Large, open datasets for human connectomics research: Considerations for reproducible and responsible data use.

Large, open datasets have emerged as important resources in the field of human connectomics. In this review, the evolution of data sharing involving magnetic resonance imaging is described. A summary of the challenges and progress in conducting reproducible data analyses is provided, including description of recent progress made in the development of community guidelines and recommendations, software and data management tools, and initiatives to enhance training and education. Finally, this review concludes with a discussion of ethical conduct relevant to analyses of large, open datasets and a researcher's responsibility to prevent further stigmatization of historically marginalized racial and ethnic groups. Moving forward, future work should include an enhanced emphasis on the social determinants of health, which may further contextualize findings among diverse population-based samples. Leveraging the progress to date and guided by interdisciplinary collaborations, the future of connectomics promises to be an impressive era of innovative research, yielding a more inclusive understanding of brain structure and function.

2020 International brain-computer interface competition: A review.

The brain-computer interface (BCI) has been investigated as a form of communication tool between the brain and external devices. BCIs have been extended beyond communication and control over the years. The 2020 international BCI competition aimed to provide high-quality neuroscientific data for open access that could be used to evaluate the current degree of technical advances in BCI. Although there are a variety of remaining challenges for future BCI advances, we discuss some of more recent application directions: (i) few-shot EEG learning, (ii) micro-sleep detection (iii) imagined speech decoding, (iv) cross-session classification, and (v) EEG(+ear-EEG) detection in an ambulatory environment. Not only did scientists from the BCI field compete, but scholars with a broad variety of backgrounds and nationalities participated in the competition to address these challenges. Each dataset was prepared and separated into three data that were released to the competitors in the form of training and validation sets followed by a test set. Remarkable BCI advances were identified through the 2020 competition and indicated some trends of interest to BCI researchers.

Range map datasets for terrestrial vertebrates across Taiwan.

Accurate data describing the geographic distribution of specific species form the basis for effective conservation management policies. However, for most species the freely available distributional information is usually confined to either expert maps or purely theoretical maps constructed by using a variety of modeling frameworks. These maps usually do not provide enough resolution for conservation applications or do not accurately describe the current distribution status. In this study, we constructed a novel workflow designed to integrate data from various species distribution models and expert knowledge into a single unified modeling process. Under this workflow, we systematically constructed current distribution maps for a selection of terrestrial vertebrates found across Taiwan. We used species distribution modeling as the base and then aggregated multiple open datasets describing species occurrence and environmental factors as data sources. Thereafter, we estimated the primary broad-scale and high spatial resolution species range maps using the MaxEnt modeling algorithm, and then consulted experts on each taxa to refine these maps. This dataset provides up-to-date species distribution maps for 379 terrestrial vertebrates in Taiwan, with members from across four taxa (27 amphibians, 52 reptiles, 264 birds, and 36 mammals). This dataset helps to fill the spatial knowledge gaps for conservation concerns and improves our understanding of the geographic distribution of more than half (61%) of the vertebrate species of Taiwan. Furthermore, by stacking the range maps of multiple species, we can identify vertebrate diversity hotspots and identify priority areas for conservation.

Open Data Resources on COVID-19 in Six European Countries: Issues and Opportunities.

Since the beginning of the COVID-19 pandemic in March 2020, national and international authorities started to develop and update datasets to provide data to researchers, journalists and health care providers as well as public opinion. These data became one of the most important sources of information, which are updated daily and analysed by scientists in order to investigate and predict the spread of this epidemic. Despite this positive reaction from both national and international authorities in providing aggregated information on the diffusion of COVID-19, different challenges have been underlined in previously published studies. Different papers have discussed strengths and weaknesses of these types of datasets by focusing on different quality perspectives, which include the statistical methods adopted to analyse them; the lack of standards and models in the adoption of data for their management and distribution; and the analysis of different data quality characteristics. These studies have analysed datasets at the general level or by focusing the attention on specific indicators such as the number of cases or deaths. This paper further investigates issues and opportunities in the diffusion of these datasets under two main perspectives. At the general level, it analyses how data are organized and distributed to scientific and non-scientific communities. Moreover, it further explores the indicators adopted to describe the spread of the COVID-19 epidemic while also highlighting the level of detail used to describe them in terms of gender, age ranges and territorial units. The paper focuses on six European countries: Belgium, France, Germany, Italy, Spain and UK.

Code-Free Development and Deployment of Deep Segmentation Models for Digital Pathology.

Application of deep learning on histopathological whole slide images (WSIs) holds promise of improving diagnostic efficiency and reproducibility but is largely dependent on the ability to write computer code or purchase commercial solutions. We present a code-free pipeline utilizing free-to-use, open-source software (QuPath, DeepMIB, and FastPathology) for creating and deploying deep learning-based segmentation models for computational pathology. We demonstrate the pipeline on a use case of separating epithelium from stroma in colonic mucosa. A dataset of 251 annotated WSIs, comprising 140 hematoxylin-eosin (HE)-stained and 111 CD3 immunostained colon biopsy WSIs, were developed through active learning using the pipeline. On a hold-out test set of 36 HE and 21 CD3-stained WSIs a mean intersection over union score of 95.5 and 95.3% was achieved on epithelium segmentation. We demonstrate pathologist-level segmentation accuracy and clinical acceptable runtime performance and show that pathologists without programming experience can create near state-of-the-art segmentation solutions for histopathological WSIs using only free-to-use software. The study further demonstrates the strength of open-source solutions in its ability to create generalizable, open pipelines, of which trained models and predictions can seamlessly be exported in open formats and thereby used in external solutions. All scripts, trained models, a video tutorial, and the full dataset of 251 WSIs with ~31 k epithelium annotations are made openly available at https://github.com/andreped/NoCodeSeg to accelerate research in the field.

Textbooks authors, publishers, formats and costs in higher education.

OBJECTIVES: There is little empirical data reported on retail prices of college textbooks beyond self-reported surveys and no published datasets. Textbooks, as an ancillary cost, can contribute to the overall rising cost of education which can impact upon students' ability to succeed in Higher Education. This study sought to understand more about costs of college textbooks by conducting a systematic collection of several thousand textbooks from faculty readings lists in one Higher Education Institution in Ireland and a retrieval and analysis of the retail prices of a selection of those books. DATA DESCRIPTION: Queries were made of the course catalogue database of a Higher Education Institution in Ireland resulting in generation of records for required and recommended textbooks for 15,414 books from 3030 unique courses for the academic year 2017-2018. This data was cleaned and processed before being used to query Google Books API. The dataset presented here represents the combination of data from the course catalogue and the Google Books API queries and comprises 2940 records of textbooks. Details for each book including title, authors, publisher, ISBN, retail price, ebook format, pdf availability, and public domain availability.