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Transformative technologies are enabling the construction of three-dimensional maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping the human body at single-cell resolution by supporting technology development, data acquisition, and detailed spatial mapping. HuBMAP will integrate its efforts with other funding agencies, programs, consortia, and the biomedical research community at large towards the shared vision of a comprehensive, accessible three-dimensional molecular and cellular atlas of the human body, in health and under various disease conditions.

Open Science is a pivotal global movement to advance science and scholarship. It includes key elements such as Open Access to scientific publications, Open Data, Open Source, and Open Methodology (Kraker et al. 2011; McKiernan et al. 2016; Stodden et al. 2016), and therefore fosters reproducibility and verification of findings (Wilkinson et al. 2016). Scientific knowledge, the product of research, is a public good and should thus be made publicly available. The vast majority of researchers agree with the idea of Open Science (Dallmeier-Tiessen et al. 2011), yet many face challenges in implementing Open Science in practice.

This report documents the program and the outcomes of Dagstuhl Seminar 18482 "Network Visualization in the Humanities", which took place November 26-30, 2019. The seminar brought together 27 researchers on Network Visualization and Digital Humanities communities. During the seminar the participants shared knowledge on the existing methods of network visualization and on network visualization challenges present in the Humanities through the introductory talks, the abstracts of which are included in this report. Multiple innovative research challenges for Network Visualisation in the Humanities have been identified and according to those four working groups have been set up that discussed the topics in detail. The summary of the discussions of the working groups is given in this report.

Dr. Katy Börner gives the opening lecture for the Places & Spaces: Mapping Science exhibit, http://scimaps.org/, held in Newman Library, https://news.lib.vt.edu/2017/08/18/the-places-spaces-mapping-science-comes-to-virginia-tech-at-the-university-libraries/. Virginia Tech. University Libraries

Macroscopes are software tools that help you focus on patterns in data that are too large or complex to see with the naked eye. Interactive by nature, you can use them to ask and answer questions of the data.Organized by the Cyberinfrastructure for Network Science Center at Indiana University, the Places & Spaces: Mapping Science exhibition showcases examples of science maps and macroscopes from around the globe and from diverse disciplines. Innovative and inspiring, these visualizations use data to tell science stories and illuminate promising new areas of research.

Informatics for Integrating Biology and the Bedside (i2b2) data is organized by the i2b2 ontology. The SCILHS public i2b2 ontology has 490,484 concepts under root path \PCORI and is 11 levels deep and can be found at github: https://github.com/SCILHS/scilhs-ontology/tree/master/Ontology.The interactive ontology visualization enables i2b2 administrators and others to see the overall structure of the ontology, to examine (in)activate paths, hide branches that are not relevant, and it will soon support ontology comparisons. The visualization also enables investigators to locate specific codes/diagnosis, determine how broad the data is. It can be explored at http://demo.cns.iu.edu/client/hsd/ontology.The static ontology visualization shown in this poster depicts a portion of the ICD-10 neoplasms diagnosis subtree. Nodes are area size coded by subtree size minus the root node; their label is printed on the left and their subtree depth plus the number of active vs. inactive child nodes to the right side of the node.

Massively open online courses (MOOCs) offer instructors the opportunity to reach students in orders of magnitude greater than they could in traditional classroom settings, while offering students access to free or inexpensive courses taught by world-class educators. However, MOOCs provide major challenges to teachers (keeping track of thousands of students and supporting their learning progress), students (keeping track of course materials and effectively interacting with teachers and fellow students), researchers (understanding how students interact with materials and each other), and MOOC platform developers (supporting effective course design and delivery in a scalable way). Along with these challenges, the sheer volume of data available from MOOCs provides unprecedented opportunities to study how learning takes place in online courses. This paper explores the use of data analysis and visualization as a means to empower teachers, students, researchers, and platform developers by making large volumes of data easy to understand. First, we introduce the insight needs of these four user groups. Second, we review existing MOOC visual analytics studies. Third, we present a framework for MOOC data types and data analyses to support different types of insight needs. Fourth, we present exemplary data visualizations that make data accessible and empower teachers, students, developers, and researchers with novel insights. The outlook discusses future MOOC opportunities and challenges.

This poster provides an overview of the Engineering Research Observatory. The goal for the project is to develop and deploy interactive data visualizations for NSF staff, researchers, and students to increase their understanding of temporal, geospatial, topical, and network patterns and trends in engineering.

Scientific research is a major driving force in a knowledge based economy. Income, health and wellbeing depend on scientific progress. The better we understand the inner workings of the scientific enterprise, the better we can prompt, manage, steer, and utilize scientific progress. Diverse indicators and approaches exist to evaluate and monitor research activities, from calculating the reputation of a researcher, institution, or country to analyzing and visualizing global brain circulation. However, there are very few predictive models of science that are used by key decision makers in academia, industry, or government interested to improve the quality and impact of scholarly efforts. We present a novel 'bibliographic bibliometric' analysis which we apply to a large collection of books relevant for the modelling of science. We explain the data collection together with the results of the data analyses and visualizations. In the final section we discuss how the analysis of books that describe different modelling approaches can inform the design of new models of science.

Presentation accepted at #altmetrics14 #WebSci14Introduction. In the fields of Physics, Mathematics and Computer science, depositing preprints or e-prints on arXiv is part of the publication cycle, as it provides access to papers, limits publication delays and establishes priority claims (Brooks, 2009). Between 1995 and 2011, about two-thirds of all arXiv e-prints could be matched to a journal article indexed in the Web of Science (Larivière et al., 2014). However, very little is known about the dissemination of these two versions across social media. The microblogging service Twitter has been identified as a tool used by academics and the general public to distribute, among other things, links to scholarly documents (Thelwall et al., 2013). Preliminary studies have demonstrated that the majority of tweets related to a scientific document appear shortly after its online availability, reaching a peak of activity much faster than citations and downloads (Eysenbach, 2011; Shuai, Pepe & Bollen, 2012). This suggests that an important share of tweets to papers in Physics, Computer science and Mathematics are likely to be made to the arXiv rather than the published version of the paper. This study investigates the challenges of measuring Twitter activity to the journal of record and arXiv versions of scientific documents.

The goals of the proposed LinkedHumanities project are to create and maintain data integration tools tailored to digital humanities collections in order to build a machine-readable "web of facts" about philosophy. The tools will interconnect structured digital representations of the humanities and leverage the created links to enrich the respective data repositories. Application testbeds include the Indiana Philosophy Ontology (InPhO) project and the Stanford Encyclopedia of Philosophy (SEP). The specific outcomes of this proposal will be (a) the implementation of a linking service between the InPhO ontology and other knowledge repositories such as DBPedia and Freebase; and (b) the population of the InPhO ontology by leveraging the previously created links to other data repositories. Most importantly, however, the data integration tools will be designed to also serve the needs of other digital humanities projects.

We will develop and implement a multi-scale workbench, called "InterDebates", with the goal of digging into data provided by hundreds of thousands, eventually millions, of digitized books, bibliographic databases of journal articles, and comprehensive reference works written by experts. Our hypotheses are: that detailed and identifiable arguments drive many aspects of research in the sciences and the humanities; that argumentative structures can be extracted from large datasets using a mixture of automated and social computing techniques; and, that the availability of such analyses will enable innovative interdisciplinary research, and may also play a role in supporting better-informed critical debates among students and the general public. A key challenge tackled by this project is thus to uncover and represent the argumentative structure of digitized documents, allowing users to find and interpret detailed arguments in the broad semantic landscape of books and articles.