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| Remote Access Data Laboratory | |
|---|---|
| Name | Remote Access Data Laboratory |
| Type | Distributed computing facility |
| Established | 21st century |
| Country | International |
| Discipline | Data science, computational research |
Remote Access Data Laboratory
A Remote Access Data Laboratory is a remotely accessible facility or service that provides researchers, engineers, and analysts with tools, datasets, and computational resources for experimental work and reproducible analysis. It combines remotely hosted hardware, virtualized environments, and curated datasets to enable collaboration across institutions, industries, and projects such as those led by CERN, NASA, European Space Agency, National Institutes of Health, and Wellcome Trust. These platforms intersect with initiatives like Open Science Framework, Dataverse, Kaggle, HPC Consortium, and FAIR data principles to standardize access and reuse.
Remote access data laboratories originated from distributed computing efforts exemplified by SETI@home, Folding@home, and grid projects such as the Worldwide LHC Computing Grid. They evolved alongside cloud services from Amazon Web Services, Google Cloud Platform, and Microsoft Azure, and research infrastructures like XSEDE, PRACE, and NeCTAR. Stakeholders include universities such as Massachusetts Institute of Technology, Stanford University, and University of Cambridge, research institutes like Max Planck Society and Lawrence Berkeley National Laboratory, and corporations including IBM and Intel. Funding and policy frameworks often reference agencies like the National Science Foundation, European Commission, and Wellcome Trust.
Typical architectures draw on virtualization and containerization technologies pioneered by projects like Docker and Kubernetes, orchestration patterns from Apache Mesos and Hadoop, and storage systems such as Ceph and GlusterFS. Compute backends may use accelerators from NVIDIA and AMD and scheduler integrations similar to Slurm Workload Manager or HTCondor. Metadata and provenance tracking often adopt standards from W3C recommendations and implementations used in Zenodo and Figshare. Identity and access management integrates with systems like OAuth 2.0, LDAP, and federations such as Shibboleth and eduGAIN.
Users connect via web interfaces inspired by Jupyter Notebook and RStudio Server, remote desktop solutions akin to VNC and X2Go, or command-line access using protocols such as SSH and APIs following RESTful API conventions. Data transfer relies on high-performance protocols used by Globus, GridFTP, and Aspera, while message queuing and event-driven patterns employ middleware like RabbitMQ and Apache Kafka. Interoperability is facilitated by standards from OpenAPI Initiative and federated identity systems like SAML.
Security models reference practices endorsed by agencies such as NIST and directives like GDPR and standards from ISO/IEC 27001. Threat models include supply-chain concerns highlighted in reports by CISA and vulnerability disclosures from CVE records. Techniques include encryption methods standardized by TLS and AES, hardware-based protections using Trusted Platform Module and Intel SGX, and auditing supported by Auditd and Splunk. Sensitive data handling aligns with guidance from HIPAA and research ethics frameworks promoted by World Medical Association.
Use cases span disciplines and projects such as genomics analyses in studies linked to 1000 Genomes Project and Human Genome Project, climate modeling in initiatives by Intergovernmental Panel on Climate Change and NOAA, particle physics workflows associated with ATLAS experiment and CMS experiment, and social science data synthesis for efforts like World Values Survey and Pew Research Center. Industry adopters include Pfizer, Siemens, Goldman Sachs, and Boeing for prototyping, while non-profits such as Bill & Melinda Gates Foundation sponsor deployment for global health research.
Scalability strategies mirror designs from Elastic scaling practices in Amazon EC2 Auto Scaling and cluster management exemplified by Apache Spark. Reliability patterns borrow from RAID storage concepts, redundancy approaches used in Content Delivery Network architectures such as Akamai, and disaster recovery playbooks from ITIL and ISO 22301. Benchmarking often uses suites associated with SPEC and domain-specific tests from MLPerf and NIST SP 800-53 evaluations.
Governance frameworks reference models used by Open Data Charter, Research Data Alliance, and institutional review boards like those at Harvard University and Johns Hopkins University. Compliance activities interact with laws and regulations including GDPR, HIPAA, and standards from ISO committees. Data management practices adopt metadata schemas from Dublin Core, licensing approaches exemplified by Creative Commons, and stewardship principles advocated by FAIR data principles and DataCite. Collaborative governance can involve consortia similar to Global Alliance for Genomics and Health and funding mechanisms like those from the European Research Council.
Category:Distributed computing Category:Research infrastructure Category:Data management