Data Inventories for the Modern Age? Using Data Science to Open Government Data

Lane, Julia; Gimeno, Ernesto; Levitskaya, Ekaterina; Zhang, Zheyuan; Zigoni, Alberto

doi:10.1162/99608f92.8a3f2336

Cited by 9 publications

(9 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Though the results of this evaluation indicate high levels of reliability, the system currently does not feature means for distinguishing the significance of one positively identified citation from another. Previous work has shown the efficacy of applying natural language processing (NLP) techniques to the textual context surrounding individual citations 1718 . Similar techniques could be applied to evaluate DCE results beyond a simple validity assessment.…”

Section: Discussionmentioning

confidence: 99%

Identifying genomic data use with the Data Citation Explorer

Byers,

Parker,

Beecroft

et al. 2024

Preprint

View full text Add to dashboard Cite

Increases in sequencing capacity, combined with rapid accumulation of publications and associated data resources, have increased the complexity of maintaining associations between literature and genomic data. As the volume of literature and data have exceeded the capacity of manual curation, automated approaches to maintaining and confirming associations among these resources have become necessary. Here we present the Data Citation Explorer (DCE), which discovers literature incorporating genomic data whether or not provenance was clearly indicated. This service provides advantages over manual curation methods including consistent resource coverage, metadata enrichment, documentation of new use cases, and identification of conflicting metadata. The service reduces labor costs associated with manual review, improves the quality of genome metadata maintained by the U.S. Department of Energy Joint Genome Institute (JGI), and increases the number of known publications that incorporate its data products. The DCE facilitates an understanding of JGI impact, improves credit attribution for data generators, and can encourage data sharing by allowing scientists to see how reuse amplifies the impact of their original studies.

show abstract

Section: Discussionmentioning

confidence: 99%

Identifying genomic data use with the Data Citation Explorer

Byers,

Parker,

Beecroft

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The search of the full-text search corpus employs three open source machine learning models, originally developed as part of the Kaggle competition "Coleridge Initiative -Show US the Data" (Coleridge Initiative, 2021a; Lane et al, 2022). The models are all available in the Coleridge GitHub repository.…”

Section: Searching the Full-text Corpusmentioning

confidence: 99%

Turning Visions Into Reality: Lessons Learned From Building a Search and Discovery Platform

Emecz,

Mitschang,

Zdawczyk

et al. 2024

Harvard Data Science Review

View full text Add to dashboard Cite

The Democratizing Data Initiative looks to demonstrate the impact of the data assets produced by U.S. federal agencies. In this article we outline the process flows and cyberinfrastructure that support the achievement of that objective. We describe how the focus on data assets evolved from the initial search for data sets to the challenges of finding how the data assets have been used by the research community. In this regard, we explain both why we need to define a search corpus of full-text records and the process by which that search corpus is created. We then explain the process of applying machine learning (Kaggle) algorithms to that full-text search corpus and the further steps we take to refine the outputs generated by those algorithms, including the manual validation by subject matter experts that we employ to minimize false positive data set identification in the final outputs. Once validation is complete, we explain how the outputs are made available to diverse stakeholders-agency staff, funding partners, general public-through a variety of channels including REST APIs, dashboards, SciServer, Jupyter Notebooks, and direct database access. We conclude by looking at potential future enhancements to the process and possible research directions.

show abstract

“…This integrated approach leads to long-term outcomes in the third stage (Figure 1, right column) that include accessible federal data, knowledgeable and empowered users, an open data community ethos, sustainable data practices, replicable research, and evidence-based policy actions, each of which are components of the core mission of democratizing data. While improving data access and usability of federal data is a primary goal of data democratization (Lane, 2024;Lane et al, 2022;Potok, 2023), another possible outcome, if strategies are implemented effectively, is a community of empowered users. Studies show that individuals who feel empowered using services remain loyal to that service provider (Vatanasombut et al, 2004) and user satisfaction has a positive impact on user retention, defined as the longevity of a user's loyalty to and interest in a product or service Gu et al, 2022).…”

Section: Theory Of Changementioning

confidence: 99%

“…The recent passage of the Foundations for Evidence-Based Policymaking Act of 2018 (hereafter Evidence Act) was a signal from the federal government of its commitment to enhance the efficiency of government programs, as well as public access to government data (Potok, 2023). Its passage also served as a call to action for federal agencies to improve government data ecosystems (Lane et al, 2022;Potok, 2023). New data access platforms like Data.gov, ResearchDataGov.org, and DemocratizingData.ai have emerged to support federal agencies as they respond to federal mandates requiring them to publish their information online (Title II, Foundations for Evidence-Based Policymaking Act of 2018, Public Law 115-435).…”

Section: Introductionmentioning

confidence: 99%

Creating Engagements: Bringing the User Into Data Democratization

Chenarides

2024

Harvard Data Science Review

View full text Add to dashboard Cite

The Foundations for Evidence-Based Policymaking Act of 2018 and supporting open science efforts offer a new opportunity for federal agencies to enhance government data ecosystems that improve public access to federal data. Public engagement will be critical to ensure that the efforts are sustainable. This article focuses on learning from other models to provide insights into building successful user engagement, drawing from fields as diverse as marketing, psychology, health care, economics, and information systems.

show abstract

Data Inventories for the Modern Age? Using Data Science to Open Government Data

Cited by 9 publications

References 21 publications

Identifying genomic data use with the Data Citation Explorer

Identifying genomic data use with the Data Citation Explorer

Turning Visions Into Reality: Lessons Learned From Building a Search and Discovery Platform

Creating Engagements: Bringing the User Into Data Democratization

Contact Info

Product

Resources

About