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Organization: Archive Team

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Formed in 2009, the Archive Team (not to be confused with the archive.org Archive-It Team) is a rogue archivist collective dedicated to saving copies of rapidly dying or deleted websites for the sake of history and digital heritage. The group is 100% composed of volunteers and interested parties, and has expanded into a large amount of related projects for saving online and digital history.

History is littered with hundreds of conflicts over the future of a community, group, location or business that were "resolved" when one of the parties stepped ahead and destroyed what was there. With the original point of contention destroyed, the debates would fall to the wayside. Archive Team believes that by duplicated condemned data, the conversation and debate can continue, as well as the richness and insight gained by keeping the materials. Our projects have ranged in size from a single volunteer downloading the data to a small-but-critical site, to over 100 volunteers stepping forward to acquire terabytes of user-created data to save for future generations.

The main site for Archive Team is at archiveteam.org and contains up to the date information on various projects, manifestos, plans and walkthroughs.

This collection contains the output of many Archive Team projects, both ongoing and completed. Thanks to the generous providing of disk space by the Internet Archive, multi-terabyte datasets can be made available, as well as in use by the Wayback Machine, providing a path back to lost websites and work.

Our collection has grown to the point of having sub-collections for the type of data we acquire. If you are seeking to browse the contents of these collections, the Wayback Machine is the best first stop. Otherwise, you are free to dig into the stacks to see what you may find.

The Archive Team Panic Downloads are full pulldowns of currently extant websites, meant to serve as emergency backups for needed sites that are in danger of closing, or which will be missed dearly if suddenly lost due to hard drive crashes or server failures.

Collection: Archive Team: URLs

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The Wayback Machine - https://web.archive.org/web/20240420213028/https://johnshopkins.academia.edu/RobertStevens

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Robert D Stevens

Johns Hopkins University, Anesthesiology Critical Care Medicine, Faculty Member

Johns Hopkins University School of Medicine, Neurology, Faculty Member

Johns Hopkins University School of Medicine, Neurosurgery, Faculty Member

Johns Hopkins University School of Medicine, Radiology, Faculty Member

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Robert Stevens, MD, is an Associate Professor at Johns Hopkins University (JHU) School of Medicine with appointments in Anesthesiology Critical Care Medicine, Neurology, Neurosurgery, and Radiology. He received his degree in medicine from the University of Geneva and completed his residency and fellowship at the University of Geneva and Johns Hopkins University. Dr Stevens’ research aims to apply computational approaches to analyse patient-specific biological signatures encountered in trauma, surgery and acute conditions such as stroke, cardiac arrest, and sepsis. To achieve this, he engages with an interdisciplinary group of basic and systems neuroscientists, biomedical and computer engineers, biostatisticians, data scientists, and neuroradiologists. Dr Stevens is the principal investigator of the Neuroimaging for Coma Emergence and Recovery (NICER) project, which examines MRI features of the brain connectome to enhance classification and prediction in patients recovering from severe brain injury. Other projects from Dr Stevens’ group analyze features captured through neurophysiologic monitoring, structural and functional MRI of the brain, serum biomarkers and wearable sensors, with the goal of enhancing classification and prediction in vulnerable, acutel ill patients. In the lab, Dr Stevens’ group studies molecular and cellular determinants of injury and recovery in experimental models of traumatic brain injury, stroke, and sepsis, with a special emphasis on neuro-immunological responses and cellular regeneration.

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Ontology for Genome Comparison and Genomic Rearrangements

Comparative and Functional Genomics, 2004

Process of building a vocabulary for the infection domain

Proceedings - IEEE Symposium on Computer-Based Medical Systems, 2008

Mining for patterns of semantic link usage: Do domain users actually like semantic browsing?

Proceedings - 2009 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2009, 2009

Using semantic web technologies to manage complexity and change in biomedical data

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2011

Developing a kidney and urinary pathway knowledge base

Thematic series on biomedical ontologies in JBMS: challenges and new directions

Logical Gene Ontology Annotations (GOAL): exploring gene ontology annotations with OWL

The Software Ontology (SWO): a resource for reproducibility in biomedical data analysis, curation and digital preservation

The Semantic Web and Knowledge Grids

Drug Discovery Today: Technologies, 2005

The Semantic Web and the Knowledge Grid are recently proposed technological solutions to distribu... more The Semantic Web and the Knowledge Grid are recently proposed technological solutions to distributed knowledge management. Early experimental applications from the Life Science community indicate that the approaches have promise and suggest that this community be an appropriate nursery for grounding, developing and hardening the current, rather immature, machinery needed to deliver on the technological visions, which thus far have been dominated by technological curiosity rather than application-led practicality and relevance. Further necessary developments in theory, infrastructure, tools, and content management should and could be steered opportunistically by the needs and applications of Life Science.:

A flexible API and editor for SKOS

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2009

The current state of SKOS vocabularies on the web

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012

Design insights for the next wave ontology authoring tools

Taverna/myGrid: Aligning a workflow system with the life sciences community

Workflows for e-Science: Scientific Workflows for Grids, 2007

Building Workflows that Traverse the Bioinformatics Data Landscape

Data Mining Techniques in Grid Computing Environments, 2009

Validating the use and role of visual elements of web pages in navigation with an eye-tracking study

Proceeding of the 17th International Conference on World Wide Web 2008, WWW'08, 2008

Draft:- Not for redistribution Semantic similarity measures as tools for exploring the Gene Ontology

Many bioinformatics resources hold data in the form of sequences. Often this sequence data is ass... more Many bioinformatics resources hold data in the form of sequences. Often this sequence data is associated with a large amount of annota- tion. In many cases this data has been hard to model, and has been represented as scientic natural language, which is not readily compu- tationally amenable. The development of the Gene Ontology provides us with a more accessible

Semantic similarity measures across the gene ontology: the relationship between sequence and annotation

OilEd: A Reason-able Ontology Editor for the Semantic Web

Lecture Notes in Computer Science, 2001

Proceedings of the 14th international conference on World Wide Web - WWW '05, 2005

Automating generation of textual class definitions from OWL to English

Journal of biomedical semantics, 2011

Text definitions for entities within bio-ontologies are a cornerstone of the effort to gain a con... more Text definitions for entities within bio-ontologies are a cornerstone of the effort to gain a consensus in understanding and usage of those ontologies. Writing these definitions is, however, a considerable effort and there is often a lag between specification of the main part of an ontology (logical descriptions and definitions of entities) and the development of the text-based definitions. The goal of natural language generation (NLG) from ontologies is to take the logical description of entities and generate fluent natural language. The application described here uses NLG to automatically provide text-based definitions from an ontology that has logical descriptions of its entities, so avoiding the bottleneck of authoring these definitions by hand. To produce the descriptions, the program collects all the axioms relating to a given entity, groups them according to common structure, realises each group through an English sentence, and assembles the resulting sentences into a paragra...

Ontology for Genome Comparison and Genomic Rearrangements

Comparative and Functional Genomics, 2004

Process of building a vocabulary for the infection domain

Proceedings - IEEE Symposium on Computer-Based Medical Systems, 2008

Mining for patterns of semantic link usage: Do domain users actually like semantic browsing?

Proceedings - 2009 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2009, 2009

Using semantic web technologies to manage complexity and change in biomedical data

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2011

Developing a kidney and urinary pathway knowledge base

Thematic series on biomedical ontologies in JBMS: challenges and new directions

Logical Gene Ontology Annotations (GOAL): exploring gene ontology annotations with OWL

The Software Ontology (SWO): a resource for reproducibility in biomedical data analysis, curation and digital preservation

The Semantic Web and Knowledge Grids

Drug Discovery Today: Technologies, 2005

The Semantic Web and the Knowledge Grid are recently proposed technological solutions to distribu... more The Semantic Web and the Knowledge Grid are recently proposed technological solutions to distributed knowledge management. Early experimental applications from the Life Science community indicate that the approaches have promise and suggest that this community be an appropriate nursery for grounding, developing and hardening the current, rather immature, machinery needed to deliver on the technological visions, which thus far have been dominated by technological curiosity rather than application-led practicality and relevance. Further necessary developments in theory, infrastructure, tools, and content management should and could be steered opportunistically by the needs and applications of Life Science.:

A flexible API and editor for SKOS

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2009

The current state of SKOS vocabularies on the web

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012

Design insights for the next wave ontology authoring tools

Taverna/myGrid: Aligning a workflow system with the life sciences community

Workflows for e-Science: Scientific Workflows for Grids, 2007

Building Workflows that Traverse the Bioinformatics Data Landscape

Data Mining Techniques in Grid Computing Environments, 2009

Validating the use and role of visual elements of web pages in navigation with an eye-tracking study

Proceeding of the 17th International Conference on World Wide Web 2008, WWW'08, 2008

Draft:- Not for redistribution Semantic similarity measures as tools for exploring the Gene Ontology

Many bioinformatics resources hold data in the form of sequences. Often this sequence data is ass... more Many bioinformatics resources hold data in the form of sequences. Often this sequence data is associated with a large amount of annota- tion. In many cases this data has been hard to model, and has been represented as scientic natural language, which is not readily compu- tationally amenable. The development of the Gene Ontology provides us with a more accessible

Semantic similarity measures across the gene ontology: the relationship between sequence and annotation

OilEd: A Reason-able Ontology Editor for the Semantic Web

Lecture Notes in Computer Science, 2001

Proceedings of the 14th international conference on World Wide Web - WWW '05, 2005

Automating generation of textual class definitions from OWL to English

Journal of biomedical semantics, 2011

Text definitions for entities within bio-ontologies are a cornerstone of the effort to gain a con... more Text definitions for entities within bio-ontologies are a cornerstone of the effort to gain a consensus in understanding and usage of those ontologies. Writing these definitions is, however, a considerable effort and there is often a lag between specification of the main part of an ontology (logical descriptions and definitions of entities) and the development of the text-based definitions. The goal of natural language generation (NLG) from ontologies is to take the logical description of entities and generate fluent natural language. The application described here uses NLG to automatically provide text-based definitions from an ontology that has logical descriptions of its entities, so avoiding the bottleneck of authoring these definitions by hand. To produce the descriptions, the program collects all the axioms relating to a given entity, groups them according to common structure, realises each group through an English sentence, and assembles the resulting sentences into a paragra...