As the amount of scientific data continues to grow at ever faster rates, the research community is increasingly in need of flexible computational infrastructure that can support the entirety of the data science life cycle, including long-term data storage, data exploration, and discovery services, and compute capabilities to support data analysis and reanalysis as new data is added and scientific pipelines are refined. The authors describe their experience developing data commons-interoperable infrastructure that collocates data, storage, and compute with common analysis tools. Across the presented case studies, several common requirements emerge, including the need for persistent digital identifier and metadata services, APIs, data portability, pay-for-compute capabilities, and data peering agreements between data commons. Although many challenges, including sustainability and developing appropriate standards remain, interoperable data commons bring us one step closer to effective data science as a service for the scientific research community.

/pdf/a-case-for-data-commons-toward-data-science-as-a-service-4skuuydjy5.pdf

A Case for Data Commons: Toward Data Science as a Service

As the amount of scientific data continues to grow at ever faster rates, the research community is increasingly in need of flexible computational infrastructure that can support the entirety of the data science lifecycle, including long-term data storage, data exploration and discovery services, and compute capabilities to support data analysis and re-analysis, as new data are added and as scientific pipelines are refined. We describe our experience developing data commons-- interoperable infrastructure that co-locates data, storage, and compute with common analysis tools--and present several cases studies. Across these case studies, several common requirements emerge, including the need for persistent digital identifier and metadata services, APIs, data portability, pay for compute capabilities, and data peering agreements between data commons. Though many challenges, including sustainability and developing appropriate standards remain, interoperable data commons bring us one step closer to effective Data Science as Service for the scientific research community.

A Case for Data Commons: Towards Data Science as a Service

Objective: Cornell University’s Research Data Management Service Group (RDMSG) surveyed NSF principal investigators (PIs) at Cornell in order to understand how well-prepared researchers are to meet the new NSF data management planning requirement, to build our own understanding of the potential impact on campus services, and to identify service gaps. Methods: We administered a 43-question online survey, which included questions about the respondents’ research and research data, their interest in assistance with the creation of data management plans, and questions for each of the five general areas cited in the NSF’s Grant Proposal Guide (2011) section on data management plans. Results and Discussion: Respondents produce a wide variety of types and formats of data, although most expect to share relatively small amounts of data. Respondents are generally uncertain as to whether the data they produce conforms to disciplinary standards. The majority create no metadata; of those that do, most do not create metadata according to a particular standard. Most researchers do not express a need for advice regarding intellectual property issues. Researchers report using a variety of strategies (oncampus and commercial) for backing up and for providing access to their data sets. Conclusions: The overarching finding from our survey is that there is much uncertainty about what the new requirement means and how to meet it, and researchers welcome offers of assistance. To the extent that Cornell researchers are representative of NSF PIs, our findings reveal something about researchers’ readiness to meet the new requirement, and their attitudes towards it. Correspondence to Gail Steinhart: gss1@cornell.edu

/pdf/prepared-to-plan-a-snapshot-of-researcher-readiness-to-4h45uai686.pdf

Prepared to Plan? A Snapshot of Researcher Readiness to Address Data Management Planning Requirements

https://works.bepress.com/judy_jeng/45/download/

Digital Public Library of America

Social science researchers increasingly make use of data that is confidential because it contains linkages to the identities of people, corporations, etc. The value of this data lies in the ability to join the identifiable entities with external data, such as genome data, geospatial information, and the like. However, the confidentiality of this data is a barrier to its utility and curation, making it difficult to fulfil US federal data management mandates and interfering with basic scholarly practices, such as validation and reuse of existing results. We describe the complexity of the relationships among data that span a public and private divide. We then describe our work on the CED2AR prototype, a first step in providing researchers with a tool that spans this divide and makes it possible for them to search, access and cite such data.

/pdf/data-management-of-confidential-data-ll1pb4yv1l.pdf

Data Management of Confidential Data

We develop the core of a method for solving the data archive and curation problem that confronts the custodians of restricted-access research data and the scientific users of such data. Our solution recognizes the dual protections afforded by physical security and access limitation protocols. It is based on extensible tools and can be easily incorporated into existing instructional materials.

/pdf/a-proposed-solution-to-the-archiving-and-curation-of-pbpmaews6h.pdf

A proposed solution to the archiving and curation of confidential scientific inputs

We describe the design, implementation, and deployment of the Comprehensive Extensible Data Documentation and Access Repository (CED2AR). This is a metadata repository system that allows researchers to search, browse, access, and cite confidential data and metadata through either a web-based user interface or programmatically through a search API, all the while re-reusing and linking to existing archive and provider generated metadata. CED2AR is distinguished from other metadata repository-based applications due to requirements that derive from its social science context. These include the need to cloak confidential data and metadata and manage complex provenance chains.

CED2AR: the comprehensive extensible data documentation and access repository

Provenance is a key component of evaluating the integrity and reusability of data for scholarship. While recording and providing access provenance has always been important, it is even more critical in the web environment in which data from distributed sources and of varying integrity can be combined and derived. The PROV model, developed under the auspices of the W3C, is a foundation for semantically-rich, interoperable, and web-compatible provenance metadata. We report on the results of our experimentation with integrating the PROV model into the DDI metadata for a complex, but characteristic, example social science data. We also present some preliminary thinking on how to visualize those graphs in the user interface.

Encoding Provenance of Social Science Data: Integrating PROV with DDI [full paper]

/pdf/encoding-provenance-of-social-science-data-integrating-prov-1ti5w3c8yr.pdf

Encoding Provenance of Social Science Data: Integrating PROV with DDI

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

William C. Block

Author Tools

Chat about Author