Developing an objective, decentralised scholarly communication and evaluation system – YouTube

“This is our proposal for how we might create a radically new scholarly publishing system with the potential to disrupt the scholarly publishing industry. The proposed model is: (a) open, (b) objective, (c) crowd sourced and community-controlled, (d) decentralised, and (e) capable of generating prestige. Submitted articles are openly rated by researchers on multiple dimensions of interest (e.g., novelty, reliability, transparency) and ‘impact prediction algorithms’ are trained on these data to classify articles into journal ‘tiers’.

In time, with growing adoption, the highest impact tiers within such a system could develop sufficient prestige to rival even the most established of legacy journals (e.g., Nature). In return for their support, researchers would be rewarded with prestige, nuanced metrics, reduced fees, faster publication rates, and increased control over their outputs….”

Crowdsourcing Scholarly Discourse Annotations | 26th International Conference on Intelligent User Interfaces

Abstract:  The number of scholarly publications grows steadily every year and it becomes harder to find, assess and compare scholarly knowledge effectively. Scholarly knowledge graphs have the potential to address these challenges. However, creating such graphs remains a complex task. We propose a method to crowdsource structured scholarly knowledge from paper authors with a web-based user interface supported by artificial intelligence. The interface enables authors to select key sentences for annotation. It integrates multiple machine learning algorithms to assist authors during the annotation, including class recommendation and key sentence highlighting. We envision that the interface is integrated in paper submission processes for which we define three main task requirements: The task has to be . We evaluated the interface with a user study in which participants were assigned the task to annotate one of their own articles. With the resulting data, we determined whether the participants were successfully able to perform the task. Furthermore, we evaluated the interface’s usability and the participant’s attitude towards the interface with a survey. The results suggest that sentence annotation is a feasible task for researchers and that they do not object to annotate their articles during the submission process.

 

Coleridge Initiative – Show US the Data | Kaggle

“This competition challenges data scientists to show how publicly funded data are used to serve science and society. Evidence through data is critical if government is to address the many threats facing society, including; pandemics, climate change, Alzheimer’s disease, child hunger, increasing food production, maintaining biodiversity, and addressing many other challenges. Yet much of the information about data necessary to inform evidence and science is locked inside publications.

Can natural language processing find the hidden-in-plain-sight data citations? Can machine learning find the link between the words used in research articles and the data referenced in the article?

Now is the time for data scientists to help restore trust in data and evidence. In the United States, federal agencies are now mandated to show how their data are being used. The new Foundations of Evidence-based Policymaking Act requires agencies to modernize their data management. New Presidential Executive Orders are pushing government agencies to make evidence-based decisions based on the best available data and science. And the government is working to respond in an open and transparent way.

This competition will build just such an open and transparent approach. …”

It’s The End Of Citation As We Know It & I Feel Fine | Techdirt

” ScholarSift is kind of like Turnitin in reverse. It compares the text of a law review article to a huge database of law review articles and tells you which ones are similar. Unsurprisingly, it turns out that machine learning is really good at identifying relevant scholarship. And ScholarSift seems to do a better job at identifying relevant scholarship than pricey legacy platforms like Westlaw and Lexis.

One of the many cool things about ScholarSift is its potential to make legal scholarship more equitable. In legal scholarship, as everywhere, fame begets fame. All too often, fame means the usual suspects get all the attention, and it’s a struggle for marginalized scholars to get the attention they deserve. Unlike other kinds of machine learning programs, which seem almost designed to reinforce unfortunate prejudices, ScholarSift seems to do the opposite, highlighting authors who might otherwise be overlooked. That’s important and valuable. I think Anderson and Wenzel are on to something, and I agree that ScholarSift could improve citation practices in legal scholarship….

Anderson and Wenzel argue that ScholarSift can tell authors which articles to cite. I wonder if it couldn’t also make citations pointless. After all, readers can use ScholarSift, just as well as authors….”

Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools – Physica Medica: European Journal of Medical Physics

“Highlights

Image pre-processing tools are critical to develop and assess AI solutions.
Open access tools and data are widely available for medical image preparation.
AI needs Big Data to develop and fine-tune a model properly.
Big Data needs AI to fully interpret the decision making process….”

 

Deep Learning in Mining Biological Data | SpringerLink

Abstract:  Recent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from different biological application domains. Categorized in three broad types (i.e. images, signals, and sequences), these data are huge in amount and complex in nature. Mining such enormous amount of data for pattern recognition is a big challenge and requires sophisticated data-intensive machine learning techniques. Artificial neural network-based learning systems are well known for their pattern recognition capabilities, and lately their deep architectures—known as deep learning (DL)—have been successfully applied to solve many complex pattern recognition problems. To investigate how DL—especially its different architectures—has contributed and been utilized in the mining of biological data pertaining to those three types, a meta-analysis has been performed and the resulting resources have been critically analysed. Focusing on the use of DL to analyse patterns in data from diverse biological domains, this work investigates different DL architectures’ applications to these data. This is followed by an exploration of available open access data sources pertaining to the three data types along with popular open-source DL tools applicable to these data. Also, comparative investigations of these tools from qualitative, quantitative, and benchmarking perspectives are provided. Finally, some open research challenges in using DL to mine biological data are outlined and a number of possible future perspectives are put forward.

 

 

Automated screening of COVID-19 preprints: can we help authors to improve transparency and reproducibility? | Nature Medicine

“Although automated screening is not a replacement for peer review, automated tools can identify common problems. Examples include failure to state whether experiments were blinded or randomized2, failure to report the sex of participants2 and misuse of bar graphs to display continuous data3. We have been using six tools4,5,6,7,8 to screen all new medRxiv and bioRxiv COVID-19 preprints (Table 1). New preprints are screened daily9. By this means, reports on more than 8,000 COVID preprints have been shared using the web annotation tool hypothes.is (RRID:SCR_000430) and have been tweeted out via @SciScoreReports (https://hypothes.is/users/sciscore). Readers can access these reports in two ways. The first option is to find the link to the report in the @SciScoreReports tweet in the preprint’s Twitter feed, located in the metrics tab. The second option is to download the hypothes.is bookmarklet. In addition, readers and authors can reply to the reports, which also contain information on solutions….”

Automated screening of COVID-19 preprints: can we help authors to improve transparency and reproducibility? | Nature Medicine

“Although automated screening is not a replacement for peer review, automated tools can identify common problems. Examples include failure to state whether experiments were blinded or randomized2, failure to report the sex of participants2 and misuse of bar graphs to display continuous data3. We have been using six tools4,5,6,7,8 to screen all new medRxiv and bioRxiv COVID-19 preprints (Table 1). New preprints are screened daily9. By this means, reports on more than 8,000 COVID preprints have been shared using the web annotation tool hypothes.is (RRID:SCR_000430) and have been tweeted out via @SciScoreReports (https://hypothes.is/users/sciscore). Readers can access these reports in two ways. The first option is to find the link to the report in the @SciScoreReports tweet in the preprint’s Twitter feed, located in the metrics tab. The second option is to download the hypothes.is bookmarklet. In addition, readers and authors can reply to the reports, which also contain information on solutions….”