Making Common Fund data more findable: catalyzing a data ecosystem

Charbonneau, Amanda; Brady, A. G.; Czajkowski, Karl; Aluvathingal, Jain; Canchi, Saranya; Carter, Robert L.; Chard, Kyle; Clarke, Daniel J B; Crabtree, Jonathan; Creasy, Heather Huot; D’Arcy, Mike; Felix, Victor; Giglio, Michelle; Gingrich, Alicia A.; Harris, Rayna M.; Hodges, Theresa K; Ifeonu, Olukemi O.; Jeon, Minji; Kropiwnicki, Eryk; Lim, Marisa; Liming, Lee; Lumian, Jessica; Mahurkar, Anup; Mandal, Meisha; Munro, James B.; Nadendla, Suvarna; Richter, Rudyard; Romano, Cia; Rocca-Serra, Philippe; Schor, Michael; Schuler, Robert; Tangmunarunkit, Hongsuda; Waldrop, Alex; Williams, C.; Word, Karen R.; Sansone, Susanna-Assunta; Ma’ayan, Avi; Wagner, Rick; Foster, Ian; Kesselman, Carl; Brown, C. Titus; White, Owen

doi:10.1093/gigascience/giac105

Cited by 12 publications

(7 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…NCI was a leader in establishing a cloud-based data system for multimodal integrative analysis ( 6 ). Additional NIH Institutes followed suit and are now providing their data to the community in a similar way [e.g., BioData Catalyst ( 16 ), AnViL ( 17 ), all of US ( 18 ), Common Fund Data Ecosystem ( 19 )]. The challenges faced by CRDC over the last 10 years have provided the NCI with key lessons learned regarding the management and sharing of cancer data and led to the development of strategies to overcome these issues and plans for the future state of CRDC.…”

Section: Lessons Learnedmentioning

confidence: 99%

NCI Cancer Research Data Commons: Lessons Learned and Future State

Kim,

Davidsen,

Davis-Dusenbery

et al. 2024

Cancer Research

View full text Add to dashboard Cite

More than ever, scientific progress in cancer research hinges on our ability to combine datasets and extract meaningful interpretations to better understand diseases and ultimately inform the development of better treatments and diagnostic tools. To enable the successful sharing and use of big data, the NCI developed the Cancer Research Data Commons (CRDC), providing access to a large, comprehensive, and expanding collection of cancer data. The CRDC is a cloud-based data science infrastructure that eliminates the need for researchers to download and store large-scale datasets by allowing them to perform analysis where data resides. Over the past 10 years, the CRDC has made significant progress in providing access to data and tools along with training and outreach to support the cancer research community. In this review, we provide an overview of the history and the impact of the CRDC to date, lessons learned, and future plans to further promote data sharing, accessibility, interoperability, and reuse.

show abstract

Section: Lessons Learnedmentioning

confidence: 99%

NCI Cancer Research Data Commons: Lessons Learned and Future State

Kim,

Davidsen,

Davis-Dusenbery

et al. 2024

Cancer Research

View full text Add to dashboard Cite

show abstract

“…The general-purpose nature of DATS and its target towards data discovery has positioned DATS to be adapted and employed by other data discovery tools like the NIH Common Fund Data Ecosystem (CFDE) (23). The CFDE employs a variation of DATS, called the Crosscut Metadata Model (C2M2), which introduces specificity to target minimal information for biomedical studies (23). Namely, C2M2 includes a hierarchy of material specified with roles: biosample → subject.…”

Section: C2m2mentioning

confidence: 99%

Enable authors to share the provenance of biological material in spreadsheets

Elizabeth

2023

Preprint

View full text Add to dashboard Cite

Scientists persistently share diverse digital assets, including analytical data and scientific methods, many of which comply with increasing mandates for open science. As scientists share investigations, the potential to transform methods and data into discoveries expands. But the potential impact of shared digital assets is restricted by limited, ambiguous, and inaccessible information about how physical materials were generated and processed, known as provenance. Scientists often share critical details of provenance in spreadsheets and many digital platforms solicit essential information via spreadsheets. Yet, scientists and scientific software engineers struggle to share accurate and sufficient provenance in spreadsheets. In this work, I first highlight concepts and challenges in sharing the provenance of biological material, regardless of the application used. Then, I identify motifs for sharing provenance in spreadsheets by evaluating diverse resources, ranging digital ecosystems to bespoke metadata management systems. I highlight efforts, ranging from frameworks to spreadsheet-based features, that target known challenges for sharing complex information like provenance. Across diverse disciplines, the scientific community has made advances to improve the presence of provenance in digital resources, which gives insight into opportunities to both advance shared science and propel new investigations.

show abstract

“…In addition, the LINCS DCIC is participating in the Common Fund Data Ecosystem (CFDE) NIH Common Fund program. This effort aims to standardize metadata across NIH Common Fund data coordination centers (DCCs) (Charbonneau et al., 2022). For the CFDE efforts, most of the LINCS data and metadata have been archived on an Amazon Web Services S3 bucket using a STRIDES account.…”

Section: Commentarymentioning

confidence: 99%

Getting Started with LINCS Datasets and Tools

et al. 2022

Self Cite

View full text Add to dashboard Cite

The Library of Integrated Network-based Cellular Signatures (LINCS) was an NIH Common Fund program that aimed to expand our knowledge about human cellular responses to chemical, genetic, and microenvironment perturbations. Responses to perturbations were measured by transcriptomics, proteomics, cellular imaging, and other high content assays. The second phase of the LINCS program, which lasted 7 years, involved the engagement of six data and signature generation centers (DSGCs) and one data coordination and integration center (DCIC). The DSGCs and the DCIC developed several digital resources, including tools, databases, and workflows that aim to facilitate the use of the LINCS data and integrate this data with other publicly available data. The digital resources developed by the DSGCs and the DCIC can be used to gain new biological and pharmacological insights that can lead to the development of novel therapeutics. This protocol provides step-by-step instructions for processing the LINCS data into signatures, and utilizing the digital resources developed by the LINCS consortia for hypothesis generation and knowledge discovery.

show abstract

Making Common Fund data more findable: catalyzing a data ecosystem

Cited by 12 publications

References 31 publications

NCI Cancer Research Data Commons: Lessons Learned and Future State

NCI Cancer Research Data Commons: Lessons Learned and Future State

Enable authors to share the provenance of biological material in spreadsheets

Getting Started with LINCS Datasets and Tools

Contact Info

Product

Resources

About