ripeta – responsible science

“Ripeta is a credit review for scientific publications. Similar to a financial credit report, which reviews the fiscal health of a person, Ripeta assesses the responsible reporting of the scientific paper. The Ripeta suite identifies and extracts the key components of research reporting, thus drastically shortening and improving the publication process; furthermore, Ripeta’s ability to extract data makes these pieces of text easily discoverable for future use….

Researchers: Rapidly check your pre-print manuscripts to improve the transparency of reporting your research.

Publishers: Improve the reproducibility of the articles you publish with an automated tool that helps evidence-based science.

Funders: Evaluate your portfolio by checking your manuscripts for robust scientific reporting.”

Wellcome and Ripeta partner to assess dataset availability in funded research – Digital Science

“Ripeta and Wellcome are pleased to announce a collaborative effort to assess data and code availability in the manuscripts of funded research projects.

The project will analyze papers funded by Wellcome from the year prior to it establishing a dedicated Open Research team (2016) and from the most recent calendar year (2019). It supports Wellcome’s commitment to maximising the availability and re-use of results from its funded research.

Ripeta, a Digital Science portfolio company, aims to make better science easier by identifying and highlighting the important parts of research that should be transparently presented in a manuscript and other materials.

The collaboration will leverage Ripeta’s natural language processing (NLP) technology, which scans articles for reproducibility criteria. For both data availability and code availability, the NLP will produce a binary yes-no response for the presence of availability statements. Those with a “yes” response will then be categorized by the way that data or code are shared….”

[2008.04541] Comprehensiveness of Archives: A Modern AI-enabled Approach to Build Comprehensive Shared Cultural Heritage

Abstract:  Archives play a crucial role in the construction and advancement of society. Humans place a great deal of trust in archives and depend on them to craft public policies and to preserve languages, cultures, self-identity, views and values. Yet, there are certain voices and viewpoints that remain elusive in the current processes deployed in the classification and discoverability of records and archives.

In this paper, we explore the ramifications and effects of centralized, due process archival systems on marginalized communities. There is strong evidence to prove the need for progressive design and technological innovation while in the pursuit of comprehensiveness, equity and justice. Intentionality and comprehensiveness is our greatest opportunity when it comes to improving archival practices and for the advancement and thrive-ability of societies at large today. Intentionality and comprehensiveness is achievable with the support of technology and the Information Age we live in today. Reopening, questioning and/or purposefully including others voices in archival processes is the intention we present in our paper.

We provide examples of marginalized communities who continue to lead “community archive” movements in efforts to reclaim and protect their cultural identity, knowledge, views and futures. In conclusion, we offer design and AI-dominant technological considerations worth further investigation in efforts to bridge systemic gaps and build robust archival processes.

WordNet | A Lexical Database for English

“WordNet® is a large lexical database of English. Nouns, verbs, adjectives and adverbs are grouped into sets of cognitive synonyms (synsets), each expressing a distinct concept. Synsets are interlinked by means of conceptual-semantic and lexical relations. The resulting network of meaningfully related words and concepts can be navigated with the browser

(link is external). WordNet is also freely and publicly available for download. WordNet’s structure makes it a useful tool for computational linguistics and natural language processing….”

Accelerating ophthalmic artificial intelligence research: the role of an open access data repository – PubMed

Abstract:  Purpose of review: Artificial intelligence has already provided multiple clinically relevant applications in ophthalmology. Yet, the explosion of nonstandardized reporting of high-performing algorithms are rendered useless without robust and streamlined implementation guidelines. The development of protocols and checklists will accelerate the translation of research publications to impact on patient care.

Recent findings: Beyond technological scepticism, we lack uniformity in analysing algorithmic performance generalizability, and benchmarking impacts across clinical settings. No regulatory guardrails have been set to minimize bias or optimize interpretability; no consensus clinical acceptability thresholds or systematized postdeployment monitoring has been set. Moreover, stakeholders with misaligned incentives deepen the landscape complexity especially when it comes to the requisite data integration and harmonization to advance the field. Therefore, despite increasing algorithmic accuracy and commoditization, the infamous ‘implementation gap’ persists. Open clinical data repositories have been shown to rapidly accelerate research, minimize redundancies and disseminate the expertise and knowledge required to overcome existing barriers. Drawing upon the longstanding success of existing governance frameworks and robust data use and sharing agreements, the ophthalmic community has tremendous opportunity in ushering artificial intelligence into medicine. By collaboratively building a powerful resource of open, anonymized multimodal ophthalmic data, the next generation of clinicians can advance data-driven eye care in unprecedented ways.

Summary: This piece demonstrates that with readily accessible data, immense progress can be achieved clinically and methodologically to realize artificial intelligence’s impact on clinical care. Exponentially progressive network effects can be seen by consolidating, curating and distributing data amongst both clinicians and data scientists.

Accelerating ophthalmic artificial intelligence research: the role of an open access data repository – PubMed

Abstract:  Purpose of review: Artificial intelligence has already provided multiple clinically relevant applications in ophthalmology. Yet, the explosion of nonstandardized reporting of high-performing algorithms are rendered useless without robust and streamlined implementation guidelines. The development of protocols and checklists will accelerate the translation of research publications to impact on patient care.

Recent findings: Beyond technological scepticism, we lack uniformity in analysing algorithmic performance generalizability, and benchmarking impacts across clinical settings. No regulatory guardrails have been set to minimize bias or optimize interpretability; no consensus clinical acceptability thresholds or systematized postdeployment monitoring has been set. Moreover, stakeholders with misaligned incentives deepen the landscape complexity especially when it comes to the requisite data integration and harmonization to advance the field. Therefore, despite increasing algorithmic accuracy and commoditization, the infamous ‘implementation gap’ persists. Open clinical data repositories have been shown to rapidly accelerate research, minimize redundancies and disseminate the expertise and knowledge required to overcome existing barriers. Drawing upon the longstanding success of existing governance frameworks and robust data use and sharing agreements, the ophthalmic community has tremendous opportunity in ushering artificial intelligence into medicine. By collaboratively building a powerful resource of open, anonymized multimodal ophthalmic data, the next generation of clinicians can advance data-driven eye care in unprecedented ways.

Summary: This piece demonstrates that with readily accessible data, immense progress can be achieved clinically and methodologically to realize artificial intelligence’s impact on clinical care. Exponentially progressive network effects can be seen by consolidating, curating and distributing data amongst both clinicians and data scientists.

Mellon Foundation grant supports development of a plan for using artificial intelligence to plumb the National Archives | Virginia Tech Daily | Virginia Tech

“A key outcome of the planning workshop will be the design of a subsequent pilot project aimed at enhancing access to National Archive collections, including the creation of new tools, techniques, and practices….”

S2ORC: The Semantic Scholar Open Research Corpus

“S2ORC is a general-purpose corpus for NLP and text mining research over scientific papers.

We’ve curated a unified resource that combines aspects of citation graphs (i.e. rich paper metadata, abstracts, citation edges) with a full text corpus that preserves important scientific paper structure (i.e. sections, inline citation mentions, references to tables and figures).
Our corpus covers 136M+ paper nodes with 12.7M+ full text papers and connected by 467M+ citation edges by unifying data from many different sources covering many different academic disciplines and identifying open-access papers using services like Unpaywall. …”

Peer Review of Scholarly Research Gets an AI Boost – IEEE Spectrum

“Open-access publisher Frontiers has debuted an AI tool called the Artificial Intelligence Review Assistant (AIRA), which purports to eliminate much of the grunt work associated with peer review. Since the beginning of June 2020, every one of the 11,000-plus submissions Frontiers received has been run through AIRA, which is integrated into its collaborative peer-review platform. This also makes it accessible to external users, accounting for some 100,000 editors, authors, and reviewers. Altogether, this helps “maximize the efficiency of the publishing process and make peer-review more objective,” says Kamila Markram, founder and CEO of Frontiers.

AIRA’s interactive online platform, which is a first of its kind in the industry, has been in development for three years.. It performs three broad functions, explains Daniel Petrariu, director of project management: assessing the quality of the manuscript, assessing quality of peer review, and recommending editors and reviewers. At the initial validation stage, the AI can make up to 20 recommendations and flag potential issues, including language quality, plagiarism, integrity of images, conflicts of interest, and so on. “This happens almost instantly and with [high] accuracy, far beyond the rate at which a human could be expected to complete a similar task,” Markram says….

The AI’s job is to flag concerns; humans take the final decisions….”

ASReview: Open Source Software for Efficient and Transparent Active Learning for Systematic Reviews | Semantic Scholar

Abstract:  For many tasks – including guideline development for medical doctors and systematic reviews for research fields – the scientific literature needs to be checked systematically. The current practice is that scholars and practitioners screen thousands of studies by hand to find which studies to include in their review. This is error prone and inefficient. We therefore developed an open source machine learning (ML)-aided pipeline: Active learning for Systematic Reviews (ASReview). We show that by using active learning, ASReview can lead to far more efficient reviewing than manual reviewing, while exhibiting adequate quality. Furthermore, the presented software is fully transparent and open source.