shared beyond the conference, could accelerate scientific discovery. This dataset contains the data files related to our analysis of poster sharing and reuse. See this inventory for all related resources, including the paper.Standards followedThis dataset is structured according to the SPARC Data Structure v3.0.2 (c.f. related manuscript and documentation) and curated using the SPARC data curation software SODA v17.0.0.Using the datasetSimply download this dataset. The main data files are in the primary folder. The derivative folder contains data files derived from the primary files. There is a manifest.xlsx file at the root level that describes all the files. See this inventory for a link to the Jupyter notebook we developed to analyze and visualize the data.LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License (see LICENSE file).How to citeIf you use this dataset, please follow the citation instruction presented on Zenodo.

AboutData Management Plans (DMPs) are now required by most funders for grant proposals to ensure researchers plan in advance for effective data management and sharing. In this work, we evaluated the performance of Large Language Models (LLMs) in drafting DMPs compliant with the National Institutes of Health (NIH)’s 2023 guidelines. This dataset contains the data files used in our analysis of how well Llama 3.3 and GPT-4.1 can draft NIH-compliant Data Management Plans (DMPs). See this inventory for all related resources, including the paper.Standards followedThis dataset is structured according to the SPARC Data Structure v3.0.2 (see related manuscript and documentation) and curated using the SPARC data curation software SODA v17.1.0.Using the datasetSimply download this dataset. The main data files are in the primary folder. The derivative folder contains data files derived from the primary files. There is a manifest.xlsx file at the root level that describes all the files. See this inventory for the Jupyter notebook used to analyze and visualize the data.LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License (see LICENSE file).How to citeIf you use this dataset, please follow the citation instructions presented on Zenodo.

AboutData Management Plans (DMPs) are now required by most funders for grant proposals to ensure researchers plan in advance for effective data management and sharing. In this work, we evaluated the performance of Large Language Models (LLMs) in drafting DMPs compliant with the National Institutes of Health (NIH)’s 2023 guidelines. This dataset contains the data files used in our analysis of how well Llama 3.3 and GPT-4.1 can draft NIH-compliant Data Management Plans (DMPs). See this inventory for all related resources, including the paper.Standards followedThis dataset is structured according to the SPARC Data Structure v3.0.2 (see related manuscript and documentation) and curated using the SPARC data curation software SODA v17.1.0.Using the datasetSimply download this dataset. The main data files are in the primary folder. The derivative folder contains data files derived from the primary files. There is a manifest.xlsx file at the root level that describes all the files. See this inventory for the Jupyter notebook used to analyze and visualize the data.LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License (see LICENSE file).How to citeIf you use this dataset, please follow the citation instructions presented on Zenodo.

OverviewThis dataset contains dataset metadata, FAIR scores, citations, mentions, and research field data collected/generated during our real world testing and validation of the S-index. The analysis of this dataset was included in our NIH S-index Challenge Phase 2 proposal. The code used to collect this data and additional details about the data collection methods and data structure are available in the related GitHub repository. We refer to the S-index Hub for more information about our S-index, the Challenge, and other related resources. DetailsAll data files are in NDJSON format. Data was collected between October 2025 and January 2026. A DuckDB file combining this data is available here.FolderDescriptionmetadataContains reduced metadata of 49M+ datasets from the Datacite database and 51k+ datasets from the Electron Microscopy Data Bank (EMDB). Includes all datasets registered/published on or before September 30th, 2025. Data is in NDSON format following the DataCite Metadata Schema.fair_scoresContains FAIR scores of all the datasets calculated using F-UJI (between October 2025 and January 2026). citationsContains 7.7M + unique citations to our datasets collected from the Make Data Count Data Citation Corpus, OpenAlex, and DataCite. mentionsContains 91k+ mentions to our datasets collected from the README files of GitHub repositories using Software Data, Model Cards of AI/ML models on Hugging Face, and patents from the USPTO. research_fieldsContains research fields assigned to our datasets following the OpenAlex Topics taxonomy. Fields assigment comes from OpenAlex and a custom classifier we developed.

OverviewThis dataset contains the DuckDB file where we combined dataset metadata, FAIR scores, citations, mentions, and research field data collected/generated during our real world testing and validation of the S-index. It also contains computed Dataset Index and S-index. The analysis of this dataset was included in our NIH S-index Challenge Phase 2 proposal. The code used to collect this data and additional details about the data collection methods and data structure are available in the related GitHub repository. We refer to the S-index Hub for more information about our S-index, the Challenge, and other related resources.Related ItemsThe NDJSON files of dataset metadata, FAIR scores, citations, mentions, and research field available here were loaded in this DuckDB fileThe code used to compute the Dataset Index and S-index is available in the related GitHub repositoryThe code to analyze this data is available in this GitHub repository.

OverviewThis dataset contains the DuckDB file where we combined dataset metadata, FAIR scores, citations, mentions, and research field data collected/generated during our real world testing and validation of the S-index. It also contains computed Dataset Index and S-index. The analysis of this dataset was included in our NIH S-index Challenge Phase 2 proposal. The code used to collect this data and additional details about the data collection methods and data structure are available in the related GitHub repository. We refer to the S-index Hub for more information about our S-index, the Challenge, and other related resources.Related ItemsThe NDJSON files of dataset metadata, FAIR scores, citations, mentions, and research field available here were loaded in this DuckDB fileThe code used to compute the Dataset Index and S-index is available in the related GitHub repositoryThe code to analyze this data is available in this GitHub repository.

OverviewThis dataset contains dataset metadata, FAIR scores, citations, mentions, and research field data collected/generated during our real world testing and validation of the S-index. The analysis of this dataset was included in our NIH S-index Challenge Phase 2 proposal. The code used to collect this data and additional details about the data collection methods and data structure are available in the related GitHub repository. We refer to the S-index Hub for more information about our S-index, the Challenge, and other related resources. DetailsAll data files are in NDJSON format. Data was collected between October 2025 and January 2026. A DuckDB file combining this data is available here.FolderDescriptionmetadataContains reduced metadata of 49M+ datasets from the Datacite database and 51k+ datasets from the Electron Microscopy Data Bank (EMDB). Includes all datasets registered/published on or before September 30th, 2025. Data is in NDSON format following the DataCite Metadata Schema.fair_scoresContains FAIR scores of all the datasets calculated using F-UJI (between October 2025 and January 2026). citationsContains 7.7M + unique citations to our datasets collected from the Make Data Count Data Citation Corpus, OpenAlex, and DataCite. mentionsContains 91k+ mentions to our datasets collected from the README files of GitHub repositories using Software Data, Model Cards of AI/ML models on Hugging Face, and patents from the USPTO. research_fieldsContains research fields assigned to our datasets following the OpenAlex Topics taxonomy. Fields assigment comes from OpenAlex and a custom classifier we developed.

This is the dataset associated with the paper titled "Publicly Available Imaging Datasets for Age-related Macular Degeneration: Evaluation according to the Findable, Accessible, Interoperable, Reproducible (FAIR) Principles". Age-related macular degeneration (AMD), a leading cause of vision loss among older adults, affects more than 200 million people worldwide. In this paper, We evaluated openly available AMD-related datasets containing optical coherence tomography (OCT) data against the FAIR principles. This is an archive of the repository that contains the data related to our evaluation. See this inventory for all related resources, including the paper. This dataset is maintained from https://github.com/fairdataihub/FAIR-AMD-OCT-paper-dataset.

This is the dataset associated with the paper titled "Publicly Available Imaging Datasets for Age-related Macular Degeneration: Evaluation according to the Findable, Accessible, Interoperable, Reproducible (FAIR) Principles". Age-related macular degeneration (AMD), a leading cause of vision loss among older adults, affects more than 200 million people worldwide. In this paper, We evaluated openly available AMD-related datasets containing optical coherence tomography (OCT) data against the FAIR principles. This is an archive of the repository that contains the data related to our evaluation. See this inventory for all related resources, including the paper. This dataset is maintained from https://github.com/fairdataihub/FAIR-AMD-OCT-paper-dataset.