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Tutorial length: 90 minutes Skill level: Any Technology requirements: None Since its inception in the Apache Software Foundation in 2011, Apache Airavata has evolved from a middleware system for supporting science gateway workflow executions to a comprehensive set of semi-autonomous subsystems that can be used to provide solutions for a wide range of science gateways. This tutorial provides a series of lightning overviews of each of these major subsystems and illustrates their usage in different science gateways. The Virtual Cluster System provides a mechanism for creating dynamic virtual clusters on OpenStack-based clouds. These virtual clusters can be used to execute both containerized serial and parallel scientific applications, providing users and gateways with their own private clusters. They can also be deployed with the JupyterHub interface, providing on-demand access to JupyterLab servers. Apache Airavata’s metadata and workflow scheduling infrastructure (the original core of Apache Airavata) builds on Apache Helix and Airavata’s own metadata management system to manage the full lifecycle for job executions, capturing the metadata needed to audit and reproduce execution outcomes. The Airavata Django Portal provides an out-of-the-box end user environment for all of the Apache Airavata middleware subsystems. Through the use of the Wagtail Content Management System and the Django Apps extension mechanism, the Airavata Django Portal can be extensively customized to create unique user interfaces that meet the usability requirements of different research communities. Airavata Custos encompasses Apache Airavata’s security services for managing user accounts; federated authentication; role, group, and attribute-based authorization; sharing and permissions; and resource credential (secrets) management. Custos services can be used independently of other Airavata services and can be integrated into other science gateway platforms such as Galaxy through the Custos API. Airavata Managed File Transfer (MFT) subsystem supports data transfer and storage endpoint management for users’ local storage systems, parallel file and mass storage systems operated by research computing systems, and cloud storage systems such as Amazon S3, Google Drive, and Box. Central MFT services and locally deployed agents can support emerging high performance transfer protocols and provide optimized transfers that are decoupled from gateway middleware. Airavata Data Lake provides secured, controlled access to data from a wide range of sources including scientific instruments, results of computations, and user and machine annotated metadata. The Airavata Data Lake system can orchestrate data movements managed by Airavata MFT and execute data pipelines to extract searchable metadata. Collectively these components enable processing of data, the movement of data from the data sources to central storage points, and distribution of data to respective authorized users. The Science Gateways Platform as a Service (SciGaP) is an operational deployment of the Airavata software stack that is run by the Indiana University Cyberinfrastructure Integration Research Center for over 40 client gateways. We conclude the tutorial with a discussion of future directions for the Apache Airavata software stack and gateways in general, including greater support for FAIR science and secure integration of a greater number of edge systems.

The Target Enablement to Accelerate Therapy Development in Alzheimer’s Disease (TREAT-AD) Center at Indiana School of Medicine and Purdue provides cutting-edge research aimed at drug discovery for the crippling disease. It consists of five core activities: medicinal chemistry, structural biology, high throughput screening, bioinformatics, and administration and data management. To enable these essential activities, the Cyberinfrastructure Integration Research Center (CIRC) at Indiana University has created the Bioinformatics and Computational Biology Portal (BCB Portal). This science gateway provides a repository for tools and data used by researchers at TREAT-AD. The BCB Portal integrates data management, diverse computational resources, visualization tools, publicly accessible user interfaces, and secure researcher interfaces. Currently, the BCB Portal is active. Several bioinformatics applications have been integrated into the Apache Airavata Framework to leverage its robust features, including data management, resource provisioning, application porting, and user-centric design. We will also provide a look at the drug discovery process, identifying target drugs, computational analysis of those targets, and finally, the final output of the TREAT-AD Center target enablement packages (TEPs). These TEPs allow promising compounds to move to laboratory and clinical testing. The initial conceptualization of the BCB Portal was presented at Gateways 2020, and this lightning talk will cover the implementation of those concepts over the past year.

Survey template for the 2020 survey of CODATA-RDA Schools for Research Data Science alumni

This recommendation describes the curriculum and example materials to give Early Career Researchers (ECR’s) the foundational skills in Data Science to work with their data. This curriculum combines technical skills, such as Software Carpentry with responsible research practices such as Open and Responsible Research. This curriculum is composed of: a set of curriculum specifications for the modules run in this curriculum, an example timetable for a 10-day intensive training event, a diagram to show how these modules are connected, a spreadsheet of links to example materials that implements this, metadata for the submission, an impact statement, a document discussing the maintenance plan of the materials. The purpose of this curriculum is to be deliberately broad and shallow to be delivered over approximately 70 contact hours. It could also form the basis for a much deeper programme which will not be explored further. In 2016 we ran one school in Trieste, Italy. In 2017 we ran two, Trieste and São Paulo, Brazil. In 2018 we ran three, Trieste, São Paulo and Kigali, Rwanda. In 2019 we will run fourschools (Addis Ababa, Ethiopia, Trieste, Abuja, Nigeria and San José in Costa Rica). At the end of this year approximately 400 students will have been taught on four continents using the curriculum developed here. Note: The submission package is available athttp://doi.org/10.5281/zenodo.3478590&nbsp

First release on the completion of the Kigali event materials.

CODATA-RDA Research Data Science Summer Schools: A Collaborative Foundational Research Data Capacity Building Initiative for 21st Century Researchers A report on the first CODATA-RDA Research Data Science Summer School, held at the International Centre for Theoretical Physics, Trieste, on 1-12 August 2016, along with a vision for the expansion of the initiative.

The Open Science Grid (OSG) includes work to enable new science, new scientists, and new modalities in support of computationally based research. There are frequently significant sociological and organizational changes required in transformation from the existing to the new. OSG leverages its deliverables to the large scale physics experiment member communities to benefit new communities at all scales through activities in education, engagement and the distributed facility. As a partner to the poster and tutorial at SciDAC 2008, this paper gives both a brief general description and some specific examples of new science enabled on the OSG. More information is available at the OSG web site: (http://www.opensciencegrid.org).

Contemporary research – particularly when addressing the most significant, transdisciplinary research challenges – cannot effectively be done without a range of skills relating to data. This includes the principles and practice of Open Science and research data management and curation, the use of a range of data platforms and infrastructures, large scale analysis, statistics, visualisation and modelling techniques, software development and annotation. A Task Group has been set up to address the critical need for skills around data science, research data management, and open science practice by pulling together existing and emerging initiatives and resources into a sustainably run, globally applicable foundational programme in Research Data Science, particularly for Low and Middle Income Countries (LMICs). The approach for this is to run a series of Schools which typically run for two weeks that provide a foundation in the wide range of topics described above to Early Career Researchers (ECR’s), with a focus on supporting those from LMIC’s. Since August 2016 these schools have run in a variety of locations, namely Trieste, Italy; Sao Paulo, Brazil; Brisbane, Australia and Kigali, Rwanda. We have delivered the curriculum to approximately 250 students from over 30 countries in a wide variety of disciplines (e.g. Bioinformatics, Earth, Atmospheric and Climate Sciences, Economics and Physics). The school covers a wide variety of technical topics such as Programming, Machine Learning and Cloud Computing but also spends a significant amount of time on more social areas such as Research Data Management and Authorship in the 21st century. Open Science in particular is delivered in a way that is both implicit and explicit. Implicit in terms of the teaching of topics that enable Open Science, such as sharing tools such as Git, the use of Persistent Identifiers and the management of data in general, and explicit in terms of running specific module on Open Science. This module, however, does not take a didactic “you must do Open Science” approach as many ECR’s are often isolated and without support of such approaches. The module is based on asking the students to reflect on aspects of Open Science, particularly from an ethical standpoint and hence to enable these students to approach Open Science in a more robust fashion, determining what practices they can adopt and the benefits they will gain. This ethical approach is amplified by the use of exercises throughout the school which relate this ethical aspect of Open Science with the specific module being taught. The impact of this approach has been exemplified by the comments on how useful the Open Science aspects have been to them and have spreading the practices in their home institutions.

Contemporary research – particularly when addressing the most significant, transdisciplinary research challenges – cannot effectively be done without a range of skills relating to data. This includes the principles and practice of Open Science and research data management and curation, the use of a range of data platforms and infrastructures, large scale analysis, statistics, visualisation and modelling techniques, software development and annotation. A Task Group has been set up to address the critical need for skills around data science, research data management, and open science practice by pulling together existing and emerging initiatives and resources into a sustainably run, globally applicable foundational programme in Research Data Science, particularly for Low and Middle Income Countries (LMICs). The approach for this is to run a series of Schools which typically run for two weeks that provide a foundation in the wide range of topics described above to Early Career Researchers (ECR’s), with a focus on supporting those from LMIC’s. Since August 2016 these schools have run in a variety of locations, namely Trieste, Italy; Sao Paulo, Brazil; Brisbane, Australia and Kigali, Rwanda. We have delivered the curriculum to approximately 250 students from over 30 countries in a wide variety of disciplines (e.g. Bioinformatics, Earth, Atmospheric and Climate Sciences, Economics and Physics). The school covers a wide variety of technical topics such as Programming, Machine Learning and Cloud Computing but also spends a significant amount of time on more social areas such as Research Data Management and Authorship in the 21st century. Open Science in particular is delivered in a way that is both implicit and explicit. Implicit in terms of the teaching of topics that enable Open Science, such as sharing tools such as Git, the use of Persistent Identifiers and the management of data in general, and explicit in terms of running specific module on Open Science. This module, however, does not take a didactic “you must do Open Science” approach as many ECR’s are often isolated and without support of such approaches. The module is based on asking the students to reflect on aspects of Open Science, particularly from an ethical standpoint and hence to enable these students to approach Open Science in a more robust fashion, determining what practices they can adopt and the benefits they will gain. This ethical approach is amplified by the use of exercises throughout the school which relate this ethical aspect of Open Science with the specific module being taught. The impact of this approach has been exemplified by the comments on how useful the Open Science aspects have been to them and have spreading the practices in their home institutions.

Contemporary research – particularly when addressing the most significant, transdisciplinary research challenges – cannot effectively be done without a range of skills relating to data. This includes the principles and practice of Open Science and research data management and curation, the use of a range of data platforms and infrastructures, large scale analysis, statistics, visualisation and modelling techniques, software development and annotation. A Task Group has been set up to address the critical need for skills around data science, research data management, and open science practice by pulling together existing and emerging initiatives and resources into a sustainably run, globally applicable foundational programme in Research Data Science, particularly for Low and Middle Income Countries (LMICs). The approach for this is to run a series of Schools which typically run for two weeks that provide a foundation in the wide range of topics described above to Early Career Researchers (ECR’s), with a focus on supporting those from LMIC’s. Since August 2016 these schools have run in a variety of locations, namely Trieste, Italy; Sao Paulo, Brazil; Brisbane, Australia and Kigali, Rwanda. We have delivered the curriculum to approximately 250 students from over 30 countries in a wide variety of disciplines (e.g. Bioinformatics, Earth, Atmospheric and Climate Sciences, Economics and Physics). The school covers a wide variety of technical topics such as Programming, Machine Learning and Cloud Computing but also spends a significant amount of time on more social areas such as Research Data Management and Authorship in the 21st century. Open Science in particular is delivered in a way that is both implicit and explicit. Implicit in terms of the teaching of topics that enable Open Science, such as sharing tools such as Git, the use of Persistent Identifiers and the management of data in general, and explicit in terms of running specific module on Open Science. This module, however, does not take a didactic “you must do Open Science” approach as many ECR’s are often isolated and without support of such approaches. The module is based on asking the students to reflect on aspects of Open Science, particularly from an ethical standpoint and hence to enable these students to approach Open Science in a more robust fashion, determining what practices they can adopt and the benefits they will gain. This ethical approach is amplified by the use of exercises throughout the school which relate this ethical aspect of Open Science with the specific module being taught. The impact of this approach has been exemplified by the comments on how useful the Open Science aspects have been to them and have spreading the practices in their home institutions.

New facilities of the 2020s, such as the High Luminosity Large Hadron Collider (HL-LHC), will be relevant through at least the 2030s. This means that their software efforts and those that are used to analyze their data need to consider sustainability to enable their adaptability to new challenges, longevity, and efficiency, over at least this period. This will help ensure that this software will be easier to develop and maintain, that it remains available in the future on new platforms, that it meets new needs, and that it is as reusable as possible. This report discusses a virtual half-day workshop on “Software Sustainability and High Energy Physics” that aimed 1) to bring together experts from HEP as well as those from outside to share their experiences and practices, and 2) to articulate a vision that helps the Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP) to create a work plan to implement elements of software sustainability. Software sustainability practices could lead to new collaborations, including elements of HEP software being directly used outside the field, and, as has happened more frequently in recent years, to HEP developers contributing to software developed outside the field rather than reinventing it. A focus on and skills related to sustainable software will give HEP software developers an important skill that is essential to careers in the realm of software, inside or outside HEP. The report closes with recommendations to improve software sustainability in HEP, aimed at the HEP community via IRIS-HEP and the HEP Software Foundation (HSF).