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Journal articles on the topic 'Data commons'

Author: Grafiati

Published: 25 May 2024

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1

Grossman, Robert L., Jonathan R. Dry, Sean E. Hanlon, Donald J. Johann, Anand Kolatkar, Jerry S. H. Lee, Christopher Meyer, Lea Salvatore, Walt Wells, and Lauren Leiman. "BloodPAC Data Commons for Liquid Biopsy Data." JCO Clinical Cancer Informatics, no. 5 (April 2021): 479–86. http://dx.doi.org/10.1200/cci.20.00179.

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PURPOSE The Blood Profiling Atlas in Cancer (BloodPAC) Data Commons (BPDC) is being developed and is operated by the public-private BloodPAC Consortium to support the liquid biopsy community. It is an interoperable data commons with the ultimate aim of serving as a recognized source of valid scientific evidence for liquid biopsy assays for industry, academia, and standards and regulatory stakeholders. METHODS The BPDC is implemented using the open source Gen3 data commons platform ( https://gen3.org ). In particular, the BPDC Data Exploration Portal, BPDC Data Submission Portal, the BPDC Workspace Hub, and the BloodPAC application programming interface (API) were all automatically generated from the BloodPAC Data Model using the Gen3 data commons platform. BPDC uses Gen3's implementation of the data commons framework services so that it can interoperate through secure, compliant APIs with other data commons using data commons framework service, such as National Cancer Institute's Cancer Research Data Commons. RESULTS The BPDC contains 57 studies and projects spanning more than 4,100 cases. This amounts to 5,700 aliquots (blood plasma, serum, or a contrived sample) that have been subjected to a liquid biopsy assay, quantified, and then contributed by members of the BloodPAC Consortium. In all, there are more than 31,000 files in the commons as of December 2020. We describe the BPDC, the data it manages, the process that the BloodPAC Consortium used to develop it, and some of the applications that have been developed using its API. CONCLUSION The BPDC has been the data platform used by BloodPAC during the past 4 years to manage the data for the consortium and to provide workspaces for its working groups.

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Fedorov, A., W. Longabaugh, D. Pot, D. Clunie, S. Pieper, R. Lewis, H. Aerts, et al. "NCI Imaging Data Commons." International Journal of Radiation Oncology*Biology*Physics 111, no. 3 (November 2021): e101. http://dx.doi.org/10.1016/j.ijrobp.2021.07.495.

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Fedorov, Andrey, William J. R. Longabaugh, David Pot, David A. Clunie, Steve Pieper, Hugo J. W. L. Aerts, André Homeyer, et al. "NCI Imaging Data Commons." Cancer Research 81, no. 16 (June 15, 2021): 4188–93. http://dx.doi.org/10.1158/0008-5472.can-21-0950.

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Hughes, LaRon, Robert L. Grossman, Zachary Flamig, Andrew Prokhorenkov, Michael Lukowski, Michael Fitzsimons, Tara Lichtenberg, and Yajing Tang. "Harmonization of clinical data across Gen3 data commons." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): e18094-e18094. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e18094.

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e18094 Background: Gen3 is an open source software platform for developing and operating data commons. Gen3 systems are now used by a variety of institutions and agencies to share and analyze large biomedical datasets including clinical and genomic data. One of the challenges of working with these datasets is disparate clinical data standards used by researchers across different studies and fields. We have worked to address these hurdles in a variety of ways. Methods: Gen3 is an open source software platform for developing and operating data commons. Detailed specification and features can be found at https://gen3.org/ with code located on GitHub ( https://github.com/UC-cdis ). Results: The Gen3 data model is a graphical representation of the different nodes or classes of data that have been collected. Examples include diagnosis, demographic, exposure, and family history. The properties and values on each node are controlled by the data dictionary specified by the data commons creator. While each commons may have a unique data model and dictionary, specifying external standards allows for easier submission of new data and assists data consumers with interpretation of results. A variety of external references can be supported, but here we demonstrate the use of the National Cancer Institute Thesaurus (NCIt). NCIt provides reference terminologies and biomedical standards that contain a rich set of terms, codes, definitions, and concepts. Using the same reference standards across commons allows for the export of clinical data between commons. The Portable Format for Biomedical Data (PFB) was created to facilitate data export and to allow the data dictionary schema as well as the raw data to be compressed and exported. This new file format, which utilizes an Avro serialization, is small, fast, easy to modify, and enables simple data export and import. PFB also has the ability to house entire external reference ontologies and it is easy to update the PFB references as changes are introduced. Conclusions: We have shown here how the Gen3 data model, use of external reference standards for clinical data, and the export/import format of PFB enable the harmonization of clinical data across different data commons.

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Heath, Allison P., Vincent Ferretti, Stuti Agrawal, Maksim An, James C. Angelakos, Renuka Arya, Rosita Bajari, et al. "The NCI Genomic Data Commons." Nature Genetics 53, no. 3 (February 22, 2021): 257–62. http://dx.doi.org/10.1038/s41588-021-00791-5.

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Spichtinger, Daniel. "Uncommon Commons? Creative Commons Licencing in Horizon 2020 Data Management Plans." International Journal of Digital Curation 17, no. 1 (September 20, 2022): 9. http://dx.doi.org/10.2218/ijdc.v17i1.840.

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As policies, good practices and mandates on research data management evolve, more emphasis has been put on the licencing of data, which allows potential re-users to quickly identify what they can do with the data in question. In this paper I analyse a pre-existing collection of 840 Horizon 2020 public data management plans (DMPs) to determine which ones mention creative commons licences and among those who do, which licences are being used. I find that 36% of DMPs mention creative commons and among those a number of different approaches towards licencing exist (overall policy per project, licencing decisions per dataset, licencing decisions per partner, licensing decision per data format, licensing decision per perceived stakeholder interest), often clad in rather vague language with CC licences being “recommended” or “suggested”. Some DMPs also “kick the can further down the road” by mentioning that “a” CC licence will be used, but not which one. However, among those DMPs that do mention specific CC licences, a clear favourite emerges: the CC-BY licence, which accounts for half of the total mentioning of a specific licence. The fact that 64% of DMPs did not mention creative commons at all is an indication for the need for further training and awareness raising on data management in general and licencing in particular in Horizon Europe. For those DMPs that do mention specific licences, 60% would be compliant with Horizon Europe requirements (CC-BY or CC0). However, it should be carefully monitored whether content similar to the 40% that is currently licenced with non- Horizon Europe compliant licences will in the future move to CC-BY or CC0 or whether such content will simply be kept fully closed by projects (by invoking the “as open as possible, as close as necessary” principle), which would be an unintended and potentially damaging consequence of the policy.

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Stein, Lincoln D., Bartha M. Knoppers, Peter Campbell, Gad Getz, and Jan O. Korbel. "Data analysis: Create a cloud commons." Nature 523, no. 7559 (July 2015): 149–51. http://dx.doi.org/10.1038/523149a.

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Schofield, Paul N., Janan Eppig, Eva Huala, Martin Hrabe de Angelis, Mark Harvey, Duncan Davidson, Tom Weaver, et al. "Sustaining the Data and Bioresource Commons." Science 330, no. 6004 (October 28, 2010): 592–93. http://dx.doi.org/10.1126/science.1191506.

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9

Plana, Alejandro, Brian Furner, Monica Palese, Nicole Dussault, Suzi Birz, Luca Graglia, Maura Kush, et al. "Pediatric Cancer Data Commons: Federating and Democratizing Data for Childhood Cancer Research." JCO Clinical Cancer Informatics, no. 5 (October 2021): 1034–43. http://dx.doi.org/10.1200/cci.21.00075.

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The international pediatric oncology community has a long history of research collaboration. In the United States, the 2019 launch of the Children's Cancer Data Initiative puts the focus on developing a rich and robust data ecosystem for pediatric oncology. In this spirit, we present here our experience in constructing the Pediatric Cancer Data Commons (PCDC) to highlight the significance of this effort in fighting pediatric cancer and improving outcomes and to provide essential information to those creating resources in other disease areas. The University of Chicago's PCDC team has worked with the international research community since 2015 to build data commons for children's cancers. We identified six critical features of successful data commons design and implementation: (1) establish the need for a data commons, (2) develop and deploy the technical infrastructure, (3) establish and implement governance, (4) make the data commons platform easy and intuitive for researchers, (5) socialize the data commons and create working knowledge and expertise in the research community, and (6) plan for longevity and sustainability. Data commons are critical to conducting research on large patient cohorts that will ultimately lead to improved outcomes for children with cancer. There is value in connecting high-quality clinical and phenotype data to external sources of data such as genomic, proteomics, and imaging data. Next steps for the PCDC include creating an informed and invested data-sharing culture, developing sustainable methods of data collection and sharing, standardizing genetic biomarker reporting, incorporating radiologic and molecular analysis data, and building models for electronic patient consent. The methods and processes described here can be extended to any clinical area and provide a blueprint for others wishing to develop similar resources.

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Volchenboum, Samuel L., Suzanne M. Cox, Allison Heath, Adam Resnick, Susan L. Cohn, and Robert Grossman. "Data Commons to Support Pediatric Cancer Research." American Society of Clinical Oncology Educational Book, no. 37 (May 2017): 746–52. http://dx.doi.org/10.1200/edbk_175029.

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The falling costs and increasing fidelity of high-throughput biomedical research data have led to a renaissance in cancer surveillance and treatment. Yet, the amount, velocity, and complexity of these data have overcome the capacity of the increasing number of researchers collecting and analyzing this information. By centralizing the data, processing power, and tools, there is a valuable opportunity to share resources and thus increase the efficiency, power, and impact of research. Herein, we describe current data commons and how they operate in the oncology landscape, including an overview of the International Neuroblastoma Risk Group data commons as a paradigm case. We outline the practical steps and considerations in building data commons. Finally, we discuss the unique opportunities and benefits of creating a data commons within the context of pediatric cancer research, highlighting the particular advantages for clinical oncology and suggested next steps.

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Eschenfelder, Kristin R., and Andrew Johnson. "Managing the data commons: Controlled sharing of scholarly data." Journal of the Association for Information Science and Technology 65, no. 9 (March 12, 2014): 1757–74. http://dx.doi.org/10.1002/asi.23086.

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Ci, Bo, Donghan M. Yang, Mark Krailo, Caihong Xia, Bo Yao, Danni Luo, Qinbo Zhou, et al. "Development of a Data Model and Data Commons for Germ Cell Tumors." JCO Clinical Cancer Informatics, no. 4 (September 2020): 555–66. http://dx.doi.org/10.1200/cci.20.00025.

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Germ cell tumors (GCTs) are considered a rare disease but are the most common solid tumors in adolescents and young adults, accounting for 15% of all malignancies in this age group. The rarity of GCTs in some groups, particularly children, has impeded progress in treatment and biologic understanding. The most effective GCT research will result from the interrogation of data sets from historical and prospective trials across institutions. However, inconsistent use of terminology among groups, different sample-labeling rules, and lack of data standards have hampered researchers’ efforts in data sharing and across-study validation. To overcome the low interoperability of data and facilitate future clinical trials, we worked with the Malignant Germ Cell International Consortium (MaGIC) and developed a GCT clinical data model as a uniform standard to curate and harmonize GCT data sets. This data model will also be the standard for prospective data collection in future trials. Using the GCT data model, we developed a GCT data commons with data sets from both MaGIC and public domains as an integrated research platform. The commons supports functions, such as data query, management, sharing, visualization, and analysis of the harmonized data, as well as patient cohort discovery. This GCT data commons will facilitate future collaborative research to advance the biologic understanding and treatment of GCTs. Moreover, the framework of the GCT data model and data commons will provide insights for other rare disease research communities into developing similar collaborative research platforms.

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Kajander, Aleksi. "Legal Perspectives on Smart City Data as a Commons." International and Comparative Law Review 22, no. 2 (December 1, 2022): 7–26. http://dx.doi.org/10.2478/iclr-2022-0012.

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Summary Smart cities are purported to produce vast amounts of data of immense value, both commercially and from a governance perspective. The control and stewardship of this smart city data remains controversial, with concerns for the role of the individual smart citizens and the control they exercise over the data they generate. Elinor Ostrom’s Nobel prize winning work on long-lasting and sustainable commons has been suggested as a solution, whereby the commons management principles would be applied to smart city data. This paper seeks to identify the current applications of Ostrom’s commons to smart city data in literature, as well as explore their legal implications. Particularly, what legal challenges may arise from the smart city data commons, and how they could be addressed through legislative frameworks. The article aims to identify and highlight these legal challenges and thereby provide a legal perspective on the concept of smart city data commons.

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Volchenboum, Samuel L., Suzanne M. Cox, Allison Heath, Adam Resnick, Susan L. Cohn, and Robert Grossman. "Data Commons to Support Pediatric Cancer Research." American Society of Clinical Oncology Educational Book 37 (2017): 746–52. http://dx.doi.org/10.14694/edbk_175029.

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15

Contreras, J. L. "Prepublication Data Release, Latency, and Genome Commons." Science 329, no. 5990 (July 22, 2010): 393–94. http://dx.doi.org/10.1126/science.1189253.

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Contreras, J. L., and J. H. Reichman. "Sharing by design: Data and decentralized commons." Science 350, no. 6266 (December 10, 2015): 1312–14. http://dx.doi.org/10.1126/science.aaa7485.

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Prainsack, Barbara. "Logged out: Ownership, exclusion and public value in the digital data and information commons." Big Data & Society 6, no. 1 (January 2019): 205395171982977. http://dx.doi.org/10.1177/2053951719829773.

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In recent years, critical scholarship has drawn attention to increasing power differentials between corporations that use data and people whose data is used. A growing number of scholars see digital data and information commons as a way to counteract this asymmetry. In this paper I raise two concerns with this argument: First, because digital data and information can be in more than one place at once, governance models for physical common-pool resources cannot be easily transposed to digital commons. Second, not all data and information commons are suitable to address power differentials. In order to create digital commons that effectively address power asymmetries we must pay more systematic attention to the issue of exclusion from digital data and information commons. Why and how digital data and information commons exclude, and what the consequences of such exclusion are, decide whether commons can change power asymmetries or whether they are more likely to perpetuate them.

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Contreras, Jorge L., and Bartha M. Knoppers. "The Genomic Commons." Annual Review of Genomics and Human Genetics 19, no. 1 (August 31, 2018): 429–53. http://dx.doi.org/10.1146/annurev-genom-083117-021552.

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Over its 30 or so years of existence, the genomic commons—the worldwide collection of publicly accessible repositories of human and nonhuman genomic data—has enjoyed remarkable, perhaps unprecedented, success. Thanks to the rapid public data release policies initiated by the Human Genome Project, free access to a vast array of scientific data is now the norm, not only in genomics, but in scientific disciplines of all descriptions. And far from being a monolithic creation of bureaucratic fiat, the genomic commons is an exemplar of polycentric, multistakeholder governance. But like all dynamic and rapidly evolving systems, the genomic commons is not without its challenges. Issues involving scientific priority, intellectual property, individual privacy, and informed consent, in an environment of data sets of exponentially expanding size and complexity, must be addressed in the near term. In this review, we describe the characteristics and unique history of the genomic commons, then address some of the trends, challenges, and opportunities that we envision for this valuable public resource in the years to come.

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Grossman, Robert L., Allison Heath, Mark Murphy, Maria Patterson, and Walt Wells. "A Case for Data Commons: Toward Data Science as a Service." Computing in Science & Engineering 18, no. 5 (September 2016): 10–20. http://dx.doi.org/10.1109/mcse.2016.92.

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Cook-Deegan, Robert, Mary A. Majumder, and Amy L. McGuire. "Introduction: Sharing Data in a Medical Information Commons." Journal of Law, Medicine & Ethics 47, no. 1 (2019): 7–11. http://dx.doi.org/10.1177/1073110519840479.

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Ottolia, Andrea, and Cristiana Sappa. "A Topography of Data Commons: From Regulation to Private Dynamism." GRUR International 71, no. 4 (December 17, 2021): 335–45. http://dx.doi.org/10.1093/grurint/ikab156.

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Abstract Knowledge is subject to enclosure through digital technology and legal rules. Data collected, stored and pooled by the Internet of Things (IoT) or Artificial Intelligence (AI) are no exception to this. Operators acting in the markets related to the algorithmic society may have a quite diversified range of intellectual property rights (IPRs) to protect the information they produce and manage. This is exploited through algorithmic processing techniques, aggregating collected data for the generation of new ones, thus creating additional information and knowledge. This paper studies whether and when data, information and knowledge, presented within the Big Data, IoT and AI structures, may be considered and exploited as commons. The analysis is not aimed at stating that commons should be the general solution for the algorithmic society. Nor does it endorse legal interpretations unilaterally favoring openness and limiting IPR protection and privacy rules (though this could be the case under certain circumstances). The question is to establish whether a certain level of commons should be provided by regulation or left to spontaneous private initiatives. In this regard, two different meanings of data commons are used in this work. The first one refers to the open access systems provided by regulation, equivalent to a public domain protection, and opposed to exclusivity mechanisms. The second refers to data commons which are privately ‘constructed’ on top of background regulation and manage resources for a limited set of claimants.

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Senabre Hidalgo, Enric, Antonio Calleja, Ricard Espelt, Sara Suárez Gonzalo, Mayo Fuster Morell, and Andreu Belsunces. "Co-creation of the Digital Democracy and Data Commons Manifesto: alternative sociotechnical visions of data." Open Research Europe 4 (March 1, 2024): 45. http://dx.doi.org/10.12688/openreseurope.17020.1.

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Amid public concern surrounding the proprietary and exploitative use of personal data by corporations and public institutions, and its consequences from a sociotechnical perspective, narratives around digital commons have recently emerged, framing potential alternatives. This paper presents the results of an experimental approach, methodology, and process, through which two main questions are addressed. Firstly, how to articulate co-creation dynamics for the structured and participatory elaboration of the Digital Democracy and Data Commons Manifesto, following principles of openness, diversity, and inclusivity. Secondly, how the manifesto, as a narrative and discursive artefact, can follow and conclude a wider process of sociotechnical discussion and positioning about data commons. Our approach is based on participatory design methods, more concretely on a collaborative writing sprint, to co-create a manifesto on alternatives to current datafication, digital inequalities, and lack of citizen control over personal data. On the one hand, we describe the process of implementing a sprint approach for collaboratively writing a topic-specific manifesto, in the context of the broader EU project DECODE (Decentralised Citizen Owned Data Ecosystems). On the other hand, we present and analyse the main results from the content structure of the manifesto over its initial and final versions, which moved progressively as a cohesive text away from a scholarly and policy-oriented tone.

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Cerami, E. G., B. E. Gross, E. Demir, I. Rodchenkov, O. Babur, N. Anwar, N. Schultz, G. D. Bader, and C. Sander. "Pathway Commons, a web resource for biological pathway data." Nucleic Acids Research 39, Database (November 10, 2010): D685—D690. http://dx.doi.org/10.1093/nar/gkq1039.

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McGuire, Amy L., Jessica Roberts, Sean Aas, and Barbara J. Evans. "Who Owns the Data in a Medical Information Commons?" Journal of Law, Medicine & Ethics 47, no. 1 (2019): 62–69. http://dx.doi.org/10.1177/1073110519840485.

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In this paper, we explore the perspectives of expert stakeholders about who owns data in a medical information commons (MIC) and what rights and interests ought to be recognized when developing a governance structure for an MIC. We then examine the legitimacy of these claims based on legal and ethical analysis and explore an alternative framework for thinking about participants' rights and interests in an MIC.

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Crețu, Valentin Bogdan. "Data, governance and tackling the “tragedy of the commons”." International Journal of Advanced Statistics and IT&C for Economics and Life Sciences 12, no. 2 (December 1, 2022): 17–23. http://dx.doi.org/10.2478/ijasitels-2022-0003.

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Abstract The purpose of this paper is to highlight the importance of active and continued public engagement in the debate regarding the regulation of artificial intelligence (AI). The results of several studies are presented, in reference to the benefits of this technology, its risks and limitations, and the shortcomings of traditional and hybrid approaches. Certain conceptual and practical approaches are presented, that aim to facilitate the participation of citizens and other stakeholders in the decision-making process of AI governance.

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Gao, Galen F., Joel S. Parker, Sheila M. Reynolds, Tiago C. Silva, Liang-Bo Wang, Wanding Zhou, Rehan Akbani, et al. "Before and After: Comparison of Legacy and Harmonized TCGA Genomic Data Commons’ Data." Cell Systems 9, no. 1 (July 2019): 24–34. http://dx.doi.org/10.1016/j.cels.2019.06.006.

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Kerlavage, Anthony. "Abstract IA24: A modern data commons approach to advance population science." Cancer Epidemiology, Biomarkers & Prevention 29, no. 9_Supplement (September 1, 2020): IA24. http://dx.doi.org/10.1158/1538-7755.modpop19-ia24.

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Abstract Since 2014, NCI has been building data science platforms to integrate genetic information about tumors with data on how patients respond to therapy. This work advances the 2011 Institute of Medicine recommendation to build a unified system to collect, integrate, and share cancer data from the widest possible set of research studies. NCI is working to fulfill this recommendation by establishing an NCI Cancer Research Data Commons (CRDC), virtual, expandable informatics infrastructure and data repositories, which are designed to support the work of the NCI intramural and extramural cancer research communities. The CRDC is a crucial component of a broader National Cancer Data Ecosystem that spans discovery research, patient participation, and population surveillance. The development of such an ecosystem was a key recommendation of the Cancer Moonshot Blue Ribbon Panel. NCI has been developing the Cancer Research Data Commons based on the understanding that big data emerging from research programs, precision medicine trials, and surveillance can be used to develop and inform models that predict how patients will response to treatment. Numerous NCI-funded programs will provide key data for this ecosystem, including a variety of discovery programs, clinical trials, cohort studies, and NCI’s Surveillance, Epidemiology, and End Results program (SEER), which is the most comprehensive source of population-based information in the country that includes stage of cancer, detailed characterization of the cancer including important biomarkers at the time of diagnosis, and performs active follow-up to determine patient survival data. The CRDC is designed to be as flexible as possible so that many types and sources of data can be accessed in a unified, interoperable manner. It is this data interoperability that facilitates novel insights into cancer biology and treatment. During the past several years, NCI has been developing key building blocks that comprise the CRDC. These building blocks include (i) Data Commons Nodes, which are repositories that house harmonized data from multiple programs; (ii) Cloud Resources, which provide workspaces, analysis capabilities, and access to elastic computing resources in commercial cloud environments; (iii) a Data Commons Framework (DCF), which is a set of modular, reusable services that maximize interoperability across cancer datatypes and repositories, increase sustainability, and reduce future resource cost; and (iv) semantics services to support data models, vocabularies, ontologies, data submission, and cross-domain queries. The modular design of the CRDC means that new services and components can be added flexibly, which allows for new data sources and new technologies to become integrated as they become available. The NCI Cancer Research Data Commons will serve as a platform for understanding fundamental cancer biology questions, including cancer initiation, progression, and recurrence, in ways that support new prevention, treatment, and surveillance strategies. Citation Format: Anthony Kerlavage. A modern data commons approach to advance population science [abstract]. In: Proceedings of the AACR Special Conference on Modernizing Population Sciences in the Digital Age; 2019 Feb 19-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(9 Suppl):Abstract nr IA24.

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Baarbé, Jeremiah, Meghan Blom, and Jeremy De Beer. "A Proposed “Agricultural Data Commons” in Support of Food Security." African Journal of Information and Communication, no. 23 (June 28, 2019): 1–33. http://dx.doi.org/10.23962/10539/27534.

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Zhang, Guo-Qiang, Licong Cui, Remo Mueller, Shiqiang Tao, Matthew Kim, Michael Rueschman, Sara Mariani, Daniel Mobley, and Susan Redline. "The National Sleep Research Resource: towards a sleep data commons." Journal of the American Medical Informatics Association 25, no. 10 (May 31, 2018): 1351–58. http://dx.doi.org/10.1093/jamia/ocy064.

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Abstract Objective The gold standard for diagnosing sleep disorders is polysomnography, which generates extensive data about biophysical changes occurring during sleep. We developed the National Sleep Research Resource (NSRR), a comprehensive system for sharing sleep data. The NSRR embodies elements of a data commons aimed at accelerating research to address critical questions about the impact of sleep disorders on important health outcomes. Approach We used a metadata-guided approach, with a set of common sleep-specific terms enforcing uniform semantic interpretation of data elements across three main components: (1) annotated datasets; (2) user interfaces for accessing data; and (3) computational tools for the analysis of polysomnography recordings. We incorporated the process for managing dataset-specific data use agreements, evidence of Institutional Review Board review, and the corresponding access control in the NSRR web portal. The metadata-guided approach facilitates structural and semantic interoperability, ultimately leading to enhanced data reusability and scientific rigor. Results The authors curated and deposited retrospective data from 10 large, NIH-funded sleep cohort studies, including several from the Trans-Omics for Precision Medicine (TOPMed) program, into the NSRR. The NSRR currently contains data on 26 808 subjects and 31 166 signal files in European Data Format. Launched in April 2014, over 3000 registered users have downloaded over 130 terabytes of data. Conclusions The NSRR offers a use case and an example for creating a full-fledged data commons. It provides a single point of access to analysis-ready physiological signals from polysomnography obtained from multiple sources, and a wide variety of clinical data to facilitate sleep research.

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Dobusch, Leonhard, Stefan Pawel, Gustav Pomberger, and René Riedl. "Open Government Data: eine Initiative der Open-Commons-Region Linz." HMD Praxis der Wirtschaftsinformatik 49, no. 1 (February 2012): 74–83. http://dx.doi.org/10.1007/bf03340665.

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Printz, Carrie. "Genomic Data Commons ushers in new era for information sharing." Cancer 122, no. 18 (September 7, 2016): 2777–78. http://dx.doi.org/10.1002/cncr.30278.

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Loomba, Johanna Jean, Glenn S. Wasson, Ravi Kiran Reddy Chamakuri, Pabitra Kumar Dash, Stephen G. Patterson, Mary M. A. Potter, Jason Edward Krisch, Martha M. Tenzer, Karen C. Johnston, and Don E. Brown. "The iTHRIV Commons: a cross-institution information and health research data sharing architecture and web application." Journal of the American Medical Informatics Association 29, no. 4 (November 25, 2021): 631–42. http://dx.doi.org/10.1093/jamia/ocab262.

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Abstract Objective The integrated Translational Health Research Institute of Virginia (iTHRIV) aims to develop an information architecture to support data workflows throughout the research lifecycle for cross-state teams of translational researchers. Materials and Methods The iTHRIV Commons is a cross-state harmonized infrastructure supporting resource discovery, targeted consultations, and research data workflows. As the front end to the iTHRIV Commons, the iTHRIV Research Concierge Portal supports federated login, personalized views, and secure interactions with objects in the ITHRIV Commons federation. The canonical use-case for the iTHRIV Commons involves an authenticated user connected to their respective high-security institutional network, accessing the iTHRIV Research Concierge Portal web application on their browser, and interfacing with multi-component iTHRIV Commons Landing Services installed behind the firewall at each participating institution. Results The iTHRIV Commons provides a technical framework, including both hardware and software resources located in the cloud and across partner institutions, that establishes standard representation of research objects, and applies local data governance rules to enable access to resources from a variety of stakeholders, both contributing and consuming. Discussion The launch of the Commons API service at partner sites and the addition of a public view of nonrestricted objects will remove barriers to data access for cross-state research teams while supporting compliance and the secure use of data. Conclusions The secure architecture, distributed APIs, and harmonized metadata of the iTHRIV Commons provide a methodology for compliant information and data sharing that can advance research productivity at Hub sites across the CTSA network.

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Wong, Janis, and Tristan Henderson. "Co-Creating Autonomy: Group Data Protection and Individual Self-determination within a Data Commons." International Journal of Digital Curation 15, no. 1 (August 11, 2020): 16. http://dx.doi.org/10.2218/ijdc.v15i1.714.

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Recent privacy scandals such as Cambridge Analytica and the Nightingale Project show that data sharing must be carefully managed and regulated to prevent data misuse. Data protection law, legal frameworks, and technological solutions tend to focus on controller responsibilities as opposed to protecting data subjects from the beginning of the data collection process. Using a case study of how data subjects can be better protected during data curation, we propose that a co-created data commons can protect individual autonomy over personal data through collective curation and rebalance power between data subjects and controllers.

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Yim, Hyung-Jun, Mikyoung Lee, Sa-Kwang Song, Dongmin Seo, and Minhee Cho. "Design and Application of Korea Research Data Commons for Data-Driven Research and Development." Journal of Korean Institute of Intelligent Systems 32, no. 5 (October 31, 2022): 392–400. http://dx.doi.org/10.5391/jkiis.2022.32.5.392.

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Bühler, Michael Max, Igor Calzada, Isabel Cane, Thorsten Jelinek, Astha Kapoor, Morshed Mannan, Sameer Mehta, et al. "Unlocking the Power of Digital Commons: Data Cooperatives as a Pathway for Data Sovereign, Innovative and Equitable Digital Communities." Digital 3, no. 3 (June 29, 2023): 146–71. http://dx.doi.org/10.3390/digital3030011.

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Network effects, economies of scale, and lock-in-effects increasingly lead to a concentration of digital resources and capabilities, hindering the free and equitable development of digital entrepreneurship, new skills, and jobs, especially in small communities and their small and medium-sized enterprises (“SMEs”). To ensure the affordability and accessibility of technologies, promote digital entrepreneurship and community well-being, and protect digital rights, we propose data cooperatives as a vehicle for secure, trusted, and sovereign data exchange. In post-pandemic times, community/SME-led cooperatives can play a vital role by ensuring that supply chains to support digital commons are uninterrupted, resilient, and decentralized. Digital commons and data sovereignty provide communities with affordable and easy access to information and the ability to collectively negotiate data-related decisions. Moreover, cooperative commons (a) provide access to the infrastructure that underpins the modern economy, (b) preserve property rights, and (c) ensure that privatization and monopolization do not further erode self-determination, especially in a world increasingly mediated by AI. Thus, governance plays a significant role in accelerating communities’/SMEs’ digital transformation and addressing their challenges. Cooperatives thrive on digital governance and standards such as open trusted application programming interfaces (“APIs”) that increase the efficiency, technological capabilities, and capacities of participants and, most importantly, integrate, enable, and accelerate the digital transformation of SMEs in the overall process. This review article analyses an array of transformative use cases that underline the potential of cooperative data governance. These case studies exemplify how data and platform cooperatives, through their innovative value creation mechanisms, can elevate digital commons and value chains to a new dimension of collaboration, thereby addressing pressing societal issues. Guided by our research aim, we propose a policy framework that supports the practical implementation of digital federation platforms and data cooperatives. This policy blueprint intends to facilitate sustainable development in both the Global South and North, fostering equitable and inclusive data governance strategies.

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LaPlante, Emily, Bingxing Huo, DR Mani, and Ratna R. Thangudu. "Abstract 7420: Optimizing proteomic data access and analysis in the cloud: Leveraging Terra's integration with the Proteomic Data Commons." Cancer Research 84, no. 6_Supplement (March 22, 2024): 7420. http://dx.doi.org/10.1158/1538-7445.am2024-7420.

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Abstract The Proteomic Data Commons (PDC) hosts cancer proteomics data with the goal of making this data available to the public to support development of cancer diagnostics, treatment, and progression tracking. As a part of the Cancer Research Data Commons (CRDC), the Terra platform provides a cloud workbench for the PDC data. FireCloud is a Broad Institute project funded by NCI to empower cancer researchers to access data, run analysis tools and collaborate securely in the cloud. It is powered by Terra, a secure, scalable cloud-native platform developed by the Broad Institute, Microsoft, and Verily, an Alphabet company. It provides batch workflow execution, interactive analysis including data visualization, and ~2,900 publicly available tools with the ability to import more tools from Dockstore. The integration of PDC and Terra enables researchers to leverage the data navigation and file-level search capabilities on the PDC web browser (pdc.cancer.gov) and export selected data manifests to a Terra workspace. This integrated hand-off specifically allows metadata and cloud links to PDC data in Portable Format for Bioinformatics (PFB) to be transferred to Terra and used in analysis with cloud resources. A featured workspace for Terra-PDC integration includes tools for (i) downloading data files and relevant additional metadata; (ii) organizing the data and metadata for running the FragPipe workflow, a comprehensive collection of tools for reading and processing raw Mass Spec (MS) data; and (iii) implementing a pre-configured FragPipe workflow to process isobarically labeled MS data from Tandem Mass Tag (TMT) or Isobaric tags for relative and absolute quantitation (iTRAQ) experiments using information automatically imported from PDC via the PFB import. The pipelines can also be customized as needed to process any type of raw MS data the PDC supports. In addition, Terra connects to the NCI Genomic Data Commons (GDC) and hosts The Cancer Genome Atlas (TCGA) and Therapeutically Applicable Research to Generate Effective Treatments (TARGET) datasets allowing users to pull in other data types for co-analysis. As a whole, using the CRDC cloud resources, in conjunction with Terra, allows analysis of data on a massive scale, enabling multi-omic integration spanning the genomic and proteomic commons data, with easy sharing of data and tools. Citation Format: Emily LaPlante, Bingxing Huo, DR Mani, Ratna R. Thangudu. Optimizing proteomic data access and analysis in the cloud: Leveraging Terra's integration with the Proteomic Data Commons [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7420.

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Nishikawa, Kai. "How are research data governed at Japanese repositories? A knowledge commons perspective." Aslib Journal of Information Management 72, no. 5 (July 31, 2020): 837–52. http://dx.doi.org/10.1108/ajim-03-2020-0072.

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PurposeThe purpose of this paper is to survey how research data are governed at repositories in Japan by deductively establishing a governance typology based on the concept of openness in the context of knowledge commons and empirically assessing the conformity of repositories to each type.Design/methodology/approachThe fuzzy-set ideal type analysis (FSITA) was adopted. For data collection, a manual assessment was conducted with all Japanese research data repositories registered on re3data.org.FindingsThe typology constructed in this paper consists of three dimensions: openness to resources (here equal to research data), openness to a community and openness to infrastructure provision. This paper found that there is no case where all dimensions are open, and there are several cases where the resources are closed despite research data repositories being positioned as a basis for open science in Japanese science and technology policy.Originality/valueThis is likely the first construction of the typology and application of FSITA to the study of research data governance based on knowledge commons. The findings of this paper provide practitioners insight into how to govern research data at repositories. The typology serves as a first step for future research on knowledge commons, for example, as a criterion of case selection in conducting in-depth case studies.

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Cook-Deegan, Robert, and Amy L. McGuire. "Moving beyond Bermuda: sharing data to build a medical information commons." Genome Research 27, no. 6 (April 3, 2017): 897–901. http://dx.doi.org/10.1101/gr.216911.116.

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Smiley, Kevin T. "The Urban Commons: How Data and Technology Can Rebuild Our Communities." Contemporary Sociology: A Journal of Reviews 49, no. 1 (December 20, 2019): 77–78. http://dx.doi.org/10.1177/0094306119889962bb.

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Phillips, Mark, and Bartha M. Knoppers. "Whose Commons? Data Protection as a Legal Limit of Open Science." Journal of Law, Medicine & Ethics 47, no. 1 (2019): 106–11. http://dx.doi.org/10.1177/1073110519840489.

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Open science has recently gained traction as establishment institutions have come on-side and thrown their weight behind the movement and initiatives aimed at creation of information commons. At the same time, the movement's traditional insistence on unrestricted dissemination and reuse of all information of scientific value has been challenged by the movement to strengthen protection of personal data. This article assesses tensions between open science and data protection, with a focus on the GDPR.

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Jensen, Mark A., Vincent Ferretti, Robert L. Grossman, and Louis M. Staudt. "The NCI Genomic Data Commons as an engine for precision medicine." Blood 130, no. 4 (July 27, 2017): 453–59. http://dx.doi.org/10.1182/blood-2017-03-735654.

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Abstract The National Cancer Institute Genomic Data Commons (GDC) is an information system for storing, analyzing, and sharing genomic and clinical data from patients with cancer. The recent high-throughput sequencing of cancer genomes and transcriptomes has produced a big data problem that precludes many cancer biologists and oncologists from gleaning knowledge from these data regarding the nature of malignant processes and the relationship between tumor genomic profiles and treatment response. The GDC aims to democratize access to cancer genomic data and to foster the sharing of these data to promote precision medicine approaches to the diagnosis and treatment of cancer.

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Fisher, Joshua B., and Louise Fortmann. "Governing the data commons: Policy, practice, and the advancement of science." Information & Management 47, no. 4 (May 2010): 237–45. http://dx.doi.org/10.1016/j.im.2010.04.001.

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Chou, Austin, Abel Torres-Espín, J. Russell Huie, Karen Krukowski, Sangmi Lee, Amber Nolan, Caroline Guglielmetti, et al. "Empowering Data Sharing and Analytics through the Open Data Commons for Traumatic Brain Injury Research." Neurotrauma Reports 3, no. 1 (April 1, 2022): 139–57. http://dx.doi.org/10.1089/neur.2021.0061.

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Grossman, Robert L. "Data Lakes, Clouds, and Commons: A Review of Platforms for Analyzing and Sharing Genomic Data." Trends in Genetics 35, no. 3 (March 2019): 223–34. http://dx.doi.org/10.1016/j.tig.2018.12.006.

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Meyer, Camille. "Social finance and the commons paradigm." Management Decision 58, no. 4 (September 13, 2019): 786–96. http://dx.doi.org/10.1108/md-01-2019-0133.

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Purpose The concept of the commons, or common goods, is becoming increasingly widespread in the world of research and among civil society. The commons are defined as resources that are shared and collectively managed by communities of users, such as natural commons (e.g. fisheries, the climate) and knowledge commons (e.g. Wikipedia, open-source software). The paper aims to discuss this issue. Design/methodology/approach This paper presents the findings of the PhD dissertation “Social finance and the commons,” recipient of the 2017 Emerald/EFMD Outstanding Doctoral Research Award, category Management and Governance, sponsored by Management Decision. Adopting an interdisciplinary perspective of the commons, this dissertation investigates how community enterprises govern financial resources as commons to serve the common good. To do so, it builds on data collected on community development banks in Brazil and complementary currencies in multiple countries. Findings The findings explain how collective action favors the implementation of new forms of governance and management potentially enabling finance to create and support communities. In doing so, this dissertation provides insights on the transformative power of some governance features for the creation of commons. Originality/value This dissertation advances theoretical and conceptual foundations for a theory of the commons in management sciences. It contributes to a new conceptualization of the commons, especially by extending the concept of commons to finance and showing the variety of commons according to governance structures and values. It also generates theoretical insights into social and community entrepreneurship research through an in-depth investigation of social finance organizations.

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Bollinger, Juli M., Peter D. Zuk, Mary A. Majumder, Erika Versalovic, Angela G. Villanueva, Rebecca L. Hsu, Amy L. McGuire, and Robert Cook-Deegan. "What is a Medical Information Commons?" Journal of Law, Medicine & Ethics 47, no. 1 (2019): 41–50. http://dx.doi.org/10.1177/1073110519840483.

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A 2011 National Academies of Sciences report called for an “Information Commons” and a “Knowledge Network” to revolutionize biomedical research and clinical care. We interviewed 41 expert stakeholders to examine governance, access, data collection, and privacy in the context of a medical information commons. Stakeholders' attitudes about MICs align with the NAS vision of an Information Commons; however, differences of opinion regarding clinical use and access warrant further research to explore policy and technological solutions.

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Jagodnik, Kathleen M., Simon Koplev, Sherry L. Jenkins, Lucila Ohno-Machado, Benedict Paten, Stephan C. Schurer, Michel Dumontier, et al. "Developing a framework for digital objects in the Big Data to Knowledge (BD2K) commons: Report from the Commons Framework Pilots workshop." Journal of Biomedical Informatics 71 (July 2017): 49–57. http://dx.doi.org/10.1016/j.jbi.2017.05.006.

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Villanueva, Angela G., Robert Cook-Deegan, Jill O. Robinson, Amy L. McGuire, and Mary A. Majumder. "Genomic Data-Sharing Practices." Journal of Law, Medicine & Ethics 47, no. 1 (2019): 31–40. http://dx.doi.org/10.1177/1073110519840482.

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Making data broadly accessible is essential to creating a medical information commons (MIC). Transparency about data-sharing practices can cultivate trust among prospective and existing MIC participants. We present an analysis of 34 initiatives sharing DNA-derived data based on public information. We describe data-sharing practices captured, including practices related to consent, privacy and security, data access, oversight, and participant engagement. Our results reveal that data-sharing initiatives have some distance to go in achieving transparency.

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Furner, B., M. Krailo, F. Shaikh, A. Fonseca, B. Fresneau, L. Klosterkemper, J. Piao, et al. "Building a scalable and sustainable data commons for germ cell tumour research." European Urology Supplements 18, no. 4 (September 2019): 7. http://dx.doi.org/10.1016/s1569-9056(19)32472-8.

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van Maanen, Gijs, Charlotte Ducuing, and Tommaso Fia. "Data commons." Internet Policy Review 13, no. 2 (April 4, 2024). http://dx.doi.org/10.14763/2024.2.1748.

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