|Timestamp||Email Address||Session Submitter Name||Session Moderator Name||Session Title||Session Short Name||Session Description||Does your session relate to any of ESIP's strategic goals?||Type||Speakers||Session Audience: consider your session as a pool of knowledge - will your presentation let folks skim the surface, jump in, or take a deep dive?||Collaboration Area Tags||Are you interested in using ESIPhub in your session?||Add Session Tags||Are you looking for additional speakers?||If you are looking for additional speakers, please describe what kind of speakers/talks you are looking for.|
|2/25/2019 15:27:email@example.com||Jeremy Fischer||Jeremy Fischer||Hands on with Jetstream Atmosphere||This tutorial will first give an overview of Jetstream and various aspects of the system. Then we will take attendees through the basics of using Jetstream via the Atmosphere web interface. This will include a guided walk-through of the interface itself, the features provided, the image catalog, launching and using virtual machines on Jetstream, using volume-based storage, and best practices.|
We are targeting users of every experience level. Atmosphere is well-suited to both HPC novices and advanced users. This tutorial is generally aimed at those unfamiliar with cloud computing and generally doing computation on laptops or departmental server resources. While we will not cover advanced topics in this particular tutorial, we will touch on the available advanced capabilities during the initial overview.
|No||Workshop||Jeremy Fischer, Sanjana Sudarshan||Jump In, We could alternatively do a short 45m presentation + Q&A, but this hands on optimally takes 2+ hours||Cloud Computing||No||Maybe||Researchers that have used Jetstream for their workflows|
|2/27/2019 6:50:firstname.lastname@example.org||Ana Pinheiro Privette||Ana Pinheiro Privette||Cloud data optimization: emerging best practices||TDB||Increase the use and value of Earth science data and information., Strengthen the ties between observations and user communities (e.g. technologies, research, education and applications).||Breakout Session||TBD||Skim the Surface, Jump In||Cloud Computing, Data Analytics, Machine Learning||Maybe||cloud, data, analysis-ready||Yes||Share lessons learned from exploring data formats, architectures, and tools to enable faster, cheaper and better data analysis in the cloud.|
|3/11/2019 14:35:email@example.com||Lynne Schreiber||Danie Kinkade||CDF General Assembly Meeting||Request Monday - 2-5pm. |
The Council of Data Facilities (CDF) is committed to working with relevant agencies, professional associations, initiatives, and other complementary efforts to enable transformational science, innovative education, and informed public policy through increased coordination, collaboration, and innovation in the acquisition, curation, preservation, and dissemination of geoscience data, tools, models, and services. Existing and emerging geoscience data facilities – through the Council – are committed to serving as an effective foundation for EarthCube. The General Assembly meeting is open to the official representatives from all member data facilities, additional member organization personnel as desired by the members, as well as observers.
|Increase the use and value of Earth science data and information.||Business Meeting||TBA||Jump In||No||Maybe|
|3/20/2019 17:08:firstname.lastname@example.org||Ethan Davis||Ethan Davis||2019 netCDF-CF Workshop (1 of 4)||The Climate and Forecast (CF) metadata convention for netCDF (netCDF-CF) is a community-developed standard first released in 2003. The CF conventions were originally developed to represent climate and forecast model output encoded in the netCDF binary format, with the specific goal of facilitating comparison of output from different models. Subsequent development of the convention has broadened its scope to include observational data and derived products.|
This workshop will focus on discussing current and future efforts and directions for the CF conventions.
|Increase the use and value of Earth science data and information.||Workshop||Ethan Davis||Jump In, Deep Dive||Data Models||Maybe||Maybe|
|3/20/2019 17:11:email@example.com||Ethan Davis||Ethan Davis||2019 netCDF-CF Workshop (2 of 4)||The Climate and Forecast (CF) metadata convention for netCDF (netCDF-CF) is a community-developed standard first released in 2003. The CF conventions were originally developed to represent climate and forecast model output encoded in the netCDF binary format, with the specific goal of facilitating comparison of output from different models. Subsequent development of the convention has broadened its scope to include observational data and derived products.|
This workshop will focus on discussing current and future efforts and directions for the CF conventions.
|Increase the use and value of Earth science data and information.||Workshop||Jump In, Deep Dive||Data Models||Maybe||Maybe|
|3/20/2019 17:12:firstname.lastname@example.org||Ethan Davis||Ethan Davis||2019 netCDF-CF Workshop (3 of 4)||The Climate and Forecast (CF) metadata convention for netCDF (netCDF-CF) is a community-developed standard first released in 2003. The CF conventions were originally developed to represent climate and forecast model output encoded in the netCDF binary format, with the specific goal of facilitating comparison of output from different models. Subsequent development of the convention has broadened its scope to include observational data and derived products.|
This workshop will focus on discussing current and future efforts and directions for the CF conventions.
|Increase the use and value of Earth science data and information.||Workshop||Jump In, Deep Dive||Data Models||Maybe||Maybe|
|3/20/2019 17:13:email@example.com||Ethan Davis||Ethan Davis||2019 netCDF-CF Workshop (4 of 4)||The Climate and Forecast (CF) metadata convention for netCDF (netCDF-CF) is a community-developed standard first released in 2003. The CF conventions were originally developed to represent climate and forecast model output encoded in the netCDF binary format, with the specific goal of facilitating comparison of output from different models. Subsequent development of the convention has broadened its scope to include observational data and derived products.|
This workshop will focus on discussing current and future efforts and directions for the CF conventions.
|Increase the use and value of Earth science data and information.||Workshop||Jump In, Deep Dive||Data Models||Maybe||Maybe|
|3/21/2019 10:56:firstname.lastname@example.org||Ziheng Sun||Annie Burgess||Advanced Geospatial Cyberinfrastructure for Deep Learning||Geospatial Cyberinfrastructure for Deep Learning||The deep stack and tremendous amount of computational parameters in deep learning models greatly increases the challenges of pre-processing, training, testing, and post- processing geospatial datasets quickly and efficiently. This session will discuss the latest progresses on constructing advanced cyberinfrastructure for deep learning on satellite-based or field-observed geospatial datasets. The goal is to bring community experiences together and collaborate on building advanced geospatial cyberinfrastructure addressing the big questions raised in solving fundamental geoscience problems using deep learning models.||Increase the use and value of Earth science data and information., Strengthen the ties between observations and user communities (e.g. technologies, research, education and applications)., Promote techniques to articulate and measure the socioeconomic value and benefit of Earth science data, information and applications., Position ESIP to play a major role in Earth science issues (e.g. addressing effects of climate change mitigation, adaptation and supporting sustainable science data infrastructure).||Working Session||Ziheng Sun, Annie Burgess, Eugene Yu||Deep Dive||Agriculture and Climate, Cloud Computing, Data to Decisions, Discovery, Energy and Climate, Enviro Sensing, Machine Learning, Science Software, Web Services||Yes||cyberinfrastructure, deep learning, esiplab, geoweaver||Yes||Earth scientists, deep learning practitioners,|
|3/25/2019 19:03:email@example.com||Ted Habermann||Ted Habermann||FAIR Metadata||FAIR Metadata||The FAIR principles provide high-level guidance for making data findable, accessible, interoperable, and reusable. Some of these principles describe repository characteristics and practices while others describe data and metadata characteristics. The metadata characteristics are described in very broad terms like “rich metadata”, “a plurality of accurate and relevant attributes”, and “detailed provenance”. Data providers in the ESIP community use many metadata dialects to serves many disciplines. Implementing the FAIR Principles in this community requires understanding specific metadata practices and elements that support these broad disciplines. The session will initiate a discussion of how this might be done with examples from several commonly used metadata dialects.||Increase the use and value of Earth science data and information.||Breakout Session||TBD||Jump In||Discovery, Documentation||No||FAIR Data, Documentation||Yes||Metadata Experts interested in FAIR|
|3/25/2019 19:05:firstname.lastname@example.org||Ted Habermann||Ted Habermann||Metadata Evaluation - Tools and Results||Metadata Evaluation||ESIP community members are actively working throughout the data life cycle from data management planning to collection and creation to archiving, discovery, and data reuse. They use many metadata dialects to address multiple data use cases and are exposed to metadata requirements and recommendations from many organizations, disciplines, and communities. Using these recommendations to guide metadata improvement requires being able to evaluate existing metadata collections with respect to these recommendations. We will present metadata evaluation tools being developed and used by ESIP members with the goal of understanding and improving their utility across ESIP.||Increase the use and value of Earth science data and information., Strengthen the ties between observations and user communities (e.g. technologies, research, education and applications).||Breakout Session||TBD||Jump In, Deep Dive||Documentation||No||Documentation, Evaluation, Assessment, Improvement||Yes||Groups that have implemented metadata evaluation tools|
|3/26/2019 16:30:email@example.com||Sophie Hou||Reid Boehm, Ruth Duerr, Denise Hills, Sophie Hou||What does it mean to be a Data Mentor?||Data Mentor Assemble||Research data can become at risk for a variety of reasons, and the risks can occur throughout the data’s lifecycle. It takes dedicated resources to ensure data can be preserved for the long term and be made available, accessible, and usable to all. At DataAtRisk.org (https://dataatrisk.org), we rely on “Data Mentors,” or people who are committed to protecting data from risk, to make data secure and facilitate data rescue activities.|
During this session, the DataAtRisk team invites attendees to help us formalize the “Data Mentor” role and its responsibilities for our Data Nomination Tool. We will first clarify the key characteristics (or personas) for the “Data Mentor”. Based on these personas, we will use user stories to describe the types of data rescue activities that the “Data Mentors” need to prioritize. Further, using these pieces of information, we will build a realistic workflow that represents the amount of effort it takes for the “Data Mentor” when facilitating data rescue activities submitted via the Data Nomination Tool. Finally, we will determine if “Data Mentor” is the appropriate name for this role.
Data Nomination Tool facilitates community-driven rescue efforts for Earth and Environmental science data. Particularly, the web-based tool connects people who can provide long term data stewardship support with those who need the assistance. The tool is created and hosted by CloudBIRST (https://cloudbirst.com/, key contact: Joan Saez). DataAtRisk.org’s current members also consist of individuals from Earth Science Information Partners (see ESIP Partners here: https://www.esipfed.org/partners), Johns Hopkins University Sheridan Libraries, and representatives from several University Research Libraries.
|Increase the use and value of Earth science data and information., Strengthen the ties between observations and user communities (e.g. technologies, research, education and applications)., Promote techniques to articulate and measure the socioeconomic value and benefit of Earth science data, information and applications., Position ESIP to play a major role in Earth science issues (e.g. addressing effects of climate change mitigation, adaptation and supporting sustainable science data infrastructure).||Working Session||TBD||Jump In||Community Data, Information Quality, Research Data Management, Usability, Data Stewardship||Maybe||Data Rescue||Yes||We are looking for two types of speakers: 1) individuals who have specific use cases for data that need rescuing, and 2) individuals who have experience in sharing their data stewardship support outside of their institutions.|
|3/29/2019 17:27:firstname.lastname@example.org||Sophie Hou||Sophie Hou||Building Data Collections with Repositories - Challenges and Lessons Learned||Although a “collection” can be simply understood by humans as “grouping of things”, the concept is much more difficult to implement in a data repositories. For example, the following areas need to be evaluated and defined before a data collection can exist:|
- How are “data collections” defined?
- How can metadata elements (such as ISO19115) be consistently and optimally used to describe the relationship between collections and their associated items (e.g. parents vs. children, etc.)?
- How can controlled vocabularies be used to help standardize the representation of a collection and its associated items?
- What are some options for displaying collections, so that the collections and their associated items are easy for users to understand and navigate?
For this session, we will be discussing the issues and recommendations for building data collections. The following is the proposed agenda:
- Background information for the need to build and represent data collections
- Data collection use cases
- Discussion of the highlighted challenges and sharing of lessons learned
- Summary and related areas to explore
|Increase the use and value of Earth science data and information., Strengthen the ties between observations and user communities (e.g. technologies, research, education and applications).||Working Session||To be finalized before the session||Jump In||Documentation, Information Quality, Research Data Management, Usability, Data Stewardship||Maybe||Metadata, Data Discovery, Data Access||Yes||We are looking for speakers who can share their repositories’ practices/guidelines for any or a combination of the following areas: 1) organizing dataset collections, 2) creating metadata records for the collections and the associated items, 3) assessing the relevance of an item to an existing collection, 4) displaying the collection information on the repository’s interface, and 5) providing access to the collections and the associated items.|
|4/2/2019 13:44:email@example.com||Margaret Mooney||Margaret Mooney, Shelley Olds, Becky Reid, LuAnn Dahlman||Teacher Workshop: Data in Action with Jupyter Notebooks and other Earth Science Tools||ESIP Teacher Workshop||This is a 1-day workshop on July 17 with the following agenda: |
8:30 - Introductions, ESIP Overview & Intro to Jupyter Notebooks
9:30 - Plenary Sessions
10:30 - Break
11:00 - Jupyter Notebooks Hurricane Activity
12:30 - Lunch
2:00 - Climate Literacy in the Classroom
3:00 – Using NASA SEDAC Online Data Products and Services
3:20 – GOES-16/17 Activities from NOAA's CIMSS
3:40 – Break
4:00 - SuAVE - Survey Analysis via Visual Exploration
5:00 - Wrap-up and Evaluations
Participants are warmly invited and strongly encouraged to attend the Research Showcase Wednesday evening from 5:30 to 8pm.
|Workshop||Shelley Olds, Sean Gordon, Patrick Chandler, Bob Downs, Margaret Mooney, Ilya Zaslavsky||Jump In||CLEAN Network, Education||Yes||Education, Jupyter Notebooks, Satellite Data, NASA, NOAA, Climate Literacy||No|
|4/3/2019 4:32:firstname.lastname@example.org||Elena Pourmal||Aleksandar Jelenak||HDF Town Hall||HDF||Data in HDF file formats continues to play an important role for Earth Scientists in the U.S. and around the world. The HDF Group will update ESIP members on the state of HDF software and HDF5 Roadmap, and will share our experience on working with HDF5 in the Cloud. We will discuss our technical approaches, and lessons learned from different projects including a NASA ACCESS project that transformed NASA HDF data into GeoTIFF in AWS. We will also update ESIP members on our involvement in standardization efforts and demonstrate how HDF tools support ESDIS data from product initial design to production, and to compliance with the standards. We will encourage ESIP members participating in the session to share their experiences with the HDF software and to contribute to the HDF5 Roadmap.||Breakout Session||Elena Pourmal, Joe Lee, Kent Yand, Aleksandar Jelenak||Skim the Surface, Jump In, Deep Dive||Cloud Computing, Documentation, Science Software, Sustainable Data Management||Maybe||HDF Group||Yes||Looking for scientists, applications developers, users of data in HDF5, HDF-EOS5 and netCDF-4 data who can speak about their experiences with HDF5 including HDF5 in the Cloud.|
|5/30/2019 11:00:email@example.com||Sudhir R Shrestha||Sudhir R Shrestha, Ana Pinheiro Privette, Joe Flasher||Geospatial Data Analytics and Visualization for Sustainability in the Cloud||Sustainability’s geospatial processes are complex since environmental, societal, and economic systems are deeply interconnected. This creates challenges for researchers working in this field because the impact from changes in one system are not always well understood or predictable for the other systems. As a result, extracting timely and meaningful insights for sustainable environmental decision making often requires large datasets from many different domains, and tools capable of capturing the multidimensional nature of the problem. To address these challenges, many users are exploring the use of cloud computing to leverage its scalable storage and geospatial analytical capabilities. In this session, we are soliciting presentations that utilizes cloud-based workflows and applications of GIS technology to derive insights for sustainability.||Increase the use and value of Earth science data and information., Strengthen the ties between observations and user communities (e.g. technologies, research, education and applications)., Promote techniques to articulate and measure the socioeconomic value and benefit of Earth science data, information and applications.||Panel||Skim the Surface, Jump In, Deep Dive||Agriculture and Climate, Cloud Computing, Community Resilience, Data Models, Data to Decisions, Data Analytics, Energy and Climate, Enviro Sensing, Machine Learning, Web Services, Geopatial Platform||No||Cloud computing, GIS, Earth Science, Sustainability, Raster Analytics||Maybe|
|4/4/2019 16:29:firstname.lastname@example.org||Adam Shepherd||Douglas Fils, Adam Shepherd||Approaches to extending schema.org for Data APIs||schema.org for Data APIs||PROBLEM: schema.org can describe static Datasets, but it's difficult to accurately describe services and APIs that provide access to data. This session will bring together data API managers and curators, conceptual modelers and ontologists to model and develop a schema.org extension address accessing data through APIs and services.||Working Session||Deep Dive||Data Models, Semantic Technologies||No||schema-org dataset api||Maybe||Technologists who manage Data access APIs and services wanting to describe them with schema.org|
|4/4/2019 16:33:email@example.com||Adam Shepherd||Charles Vardeman, Adam Shepherd, Douglas Fils||Advancing spatial and temporal aspects of schema.org||spatial temporal schema.org||PROBLEM: schema.org is currently inconsistent with standards organizations (W3C, OGC) representations of spatial and temporal information. This session will bring together data curators, conceptual modelers and ontologists to formulate solutions for extending schema.org's approach to spatial and temporal descriptions.||Working Session||Deep Dive||Data Models, Semantic Technologies, Web Services||No||schema-org spatial temporal||Maybe|
|4/8/2019 6:31:firstname.lastname@example.org||Erin Robinson||Erin Robinson||ESIP's International Connections: Sharing work that spans U.S., Australia and Europe||ESIP International||This session will highlight collaborative work between ESIP community in the U.S. and counterparts in Australia and Europe. It will introduce E2SIP (Earth & Environmental Information Partners), the emerging Australian community being incubated by ESIP and share how we have gone about establishing these connections. The session will also highlight three or four projects that are being led by international collaborators focused on drones, samples, data management training and more.||Increase the use and value of Earth science data and information., Position ESIP to play a major role in Earth science issues (e.g. addressing effects of climate change mitigation, adaptation and supporting sustainable science data infrastructure).||Panel||Jens Klump, Jane Wyngaard, Lesley Wyborn, Helen Glaves, Lindsay Powers, Kerstin Lehnert||Skim the Surface||ESIP-E2SIP||No||international, collaboration||Yes||Looking for speakers who have collaborated or are collaborating with partners in Europe and Australia.|
|4/8/2019 17:24:email@example.com||Erin Robinson||Lesley Wyborn||IGSN 2040 Governance||IGSN 2040||Governance workshop for IGSN 2040. Would like small room for 12 people all day Thurs and half day Friday||Increase the use and value of Earth science data and information., Strengthen the ties between observations and user communities (e.g. technologies, research, education and applications)., Promote techniques to articulate and measure the socioeconomic value and benefit of Earth science data, information and applications.||Working Session||Kerstin Lehnert, Lesley Wyborn, Jens Klump||Closed meeting of IGSN 2040 Steering Committee||No||Samples, PIDS, IGSN||No|
|4/8/2019 19:17:firstname.lastname@example.org||Scotty Strachan||Renée Brown, Scotty Strachan||EnviroSensing: Sensor Data, Technology, and Best Practices||EnviroSensing Practices||Sponsored by the ESIP EnviroSensing Cluster, this session is open to scientists, information managers, and technologists interested in the general topic of in-situ environmental sensing for science and management. Our community of practitioners promotes conversation around, and development and refinement of techniques to observe natural Earth system processes over short and long timescales. Short talks on new data types, interesting technology applications, project case studies, data management, related software tools, quality control processes, and other advances in the field are welcome and invited to submit!||Increase the use and value of Earth science data and information., Strengthen the ties between observations and user communities (e.g. technologies, research, education and applications)., Position ESIP to play a major role in Earth science issues (e.g. addressing effects of climate change mitigation, adaptation and supporting sustainable science data infrastructure).||Breakout Session||Renée Brown, Matt Bartos, Scotty Strachan||Skim the Surface, Jump In, Deep Dive||Discovery, Drones, Enviro Sensing, Information Quality, Research Data Management||No||Sensors, data quality, LTER, networks, telemetry, field science, ecology, hydrology, climate, weather||Yes||We are looking for scientists, data professionals, students, technologists, and program leaders who employ in-situ sensor systems for environmental science and management observations. Talks on new technologies, applications, management techniques, case studies, and tool development are encouraged to join us!|
|4/10/2019 16:18:email@example.com||SURESH VANNAN||SURESH VANNAN||Challenges and Opportunities in Adopting Cloud technologies for Data Intensive Science||Cloud Technology - Challenges and Opportunities||The amount of data generated by public and private sector organizations has increased many fold in the last decade. In recent years, consumers and providers of data are faced with an increasing challenge of managing the quantity and quality of information produced. The advent of cloud technologies has been a boon for the big data era offering a solution for the information overload. While cloud technologies have provided an excellent opportunity, challenges and opportunities on utilizing cloud technologies are still to be explored. The complex business/infrastructure aspect of the cloud technologies paradigm and the rapid changes in the technical development have made transitions complex and confusing at times. In this session, we hope to share case studies of migration/utilization of cloud technologies for data intensive science. The challenges and opportunities revealed by those case studies we hope will inform stakeholders, collaborators, and other interested parties. We hope that the lessons learned will inform future work and help expedite progress in the field of Earth Science informatics.||Increase the use and value of Earth science data and information., Strengthen the ties between observations and user communities (e.g. technologies, research, education and applications)., Promote techniques to articulate and measure the socioeconomic value and benefit of Earth science data, information and applications., Position ESIP to play a major role in Earth science issues (e.g. addressing effects of climate change mitigation, adaptation and supporting sustainable science data infrastructure).||Breakout Session||Thomas Huang, Chris Lynnes, Suresh Vannan, Other speakers will be invited||Jump In, Deep Dive||Cloud Computing, Data Models, Data to Decisions, Data Analytics, Education, Research Data Management, Science Software, Web Services||Maybe||cloud, data intensive science, data management, user communities, adoption||Yes|
|4/10/2019 19:39:firstname.lastname@example.org||Ben Roberts-Pierel||Ben Roberts-Pierel||Multi-sensor data integration for cryosphere and hydrosphere monitoring||In keeping with this year’s Summer Meeting theme of “Increasing the Use and Value of Earth Science Data and Information,” this session aims to explore different data streams used for monitoring of the hydrosphere and cryosphere. Earth science data for water resources monitoring has existed as field collected data, remote sensing, modeled and in situ data for decades but relatively recent increases in computational capabilities (e.g. cloud computing platforms), data storage and integration and processing methods like machine learning have allowed researchers to ask a suite of questions that rely on data from multiple sources and typologies to answer complex questions about water resources critical to humans and ecosystems. To emphasize the ‘use and value of earth science data’ this session will incorporate presentations on data generation and processing methods as well as applied uses of data products for water resources monitoring.||Increase the use and value of Earth science data and information., Strengthen the ties between observations and user communities (e.g. technologies, research, education and applications)., Position ESIP to play a major role in Earth science issues (e.g. addressing effects of climate change mitigation, adaptation and supporting sustainable science data infrastructure).||Breakout Session||Jump In||Agriculture and Climate, Cloud Computing, Community Data, Community Resilience, Data Models, Data to Decisions, Data Analytics, Energy and Climate, Machine Learning, Marine Data, Science Communication||Maybe||cryosphere, hydrosphere, water, data integration, water resources, remote sensing, hydrologic modeling, sensor networks||Yes||I am talking to a few people about presenting in this session but additionally those working on large monitoring networks for government agencies, researchers using cloud computing platforms or machine learning approaches to address specific, place-based water resources challenges are welcomed. Additionally, those working on integrating citizen science data and more traditional data sources are welcome to submit.|
|4/11/2019 11:38:email@example.com||Mike Little & Marge Cole||Marge Cole||How Do I Get Started In Cloud Computing Workshop||Get Started In Cloud Computing Workshop||This workshop is structured to provide physical scientists with an authentic experience in making use of current cloud computing resources and related tools and machine learning services available. The cloud, a dynamic network of computers and virtual machines can offer platform or infrastructure-as-a-service (PaaS or IaaS respectively), or host software-as-a-service (SaaS), among other features. Physical scientists working with large data sets, extended time lines, and/or models and tools they would like to share across diverse teams and user communities can achieve their objectives with these new technical capabilities. Additionally the cloud has security functions, making it a safe environment even for demanding scientific architectures. |
The cloud also provides on-demand, ‘elastic’ access to shared computing resources providing flexibility for projects and scientists who require bursts of computing power rather than sustained usage. Some of the companies that offer cloud services include Amazon, Google and Microsoft.
This workshop includes representatives from Amazon and experienced cloud users working with participants to convey data and tools to the cloud based on actual use cases. Participants should bring their own computers and plan on working through a use case, creating their own AWS cloud account and project, and completing some data analysis on the cloud.
|Workshop||Jump In||Cloud Computing||Maybe||Cloud, data analysis, workshop, science||Yes||Cloud system architects from AWS and one or two physical scientists that can help a group create an AWS account, move data and/or tools and services to the cloud and do some simple data analysis of processing to help workshop participants understand how to get started using cloud capabilities.|
|4/14/2019 16:05:firstname.lastname@example.org||Jane Wyngaard||Jane Wyngaard||Drone Data API Design Hackathon||DD API Design Hack||This will be the first of two design hackathons working to create a standards based Application Programmable Interface (API) for use in building|
data management tools for small Unmanned Aircraft Systems (sUAS) or 'drones' as used for data capture.
The 1 day event will consist of introductory lightening talks from representative speakers followed by parallel breakout groups focused on designing different components including those needed to support: (i) data storage, (ii) data movement, (iii) data annotation.
|Workshop||(Invitations pending)||Agriculture and Climate, Community Data, Data Models, Discovery, Drones, Data Analytics, Enviro Sensing, ESIP-E2SIP, Research Data Management, Semantic Technologies, Sustainable Data Management||Maybe||RPAS, sUAS, FAIR, Semantics, Hackathon|
|4/15/2019 9:22:email@example.com||David Blodgett||David Blodgett||Big Gridded Data: The transition from legacy to next generation.||Modernization of Big Gridded Data||This session aims to explore several dimensions of technology and operational systems that support archiving, cataloging, distributing, subsetting, and processing of large structured data. For this session, large structured data is defined as any data with well structured spatial, temporal, band, scenario, ensemble, dimensions and associated variables that exceed practical size constraints of commodity internet and personal computing resources. Typical examples are very high-resolution geospatial grids, outputs from ocean, landscape, weather and climate models, and multi-spectral remote sensing archives. Use cases for such data range from meta and reanalyses that require run-time access to entire datasets at once to ad-hoc investigations requiring small subsets of one or more dimension. For example, a local science project may need a small spatial subset of an ensemble climate projection or a remote sensing research project may need to sample 100 point locations from a all scenes of a multispectral remote sensing product. Data formats and computing Infrastructure to support this range of use cases, from terabyte and greater data access to custom small-subset extraction presents a great challenge especially as technology changes and what was a sound implementation and investment becomes dated and unable to meet modern expectations. |
This session will feature speakers who manage operations and maintenance of archives of large structured data, build software and standards designed to meet the needs of a wide range of large structured data use cases, and researchers working to evaluate and demonstrate the potential of next generation technical solutions.
|Increase the use and value of Earth science data and information.||Panel||Sophie Hou, Michael Rilee, Ed Armstrong, Dave Blodgett, Rich Signell ... ?||Deep Dive||Agriculture and Climate, Cloud Computing, Community Data, Data Models, Research Data Management, Sustainable Data Management, Usability, Web Services||No||Big Data, Cloud, Subsetting, Archiving||Yes||I hope to find additional speakers from other federal agencies (NOAA) and potentially software development teams (Unidata).|
|4/18/2019 18:26:firstname.lastname@example.org||Nancy J Hoebelheinrich||Nancy J Hoebelheinrich, Karl Benedict||Data Management Training Clearinghouse Advisory Board Meeting||DMTC Advisory Board Meeting||The Third Quarter 2019 Advisory Board for the ESIP-hosted and IMLS (Institute of Museum & Library Services) grant funded Data Management Training Clearinghouse would like to hold a face to face meeting at ESIP Summer. AB meetings are scheduled for each quarter of the year, and this would be the first time that AB members meet face to face (although remote participation would also be welcomed). Part of the impetus for the face to face meeting is to bring those board members to an ESIP meeting who have not yet had the opportunity to attend. Ideally, the meeting could be held for about 3 hours on the Friday after the ESIP meeting in hopes that more members could attend both.||Position ESIP to play a major role in Earth science issues (e.g. addressing effects of climate change mitigation, adaptation and supporting sustainable science data infrastructure).||Business Meeting||TBD||Deep Dive||CLEAN Network, Education, Research Data Management||No||Education Metadata, Educational resource assessment, sustainable education gateway||No|
|4/19/2019 9:08:email@example.com||Pier Luigi Buttigieg||Lewis J. Mcgibbney, Pier Luigi Buttigieg||Semantic Technologies Committee Business Meeting||SemTech Business Meeting||As the adoption of the FAIR principles accelerates, the use of semantic resources by research and operational communities worldwide is intensifying. There is thus a pressing need to coordinate our semantic development efforts in the Earth science community such that we can maintain diversity, reduce duplication of effort, and present a more unified and demonstrably interoperable interface to external stakeholders.|
We will use this session to address the need above, and develop a plan to link our activities to emerging semantic frameworks in Earth observation, such as the UN Sustainable Development Goals and the Essential Variables for Climate, Oceans, Biodiversity, and Geodiversity.
This plan will feed-forward into the planning of the 4th Geosemantics Symposium.
|Business Meeting||Open discussion format following introduction by ewis J. Mcgibbney||Jump In, Deep Dive, hear strategic goals||Community Data, Discovery, Machine Learning, Marine Data, Research Data Management, Science Software, Semantic Technologies, Sustainable Data Management, Biodiversity||No||Semantics, knowledge representation, Essential Variables, Sustainable Development Goals, Ontology, Strategy, Planning||Maybe||Speakers/discussants who have ideas on how to better apply or extend semantic technologies are always welcome|
|4/19/2019 15:22:firstname.lastname@example.org||Margaret O'Brien||Margaret O'Brien, M. Gastil-Buhl (Gastil)||Defining an EML profile||A uniform interface facilitates writing shared tools against different backend metadata storage systems, and decoupling the outward-facing appearance from the back-end implementation is typically accomplished by using an abstraction layer. If the backend is a relational database, the abstraction layer would be implemented as a set of views, but could also be implemented as simple text tables, or formatted as other data structures (e.g., JSON or YAML). In the context of an XML schema such as EML, a constrained usage pattern is called a “profile”. EML was designed to be extensible, but we have observed EML creators converging on a set of elements. De facto then, a profile is emerging, and that profile for EML can define the specs of a common interface. |
EDI is defining the EML profile based on usage patterns, which will then define an abstraction layer that allows EDI’s scripts and software to be used by a broader community. This session will summarize the patterns EDI has identified in EML usage today, and through discussion, gather input on the components that may be missed by focusing on common usage, and the impact of style diversity on coding complexity.
|Increase the use and value of Earth science data and information., Strengthen the ties between observations and user communities (e.g. technologies, research, education and applications)., Promote techniques to articulate and measure the socioeconomic value and benefit of Earth science data, information and applications.||Working Session||Margaret O'Brien||Jump In, Deep Dive||Documentation, Information Quality, Research Data Management, Metadata||Maybe||Ecological Metadata Language, EML||Maybe||Someone representing a group that uses an EML profile, or is considering one and has assembled a list of requirements|
|4/19/2019 15:30:email@example.com||Donald Petravick||Donald Petravick, Ben Galewski||Large/Mission Scale Multiplatform Data Working Group||Pain With Data||The NASA ESDIS Large/Mission Scale Multiplatform Data Working Group considers "pain" related to size and/or multiple platforms in supporting science for large mission scale analysis projects and works to understand it's root causes and work towards mitigations. Example NASA Program elements using large data are ACCESS and MEaSUREs. |
The working group has just begun and is interesting in collecting pain points to analyze, understand and work on. In the session we will present our work so far, and collect input on both “pain” and comments about results so far.
|Increase the use and value of Earth science data and information., Strengthen the ties between observations and user communities (e.g. technologies, research, education and applications)., Position ESIP to play a major role in Earth science issues (e.g. addressing effects of climate change mitigation, adaptation and supporting sustainable science data infrastructure).||Working Session||Don Petravick||Jump In||Cloud Computing, Community Data, Community Resilience, Data to Decisions, Energy and Climate, Research Data Management, Usability||No||Mission Scale Data, Multi-Cloud, Multi-Site||Yes||Investigators from programs such as NASA MEaSUREs and the people who support them. where there are multi-site issues to optimize or data volume issues to optimize.|
|4/19/2019 17:49:firstname.lastname@example.org||Nancy J Hoebelheinrich||Nancy J Hoebelheinrich||Identification of Data Skills for Data Stewards||Data Skills for Data Stewards||In this working session, participants will be identifying and discussing the kinds of data skills that people who are interested in working as professional data stewards (or data curators or information specialists, for example) need to acquire during their academic careers (including undergraduate, graduate, PhDs, PostDocs and possibly, early career professionals. The skills identified will be used by the American Geosciences Institute (AGI) to create a one page "Career Compass" handout that has been quite popular at any number of professional conferences and association meetings. An example of a Career Compass for Data Science can be seen at: https://www.americangeosciences.org/sites/default/files/CareerCompass_DataSciences.pdf. Questions about the process and intentions for the working session can be directed to the organizer or any of the proposed speakers.||Strengthen the ties between observations and user communities (e.g. technologies, research, education and applications).||Working Session||Karl Benedict, Sophie Hou, Nancy Hoebelheinrich, Wade Bishop, Jake Carlson||Jump In, Deep Dive||Education, Research Data Management, Science Communication||No||data stewardship training, capacity building for data stewards||Yes||Looking for people who have experience teaching or creating curriculum guides for prospective data stewards or data stewards looking for professional development education and training.|
|4/19/2019 18:45:email@example.com||Nancy J Hoebelheinrich||Nancy J Hoebelheinrich, Karl Benedict||Assessment Frameworks and Dimensions for Educational & Training Resources||Dimensions for Assessing Educational Resources||One of the goals of the Institute of Museum & Library Services National Leadership Grant recipient and ESIP-hosted Data Management Training Clearinghouse (DMTC) is to identify and/or develop assessment frameworks that could be applied to the educational & training resource content in the DMTC. While a number of approaches to assessing educational resources seem promising (e.g., the Kirkpatrick framework, CLEAN evaluation criteria) the working group tasked to address the question of assessment would like to know more about these approaches, how they might apply to the DMTC resources or the DMTC itself, and the mechanisms or processes that have been developed by others to evaluate educational and training resources. The session will include invited speakers to describe different frameworks and how they are used, but also allow ample time to discuss how the frameworks might apply to DMTC resources.||Strengthen the ties between observations and user communities (e.g. technologies, research, education and applications).||Breakout Session||Sophie Hou||Skim the Surface, Jump In||CLEAN Network, Education, Research Data Management||No||Training assessment, Assessment dimensions||Yes||Looking for speakers from ESIP's Education Committee and the CLEAN network to present information on known and effective frameworks who are also willing to engage in discussion re: the possible application of those frameworks (or adapted from them) to DMTC resources.|
|4/23/2019 13:00:firstname.lastname@example.org||Madison Langseth||Sophie Hou, Bob Downs, Jessica Hausman, Madison Langseth||Count 'em up -- Tracking citation metrics for research objects||Tracking research object citations||Literature citation metrics have been extremely important for researchers to advance their careers, and there are many established methods for tracking literature citation metrics. Data citation also has been recognized as being necessary to provide attribution to data, producers of data, and the data centers, archives, and repositories that enable access to data products and services. Citation techniques for other research objects, such as software and physical samples, also are being developed, but are not yet as consistent and common as literature citations.Being able to demonstrate the value, usage, and utility of other research objects is just as important as demonstrating the same for literature and data. For example, research object citation metrics will be useful for encouraging researchers to make their objects publicly accessible and for enabling repositories to sustain their funding.|
This session will strive to answer questions such as the following:
● What techniques are organizations using to track citation metrics?
● What challenges have organizations faced when tracking research object citations?
● What information are we able to glean from different types of citation metrics?
● What metrics are most important for researchers? for repositories? for funders? for authors of objects?
|Strengthen the ties between observations and user communities (e.g. technologies, research, education and applications)., Promote techniques to articulate and measure the socioeconomic value and benefit of Earth science data, information and applications.||Breakout Session||TBD||Skim the Surface, Jump In||Research Data Management, Science Software, Software & Services Citations, Web Services||No||Research Object Citations||Yes||Looking for speakers representing repositories, funding agencies, or other organizations that are tracking citations of research objects|
|4/23/2019 14:57:email@example.com||Lewis John McGibbney||Annie Burgess, John Graybeal||Community Ontology Repository (COR) Administration, Development and Planning||COR Adminstration Working Session||This session will consist of two main themes: discussion of current and pending important administrative tasks; and hands-on exercises covering the development aspect toward improvement of the software itself, as well as possible complementary tools that could be integrated (e.g., ontology viewers/visualizers). Participants will gain understanding of how this particular instance of the ORR software is deployed on Amazon and how they can contribute in various ways including further core development and integration of new tools and client libraries to leverage the powerful API and SPARQL endpoint capabilities of the COR server.||Increase the use and value of Earth science data and information., Position ESIP to play a major role in Earth science issues (e.g. addressing effects of climate change mitigation, adaptation and supporting sustainable science data infrastructure).||Working Session||Lewis John McGibbney, Carlos Rueda, Tom Narock||Jump In, Deep Dive||Agriculture and Climate, Cloud Computing, Community Data, Data Models, Data to Decisions, Discovery, Drones, Data Analytics, Enviro Sensing, Information Quality, IM Code Registry, Machine Learning, Marine Data, Science Software, Semantic Technologies, Sustainable Data Management||No||community_ontology_repository, cor, working_session, semantic_technologies, vocabularly||Yes||Anyone using COR programmatically e.g. via the REST API’s or Python API.|
|4/23/2019 15:05:firstname.lastname@example.org||Lewis John McGibbney||Pier Luigi Buttigieg||ESIP Geoscience Community Ontology Engineering Workshop (GCOEW)||Community Ontoogy Engineering Workshop||The field of Ontology engineering in computer|
science and information science is a field which
studies the methods and methodologies for building
ontologies: formal representations of a set of
concepts within a domain and the relationships
between those concepts. This workshop will crowdsource definitions for the SWEET Ontology Suite. Attendees should come prepared and ready to work on harvesting definitions for existing SWEET concepts.
|Increase the use and value of Earth science data and information., Position ESIP to play a major role in Earth science issues (e.g. addressing effects of climate change mitigation, adaptation and supporting sustainable science data infrastructure).||Workshop||Pier Luigi Buttigieg, Lewis John McGibbney, Beth Huffer||Deep Dive||Agriculture and Climate, CLEAN Network, Cloud Computing, COPDESS, Community Data, Community Resilience, Data Models, Data to Decisions, Disaster Lifecycle, Discovery, Documentation, Drones, Data Analytics, Earth Sciences Pre-Prints, Education, Energy and Climate, Enviro Sensing, ESIP-E2SIP, Information Quality, IM Code Registry, Machine Learning, Marine Data, Research Data Management, Science Communication, Science Software, Semantic Technologies, Software & Services Citations, Sustainable Data Management, Usability, VR/AR for Science, Web Services||No||crowdsourcing, ontology_engineering, vocabularly, SWEET||Maybe||Anyone who is interested in contributing towards some part of providing definitions for existing SWEET concepts.|
|4/24/2019 0:29:email@example.com||Karen Moe||Karen Moe, Dave Jones||Identifying Trusted Data Sources for Operational Decision Making & the Role of “Fitness for Use” as ORL Criteria||Trusted Data and Fitness-for-Use||The Disaster Lifecycle cluster is hosting a breakout session to explore sources of trusted datasets from various agencies and what constitutes operational readiness for these data. A key issue is how ‘Fitness for Use’ criteria can apply across ORLs, the Operational Readiness Levels. |
With FEMA’s encouragement, collaborators at the All Hazards Consortium are “operationalizing” ORLs for data-driven decision-making support to improve situational awareness in response to power outages, transportation, fuel and lodging after major disasters. An interesting development is the need to assign fixed ORLs to datasets, rather than determining the ORL value based on specific use cases. The GIS ORL team within the Sensitive Information Sharing Environment (SISE) committee of the Fleet Response Working Group (FRWG) recognizes that latency, resolution, and coverage features have a significant impact on dataset readiness for most critical infrastructure and many weather and other EO datasets. However the inherent confusion that changing a trusted dataset’s ORL assessment creates a bigger problem for operator training and response efforts. Currently, most of their critical datasets are logistical in nature (what roads have been closed by state authorities, where can truck drivers get fuel/ food/ lodging, where are the authorized staging locations, etc.) and amenable to fixed ORLs assignments.
The recent wildfires in CA and associated mud and debris flows are impacting lives and property. Earthquake exercises are leading to data needs by decision makers that can drive situational awareness and decision making criteria. For example, soil condition information in burn scar areas is critical for NWS forecasters to know so they can accurately identify rainfall thresholds for issuing flood warnings in burn scar areas.
Looking forward to successfully using more trusted EO data for disaster operations, we plan to hear about current and planned datasets for disaster response needs. We are also seeking ways to clarify fitness for use criteria (especially latency, resolution and coverage) for these datasets that otherwise would meet the current readiness criteria of ORL1.
|Breakout Session||TBD||Jump In||Agriculture and Climate, Community Resilience, Data to Decisions, Disaster Lifecycle, Information Quality, Usability||No||Trusted data, Usability|
|4/24/2019 16:25:firstname.lastname@example.org||Ana Pinheiro Privette||Ana Pinheiro Privette||The Amazon Sustainability Data Initiative: Driving Sustainability Innovation with Open Data and Cloud Technology||Amazon Sustainability Data Initiative|
Last December, Amazon announced the Amazon Sustainability Data Initiative (ASDI) to promote sustainability research, innovation, and problem-solving by making key data easily accessible and even more widely available. ASDI leverages Amazon Web Services’ technology and scalable infrastructure to stage, analyze, and distribute data.
The initiative identifies foundational data for sustainability and works closely with data providers like NOAA to stage their data in the AWS Cloud by giving them complete ownership and control over how their data is shared. While these datasets have always been freely available, they aren’t always easily accessible and researchers may not have the compute power necessary to take advantage of these resources through their own on-premises IT capabilities. By removing the burden and cost of data acquisition, ASDI enables anyone to perform modeling and analysis at a scale that was previously limited to institutions with access to super computers and dedicated data centers.
Researchers, developers and innovators are encouraged to apply for AWS Promotional Credits through the AWS Cloud Credits for Research (https://aws.amazon.com/earth/research-credits/) program. Offsetting development costs will encourage experimentation and promote innovative solutions. To support the exchange of technical knowledge, ASDI works with users to document and share lessons learned through blog posts, tutorials, and open source code.
The Amazon Sustainability Data Initiative supports researchers, developers and innovators with the data, tools, and technical expertise needed to move sustainability to the next level.
For more information, and ways to get involved, please see the links below:
* Visit the Amazon Sustainability Data Initiative (https://www.aboutamazon.com/sustainability/amazon-sustainability-data-initiative) webpage
* Explore the data available in the Registry of Open Data on AWS (https://registry.opendata.aws/tag/sustainability/)
* Request cloud credits (https://aws.amazon.com/earth/research-credits/) to prototype your solution on the AWS Cloud for free
* See Cases of Data in Action (https://www.aboutamazon.com/sustainability/data-in-action)
|Showcase||Ana Pinheiro Privette||Skim the Surface||Agriculture and Climate, Cloud Computing, Community Data, Community Resilience, Disaster Lifecycle, Discovery, Data Analytics, Education, Energy and Climate, Machine Learning||No||open data, Sustainability, ASDI, AWS||No|
|4/24/2019 17:30:50||Hampapuram.Ramapriyan@ssaihq.com||Hampapuram Ramapriyan||Hampapuram Ramapriyan, Ge Peng, David Moroni||Conveying Information Quality – Recent Progress||IQC - Recent Progress||The Information Quality Cluster (IQC) has been active since 2014 improving understanding of various aspects of information quality and fostering collaborations nationally and internationally. During this period, NASA’s Earth Science Data System Working Groups included a Data Quality Working Group, which made several recommendations that have been documented, reviewed thoroughly and published. The IQC has had plenary and breakout sessions discussing ideas about uncertainty in Earth science datasets, which have evolved into a white paper. Significant progress has been made in defining and propagating maturity matrices for various aspects of data management including information quality. The purpose of this session is to summarize the status and accomplishments in each of these areas and discuss future directions that the IQC should take. |
1. Information Quality Cluster Introduction - H. K. Ramapriyan (Rama)
2. NASA Data Quality Working Group’s Recommendations and Publications – Yaxing Wei
3. Uncertainty White Paper Status – David Moroni
4. netCDF Uncertainty Proposal Status – (to be confirmed)
5. Maturity Matrices Update – Ge Peng
6. Discussion – All – 20 mins.
|Breakout Session||Hampapuram Ramapriyan, Yaxing Wei, David Moroni, Ge Peng||Jump In||Documentation, Information Quality, Research Data Management||No||Information Quality, Data Stewardship, Uncertainty, NASA DQWG||No|
|4/25/2019 6:24:email@example.com||Lesley Wyborn||Kerstin Lehnert, Lesley Wyborn||Epic Fails in Earth Science Informatics: learning from the past to do better in the future||Epic Fails: Let's Share Our Disasters||In research we tend to only present on and/or publish our successes as they are so integral to our career progression. Yet not everything we attempt is successful: no matter how hard we try, some of our research and developments fails. For cyberinfrastructure projects, there is a high risk of failure, as technology is changing so rapidly and unpredictably, whilst the change of research culture is slow. Edwards et al. (2007) emphasized the value of honestly reporting failures “to supporting long-term and comparative learning across the varieties of cyberinfrastructural experience” and recommended that “through the disciplined and even-handed study of failure, funders and proponents of cyberinfrastructure must learn to stop hiding the bodies”. New trends in biochemical research and publishing show increased attention to sharing of negative results from early clinical trials (Kevin Kelly, “Speculations on the Future of Science”).|
The purpose of this session is to provide a free and blameless environment to encourage honest reporting of where things went wrong. It is time to bring the skeletons out of the closet and showcase Epic Fails that you know about (particularly your own) in software, data infrastructures, samples, software delivery, services, etc. From these, we can build a portfolio of lessons learned that will inform the future, and ultimately contribute to accelerating progress in Earth science informatics. (Note: for those who may find presenting in this session stressful, we will ensure a supporting environment where you can reveal your fails without having to show your face).
|Working Session||Kerstin Lehnert, Lesley Wyborn, TBD||Deep Dive||Agriculture and Climate, CLEAN Network, Cloud Computing, COPDESS, Community Data, Community Resilience, Data Models, Data to Decisions, Disaster Lifecycle, Discovery, Documentation, Drones, Data Analytics, Earth Sciences Pre-Prints, Education, Energy and Climate, Enviro Sensing, ESIP-E2SIP, Information Quality, IM Code Registry, Machine Learning, Marine Data, Research Data Management, Science Communication, Science Software, Semantic Technologies, Software & Services Citations, Sustainable Data Management, Usability, VR/AR for Science, Web Services||No||Yes||Looking for speakers who are willing to talk about where things did not really work out. If desired, we will try to enable speakers to present anonymously|
|4/25/2019 10:31:firstname.lastname@example.org||Cynthia Hall||Cynthia Hall, Teddy Gelabert, Paula Land, Kevin Ward||Help Us Help You! Developing Data Pathfinders at Earthdata.nasa.gov||NASA Earthdata Pathfinders||The NASA Earth Science Data Systems Program, which manages NASA’s Earth science data collections, is currently working to improve the discoverability of its data holdings and information through the earthdata.nasa.gov website. To this end, we have developed several data pathfinders that focus on a variety of themes in which remote Earth observation data can provide an added dimension to ground-based observations for forecasting, monitoring and responding to climate-related events. Currently we have data pathfinders for Agriculture and Water Resources, Health and Air Quality, and Wildfires. |
In this interactive working session, we would like to learn about your data needs and ask you to test our new data pathfinders. Your valuable feedback will be used to improve our tools and increase the discoverability of NASA Earth science data.
|Working Session||Cynthia Hall, Teddy Gelabert, Paula Land, Kevin Ward||Skim the Surface, Jump In||Agriculture and Climate, Data to Decisions, Discovery, Usability||No||No|
|4/25/2019 10:57:email@example.com||Christopher Lynnes||Christopher Lynnes||Surprising and Novel Ways to Integrate Community Data Systems with Each Other||Novel Inter-System Integrations||We know all the standard mechanisms for integrating data systems with each other: standards, APIs, standards-based APIs, etc., etc., etc. But new possibilities are opening up due to new technologies and approaches: Jupyter, Eclipse Che, Everything-as-a-Service, Slack, JSON-LD... Do you have a novel integration mechanism you want more developers to adopt so we can hook more things together? Come to this session and talk it up!||Strengthen the ties between observations and user communities (e.g. technologies, research, education and applications).||Breakout Session||Chris Lynnes et al.||Jump In||Cloud Computing, Data Models, Discovery, Science Software, Semantic Technologies, Web Services||Yes||In search of: talks on NEW or underappreciated ways of integrating data systems in our community. Ideally, the speaker will have a framework/approach/whatever that participants could jump in immediately.|
|4/25/2019 13:29:firstname.lastname@example.org||Jessica Hausman||Jessica Hausman, Mark Parsons||Data Citations: What Makes a Good Citation?||Data Citations||Citing data is important as it provides credit to the producers, better transparency in reproducibility of work and applies to FAIR (Findable, Accessible, Interoperable, Reproducible). As most researchers know how to cite scientific writings, citing data is not as obvious or well practiced. Therefore most data repositories are providing citation formats for their datasets so users will know how data should be cited. Repositories are also registering PIDs, typically DOIs for the datasets as tracking the PID is much easier than the actual citation text in an article. But why do the citations and registered PIDs contain the information they contain? This session will look at the citation formats registered information that goes into a PID at USGS, NOAA, NASA and other repositories. We will then compare the various citations and see why differences, if there are any, exist. Is it due to available metadata, community driven, funder driven, etc.?||Increase the use and value of Earth science data and information., Promote techniques to articulate and measure the socioeconomic value and benefit of Earth science data, information and applications.||Breakout Session||Reyna Jenkyns, Heather Brown, Madison Langseth, Jessica Hausman||Jump In, Deep Dive||COPDESS, Community Data, Data to Decisions, Discovery, Documentation, Research Data Management, Software & Services Citations||Maybe||Citation, data citation, DOI, PID||Yes||Someone that works at a data repository, outside of USGS, NOAA or NASA, that provides data citations and registers PIDs for their data holdings or facilitates it.|
|4/25/2019 14:17:email@example.com||Aleksandar Jelenak||Aleksandar Jelenak, James Gallagher||Current Status in Cloud Data Access||Cloud computing holds the promise of novel data analysis capabilities for geoscientists by providing affordable on-demand computing system resources. One of the major differences with the traditional computing systems is web-based object storage which requires new data access methods with a different set of performance parameters.|
The aim of this session is to provide the ESIP community with an opportunity to learn about the current capabilities for accessing data in cloud object stores. The emphasis will be on the actual software, data servers or libraries, which are capable of accessing cloud object stores, performance issues and bottlenecks, and best practices that can be adopted when migrating data to the cloud. When considering end-user applications, this session is about how those tools access data from the novel data storage systems available with cloud computing and not about the algorithms, etc., associated with data visualization, analytics, or machine learning.
|Breakout Session||Aleksandar Jelenak, James Gallagher||Skim the Surface, Jump In, Deep Dive||Agriculture and Climate, Cloud Computing, Data Analytics, Machine Learning, Science Software, Web Services||No||Yes||Anyone developing software for cloud data access, see the session description.|
|4/25/2019 17:25:firstname.lastname@example.org||Tim Whiteaker||Tim Whiteaker||LTER Core Metabase - schema and tools for describing data packages and generating EMLLTER Core Metabase||LTER Core Metabase||LTER-core-metabase is a relational database model (Postgres), based on the GCE LTER Metabase, with adaptations by MCR, SBC, and BLE LTER sites. The project aims to provide a database schema into which metadata about ecological data packages can be stored. The design is influenced heavily by the Ecological Metadata Language (EML), and an associated set of R scripts (currently called rMetabase2eml) pair with the database to produce EML for a given data package, enabling the information manager to quickly generate metadata in support of data package archiving. The schema and R scripts are available on GitHub, with the schema represented as a set of SQL scripts to facilitate diffs. We invite you to join us in exploring the current state of LTER-core-metabase and to discuss how we can advance the project to better support the ecological data community.||Increase the use and value of Earth science data and information.||Breakout Session||Margaret O'Brien, Gastil Gastil-Buhl, An Nguyen, Li Kui||Skim the Surface, Jump In||Data Models, Information Quality, Research Data Management, Science Software, Sustainable Data Management||No||database, PostgreSQL, R, data management||No|
|4/25/2019 17:28:08||Heather.Brown@noaa.gov||Heather Brown||Heather Brown||How do you group your data for optimal discovery?||How does your Earth Science community define a collection that is discoverable in catalogs, i.e. data.gov and Google dataset search. What are the different characterizations that define your collection(s) ranging from real-time satellite data to paleo ice cores? Help us create a cheat sheet to help the data management community.|
30 min of brief presentations (1 slide at the most or none) "I work with ______ data types and the collections are organized by ________." 45 min interactive mapping of collection types. 15 min of collating results.
|Working Session||Anna Milan, and more||Deep Dive||Agriculture and Climate, Community Data, Data to Decisions, Discovery, Documentation, Data Analytics, Energy and Climate, Enviro Sensing, Information Quality, Marine Data, Research Data Management, Science Communication, Sustainable Data Management, Usability||No||metadata, granularity||Yes||Data managers from various Earth Sciences.|
|4/26/2019 10:53:email@example.com||Bruce Caron||Bruce Caron||Meet The Maintainers: commoning for data infrastructure durability||Meet the Maintainers||Because they care about and for the infrastructure that houses every bit of data, every byte of the cloud, and every line of code, maintainers sustain the technology infrastructure that makes Earth data use possible. Maintainers work in many arenas, of course, they keep energy grids up, roadways repaired, buildings secure. Data infrastructure experts are now in conversations with other maintainers. Recently, a group of maintainers: technicians, engineers, historians, social scientists, sysadmins (the ones you call on to reboot the system when it’s down) started a conversation and created a group called The Maintainers <http://themaintainers.org>. With support from the Alfred P. Sloan Foundation, ESIP is bringing the Maintainer conversation to Tacoma. We’ve invited several of them to talk about the real issues involved in stewarding hardware and systems, not just data. By caring for your hardware, they let you focus on other tasks. Join us to discover how ESIP’s goals of sustaining the Earth science data endeavor rely upon those who chose not to innovate today, but rather to navigate the problematics of keeping everything running most of the time.||No||Breakout Session||Mark Parsons (RPI), Scarlet Galvan (Brown University), Emily Sylak-Glassman, Fred Beach (The University of Texas at Austin Energy Institute), Jason Gallo (Science and Technology Policy Institute)||Jump In||Sustainable Data Management, sustainable hardware management||No||hardware, infrastructure, commoning, maintainers||No|
|4/25/2019 17:39:firstname.lastname@example.org||Ed Armstrong||Ed Armstrong, Aleksandar Jelenak||New paradigms for alternative data packaging of geolocation information in EO satellite data||Alternative geolocation data packaging||Earth observation (EO) satellite files can encode the geolocation of their|
observations in a variety of ways. This often depends on the processing
level of information. Gridded rasters can be described by concise map
projection information while Level 1 and 2 data that are in native satellite
coordinates require more detail. Often these files encode the pixel level
geolocation information as multi dimension variables internal to the file.
In the past there have been example implementations of storing geolocation
in an external file (early NASA MODIS) or sub sampling geolocation
information (early NASA SeaWiFS) that did not work out very well for
various reasons. Storing the geolocation data or map projection references
in each file (granule) has many advantages the most important is playing
"nicely" with tools and services and software, and promoting
interoperability. However, the geolocation data for L1/L2 EO files are
often the storage heaviest individual component as its precision requires at
least float data types (its information cannot be elegantly "packed") so it
is worthwhile to revisit ideas and methodologies for reducing its
footprint. How best could geolocation information be shared across
different variables, different files from the same sensor, or even
different sensors on the same platform. Furthermore, in the age of cloud
and database tiled storage of satellite information how is geo-location (and
other) information best packaged and utilized to improve data access and
processing. In this session we will look at this problem and potential
solution space via a number of presentations, historical lessons learned and
|Breakout Session||Kwo-Sen Kuo, Aleksandar Jelenak, Ed Armstrong||Jump In||Cloud Computing, Data Models, Data Analytics, Marine Data, Web Services||Maybe||CF, geolocation, metadata, data model, data packaging||Yes||Those with experience with satellite data models and data packaging|
|4/25/2019 23:02:email@example.com||Michelle Hertzfeld||Michelle Hertzfeld, Jessica Schilling||Distributed Data Stewardship and You|
REQUEST: two, 90-min, back-to-back sessions
How can we make it possible to coordinate management, replication, and governance of data on decentralized infrastructure? What efficiencies can we gain? What practices and lessons from centralized data governance should we take care to protect or learn from? ...and what does 'decentralized infrastructure' even mean!? Let's talk, together.
**This is a working session -- there will be post-its for you to play with!** It will be light on slides, heavy on small group conversation. The outputs of our time together will be digitized and share back to the ESIP community. This is a continuation of the great conversation we started at the 2019 Winter Meeting, and will include a report-out from that meeting and other (non-ESIP) conversations that have happened since January.
|Working Session||Michelle Hertzfeld, Jessica Schilling||Jump In, Deep Dive||Cloud Computing, Community Data, Research Data Management, Usability||No||Maybe|
|4/26/2019 8:33:01||Tyler.B.Stevens@nasa.gov||Tyler Stevens||Tyler Stevens, Anna Milan||Bridging The Gap Between Discovery and Use (Data and Tools)||How do metadata repositories with vast amounts of various data help users start working with the data quickly and easily? Connecting users to the data and tools/services that can utilize the data has been an ongoing challenge. To increase the value and use of Earth science data, having tools and services that can utilize data is crucial for doing scientific research. This session will convey how metadata repositories are attempting to help users start working with their data immediately through the use of metadata modeling and intuitive discovery tools. In this session, we will also capture best practices for connecting data to tools that can be shared with other organizations who are trying to tackle this issue.||Increase the use and value of Earth science data and information.||Breakout Session||Jump In||Discovery, Documentation||No||Metadata, Modeling, Discovery, Use, Software, Services, Tools||Yes||Looking for engineers/data scientists/software developers/metadata managers who work on metadata repositories who can speak about how they are bridging the gap between discovery and use.|
|4/26/2019 13:51:firstname.lastname@example.org||John Porter||M.Gastil-Buhl||Getting Stuff Done with R, Python and Jupyter Notebooks||R, Python and Jupyter Notebooks||Sometimes the hardest part of getting started with coding is to determine which is the best software to learn or use! The goal of this session is to provide a basic introduction to three commonly-used tools for data management and analysis and to provide examples of how they can be used for managing data, visualization, exploiting cloud resources, generating metadata, using or creating web services, manipulating XML documents, and facilitating reorganization of data. |
A panel will provide brief overviews of R, Python, and Jupyter Notebooks, including examples of what they do best, drawn from real-world applications. Workshop attendees will be encouraged to participate in discussions of data challenges they have encountered and the relative merits of the different tools in meeting them. Participation in the session by coders experienced in one or more of the tools is encouraged, as is participation by those who have yet to use any of these very powerful tools.
|Increase the use and value of Earth science data and information.||Panel||Stace Beaulieu, John Porter, Colin Smith, Chris Turner||Skim the Surface||Data Analytics, IM Code Registry, Marine Data, Research Data Management, Web Services||Maybe||R, Python, Jupyter Notebooks||No|
|4/26/2019 13:04:19||Hampapuram.Ramapriyan@ssaihq.com||Hampapuram Ramapriyan||Hampapuram Ramapriyan, Peter Leonard||Data Product Developers' Guide - Workshop||Data Product Developers' Guide||The Data Product Developer's’ Guide Working Group within NASA’s Earth Science Data Systems Working Groups has been developing a guide to assist science data product developers in designing and producing products that are interoperable and conveniently usable by the community. A draft version of this document is expected to be available for broad review in early July 2019. While the initial target audience for this document are the NASA teams responsible for product generation, it is expected to be more broadly applicable. The purpose of this workshop session is to present briefly the contents of this document to interested ESIP members and promote a broader participation in the review process and facilitate improvements for the benefit of end user communities. During this session, the attendees will be divided into subgroups to review individual sections of the document and provide comments||Workshop||Chris Lynnes, Peter Leonard, Hampapuram Ramapriyan||Jump In, Deep Dive||Documentation, Information Quality, Research Data Management, Science Software||Data product development, Interoperability||No|
|4/26/2019 16:18:email@example.com||Patrick Quinn||Patrick Quinn, Peter Plofchan||Cloud Security and Compliance in Public Sector Archives||Increasing user adoption of and applications for cloud technologies as well as exponential growth in data volumes demands our public sector data archives accommodate cloud computing. Simultaneously, government-funded computing environments have constraints that present unique challenges in providing archives in the cloud, including Trusted Internet Connection mandates, funding models and legislation which do not allow unbounded costs, and security policies inherited from a pre-cloud world. Join us to discuss the progress members of the ESIP community have made in overcoming these hurdles toward moving large public sector archives to the cloud for valuable science applications.||Breakout Session||Nathan Clark, Andrew Pawloski||Jump In, Deep Dive||Cloud Computing, Community Data, Data Analytics, Sustainable Data Management, Usability, Web Services||No||Cloud, Public Sector, Government, Compliance, Big Data||Yes||Looking for people who have experiences to share regarding overcoming the security and compliance hurdles mandated by public sector computing environments|
|4/26/2019 13:35:firstname.lastname@example.org||Denise Hills||Denise Hills, Sophie Hou, Ruth Duerr||Data Risk Matrix Do-A-Thon||Data Risk Matrix Do-A-Thon||Defining risks for data can be a daunting task. The risk factors for data collections may vary from collection to collection, or vary over time for a single collection. These factors could additionally vary by the priorities and resources available at any given time. The Data Stewardship Committee held a session at the ESIP Summer Meeting 2018 (https://2018esipsummermeeting.sched.com/event/Eypr/building-a-data-risk-factor-matrix) where participants undertook a “card sorting” exercise, an established method for developing categorizations of concepts. The outcome of that exercise indicated more than one way to categorize data risks, thus indicating that any approach may need adjustment depending on the situation at hand.|
This working session is intended to further develop and evolve the Data Risk Categorization Matrix (http://bit.ly/2IX3VM5) begun by the Data Stewardship Committee, and to work through test cases for its application. We invite volunteers to use the Categorization Matrix on a data collection they are familiar with prior to the session, then during the session we will discuss issues, comments, concerns, or improvements to the matrix. Participants are encouraged to bring information on a data collection to the session to conduct live assessments with input from other participants.
|Working Session||TBD||Jump In, Deep Dive||Research Data Management, Usability, Data Stewardship||Maybe||data stewardship, data rescue, data risk||Maybe||Anyone working on determining how to prioritize data rescue/rehabilitation/conservation|
|4/26/2019 13:36:email@example.com||Ethan Davis||Ethan Davis, Ryan May||NetCDF and CF: The Basics||This workshop will teach some of the basics of CF metadata for netCDF data files with some hands-on work available in Jupyter Notebooks using Python. Along with introduction to netCDF and CF, we will introduce the CF data model and discuss some netCDF implementation details to consider when deciding how to write data with CF and netCDF. We will cover gridded data as well as in situ data (stations, soundings, etc.) and touch on storing geometries data in CF.||Increase the use and value of Earth science data and information.||Workshop||Ethan Davis, Ryan May||Jump In, Deep Dive||Data Models, Research Data Management, Science Software||Maybe||netCDF, CF metadata||No|
|4/30/2019 15:03:firstname.lastname@example.org||Deborah Smith||Deborah Smith, Amanda Leon, Helen Conover||Improving Airborne Data Discovery and Use||Airborne Data Use||Airborne earth observations are typically collected in field campaigns aimed at satellite data validation or intensive observation of a particular geophysical feature or physical relationship. This results in a wealth of coincident observations of Earth system processes from a wide variety of instruments. However, these heterogeneous data have diverse temporal and spatial scales, variables, and data formats and organization. Compared to satellite data, airborne data typically have a much smaller user community and consist of more data types containing fewer and smaller data files. In many cases, the users of airborne data may be limited to just those involved with the airborne campaign due to the complexity of the data and the difficulty visualizing and using the data. Individual data centers have developed their own particular way of serving the needs of a particular community of airborne data users effectively. In this session, we aim to bring together data providers and data users to gather effective ideas for broadening airborne data user communities beyond the campaign scientists. The session will have a few invited speakers to share ideas and will conclude with discussion time to explore participant ideas and methods for improving the discovery and use of airborne data.||Increase the use and value of Earth science data and information.||Panel||TBD||Jump In||Drones, Science Communication, Usability||No||Yes||Looking for anyone with unique ideas for improving airborne data use|
|4/26/2019 15:01:email@example.com||Corinna Gries||Corinna Gries||Preparing climate and hydrological time series data for submission to CUAHSI||Convert data to CUAHSI ODM||In this working session we will introduce CUAHSI Data services to manage point time series data, such as streamflow and precipitation. This standardized data format will enable data synthesis and comparison across different sites and locations. Specifically, we will demonstrate how to convert a climate dataset into this format and upload to CUAHSI’s data repository. Participants may follow along using their own data. Please bring one year’s worth of climate station data in your local format to this working session along with a laptop containing your favorite scripting environment. We will also provide an example dataset and expertise in various scripting languages. The goal is for a data manager to obtain a good understanding of the workflow involved for converting their local climate station data for submission to CUAHSI’s data repository.||Working Session||Martin Seul, Wade Sheldon, Margaret O'Brien, Suzanne Remillard||Jump In||Data Models, Enviro Sensing, Research Data Management||Maybe||LTER, climDB, standardize data||No|
|4/26/2019 15:13:firstname.lastname@example.org||Colin Smith||Colin Smith, Kristin Vanderbilt||The Information Management Code Registry: Software Solutions for Information Management Needs||Software Solutions for IM Needs||The Information Management Code Registry (IMCR) enhances the use and value of Earth Science data by facilitating discovery of software solutions for information management needs. Our primary goal with the IMCR is to create a comprehensive registry of information management software that is searchable by task (e.g. quality control) and other attributes (e.g. science domain). Our secondary goal is to highlight coverage gaps and help shift redundant effort to new development. In this session, we report on the accomplishment of the primary goal and present plans for attaining the second. Additionally, we will run group activities and discussions to: (1) Test and refine discoverability-, (2) inform identification of coverage gaps, (3) explore the benefit of adding non-generalized, but unique and useful, scripts developed for a single purpose, and (4) collectively cogitate on the general utility of the IMCR and how to maximize its value.||Breakout Session||Colin Smith||Jump In||IM Code Registry, Research Data Management, Science Software, Semantic Technologies, Software & Services Citations, Sustainable Data Management||No||No|
|4/26/2019 15:34:email@example.com||Scott Henderson||Scott Henderson, Rich Signell||Scalable, data-proximate cloud computing for Earth Science research||Cloud-computing research||Data intensive scientific workflows are at a pivotal time in which traditional local computing resources are no longer capable of meeting the storage or computing demands of scientists. In the Earth Sciences, we are facing an explosion of data volumes sourced from models, in-situ observations, and remote sensing platforms. Some agencies are starting to move data to commercial Cloud providers to facilitate access (e.g. NASA on Amazon Web Services). Fully leveraging these opportunities will require new approaches in the way the scientific community handles data access, processing and analysis. In particular, we need to stop downloading data and start uploading algorithms to wherever large archives reside. This session is targeted at researchers who pioneering such “data-proximate” computing on commercial Cloud infrastructure. We hope to hear current success stories, as well as failures, and identify ways to improve existing workflows.||Breakout Session||Rich Signell (USGS), Julien Chastang (UCAR-Unidata)||Jump In, Deep Dive||Cloud Computing, Community Data, Data Analytics, Research Data Management, Science Software, Web Services||Maybe||Yes||Looking for scientists interested in sharing new tools or workflows|
|4/26/2019 15:40:firstname.lastname@example.org||Scott Henderson||Scott Henderson||Using Pangeo JuptyerHubs to work with large public datasets||Pangeo JupyterHub Workshop||Bring your laptop to this hands-on workshop! Participants will learn about open-source scientific python ecosystem for analytic workflows with big data in Earth Science. Pangeo is first and foremost a community promoting open, reproducible, and scalable science (read more at https://pangeo.io). This community provides documentation, develops and maintains software, and deploys computing infrastructure to make scientific research and programming easier. The Pangeo software ecosystem involves open source tools such as xarray, iris, dask, jupyter, and many other packages. In brief workshop, participants will familiarize themselves with writing code in Jupyter Notebooks that can be run on scalable computing clusters running on the Cloud, bypassing a common bottleneck of downloading ever-increasing volumes of remote sensing or modeling data. We will introduce key Python tools and have participants write simple code to work with large public datasets hosted on Amazon Web Services and Google Cloud.||Workshop||Amanda Tan (University of Washington), Anthony Arendt (University of Washington), Andrew Pawloski (Element 84)||Jump In, Deep Dive||Cloud Computing, Community Data, Data Analytics, Science Software, Web Services||No||cloud-computing, satellite imagery, python||No|
|4/26/2019 16:03:email@example.com||Jeff Siarto||Jeff Siarto||Toward Better Earth Science UX||Earth Science UX||The “order and download” paradigm is dying. NASA and other organizations are moving their data holdings to the cloud and future missions will be producing so much data–petabytes per year in some cases–that the old way of viewing, subsetting, and analyzing this information needs to adapt. As this data grows in size and complexity, it demands more usable, accessible, and thoughtful designs and user interfaces that support science and help researchers answer important questions. This session will focus on how we’re developing better user interfaces that utilize remote sensing data–especially in a cloud environment, and the impact user experience plays on the search, discovery, and analysis of Earth science data.||Breakout Session||Jeff Siarto||Jump In||Agriculture and Climate, Community Data, Information Quality, Science Communication, Science Software, Usability, Web Services||No||ux, usability, interface design||Yes||UX and Design professionals looking to talk about new software, interfaces, user research in the Earth science and remote sensing field.|
|4/26/2019 16:21:firstname.lastname@example.org||Amber E Budden||Amber E Budden||Making Data Count: Best Practices for Tracking and Exposing Data Usage Metrics||Making Data Count||Many publishers and funders have implemented open data policies in efforts to make research more transparent and re-usable, as well as to support data as a valuable output of the research process. To give credit to researchers for data sharing, however, the community needs to take additional steps to promote standardized measurement of data usage and proper citation of data. This means different things for different stakeholders: researchers need to be educated on how and why data citations should be included in article and other publications, publishers need to promote and index data citations, repositories need to standardize and display data usage information, and institutions need to value these metrics.|
Make Data Count highlights the value of research data by providing the infrastructure for repositories to display data usage and citation metrics. The project has worked with COUNTER to develop a Code of Practice to enable standardization and has developed mechanisms for repositories to expose data usage metrics, with example implementations including California Digital Library, the Arctic Data Center and DataONE. In this session we will talk through the status of the Make Data Count project, demonstrate capabilities and future developments of the metrics dashboard available through DataONE, and provide concrete examples of what individuals, repositories and publishers within the community can do to support accurate representation of the value of research data.
|Increase the use and value of Earth science data and information., Promote techniques to articulate and measure the socioeconomic value and benefit of Earth science data, information and applications.||Breakout Session||Amber Budden, Matt Jones, Dave Vieglais||Jump In||No||Yes||We would be glad to include someone from the ESIP data citation best practices group.|
|4/26/2019 16:28:email@example.com||Kerstin Lehnert||Kerstin Lehnert||Open Forum IGSN 2040: Maturing a PID Organization toward Sustainability||IGSN2040||Globally unique, persistent, and resolvable identifiers (PIDs) are now an essential component of the modern research ecosystem and are used for many types of digital objects and research artefacts including data, software, samples, and instruments. The International Geo Sample Number (IGSN) is a specialized PID for physical samples that ensures unambiguous citation and tracking of these samples and links them to data and publications. Originally developed for the Earth Sciences, the IGSN has evolved into an international PID system and is increasingly adopted by other disciplines that need to refer to physical samples. The growing number and range of stakeholders worldwide include, but are not limited to, researchers, collection curators, and data managers. |
To date, nearly 6.9 million samples have been registered with IGSN. As the audience expands and the adoption rate accelerates, the governance and business models of the system need to be reassessed to support this growth. The IGSN 2040 project, funded in 2018 by an award from the Alfred P. Sloan Foundation, has enabled the participation of an international group of experts, from multiple domains, to re-design and improve the existing organization and technical architecture of the IGSN. The goal is to be able to respond to and support, in a sustainable manner, the rapidly growing demands of an increasingly multi-disciplinary samples user community in a landscape of maturing research data infrastructures.
The IGSN 2040 team invites the ESIP community to participate in an open forum to explore solutions for a scalable and sustainable future of the IGSN. This discussion will begin broad addressing essential criteria for trustworthiness and sustainability for PIDs in the rapidly growing global unique, persistent, and resolvable identifier (UPRI) ecosystem, and narrow to focus on the optimal organizational foundations needed to ensure longevity, scalability and effective governance of the IGSN. The results of this discussion will inform the work of the IGSN 2040 Governance Steering Committee Meeting, which is colocated with the 2019 ESIP Summer meeting.
|Working Session||Kerstin Lehnert and other members of the IGSN 2040 Governance Steering Committee||Deep Dive||Community Data, Research Data Management, Sustainable Data Management||Yes||PID, IGSN, samples, sustainability||Nofirstname.lastname@example.org|
|4/26/2019 16:39:email@example.com||Kristin Vanderbilt||John Porter, Kristin Vanderbilt||Location, Location, Location: Enabling Data Discovery by Place||Data Discovery By Place||Controlled vocabularies and ontologies are used to annotate datasets in the environmental sciences to improve data discoverability. However, they typically focus on data content and uses, rather than the location where data is collected. Although selecting terms for the theme of a dataset is usually straightforward, identifying terms for the location of data collection is a more complicated issue. Places where research is conducted vary by location and in size. Some named locations may be subsumed by other named locations (e.g., a city in a state) and sometimes multiple names need to be specified to be clear (e.g., Springfield, IL, USA vs. Springfield, MO, USA vs. Springfield, ON, CA). Moreover many geographic name databases work well for terrestrial locations, but not for aquatic ones (e.g., coral reefs). The nearest named place from a gazetteer may be quite distant from a study site in the wilderness. Additionally, data for a given study may be collected in many distinct locations with intervening gaps in between. For discoverability, is it preferable to identify a place as part of a study where many types of data are collected, or as a set of coordinates? In this working group, we will consider use cases from the perspective of environmental researchers to evaluate how well gazetteers and other resources such as the NGA GEOnet Names Server (GNS) could enable data discovery by researchers searching for data. Our aim is to provide recommendations for specifying location using geographic naming resources, or failing that, to better define how various resources might be evaluated for fitness.||Increase the use and value of Earth science data and information.||Working Session||Skim the Surface, Jump In||Discovery, Research Data Management||Maybe||No|
|4/26/2019 17:29:firstname.lastname@example.org||Sean Gordon||Sean Gordon||Metadata Improvement Lab 4: How FAIR is your metadata?||MILE 4: Visualizing Metadata FAIRness||In the fourth MILE (Metadata Improvement Lab at ESIP) workshop, participants will utilize a Jupyter notebook hosted on ESIPhub to ingest metadata and evaluate the FAIRness of the records. The DataONE operationalization of the FAIR principles will be applied using XSL, Bash, and Python to visualize the completeness of a single record or an entire catalog.||Increase the use and value of Earth science data and information.||Workshop||Sean Gordon||Jump In, Deep Dive||Documentation, Data Analytics||Yes||Metadata, data analysis, data visualization, Jupyter, Python, FAIR||Yes|
|4/26/2019 17:37:email@example.com||Matt Jones||Matt Jones, Dave Vieglais||Metadata harvesting through schema.org||Harvesting schema.org||Repositories have recognized the benefits of adopting schema.org metadata in their data catalog landing pages to improve discoverability, particularly with the incentive of inclusion in the Google Dataset search. While Google supports broad, general search and discovery, we can also use this mechanism to improve domain-specific aggregated search systems like DataONE. In this working session, we will focus on real world issues of implementing schema.org for repositories, how to link traditional metadata records into dataset landing pages, and how this can result in improved harvesting and representation by science focused aggregators such as DataONE. We will work through recommendations emerging from science-on-schema.org, optimizing JSON-LD to work with major search engines, and options for extending to include more detailed dataset information beyond the typical discovery-level metadata found in most records.||Working Session||Matt Jones, Dave Vieglais||Jump In, Deep Dive||Community Data, Discovery, Documentation, Research Data Management||No||repository, schema.org, metadata, federation||Yes||Looking for participants in science-on-schema.org group to help overview those recommendations, or others with similar recommendations.|
|4/26/2019 17:47:firstname.lastname@example.org||Mark Boyd||Mark Boyd||Getting your data into the cloud: How to deploy and use Cumulus||This session will be an interactive walkthrough of how to deploy the open-source Cumulus tool for getting your data into the cloud and a live demo of using Cumulus to ingest a new set of science data into the cloud||Workshop||Jump In, Deep Dive||Cloud Computing, Science Software, Sustainable Data Management||No||Maybe|
|4/26/2019 18:17:email@example.com||Bill Teng||Bill Teng, Nancy Hoebelheinrich, Chris Beltz, Lindsay Barbieri||Soil response to fire across time scales: Data needs and standards for near- and long-term land management||Fire-soil response, data needs and standards||Fire impacts soil hydrology and biogeochemistry at both the near (hours to days) and long (decades to centuries) time scales. Burns, especially in soils with high organic carbon stocks like peatlands, induce a loss of soil carbon stock with combustion. In addition to a loss of absolute carbon stocks, fire can shift the chemical makeup of the organic matter, potentially making the latter more resistant to decomposition. On the shorter timescales, fire can also change the water repellent or hydrophobicity properties of the soil, leading to an increased risk of landslides.|
In this session, we will focus on data needs for assessing the effects of burn across time scales, from informing risk management in the immediate post-burn period to managing carbon in a landscape decades out. Following invited presentations, group discussion will cover how data science can support developing integrated platforms to connect researchers with land managers.
|Two consecutive breakout sessions (3 hours total)||TBD||Skim the Surface, Jump In||Agriculture and Climate, Data to Decisions, Disaster Lifecycle, Machine Learning||No||Wildfire, fire, drought, soils, disasters, data, data standards, land management||Yes||TBD|
|4/26/2019 19:07:firstname.lastname@example.org||Simon Goring||Simon Goring||Beyond the cookbook: Connecting workflows, data and people for sustainable interdisciplinary Earth Sciences||This interactive workshop intends to add to the Throughput cookbook, by having participants work through and annotate workflows. Additionally, participants will use the API to look at the networks already built to ascertain what additional tools are needed to make sense of it all.||Workshop||Simon Goring||Skim the Surface, Jump In, Deep Dive||Community Data, Documentation, Research Data Management, Sustainable Data Management, Usability||Maybe||Maybe|
|4/26/2019 21:57:email@example.com||Kenton McHenry||Ben Galewsky||The Critical Zones: Supporting Place Based Research||The Critical Zones||We look at the history of data management for the the NSF CZO Network, a NSF funded network of sites focused on how components of the Critical Zone interact, shape Earth's surface, and support life. Each site has their own data management practices, with a central catalog aggregating information about well curated datasets. Each site leverages specific technologies such as Dendra, Geodashboard, Clowder, etc. We will discuss some of these local approaches and how in the last few years there has been an attempt at improving the central catalog by leveraging efforts such as CUAHSI HydroShare, together with some future looking approaches for a better federated data manager package.||Breakout Session||Collin Bode, Martin Seul, Luigi Marini||Jump In||Agriculture and Climate, Cloud Computing, Community Data, Data Analytics, Energy and Climate, Research Data Management, Software & Services Citations, Sustainable Data Management, Usability, Web Services||Maybe||Maybe||Researchers working with critical zone data.|
|4/26/2019 22:57:firstname.lastname@example.org||Anne Wilson||Anne Wilson||An ESIP community's working session on machine learning: introducing adoptable use cases and beyond||Use cases for machine learning||The ESIP ML cluster is creating some use cases for learning about machine. This working session is to introduce our working use cases and framework and seek feedback. |
The cluster is also interesting in hearing from machine learning experts. We’ll use some of this session to share knowledge about about potential speakers.
|Working Session||Jump In||Data Analytics, Machine Learning, Science Software||No||Maybe|
|4/27/2019 1:45:email@example.com||Namrata Malarout||Namrata Malarout||Cloud Engineering in Practice||Cloud Engineering in Practice||With the immense increase in volume of data acquisition and archival comes the challenge of intensive data processing that we are all trying to solve. There are many efforts underway to achieve this by infusing cloud technologies into software infrastructure. In this session we would like to cover the various approaches being taken to move towards scalable storage and auto scaled processing. We will talk about porting applications to the cloud, container based deployment models and a hybrid science data processing system. This data system utilizes both on-premise and remote compute resources to meet latency requirements while handling the large volumes of data. Cloud based infrastructure is being used for running data analytic stacks, automated workflows for reprocessing campaigns, forward keep up and much more. Several projects have invested in cloud technologies such as GRFN (Getting Ready for NISAR), PO.DAAC, SWOT and so on. We have explored running our softwares on several cloud platforms like Azure, Google Cloud Platform, Amazon Web Services, High Performance Computing and Kubernetes. We would like to shed some light on such work and lessons learned in the process.||Breakout Session||Namrata Malarout, Frank Greguska, Lewis McGibbney||Skim the Surface, Jump In||Cloud Computing, Community Data, Discovery, Data Analytics, Education, Research Data Management, Science Software, Semantic Technologies, Sustainable Data Management, Usability, Web Services||Maybe||esip-cloud||Yes||Someone working on leveraging cloud technology for science data processing|
|5/9/2019 17:42:firstname.lastname@example.org||Andrea Thomer||Andrea Thomer, Nic Weber||Conceptual modeling for earth science||Conceptual modeling for earth science||Data repositories often rely upon conceptual models that provide formal representation information and identity conditions for digital resources -- for instance, the ontologies that underlie semantic data, or conceptual models like FRBR that underlie digital libraries. Though these later two cases represent extremely well documented conceptual models, there are many other instances where underlying conceptual models are tacit or inexplicit, and rarely published by practitioners and researchers. This makes it hard to build on one another's work, identify weaknesses in our models or modeling approaches, or forge new innovative collaborations. Furthermore, even in cases were conceptual models are well articulated, we believe there is a need for further discussion related to the methods used in modeling work, and the open research questions regarding conceptual modeling.|
To that end, we'd like to see ESIP become a home for conversations about conceptual modeling for earth science data! We (https://sig-cm.github.io) are a group of information scientists who believe that sustaining a rich tradition of research and development in conceptual modeling in LIS requires collaboration with, and contributions from, communities like ESIP. This session would be the second in a series of interdisciplinary workshops, panels, and working sessions with the goal of building community and a research agenda around conceptual modeling work in libraries, archives, museums, and data repositories.
Plan for session:
- short lightning talks from presenters, setting the stage and outlining the topic
- a working session, in which participants are split into small groups to discuss areas of unmet need, and develop research questions, possible future research/development directions for ESIP + conceptual/data modeling efforts.
|Working Session||Andrea Thomer, Nic Weber||Jump In||Data Models||No||Maybe||Looking for developers and users of conceptual models in the earth sciences; and researchers interested in conceptual modeling issues. We are currently reaching out to people to try to identify speakers and participants. I know there's a nascent data modeling cluster, but the listserv has been pretty quiet - I'll reach out there first.|