Featured Dataverses

In order to use this feature you must have at least one published or linked dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

Advanced Search

41 to 50 of 2,242 Results
R Syntax - 9.9 KB - MD5: e25fd5f4b1e945bd39ca8766e7c6d4c0
R script for data cleaning and construction of composite scale scores. Must be run before 02_analysis.R. Reads fhus_vtfk.sav (not included) and writes data/df_clean.Rda
R Syntax - 26.5 KB - MD5: 3e0b30ffda825220a4381b6cb34b2a6a
R script for all analyses, figures, and tables reported in the manuscript. Reads data/df_clean.Rda produced by 01_data_preparation.R. Writes regression and correlation output files to the results/ subfolder. Plot output (ggsave) calls are commented out and must be enabled manually.
May 29, 2026
Barahmand, Zahir, 2026, "Supplementary dataset and reproducible codes for LLM-assisted mapping feedstocks of eight conversion technologies from over 121,000 studies", https://doi.org/10.18710/JM6U7B, DataverseNO, V2
This dataset was developed to systematically characterise feedstock–technology relationships across eight major biomass conversion technologies by mining a large Scopus-derived bibliographic corpus (1887–2025; partial coverage for 2025). The workflow is LLM-assisted and fully reproducible, combining automated extraction of feedstock and technology...
Plain Text - 21.2 KB - MD5: d44c4029e684e063091a7b56c176193e
Detailed overview of the repository structure, file contents, workflow steps, reuse guidance, and notes on source traceability. The public release excludes Scopus-derived abstracts and raw Scopus export files.
ZIP Archive - 12.4 MB - MD5: 1c95b307eca112047a61546f8fb2d4b4
Python scripts, configuration files, example inputs/outputs, and documentation for the LLM-assisted extraction of feedstock and technology descriptors from title and abstract fields.
ZIP Archive - 14.6 MB - MD5: b2b0300a1411ec244409217c1d6f0f12
Python scripts and documentation for rule-based cleaning, heuristic scoring, and validation-status assignment of the extracted feedstock and technology descriptors.
ZIP Archive - 30.9 MB - MD5: 7f096b9d59fcae9952608d30ecd882dc
Python scripts and documentation for targeted LLM-assisted validation of uncertain or non-accepted records after rule-based cleaning.
ZIP Archive - 138.6 MB - MD5: fab016b2ffd7b55a91de062f0abca390
Processed and derived datasets from the extraction, cleaning, validation, and manual-curation workflow, including bibliographic source identifiers, intermediate outputs, and the final curated feedstock–technology descriptors. Scopus-derived abstracts and raw Scopus exports are not included.
May 8, 2026
zhang, guoli, 2026, "Boosting Zinc-Ion Hybrid Capacitors with Mesoporous Carbon Derived from Highly Graphitized Carbon Quantum Dots", https://doi.org/10.18710/6FDKP9, DataverseNO, V1
This dataset contains electrochemical and structural characterization data that support the following publication: Zhang G., Li H., Wang K., Li G., Li K., Guan T., Wang K. Boosting Zinc-Ion Hybrid Capacitors with Mesoporous Carbon Derived from Highly Graphitized Carbon Quantum Dots. Energy & Environmental Materials, 2026. The dataset includes cycli...
Plain Text - 4.3 KB - MD5: e243af90dbf6f172f58c8ff004a595b3
README file describing dataset structure, file organization, sample naming, and experimental details.
Add Data

Log in to create a dataverse or add a dataset.

Share Dataverse

Share this dataverse on your favorite social media networks.

Link Dataverse
Reset Modifications

Are you sure you want to reset the selected metadata fields? If you do this, any customizations (hidden, required, optional) you have done will no longer appear.