Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Spatio-Temporal Signatures of User-Centric Data: How Similar Are We?
Authors: Samta Shukla, Aditya Telang, Salil Joshi, L. Subramaniam
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We are developing a framework to test our models against a real dataset of urban users. and We are currently developing a framework for testing our models on a real dataset of GPS users in Beijing. |
| Researcher Affiliation | Collaboration | Samta Shukla Rensselaer Polytechnic Institute and Aditya Telang, Salil Joshi, L Venkat Subramaniam IBM Research Lab, India |
| Pseudocode | No | The paper describes the proposed methods in text but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any specific links to source code or explicitly state that the code for the described methodology is publicly available. |
| Open Datasets | No | The paper mentions developing a framework to test models 'against a real dataset of urban users' and 'on a real dataset of GPS users in Beijing' but provides no concrete access information (link, DOI, specific repository, or formal citation with authors/year) for this dataset. |
| Dataset Splits | No | The paper does not provide specific details on dataset splits (e.g., percentages or sample counts for training, validation, or testing sets). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as library names with version numbers. |
| Experiment Setup | No | The paper focuses on the theoretical framework and proposed methods but does not include specific experimental setup details such as hyperparameter values or training configurations. |