Insights from Publishing Open Data in Industry-Academia Collaboration
- URL: http://arxiv.org/abs/2501.14841v1
- Date: Fri, 24 Jan 2025 07:30:46 GMT
- Title: Insights from Publishing Open Data in Industry-Academia Collaboration
- Abstract summary: This paper explores the motivations and lessons learned from publishing open data sets in such collaborations.<n>We surveyed participants in a European research project that published 13 data sets.<n>We found that planning the data collection is essential, and that only few datasets had accompanying scripts for improved reuse.
- Score: 3.458783333044753
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Effective data management and sharing are critical success factors in industry-academia collaboration. This paper explores the motivations and lessons learned from publishing open data sets in such collaborations. Through a survey of participants in a European research project that published 13 data sets, and an analysis of metadata from almost 281 thousand datasets in Zenodo, we collected qualitative and quantitative results on motivations, achievements, research questions, licences and file types. Through inductive reasoning and statistical analysis we found that planning the data collection is essential, and that only few datasets (2.4%) had accompanying scripts for improved reuse. We also found that authors are not well aware of the importance of licences or which licence to choose. Finally, we found that data with a synthetic origin, collected with simulations and potentially mixed with real measurements, can be very meaningful, as predicted by Gartner and illustrated by many datasets collected in our research project.
Related papers
- Open Datasets in Learning Analytics: Trends, Challenges, and Best PRACTICE [0.4666493857924357]
Open datasets play a crucial role in three research domains that intersect data science and education: learning analytics, educational data mining, and artificial intelligence in education.<n>Providing open datasets alongside research papers supports, collaboration, and trust in research findings.<n>Despite these advantages, the availability of open datasets and associated practices within the learning analytics research communities, especially at their flagship conference venues, remain unclear.
arXiv Detail & Related papers (2026-02-19T12:23:25Z) - Multi-Disciplinary Dataset Discovery from Citation-Verified Literature Contexts [0.0]
We introduce a literature-driven framework that discovers datasets from citation contexts in scientific papers.<n>Our approach combines large-scale citation-context extraction, schema-guided dataset recognition, and provenance-preserving entity resolution.<n>We release our code, evaluation datasets, and results on GitHub.
arXiv Detail & Related papers (2026-01-08T16:46:06Z) - OpenDataArena: A Fair and Open Arena for Benchmarking Post-Training Dataset Value [74.80873109856563]
OpenDataArena (ODA) is a holistic and open platform designed to benchmark the intrinsic value of post-training data.<n>ODA establishes a comprehensive ecosystem comprising four key pillars: (i) a unified training-evaluation pipeline that ensures fair, open comparisons across diverse models; (ii) a multi-dimensional scoring framework that profiles data quality along tens of distinct axes; and (iii) an interactive data lineage explorer to visualize dataset genealogy and dissect component sources.
arXiv Detail & Related papers (2025-12-16T03:33:24Z) - ForensicsData: A Digital Forensics Dataset for Large Language Models [0.0]
ForensicsData is an extensive Question-Context-Answer (Q-C-A) dataset sourced from actual malware analysis reports.<n>A unique workflow was used to create the dataset, which extracts structured data.<n> Gemini 2 Flash demonstrated the best performance in aligning generated content with forensic terminology.
arXiv Detail & Related papers (2025-08-31T19:58:24Z) - A Systematic Review of Open Datasets Used in Text-to-Image (T2I) Gen AI Model Safety [0.0]
Novel research aimed at text-to-image (T2I) generative AI safety often relies on publicly available datasets for training and evaluation.
This paper presents a review of the key datasets used in the T2I research, detailing their collection methods, compositions, semantic and syntactic diversity of prompts and the quality, coverage, and distribution of harm types in the datasets.
arXiv Detail & Related papers (2025-02-23T00:59:04Z) - SciER: An Entity and Relation Extraction Dataset for Datasets, Methods, and Tasks in Scientific Documents [49.54155332262579]
We release a new entity and relation extraction dataset for entities related to datasets, methods, and tasks in scientific articles.
Our dataset contains 106 manually annotated full-text scientific publications with over 24k entities and 12k relations.
arXiv Detail & Related papers (2024-10-28T15:56:49Z) - Data-Centric AI in the Age of Large Language Models [51.20451986068925]
This position paper proposes a data-centric viewpoint of AI research, focusing on large language models (LLMs)
We make the key observation that data is instrumental in the developmental (e.g., pretraining and fine-tuning) and inferential stages (e.g., in-context learning) of LLMs.
We identify four specific scenarios centered around data, covering data-centric benchmarks and data curation, data attribution, knowledge transfer, and inference contextualization.
arXiv Detail & Related papers (2024-06-20T16:34:07Z) - Using Large Language Models to Enrich the Documentation of Datasets for Machine Learning [1.8270184406083445]
We explore using large language models (LLM) and prompting strategies to automatically extract dimensions from documents.
Our approach could aid data publishers and practitioners in creating machine-readable documentation.
We have released an open-source tool implementing our approach and a replication package, including the experiments' code and results.
arXiv Detail & Related papers (2024-04-04T10:09:28Z) - A Survey on Data Selection for Language Models [148.300726396877]
Data selection methods aim to determine which data points to include in a training dataset.
Deep learning is mostly driven by empirical evidence and experimentation on large-scale data is expensive.
Few organizations have the resources for extensive data selection research.
arXiv Detail & Related papers (2024-02-26T18:54:35Z) - Capture the Flag: Uncovering Data Insights with Large Language Models [90.47038584812925]
This study explores the potential of using Large Language Models (LLMs) to automate the discovery of insights in data.
We propose a new evaluation methodology based on a "capture the flag" principle, measuring the ability of such models to recognize meaningful and pertinent information (flags) in a dataset.
arXiv Detail & Related papers (2023-12-21T14:20:06Z) - Assessing Scientific Contributions in Data Sharing Spaces [64.16762375635842]
This paper introduces the SCIENCE-index, a blockchain-based metric measuring a researcher's scientific contributions.
To incentivize researchers to share their data, the SCIENCE-index is augmented to include a data-sharing parameter.
Our model is evaluated by comparing the distribution of its output for geographically diverse researchers to that of the h-index.
arXiv Detail & Related papers (2023-03-18T19:17:47Z) - Documenting Data Production Processes: A Participatory Approach for Data
Work [4.811554861191618]
opacity of machine learning data is a significant threat to ethical data work and intelligible systems.
Previous research has proposed standardized checklists to document datasets.
This paper proposes a shift of perspective: from documenting datasets toward documenting data production.
arXiv Detail & Related papers (2022-07-11T15:39:02Z) - Unsupervised Opinion Summarization with Content Planning [58.5308638148329]
We show that explicitly incorporating content planning in a summarization model yields output of higher quality.
We also create synthetic datasets which are more natural, resembling real world document-summary pairs.
Our approach outperforms competitive models in generating informative, coherent, and fluent summaries.
arXiv Detail & Related papers (2020-12-14T18:41:58Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.